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Agricultural workers are essential to the supply chain for our daily food andyet, many face harmful work conditions, including garnished wages, and otherlabor violations. Workers on H-2A visas are particularly vulnerable due to theprecarity of their immigration status being tied to their employer. Althoughworksite inspections are one mechanism to detect such violations, many laborviolations affecting agricultural workers go undetected due to limitedinspection resources. In this study, we identify multiple state and industrylevel factors that correlate with H-2A violations identified by the U.S.Department of Labor Wage and Hour Division using a multilevel zero-inflatednegative binomial model. We find that three state-level factors (average farmacreage size, the number of agricultural establishments with less than 20employees, and higher poverty rates) are correlated with H-2A violations. Thesefindings provide guidance for inspection agencies regarding how to prioritizetheir limited resources to more effectively inspect agricultural workplaces,thereby improving workplace conditions for H-2A workers.

Enhancing detection of labor violations in the agricultural sector: A multilevel generalized linear regression model of H-2A violation counts