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For richer, if not for poorer? Marriage and divorce over the business cycle

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Abstract

Despite anecdotal evidence that recessions affect marriage and divorce rates, researchers do not agree about the direction and magnitude of the relationship. This paper reexamines the effect of business cycles on flows into and out of marriage, finding that increased unemployment rates are associated with reductions in both outcomes. The results are robust to the use of alternative measures of economic conditions, hold for both blacks and whites, and are concentrated among working-age individuals. Lag specifications and impulse response functions suggest that the effect of an unemployment shock on marriage is permanent, while the effect on divorce is temporary.

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Notes

  1. “In U.S., Proportion Married at Lowest Recorded Levels.” Population Reference Bureau. September 2010. Data are from the U.S. Census Bureau, 2000 Census and American Community Survey.

  2. National Vital Statistics Reports from the National Center for Health Statistics.

  3. Bureau of Labor Statistics

  4. See, for example, “Saying No to ‘I Do’ With the Economy in Mind,” in The New York Times (September 28, 2010) and “Can the Recession Save Marriage?” in The Wall Street Journal (December 11, 2009).

  5. See, e.g., Roberts (2009)

  6. I use an unbalanced panel, dropping state-years in which the relevant dependent variable is missing. Marriage data are missing for Louisiana in 2006 and Oklahoma in 2001–2003. Divorce data are missing for the following state-years: California: 1991–2009, Colorado: 1995–2000, Georgia: 2004–2009, Hawaii: 2003–2009, Indiana: 1991–2009, Louisiana: 1991–2001 and 2004–2009, Minnesota: 2005-2009, and Oklahoma: 2001–2003.

  7. This tabulation is from the vital statistics micro-data, for the period 1978–1995.

  8. The fact that my results are robust to the inclusion of controls for state-level demographic composition and remain largely unchanged when the regressions are run without weights is reassuring. I also obtain similar estimates when I run the main regression specification using marriage and divorce rates constructed using state total population estimates from the NCHS Cancer-SEER, which are adjusted population predictions based on the decennial census.

  9. As this may give disproportionate weight to large states, I also run regressions unweighted, which allows each state an equal weight. Unweighted regressions produce results that are very similar in magnitude and significance to the weighted results.

  10. Author’s calculation. Marriage (divorce) calculation based on approximate US single (married) female population in 2007.

  11. Approximately 1.5 to 2.5 % for marriages, 0.8 to 1.3 % for divorces.

  12. Note also that if reverse correlation were a significant source of bias, I would expect it to yield a negative correlation between marriage rates and employment to population ratios. However, as shown in Table 3, replacing unemployment rates with employment to population ratios yields positive coefficients in both marriage and divorce regressions.

  13. Micro-level data on marriages is available for 46 states. Micro-level data on divorces is available for 32 states.

  14. Because the denominators of marriage and divorce rates are defined using the number of females in each group, I use age and race of the female listed in each vital statistics entry to create these groups-specific counts of events.

  15. Because the sample sizes in the CPS are relatively small, these group-specific unemployment rates are likely to be measured with a larger degree of error than general unemployment rates. To account for this, I weight these estimates by group-specific population. However, differential measurement error in group-specific unemployment rates is a potential source of bias, so these results should be interpreted with caution.

  16. In 2009, according to totaldivorce.com, nine U.S. states had divorce waiting periods at least 12 months in length: Arkansas, Connecticut, Maryland, Nevada, New Jersey, North Carolina, Ohio, South Carolina, and West Virginia.

  17. For a more thorough explanation of the local projection technique and evidence demonstrating both the consistency and the efficiency of local projections, see (Jordà (2005).

  18. To allow for comparisons between the lag specification results and the impulse response function results, I run the lag specification using the 1978–1991 sample. The results are very similar to those shown in Fig. 3.

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Acknowledgements

I would like to thank the editor, Erdal Tekin, two anonymous referees, Ann Huff Stevens, Hilary Hoynes, Douglas L. Miller, and seminar participants at the University of California, Davis for their helpful comments and suggestions. I acknowledge financial support from the American Association of University Women American Dissertation Improvement Fellowship and the George and Dorothy Zolk Fellowship in Economics at the University of California, Davis.

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  1. Department of Economics, University of California, One Shields Avenue, Davis, CA, 95616, USA

    Jessamyn Schaller

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Schaller, J. For richer, if not for poorer? Marriage and divorce over the business cycle. J Popul Econ 26, 1007–1033 (2013). https://doi.org/10.1007/s00148-012-0413-0

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