2024년 영국 총선에서 정치 지도부에 대한 당파적 용서: 위반 크레딧에는 한계가 있는가?

Introduction

In the current era, marked by growing disillusionment with democratic institutions, understanding how citizens evaluate and forgive the moral and ethical failings of their leaders has become an increasingly pertinent question. The 2024 UK General Election was announced at short notice in the context of cumulative controversy over political leaders’ accountability and the rise of populism. After 14 years in power the Conservative Party was overwhelmingly rejected by the electorate, losing to the Labour Party, while the populist party Reform UK gained a substantial increase in electoral support. This offered a critical opportunity to explore how voters reconcile politicians’ and political leaders’ past transgressions with their own political allegiance.

Here we present a secondary analysis of a longitudinal election survey that investigated UK voters’ willingness to forgive party leaders and politicians in general for their past trust violations. While forgiveness may be warranted in some cases and help foster political reconciliation (Misztal, 2011) and social cohesion (Cilliers et al., 2016), it is also important that political leaders are held accountable for their actions, especially when those actions violate democratic norms, undermine public trust, or set dangerous precedents for institutional conduct.

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The election provided a valuable opportunity to test opposing predictions from the literature, which suggests on the one hand that voters would be more willing to forgive their own leader compared to outgroup leaders (i.e. an ingroup bias) or on the other hand that they would be less tolerant (i.e. black sheep effect). Advancing past findings, we test how these dynamics may change from before to after the election. We also investigate individual differences in forgiveness, specifically testing whether pre-existing levels of trust, and party and country identification, influence people’s willingness to forgive ingroup and outgroup leaders following the election. These dynamics offer insight into how voters deal with the psychological challenges or opportunities arising from political failings, and how these may shape patterns of support and rejection in times of democratic strain.

Ingroup and Outgroup Transgressors

People generally strive to regard their ingroups more positively than outgroups in order to sustain a positive social identity (Hogg & Abrams, 1988; Tajfel & Turner, 1979), often manifested as positive ingroup bias (Brewer, 1979). It is unsurprising, therefore, that people tend to be somewhat lenient toward ingroup transgressors (e.g. Hunter et al., 1991; Otten & Moskowitz, 2000). However, when the transgression threatens the group’s image and the validity of group norms, ingroup deviants may be judged even more harshly than outgroup deviants (the ‘black sheep effect’, see e.g. Abrams et al., 2000, 2002; Marques et al., 1998, 2001). This strong negative reaction to ingroup deviants helps to reaffirm the group’s norms by making a clear distinction between the group’s stance and that of the individual transgressor.

Deviant Leadership and Transgression Credit

What about transgressions from group leaders? Leadership psychology has long emphasised that followers judge leaders not only by their actions but also based on identity dynamics. When judging political leaders’ behaviour, for example, citizens often weigh ethical concerns against loyalty to their preferred party.

Past research using both experimental designs (e.g. Maier, 2011) and survey data (e.g. Davies et al., 2021) shows that trust in politicians and their parties usually declines in the aftermath of political scandals. Politicians embroiled in scandals may be punished by voters (Funk, 1996; Maier, 2011), but not always. Some studies showed that corrupt politicians may just as well be re-elected (e.g. Fernández-Vázquez et al., 2016) and others found little or no effect of politicians’ past trust violations on election outcomes (for a review, see De Vries & Solaz, 2017).

In general, studies suggest that voters are more forgiving of their own leader than of leaders of other parties, showing an ingroup bias. Ingroup leaders are indeed often granted “transgression credit”, making them more likely to be forgiven for breaking the rules than regular ingroup members (Abrams et al., 2013) including cases of blackmail or bribery (Randsley de Moura & Abrams, 2013). For example, Republican voters were more tolerant of various transgressions when those were attributed to the Republican President (i.e. Donald Trump) than when they were presented in isolation, and tolerance was positively related to perception of Trump as strengthening a Republican social identity (Davies et al., 2022). Anduiza et al. (2013) also found that voters (especially those with low political knowledge) were more dismissive of corruption reports about politicians from their party than from another party. And in another study, voters supported due process protections in corruption investigation more when it concerned an in-party than out-party leader (Magalhães et al., 2025).

