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Aggregating Performance Measures in Multi-Task Agencies

Author

Listed:
  • Veikko Thiele

    (Queen`s University)

Abstract

It has been argued in the multi-task agency literature that effort distortion can be mitigated by applying several performance measures in incentive contracts. This paper analyzes the efficient aggregation of multiple performance measures aimed at motivating non-distorted effort. It demonstrates that non-distorted effort can be induced by combining a sufficient quantity of informative performance measures. However, this is only optimal if the required aggregation concurrently maximizes the precision of the agent`s performance evaluation. This paper further illustrates how the optimal performance evaluation is affected by the ability of individual agents to perform relevant tasks.

Suggested Citation

  • Veikko Thiele, 2009. "Aggregating Performance Measures in Multi-Task Agencies," Economics Bulletin, AccessEcon, vol. 29(4), pages 2693-2699.
    • Handle: RePEc:ebl:ecbull:eb-08d80025
    as

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    References listed on IDEAS

    as
    1. Thiele, Veikko, 2007. "Performance measurement in multi-task agencies," Research in Economics, Elsevier, vol. 61(3), pages 148-163, September.
    2. Srikant Datar & Susan Cohen Kulp & Richard A. Lambert, 2001. "Balancing Performance Measures," Journal of Accounting Research, Wiley Blackwell, vol. 39(1), pages 75-92, June.
    3. Banker, Rd & Datar, Sm, 1989. "Sensitivity, Precision, And Linear Aggregation Of Signals For Performance Evaluation," Journal of Accounting Research, Wiley Blackwell, vol. 27(1), pages 21-39.
    4. George Baker, 2002. "Distortion and Risk in Optimal Incentive Contracts," Journal of Human Resources, University of Wisconsin Press, vol. 37(4), pages 728-751.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Multi-task agencies; effort distortion; performance measurement; incentives;
    All these keywords.

    JEL classification:

    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty

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