Double Burden of Malnutrition : Reexamining the Coexistence of Undernutrition and Overweight Among Women in India

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1 The Paradox of Poverty: Undernutrition and Underweight Double Burden of Malnutrition : Reexamining the Coexistence of Undernutrition and Overweight Among Women in India International Journal of Health Services 2017, Vol. 47(1) ! The Author(s) 2016 Reprints and permissions: sagepub.com/journalspermissions.nav DOI: / joh.sagepub.com Vani S. Kulkarni 1, Veena S. Kulkarni 2, and Raghav Gaiha 3 Abstract India has one of the highest rates of underweight burden, with signs of rising obesity. Coexistence of underweight and overweight persons is symptomatic of the double burden of malnutrition. The present study throws new light on the double burden of malnutrition among Indian women in the age group years. The analysis is based on a nationally representative household survey, India Human Development Survey. Our results indicate the continuing pattern of socioeconomic segregation of underweight and overweight/obese women, with a large concentration of underweight women among the low socioeconomic group and of overweight/obese women among the high socioeconomic group. Further, relative food prices of food items like cereals and vegetables are significantly associated with the risk of being underweight and overweight/obese. Additionally, we find notable rural/urban differences. The relationship between socioeconomic factors and the probability of being underweight and overweight/obese is stronger in urban than in rural areas. Given 1 University of Pennsylvania and Yale University, Philadelphia, Pennsylvania and New Haven, Connecticut, USA 2 Arkansas State University, Arkansas, USA 3 Department of Global Health and Population, Boston, Massachusetts, USA Corresponding Author: Veena S. Kulkarni, Department of Criminology, Sociology and Geography, Arkansas State University, P.O. Box 2410, State University, Arkansas 72467, USA. vkulkarni@astate.edu

2 Kulkarni et al. 109 that the health implications of being underweight and overweight/obese are equally grim, provision of healthy food items at affordable prices and implementation of programs for preventive and curative care of plausible illnesses related to underweight and overweight/obese are imperative. Keywords underweight, overweight/obesity, women, socioeconomic patterning, rural, urban, India Recent research on health and nutrition in the developing countries has drawn attention to what is often referred to as the double burden of malnutrition coexistence of a high prevalence of underweight and rising rates of overweight/obesity. 1 3 Although prevalence of underweight and overweight/obesity arise from what can be thought of as opposing factors namely, calorie deficit and (calorie) excess, respectively and cause different types of risks, 4 both pose critical individual and public health concerns. While undernutrition is associated with low bone mass, all-cause morbidity, and mortality, being overweight/obese dramatically enhances the chances of adult onset of diabetes and heart disease, among several other serious conditions. 5 7 The scholarship examining the twin burdens of undernutrition and overweight/ obesity finds socioeconomic patterning of nutritional levels. The association between poverty and low levels of nutritional status is well-documented. 8 The argument following from the process of nutrition transition suggests that as a country economically progresses, the advantaged sections of the population make a transition to consumption of a diet that is high in fat and carbohydrate and to a more sedentary lifestyle. 4,9 A comprehensive review of the evidence pertaining to the last decade of the previous century does indicate an overall positive relationship between levels of socioeconomic condition and obesity. 10 Updates of Sobal and Stunkard s 10 review corroborate the overall pattern, although conveying a more complex picture of the relationship between obesity and socioeconomic status in the developing world. 7,11 Analysis of cross-sectional data from developing countries shows that over time, as economic progress ensues, the incidence of overweight/obesity is passed on to the less privileged. 12 Hence, even though contemporarily the relationship between socioeconomic status and overweight/obesity in developing countries is in contrast to that observed in the developed ones, there are enough indications to expect the poor in the developing world to, in the long run, experience the burden of overweight/obesity and the accompanying health conditions. This shift of burden of overweight/obesity to the less wealthy in developing countries is especially disconcerting because the majority of the poor continue to grapple with the problem of high levels of undernutrition as well. There is therefore a very real possibility of the poor being exposed to the double burden of undernutition and overweight/obesity.

