Youth Sports, Gender, and Pathways to Political Involvement

Youth Sports, Gender, and Pathways to Political Involvement | 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 Youth Sports, Gender, and Pathways to Political Involvement Amanda Friesen, Noah Vanderhoeven, Lachlan Fisher This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8001537/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 Politics and sports are historically men’s domains. Increasingly, women have made strides in participating in politics, running for office, and competing in professional sports. Psychologically, participating in sports facilitates comfortability with competition, solving collective action issues as a team, increases in self-esteem, and bolstering other psychological resources. We suggest that these resources could spillover into an interest and engagement in political and civic activities. Using 8 secondary datasets across dozens of countries, we investigate the relationships between sports and politics by gender. We also control for parental socioeconomic variables and find a pattern of results that suggest youth sport participation is associated with higher levels of political engagement, particularly for girls. This broad set of evidence suggests sport might be a potential pathway to prepare youth for political behavior and calls for more research to uncover potential longitudinal effects of this activity. Introduction Politics and sports are historically and often presently men’s games, but as women have made gains in participating in politics and running for and holding office, we also see them fighting for equality on the soccer field and commanding large audiences across a number of professional sports. For women, politics and sports are often connected, with women athletes leading in activism around racial justice and LGBTQ+ rights in addition to their efforts to achieve parity with their male counterparts in sport. And all of this begins from a young age, making youth sports a fruitful place to look for pathways to political involvement. Generally, young adults participate less in politics, and of course, for younger folks in particular countries, do not have the right to vote until 18 or even 21. A vast literature has emerged to better understand political socialization from parents, peers, and schools. Our present focus is on the possibility of youth extracurricular activities– particularly organized sports – that could provide psychological resources and pathways to political interest and engagement. Politics is competitive and full of conflict so the socialization of sport competition and working as a team in the face of opposition could have an effect, specifically for girls. Though boys enjoy the competitive nature of politics, this can be an obstacle for girls who tend to stay engaged if decisions are more consensus-based (Wolak & McDevitt, 2011). Of course, sport and other forms of extracurricular activities may simply be proxies for socioeconomic resource and parental/community support but we argue that organized sports exert a different effect because of the ensuing psychological and physical benefits. In fact, a recent large-scale report commissioned by the Women’s Sport Foundation in the U.S. found that 69% of women who played sports held a leadership position and a majority indicate through sport they learned “teamwork,” “learning from mistakes,” and “handling pressure” (Sharrow, Staurowsky & Davis, 2024). Importantly, as organized sport involvement persisted across years, this increased the odds women would hold leadership roles. We are interested in how youth sport involvement translates into everyday political interest and participation. To test these claims, we identified several secondary data sources collected across dozens of countries that include both sport and political variables. Youth Political Socialisation and Gender Extensive research has tied aspects of youth socialisation to civic and political participation in adulthood; however, the specific nature of this socialisation is still uncertain. One important aspect of political engagement is the gender gap, which emerges after high school (Lawless and Fox 2014). The specific origins of this gap is debated, with explanations varying from socialisation and social role theory to conflict avoidance. Gendered socialisation theory argues that, “[gender] roles originated based on biological differences: Men’s strength and women’s childbearing meant that men occupied roles associated with hunting and laboring in the public sphere while women reared children and maintained home life.” (Schneider and Bos 2019, 175). These differences persisted as society became more industrial and meant that men were the primary economic actor and women the primary homemaker, establishing quite rigid gender norms that were difficult to shift (Wood & Eagly, 2012). In turn, men and women developed different traits and perceived norms of what a feminine woman and masculine man entailed. For women, this involved the development of communal traits (e.g., empathy, caring, kindness) that aid in caring tasks, whereas