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

We combine the New Immigrant Survey (NIS), which contains information on US legal

immigrants, with the American Community Survey (ACS), which contains information on

all immigrants to the U.S., legal and illegal ones. 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 quanti?ed

the characteristics of illegal immigrants from a random sample. We ?nd that, compared to

legal immigrants, illegal immigrants are more likely to be less educated, males, and married

with 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 certain legal and illegal characteristics match

those available from other sources, such as aggregate imputations by the Department of

Homeland Security for illegal immigrants.