Michael’s post on Charles Manski’s paper challenging prediction markets has been widely discussed. Manski’s paper is difficult and a number of people wrote asking me for further explanation. Luckily, Daniel Davies has done some of the heavy lifting. Michael also offers further comments here.

My take: Manski shows that the market price is not, for example, the mean subjective belief of the market participants. But who said it was? The argument of prediction market proponents is that the market price is a good, perhaps the best, predictor of the future event. Manski does not challenge this argument. In particular, Manski does not show (or try to show) that there is an alternative way of aggregating individual information that results in better predictions. In this sense, I think the Manski paper is something of a red herring.

I would not claim, however, that information markets cannot be improved. Movements in the price of oil tell us something about trouble in the Middle East. But the oil market was not designed to elicit information about the Middle East. The information in markets is an accidental byproduct of trading. It would be a real surprise if the rules that make for good oil trading are the same rules that make for good prediction of events in the Middle East. Sundering information markets from trading markets, therefore, is a big advance and one that is likely to lead to better market design for information revelation, perhaps with help from papers like Manski’s (contra Daniel, who argues that to work well information markets must be tied to trading markets).

Addendum: Victor, a former student of Manski’s, at the Dead Parrots Society adds considerable wisdom to the discussion.

Here is an interesting tidbit from the FTC report on spam bounty that I discussed earlier this week. The IRS has had a bounty system for tax cheats since 1967. In the first thirty years of the program more than seventeen thousand infomants earned $35.1 million, in the process helping the IRS to recover $1.2 billion. That’s a pretty good return, even if some of the ex-wives would have snitched anyway.

False. The owner can sell the forest. As a result, the owner of a forest has an incentive to continue to seed it even if seeds planted today won’t produce trees until after the owner is dead. The same idea applies to any long-lived productive asset.

I think this insight is very beautiful. It’s precisely the fact that the forest is owned that gives the owner an incentive to take into account how other people value the forest.

The basic logic doesn’t require perfect competition or fully efficient markets but if these assumptions do hold then the private owner will choose investment decisions exactly as would a “social planner.”

Bonus questions: What does the logic say about the argument that managers of publicly owned corporations will focus too much on quarterly earnings and not enough on investments that only pay off in the long-run?

What does the logic say about the incentives of a politician who controls an asset like a forest but doesn’t own it?

As usual, and in advance of the Crooked Timber complaints, the points are for thinking about the problem in a logical way, laying out the assumptions and weighing which may or may not hold in various circumstances, and not for arriving at “the answer.” Sorry for being pedantic, but I am a professor.

In From the Valley to the Summit: The Quiet Revolution That Transformed Women’s Work, Claudia Goldin informs that

…in the early to mid-1960s the labor force plans of young women, 14 to 21 years old, reflected the current labor market work of their mothers, their aunts, and possibly their older sisters. The expectations of young women regarding what they planned to do when they were 35 years-old were more in line with what older women were currently doing than with what the younger women would actually be doing in 15 to 20 years. Their expectations about their future employment were inconsistent with what they eventually did. But in the late 1960s and the early 1970s something began to change. Young women (14 to 21 years old) when asked by the NLS Young Women (1968) what they would be doing at age 35 began to offer answers that were more consistent with their actual futures. In 1968, independent of their age at the time, about 30 percent said they would be in the labor force at age 35. But in 1975 about 65 percent said they would be [see figure].

This change in labor market expectations was accompanied by an increase in educational investment and radical changes in educational concentrations as women shifted from majors that were job- or consumption-oriented to those that rewarded long-term investment in a career.

So what was the main driver behind those changes? According to Goldin, it was the birth control pill:

…the Pill lowered the costs to young, unmarried women of pursuing careers, particularly those involving substantial, upfront investments of time. The Pill fostered women’s careers in two ways. A young college woman in the mid-1960s who was considering whether or not to enter a program involving a considerable investment in her time had to factor into this decision its impact on her personal life (e.g., social life, marriage chances after the career investment period). Sex was highly risky in a world without a highly effective, female-controlled, and easy to use contraceptive such as the Pill. A pregnancy could derail a career. The Pill had a direct effect by reducing the risk, and thus the cost, of having sex. The Pill also had an indirect effect because it led to an increase in the age at first marriage and thereby produced a “social multiplier” effect. The Pill virtually eliminated one potent reason for early marriage and for many of the social trappings (e.g., going steady, engagements) that led to early marriage. With more men and women delaying marriage for many years after college graduation, the decision of any one woman to delay marriage to pursue a career meant that she would reenter a marriage market that would not be as depleted.

via The NBER Digest

Ok, bear with me for a few minutes while I tell you a little bit about my relationship with my lovely wife. I promise I’ll be brief and I’ll soon tie into some economics!

My wife calls me on the telephone more often than I call her. Sometimes she complains, “Why don’t you ever call me? Don’t you want to talk to me?” Of course I do, so why don’t I call her? Glen Whitman at Agoraphilia explains:

Say Ted would like to talk on the phone every two days, whereas Sheila would like to talk every day. You might think Sheila would call Ted about two-thirds of the time – but in fact, she will call him every time. If they talk on Monday, Ted plans to call on Wednesday; but then Sheila calls him Tuesday. His clock reset, Ted plans to call on Thursday. And then Sheila calls on Wednesday. Eventually, Sheila decides Ted doesn’t care about her, because he never calls.

Glen uses the same model to explain some other relationship disputes (you can guess which).

As long as I am promoting Agoraphilia you can also read Glen on optimal haircuts, and here is co-blogger Tom Bell on the relationship between ice-cream and cryonics and lest you think this not a serious blog here is Glen’s excellent post on health care savings accounts.

On TradeSports a contract of George Bush in the winner-take-all market is currently selling for around $7. But what does this actually mean? Most traders and researchers would argue that 0.7 is the current “market probability” that the event “George Bush wins the 2004 presidential election” occurs. But this answer drives Charles Manski, an economist who recently also published an article in the September issue of Econometrica, crazy.

He says that under not-so-far-fetched assumptions, the price of a contract reveals nothing about the dispersion of traders’ beliefs and partially identifies the central tendency of beliefs. A President.GWBush2004 contract trading at 70 reveals that 70 percent of traders believe the probability of the event “George Bush wins the 2004 presidential election” to be larger than 0.7. The mean subjective probability of this event lies somewhere in the open interval (0.49, 0.91) (price/mean belief region).

No doubt the last word on this issue hasn’t been spoken but if Manski thinks he can figure out a way that market estimates are biased (for example by narrowing the set of possible belief distributions), then he should be able to come up with a market trading strategy that consistently makes money. And if he succeeds at that, he will in the process correct the market estimate.