Punishment for intentions or outcomes: the role of gender and social norms

Dato, Simon; Friehe, Tim Article — Published Version Punishment for intentions or outcomes: the role of gender and social norms Social Choice and Welfare Suggested Citation: Dato, Simon; Friehe, Tim (2025) : Punishment for intentions or outcomes: the role of gender and social norms, Social Choice and Welfare, ISSN 1432-217X, Springer Berlin Heidelberg, Berlin/Heidelberg, Vol. 65, Iss. 4, pp. 853-882, https://doi.org/10.1007/s00355-025-01596-9 This Version is available at: https://hdl.handle.net/10419/333357 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ Social Choice and Welfare (2025) 65:853–882 https://doi.org/10.1007/s00355-025-01596-9 ORIGINAL PAPER Punishment for intentions or outcomes: the role of gender and social norms Simon Dato1 ·Tim Friehe2 Received: 26 January 2023 / Accepted: 16 March 2025 / Published online: 11 April 2025 © The Author(s) 2025 Abstract Individuals often evaluate others’ actions based on both their perceived intentions and their resulting outcomes, rewarding favorable actions and punishing unfavorable ones. This study aims to isolate the influence of these factors on punishment. We experimentally demonstrate that, when outcomes are held constant, second movers punish first movers who choose selfish actions more severely than those who select considerate ones. Conversely, when intentions are fixed, the severity of punishment does not significantly differ between fair and unfair outcomes on average. However, this average masks gender-specific variations. Men tend to prioritize punishing unkind intentions, while women are more sensitive to the perceived fairness of outcomes. Social norms help explain punishment choices and gender differences. 1 Introduction Costly punishment plays a pivotal role in fostering cooperation and sustaining the well-being of societies, as demonstrated by extensive research (e.g., Gürerk et al. 2006; Balafoutas et al. 2014). Yet, the underlying motivations for individuals to engage in costly punishment remain a subject of ongoing inquiry (e.g., Colman 2006). Empirical evidence suggests that individuals are more likely to punish those who violate clear-cut social norms, particularly when the consequences of these violations are unambiguous (e.g., Fehr and Fischbacher 2004). However, when ambiguity in this respect emerges, such as in scenarios involving chance, individuals tend to base their punishment decisions also on perceived intentions rather than solely on outcomes (e.g., Falk and Fischbacher 2006). While both outcomes and intentions can influence punishment choices, understanding their relative importance at the aggregate and individual levels is crucial BTim Friehe [email protected] Simon Dato [email protected] 1EBS University for Business and Law, Rheingaustr. 1, 65375 Oestrich-Winkel, Germany 2University of Marburg, Am Plan 2, 35037 Marburg, Germany 123 854 S.Dato,T.Friehe for predicting behavior and informing effective policy interventions. For instance, understanding employee preferences regarding procedural fairness or equitable outcomescaninformeffectiveleadershipdecisions.Moregenerally,theinterplaybetween procedural and outcome justice is a critical factor in various economic and political contexts (e.g., Bolton et al. 2005; Cappelen et al. 2007). Existing research (e.g., Charness 2004;Falketal.2008) provides valuable insights, but further investigation is needed to understand the precise interplay between these factors and their individuallevel heterogeneity. To elucidate the relative importance of intentions and outcomes in shaping punishment, we conducted an experiment where first movers chose between two lotteries with identical outcomes but differing probabilities. The Considerate lottery offered a higher likelihood of an equal split, while the Selfish lottery favored an unequal split, benefiting the first mover. Second movers, aware of the first mover’s choice and the resulting outcome, could punish their first mover. This experimental design allows us to isolate the effects of intentions and outcomes on punishment decisions. Fixing the outcome and comparing punishment across different lottery choices, we demonstrate how unkind instead of kind intentions influence punishment. Likewise, holding constant the first-mover’s lottery choice (i.e., her intention) and comparing punishment across outcomes, we isolate how the unequal compared to the equal outcome affects the second-mover’s punishment choice. Our findings reveal significantly more punishment when first movers select the selfish lottery than when they select the considerate one, indicating a strong aversion to unkind intentions. In contrast, when intentions are held constant, punishment severity does not vary when the outcome changes. This does not imply that outcomes are irrelevant, as the insignificance can also stem from counterbalancing effects such as income effects. Given the extensive literature on gender-specific social preferences (e.g., Eckel and Grossman 2008; Croson and Gneezy 2009; Niederle 2016), our study investigates the potential impact of gender on the relative valuation of outcomes and intentions when making punishment decisions. Understanding gender differences in fairness perceptions is crucial for addressing societal and organizational inequalities. As workplaces and institutions strive for gender balance, it is imperative to recognize that women may have distinct expectations regarding fairness. Recognizing these differences allows for developing more suitable compensation and promotion schemes. Our findings reveal that women and men respond differently to unkind intentions and unequal outcomes. Women are more likely to increase their punishment levels in response to unequal outcomes, suggesting a greater concern for fairness. Conversely, men are more inclined to punish selfish intentions, indicating a heightened sensitivity to unkind intentions. To investigate the underlying drivers of punishment behavior, we implemented a cooling-off period before the second movers’ decisions. This manipulation aimed to reduce emotional involvement, following established research (e.g., Cardella and Chiu 2012; Neo et al. 