Double Jeopardy: Punitive Responses to Black Women Candidates’ Anger

Double Jeopardy: Punitive Responses to Black Women Candidates’ Anger | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Double Jeopardy: Punitive Responses to Black Women Candidates’ Anger LaFleur Stephens-Dougan, Davin Phoenix, Isaiah Johnson, Andrea Benjamin, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8584399/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract How do voters respond when Black women candidates express anger–particularly when that anger is racialized? Using a preregistered survey experiment (N ≈ 5,000) that varied candidate race, gender, and expressions of anger, we test whether Black women are uniquely penalized for displays of anger. We find that although Black women are not penalized for expressing anger on average, anger produces sharp and disproportionate backlash among racially resentful White voters, resulting in substantial declines in support that exceed those faced by other candidates. White respondents high in racial resentment reduced support for an angry Black woman by half the vote likelihood scale in the general anger condition (-0.51, p<.01) and when her anger is racialized (-0.31, p<.10). Even among racially liberal White women, who initially reward non-racialized anger from Black women, support collapses once anger is framed around racial injustice, revealing the fragility of cross-racial gender solidarity. Among Black respondents, intersectionality conscious participants reward the White woman candidate but not the Black woman candidate for racial anger, while Black nationalists reward the Black man candidate and penalize the Black woman candidate for the same expression. Our findings highlight the unique barriers facing Black women candidates in using anger as a political resource. race gender anger intersectionality Black politics Introduction “I’m mad as hell and I’m not going to take it anymore!” In this famous line from the movie, “Network” (1976), news anchor Howard Beale delivers a passionate on-air rant, channeling public frustration into a call to action. The quote endures because it captures the political potential of anger, an emotion that can mobilize citizens, sharpen attention, and signal resolve (Brader 2005; Webster 2020). Yet anger is also politically risky. Candidates who display anger may be judged as unprofessional, unlikable, or unfit for leadership (Brooks 2013). Crucially, these risks may not always be distributed evenly. Some scholars argue that for women, anger violates gendered expectations of warmth and communality, rendering female candidates particularly vulnerable to backlash and trait-based attacks that challenge stereotypically feminine strengths (Eagly & Karau 2002; Rudman & Glick 1999). For Black candidates, anger–especially when expressed about racial injustice–reinforces stereotypes of aggression and fuels perceptions of racial threat (Banks and White 2024). In fact, many observers have long suspected that minority politicians must make a careful effort to not appear too strident or bellicose, to avoid losing the support of white voters (Phoenix 2019; Stephens-Dougan, 2020). Real-world examples illustrate how these constraints operate. Commentators often attributed President Obama’s famously restrained demeanor to fears of being cast as the “angry Black man” (CNN 2010; Phoenix 2019). More recently, observers noted that former Vice President Kamala Harris emphasized “joy” during her presidential campaign, in part because anger was perceived to be politically unavailable to her (Blake 2024). These examples highlight how both Black men and Black women may have to navigate the narrow bounds of emotional expression but leave open whether Black women face distinct electoral consequences when they express anger. We argue that Black women face a “double bind” in evaluations of political anger: they are penalized for violating gendered expectations surrounding emotional expression, while also facing heightened scrutiny when anger is racialized. Importantly, this bind cannot be understood by considering race and gender separately, as the expectations applied to Black women differ in kind from those applied to either group alone. Yet to date, research has not explored the nature or extent of this constraint. Existing work has focused primarily on White women and Black men (Brooks 2013; Banks et al. 2019), often collapsing across race and gender or excluding Black women altogether. Moreover, many studies rely almost exclusively on White respondents, leaving open the question of how Black voters evaluate Black women candidates who express anger. We address these gaps using a preregistered survey experiment with approximately 5,000 respondents, evenly split between White and Black Americans. Respondents evaluated a hypothetical candidate running for the U.S. Senate, whose race (Black or White), gender (male or female), and emotional expression were experimentally varied. Candidates either expressed no anger, anger about the economy, or anger about racial disparities. This design allows us to identify whether Black women are uniquely penalized for expressing anger, and whether reactions differ across White and Black voters depending on their racial and gender attitudes. Our findings reveal a consistent double jeopardy for Black women. White respondents high in racial resentment impose the sharpest penalties, reducing support for an angry Black woman by half the vote likelihood scale in the general anger condition (-0.51, p<.01) and when her anger is racialized (-0.31, p<.10). Among Black respondents, support is also withheld. Intersectionality conscious participants reward the White woman candidate but not the Black woman candidate for racial anger, while community nationalists reward the Black man candidate and penalize the Black woman candidate for the same expression. In short, this study demonstrates that anger is not a universally accessible political resource; instead, it uniquely disadvantages Black women, who incur sharp electoral penalties from White voters when they express anger and are unable to obtain the electoral benefits that comparable Black men and White women candidates receive from some Black voters. Anger as a Conditional Political Resource Anger is often understood as one of the most politically powerful emotions. Defined as a feeling of belligerence over a perceived slight or injustice, anger mobilizes participation, sharpens attention, and signals resolve and authenticity (Huddy, Feldman and Weber 2007; Neuman and MacKuen 2000; Valentino et al. 2011; Brader 2005; Banks, White and McKenzie 2019). Politicians who display anger can use it strategically to highlight threats and demonstrate leadership (Webster 2020). Moreover, displays of anger can be advantageous for people in leadership positions (Van Kleef and De Dreu, 2010), as reflecting the emotions of their constituency engenders greater approval (Garcia and Stout, 2022). Yet, despite anger being a political resource, it is not universally regarded as appropriate or effective; emotional expressions are filtered through racial and gendered hierarchies that determine whose anger is seen as legitimate (Phoenix 2019). Previous research indicates that white men frequently benefit from anger displays, which are often interpreted as consistent with strength and leadership (Bickford 2011). In contrast, for women candidates, anger violates gendered expectations. Role congruity theory holds that voters evaluate women through the lens of communal stereotypes–warmth, nurturance, and cooperation–while leadership is associated with agency and assertiveness (Eagly and Karau 2002). Women who display agentic behaviors or emotions such as anger risk backlash for being “unfeminine” or unlikeable (Rudman and Glick 1999). For Black candidates, anger carries different but equally constraining risks. White voters frequently perceive Black politicians as more liberal (Lerman and Sadin 2016) and more beholden to Black interests than their White counterparts (Stephens-Dougan 2020). Therefore, expressions of anger, particularly when directed at racial injustice, reinforce longstanding stereotypes of Black aggression and partiality toward their ingroup (Phoenix 2019; Stephens-Dougan 2020; Banks and White 2024). Black men, in particular, are often penalized for anger, given its association with aggression and violence (Livingston and Pearce 2009). Thus, we expect Black candidates to face sharper penalties than White candidates for expressing anger, especially among racially resentful voters. Black women candidates stand at the intersection of these expectations. A key insight from the literature is that stereotype profiles are not simply additive—that is, voters do not evaluate Black women by combining stereotypes about Black people and stereotypes about women—but are instead qualitatively distinct from the stereotypes associated with either category in isolation (Cassese 2019). As a result, voters hold a unique set of expectations about Black women that cannot be reduced to race or gender alone. Prior work further suggests that the political costs of anger may be especially acute when the content of that anger aligns with gendered and partisan stereotypes. Cassese and Holman (2018) show that female candidates—particularly Democratic women—are uniquely vulnerable to stereotype-based attacks on issues closely associated with their party and gender, with negative electoral consequences that are substantially larger than those faced by male candidates. 1 Although their analysis focuses on voter responses to stereotype-consistent attacks, the underlying logic—that penalties intensify when candidate behavior activates entrenched expectations about “appropriate” issue positions—suggests that similar dynamics may apply when candidates themselves engage those issues. To the extent that a Black woman candidate’s expression of anger about racial injustice activates gendered and partisan stereotypes, racialized anger may therefore be especially costly for Black women candidates, even when comparable expressions carry fewer penalties for others. At the same time, Black women face competing pressures. As women, they may be penalized for violating gender norms of warmth and civility, and as Black candidates, for reinforcing racial stereotypes (Carr 2025). Yet their dual marginalization may also render them less prototypical of either group, potentially muting backlash under some conditions (Sesko and Biernat 2010). Together, these competing logics make Black women a critical test case for understanding when and how anger functions as a political resource. In addition to candidate identity, we expect responses to candidates’ anger to vary based on voter identity. Among Black respondents, anger about racial injustice can serve as a cue of authenticity and solidarity, signaling a candidate’s commitment to shared concerns (Wamble 2024). Yet, these signals may not be interpreted uniformly. Black respondents high in intersectional consciousness–the belief that Black women’s oppression stems from interlocking forces of racism and sexism–may be especially inclined to reward Black women for expressing anger about racial inequality, seeing it as cultivating solidarity along both race and gender lines (Crowder et al. 2023; Greenwood 2008). In fact, recent research indicates that relative to Democratic Black men politicians, Democratic Black women politicians are indeed more likely to discuss substantive public policy issues and to frame them as raced and gendered (Strawbridge et al. 2025). By contrast, Black respondents with strong community nationalist orientations–emphasizing Black self-determination and often tied to patriarchal understandings of leadership–may privilege Black men as the authentic bearers of racial grievance (Alexander-Floyd 2003). We also expect voter gender to shape responses to candidates’ anger, especially racialized anger. Gendered socialization influences how men and women perceive emotional expression. Men are often more accepting of anger from other men, viewing it as an appropriate display of strength, while judging women’s anger as less legitimate or overly emotional. Women, by contrast, may be more sympathetic to anger expressed by women candidates, particularly when it reflects concerns about injustice or inequality. However, this sympathy is not guaranteed. Women may also enforce gendered norms that penalize female candidates for violating expectations of warmth and civility. Thus, we anticipate that voter gender in addition to voter race will condition evaluations of candidates who display anger. In short, anger is a conditional political resource. For Black women in particular, its effectiveness depends on the expectations that voters bring to their evaluations. These dynamics suggest that White respondents high in racial resentment will penalize Black women most heavily, while both Black and White female respondents will exhibit variation in their reactions depending on their orientations toward race and gender In sum, we contend that anger is not judged in a vacuum—it is interpreted through expectations about who is “allowed” to express it and under what circumstances. Hypotheses Because Black women candidates occupy a distinct position in voter’ evaluative frameworks, shaped by expectations that cannot be understood by considering race and gender separately, we anticipate that Black women will face especially steep and unique penalties (i.e. double jeopardy). These penalties should be most pronounced among White voters who harbor higher levels of racial resentment. Among Black voters, anger about racial injustice may serve as a cue of authenticity and solidarity, but responses are expected to diverge depending on the respondent’s orientation toward race and gender. Therefore, our hypotheses (by race of respondent) are formally stated below: White Respondents H1 (Black woman anger penalty): White respondents will be less likely to support a Black woman candidate when she expresses anger—especially racialized anger—compared to both a neutral (non-angry) condition and to non-racialized anger. H2 (Racial resentment as moderator): Among White respondents, higher levels of racial resentment will be associated with lower support for Black candidates who express anger, with the strongest negative effect observed for the Black woman candidate expressing racialized anger. Black Respondents H3 (Ingroup emotional reward): Black respondents will express greater support for Black candidates—relative to White candidates—when they express anger about racial disparities. H4a (Intersectional consciousness moderator): Higher intersectional consciousness will be associated with increased support for the Black woman candidate expressing racialized anger. H4b (Community nationalism moderator): Higher community nationalism will be associated with increased support for the Black man candidate expressing racialized anger, but not the Black woman. Research Design We test our theoretical expectations using a preregistered survey experiment conducted in Spring 2021 with approximately 5,000 respondents recruited through Qualtrics, evenly divided between Black and White Americans. 