Jeffrey Frankel, a distinguished economist, public servant, and expert on international trade, made a common but, I hope, avoidable statistical error in a recent column. Frankel wrote:

Compared to “blue-staters,” those who live in red states exhibit less responsibility, on average, in their personal behavior: they are less physically fit, less careful in their sexual behavior, more prone to inflict harm on themselves and others through smoking and drinking, and more likely to receive federal subsidies . . . Blue-state residents, who tend to be more educated and have higher incomes than residents of red states . . .

This is all fine, but Frankel is making a common, and important, mistake, which one might call “personifying the states.” In fact, as we describe in detail in our book Red State Blue State, differences between state averages do not necessarily reflect individual differences. For example, blue state residents have higher incomes, on average, than red state residents; but Democratic (“blue”) voters are poorer, on average, than Republican (“red”) voters. Even Mitt Romney knows that!

Similarly, it is misleading to write, “Statistical analysis shows that states where more residents suffer from obesity, often because they get less physical exercise and eat more junk food, tend to vote Republican. To illustrate, a mere 1% decrease in a state’s obesity on average is estimated to raise the ratio of Democratic to Republican voters from 1.00 to 1.07”—an argument that conflates individuals and states and confuses correlation with causation.

Frankel’s mistake is an easy one to make; others who have confused state-level with aggregate patterns include respected commentators Nicholas Kristof, Michael Barone, and Tucker Carlson. Seeing this mistake made by a leading scholar and former member of the Council of Economic Advisors has motivated me to post on this topic once again.

P.S. See here for the sugar-subsidy story.

[Cross-posted at The Monkey Cage]

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Andrew Gelman is a professor of statistics and political science and director of the Applied Statistics Center at Columbia University.