An Alternative Approach for Identifying a Hidden Immigrant Population

We propose an alternative method for identifying undocumented immigrants in public use household data. Our approach departs from the commonly used likely unauthorized (LU) method in that we do not resort to classifying individuals on the basis of their specific geographic origin. We instead consider health insurance status of different family members coupled with the Affordable Care Act insurance mandates, referring to this strategy for sorting individuals by documentation status as the Affordable Care Mandate (ACM) approach. The LU method is generally limited to identifying individuals originating from Latin America. By contrast, the ACM approach enables us to identify individuals who are apparently undocumented and originate from all regions of the world. We also compare our results to the Reconstructed Pew Algorithm (RPA). The RPA is not limited in terms of which adults it can classify, but it does involve making a larger number of judgment calls with respect to the final design of the algorithm. The ACM has its limits in that it can only be applied to individuals who have US-born children who currently reside in the household. Using the 2015–2017 March Supplements to the CPS, we test the ACM against the LU and RPA approaches by estimating simple models of labor force participation and fertility by presumed legal status. Using the alternative methods, we obtain similar, though not identical, results and argue that the ACM approach holds promise for studying the behavior of undocumented immigrants that does not resort to ethnic profiling and uses fewer judgment calls.