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The food and agriculture industries are critical to the U.S. economy, ensuring the daily food supply while facing significant challenges. These issues include ethical concerns related to labor exploitation and the need to improve resilience against disruptions. Addressing these issues offers an opportunity to create supply chains that are both more ethical and more resilient. This dissertation focuses on two interconnected aspects of agricultural supply chains. The first examines strategies for disrupting exploitative labor practices and ensuring better protection for farm workers. The second explores methods to enhance the resilience of ethical supply chains against various disruptions, including natural disasters and labor shortages. Together, these aspects aim to contribute to the development of agricultural supply chains that are both ethically sound and resilient to disruptions. Although farm workers play an essential role in the success of these industries, they are vulnerable to labor exploitation and trafficking. Labor violations affecting these workers often go undetected due to limited government resources for inspection. Furthermore, many farm workers face barriers to disclosing their poor working conditions due to their immigration status and mistrust of law enforcement, making them even more susceptible to exploitation. To address this issue, we conducted research to provide strategies for government agencies involved in the H-2A visa program and the screening of H-2A employers to prioritize workplaces for inspection. In the first study, we employed multilevel zero-inflated negative binomial regression analysis to extract patterns and identify factors correlated with detecting H-2A labor violations. We provide suggestions for improving inspection strategies based on our research results. This involved identifying high-risk locations and labor-intensive worksites with a greater likelihood of labor violations and emphasizing the importance of allocating sufficient task force funding and resources to prioritize inspections in these areas. Labor trafficking networks in U.S. agricultural supply chains exploit vulnerable workers, including migrants and unauthorized laborers, while evading detection through complex structures, making them difficult to disrupt. In the second study, we developed a comprehensive labor trafficking network model that maps the intricate connections and operations of these networks. Using a bi-level integer programming approach, we optimized intervention strategies to disrupt trafficking operations, balancing resource constraints with the need for maximum impact. By employing K-means clustering, we classified interventions based on their effectiveness, providing clear, data-driven guidance for anti-trafficking agencies to prioritize efforts and allocate resources efficiently. This approach offers a powerful tool for enhancing detection and improving the overall effectiveness of anti-trafficking initiatives in limited resource environments. The importance of food and agricultural supply chains in our daily lives cannot be emphasized enough. While the prior two studies sought to disrupt exploitative work conditions in agricultural supply chains, this dissertation also seeks to help supply chains that are operating ethically do so in an effective manner. Any disruption in these chains can lead to severe consequences, from food shortages to economic instability. Therefore, it is critical to develop effective strategies to mitigate the impact of disruptions in these non-exploitative supply chains. In the third study, we developed a scenario-based two-stage stochastic model to mitigate the impact of multiple disruptions in agricultural supply chains. This approach enables a detailed evaluation of strategies such as multi-sourcing and the use of backup facilities to reduce disruption impacts. The model incorporates flexibility to handle both partial and full facility disruptions, while accounting for disruptions affecting both primary and backup facilities to provide a comprehensive analysis of supply chain vulnerability and recovery. By employing a multi-period time horizon, the model evaluates supply chain performance over time, considering random disruption start times and the possibility of simultaneous disruptions across multiple echelons with varying severity. The analysis highlights the challenges posed by multiple sources of uncertainty in supply chain decision-making and emphasizes the need for further research to develop actionable strategies for improving resilience in agricultural supply chains.
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Enhancing detection of labor violations in the agricultural sector: A multilevel generalized linear regression model of H-2A violation counts
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’s 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 offer valuable insights into where H-2A violations are being detected at the state and industry levels.
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- Award ID(s):
- 1935618
- PAR ID:
- 10585049
- Editor(s):
- Abonazel, Mohamed R
- Publisher / Repository:
- PLOS ONE
- Date Published:
- Journal Name:
- PLOS ONE
- Volume:
- 19
- Issue:
- 5
- ISSN:
- 1932-6203
- Page Range / eLocation ID:
- e0302960
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1371/journal.pone.0302960
