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Modelling for Determinants of Human Trafficking

Authors: Seo-Young Cho;

Modelling for Determinants of Human Trafficking

Abstract

This study aims to identify robust push and pull factors of human trafficking. I test for the robustness of 78 push and 67 pull factors suggested in the literature. By employing an extreme bound analysis, running more than two million regressions with all possible combinations of variables for up to 180 countries during the period of 1995-2010, I show that crime prevalence robustly explains human trafficking prevalence both in destination and origin countries. My finding also implies that a low level of gender equality and development may have constraining effects on human trafficking outflows, contrary to expectations. The linkage between migration and human trafficking is less clear, and institutional quality matters more in origin countries than destinations.

Keywords

Welt, robustness, push and pull factors: robustness; extreme bound analysis [Human trafficking], extreme bound analysis, Kriminalität, Gleichberechtigung, Entwicklungsstufe, J16, Human trafficking, push and pull factors, robustness, extreme bound analysis, ddc:330, human trafficking, push and pull factors, O15, Menschenhandel, Human trafficking, J61, F22, Schätzung, jel: jel:J61, jel: jel:F22, jel: jel:J16, jel: jel:O15

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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
8
Average
Average
Top 10%
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