State Structural Determinants of Human Trafficking: A Global Study

Background: Human trafficking (HT) affects an estimated 50 million individuals globally and leads to deleterious effects for victims and their families (ILO & IOM, 2022). In response to this global human rights issue, the United Nations (UN) led the development of several legal instruments and commitments for Member States to implement for the mitigation of victimization and increase in victim reporting. This dissertation examines the patterns of compliance with UN HT-related instruments and commitments, and their effect on the relationship between State-level access to environmental resources and State-level HT reporting. Methods: I conducted three studies using data from peer-reviewed literature, UN reports, and UN datasets. In study one, I conducted a systematic literature review (SLR) of State-level structural determinants (SSDs) of HT. In study two, I used causal loop diagramming to depict the complex relationships between the SSDs for each HT type found in study one. In study three, I examined the patterns of compliance with SSDs among all UN Member States (N=193) and assessed whether the patterns of compliance moderated the effect of access to environmental resources on HT reporting. Results: The SLR yielded SSDs for four types of HT - child marriage, child conscription, organ trafficking, and sex trafficking (study one). A causal loop diagram (CLD) was depicted for each HT type, demonstrating how SSDs affect, and are affected by, other State-level factors (study two). In study three, four distinct classes emerged and were labeled based on the SSDs that they complied with. A series of latent class moderation models indicated that the effect of access to environmental resources on HT reporting significantly differed based on SSD compliance class membership. Conclusion: Research on SSDs provides opportunities to improve population health through directed regulatory action at the highest level of governance. By leveraging methodology from systems dynamics and latent class analyses, this study provides an understanding of (1) SSDs for different HT types, (2) potential factors that influence them, and (3) how they interact with access to environmental resources to affect HT reporting.