3 110 International Journal of Health Services 47(1) Although India has experienced a sustainable rise in income levels in recent years, not only does it continue to have a significantly high rate of undernutrition, there are unambiguous signs of rising obesity. A recent study using the World Health Organization (WHO) classification of underweight as having body mass index (BMI) (kg/m2) of less than 18.5 and overweight as having BMI of greater than 25 shows that among women aged 20 49, the percentage who are underweight reduced from 31.5 in 1998 to 26.6 in In contrast, the share of overweight, during the same period rose from 13% to 18.2%. 13 Hence, the enduring concern of existence of high levels of undernutrition, although declining, is accompanied by an additional apprehension of the risks posed by the rising rates of overweight/obesity. This emergence of what we call the double burden of malnutrition during a period of rapid economic growth is, not surprisingly, gaining attention among academics and media. 3,14,15 The physiological risks of this relatively new phenomenon of increasing levels of overweight/obesity in India are similar to what is now well-known from research in developed countries. 5 For instance, the WHO estimates that diabetes rates in India will rise from 19.4 million in 1994 to 57.2 million in In terms of costs, it is estimated that obesity-related conditions will translate to a $200 billion cost in productivity. 15 The few inquiries on India demonstrate that the prevalence of undernutriton and overweight/obesity continue to be segregated along the lines of socioeconomic status. An overwhelming proportion reported as experiencing undernutrition is poor and there exists a greater extent of overweight/obesity among the socioeconomically advantaged. 3,16 The present study, using the India Human Development Survey data, revisits this relationship between socioeconomic status and body weight for women aged In specific terms, our investigation contributes to the prevailing body of knowledge in the following ways. First, we account for prices of key food items that potentially serve as instruments for food consumption that directly impacts nutrition. Second, we consider a more disaggregated distribution of regional residence. Third, by employing nationally representative data that have not been previously analyzed, our findings aid in strengthening the validity of the relationship demonstrated in prior research. Background Socioeconomic Status and Nutritional Status Traditionally, the plethora of scholarship documenting the interrelationship between socioeconomic status and nutrition in the context of developing countries and in India in particular has focused on the detrimental effects of inadequate nutrition on labor market productivity and wages and vice versa. The above two-directional relationship (between levels of nutrition and labor market outcomes) is overwhelmingly evident from the theorizing and empirical

4 Kulkarni et al. 111 testing of Libenstein s efficiency wage hypothesis and its analogue, Poverty Nutrition Trap, in multiple settings. 21 In a pioneering contribution, Dasgupta 22 shows the causality in which undernutrition results in poverty under certain conditions. Further, these poverty traps can be dynastic. Once a household falls into a poverty trap, it is hard for descendants to escape it, even if the economy experiences growth in output for a while. As societies develop economically, they go through the process of nutrition transition, entailing rapid changes in the diet and physical activity. 9 The dietary changes imply increased consumption of fatty food, carbohydrates, processed food, more milled and polished grains, and animal products. 4,23 In terms of physical activity, at higher levels of economic growth there is a tendency to shift to a less manual, more mechanized, and more sedentary lifestyle. 24 An examination of the socioeconomic patterning of the nutrition transition at the more micro, individual-household level seen in the context of contemporary developing countries reveals that the incidence of overweight/obesity first occurs in the upper socioeconomic strata. The effects of excess nutrition lead to an increase in so-called diseases of affluence, such as cardiovascular ailments, diabetes, and high cholesterol. 25 Hence, the pattern seen in emerging middle-income countries, such as Brazil, China, India, and Russia, is in contrast to that found in developed nations. 10,13,26,27 In the latter group, prevalence of overweight/obesity and associated risk factors are increasingly observed in the low socioeconomic strata. Recent investigations, however, indicate a more complex relationship between socioeconomic and nutritional status. 11,12 First, over time, at higher levels of economic growth, the burden of overweight/obesity shifts to the poor. This suggests that at higher levels of income, the correlation between socioeconomic status and nutrition among the current developing countries mirrors that of the developed nations. Second, the relationship varies across gender and age groups. In developed countries, an inverse relationship between economic status and obesity is found for women. The relationship for men and children is not as consistent. 28 Third, the association between material welfare and levels of nutrition can be analyzed from a life-course perspective. Early childhood experiences play a role in determining health trajectories in adulthood. 29 Undernutrition in early life is linked to obesity in later stages of life. 23 Energy deficiency in childhood causes metabolic and hormonal changes that heighten the risk of accumulation of body fat and weaken the capacity to reduce the fat (It may be noted that genetic explanations, such as genetic polymorphism, that are known to affect individuals reaction to dietary changes in breaking down fat have also been offered. 22 ). 30 It is, therefore, likely that individuals who suffer from undernutrition in early parts of their lives will be overweight/obese in late adulthood, despite not necessarily being wealthy. However, little is known about these processes for developing countries populations. In addition, we do not delve into genetic factors, as they are beyond the scope of this study.

