Knowledge that Transforms

Several agents with privately known social values compete for a prize. The prize is allocated based on the claims of the agents, and the winner is subject to a limited penalty if he makes a false claim. If the number of agents is large, the optimal mechanism places all agents above a threshold onto a shortlist along with a fraction of agents below the threshold, and then allocates the prize to a random agent on the shortlist. When the number of agents is small, the optimal mechanism allocates the prize to the agent who makes the highest claim, but restricts the range of claims above and below.

We analyze sequential, binary voting schemes in settings where several privately informed agents have single-peaked preferences over a finite set of alternatives, and we focus on robust equilibria that do not depend on assumptions about the players' beliefs about each other. Our main results identify two intuitive conditions on binary voting trees, ensuring that sincere voting at each stage forms an ex post perfect equilibrium. In particular, we uncover a strong rationale for content-based agendas: if the outcome should not be sensitive to beliefs about others, nor to the deployment of strategic skills, the agenda needs to be built “from the extremes to the middle” so that more extreme alternatives are both more difficult to adopt, and are put to vote before other, more moderate options. An important corollary is that, under simple majority, the equilibrium outcome of the incomplete information game is always the Condorcet winner. Finally, we aim to guide the practical design of schemes that are widely used by legislatures and committees and we illustrate our findings with several case studies.

Kurmann and Otrok (2013) establish that the effects on economic activity from news on future productivity growth are similar to the effects from unexpected changes in the slope of the yield curve. This comment shows that these results become substantially weaker in the light of a recent update in the utilization-adjusted total factor productivity series produced by Fernald (2014).

This paper develops a method for forecasting the marginal abatement cost (MAC) of climate policy using three features of the failed Waxman-Markey bill. First, the MAC is revealed by the price of traded permits. Second, the permit price is estimated using a regression discontinuity design (RDD) comparing stock returns of firms on either side of the policy's free permit cutoff rule. Third, because Waxman-Markey was never implemented, I extend the RDD approach to incorporate prediction market prices which normalize estimates by policy realization probabilities. A final bounding analysis recovers a MAC range of $5 to $19 per ton CO 2 e.