DP2012-10

Nutrition, Activity I ntensity
and Wage Linkages:
Evidence from India *Katsushi S. IMA I
S am ue l Ko bina A NNIM
Ve ena S. KULKARNI
Raghav GAIH A
Revised May 28, 2014

* 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.

Nutrition, Activity Intensity and Wage Linkages: Evidence from 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
This version 28th May 2014
Abstract
The present study tests the twin hypotheses, namely, (a) the poverty nutrition trap hypothesisthat wages affect nutritional status, and (b) the activity hypothesis that activity intensity
affects adult nutrition as measured by the Body Mass Index (BMI) in the context of India.
The analyses draw upon three rounds of National Family Health Survey (NFHS) data in
1992, 1998 and 2005 and National Council of Applied Economic Research (NCAER) data in
2005. Our results indicate strong support for both the hypotheses in India. Physicallyintensive activity tends to worsen the nutritional conditions and there is evidence for apoverty nutrition trap associated with labor market participation.
JEL Codes: C21, C23, I14
Key Words: Adult Nutrition, Malnutrition, Poverty Trap, Activity Intensity, Quantile Regressions,Pseudo Panel, India*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 Undernutrition in
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, a research seminar at
Dosisha University in August 2012, and the conference in Delhi organised by National Council ofApplied Economic Research and University of Maryland in June 2013. We thank Md Azam, David
Bloom. Subhasish Dey, Per Eklund, Raghbendra Jha, and Takahiro Sato for their valuable advice atvarious stages of the project. The views expressed here are those of the author and the usual disclaimer
applies.

1

Nutrition, Activity Intensity and Wage Linkages: Evidence from India

1. Introduction
The finding of the study by Deaton and Dreze (2009) that persistent decline in calories andother nutrients in India cannot be attributed to falling per capita incomes and rising food prices,
has rekindled an interest to re-examine the long standing discourse on the relationship between
wages, efficiency and nutrition. Since the 1950s, the efficiency-wage hypothesis, which was put
forward by Leibenstein (1957) and later theorized by a number of researchers (Bliss and Stern,
1978; Dasgupta and Ray, 1986, 1987; Dasgupta, 1993; Swamy, 1997), has been examined invarying contexts (Strauss, 1986; Behrman and Deolalikar, 1989; Weinberger, 2003, Jha et al.
2009). Also, the efficiency wage hypothesis has been expanded to explore the effect of activity
intensity and nature on nutrition (Church et al., 2011). Further, an analogous and testableproposition of the efficiency-wage hypothesis is the poverty-nutrition trap (PNT) hypothesis. The
PNT hypothesis proposes that relatively high wages would enable the purchase of food whichthen strengthens adults to continue with their work. Such linkages would create a “virtuous
circle” - a cyclical relationship between wages (income), ability to acquire food, nutrition andwork efficiency. Conversely, low wages or unemployment would create a “vicious circle” where
low wages or unemployment would lead to low purchasing power and nutrition that would makepoor workers trapped into poverty through low efficiency. It is therefore not surprising that adultmalnutrition has attracted enormous attention in both academic and policy arenas. Following thepioneering contribution of Dasgupta and Ray (1986), the PNT hypothesis was tested by Jha et al.
(2009) in the context of rural India.

2

Premised on the pioneering work on the efficiency-wage hypothesis, the extant literature
has largely modeled the relationship around the effect of better nutritional status on wages.
However, given that the relationship between wages and nutritional status is complex andmediated through factors, such as sector of employment, gender, interpersonal skills, social
interactions, among several others (Jha et al., 2009), empirical research on the subject has failed
to reach a consensus. The present study contributes to this literature by examining the role ofintensity or nature of activities in addition to wages in predicting nutritional status of adults. The
central objective of this study is to estimate the effect of both wages and activity intensity or
1nature on adult nutrition, as measured by the Body Mass Index (BMI) .
The study is organized as follows. Section 2 reviews the background relating to the twocentral hypotheses on the wage - nutrition link and the activity intensity - nutrition link. Data
used for the econometric analysis are explained in Section 3. Section 4 and Section 5 discuss
econometric specifications and the results respectively. The final section lays down a few
concluding observations.

2. Background
Despite recent high levels of GDP growth and steady poverty, nutritional status has
declined, or has improved only slowly, across households with varying income levels in rural
areas in India. This has been reported as “the (Indian) empirical puzzle” and discussed by Deaton
and Dreze (2009), Palmer-Jones and Sen (2001) and Patnaik (2004, 2007).

1

2

Drawing upon

It is calculated by dividing weight in kilograms by ‘height in meters’ squared (kg/m2).

2

Using the NSS data in 1993, 2004 and 2009, Thorat and Dubey (2012) showed that poverty in terms of household
expenditure declined recently - particularly during the period between 2004 and 2009 - across different socio-religious
household groups in India. However, the evidence to support the corresponding improvement in nutritional status isstill limited. Providing a clue for “the empirical puzzle” is thus important in this compelling setting.

3

National Sample Survey (NSS) data, Deaton and Dreze (2009) argue that “the calorie Engel
curve” that plots per capita total calories or cereal calories and household per capita expenditure,3

has shifted consistently downwards between 1983 and 2005. Following the efficiency wage
hypothesis under which nutritional intakes would affect labor productivity and thus wage rates,
the above observation suggests that those undernourished remain trapped in nutrition poverty due
4

to low wage rates or exclusion from the labor market due to low nutritional intakes . Jha et al.
(2009) tested the existence of PNT using National Council of Applied Economics Research(NCAER) data in 1994 and showed that the intake of calories or micronutrients (e.g. Iron,
Riboflavin, Thiamine) affects agricultural wages for various activities, such as harvesting or
sowing and vice versa. The finding by Jha et al. (2009) lends support to the PNT hypothesis.
Examining the NCAER data this study found that the elasticities for calories, protein, and five
micronutrients (calcium, thiamine, riboflavin, carotene and iron) are positive and significant andtherefore an increase in income would increase nutrient intake.
Further, the expectation that engaging in energy sapping activities leads to undernutrition
has triggered a lively debate but with little empirical investigation. Theoretically, this is related
to the model of health production function (Thomas, 1994) where health or nutritional outcomesas an output is a function of a number of inputs (e.g. nutrient intakes and the quantity and qualityof health care, and individual and household characteristics) with a standard utility function ofhousehold members under a budget constraint for the household. In this framework, it would be

3

Deaton and Dreze (2009) offered conjectures to explain the puzzle, such as, improvements in the diseaseenvironment and a reduction in work activity intensity. On the other hand, using the NSS data in 1993 and2004, Gaiha et al. (2013, 2014) have developed an alternative explanation of changes in the consumption ofcalories, protein and fats and found significant effects of food prices on these nutrients. Also, there has beena considerable increase in eating out in India, which is not captured by the data. However, due to the data
limitation, the present study focuses on the PNT and activity hypothesis.
4
See Jha et al. (2009) for a more detailed review of the PNT hypothesis.

4natural to assume that higher activity intensity - which would normally require more calories results in lower levels of nutritional outcome.
Given the above context, the research questions that the present study proposes to address
include; (i) What are the determinants of nutritional status, as measured by BMI, of men andwomen in rural India?; and (ii) Have those determinants changed over the years?; (iii) Did thepoverty trap hypothesis or the “wage-nutrition” hypothesis hold, i.e., were there any individuals
5

who were trapped into nutrition poverty due to their low wages in the labor marker? ; and (iv)
Did the “activity-nutrition” hypothesis hold, i.e., were there any individuals who were
undernourished due to higher intensity of work or activity given that energy expenditure tends toexceed the required energy intake for those people? We will consider the effects of (predicted)
wage rates on nutrition to answer the third question. To answer the last question, we will
estimate undernutrition measured by BMI with intensity and with type of activity as a proxy for
intensity of work. We employ the data provided by India’s National Family Health Survey(NFHS henceforth) and National Council of Applied Economic Research (NCAER hereafter).

