We study workplace segregation in the United States using a unique matched employer-employee data set that we have created. We present measures of workplace segregation by education and language, and by race and ethnicity, and we assess the role of education- and language-related skill differentials in generating workplace segregation by race and (Hispanic) ethnicity. Our results indicate that there is considerable segregation by race, ethnicity, education, and language in the workplace. Only a tiny portion of racial segregation in the workplace is driven by education differences between blacks and whites, but a substantial fraction of ethnic segregation in the workplace can be attributed to differences in English-language proficiency. Finally, additional evidence suggests that segregation by language likely reflects complementarity among workers speaking the same language.

In this paper we analyze the estimation of coefficients in regression models under moment restrictions in which the moment restrictions are derived from auxiliary data. The moment restrictions yield weights for each observation that can subsequently be used in weighted regression analysis. We discuss the interpretation of these weights under two assumptions: that the target population (from which the moments are constructed) and the sampled population (from which the sample is drawn) are the same, and that these populations differ. We present an application based on omitted ability bias in estimation of wage regressions. The National Longitudinal Survey Young Men's Cohort (NLS)—in addition to containing information for each observation on wages, education, and experience—records data on two test scores that may be considered proxies for ability. The NLS is a small dataset, however, with a high attrition rate. We investigate how to mitigate these problems in the NLS by forming moments from the joint distribution of education, experience, and log wages in the 1% sample of the 1980 U.S. Census and using these moments to construct weights for weighted regression analysis of the NLS. We analyze the impacts of our weighted regression techniques on the estimated coefficients and standard errors of returns to education and experience in the NLS controlling for ability, with and without the assumption that the NLS and the Census samples are random samples from the same population.