Communication for Public Goods

This paper studies information transmission between multiple agents with di¤erent preferences and a welfare maximizing decision maker who chooses the quality or quantity of a public good (e.g. provision of public health service; carbon emissions policy; pace of lectures in a classroom) that is consumed by all of them. Communication in such circumstances suffers from the agents' incentive to "exaggerate" their preferences relative to the average of the other agents, since the decision maker's reaction to each agent's message is weaker than in one-to-one communication. As the number of agents becomes larger the quality of information transmission diminishes. The use of binary messages (e.g. "yes" or "no") is shown to be a robust mode of communication when the main source of informational distortion is exaggeration.

Sharp Bounds on Heterogeneous Individual Treatment Responses

This paper discusses how to identify individual-specific causal effects of an ordered discrete endogenous variable. The counterfactual heterogeneous causal information is recovered by identifying the partial differences of a structural relation. The proposed refutable nonparametric local restrictions exploit the fact that the pattern of endogeneity may vary across the level of the unobserved variable. The restrictions adopted in this paper impose a sense of order to an unordered binary endogeneous variable. This allows for a uni.ed structural approach to studying various treatment effects when self-selection on unobservables is present. The usefulness of the identi.cation results is illustrated using the data on the Vietnam-era veterans. The empirical findings reveal that when other observable characteristics are identical, military service had positive impacts for individuals with low (unobservable) earnings potential, while it had negative impacts for those with high earnings potential. This heterogeneity would not be detected by average effects which would underestimate the actual effects because different signs would be cancelled out. This partial identification result can be used to test homogeneity in response. When homogeneity is rejected, many parameters based on averages may deliver misleading information.