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Strong, bold, and kind: self-control and cooperation in social dilemmas

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Abstract

We develop a model that relates self-control to cooperation patterns in social dilemmas, and we test the model in a laboratory public goods experiment. As predicted, we find a robust association between stronger self-control and higher levels of cooperation, and the association is at its strongest when the decision maker’s risk aversion is low and the cooperation levels of others high. We interpret the pattern as evidence for the notion that individuals may experience an impulse to act in self-interest—and that cooperative behavior benefits from self-control. Free-riders differ from other contributor types only in their tendency not to have identified a self-control conflict in the first place.

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Notes

  1. There is also an extensive literature on neurophysiological foundations of cooperation and on emotions and punishment (e.g., Joffily et al. 2014; Boyce et al. 2015; Dickinson and Masclet 2015).

  2. The pattern emerging from dictator games largely mirrors the conflicting pattern from public good games. Piovesan and Wengström (2009) find that selfish choices in a repeated dictator game are correlated with lower response times. Studies that manipulate cognitive resources through depletion or load have yielded mixed results, namely both evidence for and against the proposition that giving requires deliberation (e.g., Hauge et al. 2009; Cornellisen et al. 2011; Schulz et al. 2014; Achtziger et al. 2015).

  3. For an application of the same treatments in a dictator game, see Martinsson et al. (2012).

  4. It is possible, for a variety of reasons, that a decision maker by default contributes a positive amount. In the spirit of parsimony and modeling convenience, we abstract from such cases, but we shall revisit this point in the Experimental Results section.

  5. The effect in Proposition 2 holds when risk aversion refers to the concavity in the utility of monetary payoffs.

  6. Each group member is assigned a number from one to four. At the end of the experimental session, and monitored by the experimenter, a randomly selected group member rolls the die.

  7. We provide the specific numbers used for this risk elicitation procedure in "Appendix C of the Supporting Electronic Material".

  8. Switching points can readily be converted into risk aversion parameters of parametric models, such as CRRA. As the choice of a model would be arbitrary, we use the switching point in our analysis.

  9. The Rosenbaum self-control schedule (1980a) is included in "Appendix A of the Supporting Electronic Material". We translated this scale to German; the same translation was used in Myrseth et al. (2015).

  10. Such self-control strategies may take a variety of forms, and common examples include counteractive self-control (e.g., Trope and Fishbach 2000; Myrseth et al. 2009) and pre-commitment (e.g., Schelling 1984).

  11. Note that the original German question clearly hinted at the normative conflict, without being too suggestive. After asking the experimental participant to recollect his or her decision about contributions, the following question was posed: “In welchem Maße fühlten Sie sich bei Ihrer Entscheidung in einem (inneren) Zwiespalt?” The term “Zwiespalt” can also be translated to English as “dilemma”.

  12. Each experimental point earned in the public goods game is exchanged at the pre-announced rate of 1 point = 0.33 euro.

  13. All instructions, written and oral, were given in German. English versions are included in this paper.

  14. Kocher et al. (2011) analyze the association between cooperation, trust, and risk (but not self-control) based on these data.

  15. The results do not depend on the inclusion of inconsistent responses; the pattern remains the same when we include only those who provided consistent answers in the Holt-Laury task.

  16. We present here and hereafter only tobit regressions, which account for the lower and the upper contribution limits, but our results also hold for OLS. Regression tables are available upon request.

  17. Note that we have 21 observations per individual, and we report robust standard errors to account for the dependence in the data.

  18. We have for expositional purposes decided to split the data according to conflict identification. When instead aggregating the data and including in the specifications a dummy for conflict identification, the same patterns obtain. When we interact the conflict dummy with the relevant variables, the interactions are significant and confirm the results in Tables 2 and 4. However, such specifications are more cumbersome to interpret, in particular the four-way interaction between conflict identification, RSS, Risk, and Others.

  19. We also included those without a weakly monotonically increasing contribution, but with a highly significant (p value < 0.01) positive Spearman rank correlation coefficient between own and others’ contributions, as in Fischbacher et al. (2001) and Fischbacher and Gächter (2010).

  20. We elect not to label this category Others, as is conventional in the literature, because the label would be identical to that used in our regression analyses. To avoid confusion, we instead refer to the residual class of contributor types as Residual.

  21. The RSS of free-riders is not significantly lower than that of either conditional cooperators or hump-shape contributors (all p values > 0.4; two-sided Mann–Whitney-U tests).

  22. The result provides ex-post evidence for the assumption in our model that no conflict identification implies low levels of contribution. See footnote 3.

  23. The regression table is available on request.

  24. This result is also reported in Kocher et al. (2011).

  25. Note that our rationale, in principle, also could account for a reduction of contribution levels over and above a certain level of average others’ contributions, as observed for hump-shape contributors. It would require, beyond that level, a strictly convex temptation function.

  26. See Myrseth and Wollbrant (2015b) for a comment.

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Acknowledgments

Financial support from the Swedish Research Council (Vetenskapsrådet), from Formas through the program Human Cooperation to Manage Natural Resources (COMMONS), and the Ideenfonds of the University of Munich is gratefully acknowledged. For valuable comments and suggestions, we thank Enrique Fatas, Amrei Marie Lahno, Kei Tsutsui, Lise Vesterlund, two anonymous referees, and seminar participants at the EWEBE Workshop 2011 in Munich; the CESifo Behavioral Economics Area Conference in Munich 2012; the Economic Science Association World Meeting in New York 2012; Victoria University Wellington; Queensland University of Technology, Brisbane; Zeppelin University; the Science of Philanthropy Conference 2013, Chicago; the University of Stavanger; Aarhus University; the Econometric Society Meeting 2014, Philadelphia; and the Frankfurt School of Finance and Management.

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  1. Department of Economics, University of Munich, Geschwister-Scholl-Platz 1, 80539, Munich, Germany

    Martin G. Kocher

  2. Department of Economics, University of Gothenburg, Box 640, 40530, Göteborg, Sweden

    Martin G. Kocher, Peter Martinsson & Conny E. Wollbrant

  3. School of Management, University of St Andrews, St Andrews, KY16 9RJ, UK

    Kristian Ove R. Myrseth

  4. School of Economics and Finance, Queensland University of Technology, Gardens Point Campus, Brisbane, 4000, Australia

    Martin G. Kocher

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  1. Martin G. Kocher
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Kocher, M.G., Martinsson, P., Myrseth, K.O.R. et al. Strong, bold, and kind: self-control and cooperation in social dilemmas. Exp Econ 20, 44–69 (2017). https://doi.org/10.1007/s10683-015-9475-7

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