We study a voluntary contributions mechanism in which punishment may be allowed, depending on subjects' voted rules. We found that out of 160 group votes, even when groups had no prior experience with unrestricted punishment, no group ever voted to allow unrestricted punishment and no group ever...

Recent experiments have shown that voluntary punishment of free riders can increase contributions, mitigating the free-rider problem. But frequently punishers punish high contributors, creating “perverse†incentives which can undermine the benefits of voluntary punishment. In our...

We introduce new treatments of a voluntary contribution mechanism with opportunities to punish in order to see how contributions, punishments and earnings change when punishment is in the form of fines the punisher distributes to other members of her group. The linked punishment-reward set up is...

When Congress delegates a policy mandate to a regulatory agency, Congress acts as a principal, choosing the institutional arrangements, or the ‘rules of the game’ for agency decision making. Individuals in the agency, acting as agents, take the rules of the game as given and do the best they...

We investigate, in an experimental setting, the effect of private information on the Coase theorem's predictions of efficiency and allocative neutrality. For a two-person bargaining game, we find significantly more inefficiency and allocative bias in the case of private information compared with...

The paper compares two approaches to the problem of achieving the social goals of sustainability and intergenerational efficiency. The first is a separated approach, where the results of standard benefit-cost analysis are (later) combined with intergenerational equity considerations. The second,...

This paper reports on an experimental study of the way in which individuals make inferences from publicly available information. We compare the predictions of a theoretical model of a common knowledge inference process with actual behavior. In the theoretical model, "perfect Bayesians," starting...