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Selecting the Best when Selection is Hard

Author

Listed:
  • Mikhail Drugov

    (New Economic School and CEPR)

  • Margaret Meyer

    (Nuffield College and Department of Economics, Oxford University, and CEPR)

  • Marc Moeller

    (University of Bern)

Abstract
In dynamic promotion contests, where performance measurement is noisy and ordinal, selection can be improved by biasing later stages in favor of early leaders. Even in the worst-case scenario, where noise swamps ability differences in determining relative performance, optimal bias is i) strictly positive; ii) locally insensitive to changes in the heterogeneity-to-noise ratio. A close relationship with expected optimal bias under cardinal information helps explain this surprising result. Properties i) and ii) imply that the simple rule of setting bias as if in the worst-case scenario achieves most of the potential gains in selective efficiency from biasing dynamic rank-order contests.

Suggested Citation

  • Mikhail Drugov & Margaret Meyer & Marc Moeller, 2022. "Selecting the Best when Selection is Hard," Working Papers w0290, New Economic School (NES).
    • Handle: RePEc:abo:neswpt:w0290
    as

    Download full text from publisher

    File URL: https://www.nes.ru/files/Preprints-resh/WP290.pdf
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    References listed on IDEAS

    as
    1. Margaret A. Meyer, 1992. "Biased Contests and Moral Hazard: Implications for Career Profiles," Annals of Economics and Statistics, GENES, issue 25-26, pages 165-187.
    2. Margaret A. Meyer, 1991. "Learning from Coarse Information: Biased Contests and Career Profiles," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(1), pages 15-41.
    3. Drugov, Mikhail & Ryvkin, Dmitry, 2017. "Biased contests for symmetric players," Games and Economic Behavior, Elsevier, vol. 103(C), pages 116-144.
    4. Andrew Schotter & Keith Weigelt, 1992. "Asymmetric Tournaments, Equal Opportunity Laws, and Affirmative Action: Some Experimental Results," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(2), pages 511-539.
    5. Jörg Franke & Christian Kanzow & Wolfgang Leininger & Alexandra Schwartz, 2013. "Effort maximization in asymmetric contest games with heterogeneous contestants," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 52(2), pages 589-630, March.
    6. repec:adr:anecst:y:1992:i:25-26:p:08 is not listed on IDEAS
    7. James Fain, 2009. "Affirmative Action Can Increase Effort," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 30(2), pages 168-175, June.
    8. Kawamura, Kohei & Moreno de Barreda, Inés, 2014. "Biasing selection contests with ex-ante identical agents," Economics Letters, Elsevier, vol. 123(2), pages 240-243.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Penghuan Yan, 2024. "Balancing Selection Efficiency and Societal Costs in Selective Contests," Papers 2409.09768, arXiv.org, revised Oct 2024.

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    More about this item

    Keywords

    Dynamic Contests; Selective Efficiency; Bias; Learning; Promotions JEL Classifications: D21; D82; D83; M51;
    All these keywords.

    JEL classification:

    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • M51 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics - - - Firm Employment Decisions; Promotions

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