PSPXXX10.1177/0146167220963577Personality and Social Psychology BulletinDiMuccio and Knowles 963577research-article2020

Empirical Research Paper

Personality and Social Psychology Bulletin Precarious Manhood Predicts Support for 1 –19 © 2020 by the Society for Personality and Social Psychology, Inc Aggressive Policies and Politicians Article reuse guidelines: sagepub.com/journals-permissions DOI:https://doi.org/10.1177/0146167220963577 10.1177/0146167220963577 journals.sagepub.com/home/pspb Sarah H. DiMuccio1 and Eric D. Knowles1

Abstract Precarious manhood (PM) theory posits that males are expected to actively maintain their reputations as “real men.” We propose that men’s concern about failing to meet masculine standards leads them to embrace policies and politicians that signal strength and toughness—or what we term political aggression. Three correlational studies support this claim. In Study 1, men’s fear of failing to meet masculine expectations predicted their support for aggressive policies (e.g., the death penalty), but not policies lacking aggressive features (e.g., affirmative action). Studies 2 and 3 utilized Google searches to assess the relationship between regional levels of PM and real-world electoral behavior. The use of search terms related to masculine anxieties correlated with Donald Trump’s vote share in the 2016 general election (Study 2) and, confirming preregistered predictions, with Republican candidates’ vote shares in 2018 congressional elections (Study 3). We close by discussing potential sources of variation in PM.

Keywords precarious manhood, masculinity, aggression, political attitudes, voting

Received May 22, 2020; revision accepted September 14, 2020

Perhaps more than any politician in recent history, Donald Precarious Manhood Trump has rooted his political persona in traditional notions of masculinity. As a candidate and as President, Trump pres- Men are expected to achieve and maintain their standing as “real men” or risk losing this highly valued status (Vandello ents himself as dominant, unyielding, and virile (Katz, 2016). 1 From threatening foreign nations with attack (Sanger et al., & Bosson, 2013). Such expectations compel men to enact 2019) to alluding favorably to the size of his penis (Krieg, culturally prescribed masculinity and to monitor their own 2016) and testosterone levels (Kurtzleben, 2016), the success at doing so. Manhood’s precariousness therefore President’s behavior suggests a desire to place his manhood leads some men to experience anxiety at the prospect beyond reproach. of not meeting masculine standards, in turn motivating a The President’s displays of masculinity appear to resonate variety of compensatory beliefs and behaviors aimed at with many American men. During the 2016 election, Trump’s restoring one’s masculine reputation (Willer et al., 2013). take-no-prisoners brand of politics attracted a considerable Although the behaviors that constitute normative mascu- number of voters—a disproportionate number of whom were linity are time- and culture-specific, pressure on men to male (Huang et al., 2016). The gender gap in support for actively earn and uphold their manhood may be nearly uni- Trump mirrors similar gender gaps in support for other versal (DiMuccio et al., 2017; Vandello & Bosson, 2013; Republican politicians (Center for the American Women and Winegard et al., 2014). Politics [CAWP], 2017) and for tough and uncompromising Dominant themes of masculinity in the United States political policies (Oliphant, 2018), suggesting that these pre- include avoiding the appearance of femininity and homo- dilections have sources in common. In this research, we sexuality, seeking status and achievement, evincing indepen- argue that support for harsh political policies, Trump, and the dence and confidence, taking risks, and being aggressive present-day Grand Old Party (GOP) reflects (in part) the psy- chology of precarious manhood (PM). On this account, some 1New York University, New York City, USA men harbor doubts about their masculinity, which they in turn seek to reaffirm through voting behavior and policy Corresponding Author: Eric D. Knowles, Department of Psychology, New York University, 6 preferences that can be characterized as “politically Washington Place, New York City, NY 10012, USA. aggressive.” Email: [email protected] 2 Personality and Social Psychology Bulletin 00(0)

(Brannon, 1976; Connell, 1995; Donaldson, 1993). American precarious men may signal to the self and others their status men who harbor chronic doubts about their masculinity as “real men.” Indeed, research shows that for political elites (Reidy et al., 2014), or men whose masculinity has been and lay-citizens alike, prevailing notions of American mas- threatened (Berke et al., 2017), may attempt to compensate culinity encourage political aggression. by engaging in hypermasculine behaviors. In the American context, these behaviors may include displays of interper- Masculinity and Support for Aggressive Policies sonal aggression (Reidy et al., 2014; Vandello & Bosson, 2013)—and, we propose, aggressive political behaviors. and Right-Wing Politicians Although previous research has linked PM to support for Observers have long identified politics as an arena in which specific politicians or policies (e.g., Carian & Sobotka, 2018; men strive to embody culturally prescribed notions of mas- Willer et al., 2013), we argue that a broad range of political culinity. In his analysis of the Vietnam War, Fasteau (1974) behaviors belong together under the rubric of political argues that the decision to escalate hostilities reflected male aggression. Indeed, the present research is novel in tracing a leaders’ preoccupation without appearing weak. Similarly, broad range of political behaviors to aggression stemming Katz (2016) and Ducat (2004) contend that male political from the psychology of PM. candidates often attempt to appeal to voters by eschewing the Threats to manhood cause a host of behavioral, cognitive, feminine, engaging in masculine pursuits, and promoting and attitudinal outcomes—including interpersonal aggres- “get tough” policies (e.g., the death penalty). These authors sion. In experiments, PM is often manipulated by directly illuminate a masculine style of politics that prescribes dis- challenging men’s masculinity. Popular paradigms have men plays of dominance and support for punitive policies. engage in stereotypically feminine tasks, such as hair braid- The link between masculinity and political aggression is ing (Bosson & Vandello, 2011), or give men false feedback also seen among male lay-citizens. For instance, “hypermas- on gender knowledge tests indicating that they score atypi- culinity” and “manhood honor” are associated with gun cally high in feminine knowledge (Vandello et al., 2008; enthusiasm and opposition to gun control—preferences that Willer et al., 2013). Men subject to such threat inductions convey one’s preparedness to confront threats with deadly tend to experience increased anxiety (Bosson et al., 2009; force (Matson et al., 2019). Although experimental studies Caswell et al., 2014; Vandello et al., 2008), punch punching linking masculinity to politics are sparse (see for a review of bags harder (Bosson et al., 2009), display more hostility the literature, DiMuccio & Knowles, 2020), Willer and col- toward women (Maass et al., 2003), administer more severe leagues (2013) found that, following a manhood threat, men shocks to confederates (Cohn et al., 2009), and behave more expressed increased support for George W. Bush’s decision aggressively toward gay men (Bosson et al., 2012). to invade Iraq and approval of his handling of the Iraq War. PM can also be captured in an individual-difference dimen- In light of this evidence, we predict that PM will be posi- sion termed gender role discrepancy stress (GRDS)—anxiety tively associated with support for a broad range of aggressive that occurs “when one fails to live up to the ideal manhood domestic and foreign policies. derived from societal mandates of masculine gender roles” Aggressive policies and politicians are unevenly distrib- (Reidy et al., 2014, p. 160).2 GRDS predicts risky and aggres- uted across the ideological spectrum in the United States. sive behavior in men, including unsafe sexual practices (Reidy Several scholars have observed that conservative and et al., 2016) and health behaviors (Eisler et al., 1988), intimate Republican candidates and policies tend to evoke masculine partner and sexual violence (Reidy et al., 2015), and anger and tropes, whereas their liberal and Democratic counterparts rely hostility toward gay men (Parrott, 2009). on more feminine themes. Lakoff (1996) argues that people understand government through implicit familial meta- Precarious Manhood and Political phors—with conservatives conceptualizing government as Aggression the “strict father” and liberals viewing it as a “nurturant par- ent.” These gendered lenses help determine which govern- We suggest that politics is an appealing avenue through ment policies people prefer, such that conservatives gravitate which men can express aggression and thereby affirm their toward harsher positions and liberals toward more compas- masculinity. The political domain extends the reach of indi- sionate stances. Similarly, Katz (2016) contends that tradi- viduals’ preferences to one’s community (in the case of local tional masculinity has played a central role in the Republican politics) and to the country as a whole (in the case of national platform since the 1970s. The modern right is “tough on politics), potentially amplifying the subjective dividends of crime” and “strong on national security,” and has garnered aggressive displays. Moreover, demonstrating masculinity much political success by painting Democrats as wimps and through one’s political views and voting behavior provides a popularizing the idea that real men vote Republican (Dueck, more socially acceptable alternative to interpersonal aggres- 2010; Katz, 2016). sion, which is strongly proscribed in many settings. Empirical work confirms the connection between stereo- We define political aggression as the endorsement of poli- typical masculinity and right-wing politics. Implicitly and cies, politicians, or parties that appear tough, strong, and explicitly, laypeople tend to associate the Republican Party forceful. By aligning themselves with aggressive politics, with masculinity and the Democratic Party with femininity DiMuccio and Knowles 3

