Oh my, this is written by Krugman:

Noah Smith has a fairly caustic meditation on the role of math in economics, in which he says that it’s nothing like the role of math in physics — and suggests that it’s mainly about doing hard stuff to prove that you’re smart.

I share much of his cynicism about the profession, but I think he’s missing the main way (in my experience) that mathematical models are useful in economics: used properly, they help you think clearly, in a way that unaided words can’t.

Keep in mind, this is coming from a guy, Krugman, who misunderstands the foundation he claims is at the bottom of his economath models.

BTW: Smith gets it mostly correct:

When I entered econ grad school, I expected - naively, it turned out - that the math that people did would be like the math I had done in physics. I expected that economists' models would largely be reliable, well-tested tools for predicting the future, just like I had predicted the cannonball with high school algebra.

And actually, some of the econ math seemed to qualify. Game theory only annoyed me slightly. Though its assumptions weren't often satisfied in the real world, seemed like itwouldwork if we could get the incentives right (and in fact, it very often does, in experiments). Consumer theory was a little more dubious - how could you measure a demand curve in practice? - but choice theory seemed like something that would work if people had stable preferences and you could nail them down empirically. I was a little disturbed by the misuse of the word "axiom" to refer to things that were actually testable (like revealed preference), but I let that one slide.

But macro was a different story.

In macro, most of the equations that went into the model seemed to just be assumed. In physics, each equation could be - and presumablyhadbeen - tested and verified as holding more-or-less true in the real world. In macro, no one knew if real-world budget constraints really were the things we wrote down. Or the production function. No one knew if this "utility" we assumed people maximized corresponded to what people really maximize in real life. We just assumed a bunch of equations and wrote them down. Then we threw them all together, got some kind of answer or result, and compared the result to some subset of real-world stuff that we had decided we were going to "explain". Often, that comparison was desultory or token, as in the case of "moment matching".

In other words, the math was no longer real. It was all made up. You could no longer trust the textbook. When the textbook told you that "Households maximize the expected value of their discounted lifetime utility of consumption", that was not a Newton's Law that had been proven approximately true with centuries of physics experiments. It was not even a game theory solution concept that had been proven approximately sometimes true with decades of economics experiments. Instead, it was just some random thing that someone made up and wrote down because A) it was tractable to work with, and B) it sounded plausible enough so that most other economists who looked at it tended not to make too much of a fuss.

We were told not to worry about this. We were told that although macro needed microfoundations - absolutelyrequiredthem - it was not necessary for the reality of any of these microfoundations to be independently confirmed by evidence. All that was necessary is that the model "worked" after all the microfoundations were thrown together. We were told this not because of any individual failing on the part of any of our teachers, but because this belief is part of the dominant scientific culture of the macro field. It's the paradigm.

Anyway, that was the beginning of my exposure to macro, but not by any means the end. The math got a lot hairier and more kludgey, though not more beautiful. Only occasionally - in a special class taught by Miles Kimball - was there the kind of elegance or deep conceptual math that I had enjoyed in college math classes. Only occasionally - as in the "matching function" of a Diamond-Mortensen-Pissarides labor search model - was there a microfoundation that people actually bothered to check rigorously against reality. Usually, the math was just a whole lot of algebra (yawn) with more made-up stuff. Kreps-Porteus preferences. Heterogeneous agent models. Investment adjustment costs. You would very formally define an "equilibrium" in terms of some functional equations, and you'd stick the system in a computer to solve for you, tossing in parameters from wherever you could grab them ("calibration").

So the math in most of the macro papers I read was easy math, implemented in a boring, kludgey, tedious way. That would have been OK if I could have convinced myself that the math representedreal stufflike in physics. But mostly, it seemed not to.

It occurred to me then that there were more uses of math than the ones I had been taught about in high school. In addition to being beautiful and representing reality, math can be used tosignal intelligence. Economists hold forth on a lot of stuff, and we often tend to listen to the ones we think are smartest. If I can do some tricks that the next guy can't, that can make me seem more like a sage. "First prove you're smart by doing some hard math thing," an economics prof once told me with a grin, "and then you can write about whatever you want." I doubt most profs are so cynical, but the incentive system is certainly there.

Math can also be used asobscurantism; if every paper in a field starts with a dense thicket of formal statements and functional equations, it will be difficult for even very smart outsiders to come in and evaluate what the people in a field are doing with their time. Again, I doubt all but the most cynical macroeconomists would be intentionally obscurantist; they would just be subtly rewarded for doing things that ended up having an obscurantist result.

Anyway, the thick, sludgy swamp of math-without-beauty-or-truth ended up discouraging me from doing math at all. My dissertation didn't really use anything beyond high-school algebra, and was all about experiments and empirics instead of theory. ButI miss math. I miss doing cool, deep, beautiful math for its own sake. But much more than that, I miss doing math that felt like it represented somethingreal.

Economics is a social science. Would you use math to study a group of baboons? No. Human behavior varies depending on the individual and the circumstances. It doesn't fit into equations.

ReplyDeleteThe most interesting (if not tragic) aspect of the post is it does not utter one word for Austrian economics or Mises, despite the wealth of literature addressing just this point, and Mises' pioneering work on the trouble with math and the social sciences. Not to mention the resurgence of the school with an ever-growing list of scholars and the exposure provided through the Internet.

ReplyDeleteThe comments also show no awareness of the Austrians.

True but have you seen the tone of the comments? It's all geeky one-uppance.

DeleteI guess krugman used the wrong equation when he pronounced Argentina a miracle in economic Keynesian intervention.

ReplyDelete