DP2012-11

Does Wom enʼs Em powerment Redu ce
Preva lence of Stunted and UnderweightChild ren in Rural India? *Katsushi S. IMA I
S am ue l Ko bina A NNIM
Raghav GAIH A
Ve ena S. KULKARNIRevised December 14, 2012

* 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, UK and RIEB, Kobe
University, Japan
Samuel Kobina Annim
Department of Economics, University of Cape Coast, Ghana &Economics, School of Social Sciences, University of Manchester, UK
Raghav Gaiha
Faculty of Management Studies, University of Delhi, India
Veena S. Kulkarni
Arkansas State University, USA
First Draft: 10thMarch 2012
This Draft: 14th December 2012
Abstract
This study investigates whether mother’s empowerment as measured by mother’s
relative (to father) bargaining power affects children’s nutritional status using the
National Family Health Survey (NFHS) and National Council of Applied Economic
Research (NCAER) data spanning the period between 1992 and 2006. First, the relative
bargaining index defined as the share of mother’s schooling years over father’s schoolingyears positively and significantly influences z scores pertaining to the short-term
measures of nutritional status of children, ‘weight-for-age’ and ‘weight- for-height’-. The
results of quantile regression suggest, however, that the bargaining power will improvethe chronic measure of nutritional status ‘height-for-age’, at the low end of conditional
distribution of z score.. Further, we find that access to health scheme or health insurance
and health-related facility, infrastructure and environment are important factors in
reducing child malnutrition.
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 of Manchester, 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, 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.

1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 experienced over the past three
decades in the rates of moderately underweight and stunted children (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 improved during the 1990s(Tarozzi & Mahajan, 2007). Another piece of research based on the survey covering 112
rural districts of nine relatively poor states conducted in 2010-11, reports that (i) theprevalence of underweight children has decreased 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 overboys aged 0-3 years is reversed in the 3-5 age category (in both height-for-age and weightfor-age) suggesting neglect of girls; 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 malnutritionand lower cognitive skills in adult years. Additionally, it is a humanitarian concern requiring
policy-makers and international communities to refocus their policy priorities on direct andindirect interventions to reduce children’s malnutrition.
The extant literature on determinants on child health and malnutrition in thedeveloping countries in general and in India in particular highlights the significance ofeconomic, social, cultural, infrastructural factors impacting at multiple levels, such asindividual, household and community (. Ackerson & Subramanian, 2008; Allendorf, 2007;
Kandpal & McNamara, 2009; Kravdal 2004; Smith et.al 2003). Recent empirical studies onchild malnutrition in India tend to focus on one of the following three factors as determinantsof children’s nutritional or health status: (i) mothers’ education, empowerment, and/or
relative bargaining power vis-a-vis fathers’, (ii) social capital at the community level; and (iii)
policy interventions such as the Integrated Child Development Services (ICDS) in addition to
taking into account variables such as household income or consumption, householdcomposition and characteristics, composition of children, caste affiliation, hygiene andsanitation facilities, and food prices. Though the results are context specific, mother’scharacteristics do consistently emerge as significant when examining child malnutrition status(Gaiha & Kulkarni 2005; Kravdal 2004; Luke & Xu 2011; Shroff et,al 2009).
The present study estimates the determinants of nutritional measures of children underage three in rural India during the period 1992-2006 with the focus on role of mother’s
empowerment as measured by mother’s relative (to father) bargaining power. The study
therefore contributes to our understanding of the factors related to child nutrition in India. We
assess the following two questions: (i) Whether the mother’s relative bargaining power
affects the nutritional status of her children? and (ii) What are the household level andinfrastructural and policy related variables that impact children’s nutrition status?.

3

Methodologically, by combining multiple rounds of one data source with another comparable
data source, our analysis overcomes the limitation of the previous studies that consideredeither fewer rounds of data or a single cross-sectional data. We employ all the three rounds of
National Family Health Survey data (NFHS henceforth) conducted in 1992-93, 1998-99 and2005-06 and called as NFHS-1, NFHS-2 and NFHS-3 respectively. We also draw upon thetwo rounds of data collected by National Council of Applied Economic Research (NCAER
hereafter) in 1994-95 and 2005. We construct pseudo panel data and hence as ,mentioned
previously are abel to obviate the shortcomings stemming from thecross-sectional naturethat majority of the previous studies suffer from. Further, we conduct robustness checks using
NCAER data to test if the results based on NFHS data could be generalised, something thatwas not possible with the previous studies.
The rest of the paper proceeds as follows. The next section briefly reviews theprevious studies on child malnutrition in India. Section 3 discusses theoretical issues andpresents the basic analytical framework that underlies our econometric analysis. After
describing the data in Section 4, we discuss the econometric models and specifications in
Section 5. Section 6 reports econometric results. The final section offers few concluding
remarks.

2.

Previous Research

The empirical findings from previous research using NFHS and NCAER data present amixed picture. For instance, drawing upon the 1994 NCAER data, Gaiha and Kulkarni (2005)
apply Poisson model and use the wage gap between men and women as a measure ofwomen’s empowerment. They find that reduction in wage gap reduces severe stunting interms of the number of stunted children in a household. Also, their analysis indicates thathousehold income, household and children compositions, and caste affiliations matter in4

reducing stunting. Using NFHS-2, Maitra (2004) shows that child health is affected only
indirectly through the improved usage of health care, which is determined by women’s
education and control over the household resources or bargaining power. Kravdal’s (2004)
study, again, employing NFHS-2 data finds that both women’s empowerment index (a sum ofbinary response variables, such as, whether a husband is justified beating his wife in certain
situations) as well as the average women’s education in the census enumeration area havesignificant association with child mortality levels.
With respect to the methodology, a couple of recent studies (Borooah, 2005; Kandpal and
McNamara, 2009) use quantile regression (QR) to estimate the effects of various factors onchild nutritional status across the distribution of nutritional levels, for example, forunderweight and overweight children separately. This can be considered to be an
improvement over OLS as the results of OLS based on the conditional mean may be
misleading due to different behavioral response towards malnourished children andoverweight children. The results from Borooah’s (2005) application of QR to 1994 NCAER
data indicate that height-for-age is positively associated with households’ access to safe
water and to good hospital care in the low to middle range region of the distribution ofnutritional status. However, the relationship (of height-for-age) with mother’s literacy issignificant only in the middle of the distribution of children’s nutritional status. Kandpal and
McNamara’s (2009) study based on NFHS-3 also finds that maternal health and education
have greater effects at the lower end. While the present study too applies QR, it is asmentioned earlier, unique as it is based on two sources of national household survey data sets
in 1992-2006, namely, three rounds of National Family Health Survey (NFHS) data and tworounds of National Council of Applied Economic Research (NCAER). The analysis therefore
enables cross-checking the results across data sources and years, which, following Tarozzi
and Mahjajan’s (2007) recommendation for, adds to the robustness to the process of5

