Responding to Stiglitz on Inequality

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There’s a Vanity Fair piece on income inequality by Nobel Price-winning economist Joseph Stiglitz, “Of the 1%, by the 1%, for the 1%”, which has been cited again and again in the commentariat lately, and it’s a frustrating piece because of the extent to which is makes logical leaps or simply distorts reality. Scott Winship of The Empiricist Strikes Back does a good job of going through the piece and addressing it point by point, including taking on a few of the talking points which are increasingly becoming things “everybody knows” in the wonk community but which don’t actually mean what they seem to.

One of the problems with our modern society’s fixation on “data” is that people, even very educated people who should know better, often fixate on a given metric (for example, the claim that “While the top 1 percent have seen their incomes rise 18 percent over the past decade, those in the middle have actually seen their incomes fall. For men with only high-school degrees, the decline has been precipitous—12 percent in the last quarter-century alone.“) without taking the time to dig into what we can discover of the realities that underlie that measure. Sometimes those realities do not fit with the ideological picture which makes the original metric so appealing. (Winship’s responses to the just quoted claim, both in the main article linked above and in this older one, are fascinating.)

Definitely worth a read.

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  1. Wait, the map isn’t the place?

    Amused to see that they did use one of the tricks I expected– bet they use some of the other ones, like counting gross income instead of net with small businesses (by that measure, ag makes BIG bucks, and so do jewelry shops) and not accounting for the effect of single parent families, worker choice, folks being restricted from working (disability, retirement, too young– being paid for not making over X is rolled in here to keep it short) and utterly ignoring the under-the-counter economy. (Illegal aliens, folks barred from working doing cash jobs, etc)

  2. I think it is called “data mining.” Employing data mining one can fabricate “facts” which bear no semblence to the truth, not even the iota found in a lie.

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