Employment contracts give a principal the authority to decide flexibly which task his agent should execute. However, there is a tradeoff, first pointed out by Simon (1951), between flexibility and employer moral hazard. An employment contract allows the principal to adjust the task quickly to...

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...

Oliver Hart and Bengt Holmström were awarded the 2016 Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel for their fundamental contributions to contract theory. This article offers a short summary and discussion of their path breaking work.

We report on several experiments on the optimal allocation of ownership rights. The experiments confirm the property rights approach by showing that the ownership structure affects relationship-specific investments and that subjects attain the most efficient ownership allocation despite starting...

We show experimentally that fairness concerns may have a decisive impact on both the actual and the optimal choice of contracts in a moral hazard context. Explicit incentive contracts that are optimal according to self-interest theory become inferior when some agents value fairness. Conversely,...

This paper reports on a two-task principal-agent experiment in which only one task is contractible. The principal can either offer a piece-rate contract or a (voluntary) bonus to the agent. Bonus contracts strongly outperform piece rate contracts. Many principals reward high efforts on both...

This paper studies the relation between discrete-time and continuoustime principal-agent models. We derive the continuous-time model as a limit of discretetime models with ever shorter periods and show that optimal incentive schemes in the discrete-time models approximate the optimal incentive...