Identifying ethnic occupational segregation

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 centur...

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Bibliographic Details
Published in:Journal of population economics 2022-07, Vol.35 (3), p.1261-1296
Main Authors: Xu, Dafeng, Zhang, Yuxin
Format: Article
Language:English
Subjects:
Censuses
Classification
Cognitive style
Demography
Economics
Economics and Finance
Ethnic groups
Ethnicity
Heterogeneity
Immigrants
Jewish people
Labor Economics
Labor market
Machine learning
Minority & ethnic groups
Multiculturalism & pluralism
Native language
Occupational segregation
Occupations
Original Paper
Population
Population Economics
Segregation
Social Policy
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Summary: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.
ISSN:0933-1433
1432-1475
DOI:10.1007/s00148-020-00796-0