The Journal of Nutrition Nutritional Epidemiology

Gender Differences in Body Mass Index in Rural Are Determined by Socio-Economic Factors and Lifestyle1

Mary Barker,2* Ginny Chorghade,2 Sarah Crozier,2 Sam Leary,3 and Caroline Fall2

2MRC Epidemiology Resource Centre, University of Southampton, Southampton General Hospital, Southampton SO16 6YD, UK and 3Avon Longitudinal Study of Parents and Children (ALSPAC), Bristol BS8 1TQ, UK Downloaded from https://academic.oup.com/jn/article/136/12/3062/4664006 by guest on 24 September 2021

Abstract A survey of the nutritional status of women in 6 villages of the district of , India, found young women to have a significantly lower BMI than their male peers. The purpose of this study was to identify social and economic factors associated with this difference in thinness and to explore the behavior in men and women that might underlie these associations. We compared men and women in 90 families in this part of Maharashtra by taking measurements of the height and weight of the married couple of child-bearing age in each family and assessing their social and economic details, fasting practices, and oil consumption. In this agricultural community, women were thinner in joint land-owning families, where the main occupation was farming, than those in nonfarming families. This was not true of men in this type of family. Men in ‘‘cash-rich’’ families had higher BMI than men in families without this characteristic. There was no corresponding difference in women’s BMI. We then examined the lifestyles of men and women in a subset of 45 of these families. Women were more likely to work full time in farming than men, to carry the burden of all household chores, to have less sleep, and to eat less food away from home than men. Women fasted more frequently and more strictly than men. Despite identifying significant differences in behavior between men and women in the same household, we did not find a direct link between behavior and BMI. We conclude that being married into a farming family is an important factor in determining the thinness of a woman in rural Maharashtra. J. Nutr. 136: 3062–3068, 2006.

Introduction the extent of these inequalities. For example, fewer girls are Indian women are thinner and shorter than women in other immunized in rural than urban areas and in the north rather than parts of Asia (1). This has consequences for their own health and the south of India (12). that of their children. India has one of the highest incidences of As they pass into adulthood, Indian girls adopt a social role that low birth weight in the world (2). This is not simply because may limit their access to education, health care, and food (13). India is economically poor; it has a higher gross national product Traditionally,they marry young and enter the groom’s family at the than many other developing countries and has shown remark- bottom of the hierarchy. They are given the most menial work and able economic growth in recent years. Gender inequality, deeply are expected to prove themselves by working hard and bearing a entrenched in Indian society, may be a factor (3). child within a year of marriage, preferably a boy. Between 1994 and 1996, a study of women living in villages Although it seems likely that this role affects women’s near Pune city in western India was conducted to examine the nutritional status, there is little direct evidence to link the two. relation of maternal nutrition to fetal growth (4–6). The study Villagers in rural Maharashtra believed their women were thin showed that thinner women had thinner babies, and also that because of the combined demands of early motherhood and work they were significantly thinner than their husbands (7); 65% of (7). This article attempts to identify the social and economic women had BMIs below 18.5 kg/m2, an indicator of chronic correlates of thinness among young women in this population energy deficiency, compared with 39% of their husbands. (survey 1), and to explore how women’s daily activities might Gender inequalities in health and nutritional status in India contribute to this phenomenon (survey 2). are the subject of an extensive literature. Females have higher mortality rates in utero due to sex-selective abortion (8), and a higher mortality in infancy and childhood (9). Girl children are Methods and Procedures more likely to be undernourished than boys (10) and often have less access to health care (11). There are regional variations in Socio-economic status: survey 1. We surveyed 101 families currently living in Pabal village. Pabal is 51 km from Pune City and comprises a 1 This study was funded by Hope (Wessex Medical Research Charity, UK). central village area surrounded by 27 hamlets. In 1991, the village had an * To whom correspondence should be addressed. E-mail: [email protected]. adult population of 8300. Most families earn a living farming cash crops, ac.uk. and women work on the farms as well as perform domestic work such as

