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Forecasting Risk Attitudes: An Experimental Study Using Actual and Forecast Gamble Choices

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Abstract

We develop and evaluate a simple gamble-choice task to measure attitudes toward risk, and apply this measure to examine differences in risk attitudes of male and female university students. In addition, we examine stereotyping by asking whether a person's sex is read as a signal of risk preference. Subjects choose which of five 50/50 gambles they wish to play. The gambles include one sure thing; the remaining four increase (linearly) in expected payoff and risk. Each subject also is asked to guess which of the five gambles each of the other subjects chose, and is paid for correct guesses. The experiment is conducted under three different frames: an abstract frame where the two highest-payoff gambles carry the possibility of losses, an abstract frame with no losses, and an investment frame that mirrors the payoff structure of the former. We find that women are significantly more risk averse than men in all three settings, and predictions of both women and men tend to confirm this difference. While average guesses reflect the average difference in choices, only 27 percent of guesses are accurate, which is slightly higher than chance.

Suggested Citation

  • Catherine C. Eckel & Philip J. Grossman, 2008. "Forecasting Risk Attitudes: An Experimental Study Using Actual and Forecast Gamble Choices," Monash Economics Working Papers archive-01, Monash University, Department of Economics.
  • Handle: RePEc:mos:moswps:archive-01
    DOI: 10.1016/j.jebo.2008.04.006
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    File URL: http://dx.doi.org/10.1016/j.jebo.2008.04.006
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    More about this item

    Keywords

    Risk Aversion; Experiment; Gender; Stereotyping;
    All these keywords.

    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination

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