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Not Equal for All: Gender and Race Differences in Salary for Doctoral Degree Recipients

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Abstract

Despite a recent increase in women and racial/ethnic minorities in U.S. postsecondary education, doctoral recipients from these groups report lower salaries than male and majority peers. With a longitudinal sample of approximately 10,000 respondents from the Survey of Doctorate Recipients, this study adds to the limited literature examining the effects of discipline, sector of employment, personal traits (e.g., marital status and number of children), and the interaction of gender and race on annual salary over the decade after degree completion, 1999–2008. Multilevel growth models reveal greater gaps in salary for women compared to men across all race/ethnic groups. The greatest rate of return was found for Asian respondents regardless of gender, and minority males had better returns than White male peers conditional on marriage. Implications for career choice, career paths, and the need for policies that address gender and race equity are discussed.

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Notes

  1. The term URM (underrepresented minority) includes participants who identified themselves belonging to Black, Hispanic, or Native American race/ethnic groups. This definition is in accordance with recent NSF reports (NSF Report 13-204, 2013).

  2. Consistent with NSF surveys, the disciplines included in our study accounted for individuals majoring in Agriculture and Biological Sciences, Computer Sciences, Mathematics, Statistics, Physical Sciences, Psychology, Engineering, and Health Sciences.

  3. Missing data accounted for 1.18 % of the total cases. In addition to the bivariate analyses, models excluding the variables with missing cases were fitted and the magnitude of the coefficients associated with variables with no missing cases remained unchanged. As such, we are confident that missing cases do not affect inferences made from these models, as that missing data appear to have happened at random (Gelman, Carlin, Stern, & Rubin, 2003). To comply with NSF standards, all numbers reported herein are rounded.

  4. The annual wage was captured in constant dollars. Models with current dollars rendered the same inferences.

  5. As Table 3 indicates (congruent with previous studies on doctorate recipients), Hispanic, African American, and Native American participants are the least represented groups in the analytic sample. This sample size limitation greatly affected the standard errors of the coefficients corresponding to models disaggregated by Hispanic males, Hispanic females, African American males, African American females, Native American males and Native American females. Based on the widely accepted definition of URMs provided by NSF, we proceeded to estimate models for URM males and URM females in order to capture potential differences in compensation across underrepresented groups. Although the aggregation of Hispanic, African American, and Native American participants could potentially be a source of bias, models where the disaggregation across those groups took place were also conducted and the magnitude of the coefficients were similar across them. As mentioned, the main differences found were the standard errors (due to sample size differences) of the coefficients across models. Table 4 shows that Native American males have the highest difference in magnitude when compared to White males. To test whether this difference would have changed the results shown in the URM male model shown in Table 5, we omitted Native Americans from that estimation. The magnitude of the coefficients was not affected, which justified the inclusion of Native American males in the final set of analyses.

  6. Analyses with sample weights were conducted, results were congruent and are available upon request.

  7. The term URM includes participants who identified themselves belonging to Black, Hispanic, or Native American race/ethnic groups.

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Authors and Affiliations

  1. Institute of Higher Education, The University of Georgia, 212 Meigs Hall, Athens, GA, 30602, USA

    Karen L. Webber & Manuel González Canché

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  1. Karen L. Webber
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  2. Manuel González Canché
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Correspondence to Karen L. Webber.

Appendix

Appendix

See Table 7.

Table 7 Models used for analyses
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Webber, K.L., Canché, M.G. Not Equal for All: Gender and Race Differences in Salary for Doctoral Degree Recipients. Res High Educ 56, 645–672 (2015). https://doi.org/10.1007/s11162-015-9369-8

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