(Winter, 2010). Moreover, a content analysis of primary support for Donald Trump in the 2016 presidential election. debates in 2012 and 2016 found that Republican candidates Support for Trump reflects aggression both because he advo- utilized more aggressive discourse (e.g., “face threats”) cates aggressive policies and evinces a tough, strong, and against their intraparty opponents than did Democrats—with forceful demeanor. Finally, we operationalize political Donald Trump proving to be the most rhetorically aggressive aggression as support for Republican congressional candi- candidate in the history of American presidential debates dates in the 2018 midterm elections. Support for these candi- (Hinck et al., 2018). Finally, a recent experiment provides dates is aggressive because, with few exceptions, they direct support for a link between PM and voting preferences. aligned themselves closely with Trump and his policy agenda In this work, threatened men were more likely to support a and belong to the more stereotypically masculine and hard- masculine president, which in turn led to increased support line American political party.3 for Donald Trump and decreased support for Hillary Clinton In Study 1, we tested whether an individual-difference in the 2016 presidential election (Carian & Sobotka, 2018). dimension related to PM (Reidy et al., 2014) predicts Despite evidence for an affinity between masculinity and men’s support for aggressive policies. In Study 2, we Republican Party politics—both in terms of the party’s pol- developed and validated a regional measure of PM based icy platform and the behavior of its candidates—the GOP on Google search volumes for selected terms; we then has historically promoted a diversity of social and economic examined whether this index predicts Trump’s electoral views (Richardson, 2014). Because not all these views are success across the country in 2016. Finally, in Study 3, we politically aggressive, our predictions regarding the link tested the preregistered hypothesis that our search-based between PM and support for GOP candidates are tentative. index of PM predicts Republican congressional candi- The Republican Party has embraced increasingly right-wing dates’ vote shares in 2018 (see https://osf.io/kufrt for pre- political positions in recent years (Grossmann & Hopkins, registration materials and data). As exploratory matters, 2016; McCarty et al., 2006; Ornstein & Mann, 2006); thus, we also investigate whether precarious masculinity relates it may be that the connection between PM and support for to support for Republican political candidates in earlier the GOP has strengthened over time. This ideological shift election cycles. We close by discussing potential sources culminated in Donald Trump’s nomination, election, and of variation in PM. subsequent consolidation of power over his party and its down-ballot candidates. An important inflection point in Study 1: Precarious Manhood and GOP fealty to Trump occurred between the 2016 midterm elections, when few expected Trump to win the presidency, Support for Aggressive Political Policies and the 2018 midterms, in which most Republican candi- Study 1 tested the hypothesis that men high in PM are drawn dates embraced Trump as their party’s standard-bearer to aggressive political policies. We conceptualize such poli- (Burns & Martin, 2019; Smith, 2018; Zeleney, 2018). In cies as those that signal strength, toughness, or forcefulness. light of Trump’s aggressive politics, and the relatively mas- We operationalized aggressive policies in two ways: (a) as culine perception of the Republican Party, we predict an those that garner support from men high in dispositional association between levels of PM at the aggregate level and aggression and (b) as those that male and female raters per- Republican candidates’ vote shares in the 2018 midterms; ceive to be “aggressive.” Nonaggressive policies were those the extent of such an association in previous congressional that failed one or both of these operational criteria. contests remains an exploratory question. After establishing a list of aggressive policies, we tested the relationship between PM and men’s endorse- The Present Research ment of those policies. We did so mindful of the possibil- ity that men’s levels of PM or policies’ levels of aggression Evidence suggests a robust connection between masculinity might be confounded with political ideology. For instance, and political aggression—that is, policies and politicians that it may be that conservative men tend to be higher in PM signal strength, toughness, and resolve. Fewer data speak to than liberal men and that aggressive policies are more the link between PM—men’s anxiety that they are falling likely to advance conservative aims than are nonaggres- short of masculine norms—and aggressive politics—espe- sive policies. In this case, a correlation between PM and cially as we are conceptualizing the construct (Carian & endorsement of aggressive policies could be a spurious Sobotka, 2018; Willer et al., 2013). Research relevant to this reflection of ideological affinities. To rule out this possi- question tends to assess aggressive politics narrowly (exam- bility, we adjusted for participants’ ideological orienta- ining only a single policy or candidate preference) and fails tions in testing the link between PM and aggressive policy to examine real-world election outcomes. Thus, the present support. To further isolate the unique relationship between work seeks to connect precarious masculinity to three instan- PM and policy preferences, we also adjusted for social tiations of aggressive politics. First, we operationalize politi- dominance orientation (SDO; Ho et al., 2015) and right- cal aggression as support for political policies that express wing authoritarianism (RWA; Altemeyer, 1996)—vari- stereotypically masculine traits (e.g., strength and forceful- ables thought to underlie a broad range of political ness). Second, we operationalize political aggression as proclivities (Duckitt & Sibley, 2007). 4 Personality and Social Psychology Bulletin 00(0)

Although Study 1 was primarily designed to test the Sample GRD items include “Most women would consider relationship between PM and men’s policy preferences, we me to be less masculine than the typical guy” and “Most guys also included a measure of voting behavior in the 2016 would say I’m not very masculine compared to them.” Par- presidential election. We predicted that, if PM is associ- ticipants made their responses on a 5-point scale anchored on ated with attempts to affirm one’s masculinity, it may also the left by strongly disagree, in the middle by neither agree predict support for an aggressive political candidate (i.e., nor disagree, and on the right by strongly agree. The GRD Donald Trump). and GRDS subscales formed reliable composites (GRDS α = .90, GRD α = .95). Method It is critical to distinguish between the constructs of GRD and GRDS. Many men feel gender-discrepant but are unboth- Participants. Five-hundred male participants were requested ered by this, and it is highly likely that subjective discrep- from Amazon’s Mechanical Turk crowdsourcing market- ancy and discrepancy stress, although correlated positively place using the TurkPrime platform (Litman et al., 2017). with one another, have different effects on political attitudes. This sample size was chosen because it fell within funding We therefore proceeded from the assumption that the politi- constraints while affording the ability to detect small correla- cal manifestations of PM are best captured by examining the tions (r ≥ .15) at 90% power. We received 504 completed link between political beliefs and GRDS while adjusting for surveys. Two participants were excluded from the analysis GRD beliefs. because they described their sex as female in the survey. The final sample of 502 males ranged in age from 19 to 76 years Trump vote. Participants were asked, “Who did you vote (M = 36.27 years, SD = 11.57 years) and included 77.5% for in the United States election for president on November White/European Americans, 6.9% Black/African Ameri- 6, 2016?” Participants chose from the following options: cans, 5.8% Asian Americans, 5.6% Latinos, 2.2% Native Hillary Clinton, Donald Trump, Jill Stein, Gary Johnson, Americans, 0.2% Pacific Islander, and 1.8% individuals of Evan McMullin, and I did not vote. A binary Trump vot- mixed or unspecified ethnicity. ing variable was created such that 1 = Donald Trump and 0 = not Donald Trump (i.e., any other choice). Measures Policy support. Participants rated their support for 24 Political orientation. Participants’ political orientation was policies chosen for their relevance to a wide range of for- measured using the following two items: “In terms of social eign and domestic affairs (see Table 1). These policies were and cultural issues, how liberal or conservative are you?” and culled from an array of sources (e.g., the Pew Research Cen- “In terms of economic issues, how liberal or conservative are ter, Gallup, and political platform statements). Participants you?” (Jost, 2006; Kroh, 2007). Responses were made on an made their ratings on a 7-point scale anchored on the left by 11-point scale anchored on the left by extremely liberal, in strongly oppose, in the middle by neutral/no opinion, and on the middle by neither, and on the right by extremely conser- the right by strongly support. vative.

