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Son Preference in Indian Families: Absolute Versus Relative Wealth Effects

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Demography

Abstract

The desire for male children is prevalent in India, where son preference has been shown to affect fertility behavior and intrahousehold allocation of resources. Economic theory predicts less gender discrimination in wealthier households, but demographers and sociologists have argued that wealth can exacerbate bias in the Indian context. I argue that these apparently conflicting theories can be reconciled and simultaneously tested if one considers that they are based on two different notions of wealth: one related to resource constraints (absolute wealth), and the other to notions of local status (relative wealth). Using cross-sectional data from the 1998–1999 and 2005–2006 National Family and Health Surveys, I construct measures of absolute and relative wealth by using principal components analysis. A series of statistical models of son preference is estimated by using multilevel methods. Results consistently show that higher absolute wealth is strongly associated with lower son preference, and the effect is 20%–40% stronger when the household’s community-specific wealth score is included in the regression. Coefficients on relative wealth are positive and significant although lower in magnitude. Results are robust to using different samples, alternative groupings of households in local areas, different estimation methods, and alternative dependent variables.

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Notes

  1. A related issue is that “pure” son preference—that is, wanting a son based on personal taste rather than a response to structural motivating factors—will be confounded in the measure. Statistically, however, such preference should cancel out with girl preference, on average.

  2. A groomprice is, according to Billig (1992), any payment in kind or cash that can be used by the husband’s family as it pleases (whereas a dowry remains the property of the daughter). Srinivas called it “modern dowry.”

  3. The term “Sanskritization,” as it was introduced by Srinivas (1962), refers to a phenomenon deeply rooted in the caste system and more specifically related to group behavior; “prosperity effect” is used more generally to apply to individual households.

  4. The relevance of this argument depends on conditions of the marriage market. The less geographically segmented the market and the less costly it is to establish contact with families further away, the less important narrowly defined relative wealth should be. Further information on this point is provided online (Online Resource 5).

  5. Details of the surveys can be found in IIPS (2000 and 2007); both reports as well as the full data sets are available online at http://www.measuredhs.com.

  6. Poorer women with no education were disproportionately represented in nonnumerical answers, but the number of cases was small enough to justify ignoring the bias (Online Resource 1, section 1).

  7. In robustness analyses, alternative definitions of the dependent variable are used: a binary variable defined as in B&Z for logit models, and a three-level ordered categorical variable defined as in P&A for the ordered logit alternative (Online Resource 4, section 3).

  8. Consider three responses: (A) 3 boys and 3 of either sex; (B) 3 boys, 2 girls, and 1 of either sex; and (C) 3 boys and 3 girls. In terms of son preference, most people would rank A above B, and B above C. Using B&Z’s measure, all three responses get a value of 1/2; with the measure chosen here A scores 1/2, B scores 1/6, and C scores 0.

  9. Details on the construction of GDP/c are given in Online Resource 1, section 2.

  10. The procedure is the same as in Gwatkin et al. (2000; see also Gaudin and Yazbeck 2006a; and Pande and Yazbeck 2003). For a list of asset variables and results on asset weights and scores obtained with NFHS-2 data, see Gaudin and Yazbeck (2006b).

  11. Online Resource 3 discusses the construction of an alternative grouping of households based on geographical identifiers available in NFHS-2.

  12. Jaffe et al. (2004) also distinguished between absolute and relative measure of socioeconomic status in their analysis of mortality in Israel.

  13. Additional background on socioeconomic and cultural variables related to son preference in Inda is given in Pande (2003) and Pande and Astone (2007).

  14. Multilevel ordered logit is theoretically feasible but proved to be too computationally intensive with such a large data set and a four-level structure.

  15. The multilevel modeling literature is divided in terms of numbering levels from the top (as here) and from the bottom. Models that consider base-level observations as level one would call this model a four-level hierarchical model rather than three-level.

  16. The notation is adopted to facilitate the intuition behind the estimation procedure. For a clear exposition of hierarchical linear multilevel analysis and advantages of its use in policy analysis, see Leyland and Groenewegen (2003); for a more rigorous treatment see, for example, Rabe-Hesketh and Skrondal (2006).

  17. The number of households is large, and 75%–80% of households contain a single observation. Although it complicated the procedure, the household-level residual was kept because its variance was consistently found significantly different from zero and higher in magnitude than variances for upper levels.

  18. Maximum likelihood is easier to implement for unbalanced panels and allows comparisons between models with different fixed portion. Restricted maximum likelihood did not yield noticeable differences on coefficient estimates and standard errors.

  19. Detailed results are not reported for these models (other than MW1) because coefficient estimates on all variables other than wealth and squared wealth did not change.

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Acknowledgments

Initial work for this article was done while the author was a visiting research associate with the University of Maryland, AREC; and a research affiliate with the World Bank, Washington DC. In addition to her host institutions, the author thanks Gautam Datta, and Jayati Datta-Mitra for substantial comments. Acknowledgments also go to David Bishai, Barbara Craig, Hirschel Kasper, Kala Krishna, Imran Lalani, Yana Rogers, and Abdo Yazbeck. This revised version owes a great deal to comments of three anonymous referees. All analyses and remaining errors are the author’s responsibility.

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  1. Department of Economics, Oberlin College, 10 North Professor Street, Oberlin, OH, 44074, USA

    Sylvestre Gaudin

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Correspondence to Sylvestre Gaudin.

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Gaudin, S. Son Preference in Indian Families: Absolute Versus Relative Wealth Effects. Demography 48, 343–370 (2011). https://doi.org/10.1007/s13524-010-0006-z

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