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Inequity Aversion and Individual Behavior in Public Good Games: An Experimental Investigation

Inequity Aversion and Individual Behavior in Public Good Games: An Experimental Investigation. Astrid Dannenberg*, Thomas Riechmann**, Bodo Sturm*, and Carsten Vogt*** *Centre for European Economic Research (ZEW) Mannheim **Otto-von-Guericke-University Magdeburg

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Inequity Aversion and Individual Behavior in Public Good Games: An Experimental Investigation

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  1. Inequity Aversion and Individual Behavior in Public Good Games: An Experimental Investigation Astrid Dannenberg*, Thomas Riechmann**, Bodo Sturm*, and Carsten Vogt*** *Centre for European Economic Research (ZEW) Mannheim **Otto-von-Guericke-University Magdeburg ***Leipzig University of Applied Sciences Supported by the German Research Foundation ESA 2007 World Meeting, Rome

  2. Objective of our study • Low explanatory power of standard theory in social dilemmas • to investigate the additional explanatory power of the Fehr and Schmidt (1999) inequity aversion model • Utility of subject i in a two-person game: • Assumptions: • αi ≥ 0 (aversion against disadvantageous inequality) • βi ≥ 0 (aversion against advantageous inequality) • βi < 1 and αi ≥ βi

  3. Experimental Design I Games A and B (N = 492) • Modified ultimatum and dictator games (similar to Blanco et al. ´06) • Pure allocation games, i.e. no strategic interaction • in order to elicit parameters αi and βi Step 1 Game C (N = 160) • certain αi-βi-types were matched in pairs • Standard two-player Public-Good game, Partner design, 10 periods Step 2 Game D (N = 160) • Stage 1: as in Game C • Stage 2: punishment option with constant marginal costs

  4. Experimental Design II Treatment variables in Game C • parameter βi • information about co-player‘s type

  5. Experimental Design III Hypotheses for Game C according to Fehr and Schmidt: • No contributions in EGO and MIX treatments • In FAIR, cooperation should be observed more frequently than in EGO and MIX. • In FAIR, cooperation should be observed more frequently than in FAIR(ni).

  6. Results: Games A&B • No dispersion of αi • Only 12% fulfill αi ≥βi. • Small negative correlation between βi and studying economics (Spearman‘s ρ= -0.137, p = 0.015)

  7. Results: Effect of βi in Game C Last period • Contributions: GFAIR > GEGO (MW U, p < 10%) and GFAIR > GMIX (MW U, p < 5%) • H0 that cooperation and defection (G < 3€) have the same probability, has to be rejected for FAIR, but not for EGO and MIX (Chi2, p < 5%).

  8. Results: Effect of Information in Game C • Last period: Contributions in FAIR are significantly higher than in FAIR(ni) (MW U, p < 5%). No difference between FAIR(ni) and EGO. • No convergence between FAIR and FAIR(ni).

  9. Conclusions • Specific composition of groups significantly influences the subjects' performance in the PG games. • Only parameter βi matters. • As long as subjects are informed about the co-player’s type, “fair” groups contribute more than “egoistic” or “mixed” groups. • This information cannot be extracted during the PG game.

  10. Thank you for your attention! www.zew.de

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