Identifying ethnic occupational segregation
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Identifying ethnic occupational segregation Dafeng Xu1
· Yuxin Zhang2
Received: 26 April 2019 / Accepted: 15 August 2020 / © Springer-Verlag GmbH Germany, part of Springer Nature 2020
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 Russianborn 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 JEL Classification J1 · N3 · R2
Responsible editor: Klaus F. Zimmermann Yuxin Zhang
yuxin [email protected] Dafeng Xu [email protected] 1
University of Washington, Evans School of Public Policy, Governance, 4105 George Washington Lane Northeast, Seattle, WA, 98105, USA
2
Mike Ilitch School of Business, Wayne State University, 2771 Woodward Avenue, Detroit, MI, 48201, USA
D. Xu, Y. Zhang
1 Introduction Scholars have long observed that US immigrants have unique labor market patterns (e.g., Borjas 1986; Yuengert 1995), and different immigrant populations also have their own labor market patterns (e.g., Fairlie and Meyer 1996). Many studies consider an immigrant population (defined by country of birth) as a whole and use country of birth interchangeably with ethnicity. However, ethnicity actually means a category of people classified on the basis of a common genealogy/ancestry (Chibnik 1991) or culture (Constant et al. 2008; Constant and Zimmermann 2008; 2009). Indeed, economists present empirical evidence that some ethnic sub-populations have unique economic outcomes: for example, Kalnins and Chung (2006) observe that social capital plays a unique role in the US lodging industry among Gujarati immigrants, Moser et al. (2014) find positive effects of the influx of German Jews on R&D in the USA, and O’Keefe and Quincy (2018) show that Jewish immigration from Russia affected native-born farmers in New Jersey in the early twentieth century. In this paper, we first present a machine learning–based algorithm of ethnic classification and then analyze occupational segregation by ethnicity among Russian-born immigrants in the USA
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