Dispositional aggression. Participants’ tendency to engage Social Dominance Orientation. Participants’ preferences for in aggressive thought and behavior was measured using hierarchy were measured using the eight-item Social Domi- Buss and Perry’s (1992) 29-item aggression questionnaire. nance Orientation (SDO) scale (Ho et al., 2015). Sample Example items include, “Once in a while I can’t control the items include “An ideal society requires some groups to be urge to strike another person” and “I can think of no good on top and others to be on the bottom” and “Some groups of reason for ever hitting a person” (reverse scored). Partici- people are simply inferior to other groups.” Responses were pants made their ratings on a 7-point scale anchored on the made on a 7-point scale anchored on the left by strongly left by extremely uncharacteristic of me and on the right by oppose, in the middle by neutral, and on the right by strongly extremely characteristic of me. A composite formed by aver- favor. The SDO items formed a reliable composite (α = .88). aging all items was reliable (α = .92). Right-Wing Authoritarianism. To assess participants’ authori- Precarious Manhood. PM was operationalized using the tarian beliefs, we administered an abbreviated six-item 5-item GRDS subscale of Reidy and colleagues’ Masculine Right-Wing Authoritarianism (RWA) scale containing items Gender Role Discrepancy Stress (MGRDS) scale. GRDS taken from previous RWA scales (Altemeyer, 1996; Man- assesses the extent to which men experience stress due to a self- ganelli Rattazzi et al., 2007). Sample items include “What perceived discrepancy with masculine norms. Sample GRDS our country really needs is a strong, determined leader who items include “Sometimes I worry about my masculinity” will crush evil and take us back to our true path” and “The and “I worry that women find me less attractive because I’m only way our country can get through the crisis ahead is to not as macho as other guys.” The MGRDS contains an addi- get back to our traditional values, put some tough leaders in tional five items measuring gender role discrepancy (GRD), power, and silence the troublemakers spreading bad ideas.” or men’s belief that they are less masculine than other males. Responses were made on a 7-point scale anchored on the left DiMuccio and Knowles 5

Table 1. Political Policies Presented in Study 1.

Average support Aggression–support association Average aggressiveness rating Categorized as Policy M CI B CI M CI aggressive? Police body cameras 0.83 [0.82, 0.85] −0.03 [−0.11, 0.05] 2.30 [1.76, 2.84] No requirement Ban guns for people on 0.77 [0.74, 0.79] −0.08 [−0.20, 0.03] 3.03 [2.42, 3.65] No “no-fly” list Marijuana legalization 0.76 [0.74, 0.79] 0.11 [−0.01, 0.24] 2.43 [1.83, 3.04] No Gender pay equality law 0.75 [0.72, 0.78] −0.11 [−0.23, 0.01] 1.93 [1.45, 2.42] No Paid maternity leave 0.74 [0.71, 0.76] −0.02 [−0.13, 0.09] 2.17 [1.63, 2.70] No requirement Marriage equality 0.72 [0.69, 0.75] 0.02 [−0.11, 0.16] 2.23 [1.55, 2.92] No Climate change regulation 0.70 [0.68, 0.73] 0.03 [−0.10, 0.15] 3.10 [2.33, 3.87] No Vaccines mandatory 0.70 [0.67, 0.73] −0.04 [−0.17, 0.08] 2.67 [1.92, 3.41] No Gun control 0.68 [0.65, 0.71] −0.10 [−0.24, 0.03] 3.60 [2.79, 4.41] No Require immigrants to 0.64 [0.61, 0.66] 0.25 [0.13, 0.37] 3.90 [3.17, 4.63] No learn English Drug decriminalization 0.64 [0.61, 0.66] 0.06 [−0.07, 0.19] 3.00 [2.30, 3.70] No Abortion rights 0.60 [0.57, 0.64] 0.03 [−0.12, 0.18] 3.10 [2.21, 3.99] No Obamacare 0.56 [0.53, 0.59] 0.06 [−0.08, 0.20] 3.40 [2.61, 4.19] No Employer-subsidized 0.55 [0.52, 0.57] 0.17 [0.05, 0.29] 2.83 [2.21, 3.46] No childcare Accept Syrian refugees 0.54 [0.51, 0.57] −0.13 [−0.26, 0.00] 3.30 [2.51, 4.09] No Death penalty 0.53 [0.50, 0.56] 0.24 [0.11, 0.37] 4.73 [4.06, 5.40] Yes Affirmative action 0.50 [0.47, 0.53] 0.06 [−0.06, 0.18] 2.97 [2.35, 3.58] No Flag burning ban 0.45 [0.42, 0.48] 0.20 [0.05, 0.35] 3.67 [2.93, 4.40] No Increased military spending 0.44 [0.41, 0.47] 0.21 [0.09, 0.34] 4.17 [3.49, 4.84] Yes Muslim immigration ban 0.43 [0.40, 0.46] 0.30 [0.16, 0.44] 4.87 [4.08, 5.65] Yes Deployment of troops to 0.43 [0.40, 0.45] 0.25 [0.13, 0.37] 4.80 [4.08, 5.52] Yes Syria Enhanced interrogation 0.42 [0.39, 0.45] 0.42 [0.29, 0.55] 5.60 [4.94, 6.26] Yes Border wall construction 0.34 [0.31, 0.38] 0.26 [0.11, 0.41] 4.97 [4.21, 5.73] Yes Increased surveillance of 0.34 [0.31, 0.36] 0.33 [0.22, 0.45] 5.07 [4.38, 5.76] Yes Muslims

Note. Bold indicates criterion for aggressiveness was met. Policies that met both criteria (significant aggression–support association and average aggressiveness rating above the scale midpoint of 4) were deemed aggressive. CI = confidence interval. by strongly disagree, in the middle by neither agree nor dis- (maximum observed value). Thus, effects represent the agree, and on the right by strongly agree. The RWA items change in outcome associated with an increase from the min- formed a reliable composite (α = .90). imum to the maximum score on a predictor.

Procedure. After initiating the survey and providing informed Aggressive and nonaggressive policies. See Table 1 for mean consent, participants were administered the policy questions endorsement of the political policies. In validating our dis- in fixed order. The aggression questionnaire, the GRD, and tinction between aggressive and nonaggressive policies, we GRDS scales were then presented in random order. Finally, reasoned that only the former should appeal disproportion- participants completed the political orientation questions, the ately to men high in dispositional aggression. To estimate presidential voting question, and a demographic question- the associations between aggression and policy support, we naire.4 After completing the survey, participants were regressed endorsement of each policy on participants’ levels debriefed and paid US$1.01. of aggression. As can be seen in Table 1, endorsement of 10 policies varied positively as a function of dispositional Results aggression. Endorsement of three policies—one liberal (child care To render the interpretation of coefficients consistent across subsidies) and two conservative (requiring immigrants to measures, predictors in this and all subsequent studies were learn English and banning the burning of the American rescaled to range from 0 (minimum observed value) to 1 flag)—correlated positively with male aggression but 6 Personality and Social Psychology Bulletin 00(0)

