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Safe options induce gender differences in risk attitudes

Author

Listed:
  • Paolo Crosetto

    (GAEL - Laboratoire d'Economie Appliquée de Grenoble - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - INRA - Institut National de la Recherche Agronomique - CNRS - Centre National de la Recherche Scientifique - UGA [2016-2019] - Université Grenoble Alpes [2016-2019], INRA - Institut National de la Recherche Agronomique)

  • Antonio Filippin

    (UNIMI - Università degli Studi di Milano = University of Milan)

Abstract

Gender differences in risk attitudes are frequently observed, although recent literature has shown that they are context dependent rather than ubiquitous. In this paper we try to rationalize the heterogeneity of results investigating experimentally whether the presence of a safe option among the set of alternatives explains why females are more risk averse than males. We manipulate three widely used risk elicitation methods finding that the availability of a safe option causally affects risk attitudes. The presence of a riskless alternative does not entirely explain the gender gap but it has a significant effect in triggering or magnifying (when already present) such differences. Despite the pronounced instability that usually characterizes the measurement of risk preferences, we show, estimating a structural model, that the effect of a safe option is remarkably stable accross tasks. This paper constitutes the first successful attempt to shed light on the determinants of gender differences in risk attitudes.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Paolo Crosetto & Antonio Filippin, 2018. "Safe options induce gender differences in risk attitudes," Post-Print hal-01994494, HAL.
    • Handle: RePEc:hal:journl:hal-01994494
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    Cited by:

    1. is not listed on IDEAS
    2. Boschini, Anne & Dreber, Anna & von Essen, Emma & Muren, Astri & Ranehill, Eva, 2018. "Gender, risk preferences and willingness to compete in a random sample of the Swedish population," Working Paper Series 10/2018, Stockholm University, Swedish Institute for Social Research.
    3. Hernandez-Arenaz, Iñigo, 2020. "Stereotypes and tournament self-selection: A theoretical and experimental approach," European Economic Review, Elsevier, vol. 126(C).
    4. Felix Holzmeister & Matthias Stefan, 2019. "The risk elicitation puzzle revisited: Across-methods (in)consistency?," Working Papers 2019-19, Faculty of Economics and Statistics, Universität Innsbruck.
    5. Gabriel Yong Ping Chua & Hui Jun Er & Shao Yi Liaw & Tai-Sen He, 2020. "Pitch Right: The Effect of Vocal Pitch on Risk Aversion," Economics Bulletin, AccessEcon, vol. 40(4), pages 3131-3139.
    6. Grimm, Stefan, 2018. "Show What You Risk - Norms for Risk Taking," Rationality and Competition Discussion Paper Series 119, CRC TRR 190 Rationality and Competition.
    7. Comeig, Irene & Holt, Charles & Jaramillo-Gutiérrez, Ainhoa, 2022. "Upside versus downside risk: Gender, stakes, and skewness," Journal of Economic Behavior & Organization, Elsevier, vol. 200(C), pages 21-30.
    8. Shastry, Gauri Kartini & Shurchkov, Olga & Xia, Lingjun Lotus, 2020. "Luck or skill: How women and men react to noisy feedback," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 88(C).
    9. repec:osf:osfxxx:pj9u2_v1 is not listed on IDEAS
    10. Holzmeister, Felix & Stefan, Matthias, 2019. "The Risk Elicitation Puzzle Revisited: Across-Methods (In)consistency?," OSF Preprints pj9u2, Center for Open Science.
    11. Felix Holzmeister & Matthias Stefan, 2021. "The risk elicitation puzzle revisited: Across-methods (in)consistency?," Experimental Economics, Springer;Economic Science Association, vol. 24(2), pages 593-616, June.

    More about this item

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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