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.