Bill Easterly's latest is a must-read for those of us who think about how economies grow, and why it happens in some places and not others. The answer to "will the data ever tell us what drives growth" is a pretty depressing 'no'. But the reason I worked for two years on the book was to argue for something slightly different than to write a page in his "History of Evolving Cluelessness." The book's main premise is that people do not know what the data they use really means. We have the IMF, or the Penn World Tables project, or whatever, telling us about quantities like capital and labor. But we "know" that institutions matter and search for "institutional variables", which involve some social scientist writing down a measure of "strength of property rights" or "central bank independence" or "corruption" and publishing a dataset that every other social scientist swarms like flies to honey. But they don't know if it's honey, it's just that it smells "like" their idea of honey.

If you don''t have a theory of honey, you'll believe just about anything might be honey. This was our point in the introduction: You need a theory of which institutions matter and why before you know what to measure. When measuring a country's capital stock you make a decision about which structures are in and which are out. Factories? Sure. Museums? Probably. The shed in back of my house that stores my patio furniture and lawnmower? No. Why? Because you want that which leads to the production of goods, and you HAVE A THEORY of what kinds of structures do this and what kinds do not.

Do we have a theory of corruption? If so, what should we measure based on that theory? Same for trade openness, democracy, or any of the other things we think belong in a cross-country growth equation, inflation equation, etc. I don't know that answering this correctly ends our evolving cluelessness, but we think it's a good place to continue the search.

Labels: economics