An Analysis of Childhood Malnutrition in Rural India: Role of Gender, Income and Other Household Characteristics

 
 
World Development Vol. 27, No. 7, pp. 1151±1171, 1999
                                                                                  Ó 1999 Elsevier Science Ltd
                                                                 All rights reserved. Printed in Great Britain
www.elsevier.com/locate/worlddev                                           0305-750X/99/$ ± see front matter
                                      PII: S0305-750X(99)00048-0

  An Analysis of Childhood Malnutrition in Rural
India: Role of Gender, Income and Other Household
                  Characteristics
                                        SARMISTHA PAL *
                                   University of Wales, Cardi€, UK
        Summary. Ð There are controversies regarding the role of individual and household
        characteristics in childhood nutritional status measured by anthropometric indicators.
        Using a nutrition index based on weight-for-age of children in rural India, the paper
        re-examines this issue. Ordered probit estimates of nutritional status suggest female
        literacy improves the nutritional status of boys at the cost of girls while higher per capita
        current income improves that of both boys and girls, though the impact is higher for
        boys; however, e€ect of income is not robust when we use instruments of longer-run
        income. But more income and literacy give more ways to discriminate between boys and
        girls. Ó 1999 Elsevier Science Ltd. All rights reserved.

             1. INTRODUCTION                            robust in equations predicting child height; its
                                                        signi®cance depends critically on the choice of
   Even after half a century of independence,           instruments for income. In this context, we
childhood malnourishment and infant mortal-             shall use the WIDER dataset from rural West
ity are of grave concern in India. ``Though             Bengal to re-examine the role of income vis-a-
quieter than famine, it (persistent under-nutri-        vis other relevant individual and household
tion) kills many more people slowly in the long         characteristics on the nutritional status index
run than famines do'' (DreÁze and Sen, 1989).           based on weight-for-age among male and
Using an ordered probit model, the paper                female children.
analyzes the nature of nutritional status derived          Existing studies use various anthropometric
from childhood weight-for-age among male                indicators such as weight, height, height-for-
and female children in the late 1980s in rural          age (e.g., see Thomas, Strauss and Henriques,
India.                                                  1990; Thomas and Strauss, 1992) to measure
   There are controversies regarding the role of        child health in di€erent countries which often
di€erent individual and household characteris-          give rise to varying results depending on the
tics in child health. The World Bank view is            choice of particular anthropometric indicator.
that ``malnutrition is largely a re¯ection of           While weight or height measures short-term
poverty: people do not have enough income for           thinness or wasting, height-for-age and weight-
food.'' Behrman and Wolfe (1984), however,              for age measure child growth relative to its
argue that the World Bank tends to overem-              potential and, therefore, re¯ects the extent of
phasize the role of income, ignoring the signif-
icance of other household characteristics,
especially female literacy. Using the empirical         * I am grateful to Amartya Sen and Sunil Sengupta for
data from Nicaragua, they suggest that the              allowing me to use the data and to two anonymous
elasticity of women's schooling is much higher          referees of this journal for very perceptive comments. I
than that for income or household size.                 would like to thank Haris Gazdar for his help to access
Thomas, Strauss and Henriques (1990) suggest            the data and also Jean Dreze, Gale Johnson and Jocelyn
that in most regions of Brazil, improvements in         Kynch for their helpful suggestions on earlier drafts of
household income increase the probability of            the paper. Any remaining errors are my own. Final
children's surviving. Income however, is not            revision accepted: 22 December 1998.
                                                    1151
1152                                 WORLD DEVELOPMENT

long-term deprivation and acute nutritional         land. By 1967±68 however the incidence of ru-
crisis (Kynch and Maguire, 1998). Though            ral poverty was above-average in West Bengal
height-for-age is considered to be a better         and the situation did not improve perceptibly in
indicator of stunting among children, weight-       the 1980s under the Left Front regime which
for-age prescribed by the World Health              came to power in 1977. For example, though
Organization is most commonly used for child        infant mortality rate (IMR) in rural West
welfare work in India. The latter has, therefore,   Bengal has declined over 1981±90, the state's
the added advantage of being directly related to    own rate of decline in the 1980s was not much
immediate policy intervention in India. Fol-        faster than the Indian average; in fact, it was
lowing this Indian tradition, the WIDER da-         surpassed or equalled by Bihar, Uttar Pradesh,
taset at our disposal has constructed childhood     Gujarat, Punjab, Kerala and Tamil Nadu
nutritional status in terms of a child's weight-    (Sengupta and Gazdar, 1997).
for-age (see further discussion in Section 3 a).
   Certain clari®cations are, however, in order.
With the cross-section (single-period) dataset at   (a) Socio-economic characteristics of the study
our disposal, we shall in this paper consider the                      villages
number of children and their birth order to be
predetermined and focus on the determination           The analysis of childhood malnutrition in
of childhood nutritional status or health1 in       this paper is based on the information from six
terms of a number of household and individual       villages in West Bengal for 1987±89.2 Much of
characteristics, including income, female liter-    the social and economic data of the WIDER
acy, family size and birth order. Second, in view   survey were based on complete enumeration of
of the controversial results on child health ob-    all households; likewise, all relevant individuals
tained from various anthoropometric indica-         were enumerated for individual information
tors like weight, height or height-for-age, we      such as educational attainment, wage earnings
would ideally like to compare and contrast          and nutritional status. In all, the survey covered
between these alternative anthropometric indi-      749 households and 3972 individuals. The pa-
cators. The WIDER dataset however contains          per makes particular use of the nutrition survey
information only on childhood nutritional sta-      data collected from 436 children below the age
tus based on weight-for-age commonly used in        of ®ve (based on complete enumeration of this
India and not on any other alternative indica-      category of children) in all six study villages.
tors. Accordingly, our analysis is based on            These six villages taken together capture a
childhood weight-for-age only. Finally, there       good deal of the diversity present in rural West
are pronounced gender biases in child health in     Bengal. The study villages are drawn from
many South Asian countries including India          di€erent agroclimatic regions of West Bengal.
(e.g., see Dasgupta, 1987; Kishor, 1993). While     While four villages are drawn from southern
most existing studies use a gender dummy in         Bengal, other two are located in North Bengal.
the regression for all children taken together to   Generally being located in di€erent districts,
examine the incidence of gender bias in child       they display interesting regional variation even
health (based on some anthropometric indica-        within the state (Table 1).3
tor), we determine separate male and female            Bhagabandasan situated in the Medinipore
health functions to examine their di€erence         district of southern Bengal is the most pros-
with respect to di€erent individual and house-      perous of the study villages while Simtuni is the
hold characteristics.                               poorest in terms of average per capita income,
                                                    modal wage rate or incidence of poverty. There
                                                    is also a demographic variation among the
       2. SAMPLE CHARACTERISTICS                    sample households across the study villages.
                                                    Family size as well as the proportion of female
   Our study focuses on six villages (hereafter     members is the highest in Kalmandasguri in
``study villages'') in India drawn from the         North Bengal. Simtuni is a tribal village where
eastern state of West Bengal. In the post-inde-     86% of the population belongs to the scheduled
pendence period, West Bengal started its            caste category while Kalmandasguri is the only
economic development in a relatively good           village with a signi®cant Muslim population.4
position among the Indian states measured by        Literacy rates are generally higher in the south
its high rate of urbanization, strong industrial    Bengal villages as compared to those in the
infrastructure and very high productivity of        North Bengal, with the exception of Simtuni.5
CHILDHOOD MALNUTRITION IN RURAL INDIA                                               1153

                                                                                                a
                        Table 1. Selected socioeconomic characteristics of the study villages

Variables                                                    Study village

                    Kuchly          Sahajpur      Bhagabandasan         Simtuni      Kalman-dasguri       Magurmari

