Distortion and Risk in Optimal Incentive

Performance measurement is an essential part of the design of any incentive system. The strength and value of incentives in organizations are strongly affected by the performance measures available. Yet, the characteristics of valuable performance measures have not been well explored in the agency literature. In this paper, I use a multi-task model to develop a two-parameter characterization of performance measures and show how these two parameters—distortion and risk—affect the value and use of performance measures in incentive contracts. I show that many complex issues in the design of real world incentive contracts can be fruitfully viewed as trade-offs between these two features of performance measures. I also use this framework to analyze the provision of incentives in several specific environments, including R&D labs and non-profit organizations. I would like to thank Nancy Beaulieu, Bob Gibbons, Brian Hall, Ed Lazear, Sherwin Rosen, Jasjit Singh, and Lars Stole for comments on this paper. This research was funded by the Harvard Business School Division of Research.