Implicit Incentive Provision with Misspecified Learning

We study misspecified Bayesian learning in principal-agent relationships, where an agent is assessed by an evaluator and rewarded by the market. The agent's outcome depends on their innate ability, costly effort -- whose effectiveness is governed by a productivity parameter -- and noise. The market infers the agent's ability from observed outcomes and rewards them accordingly. The evaluator conducts costly assessments to reduce outcome noise, which shape the market's inferences and provide implicit incentives for effort. Society -- including the evaluator and the market -- holds dogmatic, inaccurate beliefs about ability, which distort learning about effort productivity and effort choice. This, in turn, shapes the evaluator's choice of assessment. We describe a feedback loop linking misspecified ability, biased learning about effort, and distorted assessment. We characterize outcomes that arise in stable steady states and analyze their robust comparative statics and learning foundations. Applications to education and labor market reveal how stereotypes can reinforce across domains -- sometimes disguised as narrowing or even reversals of outcome gaps -- and how policy interventions targeting assessment can help.