IDEAS home Printed from https://ideas.repec.org/a/gam/jsoctx/v13y2023i4p96-d1115679.html
   My bibliography  Save this article

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

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2075-4698/13/4/96/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2075-4698/13/4/96/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    8. 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.
    9. 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.
    10. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dutta, Amitava & Puvvala, Abhinay & Roy, Rahul & Seetharaman, Priya, 2017. "Technology diffusion: Shift happens — The case of iOS and Android handsets," Technological Forecasting and Social Change, Elsevier, vol. 118(C), pages 28-43.
    2. Oscar Gutiérrez & Francisco Ruiz-Aliseda, 2011. "Real options with unknown-date events," Annals of Finance, Springer, vol. 7(2), pages 171-198, May.
    3. Shari, Babajide Epe & Dioha, Michael O. & Abraham-Dukuma, Magnus C. & Sobanke, Victor O. & Emodi, Nnaemeka V., 2022. "Clean cooking energy transition in Nigeria: Policy implications for Developing countries," Journal of Policy Modeling, Elsevier, vol. 44(2), pages 319-343.
    4. Cambier, Adrien & Chardy, Matthieu & Figueiredo, Rosa & Ouorou, Adam & Poss, Michael, 2022. "Optimizing subscriber migrations for a telecommunication operator in uncertain context," European Journal of Operational Research, Elsevier, vol. 298(1), pages 308-321.
    5. Tiruwork B. Tibebu & Eric Hittinger & Qing Miao & Eric Williams, 2024. "Adoption Model Choice Affects the Optimal Subsidy for Residential Solar," Energies, MDPI, vol. 17(3), pages 1-19, February.
    6. Simon P. Anderson & André de Palma, 2012. "Competition for attention in the Information (overload) Age," RAND Journal of Economics, RAND Corporation, vol. 43(1), pages 1-25, March.
    7. Van, Tien Linh Cao & Barthelmes, Lukas & Gnann, Till & Speth, Daniel & Kagerbauer, Martin, 2021. "Addressing the gaps in market diffusion modeling of electrical vehicles: A case study from Germany for the integration of environmental policy measures," Working Papers "Sustainability and Innovation" S05/2021, Fraunhofer Institute for Systems and Innovation Research (ISI).
    8. Ma, Peng, 2021. "Optimal generic and brand advertising efforts in a decentralized supply chain considering customer surplus," Journal of Retailing and Consumer Services, Elsevier, vol. 60(C).
    9. Sergio Currarini & Carmen Marchiori & Alessandro Tavoni, 2016. "Network Economics and the Environment: Insights and Perspectives," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 65(1), pages 159-189, September.
    10. Klingler, Anna-Lena & Luthander, Rasmus, 2018. "Market diffusion of residential PV and battery systems driven by self-consumption: A comparison of Sweden and Germany," Working Papers "Sustainability and Innovation" S18/2018, Fraunhofer Institute for Systems and Innovation Research (ISI).
    11. Robertson, Alastair & Soopramanien, Didier & Fildes, Robert, 2007. "A segment-based analysis of Internet service adoption among UK households," Technology in Society, Elsevier, vol. 29(3), pages 339-350.
    12. Edgardo Arturo Ayala Gaytán, 2009. "Social network externalities and price dispersion in online markets," Ensayos Revista de Economia, Universidad Autonoma de Nuevo Leon, Facultad de Economia, vol. 0(2), pages 1-28, November.
    13. Liberali, Guilherme & Gruca, Thomas S. & Nique, Walter M., 2011. "The effects of sensitization and habituation in durable goods markets," European Journal of Operational Research, Elsevier, vol. 212(2), pages 398-410, July.
    14. Chul-Yong Lee & Jongsu Lee, 2009. "Demand Forecasting in the Early Stage of the Technology's Life Cycle Using Bayesian update," TEMEP Discussion Papers 200903, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Apr 2009.
    15. Régis Chenavaz & Corina Paraschiv & Gabriel Turinici, 2017. "Dynamic Pricing of New Products in Competitive Markets: A Mean-Field Game Approach," Working Papers hal-01592958, HAL.
    16. Yanwen Wang & Chunhua Wu & Ting Zhu, 2019. "Mobile Hailing Technology and Taxi Driving Behaviors," Marketing Science, INFORMS, vol. 38(5), pages 734-755, September.
    17. Jakob Grazzini & Matteo G. Richiardi & Lisa Sella, 2013. "Analysis of Agent-based Models," LABORatorio R. Revelli Working Papers Series 135, LABORatorio R. Revelli, Centre for Employment Studies.
    18. Bessi, Alessandro & Guidolin, Mariangela & Manfredi, Piero, 2021. "The role of gas on future perspectives of renewable energy diffusion: Bridging technology or lock-in?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    19. White, Reilly & Marinakis, Yorgos & Islam, Nazrul & Walsh, Steven, 2020. "Is Bitcoin a currency, a technology-based product, or something else?," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    20. Shigeno, Hidenori & Matsuzaki, Taisuke & Ueki, Yasushi & Tsuji, Masatsugu, 2023. "The Effect of the Covid-19 Pandemic on the Innovation Process of Small and Medium-sized Regional Firms," 32nd European Regional ITS Conference, Madrid 2023: Realising the digital decade in the European Union – Easier said than done? 278018, International Telecommunications Society (ITS).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsoctx:v:13:y:2023:i:4:p:96-:d:1115679. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.