Everything you know is wrong"
- Weird Al Yankovic
The problem with discovering how the world works by intuition alone is that the model space is just enormous. Humans are really really smart, but very rarely are we smart enough to just sit down and think about how the world might work and actually get it right. Our most spectacularly successful leaps of theoretical insight - Newton's Principia, Einstein's relativity stuff, Mendel's theory of inheritance - were all very closely guided by data. The general pattern was that some new measurement technology would be invented - telescopes, plant hybridization experiments, etc. - that would provide some new unexplained data. Then some smart theorists would come up with a new theoretical framework (paradigm?) to explain it, and the new framework would then also explain a bunch of other stuff besides, and so people would switch to the new theory.
How about econ, though? Auction theory - a big empirical success - seems like it came out of pure intuition (though that could just be my ignorance of history). But if econ is like natural sciences, then we should expect that sort of success to be very rare. Usually, theories developed from armchair intuition - no matter how mathematical - will be wrong, just because wrong theories outnumber right theories by such a huge margin.
Right now we're in the middle of an empirical revolution in econ, and - unsurprisingly - a ton of standard, common theories are just not matching reality very well. For example:
1. If you slap some quick supply-and-demand graphs on the board, it looks like minimum wages should harm employment in the short term. But the data shows that they probably don't.
2. If there's any sort of limits to mobility, then simple labor demand theory says that a big influx of immigrants should depress the wages of native-born workers of comparable skill. But the data shows that in many cases, especially in the U.S., the effect is very small.
3. A simple theory of labor-leisure choice predicts that welfare should make recipients work less. But a raft of new studies shows that in countries around the world, welfare programs barely reduce observable work effort.
4. Most standard econ theory doesn't assume the existence of social norms. But experiments consistently show that social norms (or morals, broadly conceived) matter to people.
Again and again, standard ideas - the stuff that most of the undergrad kiddos learn in their Econ 101 classes - are being smacked down by the heavy hand of new data. We're slowly unlearning economics.
Of course, much of the new empirical literature will eventually prove to be non-replicable or poorly designed, but much of it will prove to be solid and reliable; generations of empirical economists will carefully replicate findings and try to extend them to other contexts to see how well they hold up. And the good results will hold up, and theories will be killed as a result.
Of course, much of the new empirical literature will eventually prove to be non-replicable or poorly designed, but much of it will prove to be solid and reliable; generations of empirical economists will carefully replicate findings and try to extend them to other contexts to see how well they hold up. And the good results will hold up, and theories will be killed as a result.
(Ultimately, that's good for econ theory, since the usual pattern is for better data to produce better theories. And it means more work for econ theorists, since simple theories will be killed first, and the better newer theories will take a lot of effort to make - so there will be jobs and money for theorists. Still, I expect some theorists to complain, because making simple theories is easy and making useful theories is hard.)
But anyway, what this means is that Econ 101 courses around the country probably need an overhaul. New data is rapidly producing a raft of new theoretical facts that students should be learning. Teachers should still teach the simple, classic theories that the new facts are beginning to kill...but mainly as a way to show how data can tell us when we're wrong.