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Punishment despite Reasonable Doubt – A Public Goods Experiment with Uncertainty over Contributions

  • Kristoffel Grechenig

    ()

    (Max Planck Institute for Research on Collective Goods, Bonn)

  • Andreas Nicklisch

    ()

    (Max Planck Institute for Research on Collective Goods, Bonn)

  • Christian Thöni

    ()

    (University of St. Gallen, Department of Economics)

Under a great variety of legally relevant circumstances, people have to decide whether or not to cooperate, when they face an incentive to defect. The law sometimes provides people with sanctioning mechanisms to enforce pro-social behavior. Experimental evidence on voluntary public good provision shows that the option to punish others substantially improves cooperation, even if punishment is costly. However, these studies focus on situations where there is no uncertainty about others' behavior. We investigate punishment in a world with “reasonable doubt” about others' contributions. Interestingly, people reveal a high willingness to punish even if their information about cooperation rates is inaccurate, or noisy. If there is some non-trivial degree of noise, unishment (1) cannot maintain high contributions and (2) reduces welfare even below the level of a setting without punishment. Our findings suggest that sufficient information accuracy about others' behavior is crucial for he efficiency of sanction mechanisms. If a situation is characterized by low information accuracy, precluding sanctions can be optimal.

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Paper provided by Max Planck Institute for Research on Collective Goods in its series Working Paper Series of the Max Planck Institute for Research on Collective Goods with number 2010_11.

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Date of creation: Apr 2010
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Handle: RePEc:mpg:wpaper:2010_11
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  1. M. Levati & Andrea Morone & Annamaria Fiore, 2009. "Voluntary contributions with imperfect information: An experimental study," Public Choice, Springer, vol. 138(1), pages 199-216, January.
  2. A. Mitchell Polinsky & Steven Shavell, 2005. "The Theory of Public Enforcement of Law," NBER Working Papers 11780, National Bureau of Economic Research, Inc.
  3. Greiner, Ben, 2004. "An Online Recruitment System for Economic Experiments," MPRA Paper 13513, University Library of Munich, Germany.
  4. Feess, Eberhard & Wohlschlegel, Ansgar, 2009. "Why higher punishment may reduce deterrence," Economics Letters, Elsevier, vol. 104(2), pages 69-71, August.
  5. Simon Gachter & Ernst Fehr, 2000. "Cooperation and Punishment in Public Goods Experiments," American Economic Review, American Economic Association, vol. 90(4), pages 980-994, September.
  6. Urs Fischbacher, 2007. "z-Tree: Zurich toolbox for ready-made economic experiments," Experimental Economics, Springer, vol. 10(2), pages 171-178, June.
  7. Ehrlich, Isaac, 1982. "The optimum enforcement of laws and the concept of justice: A positive analysis," International Review of Law and Economics, Elsevier, vol. 2(1), pages 3-27, June.
  8. Steven Shavell & A. Mitchell Polinsky, 2000. "The Economic Theory of Public Enforcement of Law," Journal of Economic Literature, American Economic Association, vol. 38(1), pages 45-76, March.
  9. Miceli, Thomas J., 1991. "Optimal criminal procedure: Fairness and deterrence," International Review of Law and Economics, Elsevier, vol. 11(1), pages 3-10, May.
  10. Lando Henrik, 2009. "Prevention of Crime and the Optimal Standard of Proof in Criminal Law," Review of Law & Economics, De Gruyter, vol. 5(1), pages 33-52, January.
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