2013). Given the connection between punishment and emotional expression (e.g., Xiao and Houser 2005), we hypothesized that a cooling-off period would lower punishment. Additionally, considering the literature on gender differences in emotional involvement (e.g., Croson and Gneezy 2009; Fujita et al. 1991), we anticipated a stronger impact of the cooling-off period on women’s punish123 Punishment for intentions or outcomes… 855 ment decisions. However, our results did not reveal a significant treatment effect on punishment for either men or women. To delve deeper into how social norms shape punishment decisions, we employed the methodology outlined by Krupka and Weber (2013). A growing body of research suggests that a preference for choosing socially appropriate actions can significantly influence economic behavior. In our context, second movers may be guided by perceived social norms regarding punishment, leading them to choose socially acceptable levels of punishment. By eliciting individuals’ perceptions of the social appropriateness of punishment, we demonstrate that a preference for norm compliance plays a significant role in shaping their choices. Furthermore, gender-specific social norms can partially explain the observed gender differences in punishment behavior. This suggests that understanding the interplay between individual preferences and societal expectations is essential for fully comprehending the dynamics of social punishment. Our research contributes to the existing literature in three key ways. First, we provide a comprehensive analysis of the relative importance of intentions and outcomes in shaping punishment decisions, highlighting the gender-specific nature of these preferences. While previous studies (e.g., Bolton et al. 2005;Falketal.2008; Friehe and Utikal 2018) have emphasized the distinction between outcome-based and intentionbased punishment, our research offers novel insights into how these factors differ across genders. Second, we demonstrate a strong alignment between the punishment behavior observed in our data and the prevailing social norms. Our findings contribute to the growing body of research on the relationship between social norms and decision-making, particularly in the context of punishment (Barr et al. 2018; Chang et al. 2019). Third, we address the ongoing debate raised by Fehr et al. (2018) regarding the existence and relevance of social norms in shaping punishment choices. Our results provide compelling evidence supporting their influence on decision-making processes. The paper’s structure is as follows. In Sect.2, we discuss the related literature. We explain the experimental design and procedures in Sect.3. Section4presents our main hypotheses. Section5reports our empirical findings regarding actual punishment choices and social norms on punishment choices. Section6concludes. 2 Related literature This paper examines the relative importance of intentions and outcomes in shaping individual behavior. Prior research has utilized two primary approaches to isolate intention effects: (i) comparing participant responses to the choices of others with their reactions to actions imposed exogenously (e.g., Blount 1995; Charness 2004; Cox 2004;Falketal.2008), and (ii) analyzing choices made at specific decision nodes within a game based on how these nodes were reached (e.g., Falk et al. 2003; McCabe et al. 2003). Blount (1995) analyzes ultimatum games and compares second-movers’ minimum acceptable offers. She varies whether the offer is implemented by a self-interested party, a third party, or by chance. She finds that second-movers are more willing 123 856 S.Dato,T.Friehe to accept a low offer if implemented by chance than by a self-interested party. In Cox (2004), decision-making in a standard trust game is compared to choices when allocations are exogenously imposed to match interim outcomes of the standard trust game. In other words, subjects in this latter treatment do not respond to a first mover. Similarly, Charness (2004) studies a gift-exchange game and analyzes second-mover behavior when the employer selects wages compared to an external process. In the moonlighting-game setup of Falk et al. (2008), first-mover choices were either chosen deliberately by a subject in the Intentions treatment or randomly drawn in the No Intentions treatment. In these contributions, the second-mover reaction is stronger when the first-mover choice is intentional. Despite this commonality, the different papers feature design differences that may be important. For example, in Charness (2004), efficiency motives possibly interacted with pure reciprocity concerns in a setting featuring repetition. In contrast, the game in Cox (2004) was played once, and the second mover decided about an efficiency-neutral transfer. Compared to the other two contributions, Falk et al. (2008) allows for positive and negative reciprocity. Our setup diverges from those employed in previous research, as we do not implement separate treatments to isolate intention effects. Our design lacks an intention-free condition. Instead, interim outcomes are jointly determined by the intentional choices of a first-mover and the realization of a random variable. Consequently, a specific outcome can arise from a variety of first-mover decisions. We capture the intention effect by examining how changes in first-mover behavior influence subsequent responses, holding the outcome constant. This approach contrasts with the predominant methodology in existing literature, which primarily compares reactions to intentionally implemented and randomly drawn outcomes. The distinction between our approach and previous contributions is important. For example, the previous literature in neuroeconomics has shown that participants’ emotional response to a human act is very different from the response to an act by the computer (e.g., Rilling and Sanfey 2011). For example, van’t Wout et al. (2006) find that unfair offers in an ultimatum game triggered higher skin conductance activity and rejection rates only if the offer came from a human proposer. Our intention-effect identification maintains this emotional influence by comparing responses to kind and unkind first-mover