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Ostracism and theft in heterogeneous groups

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Abstract

Ostracism, or exclusion by peers, has been practiced since ancient times as a severe form of punishment against transgressors of laws or social norms. The purpose of this paper is to offer a comprehensive analysis on how ostracism affects behavior and the functioning of a social group. We present data from a laboratory experiment, in which participants face a social dilemma on how to allocate limited resources between a productive activity and theft, and are given the opportunity to exclude members of their group by means of majority voting. Our main treatment features an environment with heterogeneity in productivity within groups, thus creating inequalities in economic opportunities and income. We find that exclusion is an effective form of punishment and decreases theft by excluded members once they are re-admitted into the group. However, it also leads to some retaliation by low-productivity members. A particularly worrisome aspect of exclusion is that punished group members are stigmatized and have a higher probability of facing exclusion again. We discuss implications of our findings for penal systems and their capacity to rehabilitate prisoners.

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Availability of data and material

All data are available at the following link: https://doi.org/10.48323/h6yj0-67120

Code availability

The z-tree codes used for the experiment is available at the following link: https://doi.org/10.48323/h6yj0-67120.

Notes

  1. Ostracism defines the act of being ignored or excluded. This type of punishment goes back to ancient Athens and was used to establish a more secure and cohesive society by excluding individuals that might have threatened peace or democracy (Williams, 2007). Nowadays ostracism rather describes ‘the practice of excluding disapproved individuals from interaction with a social group’ (Hirshleifer and Rasmusen, 1989, p. 89).

  2. http://www.statistik.at/web_de/statistiken/menschen_und_gesellschaft/soziales/kriminalitaet/index.html.

  3. Throughout the experiment and in order to avoid confusion, the word ‘team’ will typically refer to a group of four subjects who interact over the course of our experimental game, while the word ‘group’ will refer to one of two types within a team (low-productivity or high-productivity). The purpose of this terminological distinction is to avoid misunderstandings when we discuss terms such as ‘in-group’ or ‘out-group’ bias: such terms refer to bias driven by affiliation with a given type.

  4. In-group bias is broadly defined here based on Chen and Li (2009, p.432): ‘[…]group membership creates ingroup enhancement in ways that favor the ingroup at the expense of the outgroup’. More specifically, we define in-group bias as the display of preferential treatment towards a subject’s own type over subjects of the other type in stealing and exclusion decisions. In other words, in-group bias implies that type A (B) subjects direct strictly more theft and exclusion votes against type B (A) subjects than against the own type, if the behavior of the two types in the game is held constant.

  5. Nevertheless, we note that social psychology partly differentiates between ostracism, social exclusion and rejection (Williams and Zadro, 2001).

  6. This information can be deduced in the rare cases where only one subject allocated tokens to theft in a period.

  7. The majority rule works as follows: If there are currently four members in a team, each member votes on whether or not he wishes to exclude each of the other three members. Hence, each member can receive up to three exclusion votes, and majority requires at least two votes. If there are currently three members in a team (because the fourth one is excluded for the period), then each member can receive up to two votes for exclusion, and hence majority coincides with unanimity and requires two votes. If there are only two members or one member in a team in a given period, no voting takes place.

  8. Each Type A member receives 100 units through production and 75 (= 5 × 15) units through theft. At the same time, Type A is the target of 7 theft tokens on average (8 tokens from each of the Type B members plus 5 tokens from the other Type A member, divided by three possible targets). This leads to a period payoff of 175 – (7 × 15) = 70 units. Following the same reasoning, each Type B member receives 34 units through production and 120 (= 8 × 15) units through theft, and loses on average 90 units to theft (6 tokens × 15 units per token). This leads to a per period payoff of 64. These calculations assume that all team members randomly divide their theft tokens among the other members in the team.

  9. Several behavioral motives may potentially influence the decision to steal or not. These motives include, among others, moral emotions like guilt or shame. Social psychology offers insights on how moral emotions influence moral intentions and moral behavior. While guilt has found to inhibit antisocial behavior, no effect or even a negative effect was established for shame. These findings hold for children, adolescents, college students and even prison inmates (see Tangney et al., 2007 for a literature review).

