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dc.contributor.authorHokamp, Saschaen_US
dc.contributor.authorPickhardt, Michaelen_US
dc.date.accessioned2011-11-17en_US
dc.date.accessioned2011-11-17T16:40:34Z-
dc.date.available2011-11-17T16:40:34Z-
dc.date.issued2011en_US
dc.identifier.urihttp://hdl.handle.net/10419/51374-
dc.description.abstractWe 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.en_US
dc.language.isoengen_US
dc.publisherCAWM Münsteren_US
dc.relation.ispartofseriesCAWM discussion paper / Centrum für Angewandte Wirtschaftsforschung Münster 45en_US
dc.subject.jelC70en_US
dc.subject.jelC90en_US
dc.subject.jelH41en_US
dc.subject.ddc330en_US
dc.subject.keywordlinear public goods gamesen_US
dc.subject.keywordPareto-optimalityen_US
dc.subject.keywordpublic goods experimentsen_US
dc.subject.keywordbehavioral economicsen_US
dc.subject.keywordfree-rideren_US
dc.subject.keywordheterogeneous incomesen_US
dc.subject.keywordheterogeneous MPCRsen_US
dc.titlePareto-optimality in linear public goods gamesen_US
dc.typeWorking Paperen_US
dc.identifier.ppn67246960Xen_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen_US
dc.identifier.repecRePEc:zbw:cawmdp:45-
Appears in Collections:CAWM Discussion Papers, Universität Münster

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