J. Soc. Econ. Dev. https://doi.org/10.1007/s40847-017-0047-x

RESEARCH PAPER

Decomposition of gender differential in in Indian children

Shyma Jose1

Ó Institute for Social and Economic Change 2017

Abstract The nutritional status and health of Indian children is exacerbated due to poor and inadequate diet. However, intra-household gender disparity in provision of nutrition, health care and allocation of food increases the plight of the girl child. The prevalence of son preference in the Indian society has led to make the sex ratio more masculine in the recent years. The present paper tries to capture the gender differential in malnutrition and other health indicators. The paper found considerable heterogeneity existing in nutrient intake in across both genders in various states. The study showed how different child- specific, household and exogenous factors manifest in gender disparity in nutritional status. The decomposition of the gender gap in underweight children showed that the difference in effect of the determinant affected the gender gap more than the difference in distribution of the factors. This shows the prevalence of some gender favouritism toward the male chil- dren in nutritional outcomes is as effective as the difference in endowment of the factors against the girl child.

Keywords Gender gap Á Malnutrition Á Son preference Á Blinder–Oaxaca

Introduction

Child malnutrition is a major public health concern and a core issue of development in a country like India. Recent estimates show more than 40% of undernourished children in the world are in India. The nutritional status in India is characterized by malnourishment and morbidity, which aggravate the predicament of the Indian children due to poor and inadequate diet. National Family Health Survey (2005–2006) estimates show more than half of the Indian children suffer from malnutrition. Malnutrition coupled with gender

& Shyma Jose [email protected]

1 University, Room No 208, Shipra Hostel, New Delhi 110067, India 123 J. Soc. Econ. Dev. discrimination prevalent in nutritional and health measures exacerbates the plight of female children. Intra-household gender discrimination against female in provision of nutrition, health care, education and resource allocation has increased the predicament of the girl child which manifests in excess female mortality rate (Tarozzi and Mahajan 2007; Chaudhri and Jha 2011; Sen and Sengupta 1983). India has largest share of missing women1 in the world. Gender inequality is not just a social, political but also an eco- nomical issue and has accorded as one of the Millennium Development Goals (MDG). The gender disparity has become more evident in the Indian society as the gap between male and female sex ratio has increased over temporally and has become more masculine for children below 10 years of age (Arokiasamy and Pradhan 2006). This secular decline in child sex ratio over the years arises due to sex differential in child mortality rates. In India, the sex ratio declined from 972 in 1901 to 927 in 1991, but it increased to 933 in 2001 and then to 940 in 2011. The interstate differential in sex ratio is also quite abysmal with Kerala having sex ratio of 1084 and Haryana having lowest sex ratio of 879 in 2011. Intra-household gender bias is prevalent in India in allocation of food, preventive and curative health care, education, work, wages and fertility choices. Sen and Sengupta (1983) found females below 5 years of age to be more malnourished than males in two villages of West Bengal. The eradication of ill practices in child’s health and nutrition differentiating between female and male children is necessary for gender parity. This disparity in mal- nutrition is caused by factors such as allocation of food, feeding practices, birth order, sex of the sibling, poverty and parental literacy. Large household size linked with high fertility rate and low monthly per capita expenditure tends to increase gender disparity (Mishra et al. 2004; Lancaster et al. 2006). Girls are less likely to be educated and less valued than brothers because of whom they are more deprived of food and health care. The gender bias in nutritional level can create greater debility among the surviving girls and may have inter-generational effect (Patra 2008). In India, wider gender differentials exist in health care services than in food allocation (Das Gupta 1987). Many studies have reported a strong correlation between and gender inequali- ties. The health outcomes of low maternal autonomy extend beyond mothers and translate into health consequences for their children and may be a significant casual factor in child malnutrition. The literature suggests that son preference and low status of women are the two important factors contributing to the gender bias, which is prevalent due to the patriarchal intra-familial economic structure coupled with the cultural, religious and economic utility of boys over girls (Arokiasamy and Pradhan 2006). Various studies have observed that women autonomy may lead to higher survival chances of female child (Das Gupta 1987; Mehrotra 2006). The nutritional status of children has found to be affected by use of health care services, which in turn is determined by maternal education, bargaining power within households and their control over household resources. There is mutually reinforcing relationship between expanding social and economic opportunities for women. Son preference is prominent phenomenon in the India, which affects the nutritional outcomes as well as mortality rate of the girl child (Arokiasamy and Pradhan 2006; Patra 2008; Das Gupta 1987; Bhat and Zavier 1999, 2003; Miller 1981). The literature suggests that son preference is prevalent because of parental preference for boys as it yields higher returns from investment in sons (Patra 2008). The potential worth of a child to the household has both an economic and cultural dimension. The underinvestment of resources in females can be explained by the low expected returns to such investment. In a

1 Missing women refer to the millions of women who were not born due to malpractices as selective female abortions, female infanticide and female neglect leading to low sex ratio (Patra 2008). 123 J. Soc. Econ. Dev. patriarchal and male-centred kinship structure, more sons are preferred to be the source of social and political power. The present paper will study the gender differential in malnutrition indicators, health indicators and heterogeneity in nutrient intake in India across various states and also at unit level. The paper will also trace the factors involved in gender disparity in nutritional level and its consequences. The paper will make use of Blinder–Oaxaca decomposition tech- nique to decompose these factors that cause the gap in nutritional outcomes between male and female children using nonlinear Blinder–Oaxaca decomposition technique. The paper is divided into five sections. The first section will introduce the main premise of the paper. ‘‘Data and methodology’’ section deals with the data source used in the study and also discusses methodology. ‘‘Gender differential in household nutrients intake’’ section deals with the gender differential analysis with respect to nutritional intake, anthropometric indicators and other health indicators. ‘‘Factors affecting gender disparity in nutrition’’ section discusses various factors that affect gender disparity in nutritional status and also examines the decomposed gap in nutritional outcome among children to assess the exis- tence of effect of differential treatment against the female child. ‘‘Discussion’’ section gives some cautionary remark and finally, the study is concluded in ‘‘Conclusion’’ section with policy implications.

Data and methodology

The paper makes use of National Sample Survey (NSS) rounds of Consumption Expen- diture Survey (CES) for the year 2004–2005 and the National Family Health Survey (NHFS) rounds for the years 1992–1993, 1999–2000 and 2005–2006. The paper uses anthropometric indictors given in z-scores2 (standard deviation scores) for malnutrition,3 which include: underweight (weight-for-age) which is an indicator of chronic deficiency and is a composite measures of both chronic and acute undernutrition, stunting (height-for- age) is a measure of chronic undernutrition; it measures deficiency in the food energy intake over a long duration and wasting (weight-for-height) measures acute undernutrition. Children whose anthropometric measure (given in z-score) is less than 3 standard deviation (SD) below the median value of National Centre for Health Statistics (NCHS) international reference population are severely malnourished and with z-score less than 2 SD below the median value of NCHS international reference population are moderately malnourished (IIPS and ORC Macro 1994, 2000; Pathak and Singh 2011).4 The unit of analysis in the paper is children below 3 years of age and has been used unvaryingly, if otherwise specified, to measure the nutritional status of children in all three rounds of NFHS. The paper uses nonparametric Kernel density estimates using Epanechnikov kernel smoother

2 z-score is used to standardize the variable to be unit free and is calculated as z-score = (observed value - median value of the reference population)/(standard deviation of value in the reference population). 3 Malnutrition indicators utilize norms of National Centre for Health Statistics (NCHS) based on standard deviation as well as WHO standards, but this study will make use of only NCHS standards to make comparisons between the three rounds as the new WHO reference population published by WHO Multi- center Growth Reference Study Group (2006) is not available in NFHS-I and NFHS-II datasets. 4 In NFHS-I, height and weight were measured for children below 4 years of age and in NFHS-II below 3 years age. Due to the shortage of proper measuring tools, height was not measured during fieldwork in the some of the states covered by NFHS-I (IIPS and Macro 2007; Pathak and Singh 2011; Jose 2016, Arnold et al. 2009). 123 J. Soc. Econ. Dev.

