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Dowry in the absence of the legal protection of women’s inheritance rights

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Abstract

The practice of dowry is often thought to be the root cause of the unequal treatment of girls in India, as represented by sex-selective abortion and female infanticide. This is because the prospect of burdensome dowry payments in future makes the birth of a girl child unwelcome. For adult women without inheritance rights, however, dowry may function as their only source of protection. Using a nationwide dataset and exploiting a natural experimental setting, this study explores the relationship between dowry and women’s empowerment in India, a society where women do not have inheritance rights, and thus do not usually possess immovable assets. In such a society, dowry seems to enhance women’s status in the marital household. The relationship reverses when women have equal inheritance rights as their brothers. Empirical analysis suggests that the outright ban on dowry that ignores the context may not necessarily benefit women. It also implies that dowry may become unnecessary and disappear once women are assured of inheritance rights.

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Notes

  1. In empirical studies, this is often measured by the extent to which women can take decisions, have autonomy, are free from mental and physical threats within the household, and can move freely and independently by themselves.

  2. “Dowry murder” is officially defined as any instance where the death of a woman is caused by burns or bodily injury or occurs in circumstances other than under normal circumstances, within seven years of her marriage, and it is proven that before her death, the woman was subjected to cruelty or harassment by her husband or any relative of her husband for, or in connection with, any demand for dowry (the Dowry Prohibition Amendment Act, 1986). Some criticize the term “dowry murder” because it is likely to include any kind of homicide, including those presumably unrelated to dowry (for example, Kishwar 1989; Narayan 1997; Leslie 1998; Oldenburg 2002; Palriwala 2009).

  3. Datasets other than those from South Asia provide some evidence of a positive effect of dowry on women’s welfare in the marital household (e.g., Zhang and Chan 1999; Brown 2009).

  4. The inclusion of Kerala does not substantially alter the main estimation results. The results including Kerala are available upon request.

  5. In fact, the estimation results when women have “some say” as an outcome variable are different from those when women have the “most say” (see Appendix Fig. 5 and Table 10).

  6. In the regression analysis, non-wage income (i.e., total income minus wages and salaries) is used as a control variable to deal with endogeneity. The use of total income is alternatively checked and does not affect the estimation results.

  7. Inclusion of wage information does not affect the main results. The results are available upon request.

  8. Since this is the subjective measure gleaned from the answers given by the women (see the Appendix for the exact question), it can be subject to measurement errors. For robustness, we check whether the estimated coefficients change by excluding this variable, and find no difference. The results are available upon request.

  9. See the Appendix for the exact questions used for constructing the variables of dowry and bride price.

  10. On the basis of our own field survey, the paid and received amounts in a specific family are often symmetric because of the practice of arranged marriage and of assortative family matching of marriages in South Asia.

  11. It seems standard to include all expenses, both transfers and ceremonial expenses, incurred at the time of marriage in the dowry variable when conducting empirical studies (e.g., Deolalikar and Rao 1998). At any rate, the ceremonial expense is relatively small compared with that for dowry. The study by Bloch et al. (2004) report that the ceremonial expense is only one-eighth of the amount spent for dowry.

  12. In the eastern provinces of Pakistan, namely Punjab and Sindh, the practice of dowry is as common as it is in India. However, because dowry is not legally banned in Pakistan, the interviewees have no hesitation answering the actual amount of dowry paid at the time of their marriage, unlike in India.

  13. They study how kinship affects women’s decision-making power. Their measure of kinship is community-based, such as the village exogamy, and not based on whether the husband and wife are blood relatives.

  14. Precisely speaking, the amendment applies to those who were married and whose paternal grandfather died after the amendment. Moreover, women’s natal states, not their current states, determine the reform and non-reform states. Since the IHDS does not include information on the timing of a woman’s paternal grandfather’s death or her natal states, the timing of a woman’s marriage in her current resident state is used in the difference-in-differences strategy. The unavailability of information on women’s natal states may not be a serious drawback for this study, given that inter-state marriages before the amendment were not very common in the southern states where the amendment was preceded. We believe that the timing of marriage in the current resident state vis-à-vis the amendment is more important for the purpose of this study because a woman’s status can be affected not only by whether she actually inherits ancestral property but also by changes in attitude following the amendment (see Roy 2015).

  15. We also check robustness with the multivariate regression model, and the estimated association between dowry and the decision-making variables (Appendix Table 11) is not qualitatively different from the LPM estimation results. The implication of the probit model and the multivariate probit model instead of the LPM and the multivariate regression model is not substantially different. The probit and multivariate probit estimation results are available upon request.

  16. We cannot include the village fixed effects because the IV based on the “-i method” captures the village-level marriage-market situation. Also, the endogamy variable is treated by the “-i method,” and thus multicollinearity occurs with the village fixed effects.

  17. We do not include the women’s wages as an explanatory variable, since it is difficult to impute the missing values owing to various possible reasons for this not being reported. Wages are reported by only 27.2% of married women. For the remaining 72.8%, it is not clear whether the women work at home or in family enterprises without pay. The inclusion of wages is not critical to testing our model.

  18. For examples of studies using the same procedure, see Kingdon and Teal (2010) and Bellows and Miguel (2009).

  19. The estimation with IVs is also repeated by replacing the amendment with the southern indicator. The results support the estimation results given in Table 7 and are available upon request.