5 112 International Journal of Health Services 47(1) The Cultural Dimension Given that food habits, behaviors, and preferred diets are overwhelmingly cultural, 31 individuals nutritional status and perceptions regarding this trend of increasing incidence of overweight/obesity also have a cultural dimension (Anthropologists have conducted ethnographic studies to examine what they call ethnonutrition, which is the concept of how individuals construct their preferred diets. 30 ). The latter s role in health outcomes and behavior becomes especially critical when societies undergo significant social and economic changes. 32 Similar to the relationship between socioeconomic status and overweight/obesity, attitudes toward overweight/obesity in developed and developing societies appear to be divergent. In developed nations such as the United States, overweight/obesity has had a consistently negative connotation and been seen to be associated with low levels of subjective well-being Sociologists contend that the framing of fatness as a negative characteristic goes beyond the concern of fatness as a public health problem. Overweight/obesity is posed as an indicator of social deviancy and lack of personal responsibility, thereby targeting vulnerable groups such as women and the poor. 39 The focus, in doing so, arguably is to support the perception that people are responsible for their health. For women, the issue is two-fold. They wear the blame of unhealthy eating habits as both providers and consumers. Second, women are seen to experience the weight stigma differentially relative to men. 34 Fatness is inconsistent with physical attractiveness, a trait that is socially valued more for women than for men. The so-called obesity wage penalty is more pronounced for women. 40,41 In contrast, overweight/obese individuals do not seem to experience the same extent of stigma in developing societies. The results of a comparative analysis across the United States and Russia shows that in Russia, obesity and sense of well-being are positively correlated, which is the opposite of the United States. 27 In emerging economies such as Russia and South Africa, overweight/obesity is associated with prosperity. For instance, in South Africa, prevalence of overweight/obesity among women is thought to reflect on husbands ability to care for their wives and families. 42 It is therefore plausible that cultural norms among the upper classes encourage fat body shapes. 3,10 Such cultural norms can be manifested in the consumption of high-calorie food, practices of eating out, and greater restrictions on engagement in a regular regimen of physical exercises. This especially appears to be the case for women from the upper classes. A qualitative study in India shows a mix of factors, such as social barriers and lack of inclination to exercise on the part of women. 43 Socioeconomic Status and Nutritional Status in India Notwithstanding the positive association between lack of wealth and lack of nutrition, 44 there are definite signs of a nutrition transition taking place in

6 Kulkarni et al. 113 India. Mortality owing to noncommunicable diseases is on the rise. Also increasing are rates of obesity. 45 The reasons put forward for rising levels of obesity are similar to what has been seen for other developing societies, namely consumption of a rich and fatty diet, sedentary lifestyles, and urbanization. 1,23 Given that India continues to have a significantly high percentage of population that faces undernutrition, 13 the socioeconomic patterning of this phenomenon of the double burden of malnutrition seen across the developing world has been, though to a limited extent, explored in India. One of the early inquiries into the correlates of overweight/obesity and thinness in the South Indian state of Andhra Pradesh finds that socioeconomic status, not rural/urban residence, is an independent and strong predictor of women s BMI. 17 Although there were clear differences in weight status between women living in rural and urban areas, these ceased to be statistically significant when there were controls for demographic, cultural, health behavior, diet, and socioeconomic variables. The results using the second round of the nationally representative data, National Family Health Survey (NFHS-2) show that in Andhra Pradesh, more than onethird of women aged years had a BMI of less than 18.5, whereas about 27% were overweight/obese. Factors associated with underweight and overweight are largely similar. Women who report a higher standard of living, who live in households where at least one member was educated beyond high school, who work in non-manual occupations, or who watch television more than once a week were more likely to be overweight/obese. These factors were inversely related with low BMI. Thus, consistent with the experience of other countries in the early stages of nutrition transition, women (of Andhra Pradesh) in the higher socioeconomic groups were more likely to be overweight/obese, while just under half of the sample population was underweight. Age was a significant predictor of BMI, with older women more likely to be overweight or obese and younger women more likely to be underweight or severely thin. A national-level subsequent piece of research, again using NFHS-2 data, demonstrates that dietary deficit and excess are segregated by socioeconomic strata. Undernutrition is a problem of the poor whereas dietary excess plagues the rich. 2 A more detailed extension of the above finding can be seen in Subramanian and colleagues. 3 Using the second and third rounds of NFHS (NFHS-2 and NFHS-3), the authors examine changes in the socioeconomic patterning of underweight and overweight among women aged between and As this was a period of rapid economic growth, the findings are of considerable significance. The effect of wealth on BMI was positive and significant in both periods, and The BMI difference between women in the lowest wealth quintile and women in the highest quintile, regardless of location, was

7 114 International Journal of Health Services 47(1) larger in Education was also positively associated with BMI. Further, the positive association between education and BMI was observed within the urban populations at both time periods, though the association within the rural populations was not consistent. Double burden of malnutrition was thus socially disaggregated in both rural and urban areas over the period and High-SES women were more likely to be overweight, while low-ses women were more likely to be underweight. Even though the ratio of underweight to overweight decreased (from 3.3 in to 2.2 in ), there were considerably more underweight than overweight women. There was, however, a slight excess of overweight women compared to underweight women in the highest wealth quintile. This socioeconomically segregated pattern of the prevalence of undernutrition and overweight/obesity was corroborated by Corsi, Finlay, and Subramanian. 13 A related dimension of the socioeconomic patterning that is increasingly visible in India is the dietary diversification among the middle and affluent classes. While a higher Food Diversity Index (FDI) is seen to be associated with lower calorie intake, one of the manifestations of a higher FDI is eating out, which typically entails consumption of a diet high in sugar, saturated fat, and salt. 8 Empirical evidence informs a rising trend of eating in restaurants and processed (rather than home-cooked) food attributable to a growing middle class, rise in female labor force participation, nuclear two-income families, and Westernization of diets. 8 In light of the existing research, the present study employs a different data set to revisit the relationship between socioeconomic and nutritional status for Indian women. Also, we conduct a more disaggregated analysis by region of residence than that employed in previous investigations. Further, the intake of food commodities is endogenous to relative food prices. 8 Relative food prices as drivers of diets matter substantially. They are especially critical in the contemporary globalized world with the spread of developments in technology, trade liberalization, and Western culture and lifestyle via mass and social media. Globally, relative prices have shifted in favor of animal-based products and edible oils. Consequently, diets rich in legumes, vegetables, and coarse grains are becoming more expensive. 8 Absence of an explicit role of diets seen in prior research potentially overestimates the primacy of socioeconomic status. By including prices of essential food items relative to cereals, our analysis is a methodological improvement on the previous studies. For instance, Griffiths and Bentley 17 use actual intake of various food commodities without an allowance for their endogeneity to income and to relative food prices, and Subramanian and colleagues 3 do not incorporate any diet-related variables. Additionally, unlike previous analyses, we adopt a prior complete specification and not a step-wise regression procedure as the latter invariably entails data mining.