3062 0022-3166/06 $8.00 ª 2006 American Society for Nutrition. Manuscript received 4 May 2006. Initial review completed 7 July 2006. Revision accepted 3 October 2006. washing clothes and carrying water and firewood (14). Few are educated asked closed questions about the frequency of working on their own or beyond primary school level. The majority of families in the survey another’s farm, or in other jobs; how often they carried out specific belonged to agricultural castes. The King Edward Memorial Hospital household chores; whether they went to bed last in the household and Ethics Committee in Pune, India, granted permission for studies 1 and 2. got up first; how often they watched television, spent time with their Families were selected, from a survey of all married women in the friends, or had a siesta. Women were asked whether their workload village (5), to contain a husband and wife pair of child-bearing age with at increased, decreased, or remained the same after marriage. Men and least 1 son and 1 daughter in the age range of 3 to 8 y (n ¼ 101). A total of women’s eating patterns were established by asking how often they 93 men and 98 women took part in the study, producing 90 couples whose missed meals, ate outside the home, ate first or last at meal times, ate nutritional status could be compared in the 2nd half of the analysis. nonvegetarian foods, eggs, milk, fruit, and green vegetables. They were Families were visited by a researcher (G. Chlorghade), a midwife to asked whether they fasted and why, when they began to fast, how often make anthropometric measurements, and a community worker who was they fasted, and how many meals they consumed on a fasting day. known to the families. The height and weight of the father and mother in Respondents were asked about frequency of seeking medical treatment, each family were recorded. A questionnaire was completed for each how much money they spent, and why they needed treatment. We asked family by a member of the research team (G. Chlorghade), covering social, men and women about whether they worried, and what they worried economic and nutritional factors. Educational status of the target pair was about, because worrying had also been mentioned in the focus group recorded in 5 categories from illiterate to graduate. A record was made of discussions as a factor contributing to women’s thinness. whether they lived in a traditional extended family unit or a nuclear Most questions were related to activities during the past month. Downloaded from https://academic.oup.com/jn/article/136/12/3062/4664006 by guest on 24 September 2021 family. Their houses were categorized as a 1-room hut, a ‘‘kutcha’’ (more Questions about medical treatment were related to the past 3 mo. than 1 room, with mud walls and a thatched roof), ‘‘mixed pucca’’ (stone Questions about habitual practices such as fasting referred to the and cement walls, mud plaster,and a tiled roof), ‘‘pucca’’ (entirely stone or respondent’s current lifestyle. bricks and cement with a tiled roof), or rented house. Distance from Pabal The questionnaire was administered in the local language (Marathi) main village was estimated from the jeep’s mileometer. Men’s and by 1 member of the GPC research team and piloted in a neighboring women’s occupations were recorded as farm laborer on other’s land, farm village. Interviews were conducted throughout the winter of 1998 and laborer on their own and other’s land, petty employee or artisan, business the spring of 1999. person or trader, land-owning farmer, worker in service industries, or (for women) housewife. Analysis. A summary and descriptive statistics were produced to Participants estimated the acreage of irrigated and nonirrigated land compare the workload, eating habits, and fasting practices of men and owned by the household. Irrigated land was considered to be 4 times as women. Answers to questions regarding work outside the home, house- productive as nonirrigated land. A total productive acreage was created hold chores, leisure time, sleep, eating habits, fasting, and worrying were by multiplying irrigated land acreage by 4 and adding the acreage of aggregated and recoded where necessary to produce information on nonirrigated land. The interviewer recorded whether the household frequency per week. We also carried out a principal-components analysis owned the following: an iron, bicycle, tape player