District            Birbhum         Birbhum         Medinipur           Purulia         Kochbehar         Jalpaiguri
Household no.          142             227              134                75                89                49
FSize                  6.9             6.7             5.48              6.55              7.04              6.04
Female (%)            55.6            49.7             38.9              51.3              57.5              57.8
Landless               65              131              39                 2                42                 ÿ
SC (%)                38.4            37.5             15.5               1.3              33.4               2.6
ST (%)                11.7            22.3             11.8               86                8.3              1.13
Muslim (%)              ÿ               ÿ                ÿ                ÿ                40.8               ÿ
Literacy [1]       0.38 (0.29)     0.40 (0.30)      0.66 (0.55)       0.10 (0.01)       0.52 (0.39)       0.35 (0.23)
Land reform            64              56               49                13                13
Pn®nc                 0.21            0.53             0.35              0.32              0.31              0.65
PCINC [2]             1647            1545             2213              1160              1212              1441
Modal wage            3.42             3.3              3.6              2.57              2.66               ÿ
Poverty               40.4            52.3             16.5              62.5              72.7              56.6
a
  Fsize: family size; Female: Average proportion of female members; Landless: number of landless households; Land
reform: number of household who have gained from the land redistribution programme. Pn®nc: proportion of in-
come earned from nonfarm activities while PCINC is mean income per head measured in rupees; Modal wage:
Kilogram of rice per day in 1988; Poverty: % of households below poverty line. Female literacy in parentheses. There
are also much ¯uctuations in the per capita income quartiles across the study villages. Three quartiles were (903,
1111, 1455), (670, 1170, 1541), (1280, 1694, 2355), (714, 804, 951), (905, 1167, 1406) and (683, 920, 1226) respectively
in Kuchly, Sahajapur, Bhagabandasan, Simtuni, Magurmari and Kalmandasguri.

   All villages except Magurmari (close to some                 There is an intervillage variation in the
centers of traditional industry such as biri                 provision of public services too (Table 2). The
making6) are predominantly agricultural.                     percentage of villages not covered by all-
Average land holding per household varies                    weather roads (pucca roads) is as high as 49%
widely among the study villages; on average,                 in West Bengal. Among the study villages,
land holding is higher in the south than in the              Kuchly and Kalmandasguri are not connected
north. Simtuni has almost no landlessness, but               by a ``pucca road.'' Though there are primary
extremely poor soil and water conditions. In                 schools in all the study villages, access to high
all other villages, most landless households                 schools is dicult in some villages like
predominantly belong to the lowest caste                     Kalmandasguri, Simtuni and Kuchly. The
categories.                                                  scheme of household electri®cation for the
   Besides land, nonland allied activities have              rural poor had little impact in these villages.
recently emerged as the alternative source of                Only Sahajapur is enjoying the bene®ts of it
rural employment in West Bengal. In this                     while Kalmandasguri and Simtuni are not elec-
respect, too, an intervillage variation is                   tri®ed. Even in the villages with formal electri-
pronounced; whereas in Kuchly only 21% of                    ®cation, the rate of utilization is often as low as
total income is earned from nonfarm activities,              7.9%. It appears that there is a somewhat in-
the proportion is as high as 65% in Magurmari.               creasing health awareness in these villages.

                         Table 2. Distance of the village from the nearest facility (in Km s)

Village             Railway station Pucca road       Headlth centre Primary school High school          Market centre

Bhagabandasan               6                0              6                 0                  1             0
Magurmari                   4.5              0              2.5               0                  2.5           2.5
Kalmandasguri               9.5              3              3                 0                 33             3
Simtuni                    68                0              3                 0                 30             2
Kuchly                     18                3              8                 0                 18            18
Sahajapur                   8                0              3                 0                  8             8
1154                                        WORLD DEVELOPMENT

Even in Sahajapur and Magurmari, where there                 to the highest expenditure quartile. The di€er-
are no tube-wells for potable water, some poor               ences in mean per capita food expenditure
households were found collecting water from                  across per capita expenditure quartiles are sta-
the tube-wells of the neighboring villages rather            tistically signi®cant7 for all the study villages.
than taking drinking water from open and                     The contrast between average per capita food
stagnant tanks. There is no health center in any             expenditure between the lowest and the highest
of these villages, however, and villagers have to            quartiles is particularly striking (the di€erence
travel a few kilometers to see a doctor; the                 is also signi®cant at 1% level). Some intervillage
situation is worst in Kuchly and Bhagabanda-                 variation is also observed: the absolute di€er-
san. Even when there is a health center, getting             ence between the lowest and the highest quar-
expected health care is not easy. There is always            tile is the greatest for the most prosperous
a scarcity of hospital beds or of quali®ed doc-              village Bhagabandasan (and the di€erence is
tors or nurses in these rural areas.                         statistically signi®cant too).
                                                                Finally, we compare household demographic
     (b) Household expenditure and well-being                and economic characteristics of those in the
                                                             lowest and in the highest per capita expenditure
   In the context of nutrition and poverty                   quartiles (see Table 4). In all the study villages
analysis, household expenditure on basic needs               (except Magurmari) the households in the
of lifeÐincluding those on food, clothing,                   lowest expenditure quartile are generally land-
housing, education and medical careÐis often                 less or land-poor, have a larger family size and
used as a measure of household welfare                       a strikingly lower female literacy rate, and
(Glewwe, 1991; Alderman, 1993). Such expen-                  predominantly belong to the scheduled caste
diture re¯ects household command over re-                    (SC), scheduled tribe (ST) or Muslim families.
sources and, therefore, to a large extent, the               There is a signi®cant correlation between caste
health status of household members. The                      and size of land holding in the villages (except
analysis in this section is based on the expen-              Magurmari): households belonging to the lower
diture dataset in ®ve villages except Simtuni for            caste categories usually possess less land. This is
which the data were not available.                           also re¯ected in the signi®cant correlation8 be-
   Food is the major component of expenditure                tween income and caste classi®cation.9 It also
in all the study villages; the average proportion            implies that there is a one to one correspon-
of total expenditure spent on food varies be-                dence between low caste households and lower
tween 80% in Kalmandasguri and about 68% in                  per capita income in the study village (see ap-
Simtuni. Since food is one of the crucial inputs             pendix for the de®nition of the caste variable).
for the production of human health, we exam-
ine the distribution of average per capita food
expenditure across per capita expenditure                          3. ANALYSIS OF CHILD HEALTH
quartiles (Table 3). We also test the statistical
signi®cance of the di€erence between any two                    Persistence of endemic hunger and malnu-
quartile means for a given village and also the              trition is a complex socioeconomic phenome-
di€erence of the means between two villages for              non, giving rise to controversial empirical
a particular quartile with a view to assess the              evidence and necessitating careful analysis.
nutritional status of the sample children.                   Before we examine the empirical evidence
   The average food expenditure per capita in-               from the WIDER villages, we consider the
creases signi®cantly as we move from the lowest              analytical arguments to determine the nature

                                                                                                                 a
       Table 3. Average annual per capita food expenditure (in Rupees) across per capita expenditure quartiles

Village                   First quartile         Second quartile         Third quartile         Fourth quartile

Kuchly                     636   (105.45)         772   (95.27)           1281   (133.72)        1498   (555.22)
Sahajpur                   635   (127.07)         720   (89.85)            814   (104.90)        1394   (365.25)
Bhagabandasan              605   (287.11)         756   (136.87)           898   (156.40)        1566   (380.25)
Kalmandasguri              682   (97.42)          760   (103.57)           842   (88.07)         1389   (241.65)
Magurmari                  547   (156.05)         660   (81.50)            764   (55.64)         1346   (190.11)
a
    Expenditure data for Simtuni are not available. The numbers in the parentheses denote standard deviation.
CHILDHOOD MALNUTRITION IN RURAL INDIA                                           1155

