CREAT: Census Research Exploration and Analysis Tool

Separate but Not Equal: The Uneven Cost of Residential Segregation for Network-Based Hiring

October 2024

Written by: Tam Mai

Working Paper Number:

CES-24-56

Abstract

This paper studies how residential segregation by race and by education affects job search via neighbor networks. Using confidential microdata from the US Census Bureau, I measure segregation for each characteristic at both the individual level and the neighborhood level. My findings are manifold. At the individual level, future coworkership with new neighbors on the same block is less likely among segregated individuals than among integrated workers, irrespective of races and levels of schooling. The impacts are most adverse for the most socioeconomically disadvantaged demographics: Blacks and those without a high school education. At the block level, however, higher segregation along either dimension raises the likelihood of any future coworkership on the block for all racial or educational groups. My identification strategy, capitalizing on data granularity, allows a causal interpretation of these results. Together, they point to the coexistence of homophily and in-group competition for job opportunities in linking residential segregation to neighbor-based informal hiring. My subtle findings have important implications for policy-making.

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.

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:
black, minority, ethnic, heterogeneity, hiring, segregated, segregation, disadvantaged, racial, race, residential, neighborhood, neighbor, residential segregation

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:
Internal Revenue Service, Columbia University, Decennial Census, Housing and Urban Development, Employer Identification Numbers, Unemployment Insurance, Alfred P Sloan Foundation, Longitudinal Employer Household Dynamics, Employment History File, Employer Characteristics File, Individual Characteristics File, Core Based Statistical Area, Census Bureau Disclosure Review Board, Disclosure Review Board, Indian Health Service, Federal Statistical Research Data Center

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