identifying the real causes of children’s malnutrition in India. In line with the previous
studies, we estimate the effects of mother’s bargaining power relative to father’s on childmalnutrition by using an IV (instrumental variable) model where mother’s bargaining power
as well as access to health insurance scheme are treated as endogenous variables. Further, we
use QR with the focus on difference of effects of various factors for different parts ofnutritional distribution.2 QR helps us take into account the different behavioural response ofhouseholds towards malnourished children and those with children in the normal range ofheight or weight or overweight. Pseudo panel data models are also applied to identify thedeterminants of child malnutrition over the years.

3.

Analytical Framework

Theoretical Considerations
This section discuses theoretical aspects of the determination of child nutritional status andoutlines an analytical framework to explain how the bargaining power of the mother relative
to the father’s affects the nutritional status of children. In conceptualizing the determinants ofchild nutritional status, researchers choose to either specify a structural health production
function (. Thomas, 1994) or to model the intrahousehold bargaining model between amother and a father and then derive the reduced form equation for the child health or nutrition
where the bargaining or empowerment index is used as a determinant together withhousehold characteristics.
Thomas (1994) combines a health production function where child health as an output is afunction of a number of inputs (e.g. nutrient intakes and the quantity and quality of child/
health care and individual and household characteristics) with a standard utility function of
the household member under a budget constraint for the household. This process can be
typically done in the framework of unitary household models in which the household head6

makes a decision on behalf of household members, all the household resources are pooled,
and a mother and a father have an identical taste (Becker, 1974, see Park, 2007 for theapplication to child nutrition)3.
One could use the non-unitary models where a 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 ofchildren) 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 inchild 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 non- cooperativeequilibrium within marriage and define it in terms of traditional gender roles and gender 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 parents care about the average health quality or nutritional status ofchildren. 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 define A j ,7

EEP, a vector consisting of exogenous factors that determine the preferences of the individual
j. A j may depends on the factors determined outside the household, e.g. unearned income forj, as well as his or her individual characteristics.
Each individual is assumed to choose x j (own consumption) and q (child 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 (). 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

where I is a household’s income,(2)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 inhigherleads to a better nutritional outcome. In this framework, 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 or8social groups. Economic growth increases a household’s income level I and improves thehealth of children.

4.

Data

This study draws upon three rounds of NFHS data, NFHS-1 for the year 1992-3, NFHS-2 forthe year 1998-9 and NFHS-3 for the year 2005-06 and two rounds of NCAER data for theyears 1994 and 2005. The NFHS is a major nationwide, large multi-round survey conductedin a representative sample of households in India with a focus on health and nutrition ofhousehold members, especially of women and young children.5 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 data on children aged
zero to three years in rural areas for NFHS-1, 2 and 3. This is because children below age
four are covered in NFHS-1, below age three in NFHS-2, and below age five in NFHS-3. It isalso well known that nutritional conditions from zero to three years have the mostfundamental effect on stunting in later life (Maluccio et al., 2007).
We supplement NFHS data by NCAER data. NCAER data stem from multi-purpose
household survey that was designed and conducted by NCAER, an internationally known
think-tank based in New Delhi, India. The first round of the survey was carried out in 1994
covering 16 states with a multi-stage sample design where the districts were cross-classified
by income from agriculture and female literacy rate to form homogeneous strata in terms ofthese variables.6 The 2005 round, called the Indian Human Development Survey (IHDS), is acollaborative initiative between NCAER and the University of Maryland, USA. Part of the
IHDS data comprises a panel for 1994 and 2005. Both rounds (1994 and 2005) of data arenationally representative (covering all the states of India) and the thematic areas include

9education, health, livelihoods, family processes and the social structure within which thehouseholds operate.
One of the limitations of NCAER data in 2005 is that age of a child is recorded only inyears, not in months. Therefore, we construct the z scores of ‘weight-for-height’ (wasting) for
children below three years using NCAER data. For NFHS data, z scores of ‘height-for-age’(stunting), ‘weight-for-age’ (underweight) as well as ‘weight-for-height’ for children below
three years are used. On z scores, we follow the z score based on WHO (2006) which
included ‘children from a diverse set of countries: Brazil, Ghana, India, Norway, Oman andthe USA’ (ibid., 2006, p.1) as the reference group to capture the ethnic and racial diversity.
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). It is normally assumed that children with a z scorebelow -3 is classified as ‘severely stunted’, those with a z score between -3 and -2 are‘moderately stunted’. Underweight or wasting is defined in the same 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. Also, as the factors influencing
underweight and overweight children are likely to be different, we consider the factors
affecting those in other appropriate ranges, defined by z scores of -1, -2, 0, 1, 2, and 3.

5.

Econometric Specifications

10

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 bymother’s relative (to father) bargaining power affects the nutritional status of her children?’and (ii) ‘What are the factors (including those associated with children, households,
infrastructure and policy) are likely to change the their 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 (i) bargaining power of women and (ii) access to health insurance schemes.
Third, we use pseudo panel data models in order to combine the three rounds of NFHS data
(and two rounds of NCAER data).

