Punishment under threat: The role of personality in costly punishment

Journal of Research in Personality 81 (2019) 47–55

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Punishment under threat: The role of personality in costly punishment Stefan Volk a,⇑, Helena Nguyen a, Christian Thöni b a b

University of Sydney, Australia University of Lausanne, Switzerland

a r t i c l e

i n f o

Article history: Received 1 July 2018 Revised 9 May 2019 Accepted 15 May 2019 Available online 16 May 2019 Keywords: Costly punishment Punishment threat Personality Emotionality Honesty-humility Agreeableness

a b s t r a c t It is often assumed that people engage in costly punishment of third parties for prosocial reasons. In this study, we examined to what extent people engage in costly punishment of third parties in response to a perceived punishment threat rather than due to prosocial reasons. Using a modified public goods game with a punishment stage, we show that personality plays an important role in determining which of these processes drive costly punishment. We found that Honesty-Humility, which is related to prosociality, facilitates costly punishment independent of expected punishment to oneself, while Emotionality, which is related to fearfulness, facilitates punishment that is mediated by expected punishment. Agreeableness, which is related to anger and displaced aggression, had no effect on costly punishment. Ó 2019 Elsevier Inc. All rights reserved.

1. Introduction Costly punishment is an action that incurs a cost for the punished individual but also for the punishing individual (e.g., Henrich et al., 2006). People often engage in costly punishment even if they cannot expect any present or future rewards from this behavior and even if they are not directly affected by the actions of the punished individuals which raises the question as to what incites this behavior which seemingly opposes self-interest (Henrich & Boyd, 2001). In this paper we investigate the role of the three personality dimensions Honesty-Humility, Emotionality, and Agreeableness in costly punishment decisions. The dominant explanation for the underlying mechanisms driving costly punishment is that it is a form of reciprocity. For example, Ohtsuki, Iwasa, and Nowak (2009, p. 79) point out that ‘‘any discussion of the evolution of costly punishment brings us immediately into the framework of direct or indirect reciprocity”. Direct reciprocity refers here to a situation of direct retaliation where I punish you because you harmed me. A simple example of direct reciprocity are people who pay lawyers to take actions against others that have harmed them. Indirect reciprocity, on the other hand, refers to a situation where I punish you because you harmed somebody else. This can be either in response to a person’s reputation of treating others badly (e.g., Nowak & Sigmund, 2005) or in response to directly observed behavior. Take, for example, a stran⇑ Corresponding author. E-mail address: [email protected] (S. Volk). https://doi.org/10.1016/j.jrp.2019.05.005 0092-6566/Ó 2019 Elsevier Inc. All rights reserved.

ger who comes to the aid of another stranger being physically harassed and makes a citizen’s arrest of the perpetrator. In coming to this person’s defense and punishing the perpetrator by handing him over to the police, they risk their own personal safety. This type of punishment to enforce prosocial norms is often costly for the punisher in terms of time, energy and even physical risk (Jordan, Hoffman, Bloom & Rand, 2016). In this study, we investigate a third type of situation where I punish you because I expect somebody else to harm me. We argue that this type of behavior is likely to arise in situations where people cannot directly retaliate against the person or institution harming them because this actor is more powerful and/or unavailable for punishment. Our interest in this specific behavior is driven by the observation that in many real-life situations people are exposed to a harmful punishment threat by more powerful actors such as supervisors in work settings, teachers in academic settings, or referees in sports settings against whom they cannot easily retaliate. This perceived threat based on expected punishment and harm to oneself might be a motivation for some people to engage in costly punishment towards unrelated others. That is, in response to an expectation to be punished themselves and an inability to retaliate against the punisher, people might punish unrelated third parties. The motives underlying this type of punishment behavior can be various. One motivation is fear of the consequences of the potential punishment and last-place aversion or last-place loathing (Buell, 2017; Gill, Kissová, Lee, & Prowse, 2018; Kuziemko, Buell, Reich, & Norton, 2014). Last-place aversion refers more generally

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to the basic human need to avoid the feeling that one performs worse than everybody else and that there is nobody or only few one can feel superior to or look down on. When individuals fear that they might receive a punishment by a superior actor that could put them into last place or make them perform worse than everybody else, they might respond by punishing an unrelated third person, hoping the punishment will put this person behind them and in this way feel better about their own ranking and performance. Punishment can here be any action that potentially harms the punished individual such as refusing to help, sabotaging work or equipment. Take, for example, relative performance evaluations which are common in many organizations and require team leaders to compare and rank the performance of their team members and reprimand or even fire the lowest performers (Hazels & Sasse, 2008). If a team member fears that his team leader might punish him with a negative evaluation, he might respond to this perceived threat by punishing (i.e., harming) another unrelated team member by providing false advice, withholding information, or even destroying her work so that she ends up behind him in the relative team performance evaluation. Or in sports an athlete might expect a penalty from a referee which would put him in or near last place resulting in relegation and to avoid this, he manipulates the equipment of another athlete hoping this will put this competitor into last place instead of him. These types of punishment acts to harm and get ahead of others triggered by a perceived threat to one’s own standing are common in firms (Charness, Masclet, & Villeval, 2014), academia (Maher, 2010), and sports (Balafoutas, Lindner, & Sutter, 2012). They represent the type of costly punishment we are studying in that they are costly for the punished individual but also for the punishing individual in terms of the effort required to perform these sabotaging acts and the risk of getting caught. Another potential motive underlying punishment of others in response to a perceived punishment threat is displaced anger and aggression driven by threat appraisal. Displaced aggression refers to situations where individuals direct their anger and aggression to a third party not involved in the initial provocation that triggered the anger (Martinez, Zeichner, Reidy, & Miller, 2008; Twenge & Campbell, 2003). In our context, some individuals might feel anger in response to the pure anticipation of punishment and might respond to this anticipated unkindness with aggression by punishing (i.e., hurting) some available victim. This aggression can either be targeted at the punisher (i.e., direct aggression) or ‘‘redirected toward or displaced onto less powerful or more available targets” (Marcus-Newhall, Pedersen, Carlson, & Miller, 2000, p. 670) if the punisher is too powerful or not available for punishment. For example, an employee might anticipate that his team leader will punish him with a negative evaluation or even a pay cut or demotion and in response to this perceived punishment threat feel anger. Since he cannot directly retaliate against the supervisor because he fears losing his job, he engages in displaced anger and aggression by kicking a dog on the way home or acting aggressively towards his spouse. While there is substantial research studying costly punishment in situations of direct reciprocity (I punish you because you harmed me) and indirect reciprocity (I punish you because you harmed somebody else), we are not aware of any costly punishment studies that investigated the potential effects of a punishment threat (I punish you because I expect somebody else to harm me). Indeed, many traditional punishment experiments deliberately do not allow the subjects administering punishment to be punished themselves to ensure that the observed punishment is the result of prosocial motives (e.g., Fehr & Fischbacher, 2004). However, in many real-life situations people are exposed to a punishment threat by a range of actors and very often they cannot directly retaliate against these punishers because they are