(Sheather 2004) to check difference in nutritional level between male and female children at all India level. The study decomposes gender gap in malnutrition between female and male children to underscore the effect of gender differential in distribution and endowment effect using nonlinear Blinder–Oaxaca decomposition technique. The Blinder–Oaxaca decomposition technique is useful in identifying and quantifying the separate contribution of group dif- ferential in the measurable characteristics of factors affecting gender differential in nutritional outcomes and how behavioural differences or discrimination contributes to the gap. However, standard Blinder–Oaxaca decomposition technique cannot be used when the dependent variable is binary in nature. This paper makes use of an extension of Blinder– Oaxaca decomposition (Jann 2008) to nonlinear regression model developed by Fairlie (2005) using estimates from logit model for identifying categorical differences. The paper makes use of method developed by Fairlie (2005) for decomposition of nonlinear equation:

Y ¼ FðXb^Þ; where Y j is the binary outcome for the group j and F is the cumulative distribution function from logistic regression and can be written as 2  3 2  3 NA A ^A NB B ^A NB B ^A NB B ^B X FXi b X FXi b X FXi b X FXi b YA À YB ¼ 4 À 5 þ 4 À 5; NA NB NB NB i¼1 i¼1 i¼1 i¼1 where NJ is the sample size for group j. The first part captures the gap in distribution of the determinant Xj, and the second part captures the effect of differential in Xj and it also captures a portion of unobserved endowment. The study decomposes factors that cause gender differential in malnutrition among children below 3 years of age into the proportion explained by differences in characteristics (characteristics effect) and the proportion explained by differences in logit coefficients (coefficients effect). This is also called two ‘‘fold decomposition’’ (Elder et al. 2010; Hahn et al. 2008).

Gender differential in household nutrients intake

The nutritional outcomes depend upon various cofactors such as calorie intake of a person in terms of age, sex, body weight, height, nature of work (sedentary/moderate/heavy), state of health, place of residence, hygiene and environment (NSSO 2006b). The requirement of calorie per consumer unit depends upon these factors. The gender differential in nutritional outcomes especially among children depends upon the intra-household allocation of food. Many nutritionists and researchers have tried to study the gender differential in the nutritional outcomes and how allocation of food and diet within the household affects malnutrition level among female child as compared to male child. Table 1 shows amount of consumer unit assigned to person across sex and age. It is clearly evident that children aged 0–9 years irrespective of their gender require same amount of nutrients and it is only after 10 years of age that nutritional requirements of male children increase as compared to female children. However, women have higher nutritional requirements than men in the reproductive years ranging between 20 and 39 years of age. The NSSO (2006a) provides information on frequency of consumption of food items within households. The study has calculated the frequency of consumption for children below 6 years of age as shown in Table 2. At all India level, the amount of cereals 123 J. Soc. Econ. Dev.

Table 1 Consumer units assigned to a person. Source: NSS Report No. 513, nutritional intake in India, 2004–2005 Age in completed years Male Female Age in completed years Male Female

Less than 1 0.43 0.43 16–19 1.02 0.75 1–3 0.54 0.54 20–39 1.00 1.71 4–6 0.72 0.72 40–49 0.95 0.68 7–9 0.87 0.87 50–59 0.9 0.64 10–12 1.03 0.93 60–69 0.8 0.51 13–15 0.97 0.8 70 ? 0.7 0.5 consumed is almost same for both male and female children; however, examination of the state-wise pattern shows lower cereal intake among female children in most of the states. The only exceptions to that are states such as Assam, Karnataka, and Bihar with higher cereal intake among female children (Table 2). It highlights the exis- tence of gender differential in food allocation across different states. Looking at con- sumption of pulses, milk, meat, egg, fish, vegetables, fruits both fresh and dry, there is a clear indication of gender disparity in food consumption at intra-household level across all the states for 2004–2005. On the contrary, looking at the consumer unit assigned to a person (Table 1), both male and female children below 10 years of age have same nutritional requirements. This shows the disadvantaged position of female children in food allocation, which is essential to ensure their potential growth as well as survival. The nutritional status of child is primarily result of child’s food intake and health status. However, exogenous factors such as safe drinking water, hygiene, health services and mother’s caring capacity also affect the nutritional status. The food intake of children is particularly affected by mother’s caring capacity (Mehrotra 2006). It is observed that mother’s caring capacity and practices along with status of women are essential to ensure gender parity in nutritional allocation at household level. Many studies have highlighted the effect of women’s status in availability of food and household food security. The pervasive inferiority of women’s status in Indian society at large has repercussions on the child nutritional status and gender bias against girl child. The prevalence of gender bias in nutritional level highlights low status of women and women’s autonomy.

Gender disparity in health and nutritional outcomes

The gender disparity in allocation of food, health care, and lack of investment in education, fertility choice and other factors may generate debility and mortality among female chil- dren as compared to their male counterparts. The gender disparity becomes perpetual and causal as neglect of nutrition of female children to maternal undernourishment, which leads to underweight babies and to higher level of child undernutrition (Patra 2008). The gender differential in health care services between girls and boys is the direct consequence of discrimination against girls and favouritism toward boys. Gender discrimination in both immunization and treatment of illness has been reported in various studies (Das Gupta 1987; Arokiasamy and Pradhan 2006). Figure 1 shows the prevalence of various anthro- pometric indicators such as stunting, underweight and wasting according to sex for chil- dren below 3 years of age. The percentage of girls suffering from stunting and underweight

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Table 2 Percentage distribution of children below 6 years of age by frequency of consumption of specific foods (in gm/ml), 2004–2005. Source: Own calculation from Consumption and Expenditure Survey, 2004–2005 unit-level data States Sex Cereal Pulses and Milk and Edible Egg, Vegetables Fresh Dry and pulse milk oil meat or fruits fruits substitute product product fish

