IDEAS home Printed from https://ideas.repec.org/p/osf/metaar/hnefr.html
   My bibliography  Save this paper

Meta-Analysis of Empirical Estimates of Loss-Aversion

Author

Listed:
  • Brown, Alexander L.
  • Imai, Taisuke

    (California Institute of Technology)

  • Vieider, Ferdinand
  • Camerer, Colin

Abstract

Loss aversion is one of the most widely used concepts in behavioral economics. We conduct a large-scale interdisciplinary meta-analysis, to systematically accumulate knowledge from numerous empirical estimates of the loss aversion coefficient reported during the past couple of decades. We examine 607 empirical estimates of loss aversion from 150 articles in economics, psychology, neuroscience, and several other disciplines. Our analysis indicates that the mean loss aversion coefficient is between 1.8 and 2.1. We also document how reported estimates vary depending on the observable characteristics of the study design.

Suggested Citation

  • Brown, Alexander L. & Imai, Taisuke & Vieider, Ferdinand & Camerer, Colin, 2020. "Meta-Analysis of Empirical Estimates of Loss-Aversion," MetaArXiv hnefr, Center for Open Science.
    • Handle: RePEc:osf:metaar:hnefr
    DOI: 10.31219/osf.io/hnefr
    as

    Download full text from publisher

    File URL: https://osf.io/download/5fe23adc33737300b0f892a2/
    Download Restriction: no
    File URL: https://libkey.io/10.31219/osf.io/hnefr?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kpegli, Yao Thibaut & Corgnet, Brice & Zylbersztejn, Adam, 2023. "All at once! A comprehensive and tractable semi-parametric method to elicit prospect theory components," Journal of Mathematical Economics, Elsevier, vol. 104(C).
    2. Yao Thibaut Kpegli, 2023. "Smoothing Spline Method for Measuring Prospect Theory Components," Working Papers 2303, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
    3. Christina Korting & Carl Lieberman & Jordan Matsudaira & Zhuan Pei & Yi Shen, 2020. "Visual Inference and Graphical Representation in Regression Discontinuity Designs," Working Papers 638, Princeton University, Department of Economics, Industrial Relations Section..
    4. Marcela Ibanez & Sebastian O. Schneider, 2021. "Income Risk, Precautionary Saving, and Loss Aversion – An Empirical Test," Discussion Paper Series of the Max Planck Institute for Research on Collective Goods 2021_06, Max Planck Institute for Research on Collective Goods.
    5. Vincent Meisner & Jonas von Wangenheim, 2022. "Loss aversion in strategy-proof school-choice mechanisms," Papers 2207.14666, arXiv.org.
    6. Jindrich Matousek & Tomas Havranek & Zuzana Irsova, 2022. "Individual discount rates: a meta-analysis of experimental evidence," Experimental Economics, Springer;Economic Science Association, vol. 25(1), pages 318-358, February.
    7. Klimm, Felix & Kocher, Martin G. & Opitz, Timm & Schudy, Simeon, 2023. "Time pressure and regret in sequential search," Journal of Economic Behavior & Organization, Elsevier, vol. 206(C), pages 406-424.
    8. repec:cup:judgdm:v:17:y:2022:i:5:p:1015-1042 is not listed on IDEAS
    9. Meisner, Vincent & von Wangenheim, Jonas, 2019. "School Choice and Loss Aversion," Rationality and Competition Discussion Paper Series 208, CRC TRR 190 Rationality and Competition.
    10. repec:jdm:journl:v:17:y:2022:i:5:p:1015-1042 is not listed on IDEAS
    11. Haim Levy & Moshe Levy, 2021. "Prospect theory, constant relative risk aversion, and the investment horizon," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-21, April.

    More about this item

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D90 - Microeconomics - - Micro-Based Behavioral Economics - - - General
    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:osf:metaar:hnefr. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://osf.io/preprints/metaarxiv .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: OSF (email available below). General contact details of provider: https://osf.io/preprints/metaarxiv .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.