more powerful such as supervisors, referees or even law enforcement. We deem it important to study whether costly punishment occurs in such situations as it would be highly inefficient, wasting resources for both the punishing and the punished subjects without the efficiency increases often observed in costly punishment resulting from direct or indirect reciprocity (Rockenbach & Milinski, 2006). In this paper we therefore study whether subjects engage in costly punishment of innocent third parties as a result of a perceived punishment threat. We also study who is likely to engage in this type of behavior by investigating the role of the three personality dimensions Honesty-Humility, Emotionality, and Agreeableness which we hypothesize are implicated in this type of behavior. Finally, and related to these three personality dimensions, we explore some potential motives that might drive this type of punishment behavior. 1.1. Costly punishment, punishment threat, and personality There is a large body of evidence suggesting that people engage in costly punishment for prosocial motives to promote norms of cooperative behavior (Fehr & Fischbacher, 2004; Fehr, Fischbacher, & Gächter, 2002; Fehr & Gächter, 2002; Henrich et al., 2010). This type of prosocial punishment behavior has been studied within the context of direct and indirect reciprocity. However, other contexts may exist in which individuals engage in costly punishment. In this paper we study costly punishment as a result of a perceived punishment threat from actors against whom the punished individuals cannot easily retaliate. At the core of our theorizing is the role of punishment threat (i.e., the expectation of being punished oneself), which we predict can be an important antecedent of costly punishment. In this paper, we ask the important question: Who is particularly likely to respond to a punishment threat? One important factor that might explain individual differences in the propensity of people to respond to a punishment threat is personality. Yet, few studies have explored the role of individual differences, such as personality, in order to disentangle the varying mechanisms that underlie costly punishment. Studies on costly punishment have tended to adopt group-functionalist explanations with the main emphasis being on prosocial motives (Fehr & Fischbacher, 2003). These group-level explanations have predominated at the expense of idiosyncratic patterns. Knoch, Gianotti, Baumgartner, and Fehr (2010, p. 337) point out that the willingness to engage in costly punishment is ‘‘characterized by vast individual heterogeneity that is poorly understood”. Indeed, it remains poorly understood as to who is likely to engage in costly punishment and for what reasons. Besides a few exceptions (e.g., Jordan et al., 2016; Lotz, Baumert, Schlosser, Gresser, & Fetchenhauer, 2011), systematic individual differences in costly punishment remain largely under-researched. In this study, we investigate the role of personality as one key individual difference factor that makes it more or less likely for individuals to engage in costly punishment in response to a punishment threat rather than due to social cooperation or fairness considerations. Note that although studies have considered basic personality traits as predictors of behaviors in economic games and social dilemmas (Becker, Deckers, Dohmen, Falk, & Kosse, 2012; Volk, Thöni, & Ruigrok, 2011, 2012), studies have yet to investigate the influence of personality traits specifically in relation to costly punishment. We predict that punishment threat is an important antecedent of costly punishment for people with certain personality traits. Note that a punishment threat is a type of threat appraisal which relates to the ‘expectation that personal harm or loss is imminent’ (Feldman, Cohen, Hamrick & Lepore, 2004, p. 355). Our prediction is based on overwhelming evidence that threat appraisals precede

