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Voluntary contributions with imperfect information: An experimental study

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Author Info
Annamaria Fiore
M. Vittoria Levati ()
Andrea Morone

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Abstract

We use a two-person linear voluntary contribution mechanism with stochastic marginal benefits from the public good to examine the effect of imperfect information on contributions levels. To assess prior risk attitudes, individual valuations of several risky prospects are elicited via a second-price auction. We find that limited information about the productivity of the public good lowers significantly initial contributions in comparison to a setting with perfect information, whereas different information conditions do not result in qualitatively different contribution patterns. Moreover, our results show clear evidence of risk aversion, and of a negative relationship between the latter and willingness to cooperate.

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Paper provided by Max Planck Institute of Economics, Strategic Interaction Group in its series Papers on Strategic Interaction with number 2006-30.

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Length: 35 pages
Date of creation: Nov 2006
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Handle: RePEc:esi:discus:2006-30

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Related research
Keywords: Public goods experiments; Vickrey auctions; Imperfect information; Risk attitudes;

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Find related papers by JEL classification:
C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
H41 - Public Economics - - Publicly Provided Goods - - - Public Goods

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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Lisa R. Anderson & Beth A. Freeborn & Jason P. Hulbert, 2009. "Risk Aversion and Tacit Collusion in a Bertrand Duopoly Experiment," Working Papers 84, Department of Economics, College of William and Mary. [Downloadable!]
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