Data Figleaves: Statistics and their Power to Conceal Racism

If you have followed the 2024 US election coverage, you have likely come across the term “migrant crime”. A term coined by Donald Trump to refer to an alleged crime wave spurred by Joe Biden’s border policy. Conservative media outlets, like Fox News, were quick to echo Trump’s claims, often providing statistical data to fit the narrative. Plausibly, the election results are partly a testament to the success of such data-backed anti-immigration rhetoric. Similarly, you can hardly open a British tabloid, let alone X (formally Twitter), right now without encountering that particular combination. 

 Maybe you even got a taste of this in your personal life. Perhaps you had a heated discussion with some relative over the holidays. You accused (insert your relative’s favourite politician) of racism over their pronouncements on migrant crime – over their insinuations that migrants were especially prone to murder, assault or drug trafficking. In response, you were told that they couldn’t be racist since what they said was supported by statistical evidence: “Numbers don’t lie. That’s not racism, just telling it as it is.” Sounds familiar?

What’s going on here? And why is it effective? As I have argued, I find it helpful to address these questions by appealing to Jennifer Saul’s work on racial figleaves.  According to Saul, a racial figleaf is an utterance that, at least for some, acts as a cover for another utterance that would likely appear racist on its own. By itself, a phrase like “migrant crime” might well be perceived as racist. After all, it suggests some internal connection between being a migrant and a proneness to crime. However, appealing to statistical data might considerably alter this perception, at least for some. Data carries an air of objectivity and is thus likely to be viewed as unburdened by nefarious ideology: just the world as it is, captured in numbers. And this air of objectivity might well be inherited by the statements conjoined with the data references, including statements about migrant crime. Hence, thanks to the appeal to data, people who might have otherwise viewed the phrase “migrant crime” as racist might now contend themselves with the thought that the speaker is simply stating a fact. Let’s call such an appeal to statistical data a data figleaf.

Racial figleaves, says Saul, are effective because many people harbour racist prejudices yet don’t want to view themselves as racist. For these people, adding a figleaf to an utterance that would appear racist on its own allows them to embrace the sentiment expressed while avoiding the conclusion that they are thereby indulging in racism. In other words, by making utterances that might otherwise strike one as racist seem innocuous, racial figleaves can help make such utterances and the sentiments expressed by them more acceptable. Due to this, racial figleaves can, over time, negatively impact race-related attitudes within a society. Herein lies their danger. Following this train of thought, a racial figleaf seems the more dangerous, the more robust a cover for racism it provides. For instance, the well-worn phrase “I’m not a racist, but…” seems relatively harmless as it has become more a spotlight than a cover for racism. How do data figleaves fare in this regard? As mentioned, statistical data tends to adorn the accompanying statements with an air of objectivity. It likely makes them seem a sober reflection of reality and not an expression of racist vitriol. Hence, it stands to be expected that data figleaves can cover up racism pretty well. Thus, they constitute a comparatively pernicious kind of figleaf.

Don’t get me wrong. It’s not that we shouldn’t collect or use statistical data when engaging with race-related issues. Also, it goes without saying, not every opinion you disagree with or statistic that doesn’t fit your worldview automatically indicates racism.  I am merely trying to raise awareness of the dangers of uncritically swallowing certain pronouncements seemingly backed by such data. How do you tell when an appeal to statistics functions as a cover for a racist utterance? Of course, background information about the speaker, their history or the discursive context can help. But what should you do when you lack such clues? Try assessing the statement in isolation, minus the appeal to statistical data. Does it seem racist now, or more so than before? If the answer is “Yes”, chances are said appeal might function as a figleaf.

Now, imagine you are confronted with what you reasonably deem a data figleaf. How might you respond? First, you might simply ask, “What statistic are you referring to?”. Maybe the speaker just made up the numbers, and your probing forces them to come clean. In 2010, former German politician Thilo Sarrazin published an anti-Muslim book filled with statistics talk. However, when pressed on his data, Sarrazin admitted that some of his numbers were just estimation-based. Sometimes, such an admission might already deter others from accepting a racist statement accompanying the statistics reference.

What if the speaker provides the desired data? You might point out that statistics, especially crime or incarceration statistics, tend to indicate correlation, not causation. As such, they can tell you that, say, a specific racial group is overrepresented in a country’s prison population – but not why. For this reason, an incarceration statistic in itself can’t support sweeping claims about the group’s inherent criminal tendencies. But what if the speaker argues that such a tendency best explains the numbers? You might present alternative explanations that take historical, sociological, and economic factors into consideration. Perhaps the group is disproportionally affected by stop-and-frisk policies and tends to receive worse legal representation or higher sentences. Such factors can help explain crime or incarceration statistics without positing some special proneness to crime.

This list of counterspeech suggestions is certainly not exhaustive. Still, I hope it’s useful when you encounter data figleaves. Maybe it helps you get through the next holiday face-off.

Photo: Jakub Zerdzicki: https://www.pexels.com/de-de/foto/papier-statistiken-diagramm-lupe-17284804/