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

TL;DR

This paper develops a multilevel statistical model to identify key state and industry factors associated with H-2A labor violations in agriculture, aiming to improve inspection resource allocation and worker protections.

Contribution

It introduces a multilevel zero-inflated negative binomial model to analyze factors linked to H-2A violations, offering new insights for targeted inspections.

Findings

State-level factors like farm size and poverty rates are linked to violations.

The model helps prioritize inspection efforts based on identified risk factors.

Results can guide policy to better protect vulnerable agricultural workers.

Abstract

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