2 The experiment employed a fully crossed 2 x 2 x 3 factorial design varying candidate race (Black/White), gender (man/woman), and emotional expression (no anger, nonracial anger about the economy, or racialized anger about racial disparities). This design isolates the causal effects of candidate identity and anger type on vote likelihood and trait evaluations, and assesses how these effects are moderated by respondents’ racial resentment, intersectional consciousness, community nationalism, and gender. Respondents first completed questions on demographics, political orientations, and attitudes about race and gender. White participants answered the standard four-item racial resentment scale (Kinder and Sanders 1996), while Black participants completed measures of community nationalism and intersectional consciousness, along with related items on racial and gender attitudes. Participants were then randomly assigned to read a mock news article about a candidate running for the U.S. Senate, in which the candidate’s race, gender, and expression of anger were experimentally manipulated. The design replicates and extends Banks and White (2024) by incorporating gender as an additional identity dimension and by examining how reactions to candidate anger vary by respondents’ own race and gender. Our primary dependent variable is vote likelihood, measured on a seven-point scale (“How likely would you be to vote for [candidate]?”), rescaled from 0–1 for analysis. Secondary dependent variables capture stereotype-based trait attributions, including perceptions of competence and qualification, similarly rescaled. The main comparisons evaluate whether White respondents penalize the Black woman candidate who expresses anger, and whether these effects vary by racial resentment and gender (H1–H2). Among Black respondents, we test whether support for Black candidates who express anger increases with intersectional consciousness (IC) and community nationalism (CN). IC was measured with a single item asking respondents which statement best reflected their view: (1) “Black women have suffered from both sexism within the Black movement and racism within the women’s movement,” (2) “Black women mostly suffer from the same types of problems as Black men,” (3) “Neither,” or (4) “Both.” Responses were scaled 0–1, with higher values indicating greater IC. CN was measured as agreement (0–1) with four statements emphasizing Black autonomy and control over economic and political life in predominantly Black communities. Results We begin by evaluating whether candidates are penalized for expressing anger—and whether such penalties vary by the race and gender of the candidate, as well as the racial content of their anger. Our first step is to examine the main effects of the treatment conditions on vote likelihood. Columns 1–4 of Table 1 present linear regression models estimating the likelihood of voting for each candidate type (Black woman, Black man, White woman, White man) across three conditions: Control (no anger), Angry (non-racial), and Angry+Race (racialized anger). This analysis allows us to assess whether candidates are evaluated differently depending on both who they are and the object of their anger. First, we test H1, which states: “White respondents will be less likely to support a Black woman candidate when she expresses anger—especially racialized anger—compared to both a neutral (non-angry) condition and to non-racialized anger.” Table 1. Linear regression models estimating vote likelihood for each candidate (models 1-4). Models 5-8 present the conditional effect of racial resentment on vote likelihood . Models do not include controls (see Appendix B Table 2 for balance tests). Vote Likelihood by Experimental Condition and Racial Resentment (White Respondents) Vote Likelihood Main Effects Vote Likelihood as a Function of Racial Resentment Dependent variable: Black Woman Black Man White Woman White Man Black Woman Black Man White Woman White Man (1) (2) (3) (4) (5) (6) (7) (8) Angry BW 0.02 0.25*** (0.03) (0.08) Angry+Race BW -0.01 0.12 (0.03) (0.08) Angry BM -0.02 -0.04 (0.03) (0.08) Angry+Race BM -0.07** -0.05 (0.03) (0.08) Angry WW 0.03 0.04 (0.03) (0.08) Angry+Race WW 0.01 0.10 (0.03) (0.09) Angry WM 0.01 0.01 (0.03) (0.08) Angry+Race WM 0.01 -0.08 (0.03) (0.08) Racial Resentment -0.32*** -0.50*** -0.57*** -0.75*** (0.13) (0.11) (0.13) (0.12) Angry BW×RR -0.51*** (0.17) Angry+Race BW×RR -0.31* (0.17) Angry BM×RR 0.01 (0.16) Angry+Race BM×RR -0.05 (0.17) Angry WW×RR -0.08 (0.18) Angry+Race WW×RR -0.23 (0.19) Angry WM×RR 0.04 (0.17) Angry+Race WM×RR 0.23 (0.18) Constant 0.58*** 0.61*** 0.53*** 0.54*** 0.72*** 0.84*** 0.79*** 0.84*** (0.02) (0.02) (0.02) (0.02) (0.06) (0.05) (0.06) (0.06) Observations 623 592 608 614 623 592 608 614 R 2 0.002 0.01 0.001 0.0002 0.12 0.10 0.12 0.13 Adjusted R 2 -0.001 0.01 -0.002 -0.003 0.11 0.09 0.12 0.12 Note: * p<0.1; ** p<0.05; *** p<0.01 Our results indicate that H1 is not supported in the main effects. As shown in Table 1, White respondents on average do not penalize the Black woman candidate for either form of anger. Neither the coefficient for the Angry condition (0.02) nor the coefficient for the Angry+Race condition (–0.01) is statistically significant. Column 1, which reports the vote likelihood for the Black woman, makes clear that her support remains unchanged across treatment conditions. In addition, it is informative to consider baseline evaluations in the non-anger control condition. The mean vote likelihood for the Black woman candidate is 0.58, which is significantly higher than the baseline support for both the White man (0.54) and White woman (0.53) candidates (p < .05). Although none of the candidates receive overwhelming support in this low-information setting, the Black man receives the highest baseline support (0.61), though his advantage over the Black woman is not statistically significant (p = .30). Overall, White respondents evaluate the Black candidates more favorably than the White candidates in the control condition. If all candidates were being evaluated equivalently, we would expect no such differences. This relatively high baseline support for Black candidates is important context: any subsequent decline in vote likelihood following an anger treatment represents a clear penalty —a drop from an initially favorable evaluation. We contend that the elevated baseline support reflects the ideological composition of our sample. The median racial resentment score among White respondents is 0.50, meaning that half the sample falls into the racially liberal category (score < 0.5). Prior work shows that racially liberal White Democrats express heightened support for Black candidates (Mikkelborg 2025), which plausibly contributes to the favorable baseline evaluations we observe. Indeed, the only candidate to experience a statistically significant decline in support in the main-effects models is the Black man. Exposure to the Angry+Race condition produces an approximately seven-percentage-point drop in vote likelihood (p < .05). Notably, this penalty emerges only when he expresses anger about racial disparities , underscoring the conditional nature of backlash to racialized anger. While we initially theorized that the Black woman might be “doubly bound”—penalized for both her race and gender—even before expressing anger, these results show that she is not disadvantaged at baseline. However, it would be premature to conclude that Black women are immune to compounded electoral penalties for displays of anger. As we demonstrate in the next section, such penalties do emerge under specific conditions—primarily among White respondents high in racial resentment. Next, we test H2, which posits that, “ among White respondents, higher levels of racial resentment will predict lower support for Black candidates who express anger, with the strongest negative effect observed for the Black woman candidate expressing racialized anger.” The latter half of Table 1 (Columns 5-8) presents linear regression models estimating vote likelihood for the four candidate profiles among White respondents, including an interaction term for the treatment conditions with racial resentment, which enables us to test H2. While our initial models in the first half of Table 1 assessed the main effects of anger across candidates among White respondents, there are strong theoretical reasons to expect that racially resentful Whites will react more negatively to Black candidates expressing anger—particularly when that anger targets racial disparities. Our framework suggests that racially prejudiced Whites perceive such emotional displays as a threat to the racial status quo. This threat may be especially pronounced when it comes from a Black woman, whose expression of racialized anger activates both racial and gender-based stereotypes, compounding the penalty she receives. Across all models, racial resentment is a strong and consistent negative predictor of vote likelihood. White respondents with higher levels of racial resentment are significantly less likely to support any of the four candidates, with coefficients ranging from −0.32 to −0.75 (all p < .01). Notably, this includes even White candidates, suggesting that racial resentment may broadly reflect a resistance to Democratic candidates, regardless of the race of the candidate. However, the most notable treatment effects appear in the model predicting vote likelihood for the Black woman candidate. In this model, exposure to an angry emotional cue significantly increases support for the candidate ( b = 0.25, p < .01), suggesting that expressing anger—often considered politically risky for women and candidates of color—may, in this instance, enhance candidate appeal. However, this effect is significantly moderated by racial resentment. Among respondents high in racial resentment, the positive impact of the “Angry Black Woman” cue reverses ( b = −0.51, p < .01), and a similar pattern emerges for the “Angry + Race” cue ( b = −0.31, p < .10). These interactions indicate that while emotional expressiveness can enhance electoral support for marginalized candidates, its effectiveness is highly conditional on the racial attitudes of the electorate. For the remaining candidate profiles—White man, Black man, and White woman—there is little evidence that anger cues significantly influence vote likelihood, either directly or through interaction with racial resentment. This suggests that Black women candidates are uniquely penalized for emotional displays of anger among racially resentful Whites. Subsequently, we estimate the models in which we interact the treatment conditions with racial resentment, separately for White men and White women. These results are displayed in Table 2, with the first four columns of Table 2 presenting the results for White male respondents, and the latter four columns presenting the results for White female respondents. Considering that we are interested in the intersection of race and gender, it only makes sense to examine whether the effects that we see among racially resentful Whites are being driven by either men or women. Previous research, for example, has shown that White men are especially receptive to explicit racial cues (Hutchings, Walton, Benjamin 2010), so it is plausible that racially resentful White men are more likely than racially resentful White women to penalize a Black woman candidate for an emotional display of anger. Table 2. Linear regression models estimating vote likelihood for each candidate interacted with racial resentment for White male respondents (models 1-4), and White female respondents (models 5-8). Treatment conditions include Angry' and Racialized Anger. Dependent variables represent likelihood of voting for the White Man (WM), Black Man (BM), Black Woman (BW), and White Woman (WW). Models do not include controls (see Appendix B Table 2 for balance tests). Vote Likelihood by Experimental Condition and Racial Resentment (White Respondents, Subset by Gender) Male Respondents Female Respondents Dependent variable: Black Woman Black Man White Woman White Man Black Woman Black Man White Woman White Man (1) (2) (3) (4) (5) (6) (7) (8) Angry BW 0.16 0.44 *** (0.10) (0.14) Angry+Race BW 0.14 0.18 (0.10) (0.14) Angry BM 0.05 -0.20 (0.10) (0.12) Angry+Race BM -0.05 -0.01 (0.11) (0.12) Angry WW 0.06 -0.06 (0.11) (0.14) Angry+Race WW 0.13 0.04 (0.11) (0.14) Angry WM -0.01 0.04 (0.10) (0.13) Angry+Race WM -0.05 -0.37 ** (0.10) (0.16) Racial Resentment -0.47 *** -0.56 *** -0.69 *** -0.73 *** 0.02 -0.39 ** -0.43 ** -0.70 *** (0.16) (0.16) (0.19) (0.17) (0.20) (0.17) (0.19) (0.18) Angry BW*RR -0.48 ** -0.82 *** (0.23) (0.28) Angry+Race BW*RR -0.60 ** -0.30 (0.23) (0.28) Angry BM*RR -0.16 0.30 (0.22) (0.25) Angry+Race BM*RR -0.06 -0.13 (0.25) (0.25) Angry WW*RR -0.15 0.15 (0.25) (0.29) Angry+Race WW*RR -0.38 -0.05 (0.26) (0.28) Angry WM*RR -0.04 0.06 (0.23) (0.27) Angry+Race WM*RR -0.02 0.91 *** (0.23) (0.34) Constant 0.81 *** 0.85 *** 0.85 *** 0.87 *** 0.53 *** 0.80 *** 0.71 *** 0.80 *** (0.07) (0.07) (0.09) (0.07) (0.10) (0.08) (0.09) (0.08) Observations 267 253 267 282 354 334 339 329 R 2 0.25 0.16 0.23 0.19 0.06 0.05 0.04 0.08 Adjusted R 2 0.23 0.15 0.21 0.18 0.05 0.04 0.02 0.06 Note: * p<0.1; ** p<0.05; *** p<0.01 We begin by looking at the coefficient for racial resentment across all four candidate types, among White male respondents. The coefficient for racial resentment is substantively large and statistically significant, regardless of the race of the candidate. In fact, among White male respondents, the negative effect of racial resentment on vote likelihood is the largest for the White candidates. Perhaps racially resentful White men perceive White Democratic candidates as “betraying” their presumed shared racial group interests, but this is just speculation on our part. Of particular interest to us, of course, is whether racially resentful White men are uniquely penalizing the Black woman candidate when she expresses anger. The negative and substantively large coefficients on the interaction terms for racial resentment and the treatment conditions indicate that racially resentful White men are indeed, negatively penalizing the Black woman candidate when she expresses anger. First, the coefficient of -0.48 for the interaction between racial resentment and exposure to the “Angry Black Woman” condition indicates that going from no racial resentment (a racial resentment score of zero) to the maximum level of racial resentment (one), reduces the likelihood of voting for the Black woman by nearly half of one point on a 0 to 1 scale (p < .05). In addition, when we consider the interaction of racial resentment with exposure to the Black woman candidate who is angry about racial injustice, the reduction in likely vote is even greater, with a coefficient of -0.60 (p < .05). Thus, anger about racial disparities is especially off-putting to racially resentful White men. It is also noteworthy that the Black woman candidate is the only candidate for whom racial resentment significantly moderates the effect of the anger treatments. In short, she is uniquely and disproportionately penalized by racially resentful White men—particularly when her anger is linked to racial disparities. We now turn to analyzing the impact of racial resentment among White women respondents (the latter four columns of Table 2), and whether racially resentful White women, similar to their White male counterparts, also uniquely and disproportionately penalize the Black woman candidate. The results indicate that is also the case. However, the results are quite nuanced. When we consider the interaction between racial resentment and the Black Woman Angry condition, the coefficient on the interaction term indicates that the Black woman candidate who is angry receives a steep penalty of -0.82 (p <.01). In other words, moving from a racial resentment score of 0 to a racial resentment score of 1, is associated with a reduction of -0.82, which is nearly the entire range of the likely vote scale. Also, of note, however, is that among the White women with racial resentment scores of zero, the angry Black woman candidate is highly rewarded (.44, p < 0.01). In sum, there is a large divergence in the vote preferences of White women who are low in racial resentment from the vote preferences of White women who are high in racial resentment, which perhaps is to be expected. Women at the low end of the racial resentment scale handsomely reward non-racialized anger from the Black woman candidate and women at the high end of the racial resentment scale severely penalize non-racialized anger from the Black woman candidate. However, will we observe a similar divergence in vote preferences among White women, when the Black woman candidate’s anger is about racial disparities? Racially liberal women, if anything, should be favorably inclined to vote for a Black woman candidate who is indignant about racial disparities that are negatively affecting the Black community. Conversely, we might expect that White women who are high in racial resentment will penalize the Black woman candidate in the racialized anger condition. Surprisingly, the pattern of results indicates that rather than a sharp divergence in the likely vote of White women low in racial resentment and White women high in racial resentment, the gap between the two groups actually narrows when exposed to the Black woman candidate in the racialized anger condition. Among White women respondents with a racial resentment score of zero, exposure to the Angry * Race Black Woman is statistically indistinguishable from zero (b=0.18, p =.19), while racial resentment moderates exposure to the Angry*Race Black Woman condition in the opposite direction (b=-.30, p =0.29). White female respondents who are racially resentful already muster up so much dislike for the Black woman as a political candidate that there is little movement left. In short, there is no sharp divergence between low racial resentment White women and high racial resentment White women. This pattern suggests that anger about racial disparities disrupts the boost the Black woman candidate receives from those white female voters predisposed to be more supportive (low racial resentment respondents). This finding suggests the fragility of gender solidarity when anger is racialized. It also complicates findings from recent work suggesting that in an era of heightened racial strife in the post-Obama era racially liberal Whites are showing increased support for Black political figures as part of their desire to combat racism (Mikkelborg 2025). While our results affirm that racially liberal Whites may indeed be more supportive of Black candidates at baseline, this support appears to be conditional. In particular, racialized anger emerges as a threshold that some ostensibly supportive voters are unwilling to cross. A more charitable interpretation of these results, however, is that racially liberal White women may penalize the Black woman candidate when she is angry about racial disparities, not because they reject the candidate’s message, but because they perceive her racialized anger as politically risky—a sign that she lacks strategic savvy, mainstream appeal, or broader electability. From this perspective, the vote penalty is not necessarily a rejection of the candidate's message, but a judgment about how it will be received by others (see also Hassell and Visalvanich 2024). Therefore, we explore whether the vote