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Incentives to Learn Calibration: A Gender-Dependent Impact

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  • Marie-Pierre Dargnies

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

  • Guillaume Hollard

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

Abstract

Miscalibration can be defined as the fact that people think that their knowledge is more precise than it actually is. In a typical miscalibration experiment, subjects are asked to provide subjective confidence intervals. A very robust finding is that subjects provide too narrow intervals at the 90% level. As a result a lot less than 90% of correct answers fall inside the 90% intervals provided. As miscalibration is linked with bad results on an experimental financial market (Biais et al., 2005) and entrepreneurial success is positively correlated with good calibration (Regner et al., 2006), it appears interesting to look for a way to cure or at least reduce miscalibration. Previous attempts to remove the miscalibration bias relied on extremely long and tedious procedures. Here, we design an experimental setting that provides several different incentives, in particular strong monetary incentives i.e. that make miscalibration costly. Our main result is that a thirty-minute training session has an effect on men's calibration but no effect on women's.

Suggested Citation

  • Marie-Pierre Dargnies & Guillaume Hollard, 2009. "Incentives to Learn Calibration: A Gender-Dependent Impact," Post-Print hal-00649246, HAL.
  • Handle: RePEc:hal:journl:hal-00649246
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    References listed on IDEAS

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    1. Klayman, Joshua & Soll, Jack B. & Gonzalez-Vallejo, Claudia & Barlas, Sema, 1999. "Overconfidence: It Depends on How, What, and Whom You Ask, , , , , , , , ," Organizational Behavior and Human Decision Processes, Elsevier, vol. 79(3), pages 216-247, September.
    2. Cesarini, David & Sandewall, Orjan & Johannesson, Magnus, 2006. "Confidence interval estimation tasks and the economics of overconfidence," Journal of Economic Behavior & Organization, Elsevier, vol. 61(3), pages 453-470, November.
    3. Glaser, Markus & Langer, Thomas & Weber, Martin, 2005. "Overconfidence of Professionals and Lay Men: Individual Differences Within and Between Tasks?," Sonderforschungsbereich 504 Publications 05-25, Sonderforschungsbereich 504, Universität Mannheim;Sonderforschungsbereich 504, University of Mannheim.
    4. Muriel Niederle & Lise Vesterlund, 2007. "Do Women Shy Away From Competition? Do Men Compete Too Much?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 122(3), pages 1067-1101.
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    Cited by:

    1. Madiès, Thierry & Villeval, Marie Claire & Wasmer, Malgorzata, 2013. "Intergenerational attitudes towards strategic uncertainty and competition: A field experiment in a Swiss bank," European Economic Review, Elsevier, vol. 61(C), pages 153-168.
    2. Michał Krawczyk, 2011. "Overconfident for real? Proper scoring for confidence intervals," Working Papers 2011-15, Faculty of Economic Sciences, University of Warsaw.

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    More about this item

    Keywords

    Bias; Gender; Knowledge; Women;
    All these keywords.

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior

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