men prioritized agentic traits (e.g., aggressive, ambitious) that fit with their roles as hunter, farmer, worker, and eventually, leader (Wood & Eagly, 2012). Adherence to the women-as-homemaker and men-as-provider archetypes declined over time, with physical strength mattering less in post-industrialized countries and women making advances in their educational attainment; however, occupational segregation (e.g., women as nurses, men as mechanics) persists, perpetuating perceptions and selective pressures that women and men ought to adhere to communal and agentic traits, respectively (Cejka & Eagly, 1999, Wood & Eagly, 2012, Koenig & Eagly, 2014, Fox, 2017). Furthermore, direct conflict between the communal/agentic social norms of gender roles and individual/communal political norms persists. Conroy and Green (2020) find that political candidates who successfully launched a campaign after expressing preliminary interest utilised more masculine-coded agentic language, whereas candidates who failed to launch a campaign after expressing preliminary interest tended to use more communal language. Therefore, Conroy and Green (2020) argue that masculine communication norms are better suited for politics, which presents a conflict between traditional gender norms and political norms when considering political participation. As the expectations of a feminine behavior are seen as incompatible with the masculine norms of politics. The masculinity of politics is further supported by Schneider, Holman, Diekman, and McAndrew (2016) who found, in a study of high school students in the United States, that people perceive politics as more masculine because they believe it delivers masculine-coded power goals as opposed to feminine-coded communal goals. The frames that are applied to the goals of political tasks are also important. Kahalon, Shnabel, and Becker (2020) analyse the gender gap in math performance in a game setting, through role conflict and the influence of stereotypes. When performance in a math game was linked with a shared reward, women performed just as well as men (Kahalon et al., 2020). Yet, when the payoff was strictly for the individual, women performed worse than men on a math task, due to fear of a backlash from individual gains (Kahalon et al., 2020). With politics framed as conflict-heavy and women’s decreased confidence in their capacity to manage conflict in comparison with men, there is an apparent role-conflict here. This role conflict is demonstrated by Wolak and McDevitt (2011), where women are shown to have less confidence in their political knowledge, demonstrating an avenue for political exit. This is again affirmed by Wolak’s later work (2020), who writes that women may shy away from competitive settings due to, “reservations about taking risks” (1492), and, “Self-confidence rivals the effects of education and household politicization as explanations of psychological engagement in politics. Moreover, these are patterns that hold true for both young men and young women in the sample” (1495). Within the context of Wolak’s (2020) paper, self-confidence is used as a mediating effect which explains, “15% of gender’s effect on political interest” (1497). It thus appears that conflict-aversion is a piece of the gender gap in politics, acting in conjunction with socialisation to reduce women’s engagement with politics. Within the context of our study, the gap in self-confidence is theorised to be at least partially mediated by differing sports participation–where youth sports participation offers a means of socialising an individual to be more comfortable with conflict and prepare them for future civic involvement through relevant skills like confidence and leadership. Similarly, Coffé and Bolzendahl (2021) investigate the masculinity of politics using the 2012-13 Dutch Longitudinal Internet Studies for the Social Sciences panel survey data, where they determined communal traits are negatively associated with activist-type political activity while agentic traits are positively associated with political participation. However, women still participate in politics as much as men, just in the form of less resource-intensive private activism. This difference was theorised to be due to the communal/agentic gendered socialisation of women and men, with women favouring less resource-intensive private activism, while men favour more conflictive institutional politics (Coffé and Bolzendahl 2021). They also found no significant difference between men and women in their participation in collective activism (when controlling for political interest), suggesting that perceived masculinity is connected to traditional political activity, but not of informal political avenues. Similarly, for girls and boys, Cicogani, Zani, Fournier, Gavray, and Born (2012) theorize that the gender gap is due to differences in which activities they value, not necessarily because girls value politics less. In a comparison of two studies, they found that female youth are more likely to participate in social groups and are also more likely to join structured clubs, both of which are