penalty observed among low racial resentment White women for the Black woman expressing racialized anger might be explained by perceptions of her as less electable. While our survey does not include a direct measure of electability, we asked respondents to indicate whether words such as “competent” and “unqualified,” described the candidates, which we use as proxies for perceived electability. Subsequently, we conducted a mediation analysis among White women, focusing on those at the lowest and highest quartiles of racial resentment, to assess whether perceptions of electability help explain the relationship between candidate anger and vote choice. The mediators were perceptions that the candidate was unqualified or competent . The results provide limited evidence that these perceptions of electability explain differences in vote likelihood. Among low racial resentment White women, exposure to the Black woman expressing racialized anger slightly increased perceptions that she was unqualified , which in turn very modestly reduced their likelihood of voting for her (ACME = –0.022, p < .10) (See Appendix C.1). However, perceptions of competence did not mediate the effect, and there were no significant direct effects of the treatment on vote likelihood. For high racial-resentment White women, neither mediator was significant. Altogether, these results indicate that while perceptions of being unqualified play a minor role in shaping low resentment women’s reactions, they do not fully account for why low and high racial resentment White women respond similarly to the Black woman’s racialized anger. Rather, the similarity appears to arise from different mechanisms that converge on comparable vote likelihoods: low resentment women exact a small electability penalty, while high resentment women’s baseline opposition leaves little room for additional change. Overall, the results for White respondents indicate that when courting White voters, it is risky for a Black woman candidate to express anger—particularly about race. Among racially resentful White men, a Black woman’s expression of racialized anger results in a sharp electoral penalty, reducing her vote likelihood by over half a point on a 0–1 scale ( b = –0.60 , p < .05). Even non-racialized anger is punished ( b = –0.48 , p < .05), underscoring that anger is politically costly for Black women in the eyes of White men with the highest levels of racial resentment. Among White women, the patterns are more nuanced. Racially liberal White women reward the Black woman candidate when she expresses non-racialized anger ( b = 0.44 , p < .01), but this support evaporates when her anger is framed around racial disparities—the interaction with racial resentment shows no statistically significant difference from zero in that condition. In short, solidarity from White women is fragile, and emotional expression—especially when tied to race—can undermine that support. We do not find support for H1, which predicted that the Black woman candidate would lose electoral support from White voters overall when she expressed anger, particularly racialized anger. However, H2 is supported: higher levels of racial resentment are associated with significantly reduced support for the Black woman candidate when she expresses anger–especially racialized anger. Black respondents: Counterintuitive influences of intersectional consciousness and community nationalism Thus far, we have focused on how White respondents react to candidate displays of anger, conditional on the candidate’s race and gender. In this section, we turn our attention to Black respondents. Table 3 displays the results of several regression models in which we estimate vote likelihood across the various experimental conditions. Here we test H3, which posits that Black respondents will express greater support for Black candidates, relative to White candidates, when they express anger about racial disparities. Among Black respondents, we find no evidence that expressions of anger—racialized or otherwise—increase support for Black candidates. Both the Black man and Black woman candidates receive high baseline vote likelihoods (0.68 and 0.72, respectively), leaving little room for emotional cues to boost support further—a pattern consistent with a ceiling effect. By contrast, expressions of racialized anger by White candidates significantly increase support among Black respondents: vote likelihood rises for both the White man (b = 0.07, p < .05) and White woman (b = 0.07, p < .05) when their anger is framed around racial disparities, but it must be noted that their baseline vote likelihoods were relatively low (0.53 and 0.60, respectively). These results suggest that while anger is normatively accepted or expected from Black candidates, it can function as a signal of allyship when expressed by White candidates. Thus, the ingroup reward predicted in H3 does not materialize. Table 3. Linear regression models estimating vote likelihood for each candidate for Black respondents. Treatment conditions include Angry' and Racialized Anger. Dependent variables represent likelihood of voting for the White Man (WM), Black Man (BM), Black Woman (BW), and White Woman (WW). Models do not include controls (see Appendix B Table 2 for balance tests). Vote Likelihood by Experimental Condition (Black Respondents) Dependent variable: Black Woman Black Man White Woman White Man (1) (2) (3) (4) Angry BW -0.02 (0.03) Angry+Race BW -0.003 (0.03) Angry BM 0.01 (0.03) Angry+Race BM 0.04 (0.03) Angry WW 0.01 (0.03) Angry+Race WW 0.07 ** (0.03) Angry WM -0.02 (0.03) Angry+Race WM 0.07 ** (0.03) Observations 601 624 605 585 R 2 0.001 0.004 0.01 0.02 Adjusted R 2 -0.003 0.0004 0.01 0.01 Note: * p<0.1; ** p<0.05; *** p<0.01 To better understand variation in support among Black respondents, we next examine whether intersectional consciousness (IC) moderates reactions to candidates’ emotional expressions—particularly racialized anger. Intersectional consciousness reflects an awareness that overlapping identities such as race and gender create distinct experiences of oppression. We expected that Black respondents high in IC would be especially supportive of the Black woman candidate when she expressed anger about racial injustice (H4a). However, the results run counter to this expectation. Table 4 presents the results of several regression models in which we estimate vote likelihood as a function of intersectional consciousness (Columns 1-4) and as a function of community nationalism (Columns 5-8) (H4b). Table 4. Linear regression models estimating vote likelihood for each candidate interacted with intersectional consciousness (models 1-4), and community nationalism (models 5-8) for Black respondents. Treatment conditions include Angry' and Racialized Anger. Dependent variables represent likelihood of voting for the White Man (WM), Black Man (BM), Black Woman (BW), and White Woman (WW). Models do not include controls (see Appendix B Table 2 for balance tests). Vote Likelihood by Experimental Condition as a Function of Intersectional Consciousness and Community Nationalism (Black Respondents) Intersectional Consciousness Community Nationalism Dependent variable: Black Woman Black Man White Woman White Man Black Woman Black Man White Woman White Man (1) (2) (3) (4) (5) (6) (7) (8) Angry BW -0.01 0.03 (0.06) (0.10) Angry+Race BW 0.07 0.12 (0.06) (0.10) Angry BM -0.01 -0.06 (0.06) (0.09) Angry+Race BM 0.01 -0.11 (0.06) (0.09) Angry WW -0.06 -0.02 (0.06) (0.10) Angry+Race WW -0.09 -0.01 (0.06) (0.10) Angry WM 0.04 0.09 (0.06) (0.10) Angry+Race WM 0.07 -0.01 (0.07) (0.10) Intersectional Consciousness 0.22 *** 0.11 * -0.04 0.14 ** (0.06) (0.06) (0.06) (0.06) Angry BW*IC -0.01 (0.08) Angry+Race BW*IC -0.11 (0.08) Angry BM*IC 0.03 (0.08) Angry+Race BM*IC 0.06 (0.08) Angry WW*IC 0.10 (0.08) Angry+Race WW*IC 0.24 *** (0.08) Angry WM*IC -0.10 (0.09) Angry+Race WM*IC -0.01 (0.09) Community Nationalism 0.46 *** 0.30 *** 0.27 *** 0.22 ** (0.09) (0.09) (0.09) (0.09) Angry BW*CN -0.06 (0.13) Angry+Race BW*CN -0.17 (0.13) Angry BM*CN 0.11 (0.12) Angry+Race BM*CN 0.24 ** (0.12) Angry WW*CN 0.02 (0.13) Angry+Race WW*CN 0.13 (0.13) Angry WM*CN -0.16 (0.14) Angry+Race WM*CN 0.10 (0.13) Constant 0.57 *** 0.61 *** 0.62 *** 0.44 *** 0.39 *** 0.46 *** 0.40 *** 0.38 *** (0.04) (0.04) (0.04) (0.05) (0.07) (0.07) (0.07) (0.07) Observations 601 624 605 585 601 624 605 585 R 2 0.05 0.03 0.03 0.03 0.08 0.12 0.07 0.04 Adjusted R 2 0.04 0.02 0.02 0.02 0.08 0.11 0.06 0.04 Note: * p<0.1; ** p<0.05; *** p<0.01 While IC is positively associated with baseline support for Black candidates, it does not significantly amplify vote likelihood in response to either anger condition. Among respondents high in IC, support for the Black woman candidate is already extremely high—0.80 on a 0–1 scale—leaving little room for upward movement. Neither the Angry nor Angry+Race treatment significantly interacts with IC. Instead, IC appears to sustain consistently high support for the Black woman candidate across anger displays, rather than serving as a moderator of anger displays, which runs contrary to our expectations in H4a. For the Black man candidate, baseline support is also strong, though slightly lower, at 0.71. These levels of support suggest a ceiling effect: those most attuned to intersectional marginalization already view the Black woman (and to a lesser extent, the Black man) as highly qualified and electable, leaving little room for emotional expressions to further boost support. Indeed, the interaction between intersectional consciousness and the Angry+Race condition is negative and statistically insignificant, indicating that racialized anger neither enhances nor undermines candidate evaluations among this ideologically aligned subgroup. Moreover, in the non-racialized anger condition, Black respondents low in IC exhibit lower support for the Black woman candidate than for the White woman candidate. For the White woman candidate, IC significantly moderates responses to racialized anger, such that high-IC respondents are more likely to support her when she expresses anger about racial injustice. A one-unit increase in Black participants’ intersectional consciousness results in a 24% increase in support for the WW when she expresses anger about racial injustice ( p < 0 . 01). This suggests that racialized emotional expression by a White woman may signal political alignment or allyship to Black respondents high in intersectional awareness. We also examine whether community nationalism moderates support for candidates who express racialized anger (H4b). Community nationalism reflects a strong commitment to racial group uplift, pride, and solidarity, and may shape how Black respondents interpret candidates’ emotional displays. Among respondents high in community nationalism, baseline support for the Black man candidate is substantial—0.73—while support for the Black woman is even higher at 0.77. Yet the effects of emotional expression diverge by candidate gender. In the Angry+Race condition, Black respondents high in community nationalism are significantly more likely to support the Black man candidate ( b = 0.24, p < .05 ), suggesting that racialized anger from a male candidate is read as a form of advocacy on behalf of the group. For the Black woman candidate, however, the interaction is negative and statistically significant ( b = –0.17, p <.10 ), indicating that community nationalism does not translate into increased support for her when she expresses racialized anger. These results highlight how ingroup solidarity cues are filtered through gendered expectations, with Black men receiving a clearer benefit from expressing racialized anger. This may reflect persistent gendered expectations about who speaks for the racial group and how. In other words, racial solidarity does not appear to extend equally across gender lines, even within the Black electorate. No meaningful shifts are observed for the White woman or White man candidates across any of the conditions, with or without the CN interaction, suggesting that community nationalism has little bearing on how Black respondents evaluate White candidates—regardless of their emotional tone. Electability or Antipathy? Exploring Perceived Candidate Traits The question remains: what drives these electoral decisions, particularly among Black voters? Extant scholarship indicates that a candidate’s perceived electability can play a greater role in voters’ decisions than the candidate’s ideological proximity to the voter (Simas 2017). This is especially relevant in light of recent work revealing that Democratic primary voters perceive Black women candidates to be progressive, yet less electable (Chen and Sorensen 2025), thus eroding the electoral boost that Black women should receive for their perceived progressiveness. Are our results reflecting a similar calculus, specifically among Black voters? Is their withholding of electoral support for the BW candidate a reflection of their belief that a Black woman who expresses anger about racial injustice cannot win a statewide election? Or is that withholding rooted in negative sentiment toward the BW candidate? While the data do not allow us to provide definitive answers to these questions, we seek generative insight from examining how the treatment conditions affect participants’ perceptions of candidate characteristics such as intelligence and likeability. Our results, displayed in Figures 1and 2 in the Appendix C.3, indicate that across both White and Black respondents, anger displays, regardless of whether they are racialized, produce small and statistically negligible shifts in trait ratings. This suggests that the electoral penalties observed—particularly for the Black woman candidate when she expresses racialized anger—are not driven by changes in respondents’ trait-based evaluations. Instead, both groups appear to maintain consistent assessments of her competence and character even as their likelihood of voting for her decreases in the racialized anger condition. While prior research points to electability concerns as a potential mechanism in voters’ decision-making, our data do not directly measure perceptions of broader electoral viability; thus, we cannot determine whether strategic considerations or other unmeasured factors underlie these voting patterns. What the results clearly show is that anger expressions—especially when tied to race—affect electoral support without meaningfully altering underlying trait evaluations. Discussion and Conclusion This paper contributes to our understanding of how candidate race, gender, and anger, interact to shape voter evaluations in the contemporary U.S. electorate. Building on theories of racialized and gendered emotional expression, we fielded a large-scale survey experiment to assess whether Black women candidates are uniquely penalized for expressing anger, and whether the racial content of that anger further influences voter reactions. Our findings offer a nuanced picture of how intersectional identities and anger are interpreted by racially diverse electorates and by voters with varying ideological orientations. Among White respondents, we find that candidate anger does not generate uniform backlash. Instead, the content and source of the anger matter. Notably, Black male candidates are significantly penalized for expressing racialized anger, experiencing a seven-point drop in vote likelihood. In contrast, Black women are not penalized overall for expressing anger, racialized or not, suggesting that in the aggregate, they do not face a baseline anger penalty. However, this general null finding masks meaningful variation by racial attitudes: among racially resentful White voters, Black women who express anger, especially anger about racial injustice, experience sharp declines in support. These findings affirm our theory that racialized anger is particularly consequential among racially resentful White voters, who are inclined to view such expressions as threatening to the racial status quo. Among White women, we observe a striking divergence based on racial resentment. Racially liberal White women reward the Black woman candidate when she expresses non-racialized anger, while racially resentful White women severely penalize her. Yet even among racially liberal White women, this support is fragile: once the Black woman’s anger becomes racialized, the boost disappears. This erosion of support underscores the conditionality of gender-based solidarity in contexts where race is salient. Turning to Black respondents, our findings defy the prediction that Black candidates would be rewarded for expressing racialized anger (H3). Instead, we find no treatment effects for either the Black woman or Black man candidate, likely the result of ceiling effects, as both enjoy high baseline support. Interestingly, racialized anger increases support for White candidates, suggesting that Black respondents may interpret such expressions as a signal of racial allyship. However, within the Black electorate, we also observe an important asymmetry: Black community nationalists reward the Black male candidate for expressing racialized anger but penalize the Black woman for the same behavior. This divergence suggests that emotional constraints on Black women do not operate solely through White audiences; they also reflect intra-racial expectations