Implementing punishment and reward in the public goods game: The effect of individual and collective decision rules

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Abstract

Punishments and rewards are effective means for establishing cooperation in social dilemmas. We compare a setting where actors individually decide whom to sanction with a setting where sanctions are only implemented when actors collectively agree that a certain actor should be sanctioned. Collective sanctioning decisions are problematic due to the difficulty of reaching consensus. However, when a decision is made collectively, perverse sanctioning (e.g. punishing high contributors) by individual actors is ruled out. Therefore, collective sanctioning decisions are likely to be in the interest of the whole group. We employ a laboratory experiment where subjects play Public Goods Games with opportunities for punishment or reward that is implemented either by an individual, a majority, or unanimously. For both punishment and reward, contribution levels are higher in the individual than the majority condition, and higher under majority than unanimity. Often, majority agreement or unanimity was not reached on punishments or rewards. © content is licensed under a Creative Commons Attribution 3.0 License.

Figures

  • Figure 1: Average contribution in the PGGs, separated for each round and experimental condition.
  • Figure 2: Average punishment (above) and reward (below) assigned (left) and carried out (right) for different deviations from the average contribution of other group members, separated for each experimental condition.
  • Table 1: Tobit regression on contribution decisions with random effects at subject level (5460 decisions, of which 2376 censored, by 182 subjects).
  • Table 2: Tobit regression on contribution decisions in the punishment conditions with random effects at subject level (1638 decisions, of which 345 censored, by 182 subjects).
  • Table 3: Tobit regression on contribution decisions in the reward conditions with random effects at subject level (1638 decisions, of which 981 censored, by 182 subjects).
  • Table 4: Multilevel logistic regression on decisions whether to punish nested in subjects (4914 decisions by 182 subjects).
  • Table 5: Multilevel logistic regression on decisions whether to reward nested in subjects (4914 decisions by 182 subjects).

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CITATION STYLE

APA

van Miltenburg, N., Buskens, V., Barrera, D., & Raub, W. (2014). Implementing punishment and reward in the public goods game: The effect of individual and collective decision rules. International Journal of the Commons, 8(1), 47–78. https://doi.org/10.18352/ijc.426

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