less suited to political activity than the clubs boys typically join. In terms of volunteering, Riggers-Piehl, Lucchi, and Lim (2023) found that girls did a similar level or more of volunteering as boys. These considerations are also important in studying political participation, as different political activities involve different levels of conflict–such as signing a petition or volunteering in comparison with campaigning and protesting. The importance of the role conflict theory is highlighted by Lawless and Fox (2014), who write that people are more likely to pursue political careers if they show an interest in them at an early age, but the political gap itself does not appear until college begins. The gap in sports participation also appears in a similar way, thus alluding to a possible connection between conflict and gender role socialisation both contributing to the future gap in politics. While not explicitly connecting sports and politics, Jenkin et al. (2017) affirm a connection between enjoyment of competition and sports, especially with respect to continuing sport participation. It is possible that extended sport participation socialises individuals to be more favourable to conflict, which by proxy could have knock-on effects for enjoyment of politics. If the role-conflict theory and sport-competition socialisation are correct, we expect to see extended sports activity leading to an increase in enjoyment of conflict and increased political engagement. Sports and Gender Although much of sport development literature is focused on the health benefits of sports, youth sport participation has also been tied to numerous social benefits. First and foremost, an important aspect of sports to childhood social development is in its influence on pro-social behavior. Youth sports can play an important role in the development of social skills, positive socialisation to conflict, and an increase in determination and self-worth (Super, Hermens, Verkooijen, and Koelen 2018). Youth development sport programs also may improve self-worth and body esteem in girls (Rauscher and Cooky 2016). While civic clubs may have more direct effects on one’s capacity to engage future politics, sports also develop pro-social skills like teamwork, self-reliance, or grit; all of which help in future organisational participation. There are several psychological pathways sport could operate in translating to other forms of community, societal, or political participation. As mentioned, pro social behavior, working as a team to achieve a goal, and managing conflict and competition all may contribute to building confidence in other areas of life. But all sports are not equal avenues for these experiences and development, particularly as some sports closely involve teammates and others are undertaken solo. Nia and Besharat (2010) examine the different benefits generated by team versus individual sports, finding that team sports have better environment for developing trust, altruism, and compliance (sociotropy and agreeableness) while individual sports develop conscientiousness and autonomy. This may be due to self-selection, but this has not been sufficiently investigated as a causal mechanism, and Jakobsen (2014) offers some evidence that there are not significant differences between reasons for team versus individual sport participation. Turnidge, Côté, and Hancock (2014) also investigated sport’s impact on positive youth development, finding that it can occur through sports, but they are not a unique activity in this regard. Grit is also a potentially important but under-researched factor in both sports and political engagement. Grit is described by Cormier et al. as a, “person’s dispositional tendency towards passion and perseverance for long-term goals” (2024, p.2). Those with high grit tend to pursue goals amidst strife better than those with lower grit scores. In their literature review, they found that numerous studies show a positive relationship between sports participation and grit, with higher level athletes having increased grit. However, only one of 11 studies demonstrated a sex-based difference in grit values (with that single study showing women scoring significantly higher on grit scales). They also outline grit as a slightly problematic variable, as it is a composite of two subscales, which tend to have better prediction power if used individually. However, grit was still shown to be a useful measurement. In the context of sports, grit could potentially determine one’s tolerance for conflict and, by proxy, one’s enjoyment of it. Grit may therefore be useful as a mediating variable between sports and political engagement, although this has yet to be tested. There is some evidence of a connection between grit, school engagement, and civic attitude/behavior (Holbein et al. 2018). Examining the theory that education later influences civic engagement, Holbein et al. (2018) suggest most studies neglect to test what part of the education socialization experience leads to this participation. Potentially, it is due to an increase of psychosocial attributes such as prosocial behavior