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Obesity and sex ratios in the U.S.

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Abstract

This paper studies how rising male incarceration and its impact on marriage markets has affected female incentives to gain weight. Exogenous variation in marriage market conditions is obtained from differential trends in male incarceration rates across markets defined by race, location and age. We provide evidence that marriage market conditions do in fact affect the incidence of obesity. In particular, we find that increases in male imprisonment that reduced the male–female sex-ratio explain about 18 % of the increase in the female obesity rate for African-Americans in the United States over the 1990s. Results are particularly large for those in the younger age group (ages 18–23).

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Notes

  1. It is beyond the scope of this paper to provide a detailed review of theoretical and empirical research in the economics of obesity. See Cutler et al. (2003) and Philipson and Posner (2008) for surveys of economic research on obesity.

  2. See Beydoun and Wang (2007), among others.

  3. See for example Strum (2002).

  4. According to Masters et al. (2013), “overweight and obesity were likely responsible for about 18.2 % of US Black and White men’s and women’s adult deaths between 1986 and 2006”. See also Flegal et al. (2005).

  5. See Ogden et al. (2013).

  6. See Argyropoulos et al. (1998).

  7. While slenderness and evaluations of attractiveness are positively correlated among blacks, the ideal, preferred weight for black women is much higher than that for white women. See Philipson and Posner (1993).

  8. See Lemennicier (1988), Vaillant and Wolff (2011) and Malcolm and Kaya (2014).

  9. For example, Hitsch et al. (2010) show that men and women differ strongly in their preferences for the weight of a potential mate. Women with a higher BMI have a lower chance of receiving a first-contact e-mail. The effect is large: for example, a woman with a BMI of 24–26 has a roughly 10 % lower chance of being approached than a woman with a BMI of 20 or less. In addition to these preferences concerning the level of a partner’s BMI, they also find that men prefer women with a BMI that is lower than their own and dislike women with a larger BMI.

  10. See Swami (2008) for a survey of the literature.

  11. This is what Grossbard-Shechtman (1984) refers to as “compensating differentials in marriage”. See also work by Mukhopadhyay (2008) among the first to explore trade-offs between fitness and other attributes.

  12. See Averett and Korenman (1996), Averett et al. (2008), Mukhopadhyay (2008) and Tosini (2008).

  13. See Finlay and Neumark (2010) for the effect of family structure on child outcomes, Mechoulan (2011) for the impact on fertility, schooling and marriage outcomes and Cornwell and Cunningham (2007) for effects on risky behaviors and sexually transmitted diseases. Charles and Luoh (2010) find that higher male imprisonment appears to have lowered the likelihood that women marry, modestly reduced the quality of their spouses when they do marry, and shifted the gains from marriage away from women and toward men.

  14. See, for example, Iyigun and Walsh (2007), Chiappori et al. (2009) and LaFortune (2011).

  15. See Browning et al. (2014).

  16. Fitness can also generate a second return. Indeed, having a BMI in the normal range may also affect wages and opportunities in the labor market regardless of marital status.

  17. See also Galichon and Salanie (2010) and Dupuy and Galichon (2012).

  18. Some work has considered multiple attributes within a non-transferable utility framework. See Wong (2003), Coles and Francesconi (2011) and Banerjee et al. (2013).

  19. For this conclusion to hold in a model that adds labor market returns to fitness, the utility cost associated with efforts to stay fit must exceed the potential labor market returns from fitness.

  20. It is possible that there is an optimal level of fitness to secure monogamy. While we work in the context of marriage markets where polygamy is illegal, one can think of mistresses being more likely when the wife deviates from this optimal fitness level. Grossbard (1976) finds evidence for an optimal age to secure monogamy in a marriage market with legal polyginy. We thank the editor for suggesting this possibility.

  21. This effect is reminiscent of the “discouraged worker” effect that is often highlighted in discussions about unemployment. There is a “discouraged worker effect” when unemployed workers decide to stop investing in job search and withdraw from the labor force when the prospects of finding a job are very small to begin with.

  22. Therefore, we can only look at obesity after 1984. This prevents us from exploiting other censuses (i.e. 1980, 1970, etc.) but it is not a substantial limitation given that obesity and incarceration rates started to rise noticeably only after 1980.

  23. The limitations of BMI are well known. First, it is self-reported and so it might underestimate the true prevalence of obesity. However, since we are exploting differences across races, this would only be the case if the pattern of under-reporting is different across races. Also, BMI doesn’t distinguish fat from muscle mass. Controlling for BMI, African-American females have greater muscle mass, and less fat mass, than white females. This could lead us to overestimate the true female obesity gap at baseline. But, since our empirical strategy will exploit changes in race specific obesity over time, this should not be a problem as the bias is removed by taking time differences.

  24. However, in more recent years (2000–2010) the number of inter-racial marriages has increased substantially and this trend is receiving more attention. Indeed, Chiappori et al. (2011) document intresting patterns of inter-racial marriage for black and whites in the U.S. during the 2000s. They highlight how same-race marriages differ from inter-racial ones along key attributes such as education, income and BMI of the spouses.

  25. Consequently the censuses do not provide information on whether respondents are incarcerated, only on whether they are “institutionalized”. Institutional group quarters include correctional facilities, mental institutions, and retirement facilities.