behavior, whereas comparisons of responses to first-mover and random choices do not. This seems particularly relevant in light of studies identifying the role of punishment for emotional expression (e.g., Xiao and Houser 2005). Furthermore, our approach allows us to assess the outcome effect within the context of authentic first-mover decision-making rather than relying on comparisons between reactions to arbitrarily drawn outcomes. In this regard, our study resembles that of Charness and Levine (2007), where principals initially select between a high and a low wage.Crucially,bothwagechoicescanultimatelyresultinanintermediate wageforthe agent due to the realization of a random event. This feature enables investigating how a single outcome can be achieved through different first-mover intentions. Charness and Levine (2007) demonstrate the significance of both outcomes and intentions in shaping reciprocal behavior, with intentions exerting a stronger influence. However, their setup differs from ours in a crucial aspect: the lotteries associated with high and low wages involve distinct outcome distributions. In contrast, our design employs lotteries that differ only in their probability distributions while maintaining identical 123 Punishment for intentions or outcomes… 857 outcome sets. This distinction may render the lottery choice less salient and potentially influence how subjects interpret the first-mover’s intentions. We find that outcomes are relatively more important for women, whereas the reverse is true for men. We are the first to separate the relative importance of intentions and outcomes by gender, even though a large strand of the literature analyzes potential gender differences in social preferences. For example, Croson and Buchan (1999) study gender differences in trust games, finding that sender behavior is similar across genderswhile women returned ahigher proportion of their wealth. Buchan et al. (2008) find that men trust more while women are more trustworthy. In contrast, Chaudhuri and Gangadharan (2007) find no differences in reciprocal behavior. Using the ultimatum game, Eckel and Grossman (2001) find that women are more likely to accept lower offers than men. Overall, the literature presents quite mixed results (e.g., Croson and Gneezy 2009), meaning that more evidence is needed. Whereas previous contributions use experimental paradigms in which an action’s intentions and consequences were inextricably linked (e.g., the ultimatum game), our experiment disentangles intentions and outcomes. This allows us to cleanly identify gender differences in the importance of (i) outcomes and (ii) intentions on punishment. Our results also contribute to the recent and growing literature documenting the explanatory power of social norms for observed behavior. We build on Krupka and Weber (2013) and find that even gender differences in punishment decisions can be partly explained by reference to gender-specific social norms. Similar to Barr et al. (2018) and Chang et al. (2019), we can thus show that perceptions of social norms are identity-specific. Our treatment variation includes a cooling-off period. This follows contributions such as Grimm and Mengel (2011). Using an ultimatum game, they find that a delay of around 10min after the presentation of the offer and before the final acceptance choice causes a significant increase in the acceptance rate of low offers. Whereas most studies (e.g., Cardella and Chiu 2012; Neo et al. 2013) also consider a relatively short delay, Oechssler et al. (2015) study how a 24-hour delay influences ultimatum-game play, distinguishing a treatment in which subjects are paid in cash from one in which they are compensated with lottery tickets. They find that the cooling-off period influences rejection choices only when subjects receive lottery tickets. The fewer rejections that Neo et al. (2013), for example, find in their delay-treatment of the ultimatum game align with the idea that immediate decisions show the participants’ intuitive responses to the inequity. In contrast, delayed decisions result after more careful deliberation about monetary payoff consequences. Similarly, our cooling-off period was expected to reduce punishments. However, deliberation may also increase punishment. The data in Philippsen et al. (2024) is consistent with the idea that the participants’ intuitive response is a selfish payoff maximization and that a preference for costly punishment emerges only with time to deliberate. 3Design The experiment consisted of two primary parts. In Part 1, Player A selected one of two lotteries, each offering a different probability of an equal or unequal outcome. 123 858 S.Dato,T.Friehe Player B, aware of Player A’s choice and the resulting payoff allocation, then assigned punishment points. In Part 2, following the methodology of Krupka and Weber (2013), weelicitedparticipants’perceptionsofsocialnormsrelatedtothegame.Part3involved a questionnaire, including an incentivized social value orientation test and a survey on participants’ justice attitudes. Additionally, we employed the experimental task developed by Kimbrough and Vostroknutov (2018) to assess participants’ adherence to social norms. At the outset, participants were informed about the study’s structure, including the existence of Part 1 and the subsequent payoff-independent parts. In line with Dato and Nieken (2014), we collected demographic information from our subjects before Part 1 to enable gender-specific matching in Part 2. Our experiment featured two treatments: DELAY and IMMEDIATE. We will first describe treatment IMMEDIATE and then outline the key differences in treatment DELAY. 