8 Kulkarni et al. 115 Data and Methods Our analysis draws upon the India Human Development Survey 2005 (IHDS) collected by the University of Maryland and the National Council of Applied Economic Research (NCAER) in IHDS covers more than 41,000 households residing in rural and urban areas, selected from 33 states. The sample comprises 384 districts out of a total of 593 identified in the 2001 population census. Villages and urban blocks constituted the primary sampling unit from which the households were selected. The questions fielded in IHDS are organized into two separate questionnaires, one for households and one for women. While the household questionnaires were administered to the individual most knowledgeable about income and expenditure, which was often the male head of the household, the one for health and education was administered to a woman in the household, typically, the spouse of the household head. Anthropometric data (height and weight) are primarily confined to women in the age group years. Standard methods of measurement, such as using a solar-powered scale to measure weight and an adjustable wooden board to measure height, were used by trained investigators. Our sample comprises women in the age group years classified into four BMI ranges, namely, <18 BMI as underweight, 18 to < 25 BMI as normal, BMI as overweight, and >30 BMI as obese. We have deleted observations pertaining to pregnant women. Overweight and obesity are combined principally because the BMI ranges that heighten the risk of non- communicable diseases are controversial. Analytic Strategy We first present select descriptive statistics to examine the bivariate association between our dependent variables and the independent and control variables. Subsequent to the descriptive analysis, we conduct two sets of multivariate regressions. First, considering the sample of underweight and normal women in the age group years, we examine the probability of being underweight. Second, we predict the probability of being overweight/obese in the sub-sample comprising population that is normal and overweight/obese. It may be noted that we present descriptive statistics for overweight and obese separately. However, for the purposes of multivariate analyses, we combine the categories of overweight and obese. Our choice of the covariates is guided by the existing body of scholarship. The independent variables include measures of socioeconomic status, relative food prices, region of residence, and location and demographic factors. Socioeconomic status is measured by the economic indicator of monthly per capita consumption expenditure (MPCE), educational attainment, and caste affiliation. In line with the common practice followed in the context of

9 116 International Journal of Health Services 47(1) developing countries, 47 we use consumption expenditure as a measure of economic status (We also conducted the analyses using another measure of economic status, namely the various assets that households possessed. These include TV, refrigerator, motorcycle, and telephone. The estimates are similar to what we obtain from using MPCE. The results can be made available upon request.). Further, given the well-documented intertwining between caste and economic well-being, we employ caste as an index of socioeconomic status. 48 We employ the standard caste categories of Scheduled Caste (SC), Scheduled Tribe (ST), Other Backward Caste (OBC), and Other. As cereals are a major source of calories in Indian diets, ratios of cereal prices to other food commodities are constructed. Demographic factors comprise women s age category and marital status. The control variables include household-level infrastructure, access to sanitation, women s membership in organized associations, and (women s) exposure to media. It may be noted that in order to minimize potential endogenity, the regression analyses employ the values of independent and control variables at the primary sampling unit level and not those observed at the individual level. The primary sampling unit usually consists of a cluster of villages (see Desai and colleagues 46 for details). The descriptive associations depicting the prevalence of underweight, normal, overweight, and obese across the categories of independent and control variables are, however, at the individual level. Further, as prior studies identify dietary and lifestyle changes associated with urbanization as a factor contributing to rising levels of overweight/obesity, we conduct separate analyses for rural and urban locations. Given the binary nature of the dependent variables, we employ probit regression technique. The probit function we estimate takes the following form: Y i ¼ b 0 þ bx i þ dz i þ e i where Y ¼ 1 if the woman respondent is underweight or overweight/obese Y ¼ 0 if the woman respondent has a normal BMI Vector X and Z comprise independent and control variables. In the interest of easier interpretation, we present elasticities calculated at sample means. Elasticities measure a proportional (percentage) change in probability of being underweight or overweight/obesity when there is a 100% change in the independent variable. Findings Bivariate Table 1 displays distribution of women who are in the four categories of BMI across the select characteristics at the level of the individual. Additionally, in

