Popul Res Policy Rev (2007) 26:1–29 DOI 10.1007/s11113-006-9017-2
Explaining son preference in rural India: the independent role of structural versus individual factors Rohini P. Pande Æ Nan Marie Astone
Received: 21 November 2003 / Accepted: 12 May 2005 / Published online: 14 March 2007 Springer Science+Business Media B.V. 2007
Abstract Much research has been done on demographic manifestations of son preference, particularly girls’ excess mortality; however, there is less research that focuses on son preference itself. This paper analyzes the determinants of son preference in rural India. We separate the independent, relative effects of characteristics of individual women and their households, village opportunities for women and village development, and social norms. We look at both socioeconomic and sociocultural variables. Finally, we examine whether predictors of son preference differ by desired family size. Our data come from the National Family Health Survey (NFHS) India, 1992–1993. We use an ordered logit model, with dummy variables for state of residence. Our analysis shows that women’s education, particularly at secondary and higher levels, is consistently and significantly associated with weaker son preference, regardless of desired family size. Once factors measuring social norms, such as marriage customs, caste and religion, are included, economic wealth and women’s employment at household or village levels are not significant. Media access remains significant, suggesting an influence of ‘‘modernizing’’ ideas. Among social factors, caste and religion are associated with son preference but, once state of residence is controlled for, marriage patterns and cultivation patterns are insignificant. The strength and significance for son preference of many determinants differs by desired
R. P. Pande (&) International Center for Research on Women, 1717 Massachusetts Ave., N.W., Suite 302, Washington, DC 20036, USA e-mail:
[email protected] N. M. Astone Department of Population and Family Health Sciences, The Johns Hopkins School of Hygiene and Public Health, Baltimore, MD, USA
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family size. Our results suggest that policy makers seeking to influence son preference need to identify and target different policy levers to women in different fertility and social contexts, rather than try an approach of one size that fits all. Keywords
Discrimination Æ Gender Æ India Æ Inequality Æ Son preference
Introduction ‘‘May he (Pragaˆpati) elsewhere afford the birth of a female, but here he shall bestow a man!’’ This hymn (Atharva Veda, Book VII, verse 11, translation by Bloomfield, 1897) is an ancient Indian charm for women to give birth to sons. Dating to circa 800 B.C.E., it reflects a sentiment that persists in India. Much research has been done on demographic manifestations of this son preference, particularly girls’ excess mortality and, recently, sex-selective abortion. There is less research on son preference itself. In this paper we address this gap by empirically examining son preference directly as the outcome of interest. Although scholars are aware that gender inequality arises from systemic factors, they are often unable to quantitatively disentangle the social structures and norms that are the ultimate cause of gender inequality, and that contribute to the perseverance of son preference, from the individual determinants of son preference. In our analysis, we test the independent effect of both structural and social factors that set the norms for gender preference, and characteristics of individual women, their households and their immediate communities that influence how they respond to these norms.
Background Son preference and discrimination against girl children are widespread in the Middle East and North Africa (Yount, 2001) and in South and East Asia (Chan & Yeoh, 2002). In Asia, this has been documented in China (Banister, 2003), Korea (Park & Cho, 1995), Vietnam (Belanger, 2002), Nepal (Leone, Matthews, & Zuanna, 2003), and Bangladesh (Bairagi, 2001), as well as India (Mishra, Roy, & Retherford, 2004), the setting for our study. In the past, researchers interested in son preference have had as their central concern its adverse consequences, particularly excess female infant and child mortality or the poor health of girl children relative to boys (Basu, 1989; Das Gupta, 1987; Das Gupta & Shuzhuo, 1999; Makinson, 1994; Miller, 1981; Pande, 2003; Pande & Yazbeck, 2003, among others). Others have examined the role of son preference in slowing the transition to low fertility as couples bear children until they have sufficient boys (Arnold, Minja, & Roy 1998; Clark 2000; Das Gupta & Bhat 1997; Leone et al., 2003; Yount, Langsten, & Hill, 2000, among others). The advent of technology permitting pre-
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natal sex selection has shifted the focus of scholars and policymakers to sex-selective abortion and consequent distorted sex ratios as manifestations of son preference (Arnold, Kishor, & Roy, 2002; Murphy, 2003; Oomman & Ganatra, 2002). In the Indian setting, distorted sex ratios have a long history. Over the 100-year period from 1901 to 2001 the population sex ratios from Indian censuses (expressed in India as the number of women per 1,000 men) have shown more or less a continuous decline, from 972 females per 1,000 males in 1901 to 933 females per 1,000 males in 2001 (Banthia, 2001, p. 3). In this period, the only noticeable aberration was in 1981, when the situation seemed to reverse with sex ratios becoming somewhat less masculine, from 931 women per 1,000 men in 1971 to 934 in 1981. Any relief at this change was cut short, however, by the 1991 census, which showed the lowest sex ratio of the century, at only 929 women per 1,000 men. Further, between 1981 and 1991, the sex ratio for children between 0 and 6 years of age decreased at a much faster pace than the overall sex ratio (from 962 girls per 1,000 boys in 1981 to 945 in 1991). The 1991 juvenile sex ratio was lower than had been reported by any census since at least 1961. The 2001 census shows that this trend is continuing (Banthia, 2001, pp. 3 & 8). This drop in the 1991 census, reflecting that the last census of the 20th century showed the worst sex ratios in this century, resulted in active debate among academics and policymakers in India and elsewhere about the reasons for this decline, particularly following as it did on the heels of an apparent improvement between 1971 and 1981 (Griffiths, Matthews, & Hinde, 2000; Mayer, 1999). The even more serious drop in the juvenile sex ratio in the period of the late 1980s and early 1990s adds to the interest in this period, because it is a time when son preference, as manifested in these distorted sex ratios, remained high or possibly increased despite other more heartening economic and social changes such as increasing female literacy. What determines this strong son preference that affects so many demographic outcomes, in particular at the point when India faced its worst sex ratio of the 20th century? In identifying important factors, we take as our lead both the literature examining the determinants of demographic outcomes thought to be the result of son preference, as well as the broader literature on gender inequality in India.
