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So when are you loss averse? Testing the S-shaped function in pricing and allocation tasks

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  • Malul, Miki
  • Rosenboim, Mosi
  • Shavit, Tal

Abstract

In this paper, we describe three different experiments that explore participants’ risk attitude. When we analyzed the average results, we found that participants behave as the S-shape value function predicts. However, breaking the data down on the individual level reveals that the S-shape is valid just for about one-third of the cases. This result emerged from all three experiments. In the first experiment, we used lotteries with different stakes and found that in the high stake only 31% of the participants behave as the S-shape value function predicts. The percentage decreases to 16% when the stakes were lowered. In the second experiment, we used the prepayment mechanism (PPM) to create a more realistic experimental environment. In this case, 37% of the participants behaved consistently with the S-shape value function. In the third experiment, we used allocation tasks. The results revealed that most subjects could not be classified into one of the classical risk attitude groups. Our results imply that more than one value function is needed to characterize individuals’ attitudes toward risk. Deeper analysis is needed to characterize different value functions for different groups of individuals.

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  • Malul, Miki & Rosenboim, Mosi & Shavit, Tal, 2013. "So when are you loss averse? Testing the S-shaped function in pricing and allocation tasks," Journal of Economic Psychology, Elsevier, vol. 39(C), pages 101-112.
    • Handle: RePEc:eee:joepsy:v:39:y:2013:i:c:p:101-112
    DOI: 10.1016/j.joep.2013.07.007
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    More about this item

    Keywords

    S-shaped; Pricing; Allocation; Prospect theory; Value function; Loss aversion;
    All these keywords.

    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles

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