about which candidates are permitted to embody racial grievance and which are not. Intersectional consciousness and community nationalism do not significantly moderate treatment effects overall, but both are strongly and positively associated with baseline support for Black candidates, indicating that racialized anger may be expected rather than uniquely mobilizing among these voters. In sum, these findings reveal a racially asymmetric emotional double bind. White male candidates remain emotionally unencumbered: their expressions of anger—racialized or not—do not reduce support. By contrast, Black candidates, and especially Black women, must navigate a precarious emotional terrain where support is contingent on both the identity of the voter and the perceived appropriateness of anger. Our study shows that anger is not universally disqualifying for Black women—but neither is it universally safe. While some constituencies respond favorably, the expression of racialized anger remains politically risky, particularly among racially resentful White voters, racially liberal White women, and certain segments within the Black electorate. The political implications of our findings are especially acute for Black women navigating majority-White electorates. Anger has long served as a powerful political resource, used effectively by white male politicians—most notably President Donald Trump. Yet Black women are uniquely denied this political tool. This asymmetry means that Black women candidates, including those who have sought statewide or national office such as Kamala Harris, must perform a narrow emotional balancing act: projecting resolve and competence without crossing an invisible threshold that risks backlash. The result is a structural disadvantage that limits their ability to respond forcefully to racial injustice, mobilize supporters, or claim the authority that anger affords their White male counterparts. More broadly, when certain candidates cannot safely express anger, the democratic system loses an important mode of political communication—one that has historically fueled accountability, protest, and transformative change. Our findings thus make clear that emotional inequality—who may safely express anger, and who may not—constitutes a real, measurable barrier to Black women’s political viability. As American politics continues to grapple with questions of race, gender, and authenticity, candidates from marginalized backgrounds must continually calibrate their anger. Future research should explore how these dynamics unfold in higher-information contexts, across partisan lines, and in real-world electoral settings, where candidates increasingly must choose between expressing moral outrage and maintaining electoral viability. Declarations Ethics Statement This study was reviewed and approved by the Institutional Review Board of [University]. All participants provided informed consent prior to participation. Funding Information This study was supported by funding from [University]. Author Contribution L.S.D. led the development of the research question and contributed to the research design, theoretical development, and manuscript preparation. D.P. contributed to the research design, conducted portions of the statistical analysis, and contributed to manuscript preparation. I.J. executed the statistical analyses and contributed to data preparation and interpretation of results. A.B. contributed to the research design and substantive feedback on manuscript drafts. K.D. provided feedback on the manuscript and contributed to the preparation of the manuscript for submission. All authors reviewed and approved the final manuscript. Data Availability The data, code, and materials required to replicate the analyses in this article will be publicly available in the Harvard University Dataverse upon publication. The repository will include the preregistration, replication code, and supporting documentation necessary to reproduce all tables and figures in the manuscript. References Alexander-Floyd, Nikol G. "" We Shall Have Our Manhood": Black Macho, Black Nationalism, and the Million Man March." Meridians: feminism, race, transnationalism 3.2 (2003): 171-203. Banks, A. J., & White, I. K. (2024). The Anger Rule: Racial Inequality and Constraints on Black Politicians . Cambridge University Press. https://doi.org/10.1017/9781009275255 Banks, A. J., White, I. 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Stereotyping or Projection? How White and Black Voters Estimate Black Candidates’ Ideology. Political Psychology , 37 (2), 147–163. Livingston, R. W., & Pearce, N. A. (2009). The Teddy-Bear Effect: Does Having a Baby Face Benefit Black Chief Executive Officers? Psychological Science , 20 (10), 1229–1236. https://doi.org/10.1111/j.1467-9280.2009.02431.x Lumet, S. (Director). (1976). Network [Film]. Metro-Goldwyn-Mayer. Mikkelborg, A. C. (2025). White Democrats’ growing support for Black politicians in the era of the “Great Awokening”. American Political Science Review , 1-19. Phoenix, D. L. (2019). The Anger Gap: How Race Shapes Emotion in Politics . Cambridge University Press. https://doi.org/10.1017/9781108641906 Rudman, L. A., & Glick, P. (1999). Feminized management and backlash toward agentic women: The hidden costs to women of a kinder, gentler image of middle managers. Journal of Personality and Social Psychology , 77 (5), 1004–1010. https://doi.org/10.1037/0022-3514.77.5.1004 Sesko, A. K., & Biernat, M. (2010). Prototypes of race and gender: The invisibility of Black women. Journal of Experimental Social Psychology , 46 (2), 356–360. https://doi.org/10.1016/j.jesp.2009.10.016 Simas, E. N. (2017). The effects of electability on US primary voters. Journal of Elections, Public Opinion and Parties , 27 (3), 274-290. Stephens-Dougan, L. (2020). Race to the Bottom: How Racial Appeals Work in American Politics . University of Chicago Press. https://press.uchicago.edu/ucp/books/book/chicago/R/bo50271574.html Strawbridge, M. G., Clark, C. J., Mahoney, A. M., & Brown, N. E. (2025). Rhetorical Promises: Gender Diversity Among Congressional Black Caucus Members’ Representation on Twitter. Political Communication , 42 (3), 509-526. https://doi.org/10.1080/10584609.2025.2454629 Valentino, N. A., Brader, T., Groenendyk, E. W., Gregorowicz, K., & Hutchings, V. L. (2011). Election Night’s Alright for Fighting: The Role of Emotions in Political Participation. The Journal of Politics , 73 (1), 156–170. https://doi.org/10.1017/S0022381610000939 Van Kleef, G. A., & De Dreu, C. K. W. (2010). Longer-term consequences of anger expression in negotiation: Retaliation or spillover? Journal of Experimental Social Psychology , 46 (5), 753–760. https://doi.org/10.1016/j.jesp.2010.03.013 Wamble, J. J. (Ed.). (2025). We Choose You. In We Choose You: How Black Voters Decide Which Candidates to Support (pp. i–ii). Cambridge University Press. https://www.cambridge.org/core/books/we-choose-you/we-choose-you/17B8E7D575111BC9D87615A64FD70A42 Webster, S. W. (2020). American Rage . Cambridge University Press. Footnotes All candidates in the experiment are portrayed as Democrats, a design choice intended to make expressions of anger about racial disparities plausible. The pre-analysis plan can be accessed in our supplementary materials. Additional Declarations No competing interests reported. Supplementary Files DoubleJeopardyAppendix12525.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8584399","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":582630856,"identity":"80d72695-b2bc-4977-837c-721bda9fa243","order_by":0,"name":"LaFleur Stephens-Dougan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIie3NMYvCMBTA8RcKurRmTfFLVAQnsV8lJeB6guAkUjjIdO4V9Ts4Oac8qJuzoByKq4LiIoicZxXFwdDRIX8IPML78QBMpg+sAGSlrgP9f+rxbWtIDiwv3XTDG2HZiXc/kYHkEdRe/vrleXeFjXa1Q4shWW6lhtgC4p5sBuPFxMMoqTN3oKzSUEfgS6EjOa/M6oBOiMyb8VzR0RG6BjxL7pejlPxdSf6kJUwAEsnJiKVEpVcsPVlD/DPlQbRIAO1EuL0o+HYH0/eEUkGWxxb3aV9aB7tdo5SJeLdpvSfP2GMiYZb9F2IymUym1y6BbU+pOG1oBgAAAABJRU5ErkJggg==","orcid":"","institution":"Princeton University","correspondingAuthor":true,"prefix":"","firstName":"LaFleur","middleName":"","lastName":"Stephens-Dougan","suffix":""},{"id":582630857,"identity":"a37abc60-e06a-4e5e-87eb-bc5dbc47df7c","order_by":1,"name":"Davin Phoenix","email":"","orcid":"","institution":"University of California, Irvine","correspondingAuthor":false,"prefix":"","firstName":"Davin","middleName":"","lastName":"Phoenix","suffix":""},{"id":582630862,"identity":"945e663b-7d7f-496b-9952-f2871e3a81a7","order_by":2,"name":"Isaiah Johnson","email":"","orcid":"","institution":"Princeton University","correspondingAuthor":false,"prefix":"","firstName":"Isaiah","middleName":"","lastName":"Johnson","suffix":""},{"id":582630863,"identity":"195b3704-53e3-4ee5-a23a-0c47c027bac6","order_by":3,"name":"Andrea Benjamin","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Benjamin","suffix":""},{"id":582630864,"identity":"e9ea3e3e-4e1d-4b22-9316-2dea3f9ddd8e","order_by":4,"name":"Kaiyla Dartey","email":"","orcid":"","institution":"Princeton University","correspondingAuthor":false,"prefix":"","firstName":"Kaiyla","middleName":"","lastName":"Dartey","suffix":""}],"badges":[],"createdAt":"2026-01-12 17:38:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8584399/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8584399/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102297481,"identity":"83033014-a144-408f-b3b4-dd2269a7e5d0","added_by":"auto","created_at":"2026-02-10 10:27:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1161766,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8584399/v1/b59f32d1-7a06-44c3-b861-548a7060dafb.pdf"},{"id":102218199,"identity":"ade172ad-00d3-4e94-88f9-c1ce3eb044fe","added_by":"auto","created_at":"2026-02-09 13:19:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":280969,"visible":true,"origin":"","legend":"","description":"","filename":"DoubleJeopardyAppendix12525.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8584399/v1/dd31569bc2c7eb7d1eab0576.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Double Jeopardy: Punitive Responses to Black Women Candidates’ Anger","fulltext":[{"header":"Introduction","content":"\u003cp\u003e\u0026ldquo;I\u0026rsquo;m mad as hell and I\u0026rsquo;m not going to take it anymore!\u0026rdquo; In this famous line from the movie, \u0026ldquo;Network\u0026rdquo; (1976), news anchor Howard Beale delivers a passionate on-air rant, channeling public frustration into a call to action. \u0026nbsp;The quote endures because it captures the political potential of anger, an emotion that can mobilize citizens, sharpen attention, and signal resolve (Brader 2005; Webster 2020). Yet anger is also politically risky. \u0026nbsp; Candidates who display anger may be judged as unprofessional, unlikable, or unfit for leadership (Brooks 2013). \u0026nbsp; Crucially, these risks may not always be distributed evenly. Some scholars argue that for women, anger violates gendered expectations of warmth and communality, rendering female candidates particularly vulnerable to backlash and trait-based attacks that challenge stereotypically feminine strengths (Eagly \u0026amp; Karau 2002; Rudman \u0026amp; Glick 1999). \u0026nbsp;For Black candidates, anger\u0026ndash;especially when expressed about racial injustice\u0026ndash;reinforces stereotypes of aggression and fuels perceptions of racial threat (Banks and White 2024). In fact, many observers have long suspected that minority politicians must make a careful effort to not appear too strident or bellicose, to avoid losing the support of white voters (Phoenix 2019; Stephens-Dougan, 2020). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eReal-world examples illustrate how these constraints operate. Commentators often attributed President Obama\u0026rsquo;s famously restrained demeanor to fears of being cast as the \u0026ldquo;angry Black man\u0026rdquo; (CNN 2010; Phoenix 2019). More recently, observers noted that former Vice President Kamala Harris emphasized \u0026ldquo;joy\u0026rdquo; during her presidential campaign, in part because anger was perceived to be politically unavailable to her (Blake 2024). These examples highlight how both Black men and Black women may have to navigate the narrow bounds of emotional expression but leave open whether Black women face distinct electoral consequences when they express anger. \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe argue that Black women face a \u0026ldquo;double bind\u0026rdquo; in evaluations of political anger: they are penalized for violating gendered expectations surrounding emotional expression, while also facing heightened scrutiny when anger is racialized. Importantly, this bind cannot be understood by considering race and gender separately, as the expectations applied to Black women differ in kind from those applied to either group alone. Yet to date, research has not explored the nature or extent of this constraint. Existing work has focused primarily on White women and Black men (Brooks 2013; Banks et al. 2019), often collapsing across race and gender or excluding Black women altogether. Moreover, many studies rely almost exclusively on White respondents, leaving open the question of how Black voters evaluate Black women candidates who express anger.\u003c/p\u003e\n\u003cp\u003eWe address these gaps using a preregistered survey experiment with approximately 5,000 respondents, evenly split between White and Black Americans. \u0026nbsp;Respondents evaluated a hypothetical candidate running for the U.S. Senate, whose race (Black or White), gender (male or female), and emotional expression were experimentally varied. \u0026nbsp;Candidates either expressed no anger, anger about the economy, or anger about racial disparities. \u0026nbsp;This design allows us to identify whether Black women are uniquely penalized for expressing anger, and whether reactions differ across White and Black voters depending on their racial and gender attitudes.\u003c/p\u003e\n\u003cp\u003eOur findings reveal a consistent double jeopardy for Black women. \u0026nbsp;White respondents high in racial resentment impose the sharpest penalties, reducing support for an angry Black woman by half the vote likelihood scale in the general anger condition (-0.51, p\u0026lt;.01) and when her anger is racialized (-0.31, p\u0026lt;.10). Among Black respondents, support is also withheld. \u0026nbsp; Intersectionality conscious participants reward the White woman candidate but not the Black woman candidate for racial anger, while community nationalists reward the Black man candidate and penalize the Black woman candidate for the same expression. In short, this study demonstrates that anger is not a universally accessible political resource; instead, it uniquely disadvantages Black women, who incur sharp electoral penalties from White voters when they express anger and are unable to obtain the electoral benefits that comparable Black men and White women candidates receive from some Black voters.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAnger as a Conditional Political Resource\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAnger is often understood as one of the most politically powerful emotions. \u0026nbsp;Defined as a feeling of belligerence over a perceived slight or injustice, anger mobilizes participation, sharpens attention, and signals resolve and authenticity (Huddy, Feldman and Weber 2007; Neuman and MacKuen 2000; Valentino et al. 2011; Brader 2005; Banks, White and McKenzie 2019). Politicians who display anger can use it strategically to highlight threats and demonstrate leadership (Webster 2020). \u0026nbsp;Moreover, displays of anger can be advantageous for people in leadership positions (Van Kleef and De Dreu, 2010), as reflecting the emotions of their constituency engenders greater approval (Garcia and Stout, 2022).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eYet, despite anger being a political resource, it is not universally regarded as appropriate or effective; emotional expressions are filtered through racial and gendered hierarchies that determine whose anger is seen as legitimate (Phoenix 2019). \u0026nbsp;Previous research indicates that white men frequently benefit from anger displays, which are often interpreted as consistent with strength and leadership (Bickford 2011). \u0026nbsp;In contrast, for women candidates, anger violates gendered expectations. \u0026nbsp;Role congruity theory holds that voters evaluate women through the lens of communal stereotypes\u0026ndash;warmth, nurturance, and cooperation\u0026ndash;while leadership is associated with agency and assertiveness (Eagly and Karau 2002). \u0026nbsp;Women who display agentic behaviors or emotions such as anger risk backlash for being \u0026ldquo;unfeminine\u0026rdquo; or unlikeable (Rudman and Glick 1999).\u003c/p\u003e\n\u003cp\u003eFor Black candidates, anger carries different but equally constraining risks. \u0026nbsp;White voters frequently perceive Black politicians as more liberal (Lerman and Sadin 2016) and more beholden to Black interests than their White counterparts (Stephens-Dougan 2020). \u0026nbsp;Therefore, expressions of anger, particularly when directed at racial injustice, reinforce longstanding stereotypes of Black aggression and partiality toward their ingroup (Phoenix 2019; Stephens-Dougan 2020; Banks and White 2024). \u0026nbsp;Black men, in particular, are often penalized for anger, given its association with aggression and violence (Livingston and Pearce 2009). \u0026nbsp;Thus, we expect Black candidates to face sharper penalties than White candidates for expressing anger, especially among racially resentful voters.