                                                                                                                       a
Table 4. Socioeconomic characteristics of households in the lowest and the highest per capita expenditure quartiles

                    Kuchly                Sahajpur           Bhagabandasan          Magurmari        Kalmandasguri

                  Q1        Q4          Q1         Q4            Q1      Q4         Q1      Q4         Q1      Q4

Land              0.4       4.08         0.45      4.17           0.68    3.21       0.34    0.51      0.45     1.83
Fsize             5.79      5.4          5.52      3.75           5.18    4.82       5.68    4.96      5.57     3.91
Flitrate          0.17      0.58         0.14      0.61           0.2     0.69       0.18    0.54      0.21     0.47
Hindu            34.3      74.3          7.4      72.2           39.4    90.9       40      32.4       ÿ       27.3
SC               48.6      20           66.7      14.8           39.4     3         60      66.1      40.9     40.9
ST               17.1       5.7         25.9      13             21.2      6.1      ÿ        1.4      13.6      9.1
Muslim            ÿ         ÿ            ÿ         ÿ              ÿ       ÿ         ÿ        ÿ        45.5     22.7
a
  Q1 is the lowest quartile while Q4 the highest. Land is measured in acres while other religious entries are given as
the percentage of the total. Fsize is the family size and Flitrate, the female literacy rate.

and characteristics of child quality using an-                     For the purpose of child health classi®cation,
thropometric measures like weight-for-age.                         WIDER survey constructed an index of un-
                                                                   dernourishment as follows. It used the standard
(a) Use of anthropometric indicators in de®ning                    ``weight curves from birth to ®ve years of age''
                 child health                                      commonly used in most health centres in India.
                                                                   Curve I refers to as bounding the weight for
   One can identify two strands of the existing                    average well-fed healthy children (at a given
empirical literature on child quality: one relat-                  age) and is related to the international anthro-
ing to the availability of di€erent nutrients in-                  pometric standards known as the Harvard
cluding total calory (Bouis and Haddad, 1992;                      Standard. For di€erent levels of malnutrition,
Alderman, 1986) and the other relating to the                      there are accepted norms giving rise to weight
determination of child health including survival                   curves II, III and IV (for further details see Sen
rate, mortality, height, weight, height-for-age                    and Sengupta, 1983; Kynch and Maguire,
etc. (Alderman, 1993). Our analysis makes use                      1998). For example, for children aged 12
of the latter approach involving anthropomet-                      months, the points on these weight curves I, II,
ric measure of a child.                                            III and IV correspond to 7.8, 7, 6 and 5 kg
   There has been some debate whether small                        respectively; in terms of percentages, points on
size (weight, height, height-for-age, or weight-                   weight curves II, III and IV for a 12 months old
for-age) is an indicator of poor child quality or                  child correspond respectively to 90%, 77% and
health. Nutritionists, however, accept the                         64% of the accepted normal weight (7.8 kg) for
damage associated with smallness as due to the                     this age. Similarly, for children aged 24 months,
process of becoming small, i.e., growth falter-                    points on weight curves II, III and IV corres-
ing, rather than to smallness per se. Faltering                    pond to 86%, 75% and 63% of the accepted
generally occurs between six months and two                        normal weight 10 kg. (corresponding to I) for
years of age.                                                      the age. Using these weight curves I, II, III and
                                                                   IV, WIDER survey records the nutritional
    Though a stunted child may have some catch up                  status of a child below age ®ve as follows:
    growth, for the most part a child whose growth has
    faltered in the ®rst two years of life will be on a di€er-     NUTST ˆ 0 if well-nourished
    ent growth trajectory during rest of his/her life (Alder-
    man, 1993).
                                                                            if weights at or above line I†
                                                                         ˆ 1 if slightly undernourished
Three types of anthropometric index have                                    if weights between lines I and II†
commonly been used:
                                                                         ˆ 2 if moderately undernourished
  (i) weight-for-height which measures the
  short-term thinness of the body.                                          if weights between lines II and III†
  (ii) Body Mass Index (BMI) which measures                              ˆ 3 if severely undernourished
  the adult risks of morbidity.                                             if weights between lines III and IV†
  (iii) Weight-for-age or height-for-age which
  measures the child growth relative to poten-                           ˆ 4 if disastrously undernourished
  tial.                                                                     if weights fall below line IV†
1156                                            WORLD DEVELOPMENT

Any analysis based on these measures will have                 among caste Hindu children. The chi-square
an added advantage as compared to other an-                    statistic (27.02) between caste and nutritional
thropometric measures in that they directly                    status is signi®cant at the 1% level of signi®-
relate to immediate policy interventions in In-                cance for all the villages taken together.
dia. Health workers and paramedical sta€ in                       It also follows from Table 5 that irrespective
India are instructed on the basis of the nutri-                of the caste classi®cation there is a gender bias
tional status as de®ned above: slightly under-                 in child nutrition in the study villages: pro-
nourished children require nutrition education                 portion of female children well-nourished is less
of the mother and supplementary feeding at                     than that for male children for every caste.
home; moderately undernourished children re-                   Similarly, compared to male children, the pro-
quire supplementary feeding at the health cen-                 portion of a female child of being malnourished
ters; severely malnourished children require to                is much higher, especially for severe malnour-
follow doctor's advice; and disastrously mal-                  ishment. A chi-square likelihood ratio statistic
nourished children need to be hospitalized for                 between sex and nutritional status (17.33) sug-
treatment. It is worth noting here that the                    gests that there is a signi®cant association be-
WIDER dataset contains information on the                      tween these two variables.
nutritional status index of the children, but not
the actual weight-for-age measure for these                      (b) A cross-section analysis of child health
sample children; that is why we need to use the
ordered variable NUTST instead of the con-                        Economic rationale for the analysis of child
tinuous weight-for-age measure to model child                  health is usually derived from household deci-
health.                                                        sions regarding the allocation of resources
   There are 436 observations in our sample, of                which had originated with Becker (1965) and
which only 15.6% are well-nourished. About                     Becker and Lewis (1965). In the standard
15% fall under the category of severely or di-                 model, a household maximizes its utility from
sastrously undernourished while 69% are                        the quantity and quality of the children and also
slightly (36%) and moderately (33%) under-                     the consumption of other commodities subject
nourished. Distribution of nutritional status                  to a budget constraint which in turn determines
NUTST across caste (Hindu, SC, ST and                          the optimal values of consumption and also
Muslim) and sex (male and female) is shown in                  quantity and quality of children. But for the
Table 5. About 50% of scheduled caste and                      short-run analysis of child health (since we
58% of scheduled tribe children are moderately,                consider cross-section variation among the
severely and/or disastrously malnourished in                   sample children for a given year), we assume
the study villages while the proportion is 39%                 quantity of children and their birth order to be
                                                               predetermined and thus ignore the dynamics of
                                                               fertility and consumption choices and their
Table 5. Distribution of nutritional status across caste and   implications for child health. In particular, we
                         gender a                              assume that at a given time, a representative
Caste                          NUTST                           household maximizes a quasi-concave utility
                                                               (assuming household's preferences are inter-
               0         1         2        3          4       temporally separable) as a function of average
                                                               consumption c of commodities by household
Hindu          18        42       26        12        2        members and child health index w (based on,
Male           24        48       13        12        3        say, child's weight-for-age as in our case) sub-
Female         11        37       39        11        2
SC             21        29       37        10        3
                                                               ject to the current period budget constraint
Male           27        26       38         8        1        (which depends on household income and
Female         13        32       39        12        4        wealth and also prices of consumption and
ST              7        35       36        20        2        child health goods) and the child's survival
Male            8        50       27        15        ÿ        function (which depends on the duration of
Female          6        20       44        26        4        breast feeding, calorie and protein intake, child
Muslim         10        41       41         4        4        health care practices and also the individual
Male           13        50       31        ÿ         6
                                                               incidence and severity of diseases). Along with
Female          7        31       54         8        ÿ
                                                               determining the optimum value of average
a
  Each entry refers to the percentage of each group in         consumption c , this constrained maximization
the respective NUTST category. The sum total of all the        exercise determines the household demand for i-
numbers in a row is 100.                                       th child's health wi (in implicit form) as follows:
CHILDHOOD MALNUTRITION IN RURAL INDIA                             1157

