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Relative Risk Aversion: A Meta‐Analysis

Author

Listed:
  • Ali Elminejad
  • Tomas Havranek
  • Zuzana Irsova

Abstract

Estimates of relative risk aversion vary widely, but no study has attempted to quantitatively trace the sources of the variation. We collect 1021 estimates from 92 studies that use the consumption Euler equation to measure relative risk aversion and that disentangle it from intertemporal substitution. We show that calibrations of risk aversion are systematically larger than estimates thereof. Moreover, reported estimates are systematically larger than the underlying risk aversion because of publication bias. After correction for the bias, the literature suggests a mean risk aversion of 1 in economics and 2–7 in finance contexts. The reported estimates are driven by the characteristics of data (frequency, dimension, country, stockholding) and utility (functional form, treatment of durables). To obtain these results, we use recently developed techniques to correct for publication bias and Bayesian model averaging techniques to account for model uncertainty.

Suggested Citation

  • Ali Elminejad & Tomas Havranek & Zuzana Irsova, 2025. "Relative Risk Aversion: A Meta‐Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 39(5), pages 2315-2333, December.
    • Handle: RePEc:bla:jecsur:v:39:y:2025:i:5:p:2315-2333
    DOI: 10.1111/joes.12689
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    Cited by:

    1. Zuzana Irsova & Hristos Doucouliagos & Tomas Havranek & T. D. Stanley, 2024. "Meta‐analysis of social science research: A practitioner's guide," Journal of Economic Surveys, Wiley Blackwell, vol. 38(5), pages 1547-1566, December.
    2. Xin Liu & Luciano de Castro & Antonio F. Galvao, 2026. "A Smoothed GMM for Dynamic Quantile Preferences Estimation," Papers 2601.20853, arXiv.org.
    3. Canning, David, 2023. "Conducting Cost Benefit Analysis in Expected Utility Units Using Revealed Social Preferences," Working Papers 0722, University of Heidelberg, Department of Economics.
    4. Taras Bodnar & Dmytro Ivasiuk & Nestor Parolya & Wolfgang Schmid, 2023. "Multi-period power utility optimization under stock return predictability," Computational Management Science, Springer, vol. 20(1), pages 1-27, December.
    5. Qiang Chen & Xinyi Song, 2026. "Crash Risk Matters: An Option‐Implied Approach to the Expected Market Return," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 46(3), pages 511-528, March.
    6. Bravo-Melgarejo, Sai & Haritchabalet, Carole, 2025. "Prosumers: Grid vs. individual storage," Energy Economics, Elsevier, vol. 141(C).

    More about this item

    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D90 - Microeconomics - - Micro-Based Behavioral Economics - - - General

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