We modify the principal-agent model with moral hazard by assuming that the agent is expectation-based loss averse according to Köszegi and Rabin (2006, 2007). The optimal contract is a binary payment scheme even for a rich performance measure, where standard preferences predict a fully...

We extend Akerlof's (1970) "Market for Lemons" by assuming that some buyers are overconfident. Buyers in our model receive a noisy signal about the quality of the good that is at display for sale. Overconfident buyers do not update according to Bayes' rule but take the noisy signal at face...

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...

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...

We extend Akerlof (1970)'s 'Market for Lemons' by assuming that some buyers are overconfident. Buyers in our model receive a noisy signal about the quality of the good that is on display for sale. Overconfident buyers do not update according to Bayes' rule but take the noisy signal at face...

We modify the principal-agent model with moral hazard by assuming that the agent is expectation-based loss averse according to Köszegi and Rabin (2006, 2007). The optimal contract is a binary payment scheme even for a rich performance measure, where standard preferences predict a fully...

This paper extends the standard principal-agent model with moral hazard to allow for agents having reference-dependent preferences according to Köszegi and Rabin (2006, 2007). The main finding is that loss aversion leads to fairly simple contracts. In particular, when shifting the focus from...