Detecting Imperfect Substitution between Comparably Skilled Immigrants and Natives: A Machine Learning Approach

Abstract

Immigration economists often disagree about whether comparably skilled immigrants and natives are perfect substitutes in the United States and other developed countries, leading these scholars to different assessments of the labor market impacts of immigration and policy recommendations. This article attempts to provide theoretical bases for understanding the immigrant-native substitution and to introduce machine learning techniques to resolve the empirical debate. Using the male subsample from the US Census and American Community Survey, it shows that the difference in covariate selection explains substantial disagreements in estimating immigrant-native substitution. Given the difficulties in providing compelling theoretical justifications for covariates selected, this article proposes estimating via the Lasso-type (least absolute shrinkage and selection operator) estimators. My Lasso-based estimation rejects perfect substitution, but it also implies easier substitution than that preferred by Ottaviano and Peri, suggesting more direct immigrant-native competition. By extending the sample to women, I find similar immigrant-native substitution across gender. Therefore, this article casts doubt on previous immigration impact assessments. Indeed, my simulation suggests considerable precision gains concerning the immigration's wage impacts on immigrants themselves. Furthermore, this article identifies immigrant segregation as a critical source of the national-level imperfect substitution, which decreases within progressively smaller regions and almost disappears in the same city. By introducing the Lasso-type estimators into migration studies, this article makes solid progress toward evaluating and understanding imperfect immigrant-native substitution and its socioeconomic consequences.