Abstract
This study examines the immigrant-native wage gap in the United States using 2019 Annual Social and Economic Supplement data from the Current Population Survey. Ordinary least squares regression analyses were used to estimate the wages of immigrants while controlling for human capital characteristics, sociodemographic factors, and time since arrival in the United States. The results demonstrate that the wage gap converges with more time spent in the United States, likely due to increased work experience and language proficiency, but does not completely diminish. Separate regressions are conducted for different occupational groups. Occupations are shown to be a large contributor to the differential, as wages differ significantly more in unskilled occupations such as manual labor or transportation. The possibility of labor market discrimination is explored as a reason for the wage gap, with the Blinder-Oaxaca decomposition showing that only 68% of the wage gap can be explained by the included explanatory variables.
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Notes
The sampling weight used by the Census Bureau ensures that the data are representative of the population and corrects for nonresponse and under coverage (U.S. Census Bureau, 2015).
Results of the log-level regression estimation are available from the author upon request.
Individual tests for multicollinearity (utilizing variance inflation factors) and heteroskedasticity were conducted for all analyses. The results indicated no issues with multicollinearity and robust-standard errors were used to account for heteroskedasticity.
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Acknowledgements
I offer special thanks to Dr. Iris Buder for her valuable feedback and support, as well as the International Atlantic Economic Society for arranging the Best Undergraduate Paper Award Competition.
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Donahue, G. Equal Convergence? Convergence Patterns Among Immigrants by Occupation. Atl Econ J 49, 293–304 (2021). https://doi.org/10.1007/s11293-021-09728-z
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DOI: https://doi.org/10.1007/s11293-021-09728-z