Big development lessons from small questions

Tuesday’s post on Bill Gates’ plea for better GDP numbers generated an interesting debate about how to “do development” more generally.

I came away with three general lessons I think we can apply, even if you could care less about income statistics. But first the insightful criticisms that brought me to these conclusions.

1. “When Bill Gates said we need better GDP data, he didn’t say we don’t need those other good things.”

One day we’ll have the Star Trek machine that freely generates both GDP data and Earl Grey tea, hot. In the meantime, the world involves trade-offs. When policymakers say “Do more X” they are implicitly saying “Do less Y”. Some people simply don’t think in terms of the opportunity cost. Savvier ones know that more people will agree with you if you are vague about Y.

2. Even laissez-faire states still need data to manage and regulate economies.

Most statistics agencies can calculate a GDP figure. Fewer can capably run panel surveys of labor markets, a firm census, or indices of manufacturing output. The tax agencies have outdated rolls, and little ability to monitor compliance. Information on bank and credit regulation can be pretty poor. My point: You probably only think GDP is more useful data than these things if you don’t actually live in the country.

3. “We know how to fix GDP and it isn’t hard”, and “This is about building the capacity of statistics agencies”

First, there are still trade-offs. Most leaders (say, of a statistics agency) will have the time and political capital to change one or two big things a year, at best. Should the ingredients of GDP be that one big thinig?

We’re right to think in terms of cost-benefit terms when evaluating different options. Maybe GDP would pass this test. My sense is that most of the things I just mentioned would do better in cost-benefit terms. I may be wrong.

You might say “well these things improve GDP measurement anyways,” and you might be right. But the head of a statistics agency would choose to invest in different things if his outside financial incentives tilted towards getting external donors and academics the GDP and MDG measures they want for their big reports. That’s not necessarily the information governments need to govern well.

My general takeaway lessons:

First, academics, aid agencies and big foundations are biased towards funding the institutions and information that help them make the world more legible and manipulable. These are seldom the policies best at making people better off. We ought to be more self aware.

Second, to the extent academics, governments or foundations are going to mess around to try to improve other people’s welfare (and they will), I think they’re less likely to do damage if they think about their job in terms of reducing frictions or constraints. (Other people call this solving market and government failures.) Figure out the binding constraints and how to get rid of them.

Third, policy involves trade-offs, whether you say them out loud or not. Tale less seriously the people who ignore or obscure those trade-offs.

A reminder this discussion was spurred by Morten Jerven’s terrific book, Poor Numbers, which you should check out.

P.S. A reminder: I have never actually been a policymaker, and I wrote this post in 15 minutes, so you should probably not take me very seriously.