\u003c/p\u003e\n\u003cp\u003eBlack women candidates stand at the intersection of these expectations. A key insight from the literature is that stereotype profiles are not simply additive\u0026mdash;that is, voters do not evaluate Black women by combining stereotypes about Black people and stereotypes about women\u0026mdash;but are instead qualitatively distinct from the stereotypes associated with either category in isolation (Cassese 2019). As a result, voters hold a unique set of expectations about Black women that cannot be reduced to race or gender alone.\u003c/p\u003e\n\u003cp\u003ePrior work further suggests that the political costs of anger may be especially acute when the content of that anger aligns with gendered and partisan stereotypes. Cassese and Holman (2018) show that female candidates\u0026mdash;particularly Democratic women\u0026mdash;are uniquely vulnerable to stereotype-based attacks on issues closely associated with their party and gender, with negative electoral consequences that are substantially larger than those faced by male candidates.\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e1\u003c/sup\u003e Although their analysis focuses on voter responses to stereotype-consistent attacks, the underlying logic\u0026mdash;that penalties intensify when candidate behavior activates entrenched expectations about \u0026ldquo;appropriate\u0026rdquo; issue positions\u0026mdash;suggests that similar dynamics may apply when candidates themselves engage those issues. To the extent that a Black woman candidate\u0026rsquo;s expression of anger about racial injustice activates gendered and partisan stereotypes, racialized anger may therefore be especially costly for Black women candidates, even when comparable expressions carry fewer penalties for others.\u003c/p\u003e\n\u003cp\u003eAt the same time, Black women face competing pressures. As women, they may be penalized for violating gender norms of warmth and civility, and as Black candidates, for reinforcing racial stereotypes (Carr 2025). Yet their dual marginalization may also render them less prototypical of either group, potentially muting backlash under some conditions (Sesko and Biernat 2010). Together, these competing logics make Black women a critical test case for understanding when and how anger functions as a political resource.\u003c/p\u003e\n\u003cp\u003eIn addition to candidate identity, we expect responses to candidates\u0026rsquo; anger to vary based on voter identity. Among Black respondents, anger about racial injustice can serve as a cue of authenticity and solidarity, signaling a candidate\u0026rsquo;s commitment to shared concerns (Wamble 2024). Yet, these signals may not be interpreted uniformly. Black respondents high in intersectional consciousness\u0026ndash;the belief that Black women\u0026rsquo;s oppression stems from interlocking forces of racism and sexism\u0026ndash;may be especially inclined to reward Black women for expressing anger about racial inequality, seeing it as cultivating solidarity along both race and gender lines (Crowder et al. 2023; Greenwood 2008). In fact, recent research indicates that relative to Democratic Black men politicians, Democratic Black women politicians are indeed more likely to discuss substantive public policy issues and to frame them as raced \u003cem\u003eand\u003c/em\u003e gendered (Strawbridge et al. 2025). By contrast, Black respondents with strong community nationalist orientations\u0026ndash;emphasizing Black self-determination and often tied to patriarchal understandings of leadership\u0026ndash;may privilege Black men as the authentic bearers of racial grievance (Alexander-Floyd 2003).\u003c/p\u003e\n\u003cp\u003eWe also expect voter gender to shape responses to candidates\u0026rsquo; anger, especially racialized anger. \u0026nbsp;Gendered socialization influences how men and women perceive emotional expression. Men are often more accepting of anger from other men, viewing it as an appropriate display of strength, while judging women\u0026rsquo;s anger as less legitimate or overly emotional. \u0026nbsp;Women, by contrast, may be more sympathetic to anger expressed by women candidates, particularly when it reflects concerns about injustice or inequality. However, this sympathy is not guaranteed. Women may also enforce gendered norms that penalize female candidates for violating expectations of warmth and civility. Thus, we anticipate that voter gender in addition to voter race will condition evaluations of candidates who display anger.\u003c/p\u003e\n\u003cp\u003eIn short, anger is a conditional political resource. \u0026nbsp; For Black women in particular, its effectiveness depends on the expectations that voters bring to their evaluations. These dynamics suggest that White respondents high in racial resentment will penalize Black women most heavily, while both Black and White female respondents will exhibit variation in their reactions depending on their orientations toward race and gender \u0026nbsp;In sum, we contend that anger is not judged in a vacuum\u0026mdash;it is interpreted through expectations about who is \u0026ldquo;allowed\u0026rdquo; to express it and under what circumstances. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHypotheses\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBecause Black women candidates occupy a distinct position in voter\u0026rsquo; evaluative frameworks, shaped by expectations that cannot be understood by considering race and gender separately, we anticipate that Black women will face especially steep and unique penalties (i.e. double jeopardy). These penalties should be most pronounced among White voters who harbor higher levels of racial resentment. \u0026nbsp;Among Black voters, anger about racial injustice may serve as a cue of authenticity and solidarity, but responses are expected to diverge depending on the respondent\u0026rsquo;s orientation toward race and gender.\u003c/p\u003e\n\u003cp\u003eTherefore, our hypotheses (by race of respondent) are formally stated below:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eWhite Respondents\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eH1 (Black woman anger penalty):\u003cbr\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;White respondents will be less likely to support a Black woman candidate when she expresses anger\u0026mdash;especially racialized anger\u0026mdash;compared to both a neutral (non-angry) condition and to non-racialized anger.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eH2 (Racial resentment as moderator):\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e \u003cem\u003e\u0026nbsp;Among White respondents, higher levels of racial resentment will be associated with lower support for Black candidates who express anger, with the strongest negative effect observed for the Black woman candidate expressing racialized anger.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBlack Respondents\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eH3 (Ingroup emotional reward):\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e \u003cem\u003e\u0026nbsp;Black respondents will express greater support for Black candidates\u0026mdash;relative to White candidates\u0026mdash;when they express anger about racial disparities.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eH4a (Intersectional consciousness moderator):\u003cbr\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cem\u003eHigher intersectional consciousness will be associated with increased support for the Black woman candidate expressing racialized anger.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eH4b (Community nationalism moderator):\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHigher community nationalism will be associated with increased support for the Black man candidate expressing racialized anger, but not the Black woman.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eResearch Design\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe test our theoretical expectations using a preregistered survey experiment conducted in Spring 2021 with approximately 5,000 respondents recruited through Qualtrics, evenly divided between Black and White Americans.\u003ca href=\"#_ftn2\" name=\"_ftnref2\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e2\u003c/sup\u003e\u0026nbsp; The experiment employed a fully crossed 2 x 2 x 3 factorial design varying candidate race (Black/White), gender (man/woman), and emotional expression (no anger, nonracial anger about the economy, or racialized anger about racial disparities). \u0026nbsp; This design isolates the causal effects of candidate identity and anger type on vote likelihood and trait evaluations, and assesses how these effects are moderated by respondents\u0026rsquo; racial resentment, intersectional consciousness, community nationalism, and gender.\u003c/p\u003e\n\u003cp\u003eRespondents first completed questions on demographics, political orientations, and attitudes about race and gender. White participants answered the standard four-item racial resentment scale (Kinder and Sanders 1996), while Black participants completed measures of community nationalism and intersectional consciousness, along with related items on racial and gender attitudes. Participants were then randomly assigned to read a mock news article about a candidate running for the U.S. Senate, in which the candidate\u0026rsquo;s race, gender, and expression of anger were experimentally manipulated. The design replicates and extends Banks and White (2024) by incorporating gender as an additional identity dimension and by examining how reactions to candidate anger vary by respondents\u0026rsquo; own race and gender. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur primary dependent variable is\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003evote likelihood, measured on a seven-point scale (\u0026ldquo;How likely would you be to vote for [candidate]?\u0026rdquo;), rescaled from 0\u0026ndash;1 for analysis. \u0026nbsp;Secondary dependent variables capture stereotype-based trait attributions, including perceptions of competence and qualification, similarly rescaled. The main comparisons evaluate whether White respondents penalize the Black woman candidate who expresses anger, and whether these effects vary by racial resentment and gender (H1\u0026ndash;H2). Among Black respondents, we test whether support for Black candidates who express anger increases with intersectional consciousness (IC) and community nationalism (CN). IC was measured with a single item asking respondents which statement best reflected their view: (1) \u0026ldquo;Black women have suffered from both sexism within the Black movement and racism within the women\u0026rsquo;s movement,\u0026rdquo; (2) \u0026ldquo;Black women mostly suffer from the same types of problems as Black men,\u0026rdquo; (3) \u0026ldquo;Neither,\u0026rdquo; or (4) \u0026ldquo;Both.\u0026rdquo; Responses were scaled 0\u0026ndash;1, with higher values indicating greater IC. CN was measured as agreement (0\u0026ndash;1) with four statements emphasizing Black autonomy and control over economic and political life in predominantly Black communities.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWe begin by evaluating whether candidates are penalized for expressing anger\u0026mdash;and whether such penalties vary by the race and gender of the candidate, as well as the racial content of their anger. Our first step is to examine the main effects of the treatment conditions on vote likelihood. Columns 1\u0026ndash;4 of Table 1 present linear regression models estimating the likelihood of voting for each candidate type (Black woman, Black man, White woman, White man) across three conditions: Control (no anger), Angry (non-racial), and Angry+Race (racialized anger). This analysis allows us to assess whether candidates are evaluated differently depending on both who they are and the object of their anger. First, we test H1, which states: \u003cem\u003e\u0026ldquo;White respondents will be less likely to support a Black woman candidate when she expresses anger\u0026mdash;especially racialized anger\u0026mdash;compared to both a neutral (non-angry) condition and to non-racialized anger.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cem\u003eTable 1.\u0026nbsp;\u003c/em\u003eLinear regression models estimating vote likelihood for each candidate (models 1-4). \u0026nbsp;Models 5-8 present the conditional effect of racial resentment on vote likelihood\u003ca href=\"#_msocom_2\" language=\"JavaScript\" name=\"_msoanchor_2\"\u003e\u003c/a\u003e. \u0026nbsp;Models do not include controls (see Appendix B Table 2 for balance tests).\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eVote Likelihood by Experimental Condition and Racial Resentment (White Respondents)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eVote Likelihood Main Effects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eVote Likelihood as a Function of Racial Resentment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"8\"\u003e\n \u003cp\u003e\u003cem\u003eDependent variable:\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBlack Woman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBlack Man\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWhite Woman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWhite Man\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBlack Woman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBlack Man\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWhite Woman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWhite Man\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAngry BW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.25***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.03)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.08)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAngry+Race BW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.03)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.08)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAngry BM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.03)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.08)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAngry+Race BM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.07**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.03)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.08)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAngry WW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.03)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.08)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAngry+Race WW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.03)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.09)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAngry WM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.03)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.08)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAngry+Race WM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.03)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.08)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRacial Resentment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.32***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.50***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.57***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.75***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.13)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.11)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.13)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.12)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAngry BW\u0026times;RR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.51***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.17)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAngry+Race BW\u0026times;RR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.31*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.17)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAngry BM\u0026times;RR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.16)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAngry+Race BM\u0026times;RR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.17)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAngry WW\u0026times;RR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.18)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAngry+Race WW\u0026times;RR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.19)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAngry WM\u0026times;RR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.17)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAngry+Race WM\u0026times;RR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.18)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.58***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.61***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.53***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.54***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.72***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.84***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.79***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.84***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.02)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.02)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.02)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.02)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.06)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.05)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.06)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.06)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e614\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eNote:\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\"\u003e\n \u003cp\u003e\u003csup\u003e*\u003c/sup\u003e p\u0026lt;0.1; \u003csup\u003e**\u003c/sup\u003e p\u0026lt;0.05; \u003csup\u003e***\u003c/sup\u003e p\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOur results indicate that H1 is not supported in the main effects. As shown in Table 1, White respondents on average do not penalize the Black woman candidate for either form of anger. Neither the coefficient for the Angry condition (0.02) nor the coefficient for the Angry+Race condition (\u0026ndash;0.01) is statistically significant. Column 1, which reports the vote likelihood for the Black woman, makes clear that her support remains unchanged across treatment conditions.