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Competitive prizes: when less scrutiny induces more effort

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  • Dubey, Pradeep
  • Wu, Chien-wei

Abstract

We consider a principal who is keen to induce his agents to work at their maximal effort levels. To this end, he samples n days at random out of the T days on which they work, and awards a prize of B dollars to the most productive agent. The principal's policy (B,n) induces a strategic game Gamma(B,n) between the agents. We show that to implement maximal effort levels weakly (or, strongly) as a strategic equilibrium (or, as dominant strategies) in Gamma(B,n), at the least cost B to himself, the principal must choose a small sample size n. Thus less scrutiny by the principal induces more effort from the agents. The need for reduced scrutiny becomes more pronounced when agents have information of the history of past plays in the game. There is an inverse relation between information and optimal sample size. As agents acquire more information (about each other), the principal -- so to speak -- must "undo" this by reducing his information (about them) and choosing the sample size n even smaller.
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Suggested Citation

  • Dubey, Pradeep & Wu, Chien-wei, 2001. "Competitive prizes: when less scrutiny induces more effort," Journal of Mathematical Economics, Elsevier, vol. 36(4), pages 311-336, December.
  • Handle: RePEc:eee:mateco:v:36:y:2001:i:4:p:311-336
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    References listed on IDEAS

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    Cited by:

    1. Avrahami Judith & Kareev Yaakov & Uske Tobias & Gueth Werner, 2017. "On the Incentive Effects of Sample Size in Monitoring Agents – A Theoretical and Experimental Analysis," German Economic Review, De Gruyter, vol. 18(1), pages 81-98, February.
    2. Yohan Pelosse, 2014. "Dynamic Difference-Form Contests," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 16(3), pages 401-426, June.
    3. Hind Sami & Nadia Joubert & Jean-Louis Rullière, 2010. "Le mieux, ennemi du bien : approche expérimentale du contrôle comme mécanisme de filtrage," Economie & Prévision, La Documentation Française, vol. 0(4), pages 127-146.
    4. Sami, Hind, 2009. "Random monitoring in financing relationships," The Quarterly Review of Economics and Finance, Elsevier, vol. 49(2), pages 239-252, May.
    5. Yaakov Kareev & Judith Avrahami, 2006. "Choosing Between Adaptive Agents: Some Unexpected Implications of Level of Scrutiny," Levine's Bibliography 321307000000000521, UCLA Department of Economics.
    6. Dubey, Pradeep & Geanakoplos, John, 2010. "Grading exams: 100,99,98,... or A,B,C?," Games and Economic Behavior, Elsevier, vol. 69(1), pages 72-94, May.
    7. Pelosse, Yohan, 2011. "Equivalence of optimal noisy-ranking contests and Tullock contests," Journal of Mathematical Economics, Elsevier, vol. 47(6), pages 740-748.
    8. Ichino, Andrea & Muehlheusser, Gerd, 2008. "How often should you open the door?: Optimal monitoring to screen heterogeneous agents," Journal of Economic Behavior & Organization, Elsevier, vol. 67(3-4), pages 820-831, September.
    9. Yaakov Kareev & Judith Avrahami, 2006. "Choosing Between Adaptive Agents: Some Unexpected Implications of Level of Scrutiny," Discussion Paper Series dp436, The Federmann Center for the Study of Rationality, the Hebrew University, Jerusalem.
    10. Grunewald, Andreas & Kräkel, Matthias, 2022. "Information manipulation and competition," Games and Economic Behavior, Elsevier, vol. 131(C), pages 245-263.

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    More about this item

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • J41 - Labor and Demographic Economics - - Particular Labor Markets - - - Labor Contracts

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