OLS and IV
We presented a simple version of bargaining models in Section 3, but it is not easy to find thevariables 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 where the child nutritional condition is afunction of the bargaining indicators and household characteristics since the household
survey data we use (namely, NFHS or NCAER data) do not contain the variables, such as
prices specific to father or mother’s consumption or the individual 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.7 We denote i for the ith child (or an ID number identifying a particular child)11and h for the hth household (a household ID number) in a total sample at time t (year). We
estimate q ih , 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) bargaining power. The
variable,  h is our measure of women’s empowerment and comprises our central independent
variable. We proxy  h by (i) the proportion of mother’s years of schooling to father’s yearsof schooling ( [schooling years of mother]/ [schooling years of father]) (after controlling foran average schooling years of mother and father); (ii) a dummy variable on whether the father(husband) is justified for 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 themother (wife) needs permission from the father (husband) when she goes to market (1 for
Yes; 0 for No).
In case of the IV,  h is instrumented by the (proportional) difference of father’s age andmother’s age on the grounds that the relatively older father tends to have a greater bargaining
power, but it does not have a separate and direct impact on their child’s nutritional conditions.

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.

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; a wealth index; and whether a household has access to electricity; whether ahousehold has a radio (or a TV; bicycle; a flush toilet).
12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 a household has access to a health scheme or health insurance. This is
instrumented by the infrastructure variable to capture the availability of information, that is,
how many households in the village have access to a telephone in IV. R is a vector ofregional dummies (BIMARU 8, South, East, West) as well as state dummies to take accountof state fixed effects. P is a price vector (sugar, egg, cereal).

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 as behavioral response to predictors (e.g. health insurance, 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 the 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 the13error terms in each group is unlikely to be without heteroscedasticity, bootstrap estimates of
the asymptotic variances are calculated with 100 repetitions.

Pseudo Panel Data Model
One of the limitations of the above model is that each round of NFHS or NCAER 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 common across cross-sectional data
sets in different years. We apply the pseudo panel for the cohort k based on the combinationof states and mother’s age groups (15-19 years, 20-24 years, … , 45-49 years). The cohort isdenoted as k in the equation (7) below.

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, or two rounds of NCAER data in 1994 and 2005. The upper bar means thatthe average of each variable is taken for each cohort, k for each round, t . Regional variables
do not have time variation and have been dropped. A variable on health scheme or healthinsurance has been also dropped as this is available only in NFHS-3.
The 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 the equation(7), Dt is a vector of year dummies,  kt is the unobservable individual effect specific to thecohort k (e.g. cultural effects which are not captured by explanatory variables), and ekt is anerror term. The issue is whether equation (8) is a good approximation to the underlying
14

household panel models for household i in equation (8)’ below. It is not straightforward tocheck 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, k is reasonably large and thus the estimator is likely to be almost consistent. Once wetake account of the cohort population, equation (8) will become the model developed by
Deaton (1985) whereby qi kt and  i kt are considered to be error-ridden measurements ofunobservable cohort means, which leads to so-called ‘error-in-variables estimator’ (see Fuller,
1987 for more details).

6.

Results

This section discusses econometric results based on the models given in Section 4. Because
the results are voluminous, we highlight only key variables, such as bargaining variables inthis section. Table 1 summarizes the coefficient estimates of bargaining indicators estimatedby NFHS data, namely, the ratio of mother’s schooling years to father’s schooling years (for
NFHS-1, 2 and 3); whether a husband is justified in beating his wife when she is unfaithful;or 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. Average education of a father and amother is considered as a control variable for the ratio of schooling years. Table 2 shows thecorresponding results for NCAER data.
(Table 1 to be inserted)

15

The first panel of Table 1 shows the results for NFHS-1 (1992-93). It is observed that theresults of OLS do not necessarily represent the results at the ends of distributions, though
they are roughly close to those in the middle (e.g. z score from -1 to 1). The estimatesindicate that increase in mother’s relative bargaining power (in terms of schooling) tends toimprove the nutritional status of “severely and moderately stunted children” as well as“severely underweight children”. Further, average education is positive and significant. In thesecond panel for NFHS-2 (1998-99), we find that (i) mother’s relative bargaining is positiveand significant for ‘weight-for-age’ and for ‘weight-for-height’ (only for those who areundernourished in both cases), but statistically not significant for ‘height-for-age’ (ii) average
education is positive and mostly significant (iii) ‘beating’ negatively and significantly affects‘weight-for-age’ and ‘weight-for-height’ in the range ‘acutely malnourished’ and (iv)woman’s autonomy (in terms of going to the market) positively and significantly affects‘weight-for-age’ (for the malnourished) and ‘weight-for-height’ (for the acutely
malnourished).
From NFHS-3 (2005-06), we do not find many cases where a bargaining variable isstatistically significant, while average education is positive and significant in most cases. In
one case (0<z<1 for ‘weight-for-height’), the relative schooling years has a significant andpositive effect on the nutritional indicator. And in another (-2<z<-1 for ‘weight-for-height’),
it tends to be worsened by a father’s ‘beating’. We have carried out IV estimation where abargaining indicator is instrumented by the relative age difference of a husband and a wife.
As in OLS, bargaining variables remain statistically non-significant.
A main insight here is that improvement in women’s bargaining power over men tends toimprove children’s nutritional status mainly for households in the 1990s, but this effectbecame weak in 2005-06. To see if this conclusion will hold if we use NCAER data, we havecarried out OLS and quantile regressions for NCAER data in 1994 and 2005. However, we