Determinants of son preference Social norms and structures Persistent son preference is one of the strongest manifestations of gender inequality in Indian society. Though there are few studies examining son preference per se as an outcome of interest, there is substantial research on gender inequality in South Asia. Many authors argue that gender inequality is a structural phenomenon, and that patriarchal institutions and norms establish
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patterns and sometimes formal rules for the allocation of material goods, rights, opportunities and obligations between men and women (Baltiwala, 1994; Cain, 1993; Malhotra & Schuler, 2005; Morgan & Niraula, 1995). In India, a number of structural factors lay the foundation for gender inequality. These include kinship and marriage norms, the organization of the agrarian economy, and rules and rituals associated with caste and religion. In a path-breaking article in 1983, Dyson and Moore proposed that two broad differences in Indian kinship patterns—between ‘‘Northern’’ and ‘‘Southern’’ India—largely determine gender inequality and son preference (Dyson & Moore, 1983). Specifically, in the northern Indian kinship system, as Dyson and Moore and others (Arnold et al., 1998; Das Gupta, 1995; Kishor, 1995) describe it, marriage is exogamous, that is, spouses must be unrelated through kin and often by place of birth or residence. Parents of a girl often have to pay all marriage costs and provide a large dowry. After marriage, a girl typically becomes a member of her husband’s family and does not have much interaction with her natal kin. In contrast, the southern kinship pattern is characterized by endogamous marriage, that is, marriage between certain types of cousins or within a defined, contiguous geographical area. Women’s sexuality and freedom of movement are less curtailed, dowry is not a major marriage transaction, and married daughters are often likely to be on hand to render social and financial help to their parents. Recent studies using data from the late 1980s and early 1990s suggest some blurring of this North–South distinction with regard to gender inequality (Arnold et al., 1998; Dharmalingam, 1996; Rahman & Rao, 2004). Most recently, Rahman and Rao (2004), using data from 1995, dispute the idea that the regional patterns identified by Karve (1965) and Dyson and Moore (1983) still hold. They call attention to the fact that in modern India, Southern brides are as likely to pay dowry (and pay as much) as Northern brides, and that endogamous marriage can result in more inequity between wives and husbands if their prenuptial relationship was already hierarchal (e.g., uncle–niece matches). This is in accordance with other recent research that finds Indian girls equally at risk of facing discrimination in health care in the South as the North (Mishra et al., 2004). Besides region, religion plays a role in defining appropriate social and gender norms, which in turn influence son preference. In Hinduism, the major religion in India, sons are crucial. Among Hindus, a dead parent’s soul can only attain heaven if that person has a son to light the funeral pyre, and salvation can be achieved through sons who offer ancestral worship (Vlassof, 1990). Girls and women do have some importance. Giving away a daughter in marriage (kanyadaan) is considered meritorious (Miller, 1989). Studies suggest that son preference exists also among other religious groups such as the Sikhs (Das Gupta, 1987) or the Muslims (Murthy, 1996). Caste may also be associated with cultural practices that influence women’s roles, and thus son preference, such that one may expect less son preference among lower castes and tribals than among high castes. Compared to lower castes, higher castes have more rigid gender stratification systems, with strictly
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enforced rules of seclusion or purdah for women (Mandelbaum, 1988) and greater use of dowry. Lower caste and tribal women may have fewer restrictions placed on their movement or employment outside the home (Srinivas, 1976), often due to economic pressures that force them to earn an income. Rahman and Rao (2004) find that restrictive cultural practices such as strictly enforced rules of seclusion or purdah for women are significantly associated with worse gender equity measured in terms of mobility and some aspects of household decision-making, though they do not specifically consider son preference. Discussions of structural economic determinants of gender equity and son preference, specifically, the organization of the agrarian economy, follow a regional theme. In east and south India the main crop is paddy where women play a key role in weeding, transplantation, harvesting and threshing. In contrast, in the north and west, wheat and other dry-agriculture crops predominate, and—particularly where there is irrigation—the work involves more male-biased ‘‘muscle power’’ (Bardhan, 1974). Researchers have argued that the higher demand for women’s agricultural labor in rice areas makes girls and women more valuable than in wheat areas, thus contributing to less discrimination against girls in rice-growing regions. Recent research supports the argument that women’s labor force participation has positive effects on women’s autonomy (Rahman & Rao, 2004). Finally, inheritance laws that render sons crucial to retain family property (Agarwal, 1994), and the need for sons so as to exercise power in violent areas or to assure household security (Dharmalingam, 1996; Oldenburg, 1992), are also both thought to influence gender inequality and thus son preference. Research examining these issues is, however, particularly limited. Village development The evidence on the effect of village economic development on gender inequality as manifested in excess girls’ mortality is mixed. Some studies have shown a worsening of discrimination against girls with greater village development (Murthi, Guio, & Dreze, 1995). Others show more positive results. In an all-India analysis, Kishor (1993) found that after controlling for other economic, social, and cultural factors, the proportion of the labor force that was female in a district was significantly and negatively associated with excess girls’ mortality. Rahman and Rao (2004) found that village development influenced some gender equity indicators of mobility and decision-making, but they did not examine son preference or excess girls’ mortality. Household and individual characteristics Although the incentives for action ultimately flow from social and economic structures, the decisions taken to act on a preference for sons occur at the level of the individual woman and her household. Thus, given social norms, such decisions are influenced by characteristics of individual women and their
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households such as women’s education, employment, and access to media; household wealth; and household structure. A number of studies examine the relationship between household wealth and excess girls’ mortality (for example, Muhuri & Preston, 1991). Less is known about the relationship of household wealth with son preference. One may expect son preference to be lower among women from poorer households because women may be more of an economic asset in poorer households. For livelihood reasons, poor households may be less able than wealthier households to enforce purdah or seclusion for women, thus allowing women to play a more active economic role than they would in wealthier households (Miller, 1981; Rahman & Rao, 2004). On the other hand, women from wealthy households may have weaker son preference because there are alternative sources of economic support, for instance for old age, beyond having sons. Household structure may matter as well. There is some evidence of differences in women’s roles and autonomy in extended compared to nuclear households that could affect son preference. Typically, in an extended family older women would have some degree of control over the lives, decisions, and children of younger women (Barua & Kurz, 2001). In contrast, in a nuclear household even a young mother is likely to have more autonomy and a more active role in making decisions regarding herself and her children (Jejeebhoy, 1996). While no studies have directly examined the effect of household structure on gender preferences, it is possible that son preference is higher in traditional extended households where roles and opportunities for women are more constrained compared to those for men, than they are in nuclear households. Research suggests a role for women’s employment in lowering son preference. Cain (1988) argued, for Bangladesh, that where women have an economic value in the household, son preference will be weaker because it will no longer be seen as essential for lifetime, particularly old age, security. Others suggest that working women may have a more egalitarian sex ratio of child mortality because the fact of working changes gender values through an increased awareness that girls can potentially contribute an income to the household; women’s economic participation may also change their own valuation of their worth and the worth of their daughters more generally (Basu & Basu, 1991; Kishor, 1993). Evidence on the link between women’s education and son preference is less clear. A woman’s education may change her perception of ‘‘feminine worth,’’ thus decreasing her preference for sons (Bourne & Walker, 1991). Where son preference is strong, however, maternal education on its own may not be enough to improve daughters’ worth. This conclusion is based on evidence of excess girls’ mortality rather than son preference, where gender differentials are unchanged or worsened among educated compared to non-educated mothers (Bhuiya & Streatfield, 1991; Das Gupta, 1987). Recent research suggests that education beyond the primary level may have a stronger impact than primary education alone on gender equality (Malhotra, Pande, & Grown, 2003). However, there are few studies that examine the
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impact of different levels of women’s education on gender differentials in mortality or on son preference. Those that do, suggest that levels of maternal education beyond primary school benefit daughters more than sons (Govindaswamy & Ramesh, 1996). Primary education, while it might improve child health and survival in general, may not be enough to engender the change in the perceived value of women necessary to change son preference. It is with secondary or higher education that a woman’s belief structure and the opportunities available to her change sufficiently to decrease the preference for sons as her sole means of economic and social support.
