Abstract
Evidence shows that the willingness of individuals to avenge punishment inflicted upon them for transgressions they committed constitutes a significant obstacle toward upholding social norms and cooperation. The drivers of this behavior, however, are not well understood. We hypothesize that ulterior motive attribution—the tendency to assign ulterior motives to punishers for their actions—increases the likelihood of counter-punishment. We exogenously manipulate the ability to attribute ulterior motives to punishers by having the punisher be either an unaffected third party or a second party who, as the victim of a transgression, may be driven to punish by a desire to take revenge. We show that survey respondents consider second-party punishment to be substantially more likely to be driven by ulterior motives than an identical, payoff-equalizing punishment meted out by a third party. In line with our hypothesis, we find that second-party punishment is 66.3% more likely to trigger counter-punishment than third-party punishment in a lab experiment. The loss in earnings due to counter-punishment is 64.6% higher for second-party punishers than third-party punishers, all else equal.
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Data availability
Data and other replication material can be found in https://doi.org/10.7910/DVN/NVGV1N.
Notes
In line with this, Nikiforakis et al. (2012) show that the likelihood of counter-punishment is substantially higher when individuals hold conflicting normative views.
The key feature of the “taking game” is that it provides a clearer transgression—taking money from someone else who has as much as you—than in the dictator game and the prisoner’s dilemma used by Fehr and Fischbacher (2004). Incentives in this game are different to those in other games where counter-punishment has been studied (e.g., Denant-Boemont et al., 2007; Kamei and Putterman 2015; Nikiforakis 2008). As such, different norms may be driving behavior in our setting.
We used the exchange rate: 1 EMU = 2 Emirati Dirhams (AED), which is approx. 0.55 USD.
Similar to Nikiforakis et al. (2012), we chose this punishment technology to ensure that (i) that punisher and counter-punisher face symmetric monetary costs for reducing the income of others, and (ii) that the ability to counter-punish would not be impacted by the amount of punishment. Since the previous studies have shown that punishment of norm transgressions is a normal good (Carpenter, 2007; Nikiforakis and Normann, 2008), it is possible that the technology affects the demand for reducing others’ income. However, since the technology is identical across treatments in our study, it cannot affect the answer to our main research question.
Having said this, our survey data strongly suggest that individuals tend to ascribe ulterior motives to punishers for their actions when possible. Therefore, even if the informational asymmetry affects behavior in the experiment, it seems unlikely that it can fully account for the observed differences in counter-punishment in the experiment.
We did not vary the order of the vignettes, always presenting the 2P case first. The reason was to facilitate respondents’ understanding of the two scenarios as the setting in 3P was more complex.
NYU Abu Dhabi has a highly-diverse student body, with more than 115 nationalities represented in the student population at the time of the study.
In the absence of prior studies on the topic to inform our power calculations, we aimed to recruit 100 individuals for each treatment comparison. This would permit us to detect a 14 (28) percentage point difference in the willingness to counter-punish as significant at the 5-percent level, 80-percent of the time using a one-tailed (two-tailed) χ2 test. The unexpected shutdown of laboratories due to the COVID-19 pandemic, prevented us from doing so. Our smaller sample would allow us to detect a difference of 18.5 (37) percentage points in the willingness to counter-punish as significant at the 5-percent level 80-percent of the time. Given that (i) it was unknown for a long time when the forced shutdown would end, (ii) the difference in power was arguably small, especially in light of our directed hypothesis which means that one could reasonably rely on one-tailed tests, (iii) the fact that the difference in behavior we observed was much larger than anticipated, and (iv) that our sample was slightly larger than that in the analogous experiment by Fehr and Fischbacher (2004), we decided to write up our findings for publication.
In Fig. 1, we pool together the responses “biased” (Survey 1: 51%; Survey 2: 63%) and “somewhat biased” (Survey 1: 26%; Survey 2: 29%) in one category, and the responses “unbiased” (Survey 1 6%; Survey 2: 1%) and “somewhat unbiased” (Survey 1: 7%; Survey 2: 3%) in another.
The focus of our experiment is on counter-punishment behavior in the third stage of the game. Although we discuss behavior in the first and second stages in Sect. 3.3, to provide a context for our discussion, we present here some basic descriptive statistics about behavior across stages. In 2P, 40% of participants in the role of player A took either 5 or 10 EMU; individuals assigned the role of player B punished in 30% of all instances, and those in the role of player A counter-punished in 55% of all instances. In 3P, 45% of participants in the role of player A took 5 or 10; individuals assigned the role of player B player B punished in 38% of all instances, and those in the role of player A counter-punished in 33% of all instances. These statistics hint that the main differences across 2P and 3P are in counter-punishment behavior. We provide formal tests supporting this in Sects. 3.2 and 3.3.
Figure 2 pools counter-punishment across different amounts taken by Player A. Readers can find the same analysis disaggregated by amount taken in Appendix A.
Figure 2 shows that, on average, a small amount of income reduction occurs in the third stage, even when there is no punishment in the second stage. This behavior is in line with spiteful preferences (Cheung, 2014). Only 3 individuals (out of 48) individuals chose to reduce the income of Player B despite the latter not punishing them.
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Acknowledgements
The authors would like to thank the Co-Editors, Maria Bigoni and Dirk Engelmann, two anonymous reviewers, Loukas Balafoutas and Aurelie Dariel for helpful comments, as well as Hamna Kahn and Marek Mihok for research assistance. We are grateful for financial support from Tamkeen under the NYUAD Research Institute award for Project CG005.
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Muñoz-Herrera, M., Nikiforakis, N. Experimental evidence shows that ulterior motive attribution drives counter-punishment. J Econ Sci Assoc 9, 193–206 (2023). https://doi.org/10.1007/s40881-023-00137-3
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DOI: https://doi.org/10.1007/s40881-023-00137-3