3. Data
The activity-nutrition and wage-nutrition hypotheses are examined using NFHS and
NCAER datasets. The NFHS was initiated in 1992-93 and, since then, two more waves of thedata have been collected in 1998-99 and 2005-06. ICF International, via ORC Macro
MEASURE DHS (Monitoring and Evaluation to Assess and Use Results of Demographic and
Health Surveys), National AIDS Control Organization (NACO) and National AIDS Research
5

Despite its evident importance, relatively little is known about links between BMI and participation in
workfare programs, particularly in India. Using a unique data set for the Indian state of Rajasthan for
2009-10, Jha et al. (2013) attempt to fill this void and examine the association between BMI andparticipation in NREGS, the latter of which covers not only participation status but also duration andearning. They allow for the mutual endogenity, say, between BMI and earnings.

5Institute of the Ministry of Health and Family Welfare (MOHFW) are responsible for the NFHS
(International Institute for Population Sciences (IIPS) and Macro International, 2007). The
survey is nationally representative and covers fertility, family planning, maternal and childhealth, gender, HIV/AIDS, nutrition and malaria. Data are collected at the individual level(children, mothers and, lately, fathers), household and at the community level. This study uses
data on ever married women6, aged between 15 to 49 years and resident in rural areas for each of
the three rounds of the NFHS survey.
The NCAER data set in 2005, known as the IHDS (Indian Human Development Survey)
data, has been employed to explore the effects of both wages and activity intensity on adult BMI.
The IHDS data collected through collaborative research between NCAER and the University of
Maryland is a multi-topic survey. The 2005 data set is nationally representative covering all thestates of India and the thematic areas include education, health, livelihoods, family processes andthe social structure within which the households operate. To this end, the IHDS is structured into
individual, household and village level datasets.
BMI, our measure of nutrition, is used to examine both thinness and obesity. In this study,
both the raw scores and BMI classifications based on the following cut-offs; severely
underweight(BMI<16kg/m2);(18.5kg/m2<=BMI<25kg/m2);(BMI>30kg/m2) are used.

6

underweight(16kg/m2<=BMI<18.5kg/m2);(25kg/m2<=BMI<30kg/m2)

overweightandnormalobese

7

In addition to the data of women, some data of men were collected in the third round.

7(a) Recent literature has questioned the appropriateness of BMI as a proxy for adult nutrition. For instance,
WHO expert consultation (2004) addressed the need for applying the population-specific cut-off points for
BMI, e.g., as Asian populations differ from European populations in terms of the associations among BMI,
percentage of body fat, and health risks. However, WHO agreed that the WHO BMI cut-off points should
be retained as international classifications (ibid., 2004). Burkhauser and Cawley (2008) argue that BMI isflawed as it does not distinguish fat from fat-free mass such as muscle and bone. However, as the available

6

The first objective of investigating the relationship between wages and nutrition is basedon log of hourly wages as one of the explanatory variables. The second objective of examining
the relationship between activity intensity and nutrition is implemented by using the variables onnature of profession and the classification based on type of employer as an indicator for activity
intensity. The former, nature of profession, is premised on the fact that certain types ofprofessions are more manual or sedentary than others. The second case where we use employer
type, such as not working, working for a family member, working for someone else, or
self-employed, provides another perspective on examining the effect of occupational
characteristic on nutrition. Using the NCAER data, nature of profession is grouped into four
categories based on the degree of physical activity required.

8

We will provide below a brief summary of the patterns of malnutrition which our data sets
9

have revealed. We classified the entire sample into three groups, namely “underweight”(BMI<18.5kg/m2),“normal”(18.5kg/m2<=BMI<25.0kg/m2)

and“overweight/obese”(BMI>=25.0kg/m2) for both NFHS and NCAER. We have observed that the proportions ofunderweight, normal and overweight/obese are 35.3 percent, 52.3 percent and 12.5 percent,respectively, and this pattern is comparable to 35.6 percent, 51.8 percent and 12.6 percent asshown in the NFHS report (International Institute for Population Sciences (IIPS) and Macro
International, 2007). The NFHS reports show that the proportion of underweight women
declined marginally, from 35.8 percent to 35.6 percent over the period 1999 to 2006. However,data on adult nutrition are limited, we will use BMI as a proxy realising these limitations. (b) Hip
circumference and height are more correlated with percentage body fat than anything else, including waist
circumference and weight. Taking both into account, an alternative measure, the Body Adiposity Index,
was computed. BAI is a good predictor of percentage adiposity, so if the BAI is 30, then the percentage
body fat is around 30 per cent. It is reasonably accurate but not terribly accurate (George, 2011). Hence theuse of BMI remains widespread.
8
Ideally, finer categories or more direct measures should be used to capture the activity intensity, but suchdata are not easily available. We have identified the best measures given the data constraints.
9
A full set of results will be furnished on request.

7

concentrating on the rural sample for women, we observed a drop from 43.6 percent to 40.2
percent over the same period.
We compute the proportions of these three groups according to individual and household
characteristics. First, education is closely associated with the nutritional status. While a majority
of underweight women had no education, two-fifths of the overweight/obese women in rural
areas have secondary education. Second, in the context of the activity-nutrition hypothesis, weobserve that classification of employer type (working for a family member, working for someone
else, and self-employed) by nutrition shows some differences within each of the three nutrition
categories. For example, the proportion of respondents “not working” is higher for the
overweight/obese group than for the underweight. In contrast, the proportion of those “working
for someone else” is lower for the obese group than the underweight and normal groups.
Disaggregation by profession (professional/managers, clerical/sales/services, famers/fishermen
and laborers/production workers) shows that there were relatively more “farmers/fishermen”
found in the underweight group than the obese group while the share of “professional/managers
and clerical/sales/services” was higher in the obese group than in the underweight group. Theseobservations call for further examinations of the activity intensity-nutrition relationship.

4. Econometric Specifications
The main econometric techniques that we employ to examine the twin hypotheses ofwage-nutrition and activity-nutrition are a) quantile, b) pseudo panel, c) instrumental variable(IV) and Heckman sample selection regression models. In each of the different specifications,the least squares technique is used to estimate the structural model. The following motivates the
choice of different estimation techniques.

8First, previous research indicates that it is important to take into consideration different
effects of the correlates of malnutrition across different classifications of BMI. For instance,
Strauss and Thomas (1998) find that the Vietnamese war had varied height effects on different
sub-groups in the sample with different effects across short, average and tall people across thesouth and north. Similarly, the effect of activity intensity on malnutrition is expected to be moreevident among the obese than the severely underweight. That is, the obese are more likely torespond to weight changes than the severely underweight in consuming extra calories. For thisreason, quantile regression is appropriate for examining the effect of the correlates at different
points of conditional distribution of a dependent variable. Second, to take account of unobserved
effects for age and state cohort, we construct a pseudo panel that helps in addressing thispotential bias. These econometric models are used in the case of NFHS data only as the NCAER
data are limited in the coverage of the anthropometric data for adults.
Third, in an attempt to investigate causality between wages and malnutrition, on one hand,and between activity intensity and malnutrition, on the other, the IV model and Heckman sample
selection regression model are applied in the case of the 2005 NCAER data. We use the IVmodel to investigate the wage-nutrition relationship given that there may exist a two-way causal
relationship between the nutritional status and the wage rate in our hypothesis testing. The
nutritional status is premised on “how much better (or healthier) we eat given a little moreincome” and the wage rate is based on “how healthier (or more productive) we become by eating
a little bit more” (Strauss, 1986; Behrman and Deolalikar, 1989).