The current study There is little disagreement that individual characteristics and social norms are both important in understanding gender inequality in India. Nonetheless, previous research has tended not to examine the relative importance of individual socioeconomic characteristics compared to the norms and values that may influence son preference specifically, even though son preference is such a pervasive reflection of gender inequality in India. The present study contributes to the literature by focusing on son preference as an outcome of interest, and conceptualizing an individual’s son preference as emerging from a complex process that is influenced by factors at many levels—the society, the household, and the individual. We differentiate simultaneously the relative and independent importance of these different influences. Moreover, this study contributes by examining specifically what factors may have been at play to determine son preference in the time period of the late 1980s and early 1990s, when sex ratios in India were the worst of the 20th century.
Data and variables Data We use data from the rural sample of the National Family Health Survey, India (NFHS-1), 1992–1993. We focus on the NFHS-1 specifically to examine factors explaining son preference around the time of the 1991 census. The NFHS followed the format of the Demographic and Health Surveys (DHS), which are large-scale household surveys conducted in Asia, Africa, and Latin America. Data are available on individual women, their households, and their villages. For each Indian state, a multistage systematic stratified sampling design was adopted, where the primary sampling units were selected systematically, with probability proportional to size. Households were then sampled using systematic sampling with equal probability, and all eligible women in each household were interviewed. National and state-level sampling weights were created to reflect sampling design (IIPS, 1995).
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For this paper, we use a de jure sample of 50,136 ever-married women between 13 and 49 years of age at the time of the survey who have valid data on our outcome variable. We chose to use the rural sample because village data are available only for rural areas. Since about 73% of India’s population was rural at the time (IIPS, 1995, p. xxxi), the results will still be applicable to the majority of the population. We use the de jure sample (all residents) rather than the de facto sample (all those who slept in the sampled household the previous night) because detailed household structure and village data are only available for de jure women. We combine the data from the NFHS with data on rice and wheat production by state between 1989 and 1993. These data were obtained from the Economic Survey of India, 1994–1995, Directorate of Economics & Statistics, Department of Agriculture & Cooperation of the Government of India (Government of India, 1995, p. S-19). Dependent variable Respondents were asked the ideal number of children they would want. Women who had no surviving children were asked the question: ‘‘If you could choose exactly the number of children to have in your whole life, how many would that be?’’ Women who had living children at the time of the survey were asked: ‘‘If you could go back to the time you did not have any children and could choose exactly the number of children to have in your whole life, how many would that be?’’ Those who responded to either question with a number were then asked the follow-up question: ‘‘How many of these children would you like to be boys and how many would you like to be girls?’’ Responses were entered as a number of boys, girls, either, or other response. We created an ordered categorical variable that has three categories: zero when a respondent reports no son preference (including daughter preference, or a response of ‘‘either’’); one when a respondent reports an ideal of one son more than the ideal number of daughters; and two when a respondent reports an ideal of two or more sons more than the ideal number of daughters. A majority (88.6%) of sample women gave valid numeric responses to these questions (IIPS, 1995). Women whose characteristics are likely to be associated with lower son preference (for example, educated women and women with media access) are more likely to have given a valid numeric response (Pande, 1999). This could lead us to underestimate the strength of son preference in the analysis. Some women who may have higher son preference (those living in a joint family or from middle or high castes, for example) are also slightly over-represented, and thus the overall direction of any bias is not clear a priori. Given that almost 90% of sample women gave valid responses, any bias is expected to be small. Explanatory variables Following our question of interest, we conceptualize our explanatory variables as measures of broad social norms, measures of village development and
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opportunities for women, and individual or household characteristics. This division of the variables is at a conceptual level rather than at the level of the unit of measurement of the variable. Thus, for example, caste and religion are conceptualized as variables reflecting broad social norms even though they are measured, in the survey, at the level of the individual woman. Exploratory analysis showed that the data support recent research that suggests a blurring of regional patterns in son preference. Figure 1 shows the percent of women in each state, by region, who said they would ideally like more sons than daughters. There continues to be a broad regional pattern in that the southern states, on average, have lower son preference than states in the North or Central regions. At the same time, there are wide variations in son preference by state, within and across the regional classifications that were defined by Karve (1965) and used—with some modifications—by Dyson and Moore (1983) and others. For example, Andhra Pradesh in the South, and several of the states in the East, exhibit similar or higher average levels of son preference than West Bengal or Himachal Pradesh in the North. In our multivariate analysis, therefore, we do not include variables for region. Rather, we control for state-level variation by including a series of dummy variables for each state. As these state dummies are capturing any number of unobserved state-level cultural and economic characteristics that may affect son preference, it is not possible to interpret their effect and we do not show the coefficients for these dummies in our tables. Their inclusion, however, reduces the extent to which characteristics of the states that are correlated with both our explanatory variables and our outcome are biasing our results.
