Justin Wolfers

Prediction Markets: Does Money Matter?

The accuracy of prediction markets has been documented for both markets based on real money and those based on play money. To test how much extra accuracy can be obtained by using real money versus play money, we set up a real-world on-line experiment pitting the predictions of TradeSports.com (real money) against those of NewsFutures.com (play money) regarding American Football outcomes during the fall-winter 2003-2004 NFL season. As expected, both types of markets exhibited significant predictive powers, and remarkable performance compared to individual humans. Perhaps more surprisingly, the play-money markets performed as well as the real- money markets. We speculate that this result reflects two opposing forces: real-money markets may better motivate information discovery while play-money markets may yield more efficient information aggregation.

The Promise of Prediction Markets

The ability of groups of people to make predictions is a potent research tool that should be freed of unnecessary government restrictions.

Are Voters Rational? Evidence from Gubernatorial Elections

Standard agency theory suggests that rational voters will vote to re-elect politicians who deliver favorable outcomes. A second implication is that rational voters will not support a politician because of good outcomes unrelated to the politicians actions. Specifically, rational voters should try to filter signal from noise, both in order to avoid electing incompetent, but lucky politicians, and to maximize the link between their votes and optimal incentives. This paper provides insight into the information processing capacities of voters, by measuring the extent to which they irrationally reward state governors for economic fluctuations that are plausibly unrelated to gubernatorial actions. Simple tests of relative performance evaluation reveal that voters evaluate their states economic performance relative to the national economy. However, these tests only provide evidence of rule-of-thumb performance filtering. More sophisticated tests reveal that voters in oil-producing states tend to re-elect incumbent governors during oil price rises, and vote them out of office when the oil price drops. Similarly, voters in pro-cyclical states are consistently fooled into re-electing incumbents during national booms, only to dump them during national recessions. Consistent with an emerging behavioral literature, this suggests that voters make systematic attribution errors and are best characterized as quasi-rational.

Explaining the Favorite-Long Shot Bias: Is it Risk-Love or Misperceptions?

The favorite–long shot bias describes the long-standing empirical regularity that betting odds provide biased estimates of the probability of a horse winning: long shots are overbet whereas favorites are underbet. Neoclassical explanations of this phenomenon focus on rational gamblers who overbet long shots because of risk-love. The competing behavioral explanations emphasize the role of misperceptions of probabilities. We provide novel empirical tests that can discriminate between these competing theories by assessing whether the models that explain gamblers’ choices in one part of their choice set (betting to win) can also rationalize decisions over a wider choice set, including compound bets in the exacta, quinella, or trifecta pools. Using a new, large-scale data set ideally suited to implement these tests, we find evidence in favor of the view that misperceptions of probability drive the favorite–long shot bias, as suggested by prospect theory.

Using Prediction Markets to Track Information Flows: Evidence from Google

Since 2005, Google has conducted the largest corporate experiment with prediction markets we are aware of. In this paper, we illustrate how markets can be used to study how an organization processes information. We show that market participants are not typical of Google’s workforce, and that market participation and success is skewed towards Google’s engineering and quantitatively oriented employees.

Point Shaving: Corruption in NCAA Basketball

A new field of “forensic economics” has begun to emerge, applying price-theoretic models to uncover evidence of corruption in domains previously outside the purview of economists. By emphasizing the incentives that yield corruption, these approaches also provide insight into how to reduce such behavior. This paper contributes to this agenda, highlighting how the structure of gambling on college basketball yields pay-offs to gamblers and players that are both asymmetric and nonlinear, thereby encouraging mutually beneficial effort manipulation through “point shaving.” Initial evidence suggests that point shaving may be quite widespread. The incentives for gambling-related corruption derive from the structure of basketball betting. To highlight a simple example, the University of Pennsylvania played Harvard on March 5, 2005, and was widely expected to win. Rather than offering short odds on Penn winning the game, bookmakers offered an almost even bet (bet

The New Stylized Facts About Income and Subjective Well-Being

11 to win

Three Tools for Forecasting Federal Elections: Lessons from 2001

10) on whether Penn would win relative to a “spread.” In this example, the spread was 14.5, meaning that a bet on Penn would win only if Penn won the game by 15 or more points, while a bet on Harvard would be successful if Harvard either won, or lost by 14 or fewer points. The incentive for corruption derives directly from the asymmetric incentives of players, who care about winning the game, and gamblers, who care about whether a team beats (or covers) the spread. Indeed, the example above is ripe for corruption: the outcome that maximizes the joint surplus of the Penn players and the gambler occurs when Penn wins the game, but fails to cover the spread (and the gambler has bet on Harvard). The contract required to induce this outcome simply involves the gambler offering a contingent payment to the player, with the contingency being that he pays only if Penn fails to cover the spread. Given the player’s (approximate) indifference over the size of the winning margin, even small bribes may dominate his desire to increase the winning margin above 14 points, and this, in turn, yields large profits for the gambler who has bet accordingly. The betting market offers a simple technology for the gambler to commit to paying this outcomecontingent bribe: he can simply give the player the ticket from a

The Death Penalty: No Evidence for Deterrence

1,000 bet on his opponent not covering the spread. Such attempts to shave the winning margin below the point spread are colloquially referred to as “point shaving” and form the focus of my inquiry. I start by outlining the type of corruption that theory suggests will be most prevalent:

How Prediction Markets Can Save Event Studies

Economists in recent decades have turned their attention to data that asks people how happy or satisfied they are with their lives. Much of the early research concluded that the role of income in determining well-being was limited, and that only income relative to others was related to well-being. In this paper, we review the evidence to assess the importance of absolute and relative income in determining well-being. Our research suggests that absolute income plays a major role in determining well-being and that national comparisons offer little evidence to support theories of relative income. We find that well-being rises with income, whether we compare people in a single country and year, whether we look across countries, or whether we look at economic growth for a given country. Through these comparisons we show that richer people report higher well-being than poorer people; that people in richer countries, on average, experience greater well-being than people in poorer countries; and that economic growth and growth in well-being are clearly related. Moreover, the data show no evidence for a satiation point above which income and well-being are no longer related.