3.1 Part 1: First-mover’s lottery choice and second-mover’s punishment choice Part 1 comprised two stages. In Stage 1, Player A selected either the Selfish (abbreviated S) or the Considerate (abbreviated C) lottery. Lottery Selfish led to the unequal payoff allocation (abbreviated U) (π A U,πB U)=(1350,150)with probability 80% and the equal payoff allocation (abbreviated E) (π A E,πB E)=(750,750)with probability 20%. Lottery Considerate reversed the probabilities (i.e., it yielded the unequal payoff allocation with probability 20%). Player A could not dictate a division of the endowment amounting to 1500 points but skew the probability distribution towards the unequal or the equal payoff allocation. Player A’s expected payoff exceeded Player B’s in both lotteries. With Selfish, Player A (B) expects 1230 (270). With Considerate, Player A (B) expects 870 (630). Regarding the inequity aversion model of Trautmann (2009), the choice of Selfish substantially increases Player B’s disadvantageous inequity. At the end of Stage 1, Player B was informed about Player A’s lottery choice and the lottery’s outcome. Thus, a randomly matched pair of Players A and B had common knowledge about which one out of four possible scenarios is relevant to them: •Scenario SE: Player A’s choice of Selfish combined with the draw of the equal payoff allocation, •Scenario SU: Selfish combined with unequal payoffs, •Scenario CE: Considerate combined with the draw of the equal payoff allocation, or •Scenario CU: Considerate combined with unequal payoffs. 123 Punishment for intentions or outcomes… 859 Knowing the relevant scenario, Player B could deduct ppoints from Player A’s account in Stage 2 at a cost p/4.1Subjects could choose a punishment level p, where p∈{0,60,120,180,240,300}. The maximum punishment level is sufficiently high to allow B to spend a sizable share of her interim payoff on punishment. It is, however, also sufficiently low to allow a clear role of punishment depending upon the scenario: punishment increases advantageous payoff inequality in SE and CE, and decreases disadvantageous payoff inequality in SU and CU. This allows deriving clear-cut predictions regarding the outcome effect based on the model’s primitives introduced by Charness and Rabin (2002) in Sect.4. To enable emotional involvement (one of the hypothesized channels for gender differences), we purposefully implemented a direct-response format and relied on between-subject comparisons (Brandts and Charness 2011). The final payoffs amount to A i(p)=πA i−pand B i(p)=πB i−p 4, where i∈{E,U}depicts the outcome drawn in Stage 1. Before making their decisions in Part 1, participants provided incentivized belief statements. Player A indicated the expected punishment level from their respective Player B across the four scenarios. Conversely, Player B stated their anticipated lottery choice. Participants earned 200 points for each accurately predicted choice. When collecting choice and belief data from the same subject, the order of elicitation becomes a crucial consideration. Schlag et al. (2015) provide a comprehensive review of relevant research, examining various experimental paradigms. This review concludes that the impact of belief elicitation on subsequent decisions remains uncertain, both in terms of its presence and direction. Similarly, Alempaki et al. (2022), where participants ranked outcomes and considered beliefs before making choices, also found inconclusive evidence regarding the influence of prior belief elicitation. In our setup, how an incentivized belief elicitation influences subsequent decisions is also unclear. To illustrate that the effect on punishment choices is ambiguous, consider the case where Player B correctly guessed Player A’s lottery choice. As a result, B receives an additional payoff, which can have two implications. First, Player B may want to choose higher punishment due to the reduced marginal utility of income. However, second, the additional income might reduce negative emotions (if A chose the selfish lottery) or intensify positive emotions (if A chose the considerate lottery), making less punishment likely. 1We implemented a cost-effectiveness ratio of 1:4, which is similar to the 1:3 ratio used in Fehr and Fischbacher (2004) and Leibbrandt and Lopez-Perez (2012). Nikiforakis and Normann (2008) tested ratios 1:1, 1:2, 1:3, and 1:4 in a comparative-statics exercise, finding that ratios 1:3 and 1:4 perform similarly in numerous regards. Other papers use an even higher punishment effectiveness. For example, Bartling et al. (2014) used a 1:5 ratio. On a different note, we follow the literature by using the term punishment for Player B’s action in all scenarios. Note that Player B’s payoff change in scenarios involving Considerate as Player A’s lottery choice presumably served distributional preferences and not a truly punitive motivation. In a stricter understanding, punishment is a hardship imposed on someone for a wrong they have (or are believed to have) committed (e.g., Bagaric 2001). 123 860 S.Dato,T.Friehe Importantly,as we pay particular attentiontogender differences,notethat there is no gender gap in the average payoff from the belief elicitation (p=0.226, Fisher’s exact (FE)). Even though these arguments and the findings from the related literature do not hint at a systematic effect of incentivized beliefs, we can, of course, not definitively rule out that punishment behavior was affected by the incentivized belief elicitation procedure. 3.2 Part 2: Elicitation of social norms Following Barr et al. (2018), d’Adda et al. (2016), and Erkut et al. (2015), we elicited social norms from our participants regarding the game they played in Part 1.2Participants were asked to give 26 social appropriateness ratings: six punishment levels for the four scenarios possible plus the two lottery choices. The order of social appropriateness ratings was randomized at the subject level. We employed the six-point scale from Chang et al. (2019) comprising: “very socially appropriate” (later assigned a score of 5 in our empirical work), “socially appropriate” (4), “somewhat socially appropriate” (3), “somewhat socially inappropriate” (2), “socially inappropriate” (1), and “very socially inappropriate” (0). The evaluation of choices was incentivized. One of the 26 choices was randomly selected, and each participant’s evaluation of that choice was compared to that of another experimental subject (Barr et al. 2018; Erkut et al. 2015). If a participant’s evaluation matched the other subject’s rating, this participant earned 1200 points; otherwise,this participant earned nothing. Stipulating payoffs like this means that subjects play a coordination game where participants are incentivized to state the normative evaluation of their match. According to Krupka and Weber (2013), this scheme incentivizes participants to reveal their perception of what is commonly regarded as socially appropriate or inappropriate behavior in the context at hand instead of eliciting their private evaluation. We informed subjects about the gender of their randomly matched subject before they made their ratings (producing observations from single-gender and mixed-gender pairs) to accommodate the possibility of commonly known gender-specific social norms. We can use this data to assess whether participants condition their rating on the matched player’s gender. Importantly, gender differences in social norms that are not commonly known will not produce such an adjustment in response to the revelation of the randomly matched subject’s gender. 