10 Kulkarni et al. 117 Table 1. Prevalence of Underweight, Normal, Overweight and Obese and Ratio of Underweight to Overweight/Obese Women Aged by Select Characteristics, Variable Underweight Normal Overweight Obese Ratio Socioeconomic status Economic status Monthly per capita consumption expenditure (Rs.) , >1, Total Educational attainment No education Less than 6 years of education years of education Total Caste SC ST OBC Other Total Region and location of residence Region Northern Eastern Western Southern Central Total Location Rural Metro Non-metro urban Total Demographic factors Age categories (age in years) (continued)

11 118 International Journal of Health Services 47(1) Table 1. (continued) Variable Underweight Normal Overweight Obese Ratio Total Marital status Ever married Single Total Source: Authors calculation. SC, Scheduled Caste; ST, Scheduled Tribe; OBC, Other Backward Caste. order to demonstrate the two burdens more clearly, we present, in the last column, cross-tabulations using the ratio of underweight to overweight/obese at the level of the primary sampling unit. The cross-classification of this ratio by range of per capita monthly expenditure shows a negative and a positive correlation between economic status and probabilities of being underweight and being overweight/obese, respectively. The lowest two expenditure intervals accounted for a large majority of the underweight (a little < 71%). In contrast, 33.5% of the overweight and more than 27% of the obese were in these two lowest expenditure intervals. Comparison with the highest expenditure interval shows that the proportion of underweight women is considerably lower than that of overweight and obese. The highest expenditure interval accounts for less than 13% of the underweight and considerably larger shares of the overweight (<44%) and obese ( > 53%) women. In a similar vein, the higher expenditure groups account for the majority of the overweight (about 66.5%) and of the obese ( > 72.5%) and the two lowest expenditure categories account for the majority of the underweight women. This pattern is evident from the ratio of underweight to overweight/obese. With the increase in the expenditure category, the ratio steadily declines from 4.16 to The cross-classification showing the distribution of monthly per capita expenditure categories in the four BMI groups (see Figure 1) also depicts a positive association between economic well-being and BMI. The percentage of underweight women in the lowest per capita expenditure is the highest relative to the three other expenditure categories. In contrast, the highest expenditure class has the highest percentage of obese women as compared to other three expenditure categories. With respect to the caste affiliation, the disadvantaged groups, SCs, STs, and OBCs experience significant rates of undernutrition. Unsurprisingly, there is a reversal of this pattern in respect of prevalence of overweight. Of the total overweight women, those belonging to the Other group account for just under

12 Kulkarni et al. 119 Figure 1. Percentage distribution of the monthly per capita expenditure categories by underweight, normal, overweight and obese women aged half (48.21%), OBCs for about one-third, and the STs for a very small share (3.45%). As expected, the ratio of underweight to overweight/obese is the lowest for the category Other. The regional pattern indicates the highest percentage of underweight (to the total underweight) is in the Eastern region and the lowest in the Central region. Among all overweight and obese women, the Southern region accounts for the largest share, while the Central region accounts for the lowest. By contrast, the Northern region had high prevalence rates of both underweight (21.11%) and obese (25.10%). As evident from the ratio of underweight to overweight/obese, the Central region has the highest ratio followed by the Eastern and then Western regions. The lowest ratio is observed in the Southern region, with a moderately higher ratio in the Northern region. Hence the divide appears to be between the Central and Southern regions. The state distribution of the ratio (of underweight to overweight/obese) is given in the Appendix 1. The disaggregation by rural, metro, and non-metro urban locations shows that the prevalence rate of underweight among women was highest in rural areas and considerably lower in metro and non-metro urban areas. The rural/urban

13 120 International Journal of Health Services 47(1) divide appears to be more marked in respect to the prevalence of underweight (than overweight and obese), with a much sharper gap between the rates prevailing in rural (79.84%) relative to metro (6.21%) and non-metro urban (13.95%) areas. The rates of overweight are low in rural and high in metro and non-metro urban areas, as indicated by the ratio of underweight to overweight/obese. Prevalence rates of underweight declines with age. The pattern with shares of total underweight women shows that the youngest account for about onethird and the oldest for the lowest (a little over 13%). The prevalence rate of overweight among women, by contrast, rises with age. However, shares in total overweight women also rise only up to years (from 14.90% among the youngest, to 32.51% in the older group), after which the share declines (to just under one-quarter). As in the case of overweight, shares of total obesity rise from the youngest to women in the age group years (from 14.88% to 34.17%) and then the share of the oldest falls (to a little over one-fourth). In sum, descriptive associations reveal correlations between socioeconomic status, regional, rural/urban location, and age. There is an excess of underweight women (relative to overweight and obese) among the low monthly expenditure categories, historically disadvantaged social groups, rural locations, and younger age groups. In order to examine whether the associations seen in the cross-tabulations are borne out in the multivariate framework, we conduct the regression analyses. Multivariate Tables 2 and 3 depict elasticities for the estimates for the probability of being underweight and overweight/obesity for rural, urban, and all of India. The all- India analyses that consolidate the rural and urban samples are worthwhile in themselves, with the caveat that these are somewhat restrictive, as the slope coefficients are not allowed to vary. However, the locational disaggregation is more detailed, as the effects of living in metros and non-metro urban areas relative to rural are captured. Underweight Rural, Urban, and All India. In Table 2, the first specification represents the estimates for the covariates in the rural areas. The relationship between all the indicators of socioeconomic status and the probability of being overweight is in the expected direction in rural, urban, and all-india level. Women in the higher MPCE category are less likely to be underweight. The elasticities are greater in magnitude in urban locations compared to rural parts. With respect to education, similar to MPCE, we find that higher education lowers the probability of being underweight. Also, the relationship again appears to be stronger in the urban than in rural locations. The third indicator of