Assam
Manipur
Tripura
< 20 % 20 - 40% 41 - 60%
Meghalay
Mizoram
Nagaland
Andhra Goa
Karnatak Kerala TN
20
Arunacha
Rajas MP
Gujarat
Orissa
Maharash
UP
Bihar
Jammu
Punjab
Himachal
40
WBengal
60
Haryana
80
Delhi
% wanting more sons than daughters
100
61% +
0
North
Central
South
East
Fig. 1 Son preference by state and region
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We include measures of religion and caste to capture norms and beliefs that are associated with these social systems. We categorize religion as Hindu, Muslim, or ‘‘other.’’ Caste is divided into low castes (including untouchables), tribes, and ‘‘other,’’ where ‘‘other’’ includes high-caste Hindus and non-Hindus. The research is inconclusive as to the relative strength of son preference across religions, and we remain agnostic about the direction of our results for this variable. We expect women from lower castes and from tribal groups to have lower son preference than high caste women. To specifically capture the effect of marriage customs, we include two village endogamy variables as indicators of local marriage norms, one measuring the proportion of women who marry relatives (kinship endogamy) and one for the proportion who marry within the village (territorial endogamy). For both, we create weighted continuous variables, with an exponential weight chosen to bound the proportion between one and zero. The closer the proportion is to one, the more endogamous the village. Some sample villages have a small number of women only; however, the data allow us to group villages into (larger) districts. Thus, the final variable for each of these indicators is a weighted sum of the village-level and district-level aggregate for that indicator (see Pande, 1999 for details). We expect endogamy to be inversely associated with son preference. To measure the agrarian ecology argument for son preference, we examine the effect on gender preference of the extent of rice and wheat production. Using data from the Economic Survey of India 1994–1995, we created continuous variables to measure the proportion of rice, and the proportion of wheat, to total foodgrain production averaged over 1989–1990 to 1992–1993, in each state. Women in rice-growing areas are expected to have weaker son preference than others. To assess the importance of village social and economic development, we measure village-level opportunities for women, as well as general village development. We use two continuous variables to measure village opportunities for women, namely, the proportion of literate women in a village (to measure female education) and the proportion of women in a village employed outside of the home (to measure female employment). These are created in a similar manner as the endogamy variables. Measures of village development are whether a village has access to transport such as an all-weather road, bus station or rail station (to measure accessibility to the outside world), whether the village has at least a middle school (on the assumption that, when there is a school in the village girls are more likely to go to school which, in turn, could change their mother’s valuation of girls versus boys), and the number of other facilities in the village such as a bank, shop, or cinema (as an indicator of wider village economic development). We expect all indicators of village development to be inversely associated with son preference. We use two household wealth measures. First, we create an index that is a composite of variables describing consumer durables and housing quality, measured in household quintiles (Filmer & Pritchett, 2001). Second, we
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include a variable for household ownership of land, and expect women from landed families to have higher son preference than others. Further, we examine residence in an extended household defined as one that includes members in addition to the respondent, her husband and her unmarried minor children, and we expect women in extended families to have higher son preference than those in nuclear households. Our indicators for women’s characteristics are women’s level of education (distinguishing between primary school only; up to middle school; secondary school and higher); women’s employment (broadly defined as any work outside of housework); women’s cash earnings; and women’s weekly access to radio or TV and monthly access to cinema (as broad indicators of women’s exposure to new ideas). We expect highly educated women, those earning cash, and those with greater media exposure to have lower son preference than others. We include mother’s age as a control variable. Reported gender preferences may be affected by the current sex composition of children and family size (defined as the current number of surviving children), either as an ex-post rationalization of actual family size and composition, or as the result of a change—either conscious or subconscious—in preferences based on childbearing experience. Thus we control for actual sex composition and family size in our analyses.
Statistical model: ordered logit The ordered logit model is used when the outcome variable is categorized on an ordinal scale, ordered by some conceptual or subjective criteria (McCullagh & Nelder, 1989) as in our case. Following the notation of McCullagh and Nelder (1989), the probability of a response for any one category of an outcome Y can be expressed as p1,...pk for k possible values of Y. If the categories are ordered, as in an ordered logit, we can consider cumulative response probabilities pj = Pr(Y £ j) rather than the category probabilities pj. These cumulative response probabilities can be interpreted as the probability of an outcome up to a certain category, and can be written as: c1 ¼ p1 ; c2 ¼ p1 þ p2 ; . . . ; ck ¼ 1: Then, the ordered logit model can be expressed as: ! c 1 ð xÞ log ¼ hj bT x; j ¼ 1; . . . ; k 1 1 c j ð xÞ for k categories of the response variable, where cj = Pr(Y £ j|x) is the cumulative probability up to and including j, for a covariate vector x, and hj is the cut-point for the jth category. Taking an exponential of both sides of the above equation gives the odds of falling into category j or lower versus falling
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into a category higher than j, with a given set of covariates. The odds ratio for a unit change in a particular covariate, say from x = x1 to x = x2, is given by: cj ðx1 Þ= 1 cj ðx1 Þ ¼ expðbx ðx1 x2 ÞÞ cj ðx2 Þ= 1 cj ðx2 Þ where bx is the coefficient of interest. The negative sign on the coefficient means that a higher value of the variable increases the odds of a lower value of the outcome. For example, a negative coefficient for maternal education means that a higher value of maternal education is associated with higher odds for a lower value of son preference (in other words, with weaker son preference). The data in our sample are clustered such that all observations within the same household, community, and primary sampling unit are unlikely to be independent of each other. Thus if one estimates standard errors on the assumption of independence, they will be incorrect, most likely smaller than they should be (Liang & Zeger, 1993). In previous work using these data, the effects of clustering at the household and village levels were found to be minimal (Pande, 1999). We correct here for clustering at the level of the primary sampling unit, using the ‘‘cluster’’ command in Stata version 7.0. Descriptive results Patterns and extent of son preference A preference for girls is rare in this population. Only 2.6% of all respondents say they want more daughters than sons (Table 1). About half show a desire for a balanced sex composition and say they want children of either sex, or equal numbers of daughters and sons. Almost half of all respondents (46%) report some son preference. Among those who report some son preference, the majority (75.6%) want one more son than daughter. Son preference is, of course, only one of a set of preferences people have about their families and should be interpreted in that context. Particularly relevant for this study are family size preferences. Table 1 shows that the pattern of reported son preference appears to vary by the reported total number of desired children. The highest reported son preference is among those who want only three children: 88% of such women want more boys than girls. Further, the strength of reported son preference, and the percentage who report no son preference (those in the ‘‘either’’ category), vary consistently by even and odd desired family size. At every desired family size, those desiring an even number of children seem to be less likely to report son preference than those desiring an odd number of children. Further, in most cases, a majority of those desiring an even number of children are likely to report being indifferent to, or wanting equal numbers of, girls and boys.