There are, however, boundary conditions to transgression credit. The leader, for instance, must be perceived as acting in the group’s interest (Packer et al., 2018) and the transgression must not cross significant moral boundaries (Abrams et al., 2014). Fernández-Vázquez et al. (2016) similarly found that while voters readily forgave a local candidate accused of corruption if the municipality had ostensibly benefited financially from the corruption action, they demanded punishment in the absence of such benefit. Finally, other research points to viable political alternatives as a moderator of ingroup leader forgiveness: voters are much less likely to forgive (and re-elect) a corrupted politician when they feel there are other “viable alternatives” (i.e. other parties they would feel comfortable switching to) than when they do not (Jankowski et al., 2023).

In summary, ingroup leaders’ deviance can either lead to increased forgiveness or to derogation and punishment. The critical point seems to be which route will better protect and restore one’s social identity (Otten & Gordijn, 2014). Conversely, while outgroup deviants are usually perceived negatively (e.g. Hunter et al., 1991), they can also benefit from positive perceptions, especially when they deviate away from the outgroup norm (Abrams et al., 2000, 2002; Travaglino et al., 2014).

Political Transgressions and Elections

Increased Coverage of Transgressions in Election Time

Elections are opportune periods in which to study leader forgiveness. First, media coverage of various political transgressions is especially prominent during election campaigns (Garz & Sörensen, 2021), making trust and forgiveness extremely salient issues. The exact nature of these transgressions varies but they often pertain to scandals and various types of trust violation such as dishonesty, corruption, lies and broken promises – in essence, political ‘betrayals’.

In the context of the present study, during the 2024 UK General Election campaign several Conservative Party figures placed bets on the timing of the election before the date had officially been announced, which was widely interpreted as possible misuse of insider knowledge and reinforced perceptions of elite corruption (Courea, 2024). At the same time, Channel 4 (a publicly owned television channel) aired undercover footage showing Reform UK campaigners making shocking racist, Islamophobic, and homophobic slurs. The party leader, Nigel Farage, quickly distanced himself from the comments but the event raised broad concerns over extremist messaging within the party (McKiernan & Francis, 2024). In both cases, the transgression concerned a party figure, not the leader himself, but it tarnished the image of the party as a whole.

Rebalance of Power after an Election

A second reason why elections are opportune periods in which to study leader forgiveness is that political power is rebalanced after an election. Some parties gain power and some lose it, and so winner-loser dynamics are likely to emerge. A large body of literature has shown that voters of the losing side often express lower satisfaction with democracy, even a few years after the election (Hansen et al., 2019). Election losers may also voice strong dissent and undermine the political system, especially those with high levels of affective and perceived ideological polarization (Janssen, 2024).

More crucially for our present purpose, a party that just lost the election might struggle to provide a strong sense of positive social identity to its voters given the negative social comparison with the party who won (Abrams & Hogg, 1990; Tajfel & Turner, 1979). Voters might therefore turn away from the party and become less willing to forgive its leader for past misbehaviour. Conversely, voters of the winning party may be especially willing to forgive their leader, now in position of power and thus even more able to protect the group’s interest. Thus, the outcome of an election may influence which leaders are more or less likely to be forgiven (Otten & Gordijn, 2014). 

The Role of Group Identification and Trust

There are also individual differences in who might be more willing to forgive. Directly stemming from the social identity perspective, some work suggests that group members who more strongly identify with the ingroup show stronger response to ingroup deviance—in both directions. On the one hand, strong identifiers can be more forgiving of ingroup deviants than low identifiers; for example they are more likely to accept unfair offers from ingroup members in economic games than low identifiers and outgroup members are (Zhang et al., 2022). On the other hand, when the deviant’s behaviour poses a threat to the group’s image, strong identifiers may demand greater (utilitarian) punishment (Fousiani et al., 2019).

When it comes to judging outgroup deviants, stronger ingroup identification usually relates to greater harshness towards—or lower forgiveness of—the outgroup (Noor et al., 2008, 2010; Zhang et al., 2022). However, identification at a superordinate level of categorisation that encompasses both the ingroup and the outgroup (such as national identification) may foster forgiveness towards the outgroup (Cehajic et al., 2008)—as long as the superordinate group is truly perceived as such and not as a mere ingroup projection (Noor et al., 2010).

