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Ambiguity in Performance Pay: An Online Experiment

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Author Info

  • David Cooper
  • David Johnson

    (University of Calgary)

Abstract

Many incentive plans are inherently ambiguous, lacking an explicit mapping between performance and compensation. Using an online labor market, Amazon Mechanical Turk, we study the effect of ambiguity on willingness to accept contracts to do a real-effort task as well as completion and performance of this task. Ambiguity about the relationship between performance and compensation affects the willingness of individuals to accept contracts and the likelihood of quitting before completion, but not performance. These effects are non-monotonic in the level of ambiguity. Information about ability at the task reduces willingness to accept and increases quitting, but does not affect performance.

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Bibliographic Info

Paper provided by Department of Economics, University of Calgary in its series Working Papers with number 2013-27.

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Date of creation: 14 Nov 2013
Date of revision: 14 Nov 2013
Handle: RePEc:clg:wpaper:2013-27

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References

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Citations

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Cited by:
  1. Sebastian J. Goerg & David B. Johnson & Jonathan D. Rogers, 2014. "Can't Touch This! Similarity And The Willingness to Keep "Dirty Money"," Working Papers wp2014_03_01, Department of Economics, Florida State University.
  2. Oechssler, Jörg & Roomets, Alex, 2014. "A Test of Mechanical Ambiguity," Working Papers 0555, University of Heidelberg, Department of Economics.

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