Replication data for: Imperfect Public Monitoring with Costly Punishment: An Experimental Study

Principal Investigator(s):  View help for Principal Investigator(s) Attila Ambrus; Ben Greiner

Version:  View help for Version V1

Name File Type Size Last Modified
  AER-2010-1123_data 10/19/2021 01:43:PM
LICENSE.txt text/plain 14.6 KB 10/11/2019 02:02:PM

Project Citation: 

Ambrus, Attila, and Greiner, Ben. Replication data for: Imperfect Public Monitoring with Costly Punishment: An Experimental Study. Nashville, TN: American Economic Association [publisher], 2012. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2019-10-11. https://doi.org/10.3886/E112568V1

Project Description

Summary:  View help for Summary This paper experimentally investigates the effects of a costly punishment option on cooperation and social welfare in long, finitely repeated public good contribution games. In a perfect monitoring environment, increasing the severity of the potential punishment monotonically increases average net payoffs. In a more realistic imperfect monitoring environment, we find a U-shaped relationship. Access to a standard punishment technology in this setting significantly decreases net payoffs, even in the long run. Access to a severe punishment technology leads to roughly the same payoffs as with no punishment option, as the benefits of increased cooperation offset the social costs of punishing. (JEL C92, H41, K42)

Scope of Project

Subject Terms:  View help for Subject Terms imperfect monitoring; public good game
JEL Classification:  View help for JEL Classification
      C92 Design of Experiments: Laboratory, Group Behavior
      H41 Public Goods
      K42 Illegal Behavior and the Enforcement of Law
Universe:  View help for Universe people
Data Type(s):  View help for Data Type(s) experimental data

Methodology

Data Source:  View help for Data Source collected by authors in laboratory experiment
Unit(s) of Observation:  View help for Unit(s) of Observation subjects, groups,

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V1 [2019-10-11]

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