That article I just quoted (Barry Eichengreen’s “The Last Temptation of Risk,” in The National Interest) just gets better and better:

[W]here the accelerating pace of change should have prompted more caution, the routinization of risk management encouraged precisely the opposite. The idea that risk management had been reduced to a mere engineering problem seduced business in general, and financial businesses in particular, into believing that it was safe to use more leverage and to invest in more volatile assets.

Of course, risk officers could have pointed out that the models had been fit to data for a period of unprecedented low volatility. They could have pointed out that models designed to predict losses on securities backed by residential mortgages were estimated on data only for years when housing prices were rising and foreclosures were essentially unknown. They could have emphasized the high degree of uncertainty surrounding their estimates. But they knew on which side their bread was buttered. Senior management strongly preferred to take on additional risk, since if the dice came up seven they stood to receive megabonuses, whereas if they rolled snake eyes the worst they could expect was a golden parachute. If an investment strategy that promised high returns today threatened to jeopardize the viability of the enterprise tomorrow, then this was someone else’s problem. For a junior risk officer to warn the members of the investment committee that they were taking undue risk would have dimmed his chances of promotion. And so on up the ladder.

On not blowing the whistle, and the loneliness of being right:

But what of doctoral programs in economics (like the one in which I teach)? The top PhD-granting departments only rarely send their graduates to positions in banking or business—most go on to other universities. But their faculties do not object to the occasional high-paying consulting gig. They don’t mind serving as the entertainment at beachside and ski-slope retreats hosted by investment banks for their important clients.

Generous speaker’s fees were thus available to those prepared to drink the Kool-Aid. Not everyone indulged. But there was nonetheless a subconscious tendency to embrace the arguments of one’s more “successful” colleagues in a discipline where money, in this case earned through speaking engagements and consultancies, is the common denominator of success.

Those who predicted the housing slump eventually became famous, of course. Princeton University Press now takes out space ads in general-interest publications prominently displaying the sober visage of Yale University economics professor Robert Shiller, the maven of the housing crash. Not every academic scribbler can expect this kind of attention from his publisher. But such fame comes only after the fact. The more housing prices rose and the longer predictions of their decline looked to be wrong, the lonelier the intellectual nonconformists became. Sociologists may be more familiar than economists with the psychic costs of nonconformity. But because there is a strong external demand for economists’ services, they may experience even-stronger economic incentives than their colleagues in other disciplines to conform to the industry-held view. They can thus incur even-greater costs—economic and also psychic—from falling out of step.

Finally, this bit that reveals what happens when you have large stores of legacy data, combined with a dramatic drop in the costs of analyzing it:

The last ten years have seen a quiet revolution in the practice of economics. For years theorists held the intellectual high ground. With their mastery of sophisticated mathematics, they were the high-prestige members of the profession. The methods of empirical economists seeking to analyze real data were rudimentary by comparison. As recently as the 1970s, doing a statistical analysis meant entering data on punch cards, submitting them at the university computing center, going out for dinner and returning some hours later to see if the program had successfully run. (I speak from experience.) The typical empirical analysis in economics utilized a few dozen, or at most a few hundred, observations transcribed by hand. It is not surprising that the theoretically inclined looked down, fondly if a bit condescendingly, on their more empirically oriented colleagues or that the theorists ruled the intellectual roost.

But the IT revolution has altered the lay of the intellectual land. Now every graduate student has a laptop computer with more memory than that decades-old university computing center. And she knows what to do with it. Just like the typical twelve-year-old knows more than her parents about how to download data from the internet, for graduate students in economics, unlike their instructors, importing data from cyberspace is second nature. They can grab data on grocery-store spending generated by the club cards issued by supermarket chains and combine it with information on temperature by zip code to see how the weather affects sales of beer. Their next step, of course, is to download securities prices from Bloomberg and see how blue skies and rain affect the behavior of financial markets. Finding that stock markets are more likely to rise on sunny days is not exactly reassuring for believers in the efficient-markets hypothesis.

Really, just go read the whole thing. And I gotta track down Eichengreen.