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Table 1 Gender preferences by family size preferences: rural Indian women, 1992–1993 Total number of desired children 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Total Among those who Percent who want Percent who want Percent who want
Percent of women who want: More boys
More girls
50.6 7.0 9.2 0.3 88.3 4.3 27.8 0.9 82.0 10.5 49.3 3.6 74.6 18.5 47.3 5.2 65.2 27.6 41.3 3.1 79.7 20.3 50.6 0.0 100.0 0.0 0.0 0.0 34.0 0.0 67.0 0.0 45.9 2.62 report son preference: 1 more son than daughters: 2 more sons than daughters: 3+ more sons than daughters:
Either 42.4 90.5 7.4 71.3 7.5 47.1 7.0 47.5 7.2 55.6 0.0 49.4 0.0 100.0 66.0 33.0 51.5
Total number of women
Percent of total sample
1,091 17,922 17,184 9,805 2,473 1,083 209 197 35 83 4 33 2 0 2 3 50,127
2.2 35.8 34.3 19.6 4.9 2.2 0.4 0.4 0.1 0.2 0.0 0.1 0.0 0.0 0.0 0.0 100 75.6 18.6 5.8
It is possible that this reported indifference includes some degree of ‘‘unrevealed’’ son preference, where unrevealed preference is defined as a subsample of those who answer ‘‘either’’ to whether they prefer boys or girls. As noted above, among those who want more sons than daughters, 76% want only one more son than daughter. If we assume, therefore, that those with an unrevealed preference mostly would like a difference of one, then, since an even desired family size conflicts with a desired sex difference of one, it is possible that such women report being willing to settle for an equal number of boys and girls. Ninety percent of women who ideally want only two children say they would be happy with either sex, suggesting particularly high unrevealed sex preferences among this third of the total sample. To mitigate the contamination of gender preference with family size preference that Table 1 shows, as well as to try and separate out those with revealed versus unrevealed gender preferences, we present multivariate results for the whole sample, and for subgroups of women. First, we examine those who want even and odd numbers of children separately. Second, we focus attention on women who want two or three children. We expect results to be somewhat different for those who want two or three children because women who want particularly small families are likely to be different from other women in ways that may also influence their gender preferences. Moreover, this is an especially important group to consider because, as desired family size falls, it will constitute an ever larger share of the population.
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Distribution of explanatory variables Table 2 presents descriptive characteristics for the explanatory variables, for 50,127 sample women (excluding nine who said they would ideally like zero children) and by select family size preference. While many of these variables measure related concepts, a correlation matrix showed that most variables are not highly correlated, with the highest correlation that of –0.75 between rice and wheat cultivation. This is still considered acceptable (Kennedy, 1996). On the whole, women with revealed gender preferences (those who want three children only and those who want an odd number of children) do not seem to be very different from all women. Any differences are, moreover, smaller than is the case for women who report no preferences, or large potential unrevealed son preference (those who want two children only or an even number of children). Differences are highlighted in bold. In particular, women who want only two children are different from the sample as a whole in key ways that are likely to be associated with lower son preference: they are more likely to have higher education, be exposed to media, belong to wealthier households, and to live in more endogamous and more developed villages. They are also less likely to be from wheat-growing states, and more likely to live in rice-growing states.
Multivariate results We present three models for the sample as a whole to address our central question (Table 3). Model 1 presents the effects of socioeconomic characteristics of sample women and their households on son preference. Model 2 adds in the effects of variables that measure village opportunities for women in terms of employment and education, as well as village economic development. Model 3, the final model, adds variables that measure structural or social factors and norms, including 25 state dummies. This final model allows us to examine the independent influence of individual, community and macrosocial factors on son preference. We then run model 3 for subgroups of sample women as defined earlier. Independent effects of social norms, village development, and individual characteristics for all women Most individual and household characteristics are strongly associated with son preference when contextual factors are not controlled (model 1, Table 3). Women who work but don’t earn, who are educated, who have regular access to media and cinema, and who live in wealthy households are likely to have weaker son preference than other women. As hypothesized, women from landholding or extended households have higher son preference than those from landless or nuclear families. Any education works to diminish son preference. At the same time, higher levels of education are associated with
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Individual & household No education/illiterate Primary education Middle school education Higher education Doesn’t work outside the home Works but doesn’t earn cash Earns cash Listens to radio or TV weekly Goes to cinema monthly Lives in an extended family Hh wealth: poorest quintile Hh wealth: second quintile Hh wealth: middle quintile Hh wealth: fourth quintile Hh wealth: richest quintile Hh owns land Village opportunities & development Propn of literate women Propn of women working outside the home At least middle school in village Number of general facilities in village All-weather road, bus station or rail station Social factors & norms Head of household is low caste Head of household is tribal Head of household is ‘‘other’’ caste
Variable
0.54 0.18 0.13 0.15 0.60 0.26 0.15 0.24 0.18 0.59 0.14 0.14 0.18 0.22 0.31 0.63 0.43 0.31 0.72 3.13 0.71 0.12 0.08 0.80
0.38 0.29 0.68 2.79 0.64 0.13 0.11 0.76
Who want only two children
0.66 0.15 0.10 0.10 0.61 0.24 0.15 0.17 0.13 0.58 0.18 0.18 0.19 0.21 0.24 0.65
Who want even numbers of children
Percent of women
0.14 0.11 0.75
0.34 0.26 0.65 2.57 0.61
0.36 0.28 0.66 2.69 0.62 0.13 0.11 0.76
0.73 0.14 0.08 0.05 0.62 0.22 0.16 0.12 0.08 0.60 0.19 0.20 0.21 0.21 0.20 0.69
Who want only three children
0.69 0.14 0.09 0.08 0.61 0.23 0.15 0.15 0.11 0.58 0.19 0.19 0.20 0.21 0.22 0.67
All women
Table 2 Mean value for explanatory variables (weighted), for all and by family size preference: rural Indian women, 1992–1993
0.13 0.12 0.75
0.34 0.26 0.64 2.55 0.60
0.73 0.13 0.08 0.05 0.62 0.22 0.16 0.12 0.08 0.59 0.20 0.20 0.20 0.20 0.19 0.69
Who want odd numbers of children
Explaining son preference in rural India 15
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Hindu Muslim Other religion Kinship endogamy Territorial endogamy North Central South East Propn rice in foodgrains Propn wheat in foodgrains Control variables No surviving children More girls than boys Equal boys and girls More boys than girls Woman’s age N (number of women) N (number of households) N (number of villages)
Variable
Table 2 continued
0.87 0.06 0.07 0.21 0.15 0.31 0.30 0.36 0.03 0.54 0.17 0.15 0.29 0.21 0.35 29.12 17,901
0.12 0.32 0.22 0.35 30.47 29,405
Who want only two children
0.84 0.09 0.07 0.18 0.14 0.39 0.29 0.27 0.05 0.51 0.21
Who want even numbers of children
Percent of women
0.11 0.32 0.19 0.38 30.43 50,127 43,049 2,095
0.84 0.09 0.07 0.13 0.16 0.41 0.30 0.23 0.05 0.49 0.23
All women
0.10 0.30 0.17 0.43 29.85 16,887
0.86 0.08 0.06 0.14 0.11 0.44 0.33 0.18 0.04 0.46 0.26
Who want only three children
0.10 0.31 0.16 0.43 30.38 20,722
0.85 0.09 0.06 0.14 0.12 0.45 0.32 0.18 0.05 0.46 0.26
Who want odd numbers of children
16 R. P. Pande, N. M. Astone
Individual & household Women’s education (illiterate) Primary Middle school Higher Women’s employment (none) Works but doesn’t earn cash Earns cash Listens to radio or TV weekly (no) Goes to cinema monthly (no) Household wealth (poorest) Second quintile Middle quintile Fourth quintile Richest quintile Household owns land (no) Lives in extended household (no) Village opportunities & development Proportion of literate women Proportion of women working At least a middle school (no) All-weather road, bus station or rail station (none) Number of general facilities (none) Social factors & norms Territorial endogamy Kinship endogamy Proportion rice in foodgrains 0.000 0.000 0.000 0.000 0.576 0.000 0.000 0.357 0.020 0.000 0.000 0.000 0.000
–0.469 –0.602 –0.942 –0.229 –0.019 –0.223 –0.582 –0.030 –0.075 –0.142 –0.141 0.179 0.102
0.531 0.544 0.663 0.970 0.050 0.058 0.000 0.000 0.722 0.782 0.036
–1.468 –0.876 0.011 –0.009 –0.015
0.013 0.000 0.000 0.000
0.000 0.000 0.000
0.022 0.021 0.017 –0.002 0.049 0.044
–0.065 0.104 –0.173 –0.490
–0.240 –0.328 –0.605
Coef.
