Learning in Relational Contracts
Abstract: We study relational contracts between a firm and an worker who face symmetric uncertainty about their match quality. The workers actions are observed by the firm. Actions affect payoff as well as provide information about the match quality. We show that even when the worker is not protected by limited liability, and despite the absence of private information and hidden action, uncertainty about match quality precludes efficiency. The source of inefficiency is a holdup problem arising out of the separation between the entity exerting effort and the entity collecting the output. We characterize optimal relational contracts and show they may involve actions that are dominated in their informational content as well as payoff. Such actions are a modest way for the firm to provide incentives and learn about the match quality, when more efficient ways are not credible. Conditional upon strong performance, we show that the relationships move to a phase where actions that offer better learning and higher payoff are used. In this phase the worker is rewarded with a bonus upon strong performance.