This article explores racial stratification in work environments. Inequality scholars have long identified racial disparities in wage and occupational attainment, but workers’ careers and well-being are also shaped by elements of their work environment, including firm culture, managerial style, and work-life balance. I theorize two processes that could lead to racial inequality in firms’ work environments: (1) employee sorting due to exclusionary practices, and (2) spillover from racial differences in occupation and geographic location. To test this, I gathered a unique firm-level dataset composed of one million employee reviews, covering most large and medium-sized firms in the United States. I show that firms with more Black employees score lower for managerial quality, firm culture, and work-life balance, and firms with more Asian employees score higher on these dimensions. However, Asian employees’ advantage disappears when controlling for occupation, industry, and geography, whereas Black employees’ disadvantage persists, suggesting that the process of firm-level employee sorting is at work. Consistent with this, I find that Black employees’ disadvantage is strongest in areas with more conservative racial attitudes and more prevalent workplace racial discrimination. I then replicated the main findings using two entirely different data sources. Together, these results underscore racial inequality in work environments, an overlooked but important dimension of workplace inequality.

The presence of income inequality is an important problem to demographers, policy makers, economists, and social scientists. A causal link has been hypothesized between income inequality and income segregation, which measures how much households with similar incomes cluster. The information theory index is used to measure income segregation, however, critics have suggested the divergence index instead. Motivated by this, we construct both indices using American Community Survey (ACS) estimates of features of the income distribution. Since the elimination of the decennial census long form, methods of computing these indices must be updated to interpolate ACS estimates and account for survey error. We propose a novel model-based method to do this which improves on previous approaches by using more types of estimates, and by providing uncertainty quantification. We apply this method to estimate U.S. census tract-level income distributions, and in turn use these to construct both income segregation indices. We find major differences between the two indices and find evidence that the information index underestimates the relationship between income inequality and income segregation. The literature suggests interventions designed to reduce income inequality by reducing income segregation, or vice versa, so using the information index implicitly understates the value of these interventions. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.