Moral Hazard, Dynamic Incentives, and Ambiguous Perceptions

TL;DR

This paper develops a continuous-time model of dynamic moral hazard with drift ambiguity, showing how uncertainty about technology impacts optimal long-term contracts and reduces incentive intensity.

Contribution

It introduces a tractable framework for designing robust long-term contracts under drift ambiguity, linking robustness with simpler, less incentive-driven contracts.

Findings

Optimal contracts align parties' pessimistic expectations.

High-powered incentives are less effective under ambiguity.

Provides a method to characterize robust long-term contracts.

Abstract

This paper considers dynamic moral hazard settings, in which the consequences of the agent's actions are not precisely understood. In a new continuous-time moral hazard model with drift ambiguity, the agent's unobservable action translates to drift set that describe the evolution of output. The agent and the principal have imprecise information about the technology, and both seek robust performance from a contract in relation to their respective worst-case scenarios. We show that the optimal long-term contract aligns the parties' pessimistic expectations and broadly features compressing of the high-powered incentives. Methodologically, we provide a tractable way to formulate and characterize optimal long-run contracts with drift ambiguity. Substantively, our results provide some insights into the formal link between robustness and simplicity of dynamic contracts, in particular high-powered incentives become less effective in the presence of ambiguity.