S. Volk et al. / Journal of Research in Personality 81 (2019) 47–55

behaviors (Lazarus, 1984) and that beliefs about others’ punishment behavior (i.e., expected punishment) determine people’s own punishment behavior (i.e., assigned punishment) (e.g., Kamei, 2014; Szolnoki & Perc, 2013), just as beliefs about others’ behavior more generally determine people’s own behavior more generally (e.g., Fischbacher & Gächter, 2010; Frey & Meier, 2004). We argue that punishment threat plays a key role in costly punishment specifically for individuals who score high on the trait of Emotionality and low on the trait of Agreeableness. Whilst the relationship between punishment threat and punishing behavior is well-established, the role of Emotionality and Agreeableness in influencing perceptions of punishment threat and costly punishment is yet to be theorized and tested. 1.2. Emotionality In Ashton and Lee’s (2007) HEXACO model of personality, individuals high in Emotionality tend to be fearful and anxious. We hypothesize that individuals high in Emotionality respond to a perceived punishment threat with fear of the consequences of the potential punishment. More precisely, we hypothesize they respond with fear of ending up near or in last place, i.e. performing worse than everybody else, as a result of the potential punishment. We make this prediction based on findings that individuals high in Emotionality (similar to those high on Neuroticism in the BigFive model) experience higher levels of stress and anxiety and are therefore more sensitive to a punishment threat and its potentially disadvantageous consequences (Wallace & Newman, 1990). More specifically, there is evidence that individuals who experience higher levels of fear and anxiety, which is the case for individuals with high scores on Emotionality, have more fear-driven threat appraisals (Gallagher, 1990) and a chronic tendency to worry (Brinker & Wilkinson, 2014). High Emotionality is also associated with a greater anticipation of punishment and fear of threat to the self and greater sensitivity to the potential negative outcomes of a disadvantageous situation (Eisenberg, Fabes, Murphy, Maszk, Smith & Karbo, 1995; Gallagher, 1990). This makes individuals high in Emotionality particularly susceptible to a punishment threat and a related fear of performing worse than everybody else as a result of the potential punishment. We therefore hypothesize that individuals high in Emotionality are likely to respond to a perceived punishment threat from an actor who is too powerful and/or unavailable for retaliation with costly punishment of an innocent third party. We also hypothesize that they do this due to last-place aversion or last-place loathing, hoping the punishment will put this person behind them and in this way reduce their fear of ending up performing worse than everybody else. 1.3. Agreeableness HEXACO Agreeableness is defined as ‘‘the tendency to be forgiving and tolerant of others” (Ashton & Lee, 2007, p. 156). High levels of Agreeableness are typically related to forgiveness, tolerance, patience, and calmness while low levels are related to anger, irritability, and harshness. We hypothesize that individuals low in Agreeableness respond to a perceived punishment threat with anger and aggression. We also hypothesize that if the potential punisher is not available as a target, individuals low in Agreeableness will redirect this anger and aggression toward a or more available target. We make this prediction based on findings that individuals who score high on Agreeableness can easily control their temper while those scoring low tend to become easily angered in response to a perceived mistreatment (Ashton et al., 2004; Lee & Ashton, 2004). Furthermore, participants low in Agreeableness are more

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likely to engage in displaced aggression towards innocent third parties uninvolved in the provocation that triggered the aggression (Denson, Pedersen, & Miller, 2006; Lee & Ashton, 2012). We therefore hypothesize that individuals low in Agreeableness are likely to respond to a perceived punishment threat from an actor who is too powerful and/or unavailable for retaliation with displaced anger and aggression toward less powerful or more available targets. We also hypothesize that as a result of this displaced anger and aggression they engage in costly punishment of an innocent third party. 1.4. Honesty-Humility We contrast our hypotheses about how Emotionality and Agreeableness impact costly punishment through punishment threat by investigating the influence of Honesty-Humility, which is the sixth basic trait in the HEXACO model of personality (Ashton & Lee, 2009). While Agreeableness is related to reactive cooperation (i.e., non-retaliation), Honesty-Humility is the tendency toward active cooperation or ‘‘the tendency to be fair and genuine in dealing with others, in the sense of cooperating with others even when one might exploit them without suffering retaliation” (p. 156). Individuals high in Honesty-Humility tend to be sincere, cooperative, and fair-minded. Research on prosocial and altruistic behaviors has highlighted the crucial role of Honesty-Humility and there is overwhelming evidence that the trait predicts a range of positive outcomes across a variety of different situations, such as sociopolitical equality orientation (Lee, Ashton, Ogunfowora, Bourdage, & Shin, 2010), integrity and ethical decision-making (Lee, Ashton, Morrison, Cordery, & Dunlop, 2008), and a low desire for power and money (Lee, Ashton, Wiltshire, Bourdage, Visser, & Gallucci, 2013). Consistent with the extant literature, we predict that individuals high in HonestyHumility engage in costly punishment primarily for prosocial motives to promote cooperative behavior, that is, the effect of Honesty-Humility on costly punishment is independent of the effect of a punishment threat. 1.5. The present study We test these hypotheses using a public goods game (PGG) which is one of the most frequently used paradigms to study cooperation and its enforcement through costly punishment (e.g., Brandt, Hauert, & Sigmund, 2003; Fehr & Gächter, 2000; Nicklisch, Grechenig, & Thöni, 2016). Traditional punishment experiments with a focus on prosocial motives do not allow punishers to be punished themselves (e.g., Fehr & Fischbacher, 2004). To be able to study the role of punishment threat, we introduced a novel feature in our experimental design. In our extended PGG paradigm every participant had the chance to engage in costly punishment of one member of another team and every participant also faced the risk of receiving costly punishment from one member of another team (i.e., there was a punishment threat) where the punished and the punishing subject were not the same person nor in the same team. To assess to what extent the threat of being punished oneself affected participants own punishment decisions, we asked them to indicate how much punishment they expected to receive.1 Overall, we argue that researchers may have overlooked the role of punishment threat as a potential antecedent of costly punishment behaviors. We propose that the personality traits of 1 The reported study was not pre-registered. The first and third author designed the study. The first author conducted the study assisted by research assistants. The first and second author performed the data analysis. All three authors were involved in the writing of the manuscript.