or radio, moped, car of the relations among these activities using data from the 42 households or jeep, or bullock cart; and these were summed to create a material with complete information from both the man and the woman. Scores on possessions score. An amenities score was constructed for whether the each principal component were compared between men and women household had electricity or a well, or both. Men and women were asked using the Wilcoxon Signed Rank Sum test. The principal components if they fasted and, if so, how often. Ownership of milking animals and oil were then correlated with BMI for men and women as well as for the consumption were recorded. difference between men and women in the same household, using Spearman’s rank correlation. Analysis. A summary and descriptive statistics were produced to enable us to compare the BMIs of men and women in each category of social, economic, and food-related variables. Tests for trends in men’s BMIs, Results women’s BMIs, and in the percentage of difference between men’s and Socio-economic status: survey 1. Men had higher BMIs than women’s BMIs within households [(men’s BMI – women’s BMI/ men’s 2 2 BMI) 3 100], were carried out using linear regression analysis. To women (median 19.8 kg/m , interquartile range 18.4–21.9 kg/m 2 2 identify patterns of variables and how these related to the BMIs of men vs. 18.3 kg/m , interquartile range 17.3–20.2 kg/m ; P , 0.001). and women, we carried out a principal components analysis of the social, Most women were illiterate (55%), compared with 16% of economic, and food-related variables. Principal components analysis the men. Most of the men had some secondary schooling transforms the original set of variables into fewer summary variables, compared with only 45% of the women. The majority of known as the principal components (15), which are linear combinations households were joint families, and lived in ‘‘kutcha’’ or ‘‘mixed of the original variables uncorrelated with each other. The first com- pucca’’ housing. Although 40% lived within 1 km of the main ponent accounts for as much of the variation in the data as possible, with village, 32% were in hamlets .3 km away. each subsequent component explaining less. This analysis was carried In 63 families, the men considered farming either on their out on data from the 90 households where comparisons could be made own or another’s land their main occupation. The other 30 men between the man and the woman. The principal components describing patterns of social, economic, and food-related variables were correlated were petty employees or artisans, businessmen or traders, or with the BMIs of men and women separately, and then with the working in local industries. For 79 women, the main occupation difference between the BMIs of men and women in the same household, was farming. Two women worked as petty employees or using Spearman’s ranked correlation. artisans, another 2 in local industries, and 1 business-woman traded cloth. Fourteen women called themselves housewives. Women’s daily activities: survey 2. From the 101 households, the 45 Twenty households owned no land. Thirty-six percent of fam- households that showed the greatest differences between the men and ilies owned an iron, 64% a bicycle, 55% a bullock, 46% a tape- women’s BMIs were chosen for detailed interviews. Households were player or television, 11% a motorcycle or scooter, and 1% a jeep selected from above and below the median BMI. For some, the mother’s or tractor. Thirty-two percent had a personal well, and 84% had BMI was higher than the father’s, and for others it was lower. All an electricity supply. Forty-seven percent of women and 15% of mothers and all but 2 fathers were interviewed (2 men worked away from home). Men and women were interviewed separately. men reported fasting at least 1/wk. The median consumption of We administered a structured questionnaire that asked about lifestyle oil (mainly groundnut or mustard) was 0.33 kg/(person mo). characteristics (identified by the villagers in focus groups) that were Living in a joint family, farming family, poorer quality house, responsible for women’s thinness (7): the excessive workload of women, or owning a milking animal was associated with lower BMI in limited access to supplementary food sources, and fasting practices. We both sexes (Table 1). Women in families owning more farmland