3.3 Part 3: Questionnaire To assess rule-following behavior, we employed the task introduced by Kimbrough and Vostroknutov (2018), which is considered a reliable measure of an individual’s propensity to adhere to social norms (see, among others, Kimbrough and Vostroknutov 2016,2018; Gross and Dreu 2021). Participants dragged and dropped 50 balls into one of two buckets: yellow or blue. Instructions clearly stated that the rule was to 2We elicited social appropriateness ratings in Part 2 that describe injunctive social norms. In contrast, the beliefs elicited in Part 1 concern descriptive social norms. 123 Punishment for intentions or outcomes… 867 Fig. 1 Left panel: Average punishment choices by scenario. Right panel: Average punishment choices by gender and scenario Our findings provide strong evidence for a significant intention effect on punishment, thus supporting Hypothesis 1 (b). This confirms that intentions-based reciprocity plays a crucial role in driving punishment decisions. Regarding the lack of a significant outcome effect, our results suggest that the income and inequity effects offset each other. Compared to the draw of the equal payoff, the interim payoff for Player B is substantially reduced (by 80%) following an unequal outcome. Even though the absolute level of punishment remains unchanged, Players B allocate a significantly higher proportion of their interim payoff towards punishment in response to increased inequity. This finding aligns with previous research demonstrating the significant role of inequity aversion in driving second-party punishment (e.g., Leibbrandt and LopezPerez 2012). To speak about gender differences, we separate the average punishment levels by gender in the right panel of Fig.1. Player A’s choice of Selfish instead of Considerate, given the equal payoff allocation, increases males’ punishment by 87.86 points (p<0.01, WRT). In contrast, women’s punishment increases only by 35.66 points (p=0.090, WRT). Apparently, men respond more strongly to Player A’s selfish lottery choice than women. Next, we turn to potential gender differences in Player B’s response to the unequal payoff allocation. Men’s punishment conditional on unkind intentions is 24.11 points lower in SU than in SE, a difference that is not statistically significant (p=0.198, WRT). In contrast, women’s punishment in SU is 30.23 points higher than in SE, which is again not statistically significant (p=0.343). Although each gender’s reaction is insignificant, the hypothesized gender effect might still exist as women and men change their behavior in opposite directions.8 Ourresultsfrom non-parametric tests areconfirmedin ordinary least squares regressions (Table 1). In Columns (1) and (2), the dependent variable is the punishment level, whereas it is a dummy variable equal to one when positive punishment was selected in Columns (3) and (4). The interaction of the dummy variables for the selfish lottery 8Given the opposing responses of women and men, a significant reaction from either gender would suffice to demonstrate differential responses to unequal payoffs. A power analysis indicates that a sample size of roughly 800 subjects in the role of Player B is necessary to detect a significant outcome effect for women. However, Table 1leverages the contrasting responses of women and men by employing regression analyses with interaction terms. Despite our small sample size, this approach provides evidence of a gender-specific outcome effect. 123 868 S.Dato,T.Friehe Table 1 Determinants of punishment levels and incidence (1) (2) (3) (4) Punishment Punishment Punishment Punishment Level Level Dummy Dummy Unkind Intention 85.44∗∗∗ 87.69∗∗∗ 0.431∗∗∗ 0.426∗∗∗ (23.55) (24.67) (0.0923) (0.0986) Female −3.630 −7.866 −0.0179 −0.0366 (17.09) (19.12) (0.0654) (0.0728) Unkind Intention x Female −64.85∗−64.82∗−0.296∗∗ −0.288∗ (37.14) (37.54) (0.147) (0.152) Unequal Payoffs −22.42 −28.54 −0.181∗−0.193∗ (27.04) (28.09) (0.107) (0.111) Unequal Payoffs x Female 64.76 74.95∗0.344∗∗ 0.368∗∗ (39.52) (39.91) (0.159) (0.163) Constant 16.24 −144.2∗∗ 0.0719 −0.301 (13.61) (56.41) (0.0471) (0.239) Controls No Yes No Yes N190 190 190 190 R20.084 0.120 0.114 0.129 Notes: Analysis of punishment levels (Columns (1) & (2)) and punishment dummy (=1 if positive punishment was chosen; Columns (3) & (4)). We report results from ordinary least squares regressions. Unkind Intention is a dummy variable equal to one when Player A chose lottery Selfish.Unequal Payoffs is a dummy variable equal to one when the payoff allocation (1350,150)was drawn. Controls include age, number of siblings, social value orientation, justice sensitivity, and rule-following propensity. Standard errors in parentheses. ∗p<0.1, ∗∗ p<0.05, ∗∗∗ p<0.01 choice and female is negative and at least weakly significant in every specification. The magnitude of the coefficients indicates the economic significance of the gender effect. The interaction of the dummy variables for unequal payoffs and female is positive and significant in Columns (2)-(4). Accordingly, as a reaction to a draw of unequal payoffs, women raise their punishment and are more likely than men to choose a positive punishment level. Comparing the results in Columns (1) and (3), we find that both interaction terms’ significance levels are higher for the binary punishment decision than for punishment levels. This indicates that the gender effects emerge mainly due