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Towards a typology of risk preference: Four risk profiles describe two-thirds of individuals in a large sample of the U.S. population

Author

Listed:
  • Renato Frey

    (University of Zurich
    Princeton University)

  • Shannon M. Duncan

    (Columbia University
    University of Pennsylvania)

  • Elke U. Weber

    (Princeton University)

Abstract

It has been a longstanding goal of the behavioral sciences to measure and model people’s risk preferences. In this article, we adopt a novel theoretical perspective of doing so and test to what extent specific types of individuals share similar risk profiles (i.e., configurations of multidimensional risk preferences). To this end, we analyzed data of a U.S. sample (N = 3,123) in a comprehensive and rigorous way, resulting in a twofold contribution. First, based on data from the Domain-Specific Risk-Taking scale (DOSPERT) and using a cross-validation procedure, we established a multidimensional trait space including general and domain-specific dimensions of risk preference. Second, we employed model-based cluster analyses in this multidimensional trait space, finding that 66% of participants can be described well with four basic risk profiles. In sum, the typological perspective proposed in this article has important implications for current theories of risk preference and the measurement of individual differences therein.

Suggested Citation

  • Renato Frey & Shannon M. Duncan & Elke U. Weber, 2023. "Towards a typology of risk preference: Four risk profiles describe two-thirds of individuals in a large sample of the U.S. population," Journal of Risk and Uncertainty, Springer, vol. 66(1), pages 1-17, February.
    • Handle: RePEc:kap:jrisku:v:66:y:2023:i:1:d:10.1007_s11166-022-09398-5
    DOI: 10.1007/s11166-022-09398-5
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    References listed on IDEAS

    as
    1. Thomas Dohmen & Armin Falk & David Huffman & Uwe Sunde & Jürgen Schupp & Gert G. Wagner, 2011. "Individual Risk Attitudes: Measurement, Determinants, And Behavioral Consequences," Journal of the European Economic Association, European Economic Association, vol. 9(3), pages 522-550, June.
    2. Martin Gerlach & Beatrice Farb & William Revelle & Luís A. Nunes Amaral, 2018. "A robust data-driven approach identifies four personality types across four large data sets," Nature Human Behaviour, Nature, vol. 2(10), pages 735-742, October.
    3. Armin Falk & Anke Becker & Thomas Dohmen & David Huffman & Uwe Sunde, 2023. "The Preference Survey Module: A Validated Instrument for Measuring Risk, Time, and Social Preferences," Management Science, INFORMS, vol. 69(4), pages 1935-1950, April.
    4. Arslan, Ruben C. & Brümmer, Martin & Dohmen, Thomas & Drewelies, Johanna & Hertwig, Ralph & Wagner, Gert G., 2020. "How people know their risk preference," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 10.
    5. Ann-Renée Blais & Elke U. Weber, 2006. "A Domain-Specific Risk-Taking (DOSPERT)Scale for Adult Populations," CIRANO Working Papers 2006s-24, CIRANO.
    6. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    7. repec:cup:judgdm:v:1:y:2006:i::p:33-47 is not listed on IDEAS
    8. Elke U. Weber & Richard A. Milliman, 1997. "Perceived Risk Attitudes: Relating Risk Perception to Risky Choice," Management Science, INFORMS, vol. 43(2), pages 123-144, February.
    9. Robert Jennrich & Peter Bentler, 2011. "Exploratory Bi-Factor Analysis," Psychometrika, Springer;The Psychometric Society, vol. 76(4), pages 537-549, October.
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    Cited by:

    1. Baláž, Vladimír & Chen, Jason Li & Williams, Allan M. & Li, Gang, 2024. "Stability of risk and uncertainty preferences in tourism," Annals of Tourism Research, Elsevier, vol. 105(C).

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    More about this item

    Keywords

    Risk preference; Psychometric modeling; DOSPERT; Bayesian latent profile analyses;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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