Gender difference in BMI in India 3063 TABLE 1 BMI of men and women and the mean percentage difference in BMI by social, economic, and nutritional factors1,2

Men's Women's % Diff. Factors n BMI P-value3 BMI P-value in BMI4 P-value

Social factors kg/m2 kg/m2 Men's education 0.56 0.98 0.66 Illiterate 15 19.8 19.3 2.1 Neo-literate 4 19.9 19.0 3.3 Up to standard 8 40 20.5 18.4 9.3 Up to standard 12 29 19.6 18.9 2.9 Graduate 5 22.5 19.9 11.0 Women's education 0.02 0.11 0.58 Illiterate 54 19.7 18.4 5.5

Neo-literate 0 Downloaded from https://academic.oup.com/jn/article/136/12/3062/4664006 by guest on 24 September 2021 Up to standard 8 31 20.4 18.8 6.1 Up to standard 12 11 21.2 19.9 5.4 Graduate 2 23.9 19.0 20.3 Family type 0.03 0.01 0.54 Nuclear 37 20.9 19.6 4.8 Joint 64 19.7 18.2 6.8 House type 0.01 0.06 0.59 Hut 10 18.9 19.4 25.2 Kutcha 34 19.9 18.2 7.4 Mixed pucca 32 19.9 18.1 8.1 Pucca 20 21.1 19.2 7.9 Rented/not owned 5 22.5 22.7 23.3 Distance from main village, km 0.45 0.05 0.43 #1 40 20.5 19.5 3.9 1–3 29 19.8 17.8 8.5 .3 32 20.1 18.4 6.7 Economic factors Men's occupation 0.004 0.002 0.78 Farming 63 19.6 18.2 6.3 Nonfarming 30 21.4 19.9 5.4 Women's occupation ,0.001 0.001 0.61 Farming 79 19.7 18.3 5.6 Nonfarming 19 22.3 20.3 7.6 Land ownership score in 0.57 0.01 0.08 dryland value, acres #2 33 20.7 19.6 3.3 #6 20 20.1 18.8 6.1 #16 25 18.9 17.9 3.2 .16 23 20.8 18.1 12.2 Material possessions score 0.47 0.31 0.16 None 13 19.9 19.1 3.1 1 possession 21 19.7 19.2 0.5 2 possessions 30 20.4 18.5 8.8 3 possessions 19 20.2 18.0 8.5 4 possessions 13 20.6 18.7 8.7 5 possessions 5 20.0 18.9 5.0 Amenities score 0.54 0.32 0.27 None 14 19.9 19.1 3.6 Well or electricity 57 20.1 18.7 5.1 Well and electricity 30 20.4 18.4 8.6 Nutrition factors Men's fasting frequency 0.37 0.75 0.51 Never 50 20.3 18.7 7.3 1/y 10 20.1 18.8 5.0 2/mo 19 20.3 19.5 3.1 1/wk 14 19.4 17.9 6.1 (Continued)