10

10

To address this from an

Swamy (1997) focused more on the effect of nutrition on worker’s productivity in the context of rural
India. According to him, the nutrition-based efficiency wage theory predicts that wages should be rigid
because lowering them would reduce worker’s productivity and would increase the cost per efficiency unit,
but he found that a wage cut lowers the cost per efficiency unit of labor. While this aspect is important, we
focus more on the effect of wages (which are determined by proxies for activity intensities) on nutrition to
make the estimation tractable under the data constraint.

9

econometric perspective, we use the availability of trade-union in a community and thestate-level consumption inequality as instruments for wages in the BMI equation. The rationale
for the former is that the presence of a trade union enhances the bargaining power of workers tonegotiate for better wages for vulnerable people (such as the malnourished). The availability of a
trade union is therefore more likely to be correlated with wages than BMI.

11

The validity of the

instrument is supported by the specification tests. Also, the consumption inequality as an
instrument for wages is based on the traditional income-consumption relationship. Although astudy for the United States (Krueger and Perri, 2006) suggests that the increase in income
inequality has not been accompanied by substantial increase in consumption inequality, theassociation between consumption inequality and income (wages) is far from fully refuted.
Finally, to carefully explore the effect of wages and activity intensity on nutrition, we
compare the OLS and IV estimates for the case in which we eliminate the effect of sample
selection in the wage equation. In this case, we use predicted wages from the corrected wage
equation in the nutrition equation. Although this may be subject to the criticism that the wage
values are only the estimates, and not real values, it offers an opportunity to identify the effect ofwages on nutrition albeit with possible errors (sample selection bias). In the Heckman
sample-selection model, the probit model is estimated to exclude the respondents who were
unemployed in the first step, and then the wage equation is estimated in the second step whichcorrects the sample selection bias through the inverse Mills ratio derived by the probit model inthe first step. This implies that the estimated wage effect will not capture the characteristics of

11

While having a trade union in the community is not very usual in rural India, the rural trade union, suchas, AIAWU (All India Agricultural Workers Union) has played an important role in some areas (e.g.
Haryana) in recent years (Byres et al., 2013). One could suspect that the presence of a trade union may becorrelated with the community unobservables which may affect BMI, but the statistical correlation between
the availability of trade union and BMI is weak and the specification tests validate our instruments (Table
4).

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the unemployed. In this study, we use the infant dependency ratio as an exclusion restriction
variable for employment. The choice of infant dependency ratio is based on the hypothesized
positive relationship between higher economic dependency and labor market participation. Thatis, a household with higher dependency burden has greater impetus to search for a job andparticipate in the labor market than households with lower dependency burden. However, thishypothesized positive relationship in the case of female labor market participation is likely to be
rejected, at least in the short-run, due to health constraints around delivery period. This is a topic
of long standing academic discourse (see, for example, Bilsborrow, 1977).

Model 1 – Ordinary Least Squares Regression
Equation 1 below represents the ordinary least squares (OLS) estimation of the functional
relationship between BMI and its correlates.
BMI i   0  1 INDi   2 HH h   3 ST  i(1)where i stands for individual (or the i-th individual), h for household, and  for state.

12

BMI isthe adult’s body mass index. IND is a vector of individual factors, specifically, the age and its
square (to account for non-linearity between age and BMI), education, measured as a categoricalvariable (“no education”, “attempted or completed primary”, “attempted or completed
secondary” and “attempted or completed any higher than secondary education”), working status,
measured as a categorical variable (“not working”, “working for a family member”, “working forsomeone else” and “self-employed”) and marital status (“currently married”, “formerly married”

12

Ideally, variance should be clustered at household level, but we do not take account of the clustering
effects as the commands for the ‘robust’ estimator or quantile regression do not allow us to incorporate theclustering effects. However, the regression results of OLS with clustering effects and those without provide
us with very similar results, suggesting that the clustering effects would not significantly affect the resultsin our case.

11and “never married”). HH is a vector of household level characteristics, that is, wealth, measured
13

as a continuous variable , religious affiliation of the household head measured as a categoricalvariable (Hindu, Muslim, Christian, Sikh, Buddhist/Neo-Buddhist, Jain and Other), social groupof the household head measured as a categorical variable (scheduled caste, scheduled tribe, otherbackward group and non-backward group), household size, measured as a non-categoricalvariable, distance to water measured as a continuous variables, agricultural land size owned by
the household and its square). ST is a vector of state level indicators- specifically, characteristics
of different locations classified into BIMARU (Rajasthan, Uttar Pradesh, Bihar and Madhya
Pradesh), North (Jammu & Kashmir, Himachal Pradesh, Punjab, Uttaranchal and Haryana),South (Maharashtra, Gujarat, Goa, Andhra Pradesh, Karnataka, Kerala and Tamil Nadu), East
(Sikkim, Arunachal Pradesh, Nagaland, Manipur, Mizoram, Tripura, Meghalaya, Assam, West
Bengal, Jharkhand, Orissa and Chhattishgarh) and Delhi and state level prices of commodities(sugar, eggs and cereals) 14 .  is the error term which is independently and identically
distributed (i.i.d).
Model 2 – Quantile Regression
As stated above, we augment the OLS regression with a quantile regression. Koenker and Basset(1978) prove that for any distribution the median is a better measure of location. The regression
median15 is more efficient compared to OLS which is underpinned by the Gaussian assumptions.
In contrast to OLS, quantile regression sorts the data and identifies a threshold (τ) to estimate the13

The wealth score in the NFHS data is computed based on the asset index, a composite index weighting a
number of different proxies for household assets, such as type of flooring, number of bedrooms, radio, TV,radio or telephone. See Rutstein and Johnson (2004) for details of the wealth score.
14
For a similar specification but a different method of estimation, see Jha et al. (2013).
15

The proof of the median regression can be easily replicated for other percentiles (quantiles).

12coefficient (β) that minimizes the sum of absolute residuals. The general set-up of quantile
regression, Equation (2), is solved from an optimization perspective using linear programming:

ˆt   arg min   yi  x ' i  n  Ki 1(2)where estimated β(τ) called ‘tauth’ (τth) regression quantile estimates the coefficient at a
specified threshold (τ). τ is the sample quantile and takes on any value between 0 and 1. The
expression  yi  x ' i   , the absolute value function, weights the absolute difference between

y i and x ' i  with τ and by (1 – τ) for all observations below the estimated hyperplane. Koenker
and Basset (1978) estimate conditional quantiles using the minimization procedure synonymouswith least squares.
In contrast to the previous research (Bassole, 2007; Aturupane et al., 2008) that use
traditional thresholds (for example, 10th, 25th and 50th), the present study identifies respective
thresholds that characterize the following group of respondents; severely underweight (BMI<16.0),

underweight(16.0<=BMI<18.5),

normal(18.5<=BMI<25.0),

overweight(25.0<=BMI<30.0) and obese (BMI >= 30.0). The rationale is to help place the observations in acontext for policy targeting.

