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Pareto-optimality in linear public goods games

Authors: Sascha Hokamp; Michael Pickhardt;

Pareto-optimality in linear public goods games

Abstract

We derive a generalized method for calculating the total number of Paretooptimal allocations (NOPA) in typical linear public goods games. Among other things, the method allows researchers to develop new experimental designs for testing the relevance of Pareto-optimality in experimental settings, for investigating alternative causes of the decline of voluntary contributions, or for analyzing the contribution behavior of the rich and poor in heterogeneous income settings. Further findings include that the NOPA is related to the marginal per capita return (MPCR) of a contribution to the public good and that the maximum number of free-riders tolerated by the Paretooptimality concept is independent from the group size and income distribution. Finally, we apply our findings to a number of published linear public goods games, suggest an agenda for future research and provide a MATLAB code.

Keywords

ddc:330, linear public goods games,Pareto-optimality,public goods experiments,behavioral economics,free-rider,heterogeneous incomes,heterogeneous MPCRs, free-rider, public goods experiments, behavioral economics, linear public goods games, Pareto-optimality, heterogeneous incomes, heterogeneous MPCRs, C90, C70, H41, linear public goods games, Pareto-optimality, public goods experiments, behavioral economics, free-rider, heterogeneous incomes, heterogeneous MPCRs, jel: jel:C70, jel: jel:C90, jel: jel:H41

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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