An Experimental Comparison of the Fairness Models by Bolton and Ockenfels and by Fehr and Schmidt
We present an experiment to compare the two fairness theories by Bolton and Ockenfels [ERC] and by Fehr and Schmidt [F&S]. If one wants to compare their predictive power, most of the experiments that are interpreted retrospectively are not helpful, since both theories make equal or very similar predictions. Both models rely on inequality aversion. The fundamental difference between them is that ERC assumes that subjects like the average payoff to be as close as possible to their own payoff while F&S assumes that subjects dislike a payoff difference to any other individual. To obtain explicitly opposite predictions by the two theories we chose a game that focuses on their fundamental difference. A person received a fixed payoff and chose between three different allocations of money between a person who received in all allocations more than her and a person who always received less. The allocations with an average payoff for the other two persons closer to her's, had both individual payoffs more distant from her's. ERC predicts that she chooses the allocation that is most unequal between the other two persons. The choice of the opposite allocation is predicted by F&S. Subjects knew that their decision could never influence their own payoff. To prevent interference of preferences for efficiency with our objective, we designed two treatments, one where following the ERC prediction leads to a maximization of total payoff, one where maximization of total payoff is in line with the F&S prediction. In the second treatment the results clearly confirm the F&S prediction. In the first treatment subjects chose in about equal proportions the two extreme allocations. Hence the performance of F&S is much better than that of ERC, although both theories ignore the importance that subjects assign to efficiency.
Year of publication: |
2000-08-01
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Authors: | Engelmann, Dirk ; Strobel, Martin |
Institutions: | Econometric Society |
Saved in:
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