CREAT: Census Research Exploration and Analysis Tool

Workplace Segregation in the United States: Race, Ethnicity, and Skill

January 2007

Working Paper Number:

CES-07-02

Abstract

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 . since skill is often correlated with race and ethnicity we assess the role of education- and language-related skill differentials in generating workplace segregation by race and ethnicity. We define segregation based on the extent to which workers are more or less likely to be in workplaces with members of the same group, and we measure segregation as the observed percentage relative to maximum segregation. Our results indicate that there is considerable segregation by education and language in the workplace. Among whites, for example, observed segregation by education is 17% (of the maximum), and for Hispanics, observed segregation by language ability is 29%. Racial (blackwhite) segregation in the workplace is of a similar magnitude to education segregation (14%), and ethnic (Hispanic-white) segregation is somewhat higher (20%). Only a tiny portion (3%) 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 (32%) can be attributed to differences in language proficiency. Finally, additional evidence suggests that segregation by language likely reflects complementarity among workers speaking the same language.

Document Tags and Keywords

Keywords Keywords are automatically generated using KeyBERT, a powerful and innovative keyword extraction tool that utilizes BERT embeddings to ensure high-quality and contextually relevant keywords.

By analyzing the content of working papers, KeyBERT identifies terms and phrases that capture the essence of the text, highlighting the most significant topics and trends. This approach not only enhances searchability but provides connections that go beyond potentially domain-specific author-defined keywords.
:
employee, black, minority, ethnicity, ethnic, hispanic, immigrant, white, segregated, workplace, discrimination, workforce, segregation, occupation, racial, race, educated, socioeconomic

Tags Tags are automatically generated using a pretrained language model from spaCy, which excels at several tasks, including entity tagging.

The model is able to label words and phrases by part-of-speech, including "organizations." By filtering for frequent words and phrases labeled as "organizations", papers are identified to contain references to specific institutions, datasets, and other organizations.
:
Metropolitan Statistical Area, Characteristics of Business Owners, Bureau of Labor Statistics, Center for Economic Studies, New York Times, Harvard University, Federal Reserve Bank, WECD, Postal Service, Sample Edited Detail File, Business Register, National Institutes of Health

Similar Working Papers Similarity between working papers are determined by an unsupervised neural network model know as Doc2Vec.

Doc2Vec is a model that represents entire documents as fixed-length vectors, allowing for the capture of semantic meaning in a way that relates to the context of words within the document. The model learns to associate a unique vector with each document while simultaneously learning word vectors, enabling tasks such as document classification, clustering, and similarity detection by preserving the order and structure of words. The document vectors are compared using cosine similarity/distance to determine the most similar working papers. Papers identified with 🔥 are in the top 20% of similarity.

The 10 most similar working papers to the working paper 'Workplace Segregation in the United States: Race, Ethnicity, and Skill' are listed below in order of similarity.