3064 Barker et al. TABLE 1 Continued

Men's Women's % Diff. Factors n BMI P-value3 BMI P-value in BMI4 P-value

Nutrition factors kg/m2 kg/m2 Women's fasting frequency 0.19 0.87 0.57 Never 31 19.8 18.8 4.4 1/y 8 19.2 17.8 5.2 2/mo 13 21.1 18.7 10.4 1/wk 46 20.5 18.8 6.0 Milking animal 0.01 ,0.001 0.48 No 28 21.4 20.2 4.2 Yes 73 19.7 18.1 6.7 Oil consumption, kg 0.002 0.52 0.04 21 21 person mo Downloaded from https://academic.oup.com/jn/article/136/12/3062/4664006 by guest on 24 September 2021 0.21 26 19.8 18.9 3.0 0.33 26 19.1 18.8 0.0 0.57 28 19.8 17.5 11.0 .0.57 21 22.4 20.0 9.1

1 BMI are geometric means. 2 Analysis based on data from 93 men, 98 women, and 90 husband and wife pairs in 101 households in Pabal village. 3 P-value for association. 4 % difference in BMI ¼ (men’s BMI 2 women’s BMI/men’s BMI) 3 100.

and living farther from the main village had lower BMIs. Higher The first principal component was a grouping of variables that educational status on the part of the women was associated with defined the extent of the household’s involvement in farming. higher BMI in men but not the women themselves. Greater Higher scores described households where the men and women household oil consumption was associated with an increase in men’s were farmers, owned more land, lived farther from the main BMI but not women’s. BMI was higher in men relative to women in village, had a joint family structure, and owned a milking animal. the same household in families with higher oil consumption. This principal component was negatively correlated with BMI in women (r ¼ 20.27, P ¼ 0.01) but unrelated to BMI in men. Principal components analysis. To simplify interpretation, The second principal component included variables that we present only those variables that loaded .0.3 (Table 2). defined cash wealth as opposed to land wealth. Higher scores Within a component, positive loading indicates a direct associ- described families living in better housing, consuming more oil, ation of the variable with the component and negative loading and having more possessions and amenities. This component an inverse association. was positively associated with BMI in men (r ¼ 0.23, P ¼ 0.03) but not women. In households with more cash wealth, men had higher BMIs than women in the same household (correlation with % difference in BMI, r ¼ 0.24, P ¼ 0.02). TABLE 2 Variable loadings on the first 2 principal components in the analysis of social, economic, and Women’s daily activities: survey 2. The median (interquartile 2 2 food-related variables1 range) BMI was 18.2 kg/m (17.4–21.0 kg/m ) for the 45 women interviewed, and 20.4 kg/m2 (17.8–23.2 kg/m2) for the 1: Farming 2: Cash-wealthy 43 men. Variable households households Work. A third of the women worked full time in farming, mostly n ¼ 90 on their own family’s land, compared with 14% of men (Table 3). Men's level of education – – Five households owned no land, and the women worked on Women's level of education – – other’s farms as daily laborers. Nine women rarely or never went Joint family vs. nuclear family 0.37 – out to work. Most of the men (n ¼ 25) did work other than Distance from main village 0.31 – farming, such as working in village shops or local industry. House type – 0.41 Household chores were predominantly a female activity Nonfarming men vs. farming men 20.39 – (Table 3). Men performed a median of 4–5 chores/wk, whereas Nonfarming women vs. farming women 20.37 – the women’s median was 70/wk. Forty-three women and no men Land ownership 0.34 – prepared food $1/d. The majority of women washed clothes, Material possessions – 0.48 washed-up, fetched water, and tended to the animals every day. Amenities score – 0.45 Women were also largely responsible for cleaning the animal shed Men's fasting – – and collecting firewood. In contrast, men were more likely to run Women's fasting – – errands, such as buying groceries and going to the main village. Milking animal 0.38 – Both men and women in the same household often reported Oil consumption – 0.38 going to bed last and waking first, casting doubt on the reliability 1 Loadings below 0.30 shown as –. of this data. Women were more likely to report being the first to