Finally, pre-violation levels of trust may also affect one’s willingness to forgive. Trust acts as a buffer, making people more likely to interpret a violation as a temporary lapse and to attribute it to situational or accidental factors (see Lewicki & Bunker, 1996; Tomlinson et al., 2004). High trust thus often translates into greater forgiveness. Interestingly, this seems to be the case for both ingroup and outgroup transgressors: ingroup trust tends to be generally higher (e.g. Brewer, 1981) but if outgroup trust emerges, it then has a strong positive effect on forgiveness (Cehajic et al., 2008; Hewstone et al., 2008; Noor et al., 2008).

The Present Research

This research aims to shed light on dynamics of forgiving political leaders for past transgressions, and more specifically for past trust violations, in the aftermath of an election. We present a secondary analysis of a longitudinal election survey conducted around the UK General Election of 2024. Focusing on the main four parties in this election, we defined group membership as the party for which participants voted. The ‘ingroup’ leader is thus the leader of that party, while those of the other parties are considered ‘outgroup’ leaders.

As candidates in the election, Rishi Sunak was the incumbent Prime Minister and had been leader of the Conservative Party since 2022. The Conservative Party secured only 23.7% of the votes, losing 251 seats in the House of Commons for a new total of 121. Sunak resigned after losing the election. The official opposition party leader, Keir Starmer, had been leader of the Labour Party since 2020 and became Prime Minister following Labour’s election victory. Labour secured 32.1% of the votes and 411 seats, gaining a substantial majority in the House of Commons. The election was also marked by the rise of Reform UK, a populist party led by Nigel Farage that polls predicted would achieve the third-highest vote share (which it did in fact do, securing 14%). However, as a result of the UK’s first-past-the-post voting system, Reform only obtained 5 seats in the House of Commons.

In this context, we measured British voters’ willingness to forgive Sunak, Starmer and Farage as well as politicians in general for past trust violations, before and after the election. This allows us to test the opposing predictions from the literature that we described above, namely, the transgression credit hypothesis which suggests that voters would be more willing to forgive their own leader compared to outgroup leaders, and the black sheep effect hypothesis, which predicts the opposite.

Our main analysis considers how each party leader is perceived by voters of each party (including his own). Thus, we are able not just to contrast ingroup and outgroup forgiveness but also to investigate differences between responses to more mainstream (Starmer, Sunak) and populist (Farage) figures (see Jankowski et al., 2023). As a robustness check we also consider perceptions by ingroup voters (i.e. those who voted of a given party) versus outgroup voters more globally (i.e. all others). Second, the longitudinal design allows us to test how forgiveness may change from before to after the election and whether it follows winner-loser dynamics. Third and finally, we explore different mechanisms underlying forgiveness by testing whether party identification (i.e. ingroup identity; see Greene, 1999), country identification (superordinate identity), and levels of trust in each political leader, influence willingness to forgive post-election.

This secondary analysis was not preregistered. However, the general design of the study, sample size and justification for exclusions, and the full list of variables, had been preregistered with respect to separate research question (e.g. https://aspredicted.org/jvb4-3v3z.pdf). We applied the same criterion for exclusion here. This research was approved by the ethics committee of the School of Psychology at the University of Kent. Data, materials, and code for analysis are available on the OSF: https://osf.io/ejryt/.

Method

Participants and Procedure

Participants were UK Prolific respondents, pre-screened according to their projected vote to be approximately representative of the four main parties in the election (see Supplemental Material SM1). In addition to Conservative, Labour, and Reform voters, we also sampled Liberal Democrat voters. While representing the third-largest party in the UK, the Liberal Democrats are less often in the media spotlight. In previous surveys we had found that a significant percentage of (non-LibDem) respondents did not know who the party leader, Ed Davey, was. We therefore decided to include Liberal Democrat voters but not to consider their leader in analysis. The sample size was based on feasibility and budget constraints.