Coef.
P > |z|
Village opportunities & development
Individual & household factors P > |z|
Model 2
Model 1
Table 3 Determinants of son preference among rural Indian women, 1992–1993: all sample women
0.039 –0.073 –0.157
–0.529 –0.131 0.003 –0.028 –0.012
0.041 0.009 –0.006 –0.108 0.020 0.015
0.006 0.049 –0.169 –0.219
–0.188 –0.285 –0.594
Coef.
0.768 0.586 0.331
0.000 0.147 0.911 0.345 0.164
0.243 0.786 0.871 0.025 0.407 0.525
0.819 0.102 0.000 0.000
0.000 0.000 0.000
P > |z|
Social factors and norms
Model 3
Explaining son preference in rural India 17
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123 –0.003 2.043 49,495 0.051 –43,765.404
–0.291 –0.301 0.455 0.128 –0.011
Note: regression includes dummy variables for state
0.000 0.000 0.000 0.000 0.000 –0.524 1.558 47,269 0.065 –41,144.48
–0.254 –0.265 0.480 0.114 –0.005
Coef.
Coef.
0.000 0.000 0.000 0.000 0.000
P [ |z|
Village opportunities & development
Individual & household factors P [ |z|
Model 2
Model 1
* Variable for proportion of wheat dropped due to collinearity
Proportion wheat in foodgrains Caste (‘‘other’’) Low caste Tribal Religion (Hindu) Muslim Other Control variables Composition of surviving children (none) More girls than boys Equal girls and boys More boys than girls Family size (number of living children) Woman’s age Cut points for outcome variable _cut1 _cut2 Sample size (N) Pseudo-R2 Log-likelihood
Table 3 continued
0.144 2.266 47,045 0.083 –40,178.158
0.000 0.000 0.000 0.000 0.002
0.042 0.013
0.090 –0.121
–0.233 –0.245 0.510 0.109 –0.004
0.003 0.003
P [ |z|
0.096 –0.121
*
Coef.
Social factors and norms
Model 3
18 R. P. Pande, N. M. Astone
Explaining son preference in rural India
19
significantly weaker son preference than are lower levels of education, possibly because higher levels of education provide a woman with more avenues to increase her social and economic standing by means other than being a mother of sons. In model 2 we introduce characteristics of village opportunities and development. While the effects of education, work, and media access remain significant, household wealth is no longer important. Overall village development in terms of facilities available in the village is inversely related with son preference, as hypothesized. Village-level women’s employment and education are particularly important and significantly related to lower son preference. In model 3, the complete model, we introduce variables to measure social and cultural norms. Overall, this model suggests that women’s opportunities and characteristics as measured by education and access to media are critical, at both individual and community levels. Women’s work and household wealth, however, are not important, and neither is village development. Among variables measuring social norms, only caste and religion significantly influence son preference. Independent of all other factors, women’s education at individual and village levels continues to be strongly associated with weaker son preference. The effect of secondary education here is particularly noteworthy, and women with secondary or higher education have significantly weaker son preference than women with lower levels of education. In fact, the coefficient on this variable is the largest of any variable in the model. This strong result supports the view in the literature that higher levels of education change women’s view of female worth (Malhotra et al., 2003). Similarly, regular access to media and cinema remain significantly associated with lower son preference, suggesting that access to ‘‘modern’’ information and ways of life can influence gender preferences independent of other individual or structural factors. On the other hand, women’s employment, at the individual or community level, is not significant. This may reflect the study’s context of rural areas with low female education and thus limited professional skills, wherein employment may be more associated with economic necessity rather than signifying greater gender equality as such. Household wealth variables are also not significant. The effect of land ownership becomes small and statistically nonsignificant, and only women in the wealthiest quintile of households have significantly weaker son preference than the poorest, though the coefficient is small. One interpretation of this finding is that when land ownership is controlled, women in wealthier households are less conservative in their gender preferences than those in the poorest households. Another interpretation is that a woman has to be in an extremely wealthy household before she has alternatives to sons as economic assets. Seventeen of the 25 state dummies are statistically significant, with 13 of them associated with higher son preference. Though these dummies cannot be substantively interpreted, this pattern is consistent with the existence of
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factors at the state level that determine son preference, above and beyond what can be measured by the variables included in this analysis. In this full model, neither the endogamy variables nor the agrarian ecology variables remain significant. In other words, the factors that have historically been thought to mediate regional differences in son preference (and gender equity more generally) are not found to be influential. This finding is consistent with recent research suggesting that broad regional cultural and economic patterns of endogamy or agricultural production are becoming less important determinants of gender inequality—and thus, presumably, son preference—than are more local cultural and social norms (Rahman & Rao, 2004). On the other hand, caste and religion remain strong determinants of son preference, regardless of which state the respondent may reside in, suggesting that these social characteristics continue to influence son preference. Contrary to our hypothesis, lower caste women have slightly higher son preference than high castes. This may be related to the process of Sanskritization, whereby lower castes try to emulate the higher castes by adopting customs that contribute to confining women to the home (Srinivas, 1976). Tribal women, on the other hand, have significantly lower son preference than reference women. The religion coefficients suggest that women from Muslim households have slightly higher son preference, and women from other, non-Hindu, non-Muslim religions, have slightly lower son preference than do Hindu women. Comparing final models for all women and women with different family size preferences The effects of different individual and structural factors on son preference for those who have revealed son preference (odd ideal family size) and those who have unrevealed preferences (even ideal family size) are, on the whole, relatively similar to the full sample (Table 4). Among individual characteristics, the effects of women’s education remain strong, as does the greater influence of post-primary education in reducing son preference. Similarly, exposure to weekly media continues to be significant. Among the variables measuring social norms, the weaker son preference among tribal women remains significant across all three groups. Women who want particularly small families, on the other hand, differ from the full sample in some ways (Table 5; models 6 and 7 are logit models, since the third category for the ordered logit dependent variable was not defined for these subsamples). For instance, the effects of media and religion vary across subgroups. The rice production coefficient, in addition, is unstable across model specifications. Education and the effect of tribe are consistent across models. In all the models in Tables 4 and 5, individual women’s education (though not village female literacy) remains a strong predictor of son preference, especially middle or higher education. Similarly, in all the models, women from tribal households have weaker son preference than high/other caste women.