Gender difference in BMI in India 3065 TABLE 3 Numbers of men and women performing work made with sago, mashed potato, coriander, peanuts, gram dal, and taking leisure every day and green chili), whereas only 18 of 45 women ate 2 meals a day. Most women (n ¼ 25) said they began to fast after marriage, Variable Men, n ¼ 43 Women, n ¼ 45 whereas men were more likely to have fasted before they were married. Women said they took up fasting because that is what Work activities n women in the area do (n 14), because it was a family tradition Farming 6 18 ¼ (n 9), and for religious reasons (n 8). Men fasted for Working in other jobs 7 2 ¼ ¼ religious reasons (n 16), to ‘‘rest their stomachs’’ (n 12), or Preparing food 0 43 ¼ ¼ because of family tradition (n 10). Respondents recorded Washing clothes 0 28 ¼ .1 response to this question. Washing utensils 0 29 Twenty-five women and 15 men said that they had worries. Fetching water 0 35 Tending to animals 19 23 Women and men worried about similar things: the future for their sons (9 women, 4 men) and daughters (13, 5), unpaid debts, Cleaning the animal shed 0 16 and money problems (2, 1). In addition, 7 women worried about Collecting firewood 0 13 unfinished chores.

Running errands 2 0 Downloaded from https://academic.oup.com/jn/article/136/12/3062/4664006 by guest on 24 September 2021 Going to the village 28 0 Principal components analysis. Data from both sexes were Leisure activities combined to derive the variable loadings in the principal Spending time with friends 29 5 components analysis. The first component defined working Watching television 9 5 patterns (Table 5); high scores reflected more farm work, more household chores, less sleep, and fewer snacks. Women had wake and the last to bed (n ¼ 28) than men (n ¼ 12). Men higher scores than men (P , 0.001). watched TV and saw their friends more often than women The second principal component primarily described eating (Table 3), and more men reported usually relaxing or sleeping patterns. Higher scores reflected less nonvegetarian food, less after work (8 women, 25 men). fruit and vegetables, being served last at meals, and missing meals. Higher scorers also worked fewer hours on the farm and Unequal access to food. The majority of women (n ¼ 27) and worried more. This component did not discriminate between nearly half the men (n ¼ 20) said they rarely or never missed men and women. meals. More men (n ¼ 17) reported missing 1 meal/d than Neither principal component was correlated with BMI in ei- women (n ¼ 11). However men consumed snacks away from ther men or women or with differences in BMI between spouses. home more often (Table 4). Women were more likely to eat last at meal times. Nonvegetarian foods (meat and fish) were eaten rarely by both men and women. Eggs, milk, and milk products Discussion were consumed frequently by both sexes, although men were more likely to drink milk than women. The majority of men and We found that both men and women from farming households women ate green vegetables .1/wk. However, men were more were thinner than those whose households were engaged in other likely to eat fruit than women. types of work. Women, but not men, were significantly thinner in Men and women did not differ in frequency of visiting a farming households that owned more land and milking animals, doctor or in expenditure for medical treatment. The majority (29 lived farther from the main village, and had a traditional joint men and 30 women) had had no medical treatment. family structure. In contrast, men, but not women, had higher BMIs if they lived in ‘‘cash-wealthy’’ households (families living Fasting. The majority of women (n ¼ 24), but fewer men (n ¼ in better housing, possessing more material items, having more 14), reported fasting $1/wk. On average, women fasted 1/wk, amenities, and consuming more oil). When we looked at whereas men fasted once every 3 wk. Sixteen women and 15 men rarely or never fasted. When fasting, most men (n ¼ 29) ate 2 meals a day, 1 of which would be fasting snacks such as roasted TABLE 5 Variable loadings on the first 2 principal components sweet potato or a sago vada (a deep-fried, savory snack usually for men and women1

