Abstract
Previous work shows that higher male wage inequality decreases the share of ever-married women in their 20 s, consistent with the theoretical prediction that greater male wage dispersion increases the return to marital search. Consequently, male wage inequality should be associated with higher husband quality among those “early-mover” women who choose to forgo these higher returns to search. We confirm using US decennial Census and American Community Survey (ACS) data from 1980 to 2018 that married women ages 22–30 in marriage markets with greater male wage inequality are more likely to marry up in education and in husband’s occupation. We additionally consider whether male wage inequality increases wage uncertainty, leading women to prefer older husbands who can send stronger signals of lifetime earnings. We confirm that higher male wage inequality is also associated with a larger marital age gap.
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Notes
Gould and Paserman (2003) and Coughlin and Drewianka (2011) both point out that it is possible for the reservation wage to increase in this case without prolonging search. If the reservation wage previously exceeded the average male wage, then the increased upper tail wage inequality could increase the probability of a draw above even the new higher reservation wage value, reducing search duration. The literature has confirmed a positive relationship between male wage inequality and search duration suggesting that this upper tail probability effect has not dominated.
There is also a related literature studying how changes in assortative matching affect between-household income inequality (e.g., Ciscato and Weber, 2020).
Bergstrom and Bagnoli (1993) develop a related model of the marital age gap in which high ability men delay marriage to reveal their higher potential earnings, but low-ability men do not delay because their signal will not improve with time. No women delay marriage, as delay does not improve the value of their household production, but high value women marry older high-ability men and low-value women marry younger low-ability women. In this model, search is costless, but uncertainty about men’s potential earnings generates marriage delays for high-quality men and larger age gaps for high-quality spouses.
6 If men in high inequality markets respond by becoming less picky, early mover women in the higher inequality markets could be marrying at the same rates as those in lower quality markets because they are adequately compensated in husband quality for foregoing the greater returns to search in their market. Early mover women in high inequality markets should still therefore on average be accepting higher quality offers than those in low inequality markets.
Loughran (2002) and Gould and Paserman (2003) define marriage markets based on metropolitan areas. Coughlin and Drewianka (2011) define marriage markets at the state level and find that their estimates are very similar to those of Loughran (2002) and Gould and Paserman (2003) when they restrict their sample to the same years analyzed in those earlier papers. We also define the market at the state level to avoid issues with consistent definitions of metro areas over time. State-level definitions for marriage markets have been common in recent work (e.g., Bertrand, Kamenica and Pan, 2015; Shenhav, forthcoming).
Hourly wages are calculated for each worker by dividing annual earnings by annual hours. Annual hours are calculated by multiplying weeks worked last year times usual hours per week. Because weeks of work are reported in intervals in 2006–2018, weeks of work are taken as the midpoint of the reported interval. Specifically, week values of 7, 20, 33, 44, 48, and 51, are used, respectively, for the reported intervals 1–13, 14–26, 27–39, 40–47, 48–49, and 50–52.
This restriction results in dropping 260 marriage markets which is about 0.18% of analysis sample of women ages 22–30.
For example, Furtado and Theodoropolous (2011) demonstrate that endogamous matching on race/ethnicity becomes somewhat less common as education increases. This means that male wage dispersion by race/ethnicity may be slightly less salient for highly educated women. This would attenuate our estimates but would not bias the results towards finding a positive effect of male wage dispersion in own marriage market on husband’s education.
If the age distribution for women ages 22–30 is sufficiently similar across marriage markets and time periods, then it is not necessary to stratify by woman’s age and control for age fixed effects. But, because marital status is strongly related to age in this age range, it is important to adequately control for any differences in the age distribution.
Appendix Table 11 reports the coefficients on the control variables for the Table 1 regressions using standard deviation of log wages as the inequality measure. In the first two columns, a higher average male wage and a higher ratio of men to women are both associated with a larger share of women ever married, consistent with expectations, but these effects become small and statistically insignificant when we add the full set of fixed effects in column 3. In column 4, where we add controls for female wages and employment, a higher ratio of average male wage to female wage is associated with a higher share ever married, also consistent with expectations. Perhaps counterintuitively, however, a higher share of women ages 22–30 employed full-time is associated with a higher share ever married.
Shenhav (2021) finds that higher wages for women relative to men reduce marriage but have no effect on cohabitation. Our coefficient estimates for the male–female wage ratio are consistent with Shenhav’s findings. The coefficient estimate is positive and significant in the shared ever married regression (shown in Appendix Table 11) but insignificant in the share cohabiting regression.
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Acknowledgements
We gratefully acknowledge helpful comments from editor Klaus F. Zimmermann, three anonymous reviewers, and participants in the Society of Economics of the Household (SEHO) 2021 meeting, the Population Association of America (PAA) 2021 meeting, the WEAI 2021 meeting, as well as seminar participants at the University of Kentucky, the University of Southern Florida, the US Census Bureau, and San Diego State University.
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Mansour, H., McKinnish, T. Male wage inequality and characteristics of “early mover” marriages. J Popul Econ 36, 115–138 (2023). https://doi.org/10.1007/s00148-022-00898-x
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DOI: https://doi.org/10.1007/s00148-022-00898-x