14 Table 2. Elasticties (Robust Standard Error) of the Probability of Being Underweight for Women Aged Residing in Rural, Urban and in All India, Rural Urban All India Socioeconomic status Economic status Monthly per capita consumption expenditure Rs (0.013) (0.025)** (0.011)** Monthly per capita consumption expenditure Rs , (0.008) (0.024)** (0.008)** Monthly per capita consumption expenditure Rs. > 1, (0.010)** (0.046)** (0.013)** Educational attainment No education (0.007) (0.017) (0.007) Less than 6 years of education (0.007)* (0.028)** (0.009)** Caste SC (0.008)** (0.005)** (0.013)** ST (0.005) (0.004) (0.003) OBC (0.013) (0.021) (0.011) Relative food prices Ratio of cereal to pulse prices (0.057) (0.094) (0.048) Ratio of cereal to milk and milk product prices (0.024)* (0.060)** (0.023) Ratio of cereal to meat prices (0.011)** (0.059)** (0.010)** Ratio of cereal to eggs prices (0.072) 0.15 (0.198) (0.073) Ratio of cereal to vegetables prices (0.051)** (0.121)** (0.048)** Ratio of cereal to edible oil prices (0.123)** (0.264)** (0.113)** (continued) 121

15 Table 2. (continued) Rural Urban All India Region and location of residence Eastern (0.010)* (0.023)* (0.010)** Western (0.009)** (0.016)** (0.008)** Southern (0.011)* (0.026) (0.011)** Central (0.006) (0.007) (0.005) Metro n.a (0.023)** n.a Metro-urban n.a n.a (0.007)** Nonmetro-urban n.a n.a (0.009) Demographic factors Age categories Age (0.011)** (0.022)** (0.010)** Age (0.010)** (0.02)** (0.009)** Age (0.007)* (0.015)** (0.006)** Ever married (0.431) (0.423) (0.289) Number of observations 16,823 8,575 25,340 Wald chi-squared Log pseudolikelihood Robust standard errors in brackets. ** p 0.01 ; * p 0.05; + p 0.10 (two-tailed). n.a, not included Reference categories: Monthly per capita expenditure 0 400; More than 6 years of education; Other (caste); Northern states; Metro; Age 22 28, Never married. 122

16 Table 3. Elasticties (Robust Standard Error) of the Probability of Being Overweight/Obese for Women Aged Residing in Rural, Urban and in All India, Rural Urban All India Socioeconomic status Economic status Monthly per capita consumption expenditure Rs (0.025)** (0.020)* (0.016)** Monthly per capita consumption expenditure Rs , (0.011)** (0.017)** (0.009)** Monthly per capita consumption expenditure Rs. > 1, (0.010)** (0.028)** (0.011)** Educational attainment No education (0.009)** (0.009)** (0.006)** Less than 6 years of education (0.006)** (0.013)** (0.006)** Caste SC (0.015)** (0.008)* (0.008)** ST (0.011)** (0.003)** (0.006)** OBC (0.020)** (0.013)** (0.012)** Relative food prices Ratio of cereal to pulse prices (0.087)** (0.059)** (0.051) Ratio of cereal to milk and milk product prices (0.038) (0.031)** (0.024) Ratio of cereal to meat prices (0.031) (0.013)** (0.011)** Ratio of cereal to eggs prices (0.106)* (0.100)** (0.071) Ratio of cereal to vegetables prices (0.084)** (0.062)** (0.049)** Ratio of cereal to edible oil prices (0.159) (0.128)** (0.100) (continued) 123

17 Table 3. (continued) Rural Urban All India Region and Location of Residence Eastern (0.018)** (0.012) (0.010)** Western (0.016)** (0.010) (0.009)** Southern (0.016)** (0.013)** (0.010) Central (0.013)** (0.005) (0.006)** Metro n.a (0.011)** n.a Metro-urban n.a n.a (0.003)** Nonmetro-urban n.a n.a (0.007)** Demographic factors Age categories Age (0.019)** (0.013)** (0.011)** Age (0.015)** (0.009)** (0.008)** Age (0.008)** (0.005)** (0.004)** Ever married (0.575) (0.367)* (0.350) Number of observations 14,967 10,504 25,471 Wald chi-squared Log pseudolikelihood Robust standard errors in brackets. **p 0.01; *p n.a ¼ not included. Reference categories: Monthly per capita expenditure 0 400; More than 6 years of education; Other (caste); Northern states; Metro; Age

