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How Serious is the Measurement-Error Problem in a Popular Risk-Aversion Task?

Author

Listed:
  • Fabien, Perez

    (ENSAE)

  • Guillaume, Hollard

    (Ecole Polytechnique)

  • Radu, Vranceanu

    (ESSEC Research Center, ESSEC Business School)

  • Delphine, Dubart

    (ESSEC Research Center, ESSEC Business School)

Abstract

This paper uses the test/retest data from the Holt and Laury (2002) experiment to provide estimates of the measurement error in this popular risk-aversion task. Maximum likelihood estimation suggests that the variance of the measurement error is approximately equal to the variance of the number of safe choices. Simulations confirm that the coefficient on the risk measure in univariate OLS regressions is approximately half of its true value. Unlike measurement error, the discrete transformation of continuous riskaversion is not a major issue. We discuss the merits of a number of different solutions: increasing the number of observations, IV and the ORIV method developed by Gillen et al. (2019).

Suggested Citation

  • Fabien, Perez & Guillaume, Hollard & Radu, Vranceanu & Delphine, Dubart, 2019. "How Serious is the Measurement-Error Problem in a Popular Risk-Aversion Task?," ESSEC Working Papers WP1911, ESSEC Research Center, ESSEC Business School.
    • Handle: RePEc:ebg:essewp:dr-19011
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    References listed on IDEAS

    as
    1. John Hey & Andrea Morone & Ulrich Schmidt, 2009. "Noise and bias in eliciting preferences," Journal of Risk and Uncertainty, Springer, vol. 39(3), pages 213-235, December.
    2. Gary Charness & Thomas Garcia & Theo Offerman & Marie Claire Villeval, 2020. "Do measures of risk attitude in the laboratory predict behavior under risk in and outside of the laboratory?," Journal of Risk and Uncertainty, Springer, vol. 60(2), pages 99-123, April.
    3. Ben Gillen & Erik Snowberg & Leeat Yariv, 2019. "Experimenting with Measurement Error: Techniques with Applications to the Caltech Cohort Study," Journal of Political Economy, University of Chicago Press, vol. 127(4), pages 1826-1863.
    4. Shane Frederick, 2005. "Cognitive Reflection and Decision Making," Journal of Economic Perspectives, American Economic Association, vol. 19(4), pages 25-42, Fall.
    5. Giuseppe Attanasi & Nikolaos Georgantzís & Valentina Rotondi & Daria Vigani, 2018. "Lottery- and survey-based risk attitudes linked through a multichoice elicitation task," Theory and Decision, Springer, vol. 84(3), pages 341-372, May.
    6. Paolo Crosetto & Antonio Filippin, 2016. "A theoretical and experimental appraisal of four risk elicitation methods," Experimental Economics, Springer;Economic Science Association, vol. 19(3), pages 613-641, September.
    7. Ted O'Donoghue & Jason Somerville, 2018. "Modeling Risk Aversion in Economics," Journal of Economic Perspectives, American Economic Association, vol. 32(2), pages 91-114, Spring.
    8. Engel, Christoph & Kirchkamp, Oliver, 2019. "How to deal with inconsistent choices on multiple price lists," Journal of Economic Behavior & Organization, Elsevier, vol. 160(C), pages 138-157.
    9. Arnaud Reynaud & Stéphane Couture, 2012. "Stability of risk preference measures: results from a field experiment on French farmers," Theory and Decision, Springer, vol. 73(2), pages 203-221, August.
    10. Chuang, Yating & Schechter, Laura, 2015. "Stability of experimental and survey measures of risk, time, and social preferences: A review and some new results," Journal of Development Economics, Elsevier, vol. 117(C), pages 151-170.
    11. Charles A. Holt & Susan K. Laury, 2002. "Risk Aversion and Incentive Effects," American Economic Review, American Economic Association, vol. 92(5), pages 1644-1655, December.
    12. Rui Mata & Renato Frey & David Richter & Jürgen Schupp & Ralph Hertwig, 2018. "Risk Preference: A View from Psychology," Journal of Economic Perspectives, American Economic Association, vol. 32(2), pages 155-172, Spring.
    13. Sarah Jacobson & Ragan Petrie, 2009. "Learning from mistakes: What do inconsistent choices over risk tell us?," Journal of Risk and Uncertainty, Springer, vol. 38(2), pages 143-158, April.
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    More about this item

    Keywords

    ORIV; Experiments; Measurement error; Risk-aversion; Test/retest;
    All these keywords.

    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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