Income Inequality: It’s Easy to be Poor When We Don’t Count the Safety Net
The American Enterprise Institute’s Kevin Hassett and Aparna Mathur have an important (and devastating) piece in today’s Wall Street Journal breaking down the misleading facets of the left’s argument that the U.S. is currently suffering through a crisis of economic inequality. Here’s a particularly eye-opening excerpt:
In the first place, studies that measure income inequality largely focus on pretax incomes while ignoring the transfer payments and spending from unemployment insurance, food stamps, Medicaid and other safety-net programs. Politicians who rest their demands for more redistribution on studies of income inequality but leave out the existing safety net are putting their thumb on the scale.
Second and more important, it is well known that people’s earnings in general rise over their working lifetime. And so, for example, a person who decides to invest more in education may experience a lengthy period of low income while studying, followed by significantly higher income later on. Snapshot measures of income inequality can be misleading.
Thomas Sowell frequently makes a point complimentary to Hassett and Mathur’s second observation above: that measuring income inequality over time tends to be deeply misleading because membership in any given income bracket is highly fluid, with people’s income often shifting dramatically over time. Thus, someone who’s in the bottom quintile of income in today’s measurements may be in the second quintile from the top in 15 years’ time. But we tend to analyze these groups as if their composition is static.
Hassett and Mathur’s first point, however, is the one that always bowls me over. If the point of a safety net is to remove people from the perils of indigence, yet the government refuses to factor those provisions into measurements of income, we end up with a perpetually imperiled underclass that only exists on paper. As Mark Twain said (supposedly quoting Disraeli), there are three kinds of lies: “Lies, damned lies, and statistics.”
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