Andhra M 17.9 58.9 134.8 85.6 90.3 105.3 34.3 17.8 Pradesh F 9.6 57.8 127.6 83.9 83.0 103.5 34.8 16.1 Assam M 20.5 56.7 120.3 86.9 187.2 185.3 24.7 13.2 F 33.3 57.5 117.6 84.5 170.5 174.1 23.9 12.8 Bihar M 197.9 51.1 155.4 73.5 50.2 107.5 21.2 18.2 F 246.9 50.0 143.0 70.9 49.7 104.0 20.3 16.5 Gujarat M 11.8 65.1 283.9 141.6 75.6 148.4 41.1 23.2 F 9.7 65.0 265.4 133.5 67.9 142.5 39.1 22.3 Haryana M 9.3 50.2 499.1 71.8 88.6 125.1 54.2 24.8 F 3.3 48.4 489.8 69.0 88.5 121.9 57.1 23.7 Himachal M – 93.9 330.6 105.3 78.1 102.6 41.6 37.6 Pradesh F 10.6 90.6 315.3 102.6 79.5 96.7 39.2 51.5 Jammu and M – 57.9 295.1 118.4 135.4 153.2 42.2 31.2 Kashmir F – 57.7 284.4 119.8 123.7 155.1 42.2 26.2 Karnataka M 7.4 66.9 139.0 77.1 96.3 82.3 45.3 15.7 F 10.6 63.9 133.9 75.0 93.9 82.2 46.7 15.7 Kerala M 20.5 53.7 173.6 78.8 213.1 105.5 137.4 23.6 F 19.1 51.7 162.2 77.3 206.5 104.3 136.4 23.9 Madhya M 11.3 55.9 153.0 66.1 53.4 83.8 22.1 21.4 Pradesh F 12.3 53.7 144.9 63.8 52.3 79.8 19.6 17.9 Maharashtra M 13.0 71.3 168.1 110.3 92.6 110.0 43.4 32.6 F 12.6 70.9 152.9 106.5 90.2 107.0 42.2 31.0 Orissa M 10.5 36.3 89.7 47.9 63.0 111.3 22.0 11.6 F 10.5 37.2 96.6 50.3 60.8 115.2 23.4 12.9 Punjab M 200.8 72.5 428.3 107.4 63.8 129.4 50.5 30.4 F 197.0 70.5 392.9 103.2 63.3 124.2 46.5 29.2 Rajasthan M 19.6 35.7 326.2 71.3 91.9 93.9 28.7 27.7 F – 35.4 321.4 70.5 90.6 94.0 29.1 41.7 Tamil Nadu M 10.5 74.0 171.5 79.8 99.7 120.3 45.2 15.8 F 10.3 73.9 164.8 78.4 100.5 123.5 47.0 14.1 Uttar M 71.0 63.0 197.9 74.3 73.5 111.2 26.9 21.7 Pradesh F 69.7 60.9 184.5 74.1 76.2 109.1 24.8 19.0 West M 7.7 38.1 117.5 87.7 149.5 151.6 32.5 12.5 Bengal F 6.6 36.9 105.1 83.9 138.9 144.8 29.4 10.8 All India M 15.4 57.2 204.1 82.7 97.7 116.3 36.2 23.3 F 15.1 55.6 188.6 79.9 94.9 113.3 35.4 22.0

‘–’ not available

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60

50

40

30

20

Percentage of children 10

0 Male Female Male Female Male Female NFHS 1 NFHS 2 NFHS 3 Sex/ year

stunting underweight wasting

Fig. 1 Nutritional Status of children below 3 years of age measured by z score below median- 2 SD. Source: NFHS-I, NFHS-II, NFHS-III is higher for all the three rounds from NFHS-II to NFHS-III except for NFHS-I where the gap is negligible. Figure 2 shows prevalence of severe malnutrition among children measured by severe stunting, severe underweight and severe wasting. The figure shows slightly higher prevalence of both severe stunting and severe underweight in girls than boys in NFHS-II and NFHS-III and only severe stunting in NFHS-I. Severe wasting was higher in male children as compared to females for all three NFHS rounds. The anthropometric indicators provide with information to gauge the nutritional status about body and growth compositions of the children. These three indices of physical growth are used to describe nutritional status of children in Fig. 3. The proportion of children who are stunted or underweight increases rapidly with age, especially in 20–23 months, whereas wasting among children decreases with increasing child’s age. The figure shows proportion of stunting, underweight and wasting among children aged 0–36 months across both genders to evaluate their nutritional status. Fig. 3a and b shows prevalence of stunting and underweight increase through age 20–23 months for girls as compared to boys. The initiation and continuous breastfeeding of girls are delayed, and

30 25 20 15 10 5 Percentage of children 0 Male Female Male Female Male Female NFHS 1 NFHS 2 NFHS 3 Sex/ year

severe stunting severe underweight severe wasting

Fig. 2 Nutritional Status of children below 3 years of age measured by z score below median - 3 SD. Source: NFHS-I, NFHS-II, NFHS-III 123 J. Soc. Econ. Dev.

70 60 50 40 30 children 20 10

a.Percentage of Stunted 0 0 2 4 6 8 1012141618202224262830323436 Ages in months Male Female

70 60 50 40 30 children 20 10

b.Percentage of underweight 0 0 2 4 6 8 1012141618202224262830323436 Ages in months Male Female

35 30 25 20 15 10 5 0 0 2 4 6 8 1012141618202224262830323436 c.Percentage of Wasted children Ages in months Male Female

Fig. 3 Percentage of children aged 0–36 months affected by stunting, underweight and wasting according to age in months and sex for NFHS-III. Source: NFHS-III; IIPS, MoHFW, GOI

123 J. Soc. Econ. Dev. they are not given supplementary nutrients as compared to boys. The delay in feeding of girls manifests mother’s preference for son and prevalence of gender disparity in food allocation at early stages of growth of girl child. However, prevalence of wasting (shown in Fig. 3c) remains the same for boys and girls below 20–23 months and beyond that, it decreases rapidly for girls as compared to boys. All India data do not show significant gender gap in anthropometric indicators (see Fig. 4). The figure depicts nonparametric kernel density estimates for stunting, under- weight and wasting for NFHS-III (2005–2006) for both male and female children below 3 years of age. These densities are calculated using kernel smoother with Epanechnikov kernel with bandwidth according to the criterion proposed by Silverman (1986), which gives robust version of the optimal bandwidth. The kernel densities of both male and female children

a Height–for-age b Weight-for-age .3 .4 .3 .2 .2 .1 .1 Kernal Density Estimates Kernal Density Estimates 0 0 -5 0 5 -5 0 5 Height-for-age Z-score Weight-for-age Z-score

Stunting female Stunting male Underweight female Underweight Male

c Weight-for-height .4 .3 .2 .1 Kernal Density Estimates 0 -4 -2 0 2 4 6 Weight-for-Height Z-score

Wasting male Wasting Female

Fig. 4 Kernel density estimates of the anthropometric indicators for children below 3 years of age, NFHS- III. a Height-for-age b Weight-for-age. c Weight-for-height. Note: The bandwidth for the kernel estimate of height-for-age is 0.18. The bandwidth for the kernel estimate of weight-for-age is 0.15. The bandwidth for the kernel estimate of weight-for-height is 0.15 Source: NFHS-III; IIPS, MoHFW, GOI 123 J. Soc. Econ. Dev.

