Abstract
Racial segregation has long characterized urban life in the U.S., with research consistently showing that minority groups occupy different social spaces than whites. While past scholarship has focused largely on residential contexts, a considerable portion of individuals’ days is spent outside of the home and existing research misses the potential for cross-group contact in non-residential contexts. In this paper, we assess the levels and patterns of segregation in the environments where people spend their workday, for white, black, Hispanic, and Asian workers. Using commuting data from the Census Transportation Planning Package, we construct measures of racial composition in “workhoods” and compare metropolitan-level segregation in places of work and home. Results indicate that workhood segregation is substantially lower than residential segregation. Black-white segregation in work settings is, for example, half the level of black-white segregation in residential settings. Multivariate analyses also reveal that workhood segregation, for all groups, is higher in metropolitan areas with greater residential segregation. For Hispanic workers, areas with larger immigrant populations have higher workhood segregation, and for blacks, workhood segregation is lower in metropolitan areas with large military populations. Our findings also consistently show that black and Hispanic workhood segregation is lower in areas where minority groups are more occupationally disadvantaged.
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Notes
The main assumption of this operationalization is that workers are present in a workhood at the same time during the day and that they have no contact with non-working persons during the workday. This is clearly a strong assumption to make, so we considered multiple alternative measures of the workhood that incorporate residential populations plausibly present during the workday: unemployed and out-of-the-labor-force adults, elderly persons, and school-aged children. The inclusive approach is similar to the Census Bureau’s “daytime” population estimates (McKenzie et al. 2013) but is less ideal for the purposes of this project given that non-workers and children are not strictly home-based (e.g., many non-working adults have responsibilities that require them to leave their residential neighborhood and children are likely to be in schools that may be located outside of their census tracts of residence). Nevertheless, segregation scores based on alternative conceptualizations of the workhood are shown in Appendix Table 5. Our conclusions do not change when using alternative definitions of the workhood population.
Dissimilarity scores for workers are moderately lower than for the total population. In 2010, black-white residential dissimilarity for workers was 60.4 and, for all residents, it was 64.9 (see Appendix Table A1). The two measures are, however, nearly perfectly related (r = 0.98).
For disclosure protection, the Census imposes rounding rules on CTPP population estimates (e.g., 0 kept as 0; values between 1 and 7 rounded to 4; values above 7 rounded to the nearest multiple of 5) (Srinivasan 2004). We simulated the impact of these rounding rules (available on request) on residential data and found these rules produce segregation scores that are virtually identical to scores based on unrounded data, suggesting that the CTPP rounding rules are unlikely to influence our results.
The index of net difference (ND) is an ordinal measure of occupational differentiation and measures the extent to which a randomly selected minority worker is likely to be working in a higher- or lower-ranked job than a randomly selected white worker. It ranges from − 1 to + 1, with negative values indicating that minority workers tend to work in higher-ranking jobs than whites; positive values implying that minorities work in lower-ranking jobs; and a value of 0 meaning that they work in equally ranked jobs. For our measure, we rank jobs based on reported wages in the 2006–2010 ACS PUMS and then map these to the Census Bureau’s 2006–2010 EEO tabulations of workers in specific occupations to summarize occupational inequality from whites for each metropolitan area. In our analytic sample, the black-white ND scores are positive for all metropolitan areas indicating that white workers are more likely to be employed in higher-ranked occupations than are black workers in every metropolitan area.
In supplemental analysis, we considered a range of additional metropolitan-level correlates including measures of modes of transportation (e.g., percent of workers that drive to work), industrial heterogeneity, cost of living, and residential and occupational sprawl and density. None of these measures explained substantively meaningful variation in workhood segregation and their inclusion does not alter the point estimates shown in Table 4.
Given well-known regional differences in residential segregation, we also considered statistical controls for Census region. In the final models, the coefficients on these terms never reached statistical significance (at 10% level) and their inclusion did not meaningfully alter the estimates of other variables.
Corresponding models of residential dissimilarity are shown in Appendix Table 6, and indicate that the correlates predict residential segregation in the expected directions.
Parallel estimates for 2000 data are shown in Appendix Table 7.
The correlations between occupational inequality and group income ratios are moderate (for blacks, r = − 0.63; for Hispanic, r = − 0.42; and for Asians, r = − 0.24) but variance inflation statistics are low (e.g., \(\sqrt {\text{VIF}} = 1.57\) for black occupational inequality), suggesting that multicollinearity is not a serious threat to estimation of these models. In addition, we considered—in supplemental analysis—several alternative SES measures, including indicators of occupational segregation (D), group educational attainment, and group occupational concentration. These measures all produce substantively similar findings indicating that workhood segregation between blacks and whites is lower in areas with more disadvantaged black populations.
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Acknowledgements
We are grateful to Steven Alvarado, Mike Bader, Kendra Bischoff, Erin York Cornwell, Anna Haskins, Dan Lichter, Brian McKenzie, Jeremy Pais, Jake Rugh, Laura Tach, and Nate Walters for comments on earlier versions of the paper.
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Hall, M., Iceland, J. & Yi, Y. Racial Separation at Home and Work: Segregation in Residential and Workplace Settings. Popul Res Policy Rev 38, 671–694 (2019). https://doi.org/10.1007/s11113-019-09510-9
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DOI: https://doi.org/10.1007/s11113-019-09510-9