Estimating Labor Trafficking Among Farmworkers: An Inverse Sampling Strategy Based on Reliable Housing Predictions

Authors: Pitts, Wayne; Barrick, Kelle; Zhang, Sheldon & Lattimore, Pamela

Abstract: The lack of knowledge on the magnitude of the human-trafficking problem regionally and internationally remains the weakest link in the current countertrafficking movement and has given rise to estimates and unsubstantiated claims. However, difficult-to-count populations such as trafficking victims pose significant sampling challenges for social scientists because a sampling frame cannot be reliably established. As a result, field researchers often choose to accept nonparametric strategies such as convenience sampling at the cost of weakened generalizability and inferential potential. We launched a pilot in 2013 to test whether a new sampling frame enumeration strategy, based on Global Positioning System (GPS)-enabled field inspections, would be economically feasible and methodologically reliable. The pilot study included 543 dwellings randomly drawn from U.S. Census block and block clusters in four North Carolina counties. Findings support the rationale behind our field enumeration strategies and its cost effectiveness in establishing a sound sampling frame for our target population. We believe that this sampling strategy may offer considerable value for future fieldwork among the difficult-to-count migrant farmworker population.

Keywords: GeoFrame™ field enumeration, labor trafficking, migrant farm workers, probability sampling design, trafficking-prevalence estimates