Table 3 Nutritional status measured by stunting of children aged 0–36 months by sex, NFHS-I to NFHS- III. Source: NFHS surveys-I, II, III; IIPS, MoHFW, GOI State NFHS-I NFHS-II NFHS-III

Male Female Male Female Male Female

Andhra Pradesh – – 37.8 40.2 32.3 37.5 Assam 55.6 48.4 50.7 50.5 37.2 33.6 Bihar 58.6 53.3 53.1 54.5 40.2 47.1 Gujarat 45.1 43.8 42.5 45.8 43.5 42.8 Haryana 41.1 46.1 47.7 53.7 28.6 27.0 Himachal Pradesh – – 46.1 36.4 36.5 36.0 Jammu and Kashmir 38.7 37.3 40.2 38.1 26.5 29.4 Karnataka 40.9 41.7 35.4 38.1 39.6 36.2 Kerala 26.8 24.7 22.3 21.8 22.2 20.2 Madhya Pradesh – – 49.7 53.2 38.9 38.5 Maharashtra 39.8 43.8 39.0 41.1 38.7 41.7 Orissa 46.5 44.0 44.6 44.0 39.0 39.2 Punjab 36.6 40.1 38.9 39.6 28.8 28.8 Rajasthan 44.3 40.0 50.8 54.4 33.2 34.2 Tamil Nadu – – 30.1 28.9 24.1 26.0 Uttar Pradesh 54.8 54.1 53.6 58.1 45.5 47.5 West Bengal – – 37.1 48.1 33.4 33.5 All India 48.1 48.0 43.9 46.7 38.2 39.7

‘–’ indicates states where height and weights were not measured in NFHS-I below 3 years of age did not show much difference in weight-for-age, height-for-age and weight-for-height at all India level. Therefore, it makes it necessary to look into state-wise assessment to check for interstate variability in nutritional outcome measures. Table 3 shows the presence of gender gap in stunting in most of the states but signif- icantly higher in some states such as Haryana, Karnataka, Maharashtra and Punjab in NFHS-I; Haryana, Uttar Pradesh, West Bengal, Gujarat, Madhya Pradesh and Rajasthan in NFHS-II; and Andhra Pradesh, Bihar, Jammu and Kashmir, Maharashtra, Jammu and Kashmir, Uttar Pradesh, Tamil Nadu and Rajasthan in NFHS-III. The states that showed lower stunting among girls were Gujarat, Orissa, Jammu and Kashmir, Bihar, Assam, Rajasthan and Kerala in NFHS-I; Kerala, Jammu and Kashmir, Himachal Pradesh and Tamil Nadu in NFHS-II; and Gujarat, Haryana, Kerala and Assam in NFHS-III. The state- wise analysis shows considerable interstate heterogeneity among all the indicators. Inad- equate nutrition leading to undernourishment is predominant at all India level for NFHS-II and NFHS-III. The prevalence of underweight (see Table 4) among female children is higher in most of the states for all three NFHS rounds. Some states Assam, Bihar, Karnataka, Madhya Pradesh, Kerala, Orissa, Uttar Pradesh and Rajasthan show better nutritional status among girls than boys in NFHS-I; Himachal Pradesh and Jammu and Kashmir in NFHS-II; and Himachal Pradesh, Haryana, Tamil Nadu and Gujarat in NFHS-III. The trend of wasting shows better nutritional level among girls than boys in all the three rounds except Haryana, Jammu and Kashmir in NFHS-I, Gujarat and Maharashtra in NFHS -II and Andhra

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Table 4 Nutritional status measured by underweight of children aged 0–36 months by sex, NFHS-I to NFHS-III. Source: NFHS surveys-I, II, III; IIPS, MoHFW, GOI State NFHS-I NFHS-II NFHS-III

Male Female Male Female Male Female

Andhra Pradesh 46.2 50.1 36.0 40.6 37.4 37.9 Assam 53.7 47.7 35.2 38.3 40.5 41.1 Bihar 66.2 59.1 53.3 56.5 56.0 62.2 Gujarat 45.9 51.0 41.3 50.3 48.8 47.1 Haryana 33.1 37.7 32.2 38.3 37.1 36.5 Himachal Pradesh 46.3 46.4 46.1 42.4 43.9 41.9 Jammu and Kashmir 40.3 44.1 36.4 33.0 30.8 30.3 Karnataka 51.7 50.4 42.4 45.7 41.2 42.1 Kerala 28.9 26.9 26.6 28.0 28.9 28.8 Madhya Pradesh 61.0 58.7 52.9 58.1 40.0 41.0 Maharashtra 50.2 53.5 50.0 50.3 59.3 61.6 Orissa 54.1 51.3 55.0 54.8 43.1 46.3 Punjab 44.4 48.3 27.6 31.2 25.7 31.4 Rajasthan 46.0 44.3 49.8 52.4 43.5 44.7 Tamil Nadu 45.1 49.8 36.0 38.3 39.1 29.6 Uttar Pradesh 59.6 55.4 50.0 53.9 46.1 50.0 West Bengal 56.4 58.7 46.1 52.6 42.2 46.0 All India 53.2 52.4 45.5 48.9 45.5 47.6

Pradesh, Kerala and Assam in NFHS-III (see Table 5). The index of wasting measures acute malnourishment preceding the survey due to inadequate nutrition or illness. Anaemia is one of the outcomes of inadequate diet and is measured by low level of haemoglobin in the blood. The prevalence of anaemia affects women and subsequently leads to low-weight babies, maternal mortality and . There exists a positive relationship between haemoglobin level of mothers and prevalence of anaemia in children (Arnold et al. 2009). Table 6 shows prevalence of anaemia by sex in children aged 0–36 months for NFHS-II and NFHS-III. The NFHS-II shows lower prevalence of anaemia among girls as compared to the boys but the gap is not sig- nificant enough. The level of anaemia increased in NFHS-III from NFHS-II for both girls and boys; however, it was surprising to note the prevalence of higher anaemia among boys in NFHS-III. The state-wise analysis shows most states have higher level of anaemia among girls as compared to boys and the gap is more pronounced in NFHS-III. States such as Madhya Pradesh, Jammu and Kashmir, Kerala, Assam, Bihar and Gujarat have higher level of anaemia among girls than boys in NFHS-II, whereas states such as Andhra Pradesh, West Bengal, Himachal Pradesh, Haryana and Rajasthan have higher prevalence of anaemia among girls than boys in NFHS-III. Some economists have argued that mis- conception about breastfeeding leads to low nutritional level among children. The poor education among mother is responsible for lack of awareness about breastfeeding, which is widespread in India. Male children are given supplementary nutrients earlier than the weaning period as compared to girls because of son preference. This is because of 123 J. Soc. Econ. Dev.

