Identifying ethnic occupational segregation

Dafeng Xu () and Yuxin Zhang ()
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Dafeng Xu: Evans School of Public Policy, Governance
Yuxin Zhang: Wayne State University

Journal of Population Economics, 2022, vol. 35, issue 3, No 14, 1296 pages

Abstract: Abstract Many studies consider occupational segregation among the immigrant population from a given birth country as a whole. This ignores potential ethnic heterogeneity within an immigrant population and may underestimate occupational segregation. We focus on Russian immigrants in the early twentieth century USA—then a major immigrant population with a high degree of ethnic diversity, including Russian, Jewish, German, and Polish ethnics—and study occupational segregation by ethnicity. We apply a machine learning ethnicity classification approach to 1930 US census data based on name and mother tongue. Using the constructed ethnicity variable, we show high degrees of occupational segregation by ethnicity within the Russian-born immigrant population in the USA. For example, Jews, German ethnics, and Polish ethnics were concentrated in trade, agriculture, and manufacturing, respectively. We also find evidence that Russian-born immigrants’ labor market outcomes were associated with networks measured by the spatial concentration of co-ethnics—particularly more established ones—but not by the concentrations of other ethnic groups.

Keywords: Immigration; Occupational segregation; Ethnicity; Network; Machine learning (search for similar items in EconPapers)
JEL-codes: J1 N3 R2 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s00148-020-00796-0

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