Table 2. Variables Assessed in Study 1 (N = 502). Variable M SD 1 2 3 4 5 6 7 8 9 10 1. Gender role discrepancy 0.25 0.24 — .59** .30** .08 −.03 .04 .08 .09* −.00 .05 stress 2. Gender role discrepancy 0.30 0.27 — .17** −.13* −.12* −.11* −.18** −.19** .18** −.11* 3. Dispositional aggression 0.39 0.22 — .12** .03 .19** .17** .26** .04 .06 4. Social conservatism 0.39 0.29 — .66** .52** .69** .60** −.61** .51** 5. Economic conservatism 0.53 0.28 — .48** .52** .51** −.57** .44** 6. Social dominance 0.33 0.22 — .57** .50** −.61** .40** orientation 7. Right-wing authoritarianism 0.33 0.22 — .75** .55** .56** 8. Aggressive policy support 0.42 0.25 — −.43** .58** 9. Nonaggressive policy 0.65 0.16 — −.44** support 10. Trump vote in 2016 0.25 0.43 —

Note. Trump vote is coded as 1 = Trump and 0 = not Trump; mean Trump vote is interpreted as the proportion of the sample that reported voting for Trump. *p < .05. **p < .01.

arguably lack clear aggressive content. To resolve these emerged between GRDS and nonaggressive policy endorse- inconsistencies, we introduced a second criterion for ment. However, we also observed that GRDS (anxiety about aggressive policies: men’s and women’s subjective judg- one’s masculinity) and GRD (beliefs about one’s masculinity) ments of policies’ aggressiveness (see Study 1 Supplemental were positively correlated with one another, yet bore opposite Material for full details). To this end, a politically diverse associations with aggressive policy endorsement. This pattern sample of 30 people from the Prolific Academic crowd- suggests that the unique relationship between GRDS and sup- sourcing platform (Peer et al., 2017) answered the follow- port for aggressive policies may have been underestimated ing question about each policy: “How aggressive do you due to the positive association between GRDS and GRD. It is perceive this policy to be?” Participants made their ratings also possible that the correlation between GRDS and aggres- on a 7-point scale anchored on the left by not at all aggres- sive policy support reflects these variables’ relationships to sive and on the right by extremely aggressive. Reliability political ideology, SDO, and RWA. We therefore regressed of the average aggressiveness ratings for the policies was aggressive policy endorsement simultaneously on GRDS and high, ICC(2, k) = .90 (Cicchetti, 1994; Shrout & Fleiss, GRD—with and without social and economic conservatism, 1979), and policies’ aggressiveness ratings correlated SDO, and RWA as additional covariates—using ordinary strongly with the magnitude of their respective aggres- least squares (OLS) regression (Table 3). The association sion–support associations, r(24) = .82, p < .001 between GRDS and aggressive policy endorsement increased (Supplemental Figure S1). in magnitude with the addition of GRD as a covariate (Model Seven policies both correlated significantly with dispo- 1) and remained significant when adjusting for the full range sitional aggression and scored above the scale midpoint of covariates (Model 2). on perceived aggressiveness (Table 1). These policies— These analyses were repeated for nonaggressive policy increased Muslim surveillance, border wall construction, support (Table 4). The association between GRDS and non- enhanced interrogation techniques, deployment of troops aggressive policy endorsement became significantly nega- to Syria, Muslim immigration ban, and increased military tive after adjusting for GRD alone (Model 1), but disappeared spending—were thus categorized as aggressive. The 17 with the addition of political ideology, SDO, and RWA as remaining policies, which failed one or both of the aggres- covariates (Model 2).5 sion criteria, were categorized as nonaggressive. Support for the aggressive policies was averaged to form a Precarious manhood and Trump voting. We did not observe a composite measuring aggressive policy endorsement significant bivariate correlation between GRDS and Trump (α = .86), and support for the nonaggressive policies was voting (see Table 2). However, it may be that the positive averaged to form a composite measuring nonaggressive relationship between GRDS and GRD (which is negatively policy endorsement (α = .87). associated with support for Trump) obscures a unique rela- tionship between GRDS and Trump support. Therefore, Precarious Manhood and support for aggressive policies. As using logistic regression, we regressed the binary Trump can be seen in Table 2, we observed a significant bivariate variable simultaneously on GRDS and GRD—with and correlation between GRDS and men’s endorsement of aggres- without the inclusion of social and economic conservatism, sive political policies. In contrast, no bivariate relationship SDO, and RWA in the model (Table 5). The association DiMuccio and Knowles 7

Table 3. OLS Regression Analysis Predicting Aggressive Policy Endorsement (Study 1, N = 502). Model 1 Model 2

95% CI 95% CI Variable Estimate [LL, UL] β Estimate [LL, UL] β Gender role discrepancy stress 0.31** [0.21, 0.42] .31 0.11** [0.03, 0.16] .11 Gender role discrepancy −0.35** [−0.44, −0.25] −.37 −0.11** [−0.13, −0.02] −.12 Social conservatism 0.06 [−0.03, 0.10] .07 Economic conservatism 0.10* [0.02, 0.13] .12 Social dominance orientation 0.08 [−0.05, 0.08] .07 Right-wing authoritarianism 0.63** [0.44, 0.60] .56

Note. OLS = ordinary least squares; CI = confidence interval; LL = lower limit; UL = upper limit. *p < .05. **p < .01.

Table 4. OLS Regression Analysis Predicting Nonaggressive Policy Endorsement (Study 1, N = 502). Model 1 Model 2

95% CI 95% CI Variable Estimate [LL, UL] Β Estimate [LL, UL] β Gender role discrepancy stress −0.11* [−0.18, −0.04] −.17 −0.01 [−0.06, 0.04] −.02 Gender role discrepancy 0.16** [0.10, 0.23] .28 0.05* [0.00, 0.10] .08 Social conservatism −0.13** [−0.19, −0.08] −.24 Economic conservatism −0.11** [−0.16, −0.06] −.20 Social dominance orientation −0.24** [−0.30, −0.18] −.33 Right-wing authoritarianism −0.05 [−0.12, 0.01] −.07

Note. OLS = ordinary least squares; CI = confidence interval; LL = lower limit; UL = upper limit. *p < .05. **p < .01.

Table 5. Logistic Regression Analysis Predicting Trump Voting (Study 1, N = 502). Model 1 Model 2

95% CI 95% CI Variable Estimate [LL, UL] OR Estimate [LL, UL] OR Gender role discrepancy stress 1.87** [0.81, 2.93] 6.49 1.21 [−0.19, 2.62] 3.37 Gender role discrepancy −2.10** [−3.12, −1.07] 0.12 −1.16 [−2.59, 0.27] 0.31 Social conservatism 0.71 [−0.68, 2.11] 2.04 Economic conservatism 2.63 [1.18, 4.09] 13.93 Social dominance orientation 1.60 [−0.18, 3.39] 4.98 Right-wing authoritarianism 6.14 [4.02, 8.26] 466.36

Note. OR = odds ratio; CI = confidence interval; LL = lower limit; UL = upper limit. **p < .01. between GRDS and Trump support became significantly operationalization of political aggression). PM was opera- positive with the addition of GRD as a covariate (Model 1), tionalized in terms of GRDS (Reidy et al., 2014), and but fell to nonsignificant when adjusting for the full range of aggressive policies were defined as those that signal covariates (Model 2). toughness, strength, or forcefulness, and met two separate indices of aggressiveness. Confirming our hypothesis, Discussion GRDS was significantly and positively related to men’s support for aggressive policies (e.g., increased military In Study 1, we hypothesized that PM—an aversive sense spending and the use of “enhanced interrogation” tech- that one is insufficiently masculine—would predict men’s niques) after adjusting for GRD beliefs and levels of polit- endorsement of aggressive political policies (our first ical conservatism, SDO, and RWA. Although we predicted 8 Personality and Social Psychology Bulletin 00(0)

Table 6. Provisional and Retained Search Terms in Study 2 Pretest (N = 272). Provisional (pretest GRDS predicting past search GRDS predicting future interest Retained (Google Trends phrasing) (estimate) (estimate) phrasing) Erectile dysfunction .18 .38** Erectile dysfunction (topic) Hair loss .36* .36* Hair loss (topic) Hair plugs .15 .26* — How to get girls .73** .13** how to get girls (exact phrase) Penis enlargement .35** .33** Penis enlargement (topic) Penis size .36* .15* Human penis size (topic) Porn .04 −.02 — Steroids .36* .25 Anabolic steroid (topic) Tattoos .21 .15 — Testosterone .37* .34* Testosterone (topic) Viagra .17 .26** Sildenafil (topic)