Table 5 Nutritional status measured by wasting of children aged 0–36 months according to sex, NFHS-I to NFHS-III. Source NFHS surveys-I, II, III; IIPS, MoHFW, GOI State NFHS-I NFHS-II NFHS-III

Male Female Male Female Male Female

Andhra Pradesh – – 9.7 9.0 12.7 13.7 Assam 13.6 9.8 14.5 11.7 11.7 14.9 Bihar 28.3 19.2 21.8 20.7 28.4 26.7 Gujarat 20.4 19.4 14.7 18.4 18.8 15.5 Haryana 5.6 6.2 5.9 4.8 20.0 17.5 Himachal Pradesh – – 17.1 16.9 17.6 16.3 Jammu and Kashmir 14.5 18.6 12.8 10.3 17.9 13.8 Karnataka 20.7 18.3 21.9 18.9 17.3 17.9 Kerala 13.3 12.7 12.4 9.7 15.8 17.2 Madhya Pradesh – – 20.1 19.8 14.7 14.8 Maharashtra 23.6 22.5 20.5 22.3 35.1 31.6 Orissa 24.7 22.6 25.5 24.1 20.7 16.0 Punjab 21.5 20.5 8.7 5.6 7.7 11.1 Rajasthan 22.1 21.3 12.0 11.9 21.4 18.0 Tamil Nadu – – 20.8 19.4 24.5 19.6 Uttar Pradesh 20.5 15.9 11.6 10.7 13.4 13.8 West Bengal – – 15.4 12.5 19.0 19.1 All India 21.1 17.5 16.3 15.5 19.6 18.8

mother’s misconception that breastfeeding is insufficient for male infants while girls are more breastfed exclusively and not given supplementary nutrients. This may be the reason for higher prevalence of anaemia among boys than girls in age group 0–36 months.

Gender disparity, son preference and mortality

There are considerable studies to suggest that gender bias affects malnutrition level in India. Son preference and gender bias in childhood nourishment including breastfeeding, nutritional supplement practices, vaccination and health care services contribute to excess female infant mortality rate (Agnihotri et al. 2002; Mishra et al. 2004). Son preference often leads to gender-selective abortion and malpractices such as inadequate feeding of girl child, which is associated with higher infant mortality among females. The differential treatment of children by sex can have severe repercussions and lead to differences in mortality of boys and girls (Das Gupta 1987). Chronic and acute malnutrition coupled with gender disparity in nutritional intake leads to excess infant mortality rate, morbidity and nutritional deficiency among girls. Sex differential in infant mortality rate has been studied to understand how the son preferences are embedded in Indian society. The sex compo- sition of children in the family affects subsequent fertility behaviour. There are studies that examined how female autonomy and control over resources determine the quality of care offered to children and subsequently affect mortality rate. Figure 5 shows finding of

123 J. Soc. Econ. Dev.

Table 6 Percentage of children State NFHS-II NFHS-III aged 0–36 months with anaemia according to haemoglobin level Male Female Male Female by state, NFHS-II and NFHS-III. Source: NFHS surveys-II, III; Andhra Pradesh 72.9 71.8 77.7 80.7 IIPS, MoHFW, GOI Assam 61.1 65.8 77.9 75.1 Bihar 80.5 82.1 87.1 87.3 Gujarat 73.4 75.8 79.9 78.8 Haryana 86.3 81.0 58.2 64.8 Himachal Pradesh 70.8 68.8 81.2 84.4 Jammu and Kashmir 70.3 72.1 69.0 68.1 Karnataka 72.7 68.4 83.3 82.2 Kerala 42.3 45.7 56.0 55.7 Madhya Pradesh 74.2 75.8 76.3 66.2 Maharashtra 78.7 72.9 82.2 81.3 Orissa 72.8 71.7 74.2 74.3 Table is based on children who Punjab 82.2 77.6 84.1 74.1 stayed in the household the night Rajasthan 82.0 82.7 78.0 82.2 before the interview. Prevalence Tamil Nadu 72.4 65.3 76.9 67.9 of anaemia, based on haemoglobin levels, is adjusted Uttar Pradesh 76.4 71.1 84.4 84.8 for altitude using formula in CDC West Bengal 78.1 78.5 68.2 69.5 (1998). Haemoglobin in All India 75.1 73.3 79.2 78.4 g/dl = grams per decilitre

120 110 100 90 80 70 IMR 60 50 40 30 20 1982 1985 1991 1995 1999 2001 2003 2004 2005 Year

Total_R Male_R Female_R Total_U Male_U Female_U

Fig. 5 Infant mortality rate by sex and residence from 1982 to 2005. Note: The figure excludes Jammu and Kashmir from 1991 to 1999. Source: Sample Registration System, Office of the Registrar General, India, Ministry of Home Affairs (2006)

Sample Registration System (SRS) of infant mortality rate by sex and residence. The overall infant mortality rate in India is steadily declining; however, prevalence of infant mortality rates is higher in rural areas. The gender gap in infant mortality rate is higher in urban areas. The infant mortality rate among boys has been more or less below girls for most of the time and the gender gap has increased from 1982 to 2005. This shows India’s inability in achieving the MDG target of reducing child mortality by two-thirds between 1990 and 2015.

123 J. Soc. Econ. Dev.

Table 7 Percentage of women and men age 15–49 years who want more sons than daughters and per- centage who want more daughters than sons, NFHS-I to NFHS-III. Source: NFHS surveys-II, III; IIPS, MoHFW, GOI States % who want more sons than daughters % who want more daughters than sons

NFHS-III NFHS-II NFHS-I NFHS-I NFHS-II NFHS-III (2005–2006) (1998–1999) (1992–1993) (1992–1993) (1998–1999) (2005–2006)

Andhra 32.9 19.8 10.7 4.7 2.7 2.6 Pradesh Assam 43.6 38.2 28.3 4.3 2.9 1.7 Bihar 55.8 50.8 42.2 n.a 1.7 1.2 Gujarat 42.4 33.2 25.9 1.4 1.8 2.3 Haryana 45.1 37.5 25.1 0.6 0.5 1.1 Himachal 36.7 25.9 13.5 0.9 0.6 1.4 Pradesh Jammu 49.1 38.0 29.0 n.a 2.7 3.6 Karnataka 27.0 13.0 13.6 2.1 1.9 4.1 Kerala 18.3 14.6 11.8 4.7 5.2 5.9 Madhya 51.5 43.7 34.6 n.a 2.2 1.8 Pradesh Maharashtra 35.9 27.1 16.6 3.8 1.9 2.4 Orissa 45.1 37.6 28.6 2.3 2.1 2.5 Punjab 48.0 29.1 20.7 0.5 0.4 1.3 Rajasthan 57.6 47.5 38.7 1.1 1.3 1.7 Tamil Nadu 11.5 9.6 6.4 2.0 1.9 2.7 Uttar 56.6 54.2 38.1 n.a 1.4 1.6 Pradesh West 31.9 20.7 17.3 3.3 3.4 2.7 Bengal All India 25.4 33.2 41.4 2.4 2.2 2.6 n.a not available

Table 7 gives the percentage of men and women who prefer more sons than daughters in their ideal family size from NFHS-I, II, III in India. It is a measure of son preference, and the predominance of son preference is higher in northern states as compared to southern states and is consistent with earlier studies (Bhat and Zavier 1999, 2003; Arnold et al. 1998; Das Gupta 1987). The daughters born in families that already have a girl child are found to have greater risk of mortality (Das Gupta 1987). In comparison with son preference, girl preference is appalling. The interstate variability in girl preferences is negligible. States such as Kerala, Tamil Nadu, Himachal Pradesh, Andhra Pradesh and Karnataka have lower son preference as compared to Uttar Pradesh, Bihar, Rajasthan and Madhya Pradesh. Son preference has declined over the decade from 1992–1993 to 2005–2006. There are significant variations across states in son preference, which are likely to persist in India, especially in the northern region. In a male-dominated society like India, where son preference is marked, women may make health care decisions deleterious to health of daughters. The repercussion of son preference is seen in declining fertility rate manifesting gender bias reflected in excess mortality rate of girls.