Of the 605 participants who completed the first questionnaire, 564 returned to complete the second one. No one failed the two attention checks in the pre-election questionnaire (our preregistered criteria for exclusion) but six participants did so in the post-election questionnaire and were excluded from analysis.[1] When reporting their actual vote in the post-election questionnaire, 10 participants reported eventually voting for a different party (Green) and 15 did not vote. To ensure a clear ingroup/outgroup categorisation based on party vote, we also excluded them. The sample size was thus N = 533, including 229 men, 300 women, 3 non-binary and 1 undisclosed, of a mean age of 48.6 years (SD = 14.2). They were 167 Conservative, 159 Labour, 117 Reform, and 90 Liberal Democrat voters. A sensitivity power analysis indicated that this sample size would allow detection of differences between groups as small as d = .30 with .80 power (G*Power 3.1.9.2, Faul et al., 2007).

Measures

Willingness to forgive leaders. At both time points, we first asked participants to report whether they had felt betrayed by different political figures “over the past year or so” on scales ranging 1 = No sense of betrayal to 7 = Felt completely betrayed. A second set of questions followed that measured willingness to forgive them for these past betrayals or trust violations and we focus on those for the present analysis. To ensure face validity, these questions were not presented to participants who ticked 1 = No sense of betrayal for a given political figure, resulting in slightly smaller n for some analyses. The exact item read, “To what extent do you think [name] should be forgiven for these actions?”, 1 = Absolutely not, to 7 = Completely. One item was presented for each of: Rishi Sunak (Conservative Party leader), Keir Starmer (Labour), Nigel Farage (Reform), and “politicians in general”.

Trust. At both time points, trust in each political leader was measured with a single item for each: “How much do you trust or distrust the following political leaders?”, 1 = Strongly distrust, 4 = Neither trust nor distrust, 7 = Strongly trust. Overall, participants expressed more distrust than trust in each figure (T1: Sunak: M = 3.21, SD = 1.98; Starmer: M = 3.33, SD = 1.90; Farage: M = 3.02, SD = 2.16; T2: Sunak: M = 3.10, SD = 1.92; Starmer: M = 3.69, SD = 1.93; Farage: M = 2.96, SD = 2.12).

Social identification. At both time points, three items measured the strength of identification to one’s political party (e.g. T1: “How do you feel about the party you intend to vote for? / T2: “Following the election how do you now feel about the party you voted for?” – I feel close to this party”, 1 = Not at all, 7 = Extremely; T1: α = .92, M = 4.37, SD = 1.43; T2: α = .93, M = 4.39, SD = 1.43). Another set of three items measured national identification (e.g. “I feel British”, 1 = Not at all, 7 = Extremely; T1: α = .90, M = 5.13, SD = 1.56; T2: α = .92, M = 5.07, SD = 1.57). These items were adapted from past research in the UK (Abrams et al., 2021) and broadly correspond to the centrality dimension of ingroup identification (Leach et al., 2008).

Demographics. Various demographics were measured in the pre-election questionnaire. Specifically, participants reported: their gender (man/woman/non-binary), their age in years, education on a 14-point scale, annual household income with 6 bands, and subjective socioeconomic status on an 8-point status ladder (SM1).

Results

Forgiveness of Ingroup and Outgroup Leaders

To test the transgression credit hypothesis versus the black sheep effect, we first examined differences between the four groups of voters in their willingness to forgive each political figure before and after the election. To this end we conducted a 4 Party Vote (between-participant) ´ 4 Party Leader (within-participant) ´ 2 Time (within-participant) mixed model (fit by REML, random intercepts only; see Table 1). The analysis revealed three significant main effects so that main rates of forgiveness differed between leaders, bwetween groups of voters, and across time. Specifically, forgiveness increased from before (M = 2.78, SD = 1.13) to after the election (M = 2.92, SD = 1.24).

Table 1

Mixed-Model Results: Willingness to Forgive Political Leaders as Function of Party Vote, Party Leader, and Time

Importantly, these main effects were qualified by a Party Leader ´ Time as well as Party Leader ´ Party Vote two-way interaction. However, the Time ´ Party Vote two-way interaction and Party Leader ´ Time ´ Party Vote three-way interaction, were not significant.

