American Political Science Review

Research Article

Attitudes toward Highly Skilled and Low-skilled Immigration: Evidence from a Survey Experiment


a1 Massachusetts Institute of Technology

a2 Harvard University


Past research has emphasized two critical economic concerns that appear to generate anti-immigrant sentiment among native citizens: concerns about labor market competition and concerns about the fiscal burden on public services. We provide direct tests of both models of attitude formation using an original survey experiment embedded in a nationwide U.S. survey. The labor market competition model predicts that natives will be most opposed to immigrants who have skill levels similar to their own. We find instead that both low-skilled and highly skilled natives strongly prefer highly skilled immigrants over low-skilled immigrants, and this preference is not decreasing in natives' skill levels. The fiscal burden model anticipates that rich natives oppose low-skilled immigration more than poor natives, and that this gap is larger in states with greater fiscal exposure (in terms of immigrant access to public services). We find instead that rich and poor natives are equally opposed to low-skilled immigration in general. In states with high fiscal exposure, poor (rich) natives are more (less) opposed to low-skilled immigration than they are elsewhere. This indicates that concerns among poor natives about constraints on welfare benefits as a result of immigration are more relevant than concerns among the rich about increased taxes. Overall the results suggest that economic self-interest, at least as currently theorized, does not explain voter attitudes toward immigration. The results are consistent with alternative arguments emphasizing noneconomic concerns associated with ethnocentrism or sociotropic considerations about how the local economy as a whole may be affected by immigration.


Both authors are affiliated with Harvard's Institute for Quantitative Social Science (IQSS) which generously provided funding for the survey. We thank Alberto Abadie, George Borjas, Giovanni Facchini, Gordon Hanson, Gary King, David Lynch, Anna Mayda, Dani Rodrik, Ken Scheve, Matthew Slaughter, Dustin Tingley, the co-editors, and five anonymous reviewers for very helpful comments. The usual disclaimer applies.