Empirical Characteristics of Legal and Illegal Immigrants in the U.S

39 Pages Posted: 6 Apr 2013

See all articles by Vincenzo Caponi

Vincenzo Caponi

Ryerson University - Department of Economics; National Institute of Statistics and Economic Studies (INSEE) - Center for Research in Economics and Statistics (CREST); École Nationale de la Statistique et de l'Analyse de l'Information (ENSAI); Institute for the Study of Labor (IZA); University of Kiel - Institute for World Economics (IfW)

Miana Plesca

University of Guelph - Department of Economics

Abstract

We combine the New Immigrant Survey (NIS), which contains information on US legal immigrants, with the American Community Survey (ACS), which contains information on legal and illegal immigrants to the U.S. Using econometric methodology proposed by Lancaster and Imbens (1996) we compute the probability for each observation in the ACS data to refer to an illegal immigrant, conditional on observed characteristics. The results for illegal versus legal immigrants are novel, since no other work has quantified the characteristics of illegal immigrants from a random sample.We find that, compared to legal immigrants, illegal immigrants are more likely to be less educated, males, and married with their spouse not present. These results are heterogeneous across education categories, country of origin (Mexico) and whether professional occupations are included or not in the analysis. Forecasts for the distribution of legal and illegal characteristics match aggregate imputations by the Department of Homeland Security. We find that, while illegal immigrants suffer a wage penalty compared to legal immigrants, returns to higher education remain large and positive.

Keywords: legal immigrants, illegal immigrants, contaminated controls

JEL Classification: J15, F22

Suggested Citation

Caponi, Vincenzo and Caponi, Vincenzo and Plesca, Miana, Empirical Characteristics of Legal and Illegal Immigrants in the U.S. IZA Discussion Paper No. 7304, Available at SSRN: https://ssrn.com/abstract=2245974 or http://dx.doi.org/10.2139/ssrn.2245974

Vincenzo Caponi (Contact Author)

National Institute of Statistics and Economic Studies (INSEE) - Center for Research in Economics and Statistics (CREST) ( email )

15 Boulevard Gabriel Peri
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Ryerson University - Department of Economics ( email )

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HOME PAGE: http://www.caponi.ca

École Nationale de la Statistique et de l'Analyse de l'Information (ENSAI) ( email )

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Institute for the Study of Labor (IZA) ( email )

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University of Kiel - Institute for World Economics (IfW) ( email )

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Germany

Miana Plesca

University of Guelph - Department of Economics ( email )

50 Stone Road East
Guelph, Ontario N1G 2W1
Canada

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