\u003c/p\u003e\n\u003cp\u003eIn addition, it is informative to consider baseline evaluations in the non-anger control condition. The mean vote likelihood for the Black woman candidate is 0.58, which is significantly higher than the baseline support for both the White man (0.54) and White woman (0.53) candidates (p \u0026lt; .05). Although none of the candidates receive overwhelming support in this low-information setting, the Black man receives the highest baseline support (0.61), though his advantage over the Black woman is not statistically significant (p = .30). Overall, White respondents evaluate the Black candidates more favorably than the White candidates in the control condition. If all candidates were being evaluated equivalently, we would expect no such differences.\u003c/p\u003e\n\u003cp\u003eThis relatively high baseline support for Black candidates is important context: any subsequent decline in vote likelihood following an anger treatment represents a clear \u003cem\u003epenalty\u003c/em\u003e\u0026mdash;a drop from an initially favorable evaluation. We contend that the elevated baseline support reflects the ideological composition of our sample. The median racial resentment score among White respondents is 0.50, meaning that half the sample falls into the racially liberal category (score \u0026lt; 0.5). Prior work shows that racially liberal White Democrats express heightened support for Black candidates (Mikkelborg 2025), which plausibly contributes to the favorable baseline evaluations we observe.\u003c/p\u003e\n\u003cp\u003eIndeed, the only candidate to experience a statistically significant decline in support in the main-effects models is the Black man. Exposure to the Angry+Race condition produces an approximately seven-percentage-point drop in vote likelihood (p \u0026lt; .05). Notably, this penalty emerges only when he expresses anger \u003cem\u003eabout racial disparities\u003c/em\u003e, underscoring the conditional nature of backlash to racialized anger.\u003c/p\u003e\n\u003cp\u003eWhile we initially theorized that the Black woman might be \u0026ldquo;doubly bound\u0026rdquo;\u0026mdash;penalized for both her race and gender\u0026mdash;even before expressing anger, these results show that she is not disadvantaged at baseline. However, it would be premature to conclude that Black women are immune to compounded electoral penalties for displays of anger. As we demonstrate in the next section, such penalties do emerge under specific conditions\u0026mdash;primarily among White respondents high in racial resentment.\u003c/p\u003e\n\u003cp\u003eNext, we test H2, which posits that, \u0026ldquo;\u003cem\u003eamong White respondents, higher levels of racial resentment will predict lower support for Black candidates who express anger, with the strongest negative effect observed for the Black woman candidate expressing racialized anger.\u0026rdquo;\u0026nbsp;\u003c/em\u003e The latter half of Table 1 (Columns 5-8) presents linear regression models estimating vote likelihood for the four candidate profiles among White respondents, including an interaction term for the treatment conditions with racial resentment, which enables us to test H2. \u0026nbsp;While our initial models in the first half of Table 1 assessed the main effects of anger across candidates among White respondents, there are strong theoretical reasons to expect that racially resentful Whites will react more negatively to Black candidates expressing anger\u0026mdash;particularly when that anger targets racial disparities. Our framework suggests that racially prejudiced Whites perceive such emotional displays as a threat to the racial status quo. This threat may be especially pronounced when it comes from a Black woman, whose expression of racialized anger activates both racial and gender-based stereotypes, compounding the penalty she receives.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Across all models, racial resentment is a strong and consistent negative predictor of vote likelihood. White respondents with higher levels of racial resentment are significantly less likely to support any of the four candidates, with coefficients ranging from \u0026minus;0.32 to \u0026minus;0.75 (all \u003cem\u003ep\u003c/em\u003e \u0026lt; .01). Notably, this includes even White candidates, suggesting that racial resentment may broadly reflect a resistance to Democratic candidates, regardless of the race of the candidate. \u003csup\u003e\u0026nbsp;\u003c/sup\u003eHowever, the most notable treatment effects appear in the model predicting vote likelihood for the Black woman candidate. In this model, exposure to an angry emotional cue significantly increases support for the candidate (\u003cem\u003eb\u003c/em\u003e = 0.25, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01), suggesting that expressing anger\u0026mdash;often considered politically risky for women and candidates of color\u0026mdash;may, in this instance, enhance candidate appeal. However, this effect is significantly moderated by racial resentment. Among respondents high in racial resentment, the positive impact of the \u0026ldquo;Angry Black Woman\u0026rdquo; cue reverses (\u003cem\u003eb\u003c/em\u003e = \u0026minus;0.51, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01), and a similar pattern emerges for the \u0026ldquo;Angry + Race\u0026rdquo; cue (\u003cem\u003eb\u003c/em\u003e = \u0026minus;0.31, \u003cem\u003ep\u003c/em\u003e \u0026lt; .10). These interactions indicate that while emotional expressiveness can enhance electoral support for marginalized candidates, its effectiveness is highly conditional on the racial attitudes of the electorate. \u0026nbsp;For the remaining candidate profiles\u0026mdash;White man, Black man, and White woman\u0026mdash;there is little evidence that anger cues significantly influence vote likelihood, either directly or through interaction with racial resentment. This suggests that Black women candidates are \u003cem\u003euniquely\u0026nbsp;\u003c/em\u003epenalized for emotional displays of anger among racially resentful Whites.\u003c/p\u003e\n\u003cp\u003eSubsequently, we estimate the models in which we interact the treatment conditions with racial resentment, separately for White men and White women. These results are displayed in Table 2, with the first four columns of Table 2 presenting the results for White male respondents, and the latter four columns presenting the results for White female respondents. \u0026nbsp; Considering that we are interested in the intersection of race and gender, it only makes sense to examine whether the effects that we see among racially resentful Whites are being driven by either men or women. \u0026nbsp;Previous research, for example, has shown that White men are especially receptive to explicit racial cues (Hutchings, Walton, Benjamin 2010), so it is plausible that racially resentful White men are more likely than racially resentful White women to penalize a Black woman candidate for an emotional display of anger.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 2.\u0026nbsp;\u003c/em\u003eLinear regression models estimating vote likelihood for each candidate interacted with racial resentment for White male respondents (models 1-4), and White female respondents (models 5-8). Treatment conditions include Angry\u0026apos; and Racialized Anger. Dependent variables represent likelihood of voting for the White Man (WM), Black Man (BM), Black Woman (BW), and White Woman (WW). \u0026nbsp;Models do not include controls (see Appendix B Table 2 for balance tests).\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eVote Likelihood by Experimental Condition and Racial Resentment (White Respondents, Subset by Gender)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale Respondents\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale Respondents\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"8\"\u003e\n \u003cp\u003e\u003cem\u003eDependent variable:\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"8\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBlack Woman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBlack Man\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWhite Woman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWhite Man\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBlack Woman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBlack Man\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWhite Woman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWhite Man\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAngry BW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.44\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAngry+Race BW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.10)\u003c/p\u003e\n \u003c/td\u003e\n 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\u003cp\u003e-0.47\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.56\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.69\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.73\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.39\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.43\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.70\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAngry BW*RR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.48\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.82\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAngry+Race BW*RR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.60\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAngry BM*RR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAngry+Race BM*RR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAngry WW*RR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAngry+Race WW*RR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAngry WM*RR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAngry+Race WM*RR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.91\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.81\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.85\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.85\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.87\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.53\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.80\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.71\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.80\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e329\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eNote:\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\"\u003e\n \u003cp\u003e\u003csup\u003e*\u003c/sup\u003ep\u0026lt;0.1; \u003csup\u003e**\u003c/sup\u003ep\u0026lt;0.05; \u003csup\u003e***\u003c/sup\u003ep\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe begin by looking at the coefficient for racial resentment across all four candidate types, among White male respondents. The coefficient for racial resentment is substantively large and statistically significant, regardless of the race of the candidate. \u0026nbsp;In fact, among White male respondents, the negative effect of racial resentment on vote likelihood is the largest for the White candidates. \u0026nbsp; Perhaps racially resentful White men perceive White Democratic candidates as \u0026ldquo;betraying\u0026rdquo; their presumed shared racial group interests, but this is just speculation on our part.\u003c/p\u003e\n\u003cp\u003eOf particular interest to us, of course, is whether racially resentful White men are uniquely penalizing the Black woman candidate when she expresses anger. \u0026nbsp;The negative and substantively large coefficients on the interaction terms for racial resentment and the treatment conditions indicate that racially resentful White men are indeed, negatively penalizing the Black woman candidate when she expresses anger. \u0026nbsp;First, the coefficient of -0.48 for the interaction between racial resentment and exposure to the \u0026ldquo;Angry Black Woman\u0026rdquo; condition indicates that going from no racial resentment (a racial resentment score of zero) to the maximum level of racial resentment (one), reduces the likelihood of voting for the Black woman by nearly half of one point on a 0 to 1 scale (p \u0026lt; .05). \u0026nbsp;In addition, when we consider the interaction of racial resentment with exposure to the Black woman candidate who is angry about racial injustice, the reduction in likely vote is even greater, with a coefficient of -0.60 (p \u0026lt; .05). \u0026nbsp;Thus, anger about racial disparities is especially off-putting to racially resentful White men. It is also noteworthy that the Black woman candidate is the only candidate for whom racial resentment significantly moderates the effect of the anger treatments. In short, she is uniquely and disproportionately penalized by racially resentful White men\u0026mdash;particularly when her anger is linked to racial disparities. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe now turn to analyzing the impact of racial resentment among White women respondents (the latter four columns of Table 2), and whether racially resentful White women, similar to their White male counterparts, also uniquely and disproportionately penalize the Black woman candidate. \u0026nbsp;The results indicate that is also the case. \u0026nbsp; However, the results are quite nuanced. \u0026nbsp; When we consider the interaction between racial resentment and the \u003cem\u003eBlack Woman Angry\u003c/em\u003e condition, the coefficient on the interaction term indicates that the Black woman candidate who is angry receives a steep penalty of -0.82 (p \u0026lt;.01). \u0026nbsp;In other words, moving from a racial resentment score of 0 to a racial resentment score of 1, is associated with a reduction of -0.82, which is nearly the entire range of the likely vote scale. Also, of note, however, is that among the White women with racial resentment scores of zero, the angry Black woman candidate is highly rewarded (.44, p \u0026lt; 0.01). \u0026nbsp;In sum, there is a large divergence in the vote preferences of White women who are low in racial resentment from the vote preferences of White women who are high in racial resentment, which perhaps is to be expected. Women at the low end of the racial resentment scale handsomely reward non-racialized anger from the Black woman candidate and women at the high end of the racial resentment scale severely penalize non-racialized anger from the Black woman candidate.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;However, will we observe a similar divergence in vote preferences among White women, when the Black woman candidate\u0026rsquo;s anger is about racial disparities? \u0026nbsp;Racially liberal women, if anything, should be favorably inclined to vote for a Black woman candidate who is indignant about racial disparities that are negatively affecting the Black community. Conversely, we might expect that White women who are high in racial resentment will penalize the Black woman candidate in the racialized anger condition. Surprisingly, the pattern of results indicates that rather than a sharp divergence in the likely vote of White women low in racial resentment and White women high in racial resentment, the gap between the two groups actually \u003cem\u003enarrows\u0026nbsp;\u003c/em\u003ewhen exposed to the Black woman candidate in the racialized anger condition. Among White women respondents with a racial resentment score of zero, exposure to the \u003cem\u003eAngry * Race Black Woman\u0026nbsp;\u003c/em\u003eis statistically indistinguishable from zero (b=0.18, p =.19), while racial resentment moderates exposure to the Angry*Race Black Woman condition in the opposite direction (b=-.30, p =0.29). \u0026nbsp; White female respondents who are racially resentful already muster up so much dislike for the Black woman as a political candidate that there is little movement left. In short, there is no sharp divergence between low racial resentment White women and high racial resentment White women. \u0026nbsp;This pattern suggests that anger about racial disparities disrupts the boost the Black woman candidate receives from those white female voters predisposed to be more supportive (low racial resentment respondents). \u0026nbsp;This finding suggests the fragility of gender solidarity when anger is racialized. \u0026nbsp;It also complicates findings from recent work suggesting that in an era of heightened racial strife in the post-Obama era racially liberal Whites are showing increased support for Black political figures as part of their desire to combat racism (Mikkelborg 2025). While our results affirm that racially liberal Whites may indeed be more supportive of Black candidates at baseline, this support appears to be conditional. In particular, racialized anger emerges as a threshold that some ostensibly supportive voters are unwilling to cross.