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How do sex ratios in China influence marriage decisions and intra-household resource allocation?

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Abstract

This article examines how imbalanced sex ratios influence marriage decisions and household bargaining. Using data from the 1982 Chinese census, the traditional “availability ratio” is modified to reflect the degree to which men tend to marry women from different cohorts. This ratio reflects the average tendency of men to prefer women who are close in age to women who are several years younger than them by weighting cohort sizes using the proportion of people in the population who marry someone born in a different cohort. Given that men generally marry younger women, this ratio varies independently of the size of one’s own birth cohort. Yet, the ratio fluctuates considerably across individuals, as the sizes of birth cohorts in China vary across time and regions. This enables us to examine how variability in such ratios may influence marriage decisions and household bargaining. The findings suggest that women exercise greater bargaining power once married. Results indicate that as women become scarcer in the marriage market, they have healthier sons. Men also delay marriage, and consume less tobacco and alcohol. This paper also highlights how sensitive findings may be to using this modified weighted availability ratio rather than a traditional unweighted availability ratio.

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Notes

  1. See the following studies: Coale (1984, 1991); Coale and Banister (1994); Oster (2005); Das Gupta (2005); Ebenstein (2007); Lin and Luoh (2008); Oster et al. (2008); Qian (2008); Almond et al. (2010); and Hamoudi (2010). As of 2005, China’s sex ratio at birth was 119 boys for every 100 girls. Among children ages one to four this figure rose to 125 in the overall population, reaching as high as 142 in some provinces (Zhu et al. 2009).

  2. In related work in the U.S. and Taiwan, a high sex ratio leads to improved bargaining power for women (Grossbard-Shechtman 1993; Angrist 2002; Grossbard and Amuedo-Dorantes 2007; Francis 2011).

  3. In prior research, increases in female income or greater control of household assets leads to greater investment in children (e.g., Thomas 1990; Duflo 2000, 2003; Rangel 2006; Qian 2008; Brown 2009; Rubalcava et al. 2009).

  4. For example, see: Grossbard-Shechtman (1993), Bergstrom and Lam (1994), Angrist (2002), Chiappori et al. (2002), Grossbard and Amuedo-Dorantes (2007), Brandt et al. (2008), and Francis (2011).

  5. One consequence of declines in fertility is a "marriage squeeze" (Schoen 1983), as men generally marry women who are several years younger. Women born during periods of low fertility marry men born earlier in larger cohorts. As a result, these men are faced with a relative scarcity of women, particularly if the women they most want to marry (those generally two to three years younger) cannot be easily substituted for slightly younger cohorts, also born during low fertility periods. The largest difference in sex ratios in this article is roughly 23 %, where sex ratios range between 102 and 125. One related study has examined how a very large influx of mainland Chinese soldiers to Taiwan influenced household bargaining (Francis 2011), where the influx of Chinese soldiers raised the sex ratio for those ages 20–24 by about 57 % (from 97 men per 100 women to 152 men). While this is an important and unique study, it is unclear how generalizable the findings are beyond this particular case.

  6. For a comprehensive survey of research studies implementing this ratio, see Chiswick and Houseworth (2011).

  7. To illustrate this point further, while 78 % of women over 15 in China were ever married, only 70 % of men over 15 were ever married. While the total sex ratio was 1.05, the single sex ratio was 1.16. The single sex ratio by age also shows a strongly increasing pattern by age, until ages 36–40, when the single sex ratio declines.

  8. In the 1982 Chinese census, only 10 % of women were married to men born in their same year.

  9. Choo and Siow (2006a, 2006b) propose a static transferable utility marriage market model to demonstrate that the probability of a woman of type j marrying a husband of type i is proportional to her net gains from doing so. While their model refers to different types more generally, I define type by age here, since my focus is on people marrying across different ages.

