Performance Measure Properties and Incentives


Speaker


Abstract

We provide a comprehensive empirical analysis of incentive design, focusing on effects of performance measure properties (controllable and uncontrollable risk, distortion, and manipulation). The data are from auto dealership manager incentive systems. We find that dealerships put the most weight on measures that are “better” with respect to these properties, for both explicit incentives (bonuses) and implicit incentives (e.g., promotions or autonomy). In addition, a measure’s properties relative to those of other measures also affects its weight for incentives. Implicit incentives, which are awarded ex post, are used to deter manipulation of the performance measure. Our results are consistent with a setting where employees have multitask jobs, and face controllable risk. Firms use multiple bonuses and implicit rewards to balance multitask incentives. Controllable risk affects incentive design in two ways: it motivates using the information to improve firm value, and deters manipulation of the performance measure.

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Anna Nöteberg

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