Model 3 – Pseudo Panel Regression
The econometric analysis further makes a case to incorporate unobserved unit-specificcharacteristics. The unobserved unit-specific (individual, household and community) also affects
adult nutritional status. The use of pseudo panel is useful in our study of India where real panel
data on nutritional issues at the national level is rare. In this study, we generate a pseudo panel
based on mother’s age cohorts for each state over the three waves of NFHS survey. Deaton

13

(1986) puts forward a pseudo panel when more than one cross-sectional data has a common
variable, for example, age, education and location. The use of such variables is premised on theassumption that the classifications rarely change overtime and they are exogenously determinedoutside the model.
We specify the functional form relationship (Equation (3)), followed by multiple
regression of the pseudo panel (Equation (3a)). The specification of Equation (3a) below is the
fixed effects (FE) model applied to pseudo panel data. We do not specify the random effects(RE) model specification of Equation (3). The rationale for choosing FE is a counter test on theassumption that the unit-specific effects are constant over time and as such arbitrary correlation(clumsy construction) is assumed between the unobserved heterogeneity term and the
explanatory variables (see Wooldridge, 2009).
The functional form of the pseudo panel is specified as:

BMI i gt  f INDi gt , HH i gt

(3)

where subscript g is the cohort captured by age cohorts (classified into seven categories) for 18
states16 andt stands for year. This yields a sample size of 378 (7 x 18 x 3). All the notations

remain the same as in Equation (1).
The estimable form of Equation (3) which takes the form of a real panel is specified asfollows:q

 X gt  Dt  ag   gt
BMI gt  
r 1
r

(3a)

r

16

Over time states in India have been reclassified and that results in difference in the states across the threewaves of the NFHS. The states used for the pseudo panel are; Arunachal Pradesh, Assam, Bihar, Gujarat,
Haryana, Jammu and Kashmir, Karnataka, Kerala, Maharashtra, Manipur, Meghalaya, Mizoram,
Nagaland, Orissa, Punjab, Rajasthan, Tripura and Utter Pradesh.

14where the first term on the right hand side is a simple aggregation of the set of all the
explanatory variables as specified in Equation (1) above.

17

We denote the subscript of thiscomposite term by r to represent the individual and household explanatory variables. Subscripts
g indicates the cohort constructed by mother’s age and state and t stands for each round of the
NFHS survey. The term D captures the time effect and the last two variables a g and  g t are
made up of the (time-invariant) unobserved heterogeneity term (or fixed effects term) and theidiosyncratic error term. The daunting issue is to examine the extent to which the pseudo panel
approximates a real panel. With the absence of real panel data it is virtually impossible toaddress this issue, hence we rely on the argument by Verbeek and Nijman (1992) and Verbeek(1996) that the estimator is consistent if the number of observations in cohort g tends to infinity,   . Since the number of households within each cohort is large in our case, we are
*

gt

g

confident that the estimator is likely to be almost consistent.

Model 4 – Instrumental Variable Regression
The theoretical underpinning of the PNT suggests that wages and nutrition are endogenous in therespective equations of the other (see Jha et al., 2009). This implies that while in the estimationof BMI wages are endogenous, BMI is endogenous in the prediction of wages. Ideally, this will
require the use of an instrument each for BMI and wages to estimate systems equation. In thisstudy, however, we attempt to resolve the endogeneity inherent in the BMI equation. As
discussed previously, the instruments used for wages are: availability of trade union in a village
and district level consumption inequality. Equation (4) below shows the econometricspecifications for the reduced form equations.
17

Subscripts, i, h and  in Equation (1) are omitted in Equation (3a).

15

Lnwagesi   0  1 INDi   2 HHh   3 STh   4TU   5Consumption _ Gini  i(4)where Lnwagesi is the log of wage at the individual level and TU and Consumption Gini are the two
instruments used in correcting for endogeneity of wages in the BMI equation. The vector
notations remain the same as specified in Equation (1). However, when we use the NCAER data,itisfeasibletoexamine‘Professionals/managers’,

occupation-based‘Clerical/Sales/Services’,

physical

activity

disaggregated‘Famers/Fishermen’

and

into

‘Laborer/

Production workers’.
Following the standard approach for IV, Equation (4) is estimated in the first stage andthe predicted values are jointly plugged into the structural equation, Equation (1). In estimatingIV, efficiency is compromised due to large standard errors as a result of the two- stage
estimations. Hence IV is preferred to OLS if only the former yields consistent results. Thislargely depends on the validity of choice of instruments. In this study, the validity (strength andrelevance) and identification of the instruments are examined by using the Kleibergen-Paap rk
LM statistic (underidentification), Cragg-Donald Wald F statistic (weak identification) and
Hansen J statistic (overidentification). The first stage results and the Hausman test are used forchoosing between OLS and IV estimations.

Model 5 – Heckman Sample Selection Regression
The Heckman sample selection model is premised on the argument that the estimation of wage
equation should not be only based on truncated data since the unemployed are purposively (ornon-randomly) excluded from the model. The model based on the non-randomly selected sample
yields biases estimators. To address this, we estimate the probit model for both employed andunemployed respondents (employment probability equation) that are randomly selected in the16

first stage. The main requirement for the Heckman sample selection is the exclusion restriction
condition. The inclusion of the exclusion restriction entails that the employment probability
equation (also termed as the selection equation) should incorporate a variable that explains theprobability of getting employed but not wages. In the present analysis, we opt for the infant
dependency ratio as the exclusion restriction. Like the IV, the two stage Heckman modelcompromises on efficiency of the coefficients, hence the model should only be chosen over OLS
if it yields consistent estimates, which would require a large sample. Otherwise the OLS basedon the sub-sample should be chosen over the Heckman model.
Equations (5) and (5a) below present the selection and outcome equations for the
Heckman sample selection estimation. Equation (5) is a binary outcome model (probit) andEquation (5a) is an OLS regression. The first stage is the probit model estimation, based on
which the inverse Mills ratio (ratio of the probability density function to the cumulative
distribution function of a distribution) is estimated and plugged into the OLS equation.

1st stage: selection equation (probit)
Emp*i   0  1 PIDi   2 PIDh   3 INFh  i

(5)

2nd stage: wage equation (OLS)
Lnwagesi   0  1 PIDi   2 PIDh   i

(5a)



ELnwagesi Lnwagesi is observed  E Lnwagesi Emp * i  0



(5b)

where Emp*i is a binary response (= 1 if respondent is employed and 0 otherwise). PIDi stands
for variables at the individual level, that is, age and its square, sex and education (measured as
categorical variables). The variables at the household level (denoted by PID'h) include household
size and its square, social group of household head, presence of trade union in a village, district
17

level consumption inequality and location dummies. INFh represents the ‘exclusion restriction’variable, that is, infant dependency ratio. In Equation (5a), Lnwagesi represents the log of hourly
wages and all other variables have the same notations as in Equation (5).  i and  i in both
Equations (5) and (5a) are error terms with the following properties:  ,   ~ N(0, 0, σ2u, σ2ε,ρεu). The first two terms in the description of the distribution of the error terms are the zero mean
condition for the two equations, the third and fourth terms, σ2u, σ2ε, represent constant variancefor the respective equations and the last term, ρεu, is the correlation between the error terms of thetwo equations. Estimations of Equations (5) and (5a) give the expected value of log of wage
conditional on the probability that log of wage is observed as in Equation (5b).

5. Results
The econometric results are given in Tables 1-4. We report (i) the results for rural women for
18

NFHS data 1, 2, and 3 in Table 1; (ii) those for rural men for NFHS 3 in Table 2 ; (iii) those forselected variables for pooled OLS as well as pseudo panel model for rural women for NFHS 1, 2,
and 3 in Table 3; and (iv) those for all the variables for NCAER data. Column 1 of Tables 1–2
gives the OLS results and columns 2–5 give the quantile regression results. A brief summary of
the results is given below.

(i)

The Results based on NFHS data (Tables 1-3)

Among various explanatory variables on household, individual, and state characteristics, wehighlight age, household wealth, education, working status - our proxies for physical intensity,

18

BMI of men are unavailable in the first and the second surveys.

18

and food price to save the space. For simplicity, we denote 1992-3 (NFHS-1) as 1992, 1998-9(NFHS-2) as 1998 and 2005-6 (NFHS-3) as 2005.