\u003c/p\u003e\n\u003cp\u003eA more charitable interpretation of these results, however, is that racially liberal White women may penalize the Black woman candidate when she is angry about racial disparities, not because they reject the candidate\u0026rsquo;s message, but because they perceive her racialized anger as politically risky\u0026mdash;a sign that she lacks strategic savvy, mainstream appeal, or broader electability. From this perspective, the vote penalty is not necessarily a rejection of the candidate\u0026apos;s message, but a judgment about how it will be received by others (see also Hassell and Visalvanich 2024). Therefore, we explore whether the vote penalty observed among low racial resentment White women for the Black woman expressing racialized anger might be explained by perceptions of her as less electable. While our survey does not include a direct measure of electability, we asked respondents to indicate whether words such as \u0026ldquo;competent\u0026rdquo; and \u0026ldquo;unqualified,\u0026rdquo; described the candidates, which we use as proxies for perceived electability. \u0026nbsp;Subsequently, we conducted a mediation analysis among White women, focusing on those at the lowest and highest quartiles of racial resentment, to assess whether perceptions of electability help explain the relationship between candidate anger and vote choice. \u0026nbsp;The mediators were perceptions that the candidate was \u003cem\u003eunqualified\u003c/em\u003e or \u003cem\u003ecompetent\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results provide limited evidence that these perceptions of electability explain differences in vote likelihood. Among low racial resentment White women, exposure to the Black woman expressing racialized anger slightly increased perceptions that she was \u003cem\u003eunqualified\u003c/em\u003e, which in turn very modestly reduced their likelihood of voting for her (ACME = \u0026ndash;0.022, \u003cem\u003ep\u003c/em\u003e \u0026lt; .10) (See Appendix C.1). However, perceptions of competence did not mediate the effect, and there were no significant direct effects of the treatment on vote likelihood. For high racial-resentment White women, neither mediator was significant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAltogether, these results indicate that while perceptions of being \u003cem\u003eunqualified\u003c/em\u003e play a minor role in shaping low resentment women\u0026rsquo;s reactions, they do not fully account for why low and high racial resentment White women respond similarly to the Black woman\u0026rsquo;s racialized anger. Rather, the similarity appears to arise from different mechanisms that converge on comparable vote likelihoods: low resentment women exact a small electability penalty, while high resentment women\u0026rsquo;s baseline opposition leaves little room for additional change.\u003c/p\u003e\n\u003cp\u003eOverall, the results for White respondents indicate that when courting White voters, it is risky for a Black woman candidate to express anger\u0026mdash;particularly about race. Among racially resentful White men, a Black woman\u0026rsquo;s expression of racialized anger results in a sharp electoral penalty, reducing her vote likelihood by over half a point on a 0\u0026ndash;1 scale (\u003cem\u003eb = \u0026ndash;0.60\u003c/em\u003e, \u003cem\u003ep\u003c/em\u003e \u0026lt; .05). Even non-racialized anger is punished (\u003cem\u003eb = \u0026ndash;0.48\u003c/em\u003e, \u003cem\u003ep\u003c/em\u003e \u0026lt; .05), underscoring that anger is politically costly for Black women in the eyes of White men with the highest levels of racial resentment. Among White women, the patterns are more nuanced. Racially liberal White women reward the Black woman candidate when she expresses non-racialized anger (\u003cem\u003eb = 0.44\u003c/em\u003e, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01), but this support evaporates when her anger is framed around racial disparities\u0026mdash;the interaction with racial resentment shows no statistically significant difference from zero in that condition. In short, solidarity from White women is fragile, and emotional expression\u0026mdash;especially when tied to race\u0026mdash;can undermine that support. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe do not find support for H1, which predicted that the Black woman candidate would lose electoral support from White voters overall when she expressed anger, particularly racialized anger. However, H2 is supported: higher levels of racial resentment are associated with significantly reduced support for the Black woman candidate when she expresses anger\u0026ndash;especially racialized anger.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBlack respondents: Counterintuitive influences of intersectional consciousness and community nationalism\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThus far, we have focused on how White respondents react to candidate displays of anger, conditional on the candidate\u0026rsquo;s race and gender. In this section, we turn our attention to Black respondents. Table 3 displays the results of several regression models in which we estimate vote likelihood across the various experimental conditions. \u0026nbsp;Here we test H3, which posits that Black respondents will express greater support for Black candidates, relative to White candidates, when they express anger about racial disparities. Among Black respondents, we find no evidence that expressions of anger\u0026mdash;racialized or otherwise\u0026mdash;increase support for Black candidates. Both the Black man and Black woman candidates receive high baseline vote likelihoods (0.68 and 0.72, respectively), leaving little room for emotional cues to boost support further\u0026mdash;a pattern consistent with a ceiling effect. By contrast, expressions of racialized anger by White candidates\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003esignificantly increase support among Black respondents: vote likelihood rises for both the White man (b = 0.07, p \u0026lt; .05) and White woman (b = 0.07, p \u0026lt; .05) when their anger is framed around racial disparities, but it must be noted that their baseline vote likelihoods were relatively low (0.53 and 0.60, respectively). These results suggest that while anger is normatively accepted or\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eexpected from Black candidates, it can function as a signal of allyship when expressed by White candidates. Thus, the ingroup reward predicted in H3 does not materialize.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 3.\u0026nbsp;\u003c/em\u003eLinear regression models estimating vote likelihood for each candidate for Black respondents. Treatment conditions include Angry\u0026apos; and Racialized Anger. Dependent variables represent likelihood of voting for the White Man (WM), Black Man (BM), Black Woman (BW), and White Woman (WW). Models do not include controls (see Appendix B Table 2 for balance tests).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"482\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 482px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVote Likelihood by Experimental Condition (Black Respondents)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 482px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 357px;\"\u003e\n \u003cp\u003e\u003cem\u003eDependent variable:\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 357px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eBlack Woman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eBlack Man\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eWhite Woman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eWhite Man\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cem\u003e(1)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cem\u003e(2)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cem\u003e(3)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cem\u003e(4)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 482px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eAngry BW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cem\u003e(0.03)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eAngry+Race BW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cem\u003e(0.03)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eAngry BM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cem\u003e(0.03)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eAngry+Race BM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cem\u003e(0.03)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eAngry WW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cem\u003e(0.03)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eAngry+Race WW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.07\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cem\u003e(0.03)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eAngry WM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cem\u003e(0.03)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eAngry+Race WM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.07\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cem\u003e(0.03)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 482px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e585\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 482px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cem\u003eNote:\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 357px;\"\u003e\n \u003cp\u003e\u003csup\u003e*\u003c/sup\u003ep\u0026lt;0.1; \u003csup\u003e**\u003c/sup\u003ep\u0026lt;0.05; \u003csup\u003e***\u003c/sup\u003ep\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTo better understand variation in support among Black respondents, we next examine whether intersectional consciousness (IC) moderates reactions to candidates\u0026rsquo; emotional expressions\u0026mdash;particularly racialized anger. Intersectional consciousness reflects an awareness that overlapping identities such as race and gender create distinct experiences of oppression. We expected that Black respondents high in IC would be especially supportive of the Black woman candidate when she expressed anger about racial injustice (H4a). However, the results run counter to this expectation. Table 4 presents the results of several regression models in which we estimate vote likelihood as a function of intersectional consciousness (Columns 1-4) and as a function of community nationalism (Columns 5-8) (H4b). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cem\u003eTable 4.\u0026nbsp;\u003c/em\u003eLinear regression models estimating vote likelihood for each candidate interacted with intersectional consciousness (models 1-4), and community nationalism (models 5-8) for Black respondents. Treatment conditions include Angry\u0026apos; and Racialized Anger. Dependent variables represent likelihood of voting for the White Man (WM), Black Man (BM), Black Woman (BW), and White Woman (WW). Models do not include controls (see Appendix B Table 2 for balance tests).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eVote Likelihood by Experimental Condition as a Function of Intersectional Consciousness and Community Nationalism (Black Respondents)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntersectional Consciousness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommunity Nationalism\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"8\"\u003e\n \u003cp\u003e\u003cem\u003eDependent variable:\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBlack Woman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBlack Man\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWhite Woman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWhite Man\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBlack Woman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBlack Man\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWhite Woman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWhite Man\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(1)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(2)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(3)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(4)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(5)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(6)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(7)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(8)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAngry BW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.06)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.10)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAngry+Race BW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.06)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.10)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAngry BM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.06)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.09)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAngry+Race BM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.06)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.09)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAngry WW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.06)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.10)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAngry+Race WW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.06)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.10)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n 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\u003cp\u003eAngry BW*CN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.13)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n 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\u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.13)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAngry BM*CN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.12)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAngry+Race BM*CN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.24\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n 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WW*CN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.13)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n 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WM*CN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.14)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAngry+Race WM*CN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.13)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.57\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.61\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.62\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.44\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.39\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.46\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.40\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.38\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.04)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.04)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.04)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.05)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.07)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.07)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.07)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e(0.07)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e585\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eNote:\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\"\u003e\n \u003cp\u003e\u003csup\u003e*\u003c/sup\u003ep\u0026lt;0.1; \u003csup\u003e**\u003c/sup\u003ep\u0026lt;0.05; \u003csup\u003e***\u003c/sup\u003ep\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWhile IC is positively associated with baseline support for Black candidates, it does not significantly amplify vote likelihood in response to either anger condition. Among respondents high in IC, support for the Black woman candidate is already extremely high\u0026mdash;0.80 on a 0\u0026ndash;1 scale\u0026mdash;leaving little room for upward movement. Neither the \u003cem\u003eAngry\u003c/em\u003e nor \u003cem\u003eAngry+Race\u003c/em\u003e treatment significantly interacts with IC. Instead, IC appears to sustain consistently high support for the Black woman candidate across anger displays, rather than serving as a moderator of anger displays, which runs contrary to our expectations in H4a.