wi ˆ g XI ; Xh ; XP ; vi †;                      1†   tional intakes has not always been signi®cant
where XI the individual characteristics (e.g.,         (Deaton, 1989), but Morduch and Stern (1997)
gender, physiology, birth order) of the child, Xh      ®nd that there is gender bias in childhood
is the set of household characteristics (e.g.,         height-for-age in Bangladesh.
family size, parental health, parental care, in-
come) and XP the public environment (e.g.,                      (ii) Household characteristics
medical and health care practices and facilities
available) into which the child is born. As-              Income13 is one of the most signi®cant vari-
suming, that all the right hand side variables         ables in the child's health function. To a large
are exogenous, Eqn. (1) can be considered as a         extent, it determines the amount of di€erent
reduced form equation which forms the basis of         inputs (e.g., food, clothing, residence, sanita-
much discussion of the socioeconomic litera-           tion, medical care etc.) into child health pro-
ture on child anthropometry (e.g., see Heller          duction function (Behrman and Wolfe,
and Drake, 1979; Behrman and Wolfe, 1982;              1982, 1984; Thomas, Strauss and Henriques,
Thomas, Strauss and Henriques, 1990; Thomas            1990, Strauss and Henriques, 1991). Given the
and Strauss, 1992). Care must be taken how-            close correlation between caste and income
ever to interpret the estimated coecients if all      distribution in the study villages (see Section 2),
the explanatory variables in reality are not ex-       caste may also have a similar e€ect on the
ogenous.10                                             earning capacity of a household in the study
   We also extend Eqn. (1) to include the pos-         villages. Family size re¯ects the number of units
sible discrimination against female children in        among which household resources need to be
survival and nutrition observed in many south          allocated according to the weights of each unit.
Asian countries including India (Behrman,              Family size may have an ambiguous role in
1988a, b; Dasgupta, 1987). It has been argued          nutritional status depending on the relative
that household's allocation of resources be-           strength of size economies in consumption as
tween male and female children is overwhelm-           against the diminishing returns to scale in nu-
ingly determined by the household's expected           tritional status. Some empirical studies suggest
future gain from male and female children (e.g.,       that the correlation between family size and
see Rosenzweig and Schultz, 1982; Sen and              child health is weak (Lanjouw and Ravallion,
Sengupta, 1983). This gender discrimination            1995). A particularly distinctive role has been
among children is largely household-speci®c            attributed to literacy (male/female/overall) in
though it may depend on the market opportu-            that it determines the technology of a child's
nities as well as the social set-up of the region.     health function. The available evidence sug-
This necessitates us to modify the child health        gests that female literacy does have a signi®cant
function (1) to derive gender-speci®c health           impact on child's health while male literacy
fucntion wis of the i-th child of a given sex s,       does not (Behrman and Wolfe, 1984; Thomas,
s ˆ m, f as follows:                                   Strauss and Henriques, 1991; Murthi et al.,
                                                       1995).
wis ˆ / XIs ; Xhs ; XPs ; vis †;                  2†
where vis is individual-speci®c random term of                     (iii) Public environment
the i-th child belonging to s-th sex.
                                                         Heller and Drake (1979) and also Thomas
             (i) Individual characteristics            and Strauss (1992) suggest the signi®cance of
                                                       public environment on a child's nutritional
   For a child of a given sex, age is an important     status. Among other things, this includes the
determinant of the physiological characteristics       provision of public utility services such as
which convert consumption into nutrition11             sewerage, drinking water, medical facilities and
and nutrition into higher productivity and,            market opportunities. To a certain extent,
therefore, higher earning potential. Sex               availability of these public services a€ects
(Rosenzweig and Schultz, 1982; Morduch and             household resources as well as child's health.
Stern, 1997) and birth order (Dasgupta, 1987;          For example, the greater the distance of a
Behrman, 1988a, b) may also re¯ect the weights         primary health center from the village, the
a household attaches to di€erent children in the       greater the cost of getting some medical care
allocation of its resources.12 The empirical ev-       during illness, thus worsening the health situ-
idence of bias against female children in nutri-       ation.
1158                                 WORLD DEVELOPMENT

           (iv) Random disturbance term             ished children with those more seriously mal-
                                                    nourished, especially in a population where
   vis refers to any individual, household or       only a minority of the children are adequately
community speci®c unobservable characteris-         nourished (see discussion in Section 3). A bet-
tics that may a€ect health of a child of a given    ter way to model NUTST is, therefore, to use
sex. For example, among other things, health        an ordered probit model where one can dis-
status of a child may be a€ected by the random      tinguish the children according to each level of
illness of the child or breakdown of parental       their nutritional status as coded in NUTST.
health, a sudden outburst of an infection or           Ordered probit model di€ers from a univar-
pollution in the local community.                   iate probit one in that the dependent variable is
                                                    no longer a dummy variables, but an ordered
                                                    variable taking values 0, 1, 2, 3, 4 according to
 4. MODELING NUTRITIONAL STATUS                     the level of nourishment of the children. As in a
                                                    univariate probit model, the model is built
  In this section we econometrically analyze        around a latent regression variable. Suppose
the factors determining childhood nutritional
                                                    w0    0
                                                     is ˆ x b ‡ vis ;                              4†
or health status of boys and girls below age ®ve
in the study villages. Given the ordered nature     where  w0
                                                            isis unobserved, b the set of regression
of the nutritional status index NUTST provid-       parameters and v the random disturbance term
ed by the WIDER data, we shall use an ordered       following a normal distribution with zero mean
probit model to analyze the nutritional status      and constant variance r2 . What we observe is
of male and female children.                        as follows:
                                                    wis ˆ 0 if w0
                                                                 is 6 0 if wellnourished†
            (a) An ordered probit model
                                                        ˆ 1 if 0 6 w0is 6 l1
  Suppose we observe the nutritional status                if slightly undernourished†
NUTST of male and female children in the                ˆ 2 if l1 6 w0
                                                                      is 6 l2
study villages. This can be modeled at least in
two ways.                                                  if moderately undernourished†
  We could model the probability of ®nding a            ˆ 3 if l2 6 w0
                                                                      is 6 l3
child that is (slightly, moderately or severely)
                                                           if severely undernourished†
malnourished. To this end, we generate a
variable MALNUT which takes a value 1 if any            ˆ 4 if l3 6 w0
                                                                      is
child is malnourished (i.e., if NUTST P 1) and             if disastrously undernourished†
zero otherwise. Now suppose for each child i of
a given gender s ˆ m, f, there is an underlying     Here l0 s are the unknown threshold parameters
response variable wis de®ned by the relation-      to be estimated along with the regression pa-
ship:                                               rameters bs. Given this classi®cation, we can
                                                    derive the probabilities of malnutrition of dif-
wis ˆ x0 a ‡ uis ;                           3†    ferent degrees as follows:
where a is the vector of parameters and u the       Prob‰w ˆ 0Š ˆ U ÿx0 b†
random disturbance term which follows a
normal distribution with zero mean and con-         Prob‰w ˆ 1Š ˆ U l1 ÿ x0 b† ÿ U ÿx0 b†
stant variance. In practice wis is unobservable;   Prob‰w ˆ 2Š ˆ U l2 ÿ xb† ÿ U l1 ÿ x0 b†        5†
what we observe is the variable MALNUT                                       0               0
                                                    Prob‰w ˆ 3Š ˆ U l3 ÿ x b† ÿ U l2 ÿ x b†
de®ned as follows:
                                                    Prob‰w ˆ 4Š ˆ 1 ÿ U l3 ÿ x0 b†
MALNUT ˆ 1 if wis > 0;
                                                    where U is the cumulative normal distribution
       ˆ 0 otherwise:
                                                    function such that the sum total of above
  Using MALNUT as the dependent variable,           probabilities is equal to one. Also note that
one can use a univariate probit model to esti-      here we drop the subscripts `is' for notational
mate the probability if the i-th child being        simplicity. We maximize the log-likelihood
malnourished or not. It means however that we       function to obtain the estimates of b0 s and l0 s.
are not using all the information contained in        Following our speci®cation of Eqns. (1) and
NUTST: we are combining slightly malnour-           (2) in Section 3, three sets of explanatory
CHILDHOOD MALNUTRITION IN RURAL INDIA                                       1159