123 J. Soc. Econ. Dev.

Factors affecting gender disparity in nutrition

The exogenous and endogenous factors that affect gender disparity for all the three rounds of NFHS-I, NFHS-II and NFHS-III will be discussed in the present section. Table 8 shows the summary statistics for prevalence of underweight (weight-for-age lower than - 2 SD) among male and female children across various household, individual factors for NFHS-I, NFHS-II and NFHS-III. The paper tries to understand factors associated with gender bias in child malnutrition using multivariate analysis to control the effect of other potentially confounding factors. The main factors used for examining gender differential in malnu- trition are birth order, birth interval, parental literacy, mother’s work status, place of delivery (as proxy for health care facility), caste, region, place of residence, religion and standard of living using proxies such as source of lighting, source of drinking water and provision of toilet facility. Parental literacy especially maternal has been found to play a key role in child nutrition in India. It has significant effect on gender-related disparity in nutritional outcomes as parental education is associated with better provision of health care and childcare facilities (Tarozzi and Mahajan 2007). Literacy and educational level of the mother matters not only in reducing malnutrition but also in reducing gender discrimination against girl child in nutrient allocation as mother is principal caregiver. The incidence of malnutrition increased with lower literacy level of parents as well as among disadvantaged and marginalized socio-religious groups (Table 8). The prevalence of malnutrition is higher among scheduled population, but the gender gap is higher among the non-scheduled population especially others (forward castes) in NFHS-III. There are various studies that have examined aspects of socio-economic condition on gender bias with female disad- vantage in nutritional level. Women belonging to scheduled population have higher autonomy, empowerment and higher labour force participation rate than the forward castes. The increased autonomy of females has positive association with lower gender disparity in nutritional status. Table 8 shows children belonging to households living in rural areas with no electricity, proper sanitation and drinking water facility (used as proxies to measure living standard) have higher prevalence of gender gap in malnutrition. The fact that socio-economic status has positive impact on the nutritional level of children is consistent with earlier studies. Birth order and birth interval have been found to play significant role in allocation of food resources within the family. Birth order takes into account number of children and sex of the child. There are many studies found increasing neglect of female child of higher birth order (Das Gupta 1987, Arokiasamy and Pradhan 2006). The mother usually feeds the husband and sons before eating last with daughters, especially in poorer households (Mehrotra 2006). The high birth order is associated with poor nutritional status of the young infants as it increases competition for food, maternal care and resources (Rad- hakrishna and Ravi 2004). Mortality risk during childhood has consistently been found to be positively related to short birth interval, especially for girls (Arnold et al. 2009). Table 8 shows that higher birth order and lower birth interval (proxy for family planning) lead to disparity against the girl child. The prevalence of malnutrition among girls is compara- tively higher among the working women. Looking at region-wise trend in the summary statistics, the level of gender gap in malnutrition is lower in southern, north-eastern and western states, especially in NFHS-III. The central and northern regions have higher prevalence of malnutrition among female children. This is consistent with the literature which reported that there exists regional

123 J. Soc. Econ. Dev.

Table 8 Nutritional status measured by weight-for-age z score below median- 2SD for children aged 0–36 months across various factors, NFHS-I to NFHS-III. Source: NFHS-I, NFHS-II, NFHS-III, IIPS, MoHFW, GOI Factors NFHS-I NFHS2 NFHS-III

Male Female Male Female Male Female

Place of residence Urban 44.51 44.17 37.07 39.94 36.18 38.20 Rural 55.86 54.97 48.05 51.65 48.56 50.57 Source of lighting Not electrified 60.93 58.45 54.79 57.75 55.26 58.87 Electrified 45.36 46.11 38.83 42.51 40.06 41.25 Mother’s education Illiterate 59.11 58.97 53.65 57.75 54.37 57.09 Literate, \ middle 51.43 51.42 48.78 49.33 44.30 48.82 Middle school 40.53 36.83 34.67 37.56 37.37 37.31 High school and above 22.46 25.33 22.88 24.57 20.73 20.81 Father’s education Illiterate 59.90 60.59 56.38 58.58 55.00 58.36 Literate, \ middle 56.33 55.96 51.16 55.25 50.17 52.45 Middle school 56.31 52.82 41.25 46.14 42.62 43.97 High school and above 47.71 46.55 32.65 33.43 27.35 27.36 Birth interval \ 24 months 58.79 53.69 50.69 54.63 49.81 50.85 24–47 months 53.49 55.69 47.50 52.42 49.39 52.74 48 ? months 51.32 50.23 43.29 46.95 42.80 45.90 Birth order 1 51.44 51.56 40.79 41.81 39.43 40.91 2–3 56.75 57.29 44.19 48.44 44.68 46.57 4–5 61.65 58.16 50.32 56.80 51.55 54.43 6 ? 53.23 52.44 57.77 59.90 58.90 64.58 Age of child 0–3 months 12.65 12.12 6.90 10.98 6.39 8.67 4–9 months 34.88 33.01 25.87 28.40 24.79 28.07 10–15 months 62.71 58.49 55.70 55.28 52.90 52.24 16–21 months 65.61 64.36 57.44 58.54 57.13 58.18 22–27 months 63.81 62.44 58.06 60.50 55.06 58.13 28–36 months 61.15 65.16 55.49 62.55 52.41 58.27 Mother’s work status Not working 51.11 49.76 41.65 44.93 41.21 43.16 Working 59.12 59.62 54.04 57.26 53.90 55.99 Place of delivery Home 57.82 57.04 51.30 55.73 52.40 54.35 Institution 41.22 40.38 35.61 37.05 35.99 36.96 Sex of household head Male 53.32 52.84 45.68 49.00 45.44 47.44