16have found that while average education is generally positive and significant, the bargaining
variable is not significant. Hence, the results in Table 1 will have to be interpreted withcaution because conflicting results imply that regression results are likely to be contextspecific.
(Table 2 to be inserted)
In the interest of brevity, we summarise the results based on the cross-sectional
regressions (OLS, IV and QR) for three rounds of NFHS data and two rounds of NCAER
data.9 The first column of each panel summarises the results of OLS and IV where ‘+’ or ‘-’
are shown in case the coefficient estimate is significant. If it is significant only for OLS or IV,
it is shown as, e.g., “+(ols)”. 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 thecase where a positive 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’.
(Table 3 to be inserted)
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 ‘normal’ group (which is closer to the median regression). The
results of QR will be useful for us to check whether those of OLS will hold for all thenutritional groups across the entire conditional distribution of z. In a few cases, the results of
QR are not only different from those of OLS, but changes the signs at different points ofconditional distributions. For example, a child’s age is not statistically significant for ‘weightfor-height’ (wasting) in OLS or IV for NFHS-3, but it is positive and significant up to thegroup with z score <-1.0, not significant for the group with z between -1 and negative and17significant for those with z score >1.0. This implies that the change of ‘weight for height’ isin the direction of being equalized which is statistically significant, but OLS is not able tocapture that. This is the point emphasized by Borooah (2005).
However, such cases are few and far between and we get results mostly consistent acrossdifferent estimation methods. Again, given the space limitations, we highlight the importantresults. Consistent with previous studies (Borooah, 2005;, Kandpal & McNamara, 2009),
whether a child is male is negative and significant in most cases. However, as suggested byCharmarbagwala et al. (2004), the sign of a sex dummy of a child can differ across countries,
over the years, and our results are likely to be context specific. Age of a child is negative andsignificant with its square positive and significant, 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 suggest that higher birth order negatively affects
nutritional status in case of the NFHS data, irrespective of which measure is used. In case of
the NCAER data, however, a negative effect of higher birth order is not clearly observed for az score of weight-for-height.
The variable on whether a household has access to health scheme or health insurance isavailable for only NFHS-3. Only in case of IV applied to ‘weight-for-age’, where it isinstrumented by the availability of telephone in the region10,the result suggests that household
access to heath scheme and health insurance improves the child’s nutritional condition (interms of underweight measure) after taking account of the endogeneity problem.
‘Time necessary for getting water’ has an expected negative and significant sign in somecases, in particular, for underweight and wasting. As women are responsible for fetchingwater, there is an unavoidable trade-off between this activity and child care. Access toelectricity has a positive and significant coefficient for underweight and wasting measures.
Among the variables on household characteristics, mother’s age is highly significant and18

positive with its square negative and significant for all the three measures. Access to a radiois positive and significant only for children malnourished or in the normal range in 1992-93.
Access to a TV is positive and significant mainly in 1998-99. Having fridge is positive andsignificant only in 2005-06. Children from scheduled castes or scheduled tribes tend to havelower levels of z scores.
As hypothesised, we obtained negative and significant coefficient estimates for food pricefor NFHS-1 and NCAER data in 2005. The coefficient estimate, however, became positiveand significant for NFHS-2. Prices of sugar, egg and cereal are negative and significant forweight-for-age in 2005-06 (for NFHS-3). While the coefficient estimates of cereal prices
were positive and significant for NCAER in 1994 data, those for sugar and cereal were
negative and significant in 2005. The reasons for this inconsistency across years call forfurther examination.
Table 4 reports the results of pseudo panel model based on NFHS data. As the variables
available for NFHS-1 are limited, we present two cases – NFHS 1, 2 and 3 and NFHS 2 and 3- for three different child nutrition indicators. These are presented shown in columns (1)through (6) in Table 4. The choice between fixed effects model and random effects model isbased on the Hausman test and except for one estimation for ‘weight-for-age’ (column (2),Table 4), we have chosen the fixed effects model.
(Tables 4 to be inserted)

The relative bargaining power of mother in terms of schooling years is positive andstatistically significant for ‘weight-for-age’ and ‘weight-for-height’ in columns (2), (3), (5)and (6) of Table 4. The coefficient estimate of the relative bargaining indicator for mothers is
also positive and significant for ‘weight-for-height’ in column (1) of Table 5, based on the
fixed effects model. Our hypothesis that the role of mother’s relative bargaining power or19

empowerment in improving her children’s nutritional status in terms of short-term nutritional
deprivation is corroborated by both NFHS data and NCAER data. However, the relative
bargaining indicator is not significant for ‘height-for-age’ which represents chronic or longterm nutritional deprivation in Table 4, although as discussed earlier, the bargaining
variable’s effect is positive and significant on those malnourished in QR for NFHS-1.
However, ‘whether a husband is justified in beating his wife’ is positive and significant forweight-for-age in the column (5), contrary to our prediction. Looking more closely at thecorresponding case of QR for NFHS-2 and 3 in Table 1, we find that ‘beating’ is not
significant for NFHS-2 (except for the first category of ‘acutely malnourished’ where‘beating’ is negative and significant) or for NFHS-3. It is likely that positive and statisticallyinsignificant results in OLS and QR for NFHS-3 become dominant once we highlight interstate variations. ‘Whether a wife is allowed to go to the market without permission fromhusband’ is positive and significant for ‘height-for-age’ and ‘weight-for-age’. 11
Table 5 presents the results of the pseudo panel data model based on two rounds ofNCAER data in 1994 and 2005 for ‘weight-for-height’. The results of both fixed effects andrandom effects models are shown in columns (1) and (2), while the Hausman test result
favours fixed effects model. While the share of children under age five is negative andsignificant for ‘weight-for-height’ in column (1) of Table 5 for NCAER data, it is positive but
statistically insignificant in column 3, and positive and significant in column (6) for NFHS
data. But it is negative for ‘height-for-wage’ and ‘weight-for-age’. The results are mixed, but
by and large, having more children under five tends to result in worse nutritional conditionsof children. Consistent with previous results, boys under three are worse nourished than girls.
A child’s age is mostly negative and significant with its square positive.
(Table 5 to be inserted)20

As before, mother’s age is positive and significant (with square positive and significant)for ‘weight-for-age’ and ‘weight-for-height’. The effect of having a ‘flush toilet’ on childnutritional status is more clearly observed in case of pseudo panel (Table 5) where it ispositive and significant at the 1% level.
The effect of ‘time for getting water’ on child nutrition is also more clearly observed inpseudo panel models in Tables 4 and 5. It is negative and significant for ‘weight-for-height’in columns 3 and 6 of Table 4 and column 1 of Table 5. Electricity is also negative andsignificant for ‘weight- for- height’ in column 3. ‘Access to a TV’ positively increases z scoreirrespective of its definition when NFHS-2 and 3 are used, while it is significant only for‘weight-for-height’ for NFHS 1, 2 and 3. Children belonging to scheduled castes (SC) are
more likely to be undernourished for all the measures for NFHS. 12 Food price is not
significant in most of the cases except for ‘weight-for-age’ in column (5) of Table 4 where
the coefficient of food price is positive and significant.

