Abstract

This paper provides empirical estimates of the short-run impacts of immigration on the employment opportunities of US-born workers based on a novel sectoral approach. We focus on six economic sectors with low skill requirements and high shares of immigrant workers. Our inference is based on panel data at the metropolitan area-year level of aggregation. We instrument for the share of immigrants in a given sector using the share of immigrants in all other sectors of the economy. Our strategy yields conservative estimates of the effects of immigration on native labor outcomes because (i) movement of capital and native labor across metropolitan areas reduces the extent to which intercity comparisons can account for shocks to native employment conditions, and (ii) our instrument for the immigrant share likely remains correlated with sectoral native labor demand shocks, albeit less so than the sectoral immigrant share itself. We find evidence of negative short-run effects of immigration on native earnings in the construction, food service, and personal service sectors. Upper bounds on the annual earnings impact of a 10 percentage point increase in the share of immigrant workers range from -2.9% to -6.6%. Earnings impact estimates in other sectors are generally negative but not always statistically significant. We find negative and significant effects on the native employment rate across all six sectors.

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Zach Rutledge
Ph.D. Student, Agricultural and Resource Economics

Zach Rutledge studies academic journal articles and other relevant material to understand econometric modelling methodology for immigration-related research, use statistical software to process US Census data (including summary statistics, graphs, and regression analysis), develop econometric models to estimate the effect of immigration on wages and employment levels of construction workers in the US, and write research papers. His work consists of lengthy hours reading complicated economic journal articles, sitting at a computer writing computer code to clean and analyze data, and writing reports and research papers.

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