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Occupational segregation by sexual orientation in the U.S.: exploring its economic effects on same-sex couples

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Abstract

This paper examines the importance of the occupational sorting of individuals in same-sex couples in explaining the economic position of lesbian women and gay men beyond controlling for occupation in the estimation of their respective wage gaps, as usually done in the literature. The analysis reveals that the distribution of partnered gay men across occupations brings them a monetary gain, with respect to the average wage of coupled workers, whereas the occupational sorting of partnered lesbian women only allows them to depart from the large losses that straight partnered women have. The results show that when controlling for educational achievements, immigration profile, racial composition, and age structure, the gain for gay men associated with their occupational sorting shrinks substantially. Moreover, the small gain that lesbian women derive from their distribution across occupations turns into an earning disadvantage when one controls for characteristics. This leaves them with a loss, with respect to the average wage of coupled workers, that is not too different from to the one partnered straight women have. It is their higher educational attainments and, to a lower extent, their lower immigration profile that protects workers in same-sex couples, revealing that gay men do not enjoy the privilege of straight partnered men and that lesbian women are not free from the mark of gender.

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Notes

  1. See Bianchi and Rytina (1986), Reskin et al. (2004), Levanon et al. (2009), Blau et al. (2013), and Alonso-Villar and Del Río (2017b), inter alia.

  2. Other investigations point out that the timing of the first same-sex sexual experience may affect individuals’ cognitive skills, although no consensus has been reached with respect to the sign of these effects (Baumle et al. 2009; Ueno et al. 2013).

  3. This approach is different from the one usually followed in the literature. Thus, for example, the popular index of dissimilarity would require to compare the occupational sorting of lesbian women with that of another group (straight women, straight men, or gay men).

  4. The total list includes 458 occupations but in 5 of them there is no employment during this period. The hourly wage of each worker is calculated by dividing the annual wage by the product of usual hours worked per week and weeks worked last year. Since the latter is an intervalled variable, we take the midpoint value.

  5. The characteristics of gay people in the ACS may be influenced by the fact that some individuals in same-sex households may not report the true information about their relationship with the householder (Berg and Lien 2009). This may cause some bias if individuals hiding a same-sex couple relationship are those whose attributes make them more vulnerable in case of disclosure. On the other hand, assortative matching may be different for homosexuals and heterosexuals (Schwartz and Graf 2009), which may also affect the characteristics of our couples.

  6. Something similar happens to unpartnered workers (see Tables 1 and 2).

  7. Unpartnered workers have a demographic composition that is clearly different from that of partnered workers, either heterosexuals or homosexuals. They are younger, have lower educational achievements, and a lower proportion of whites (see Table 7 in the Appendix). This is likely to explain some of the discrepancies that exist between the occupational sorting of this group and that of the other groups.

  8. Ψ1 and Ψ2 are the well-being gains due to the occupational sorting of the group, Ω1 and Ω2 are the well-being gains arising within occupations, and WAD1 and WAD2 are the total well-being gains, for ε = 1 and 2.

  9. To obtain the contribution of education, for example, we use the logit coefficients as follows: First, we calculate the prediction of \(\Pr (W = 1\left| z \right.)\) by assuming that all coefficients except those of education dummies are zero, and then we compare the conditional loss (gain) resulting from this counterfactual to the unconditional loss (gain) of the group. Next, we calculate the prediction of that probability while assuming zero coefficients for all covariates except for education and one other covariate (e.g., immigration). The resulting counterfactual is compared to the counterfactual where only immigration is taken into account. The analysis is repeated but with race (rather than immigration) as the other covariate accounted for, and so on. This is how we obtain the marginal contribution of education when this is the second factor we control for. We follow the same procedure while considering all possible sequences where education is the third (rather than the second) factor to change. By averaging over all possible marginal contributions of education, we compute the contribution of this covariate to explain the difference between conditional and unconditional losses (gains) of the group.

  10. Due to their small group size, Native Americans have been joined with the group of individuals from other races.

  11. In any case, the lesbian wage advantage seems to strongly depend, though, on the indicator used for sexual orientation and also on how labor intensity and experience are accounted for, whether the analyses take into account that lesbian women previously married to men may have different experiences than other lesbian women (Daneshvary et al. 2009), or even the household division of labor within same-sex female couples, with a “primary” earner and a “secondary” one (Schneebaum 2013). There are also recent works that show situations where lesbian women get lower wages than straight women (Curley 2018) and also lower economic outcomes when other dimensions are taken into consideration (harassment at work, difficulty in finding a job, stress, etc.), as is the case of young lesbian women in Australia (Carpenter 2008). Conducting an experiment based on job applications for clerical jobs in Austria, Weichselbaumer (2003) finds that there exists discrimination against lesbian women. Using a similar methodology, Drydakis (2011) also shows that low-qualified lesbian women in Greece have a lower probability to receive an invitation for an interview, and if they are hired, their wages are lower than those of straight women.

  12. These numbers can be seen in the Online Resource (Table A2). As also shown in that table, the role of the different factors is similar when exploring the EGap.

  13. Murray-Close and Schneebaum (2017) claim that having a bachelor’s degree brings this sexual minority advantages beyond the economic ones since it allows gay people to get into more tolerant workplaces.

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Acknowledgements

We gratefully acknowledge financial support from the Ministerio de Economía, Industria y Competitividad, the Agencia Estatal de Investigación, and Fondo Europeo de Desarrollo Regional (ECO2016-76506-C4-2-R and ECO2017-82241-R) and Xunta de Galicia (GRC 2015/014). We want to thank Lee Badgett, Randy Albelda, and Alyssa Schneebaum for helpful comments. We are also indebted to Carlos Gradín for technical discussions on the propensity score procedure. We thank the referees and editors of the journal for their insightful suggestions.

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  1. Departamento de Economía Aplicada, Facultade de Económicas, Universidade de Vigo, Campus Lagoas-Marcosende, 36310, Vigo, Spain

    Coral del Río & Olga Alonso-Villar

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  1. Coral del Río
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  2. Olga Alonso-Villar
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Correspondence to Olga Alonso-Villar.

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Table 7 and Fig. 10

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del Río, C., Alonso-Villar, O. Occupational segregation by sexual orientation in the U.S.: exploring its economic effects on same-sex couples. Rev Econ Household 17, 439–467 (2019). https://doi.org/10.1007/s11150-018-9421-5

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