  10. Data was provided by the Minnesota Population Center (2006).

  11. See the following studies: Fan and Huang (1998), Chan et al. (1999), Chan (2001), and Huang and Zhan (2005).

  12. According to the 1982 census, the sex ratio by year of birth declines significantly for the years of the Great Famine and those immediately following. Boys born during this time period were less likely to survive to marriageable ages.

  13. The mean age at marriage is 25 for men and 23 for women.

  14. We would similarly estimate availability ratios from a woman’s perspective.

  15. The sample is restricted to these years because mortality rates for men tend to increase after age 50 and men cannot legally marry when under the age of 18.

  16. There are some limitations to this approach. First, weights determined by earlier cohorts introduce some measurement error in proxying for the weights relevant to the population under study, to the extent that these population averages differ across the two sets of cohorts. Second, these weights may be determined by some omitted socioeconomic factors which may also influence outcomes examined in the analysis here. I am grateful to a reviewer for pointing this out.

  17. In general, distributions estimated from the two samples are quite similar. In comparison to the wider sample of men born between 1932 and 1964, men born between 1932 and 1937 are less likely to marry women closer in age, and are more likely to marry women who are increasingly younger (four to nine years younger in rural areas and three to nine years younger in urban areas).

  18. For each bootstrap iteration, I impose the null hypothesis that the sex ratio has no effect on outcome measures. I resample residuals at the cluster level to preserve any correlation between individuals within a province and hukou type. These residuals and the covariates are then used to obtain a predicted outcome y that does not include the effect of the sex ratio. This predicted y is then regressed on the complete set of covariates that includes the sex ratio. For the ith iteration, estimates of β * 1, i , and its standard error yield corresponding Wald statistics. The distribution of these Wald statistics (under the null hypotheses) is collected over 999 iterations in order to compute critical values.

  19. These regressions were estimated at the province-residence type-birth year level. Results are available upon request from the author.

  20. In regressions on famine exposure averaged to the province-year-residence type level, availability ratios do not predict this measure. Results are available upon request from the author.

  21. Results are available upon request from the author.

  22. Results are available upon request from the author.

  23. A detailed description of this data can be found at http://www.cpc.unc.edu/projects/china.

  24. According to World Bank World Development Indicators, in China, male life expectancy at birth in 2004–2008 was 71 years of age, while female life expectancy at birth was 75 years of age in 2008.

  25. Previous regressions do not include the mother’s characteristics because they are endogenous choices made by the father.

  26. Marriage has been virtually universal for these cohorts (Zeng 1995). According to the 1982 census, by the time men reach the age of 46–50, 96 % of them have been married, and 99 % of women in this age group have married. While nearly all eventually marry, the interest here is at what age they marry and who they marry.

  27. A marriage matching model which incorporates these different matching characteristics and constraints may help to distinguish between these different dynamics (e.g., see Brandt et al. 2008).

  28. Results are available upon request from the author.

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Acknowledgments

I am very grateful to Gary Becker, Marianne Bertrand, Markus Eberhardt, Marcel Fafchamps, Soshana Grossbard, Ali Hortacsu, Steven Levitt, Albert Park, and two anonymous referees for their helpful comments and suggestions. I am also grateful to Douglas Almond for providing data on Great Famine death rates. I thank the Oxford Institute of Population Ageing, the Oxford Martin School, the University of Chicago Center on Aging, and the Chicago Center for Excellence in Health Promotion Economics for providing financial support for this project.

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  1. Department of Agricultural, Food, and Resource Economics, Justin S. Morrill Hall of Agriculture, Michigan State University, 446 West Circle Drive, Room 202, East Lansing, MI, 48824-1039, USA

    Maria Porter

  2. Oxford Institute of Population Ageing, University of Oxford, Oxford, UK

    Maria Porter

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Correspondence to Maria Porter.

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Porter, M. How do sex ratios in China influence marriage decisions and intra-household resource allocation?. Rev Econ Household 14, 337–371 (2016). https://doi.org/10.1007/s11150-014-9262-9

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