This study adopts behavioral contract theory through a mathematical model and clarifies the situation in which a fixed–salary contract is preferable to incentives–based one for the principal. Theoretically, the expected utility for the principal is higher under an incentives–based contract...

I examine optimal managerial compensation and turnover policy in a principal-agent model in which the firm output is serially correlated over time. The model captures a learning-by-doing feature: higher effort by the manager increases the quality of the match between the firm and the manager in...

We study the optimal dynamics of incentives for a manager whose ability to generate cash .ows changes stochastically with time and is his private information. We show that, in general, the power of incentives (or "pay for performance") may either increase or decrease with tenure. However, risk...

We introduce uncertainty into Holmstrom and Milgrom (1987) to study optimal long-term contracting with learning. In a dynamic relationship, the agent's shirking not only reduces current performance but also increases the agent’s information rent due to the persistent belief manipulation...

This paper investigates relational incentive contracts with continuous, privately-observed agent types that are persistent over time. With fixed agent types, full separation is not possible when continuation equilibrium payoffs following revelation are on the Pareto frontier of attainable...

The marginal cost of effort often increases as effort is exerted. In a dynamic moral hazard setting, dynamically increasing costs create information asymmetry. This paper characterizes the optimal contract and helps explain the popular yet thus far puzzling use of non-linear incentives, for...

I study firm characteristics that justify the use of options or refresher grants in the optimal compensation packages for CEOs in the presence of moral hazard. I model explicitly the determination of stock prices as a function of the output realizations of the firm: Symmetric learning by all...