through school involvement. As evidenced by previous studies, grit has been shown to positively contribute to success outcomes, such as higher scores in math and reading, increased volunteering, increased political efficacy, and increased likelihood of planning to vote (Holbein et al. 2018). By focusing on grit as resilience and self-efficacy, they argue grit can predict one’s ability to overcome the difficult and/or costly behaviors accompanying civic activity, in addition to supporting one’s self-confidence. Thus, if sport helps develop grit and grit provides a foundation for political involvement, this may be an additional pathway from sport to politics. Another such pathway is through leadership development, a skill set tightly connected to both sport and politics as well. Leadership In the midst of building the current project and identifying key data sources to test our hypotheses, the Women’s Sport Foundation released a comprehensive and likely the most complete study of sport and women’s leadership to date (Sharrow, Staurowsky, and Davis 2024). Although the report does not meaningfully disentangle economic status from leadership and access to sports, they, nonetheless, illustrate a relationship between extensive sport involvement and future leadership positions (both formal and informal) for women. The key takeaways from their report were a connection between fundamental skills of leadership and sport development, as evidenced by survey responses linking sport activity and leadership skills as well as a correlation between named leadership roles in adulthood and youth sport participation. While the report has the depth and coverage currently missing from sport and politics literature, the survey questions are aimed at current women leaders so we are not able to compare the leadership of nonathletes or those who may answer about their sport experiences differently. Although the authors acknowledge socioeconomic barriers to sport, we cannot untangle how family income or other characteristics may lead to both sports and politics. Overall, the report is an important addition to investigations on sport and politics, and we intend to replicate several of their measures in our new data collection. For now, we see this set of findings as proof of concept about the different motivations, personality and skill development, and other accompanying benefits that translate youth athletic participation to leadership in adulthood. Athlete Activism Though our present study is concerned with a variety of political activities, focusing on particular forms of activism might also uncover the sport-to-politics pipeline. Professional athletes have the unique position of celebrity status due to their achievement within a physical field, leading to the organic development of a platform via social media. Many athletes have utilized their platforms and status to spread awareness for causes, such Black National Football League players kneeling during national anthems in protest of police brutality, or the strong support of the Black Lives Matter movement by both WNBA and NBA athletes (Thomas and Wright 2022). The advent of Title IX in the USA also provided another avenue for the development of advocacy skills, through which young women advocated for equality in both professionalised post-secondary sports and lower-level access. Title IX is a part of a series of amendments to the Education Act in 1972, and after a period of legal challenges, it established that college athletes in the United States are guaranteed the right to equal resources regardless of sex (Carrick et al., 2021, Druckman and Sharrow, 2023). In the fallout of Title IX, it was theorised women were provided with a framework through which they could advocate for equality in a traditionally male-dominated field, thus enhancing their civic skills. More recently, in the United States and other parts of the world, debates around the inclusion or exclusion of trans athletes in various competitions have highlighted the very political nature of who gets to play (Sharrow, 2025). Sports and civic involvement We have argued that there is a sport and politics overlap, both in motivations and selection into participating as well as the kinds of skills and dispositions associated with both realms. We are not the first to connect athletic endeavours to political participation. In a study of Japanese adults, Hayashida and Shimizu (2022) found continuous sport participation from youth to young adulthood is linked to increasing adult civic participation. Perks (2007) affirms this finding using Canadian data, “that the effects of youth sport participation on adult participation in community activities lasted throughout the lifecycle” (378), though the correlation is quite modest. In a longitudinal study of third graders, there is further evidence of this connection between sports and civic participation, even when accounting for socioeconomic status (Rotolo et al. 2020). Moreover, Donovan, Bowler, and Hanneman (2004) found a strong connection between athletics and passive political participation (e.g. voting), and individual sport