Globally, there has been enormous investment in interventions to prevent human trafficking and modern slavery, many of which have relied on limited evidence and best-guesses about individual vulnerability.

The results from our paper, “The use of Bayesian Networks for realist evaluation of complex interventions: evidence for prevention of human trafficking”, contradict widely held assumptions about individual level vulnerability. Our findings show that among female Nepalese migrants risks to forced labour are most strongly associated with the destination country, work sector and mode of recruitment. Women’s individual characteristics, such as age, caste, education, awareness and participation in training did not influence their risk of forced labour at destination, either directly or indirectly.  If these findings are generalizable to other migration corridors, they suggest that many interventions to prevent human trafficking are misconceived, and thus resources are misallocated.

The Effectiveness of the Work in Freedom Programme

The paper used Bayesian Networks to model data collected during the theory-based evaluation of the Work in Freedom (WiF) Programme conducted by our team at the London School of Hygiene and Tropical Medicine. WiF was implemented by the International Labour Organisation (ILO) and funded by the UK Department for International Development. The findings suggest that the ILO’s activities on pre-departure rights-based awareness and empowerment were most likely ineffective. Furthermore, as findings from our evaluation of the WiF component in Bangladesh showed, these pre-departure activities have even resulted in harm to some women.

However, the conclusion from this paper should not be that any pre-departure intervention is a poor use of resources, but instead that uninformed and untested assumptions about safe migration will result in ineffective interventions. In other words, for limited funding to be effectively utilized, interventions must be designed based on strong evidence of the actual risks and protective factors for each setting, especially the larger structural drivers along the pathways to exploitation. Weak assumptions in the WiF interventions included the supposition that women can or will assert their rights in contexts marked by significant power imbalances and by laws and regulations that favour those in positions of power, namely the employers, recruiters and the state. For example, knowing your rights when exploited by an abusive employer or extorted by a recruiter is of little use if there are not any systemic guarantees to ensure that these rights will be enforced, particularly in destination countries. For migrants to avoid or counter exploitation and abuse based on pre-departure information, they need information that they can realistically assert when needed.

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