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Table 8 continued

Factors NFHS-I NFHS2 NFHS-III

Male Female Male Female Male Female

Female 51.74 46.07 42.63 47.10 46.15 49.15 Caste Schedule caste 56.59 57.13 52.00 54.63 51.04 54.01 Schedule tribe 59.13 56.11 55.17 57.33 55.49 58.16 OBC – – 45.49 49.30 47.29 47.21 Others 52.01 51.23 39.67 42.90 35.95 39.82 Religion Hindu 53.31 52.73 46.08 49.83 45.91 48.57 Muslims 57.04 54.39 47.00 49.39 46.07 45.34 Christians 38.28 34.36 33.33 28.47 35.11 36.54 Others 43.69 47.51 32.73 36.94 38.97 41.95 Source of drinking water Piped 44.89 46.21 38.69 41.47 38.59 39.60 Open source 56.88 55.17 49.05 52.80 49.79 52.58 Others 46.35 46.00 43.88 55.17 39.89 39.37 Type of toilet facility Flush toilets 37.00 37.93 31.83 32.62 34.20 35.13 Pit toilets 48.45 40.97 32.67 39.14 41.39 41.97 Others/no facility 57.58 57.32 51.57 55.09 50.67 52.97 Region North 42.23 43.44 41.26 43.57 38.75 40.28 Central 60.01 56.40 51.05 55.44 49.80 53.11 East 60.85 57.86 51.05 54.89 50.81 54.80 West 48.71 52.52 47.20 50.11 43.37 43.37 South 44.94 47.09 36.29 39.51 37.72 35.72 North-East 50.00 44.42 34.19 34.68 38.54 38.87 Total 53.22 52.43 45.49 48.88 45.52 47.63 variation in malnutrition in female children, which is predominant in northern India than in southern parts (Das Gupta 1987, Arokiasamy and Pradhan 2006). The prevalence of malnutrition is lower in male-headed households, but gender gap is higher in female- headed households. The gender gap in nutritional status of girls increases with age of the child and is quite significant in 22–36 months. This could be mainly due to the fact that feeding of supplementary nutrients along with weaning increases the exposure to illness in this period. The NFHS-III data show higher gender gap among the children belonging to Hindus and other religious groups. The gender gap was quite significant in NFHS-II and NFHS-III at all India level. It is seen that girls had better nutritional status in NFHS-I; however, the gap has augmented over the years even though the level of underweight children has come down.

123 J. Soc. Econ. Dev.

Decomposition of the factors

The determinants of gender disparity in malnutrition measured by weight-for-age less than median - 2 SD of reference population are influenced by many exogenous and endoge- nous factors. The variables considered in the analysis can be broadly classified into household specific, child-specific and exogenous variables. Disaggregating the effects of the determinants of malnutrition across both genders provide new insights on gender disparity. The paper uses nonlinear Blinder–Oaxaca decomposition technique to explain the difference in logistic estimates for malnutrition between the male and female children. This technique decomposes the gap into differences in distribution of determinants and also differences in effects of determinants. The female children below 3 years of age have an average (negative) weight-for-age z- score of 0.51 compared to 0.49 for the male; the difference is highly significant (q \ 0.00) in NFHS-III (Fig. 6a) and 0.52 average weight-for-age z-score for the females and 0.47 for the males in NFHS-II (Fig. 6b). The difference in the two gender groups is highly sig- nificant for NFHS-II as well. The decomposition of factors is shown in Figs. 7 and 8. The gap between the two genders can be decomposed into two components: difference in distribution of the determinants which accounts for 29% of the gap and differences in effect of distribution which accounts for 71% of the gap in NFHS-III (both the components are highly significant). The first part of the gap explains the difference in endowment of factor between the two genders which is 29, and 71% of the gap measures the effect of

a NFHS -III b NFHS -II

Male Male

Female Female

0.350 0.400 0.450 0.500 0.550 0.350 0.400 0.450 0.500 0.550 Weight-for-age Zscore NFHS3 Weight-for-age Zscore NFHS2 Average weight for age Average weight for age

Gap due to difference in distribution of Gap due to difference in distribution of determinants (0.007) determinants (0.006)

Gap due to difference in effect of Gap due to difference in effect of determinants (0.018) determinants (0.038)

Fig. 6 Decomposition of the gender gap in malnutrition in children below 3 years of age for NFHS-II and NFHS-III. a. NFHS-III, b. NFHS-II. Source: Author’s calculation from NFHS-II, NFHS-III, IIPS, MoHFW, GOI 123 J. Soc. Econ. Dev.

0.0010

0.0005

0.0000

-0.0005 Toilet Caste Water Region -0.0010 Religion Birth order Contribution Birth interval Watching TV Mother's age -0.0015 Place of delivery Source of lighting Listening to Radio Place of residence Father’s education Mother’s education

-0.0020 Reading Newspaper Mother’s work status

-0.0025 head Household Sex of

Decomposed Gap

Fig. 7 Contribution of differences in the distribution of the determinants of malnutrition to the total gap between male and female children NFHS-III. Note: Grey bars indicate insignificance at the 10% level. Dash outlined bars show significant variables. Source: Author’s calculation from NFHS-III, IIPS, MoHFW, GOI

0.0600

0.0400

0.0200

0.0000

-0.0200 Toilet Caste Water Region Religion Constant -0.0400 Electricity Birth order Contribution Birth interval Watching TV Mother's age -0.0600 Place of delivery Listening to Radio Listening Place of residence Father’s education Mother’s education Reading Newspaper Reading

-0.0800 Mother’s work status Sex of Household head -0.1000

-0.1200 Decomposed Gap

Fig. 8 Contribution of differences in the effect of determinants of malnutrition to the total gap between male and female children NFHS-III. Note: Grey bars indicate insignificance at the 10% level. Dash outlined bars show significant variables. Source: Author’s calculation from NFHS-III, IIPS, MoHFW, GOI difference in endowment, which measures discrimination against girl child or son preference. The decomposition analyses interaction between gender and socio-economic factors to study the existence of gender discrimination in child malnutrition level. Figure 7 shows how differences in the distribution of each factors affecting the malnutrition among children contributed to the first part of the gap also called the endowment effect. A

123 J. Soc. Econ. Dev. negative contribution means that the determinant narrows down the gap between the two groups (Van de Poel and Speybroeck 2009). The place of delivery, sex of the household head, toilet and source of electricity are significant in narrowing down the gender gap in malnutrition; however, contributions of other factors were insignificant at 10% level in NFHS-III (see Table 9). These factors such as place of delivery at a health facility or hospital and better sanitation facility and electrification of house (both proxy for standard of living) highlight that economic conditions of the households help in reducing gender gap and improving female children’s nutritional intake. The focus on child nutritional status avoids reverse causality between income and health care that is usually present in empirical studies. Female-headed households are particularly vulnerable, and they often lack access to resources needed to improve food security but given higher autonomy and education of women can play important role in reducing gender gap. These factors can be used in policy design to bring down the gender gap in nutritional status. The policy perspective should

Table 9 Blinder–Oaxaca decomposition of undernutrition across sex of the child NFHS-II and NFHS-III. Source: Author’s calculation from NFHS-II and NFHS-III, IIPS, MoHFW, GOI Factors NFHS-II NFHS-II

