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Nobel and Novice: Author Prominence Affects Peer Review

Author

Listed:
  • Jurgen Huber

    (University of Innsbruck)

  • Sabiou M. Inoua

    (Chapman University)

  • Rudolf Kerschbamer

    (University of Innsbruck)

  • Christian Konig-Kersting

    (University of Innsbruck)

  • Stefan Palan

    (University of Graz)

  • Vernon L. Smith

    (Chapman University)

Abstract

Peer-review is a well-established cornerstone of the scientific process, yet it is not immune to biases like the status bias, which we explore in this paper. Merton described this bias as prominent researchers getting disproportionately great credit for their contribution while relatively unknown researchers getting disproportionately little credit (1). We measured the extent of this bias in the peer-review process through a pre-registered field experiment. We invited more than 3,300 researchers to review a finance research paper jointly written by a prominent author (a Nobel laureate) and by a relatively unknown author (an early-career research associate) varying whether reviewers saw the prominent author’s name, an anonymized version of the paper, or the less well-known author’s name. We found strong evidence for the status bias: more of the invited researchers accepted to review the paper when the prominent name was shown, and while only 23 percent recommended “reject†when the prominent researcher was the only author shown, 48 percent did so when the paper was anonymized, and 65 percent did when the little-known author was the only author shown. Our findings complement and extend earlier results on double-anonymized vs. single-anonymized review (2–10).

Suggested Citation

  • Jurgen Huber & Sabiou M. Inoua & Rudolf Kerschbamer & Christian Konig-Kersting & Stefan Palan & Vernon L. Smith, 2022. "Nobel and Novice: Author Prominence Affects Peer Review," Working Papers 22-15, Chapman University, Economic Science Institute.
  • Handle: RePEc:chu:wpaper:22-15
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    File URL: https://digitalcommons.chapman.edu/esi_working_papers/376/
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    Cited by:

    1. Sun, Zhuanlan, 2024. "Textual features of peer review predict top-cited papers: An interpretable machine learning perspective," Journal of Informetrics, Elsevier, vol. 18(2).
    2. Tol, Richard S.J., 2023. "Nobel begets Nobel in economics," Journal of Informetrics, Elsevier, vol. 17(4).
    3. Sun, Zhuanlan & Clark Cao, C. & Ma, Chao & Li, Yiwei, 2023. "The academic status of reviewers predicts their language use," Journal of Informetrics, Elsevier, vol. 17(4).

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