Estimating Effectiveness of Identifying Human Trafficking via Data Envelopment Analysis

TL;DR

This paper presents a novel application of Data Envelopment Analysis (DEA) to evaluate and improve the efficiency of transit monitoring stations in identifying human trafficking, providing a data-driven decision support tool.

Contribution

It introduces the first DEA-based model for assessing and enhancing the performance of anti-human trafficking transit stations, incorporating multiple inputs, outputs, and homogeneity criteria.

Findings

Identified efficient stations and their characteristics.

Compared station performance rankings.

Recommended operational improvements for efficiency.

Abstract

Transit monitoring is a preventative approach used to identify possible cases of human trafficking prior to exploitation while an individual is in transit or before one crosses a border. Transit monitoring is often conducted by non-governmental organizations (NGOs) who 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 (e.g. staff, etc.) available and make specific operational improvement recommendations. Our model consists of 91 decision-making units (DMUs) from 7 stations over 13 quarters and considers three inputs, four outputs, and 3 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.