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A Framework to Develop Interventions to Address Labor Exploitation and Trafficking: Integration of Behavioral and Decision Science within a Case Study of Day Laborers

Author

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  • Matt Kammer-Kerwick

    (BBR, IC2 Institute, The University of Texas at Austin, 2815 San Gabriel Street, A0300, Austin, TX 78705, USA)

  • Mayra Yundt-Pacheco

    (BBR, IC2 Institute, The University of Texas at Austin, 2815 San Gabriel Street, A0300, Austin, TX 78705, USA)

  • Nayan Vashisht

    (BBR, IC2 Institute, The University of Texas at Austin, 2815 San Gabriel Street, A0300, Austin, TX 78705, USA)

  • Kara Takasaki

    (BBR, IC2 Institute, The University of Texas at Austin, 2815 San Gabriel Street, A0300, Austin, TX 78705, USA)

  • Noel Busch-Armendariz

    (IDVSA, Steve Hicks School of Social Work, The University of Texas at Austin, 1925 San Jacinto Blvd., Austin, TX 78712, USA)

Abstract

This paper describes a process that integrates behavioral and decision science methods to design and evaluate interventions to disrupt illicit behaviors. We developed this process by extending a framework used to study systems with uncertain outcomes, where only partial information is observable, and wherein there are multiple participating parties with competing goals. The extended framework that we propose builds from artefactual data collection, thematic analysis, and descriptive analysis, toward predictive modeling and agent-based modeling. We use agent-based modeling to characterize and predict interactions between system participants for the purpose of improving our understanding of interventional targets in a virtual environment before piloting them in the field. We apply our extended framework to an exploratory case study that examines the potential of worker centers as a venue for deploying interventions to address labor exploitation and human trafficking. This case study focuses on reducing wage theft, the most prevalent form of exploitation experienced by day laborers and applies the first three steps of the extended framework. Specifically, the case study makes a preliminary assessment of two types of social interventions designed to disrupt exploitative processes and improve the experiences of day laborers, namely: (1) advocates training day laborers about their workers’ rights and options that they have for addressing wage theft and (2) media campaigns designed to disseminate similar educational messages about workers’ rights and options to address wage theft through broadcast channels. Applying the extended framework to this case study of day laborers at a worker center demonstrates how digital technology could be used to monitor, evaluate, and support collaborations between worker center staff and day laborers. Ideally, these collaborations could be improved to mitigate the risks and costs of wage theft, build trust between worker center stakeholders, and address communication challenges between day laborers and employers, in the context of temporary work. Based on the application of the extended framework to this case study of worker center day laborers, we discuss how next steps in the research framework should prioritize understanding how and why employers make decisions to participate in wage theft and the potential for restorative justice and equity matching as a relationship model for employers and laborers in a well-being economy.

Suggested Citation

  • Matt Kammer-Kerwick & Mayra Yundt-Pacheco & Nayan Vashisht & Kara Takasaki & Noel Busch-Armendariz, 2023. "A Framework to Develop Interventions to Address Labor Exploitation and Trafficking: Integration of Behavioral and Decision Science within a Case Study of Day Laborers," Societies, MDPI, vol. 13(4), pages 1-31, April.
  • Handle: RePEc:gam:jsoctx:v:13:y:2023:i:4:p:96-:d:1115679
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    References listed on IDEAS

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    1. Charlotte C. Greenan, 2015. "Diffusion of innovations in dynamic networks," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(1), pages 147-166, January.
    2. Busby, J.S. & Onggo, B.S.S. & Liu, Y., 2016. "Agent-based computational modelling of social risk responses," European Journal of Operational Research, Elsevier, vol. 251(3), pages 1029-1042.
    3. Gomes, Sharlene L. & Hermans, Leon M. & Thissen, Wil A.H., 2018. "Extending community operational research to address institutional aspects of societal problems: Experiences from peri-urban Bangladesh," European Journal of Operational Research, Elsevier, vol. 268(3), pages 904-917.
    4. Utomo, Dhanan Sarwo & Onggo, Bhakti Stephan & Eldridge, Stephen, 2018. "Applications of agent-based modelling and simulation in the agri-food supply chains," European Journal of Operational Research, Elsevier, vol. 269(3), pages 794-805.
    5. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    6. Seo-Young Cho & Axel Dreher & Eric Neumayer, 2014. "Determinants of Anti-Trafficking Policies: Evidence from a New Index," Scandinavian Journal of Economics, Wiley Blackwell, vol. 116(2), pages 429-454, April.
    7. Sheldon X. Zhang & Michael W. Spiller & Brian Karl Finch & Yang Qin, 2014. "Estimating Labor Trafficking among Unauthorized Migrant Workers in San Diego," The ANNALS of the American Academy of Political and Social Science, , vol. 653(1), pages 65-86, May.
    8. McLane, Adam J. & Semeniuk, Christina & McDermid, Gregory J. & Marceau, Danielle J., 2011. "The role of agent-based models in wildlife ecology and management," Ecological Modelling, Elsevier, vol. 222(8), pages 1544-1556.
    9. Luca Coscieme & Paul Sutton & Lars F. Mortensen & Ida Kubiszewski & Robert Costanza & Katherine Trebeck & Federico M. Pulselli & Biagio F. Giannetti & Lorenzo Fioramonti, 2019. "Overcoming the Myths of Mainstream Economics to Enable a New Wellbeing Economy," Sustainability, MDPI, vol. 11(16), pages 1-17, August.
    10. Sean M. Crotty, 2017. "Can the Informal Economy Be “Managed†?: Comparing Approaches and Effectiveness of Day†Labor Management Policies in the San Diego Metropolitan Area," Growth and Change, Wiley Blackwell, vol. 48(4), pages 909-941, December.
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    1. Kirsten Foot & Marcel Van der Watt & Elizabeth Shun-Ching Parks, 2023. "Special Issue “Frontiers in Organizing Processes: Collaborating against Human Trafficking/Modern Slavery for Impact and Sustainability”," Societies, MDPI, vol. 13(4), pages 1-3, April.

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