Estimating Effectiveness of Identifying Human Trafficking via Data Envelopment Analysis

Geri L. Dimas (), Malak El Khalkhali (), Alex Bender (), Kayse Lee Maass (), Renata A. Konrad (), Jeffrey S. Blom (), Joe Zhu () and Andrew C. Trapp ()
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Geri L. Dimas: Data Science Program, Worcester Polytechnic Institute, Worcester, Massachusetts 01609
Malak El Khalkhali: School of Business, Worcester Polytechnic Institute, Worcester, Massachusetts 01609
Alex Bender: Department of Mechanical and Industrial Engineering, Northeastern University, Boston, Massachusetts 02115
Kayse Lee Maass: Department of Mechanical and Industrial Engineering, Northeastern University, Boston, Massachusetts 02115
Renata A. Konrad: School of Business, Worcester Polytechnic Institute, Worcester, Massachusetts 01609
Jeffrey S. Blom: Love Justice International, Lincoln, Nebraska 68506
Joe Zhu: School of Business, Worcester Polytechnic Institute, Worcester, Massachusetts 01609
Andrew C. Trapp: Data Science Program, Worcester Polytechnic Institute, Worcester, Massachusetts 01609; School of Business, Worcester Polytechnic Institute, Worcester, Massachusetts 01609

Interfaces, 2023, vol. 53, issue 6, 408-424

Abstract: Transit monitoring is a preventive approach used to identify possible cases of human trafficking before exploitation while an individual is in transit or before crossing a border. Transit monitoring is often conducted by nongovernmental organizations (NGOs) that train staff to identify and intercept suspicious activity. Love Justice International (LJI) is a well-established NGO that has been conducting transit monitoring for years along the Nepal-India border at multiple monitoring stations. In partnership with LJI, we developed a system that uses data envelopment analysis (DEA) to help LJI decision makers evaluate the performance of these stations at intercepting potential human trafficking victims given the amount of resources (staff, etc.) available and make specific operational improvement recommendations. Our model consists of 91 decision-making units from seven stations over 13 quarters and considers three inputs, four outputs, and three homogeneity criteria. Using this model, we identified efficient stations, compared rankings of station performance, and recommended strategies to improve efficiency. To the best of our knowledge, this is the first application of DEA in the anti-human trafficking domain.

Keywords: human trafficking; data envelopment analysis; nonprofit operations; efficiency modeling (search for similar items in EconPapers)
Date: 2023
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