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When Performance Trumps Gender Bias: Joint vs. Separate Evaluation

Author

Listed:
  • Iris Bohnet

    () (John F. Kennedy School of Government, Harvard University, Cambridge, Massachusetts 02138)

  • Alexandra van Geen

    () (Erasmus School of Economics, 3062 PA Rotterdam, Netherlands)

  • Max Bazerman

    () (Harvard Business School, Harvard University, Boston, Massachusetts 02163)

Abstract

Gender bias in the evaluation of job candidates has been demonstrated in business, government, and academia, yet little is known about how to overcome it. Blind evaluation procedures have been proven to significantly increase the likelihood that women musicians are chosen for orchestras, and they are employed by a few companies. We examine a new intervention to overcome gender bias in hiring, promotion, and job assignments: an “evaluation nudge” in which people are evaluated jointly rather than separately regarding their future performance. Evaluators are more likely to base their decisions on individual performance in joint than in separate evaluation and on group stereotypes in separate than in joint evaluation, making joint evaluation the profit-maximizing evaluation procedure. Our work is inspired by findings in behavioral decision research suggesting that people make more reasoned choices when examining options jointly rather than separately and is compatible with a behavioral model of information processing.Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2015.2186 . This paper was accepted by Uri Gneezy, behavioral economics .

Suggested Citation

  • Iris Bohnet & Alexandra van Geen & Max Bazerman, 2016. "When Performance Trumps Gender Bias: Joint vs. Separate Evaluation," Management Science, INFORMS, vol. 62(5), pages 1225-1234, May.
  • Handle: RePEc:inm:ormnsc:v:62:y:2016:i:5:p:1225-1234
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    File URL: http://dx.doi.org/10.1287/mnsc.2015.2186
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    Cited by:

    1. Reuben, Ernesto & Timko, Krisztina, 2017. "On the Effectiveness of Elected Male and Female Leaders and Team Coordination," IZA Discussion Papers 10497, Institute for the Study of Labor (IZA).
    2. repec:eee:touman:v:65:y:2018:i:c:p:29-40 is not listed on IDEAS
    3. Sezer, Ovul & Zhang, Ting & Gino, Francesca & Bazerman, Max H., 2016. "Overcoming the outcome bias: Making intentions matter," Organizational Behavior and Human Decision Processes, Elsevier, vol. 137(C), pages 13-26.

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