18 Kulkarni et al. 125 socioeconomic status, namely, the caste affiliation, is not consistently significant across all the groups. Being an SC increases the risk of underweight in rural, urban, and all-india levels. Of the six relative food prices, four have significant estimates in rural, urban, and all-india areas alike. They are cereal to milk and milk product prices (positive association), cereal to meat prices (negative association), cereal to vegetable prices (positive association), and cereal to edible oil prices (negative association). Higher cereal prices relative to those of milk and milk products and vegetables increases the probability of being underweight in rural areas and nationally. The relationship is contrary in urban parts. Also, higher price of cereal relative to meat and edible prices reduces the probability of being underweight in urban areas. The elasticities in urban locations indicate a stronger relationship between food prices and BMI than what is seen in rural ones. Regionally, except for Eastern, women residing in other areas experience a greater probability of being underweight. The estimates for the Central region are not significant. Another locational indicator pertains to metro vs non-metro residence. The estimates for both urban areas and for all of India show that living in metro areas is associated with a lower probability of being underweight. The results across the age categories indicate that as women get older, they are less likely to be underweight. Marital status does not emerge as a significant factor in predicting the probability of being underweight for rural and urban women. Overweight/Obesity Rural, Urban, and All India. Table 3 presents the estimates predicting the probability of being overweight/obese. The relationship between all the measures of socioeconomic status and the probability of being overweight/ obese is expectedly positive and opposite of what is observed in case of probability of being underweight in Table 3. Further, unlike the case of probability of being underweight, the elasticities for all three measures are greater in magnitude in rural than in urban areas. This suggests that the relationship between socioeconomic status and the probability of being overweight/obese is stronger in villages than in towns/cities. Also, in case of the caste background, risks of being obese/overweight are lower in the relatively vulnerable groups in the social hierarchy, namely, SC, ST, and OBC. With respect to food prices, the ratios of cereal to egg prices and cereal to vegetable prices are significant in rural and urban areas. However, the direction of the relationship is opposite. While in rural areas, the higher ratio of cereal to egg prices and vegetable prices reduces the risk of overweight/obesity, the correlation is reversed in urban regions. The two other food prices that are significant in urban areas are ratio of cereal to meat prices and edible oil prices. The signs pertaining to the above two elasticities are negative (in urban areas), indicating lower price ratio is related to higher probability of obesity/overweight.

19 126 International Journal of Health Services 47(1) Regionally, all the regions relative to Northern in the rural parts experience a lower risk of being overweight/obese. In the case of urban women, those residing in the Southern region experience a greater probability of being overweight/ obese relative to women living in the Northern region. In urban locations, women living in metro areas are less likely to be overweight/obese, albeit with low elasticity. At the national level, both metro and non-metro urban areas are more likely to have a higher BMI. Among the demographic factors, both age and marital status emerge as significant. Age and overweight/obesity are positively associated, implying older women are more prone to being overweight/ obese. The elasticities are slightly higher in the age group Marital status of being ever married in urban areas is associated with considerably higher risks of overweight/obesity. Marital status is not of significance in rural region nor at the all-india level. Discussion We reexamine the socioeconomic patterning of the double burden of malnutrition for India employing a recent and rich data set. Also, our study, by considering relative food prices, helps account for the endogenity between food intake and nutritional status. Similar to previous research, we find that double burden of malnutrition remains socioeconomically segregated along the three dimensions of socioeconomic status, namely, MPCE category, educational attainment, and caste. Underweight women are concentrated in low-mpce categories while there are more overweight/obese women in the high-mpce category. Though the relationship between education and underweight is not so robust, having more than five years of education reduces the prevalence of underweight among women. However, a positive relationship between education and overweight is more robust, which echoes with what has been observed in prior work. Subramanian, Perkins, and Khan 3 report a positive association between education and overweight, as do Griffiths and Bentley. 17 They find that women residing in households where at least one member is educated for more than 10 years are more likely to be overweight. Similar evidence exists for Bangladesh, where more educated women were more likely to be overweight in rural areas (In rural areas, women with 14 years of schooling had an eight-fold higher risk of being overweight compared with their non-educated peers 57 ). This finding is at variance with what is observed in many developed countries, where either no relationship is found between education and obesity among women or a negative one is seen. 49,50 The socially and economically disadvantaged groups comprising SCs, STs, and OBCs have more underweight than overweight women, and the category Other has more overweight women. Our analyses therefore resonate with the observation that the relationship between socioeconomic status and prevalence

