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Looming Large or Seeming Small? Attitudes Towards Losses in a Representative Sample

Author

Listed:
  • Jonathan Chapman
  • Erik Snowberg
  • Stephanie W. Wang
  • Colin Camerer

Abstract

We measure individual-level loss aversion using three incentivized, representative surveys of the U.S. population (combined N=3,000). We find that around 50% of the U.S. population is loss tolerant, with many participants accepting negative-expected-value gambles. This is counter to earlier findings—which mostly come from lab/student samples—and expert predictions that 70-90% of participants are loss averse. Consistent with the difference between our study and the prior literature, loss aversion is more prevalent in people with high cognitive ability. Loss-tolerant individuals are more likely to report recent gambling and to have experienced financial shocks. These results support the general hypothesis that individuals value gains and losses differently, although the tendency in a large proportion of the population to emphasize gains over losses is an overlooked behavioral phenomenon.

Suggested Citation

  • Jonathan Chapman & Erik Snowberg & Stephanie W. Wang & Colin Camerer, 2022. "Looming Large or Seeming Small? Attitudes Towards Losses in a Representative Sample," NBER Working Papers 30243, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:30243
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    Cited by:

    1. Gallo, Edoardo & Barak, Darija & Langtry, Alastair, 2023. "Social distancing in networks: A web-based interactive experiment," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 107(C).
    2. Sanjit Dhami & Narges Hajimoladarvish & Konstantinos Georgalos, 2023. "Precautionary Savings, Loss Aversion, and Risk: Theory and Evidence," CESifo Working Paper Series 10570, CESifo.
    3. Duraj, Jetlir & He, Kevin, 2024. "Dynamic information preference and communication with diminishing sensitivity over news," Theoretical Economics, Econometric Society, vol. 19(3), July.

    More about this item

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C9 - Mathematical and Quantitative Methods - - Design of Experiments
    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D9 - Microeconomics - - Micro-Based Behavioral Economics

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