Bryan Caplan asks the question of why more academic economists don't use Bayesian methods. It's a question academic Bayesian economists ask ourselves pretty often.
One part of the answer is that many already are informal Bayesians. For example, the whole DSGE literature would be uninteresting (or even more uninteresting, depending on how you feel about it) if there were not a broad consensus about what the plausible range of certain key parameters is.
But the main answer surely involves hysteresis in the teaching of econometrics. Before 1985 or so, anyone who wanted to make use of Bayesian methods was limited to simple models (eg: linear normal regression) for which analytical expressions for certain key integrals are available. Anyone who wanted to apply Bayesian methods to more sophisticated models had to evaluate those integrals using existing numerical techniques; the 'curse of dimensionality' limited Bayesian econometricians to low-dimension problems.
Even if econometricians could be convinced about the soundness of Bayesian methodology (and it's pretty easy to convince them of that; the usual classical story of repeated, infinite samples makes little sense for the analysis of the finite non-experimental data sets that economists are invariably obliged to work with), they could quite sensibly make the point that classical methods were at least able to provide estimates for models more complex than the linear normal model. It didn't make much sense to spend valuable class time on a methodology that could be applied to a proper subset of models that could be estimated by classical methods. And when those students became teachers of econometrics, they would teach what they knew. And they didn't know anything about Bayesian methods.
So when the curse of dimensionality was lifted by the development of Monte Carlo techniques and computers that could run them quickly and cheaply, there were very, very few teachers of econometrics who realised that Bayesian methods could be now applied to a much wider class of models. There are many important cases (multinomial probit, stochastic volatility, and indeed many if not most latent variable models) where classical estimation is much more costly than Bayesian estimation.
Econometrics syllabuses are path-dependent: academic econometricians who didn't see Bayesian methods as students don't teach them to their students. But the original reason for marginalising Bayesian methodology was that its applicability was too limited for most practitioners - and this is no longer the case. Economists are familiar with the notion of hysteresis - the persistence of a phenomenon after its cause has been removed - and it would appear that the teaching of econometrics is another good example.