It looks like some version of the Policy Analysis Markets will be launched after all, but without any government involvement. As noted in my previous post, such markets could be effective aggregators of information if structured properly. As far as I can tell, the markets will be run by a private company (San Diego-based Net Exchange). If profit condiderations drive decisions about market structure, then it seems unlikely that the market rules and procedures will be optimal from the perspective of aggregating information.
Saturday, December 06, 2003
Tuesday, August 19, 2003
In an extraordinary sequence of events last month, a Defense Department project aimed at constructing a futures market in catastrophic events appeared briefly in the media spotlight, received blistering attacks from all corners of the political spectrum, and was hastily dismantled, all in the space of a few days.
Notwithstanding the sudden demise of this particular initiative, the basic idea underlying it is important enough to deserve a calm, sensible and non-partisan public debate. Similar markets already exist. Dublin-based Tradesports.com offers futures contracts linked to the capture of Saddam Hussein and Osama Bin Laden, as well as the search for unconventional weapons in Iraq. The Iowa Electronic Markets offers contracts tied to the outcome of the Democratic presidential nomination process, as well as vote shares in the 2004 election. The Iowa Markets have managed to forecast vote shares consistently better than most opinion polls. Perhaps such markets can forecast attacks, assassinations and social upheavals and in doing so, reduce their incidence and mitigate their most harmful effects. At the very least, it's worth giving the idea a thoughtful public airing.
The main argument in favor of such markets is that they efficiently aggregate dispersed information to produce a more reliable forecast than could be obtained by more conventional methods. If this argument is to be persuasive, then the same asset traded in different markets should fetch roughly the same price. A quick comparison of the Democratic nominee contracts in IEM and Tradesports is enough to give pause even to the most ardent market enthusiast. It is easy to find significant and persistent pricing disparities for identical contracts across the two markets.
At the time of this writing, contracts that pay $10 if Gephardt is chosen to be the nominee can be purchased for 70 cents on Tradesports but cost $1.19 on IEM. Over the first two weeks of August, such contracts have been available for purchase in large quantities on Tradesports at prices never exceeding 80 cents. Over the same period, equivalent contracts have traded on IEM for prices as high as $1.59, and for an average price of $1.18. Similar disparities have persisted in the pricing of Lieberman contracts, but in the opposite direction: such contracts have been significantly cheaper on IEM relative to Tradesports over this period. Why the difference? What prevents the equalization of prices through cross-market arbitrage? And which, if any, of these market forecasts should be taken seriously?
While there is no substantive difference in the terms of the individual contracts, there are several differences in the rules and procedures governing the two markets. The most important of these concern the information available to traders about orders placed by others, and the types of orders that market participants are permitted to place. Such differences in rules can affect the ability of markets to serve as efficient aggregators of information.
Traders in the Iowa Markets can see only the best available prices at which the asset can be currently sold or purchased (the bid and ask prices). They cannot see the amounts that could be traded at these prices, nor can they see the range of other prices and quantities at which orders have been placed. In contrast, Tradesports allows participants to see the entire order book, consisting of the prices and quantities associated with every active and unfilled order. Having access to the order book allows traders to make inferences about the information available to others, which can potentially lead to better information aggregation. In this respect, the Tradesports design is superior.
A second major difference in market structure pertains to the kinds of orders that can be placed. The Iowa Markets allow for the purchase and sale of contract “bundles” either directly from the exchange at a fixed price, or from other traders at currently posted prices. A bundle consists of the entire set of contracts, exactly one of which will eventually pay off while the others expire worthless. The exchange buys and sells bundles at a dollar apiece, since this is exactly what the eventual payoff to the bundle will be. Having bought a bundle, traders can sell off one or more of its individual components on the market: this is how they can bet “against” an event.
The ability to buy and sell bundles can have dramatic effects on the speed and efficiency with which markets respond to new information. It ensures that when the price of one asset rises, the others almost simultaneously decline. Furthermore, it allows traders with good information but small budgets to periodically recycle their funds and have a disproportionate impact on the market. For instance, if they are able to systematically identify temporarily underpriced contracts, they can accumulate bundles over time and sell them back to the exchange, thus freeing up funds for subsequent purchases. In Tradesports they would have to sell these contracts to other traders on an individual basis, which is more time-consuming and less reliable. Along this dimension, therefore, the Iowa design is superior.
There are other differences in market structure. Trading is free in the Iowa Markets, while Tradesports participants pay a small percentage of the contract face value on each trade. Prices on IEM can be posted in multiples of one-tenth of a cent, while the tick size in Tradesports markets can be as high as 10 cents. Tradesports shuts down for an hour a day, while trading is continuous on IEM. In general, lower fees, smaller tick sizes, and continuously open markets can be expected to result in broader participation and better information aggregation.
If the original purpose of a market in catastrophic events is to be met, the structure of the new market should combine the best features of existing ones. Even the most carefully constructed market, however, may fail to yield reliable forecasts. Policy Analysis Markets are likely to be thin, subject to large price swings and relatively easy to manipulate. Low probability events would tend to be overpriced since it is expensive and risky to bet against them. For similar reasons, high probability events could be systematically underpriced. Despite these potential pitfalls, it is worth paying some attention to the question of how such markets should be structured, because their emergence, with or without governmental involvement, is surely inevitable.
Saturday, January 18, 2003
A recent paper by Marianne Bertrand and Sendhil Mullainathan has been getting a lot of press in the shadow of the debate over the University of Michigan’s affirmative action policies. The paper, entitled Are Emily and Brendan More Employable than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination, is accessible to the non-specialist and well worth reading.
The authors started with a collection of resumes that were downloaded from web sites catering to jobseekers, and modified them to remove identifying information. They then sent out resumes in response to vacancies advertised in Boston and Chicago, after first randomly assigning to each resume either a "white-sounding" or a "black-sounding" name. The names were obtained from historical birth records, and were based on the relative frequencies of names in black and white households. Hence names that were either much more common in white households than in black ones (such as Meredith and Todd) or much more common in black households than in white ones (such as Tanisha and Hakim) were selected for the study. Names such as Michael and Vanessa, which are common in both communities and hence carry little or no racial association, were not used. The authors found that resumes which had been randomly assigned white-sounding names elicited significantly higher callback rates than those assigned black-sounding names.
Many economists will be tempted to interpret these findings through the lens of statistical discrimination theory: if names carry information about the populations from which subjects are drawn, and these populations differ with respect to their productive characteristics, then names will carry statistical information about worker productivity. The authors try to test this interpretation by selectively improving some resumes prior to the random assignment of names. This is done, for instance, by filling in gaps in employment history and adding volunteer work. They find that such improvements significantly increase callback rates for resumes assigned white-sounding names but have negligible effects for those assigned black-sounding names. This flies in the face of the standard statistical discrimination model, in which greater information about candidates should narrow rather than widen discriminatory treatment.
So what’s going on? The authors are careful to leave open the possibility that more sophisticated models of statistical discrimination can account for their findings. But sometimes the simplest explanation really is the right one, and in my own view this is the case here. It seems to me that there is a subset of employers who have a strong negative gut-reaction to a black-sounding name and don’t bother to scan resumes for additional information once this reaction is triggered. The number of such individuals may be small relative to the population of employers, but they must be sufficiently numerous for their behavior to result in statistically discernible aggregate effects.