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 USA.
Using an econometric methodology proposed by Lancaster and Imbens (J Econ 71:145–160, ) we compute the probability for each observation in the ACS data to refer to an illegal immigrant, conditional on observed characteristics. These results are novel, since no other work has quantified the characteristics of illegal immigrants from a random sample representative of the population. Using these conditional probability weights on the ACS data, we are able to uncover some interesting facts on illegal immigrants. We find that, while illegal immigrants suffer a large wage penalty compared to legal immigrants at all education levels, the penalty decreases with education. We also find that the total fertility rate among illegal immigrant women is significantly higher than that among legal ones, in particular for middle and higher educated women. Looking at the sector of activity, we document that the sectors attracting most illegal immigrants are constructions and agriculture. We also generate empirical distributions for state of residence, country of origin, age, sex, and number of legal and illegal immigrants. Our forecasts for the aggregate distribution of legal and illegal characteristics match imputations by the Department of Homeland Security.