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 quantied
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.