19[Place Table 1 here]

Age
In Tables 1 and 2, fairly consistent and expected results are observed for BMI among rural
women across 1992, 1998 and 2005 (Table 1) and for rural men in 2005 (Table 2). For instance,in the case of OLS, the coefficient estimate of age is positive and significant for women in 1998
and 2005 and for men in 2005, while it is not significant for women in 1992. The presence of anon-linear quadratic term (square of age) shows that the marginal effect of age on BMI getssmaller, that is, the observed positive effect weakened at older ages for women in 1998 and 2005
and for men in 2005. This evidence supports a finding by Jha et al. (2013). However, inspecting
the quantile regression results for rural women, the age effect is not necessarily significant for all
the groups. For women positive and significant effects of age tend to be observed for the(relatively) overweight, not for the underweight in 1998 and 2005, whilst for men age is positiveand significant for all the groups with a greater coefficient estimate as they shift from‘underweight’ to ‘obese’ in 2005. Comparing the results in 1992 with those in 1998, we
conjecture that older and relatively overweight women became more overweight or obese in the
1990s possibly due to their better nutritional intakes. In Table 3 where pooled model or
pseudo-panel model (or fixed effects model - which is chosen over random effects model guided
19

The findings based on NFHS data but not presented in the tables include: (i) respondents with heads
belonging to scheduled castes have lower BMI compared to their counterparts in 1998; (ii) in someinstances, household heads who are Hindus, Muslims, Sikhs and Jains have lower BMI than the rest (e.g.
Christian); (iii) use of a flush toilet is associated with higher BMI; and (iv) size of household is negatively
associated with BMI of adults in the case of OLS for 1998 and 2005.

19

by Hausman test) is applied for three rounds, age is positive and significant with a negative
coefficient for age squared, which is by and large consistent with the results of Tables 1 and 2.

[Place Table 2 here]

WealthIn all the cases of Tables 1 - 3, the coefficient estimate of wealth score is positive and highly
significant, suggesting that BMI of both men and women increases with household wealth. Thisis presumably because wealthier households are able to afford more food which will improvetheir BMI. In Tables 1 - 2, the results of quantile regression indicate that the coefficient estimate
for wealth scores is higher for more overweight or obese groups than underweight groups, or, themarginal effect of wealth on BMI (or our proxy for nutritional poverty) is non-linear. Wealthscore is positive and significant in Table 3 (pooled or pseudo panel model).

[Place Table 3 here]

Education
The role of education in BMI for women changed dramatically from 1992 to 1998 and 2005. The
results of OLS in Table 1 show that education is not statistically significant in 1992, while all thecategories, “primary school”, “secondary school” and “higher than secondary” are positive andsignificant in 1998, and “primary” and “secondary” are positive and significant in 2005. On the
results of quantile regression for 1992, we find a positive and significant role of secondary
school education in increasing BMI for the severely undernourished with the marginal effect20

0.25 (i.e. attempting or completing secondary school would improve BMI by 0.25 ceterisparibus), but in the meantime secondary education would significantly reduce BMI for
overweight or obese women. The coefficient estimates of education are statistically insignificant
in many cases of quantile regression for 1992.
However, in case of quantile regression for 1998, education becomes more important in
improving women’s BMI, but a positive effect was observed for the relatively well-nourishedwomen and not so for the undernourished. Neither primary school nor secondary school
education turned out to be significant for “severely underweight” or “underweight”, while higher
education significantly increases BMI of underweight women. For 2005, however, education
appears to have a more important role in improving BMI for the relatively underweight women.
The coefficient estimate of “primary school” is positive and significant for both “severely
underweight” and “underweight” women and “secondary school” is positive and significant for“underweight” women. We observe in Table 2 that the pattern of the results of education inquantile regression, in year 2005, for rural men is similar to that for rural women. In Table 3, allcategories of education are positive and significant in Column (1) wherein all the samples ofrural women across three rounds are pooled. In pseudo model (fixed effects model, Column (2)),
the category, “no education”, tends to reduce BMI of rural women significantly, as expected.

20

Working Status
As discussed in the previous section, we test whether more physically intensive work reduces
BMI. In 1992, “working for someone else” positively and significantly affects women’s BMI(OLS, the first panel of Table 1). In case of the quantile regression, the relationship between the20

Categorical variables of education are not used for pseudo panel as they are correlated once aggregated at
the cohort level.

21

category, “working for someone else”, and BMI is observed to be positive and significant for“severely underweight” women. Further, as per the quantile regression estimates, while thecoefficient of “working for family member” is positive and significant only for “severely
underweight” women, it is negative and statistically insignificant for “overweight” or “obese”
women. This implies that those severely underweight tended to be poor and could easily findjobs and so finding the job itself was likely to improve their nutritional status even if they workfor family member or somebody else.
From the second and third panels of Table 1, we observe that working for someone else ora family member is associated with lower BMI in 1998 and 2005. In both years, this relationshipis observed in the case of the OLS and for underweight, normal, overweight and obese inquantile regression. The effect of working for someone else in the case of severely underweight
women weakens and turns positive and significant again in Column 2 (“severely underweight”)
for 2005.
Column (2) of Table 3 (fixed effects for the pseudo panel) shows that employer type, aproxy for occupation based physical activity affects women’s BMI significantly. That is, BMI ofwomen “working for someone else” or “being self-employed” is lower than that of women ‘notworking’. This is consistent with the previous findings based on the cross-sectional estimations.
Cohorts with a higher proportion of wealthy people are associated with higher BMI. From an
education perspective, greater proportion of non-educated individuals in a cohort is related tolower BMI scores. In a nutshell, both occupation-based physical activity intensity and wealth(proxy for wages) are related to BMI of adults.

Food prices22

On the effect of food prices on BMI, we observed that price of higher price of eggs is associatedwith lower BMI for women in 1998 and 2005 as well as for men in 2005, but such an effect was
not observed in 1992 (Tables 1 and 2). Price of eggs is negative and significant in case where
pseudo panel model is applied (Table 3). On the other hand, higher price of cereals is associated21with higher BMI in most of the cases (Tables 1, 2 and 3) . This may sound counter-intuitive, but
it could be the case that higher prices would increase farmers’ income, which would improve thenutritional status due to the increased purchasing power of the household.

(ii)

Results based on NCAER data (Table 4)

In Table 4, the NCAER data are used to further explore the relationship between
occupation-based physical activity intensity and nutrition, on one hand, and investigate thenutrition-wage relationship, on the other. As indicated earlier, three variants of econometricanalysis, namely; OLS, IV and Heckman sample-selection models are used. To examine theeffects of the two hypotheses separately the OLS equation is estimated twice. The first equation
estimates a restricted model, that is, without occupation-based physical activity variables, while
the second OLS equation estimates an unrestricted model. The results based on the OLS
equations (Columns (1) and (2) of Table 4) show that wages in both equations are highly
significant and positive in explaining the BMI of adults. On the other hand, the results in Column(2) of Table 4 indicate that adults engaged in occupations that are physically (manually)strenuous (farmers, fishermen, laborers and production workers) have lower BMI compared toadults engaged in more or less sedentary work (professionals, managers, clerical and sales
personnel).

21

It is positive and significant only for men with “normal” range of BMI in Table 2.

