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Opportunities and Obstacles: Consulting Civil Society Organizations and Human Trafficking Policy

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Abstract

Efforts to address human trafficking require a multi-faceted approach. The “3-P” index outlined in the U.N. protocol on human trafficking highlights the importance of prosecuting criminals, protecting survivors, and preventing the crime. In addition to states, civil society organizations (CSOs) play a crucial role in fighting this issue. Cooperation between the state and CSOs is increasingly recognized as a vital strategy for combatting human trafficking. Does the consulting of CSOs by policymakers affect outcomes in prosecution, protection, and prevention? Using data from an original data set of 183 countries, my findings suggest that cooperation significantly influences prosecution and prevention efforts, but not protection efforts. I theorize that this is because there are low technical and political costs to implement prosecution and prevention policies, but high technical and political costs to implement protection policies. Furthermore, I argue that CSO consultation is more likely to be associated with shallow, rather than deep, policy changes.

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Notes

  1. I do not want to inadvertently create a dichotomy between those who are seen to have been exploited under the law, versus those who are seen to have broken the law. The conflation of sex trafficking and sex work has increased the criminalization and stigmatization of sex workers (see Bettio et al., 2017).

  2. One of the most common critiques of the TIP reports is that they do not report neutral judgments on compliance with international standards, but rather on standards that the United States has set as the “self-appointed hegemon” in examining efforts to address human trafficking (Merry, 2016; Cho & Vadlamannati, 2012).

  3. I include models without collapsing these variables in the appendix (4 and 5) by running ordered probit regressions instead of logistic regressions. I include predicted probability graphs to assist with the interpretation of the regression results. These models show similar results to the logistic regressions.

  4. As an additional robustness check, I include logistic regression results for models with country-level fixed effects in the appendix (4, 5, and 6). I include odds ratios to assist with the interpretation of the model results.

  5. Because there is little variation within country in the corruption variable, I do not include it in the fixed effects models. Any cross-country variation in political corruption will be picked up by the fixed effects and will produce biased results.

  6. For more detail on the politicized nature of the TIP reports, see Gallagher (2011).

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  1. Department of Political Science, Center for Environmental Politics, University of Washington, Seattle, USA

    Rachel Castellano

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Appendix

Appendix

See Figs.

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Heat Maps of Dependent Variables

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Logistic Regression Results with Fixed Effects—Prosecution Model

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Logistic Regression Results with Fixed Effects—Prevention Model

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Logistic Regression Results with Fixed Effects—Protection Model

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Ordered Probit Regression Results and Predicted Probabilities: Prevention Model

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Ordered Probit Regression Results and Predicted Probabilities: Prosecution

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Castellano, R. Opportunities and Obstacles: Consulting Civil Society Organizations and Human Trafficking Policy. Voluntas 34, 1050–1067 (2023). https://doi.org/10.1007/s11266-022-00522-2

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