Descriptive statistics, causal inference, and story time

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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 two books is in the end a morality tale. The tale is about those countries and individuals with the gumption to pull themselves up by their bootstraps or the courage to speak truth to power, and those power- drunk bottom billion elites, toadying sycophants, and soft-hearted academics too blinded by misplaced utopian dreams to recognize the real causes of economic stagnation and civil war. By insisting on the credo of “just the facts, ma’am,” the books introduce many of their key analytical moves on the sly, or via anecdote. . . . This is one explana- tion of how he comes to the point of effectively arguing for an international regime that would chastise undemocratic leaders by inviting their armies to oust them–a proposal that overestimates the virtuousness of rich countries (and poor countries’ armies) while it ignores many other potential sources of political change . . .

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. . . . But the rhetorical tics of Collier’s books deserve some attention. . . . if his European and North American audiences are so deeply (and, it would seem, so easily) misled, why is he quick to presume that the “bottom billion” are rational actors? Mightn’t they, too, be resistant to the good sense purveyed by economists and other demystifiers?

Now to the statistical modeling, causal inference, and social science. 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. Having run a series of regressions, he identifies counterintuitive findings . . . However, his analysis is typically a two-step process. First, he states the correlation, and then, he suggests an explanation of what the causal process might be. . . . Much of the intellectual heavy lifting in these books is in fact done at the level of implication or commonsense guessing.

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.” 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.

The story of story time

But story time can’t be avoided. 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. (Remember the first principle of decision analysis: Not making a decision is itself a decision.)

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. The challenge is to go back and forth between the storytelling and the data. You find some interesting result (perhaps an observational data summary, perhaps an analysis of an experiment or natural experiment), this motivates a story, which in turn suggests some new hypotheses to be studied. Yu-Sung and I were just talking about this today in regard to our article on public opinion about school vouchers.

The question is: How do quantitative analysis and story time fit into the big picture? Mike McGovern writes that he wishes Paul Collier had been more modest in his causal claims, presenting his quantitative findings as “intriguing and counterintuitive correlations” and frankly recognizing that exploration of these correlations requires real-world understanding, not just the rhetoric of hard-headed empiricism.

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. Once they recognize that, indeed, they are in story time, they can think harder about the empirical implications of their stories.

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. Rogeberg has a theoretical model explaining how economists can be so rigorous in parts of their analysis and so unrigorous in others. Rogeberg sounds very much like McGovern when he writes:

The puzzle that we try to explain is this frequent disconnect between high-quality, sophisticated work in some dimensions, and almost incompetently argued claims about the real world on the other.

The virtue of description

Descriptive statistics is not just for losers. There is value in revealing patterns in observational data, correlations or predictions that were not known before. For example, political scientists were able to forecast presidential election outcomes using information available months ahead of time. This has implications about political campaigns–and no causal identification strategy was needed. Countries with United Nations peacekeeping take longer, on average, to revert to civil war, compared to similarly-situated countries without peacekeeping. A fact worth knowing, even before the storytelling starts. (Here’s the link, which happens to also include another swipe at Paul Collier, this time from Bill Easterly.)

I’m not convinced by every correlation I see. For example, there was this claim that warming increases the risk of civil war in Africa. As I wrote at the time, I wanted to see the time series and the scatterplot. A key principle in applied statistics is that you should be able to connect between the raw data, your model, your methods, and your conclusions.

The role of models

In a discussion of McGovern’s article, Chris Blattman writes:

Economists often take their models too seriously, and too far. Unfortunately, no one else takes them seriously enough. In social science, models are like maps; they are useful precisely because they don’t explain the world exactly as it is, in all its gory detail. Economic theory and statistical evidence doesn’t try to fit every case, but rather find systematic tendencies. We go wrong to ignore these regularities, but we also go wrong to ignore the other forces at work-especially the ones not so easily modeled with the mathematical tools at hand.

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. My take-away from McGovern is that the statistical evidence of Collier et al. is fine; the problem is with the economic models which are used to extrapolate from the evidence to the policy recommendations. 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. (This is related to my trick of understanding instrumental variables by directly summarizing the effect of the instrument on the treatment and the outcome without taking the next step and dividing the coefficients.)

To put it another way: I would separate the conceptually simple statistical models that are crucial to understanding evidence in any complex-data setting, from the economics (or, more generally, social science) models that are needed to apply empirical correlations to real-world decisions.

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