Freakonomics and Data
There’s a really interesting article in the New Republic, “Freaks and Geeks:”
In 2000, a Harvard professor named Caroline Hoxby discovered that streams had often formed boundaries to nineteenth-century school districts, so that cities with more streams historically had more school districts, even if some districts had later merged. The discovery allowed Hoxby to show that competition between districts improved schools. It also prompted the Harvard students to wrack their brains for more ways in which arbitrary boundaries had placed similar people in different circumstances.
…In retrospect, I have come to see this as the moment I realized economics had a cleverness problem. How was it that these students, who had arrived at the country’s premier economics department intending to solve the world’s most intractable problems–poverty, inequality, unemployment–had ended up facing off in what sometimes felt like an academic parlor game?
It’s a very interesting article on the economics of academic economics, and some of the perverse incentives which exist in the field.
Me, I look forward to the day when we have so much data that we can start looking for arbitrary differences and boundaries. I look forward to the day when security has a cleverness problem. No doubt we’ll end up calling it database pharming.
I’m not sure where to start in reaction to this article, because I both agree with it, and think that it’s ridiculous.
He seems to have several key points that he conflates into the “Freakonomics” mentality. (I should add that the whole book was pleasant, but wholly overwrought and overfluffed. Clark medal winners aren’t ‘rogues’.)
1. Properly identified models are now critical for empirical work.
True. It makes it harder to crank out papers, but I’m totally ok with the fact that econ–and social science in general–is responding to the increase in data by paying close attention to causality directions, lack of bias, etc. Keeping your left side and your right side variables independent will never, ever be a vice in my mind.
2. You need clever tricks to properly identify a model. Instrumental variables and natural experiments are not obvious.
Also true. But is there any empirical work in which you don’t need some clever tricks to measure things that other people have a hard time measuring? One of the hardest challenge of any good research program is designing your experiment. Measuring the speed of light *(Romer) or the gravitational effects on it (Einstein) took some serious skull sweat.
3. People are abandoning theory to do sexy empirical work
I don’t have all that much patience for formal theory, myself. A few brilliant people have come up with revolutionary ideas that seem obvious in retrospect (Nash, Vickrey, Schelling) but it seems that much of the rest of it is just pushing way, way too hard. How many people need to compare the third order conditions of every single theory of development?
4. The academic system does not properly reward smart people for doing the work you or I think they should
Is there anyone who thinks that the current publish-like-mad environment is rewarding risk taking and bold thinking by junior academics, anywhere in academe? Is there anyone who has proposed a realistic alternative that can actually be implemented?
5. Economists need to make more data and check in more often with reality
Here, I whole-heartedly agree with Scheiber, but a) surely this is far more crucial for the theorists than the empiricists and b) this applies to all of the social scientists. A friend studies the relationship between marriage and poverty, and credits the course on qualitative research (anathema to any real economist) as one of the most valuable things he’s ever done.
Yes, people are now applying econ to sexual orientation and what kind of bread they like with the pastrami and everything else. The field is exploding because we have more data than ever before, and anyone can run a decent model on their laptop with today’s hardware. I’d much rather a field with too wide a focus–and an emphasis on finding new ways to unlock the secrets of data–than an economics that is too formal and traditionalist.