Comparing the Effectiveness of Assignment Strategies for Estimating Likely Undocumented Status in Secondary Data Sources for Latino and Asian Immigrants

Abstract

Researchers are increasingly interested in the role of undocumented status in immigrant economic, social, and health outcomes. A major obstacle to this work is that detailed immigration status is not widely collected in representative data sources. Some secondary data sources collect enough information to identify immigrants without a green card (non-LPRs), and researchers take different approaches to assign undocumented status to immigrants within this population. These approaches have not been compared to one another, nor do we know if they work equally well for Latino and Asian immigrants. In this research note, we test the validity of several assignment strategies using the 2001, 2004, and 2008 panels of the restricted version of the Survey of Income and Program Participation (SIPP) to measure differences in health-related outcomes (e.g., health insurance coverage and self-rated health) by immigration status. We compare results when immigration status is directly measured using the detailed information in the SIPP to several strategies to assign undocumented status among non-LPRs. The probabilistic approach produced the smallest biases, but Asian immigrants had larger biases compared to Latinos across all strategies.