to a change at the extensive margin (the decision whether or not to punish): a draw of the unequal payoff motivates females more strongly than males to punish A, whereas the choice of Selfish more strongly prompts males to punish A than females. We summarize our results regarding a gender-specific relative importance of intentions and outcomes for punishment as follows: Result 2 (a) After a draw of the equal payoff allocation, men assign greater incremental punishment than women for Player A’s choice of the selfish lottery instead of the considerate one. (b) After a selfish lottery choice of Player A, women assign greater incremental punishment than men when the unequal outcome resulted instead of the equal one. 123 Punishment for intentions or outcomes… 869 Table 2 Punishment levels: latent class analysis for men and women Punishment Level Men Women Class 1 Class 2 Class 1 Class 2 (1) (2) (3) (4) Unkind Intention 6.53 286.11∗∗∗ 3.01 −8.42 (13.16) (20.82) (10.19) (23.90) Unequal Payoffs −22.50∗∗ −10.84 10.18 75.58∗∗∗ (11.23) (18.07) (9.67) (18.68) Constant 21.20∗∗ 2.28 −0.783 202.21∗∗∗ (10.65) (16.50) (6.08) (23.67) Latent Class Marginal Probabilities 72% 28% 81% 19% Notes: Analysis of punishment levels using GSEM regression. Unkind Intention is a dummy variable equal to one when Player A chose lottery Selfish.Unequal Payoffs is a dummy variable equal to one when the payoff allocation (1350,150)was drawn. Standard errors in parentheses. ∗p<0.1, ∗∗ p<0.05, ∗∗∗ p<0.01 Our findings indicate that, on average, women exhibit greater concern for equitable payoffs than men, who prioritize kind intentions. To investigate the prevalence and characteristics of distinct types within each gender, we conducted a latent class analysis. This analysis aimed to determine (i) the existence and proportion of different player types within each gender and (ii) how these types differentially respond to unequal payoffs and unkind intentions in their punishment decisions. Table 2reveals distinct player types for each gender. Both men and women exhibit a prevalent Class 1 type that is largely indifferent to unkind intentions and unequal payoffs, exhibiting no increase in punishment in response to either. Notably, within this class, men tend to decrease punishment following unequal payoffs, potentially driven by an income effect. This type, characterized by low punishment levels, aligns with a narrowly self-interested decision-making style. Crucially, gender-specific types emerge. Men display a second type that strongly punishes unkind intentions while demonstrating indifference to unequal payoffs. Conversely, the second type among women prioritizes punishing unequal payoffs while exhibiting little concern for intentions.Our structural estimation resultsthushighlightastrikinggender disparity.Within our sample and experimental design, only women demonstrate a propensity to punish unequal payoffs, while only men tend to punish unkind intentions. 5.2 Social norms (part 2) When eliciting social appropriateness ratings, we informed each subject about the gender of the subject whose norm rating they must match to obtain additional payment. However, none of the 24 punishment ratings depends on the announced gender of the paired subject (p>0.150 for women and p>0.237 for men). Regarding the appropriateness of lottery choices, ratings do not depend on the matched subject’s gender except that women rate the Selfish choice as more appropriate when matched 123 870 S.Dato,T.Friehe Fig. 2 Mean norm ratings of punishment in scenarios SU, SE, CU, and CE with a man (p=0.064). This suggests that any gender differences in social norms are not commonly known. In our analysis, we pool the data of same-sex and mixed-sex pairs. 5.2.1 Punishment We identify how unkind intentions and the unequal outcome influenced the normative evaluation of punishment before we explore potential gender differences. Zero punishment receives the highest average appropriateness rating in all scenarios (Fig.2).9This speaks to the question recently raised by Fehr et al. (2018) about whether a social norm of punishment exists. The social appropriateness of punishment strongly depends on Player A’s lottery choice and the drawn payoff allocation (Fig.2). Below, we state that punishment is more appropriate in Scenario X than Y if positive punishment levels are more and zero punishment is less appropriate in Scenario X than in Y. Independent of the outcome, punishment is more socially appropriate when Player A’s intentions were unkind instead of kind (p<0.0001 for every comparison, WSR). Hence, Player A’s choice of Selfish promotes punishment. Conditional on the (un)kind intention of Player A, punishment is more socially appropriate when the unequal instead of the equal outcome was drawn (p<0.0001 for every comparison, WSR). Thus, inequity legitimizes punishment. These results imply that punishment is least (most) appropriate in Scenario CE (SU). Comparing Scenarios SE and CU (i.e., scenarios with intentions and outcomes of opposite valence), we find that zero punishment 9There is some heterogeneity in this regard at the subject level. For some subjects, the maximal appropriateness rating applies to a positive punishment level (at least in some scenarios). 123 Punishment for intentions or outcomes… 871 Table 3 Determinants of punishment levels and incidence conditional on norm information Punishment Level (1) (2) (3) (4) Unequal Payoffs 3.520 −8.941 4.793 −7.145 (20.23) (19.57) (20.73) (19.94) Unkind Intention 59.37∗∗∗ 37.11∗62.64∗∗∗ 39.66∗∗ (19.12) (19.78) (19.20) (19.91) Inappropriateness of Punishment −10.05∗∗∗ −9.718∗∗∗ (3.008) (3.017) Constant 14.53∗73.27∗∗∗ −112.8∗∗ −37.42 (8.568) (20.20) (53.09) (60.21) Controls No No Yes Yes N190 190 190 190 R20.063 0.147 0.089 0.166 Notes: Analysis of punishment levels. We report results from ordinary least squares regressions. Unkind Intention is a dummy variable equal to one when Player A chose the lottery Selfish.Unequal Payoffs is a dummy variable equal to one when the payoff allocation (1350,150)was drawn. The variable Inappropriateness of Punishment is thedifferencein appropriatenessrating betweenzero andmaximum punishmentfor the relevant scenario. Controls include