Almost all economic models assume that human beings are Bayesians: They start with some prior beliefs about how the world works, and update those beliefs using Bayes’ Theorem as new information arrives.  Behavioral economists often question whether people are in fact Bayesians, but they agree that we should be.  (See e.g. the epilogue to The Winner’s Curse).  It is striking, then, to realize that academic economists are not Bayesians.  And they’re proud of it!

This is clearest for theorists.  Their epistemology is simple: Either something has been (a) proven with certainty, or (b) no one knows – and no intellectually respectable person will say more.  If no one has proven that Comparative Advantage still holds with imperfect competition, transportation costs, and indivisibilities, only an ignoramus would jump the gun and recommend free trade in a world with these characteristics.

Empirical economists’ deviation from Bayesianism is more subtle.  Their epistemology is rooted in classical statistics.  The respectable researcher comes to the data an agnostic, and leaves believing “whatever the data say.”  When there’s no data that meets their standards, they mimic the theorists’ snobby agnosticism.  If you mention “common sense,” they’ll scoff.  If you remind them that even classical statistics assumes that you can trust the data – and the scholars who study it – they harumph.

I frequently encountered this anti-Bayesian mind-set among Princeton labor economists, who scorned e.g. all common-sense doubts about Card-Krueger’s research on the minimum wage.  Most of the skeptics quibbled with the quality of the research.  They couldn’t bear to admit that (a) the research was high-quality, but (b) it would take vastly more research of vastly higher quality to convince them that employers buy just as much labor (or more!) when its price rises.  If the critics had been thorough Bayesians, they would have said something like what I say during the first week of my Ph.D. Micro class:

Bayes’ Rule provides a natural
framework for scientists to relate hypotheses to evidence. Let A be your
hypothesis and B be some evidence; then calculate P(A|B).

Ex: The P(minimum wage causes unemployment|Card/Krueger
study’s findings).  Suppose P(CK findings|m.w. does cause
unemployment)=.3, P(CK findings|m.w. does not cause unemployment)=.8, P(m.w.
does cause unemployment)=.99, and P(m.w. does not cause
unemployment)=.01.  Then the conditional probability comes out to

My question: Why aren’t academic economists Bayesians?  If even iconoclastic behavioral economists agree that rational agents should be Bayesians, what excuse have academic economists got to be anything else?