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Honesty-Humility, Agreeableness, and Emotionality might reveal differences in the processes underlying costly punishment. We argue that unlike Honesty-Humility, Emotionality and Agreeableness might make people more likely to engage in costly punishment to respond to a perceived punishment threat rather than to act prosocially. 2. Method 2.1. Participants 205 undergraduate students (mean age 22.6, SD = 2.4, 48.8% male) participated in the study which was conducted at a large public university in Indonesia. Our sample size was determined by the following considerations: Gignac and Szodoria (2016) point out that correlations of 0.2 are a typical effect size in individual difference research including personality research. For a two-sided alpha of 0.05, a power of 0.80 and a rho of 0.20, the necessary sample size is 193 subjects (Faul, Erdfelder, Buchner, & Lang, 2009). We increased our desired sample size to 205 to allow for possible drop outs. Participants were paid 60,000 Indonesian Rupiah (US$ 5) for participating, in addition to the money earned from the decisions they made during the study. During the experiment payments were calculated in tokens. Payoffs were converted at an exchange rate of 1 token = 3000 Indonesian Rupiah (US$ 0.2). Subjects earned in the experiment on average 55,478 (SD 25,290) Indonesian Rupiah (US$ 4) in addition to the show up fee. No deception was used at any point in this study. 2.2. Procedure Participants were randomly seated at tables which were separated by cardboard partitions to ensure that decisions were made in anonymity from other participants. The entire study was conducted with pen and paper; communication between participants was strictly prohibited. Participants received written instructions explaining their tasks in the study in great detail and had to answer a number of control questions to prove their understanding of the task. At the end of the study participants were paid their experimental earnings. 2.2.1. Personality measure Participants completed the HEXACO-60 measure of personality (Ashton & Lee, 2009) consisting of six ten-item scales for the personality traits Honesty-Humility, Emotionality, Extraversion, Agreeableness, Conscientiousness, and Openness to Experience on a scale from 1 (strongly disagree) to 5 (strongly agree). In line with our above theorizing we were specifically interested in participants’ scores on the Honesty-Humility, Emotionality, and Agreeableness scales. The means, standard deviations, and Cronbach’s alphas of our sample for Honesty-Humility (a = 0.74, M = 3.32, SD = 0.65), Emotionality (a = 0.77, M = 3.18, SD = 0.67), and Agreeableness (a = 0.69, M = 3.12, SD = 0.57) are very similar to those in a North-American undergraduate sample (n = 1126) provided as reference statistics on the HEXACO website (Lee & Ashton, 2009). This indicates the comparability of our sample with other samples. 2.2.2. Public goods game under punishment threat After completing the personality measure, participants played a two-person public goods game (PGG). Team members were not able to identify each other and no information about the other person’s decisions was provided during the experiment. Each subject received 20 tokens, any integer portion of which they could either keep privately or contribute to a team account. Contributions to the team account benefited both team members alike, that is,

regardless of the amount contributed both team members received 0.75 times the sum of all contributions. Since participants earned exactly one token for each token they kept privately but only 0.75 token for each token they contributed to the team account, it was in their material self-interest to keep all tokens privately independent of the other group member’s decision. When subjects made their contribution decision in the PGG they were aware that after the game one member of another team could punish them for this decision. They also knew that they themselves would have the opportunity to punish one member of another team for their PGG contribution. In both cases the punishing subject was not affected by the contribution decision of the person they were allowed to punish. Fig. 1 illustrates how the punishment design was explained to the participants. Take subject B1 for example. B1 forms a team with B2, they both play the PGG. After the PGG, B1 can punish subject C1 from team C for his or her contribution decision. At the same time subject A1 from team A can punish B1 for his or her own contribution decision. The punishment procedure was designed to make sure that A1 and C1 are not the same person and not in the same team to rule out any potential direct reciprocity effects in punishment. Participants had the opportunity to punish other subjects by assigning deduction points. However, punishment was costly, for each assigned deduction point the income of the punishing subject was reduced by one token while the income of the punished subject was reduced by three tokens. Participants received an extra endowment of 10 tokens for the punishment round, any amount of which they could either keep or use for the punishment. 2.2.3. Expected punishment To be able to assess in objective, behavioral terms to what extent the threat of being punished oneself affected participants own punishment decisions, we asked them to indicate how much punishment they expected to receive. This was implemented using the strategy method (e.g., Jordan, McAuliffe, & Rand, 2015). More precisely, we used a table showing in ascending order the subject’s own 21 (0–20) possible contribution levels in the PGG. For each of these 21 possibilities participants had to indicate how many deduction points they expected to receive. The table elicited from each participant an expected punishment schedule as a function of their own possible contributions in the PGG. This schedule allows to study the extent to which subjects expect to be punished by others in much greater detail than a design that only asks for a single punishment expectation in response to their actual contribution decision in the PGG. For example, assume that the subject contributes the maximum amount of 20 tokens. In this case most participants would expect to receive zero deduction points. However, this would provide us with very limited insight into their general punishment expectations. When eliciting an expected punishment schedule for each possible contribution level, we still get full information about this person’s general punishment expectations. 2.2.4. Assigned punishment In addition to indicating how much punishment they expected to receive, participants also had to make a punishment decision themselves. This was again implemented using the strategy method. Participants had to fill in a second table, this time showing the 21 (0–20) possible PGG contribution levels of one member of another team. For each of these 21 possibilities they had to indicate their corresponding punishment by assigning deduction points (any amount between 0 and 10 in steps of 0.5). After the experiment, the experimenter implemented the punishment indicated in the table for the actual contribution level of the punished subject. Also, after the experiment, the experimenter compared the assigned punishment table of the punishing subject with the

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Team A

Team B

Team C

Subject A1

can assign deduction points

Subject B1

can assign deduction points

Subject C1

Subject A2

can assign deduction points

Subject B2

can assign deduction points

Subject C2

Fig. 1. Experimental punishment design.