23

[Place Table 4 here]

The IV and OLS (based on Heckman wage regression) results are given in Columns (3)and (4) of Table 4. In both estimations the positive effect of wages on nutrition is confirmed.
However, the effect of occupation-based physical activity intensity on nutrition is observed onlyin the OLS results which are based on predicted wages obtained from Heckman wage estimation
model. The OLS results (Column (4) of Table 4) support the hypothesis that adults engaged in
occupation-based physically intensive job have lower BMI. Indeed, the result is more revealing
as we observe a 10 percent statistical significance on the dummy for clerical/sales/services
personnel. That is, compared to professionals, clerical and sales personnel have lower BMI,
though both are engaged in seemingly sedentary form of work. A possible reason is that whencomparing managers to clerks, the latter are more likely to be active.
Interpretations of both the IV and the OLS (based on Heckman wage model) are supportedby the first stage regressions shown in Appendix and the post estimation tests in the last six rowsof Table 4. In Appendix, the first stage regression for the IV shows that both instruments(availability of trade union in a village and district level consumption inequality) are significant
at the 1 percent level. 22 To verify validity of our instruments, the null hypotheses of

22

The Appendix reports the econometric results of the labor market participation equation and the wage
equation where we used the dummy variable on whether a worker is male (taking the value 1) or not (0) to
capture the gender effect, which has turned out to be positive and statistically significant. Underlying thisare the active debates on female labor force participation (FLP) in India. FLP varies considerably acrossdifferent regions reflecting, e.g., the social and cultural norms, opportunities for education for women andchild care facilities. For instance, a woman not in the labor force may have chosen to do so due to thesocio-cultural reason and this may not necessarily imply low nutritional status. However, as we do not haveenough data to disentangle these complex causal relationships, we have estimated the simpler version of
wage and labor market participation equations where the gender dummy is used, while we realise thelimitations of our approach.

24

underidentification and weak identification are rejected (Table 4). Also, from the last row ofTable 4, we fail to reject the null hypothesis that the instruments are jointly valid. Finally, tomake a choice between the OLS and IV, the Hausman test rejects the null hypothesis that both
OLS and IV are consistent, but OLS is efficient. That is, rejecting the null hypothesis means that
it is only the IV that yields consistent estimates.
In the case of the Heckman-wage regression, infant dependency ratio is significant in theemployment probability equation at one percent level and the rho (correlation between the error
terms in the participation and outcome equations) is significant. The latter suggests a rejection of
the null hypothesis that there is no correlation between the employment probability and wage
equations. This supports the use of the Heckman sample-selection in a wage equation.

6. Conclusion
The present study, drawing upon three rounds of NFHS data in 1992, 1998 and 2005 andNCAER data in 2005, tests two hypotheses; (a) the poverty nutrition trap hypothesis predicting
wage affects nutritional status and (b) the activity hypothesis postulating that activity intensity
affects adult nutrition in terms of Body Mass Index (BMI). We employ the following three
econometric models. First, we apply quantile regressions to each round of cross-sectional data totake account of different behavioral response among different nutritional groups. Second, weconstruct a pseudo panel model to see any common pattern over the years. Finally, instrumental
variable (IV) and Heckman sample-selection regression models are employed to test the poverty
nutrition trap hypothesis, taking account of the sample selection bias associated with the labormarket participation and the endogeneity of wages in the BMI equation.

25

Our results strongly support both hypotheses. That is, there exists a poverty nutrition trap
associated with the labor market participation. After taking account of the sample selection bias
associated with the labor market participation and the endogeneity of wages, we find that those
who are left out from the labor market or experience lower wages tend to have lower levels ofnutrition in terms of BMI. Further, our estimates show that those who are doing manual labor ormore physically intensive and demanding activities (e.g. farmers, fishermen, laborers andproduction workers) are more likely to be undernourished than those who are doing lessintensive activities (e.g. professionals, managers).
Both results would explain why the improvement of BMI has been relatively slow at all theranges of nutritional groups. At the low end of the income distribution, people would need toenter into the labor market and earn wages to escape poverty. Additionally, even when they
manage to find jobs, low wages and/or physically demanding jobs would prevent them from
improving nutritional conditions. Only if they are able to earn higher wages, enter into the jobs
which would require less physically demanding work and/or are self- employed possibly withhigher education, they would be able to improve their nutritional status. However, thisopportunity appears to be still relatively limited. In terms of policy implications, facilitating
diversifications of activities of the poor, for example, through providing employment in
non-farm or service sectors would be effective as a poverty alleviation strategy in reducing theprevalence of malnutrition.
Although we have not analysed explicitly the factors underlying the continued reduction ofnutritional intakes, our results suggest that it is too optimistic to relate this reduction to the factthat more and more people are now doing physically less demanding work as a result of
economic growth. It is more likely that a substantial number of rural people have found it

26

difficult to escape from the poverty nutrition trap, or to shift to physically less demanding
activities. Hence poverty alleviation programmes aimed at directly and/or indirectly addressing
the problem of nutritional deprivations should continue to serve as an important role in rural
India.

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30

TABLE 1
Summary of Econometric Results for Rural Women: NFHS 1, 2 & 3 (1992-3, 1998-9, & 2005-6)
Explanatory Variables

(1)

(2)

(3)

OLS

Severely
underweightUnderweight(4)
Quantile Regression$
Normal

(5)

(6)

Over
weightObese

NFHS 1 (1992-3) Women
Age
Age Squared
Wealth Scores
Education (Attempted orCompleted Primary)1
Education (Attempted orCompleted Secondary)
Education (Attempted or Comppleted Higher than Secondary)Working for family member
Working for someone else
Self employed
Price of Sugar (State level)
Price of eggs (State level)
Price of Cereals (State level)
N

-0.03
-0.02
[-0.80][-0.92]0.00
0.00[0.70][1.11]
0.44
0.22[5.71]**[4.15]**
0.02
0.06[0.25][0.85]
0.25
0.04[4.07]**[0.38]
0.02
0.30[0.07][1.13]
0.16
0.10[2.95]**[1.14]
0.17
0.18[1.95]+[2.91]**
-0.56
-0.20
[-3.60]**[-1.24]-0.41-0.15[-6.07]**[-3.13]**0.02
0.01[1.05][0.54]
0.02
0.03[2.05]*[3.33]**
10336
10336
Adj. R2 0.034; F-statistics

-0.02
-0.14
-0.27
[-0.44][-1.06][-0.64]
0.00
0.00
0.00
[0.35][0.69][0.36]
0.32
1.41
2.20
[3.92]**[5.32]**[2.73]**
0.01
0.03
-0.63
[0.10][0.10][-0.53]
-2.57
0.09
-0.45
[-2.62]**[0.88][-1.42]
-1.69
-4.72
0.11
[-1.83]+[-2.62]**[0.23]
0.02
-0.03
-1.13
[0.19][-0.13][-1.21]
0.07
0.20
-1.18
[0.81][0.59][-1.26]
-0.88
-3.96
-0.35
[-2.14]*[-3.99]**[-1.57]
-0.53
-1.10
-1.82
[-6.41]**[-5.79]**[-3.26]**
0.04
0.07
0.22
[2.14]*[0.99][1.05]
0.00
-0.02
0.09
[0.16][-0.35][0.72]
10336
10336
10336
9.43; Log-likelihood -2.6e+04

0.31[0.46]-0.01[-0.50]3.20[2.44]*-1.41[-0.62]-4.54[-2.80]**-3.67[-1.08]-0.14[-0.05]-2.05[-1.21]-7.30[-4.44]**-4.40[-3.03]**0.82[2.35]*-0.21[-1.00]10336

NFHS 2 (1998-9) Women
Age
Age Squared
Wealth Scores
Education (Attempted orCompleted Primary)1
Education (Attempted orCompleted Secondary)
Education (Attempted or Completed Higher than Secondary)Working for family member
Working for someone else
Self employed
Price of Sugar (State level)
Price of eggs (State level)
Price of Cereals (State level)
N