  10. An additional implication of exclusion is that it is not possible to steal from excluded members. However, this does not add to the costs of exclusion since one can always steal from remaining team members. One exception is the case of only one person left in the team. This possibility does not change our predictions, which state that only the two Type B members will be excluded and the two Type A members will remain in the team.

  11. Sample instructions for treatment MAIN are provided in Supplementary Appendix A.4.

  12. In this respect, it is important to note that negative payoffs occurred in only 204 out of 6,660 subject-period observations, or 3.05% of the time, while the accumulated wealth never turned negative for any team member in any period.

  13. Throughout the analysis, all non-parametric tests are based on conservative definitions of what constitutes an independent observation. In between-subjects comparisons, we treat one average per team (over all periods) as one independent observation. In paired comparisons entailing related samples, we treat the average behavior of all subjects in a sample and over all periods within a team as one independent observation (e.g., comparing Type A vs. Type B members, or comparing members who have just been re-admitted into the team vs. the rest).

  14. In order to visualize the heterogeneity in stealing behavior across teams, we also present in Fig. A.5 in The Supplementary Appendix mean stealing over time for each team separately.

  15. We have replicated the Table 3 regressions replacing the variables on cumulative theft over all preceding periods with 3-period moving averages. This leads to no qualitative change in the findings.

  16. This leads us to exclude 392 observations (27.2% of the sample) in the regressions for treatment MAIN, and 488 observations (23.92% of the sample) in the regressions for the homogeneous treatments.

  17. This effect does not differ significantly between high theft and low theft teams, as revealed by a separate regression including an (insignificant) interaction term between exclusion and a dummy for high theft teams. Moreover, as an additional robustness check, we have estimated the Table 3 regressions excluding the last period of play, since theft increases sharply in that period. All results hold in direction and magnitude.

  18. In the regressions of Table 5, the list of explanatory variables includes two different versions of theft. In columns 1 and 2, following Table 3, we use mean theft and mean received theft as explanatory variables. In columns 3 and 4, following Table 4, we use theft and received theft in the current period as explanatory variables. To simplify the presentation of the table, we do not include separate rows for these different theft variables, but instead call them Mean (Current) Theft and Mean (Current) Theft_Received. This joint notation refers to mean theft in the first two specifications, and to current theft in the latter two specifications.

  19. This is made possible by the fact that subjects can be identified by means of their fixed player numbers within a team.

  20. This finding is robust to replacing the current theft variables with 3-period moving averages of committed and suffered theft. The regressions are shown in the Supplementary Appendix (columns 3 and 4 in Table A.2.).

  21. The magnitude of this effect is slightly higher for Type A (1.90 tokens) than for Type B members (2.42 tokens). However, a linear regression reveals that the difference in differences is not significant.

  22. In regards to these comparisons, it must be kept in mind that the behavior of a particular type may differ between the heterogeneous and homogeneous treatments due to several reasons, related to team dynamics and experiences over the course of the game as well as to the different average productivity and income levels (which are highest in HOA, lowest in HOB and intermediate in MAIN).

  23. The fact that we find evidence for an in-group bias in MIB but not in MIA could be driven by the higher average levels of theft and exclusion in MIB, meaning that there is also more power to detect the presence of an in-group bias in that treatment.

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Acknowledgements

We are thankful to Marie-Claire Villeval and seminar participants at the University of Düsseldorf, Max Planck Institute for Tax Law and Public Finance, Monash University and University of Queensland, Ca’Foscari University of Venice, EconomiX at Université Paris Nanterre and participants at the 2019 ESA conference in Dijon for helpful comments and suggestions.

Funding

The project has been approved and received funding by the vice-rectorate for research of the University of Innsbruck (Project Number 282227).

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  1. Department of Public Finance, University of Innsbruck, Innsbruck, Austria

    Alexandra Baier & Loukas Balafoutas

  2. Economix, UPL, Université Paris Nanterre, CNRS, 200, Avenue de la République, 92000, Nanterre, France

    Tarek Jaber-Lopez

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Baier, A., Balafoutas, L. & Jaber-Lopez, T. Ostracism and theft in heterogeneous groups. Exp Econ 26, 193–222 (2023). https://doi.org/10.1007/s10683-022-09758-7

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