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True Statistics Can Create False Impressions

In my latest column for AIER, I offer three real-world examples of how statistics that are true – if reported or read without sufficient care – can create impressions that are false. A slice:

Economists define a “normal good” as a good that people demand more of as their expected purchasing power rises. Examples of normal goods are fine wine (rather than box wine), hotel (rather than motel) accommodations, and new (rather than used) automobiles. When we observe people consuming greater amounts of a normal good when that good’s price hasn’t fallen, one possible explanation for this increase in demand is that people’s expected purchasing power has risen — and, in turn, that people are economically better off than they were earlier.

Suppose that workers today in locales hit with significant negative employment ‘shocks’ suffer longer periods of unemployment than did similarly situated workers in the past. One result is an increase in the average duration of unemployment. What might explain this increase in the duration of unemployment? One possibility is that the economy’s rate of creating new jobs has slowed relative to the rate at which it loses jobs. If so, this reality would be evidence of worsening economic performance.

But this reality in America today is likely better explained by another, very different possibility: working-class people are wealthier than in the past; their lifetime purchasing power is higher. Powerful evidence that ordinary Americans are today much wealthier than in the past is found (among very many other places) in Michael Strain’s 2020 book, The American Dream Is Not Dead (But Populism Could Kill It), and in Phil Gramm’s, Robert Ekelund’s, and John Early’s 2022 study,  The Myth of American Inequality: How Government Biases Policy Debate.

Compared to worker Jones today, worker Smith and his family in, say, 1974 would have suffered deeper economic distress if he remained unemployed for many months. So worker Smith didn’t wait long before moving himself and his family from the hometown that they love to a different town where his employment prospects were brighter. In contrast, worker Jones and his family today have higher purchasing power than did worker Smith and his family in the 1970s. If worker Jones loses his job, he can better afford than could worker Smith to ‘consume’ for a longer time his love of his hometown.

Yet worker Jones’s decision not to move in search of employment causes economic statistics to look worse than they would have looked had worker Jones been less wealthy. Specifically, because worker Jones’s greater prosperity allows him to remain longer in his hometown waiting for new employment to come to him — rather than him finding new employment sooner by moving to another town — the measured average duration of unemployment is higher than it would have been had Jones been poorer and moved away in search of a new job.

In short, the rising material prosperity of even ordinary Americans makes the consumption of locational preferences more attractive. Although this outcome is unambiguously desirable, it can result in statistics seeming to indicate a worsening of ordinary Americans’ economic lot.