age, number of siblings, social value orientation, justice sensitivity, and rule-following propensity. Standard errors in parentheses. ∗p<0.1, ∗∗ p<0.05, ∗∗∗ p<0.01 is considered equally appropriate in both cases (p=0.834, WSR). In contrast, all positive punishment levels are significantly more appropriate in SE (p<0.0001, WSR). This demonstrates that unkind intentions increase the appropriateness of punishment by more than a draw of unequal payoffs and resonates well with observed punishment choices. We summarize our results regarding the averaged social norm of punishment as follows: Result 3 (a) Punishment is more socially appropriate if intentions are unkind instead of kind, and if outcomes are unequal instead of equal. (b) Unkind intentions increase the social appropriateness of punishment by more than unequal payoffs, relative to a scenario with kind intentions and equal payoffs. To assess the explanatory power of social norms regarding punishment, we exploit individual heterogeneity regarding the inappropriateness of punishment. Controlling for Player A’s intentions and the drawn outcome, Players B who rated punishment as more inappropriate should punish Player A less. The regression results displayed in Columns (2) and (4) of Table 3confirm this prediction: the coefficient Inappropriateness of Punishment, which is calculated as the difference in appropriateness ratings between zero and maximum punishment, is negative and highly significant. Strikingly, the Unkind Intention coefficient, which captures the impact of unkind intentions, becomes smaller and less significant. A two-tailed t-test on the equality of the Unkind Intention coefficients in Columns (1) and (2) (and in Columns (3) and (4)) reveals that the difference of coefficients is highly significant (p<0.01). Hence, 123 872 S.Dato,T.Friehe the effect of unkind intentions on punishment can (at least partly) be explained by a preference for norm compliance. Next, we analyze gender differences in norm ratings. First, we aim to understand whether the impact of unkind intentions on the perceived social appropriateness of punishment is different for women and men. We run regressions with the appropriateness rating of punishment as the dependent variable. As independent variables, we consider the gender of the rater, Player A’s intention, and whether a zero or a positive punishmentlevelwasrated.Fixingtheoutcome(toE in Column (1) and toUinColumn (2)), the coefficient of the triple interaction shows that, for women, unkind intentions are associated with a smaller increase in the appropriateness ratings of positive punishment levels relative to zero punishment than for men. In other words, men’s perceived social norm of punishment is more strongly affected by a change in intentions than the corresponding perception of women. Likewise, we explore the implications of the outcome, fixing Player A’s lottery choice (to Considerate in Column (3) and Selfish in Column (4)). The coefficient of the triple interaction is insignificant in (3) and only weakly significant in (4). Thus, the impact of the outcome draw on the perceived social norm of punishment seems not to be gender-specific. Second, we evaluate whether gender differences in perceived social norms can help to explain gender differences in punishment. Table 5presents results from augmenting the empirical model from Table 1by incorporating individual appropriateness ratings. The interaction of Selfish and Female becomes insignificant in our analyses of punishment levels (Columns (1) and (2)). In contrast, the gender-specific punishment response to unequal payoffs cannot be similarly explained by heterogeneity in norm ratings. The interaction of Unequal Payoffs and Female remains significant in our analysis of punishment levels. In sum, gender-specific punishment norms (i) can help to explain the gender effect in terms of punishing unkind intentions, but (ii) have little explanatory power regarding the gender-specific outcome effect.10 Result 4 Incorporating social-norm ratings at the subject level helps to explain the (general as well as the gender-specific) impact of intentions on punishment choices. 5.2.2 Lottery choice Selfish is perceived as significantly less socially appropriate than Considerate (Fig.3). Accordingly, A’s choice of Selfish is a clear norm violation. This holds for both genders (p<0.01, WRT). It is well established that norm violations are frequently punished (Fehr and Fischbacher 2004) and that such punishments help to sustain cooperation. Accordingly, Players B might have punished A for violating the lottery choice norm. This motive could be reflected in Player B’s punishment norm: this is true if A’s norm violation renders B’s punishment more appropriate. It could, however, also operate independently from punishment norms: one possibility would be that A’s norm violation triggers a socially inappropriate retaliation motive in B. 10 Accordingly, we have tested two potential explanations for the gender-specific punishment response to unequal payoffs (emotions and social norms) and have to reject both. Hence, further research is needed to explain this result. 123 Punishment for intentions or outcomes… 873 Table 4 Determinants of the appropriateness ratings for punishment levels Punishment Appropriateness Rating (1) (2) (3) (4) SE vs. SU vs. CE vs. SE vs. CE CU CU SU Female 0.130 0.510∗∗∗ 0.130 0.296∗ (0.114) (0.169) (0.114) (0.151) Punishment Dummy −4.864∗∗∗ −3.365∗∗∗ −4.864∗∗∗ −2.707∗∗∗ (0.139) (0.221) (0.139) (0.193) Female x Punishment Dummy −0.126 −0.318 −0.126 −0.717∗∗∗ (0.191) (0.281) (0.191) (0.257) Unkind Intention −0.490∗∗∗ −0.898∗∗∗ (0.126) (0.164) Female x Unkind Intention 0.166 0.333 (0.158) (0.209) Unkind Intention x Punishment Dummy 2.157∗∗∗ 2.856∗∗∗ (0.207) (0.235) Female x Unkind Intention x Punishment Dummy −0.591∗∗ −0.862∗∗∗ (0.272) (0.310) Unequal Payoffs −0.672∗∗∗ −1.081∗∗∗ (0.148) (0.139) Female x Unequal Payoffs 0.380∗∗ 0.547∗∗∗ (0.175) (0.185) Unequal Payoffs x Punishment Dummy 1.499∗∗∗ 2.198∗∗∗ (0.216) (0.203) Female x Unequal Payoffs x Punishment Dummy −0.191 −0.463∗ (0.265) (0.270) Constant 5.686∗∗∗ 5.014∗∗∗ 5.686∗∗∗ 5.196∗∗∗ (0.0919) (0.137) (0.0919) (0.122) N4560 4560 4560 4560 R20.489 