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Understanding outcome bias

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  • Brownback, Andy
  • Kuhn, Michael A.

Abstract

Disentangling effort and luck is critical when evaluating outcomes. In a principal-agent experiment, we demonstrate that principals' judgments of agents are biased by luck, despite perfectly observable effort. This erodes the power of incentives to stimulate effort. We explore two potential solutions to this “outcome bias”–information control, and outsourcing judgment to independent third parties. Both are ineffective. When principals control information about luck, they do not avoid it. When agents control information, they manipulate principals' outcome bias to minimize punishments. We also find that even independent third parties exhibit outcome bias. These findings suggest that outcome bias cannot be driven solely by disappointment nor distributional preferences. Instead, we hypothesize that luck directly affects principals' inference about agent type even though effort is observed. We elicit the beliefs of third parties and principals and find that lucky agents are believed to be harder workers than identical, unlucky agents.

Suggested Citation

  • Brownback, Andy & Kuhn, Michael A., 2019. "Understanding outcome bias," Games and Economic Behavior, Elsevier, vol. 117(C), pages 342-360.
  • Handle: RePEc:eee:gamebe:v:117:y:2019:i:c:p:342-360
    DOI: 10.1016/j.geb.2019.07.003
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    7. Nisvan Erkal & Lata Gangadharan & Boon Han Koh, 2022. "By chance or by choice? Biased attribution of others’ outcomes when social preferences matter," Experimental Economics, Springer;Economic Science Association, vol. 25(2), pages 413-443, April.
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    9. Nisvan Erkal & Lata Gangadharan & Boon Han Koh, 2021. "Gender Biases in Performance Evaluation: The Role of Beliefs Versus Outcomes," University of East Anglia School of Economics Working Paper Series 2021-09, School of Economics, University of East Anglia, Norwich, UK..
    10. Friedrichsen, Jana & Momsen, Katharina & Piasenti, Stefano, 2022. "Ignorance, intention and stochastic outcomes," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 100, pages 1-1.
    11. Elias Bouacida & Renaud Foucart, 2020. "The acceptability of lotteries in allocation problems," Working Papers 301646245, Lancaster University Management School, Economics Department.
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    13. Peter Andre, 2022. "Shallow Meritocracy," CRC TR 224 Discussion Paper Series crctr224_2022_318v3, University of Bonn and University of Mannheim, Germany.
    14. Andre, Peter, 2023. "Shallow meritocracy," SAFE Working Paper Series 405, Leibniz Institute for Financial Research SAFE.
    15. Raphael Flepp & Pascal Flurin Meier, 2024. "Struck by Luck: Noisy Capability Cues and CEO Dismissal," Working Papers 389, University of Zurich, Department of Business Administration (IBW).
    16. Meier, Pascal Flurin & Flepp, Raphael & Meier, Philippe & Franck, Egon, 2022. "Outcome bias in self-evaluations: Quasi-experimental field evidence from Swiss driving license exams," Journal of Economic Behavior & Organization, Elsevier, vol. 201(C), pages 292-309.
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    18. Nisvan Erkal & Lata Gangadharan & Boon Han Koh, 2023. "Discrimination in Evaluation Criteria: The Role of Beliefs versus Outcomes," Discussion Papers 2316, University of Exeter, Department of Economics.
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    More about this item

    Keywords

    Experiment; Reciprocity; Outcome bias; Attribution bias; Blame;
    All these keywords.

    JEL classification:

    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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