Value by design : performance evaluation and learning from wages

We study a dynamic principal-agent setting in which both sides learn about the importance of effort. The quality of the agent's output is not observed directly. Instead, the principal jointly designs an evaluation technology and a wage schedule. More precise performance evaluation reduces current agency costs but promotes learning, which can increase future agency costs. As a result, the optimal evaluation technology is noisy. Performance information that solely pertains to effort is revealed, while the principal optimally conceals information about the agent's ability. The optimal evaluation features a censorship pattern with a base wage and tailored bonuses/penalties when the threshold is exceeded. With binary output, it is both imprecise and tough if effort and ability are strong complements: a bad performance is always sanctioned, but a good one is not always recognized.