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; For the Black man candidate, baseline support is also strong, though slightly lower, at 0.71. These levels of support suggest a ceiling effect: those most attuned to intersectional marginalization already view the Black woman (and to a lesser extent, the Black man) as highly qualified and electable, leaving little room for emotional expressions to further boost support. Indeed, the interaction between intersectional consciousness and the \u003cem\u003eAngry+Race\u003c/em\u003e condition is negative and statistically insignificant, indicating that racialized anger neither enhances nor undermines candidate evaluations among this ideologically aligned subgroup.\u003c/p\u003e\n\u003cp\u003eMoreover, in the non-racialized anger\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003econdition, Black respondents low in IC exhibit lower support for the Black woman candidate than for the White woman candidate. \u0026nbsp;For the White woman candidate, IC significantly moderates responses to racialized anger, such that high-IC respondents are more likely to support her when she expresses anger about racial injustice. \u0026nbsp; A one-unit increase in Black participants\u0026rsquo; intersectional consciousness results in a 24% increase in support for the WW when she expresses anger about racial injustice (\u003cem\u003ep \u0026lt;\u0026nbsp;\u003c/em\u003e0\u003cem\u003e.\u003c/em\u003e01). This suggests that racialized emotional expression by a White woman may signal political alignment or allyship to Black respondents high in intersectional awareness.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe also examine whether community nationalism moderates support for candidates who express racialized anger (H4b). Community nationalism reflects a strong commitment to racial group uplift, pride, and solidarity, and may shape how Black respondents interpret candidates\u0026rsquo; emotional displays. Among respondents high in community nationalism, baseline support for the Black man candidate is substantial\u0026mdash;0.73\u0026mdash;while support for the Black woman is even higher at 0.77. Yet the effects of emotional expression diverge by candidate gender. In the \u003cem\u003eAngry+Race\u003c/em\u003e condition, Black respondents high in community nationalism are significantly more likely to support the Black man candidate (\u003cem\u003eb = 0.24, p \u0026lt; .05\u003c/em\u003e), suggesting that racialized anger from a male candidate is read as a form of advocacy on behalf of the group. For the Black woman candidate, however, the interaction is \u003cem\u003enegative and statistically significant\u003c/em\u003e (\u003cem\u003eb = \u0026ndash;0.17, p \u0026lt;.10\u003c/em\u003e), indicating that community nationalism does not translate into increased support for her when she expresses racialized anger. These results highlight how ingroup solidarity cues are filtered through gendered expectations, with Black men receiving a clearer benefit from expressing racialized anger. \u0026nbsp;This may reflect persistent gendered expectations about who speaks for the racial group and how. In other words, racial solidarity does not appear to extend equally across gender lines, even within the Black electorate. No meaningful shifts are observed for the White woman or White man candidates across any of the conditions, with or without the CN interaction, suggesting that community nationalism has little bearing on how Black respondents evaluate White candidates\u0026mdash;regardless of their emotional tone.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eElectability or Antipathy? Exploring Perceived Candidate Traits\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe question remains: what drives these electoral decisions, particularly among Black voters? Extant scholarship indicates that a candidate\u0026rsquo;s perceived electability can play a greater role in voters\u0026rsquo; decisions than the candidate\u0026rsquo;s ideological proximity to the voter (Simas 2017). This is especially relevant in light of recent work revealing that Democratic primary voters perceive Black women candidates to be progressive, yet less electable (Chen and Sorensen 2025), thus eroding the electoral boost that Black women should receive for their perceived progressiveness.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAre our results reflecting a similar calculus, specifically among Black voters? Is their withholding of electoral support for the BW candidate a reflection of their belief that a Black woman who expresses anger about racial injustice cannot win a statewide election? Or is that withholding rooted in negative sentiment toward the BW candidate? While the data do not allow us to provide definitive answers to these questions, we seek generative insight from examining how the treatment conditions affect participants\u0026rsquo; perceptions of candidate characteristics such as intelligence and likeability. Our results, displayed in Figures 1and 2 in the Appendix C.3, indicate that across both White and Black respondents, anger displays, regardless of whether they are racialized, produce small and statistically negligible shifts in trait ratings.\u003c/p\u003e\n\u003cp\u003eThis suggests that the electoral penalties observed\u0026mdash;particularly for the Black woman candidate when she expresses racialized anger\u0026mdash;are not driven by changes in respondents\u0026rsquo; trait-based evaluations. Instead, both groups appear to maintain consistent assessments of her competence and character even as their likelihood of voting for her decreases in the racialized anger condition. While prior research points to electability concerns as a potential mechanism in voters\u0026rsquo; decision-making, our data do not directly measure perceptions of broader electoral viability; thus, we cannot determine whether strategic considerations or other unmeasured factors underlie these voting patterns. What the results clearly show is that anger expressions\u0026mdash;especially when tied to race\u0026mdash;affect electoral support without meaningfully altering underlying trait evaluations.\u003c/p\u003e"},{"header":"Discussion and Conclusion","content":"\u003cp\u003eThis paper contributes to our understanding of how candidate race, gender, and anger, interact to shape voter evaluations in the contemporary U.S. electorate. Building on theories of racialized and gendered emotional expression, we fielded a large-scale survey experiment to assess whether Black women candidates are uniquely penalized for expressing anger, and whether the racial content of that anger further influences voter reactions. Our findings offer a nuanced picture of how intersectional identities and anger are interpreted by racially diverse electorates and by voters with varying ideological orientations.\u003c/p\u003e\n\u003cp\u003eAmong White respondents, we find that candidate anger does not generate uniform backlash. Instead, the content and source of the anger matter. Notably, Black male candidates are significantly penalized for expressing racialized anger, experiencing a seven-point drop in vote likelihood. In contrast, Black women are not penalized overall for expressing anger, racialized or not, suggesting that in the aggregate, they do not face a baseline anger penalty. However, this general null finding masks meaningful variation by racial attitudes: among racially resentful White voters, Black women who express anger, especially anger about racial injustice, experience sharp declines in support. \u0026nbsp;These findings affirm our theory that racialized anger is particularly consequential among racially resentful White voters, who are inclined to view such expressions as threatening to the racial status quo.\u003c/p\u003e\n\u003cp\u003eAmong White women, we observe a striking divergence based on racial resentment. Racially liberal White women reward the Black woman candidate when she expresses non-racialized anger, while racially resentful White women severely penalize her. Yet even among racially liberal White women, this support is fragile: once the Black woman\u0026rsquo;s anger becomes racialized, the boost disappears. This erosion of support underscores the conditionality of gender-based solidarity in contexts where race is salient.\u003c/p\u003e\n\u003cp\u003eTurning to Black respondents, our findings defy the prediction that Black candidates would be rewarded for expressing racialized anger (H3). Instead, we find no treatment effects for either the Black woman or Black man candidate, likely the result of ceiling effects, as both enjoy high baseline support. Interestingly, racialized anger increases support for White candidates, suggesting that Black respondents may interpret such expressions as a signal of racial allyship. However, within the Black electorate, we also observe an important asymmetry: Black community nationalists reward the Black male candidate for expressing racialized anger but penalize the Black woman for the same behavior. This divergence suggests that emotional constraints on Black women do not operate solely through White audiences; they also reflect intra-racial expectations about which candidates are permitted to embody racial grievance and which are not. Intersectional consciousness and community nationalism do not significantly moderate treatment effects overall, but both are strongly and positively associated with baseline support for Black candidates, indicating that racialized anger may be expected rather than uniquely mobilizing among these voters.\u003c/p\u003e\n\u003cp\u003eIn sum, these findings reveal a racially asymmetric emotional double bind. White male candidates remain emotionally unencumbered: their expressions of anger\u0026mdash;racialized or not\u0026mdash;do not reduce support. By contrast, Black candidates, and especially Black women, must navigate a precarious emotional terrain where support is contingent on both the identity of the voter and the perceived appropriateness of anger. Our study shows that anger is not universally disqualifying for Black women\u0026mdash;but neither is it universally safe. While some constituencies respond favorably, the expression of racialized anger remains politically risky, particularly among racially resentful White voters, racially liberal White women, and certain segments within the Black electorate.\u003c/p\u003e\n\u003cp\u003eThe political implications of our findings are especially acute for Black women navigating majority-White electorates. Anger has long served as a powerful political resource, used effectively by white male politicians\u0026mdash;most notably President Donald Trump. \u0026nbsp;Yet Black women are uniquely denied this political tool. This asymmetry means that Black women candidates, including those who have sought statewide or national office such as Kamala Harris, must perform a narrow emotional balancing act: projecting resolve and competence without crossing an invisible threshold that risks backlash. The result is a structural disadvantage that limits their ability to respond forcefully to racial injustice, mobilize supporters, or claim the authority that anger affords their White male counterparts. More broadly, when certain candidates cannot safely express anger, the democratic system loses an important mode of political communication\u0026mdash;one that has historically fueled accountability, protest, and transformative change. Our findings thus make clear that emotional inequality\u0026mdash;who may safely express anger, and who may not\u0026mdash;constitutes a real, measurable barrier to Black women\u0026rsquo;s political viability.\u003c/p\u003e\n\u003cp\u003eAs American politics continues to grapple with questions of race, gender, and authenticity, candidates from marginalized backgrounds must continually calibrate their anger. Future research should explore how these dynamics unfold in higher-information contexts, across partisan lines, and in real-world electoral settings, where candidates increasingly must choose between expressing moral outrage and maintaining electoral viability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics Statement\u003c/h2\u003e\n\u003cp\u003eThis study was reviewed and approved by the Institutional Review Board of [University]. \u0026nbsp;All participants provided informed consent prior to participation.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eFunding Information\u003c/h2\u003e\n\u003cp\u003eThis study was supported by funding from [University].\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eL.S.D. led the development of the research question and contributed to the research design, theoretical development, and manuscript preparation. D.P. contributed to the research design, conducted portions of the statistical analysis, and contributed to manuscript preparation. I.J. executed the statistical analyses and contributed to data preparation and interpretation of results. A.B. contributed to the research design and substantive feedback on manuscript drafts. K.D. provided feedback on the manuscript and contributed to the preparation of the manuscript for submission. All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data, code, and materials required to replicate the analyses in this article will be publicly available in the Harvard University Dataverse upon publication. The repository will include the preregistration, replication code, and supporting documentation necessary to reproduce all tables and figures in the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlexander-Floyd, Nikol G. \u0026quot;\u0026quot; We Shall Have Our Manhood\u0026quot;: Black Macho, Black Nationalism, and the Million Man March.\u0026quot; \u003cem\u003eMeridians: feminism, race, transnationalism\u003c/em\u003e 3.2 (2003): 171-203.\u003c/li\u003e\n\u003cli\u003eBanks, A. J., \u0026amp; White, I. K. (2024). \u003cem\u003eThe Anger Rule: Racial Inequality and Constraints on Black Politicians\u003c/em\u003e. Cambridge University Press. https://doi.org/10.1017/9781009275255\u003c/li\u003e\n\u003cli\u003eBanks, A. J., White, I. K., \u0026amp; McKenzie, B. D. (2019). 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Cambridge University Press.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e All candidates in the experiment are portrayed as Democrats, a design choice intended to make expressions of anger about racial disparities plausible.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The pre-analysis plan can be accessed in our supplementary materials.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"race, gender, anger, intersectionality, Black politics","lastPublishedDoi":"10.21203/rs.3.rs-8584399/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8584399/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHow do voters respond when Black women candidates express anger\u0026ndash;particularly when that anger is racialized? Using a preregistered survey experiment (N\u0026thinsp;\u0026asymp;\u0026thinsp;5,000) that varied candidate race, gender, and expressions of anger, we test whether Black women are uniquely penalized for displays of anger. We find that although Black women are not penalized for expressing anger on average, anger produces sharp and disproportionate backlash among racially resentful White voters, resulting in substantial declines in support that exceed those faced by other candidates. White respondents high in racial resentment reduced support for an angry Black woman by half the vote likelihood scale in the general anger condition (-0.51, p\u0026lt;.01) and when her anger is racialized (-0.31, p\u0026lt;.10). Even among racially liberal White women, who initially reward non-racialized anger from Black women, support collapses once anger is framed around racial injustice, revealing the fragility of cross-racial gender solidarity. Among Black respondents, intersectionality conscious participants reward the White woman candidate but not the Black woman candidate for racial anger, while Black nationalists reward the Black man candidate and penalize the Black woman candidate for the same expression. Our findings highlight the unique barriers facing Black women candidates in using anger as a political resource.\u003c/p\u003e","manuscriptTitle":"Double Jeopardy: Punitive Responses to Black Women Candidates’ Anger","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-09 13:19:17","doi":"10.21203/rs.3.rs-8584399/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"48d9bceb-5fb8-4ca8-84a7-3ee9dc7e5a42","owner":[],"postedDate":"February 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-15T21:53:49+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-09 13:19:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8584399","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8584399","identity":"rs-8584399","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}