  20. The estimation with IVs is done similarly. The results support the estimation results given in Table 8 and are available upon request.

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Acknowledgements

I am grateful to Claus Pörtner for his advice and guidance throughout the development of this paper. I owe thanks to Shelly Lundberg, Seik Kim, Aimee Chin, and seminar participants at the University of Washington for their insightful suggestions. I am very grateful to two anonymous referees for comments on the early version of the paper. I greatly appreciate the financial support from the Department of Economics at the University of Washington, the James K. and Vilola M. Hall Fellowship, and the Grover and Creta Ensley Fellowship in Economic Policy. Any errors, omissions, or misrepresentations are, of course, my own.

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Appendix

Appendix

The India Human Development Survey (IHDS), 2005

The data for this study comes from the India Human Development Survey (IHDS), which is a nationally representative survey covering 41,554 households in 1503 villages and 971 urban neighborhoods across India, collected between November 1, 2004 and October 30, 2005. The survey consists of three parts—the household questionnaire, the education and health questionnaire, and the children’s learning tests questionnaire. The household questionnaire includes standard questions on socioeconomic characteristics and demographics that can be answered by anybody in the household. The education and health questionnaire is also called women’s questionnaire because it is answered by “eligible women,” defined as those who were ever married between the ages of 15 and 49. The women’s questionnaire contains unique questions such as marriage practice and gender relations within the household. Given that this study is concerned with gender relations between the husband and wife, the respondents of the women’s questionnaire in this study are effectively the wives of the heads of households. The children’s learning tests questionnaire includes the test scores of all children aged 8 to 11, obtained from reading, math, and writing tests administered by the interviewers.

Some critical variables used in this study are unique because they are constructed based on information in the women’s questionnaire. Below, we describe how all these variables are constructed.

Decision-making variables (outcome variables): The variables take the value one when the wife answers yes to the question “Who has the most say in the decision?” with respect to the following matters: “What to cook on a daily basis?,” “Whether to buy an expensive item such as a TV or fridge?,” and “How many children you have?,” and if she has any children, “What to do if a child falls sick?”, “To whom your children should marry?” The question comes with the specific instruction that the interviewer should ask the wife who has the most decision-making power, if she answers that such power is vested in more than one household member.

Dowry: The specific question, “At the time of marriage, how much money is usually spent by the girl’s family?” follows the instruction “I would like to ask you some questions about marriage customs in your community (jati) for a family like yours.” The note for the interviewer goes, “Probe to get the amount for a typical wedding. Try to get one number, but accept a range if that is what is given.” Since the question allows for some range in the response amount, we assign the median of the maximum and minimum values spent by the girl’s family on dowry at the time of marriage. It should be noted that the difference between the maximum and minimum values given by the majority of respondents is not large. The median of the difference is Rs.10,000, and the 90th percentile is Rs.50,000 (see Appendix Fig. 3 Panel A for the distribution of the difference between the maximum and minimum values).

Bride price: As with dowry, the specific question asked is, “At the time of marriage, how much money is usually spent by the boy’s family?” We assign the median of the maximum and minimum values spent by the boy’s family at the time of marriage to bride price. Similar to dowry, the difference between the maximum and minimum values given by the majority of respondents is not large: the median of the difference is Rs.10,000, and the 95th percentile is Rs.50,000 (see Appendix Fig. 3 Panel B for the distribution of the difference between the maximum and minimum values).

For a robustness check, we repeat the estimation procedure with the subsample in which the difference between the maximum and minimum values of dowry/bride price given in answers is below Rs.50,000. The results with the subsample are not qualitatively different from the full-sample estimation results (Appendix Table 9).

Wealth (natal > husband’s family, =1): The variable takes the value one when the wife answers yes to the question “At the time of your marriage, if you compared the economic status of your natal family with your husband’s family. Would you say your natal family was better off?”

Endogamous marriage (yes =1): The variable takes the value one when the wife answers yes to any one of the following questions: “Are any women from your natal family married into this family?,” “Are you related to your husband by blood?,” “Did you grow up in the same village/town as your husband?”

Fig. 3
figure 3

Distribution of the difference between the maximum and minimum values in answers by respondents. Panel A: Jati-based dowry payment (Rs.) and Panel B: Jati-based bride price payment (Rs.).

Fig. 4
figure 4

Correlation between jati-based dowry and the personal dowry amount. Source: Pakistan rural household survey conducted by the author in 2013

Fig. 5
figure 5

Women’s decision-making power within the household (woman has some decision-making power = 1)

Table 9 Effects of dowry/bride price on women’s decision-making power in the reform and non-reform states with control variables, using the subsample in which the difference between the maximum and minimum values of dowry/bride price < Rs.50,000 (only the coefficient estimates of interest are reported)
Table 10 Effects of dowry/bride price on women’s decision-making power in the reform and non-reform states with control variables (woman has some decision-making power = 1): Estimated using an LPM (only the coefficient estimates of interest are reported)
Table 11 Effects of dowry/bride price on women’s decision-making power in the reform and non-reform states with control variables: Estimated using a multivariate regression model (only the coefficient estimates of interest are reported)

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Makino, M. Dowry in the absence of the legal protection of women’s inheritance rights. Rev Econ Household 17, 287–321 (2019). https://doi.org/10.1007/s11150-017-9377-x

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