Explained Unexplained Explained Unexplained

Caste - 0.0003 - 0.0003 - 0.0002 0.0383** Birth order 0.0000 0.0000 0.0000 - 0.0219 Father’s education - 0.0006 - 0.0006 0.0001 - 0.0244 Birth interval - 0.0014** - 0.0014 - 0.0005 0.0303 Religion 0.0000 0.0000 0.0000 0.0095 Place of delivery - 0.0001 - 0.0001*** - 0.0020* - 0.0417 Mother’s work status - 0.0008*** - 0.0008 - 0.0007 0.0158 Reading Newspaper - 0.0005 - 0.0005 0.0000 0.0058 Listening to Radio - 0.0003 - 0.0003 0.0000 - 0.0095 Watching TV - 0.0006 - 0.0006 - 0.0004 0.0048 Mother’s age 0.0002 0.0002 0.0001 0.0047 Region 0.0003 0.0003 0.0000 0.0359** Place of residence - 0.0001 - 0.0001 0.0001 - 0.0189 Mother’s education 0.0001 0.0001 0.0000 - 0.0171 Sex of household head - 0.0011* - 0.0011 - 0.0020* 0.0007 Toilet - 0.0006 - 0.0006 - 0.0008*** 0.0300 Water 0.0000 0.0000 - 0.0003 - 0.0057 Electricity - 0.0006 - 0.0006 - 0.0006*** 0.0458 Constant - 0.1196 - 0.1007 No of Male observation 9205 7663 No of female observation 8219 6901 Male 0.4740* 0.4858* Female 0.5183* 0.5114* Gap - 0.0443* - 0.0256* Explained - 0.0064* - 0.0073* Unexplained - 0.0379* - 0.0183* *Significant at 1% level, **significant at 5% level and ***significant at 10% level 123 J. Soc. Econ. Dev. concentrate on targeting the underlying causes of gender bias instead of mitigating the immediate causes. Figure 8 shows decomposition by the difference in effect of the determinants. Caste and region are the only statistically significant variables that explain the characteristic effect, and both the variables increase the gender gap with respect to malnutrition. The positive contributions of these variables come from the fact that belonging to lower social hierarchy level increases the prevalence of undernutrition but higher gender gap is observed in among the forward caste. This again emphasizes the role of women in children’s nutritional level as scheduled population have higher autonomy for the women as compared to for- ward caste as suggested in earlier studies (Van de Poel and Speybroeck 2009). Gender bias established in this analysis has serious implication for achieving gender parity while concentrating on underlying causes like caste and region which affect the nutritional status of children. The variable region also adds to the gender gap as some regions such as central and northern regions have higher prevalence of gender differential in malnutrition due to higher son preferences in the ideal family size in NFHS-III. The differences in the effect of distribution measure for the behavioural pattern favouring male children over female in the nutritional status. The results show that some part of the gap can be attributed to differences in the distribution of determinants which play an important role but the majority of the gap is due to differences in the effect of distribution of determinants in malnutrition between male and female for NFHS-III, which measures favouring of male children. These results are subject to usual caveats of cross- sectional analysis regarding the causal interpretation. The result of NFHS-II is not dis- cussed for the sake of parsimony but is given in Table 9.

Discussion

The above simulation brings into focus the linkage between gender and malnutrition that needs to be targeted to eliminate gender bias in both determinants and effect of determi- nants. The study highlights the need to focus on women’s relative status in household to sex differential in child nutritional level. The emphasis on correlation between women’s autonomy and gender bias in children’s nutritional level will help in formulating policy to eliminate the disadvantaged position of female children at intra-household level, especially in health care services and food allocation. There is sufficient evidence provided in lit- erature that the link between women’s education and child’s nutritional level is stronger in a highly stratified society. The present study attempted to examine the sex differential in socio-economic, individual, household and environmental factors that lead to gender bias. The literature highlights the main reason for underlying inverse relationship between women autonomy and child’s nutritional level is due to greater propensity of women to care for the children, to use better health facility, to adopt modern health practices and to eliminate gender discrimination in household allocation of resources. Mothers have greater potential to discriminate in food allocation as child stops breast feeding; hence, mother’s education and autonomy will help in reducing gender bias in malnutrition with respect to distribution of determinants. The underlying causes of gender bias against girl child act via constraint on mother, and the potential significance for these outcomes of women status in society receives little attention from policy maker. The empirical evidence presented in previous sections can help to draw various policy implications. The household with gender

123 J. Soc. Econ. Dev. parity in food allocation will be determined in long run by women autonomy, which can be enhanced by ensuring educational level and employment of females. Improvement in women’s autonomy will empower in their household decision making as well as higher control over resources that will check gender bias among children. The high heterogeneity in the prevailing status of undernutrition across gender requires spe- cially designed region-wise approach in addressing such issues, which will bring signifi- cant outcome. The evidence supports the suggestion that gender inequality is a social impairment which tends to encourage gender inequality of other kind. The nutritional status being the basic capabilities, reduction and elimination of gender differential in child’ nutritional level is imperative in achieving gender parity in other dimensions of human development. The persistent gender gap in nutritional status of children requires policy makers to focus on improving the nutritional level of girls.

Conclusion

Gender differential in malnutrition showed the presence of inequality existing against females as compared to male children in the Indian society. At all India level, the analysis does not show the prevalence of gender gap with respect to malnutrition as well as health indicators according to NFHS-III (2005–2006). However, the state level analysis varies from the all India figures and shows gender differential in malnutrition level in many states. The interstate heterogeneity in gender disparity with respect to malnutrition is quite significant. The North–South divide is evident with respect to gender gap in nutrient intake as well as son preference in an ideal family size. The analysis showed evidence of gender gap in nutrient intake comprising of consumption of specific foods including staple food among children below 6 years of age. The age-wise classification of food consumption showed lower intake among girls as compared to boys though biologically the level of consumption and nutrients needed is same for both boys and girls below 10 years of age. An interesting observation in this study was the prevalence of low level of wasting among the girls than boys. The paper showed how different child-specific factors, household factors and exogenous factors manifested in gender disparity in health using malnutrition indicators. The decomposition of gap showed the difference in effect of the determinant dominated the gender gap more than the difference in distribution of the factors. This shows the preva- lence of gender favouritism toward male child in nutritional outcomes is as effective as the difference in endowment of factors against girl child. The son preference in the society has put the women and girls in disadvantaged position. The patriarchal mindset reflected in households has limited the female autonomy in order to justify lesser role of women in decision making, especially about her children, their nutritional intake and health care, which affects the nutritional outcomes and mortality rate of the children especially girls. Addressing gender inequalities in food consumption within the family is a key factor in successful nutrition programs and policies. Improving household access to food is not enough if it is distributed unequally amongst members to bring gender parity. The variables place of delivery emphasizing delivery in a health facility, female headed household reflecting more autonomy for female, better standard of living which includes better sanitation facility along with electrification of the household with special emphasis on children from the lower strata of social hierarchy and certain geographical regions should

123 J. Soc. Econ. Dev. be addressed, if policy makers want to reduce gender gap in nutritional, health and mor- tality rates to bring about the gender parity. The inference drawn suggests that policies which enhance the women autonomy along with female education will have larger effect on their decision making as well as control over economic and household resources which will narrow gender differential in nutrition. The policy implication should work toward reducing obstacles in accessing health care services as well as quality of services. The existing programmes should endeavour to build on synergy to help overcome difficulties in accessing health and social services that are essential to bring down gender differential in nutritional level. Improvement in girl’s nutritional level to the level of boys needs not only targeted intervention but also policies aimed at changing value system and demographic behaviour.

Acknowledgements I am grateful to Prof. S.K Thorat for his insights and comments. Errors, if any, will solely be my responsibility.

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