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Diffusing Human Trafficking Policy in Eurasia$
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Laura A. Dean

Print publication date: 2020

Print ISBN-13: 9781447352839

Published to Policy Press Scholarship Online: January 2021

DOI: 10.1332/policypress/9781447352839.001.0001

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Empirical comparisons of human trafficking policy across Eurasia

Empirical comparisons of human trafficking policy across Eurasia

(p.149) 6 Empirical comparisons of human trafficking policy across Eurasia
Diffusing Human Trafficking Policy in Eurasia

Laura A. Dean

Policy Press

This chapter broadens the analysis back to whole region of Eurasia to analyze the determinants of policy adoption and implementation with original dataset, adapting variables for internal determinants and external pressure to the international policymaking environment. It examines the quantitative determinants of policy adoption and implementation across all fifteen counties in Eurasia in a similar time period as the case study analyses from 2003-2015 in a pooled time series. It presented a new and innovative Human Trafficking Policy Index which measures the scope of human trafficking policies in Eurasia and ranks it on a 15-point scale every year. The findings mirror the qualitative results and reveal that internal political conditions and monetary factors inside the country such as state commitment, policy entrepreneurs, bureaucracy, and state capacity determine how the countries adopted policy while policy entrepreneurs, bureaucracy, and police effectiveness influence policy implementation. The mixed method comparison demonstrates that state commitment and policy entrepreneurs had a positive influence on human trafficking policy adoption while bureaucratic impediments inhibited policy adoption. The results were less cohesive with policy implementation model but revealed the influence of internal determinants such as street-level bureaucrats, bureaucratic impediments, and conflicting policing results.

Keywords:   human trafficking policy index, internal determinants, external pressures, international policymaking, pooled time series

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