Note. Relationships between search terms and GRDS were tested by regressing self-reported past searches or future interest on GRDS while adjusting for gender role discrepancy (GRD). Although hair plugs was significantly associated with GRDS in the pretest, Google Trends search volumes were too rare to produce data for most regions; this term was, therefore, not retained in the final set. With the exception of “how to get girls,” retained phrases are Google “topics”—groups of search terms that the Google algorithm deems to be synonymous—rather than exact phrases. GRDS = gender role discrepancy stress. *p < .05. **p < .01.

a null relationship between GRDS and nonaggressive States, so should levels of PM. Study 2 leveraged geographi- policy endorsement (e.g., climate regulations and mari- cal variation in internet search patterns to test the hypothesis juana legalization), we found that PM was negatively that PM predicted support for Donald Trump. associated with support for such policies after adjusting The Google Trends service (http://trends.google.com) is a for GRD—although this relationship was not robust to the tool for assessing the frequency with which people enter spe- inclusion of ideology, SDO, and RWA as covariates. This cific terms into the Google search engine. Google Trends has suggests that PM is not only positively associated with proven useful in tracking a diverse array of physical and psy- support for aggressive policies, but is also associated with chological phenomena—including infectious disease out- less support for policies that lack aggressive qualities.6 breaks (Carneiro & Mylonakis, 2009) and racial prejudice Our analysis of self-reported voting in Study 1 suggests (Stephens-Davidowitz, 2014). Internet searches are particu- that support for Donald Trump—a politician who promotes larly useful for studying sensitive topics, as people feel that aggressive policies and displays an aggressive demeanor— their search behavior is private (Stephens-Davidowitz, may be stronger in men high (vs. low) in PM. The next study 2017). In the present context, we sought to correlate the revisits the relationship between PM and voting in the con- regional popularity of PM-related search terms with Trump’s text of the 2016 presidential election, this time using real- vote share in the 2016 general election. world voting data and internet search patterns indicative of We first generated a list of search terms that might be dis- PM. We thus shift from examining individual-level variables proportionately popular among men high in PM (Table 6).7 to aggregate levels of PM and voting outcomes across geo- This search set was identified based on our reading of the graphical regions. Although inferences based on aggregate- masculinities literature, which shows that the prevailing level data must always be interpreted with caution (King, masculinity in the United States is associated with strength 1997), the results of Study 1 motivate the hypothesis that PM and muscularity, sexual prowess, virility, youth, and health and voting, each measured at the aggregate level, will be (Chapple & Ziebland, 2002; Hunt et al., 2013; Johnston positively associated with one another. et al., 2014; Kimmel, 2006; O’Brien et al., 2007). Indeed, seeming to fall short in these areas can threaten American Study 2: Precarious Manhood and the men’s sense of masculinity (Courtenay, 2000; Fergus et al., 2002; Frederick et al., 2017; Loe, 2001; R. Ricciardelli, 2016 Presidential Election 2011; Thompson & Barnes, 2013; Tsang et al., 2019). Thus, PM is theorized to be a sociocultural phenomenon (Bosson our PM search terms tapped men’s concern for their standing et al., 2009; Vandello & Bosson, 2013; Vandello et al., 2008). in the domains of sexuality, youth, and health. That is, PM has its genesis in unyielding norms of male As described in the Study 2 Supplemental Material, we behavior and in environmental shocks that threaten men’s validated our final list of eight search terms (Table 6) by sense of masculinity (Michniewicz et al., 2014). To the retaining only those in which male survey participants extent that these forces vary between regions of the United scoring high in GRDS (Reidy et al., 2014) expressed DiMuccio and Knowles 9

Figure 1. Prevalence of Google search composite consisting of erectile dysfunction, hair loss, how to get girls, penis enlargement, human penis size, anabolic steroid, testosterone, and Sildenafil. Note. DMAs are color coded such that darker shades indicate higher search volumes. DMA = designated market area. heightened interest. In an additional validity test, also Search volumes provided by Trends are scaled relative to detailed in the Study 2 Supplemental Material, we show 100, with 100 representing the DMA with the highest search that rates of PM searches correlate with polling data theo- volume for a term and values below 100 representing percent- retically related to PM. ages of that highest volume. Search volumes are gauged rela- With the exception of the exact phrase how to get girls, the tive to total Google searches in a region, and thus are not final set of search terms were translated into Google Trends sensitive to differences in population or device usage rates. “topics” (Table 6). Topics represent groups of related searches Volumes for the eight terms tended to correlate with one that the Google algorithm regards as approximate synonyms. another across DMAs (α = .66) and were thus averaged to (Thus, the topic erectile dysfunction includes searches for form a composite PM score.9 Figure 1 depicts composite erectile dysfunction and impotence.) Requesting search vol- search volumes for DMAs across the United States. umes for topics rather than exact phrases has the dual advan- tages of capturing the diversity of phrases that people use to 2016 General election voting data. The 2016 general election search for the same information and maximizing the number results by DMA were obtained from the Daily Kos Elections of geographic regions for which Google Trends provides data. data archive.10 The critical variable in this dataset was the percentage of votes cast for Donald Trump. The election data Method were merged with the Google search file, yielding a dataset that mapped PM composite scores to election returns for Acquisition of Google Trends data. Search volumes for each of every DMA. the eight PM search topics were downloaded from Google Trends. Data were acquired for the year immediately preced- Control variables. In testing the relationship between regional ing the 2016 general election (November 7, 2015, to Novem- PM scores and Trump vote returns, we controlled for several ber 7, 2016) at the level of designated market areas (DMAs).8 search-related and demographic variables. Search-related 10 Personality and Social Psychology Bulletin 00(0)