0.274 0.488 0.205 Notes: Analysis of appropriateness ratings for different punishment levels. We report results from ordinary least squares regressions. Unkind Intention (Unequal Payoffs) is a dummy variable equal to one when Player A chose the lottery Selfish (when the payoff allocation (1350,150)was drawn) in the relevant scenario that the subject evaluated. Punishment Dummy is equal to one when the circumstance to be evaluated features a positive punishment by Player B. Standard errors (in parentheses) are clustered at the subject level. ∗p<0.1,∗∗ p<0.05,∗∗∗ p<0.01 123 874 S.Dato,T.Friehe Table 5 Determinants of punishment levels and incidence conditional on norm information (1) (2) (3) (4) Punishment Punishment Punishment Punishment Level Level Dummy Dummy Unkind Intention 58.88∗∗ 61.96∗∗ 0.341∗∗∗ 0.335∗∗∗ (24.74) (25.46) (0.0971) (0.102) Female −0.737 −3.038 −0.00803 −0.0195 (15.41) (17.46) (0.0609) (0.0684) Unkind Intention x Female −54.65 −55.72 −0.261∗−0.256∗ (35.73) (36.22) (0.143) (0.148) Unequal Payoffs −38.23 −41.96 −0.235∗∗ −0.240∗∗ (27.01) (27.81) (0.107) (0.111) Unequal Payoffs x Female 68.60∗77.99∗∗ 0.357∗∗ 0.379∗∗ (38.48) (39.10) (0.156) (0.160) Inappropriateness −10.07∗∗∗ −9.563∗∗∗ −0.0343∗∗∗ −0.0339∗∗∗ of Punishment (3.081) (3.104) (0.0115) (0.0116) Constant 73.78∗∗∗ −63.71 0.268∗∗∗ −0.0159 (22.61) (62.72) (0.0818) (0.258) Controls No Yes No Yes N190 190 190 190 R20.164 0.189 0.171 0.182 Notes: Analysis of punishment levels (Columns (1) & (2)) and punishment dummy (=1 if positive punishment was chosen; Columns (3) & (4)). We report results from ordinary least squares regressions. Unkind Intention is a dummy variable equal to one when Player A chose the lottery Selfish.Unequal Payoffs is a dummy variable equal to one when the payoff allocation (1350,150)was drawn. The variable Inappropriateness of Punishment is thedifferencein appropriatenessrating betweenzero andmaximum punishmentfor the relevant scenario. Controls include age, number of siblings, social value orientation, justice sensitivity, and rule-following propensity. Standard errors in parentheses. ∗p<0.1, ∗∗ p<0.05, ∗∗∗ p<0.01 Fig. 3 Mean Norm Ratings and 95% confidence intervals of Lottery Choice by Gender 123 Punishment for intentions or outcomes… 875 Table 6 The impact of the social appropriateness of lottery choices on punishment (1) (2) (3) (4) (5) (6) SU SU SE SE CE CE Lottery Norm −3.161 −6.172 1.319 −4.372 −9.076∗∗ −2.572 (7.366) (7.842) (6.581) (6.464) (4.054) (3.500) Punishment Norm −7.649 −10.51∗∗∗ −35.83∗∗∗ (6.985) (3.041) (7.172) Constant 90.02∗∗∗ 129.0∗∗ 70.46∗∗ 118.0∗∗∗ 48.42∗∗ 228.7∗∗∗ (32.83) (48.34) (28.83) (30.66) (18.40) (38.98) N35 35 103 103 45 45 R20.006 0.041 0.000 0.107 0.104 0.438 Notes: Analysis of the punishment level using ordinary least squares regressions. Lottery Norm is the difference in appropriateness ratings between the choices Considerate and Selfish.Punishment Norm is the difference in appropriateness ratings between zero and maximum punishment for the relevant scenario. ∗ p<0.1, ∗∗ p<0.05, ∗∗∗ p<0.01 The results in Table 6document that the punishment level is significantly correlated with the lottery norm only in CE: the more inappropriate the Selfish choice is, the less B punishes A when she complies with the norm by choosing Considerate. For all three scenarios, the coefficient for the lottery norm is insignificant when controlling for the punishment norm. In summary, our results indicate that, at least to some extent, the social appropriateness of lottery choices affects punishment norms and determines punishment choices this way. Our results do not provide evidence in favor of a separate channel, unrelated to punishment norms. 6 Conclusion Costly punishment plays a pivotal role in fostering cooperation and promoting societal well-being (e.g., Bowles and Gintis 2004). To effectively assess and respond to behavior, individuals evaluate others’ choices based on both intentions and outcomes. While previous research has explored the influence of these factors on punishment decisions, the relative importance of intentions and outcomes remains a subject of inquiry. Our study demonstrates that unkind intentions significantly increase punishment levels at the aggregate level. In contrast, when intentions are held constant, the severity of punishment does not vary substantially with the outcome. We observe genderspecific differences in the relative importance of intentions and outcomes. Men tend to prioritize punishing unkind intentions, aligning with their preference for adhering to principles (e.g., Del Giudice et al. 2012; Eckel and Grossman 1996). In contrast, women respond more strongly to unequal outcomes, suggesting a greater concern for equal payoffs. By examining elicited social norms, we shed light on the underlying mechanisms drivingthese gender differences.Ourfindingssuggestthatmenandwomenmayadhere to distinct perceptions of social norms regarding punishment. Our results underscore the importance of considering gender-specific preferences in various domains. For example, we may consider similarities to the question about the 123 876 S.Dato,T.Friehe relative desirability of equal opportunities (i.e., a fair procedure) and similar outcomes (i.e., a fair outcome). This question is important in an organizational and a wider societal context (e.g., in terms of preferences for redistribution). Considering this reality, additional research about gender differences regarding procedural and outcome fairness using different experimental setups is warranted. Acknowledgements We thank two anonymous reviewers for their valuable suggestions on earlier manuscript versions. In addition, we gratefully acknowledge the helpful comments received from Florian Baumann, Andreas Grunewald, Zohal Hessami, Mario Mechtel, Cat Lam Pham, Christoph Rössler, Hannes Rusch, and participants of the CESifo Area Conference on Public Economics, the seminar LIEN at Paris Nanterre, and the LawEcon Workshop at the University of Bonn. Funding Open Access funding enabled and organized by Projekt DEAL. Data availability Data are available from the authors upon request. Declarations Conflict of interest None. 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