A Comparative Latent Class Analysis on Data Collection Methods on Labor Exploitation on Two Samples of Workers in the Costa Rican Fishing Industry.

• In Event: Frontiers of Research on Human Trafficking

Abstract

Due to the challenging nature of sampling exploited workers—a highly marginalized and often hard-to-reach population—scholars are often limited in their ability to standardize data collection efforts. Using two samples of survey data from workers in the Costa Rican fishing industry, we compare latent classes of exploitative labor between a subset of individuals surveyed through a proportional probability sample (PPS) and others surveyed using link tracing sampling (LTS).
Results suggest there are qualitative differences between the latent classes across the two differently sampled groups. Those sampled through PPS had their exploitation more clearly defined by wage-related issues of physical abuse, while those in the LTS group indicated more “traditional” labor trafficking markers. Despite these differences, latent classes largely looked similar, with each group having a more highly exploited class and a more minimally exploited class. This study suggests that sampling techniques may affect the broader understanding of the issue, but most of the variation of exploitative experiences may lie in idiosyncrasies of specific contexts or occupations.

Authors

• Stephen Abeyta, New York University

• Lauren Moton, New York University

• Meredith Dank, New York University