We model private politics in the presence of two-sided incomplete information. An interest group (IG) threatens to inform consumers about a firm鈥檚 damaging technology should the firm not adopt a clean technology. The IG does not know how costly adopting the clean technology is and the firm does not know how much the IG cares about the damages. In equilibrium an IG who cares a lot signals its type to the firm and the firm is more inclined to adopt the clean technology if it receives such a signal. However, impasses can occur: the firm does not adopt the clean technology despite the fact that the IG has signaled that it cares a lot and threatens to inform a large fraction of the consumer population. The IG never informs all consumers: as soon as a certain fraction of the consumers is informed by the IG the firm reduces its price and thereby reveals to the remaining consumers that it is employing a damaging technology. The IG鈥檚 actions increase consumer well-being, but decrease total welfare unless the cost of adopting the clean technology is likely to be low. Yet, since the IG is inclined to target firms with this property, a regulator might want to delegate information provision to the IG.
Allard van der Made I explain the ubiquitous use of graduated punishments by studying a repeated public good game in which a social planner imperfectly monitors agents to detect shirkers. Agents' cost of contributing is private information and administering punishments is costly. Using graduated punishments can be optimal for two reasons. It increases the price of future wrongdoing (temporal spillover effect) and it can lead to bad types revealing themselves (screening effect). The temporal spillover effect is always present if graduated punishments prevail, but screening need not occur if agents face a finite horizon. Whether or not a screening effect is exploited has a substantial impact on both outcomes and actual punishments. If the temporal spillover effect is sufficiently strong, then first-time shirkers are merely warned.
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