This paper aims to characterise a dynamic, incentive-compatible contract for the provision of health services, allowing for both moral hazard and adverse selection. Patients' severity changes over time following a stochastic process and is private information of the provider. We characterise the...

The discussion about health care systems focuses on the dynamics of expenditures and on the weak growth of the revenue base. In this discussion it is widely overseen that medical expenditures and supply of medical services crucially depend on the compensation of physician services. The paper...

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 consider a model of moral hazard with limited liability of the agent and effort that is two-dimensional. One dimension of the agent's effort is observable and the other is not. The principal can thusmake the contract conditional not only on outcome but also on observable effort. The...

We show that concerns for fairness may have dramatic consequences for the optimal provision of incentives in a moral hazard context. Incentive contracts that are optimal when there are only selfish actors become inferior when some agents are concerned about fairness. Conversely, contracts that...

This paper proposes a simple partial equilibrium model to investigate the effects of government policy on venture capital backed investments. Giving up an alternative career, entrepreneurs focus their effort on a single, high risk venture each. Venture capitalists acquire an equity stake and...

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...