Efficiency of the Minimum Approval Mechanism With Heterogeneous Players

ABSTRACT The minimum approval mechanism (MAM) was introduced by Masuda et al. (2014) as a mechanism aimed at mitigating free riding in the social dilemma context of a public good game. The MAM is a two‐stage mechanism which theoretically achieves the socially optimum level of public good provision, according to various equilibrium concepts (e.g., backward elimination of weakly dominated strategies, level‐k, or minimax regret). We study the robustness of this mechanism to the introduction of endowment heterogeneity. We explore, theoretically and experimentally, how endowment inequalities affect the effectiveness of the MAM at improving the level of provision. We find that the mechanism is still Pareto‐improving under endowment heterogeneity, but that its efficiency diminishes as inequality is increased. Our experimental findings indicate a significant weakening of the mechanism under endowment inequalities, surpassing our theoretical predictions. A close examination of individual behaviors reveals a significant drop in contributions compared to the uniform case, prompted by even minor inequalities. Intriguingly, our findings challenge conventional assumptions by showing that inequality aversion drives contributions in a public good game with endowment disparities only under certain assumptions. We explore the impact of beliefs about the contributions of advantaged players as potential motivations through guilt aversion and Kantian preferences.

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Journal
Journal of Public Economic Theory
Published
2025-07-20
DOI
https://doi.org/10.1111/jpet.70043
Primary Topic
Experimental Behavioral Economics Studies
Type
article
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article

Efficiency of the Minimum Approval Mechanism With Heterogeneous Players

Marc Willinger, Gabriel Bayle
Journal of Public Economic Theory
Experimental Behavioral Economics Studies
article

Efficiency of the Minimum Approval Mechanism With Heterogeneous Players

Marc Willinger, Gabriel Bayle
article en

Abstract

ABSTRACT The minimum approval mechanism (MAM) was introduced by Masuda et al. (2014) as a mechanism aimed at mitigating free riding in the social dilemma context of a public good game. The MAM is a two‐stage mechanism which theoretically achieves the socially optimum level of public good provision, according to various equilibrium concepts (e.g., backward elimination of weakly dominated strategies, level‐k, or minimax regret). We study the robustness of this mechanism to the introduction of endowment heterogeneity. We explore, theoretically and experimentally, how endowment inequalities affect the effectiveness of the MAM at improving the level of provision. We find that the mechanism is still Pareto‐improving under endowment heterogeneity, but that its efficiency diminishes as inequality is increased. Our experimental findings indicate a significant weakening of the mechanism under endowment inequalities, surpassing our theoretical predictions. A close examination of individual behaviors reveals a significant drop in contributions compared to the uniform case, prompted by even minor inequalities. Intriguingly, our findings challenge conventional assumptions by showing that inequality aversion drives contributions in a public good game with endowment disparities only under certain assumptions. We explore the impact of beliefs about the contributions of advantaged players as potential motivations through guilt aversion and Kantian preferences.

Journal of Public Economic TheoryVol. 27(4)
Centre National de la Recherche Scientifique (FR), Université de Montpellier (FR), Institut Agro Montpellier (FR), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), Institut Agro Montpelier (FR), Center for Environmental Economics - Montpellier (FR), Université Jean Monnet (FR)
Reduced inequalities
Openalex Percentile: Top 83%
Experimental Behavioral Economics Studies
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Efficiency of the Minimum Approval Mechanism With Heterogeneous Players — Marc Willinger, Gabriel Bayle · Journal of Public Economic Theory (2025) | TGRS Research Map | TGRS