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 in
1992-93, 1998-99, 2005-06 as well as the National Council of Applied Economic Research(NCAER) data in 1994 and 2005. NCAER data are used as a robustness check to see if theresults based on NFHS data could be generalised. OLS, IV, QR and pseudo panel model areapplied to these data sets. We summarise our central findings in the following paragraphs.
First, the relative bargaining index defined as the share of mother’s schooling years overfather’s schooling years positively and significantly influences z scores pertaining to theshort-term measures of nutritional status of children, namely, ‘weight-for-age’ and ‘weightfor-height’. The results of QR suggest, however, that the bargaining power will improve a21chronic measure of nutritional status, ‘height-for-age’, at the low end of conditional
distribution of z score or those stunted.
Second, the result of IV estimation indicates that access to health scheme or healthinsurance - which is instrumented by the proxy for infrastructure - tends to improve ‘weightfor- age’ in 2005-06.
Third, health-related facility, infrastructure and environment are important factors in
reducing the prevalence of child malnutrition. For example, wider access to a flush toilet islikely to improve nutritional status of children in terms of stunting and underweight. Easieraccess to water is important in reducing ‘wasting’. The results of QR imply that access toradio and TV is important for improving the measures of ‘stunting’ and ‘underweight’
particularly at the lower parts of the conditional distribution. Also, children belonging toscheduled caste are more likely to be undernourished.

Fourth, QR yields an additional

insight into child malnutrition because behavioural responses towards undernourished andover-nourished children are likely to differ.
Despite the steady decline in the prevalence of undernourished children, India is still oneof a few countries with the worst levels of low birth weight, underweight and wasting amongchildren in BRIC and SAARC countries. In terms of policy implications, more provisions ofhealth scheme or health insurance are likely to be effective in reducing the temporary
nutritional deprivation of children. Infrastructure (e.g. access to electricity) and healthfacilities are found to be also important. But policies to empower women in poor households
that tend to have more malnourished children are required for reducing not only short-term
nutritional deprivations but also chronic deprivations. If we go by the predictions ofhousehold models, both Beckerian and bargaining, expanding outside employment options
for women is key to their empowerment.

22

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26

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

Quantile Regression(1)
Z score 4.0(2)
Z score-3.0(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

NFHS-1 Rural 1992-3
Heightfor Age

Heightfor Age[Mother’s Schooling Yrs/
Father’s Schooling Yrs]

0.05080.000238

0.122**

0.0750*0.04420.00342-0.0315-0.0740-0.179(Bargaining)(1.504)(0.00364)(2.755)(2.127)(1.235)(0.0637)(-0.397)(-0.643)(-1.523)

Average Education

0.0343+0.01100.04020.0490*0.0415+0.0465+0.02270.05760.0866(1.784)Weightfor Age(0.354)Weightfor Age(1.118)(2.265)(1.847)(1.750)(0.525)(0.672)(0.756)[Mother’s Schooling Yrs/
Father’s Schooling Yrs]

0.0400+0.0741

0.0672*0.0406

0.0591*0.00499

0.00794-0.0656-0.127(Bargaining)(1.901)(1.384)(2.170)(1.494)(2.284)(0.188)(-0.241)(-1.327)(-0.550)

Average Education

0.0446**0.02140.0435**0.0505**0.0544**0.0613**0.0597**0.0684*0.00818(3.676)Weightfor
Height(0.768)Weightfor
Height(2.598)(3.337)(3.692)(4.005)(2.994)(2.334)(0.0689)[Mother’s Schooling Yrs/
Father’s Schooling Yrs]

-0.00508-0.0830-0.0387

0.06300.01734.13e-06-0.0427-0.0349-0.0708(Bargaining)(-0.177)(-0.919)(-0.499)(1.540)(0.449)(0.000145)(-0.936)(-0.431)(-0.509)

Average Education

0.0381*

0.06610.0476

0.0548+0.03190.0376+

0.03100.05650.0283(1.106)(1.158)(1.737)(1.546)(1.936)(1.583)(1.455)(0.417)(7)
Z score2.0(8)
Z score3.0(2.356)
OLS

IV

Quantile Regression(1)
Z score 4.0(2)
Z score-3.0(3)
Z score-2.0(4)
Z score-1.0(5)
Z score -0(6)
Z score1.0

NFHS-2 Rural 1998-9
Heightfor Age

Heightfor Age[Mother’s Schooling Yrs/
Father’s Schooling Yrs]

-0.00176

0.000731

0.001620.02770.01830.001620.009400.004070.00550-0.0177-0.0284(Bargaining)(-0.147)(-0.0108)(-0.125)(1.035)(1.189)(-0.126)(-0.677)(0.259)(0.228)(-0.762)(-0.632)

Average Education

0.0421**

0.04300.0424**0.0416**

0.0551**0.0490**0.0470**0.0398**0.0310**0.01750.0303(7.865)(0.872)(3.347)(3.768)(8.449)(8.125)(7.934)(5.328)(2.904)(1.073)(1.215)27

Whether a husband beats

0.000834

0.120

0.02900.01610.0128-0.0168

0.03550.0620-0.139-0.474**

if a wife is unfaithful(0.0243)(0.0145)(0.464)(0.369)(0.319)(0.688)(0.844)(-1.341)(-2.693)

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

0.03530.06070.07010.0318(-0.416)0.00761

0.07250.0794-0.09394.70e-05(0.918)Weightfor Age(1.425)(0.800)(-0.170)(1.302)(1.001)(-0.798)(0.000256)-0.0518(0.0144)(0.937)Weightfor Age[Mother’s Schooling Yrs/
Father’s Schooling Yrs]

0.0222*0.03680.0231*

0.0649**0.0364*0.0272**0.006540.02210.01180.00631-0.0423(Bargaining)(2.327)(0.483)(2.263)(3.157)(2.384)(3.150)(0.637)(1.422)(0.612)(0.197)(-0.826)