Decomposing these interactions revealed clear leniency for one’s own party (Table 2): voters of each party were much more willing to forgive their party leader for past trust violations than were voters of other parties, both pre- and post-election (reflecting the Leader ´ Party Vote interaction)—a finding that supported the transgression credit hypothesis. In addition, Conservative were more willing to forgive “politicians in general” than other party voters.

Table 2

Willingness to Forgive Political Leaders Before (T1) and After (T2) the 2024 General Election, Depending on Party Vote (Conservative, Labour, Liberal Democrats, and Reform UK)

We computed difference scores (post-election – pre-election) to probe differences due to time. In line with the main effect of Time identified in the analysis, willingness to forgive generally increased after the election. Specifically, respondents became more forgiving of both the election winner, Starmer (intercept or difference to zero: F(1, 391) = 30.44, p < .001, η²_p = .07), and the election loser, Sunak, F(1, 454) = 29.26, p < .001, η²_p = .06. The same was true of forgiveness of politicians in general, F(1, 487) = 12.13, p < .001, η²_p = .02. In contrast (and reflecting the Leader ´ Time interaction), the Reform leader, Farage, did not benefit from this post-election leniency effect, F(1, 394) = 0.37, p = .543, η²_p = .00.

Interestingly, this change through time was not moderated by party affiliation (i.e., no significant three-way interaction with Party Vote). In other words, disparities in levels of forgiveness between parties persisted as all scores increased after the election (see Table 2 and Figure 1).

As robustness check, we also tested a simpler model that merely contrasted ingroup versus outgroup leader perceptions. This analysis consistently revealed a main effect of time, a main effect of ingroup/outgroup membership, and no interaction effect. Details are reported in SM2.

Figure 1

Willingness to Forgive Political Leaders Before and After the 2024 General Election, Depending on Party Vote (Labour, Liberal Democrats, Conservative, and Reform UK)

The Effect of Trust and Social Identification

We then turned to the different mechanisms potentially underlying forgiveness. To this end, we conducted a series of cross-lagged panel models to test the longitudinal associations between forgiveness, trust, and national / party identification. Our goal was to test how the different mechanisms at T1 would relate to forgiveness at T2 while accounting for measurement error (and autoregressive effects) of all constructs. National and party identification (three items each) were entered as latent variables. Forgiveness and trust were entered as manifest variables since they were measured with one item only. We used maximum likelihood with robust standard errors (MLR) as method of estimation and followed the computational approach suggested by Mackinnon et al. (2022).

We initially conducted measurement invariance tests to investigate the equivalence of the measurement model in the pre- and post-election assessment. This led us to favour the more parsimonious model of residual invariance which yielded good fit indices, χ²(60) = 166.9, χ²/df = 2.78, CFI = .983, RMSEA = .058, 90% CI [.048, .068], SRMR = .028 (see SM3).

We regressed all variables at T2 on all variables at T1. In addition, we used multiple-group analysis to compare the dynamics of forgiveness of one leader among his partisans (i.e. ingroup) versus all others (i.e. outgroup). For simplicity, we only report here the results pertaining to predicting forgiveness at T2 (see Table 3 and Figure 2). The full CLPM output can be found in SM4.

Table 3

Cross-Lagged Panel Model Results: Willingness to Forgive Political Leaders Post-Election Depending on Party Vote (Ingroup vs. Outgroup Voters), Trust, and Social Identification (Party Identification and National Identification)

Figure 2

Ingroup and Outgroup Voters’ Willingness to Forgive the Three Main Party Leaders

Some consistent results appeared across political leaders. As pertaining to Sunak, Starmer and Farage alike, willingness to forgive at T2 (controlling for the auto-regressive effect) was greater among outgroup voters who expressed great trust at T1 (β_s ranging .36-.41, p_s < .001), but this was not significant for ingroup voters (β = .02, p = .698). Stronger party identification was also related to outgroup voters’ lower willingness to forgive Sunak (β = -.13, p = .005) and Starmer (β = -.13, p = .009); for Farage, the effect was descriptively negative but not significant (β = -.07, p = .097). In contrast, party identification was not significantly related to forgiveness of the ingroup leader (β= .01, p = .830).