expected punishment table of the punished subject. Every time the punished subject estimated the correct amount of deduction points that was assigned to him or her for a specific contribution level by the punishing subject he or she earned one token. All parts of the experiment were common knowledge. Thus, in addition to the expected punishment schedule we also elicited an assigned punishment schedule from every participant. In our analysis we were mainly interested in the personality data and the assigned punishment schedule as well as the expected punishment schedule. The contribution decisions in the PGG were less relevant for us, the game was merely a vehicle to implement our punishment protocol. 2.2.5. Behavioral predictions Based on our theorizing about the role of punishment threat and the three personality traits of Honesty-Humility, Agreeableness, and Emotionality in costly punishment, we made the following behavioral predictions for our experimental design. Individuals scoring high on Honesty-Humility will be unaffected by the expectation of receiving deduction points. They will assign deduction points to a member of another team to punish them for low contributions, i.e. for prosocial reasons. Individuals scoring high on Emotionality will respond to the expectation of receiving deduction points with fear of the consequences of the potential punishment and last place aversion. More specifically, in our experimental design we hypothesize they fear to perform worse than everybody else and walk out with the least amount of experimental earnings as a result of the potential punishment. In order to avoid this feeling, they assign deduction points to a member of another team to put this person behind them and in this way reduce their fear of ending up performing worse than everybody else. Our experimental design allowed subjects to get ahead of others through punishment because it reduced the income of the punished subject three times as much as the income of the punishing subject. Finally, individuals scoring low on Agreeableness will respond to the expectation of receiving deduction points with anger and aggression. Since they cannot retaliate against the person assigning deduction to them they will redirect their anger and aggression toward a member of another team who is available for punishment by assigning deduction points to this subject. In our study, while we cannot directly observe motives, we can observe the behavioral representations of these hypothesized motives and interpret them in relation to the personality traits involved. 3. Results For our analysis we calculated for each participant how many deduction points they expected to receive on average across all 21 contribution levels in the PGG (i.e., expected punishment). We also calculated for each participant how many deduction points they assigned on average across all contribution levels (i.e.,

assigned punishment). This provided us with measures of the extent to which subjects expected punishment by others and the extent to which they were willing to engage in costly punishment themselves across the full range of possible cooperation levels in the PGG. Out of 205 participants, 168 (82%) expected to receive punishment (expected punishment > 0) and 136 (66.3%) engaged in punishment (assigned punishment > 0).2 Means, standard deviations, correlation coefficients, and reliability estimates for all variables are presented in Table 1. As can be seen in Table 1, participants expected slightly more punishment (M = 2.35, SD = 1.89) than they assigned (M = 2.07, SD = 2.13). In terms of intercorrelations, there was a positive correlation between expected and assigned punishment (rs = 0.66, p < .01). The personality dimension Honesty-Humility was positively correlated with assigned punishment (rs = 0.21, p < .01) but not with expected punishment (rs = 0.11, p = .11). The personality dimension Emotionality correlated positively with both expected punishment (rs = 0.31, p < .01) and assigned punishment (rs = 0.35, p < .01). The personality dimension Agreeableness was neither correlated with expected punishment (rs = 0.04, p = .55) nor with assigned punishment (rs = 0.02, p = .81). Gignac and Szodoria (2016, p.74) recommend for individual difference research, including personality research, ‘‘to consider correlations of 0.10, 0.20, and 0.30 as relatively small, typical, and relatively large”. The effect of Honesty-Humility on assigned punishment (rs = 0.21) can therefore be considered typical while the effect of Emotionality on both expected punishment (rs = 0.31) and assigned punishment (rs = 0.35) is relatively large. Overall, these patterns of correlations are consistent with our hypotheses in that we expect the effects of Emotionality on costly punishment to be driven by the threat of being punished oneself, whilst the effects of Honesty-Humility on costly punishment to be relatively independent of this threat. However, our hypothesis regarding the effects of Agreeableness on costly punishment is not supported by our initial correlation analysis. In order to test our hypotheses that expected punishment would mediate the relationship between Emotionality and assigned punishment as well as between Agreeableness and assigned punishment, but not the relationship between HonestyHumility and assigned punishment, we ran three mediation analyses, one for each personality dimension as the independent 2 An alternative measure instead of the average would have been the slope of the schedules. We used the average instead of the slope because the average provides a measure of the overall level of punishment while the slope provides a measure of the extent to which the punishment is adjusted to the punished subject’s level of contribution in the PGG. However, for our hypothesized costly punishment motives based on Emotionality (fear to perform worse than everybody else) and Agreeableness (anger and displaced aggression) the level of the punished subject’s contribution in the PGG is less relevant. In both cases subjects punish to either reduce another subjects’ income or to vent anger onto an available target but not in response to the punished subject’s contribution in the PGG. As such, using the average instead of the slope in our below reported mediation models was in line with our theoretical arguments and predictions.

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Table 1 Descriptive statistics.1,2

1. 2. 3. 4. 5. 6. 7. 8. 9.

PGG contribution Expected punishment Assigned punishment Honesty-Humility Emotionality Agreeableness Extraversion Conscientiousness Openness to Experience

M

SD

1

2

3

4

5

6

7

8

9

11.34 2.35 2.07 3.32 3.18 3.12 3.74 3.57 3.42

6.08 1.89 2.13 0.65 0.67 0.57 0.53 0.57 0.53

0.053 0.073 0.05 0.04 0.03 0.05 0.15* 0.01

0.66** 0.11 0.31** 0.04 0.10 0.03 0.03

0.21** 0.35** 0.02 0.13 0.15* 0.08

(0.74) 0.15* 0.35** 0.04 0.14 0.16*

(0.77) 0.03 0.34** 0.00 0.08

(0.69) 0.09 0.08 0.18*

(0.77) 0.22** 0.32**

(0.74) 0.13

(0.65)