Average Education

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)Weightfor
Height(2.474)(1.803)(1.068)(2.323)(0.182)(1.205)(0.532)[Mother’s Schooling Yrs/
Father’s Schooling Yrs]

0.00592

0.175

0.02790.03240.0623**0.0401*0.01360.02010.00560-0.0321-(Bargaining)(0.415)(0.289)(0.648)(0.863)(2.655)(2.394)(1.140)(1.412)(0.260)-

Average Education

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**-9.1180.188*-0.0183

0.0746

0.0663+0.105*

0.162*

0.104-(2.645)(-0.738)(2.381)(-0.243)(1.328)(1.710)(2.331)(2.405)(1.471)--

NFHS-3 Rural 2005-6
Heightfor Age

Heightfor Age[Mother’s Schooling Yrs/
Father’s Schooling Yrs]

0.00427

0.01240.00473

0.0688**0.0306*0.01040.00604-0.0175

0.00783(Bargaining)(0.421)(1.019)(-0.216)(4.810)(2.307)(0.991)(-0.507)(-1.127)(-0.314)

Average Education

0.0283**0.01620.01690.0381**

0.0428**0.0399**0.0345**0.0277**(5.610)(1.485)(0.561)(3.030)(5.700)(6.956)(7.304)(3.999)28-0.0199-0.0669(-0.922)0.0140(-0.614)0.00199(1.553)(-0.121)(-1.395)-0.0425

Whether a husband beats

0.0455-1.634-0.0398-0.0683-0.0291

0.04480.0995+0.188*

0.354**

0.365+

if a wife is unfaithful(1.316)(-0.358)(-0.606)(-1.351)(-0.751)(1.300)(1.917)(2.573)(2.771)(1.864)

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

0.0551+0.02000.05680.02840.0604+0.0247

0.0963

0.161

0.125

(0.276)(1.270)(0.727)(1.669)(0.533)(1.373)(1.326)(0.683)

Health Insurance Scheme-0.0204

4.092-0.2320.132

0.0131

0.0413-0.0936

0.127-0.220(-0.220)
Weightfor Age(1.254)(-0.728)Weightfor Age(0.904)(0.124)(0.417)(-0.463)-0.0103(0.0596)(0.397)(-0.523)[Mother’s Schooling Yrs/
Father’s Schooling Yrs]

0.00546

0.01550.0139-0.0279

0.02080.00852

0.004180.00859

0.02520.007900.00845(Bargaining)(0.668)(1.526)(0.878)(-0.832)(1.185)(0.854)(0.455)(0.705)(1.476)(-0.272)(0.124)

Average Education

0.0334**0.0193*0.0464*0.0544**0.0513**0.0335**0.0283**0.0267**0.0306**0.00800-0.0184(8.636)(2.161)(2.309)(3.064)(6.988)(5.718)(6.096)(4.802)(4.204)(0.553)(-0.926)(1.673)

Whether a husband beats

0.01562.067-0.0125-0.05080.004420.01180.03500.05990.0742-0.0836

if a wife is unfaithful(0.604)(0.701)(-0.169)(-1.018)(0.121)(0.399)(0.880)(1.178)(0.711)(-0.560)

Whether a wife is allowed
to go to market without
permission from a husband-0.00379

0.03190.03830.0220-0.0115-0.0455-0.04120.02020.201(-0.154)(0.303)(0.751)(-0.413)(-1.335)(-0.657)(0.240)(1.210)

Health Insurance Scheme

0.0412

5.267+

0.08480.02140.0607-0.0208

0.139

0.479

0.117

(0.528)Weightfor
Height(1.865)(0.476)Weightfor
Height(0.183)(0.677)0.00635(0.0556)(0.903)(-0.151)(0.670)(1.324)(0.247)[Mother’s Schooling Yrs/
Father’s Schooling Yrs]

0.00891

0.01120.01980.03250.02410.00455

0.002150.00526

0.0233+0.01700.0107(Bargaining)(0.972)(1.110)(1.045)(1.218)(1.428)(0.313)(0.184)(0.446)(1.940)(0.744)(0.284)

Average Education

0.0215**0.0184*0.04170.0384**0.0552**0.0392**0.0190**0.0162**0.0141*0.0188+0.00877(5.149)(2.473)(1.523)(3.135)(4.158)(5.476)(3.347)(2.453)(1.708)(0.442)

Whether a husband beats-0.0512+3.019

0.0999-0.0823-0.0542(3.526)0.0771+-0.0249-0.0508-0.158*-0.319*if a wife is unfaithful(-1.850)(0.789)(1.150)(-1.031)(-1.210)(-1.944)(-0.798)(-1.283)(-2.167)(-2.411)

Whether a wife is allowed
to go to market without
permission from a husband-0.0467+

0.0509-0.113-0.0636-0.0312-0.0331-0.0367-0.0203

0.0734(0.567)(-1.532)(-1.575)(-0.963)(-1.043)(-0.918)(-0.324)(0.489)Health Insurance Scheme

0.02451.224

0.578*

0.0378-0.0464-0.0182

0.05660.01030.0718-0.272(0.309)(0.558)(2.333)(0.270)(-0.388)(-0.146)(0.613)(0.0796)(0.360)(-0.827)(-1.759)

29

Table 2. Effects of Bargaining Power of Mother on child malnutrition in Rural India (based on NCAER data)
(1)

(2)

(3)

(4)

(5)

(6)

Quantile Regression$
Explanatory Variables

OLS

Severely
Malnourished-3.72(4.80)a

Acutely
Malnourished-2.41(15.1)aSlightly
Malnourished-1.49(29.5)aMalnourished-0.55(49.5)

aNormal1.22(80.2)a

NCAER data in 1993
[Mother’s Schooling Yrs] /[Father’s
Schooling Yrs]
[Mother’s Schooling Yrs] + [Father’s
Schooling Yrs]/2

0.00

-0.00

-0.01

0.01

-0.00

0.01

[0.34][-0.23][-0.42][0.93][-0.08][0.45]

0.02

0.03

0.00

0.01

0.03

0.04

[1.71]+[1.56][0.23][0.52][1.87]+[2.36]*

(1)

