There was a bunch of Twitter discussion this week inspired by a Vox article by Dylan Matthews about Raj Chetty’s introductory economics course at Harvard. TLDR: The course is basically a review of recent, large-scale empirical work on important social problems like inequality. And it has little to no theory. I’ve watched some of the lectures and I’d recommend them. But the discussion on Twitter was about whether this was a good way to introduce students to economics. Some stuck up for traditional Econ 101; some argued it complemented Chetty’s big data course; some plugged the excellent new(ish) CORE introductory text.
My reaction was this:
About fixing Econ 101… whatever one decides to change content-wise, the biggest thing IMO would be to open w/ a full lecture on models & many-model thinking. Then say: What follows are not ironclad truths but partial explanations that will sometimes prove to be useful.
— Walt Frick (@wfrick) May 23, 2019
Whereas academic economists are concerned with the science of economics, including making their models as accurate as possible, most people who take econ 101 need to learn how to use those models in some way in their work and lives. They need to learn to be analysts, not scientists.
And the way to use economic models is to recognize that they are deeply incomplete approximations of the world, that they’ll be more useful in some cases than in others, and that the best results come from combining them with other useful models from other disciplines. I’m all for more people learning economic models other than just perfectly competitive markets, of course. But even more important is for people who do learn about competitive markets to realize that it’s an approximation that is never true but can sometimes be useful.
As good as the Chetty course is, my preferred update would also borrow heavily from, say, Scott Page or Philip Tetlock.
beyond the times 