Finding Labor Trafficking Violations Among Migrant Farmworkers: A GIS-Enabled Multi-Stage Sampling

• In Event: Detecting and Measuring Human Trafficking Violations

Abstract

This paper presents preliminary findings on labor trafficking violations from a sample of migrant farmworkers recruited through a state-wide multistage probability-based sampling. Research on human trafficking faces many methodological challenges, one of which is how to obtain a fair sample of the “hidden” populations where victimization can be measured and estimated. Some progress has been made in recent years applying different methods and field techniques. Some have produced impressive results. However many of these methods cannot adequately address the fact that trafficking victims are highly skewed in their distributions within informal or unregulated economies. In this project, our team focuses on one particular labor sector, migrant farmworkers in North Carolina, and applies an innovative GIS-based enumeration strategy to build a reasonable sampling frame for multistage probability-based sampling. With a probability sample, we will be able to derive reasonable estimates of the scope and nature of labor trafficking violations among this population.

Authors

• Sheldon Zhang, San Diego State University

• Kelle Barrick, RTI International

• Wayne Pitts, RTI International