participation was significantly related to voting, for Black young adults, even when accounting for family socioeconomic status and individual-level efficacy (Braddock and Dawkins 2007). They did not find these same increases for those participating in varsity team sports. But the evidence overall is definitely mixed. MacFarland and Thomas (2006) aggregated NELS and Add Health datasets to determine that while youth voluntary associations increase adult political participation, sports do not when controls for socioeconomic status were applied to their models. Moreover, Kirlin (2003) argues sports and hobby clubs have limited impact on later civic activity, while debate clubs and other community groups increase the likelihood of later civic activity. Rotolo, Johnson, and McCall (2020) also find that high school sport participation is not linked with civic engagement and countered the assumption that youth club activity increases adult civic behavior and prosocial behavior. Additionally, MacFarland and Thomas (2006) found non-school and school sports are not correlated with long-term political activity once background characteristics are considered. This data was collected at various time points, with a mix of measures, depending on data availability, and generally, scholars have not considered the specific focus on gender. Is it sports or is it socioeconomics? Socioeconomic status is a vital qualifying consideration within sports and politics literature. This is known as the selection hypothesis, which predicts that socioeconomic or background effects explain the predicted effect of organised/formal youth sports on civic engagement (Rotolo et al. 2020, p. 180). This theory was tested by Rotolo et al. (2020) utilising exclusively American, longitudinal data, which they note is unique from previous studies in the field which garnered mixed results (MacFarland and Thomas 2006; Perks 2007). Importantly, Rotolo et al. (2020) found that once background characteristics were taken into account, the sports activity of surveyed American youth did not have a significant impact on civic or political participation (p. 193). Moreover, aspects such as parental expectation of the youth’s academic performance, their “academic self-concept,” and grades, all also rendered sports insignificant to their future voting likelihood. There is no discounting the value of Rotolo et al.’s (2020) article to the sport-politics research paradigm, with its well-developed measures of sports, types of civic and political engagement, and background variables. However, it is a small study, at only 703 participants, which lies in stark contrast of Perks’ (2007) survey of 13,000 Canadians. Yet, Rotolo et al. (2020) perform longitudinal analysis while Perks (2007) does not. It is possible that the consequences of socioeconomic factors are not fully apparent without a longitudinal study, given it is challenging to accurately identify someone’s childhood wealth and current familial economic support; parental education can only approximate so much. Moreover, both Rotolo et al. (2020) and Perks (2007) leave the gender gap of both sports and politics unexplored, diminishing either study’s full support or disapproval of future research into the specific relationship between girls and women in sports and politics. These studies also neglect to analyse sport motivations, which may or may not align with the educational expectation Rotolo et al. (2020) performed. There is simply no data here exploring a connection between dedication to athletics correlating with a dedication to academia, which may both correlate with political and/or civic engagement. Though we cannot discern the nature of these relationships from current extant surveys, we do a broad survey of the field of sport and politics data, identifying key datasets with which we test the relationships between sport and politics, by gender, and eventually build multivariate models with a subset of this data. Finally, we propose a new study to better understand the mechanisms involved in the selection into sport, the skills developed by athletes, how this compares to other kinds of activities, and what this means for political engagement. Hypotheses H1: There is a positive relationship between youth sport participation and political engagement, even when controlling for parental socioeconomics. H2: There is an interaction with gender such that girls experience a higher positive relationship between sport and political engagement, as compared to boys. Data and Methods In an extensive review of the youth sports and civics literature, we uncovered a variety of data sources, which we narrowed to eight key datasets (see Table A.1 in the appendix for details on samples, measures, and bivariate relationships). The eight remaining datasets were chosen because their survey questions were most pertinent to our hypothesis, with relevant measures for socioeconomic status and sufficiently large sample sizes for generalisability and significance testing. Below we detail the significant, bivariate correlations, noted in Table A.1. Flash Eurobarometer 408 (2014). We utilised