  26. In order to have a precise estimate of male incarceration rates and sex-ratio among non-institutionalized singles, we dropped some markets (i.e. combinations of age, race, and state) if they failed to reach some criteria. First, the number of males in a market should be larger than 100 for us to calculate the male incarceration rate for that market. Second, the number of non-institutionalized single males and females in a given market should both be above 100 for us to calculate the sex ratio among non-institutionalized singles for that market. Any market that failed to satisfy these two criteria was not included in the final sample. Theoretically, there should be 51 (states) \(\times\) 3 (age groups) \(\times\) 2 (races) = 306 markets. After applying our selection criteria we end up with 259 markets in 2000 and 224 markets in 1990. The sample thus excludes very small markets. This is because we want to be conservative and make sure that we have enough microdata within each market (i.e. within cells defined by age group, race, location and year) so as to be able to reliably compute our aggregate measures of sex-ratio and male incarceration rate. We face a tradeoff. If we relax the restriction, we will indeed have more markets, but the data from these additional markets will not be as reliable. We could also impose a stricter cutoff to gain even more precision, but then we will be excluding additional markets. We think our current sampling strategy strikes the right balance between these two concerns.

  27. To avoid using males institutionalized in state \(s\) who were actually living in state \(s^{\prime }\) before being institutionalized, we use only males who were living in the same state in the past 5 years to compute our institutionalization rates. All our results are similar if we do not impose this restriction.

  28. As Charles and Luoh (2010) argue, the increase in incarceration was mostly due to a set of policies known collectively as the “War on Drugs”. These reforms increased the number of convictions involving prison time and also led to an increase in the length of sentences.

  29. To be precise, we compute the sex-ratio by counting the number of single, non-institutionalized, males and females in each market for 1990 and 2000. Each market \(m\) represent a particular \(\left( a,r,s\right)\) combination. So, in addition to race and location, our markets are specific to each of our three age groups. When constructing sex-ratios we use the same ages for both men and women in these groups. An alternative would be to use an age difference based on average difference in age at marriage. See Akers (1967), Heer and Grossbard-Shechtman (1981), Goldman et al. (1984) and Grossbard and Amuedo-Dorantes (2007).

  30. Another possibility is that over and above baseline differences, the effect of a change in the sex-ratio on obesity could be different for blacks and whites. However, the available variation in the data is not enough to separately identify these race-specific responses.

  31. For example, Grossbard and Amuedo-Dorantes (2007) use 2-year age difference to construct sex ratios by age group and therefore exploit a different source of variation: birth cohort size fluctuations for adjacent cohorts induced by fertility booms/busts. Using that definition, even without increases in male incarceration, there is a deterioration of marriage market conditions for women (18–23) from 1990 to 2000. This is because women in that age group in 1990 belong to high sex ratio generations born during a fertility bust whereas those in the same age group in 2000 belong to generations with more balanced sex ratios. Then one concern could be that our measurement of sex-ratio in 1990 for women in the age group 18–23 is slightly biased downwards. Our two-way interaction \(\lambda _{at}\) between our indicators for “age group (18–23)” and “year=2000” captures any reason why women in that age group are more likely to be obese in 2000 relative to 1990. Therefore our identification does not rely on these broad cohort trends whose sex ratio dynamics may be affected by other forces.

  32. As Finlay and Neumark (2010) argue “\(\ldots\) incarceration rates are plausibly excludable from the outcome equation because recent increases in incarceration rates have not been caused primarily by corresponding changes in criminal behavior. Rather, some states have adopted harsher punishments, especially for drug and repeat offenses, while the general level of reported crime has not increased much\(\ldots\)”

  33. Of course, we can only measure whether a woman is single as of time of interview. Some of these women may go on to marry.

  34. The definitions of Obese I, II and III correspond to the following BMI ranges (I) 30.00–34.99, (II) 35.00–39.99, (III) 40.00+.

  35. Everything from commuting patterns to occupational and food choices are well studied factors affecting obesity whose effects could be race specific.

  36. For example, Cawley (2004) documents that only white females face a significant wage penalty due to obesity. It is possible that this penalty grew larger between 1990 and 2000, providing stronger incentives for white females to remain fit, relative to black females.

  37. Note that there is nothing specific about black females that explains these results. In other words, our model would predict that white females would had experienced similar increases in obesity, had they been confronted with a similar deterioration in marriage market conditions.

  38. See Lochner and Moretti (2004), among others.

  39. See Mare and Schwartz (2005) and Negrusa and Oreffice (2010).

  40. See, for example, Bolin and Cawley (2006).

  41. See Lakdawalla and Philipson (2002), Cutler et al. (2003), Lakdawalla et al. (2005) and Philipson and Posner (2008).

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Acknowledgments

We thank Marianne Bitler, Michele Boldrin, Pierre-Andre Chiappori, Charles Courtemanche, Scott Cunningham, Janet Currie, Keith Finlay, Sebastian Galiani, Donna Gilleskie, Sukkoo Kim, Enrico Moretti, Derek Neal, Karen Norberg, Bob Pollak and Bruce Petersen for helpful comments. We also thank Tim Krah, O Hyun Kwon, Enyu Liu, Fulin Li and Hanxiao Cui for their excellent research assistance. This paper is based, in part, on Kathryn Hoelzer-McEvilly’s thesis at Washington University. Lin acknowledges research support from the Humanities and Social Science Foundation from China Ministry of Education (Project No. 13YJA790064).

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  1. Guanghua School of Management, Peking University, Rm. 325, Hall 2, Beijing, 100871, China

    Wanchuan Lin

  2. Duke Law School, Duke University, 210 Science Drive, Box 90362, Durham, NC, 27708, USA

    Kathryn McEvilly

  3. Department of Economics, Washington University in St. Louis, 1 Brookings Dr., Campus Box 1208, St. Louis, MO, 63144, USA

    Juan Pantano

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Lin, W., McEvilly, K. & Pantano, J. Obesity and sex ratios in the U.S.. Rev Econ Household 14, 269–292 (2016). https://doi.org/10.1007/s11150-014-9269-2

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