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How serious is the measurement-error problem in risk-aversion tasks?

Author

Listed:
  • Fabien Perez

    (ENSAE Paris - École Nationale de la Statistique et de l'Administration Économique - Groupe ENSAE-ENSAI - Groupe des Écoles Nationales d'Économie et Statistique - IP Paris - Institut Polytechnique de Paris)

  • Guillaume Hollard

    (X - École polytechnique - IP Paris - Institut Polytechnique de Paris)

  • Radu Vranceanu

    (ESSEC Business School, THEMA - Théorie économique, modélisation et applications - CNRS - Centre National de la Recherche Scientifique - CY - CY Cergy Paris Université)

Abstract

This paper analyzes within-session test/retest data from four different tasks used to elicit risk attitudes. Maximum-likelihood and non-parametric estimations on 16 datasets reveal that, irrespective of the task, measurement error accounts for approximately 50% of the variance of the observed variable capturing risk attitudes. The consequences of this large noise element are evaluated by means of simulations. First, as predicted by theory, the coefficient on the risk measure in univariate OLS regressions is attenuated to approximately half of its true value, irrespective of the sample size. Second, the risk-attitude measure may spuriously appear to be insignificant, especially in small samples. Unlike the measurement error arising from within-individual variability, rounding has little influence on significance and biases. In the last part, we show that instrumental-variable estimation and the ORIV method, developed by Gillen et al. (2019), both of which require test/retest data, can eliminate the attenuation bias, but do not fully solve the insignificance problem in small samples. Increasing the number of observations to N=500 removes most of the insignificance issues.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Fabien Perez & Guillaume Hollard & Radu Vranceanu, 2021. "How serious is the measurement-error problem in risk-aversion tasks?," Post-Print hal-03834842, HAL.
    • Handle: RePEc:hal:journl:hal-03834842
    DOI: 10.1007/s11166-021-09366-5
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    Cited by:

    1. Héloise Cloléry & Guillaume Hollard & Fabien Perez & Inès Picard, 2022. "Should we trust measures of trust?," Working Papers 2022-13, Center for Research in Economics and Statistics.
    2. Schröder, David, 2025. "Lotto lotteries — Decision making under uncertainty when payoffs are unknown," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 114(C).

    More about this item

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • 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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