1

The table shows mean scores (M), standard deviations (SD) and correlation coefficients. Coefficient alphas are shown in parentheses on the diagonal. 2 * indicates p < .05; ** indicates p < .01. 3 Interestingly, there was no correlation between PGG contributions and expected punishment or between PGG contributions and assigned punishment. This is likely due to the fact that our measures of expected and assigned punishment are calculated as averages across all possible contribution levels and hence not directly related to subjects’ actual contribution decisions in the PGG.

variable. We used bootstrapping which uses resampling of raw data to estimate the confidence intervals (CIs) of the indirect effects of the mediation model (Hayes, 2013; Preacher & Hayes, 2008). In testing for mediation, bootstrapping is an analytical approach that overcomes the Sobel test’s assumption that the indirect effect follows a normal distribution under the null hypothesis. Bootstrapping is a nonparametric resampling approach that is the preferred strategy for testing for mediation effects since it ‘‘makes no assumptions about the shape of the distributions of the variables or the sampling distribution of the statistic” (Preacher & Hayes, 2004, p. 722). Results of the mediation analyses based on the Preacher and Hayes (2004) bootstrapping approach can be seen in Figs 2–4. In all analyses, we controlled for the initial PGG contribution to take into account any potential relationships between how much participants contributed at the onset of the PGG game and how much they ended up expecting and assigning punishment. Given that the three personality dimensions of Honesty-Humility, Emotionality, and Agreeableness are partially correlated, we also controlled for the effects of the other personality dimensions in our mediation analyses (e.g., the mediation analysis for Emotionality controls for the effects of Honesty-Humility and Agreeableness). Results revealed that participants with higher scores on Emotionality expected harsher punishments from others (i.e., higher expected punishment) (b = 0.77, SE = 0.19, t = 4.00, 95% CI [0.39; 1.15]). As expected, the total indirect effect of Emotionality on assigned punishment was significantly positive (b = 0.54, 95% CI [0.28; 0.84]). That is, the bootstrapping mediation analysis indicated that the effects of Emotionality on assigned punishment was mediated by expected punishment (see Fig. 2). As an estimate of effect size, we also calculated the ratio of the indirect effect relative to the total effect (MacKinnon, Warsi, & Dwyer, 1995). This

Expected Punishment 0.77**

0.71**

Emotionality 0.80**

Assigned Punishment

Fig. 2. Mediation analysis Emotionality (Results of the mediation analysis showing the influence of Emotionality on assigned punishment, as mediated by expected punishment. Asterisks indicate significant paths (**p < .0001)).

Expected Punishment 0.71**

-0.26 (n.s.)

Agreeableness -0.24 (n.s.)

Assigned Punishment

Fig. 3. Mediation analysis Agreeableness (Results of the mediation analysis showing the influence of Agreeableness on assigned punishment, which was insignificant and not mediated by expected punishment. Asterisks indicate significant paths (**p < .0001)).

Expected Punishment 0.71**

0.34 (n.s.)

Honesty– Humility

0.54*

Assigned Punishment

Fig. 4. Mediation analysis Honesty-Humility (Results of the mediation analysis showing the influence of Honesty-Humility on assigned punishment, which was not mediated by expected punishment. Asterisks indicate significant paths (**p < .0001)).

analysis revealed that 68% of the effect of Emotionality on assigned punishment occurred indirectly through expected TPP. Mediation analysis for Agreeableness revealed that there was a non-significant negative relationship between Agreeableness and expected punishment (b = 0.26, SE = 0.24, t = 1.09, 95% CI [ 0.73, 0.21]) and between Agreeableness and assigned punishment (b = 0.24, SE = 0.27, t = 0.89, 95% CI [ 0.77, 0.29]). The bias-corrected bootstrap confidence intervals for the indirect effects include zero (95% CI [ 0.50; 0.14]) indicating no mediation. Finally, mediation analysis for Honesty-Humility revealed that there was a non-significant relationship between HonestyHumility and expected punishment (b = 0.34, SE = 0.21, t = 1.59, 95% CI [ 0.08, 0.76]). The bias-corrected bootstrap confidence intervals for the indirect effects include zero (95% CI [ 0.07;

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0.53]) indicating no mediation. Only the direct relationship between Honesty-Humility and assigned punishment was significant (b = 0.54, SE = 0.24, t = 2.27, 95% CI [0.07, 1.02]) (see Fig. 4), thus supporting our theoretical prediction that the effects of Honesty-Humility on assigned punishment is direct and independent of expected punishment.

4. Discussion Previous studies have examined costly punishment within the framework of direct and indirect reciprocity and explained the occurrence of this type of punishment with prosocial motives promoting norms of cooperative behavior (Fehr & Fischbacher, 2004; Fehr et al., 2002; Fehr & Gächter, 2002; Henrich et al., 2010). In this study, we examined costly punishment in the context of an imminent punishment threat and a situation in which individuals cannot retaliate against the person from whom they expect punishment to explore alternative motives for costly punishment. Our interest in these types of situations is driven by the observation that in many real-life situations people are exposed to a punishment threat by a range of superior actors against whom they cannot easily retaliate such as supervisors, referees, judges, or teachers. In our experiment, we therefore allowed subjects administering punishment to be punished themselves to study the extent to which people engage in costly punishment of third parties in response to a perceived punishment threat rather than due to purely prosocial motives. Our finding of a positive effect of Honesty-Humility on assigned punishment that was not mediated by expected punishment is in line with the established view that many people engage in costly punishment for prosocial reasons (Fehr & Fischbacher, 2004; Fehr et al., 2002; Fehr & Gächter, 2002; Henrich et al., 2010). However, the results of our analysis also showed that Emotionality was positively related to both assigned and expected punishment. Moreover, we found that the effect of Emotionality on assigned punishment was mediated by expected punishment, i.e. the fear of being punished oneself. These findings suggest that for some people alternative motives might be the driving factor behind costly punishment decisions. For example, our results indicate that people with higher scores on Emotionality expected higher punishment for the same cooperation levels than people scoring low on this trait. Our mediation analysis suggests that this experienced threat in turn motivated these subjects to engage in costly punishment. Finally, we found no effect of Agreeableness on either assigned or expected punishment which indicates that the hypothesized effects of displaced anger and aggression in response to a perceived punishment threat from an actor who is too powerful and/or unavailable for retaliation are less relevant for costly punishment decisions. Overall, our findings are consistent with previous studies demonstrating the importance of individual differences in costly punishment (Jordan et al., 2016; Lotz et al., 2011). For example, the study by Jordan and colleagues examined the trait of trustworthiness and found that for these individuals, engaging in costly punishment signals that they themselves are not selfish. Similarly, in the present study, we found that the personality trait of Emotionality influenced costly punishment in response to a punishment threat. It is important to note that these processes can only be observed and disentangled when considering individual difference variables such as personality. Given that individuals can also score high/low on Emotionality, Honesty-Humility, and Agreeableness simultaneously we also conducted a post-hoc analysis wherein we analyzed the same mediation model with an additional three-way interaction term between the personality traits as a predictor. The interaction term was non-

