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Lies, Damned Lies, and Statistics

The Devil is indeed in the details. Writing in Slate, Matthew Yglesias argues that significant policy decisions can hinge on which statistics legislators choose to heed. Unfortunately, they often choose bad ones, particularly when it comes to the economy:

Even the smartest theoretical or policy work is basically valueless if it’s based on flawed or misleading data. . . . Absent laboratory conditions, it’s very difficult to obtain precise measurements of key figures, leaving us all too often with big theories based on poor numbers. And when theories move out of the ivory tower and into the policy realm, the problem gets worse as pressure for timeliness and relevance escalates.

Yglesias presents the Obama administration’s recession policy as an example of this kind of failure. When he took office, Obama based his recovery policies on the belief that “the economy had shrunk at an alarming 3.8 percent annualized rate. After several rounds of revisions, the correct figure seems to have been an 8.9 percent annual rate.” Yglesias doesn’t think the pressures that muck up the data collection process can ever be eliminated, but he suggests that we be more attentive to improvements in technique. This seems like a highly important service that the academy, as a laboratory for experiments on new kinds of data modeling, can offer society.

Read the whole thing, and keep it in mind next time you hear a politician quote a statistic in defense of his preferred policy.

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