Disagreement and learning in a dynamic contracting model

We present a dynamic contracting model in which the principal and the agent disagree about the resolution of uncertainty, and we illustrate the contract design in an application with Bayesian learning. The disagreement creates gains from trade that the principal realizes by transferring payment to states that the agent considers relatively more likely, a shift that changes incentives. In our dynamic setting, the interaction between incentive provision and learning creates an intertemporal source of ?disagreement risk? that alters optimal risk sharing. An endogenous regime shift between economies with small and large belief differences is present, and an early shock to beliefs can lead to large persistent differences in variable pay even after beliefs have converged. Under risk-neutrality, ?selling the firm? to the agent does not implement the first-best outcome because it precludes state-contingent trades.