its measure of participating in activities of a political party in the past year compared with membership in a sports club. For girls, there was a significant positive correlation between sports and politics. (0.077, p < 0.001). Sample size: 13,454; ages, 15–30. Eurobarometer 62 (2004). We compared frequency of political discussion and frequency of sport participation. Sports and politics were significantly correlated for both girls/women (0.147, p = 0) and boys/men (0.036, p < 0.001). Sample size: 24,603; ages 15–97. Add Health, Waves 3 and 4 (2001–2002; 2007–2008) . This is a longitudinal dataset tracking primarily health outcomes of Americans, but during Waves 3 and 4, both sport and political questions were asked. The waves were considered both separately and together as there were some variables which were not consistent across waves. In Wave 3, participants were aged 18–26, while Wave 4 they were aged 24–32. In Wave 3, individual sport participation and political rally support saw positive correlations for both women (0.09, p < 0.001) and men (0.078, p < 0.001). Wave 4 uncovered a positive correlation for men between voting in local elections and both team (0.0316, p < 0.05) and individual (0.047, p < 0.05) sport participation. We also considered a longitudinal comparison, where sport participation in Wave 3 was compared with the voting in a location election variable from Wave 4. Again, there was only a positive correlation for men for both team (0.038, p < 0.01) and individual (0.041, p < 0.01) participation. Samples size: 4,856, ages 18–32. International Social Survey Programme 2007, Leisure time and sports . We compared participation in sport, civic, and cultural groups with political participation. For all measures, there was a significant correlation (p < .001) between club participation and political participation (girls/women r = .178; boys/men r = .16). Civic group participation and politics were more strongly related (girls/women r = .331; boys/men r = .356) as were cultural groups and politics (r = 0.263 for girls/women and r = 0.299 for boys/men). Sample size: 27,599; ages 15–97. University of Washington (2000–2008), Beyond Highschool (ICPSR 33321) (2000–2010) . We compared participation in sports, debate, and drama clubs of Washington grade 12s with voting in the presidential election of 2008. The only significant correlation was a negative relationship between sports and voting for both girls/women (-0.136, p < 0.01) and boys/men (-0.154, p < 0.01). There was no significant correlation between debate or drama clubs and presidential voting. Sample size: 831; ages 16–18 (First survey) and 26–28 (second survey, 10 years later). The Civic and Political Health of the Nation survey (2002). We compared voter registration and various activities both inside and outside high school. For boys/men, there were only significant correlations between non-high school sports participation (0.235, p < 0.05) and non-high school political groups (-0.25, p < 0.05). For girls/women, there were only significant correlations with voter registration between student council (-0.100, p < 0.05), debate club (0.035, p < 0.05) and non-high school political group participation (-0.287, p < 0.01). Sample size: 209–910 (varies for each activity); ages 15–25. Eurobarometer 88.4 (2017) . We compared a measure of political interest with sport club membership, finding a significant correlation for both girls/women (0.082, p < .001) and boys/men (0.028, p ≤ 0.05). Sample size: 27,933; ages 15–99. Eurobarometer 97.3 (2022) . We compared a measure of political interest with sports participation frequency, finding a significant correlation for both girls/women (0.123, p < .001) and boys/men (0.038, p < 0.001). Sample size 26,569; ages 15–99. In summary, for girls/women, there were nine significant positive correlations between extracurricular activities and political engagement, with five for sports and four for non-sports; and three significant negative correlations (one sport, two nonsports). For boys/men, there were 12 significant positive correlations with politics, with 10 for sports and two for non-sports and two negative correlations (one each for sports and nonsports). If we compare the significant correlations across all datasets, with both girls/women and boys/men, 6/8 show girls and/or women having a higher correlation between an activity and political participation. Of these, all six relate to sports measures. Importantly, for one of these, girls/women have a smaller negative correlation than boys/men by a small margin (The University of Washington Longitudinal study). Of course, there is also evidence of no relationship between politics and extracurricular activities. For girls/women, this included the Add Health measures of sport and local voting and the Civic and Political Health of the Nation measure of non-high school sports and voter registration. The only nonsignificant correlation between political discussion and sport for boys/men were Flash 408 and the non-sport measures from the Civic and Political Health of the Nation data set (student council, debate, and