20 Kulkarni et al. 127 of obesity in developed countries is opposite of that in developing countries. Also, the greater prevalence of overweight/obese women in higher socioeconomic status may reflect a cultural change to a greater acceptance of moving away from eating home-cooked meals and a vegetarian diet. Additionally, among those in higher socioeconomic groups, being overweight is associated with affluence and physical comfort and therefore a likely symbol of status. Second, relative food prices emerge as significant with respect to the probability of being underweight and overweight/obese. There is, for example, a consistently positive relationship between ratio of cereal to vegetable prices with the probability of being underweight. This implies that high cereal prices deter the consumption of cereals and increase the risk of being underweight. However, a more detailed food commodities classification would yield richer insights into what food items are complements and substitutes. Third, the spatial pattern of the distribution of underweight and overweight/ obese women reveals differentials on the lines of regional, rural/urban, metro/ non-metro, and regional residence. The regional differences appear to belie the significance of the North-South divide seen in previous studies on female survival rates. 51,52 Women from the Eastern region experience a lower risk of being underweight and overweight/obese relative to those from the Northern region. In the case of the probability of being overweight/obese, only women from the urban parts of the Southern region (relative to Northern) show a greater risk of being so. A possible explanation is that the cultural norms of fatness as a sign of beauty as conjectured by Subramanian, Perkins, and Khan 3 are more prominent in the urban parts of the Southern region. The studies on the South Indian states of Andhra Pradesh and Karnataka indicating the role of culture in shaping perceptions regarding fatness and on imposing barriers (on women) for engaging in physical exercises 17,43 can be seen as supporting the above conjecture. Additionally, future studies based on emerging data will potentially contribute in enhancing our understanding of interregional variations. The rural/urban differentials are noteworthy. The relationship between economic status and the risk of being underweight and overweight/obese is stronger is urban than in rural areas. This may be because at high economic levels, physical activities are less strenuous, access to fast food is easier, and diets are more diversified in urban areas. With respect to relative food prices, too, the elasticities of being underweight with respect to the ratios of cereal to vegetable prices and cereal to meat prices are higher in urban than in rural areas. This suggests that food and nutrient intake is more sensitive to prices in urban areas. A plausible explanation of this greater sensitivity is that rural residents tend to rely more on homegrown food and on wholesale prices, the latter having lower fluctuation than retail prices A caveat may be noted here. In recent work, the adoption of BMI, a general measure of relative weight, to optimally capture metabolic obesity for Asians is frequently questioned. It has been seen that abdominal obesity as measured by

21 128 International Journal of Health Services 47(1) waist-hip ratio exists to a noticeable extent among Asians. Inquiries on the effect of high waist-hip ratio show a positive relationship between the former and allcause mortality and acute myocardial infarction among urban Chinese and Indian women, respectively. Another recent discussion around the obesity paradox that the overweight are less likely to die than those of normal weight over the same time period is a subject of debate among nutrition experts. Being overweight increases a person s risk of diabetes, heart disease, cancer, and many other chronic illnesses. But evidence also suggests that for those who are middle-aged or older, or already sick, a bit of extra weight is not particularly harmful and may even be helpful. 53 The opposite camp, however, insists that with more appropriate controls for sickness-related loss of weight and smoking, the paradox disappears. The complexity of the metabolic syndrome makes the obesity paradox far from being fully resolved A limitation more specific to our analysis, as mentioned earlier, is the lack of sufficiently detailed disaggregation of food commodities that would have enabled a clearer understanding of food complements and substitutes and of compensated and uncompensated food price effects on nutrient intake. Persistence of socioeconomic patterning of double burden of malnutrition has potential policy implications. Though life expectancy in India is improving, levels of physical well-being remain a pivotal concern. There is a double burden of disease with noncommunicable diseases being on the rise coupled with significant prevalence of water-borne and vector-borne communicable diseases, including tuberculosis and respiratory infections. As the rural population migrates to urban areas, the associated lifestyle changes and dietary transition are likely to elevate the risks related to noncommunicable diseases. Since type of food intake is a critical variable in predicting nutrition levels, food subsidy and policies to contain the rise in relative prices of healthy items such as cereals and legumes would be helpful. With more health care currently financed with private, out-of-pocket resources, the increasing burden of communicable and noncommunicable illnesses will make it harder for households to escape poverty and to prevent them from falling into poverty. Extreme poverty and fetal and early childhood undernutrition are likely to create a large pool of those at heightened risk of being underweight. In addition, children born to undernourished women, if they survive, are more likely to succumb to diabetes and to cardiovascular and other chronic diseases in adulthood. Further, childhood malnourishment enhances the chances of being overweight/obese as an adult. Given the likelihood of the burden of obesity shifting to the poor in the long run, programs such as providing subsidized health insurance coverage, increasing awareness of the risks of obesity, and education about the significance of regular physical activity are imperative. Finally, a balanced strategy of preventive and curative care aimed at simultaneously addressing the twin problems of undernourishment and overweight/obesity requires serious consideration.