controls included searches for terms that, although not Table 7. OLS Regression Analysis Predicting Trump’s General indicative of PM, might be regionally confounded with our Election Vote Percentage (Study 2, N = 209 DMAs). PM-related topics. These control topics included breast 95% CI augmentation (a body modification analogous to penis Predictor B [LL, UL] β r enlargement), estrogen (the female counterpart to testoster- one), menopause (like erectile dysfunction, an often-dis- Precarious 18.99** [10.64, 27.35] .21 .45** tressing physiological event), and internet pornography (a manhood Breast 10.05** [3.88, 16.23] .13 .10 proxy for general interest in sexual themes). Other control augmentation terms were chosen to adjust for possible political and cul- Estrogen 7.41* [0.47, 14.34] .07 −.00 tural differences between high- and low-PM DMAs: illegal Menopause −7.51 [−15.99, 0.98] −.07 .09 immigration and Mexicans (two terms that might be of Internet −4.27 [−13.67, 5.12] −.05 .42** higher interest to conservative residents), and NASCAR (an pornography automotive sport disproportionately popular in the conser- Illegal 5.56 [−1.90, 13.02] .06 −.15* vative states). immigration Demographic controls included a host of factors that Mexicans 20.72** [11.52, 29.92] .32 −.06 might otherwise predict Trump voting, including DMAs’ NASCAR 6.21 [−1.01, 13.44] .08 .45** total population and population density (proxies for rurality), Population 0.11 [−9.42, 9.63] .00 −.42** the percentages of residents who are male, White, Black, Population 14.09* [2.31, 25.87] .09 −.33** Asian, Hispanic, above 45 years of age, and college educated density (politically relevant voting blocs); the unemployment rate Male % 10.74* [1.65, 19.82] .11 .12 and median household income (economic factors that predict Above 45% −6.20 [−23.02, 10.63] −.09 .28** political preferences); and a dummy variable indicating White % −15.83* [−31.42, −0.25] −.25 −.02 Black % −29.41** [−46.57, −12.24] −.23 −.49** whether a DMA is located in the Southern United States (a Asian % −53.95** [−64.89, −43.00] −.75 −.36** perennially important divide in electoral politics).11 All Hispanic % −1.40 [−9.64, 6.85] −.02 .22** demographic data were derived from American Community 12 College % −27.69** [−36.69, −18.69] −.46 −.63** Survey (ACS) 5-year estimates. Unemployed % −20.33** [−28.16, −12.51] −.25 −.16* Median income 2.15 [−8.23, 12.53] .03 −.49** Previous elections. Any observed relationship between PM South 6.46** [3.99, 8.93] .26 .28** and Trump support in the 2016 presidential election might represent a “Trump effect.” Alternatively, such an associa- Note. Search-based control variables in italics. r indicates the bivariate tion could reflect a more general affinity among precarious correlation between a predictor and the Trump vote. OLS = ordinary least squares; DMA = designated market area; CI = confidence interval; men for Republican presidential candidates. To examine this LL = lower limit; UL = upper limit. question, we repeated the above procedures for the previous *p < .05. **p < .01. two presidential election cycles, extracting Google Trends data for the year immediately preceding each election. Previous elections. Analyses of Mitt Romney and John McCain’s vote percentages in the 2012 and 2008 elections Results are shown in Supplemental Tables S2 to S5. See Figure 2 2016 Presidential election. See Supplemental Table S1 for (middle panel) for a partial regression plot depicting the correlations between all variables assessed in Study 2. The unique relationship between PM and Romney’s vote share; bivariate correlation between PM scores and Trump’s vote Figure 2 (right panel) shows the unique relationship between percentage across DMAs was moderately positive and sig- PM and McCain’s vote percentage. The confidence intervals nificant, r(209) = .45, p < .01. However, PM was correlated for these associations include 0; thus, we see little evidence with a number of other variables that themselves predicted for reliable associations between PM and vote shares for Trump voting—necessitating regressions to test the unique either previous Republican candidate after adjusting for the relationship between PM and support for Trump. Table 7 covariates. displays results of an OLS linear regression analysis in which PM and all search-based and demographic control variables Discussion were used to predict Trump’s vote percentage. Net of all con- trol variables, PM remained a highly significant predictor of Study 2 tested whether PM may have been associated with Trump’s vote percentage; see Figure 2 (left panel) for a par- Trump’s victory in the 2016 general election—our second tial regression plot depicting the unique relationship between operationalization of political aggression. Regional levels of PM scores and Trump’s vote percentage across the 209 PM were indexed via the prevalence of Google searches DMAs. The fact that the confidence interval for this relation- that, according to our pretest, are popular among precarious ship excludes 0 suggests that it is statistically reliable. men. We found that Trump received a higher share of votes DiMuccio and Knowles 11

Figure 2. Partial regression plot showing the unique relationship between precarious manhood and Republican presidential candidates’ vote percentages after adjusting for search-based and demographic control variables (Study 2). Note. N = 209 DMAs. DMA = designated market area. in media markets where PM-related searches were particu- the President to produce a relationship between PM and vot- larly popular, and that this relationship held after adjusting ing for other Republicans. We take up this question in the for a range of search-based and demographic covariates. next study. Because our PM composite included terms of demonstrated In Study 3, we sought to replicate the relationship between interest to men high in precariousness, and because precari- PM and voting, this time for GOP candidates in the 2018 elec- ousness predicted self-reported voting for Trump and many tions for the U.S. House of Representatives. The 2018 mid- of the policies he supports in Study 1, we believe that the term elections were widely seen as a referendum on President most plausible interpretation of the present findings is that Trump, and most of the Republican House candidates fell men high in PM were particularly likely to vote for Trump solidly in line behind the President—advocating his policies in the 2016 general election. and even at times emulating his aggressive personal style Regarding our search-based index of PM, we do not (Smith, 2018; Zeleney, 2018). The Republican Party— claim that PM is the only reason men search for PM-related already the more “masculine” of the two major American topics on the internet. Search volumes for any one of our political parties—had by 2018 coalesced around an aggres- terms are likely driven by a number of concerns, including sive brand of politics potentially affirming to men high in pre- age-related and medical considerations. Our terms, how- carious masculinity. We therefore tested the preregistered ever, appealed to men who scored high on a validated mea- prediction that Republicans would be successful in the House sure of PM—suggesting that precariousness is one source of elections to the extent that their districts are high in PM. variation in their use. Moreover, the fact that the predicted associations between PM search volumes and political out- Study 3: Precarious Manhood and the comes held after controlling for a wide range of potential confounds (including regions’ age composition) suggests 2018 Congressional Election that our PM search set validly proxied masculine concerns. In Study 3, we predicted that Republican candidates’ success Additional confidence in the validity of our PM index comes in the 2018 elections for the U.S. House of Representatives from the fact that it correlated negatively with polling data would vary as a function of congressional districts’ (CDs) assessing the amount of respect men report receiving in their levels of PM. Files containing district-level PM estimates daily lives (see Study 2 Supplemental Material) and posi- and control variables were uploaded to the Open Science tively with a known predictor of masculine anxiety— Foundation repository prior to the midterm elections (https:// namely, unemployment rates (Michniewicz et al., 2014). osf.io/kufrt). Interestingly, we saw little evidence for a relationship between PM and voting for the Republican presidential can- Acquisition of Google Trends Data didate in either of the previous general elections. It therefore appears that voting for a Republican is not, in and of itself, As in Study 2, search volumes for the eight PM indicators related to PM. In contrast, voting for Trump, an unabashedly (Table 6) were extracted from Google Trends at the level of aggressive politician, was related to PM above and beyond DMAs. A search window encompassing the year before the other predictors. This raises the question of whether the GOP 2018 midterm elections (November 5, 2017, to November 5, since Trump’s election is associated strongly enough with 2018) was selected. 12 Personality and Social Psychology Bulletin 00(0)