a non-high school political group). Overall, the significant correlations are small but are comparable in size to those found by Perks ( 2007 ). There is a severe lack of long-term data on the influence of sports on political activity. Of what is measured, there are limited activities considered. Most importantly, the majority of correlations across all datasets considered were found to have no statistical significance. This perhaps explains some of the diversity in results for previous studies. Moreover, the year the study took place appears to be irrelevant for determining correlation level, as the datasets with significant results collected data from across the 2000s (2000–2022). There was heavy variation in the type of political measure utilised by the surveys, but it is uncertain what impact this had on the connection between activity and outcome; it ranged from political interest, support, and actual participation–and there were significant correlations for all of these in at least one dataset. The potential differential impact of team versus individual sport is also uncertain, with few datasets differentiating sport type. Add Health saw a slight increase with respect to individual over team sport for local voting in both 2008 and the longitudinal measure, but this increase is quite small (+ 0.016 for W4, + 0.003 for longitudinal). The women’s result also showed no significance, so a gendered comparison is not possible. Very few datasets cover both in-depth sport and political questions. Generally, they fail in one of three areas: (1) the political or sport measure is too simplistic or only tracks one aspect like voter registration or frequency of activity, (2) lack of longitudinal data, (3) small sample size within an otherwise large dataset. Importantly, even some longitudinal datasets like the Add Health survey from 1994–2018 lack repeated questions between years, meaning both sports activity and political participation were measured only during a single wave. Because of these additional shortcomings, we further reduce our corpus of data to the three best surveys for our purposes and test these further below. Multivariate Analyses Taking all of this into account, we identified three surveys with sufficient measures and sample sizes to analyze using various regression models to further unpack the gendered relationship between sports participation and political participation. We chose these three datasets because we felt they had the best combinations of the highest quality measures of sports club or team participation and political participation: Waves 3 and 4 of Add Health, ISSP 2007 Sports and Leisure wave, and the Flash Eurobarometer 408 (2014). For the measures of sports participation, the main exclusion criteria when we were identifying datasets for this paper were whether the measure used a direct question about sports club participation of membership. If the dataset has a proxy for sport participation, like time spent doing other leisure activities, we excluded it from our paper and appendix. If the sports participation measure is about explicit sport participation as an extracurricular activity, then we feel it is a strong measure for our purposes and included it in our correlation analysis. The other dimension is the kind of political measures, divided between participation and political interest measures. Our research question for this paper concerns the relationship between youth sports and political participation, whether that be participating in discussion groups, political clubs, political acts/behaviors, voting and/or eventual candidacy. The Flash Eurobarometer 408 (2014) was a study of 28 countries within the European Union, surveying “education, job prospects and volunteering of the European Youth.” (European Commission, 2015). We utilised its measure of participating in activities of a political party in the past year compared with membership in a sports club. For Add Health we used Waves 3 and 4 (1994–2018, with Wave 3 taking place from 2001–2002 with participant age ranging from 18–26 and Wave 4 taking place from 2008–2009 with participant age ranging from 24–32). This is a longitudinal dataset tracking primarily health outcomes of Americans, but during Waves 3 and 4, both sport and political questions were asked. More specifically, participants were asked in Wave 3 whether they attended a political rally in the past 12 months, if they participated in individual and/or team sports over the past 12 months, and for Wave 4, whether they voted in recent local elections or participated in individual and/or team sports over the past 12 months (Harris and Urdy, 1994–2018). Finally, we used the ISSP 2007, Leisure Time and Sports module (ISSP Research Group, 2009 ). From this data, we compared participation in sport, civic, and cultural groups with political participation. Results The first series of models used the data from ISSP 2007 Leisure Time and Sport module. We used ordinary least squared regression, as the measures of sports participation and political participation used continuous scales of the number of days during a week people