In comparison, the role of national identification was less clear. National identification was related to greater forgiveness of Sunak and Farage among outgroup voters (β= .11, p = .034; and β= .13, p = .004, respectively), but it was not significantly related to forgiveness of Starmer (β= .05, p = .311). Among ingroup voters, national identification was related to greater forgiveness of Starmer only (β= .28, p = .008); for Farage the effect was not significant (β= .00, p = .983), and for Sunak it was non-significant and directionally negative (β= -.10, p = .176).

Discussion

This research investigated voters’ willingness to forgive political leaders for their past trust violations in the context of the 2024 UK General Election. It aimed to contrast opposing predictions from the transgression credit hypothesis on the one hand, and the black sheep effect hypothesis on the other hand. Our results revealed clear ingroup bias in the form of transgression credit: Voters were much more forgiving of their own party leader than of other leaders. This effect appeared both before and after the election and time did not seem to moderate the magnitude of the ingroup bias. However, on average, levels of forgiveness increased after the election. This was the case for all leaders (winner and losers) as judged by their ingroup, and also in many cases as judged by the outgroup. The results thus point at dynamics more complex than a mere winner-loser effect which would have seen forgiveness of the winner increase more than that of the losers. Instead, they might reflect an overall diminution of competitiveness following the result of the election, but also other factors potentially in play.

Forgiveness for the Election Winner and Losers

Increased forgiveness for the election winner (by both ingroup and outgroup voters) may reflect their perceived right to be supported following a democratic election (‘conferral’, Abrams et al., 2018), as well as their incumbency in a new leadership role of Prime Minister. That this forgiveness is granted by voters of all parties is remarkable; it suggests that, at least initially, voters of the losing parties are willing to “reach across the aisle” and cooperate with others for the sake of the superordinate group’s future (i.e. the nation, Ismail & Jia, 2025).

The, perhaps surprising, increased forgiveness for the “big” election loser (the Conservative Party leader Rishi Sunak) may reflect a motivation amongst a somewhat liberally minded electorate in the UK to bury the hatchet across parties (Christian et al., 2018) or perhaps a degree of sympathy or empathy for the leader’s personal suffering. Sunak was not just the Conservative Party leader but also the nation’s Prime Minister, and he might have benefitted from transgression credits deriving from his past role (another expression of conferral).

On the other hand, Nigel Farage—a prominent Eurosceptic, former leader of the Brexit Party, and leader of the populist party Reform UK—was not more likely to be forgiven by voters for other parties after than before after the election. It could be that post-election leniency applies only to ‘mainstream’ or well-established political figures, and that Farage’s status as a political outsider, strengthened by his anti-establishment narratives, marked him as sufficiently distinct as to preclude his inclusion into the fold with other leaders. In this specific election, the case of Reform was ambivalent, since despite ‘losing’ in absolute terms (i.e. obtaining a small number of seats in the House of Commons), Reform gained substantially in relative terms (obtaining a large percentage of votes and actually entering the House for the first time of their history).

Overall, a simple winner-loser dynamics provides a poor explanation for the present results. Our insights are, however, limited by the reliance on a single election context and future studies comparing how populist versus mainstream, but also incumbent versus emerging, party leaders may be evaluated in other contexts would help clarifying the exact mechanisms at play.

The Role of Group Identification and Trust

We found that trust, national identification and party identification play distinct roles when it comes to forgiving the past trust violations of ingroup and outgroup leaders. Pre-election trust in each outgroup leader was a significant predictor of forgiveness; an effect that replicates previous work (e.g. Cehajic et al., 2008; Hewstone et al., 2008; Noor et al., 2008). However, pre-election trust did not predict forgiveness of ingroup leaders. To put this differently, forgiveness of outgroup but not ingroup leaders appears to be contingent on trust. This might suggest that ingroup forgiveness is underpinned by different considerations. If one views expressions of forgiveness in such contexts as a form of intergroup communication (Abrams & Lalot, 2025; Giles et al., 2025), one of these considerations may be the reputational challenge of accounting for the ingroup’s performance in the aftermath of the election (see also Emler, 1990; Hall et al., 2004) and of dealing with ingroup normative expectations (see Dunning et al., 2014), regardless of one’s true feelings (or trust) toward the leader. Work on interpersonal forgiveness has shown that forgiveness could be related either to trust in the transgressor or commitment to the relationship, depending on self-regulation mode (i.e. focusing on the benefits of repairing the relationship vs. perceived costs of further deterioration, respectively; Molden & Finkel, 2010). In a similar vein, different motivations might be at play when deciding whether to forgive an ingroup or outgroup leader—a possibility that future research should explore.

