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A Positive Spin on the Data Problem
June 9, 2022

Back in 2020, there was a huge surge of social media interest around child sex trafficking when people were sharing numbers about missing children that had been based on faulty data. The problem with tracking numbers on trafficking cases is that it can be so difficult to accurately measure anything. This leads people to call into question the veracity of claims, and by extension, the legitimacy of the anti-trafficking movement as a whole. However, while numbers are notoriously difficult to track, that doesn’t mean the problem doesn’t exist. The good news is that people from across the field are finding multitudinous ways to study the problem. While we still might not have precise data, we are coming at the data problem from a variety of angles–and that might end up giving us a fuller understanding of the picture than we would if we rested on one or two more precise measures alone.

Have you ever heard the parable of the blind men and the elephant?

Here’s How the Parable Goes

In a quick summary, the parable describes several blind men who come across an elephant. One touches its side and declares the elephant is like a wall. Another feels its tusk and declares the elephant is rather like a spear. A different blind man feels the trunk and declares the elephant is like a snake, while another grabs the tail and says the elephant is like a rope. And so on. No one is able to describe the elephant accurately–they are all only capable of conveying their own limited perspectives. But collectively, they get closer – though not all the way – to the truth.

In many ways, this is similar to how we’re approaching the data problem in anti-trafficking.

We Use Multiple Perspectives to Assess Data

Trying to measure victims of trafficking straight-up is problematic. Even if you look at the numbers of victims reported to the police, this can be problematic because not all victims get reported or because some cases are identified as something else (e.g. prostitution, illegal migration, etc.).

However, we can use those official numbers in conjunction with other types of measures, to get a wider look at the problem. For example:

  • Household surveys: One way to examine how many people have been subjected to forced labor would be to conduct household surveys. This is a way to get an overview from the individual level, and with anonymous surveys one can relatively easily ensure the safety of survey participants, knowing they can answer more freely.
  • Another tactic is sector surveys: Governments and businesses increasingly call for surveys of sectors such as cotton production, coffee, or cocoa to examine whether people employed in the sector are subjected to forced or exploitative labor. This gives a more pinpointed, targeted view of certain industries. However, there are ethical issues with ensuring the safety of employees participating in the surveys and whether or not they can answer the questions fully and honestly. 
  • A very different tactic is statistical modeling: This approach takes into account a variety of information to project estimates of things like how prevalent trafficking is or how to assess vulnerability. There is currently a move to shift from assessing vulnerability on a global scale to a regional one, accounting for a wide variety of factors such as: individual-level factors like the presence or absence of a capable guardian, a willing offender, and a vulnerable target alongside systemic level factors like environmental insecurity, health insecurity, food insecurity, cultural conventions that promote insecurity, and so on. While these models won’t bring quite the precision of something like a survey, what they do is help contribute to the broader picture and allow a much bigger overview of all the different things that might contribute to vulnerability. Different regions will have different models because trafficking can operate differently based on context. For example, when climate change produces reliable seasonal crises, trafficking vulnerability might be a seasonal phenomenon, compared to acute disasters which are less predictable. What it loses in its ability to tell us with individual level precision, like with a household survey, it gains in filling out the broader context–which can then inform policy decisions about what we need, not just for individuals, but also for communities and regions to promote resilience.

So yes, it is true that there is no perfect measure for anything in trafficking. But that doesn’t mean that the problem doesn’t exist. Instead, we can be much more sophisticated in how we think about the scale, scope, and dimensions of the problem. Trafficking is complex and complicated. It makes sense that we need sophisticated tools to track it.

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