Political scientists who write clearly for a broader audience are upset with Nick Kristof for saying that political scientists no longer write for a broader audience. I’m not going to get into that fight. I do want to register one point, however: In my field there is indeed a problem with abstruseness, with the many academics who never even try to put their thoughts in plain language.

And what is the nature of that problem? It’s not that laypeople don’t understand what the academics are saying. It is, instead, that the academics themselves don’t understand what they’re saying.

Don’t get me wrong: I like mathematical modeling. Mathematical modeling is a friend of mine. Math can be a powerful clarifying tool. So, in some cases, can jargon, which used right can both save time and add clarity to the discussion. If I talk about Dixit-Stiglitz preferences, or for that matter the zero lower bound, technically trained economists immediately know whereof I speak, where plain English would both take longer and leave room for misunderstanding.

But it’s really important to step away from the math and drop the jargon every once in a while, and not just as a public service. Trying to explain what you’re doing intuitively isn’t just for the proles; it’s an important way to check on yourself, to be sure that your story is at least halfway plausible.

Take real business cycle theory – I know it’s a horse I beat a lot, but it’s not dead, and it’s a prime example within economics of what I have in mind. I still want to spend at least some time explaining that theory to my undergrads, so I’ve been looking for a simple, intuitive explanation by an RBC theorist of what’s going on. And I haven’t been able to find one!

I mean, I could do it myself. Strip the story down to basics – make it a steady-state model, not a growth model, and drop the capital accumulation; what you’re left with is fluctuations in the marginal productivity of labor, which have a magnified impact on output because workers choose to work less when the technology is bad and more when the technology is good. As I’ve written before someplace, it’s the story of a farmer who stays inside when it’s raining and puts in extra hours when the sun is shining.

But the RBC theorists never seem to go there; it’s right into calibration and statistical moments, with never a break for intuition. And because they never do the simple version, they don’t realize (or at any rate don’t admit to themselves) how fundamentally silly the whole thing sounds, how much it’s at odds with lived experience.

I once talked to a theorist (not RBC, micro) who said that his criterion for serious economics was stuff that you can’t explain to your mother. I would say that if you can’t explain it to your mother, or at least to your non-economist friends, there’s a good chance that you yourself don’t really know what you’re doing.

Math is good. Sometimes jargon is good, too. But plain language and simple intuition are important to keep you grounded.