0.05
-0.01
-0.01
[4.08]**[-0.24][-0.98]
-0.00
-0.00
0.00
[-0.75][-0.65][1.07]
1.03
0.35
0.60
[26.32]**[7.87]**[12.95]**
0.21
-0.03
-0.01
[4.55]**[-0.54][-0.14]
0.19
-0.09
-0.02
[3.63]**[-1.10][-0.37]
0.43
0.30
-0.02
[3.40]**[2.26]*[-0.09]
-0.11
0.08
0.12
[-2.84]**[2.54]*[1.64]
-0.25
-0.09
0.03
[-5.99]**[-1.82]+[0.60]
0.14
-0.02
0.18
[2.13]*[-0.33][1.29]
-0.08
0.03
-0.00
[-1.90]+[1.16][-0.09]
-0.03
-0.03
-0.03
[-3.68]**[-2.25]*[-2.39]*
0.02
0.03
0.04
[4.43]**[3.86]**[9.25]**
36227
36227
36227
Adj. R2 0.128; F-statistics 116.71

0.02[0.83]
0.00[1.51]
1.02[21.96]**0.25[6.93]**
0.28[2.64]**
0.63[2.70]**-0.09[-2.37]*-0.23[-4.67]**-0.08[-0.69]0.05[1.61]-0.02[-2.40]*0.03[5.01]**
36227

0.05
[1.69]+
0.00[2.30]*
1.80[13.69]**0.46[3.25]**
0.45[2.53]*
0.95[2.36]*-0.38[-2.41]*-0.63[-4.63]**0.03[0.12]
0.17[1.90]+-0.02[-0.62]0.01[1.01]
36227

0.27[2.72]**-0.00[-1.18]2.27[5.69]**
1.30[2.10]*
0.35[0.60]
1.39[1.31]-1.22[-3.74]**-1.21[-2.48]*-0.76[-1.04]0.04[0.13]
0.03[0.34]-0.03[-0.87]36227

0.08[7.02]**
-0.00

0.05
[3.65]**
0.00

0.25[7.14]**
-0.00

0.57
[6.13]**-0.01

NFHS 3 (2005-6) Women
Age
Age Squared-0.01[-0.62]0.00-0.01[-0.63]0.00

31

[-1.83]+[1.82]+[-3.07]**[-3.81]**[0.15][1.08]
0.12
0.05
0.08
0.12
0.17
0.21
[31.58]**[8.90]**[21.55]**[24.58]**[18.63]**[5.99]**
0.43
0.15
0.23
0.39
0.54
0.62
Education (Attempted or[10.32]**[2.12]*[6.33]**[7.90]**[4.21]**[2.01]*
Completed Primary)1
0.33
0.13
0.30
0.30
Education (Attempted or
0.10
0.35
[8.26]**[3.38]**[6.25]**[3.10]**
Completed Secondary)[1.50][1.04]
0.22
Education (Attempted or Comp0.06
-0.13
0.17
-0.39
-0.18
[2.23]*
leted Higher than Secondary)[0.58][-0.77][1.49][-1.63][-0.27]
-0.31
-0.08
-0.25
-0.83
-1.48
Working for family member
0.03[-9.08]**[-2.04]*[-5.81]**[-8.72]**[-5.74]**[0.58]
-0.25
0.13
-0.07
-0.25
-0.65
-0.96
Working for someone else[-6.24]**[2.08]*[-1.72]+[-5.12]**[-6.85]**[-2.77]**
-0.23
-0.24
-0.67
-1.47
Self employed
0.10
-0.01
[-4.23]**[-4.06]**[-4.27]**[-4.47]**[0.95][-0.20]
-0.04
0.07
-0.05
-0.18
-0.62
Price of Sugar (State level)-0.00[-1.79]+[1.84]+[-1.82]+[-2.11]*[-3.51]**[-0.16]
-0.05
-0.03
-0.03
-0.05
-0.03
Price of eggs (State level)
0.02[-6.72]**[-3.07]**[-3.68]**[-4.98]**[-1.98]*[0.32]
0.02
0.01
0.02
0.02
0.02
Price of Cereals (State level)-0.01[5.05]**[2.50]*[8.12]**[6.96]**[2.33]*[-0.66]
N
51888
51888
51888
51888
51888
51888
Adj. R2 0.173; F-statistics 236.62
t statistics in brackets----- + p<.10, * p<.05, ** p<.0; Base categories: 1 Education (None); 2 Work Status(Not working) 3 Marital
Status (Never Married); 4 Religion (Other); 5 Social Group (Non-Backward Group); 6 Location (North); $ Quantile regression standard
errors are bootstrapped based on 100; a The median z-score and the corresponding percentile for the group; Only key explanatory
variables are selected; Significant coefficient estimates are shown in bold.
Wealth Scores

32

TABLE 2
Summary of Econometric Results for Rural Men: NFHS 3 (2005-6)
Explanatory Variables

(1)

(2)

(3)

(4)
Quantile Regression$

OLS

Severely
UnderweightUnderweight0.29[23.54]**-0.00[-20.03]**0.12[25.75]**0.23[4.61]**
0.38[7.92]**
0.89

0.20[8.43]**-0.00[-8.41]**
0.04[5.15]**-0.03[-0.34]0.04[0.42]
0.36

0.23[18.05]**-0.00[-16.48]**0.06[14.67]**0.11[2.12]*
0.19[3.89]**
0.64

(5)

(6)

NormalOverweightObese

0.28[18.43]**-0.00[-15.34]**0.11[21.95]**0.26[4.01]**
0.32[5.45]**
1.05

0.36[11.97]**-0.00[-9.46]**0.18[19.39]**0.31[2.49]*
0.47[4.54]**
1.08

0.53[6.38]**-0.01[-5.23]**0.27[8.79]**
0.24[0.86]
0.46[1.59]
0.22

NFHS 3 (2005-6) Men
Age
Age Squared
Wealth Scores
Education (Attempted orCompleted Primary)1
Education (Attempted orCompleted Secondary)
Education (Attempted orCompleted Higher than Secondary)Price of Sugar (State level)[10.29]**[2.34]*[7.53]**[10.73]**[6.93]**[0.42]
0.12
0.07
-0.22
0.00
-0.04
-0.07
[2.11]*[2.21]*[-2.84]**[0.04][-1.00][-0.33]-0.05-0.04
Price of eggs (State level)
-0.01
-0.01
-0.03
-0.08
[-4.22]**[-3.34]**[-0.69][-1.09][-1.04][-1.25]0.01
Price of Cereals (State level)
0.00
-0.00
0.01
0.01
0.01
[3.05]**[0.79][-0.09][1.34][1.09][0.52]
N
28705
28705
28705
28705
28705
28705
Adj. R2
0.200
F-statistics
204.10Log-likelihood-7.0e+04
t statistics in brackets----- + p<.10, * p<.05, ** p<.0; Base categories: 1 Education (None); 2 Marital Status (Never Married); 3
Religion (Other); Social Group (Non-Backward Group); 5 Location (North); $ Quantile regression standard errors are bootstrapped
based on 100; a The median z-score and the corresponding percentile for the group; Only key explanatory variables are selected;
Significant coefficient estimates are shown in bold.