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significant (and all other results remained unchanged) suggesting that in this study the various motives driven by different personality traits did not coexist in determining costly punishment decisions. The results of this study have important theoretical implications. Our results highlight the need for research to investigate multiple mechanisms underlying costly punishment. In this study, we demonstrate that previous research may have overlooked perceived punishment threat as an important proximate mechanism underlying costly punishment which can cause punishment responses that are more in line with threat appraisal rather than prosociality. Given the complex and multi-faceted nature of human behavior, we argue that there is a need for research to adopt an ‘inclusive’ (rather than exclusive) perspective on costly punishment, hence the need to investigate multiple, co-existing mechanisms. A limitation of this study is that it relies on a statistical mediation analysis to test for the hypothesized causal relationships, that is, expectations about others’ choices (i.e., expected punishment) temporally precedes people’s own choices (i.e., assigned punishment). This study was not intended to be a tightly controlled experiment as we cannot manipulate personality traits and our aim was to observe assigned and expected punishment. In line with Costantini and Perugini’s (2018) recommendation that causal explanations in personality research require conceptual clarity, we based our hypothesized model and predictions on rigorous theories and research on the role of punishment threat (i.e., expected punishment) which we predict precedes assigned punishment. This is based on overwhelming evidence that threat appraisals precede behaviors (Lazarus, 1984). From a theoretical perspective there are two alternative mechanisms that could explain the relationship between expected punishment and assigned punishment. One is the here proposed reaction to a perceived punishment threat which predicts that expected punishment causally precedes assigned punishment. The alternative mechanism is a false consensus effect (Ross, Greene, & House, 1977) according to which people tend to assume that their own behavior is predictive of other people’s behavior. This perspective would predict that assigned punishment causally precedes expected punishment. Our finding that expected punishment only matters for people high in Emotionality but not for people high in Honesty-Humility provides a strong indication which of the two mechanisms explains our findings. From a trait perspective, there is no theoretical reason to assume that only people high in Emotionality but not people high in Honesty-Humility would be affected by a false consensus effect. For a false consensus effect, we would expect all participants to be equally affected (we are not aware of any study showing a relationship between personality and false consensus). However, as we outlined in this paper, there are strong theoretical reasons to assume that only people high in Emotionality but not people high in Honesty-Humility perceive a punishment threat and subsequently respond to it by engaging in costly punishment themselves. Our interpretation is also in line with extensive research on conditional behavior. There is overwhelming evidence that a large majority of people make behavioral choices conditional on their expectations about other people’s choices. This has been found for many types of choices including cooperation (e.g., Fischbacher, Gächter, & Fehr, 2001; Thöni & Volk, 2018) as well as costly punishment (e.g., Kamei, 2014). The reverse causal relationship, i.e. that people’s own choices determine their beliefs or expectations about other people’s choices, is not well documented in economic games and social dilemmas. For example, in their analysis of belief formation about other players’ choices in public goods experiments similar to ours, Fischbacher and Gächter (2010) found no evidence that subjects’ own choices mattered.

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We also conducted a post-hoc analysis wherein we tested whether a reverse causal model where expected punishment is the outcome and assigned punishment the mediator shows better or poorer fit than our proposed theoretical model with expected punishment as mediator and assigned punishment as outcome. We found that the reverse causal model is a poorer fitting model by all popular fit indices based on recommended cut-offs indicating a good fit (Kline, 2005). In this study, we also ruled out other possible explanations by controlling for the initial contributions in the PGG game, hence the results we found are over and above the initial gaming contributions that individuals gave. Our analyses also controlled for the traits of Emotionality, Honesty-Humility, and Agreeableness interchangeably (in case these traits have overlapping variance). Nonetheless, a limitation we acknowledge is that we have used a brief measure of personality with only 10 items for each personality dimension which does not allow for finer grained analyses at the facet level. Given the complex nature of our experimental design with three decision making stages and a complex set of instructions we chose to adopt the HEXACO-60 measure rather than a longer measure. While there are even shorter personality measures available with only 2 items for each personality dimension (e.g., Gosling, Rentfrow, & Swann, 2003), we chose an instrument with medium length. We did so to reduce transient measurement errors resulting from participant fatigue, boredom, or frustration associated with completing a personality survey in combination with a lengthy experimental session while maintaining sufficient depth in our personality assessment. Furthermore, the results of this study are not a direct, objective assessment of motives (i.e., we only infer motives from the personality traits that we measured in line with the literature and the conceptualization of these traits in the HEXACO model). As such, our study remains at an exploratory level when it comes to the assessment of motives underlying the costly punishment behaviors we observed. The main purpose of this study was to test whether individuals engage in costly punishment of innocent third parties as a result of a perceived punishment threat and who is likely to engage in this type of behavior. We demonstrate that people scoring high on the personality trait Emotionality are likely to engage in this type of behavior and hypothesized an underlying fear-driven motive. However, whether it is indeed the fear-driven motive in combination with last-place aversion as we hypothesized, or other motivations that drive the behavior observed in our experiments remains an open question. Future research should try to get more directly at the link between personality, motives, and punishment behaviors, for example by collecting open responses regarding participants’ motives. Finally, it is important to point out that since we are the first to demonstrate a relationship between Emotionality and costly punishment, and given the relatively modest sample size of our study, our findings should be treated with caution. More research is needed to more clearly establish when and how Emotionality (and other personality traits) are important predictors of costly punishment decisions.

