Noah Smith joins sides with Paul Romer who recently condemned the overuse of mathematical modeling in economics. (I wrote about it here.) Smith isn’t exactly known for his love of Austrian methods, so that should be your tip off that criticizing “mathiness” isn’t necessarily support for the logical, a priori approach. Nevertheless, Smith and Romer do make some valid points in the context of what is really an attack on the Chicago school and its descendants.
Many of our own readers think that since Austrians are opposed to mathematical modeling, then we must be opposed to measuring things, or to economic history that uses statistics, or to any kind of math at all. That’s not true, of course. All those things can be helpful and add insight to economic analysis. Rothbard, for example, was quite fond of the work of Richard Vedder, who uses math to help us understand a variety of relationships.
The sorts of models condemned by Romer are something else entirely, as can be sensed from Smith’s description here:
Economics has a lot of math. In no other subject except mathematics itself will you see so many proofs and theorems. Some branches of econ, such as game theory, could legitimately be housed in university math departments. But even in fields such as macroeconomics, which ostensibly deal with real-world phenomena, math is central to everything that economists do.
But the way math is used in macroeconomics isn’t the same as in the hard sciences. This isn’t something that most non-economists realize, so I think I had better explain.
In physics, if you write down an equation, you expect the variables to correspond to real things that you can measure and predict. For example, if you write down an equation for the path of a cannonball, you would expect that equation to let you know how to aim your cannon in order to actually hit something. This close correspondence between math and reality is what allowed us to land spacecraft on the moon. It also allowed engineers to build your computer, your car and most of the things you use.
Some economics is the same way, especially in microeconomics, or the study of individuals’ actions -- you can predict which kind of auction will fetch the highest prices, or how many people will ride a train. But macroeconomics, which looks at the broad economy, is different. Most of the equations in the models aren’t supported by evidence. For example, something called the consumption euler equation is at the core of almost every modern macroeconomic model. It specifies a relationship between consumption growth and interest rates. But when researchers looked at real data on consumption growth and interest rates, they found that the equation gives exactly the wrong predictions! Yet it continues to be used as the core of almost every macro model.
If you read the macro literature, you see that almost every famous, respected paper is chock full of these sort of equations that don’t match reality. This paper predicts that everyone will hold the same amount of cash. This paper predicts that people buy financial assets that only pay off if people are able to change the wage that they ask to receive. These and many other mathematical statements don’t remotely correspond to observable reality, nor do they have any evidence in support of them. Yet they are thrown into big multi-equation models, and those models are then judged only on how well they fit the aggregate data (which usually isn’t very well).
That whole approach would never fly in engineering. Engineering is something you expect to work. But macroeconomists often treat their models as simply ways, in the words of David Andolfatto, vice president of the Federal Reserve Bank of St. Louis, to “organize our thinking” about the world. In other words, macroeconomists use math to make their thoughts concrete, to persuade others, and to check the internal consistency of their (sometimes preposterous) ideas, but not to actually predict things in the real world.
