This paper studies the design of optimal contracts in dynamic environments where agents learn by doing. We derive a condition under which contracts are fully incentive compatible. A closed-form solution is obtained when agents have CARA utility. It shows that human capital accumulation...

Several empirical findings have challenged the traditional view on the trade-off between risk and incentives. By combining risk aversion and limited liability in a standard principal-agent model the empirical puzzle on the positive relationship between risk and incentives can be explained....

This paper analyzes the principal-agent relationship between a plaintiff and his or her lawyer when the lawyer's investment in discovery is private information. The plaintiff uses the level of the contingency fee and potentially also restrictions on settlements to guide the lawyer's...

Severance pay, a fixed-sum payment to workers at job separation, has been the focus of intense policy concern for the last several decades, but much of this concern is unearned. The design of the ideal separation package is outlined and severance pay emerges as a natural component of job...

The paper analyzes the choice of organizational structure as solution to the trade-off between controlling behavior based on authority rights and minimizing costs for implementing high efforts. The analysis includes the owner of a firm, a top manager and two division heads. If it is more...

A standard tournament contract specifies only tournament prizes. If agents' performance is measured on a cardinal scale, the principal can complement the tournament contract by a gap which defines the minimum distance by which the best performing agent must beat the second best to receive the...

We consider a two-stage principal-agent model with limited liability in which a CEO is employed as agent to gather information about suitable merger targets and to manage the merged corporation in case of an acquisition. Our results show that the CEO systematically recommends targets with low...