Workers competing in a tournament for a prize (e.g., a promotion) often perform sequentially in multiple stages. When the firm is privately informed about the workers’ performance, it can sharpen incentives by strategically disclosing the intermediate results. But the policies that enhance final-stage effort may dampen incentives at the intermediate stage. In our model, the optimal disclosure policy has a simple form: disclose only if all workers perform poorly. This result offers a novel justification for partial disclosure in performance feedback. Also, it contrasts with the existing literature that focuses on the extreme policies of full disclosure and no disclosure.

Abstract An agent may privately learn which aspects of his job are more important by shirking on some of them, and use that information to shirk more effectively in the future. In a model of long-term employment relationship, we characterize the optimal relational contract in the presence of such learning-by-shirking and highlight how the performance measurement system can be managed to sharpen incentives. Two related policies are studied: intermittent replacement of existing measures, and adoption of new ones. In spite of the learning-by-shirking effect, the optimal contract is stationary, and may involve stochastic replacement/adoption policies that dilute the agent’s information rents from learning how to game the system.