(2)

(3)

(4)

(5)

(6)

$

Quantile Regression
Explanatory Variables

Severely
Malnourished-3.72(4.80)a

Acutely
Malnourished-2.41(15.1)aSlightly
Malnourished-1.49(29.5)a

0.00

-0.00

-0.01

[0.34][-0.23]

0.02

0.03

[1.71]+[1.56]

OLS

MalnourishedaNormal-0.55(49.5)

1.22(80.2)a

0.01

-0.00

0.01

[-0.42][0.93][-0.08][0.45]

0.00

0.01

0.03

0.04

[0.23][0.52][1.87]+[2.36]*

NCAER data in 1993
[Mother’s Schooling Yrs/Father’s
Schooling Yrs]
[Mother’s Schooling Yrs] + [Father’s
Schooling Yrs]/230

Table 3. Summary of Results of OLS, IV and Quantile Regressions (QR) based on NFHS and NCAER Data
VARIABLES

HAZ*1
OLS/IVQR

NFHS-1 (1992/3)
WAZ*1
HWZ*1
OLS/IVQR
OLS/IVQR

HAZ*1
OLS/IVQR

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

HAZ*1
OLS/IVQR

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

NCAER-1994

NCAER-2005

OLS

QR

OLS/IV

QR

-

-

-N

-

+M
-N

Child Characteristics
child_male

-

-M

age

-

-

age2

+

-MN
-

-

+

+

second child-N-

third child

-O

+

-

-NO

-

-O
NO

+

+
M

+

+
M
+
MN

forth more

-MN

+
+M/N

+

-

-

-

-

-M

-

-MN

-

-MN

-

-N

-

-

-

-

-

-M

-

-

-

-

+NO

+

+

+

+

+

+O

-

-MN

-N

-

-MN

-

-MN

-

-N

-NO

-

-MN

-

-MN

-

-N

+

+M
+
MN

-N

+MN

+

+

+

+

-

-M

-

-NO

-MN

-

-

-N

-N

-MN

-

-MN
MN

-N

-N

+

+

-N

-

-MN
+M
-NO
-M
+O

NA
NA
NA
NA
NA
NA

Bargaining
Schooling_Ratio
AV_Schooling
Yrs

+

+

beat_unfaithfulNAallow_market

NA

+

+N

+

+MN

+

+M
+(ols)

-O

+MN

+M
+

-M
+M

+(ols)+MN++NO+(ols)

+N

+(iv)

+

+

+

+M

-(ols)

NA-(ols)

NA

Policyhealth_scheme

NA

NA

+(iv)

NA

Environment
time_water

-N

-N
+

+
MN

+

+N

Household Composition Characteristics+
mother_age
+
NO
+

+

+

+N

mother_age2--

electricity

+NO

-

+
-

hhsize

radio
TV

-M

-N

+

+M

+MN

+

+MN

+(ols)

+N

+(iv)

-MN

-

-MN

-(ols)

-N

-(iv)

+M

-MO

child5_share

fridge-(ols)

+

+
M
+
M

+

-O
+
MN
+
M

-

-NO

+

+MN

-

-NO

+

+

-

-

+MN
MN

+O

+

-

-O

+

+MN

+

+MN
+M

31

+MN
+(ols)

+

+

+

+N

+

-MN

-

-N

-NO

NA

NA

-O

+N

+O

-N

+(ols)

+MN

+

+N

+N
NA

NA

+

+N

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

bicycle-Nflushtoilet

+M

-O

+

+M
+

+
NO

-NO
+

wealth_index

NA

NA

Income pc
Incomepc*Toilet

NA

NA

NA

NA

NA

NA

+
N

hh_SC

-

-N

+

hh_ST
hh_Other

-

Hindu

-M
+O

-

-MN

-N

-

+MO

-(ols)

-

-MN

-NO

-

-MN

+N

-(ols)-MN-(ols)-MN-(ols)+O-(ols)-M

MuslimChristian+(ols)

+

+NO

+

-NO

-MO

+

+
+

+
+

-

+N

+
+M

-

-

-MN

-M

-

-MN

-MN

-(iv)

+M

-MN

-

-MN

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

+NO

+

+N

-N

+

+N

-N

+

+

+M

-

-N

+O

Sikh

+M

-M

-M

-MN

-

-MN

+N

+N

-N
-

-N

Regional Dummies
BIMARU-(iv)-+(ols)

+
N

South

+

O
+
MO

-

M

East
West

-M
+O

-

-

-NO

-(iv)

+M
-NO

+(ols)

-

-

-

-

-

+

-

-(ols)

+

-

+M
-O

-(ols)
+M

-MN

-MN

-

-

-

-

-

-

-

-M

-

-

-

-

-

-N

-N

Price
Food PriceN+NO+(ols)+(ols)NA

Sugar Price

NA

NA

-

Egg PriceNANA-(ols)

-NO

-

NA
+

-

Cereal Price
NA
NA
-(iv)
*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’.
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 given in Appendix Tables A2- Appendix Tables.

32

+N
+NO
+

+M

Table 4. Pseudo Panel for Z Score of Children, NFHS
(1)
Height for
Age

VARIABLES
Fixed or Random
Effects Model
Schooling_Ratio
AV_Schooling Yrs
beat_unfaithfulallow_market

child5_sharechild_maleage
age2
second childthird childforth moremother_age

mother_age2
flushtoilet
time_waterelectricity
radioTV
fridge(2).
Weight for
Age

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

Based o NFHS 1, 2 and 3(5).
Weight for
Age

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

FE

RE

FE

FE

FE

FE

0.04320.197**

0.280**

0.06010.243**

0.281*(0.476)(3.372)(3.451)(0.497)(3.047)(2.484)0.01430.00645-0.03540.03530.0231-0.0298(0.367)(0.316)(-1.012)(0.814)(0.809)(-0.736)

-

-

-

0.388

0.376*

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

0.342*

0.136(1.946)(2.230)(0.627)(0.437)(-0.407)(0.728)(-1.037)(0.946)(2.321)-0.485-0.381+

