More on this later, but my vote for the "One Post You Must Read On Our Current Financial Crisis, If You Only Have Time To Read One" award goes to this post from Brad DeLong. What I like is that it provides a good summary of "how we got here" while doing so in a way that generally avoids the perfect hindsight-driven tendency to pronounce Greenspan, et al nothing more than a bunch of fools and knaves. If your explanation is based on everyone having been fools, you are likely to miss something big.

So Bear Stearns has essentially collapsed. Megan and Felix Salmon have commentary.

This is going to be a verrrrry interesting week in the markets. If you don't typically tune into the business news, this might be a good time to start. Will Lehman make it to Wednesday? Tune in tomorrow, on CNBC!

If things go badly, we could be in a financial version of "Ten Little Indians."

Here's Jim Cramer on his friend Eliot Spitzer:

Very touching and sad.

(Via Felix Salmon.)

Via Greg Mankiw, here's a useful interactive guide to the subprime mortgage mess.

And in honor of Paul Krugman's horrifying-yet-too-convincing column (in which he reminds us all that it is easier to make a tightly constructed argument for doom if you don't spend all your time ranting about the evils of GWB or Barack Obama), here's an excerpt from my favorite personal-finance book, Andrew Tobias' The Only Investment Guide You'll Ever Need. Tobias has a chapter about miscellaneous investment topics, and here (quote may be slightly off) is his definition of a margin call:

A margin call is what alerts you to the fact that your life is going to hell and that you never should have gotten into the market when you did, let alone on margin.

This article advocating "settling" for a not-so-perfect husband has attracted a lot of attention, and there certainly is much to say about it. For some reason, it reminded me of a recent piece by economists Betsey Stevenson and Justin Wolfers, which does one of the best jobs I have seen of summarizing the relationships between economic changes and the family. I find it hard to argue against their observations. Their conclusion:

So what drives modern marriage? We believe that the answer lies in a shift from the family as a forum for shared production, to shared consumption. In case the language of economic lacks romance, let's be clearer: modern marriage is about love and companionship. Most things in life are simply better shared with another person: this ranges from the simple pleasures such as enjoying a movie or a hobby together, to shared social ties such as attending the same church, and finally, to the joint project of bringing up children. Returning to the language of economics, the key today is consumption complementarities - activities that are not only enjoyable, but are more enjoyable when shared with a spouse. We call this new model of sharing our lives "hedonic marriage".

...Thus marriage isn't dead, it is, again, transforming. Hedonic marriage is different from productive marriage. In a world of specialization, the old adage was that "opposites attract," and it made sense for husband and wife to have different interests in different spheres of life. Today, it is more important that we share similar values, enjoy similar activities, and find each other intellectually stimulating. Hedonic marriage leads people to be more likely to marry someone of their similar age, educational background, and even occupation. As likes are increasingly marrying likes, it isn’t surprising that we see increasing political pressure to expand marriage to same-sex couples.

Lori Gottleib summarizes marriage differently:

Once you're married, it's not about whom you want to go on vacation with; it's about whom you want to run a household with. Marriage isn't a passion-fest; it's more like a partnership formed to run a very small, mundane, and often boring nonprofit business. And I mean this in a good way.

If that's not a description of marriage as a "forum for shared production" (at least in the post-industrial age) rather than "hedonic," I'm not sure what is. Her article is all about deemphasizing the hedonic function of marriage in favor of the more prosaic and productive aspects - and not just children, but also the more practical benefits of having a permanent partner. Perhaps her article should have been published at Cato Unbound as a response to Stevenson & Wolfers' piece.

I am currently reading Ian Ayres' Super Crunchers, and it's quite good. But in the introduction, he makes some mistakes regarding one of his examples of the analysis of large databases (the "Super Crunching" of the title). Specifically, he cites the now-famous example from Moneyball about how Billy Beane instituted a new draft philosophy for the 2002 draft, allegedly de-emphasizing the opinions of scouts in favor of a more statistically-driven approach. Ayres specifically cites the example of Jeremy Brown, the infamous "fat catcher" whom scouts hated but was drafted anyway on Beane's orders due to his great hitting stats in college. (This blog may have discussed Moneyball once or twice.

First, while Michael Lewis' account of the A's approach to the 2002 draft - especially regarding Brown - was an all-time classic of journalism, the philosophy of that draft has not held up well. Specifically, Ayres wrote that Brown "has progressed faster than anyone else the A's drafted that year," and then cites his brief 2006 callup. Those two sentences create a very misleading impression (especially the "has" tense). Brown did rise through the high minors quickly and was (according to Moneyball's epilogue) the first 2002 draftee invited to the A's major-league spring training, but Brown's career stalled soon thereafter. Fellow 2002 draftees Nick Swisher, Joe Blanton and Mark Teahen all reached the major leagues long before Brown's 2006 debut. Those three have gone on to major league careers of varying levels of success, but Brown only appeared in 5 games before being designated for assignment in 2007 (probably around the time Ayers' book was going to press). Brown in fact just announced his retirement.

More generally, the data-driven approach taken by the A's in the 2002 draft was not particularly successful. Another major part of their philosophy, as detailed by Lewis, was the near-categorical rejection of high school players in favor of college players, supported by old research conducted by Bill James among others. Well, we now know that those conclusions haven't been accurate for a while. ($$) The A's themselves have in recent years drafted many high school pitchers, generally regarded as the riskiest possible category of prospect. Moreover, as Derek Jacques notes ($$), most of the other prospects specifically identified in Moneyball as draft targets identified through the A's statistical analysis did not come close to making the majors. It is incorrect to say that scouts' importance to the identification of prospects has decreased in the years following Moneyball's publication - if anything, the opposite is true.

Finally, I'm not sure that Ayres picks the righ theoretical example to illustrate the data-mining that is at the heart of his book. While there are thousands of baseball prospects considered for drafting every year, the differences in their playing contexts (high school vs. college, different areas of the country and levels of competition, etc.) work against the idea that a large database of common baseline experiences can be constructed and analyzed. Baseball people look at their statistics, but the contexts are so different as to make it difficult to analyze usefully in the aggregate - which is what "Super Crunching" is about.

But sabermetrics does present a really good example of what Ayres is looking for: the efforts in recent years to build better defensive metrics. Whether it's David Pinto's "Probablistic Model of Range," Bill James and John Dewan's "Plus-Minus System" or an alternate model, the new measures of defensive performance rely on analyzing thousands of plays in the field. So let's pretend this was the example Ayres meant to cite.

I like this analogy:

[W]hy did the Houston Rockets draft Yao Ming? They couldn't not draft him. The lessons for financial markets are obvious. Drafting Yao Ming is like writing the disguised naked put. You see the money in front of you, you see the return in front of you, you see the potential in front of you, none of the alternatives are so glamorous, and so you can't not do it.

The comments on that post are interesting as well. I don't know whether the 7'4" injury tendency is real or an artifact of small sample size, but I know that in baseball, 6'4" seems to be the biological limit for catchers' ability to have long careers (may be $$)