Snapshot Models of Undocumented Immigration

Area of Science:

• Demography
• Sociology
• Public Health

Background:

• Accurate estimation of the undocumented immigrant population is vital for understanding health and security risks.
• Existing U.S.-based surveys may underestimate population size due to sampling biases.

Purpose of the Study:

• To develop a data-driven model for estimating the size of the undocumented Mexican immigrant population in the U.S.
• To account for sampling biases inherent in studies of returned migrants.

Main Methods:

• Utilized data from the Mexican Migration Project (MMP), focusing on migrant sojourn dates and journey counts.
• Developed a statistical model to jointly analyze trip timing and duration, correcting for the bias of excluding individuals still in the U.S.
• Applied models to 30 annual random snapshot surveys of returned migrants from 1980 to 2016.

Main Results:

• Characterized undocumented migration flows, including single visits, repeat visits, and cessation of circular migration.
• Produced lower bound estimates for the undocumented immigrant population significantly larger than traditional estimates.
• Findings are broadly consistent with recent high-end estimates of the undocumented population.

Conclusions:

• The study provides a more robust methodology for estimating undocumented populations by addressing critical sampling biases.
• The findings highlight the need for revised population estimates to inform policy and risk assessment.
• This data-driven approach offers a valuable tool for ongoing demographic analysis of undocumented immigration.