0.0672-3.348**0.398

2.541*(-1.521)(-1.885)(0.235)(-3.012)(0.544)(2.448)-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)-1.041*0.480-0.721**-0.844*

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

0.02590.124**

0.115*-0.0730

0.02630.113+(0.412)(2.040)(-1.029)(0.564)(1.710)-0.000351(2.979)0.00173**-0.00158*

0.000809-0.000455-0.00148(-0.405)(-2.982)(-2.030)(0.824)(-0.705)(-1.614)0.949**

0.654**-0.647*0.374-0.0266-0.688*(3.274)(4.184)(-2.495)(1.024)(-0.111)(-2.017)0.00188-0.00105-0.01000**

0.00276

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

0.349-0.0861-0.463+0.296-0.169-0.447(1.273)(-0.638)(-1.890)(0.988)(-0.858)(-1.595)(-2.331)

0.400

0.237

-0.324-0.05360.0240-0.251(1.533)(1.492)(-1.390)(-0.181)(0.123)(-0.907)0.914**

0.004820.165

0.767**

0.752*

0.675**(0.0163)(0.905)(2.893)(2.155)(2.939)(2.803)0.311

0.0471

0.06490.0581

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

bicycle

0.363

0.161

-0.112-0.0972

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

hh_SC-1.191**-0.699**-0.499+-1.402**-1.036**-1.000**(-3.884)(-3.731)(-1.819)(-3.845)(-4.318)(-2.937)0.378-0.00342-0.0479-0.217-0.382-0.427(1.078)(-0.0197)(-0.153)(-0.502)(-1.344)(-1.059)-0.1370.129

0.388+-0.129

0.112

0.110

(-0.522)(0.895)(1.658)(-0.422)(0.556)(0.385)-0.389-0.431+

0.157-0.592

0.118

0.185

hh_ST
hh_OtherHindu

33

MuslimChristianSikh(-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

(-1.925)(-0.999)(0.292)(-1.910)(0.723)(0.634)-2.442*-0.3751.871*-2.605*0.02852.176*(-2.344)(-1.033)(2.007)(-2.377)(0.0396)(2.125)-0.629

-

-

-

rural

-

(-0.906)
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
D_1998D_2005Constant-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)

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

2

Chi (29)=2

Chi (30)=2

Chi (29)=2

Chi (30)=2

Chi (30)=2

Chi (31)=93.17**

19.23

280.85**59.79**

138.07**66.55**

Prob>chi2

0

0.935

0

0.001

0

0.0002Chosen Model

FE

RE

FE

FE

FE

FE

Fixed or random-effects
t-statistics in parentheses (** p<0.01, * p<0.05, + p<0.1).
FE stands for Fixed-Effects Model and RE random effects model.

34

Table 5. Pseudo Panel for Z Score of Children (Weight for Height), NCAED data
Explanatory Variables
Mother’s Education/Father’s Education(Mother’s Education + Father’s Education)/2
Child’s Sex (Male)Child’s age
Child’s age SquaredHousehold Size
Share of kids less than 5 years in a household
BIMARU1South
East
Price of Sugar
Price of Eggs
Price of Cereals
Hindu
MuslimChristianSikh
Distance to Water
Household owns toilet
Log of per capita income
Log of per capita income * Toilet
Scheduled caste
Scheduled tribe
ConstantN
Adj. R2
F-statistics
F-test (Unobserved=0)
Hausman test

(1)
FE
0.38
(2.77)**
0.11(0.99)-0.01(-0.01)-0.36(-1.36)-0.01(-0.13)0.34(3.03)**-4.66(-1.71)+0.00(0.65)
0.02(0.87)
0(-1.37)
0.28
-0.12
-1.16
(-0.45)-1.12(-0.29)-3.9(-1.05)-0.05(-3.56)**-4.46(-0.63)
0.13
-0.36
0.71
-0.88
2.69
(3.16)**
2.12(1.91)+-3.52(-0.76)266-0.593
3.82
-

(2)
RE
0.09
(1.26)
0.13(2.07)*-0.15(-0.35)-0.16(-1.07)
0
-0.09
0.22
(3.87)**-2.2(-1.50)-0.44(-1.90)+-0.21(-1.05)-0.43(-1.85)+0(-1.05)-0.01(-1.47)0(-0.91)
1.05
-0.59
0.26
-0.15
0.59
-0.25
1.2
-0.61
-0.01
(-1.37)-7.24(-1.57)0.5(2.21)*
0.89(1.68)+
0.99(2.17)*
1.72(2.72)**-6.24(-2.20)266
40.50(0.00)

t statistics in brackets ----- + p<.10, * p<.05, ** p<.01; 1 Base reference for location is North.
FE stands for Fixed-Effects Model and RE random effects model.

35

NOTES
1

BRIC comprises the contemporarily economically fast growing countries of Brazil, Russia,

India and China. SAARC stands for The South Asian Association for Regional Cooperation.
2

Aturupane et al. (2008) applied QR to estimate the determinants of weight (as well asheight) for age and found that mother’s education is important in reducing underweight at
higher percentiles.
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

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

6

See Gaiha and Kulkarni (2005) for further details of NCAER data for the year 1994.

7

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.
8

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

9

A full set of results will be furnished on request.

10

In the first stage, the instrument is significant with a t value of 3.12. While over-identification is ruled out (the equation is exactly identified), the results of under-36

identification and weak identification tests imply that the excluded instruments are correlatedwith the endogenous regressors and the correlation is not weak.
11

Because the pseudo panel model involves averaging the values for each cohort,interpretation of the coefficient estimate is not straightforward. However, it appears that themarginal effect is not large since a one unit increase of ‘beat_unfaithful’ (equivalent to 100%
increase in the probability of a wife being allowed to go to the market without permission ofher husband) only increases z score of height-for-age by 0.453 (0.342 for weight-for-age).
12

However, in case of NCAER data, coefficients for SCs and STs are positive andsignificant. A plausible reason may be due to the fact that ‘weight-for-height’ is sensitive tochanges or measurement errors in two factors (weight and height) rather than one. Ourpreferred measures using the NFHS data are, therefore, ‘height-for -age’ and ‘weight-forage’.

37