Behavior 1: Working life 2: Eating habits TABLE 4 Numbers of men and women missing meals, eating n ¼ 88 snacks, eating last at family meals, eating Farm work 0.39 20.34 nonvegetarian foods, green vegetables, fruit, Other work 20.42 – and drinking milk 1/wk . Leisure activities – – Household chores 0.51 – Variable Men Women Lack of sleep score 0.34 – n Eating last at family meals – 0.33 Missing meals 22 14 Eating nonvegetarian food – 20.39 Eating snacks or food outside the home 17 4 Eating fruit and vegetables – 20.34 Eating last at family meals 17 27 Fasting – – Eating nonvegetarian foods 0 0 Missing meals – 0.39 Eating green leafy vegetables 33 35 Eating snacks 20.33 – Eating fruit 19 7 Worrying – 0.39 Drinking milk 20 12 1 Loadings below 0.3 shown as –.

3066 Barker et al. behaviors underlying these associations, we found that women in for living in a nuclear family, despite having fewer women with farming households spent more time in farm work than their whom to share the farm and household tasks (7). husbands in addition to performing the majority of household The second family pattern we identified was ‘‘cash wealth.’’ chores. They also had less sleep, ate fewer snacks, and worried The cash wealthy lived in better housing, had more access to more. However, we found no direct link between this pattern of amenities, owned more possessions, and consumed more oil. behavior and women’s thinness relative to men. Why this pattern was associated with men being better nourished, We conclude that being a part of a farming household is a but not women, is not clear. It suggests that household resources factor contributing to the thinness of women in this village. Our are not allocated equally to men and women, even in wealthier data suggest this is not simply because farming women are households. poorer. Women in families with more land were thinner than Gittelsohn (23) described in detail the distribution of food those who owned less land. Land wealth seems to have a within households in rural Nepal, and found that adult women negative rather than positive effect on women’s BMIs, but was ate last, least, and worst. Men received more ‘‘high status’’ foods unrelated to men’s BMIs. Though both men and women un- such as animal products. Our own observations were that men doubtedly have hard-working lives, the farming women seemed consumed more milk, fruit, and snacks than women, although to have time for little else other than work. Lukmanji (16) we found no differences in the consumption of nonvegetarian describes rural women in developing countries as bearing the food. Women in Naved’s (24) study in Bangladesh justified their Downloaded from https://academic.oup.com/jn/article/136/12/3062/4664006 by guest on 24 September 2021 burden of a ‘‘double day’’ to fulfill both their working and preferential distribution of food to the adult males in the domestic roles. In another study of women in this part of India, household by saying that men are the main breadwinners and Rao et al. (14) found that farming women had a similar domestic work hardest. This may be a false perception. A study of Indian workload to that of nonfarming women. These authors cite a male and female farmers concluded that although energy intake study of female farmers in Burkina Faso, West Africa (17,18), in women was lower than that of men, they spend more time on and concluded that time spent farming by women in their study economically productive work (25). However, Naved’s study was similar to that spent by the West African women, but that might explain some of our findings. This was a qualitative the Indian women spent considerably more time in domestic evaluation of the impact of ‘‘cottage-industry’’ schemes to farm work. This suggests that even by the standards of poor farmers vegetables and fish on micronutrient intakes of men and women. in other countries, rural Indian women work excessively hard. Women involved in the fish-farming schemes rarely ate the fish Previously held focus group discussions (7) suggested that farming they produced, which were instead given to their guests, men, women in our population had a particularly hard life, and that and children. In contrast, the women vegetable growers ate more parents were prepared to go into debt to provide dowries to of the new varieties of vegetables because other family members ensure their daughters married boys who were not farmers. did not like them. If the same applies in our study area, women Recent industrialization in rural Maharashtra may be increasing in cash-wealthy households may benefit less from the house- the burden on women. More women than men in our study listed hold’s relative wealth because resources are not distributed their occupation as farming, and more women were working full equally among household members. time on the farm. This may be because men were increasingly Our study demonstrates that women’s lack of autonomy, seeking jobs in the newly opened local factories where they could especially limits on their freedom of movement, has a measurable earn cash and leave their women to tend the family’s land. effect on the quality of their diets. They have less access to food It is unclear why the differences we observed in the workload prepared outside the home. Unlike men, they rarely go to snack and leisure time of men and women did not explain differences shops and food markets in the main village or even to local shops. in their BMIs. Although work patterns described by the men and The focus group discussions revealed that most women had no women broadly concur with directly observed activity data from access to cash (7). Therefore, women have fewer opportunities to this area, our study may have been inadequately powered, given supplement their diet than men. There is epidemiological the qualitative nature of some of our data and the small sample evidence to suggest that women’s freedom of movement is a size. A study in Ghana showed that farm work had a strong factor in the health of themselves and their children (9,12). A negative effect on women’s BMI (19). Alternatively, we may woman’s level of education is also known to be an important have lacked data on other crucial factors. We did not record the factor in the health of her family, although the main focus of reproductive history of the women, such as parity and breast- research has been on the health of children [for a discussion see feeding, or symptoms of reproductive tract infection. Because of (26)]. We found that men had higher BMIs in the households of difficulties in obtaining an accurate age in this population, we better-educated women; although, for women, being better- did not collect data on the age of the men and women, or the age educated was unrelated to their own BMIs. Men’s level of differences between spouses. Skinfold measurements may have education was unrelated to their own or to their wive’s BMI. We been preferable to BMI as assessments of energy status. have been unable to find additional evidence indicating that Joint families have been associated with worse health maternal education improves the nutritional status of husbands. outcomes for children and young women (20,21). Young women This finding needs to be substantiated in a larger study. in joint families are subject to the authority of their mother-in- A dietary issue that we explored in some depth is fasting. As law, and have little autonomy and decision-making power. We far as we know, there is no literature on this topic. In India, found both men and women in joint families to be thinner than fasting is undertaken by both sexes, not only for religious those living in nuclear families. However, the pattern that reasons, but also as a social ritual and for the perceived health described farming families with a joint family structure disad- benefits. However, our data showed that women adhere more vantaged women more than men. This pattern may be identi- strictly to fasting practices than men. Barbara Harriss’ (27) fying traditional family units where young married women are explanation of gender differences in fasting is that men fast for at the bottom of the hierarchy of decision making and access to their own individual, spiritual purposes, whereas women fast for resources. A study in Zimbabwe found that women who had the benefit of the whole household. Hinduism promotes self- more say in household decisions had higher BMIs (22). It was sacrifice in women as a way of pleasing the deities and notable that women in our focus groups expressed a preference preventing bad luck. For women, fasting is therefore part of