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Individual & household Women’s education (illiterate) Primary Middle school Higher Women’s employment (none) Works but doesn’t earn cash Earns cash Listens to radio or TV weekly (no) Goes to cinema monthly (no) Household wealth (poorest) Second quintile Middle quintile Fourth quintile Richest quintile Household owns land (no) Lives in extended household (no) Village opportunities & development Proportion of literate women Proportion of women working At least a middle school (no) All-weather road, bus station or rail station (none) Number of general facilities (none) Social factors & norms Territorial endogamy Kinship endogamy Proportion rice in foodgrains 0.243 0.786 0.871 0.025 0.407 0.525 0.000 0.147 0.911 0.345 0.164
0.041 0.009 –0.006 –0.108 0.020 0.015 –0.529 –0.131 0.003 –0.028 –0.012 0.768 0.586 0.331
0.819 0.102 0.000 0.000
0.006 0.049 –0.169 –0.219
0.039 –0.073 –0.157
0.000 0.000 0.000
–0.188 –0.285 –0.594
–0.213 0.298 –1.279
–0.262 0.048 0.025 –0.070 –0.013
0.021 0.014 0.023 –0.167 0.054 0.040
–0.032 –0.056 –0.136 –0.058
–0.246 –0.190 –0.569
Coef.
Coef.
0.318 0.205 0.000
0.111 0.737 0.638 0.162 0.316
0.770 0.836 0.732 0.028 0.247 0.334
0.543 0.299 0.022 0.448
0.000 0.005 0.000
P > |z|
Women with odd desired family size
Final model for all rural women P > |z|
Model 4
Model 3
Table 4 Determinants of son preference among rural Indian women, 1992–1993: by odd and even desired family sizes
0.079 –0.037 –0.311
–0.751 –0.141 –0.017 –0.026 –0.029
0.077 0.034 –0.028 –0.107 0.001 0.034
0.037 0.146 –0.237 –0.279
–0.249 –0.515 –0.518
Coef.
0.705 0.887 0.280
0.000 0.356 0.738 0.638 0.029
0.186 0.556 0.659 0.169 0.983 0.366
0.480 0.009 0.000 0.004
0.000 0.000 0.000
P > |z|
Women with even desired family size
Model 5
Explaining son preference in rural India 21
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123 0.042 0.013
0.000 0.000 0.000 0.000 0.002
0.090 –0.121
–0.233 –0.245 0.510 0.109 –0.004 0.144 2.266 47,045 0.083 –40,178.158
0.169 –0.214
0.003 0.003
0.096 –0.121
Note: regression includes dummy variables for state
–2.353 2.961 19,415 0.119 –10,497.907
–0.712 –0.030 0.308 0.168 –0.007
–0.047 –0.196
*
Coef.
Coef. *
Women with odd desired family size
Final model for all rural women P [ |z|
Model 4
Model 3
* Variable for proportion of wheat dropped due to collinearity
Proportion wheat in foodgrains Caste (‘‘other’’) Low caste Tribal Religion (Hindu) Muslim Other Control variables Composition of surviving children (none) More girls than boys Equal girls and boys More boys than girls Family size (number of living children) Woman’s age Cut points for outcome variable _cut1 _cut2 Sample size (N) Pseudo-R2 Log-likelihood
Table 4 continued
0.000 0.734 0.000 0.000 0.018
0.578 0.014
0.003 0.003
P [ |z|
1.609 1.775 27,622 0.129 –12,196.578
–0.329 –0.381 0.681 0.131 –0.002
0.244 –0.049
0.059 –0.171
*
Coef.
Women with even desired family size
Model 5
0.000 0.000 0.000 0.000 0.341
0.003 0.657
0.309 0.013
P [ |z|
22 R. P. Pande, N. M. Astone
Individual & household Women’s education (illiterate) Primary Middle school Higher Women’s employment (none) Works but doesn’t earn cash Earns cash Listens to radio or TV weekly (no) Goes to cinema monthly (no) Household wealth (poorest) Second quintile Middle quintile Fourth quintile Richest quintile Household owns land (no) Lives in extended household (no) Village opportunities & development Proportion of literate women Proportion of women working At least a middle school (no) All-weather road, bus station or rail station (none) Number of general facilities (none) Social factors & norms Territorial endogamy Kinship endogamy Proportion rice in foodgrains 0.243 0.786 0.871 0.025 0.407 0.525 0.000 0.147 0.911 0.345 0.164
0.041 0.009 –0.006 –0.108 0.020 0.015 –0.529 –0.131 0.003 –0.028 –0.012 0.768 0.586 0.331
0.819 0.102 0.000 0.000
0.006 0.049 –0.169 –0.219
0.039 –0.073 –0.157
0.000 0.000 0.000
–0.188 –0.285 –0.594
0.016 0.024 –1.682
–0.333 0.458 0.024 0.091 –0.048
–0.060 0.035 0.006 –0.023 0.007 0.063
0.006 0.269 –0.263 –0.155
–0.153 –0.600 –0.352
Coef.
Coef.
0.968 0.959 0.000
0.147 0.046 0.770 0.256 0.019
0.611 0.763 0.961 0.863 0.919 0.268
0.945 0.002 0.000 0.228
0.081 0.000 0.002
P > |z|
Women who ideally want two children
Final model for all rural women P > |z|
Model 6
Model 3
Table 5 Determinants of son preference among rural Indian women, 1992–1993: by desired family size of two or three children
–0.318 0.250 –2.411
–0.242 –0.019 0.064 –0.119 0.014
0.041 0.033 0.062 –0.128 0.068 0.067
–0.039 –0.064 –0.080 –0.062
–0.196 –0.064 –0.332
Coef.