variables are included: Individual characteristics   PCINC by PCLAND (e.g., LANDFLIT,
of the child, namely, sex (FEMALE), age              LANDC2, LANDC3, LANDC4), and PCEXP
(AGE), birth order (ORDER); and Household            respectively (e.g., EXPFLIT, EXPC2, EXPC3,
characteristics namely, family size (FSIZE),         EXPC4; also see note to Table 6).
family literacy rate (LITRATE), presence of an         Finally, in order to pick up any nonlinearity
adult, literate female (FLIT)14 and caste (SC,       present in the data, we include some additional
ST, Muslim respectively for scheduled caste,         squared terms for the continuous variables: the
scheduled tribe and Muslim households). So far       square of age (SQAGE), square of family size
as the income measure is concerned, one needs        (SQFSIZE), square of income (SQPCINC,
to be careful in the choice of right instrument.     SQPCEXP, SQPCLAND) and square of liter-
To the extent that household smooths con-            acy rate (SQLITRT). The means and standard
sumption, permanent income or household ex-          deviations of the explanatory variables are
penditure may be regarded as a better measure        given in Table 6.
of long-run resource availability than current         Since the dependent variable is an ordered
income which tends to have a larger transitory       variable, estimated parameters do not re¯ect
component. Cross-section nature of the WID-          the marginal e€ects. The derivation of the
ER data at our disposal does not, however,           marginal e€ects in the ordered probability
allow us to construct a measure of permanent         models is quite complex; we calculate the e€ects
income. Consequently, we have considered             of change in covariates on the cell probabilities
three instruments of income, namely, (i) per         as follow:
capita current income (PCINC),15 (ii) per cap-
ita expenditure (PCEXP, for all villages except      @Prob‰cell jŠ
                                                                   ˆ ‰/ ljÿ1 ÿ b0 xk † ÿ / lj ÿ b0 xk †Š
Simtuni since the expenditure data for Simtuni           @xk
are not available) and (iii) per capita land-                         b
holding (PCLAND) to instrument current in-
come. Instruments (ii) and (iii) could be            where /(.) is the normal density function, lj the
considered as longer-term income measure in          threshold parameter and xk the k-th explana-
the analysis of child health. (c) Locational fac-    tory variable.
tors, namely, ®ve village dummies for six vil-
lages are also considered (however, in case                       (b) Parameter estimates
PCEXP is used as an instrument of income, we
use four village dummies for the ®ve villages for       First we consider the ordered probit esti-
which data are available). Inclusion of these        mates of b and l for all sample children as
village dummies account for the village-level        shown in Table 7.17 Columns (1), (2) and (3) of
variation not only in the provision of public        the table gives the estimates for three instru-
services16 but also in prices and in market op-      ments of income, namely, PCINC, PCEXP (for
portunities across the study villages.               all villages except Simtuni) and PCLAND,
   It follows from our discussion in Sections 2      other variables remaining the same. A com-
and 3 however, that there has been some cor-         parison of the likelihood ratio statistic suggests
relation between/among variables like income/        that the goodness of ®t of the regression
expenditure, literacy and caste. This in turn        equation is the highest when we consider
means that these variables are not randomly          PCINC variable instead of PCEXP or
distributed in our sample, which in turn may         PCLAND. Moreover, estimates presented in
introduce some bias in the estimates. For ex-        columns (2) and (3) generally have lower t-ra-
ample, wealthier households generally belong         tios. AGE is highly signi®cant among the sam-
to upper caste Hindu families or literate female     ple children such that older children have
members are more likely to belong to wealthier,      greater likelihood of being malnourished;
upper caste households. In order to control for      however, SQAGE is signi®cantly negative sug-
this possible bias in our estimation, we include     gesting that probability of being malnourished
a set of interaction terms between: family in-       increases less than proportionately with age.
come and female literacy (INCFLIT); female           This result holds good in all three speci®ca-
literacy and caste (FLC2, FLC3, FLC4); and           tions. Second, PCINC and SQPCINC are both
income and caste variables (INC2, INC3,              highly signi®cant: children from households
INC4). In addition, for the instruments of in-       with higher per capita income are much less
come, namely, PCLAND and PCEXP, we                   likely to be malnourished. But, the coecients
generate similar interaction terms by replacing      of PCLAND and PCEXP though negative are
1160                                          WORLD DEVELOPMENT

                                                                                            a
                           Table 6. Mean and standard deviation of explanatory variables

Variables                              Male                             Female                           All