Generating PM Estimates for CDs Table 8. OLS Regression Analysis Predicting Republican Vote Share in the 2018 House of Representatives Elections (Study 3, Although both DMAs and CDs cover the United States to N = 394 Congressional Districts). a nearly exhaustive extent (i.e., almost every U.S. location is contained in both a DMA and a CD), there is no system- 95% CI Predictor B [LL, UL] β r atic correspondence between these geographic units. Thus, a procedure was developed to map DMAs onto CDs Precarious 21.67* [3.79, 39.55] .22 .47** using ArcGIS software (Environmental Systems Research manhood Institute [ESRI], 2017). As described in the Study 3 Breast 13.71** [3.86, 23.55] .20 .12* Supplemental Material, this process involved first deter- augmentation mining the degree of CDs’ population overlap with one or Estrogen 3.64 [−8.21, 15.48] .03 .27** more DMAs, and then using weighted information about Menopause −10.89 [−23.01, 1.23] −.11 .02 Google searches within DMAs to “characterize” CDs in Internet −10.92 [−30.36, 8.51] −.09 .44** pornography terms of PM. Illegal 10.14 [−5.45, 25.74] .08 −.17** immigration 2018 Congressional Election Voting Data Mexicans 11.64 [−9.48, 32.77] .15 −.03 NASCAR 8.34 [−3.75, 20.43] .09 .38** The 2018 midterm election results were scraped from the Population −2.50 [−11.21, 6.21] −.04 −.32** New York Times website.13 Additional race information (e.g., Population 4.67 [−5.12, 14.47] .06 −.34** candidates’ party and gender) was scraped from Wikipedia14 density and combined with the voting file. Only the 394 races pitting Male % 11.31 [−6.78, 29.39] .09 .13* at least one Republican against at least one Democrat were Above 45% 20.45 [−14.52, 55.42] .20 .34** retained for analysis. The critical outcome variable in the White % 20.05 [−9.07, 49.17] .21 .05 resulting dataset was the percentage of the vote received by Black % −4.09 [−37.09, 28.91] −.03 −.47** the Republican candidate or candidates. The election data Asian % −31.76** [−52.40, −11.11] −.35 −.24** were merged with the CD-based Google search file, yielding Hispanic % 6.78 [−11.01, 24.57] .06 .07 a dataset that mapped PM composite scores to election College % −26.74* [−47.74, −5.75] −.34 −.54** returns for every CD. Unemployed % −11.80 [−25.43, 1.83] −.12 −.08 Median income 6.36 [−16.17, 28.90] .08 −.54** South 2.00 [−2.99, 6.98] .06 .31** Control Variables Note. Search-based control variables in italics. r indicates the bivariate In testing the relationship between regional PM scores and correlation between a predictor variable and the Republican vote. Republican vote share, we controlled for the same search- OLS = ordinary least squares; CI = confidence interval; LL = lower limit; UL = upper limit. related and demographic covariates as in Study 2. Control *p < .05. **p < .01. variables were calculated according to the procedure described above for mapping DMA-based scores onto CDs. panel) is a partial regression plot depicting the independent relationship between PM scores and Republican vote per- Results centage across the 394 CDs. The confidence interval for this 2018 Congressional elections. We tested the preregistered relationship excludes 0, suggesting a reliable association hypotheses that Republican candidates would receive a between PM and Republican voting in the 2018 congressio- higher percentage of the vote in House districts high (vs. nal elections. low) in PM before and after adjusting for a predetermined set of control variables (https://osf.io/abyxs). Supplemental Previous elections. As with our examination of presidential Table S6 displays correlations between variables assessed in election returns, we repeated our analysis of PM and con- Study 2. As hypothesized, the bivariate correlation between gressional voting for the previous two election cycles, PM scores and Republican House candidates’ vote shares extracting Google Trends search volumes for the years across districts was moderately positive and significant, immediately preceding the 2016 and 2014 House contests. r(394) = .47, p < .01. We next ran OLS regressions to test See Supplemental Tables S7 to S12 for full results of these whether this relationship was robust to the preregistered analyses, and Figure 3 for partial regression plots depicting covariates. Table 8 displays results of an OLS linear regres- the unique relationship between PM and Republican vote sion analysis in which PM and all search-based and demo- shares in the 2016 and 2014 House elections. The confidence graphic control variables were used to predict Republicans’ intervals for the 2016 and 2014 relationships include 0, sug- vote percentage. Net of all control variables, PM remained a gesting the absence of a reliable association between PM and significant predictor of Republican vote share. Figure 3 (left voting in these election cycles. DiMuccio and Knowles 13

Figure 3. Partial regression plots showing the unique relationship between district-level precarious manhood and Republican vote percentage in House elections after adjusting for search-based and demographic control variables (Study 3).

Discussion Study 2, Google searches for terms correlated with PM pre- dicted regional support for Donald Trump in the 2016 presi- Consistent with preregistered predictions, Republican can- dential election after adjusting for a wide range of search-based didates’ vote share in the 2018 elections for the U.S. House and demographic control variables. Finally, in Study 3, our of Representatives—our third operationalization of political search-based index of PM predicted support for Republican aggression—was higher in CDs where PM-related searches candidates in the 2018 congressional elections after applying were particularly popular. This relationship was robust to an the same controls. Taken together, our findings support the array of predetermined search-based and demographic notion that men who are likely to doubt their masculinity may covariates. Moreover, as described in the Study 3 Supple- support aggressive policies, politicians, and parties, possibly mental Material, finer grained analyses of the elections sug- as a means of affirming their manhood. The present research gest that Republican candidates benefited from running in and findings are the first to link precarious masculinity (as high-PM districts regardless of whether their Democratic opposed to masculinity alone) to three distinct but related opponent was a man or a woman. This, in turn, implies that instantiations of political aggression—a broad operationaliza- PM creates a pro-Republican—rather than antifemale—bias tion previously underexplored in the literature. among voters. Paralleling our analysis of presidential voting, the rela- tionship between PM and Republican congressional candi- Why Now? dates’ vote shares appears to be a relatively recent Although we observed significant relationships between PM development: Only in 2018 did we uncover a significant and voting patterns in the 2016 presidential election and the association between PM and GOP voting. Thus, it appears 2018 congressional elections, the PM–voting relationship was that the association between the Republican Party and tradi- not robust in the previous elections we examined. In the case tional masculinity (Ducat, 2004; Katz, 2016; Lakoff, 1996; of previous general elections, the significant bivariate correla- Winter, 2010) is not sufficient to render Republican voting a tion between PM and the 2012 Romney vote did not hold after potent manhood affirmation. Yet in 2018, perhaps owing to the inclusion of covariates in the model (Supplemental Table Donald Trump’s consolidation of power as the GOP’s stan- S3), and no correlation emerged between PM and McCain vot- dard bearer, a significant association between male PM and ing in 2008 (Supplemental Table S5). In the case of the 2016 Republican voting emerged. and 2014 congressional elections, we observed significant correlations between PM and Republican candidates’ vote General Discussion shares, but these correlations did not survive the inclusion of covariates (Supplemental Tables S8 and S10). This raises a Across three studies, we find that PM is associated with sup- crucial question: Why is the unique relationship between PM port for political aggression, namely, aggressive political and voting just now emerging? policies, Donald Trump, and GOP congressional candidates. Multiple factors are likely strengthening the link between In Study 1, an established measure closely related to PM PM and men’s political behavior. First, women have made (Reidy et al., 2014) predicted individuals’ support for policies significant inroads into male-dominated professions in the that signal strength, toughness, and forcefulness—including last several years (Kolko & Miller, 2018), and it may be increased military spending, torture, and the death penalty. In that precarious men seek to redress this intrusion into 14 Personality and Social Psychology Bulletin 00(0) traditionally masculine roles by voting for Trump and other Clarifying Our Claims GOP politicians. Second, voices in professional and aca- demic circles, as well as news and social media, have high- Efforts to understand the social factors that shape gender lighted masculinity itself as a topic of national discourse. In identity have aroused harsh criticism and resistance in recent 2019, for example, the American Psychological Association years (Redden, 2018). Given this fact, along with current released its first “Guidelines for Psychological Practice sensitivities around masculinity and politics, we feel it is with Boys and Men” (American Psychological Association, critical to clarify what we are—and are not—claiming in the 2018). The report received a great deal of highly polarized present work. media attention, with many observers decrying what they First, we do not claim that PM is the only reason people saw as an attack on traditional masculinity (Fortin, 2019; support aggressive policies, Donald Trump, or the GOP. French, 2019). Moreover, data from Google Trends indi- Any given pattern of political behavior is likely multideter- cate that interest in the phrase “toxic masculinity” increased mined, and we have sought to examine just one potential markedly between 2016 and 2020—peaking in January influence on these preferences. Consequently, it would be 2018, with the release of Procter & Gamble’s “The Best an error to assume that men who support aggressive poli- Men Can Be” advertising campaign. The short film accom- cies, Trump, or the Republican Party are high in precarious panying this campaign, which urges men to reject a culture masculinity. Nor do we claim that political behavior driven of bullying, harassment, and misogyny, was assailed by by PM is necessarily immoral or undesirable. Identifying conservative critics (Hsu, 2019) and is currently the 48th motives that contribute to a pattern of behavior entails most disliked video on YouTube (16th in terms of percent- nothing about that behavior’s inherent value. age of all reactions; “List of most-disliked YouTube vid- Second, we do not claim that support for aggressive eos,” 2020). Masculinity’s increasing politicization suggests policies, Trump, or the GOP are the only possible political that precarious men may seek to reinforce traditional man- manifestations of PM. In fact, we believe that future stud- hood through political means. ies should look for potential manifestations of PM on the The absence of a robust association between PM and political left. Although left-leaning policies are typically Republican voting before 2016 may also reflect changes less aggressive in their goals, the methods by which politi- within the GOP itself. A political party is an institutional cians and their supporters pursue those goals vary consid- entity and, therefore, subject to ideological evolution and erably. It may thus be that, on the left, PM manifests in a even reversal, often resulting from electoral strategies engi- preference for uncompromising, hardball political tactics. neered by party elites (e.g., Southern realignment under Anecdotal evidence for a pattern of online harassment by Richard Nixon and Barry Goldwater; Richardson, 2014). As male followers of Bernie Sanders (so-called “Bernie noted earlier, the Republican Party has shifted to the ideo- Bros”; Stephens, 2020) and accusations of misogyny logical right in recent years (Grossmann & Hopkins, 2016; against members of the self-styled “dirtbag left” (e.g., the McCarty et al., 2006; Ornstein & Mann, 2006), and in so hosts of the popular Chapo Trap House podcast; Bowles, doing increasingly adopted stances that might appeal to pre- 2020) suggest that this is a question worthy of empirical carious men (e.g., hardline immigration policies). Despite study. the historically gendered connotations of the Republican Third, we do not claim that men who support aggressive and Democratic parties (Lakoff, 1996; Winter, 2010), then, politics, or search for particular topics on the internet, are it is perhaps not surprising that no relationship between PM physically different from those who do not. This research and Republican voting emerged until the rise of Trump and proceeds from the idea that some men, more than others, a GOP under his influence. Trump, and the Republican Party are exposed to unyielding masculine cultures and/or envi- he leads, appear more consistently aggressive than high- ronment shocks (e.g., spikes in unemployment or the loss profile GOP politicians of the recent past—including Mitt of traditionally male industries) that are known to threaten Romney (who, despite describing himself as “severely con- men’s sense of (socially defined) value. servative,” fought to expand health coverage as governor of Finally, we do not claim that traditional masculinity is Massachusetts) and John McCain (who, despite his hawk- bad. Indeed, any culturally prescribed mode of masculinity ishness, pushed for conciliation with Vietnam and defended or femininity is likely to include features that are functional Barack Obama against xenophobia attacks). Although the and dysfunctional in certain situations. For instance, being recent ideological evolution of the Republican Party may assertive, competitive, and strong (stereotypically masculine not have occurred without Trump—just as party realign- traits) is advantageous across many life domains. Likewise, ment in the 1950s and 1960s required strategic decisions by being forgiving, compassionate, and accommodating (ste- Nixon, Goldwater, and other elites—it is likely that these reotypically feminine traits) can be undesirable in the wrong changes will far outlast Trump as a political force. Thus, we contexts (Nelson, 1994; L. A. Ricciardelli & Williams, 1995; believe the link between precarious masculinity and Woodhill & Samuels, 2003). Our goals in this research are Republican voting will generalize to future elections. descriptive, not prescriptive. DiMuccio and Knowles 15

