andrew_gelman_stats andrew_gelman_stats-2011 andrew_gelman_stats-2011-789 knowledge-graph by maker-knowledge-mining
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Introduction: Dave Backus points me to this review by anthropologist Mike McGovern of two books by economist Paul Collier on the politics of economic development in Africa. My first reaction was that this was interesting but non-statistical so I’d have to either post it on the sister blog or wait until the 30 days of statistics was over. But then I looked more carefully and realized that this discussion is very relevant to applied statistics. Here’s McGovern’s substantive critique: Much of the fundamental intellectual work in Collier’s analyses is, in fact, ethnographic. Because it is not done very self-consciously and takes place within a larger econometric rhetoric in which such forms of knowledge are dismissed as “subjective” or worse still biased by the political (read “leftist”) agendas of the academics who create them, it is often ethnography of a low quality. . . . Despite the adoption of a Naipaulian unsentimental-dispatches-from-the-trenches rhetoric, the story told in Collier’s
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1 Dave Backus points me to this review by anthropologist Mike McGovern of two books by economist Paul Collier on the politics of economic development in Africa. [sent-1, score-0.265]
2 Despite the adoption of a Naipaulian unsentimental-dispatches-from-the-trenches rhetoric, the story told in Collier’s two books is in the end a morality tale. [sent-9, score-0.214]
3 My [McGovern's] aim in this essay is not to demolish Collier’s important work, nor to call into question development economics or the use of statistics. [sent-18, score-0.16]
4 Now to the statistical modeling, causal inference, and social science. [sent-28, score-0.179]
5 McGovern writes of Collier (and other quantitatively-minded researchers): Portions of the two books draw on Collier’s academic articles to show one or several intriguing correlations. [sent-29, score-0.171]
6 This pattern (of which McGovern gives several convincing examples) is what statistician Kaiser Fung calls story time –that pivot from the quantitative finding to the speculative explanation My favorite recent example remains the recent claim that “a raise won’t make you work harder. [sent-39, score-0.404]
7 ” As with McGovern’s example, the “story time” hypothesis there may very well be true (under some circumstances) but the statistical evidence doesn’t come close to proving the claim or even convincing me of its basic truth. [sent-40, score-0.256]
8 The story of story time But story time can’t be avoided. [sent-41, score-0.455]
9 On one hand, there are real questions to be answered and real decisions to be made in development economics (and elsewhere), and researchers and policymakers can’t simply sit still and say they can’t do anything because the data aren’t fully persuasive. [sent-42, score-0.216]
10 ) From the other direction, once you have an interesting quantitative finding, of course you want to understand it, and it makes sense to use all your storytelling skills here. [sent-44, score-0.184]
11 The question is: How do quantitative analysis and story time fit into the big picture? [sent-48, score-0.305]
12 I agree completely with McGovern–and I endeavor to follow this sort of modesty in presenting the implications of my own applied work–and I think it’s a starting point for Coliier and others. [sent-50, score-0.161]
13 Once they recognize that, indeed, they are in story time, they can think harder about the empirical implications of their stories. [sent-51, score-0.178]
14 The trap of “identifiability” As Ole Rogeberg writes (following up on ideas of James Heckman and others), the search for clean identification strategies in social research can be a trap, in that it can result in precise but irrelevant findings tied to broad but unsupported claims. [sent-52, score-0.223]
15 This has implications about political campaigns–and no causal identification strategy was needed. [sent-58, score-0.238]
16 Countries with United Nations peacekeeping take longer, on average, to revert to civil war, compared to similarly-situated countries without peacekeeping. [sent-59, score-0.247]
17 For example, there was this claim that warming increases the risk of civil war in Africa. [sent-63, score-0.187]
18 I generally agree with what Chris writes, but here I think he’s a bit off by taking statistical evidence and throwing it in the same category as economic theory and models. [sent-71, score-0.254]
19 is fine; the problem is with the economic models which are used to extrapolate from the evidence to the policy recommendations. [sent-73, score-0.272]
20 I’m sure Chris is right that economic models can be useful in forming and testing statistical hypotheses, but I think the evidence can commonly be assessed on its own terms. [sent-74, score-0.331]
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