Educational Variations in Cohort Trends in the Black-White Earnings Gap Among Men: Evidence From Administrative Earnings Data

Abstract

Despite efforts to improve the labor market situation of African Americans, the racial earnings gap has endured in the United States. Most prior studies on racial inequality have considered its cross-sectional or period patterns. This study adopts a demographic perspective to examine the evolution of earnings trajectories among white and black men across cohorts in the United States. Using more than 40 years of longitudinal earnings records from the U.S. Social Security Administration matched to the Survey of Income and Program Participation, our analyses reveal that the cohort trends in the racial earnings gap follow quite different patterns by education. Race continues to be a salient dimension of economic inequality over the life course and across cohorts, particularly at the top and the bottom of the educational distribution. Although the narrowing of the racial gap among high school graduates is in itself a positive development, it unfortunately derives primarily from the deteriorating economic position for whites without a college degree rather than an improvement in economic standing of their black counterparts.

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Notes

  1. 1.

    We also exclude a tiny fraction of respondents who received a Social Security disability benefit (either through the disability insurance or needs-based Supplemental Security Income programs) before age 25 using a merged administrative variable. To construct earnings trajectories, we also require individuals to have more than one year of positive earnings over their observation period.

  2. 2.

    As far as we are aware, this is the first study to look at racial gaps in annual earnings by cohort using the SIPP-matched data. Sakamoto et al. (2018) used similar data, but their work focused on cumulative 20-year earnings, used different methodologies, and did not examine cohort differences. Others have used these data to examine other aspects of earnings inequality, such as lifetime earnings by field of study (Kim et al. 2015), intergenerational income persistence (Dahl and DeLeire 2008; Mazumder 2005), and women’s life cycle employment patterns (Goldin and Mitchell 2017). Our use of these data is generally consistent with their work.

  3. 3.

    The SER file contains annual earnings covered by the Social Security program. See the online appendix, section B, for a description of earnings measures in the DER and SER files. We switched our earnings measure from the SER file to the DER file in 1980 (the first year available) onward because the DER includes earnings that are not limited to the Social Security taxable maximum as well as earnings from jobs not covered by Social Security. Importantly, we replicated our regression models using individuals’ Social Security earnings (from the SER) for the entire analysis, up to 2014 (instead of switching to earnings from the DER beginning in 1980). Results were unchanged. We include taxable earnings from self-employment in our earnings measure in order to observe earnings in the most comprehensive way. Annual self-employment income comes from IRS Form 1040 Schedule SE. Our models include a dummy variable for self-employment as a control.

  4. 4.

    We define high school graduates as those with 12 completed years of schooling and/or a high school diploma or G.E.D. In this paper, we consider a G.E.D. equivalent to a high school diploma.

  5. 5.

    In our context, the multilevel growth curve model refers to the random-effects model, where the random effects take the form of either random intercepts or random slopes. The model was fit via maximum likelihood estimation. We specify the residual term as an autoregressive process of order 1 (AR(1)): ϵit = ρϵi,t – 1 + vit. This specification implies that the residual term contains a nontransitory part and a transitory part: the coefficient ρ captures the dependence of the residual on the residual in the previous year. The latter term of the residual structure, vit, is assumed to be independently distributed at different ages. The results are also robust with and without the inclusion of SIPP weights (see online appendix, section H).

  6. 6.

    The baseline and additive models are presented in the online appendix, sections F and G. Because the additive models may mask important heterogeneity by race across educational groups, we focus on interactive growth curve models in our discussion.

  7. 7.

    Section I of the online appendix presents the predicted earnings trajectories with their 95% confidence intervals.

  8. 8.

    The narrowing racial gap in educational level, per se, will not affect our estimation of the racial earnings gap because we conduct our analyses separately for different education groups. However, the relative ranking of a given level of educational attainment may change over time as a result of the compositional changes within race-education groups.

  9. 9.

    The sample sizes for blacks without a high school diploma in our linked data are still quite small, typically less than 100 individuals at each age (see Tables D1, D2, and D3, online appendix). Larger sample sizes are needed to examine these potential mechanisms.

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Acknowledgments

Siwei Cheng acknowledges support from the Russell Sage Foundation. We thank Maria Abascal, Mike Hout, and Ted Mouw for comments on earlier versions of this article. This article was presented at the 2017 annual meeting of the Population Association of America and the 2017 annual meeting of the American Sociological Association. The views expressed in this article are those of the authors and do not represent the views of the Social Security Administration (SSA) or any federal agency. Access to SSA data linked to U.S. Census Bureau survey data is subject to restrictions. The data are accessible at a secured site and must undergo disclosure review before their release. For researchers with access to these data, our programs used in this analysis are available on request.

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Cheng, S., Tamborini, C.R., Kim, C. et al. Educational Variations in Cohort Trends in the Black-White Earnings Gap Among Men: Evidence From Administrative Earnings Data. Demography 56, 2253–2277 (2019). https://doi.org/10.1007/s13524-019-00827-w

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Keywords

  • Life course
  • Cohort trends
  • Racial and ethnic inequalities
  • Labor market
  • Administrative data