AGE [1]                        29.65 (1.717)                      3.142 (1.598)                   3.054 (1.658)
SQAGE [1]                      11.7248 (10.7547)                 12.4122 (10.4508)               12.0732 (10.5951)
ORDER                           0.02 (0.15)                       0.04 (0.19)                     0.03 (0.18)
SIZE                            6.55 (3.28)                       6.55 (3.05)                     6.55 (3.16)
SQSIZE                         53.58 (62.76)                     52.14 (52.15)                   52.85 (57.56)
PCINC [2]                       1.389 (0.9358)                    1.346 (0.8871)                  1.367 (0.9107)
SQPCINE [2]                     2.8009 (5.4092)                   2.5950 (5.0069)                 2.6965 (5.2042)
PCEXP [2]                       1.2180 (0.5198)                   1.2091 (0.5876)                 1.2135 (0.5545)
SQPCEXP [2]                     1.75 (1.9296)                     1.8055 (2.2980)                 1.7793 (2.1217)
LITRATE                         0.40 (0.39)                       0.43 (0.50)                     0.42 (0.39)
SQLITRT                         0.31 (0.38)                       0.34 (0.39)                     0.33 (0.39)
FLIT                            0.38 (0.49)                       0.43 (0.49)                     0.41 (0.49)
SC                              0.33 (0.47)                       0.38 (0.49)                     0.36 (0.48)
ST                              0.24 (0.43)                       0.24 (0.43)                     0.24 (0.43)
MUSLIM                          0.07 (0.26)                       0.06 (0.24)                     0.07 (0.25)
FLC2                            0.0884 (0.2845)                   0.1222 (0.3282)                 0.1055 (0.3076)
FLC3                            0.0047 (0.0682)                   0.0136 (0.1160)                 0.0092 (0.0955)
FLC4                            0.0186 (0.1354)                   0.0181 (0.1336)                 0.0183 (0.1344)
INCFLIT                       713.0361 (1187.7772)              732.3180 (1153.8353)            722.8098 (1169.3880)
EXPFLIT                       561.0961 (828.1882)               615.7612 (855.7972)             588.8048 (841.7728)
LANDFLIT                        0.1401 (0.2807)                   0.1456 (0.2938)                 0.1428 (0.2871)
INC2                          364.1399 (572.6027)               439.3844 (622.0920)             402.2799 (598.6991)
INC3                          266.0134 (520.2095)               247.6766 (461.0789)             256.7189 (490.6470)
INC4                           73.5633 (269.6706)                57.6186 (241.3429)              65.4812 (255.5340)
EXPC2                         326.7020 (484.2186)               395.5128 (552.3618)             361.5809 (520.4217)
EXPC3                         183.2426 (430.9795                159.3441 (379.9271)             171.1289 (405.6133)
EXPC4                          73.4927 (265.5296)                57.4559 (241.0020)              65.3640 (253.2290)
LANDC2                          0.0244 (0.0823)                   0.0385 (0.1096)                 0.0316 (0.0973)
LANDC3                          0.0434 (0.1837)                   0.0400 (0.1306)                 0.0417 (0.1588)
LANDC4                          0.0034 (0.0277)                   0.0057 (0.0369)                 0.0046 (0.0327)
Kuchly                          0.15 (0.36)                       0.18 (0.39)                     0.17 (0.37)
Sahajapur                       0.34 (0.48)                       0.33 (0.47)                     0.34 (0.47)
Bhagabandasan                   0.15 (0.36)                       0.10 (0.29)                     0.12 (0.33)
Simtuni                         0.09 (0.28)                       0.09 (0.29)                     0.09 (0.29)
Kalmandasguri                   0.14 (0.35)                       0.19 (0.39)                     0.17 (0.37)
No. of observations           215                               221                             436
a
  AGE: age of the child in months; FEMALE: 1 if the child is female and zero if male; ORDER: 1 if the child
concerned is the eldest in the birth order, 2 if second eldest and 3 for the third eldest in the family and so on; SIZE ±
number of members in the household the child belong to; PCINC: annual per capita income of the household the
child belongs to; FLIT: If some female member of the household is literate; INCFLIT: Interaction between PCINC
and FLIT; LITRATE: literacy rate of the members in the household; SC: 1 if the child belongs to scheduled caste
household and zero otherwise; ST: 1 if the child belongs to scheduled tribe household and zero otherwise; MUSLIM:
1 if the child belongs to Muslim household and zero otherwise [Reference group is upper caste Hindu]. SQAGE,
SQSIZE, SQPCINC, SQLITRT: square of age, family size, PCINC, LITRATE respectively. FLC2, FLC3, FLC4:
interaction among FLIT and SC, ST and MUSLIM respectively; INC2, INC3, INC4: interaction among PCINC
and SC, ST and MUSLIM respectively; EXPC2, EXPC3, EXPC4: interaction among PCEXP and SC, ST and
MUSLIM respectively; LANDC2, LANDC3, LANDC4: interaction among PCLAND and SC, ST and MUSLIM
respectively; INCFLIT, EXPFLIT, LANDFLIT: interaction among FLIT and PCINC, PCEXP and PCLAND
respectively. Age has been scaled down by a factor of 10; PCINC, PCEXP have been scaled down by a factor of
1,000. This scaling was necessary to ensure the convergence of the log-likelihood function of the ordered probit
model.

both insigni®cant (while the coecient of                     termining childhood health status among the
SQPCEXP is signi®cant). In other words, while                 sample children. These estimates may, there-
current per capita income is highly signi®cant,               fore, cast doubts about the role of permanent
longer-term instrumemt of income like land or                 income instruments on child health derived
expenditure per capita is not signi®cant in de-               from weight-for-age. Finally, the gender dummy
CHILDHOOD MALNUTRITION IN RURAL INDIA                                                       1161

                                                                                                  a
                      Table 7. Ordered probit estimates of childhood nutritional status for all

Variables                      Coecient (T-ratio)              Coecient (T-ratio)               Coecient (T-ratio)

Intercept                       1.44 (2.659)                    0.84 (1.190)                         0.44 (0.928)
AGE                             0.35 (2.724)                    0.32 (2.350)                        0.33 (2.580) 
SQAGE                          ÿ0.04 (2.058)                    ÿ0.04 (1.747)                       ÿ0.04 (1.926) 
FEMALE                          0.35 (3.225)                    0.34 (2.895)                       0.36 (3.211) 
ORDER                          ÿ0.02 (0.157)                      0.04 (0.410)                         0.03 (0.248)
SIZE                            0.09 (1.224)                      0.07 (0.928)                         0.09 (1.257)
SQSIZE                         ÿ0.008 (1.887)                   ÿ0.006 (1.469)                       ÿ0.007 (1.644) 
PCINC                          ÿ1.28 (3.938)                           ÿ                                   ÿ
SQPCINC                         0.17 (3.702)                           ÿ                                   ÿ
PCEXP                                  ÿ                         ÿ0.80 (1.324)                               ÿ
SQPCEXP                                ÿ                          0.25 (2.351)                              ÿ
PCLAND                                 ÿ                                 ÿ                            ÿ1.03 (0.902)
SQPCLAND                               ÿ                                 ÿ                            ÿ0.045 (0.082)
LITRATE                         0.18 (0.285)                      0.22 (0.339)                         0.11 (0.170)
SQLITRT                         0.17 (0.267)                      0.003 (0.004)                        0.14 (0.204)
FLIT                           ÿ0.55 (1.317)                      0.14 (0.221)                        ÿ0.59 (1.560)
SC                             ÿ0.95 (2.321)                    ÿ0.78 (1.356)                        ÿ0.46 (1.974) 
ST                             ÿ0.47 (0.991)                     ÿ0.49 (0.650)                        ÿ0.31 (1.098)
MUSLIM                         ÿ1.12 (0.906)                     ÿ0.72 (0.466)                        ÿ0.53 (1.131)
INCFLIT                         0.08 (0.349)                             ÿ                                   ÿ
EXPFLIT                                ÿ                         ÿ0.52 (1.095)                               ÿ
LANDFLIT                               ÿ                                 ÿ                             0.45 (0.443)
FLC2                            0.34 (1.046)                      0.27 (0.832)                         0.48 (1.343)
FLC3                            1.09 (1.656)                     1.12 (1.634)                         1.23 (2.014) 
FLC4                            1.20 (2.077)                     1.09 (1.856)                        1.39 (2.389) 
INC2                            0.48 (1.637)                             ÿ                                   ÿ
INC3                            0.32 (0.913)                             ÿ                                   ÿ
INC4                            0.71 (0.631)                             ÿ                                   ÿ
EXPC2                                  ÿ                          0.40 (0.825)                               ÿ
EXPC3                                  ÿ                          0.33 (0.517)                               ÿ
EXPC4                                  ÿ                          0.36 (0.249)                               ÿ
LANDC2                                 ÿ                                 ÿ                            ÿ0.002 (0.002)
LANDC3                                 ÿ                                 ÿ                             2.13 (1.896) 
LANDC4                                 ÿ                                 ÿ                            ÿ0.49 (0.190)
Kuchly                          0.23 (0.840)                      0.11 (0.400)                         0.06 (0.223)
Sahajapur                       0.28 (1.089)                      0.20 (0.766)                         0.09 (0.311)
Bhagabandasan                   0.38 (1.246)                      0.08 (0.267)                        ÿ0.05 (0.149)
Simtuni                         0.33 (0.999)                             ÿ                            ÿ0.11 (0.228)
Kalmandasguri                   0.29 (1.129)                      0.20 (0.764)                         0.06 (0.223)

                                                                                                                 
l1                              1.18 (13.924)                    1.15 (13.528)                   1.16 (13.966)
                                                                                                                 
l2                              2.27 (20.723)                    2.22 (20.023)                   2.22 (21.960)
                                                                                                                 
l3                              3.32 (17.971)                    3.19 (16.228)                   3.26 (17.671)
L                            ÿ557.6878                        ÿ515.2573                       ÿ564.921
L0                           ÿ599.023                         ÿ542.1376                       ÿ599.023
LR                             82.6703                        53.7606                       68.2040 
Observations                  436                              397                             436
a
   FLC2, FLC3, FLC4: interaction among FLIT and SC, ST and MUSLIM respectively; INC2, INC3, INC4:
interaction between PCINC and SC, ST and MUSLIM respectively; EXPC2, EXPC3, EXPC4: interaction among
PCEXP and SC, ST and MUSLIM respectively. LANDC2, LANDC3, LANDC4: interaction among PCLAND and
SC, ST and MUSLIM respectively; INCFLIT, EXPFLIT, LANDFLIT: interaction among FLIT and PCINC,
PCEXP and PCLAND respectively. The dependent variable of the ordered probit model is NUTST. LR ˆ 2(LÿL0 ) is
the likelihood ratio statistic with a chi-square distribution where L is the log-likelihood function, L0 the restricted
log-likelihood function.