0.186 0.412 0.000
0.341 0.932 0.440 0.113 0.427
0.657 0.723 0.494 0.204 0.248 0.226
0.585 0.450 0.300 0.547
0.013 0.515 0.003
P > |z|
Women who ideally want three children
Model 7
Explaining son preference in rural India 23
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123 0.042 0.013
0.000 0.000 0.000 0.000 0.002
0.090 –0.121
–0.233 –0.245 0.510 0.109 –0.004
47,045 0.083 –40,178.158
0.144 2.266
0.003 0.003
0.096 –0.121
Note: regression includes dummy variables for state
–1.440 16,861 0.120 –4,675.195
–0.026 –0.212 0.902 0.079 –0.013
–0.181 0.030
–0.131 –0.311
*
Coef.
Coef. *
Women who ideally want two children
Final model for all rural women P [ |z|
Model 6
Model 3
* Variable for proportion of wheat dropped due to collinearity
Proportion wheat in foodgrains Caste (‘‘other’’) Low caste Tribal Religion (Hindu) Muslim Other Control variables Composition of surviving children (none) More girls than boys Equal girls and boys More boys than girls Family size (number of living children) Woman’s age Cut points for outcome variable _cut1 _cut2 Constant Sample size (N) Pseudo-R2 Log-likelihood
Table 5 continued
0.000
0.830 0.082 0.000 0.001 0.002
0.327 0.836
0.182 0.015
P [ |z|
3.081 15,795 0.167 –4,907.401
–0.798 0.007 0.250 0.154 –0.004
–0.075 –0.305
0.154 –0.264
*
Coef.
Women who ideally want three children
Model 7
0.000
0.000 0.956 0.023 0.000 0.313
0.475 0.005
0.088 0.013
P [ |z|
24 R. P. Pande, N. M. Astone
Explaining son preference in rural India
25
The variation in results between the sample as a whole and the different subsamples examined here is difficult to interpret as it is likely to be a combination of differences in sample sizes, differences in the extent of revealed son preference and ideal desired family sizes between groups, and systematic differences in sample characteristics for the subsamples that want particularly small families. At a minimum, however, this analysis suggests that it is important to take account of ideal family size preferences in measuring ideal gender preferences. In particular, women who go against the norm in wanting small families may be different from other women in ways that warrant further exploration when examining gender preferences.
Discussion In this paper we have tested the hypothesis that a number of factors that have long been associated, at the level of individual characteristics or social norms, with demographic phenomena thought to reflect son preference, are also important predictors of son preference when measured directly. We have analyzed the independent, relative effects of individual and household characteristics, village opportunities and development, and social norms and structures. Finally, we have examined whether the predictors of son preference differ by desired family size. The data we use, while not exhaustive, provide a unique opportunity to examine these issues quantitatively. Our study is informed by a theoretical orientation whose proponents see gender stratification as emerging from the simultaneous influence of factors at many levels: a woman’s own experience in her household, the characteristics of the people with whom she lives and the household in which she resides, the local community, and macrosocietal norms (Malhotra & Schuler, 2005). As such, our results are consistent with this view of gender and how gender preferences are formed: while an individual woman’s and her household’s characteristics do influence her gender preferences, social norms are important. In other words, it is not enough in an analysis of son preference to focus on an individual woman’s characteristics alone or structural characteristics alone; rather, we need to examine both simultaneously. At the same time, our paper demonstrates an enormous influence of women’s education, particularly education beyond primary schooling, regardless of desired family size. Given the multivariate nature of this analysis, we can conclude that there is something in the nature of women’s education—particularly higher education—that significantly weakens son preference, beyond any role it may play in allowing women to be employed or have access to media, beyond its association with higher socioeconomic status, and net of social norms. This finding is consistent with recent research on other aspects of gender inequality that shows a much stronger effect of post-primary education, compared to primary education, in improving gender equality and women’s lives (Malhotra et al., 2003).
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While our data do not give us information on why, in particular, education beyond primary school levels has such an important effect net of other factors, we can hazard some possible reasons. There may be an intergenerational process at work. Particularly in rural India where few women go beyond primary schooling, women who are more educated are likely to be regarded differently by their households and communities and allowed a wider range of roles and opportunities than is traditionally sanctioned. Consequently, their own normative view of permissible roles for daughters and sons, and thus relative values of daughters and sons, may differ from the norm and they have weaker son preference. There may also be other factors at work that make certain women both more likely to be more educated and more likely to have weaker son preference that we were not able to capture in our analysis, such as other aspects of women’s empowerment, characteristics of their natal family, or the sociopolitical character of their village. Thus, while our paper suggests that women’s education may be a trigger for change insofar as gender preferences are concerned, it also indicates that more in-depth research is needed to better understand what it is about education, net of other related factors, that has this effect, and how education can be best manipulated to help change underlying gender norms and preferences. Our results endorse the view that economic factors are not enough to change deeply entrenched norms about gender preferences. The weak explanatory power of the household wealth and village development variables is testimony to the tenacity with which centuries-old norms about women persist in rural India, independent of increased wealth. Recent reports of growing sex-selective abortion in India suggest that women and families in wealthier households and communities merely change the way they implement son preference, not the preference itself (Oomman & Ganatra, 2002). This paper adds to the existing literature by rigorously disentangling the multitude of simultaneous influences on son preference among rural Indian women and suggests possible avenues on which to focus future research and policy. The broad causes of son preference are well-understood. Next steps need to focus on better understanding the processes by which factors associated with weaker son preference operate, so as to better identify at what stage to intervene. A better understanding of how to focus policy to weaken the motivation for son preference is particularly urgent in the face of the 2001 census, which shows that son preference continues to exert its influence on Indian society, despite strong economic and social development in the decade of the 1990s, and despite policy alarm bells that were set off by the abysmal sex ratios in the 1991 census. Policy levers need to be chosen with care, however. As India goes through and completes the fertility transition, an increasing percentage of women will want smaller families. We have shown that the determinants of son preference may be different for such women. Moreover, our paper joins other recent research in pointing out that the context of son preference in India is no longer broadly regional; rather, it is context-specific, where the
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Explaining son preference in rural India
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context has to be examined at state or even more micro levels. Therefore it is imperative that policymakers identify and target different policy levers to women in different fertility and social contexts, rather than try an approach of one size that fits all. Acknowledgments This paper is based on the first author’s doctoral thesis while at the Johns Hopkins Bloomberg School of Public Health. Support for this research was provided by the Hewlett Foundation, The Population Council, and the Mellon Foundation. The authors thank Drs. Ken Hill, Anju Malhotra, and Kathryn Yount for their comments.
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