by Russ Roberts on June 11, 2009

in Data

My colleague Bryan Caplan at EconLog on how economics is evolving:

I've studied economics for over twenty years.  The more I think about
it, though, the more I realize that I don't know what "economics" means

Textbooks may say that economics is about "incentives" or "trade-offs."  But you can publish papers in econ journals about the effect of birth weight on educational attainment.  I don't see any incentives or trade-offs there.  Or take Emily Oster's early research
arguing that hepatitis, not infanticide or selective abortion,
explained a lot of Asia's gender imbalance.  Some economists asked,
"How is this economics?"  But if some economists argue that the gender
imbalance is driven by incentives, how can you object if other
economists say that the real explanation is medical?  Or consider
happiness research.  Economists like Justin Wolfers are in the vanguard; but the connection to incentives or trade-offs is unclear.

could deplore all this as a loss of focus.  But I see massive
progress.  Economics has grown hard to define because we now focus
primarily on real-world problems, not "literatures."  If we want to
understand income determination, we don't waste time with topological
proofs.  We still think about supply and demand, but we also think
about policy, psychology, behavioral genetics, and much more.  As a result, we come to understand the world, instead of solving unusually difficult homework problems.

He goes on to say that what economists do, might best be called "sociology" the word we already have to describe the study of the social world, which is what Bryan is arguing economists increasingly study.

I am reminded of what George Stigler said: "There is only one social science and we are its practitioners."

Having noted that, I have to disagree with Bryan on a few things. I think too much of modern empirical economics is the economics-free application of sophisticated statistical techniques that does little to actually advance our understanding of the social world. It's not just that it isn't about trade-offs or incentives, the role of trade-offs and incentives are ignored. I also don't think we've made "massive progress" in understanding the social world. We've made massive progress in publishing papers on the social world. But understanding? Not so much. We treat the natural world as if the sophisticated tools of statistics can turn reality into a natural experiment. But the world is usually (always?) too complex for the results to be reliable.

I continue to ask the question: name an empirical study that uses sophisticated statistical techniques that was so well done, it ended a controversy and created a consensus—a consensus where former opponents of one viewpoint had to concede they were wrong because of the quality of the empirical work.

When I asked Ian Ayres that question, someone who advocates increased use of statistical techniques in various aspects of life, his answer was the Levitt and Donahue study of abortion and crime. A strange answer as it is highly controversial and widely dismissed by skeptics.

My example used to be the Monetary History of the United States by Friedman and Schwartz that created a consensus that the Fed and the money supply play a crucial role in inflation and business cycles. But The Monetary History is a collection of facts rather than the use of the fancy techniques so in vogue today. I'm not sure there's anything sophisticated in the empirical work. Is there even a single regression in it? I don't know. But the point is that I'm not saying that facts are irrelevant to our understanding of the world. I'm saying that attempts to use statistical technique to tease out causation in a complex world is incredibly un-credible.

I am open to other suggestions. I'd like one example, please. One example, from either micro or macro where people had to give up their prior beliefs about how the world works because of some regression analysis, ideally usually instrumental variables as that is the technique most used to clarify causation.

One example. There should be dozens. Or hundreds. But I'll take one.


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