Limitations and Future Directions Funding Although the present results conform well to our theoretical The author(s) disclosed receipt of the following financial sup- port for the research, authorship, and/or publication of this arti- claims, some caveats are in order. Most importantly, the find- cle: S.H.D. received support from a National Science Foundation ings reported here are correlational, and thus do not afford Graduate Research Fellowship. strong claims about the causal effect of PM on men’s politi- cal behavior. Despite the fact that we controlled for a wide range of ideological, demographic, and search-based vari- ORCID iDs ables across studies, it is still possible that voting behavior Sarah H. DiMuccio https://orcid.org/0000-0002-7311-5787 affects PM—or that voting and PM are influenced by some Eric D. Knowles https://orcid.org/0000-0001-8525-1930 unmeasured third variable. Nonetheless, our interpretation of the data receives outside support from experiments demon- Supplemental Material strating that masculinity threats have a causal influence on Supplemental material is available online with this article. male aggression (Bosson et al., 2009) and political attitudes (Willer et al., 2013). Notes Moreover, our studies examining the relationship between PM and voting (Studies 2 and 3) are subject to 1. This is in contrast to the predominant (American) view of limitations intrinsic to “ecological inference” (King, womanhood, which is perceived as a status conferred automati- 1997). Interpretative ambiguities are unavoidable when- cally and through naturally occurring developmental changes. Because of this perception, womanhood is seen as a status that ever researchers attempt to infer the behavior of individu- is relatively assured; it is neither expected nor required that als on the basis of aggregate statistics. In the present women earn or protect their gender status as women (Vandello & context, we cannot be certain that the same individuals are Bosson, 2013; Vandello et al., 2008). It is theorized that this is the responsible for variation in both PM-related search vol- case because men hold the high-status gender identity and must, umes and candidates’ vote shares across regions of the therefore, fight for full membership in this dominant group. country. The findings of Study 1, however, suggest that 2. It should be noted that “precarious manhood” (PM) is the ours is the most plausible interpretation of the data. In that expectation that men’s status as “real men” is one that is dif- study, participants high in an individual-difference mea- ficult to achieve and easily lost (Vandello et al., 2008) This sure of PM were significantly more likely to support phenomenon primarily manifests itself situationally, whereby aggressive political policies—and to report voting for men who experience gender-threatening cues feel anxiety and Donald Trump—than were participants low in PM. We are momentarily motivated to alleviate this anxiety and prove their manliness by engaging in masculine behaviors. However, believe that this fact increases the likelihood that our men can also experience chronic stress about their gender role, aggregate-level findings in Studies 2 and 3 reflect the vot- whereby they feel anxious about not living up to masculine ing behavior of men high in PM. Future research should standards. The latter is well captured by gender role discrep- utilize experimental methodologies to manipulate man- ancy stress and can be conceptualized as a trait measure of hood threats to assess the causal outcomes of such threats anxiety about the male role (Vandello & Bosson, 2013). on men’s endorsement of aggressive political policies and 3. We regard some political behaviors (e.g., voting for Republican aggressive politicians. candidates in the midterms) as having multiple aggressive qualities. Although this does not detract from these behaviors’ status as aggressive, it does limit our ability to discern which Conclusion of their aggressive features connects them to PM. Our results suggest that politics is a domain in which males 4. Two other questionnaires were included in the survey for exploratory purposes: the Pacific Attitudes Toward Gender who are anxious about their levels of masculinity—that is, Scale (Vaillancourt & Leaper, 1997) and the General System men high in PM—attempt to affirm their status as “real Justification Scale (Kay & Jost, 2003). men.” If this is correct, then the remedy to politics unduly 5. Our conclusions are unaffected by the choice of different ana- influenced by masculine insecurity is not to force men either lytic strategies. In particular, a distinction between aggres- to more securely embrace, or abandon entirely, a commit- sive and nonaggressive policies based on only one criterion ment to traditional norms of masculine behavior. Rather, (i.e., policies’ perceived aggressiveness or the magnitude of because PM has its genesis in unyielding masculine norms, the support–aggression association) produces similar results. we suggest that social settings be designed to communicate Moreover, using either criterion (or a composite of both) as a flexibility and the freedom to choose whatever form of mas- continuous predictor in interaction with GRDS yields the same culinity (traditional or otherwise) one prefers. conclusion: Masculine concerns significantly and positively predict support for more aggressive policies but not support for less aggressive policies. Declaration of Conflicting Interests 6. It is theoretically possible that aggressive political prefer- The author(s) declared no potential conflicts of interest with respect ences affect precarious masculinity rather than vice versa. to the research, authorship, and/or publication of this article. Unfortunately, our correlational design prevents us from 16 Personality and Social Psychology Bulletin 00(0)

assessing this possibility. Note that we cannot simply rerun References our linear regression analyses with gender-role discrepancy Altemeyer, B. (1996). The authoritarian specter. Harvard University stress as the outcome variable and policy support as the pre- Press. dictor variable, as these models yield mathematically equiva- American Psychological Association. (2018). APA guidelines for lent results concerning the association between GRDS and psychological practice with boys and men. policy support. Berke, D. S., Reidy, D. E., Miller, J. D., & Zeichner, A. (2017). 7. Critically, we do not claim that searching for any particular Take it like a man: Gender-threatened men’s experience of term on the list means that a man is precarious, or that PM is gender role discrepancy, emotion activation, and pain toler- necessarily high wherever any individual terms are popular. ance. Psychology of Men & Masculinity, 18, 62–69. https://doi. 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