Gender difference in BMI in India 3067 their duty to promote the health and welfare of their household. 10. Choudhury KK, Hanifi MA, Rasheed S, Bhuiya A. Gender inequality This sense of responsibility may underlie the fact that women and severe malnutrition among children in a remote rural area of Bangladesh. J Health Popul Nutr. 2000;18:123–30. report more worries than men. Unfortunately, fasting may 11. Pandey A, Sengupta PG, Mondal SK, Gupta DN, Manna B, Ghosh S, further reduce women’s nutritional status. Sur D. Bhattacharya SK. Gender differences in healthcare-seeking In a situation where everyone works hard and most people during common illnesses in a rural community of west Bengal, India. are thin, women in farming families work the hardest and are the J Health Popul Nutr. 2002;20:306–11. thinnest. Villagers in rural Maharashtra know how hard life is 12. Pande RP, Yazbeck AS. What’s in a country’s average? Wealth, gender, for young farming women. Their solution is to provide dowries and regional inequalities in immunization in India. Soc Sci Med. sufficient to attract grooms working in business or industry to 2003;57:2075–88. ensure their daughters an ‘‘easy lifestyle with enough sleep’’ (7). 13. Santow G. Social roles and physical health: the case of female disadvantage in poor countries. Soc Sci Med. 1995;40:147–61. The women themselves suggest that if they had regular paid 14. Rao S, Kanade A, Margetts BM, Yajnik CS, Lubree H, Rege S, Desai B, work, much of their anxiety would be alleviated. Jackson A, Fall CHD. Maternal activity in relation to birth size in rural India. The Pune Maternal Nutrition Study. Eur J Clin Nutr. 2003; Acknowledgments 57:531–42. We thank Dr. C. S. Yajnik and the staff of the Diabetes Unit, 15. Joliffe IT, Morgan BJT. Principal component analysis and exploratory King Edward Memorial Hospital, Pune, India, and Dr. Shobha Rao, factor analysis. Stat Methods Med Res. 1992;1:69–95. Downloaded from https://academic.oup.com/jn/article/136/12/3062/4664006 by guest on 24 September 2021 Agharkar Research Institute, Pune. We thank Stephanie Lemke for 16. Lukmanji Z. Women’s workload and its impact on their health and nutritional status. Prog Food Nutr Sci. 1992;16:163–79. her helpful and detailed comments on earlier drafts of this paper 17. Bleiburg F, Brun TA, Goihman S, Lippman D. Food intake and energy and Jason Poole for his help with later stages of the analysis. expenditure of male and female farmers from Upper-Volta. Br J Nutr. 1981;45:505–15. 18. Bleiberg F, Brun TA, Goihman S, Gouba E. Duration of activities and Literature Cited energy expenditure of female farmers in dry and rainy seasons in Upper- 1. World Health Organization. Maternal anthropometry and pregnancy Volta. Br J Nutr. 1980;43:71–82. outcomes. Bulletin of the WHO. 1995;73: Suppl:1–98. 19. Higgins PA, Alderman H. Labor and women’s nutrition: the impact of 2. UNICEF. The state of the world’s chidren 2001—early childhood. New work effort and fertility on nutritional status in Ghana. J Hum Resour. 1997;32:577–95. York: UNICEF; 2001. 20. Das Gupta M. Lifeboat versus corporate ethic: social and demo- 3. Sen A. Many faces of gender inequality. Frontline 18 22, Oct 27–9 Nov. graphic implications of stem and joint families. Soc Sci Med. 1999;49: 2001. 173–84. 4. Yajnik CS, Fall CH, Coyaji KJ, Hirve SS, Rao S, Barker DJ, Joglekar C, 21. Bloom S, Wypij D, Das Gupta M. Dimensions of women’s autonomy Kellingray S. Neonatal anthropometry: the thin-fat Indian baby. The and the influence on maternal health care utilization in a north Indian Pune Maternal Nutrition Study. Int J Obes Relat Metab Disord. city. Demography. 2001;38:67–78. 2003;27:173–80. 22. Hindin MJ. Women’s power and anthropometric status in Zimbabwe. 5. Fall CHD, Yajnik CS, Rao S, Coyaji KJ, Shier RP. The effects of Soc Sci Med. 2000;51:1517–28. maternal body composition before birth on fetal growth: the Pune 23. Gittelsohn J. Opening the box: intrahousehold food allocation in rural Maternal Nutrition and Fetal Growth Study. In: O’Brien PMS, Wheeler Nepal. Soc Sci Med. 1991;33:1141–54. T, Barker DJP, editors. Fetal programming: influences on development and disease in later life. London: RCOG Press, 1999: 231–242. 24. Naved RT. Intrahousehold impact of the transfer of modern agricultural technology: a gender perspective. FCND Discussion Paper no. 85, 6. Rao S, Yajnik CS, Kanade A, Fall CHD, Margetts B, Jackson AA, Shier 1–103. 2000. Washington (DC): International Food Policy Research R, Joshi S, Rege S, et al. Intake of micronutrient-rich foods in rural Institute. Indian mothers is associated with the size of their babies at birth: Pune 25. Edmundson WC, Edmundson SA. Food intake and work allocation of Maternal Nutrition Study. J Nutr. 2001;131:1217–24. male and female farmers in an impoverished Indian village. Br J Nutr. 7. Chorghade GP, Barker M, Kanade S, Fall CHD. Why are rural Indian 1988;60:433–9. women so thin? Findings from a village in Maharashtra. Public Health 26. Basu AM, Stephenson R. Low levels of maternal education and the Nutr. 2006;9:9–18. proximate determinants of childhood mortality: a little learning is a 8. Retherford RD, Roy TK. Factors affecting sex-selective abortion in dangerous thing. Soc Sci Med. 2005;60:2011–23. India. Natl Fam Health Surv Bull. 2003;17:1–4. 27. Harriss B. The intra-family distribution of hunger in South Asia. 9. Filmer D, King EM, Pritchett L. Gender disparity in South Asia: In: Dre`ze J, Sen A, Hussain A, editors. The intrafamily distribution comparisons between and within countries. World Bank policy research of hunger—selected essays. Oxford: Clarendon Press, 1995; p. working paper no. 1867. World Bank, 1998. 224–297.

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