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Rethinking the Two-Body Problem: The Segregation of Women Into Geographically Dispersed Occupations

Abstract

Empirical research on the family cites the tendency for couples to relocate for husbands’ careers as evidence against the gender neutrality of household economic decisions. For these studies, occupational segregation is a concern because occupations are not random by sex and mobility is not random by occupation. I find that the tendency for households to relocate for husbands’ careers is better explained by the segregation of women into geographically dispersed occupations rather than by the direct prioritization of men’s careers. Among never-married workers, women relocate for work less often than men, and the gender effect disappears after occupational segregation is accounted for. Although most two-earner families feature husbands in geographically clustered jobs involving frequent relocation for work, families are no less likely to relocate for work when it belongs to the wife. I conclude that future research in household mobility should treat occupational segregation occurring prior to marriage rather than gender bias within married couples as the primary explanation for the prioritization of husbands’ careers in household mobility decisions.

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Fig. 1
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Notes

  1. 1.

    The study of the segregation of women into low-paying jobs within firms is a familiar application of this approach; fine-grained controls reduce and virtually eliminate the estimated effect of sex (see, for example, Petersen and Morgan 1995 or Reskin and Bielby 2005). However, because relocations for work are rare, introducing occupational fixed effects makes tests very weak. Including a linear term for clustering instead maintains the statistical power of the test while tying results to the specific theoretical construct provided by clustering.

  2. 2.

    Available online (http://www.usa.ipums.org). See Ruggles et al. (2010).

  3. 3.

    The 2000 PUMS distinguishes 337 occupations by (up to) a six-digit SOC code. By exploiting the hierarchical nature of SOC codes, it is easy to show that aggregating occupations (for example, to three-digit categories) reduces the magnitude of the effects, suggesting potential aggregation bias. Reskin (1993:243) also noted this bias: “. . . ‘college and university teacher’ includes someone teaching night classes on repairing office machines at Parkland Community College as well as a distinguished professor of mathematics at Harvard.” Likewise, segregation into highly mobile subspecialties within occupations would not be captured by aggregate measures like SOC codes. The resulting measurement error (in the independent variable) is expected to cause attenuation bias, reducing the magnitude of the estimated coefficients and increasing the standard errors.

  4. 4.

    The March CPS features six-digit SOC codes beginning in 2003. Unlike the census or PSID, it asks relocating households to report the primary reason for relocating in the prior year, including “for work or job transfer.” Only 10 % of relocations are for work, and geographic flexibility may lead workers to relocate for reasons other than work, confounding estimates that use observed relocations for all reasons. The March CPS also has much greater statistical power than the PSID. Data are available on the IPUMS website (http://www.cps.ipums.org) (see Ruggles et al. 2010).

  5. 5.

    Unfortunately, although the CPS reports the principal reason for household relocations, its sample size is far too small to estimate relocation likelihoods by occupation.

  6. 6.

    “Physicists and astronomers” are treated as distinct from “postsecondary teachers” by the U.S. Bureau of Labor Statistics.

  7. 7.

    To sign the omitted variable bias posed by the correlation between sex and occupational clustering, I examine pairwise correlations between a female dummy variable, a worker’s occupational clustering score, and an indicator variable denoting that the worker relocates for work. The correlation between the female dummy variable and the clustering score is negative (Hypothesis 1A), and the correlation between the clustering score and relocation for work is positive (Hypothesis 1B), both with p < .01. Therefore, without controlling for clustering, the raw correlation between female and relocation will be biased downward. These correlations are each robust and significant in all nine explored specifications: the full sample, by age less than 35, for never-married and married workers, and by college education (i.e., 1 + 23). Although correlations were strongest among the young and college educated, this check suggests that the OVB very broadly leads to downwardly biased estimates of the independent effect of sex on relocation for work.

  8. 8.

    Primary reason for relocation is a self-reported variable, and I also examine relocation reported for reasons other than work (results are available upon request). As shown in Table 2, treating “all relocations” as the dependent variable has the following effects: the magnitude of all coefficients is reduced, the coefficient for clustering remains a statistically significant predictor of relocations, and the coefficient for the female variable loses significance in columns 3 and 7. Treating relocations primarily “for family” as the dependent variable has the following effects: the coefficient on clustering loses significance, and the coefficient for the female variable rises. I interpret results to suggest that never-married women are more likely than men to cite family as the primary reason why they relocate, and the geographic clustering of a job is a better predictor of relocations that households report are primarily “for work.”

  9. 9.

    Among dual-earner couples, 5.09 % of men and women have the same occupation. To avoid bias, the terms for maximal clustering is zero for both spouses when occupations match. These couples are significantly more likely to relocate for work than spouses where the occupations do not match (23.9 %, with a standard error of 9.3 %). One possibility is that work relocations are easier when the husband and wife have the same occupation.

  10. 10.

    There are also exceptions to the generalization that engineering and technical occupations are geographically clustered; for example, civil engineers, accountants, and auditors are technical occupations, and they are dispersed. They also have rapidly absorbed highly educated women.

  11. 11.

    This approach would also build on research on the migration of college-educated couples into large metropolitan areas that may be amenable to supporting two careers (see, e.g., Costa and Kahn 2000).

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Acknowledgments

I thank Dan Fehder, Roberto Fernandez, Stephanie Hurder, Erin Kelly, George Lan, Colleen Manchester, Matt Marx, Paul Osterman, Gina Rumore, workshop participants at MIT and the Minnesota Population Center, IWER Workshop participants, and MPC Workshop participants for their helpful feedback. I also gratefully acknowledge support from the Minnesota Population Center (5R24HD041023). Replication materials are available upon request. The usual disclaimer applies.

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Benson, A. Rethinking the Two-Body Problem: The Segregation of Women Into Geographically Dispersed Occupations. Demography 51, 1619–1639 (2014). https://doi.org/10.1007/s13524-014-0324-7

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Keywords

  • Household mobility
  • Occupational segregation
  • Family