A standard hidden information model is considered to study the influence of the a priori productivity distribution on the optimal contract. A priori more productive (hazard rate dominant) agents work less, enjoy lower rents, but generate a higher expected surplus.

This paper offers a rationale for limiting the delegation of (real) authority, which neither relies on insurance arguments nor depends on ownership structure. We analyse a repeated hidden action model in which the actions of a risk neutral agent determine his future outside option. Consequently,...

We examine a situation where efforts on different tasks positively affect production but are not separately verifiable and where the manager (principal) and the worker (agent) have different ideas about how production should be carried out: agents prefer a less efficient way of production. We...

When designing incentives for a manager, the trade-off between insurance and a good allocation of effort across various tasks is often identified with a trade-off between the responsiveness (sensitivity, precision, signal-noise ratio) of the performance measure and its similarity (congruity,...

What is the motivational effect of imposing a minimum effort requirement? Agents may no longer exert voluntary effort but merely meet the requirement. Here, we examine how such hidden costs of control change when control is considered legitimate. We study a principal-agent model where control...

Consider a principal-agent relationship in which more effort by the agent raises the likelihood of success. Does rewarding success, i.e., paying a bonus, increase effort in this case? I find that bonuses have not only an incentive but also an income effect. Overall, bonuses paid for success may...

We investigate a team setting in which workers have different degrees of commitment to the outcome of their work. We show that if there are complementarities in production and if the team manager has some information about team members, interventions that the manager undertakes in order to...