   Signi®cant at the 10% level.

    Signi®cant at the 1% level.
1162                                  WORLD DEVELOPMENT

FEMALE is highly signi®cant and positive so          Table 8. Ordered probit estimates of childhood nutritional
that other things remaining identical female                                  status
children are more likely to be malnourished
                                                     Variables          Female coecient Male coecient
than comparable male children.                                             (T-ratio)       (T-ratio)
   In view of the latter result, we next examine
the nature of separate nutritional status func-      Intercept             0.81 (0.766)       2.06 (2.445) 
tions for male and female children in the study      AGE                   0.77 (3.337)     0.11 (0.501)
               
villages vis-a-vis    di€erent individual and        SQAGE                ÿ0.11 (2.943)    ÿ0.001 (0.037)
household characteristics as speci®ed in             ORDER                  0.02 (2.118)     0.058 (0.300)
Eqn. (2). Hence, we re-estimate the nutritional      SIZE                  0.13 (0.903)       0.06 (0.614)
                                                     SQSIZE               ÿ0.01 (1.394)      ÿ0.005 (1.072)
status function by excluding the gender dummy        PCINC                ÿ1.17 (1.372)      ÿ1.48 (3.067) 
(see Table 8). The likelihood ratio statistic re-    SQPCINC               0.23 (3.173)     0.15 (2.312) 
¯ecting the goodness of ®t of the model is sig-      LITRATE               0.37 (0.362)       0.21 (0.230)
ni®cant in each case.18 Parameter estimates for      SQLITRT              ÿ0.42 (0.408)       0.23 (0.241)
alternative speci®cation with respect to longer-     FLIT                  0.71 (0.674)      ÿ1.35 (2.205) 
term instruments of income, namely, PCEXP            SC                   ÿ0.42 (0.462)      ÿ1.34 (2.197) 
and PCLAND are shown in the Appendix                 ST                   ÿ0.18 (0.142)      ÿ1.39 (1.379)
                                                     MUSLIM              ÿ1.56 (0.693)       ÿ0.93 (0.616)
(Table 14).
                                                     INCFLIT              ÿ0.42 (0.569)      ÿ0.41 (1.084)
   Results obtained establishes the contrast in      FLC2                  0.32 (0.581)       0.13 (0.280)
the nature of nutritional status functions be-       FLC3                  0.89 (0.856)       1.46 (0.000)
tween male and female children. In particular,       FLC4                  0.73 (0.653)       1.7 (1.287)
age, birth order and family income are found to      INC2                  0.11 (0.152)       0.85 (1.586)
be signi®cant for girls; signi®cant variables for    INC3                  0.17 (0.169)       1.06 (1.259)
boys' health are income and female literacy.19       INC4                  1.2 (0.611)        0.42 (0.300)
This at once implies that the nutritional status     Kuchly                0.07 (0.199)       0.33 (0.660)
                                                     Sahajapur             0.48 (1.426)       0.06 (0.130)
estimates obtained from the combined sample          Bhagabandasan         0.42 (0.964)       0.47 (0.883)
(with a gender dummy) fail to capture the            Simtuni               0.39 (0.799)       0.44 (0.751)
complex di€erences in the nature of the nutri-       Kalmandasguri        ÿ0.09 (0.244)       0.61 (1.301)
tional status functions between male and fe-
male children, thus providing a further              l1                    1.19 (8.309)    1.28 (10.452) 
justi®cation for determining child health func-      l2                    2.47 (14.123)  2.22 (13.391) 
tions separately for male and female children.       l3                    3.56 (12.796)  3.32 (9.539) 
   The likelihood of a girl of being malnour-        L                  ÿ272.8278         ÿ266.8102
ished increases with age (though the rate of         L0                 ÿ299.9716         ÿ290.3857
                                                     LR                   54.2876         47.1511 
increase decreases as age increases). The prob-      Observations        221               215
ability of malnourishment is signi®cantly higher
if the girl has a higher birth order, possibly       a
                                                        The dependent variable of the ordered probit model is
suggesting a discrimination against female           NUTST. LR ˆ 2(L ÿ L0 ) is the likelihood ratio statistic
children (this issue will be further examined        with a chi-square distribution where L is the log-likeli-
later in this section) while birth order is not      hood function, L0 the restricted log-likelihood function.
signi®cant for boys. But, per capita household       FLC2, FLC3, FLC4: interaction between FLIT and SC,
income and female literacy are highly signi®-        ST and MUSLIM respectively; INC2, INC3, INC4: in-
                                                     teraction among PCINC and SC, ST and MUSLIM
cant determinants of boys' health. Per capita        respectively; INCFLIT: interaction among FLIT and
family income is also a highly signi®cant factor     PCINC.
in improving the health of baby girls: nutri-        
                                                        Signi®cant at the 10% level.
tional status of a boy or girl signi®cantly im-      
                                                         Signi®cant at the 1% level.
proves as per capita family income increases.20
Female literacy rate however, exerts opposite
e€ects on boys and girls: presence of a literate     childhood nutritional status: the variable SIZE
adult female member in the family improves the       is insigni®cant in all speci®cations. This per-
nutritional status of a baby boy, ceteris paribus,   haps suggests that the positive e€ect of size
while it lowers that of a girl (even after con-      economies of scale in consumption just com-
trolling for the interaction between income and      pensates the negative e€ect of diminishing re-
female literacy).21                                  turns to scale in nutritional status so that the
   There is, however, no evidence that family        total e€ect is insigni®cant. In addition, the ag-
size in the WIDER sample signi®cantly a€ects         gregate family literacy rate does not have a
CHILDHOOD MALNUTRITION IN RURAL INDIA                                             1163

signi®cant e€ect on the nutritional status of                   female literacy is, however, di€erent for male
male or female children. Finally, we consider                   and female children: it improves the probability
the e€ect of caste variables on child health.                   of being well-nourished by about 35% for male
After controlling for the interactions between                  children while it lowers that for female children
per capita income, caste and female literacy                    by about 11%. Increase in per capita income by
rate, none of the caste variables is signi®cant in              one unit would raise the probability of being
the determination of female health. Boys from                   well-nourished for both male and female chil-
scheduled caste households however are less                     dren though the extent is still higher for male
likely to be malnourished (since the dummy for                  children (38% as against only 17% for female
SC is negative and signi®cant).22                               children).
   Finally, given that the dependent variable of
our regression NUTST is an ordered variable,                       (c) Predicted probability of nutritional status
we calculate the marginal e€ects of a unit
change in a number of explanatory variables                        In this section, we use the male and female
for both male and female children which, to                     ordered probit estimates to calculate the pre-
some extent, would re¯ect the e€ect of a unit                   dicted probability of childhood nutritional
change in any explanatory variable on the                       status of di€erent degrees (see Table 10). These
probability of the child of being well nourished                predicted probability estimates further charac-
(NUTST ˆ 0),        slightly     undernourished                 terize the extent of the di€erence in male-female
(NUST ˆ 1),       moderately     undernourished                 health status in the study villages. To this end,
(NUST ˆ 2),        severely      undernourished                 we only consider the estimates corresponding
(NUST ˆ 3) and disastrously undernourished                      to per capita current income which plays a
(NUTST ˆ 4). These estimates are shown in                       signi®cant role (Table 8) unlike the estimates
Table 9. These marginal e€ects ®gures further                   using longer term instruments of income. Sub-
strengthen the inferences obtained from the                     stituting these ordered probit estimates from
parameter estimates (see Table 8). In particu-                  Table 8 for male and female into Eqn. (5), we
lar, we focus on the marginal e€ects of age, per                calculate the predicted probability of a child of
capita income and female literacy which are                     a given sex aged 30 months from a scheduled
signi®cant in determining male/female nutri-                    tribe household with (shown in the parenthesis)
tional status. An increase in age by one unit                   and without literate female of being slightly,
would lower the probability of being well-                      moderately, severely and disastrously malnour-
nourished and slightly malnourished for both                    ished, values of continuous regression variables
male and female children, though the extent is                  being maintained at their gender-speci®c vil-
higher for female children. Marginal e€ect of                   lage-level averages unless otherwise stated.