Party identification was negatively related to forgiveness of outgroup leaders, again replicating past work (Noor et al., 2008, 2010; Zhang et al., 2022). Like trust, however, it was not related to forgiveness of ingroup leaders. Past literature has revealed contrasting findings in the relationship between group identity and evaluations, showing on the one hand that stronger identification may intensify ingroup bias (e.g. Zhang et al., 2022) and on the other that it intensifies the black sheep effect (e.g. Fousiani et al., 2019). The absence of an overall effect may indicate either that neither or that both of these contrasting processes may be operating in forgiveness of political leaders. However, both inferences seem inconsistent with the overall ingroup bias observed in the study, and with the increase in forgiveness post-election. Therefore, the role of possible moderators in the relationship between ingroup identification and ingroup forgiveness is clearly an interesting avenue for future research.

National identification was conceived here as a form of superordinate identity which may drive a desire to move past political divisions and focus on building a better nation (see Cehajic et al., 2008; Ismail & Jia, 2025; Noor et al., 2010). Our results suggest voters who identified strongly with their country were willing to forgive the defeated outgroup leaders. However, only those who happened to support the winning party were also more forgiving of their own leader, probably as a result of conferral effects (Abrams et al., 2018) once their leader assumed the position of leading the nation as a whole. In contrast, the election losers with strong national identification did not afford any extra or lesser latitude to their leaders, perhaps regarding them as no longer relevant for the country’s future prospects.

Limitations and Constraints on Generality

A strength of the present work is the inclusion of (almost) equal numbers of voters from the four main political parties, allowing for direct comparisons. The sample was balanced in terms of gender and encompassed a range of different ages, incomes, and status, as well as being typical of the voting profile for each party nationally; however, it is not a representative population sample and the sample size is somewhat modest.

We relied on a real-life election context which implies that many factors covary, especially when it comes to distinguishing different politicians. In this context we identified not only differences in ingroup-outgroup perceptions but also similarities between parties that are ideologically closer. Specifically, Liberal Democrats (typically centre-left) gave forgiveness ratings closer to Labour voters (left), while Reform (typically right-populist) gave ratings closer to Conservative voters (right). This suggests that voters judge political leaders not just based on discrete party boundaries but also more gradually on ideological proximity, an effect that could be especially relevant in multi-party contexts. This, however, remains speculative. Replications in different contexts, combined by experimental studies, will be important to better elucidate whether the differences between leaders are imputable to whether they win or lose the election; whether they represent more conservative or liberal, as well as mainstream versus populist, parties; whether voters feel they have viable alternatives; and also how this upholds in strong bipartisan versus multi-party contexts. Additionally, including non-voters would enable comparison directly with voters, potentially uncovering different dynamics among the politically less invested.

We conceptualised the political ingroup and outgroup based on party vote (see Greene, 1999), and thus defined group identification based on one’s sense of identification with the party rather than the leader. However, people may also feel a sense of identification, or perceived oneness, with the leader themselves. It would be important for future research to examine further how the two types of identification may differentially relate to forgiveness for this leader’s past transgressions.

Extreme forms of identification, such as identity fusion (a visceral sense of oneness with a group; Swann & Buhrmester, 2015) should also be considered. Identity fusion is related to a greater willingness to defend the group’s reputation. As such, it was found to amplify both the decrying of publicly known ingroup transgressions (consistent with the black sheep effect) and the overlooking of publicly unknown transgressions (consistent with the transgression credit hypothesis; Ashokkumar et al., 2019). Interestingly, recent findings suggest that fusion can pertain to either a group or another individual, such as a political leader (Gómez et al., 2020), albeit with different consequences; for example, only the latter increased participation in political violence (Kunst et al., 2019). Future studies might extend the present findings by investigating how fusion with the party versus the leader affects partisans’ willingness to forgive.  

