(Part 1 of my ongoing review of Big Ideas in Macroeconomics, by Kartik Athreya, can be found here.)
Is Macroeconomics a science?
In the first section 1.2.1 of Big Ideas in Macroeconomics - yes, the sections are numbered like that - Kartik Athreya writes:
I will not go into the sterile - and crashingly boring - discussion of whether economics is a science or not, since relabeling it would change neither the questions we asked nor how we approached them.This is a little odd, since he spends the entire section discussing exactly that question. His answer to the question of "Is econ a science?" is basically: "No, and that's fine."
For example, he writes:
My view is that a part of what we do is "organized storytelling, in which we use extremely systematic tools of data analysis and reasoning, sometimes along with more extra-economic means, to persuade others of the usefulness of our assumptions and, hence, of our conclusions...This is perhaps not how one might describe "hard sciences"...
[E]conomics is replete with "observational equivalence" whereby two (or more) sets of assumptions match a given set of data equally well. The paucity of data [with the power] to winnow the set of assumptions...is a huge problem.
One important difference between economics and physical sciences is that we economists have a hard time verifying the closeness of our standard assumptions to reality...Economists also lack axioms that closely approximate conditions in the real world the way that, for example, Newtonian axioms for projectile motion seem to. Seen this way, it is actually the collection of assumptions that we "like most" which constitutes our understanding of the world.
There is a second, even more substantial difference from the physical sciences: for most important macroeconomic questions, macroeconomists cannot conduct controlled experiments.To recap, Athreya sees the following major differences between macroeconomics and "hard sciences":
1. Unlike hard scientists, macroeconomists must spend considerable effort persuading people of the likability of assumptions. The assumptions that macroeconomists "like most" are the ones around which consensus forms.
2. Because of uninformative data, macroeconomics can't offer the kind of robust, definitive answers to real-world questions that hard scientists can often provide.
That is basically my view of how things stand as well (though I'm more annoyed by the situation than Athreya is). By most people's definition, this would mean that macroeconomics isn't a "science". Athreya says that discussing that simple fact is "crashingly boring"...but one thing Athreya's book clearly demonstrates that he is not a man who is easily bored. More likely, Athreya realizes that our society has an unfortunate tendency to sneer and look down its nose at any academic discipline that is not labeled a "science". He realizes that relabeling macro a "non-science", while seemingly a pointless semantic question, will cause the field to lose prestige in the eye of the public.
I do think that the cultural importance of the "science" label is counterproductive. It's not the fault of historians, for example, that Francis Bacon's method offers only limited clues to history's mysteries.
But unlike Athreya, I'm deeply bothered by this "persuasion" thing. Athreya says macroeconomists' job is to persuade others of the "usefulness" of assumptions - basically, that macroeconomists are sort of halfway between scientists and lawyers. But persuasion, when employed on a mass scale, can turn the "wisdom of crowds" into the madness of herd behavior. People have an instinct for social conformity. And we tend to be overconfident in our beliefs, especially about complicated and difficult subjects.
Suppose - and I'm pulling this purely from my lower digestive tract here - that the data tell us that it's 63% likely that recessions are caused by financial market disruptions, and 37% likely that recessions are caused by productivity slowdowns. That's the best the data can do for us - there's no "falsification" here. The rational thing is for everyone to hold that 63/37 split in their minds as a Bayesian belief. But the rational thing is incredibly hard for real humans to do. Our brains urge us to turn our belief into a 100/0 thing - to decide that one assumption is right and the other is wrong. After we make up our mind, we try to persuade other people to do the same. Herd behavior and conformity work their magic. Political bias and other personal biases may come into play. Consensus forms. But the consensus has a big chance of being dead wrong.
Which is better - a firm consensus with a 37% chance of being wrong, or a distribution of beliefs with large confidence intervals, centered on the best possible guess? It depends, I guess. Sometimes, when you need quick action, and when human indecision is holding you back, the firm consensus might be better. But for the slow, ponderous task of figuring out how the world works, I'd prefer the latter. Consensus too often sends us down blind alleys.
I think a lot of people instinctively agree with me, and this is behind the calls for macroeconomists to be more "humble", or to "admit their ignorance", etc. It's probably one reason that macro doesn't often reach a general consensus. But I kind of worry that the amount of consensus that does get reached is too much.
But I feel like Athreya doesn't fully share my distrust of consensus. Section 18.104.22.168 of Big Ideas (yes, the book has subsubsubsections) is entitled "It Takes a Model to Beat a Model". This is a line I've heard before from a number of macroeconomists. The idea that models "beat" other models seems to indicate a preference for 100/0-type beliefs, at the individual level or at the group level.
Mathematics in Macroeconomics
Big Ideas has some other bits of philosophy that might interest people. Chapter 4 has a section (Subsubsection 4.2.4, to be precise) defending the use of mathematics in macroeconomics. The main reasons for math, Athreya says, are that it allows us to be quantitative (which we need in order to make policy), and it allows us to be precise about our meaning. That's pretty similar to what I wrote in this post a while back. And I still think these are good reasons to use math whenever possible.
Athreya makes an important point: Without math, discussions about theory often degenerate into arguments about "what Keynes really meant", or "what Hayek really meant", etc. Like prophets and philosophers, "literary" economists will inevitably get reinterpreted, misinterpreted, and conflictingly interpreted. Spend some time talking with those "heterodox" economists who believe that econ should be a purely literary field, and you'll see what I mean.
But Athreya goes further. He doesn't just defend the use of math in macro; he frowns heavily on any economic discussion that doesn't use math. From Subsubsubsection 22.214.171.124:
The unwillingness [of economists] to couch things in [mathematical] terms (usually for fear of "losing something more intangible") has, in the past, led to a great deal of essentially useless discussion.
The plaintive expressions of "fear of losing something intangible" are concessions to the forces of muddled thinking.That's an interesting claim coming from a book that contains no equations...
But anyway, I think Athreya overlooks something important, which is the role of non-mathematical discussion in idea generation. Even when you use math to make a model, you don't start with the equations - you start by thinking about the concepts. Sometimes you need a lot of thinking before you come up with concepts that are interesting enough to model formally. Sometimes that thinking can't be done by just one person, and instead requires a discussion between people.
Sometimes, hidden among the reams of useless discussion, is a great idea that ends up turning into a great mathematical model. If you always start with the math, you can crank out models, but I think you might miss some of those deeper, bigger insights.
Anyway, in Part 3, I'll discuss the main "Big Idea" in Big Ideas: Arrow-Debreu equilibrium. Stay tuned!