                                                                                              a
                                Table 9. Marginal e€ects of selected explanatory variables

Variable                                           NUTST

                      0                        1                   2                     3                   4

                F           M           F            M         F         M        F               M      F       M

AGE          ÿ0.11        ÿ0.03       ÿ0.19        ÿ0.01      0.13      0.02     0.14         0.02      0.03    0.002
SQAGE         0.02        ÿ0.0003      0.03        ÿ0.0002   ÿ0.02     2Eÿ04    ÿ0.02         0.0002   ÿ0.004   0
ORDER        ÿ0.003       ÿ0.015      ÿ0.005       ÿ0.007     0.003     0.012    0.004        0.009     0.001   0.001
SIZE         ÿ0.02        ÿ0.016      ÿ0.03        ÿ0.008     0.02      0.012    0.03         0.009     0.005   0.001
SQSIZE        0.002        0.001       0.003        0.0007   ÿ0.002    ÿ0.001   ÿ0.002       ÿ0.0008   ÿ0.0005 ÿ0.0001
PCINC         0.17         0.38        0.28         0.18     ÿ0.19     ÿ0.29    ÿ0.22         0.23     ÿ0.05   ÿ0.03
SQINC        ÿ0.03        ÿ0.04       ÿ0.06        ÿ0.02      0.04      0.03     0.04         0.02      0.01    0.004
FLIT         ÿ0.11         0.35       ÿ0.17         0.17      0.12     ÿ0.27     0.13        ÿ0.21      0.03   ÿ0.03
a
 Each entry refers to the marginal e€ect of a unit change in the respective explanatory variable listed in column 1 for
male (M) and female (F) children on the probability of being well-nourished (NUTST ˆ 0), slightly undernourished
(NUST ˆ 1), moderately undernourished (NUST ˆ 2), severely undernourished (NUST ˆ 3) and disastrously un-
dernourished (NUTST ˆ 4).
1164                                        WORLD DEVELOPMENT

                                                                                                   a
                     Table 10. Predicted probability of nutritional status in the study villages

Village               Sex          NUTST ˆ 0        NUTST ˆ 1          NUTST ˆ 2       NUTST ˆ 3        NUTST ˆ 4

Kuchly                Female      0.09   (0.006)    0.34   (0.01)      0.44   (0.20)   0.12 (0.41)     0.01 (0.37)
                      Male        0.37   (0.39)     0.46   (0.45)      0.15   (0.14)   0.02 (0.02)     0.0014 (0.0012)
Sahajapur             Female      0.04   (0.0001)   0.24   (0.005)     0.48   (0.09)   0.09 (0.33)     0.04 (0.57)
                      Male        0.45   (0.53)     0.42   (0.39)      0.11   (0.08)   0.01 (0.0092)   0.007 (0.0004)
Bhagabandasan         Female      0.05   (0.0001)   0.28   (0.006)     0.47   (0.10)   0.17 (0.33)     0.03 (0.56)
                      Male        0.54   (0.62)     0.38   (0.33)      0.07   (0.05)   0.008 (0.005)   0.003 (0.0001)
Simtuni               Female      0.02   (0.0001)   0.18   (0.007)     0.47   (0.12)   0.27 (0.35)     0.06 (0.53)
                      Male        0.09   (0.08)     0.39   (0.36)      0.35   (0.36)   0.15 (0.17)     0.0002 (0.03)
Kalaman dasguri       Female      0.08   (0.0003)   0.34   (0.013)     0.44   (0.16)   0.13 (0.39)     0.02 (0.44)
                      Male        0.36   (0.44)     0.46   (0.44)      0.15   (0.11)   0.03 (0.01)     0.0002 (0.0008)
a
   These probabilities are calculated for children aged 30 months belonging to a scheduled tribe household without a
literate female, values of other variables being kept at their respective village-level averages. Numbers in the pa-
rentheses denote the probability for households with a literate female.

   First, we consider the case when the house-                male and female health status if Simtuni had
hold concerned does not have any literate                     the same per capita current income as the
female. In this case, the likelihood of being                 richest village Bhagabandasan (see Table 11).
well-nourished (NUTST ˆ 0) and of slightly                    In this case, the probability of being well-
malnourished (NUTST ˆ 1) is much higher for                   nourished increases signi®cantly for both male
boys while the likelihood of being moderately                 and female children, though the margin is still
or seriously malnourished (NUTST P 2) is                      higher for boys. For example, other things re-
much higher for girls. There is also a pro-                   maining unchanged, the probability of being
nounced intervillage variation in child health:               well-nourished (NUTST ˆ 0) increases by
(i) probability of male and female children be-               about 35% for male and only 2% for female
ing well-nourished is less in the tribal domi-                children in Simtuni; however, the probability of
nated village Simtuni; (ii) the likelihood of                 slight malnourishment (NUST ˆ 1) increases by
female children being well-nourished is rela-                 4% for male and 8% for female children. The
tively higher in the Muslim dominated North                   probability of disastrous malnourishment
Bengal village Kalmandasguri and (iii) the dif-               (NUTST ˆ 4) decreases by 15% for male and
ference in male and female health status is rel-              3% for female children.24
atively less pronounced in the tribal dominated
village Simtuni or Muslim dominated village                          (d) Signi®cance of birth order on female
Kalmandasguri compared to the villages dom-                                     nutritional status
inated by upper caste Hindus.
   Second, we consider the nature of child                       In the Indian society with a pronounced
health in the presence of literate female in the              preference for male children (Kishor, 1993),
household. In this case, the probability of boys              signi®cance of birth order for girls cannot be
being well-nourished increases while that of                  ignored. In particular, Dasgupta (1987) has
girls decreases in all the study villages. The                emphasized the disadvantage of being a second
probability     of    severe      malnourishment              (or higher) order daughter. Our regression re-
(NUST > 2) also increases for female children.                sults from the WIDER villages in West Bengal
In other words, the e€ect of female literacy on               (as presented in Table 8) too suggest that birth
childhood health status di€ers between male                   order matters for girls though not for boys. We
and female children: it improves the health of                shall, in this section, further examine the e€ect
the boys at the cost of girls in the study villages.          of birth order on the nutritional status of baby
   Finally, given the relative importance of in-              girls only. In particular, following Dasgupta's
come in the existing literature, we examine the               argument, we shall examine if second or higher
e€ect of increasing per capita current income23               order girls in the sample are signi®cantly at a
on child health when FLIT ˆ 0, using the esti-                disadvantage with respect to their health status.
mates shown in Table 8 as before. To this end,                The simplest way to model this is to include a
we focus on the poorest of the study villages                 dummy for whether the female child has an
Simtuni and examine what would happen to                      older sister or not (OLDSISTER). Alterna-
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