5. Conclusion Previous research has focused on prosocial motives promoting norms of cooperative behavior to explain why subjects engage in costly punishment of third-parties that have not harmed them. Our results indicate that researchers may have overlooked an important alternative explanation for costly punishment of thirdparties. Some people might engage in costly punishment of unrelated others in response to a punishment threat from a superior actor who is not available for retaliatory punishment such as

supervisors, referees, judges, teachers, or police officers. This type of third-party punishment is highly inefficient because it wastes resources of both the punished and the punishing individual without the efficiency increases of costly punishment resulting from direct or indirect reciprocity that often promotes future cooperation (Rockenbach & Milinski, 2006). Our results indicate that the personality traits Emotionality and Honesty-Humility play an important role in determining whether costly punishment of third parties is more likely to be driven by a perceived punishment threat or by prosocial considerations. Agreeableness had no effect on costly punishment decisions in our study. To our knowledge, our study is the first to investigate the role of Emotionality, Agreeableness, and Honesty-Humility in costly punishment. More research is needed to investigate a range of individual difference factors, including more in-depth investigations of different types of personal traits to understand different kinds of motives underling costly punishment. This will help us better understand when costly punishment interventions are likely to be effective because driven by prosocial motives and when they are likely to be ineffective because driven by alternative motives that can lead to inefficient or antisocial punishment. Acknowledgements We would like to acknowledge funding for this research project from the German Research Foundation (DFG), project VO1811/1-1. References Ashton, M. C., & Lee, K. (2007). Empirical, theoretical, and practical advantages of the HEXACO model of personality structure. Personality and Social Psychology Review, 11, 150–166. https://doi.org/10.1177/1088868306294907. Ashton, M. C., & Lee, K. (2009). The HEXACO-60: A short measure of the major dimensions of personality. Journal of Personality Assessment, 91, 340–345. https://doi.org/10.1080/00223890902935878. Ashton, M. C., Lee, K., Perugini, M., Szarota, P., de Vries, R. E., Di Blas, L., et al. (2004). A six-factor structure of personality-descriptive adjectives: Solutions from psycholexical studies in seven languages. Journal of Personality and Social Psychology, 86, 356–366. Balafoutas, L., Lindner, F., & Sutter, M. (2012). Sabotage in tournaments: Evidence from a natural experiment. Kyklos, 65(4), 425–441. https://doi.org/10.1111/ kykl.12000. Becker, A., Deckers, T., Dohmen, T., Falk, A., & Kosse, F. (2012). The relationship between economic preferences and psychological personality measures. Annual Review of Economics, 4, 453–478. https://doi.org/10.1146/annurev-economics080511-110922. Brandt, H., Hauert, C., & Sigmund, K. (2003). Punishment and reputation in spatial public goods games. Philosophical Transactions of the Royal Society B: Biological Sciences, 270, 1099–1104. https://doi.org/10.1098/rspb.2003.2336. Brinker, J. K., & Wilkinson, C. R. (2014). Ruminative thought style and personality. Personality and Individual Differences, 60, S41. https://doi.org/10.1016/ j.paid.2013.07.112. Buell, R. W. (2017). Last place aversion in queues. Harvard Business School Technology & Operations Mgt. Unit Working Paper No. 18-053. doi: 10.2139/ ssrn.3090591. Charness, G., Masclet, D., & Villeval, M. C. (2014). The dark side of competition for status. Management Science, 60(1), 38–55. https://doi.org/10.1287/ mnsc.2013.1747. Costantini, G., & Perugini, M. (2018). A framework for testing causality in personality research. European Journal of Personality, 32, 254–268. https://doi. org/10.1002/per.2150. Denson, T. F., Pedersen, W. C., & Miller, N. (2006). The displaced aggression questionnaire. Journal of Personality and Social Psychology, 90, 1032–1051. https://doi.org/10.1037/0022-3514.90.6.1032. Eisenberg, N., Fabes, R. A., Murphy, B., Maszk, P., Smith, M., & Karbo, M. (1995). The role of emotionality and regulation in children’s social functioning: A longitudinal study. Child Development, 66, 1360–1384. https://doi.org/ 10.1111/j.1467-8624.1995.tb00940.x. Faul, F., Erdfelder, E., Buchner, A., & Lang, A. G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41, 1149–1160. https://doi.org/10.3758/BRM.41.4.1149. Fehr, E., & Fischbacher, U. (2003). The nature of human altruism. Nature, 425(6960), 785–791. https://doi.org/10.1111/j.1750-4716.2011.00084.x. Fehr, E., & Fischbacher, U. (2004). Third-party punishment and social norms. Evolution and Human Behavior, 25(2), 63–87. https://doi.org/10.1016/S10905138(04)00005-4.

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