On the measurement side, we note that our single item measure of forgiveness limits our confidence in its reliability, and it might also be interesting to consider the perceptions of those participants that we excluded because they did not perceive any trust violation from the leader in the first place (though these were few). It will also be important for future studies to better distinguish between different forms of trust violation, in terms of their severity first, and also in terms of their nature, relating for example to breach of competence versus integrity (PytlikZillig et al., 2016). In combination, it will be worthwhile to consider the leader’s response to the accusation: Whether the person apologises or denies the violation can indeed shape the public perception of the act and thus potentially their willingness to forgive (Kim et al., 2004, 2006).

As our longitudinal design included only two measurement points, our analysis strategy was limited to ‘classical’ CLPMs with no possibility to integrate random intercepts (i.e. RI-CLPM). CLPM conflate between-group differences with within-person change and can thus only detect temporal relationships but not causal ones (Singh et al., 2024). Future studies including more measurement points will help to ascertain the causal relationships from trust and identification to forgiveness.

The present findings are from a large democratic country during a single election. The post-election leniency effect was observed for all ingroup parties as well as to the three mainstream parties as outgroups but not the more extreme (Reform) party. This might imply limits on what people might tolerate from populist leaders. Alternatively, as the UK gravitates politically back towards Europe following the failure of Brexit to deliver its promised benefits, perhaps many voters will continue to feel unable to forgive Nigel Farage for his role as Brexit’s main advocate.

Conclusions

This longitudinal study reveals clear support for the transgression credit rather than the black sheep effect hypothesis. Voters grant transgression credit to their party leaders, forgiving them more than leaders from other parties for their past trust violations. Further, willingness to forgive increased overall in the aftermath of the election, for both the election winner and loser and from both ingroup and outgroup voters. We found that pre-existing levels of trust and national identification increase forgiveness for outgroup (but not ingroup) leaders, while party identification decreases it.

Although forgiveness may be warranted in some cases, it is somewhat surprising and of concern that voters are not keener on holding their political leaders accountable for their misdemeanours and breaches of trust. These findings suggest that forgiveness, shaped by loyalty and identity, can subtly resist accountability and in doing so, may erode democratic safeguards.

The present findings remain a snapshot of a single, albeit highly consequential, election. Comparison both over time and with evidence from other countries will illuminate both the ubiquity of transgression credit and its limits in terms of leader accountability and forgiveness.

Endnotes

[1] Attention checks at T1: “For this question, please select the answer that says Africa”: 1 = Europe, 2 = North America, 3 = Africa, 4 = South America, 5 = Oceania (embedded in the demographic questions block); “For this question please tick moderately better”: 1 = Much worse, 2 = Moderately worse, 3 = Slightly worse, 4 = About the same, 5 = Slightly better, 6 = Moderately better, 7 = Much better (embedded in a set of questions on relative deprivation). At T2: “For this statement please tick strongly agree”: 1 = Strongly disagree to 7 = Strongly agree (embedded in a matrix table with other items), “For this question please tick moderately better”: 1 = Much worse, 2 = Moderately worse, 3 = Slightly worse, 4 = About the same, 5 = Slightly better, 6 = Moderately better, 7 = Much better (embedded in a set of questions on relative deprivation).

Conflicts of Interest

The authors declare no competing interests.

Author Contributions

FL: Conceptualization; Methodology; Formal analysis; Investigation; Data Curation; Writing – Original Draft. DA: Conceptualization; Methodology; Investigation; Writing – Original Draft; Project administration; Funding acquisition.

Data Availability Statement

Data, materials, and code for analysis are publicly available on the OSF: https://osf.io/ejryt/

Acknowledgements

FL is supported by the Swiss National Science Foundation (Grant: PZ00P1_216373 / 1). DA is supported by a British Academy Senior Research Fellowship (SRF25\250410).

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