33

TABLE 3
Econometric Results: National Family Health Survey: Female-Rural Sample (Pseudo Panel for
1992-3, 1998-9 and 2005-6)
Explanatory variables
Age
Age Squared
Wealth Score
Proportion of cohort with no education
Education (Attempted or Completed
Primary)1
Education (Attempted or Completed
Secondary)
Education (Attempted or Completed Higher
than Secondary)Self employed2
Working for someone else
Working for family member
Price of Sugar (State level)
Price of eggs (State level)
Price of Cereals (State level)
Survey round dummy (1998 -9)6
Survey round dummy (2006 -3)
Constant(1)
Pooled
0.15[6.49]**-0.00[-3.64]**0.07[10.63]**0.48[3.57]**
0.51[4.69]**
0.42[3.93]**-0.10[-0.56]-0.20[-1.37]-0.12[-1.39]-0.04[-1.42]-0.05[-3.56]**0.01[2.98]**
4.25[33.47]**
4.54[68.61]**
15.25[21.40]**1035360.024
860.57
0.97(2)
Fixed Effects
2.91[2.81]**-0.03[-1.62]0.19[1.97]*-2.39[-1.83]+-3.25[-1.21]-4.48[-2.42]*-1.62[-1.32]
4.61[9.29]**
5.27[5.62]**-50.36[-2.79]**
378
0.581
30.47
-

N
Adj. R2
F-statistics
Correlation between the error andregressors
Hausman Test
41.92(0.00)
F-test (Unobserved heterogeneity=0)1.26(0.07)
Test for time effect45.67(0.00)
t statistics in brackets----- + p<.10, * p<.05, ** p<.0; Base categories: 1 Education (None); 2 Work Status(Not working) 3
Religion (Other); 4 Social Group (Non-Backward Group); 5 Location (North);6 Round Dummy(1993 -1); Only key explanatory variables
are selected; Significant coefficient estimates are shown in bold.

34

TABLE 4
1
Econometric Results: NCAER data– Rural Sample (2005)
Explanatory variables

(1)
OLS£

(2)
OLS$

(3)
IVα

(4)
OLS (Based on
Heckman wage
regression)π1.40[3.05]**-0.55[-1.79]+0.02[0.68]-0.00[-0.83]-0.09[-0.78]-0.12[-0.93]-0.25[-1.17]-1.10[-2.77]**-1.53[-1.93]+-0.93[-1.52]-0.18[-2.51]*-0.14[-2.95]**0.01[2.70]**-0.01[-1.64]-0.18[-0.65]-0.42[-1.78]+-0.51[-1.99]*-1.52[-1.88]+-0.01[-0.99]-0.58[-1.69]+-1.09[-3.26]**-1.21[-3.57]**19.47[24.93]
5791
0.019
6.74

0.43
0.40
1.84
[6.24]**[5.69]**[3.12]**
Predicted log of Wage
-0.66
Sex(Male)
-0.12
-0.03
[-2.00]*[-0.56][-0.15]
0.07
0.07
0.06
Age years[2.75]**[2.55]*[2.34]*
-0.00
-0.00
-0.00
Age years squared[-2.21]*[-2.07]*[-2.04]*
2
Education (Lower Primary)
-0.01
-0.03
-0.03
[-0.06][-0.23][-0.22]
Education (Upper Primary)
0.11
0.08
0.02
[1.10][0.80][0.14]
0.32
Education (Secondary)
0.17
0.00
[2.09]*[1.08][0.01]-0.90
Education (Higher Secondary)
-0.13
-0.51
[-2.45]*[-0.42][-1.58]-1.64
Education (Undergraduate)
-0.34
-0.89
[-1.87]+[-0.53][-1.20]0.89
Education (Graduate)
0.28
-0.61
[2.86]**[0.74][-1.08]
-0.17
-0.17
-0.23
Scheduled Caste[-2.44]*[-2.38]*[-2.93]**
-0.14
-0.14
-0.11
Household size[-3.03]**[-3.04]**[-2.28]*
0.01
0.01
0.01
Household Size Squared[2.80]**[2.82]**[1.99]*
-0.01
-0.01
-0.01
Distance to market[-1.92]+[-1.95]+[-2.25]*
-0.69
-0.63
BIMARU2
0.05[-3.87]**[-3.50]**[0.14]
-0.80
-0.75
South
-0.14
[-4.69]**[-4.31]**[-0.44]
-0.94
-0.88
East
-0.24
[-5.18]**[-4.83]**[-0.73]
-1.33
-1.40
-1.61
Others[-1.75]+[-1.76]+[-1.98]*
-0.01
-0.01
-0.02
Price of cereals[-1.74]+[-1.82]+[-2.51]*
Activity Intensity
-0.44
0.19
(Clerical/Sales/Services)3[-1.28][0.41]-0.84
Activity
0.30[-2.45]*Intensity(Farmers/Fishermen)[0.49]-1.01
Activity Intensity (Laborers and-0.18[-2.94]**
Production workers)[-0.34]
Constant
18.95
19.91
16.39
[36.62][31.55][10.17]
N
5811
5803
5790
Adj. R2
0.021
0.023-0.045F-statistics
8.54
7.93
5.70
Hausman Test
9.30(0.98)
Under identification test
78.91(0.00)
Weak identification test
48.87
Over identification test
0.51(0.48)
t statistics in brackets---+ p<.10, * p<.05, ** p<.01; 1 The sample has been restricted to BMI less than 25; 2 Reference category
for education is No education; 3 Base group for activity intensity is Professionals/Managers; £ Basic OLS without activity intensity
variables; $ Basic OLS with activity intensity variables; α The instruments for correcting for wage endogeneity are the available of a
trade union in the village and state level gini coefficient π Exclusion restriction variables used for the probability get employed ishousehold’s infant dependency ratio.; Significant coefficient estimates are shown in bold.
Log of Wage

35

Appendix: First Stage IV and Heckman Selection Results(1)IV
Exp. Var.

Dep. Var.

Availability of Trade Union in a village
District level consumption inequality
Infant dependency Ratio
Gender (whether male)Age
Age Squared
Education (Lower Primary)1
Education (Upper Primary)
Education (Secondary)
Education (Higher Secondary)
Education (Undergraduate)
Education (Graduate)
Scheduled Caste
Household Size
Household Size Squared
Distance to Market
BIMARU2South
East
OthersPrice of cereals
Activity Intensity (Clerical/Sales/Services)2
Activity Intensity(Farmers/Fishermen)
Activity Intensity (Laborers and Production
workers)
Constant
Correlation between the error terms in theparticipation and outcome equations
N
Adj. R2
F-Statistics

(2)

(3)
HeckmanLog of Hourly Wages

Participation Eq.
Employed or otherwise

Outcome Eq.
Log of Hourly Wages

0.16[18.15]**0.36[13.25]**0.35[51.07]**0.02[18.60]**0.03[3.40]**
0.11[13.39]**0.20[20.65]**0.27[17.94]**0.31[6.12]**
0.49[24.53]**-0.00[-13.81]**-0.00[-0.54]-0.00[-0.33]0.00[2.64]**-0.00[-6.86]**-0.40[-37.16]**-0.28[-27.40]**-0.38[-34.93]**-0.01[-0.19]0.01[11.00]**-0.40[-21.74]**-0.89[-50.50]**-0.60[-34.35]**1.73[42.93]**
30852
0.443
1066.57-0.04[-3.01]**-0.10[-2.18]*0.64[16.78]**1.00[91.63]**0.14[59.37]**-0.00[-58.63]**-0.24[-13.19]**-0.40[-29.25]**-0.66[-41.80]**-0.87[-39.06]**-1.24[-19.06]**-0.63[-22.86]**0.43[35.89]**-0.09[-19.32]**
0.00[5.70]**
0.24[14.36]**0.52[33.32]**
0.33[19.31]**
0.31[5.09]**-2.74[-46.13]**
80707
-

0.17
[18.19]**0.47[15.62]**0.47[16.25]**0.03[6.85]**
0.04[3.30]**
0.16[11.41]**
0.32[15.52]**0.50[17.40]**0.60[9.11]**
1.02[40.83]**-0.00[-4.70]**0.01[1.08]-0.02[-4.00]**0.00[4.92]**-0.41[-30.11]**-0.37[-20.60]**-0.39[-25.99]**
0.03[0.59]
0.67[5.47]**
2.89[0.00]**
80707-

t statistics in brackets + p<.10, * p<.05, ** p<.01; 1 Reference category for education is No education; 2 Base group for activity intensity
is Professionals/Managers.

36