Over the last few years, a whole bundle of things that happened in the state and local government world were attributed in some way to the pandemic.
To be sure, the devastating impact of COVID has found its way into many corners of our lives. But just because a shift in major societal trends occurred during or after the pandemic, that doesn’t mean that one was the cause of the other, despite what you may read elsewhere.
We spoke with Stefaan Verhulst a professor at the NYU Center for Urban Science, and co-founder of the GovLab at NYU, about this. He’s one of the most data-savvy people we’ve ever met, and here’s what he had to say: “"The pandemic is now frequently attributed as the explanation for various trends. It has become a dominant variable in interpretations of contemporary phenomena. However, it's important to consider that observing a spike during the pandemic might indicate a correlation rather than a direct causation.”
As many readers of the B&G Report doubtless know, confusing two events that happen simultaneously (correlation) with one event that causes the other (causation) can lead to all sorts of false conclusions.
As the extensively published physician Frank Messerli wrote in the New England Journal of Medicine over a decade ago, it’s possible to find a correlation between chocolate consumption by country and the number of Nobel laureates. It seems intuitively clear that Hershey Bars aren’t the key to genius, and he wrote his piece as a caution to scientists about jumping to false conclusions based on correlation data.
Here’s a powerful example of this kind of flawed thinking from Verhulst: “Consider remote working,” he said. “If you look at productivity lines, they have been going down for fifty years and it’s been a trend not just because of the remote work that was the result of the pandemic. But there were all kinds of assumptions made despite the fact that there was very little causation between whether people worked remotely or not and their productivity.”
This is an easy trap to fall into, and in the years when we worked as consultants to the Pew Charitable Trusts we became accustomed to the careful analysis that went into avoiding confusing correlations with causations. We just wish that others did the same.
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