The Social, Spatial and Temporal Systems Behind Human Trafficking
Without a strong evidence base on human trafficking, responses risk being shaped by myths, stereotypes, assumptions and agendas. Although many hundreds (thousands even) of millions of dollars have been spent on counter-trafficking, the underlying empirical literature remains notoriously fragmented and underdeveloped. A fundamental barrier to its expansion lies in difficulties accessing and generating data. Trafficking is a sensitive topic and those affected belong to hidden populations: hard-to-reach and often stigmatized groups. It is also a complex social phenomenon, with fuzzy conceptual boundaries and obvious intersections with social inequality, precarity, migration, labour rights, child exploitation, corporate practices and more. Even with the best of intentions, ill-informed responses can be costly, ineffective and even actively detrimental. A recent Delta 8.7-developed tool revealed, for example, mismatches between where risk of exploitation may be highest and financial commitment on counter-measures concentrates. Meanwhile, the widespread tendency to conflate trafficking with sex work can fuel discrimination and harm already marginalized groups.
Building a better evidence-base requires a combination of robust top-down research and rigorous bottom-up enquiries that are sensitive to nuance and local context. We were delighted to secure Research Council funding for an innovative new study on trafficking in the United Kingdom. The project, which will run from 2019-2022, focuses on three key dimensions: social structures, geographical space and time. It combines hard-to-access datasets and methods that have proven valuable elsewhere but been under-used in the trafficking domain. Our ultimate goal is to help make analyses and interventions more nuanced, data-driven and carefully targeted. Conscious that identified cases are no perfect mirror of reality, we will identify factors shaping the data and the biases they contain. We will interpret our findings, their implications and limitations sensitively and accurately.
The project has four key components. First, we will examine the social structures in which traffickers and their victims are embedded, using social network analysis to identify influential actors, functions, structural strengths and vulnerabilities. Although trafficking is popularly characterized as sophisticated organized crime, relatively little is known about the structures involved. The work builds on earlier studies into trafficking networks. What distinguishes it is that we will move beyond a small number of case studies to analyse an entire year’s worth of identified labour trafficking networks, plus a sample of sexual exploitation and domestic servitude networks. Our data derive from in-depth victim case files from the UK’s National Referral Mechanism (NRM) system.
Second, we will analyse patterns and trends in identified trafficking cases, using time-series analysis and other statistical techniques. Since trafficking is a broad and varied phenomenon, we will examine variation by trafficking types and other factors. We will assess how the UK situation has evolved, examining relationships with external events like legislative changes, spending programmes and publicity campaigns. Although responses to trafficking are thought sensitive to fluctuations in prioritization, funding and awareness, these relationships are rarely examined systematically. We will compare and contrast data from three sources: the NRM, the Duty to Notify system (for suspected victims who decline referral to the NRM) and the UK Modern Slavery Helpline. Doing so should help tease out differences between the official and unofficial pictures and inform more inclusive responses.
Third, we will explore how key stages in the trafficking process are distributed across geographical space, examining the spatial characteristics of critical locations and determining connections between them. Despite the fact that trafficking is a process in which movement is an key factor, research into the geographies of human trafficking is vastly underdeveloped. With a few exceptions, existing reporting rarely goes beyond country-level mapping. Better use of geospatial analysis could advance our theoretical and empirical understanding of how (known) abuses are concentrated and what environmental factors are associated with them. The results could identify gaps and inform far more finely-targeted interventions. Alongside data from the NRM and the UK Modern Slavery Helpline, here we will draw on publicly available spatial datasets.
Finally, along with our project partners and a broader range of stakeholders—including, we hope, trafficking survivors and workers’ organizations—we will work to translate research findings into actionable insights for policy and practice, tools and skills development. In our outputs, we will highlight factors that could potentially be altered to deter, detect and disrupt trafficking, without inadvertently causing harm to victims or related groups.
The project is led by Dr Ella Cockbain with researchers at University College London (Professor Kate Bowers, Dr Lisa Tompson, Dr Aiden Sidebottom and Oli Hutt). Relatively unusually, it also involves end-to-end collaboration with a team of partners from non-governmental organizations (Unseen and Stop the Traffik), law enforcement (National Crime Agency) and government (Home Office), plus three international expert advisors (Professors Wim Bernasco, Aili Malm and Sheldon Zhang). For researchers, the benefits of such collaboration include the ability to tap into partners’ experiential knowledge and insights, datasets, expertise and ability to influence policy and practice. For partners, there is the opportunity to have a more evidence-informed and data analytical response to the issues surrounding human trafficking/modern slavery.
For too long, local, national and international responses—policy, strategy and legislation—and funding have been based on minimal data, anecdotal evidence and an inability to assess the effectiveness of responses. Collaboration has challenges too. For researchers, it is vital to maintain academic independence and integrity, especially where tensions exist or if findings prove controversial. Even with support, securing data access can be a long and challenging process, and analyses are constrained by the original datasets. For partners, involvement in research carries costs and requires trust and a willingness to open up sensitive datasets and working practices to outside scrutiny. On balance, however, we firmly believe that improved partnerships have much to offer trafficking research.
Acknowledgments: We would like to recognize and thank our funder (The Economic and Social Research Council of the UK; grant reference: ES/S008624/1) and all our collaborators and project partners. If you have any comments or questions, please contact Dr Ella Cockbain (firstname.lastname@example.org).