We modify the principal-agent model with moral hazard by assuming that the agent is expectation-based loss averse according to K o szegi 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 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 consider a brand manufacturer who can offer, next to its high-quality product, also a decoy good and faces competition by a competitive fringe that produces low quality. We show that the brand manufacturer optimally provides a decoy good to boost the demand for its main product if...

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...

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 consider a brand manufacturer who can offer, next to its high-quality product, also a decoy good and faces competition by a competitive fringe that produces low quality. We show that the brand manufacturer optimally provides a decoy good to boost the demand for its main product if consumers'...