participated in these activities on average. Our dependant variable is whether an individual participated in the political party or organization in the last twelve months, with responses ranging from never to at least once a week. Our independent variable concerns participation in sports clubs or teams and uses the same scale as our dependent variable. We also controlled for self-perceptions of social class, as respondents were asked to rate their place on a ten-point scale, where ten was the highest and one was the lowest, as access to organized sport clubs and sport equipment is mediated by wealth in many cases (Rotolo et al. 2020). Gender is included as we are interested in how sport may act as a form of gendered socialization that encourages girls to participate politically later in life, and age as another relevant sociodemographic factors. These models are shown in Table 1. In our first model, we use the entire sample of 42,790 respondents and find that sport participation is a statistically significant predictor of political participation at the 95% significance level. As people participate in sport clubs more, they are more likely to participate in political parties and organizations ( p < <0.001 ). We can also see from our second model that the interaction between gender and sports participation is statistically significant ( p < <0.001 ) though substantively small. As another way to look at these effects, we split the sample by men (19,390) and women (23,400) and find that women are slightly more likely than men to participate in political parties if they are greater participants in sports clubs, finding support for our hypotheses ( p < <0.001 ). As a robustness test, we also have ordinal logistic regression models in the appendix (Table A.2) that replicate our findings using OLS. Table 1: ISSP 2007, political participation regressed on sport participation by gender Model 1 Model 2 Model 3 - Men only Model 4 - Women only Predictors Estimates std. Error p Estimates std. Error p Estimates std. Error p Estimates std. Error p Sports participation 0.08 0.00 <0.001 0.07 0.00 <0.001 0.07 0.00 <0.001 0.08 0.00 <0.001 Social Class 0.01 0.00 <0.001 0.01 0.00 <0.001 0.01 0.00 <0.001 0.01 0.00 0.018 Gender (Female) -0.04 0.01 <0.001 -0.06 0.01 <0.001 Age 0.00 0.00 <0.001 0.00 0.00 <0.001 0.00 0.00 <0.001 0.00 0.00 0.076 Gender x Sports participation 0.01 0.00 0.030 Random Effects σ 2 0.40 0.40 0.46 0.34 τ 00 0.04 V5 0.04 V5 0.05 V5 0.03 V5 ICC 0.08 0.08 0.10 0.07 N 31 V5 31 V5 31 V5 31 V5 Observations 42790 42790 19390 23400 Marginal R 2 / Conditional R 2 0.028 / 0.110 0.028 / 0.110 0.023 / 0.117 0.029 / 0.098 AIC 82320.848 82327.056 40322.581 41506.390 Our next series of models rely on data from the Flash Eurobarometer 408 and use binomial logistic regression, as our dependent variable in this dataset concerned whether or not a participant had voted in a local, regional, or national election in the last year. Our independent variable was also binary and concerned whether or not a respondent had participated in a sports club in the last 12 months. Finally, we controlled for participant education as a measure of socioeconomic status, as access to organized sport clubs and sport equipment is mediated by wealth in many cases (Rotolo et al. 2020). We report odds ratios for our binomial regression models, where coefficients greater than one represent a variable that has a positive relationship with the dependent variable and variables with coefficients that are smaller than 1 representing a negative relationship (Persoskie and Ferrer, 2017). These results are shown in Table 2, which shows the coefficient for the effect of sports participation and education level on likelihood of voting in a local election from models 7 and 8. In our fifth model, we use the complete sample of 13,286 participants. We find that respondents who participated in sports clubs were more likely to have voted in the last 12 months than those who did not participate in sports clubs. However, we find that women were less likely to have voted than men. Both results were statistically significant at the 95% significance level ( p < 0.001 ); however, the interaction effect between gender and sports participation in model six was not statistically significant ( p = 0.218 ). Nonetheless, we ran models by splitting the sample into men (6,416) and women (6,870). Here, we only found evidence in the women-only sample that those who participated in sports clubs were more likely to have voted, and these results were statistically significant ( p < 0.001 ). Overall, we found evidence in support of our hypothesis as despite women being less likely to political participate than men, women who participated in sports clubs were more likely to have voted than women who did not participate in sports clubs. Table 2: Eurobarometer 408, political participation regressed on sport participation by gender Model 5 Model 6 Model 7 - Men only Model 8 - Women only Predictors Odds Ratios std. Error p Odds Ratios std. Error p Odds Ratios std. Error p Odds Ratios std. Error p Sports participation 1.18 0.06 0.001 1.12 0.07 0.085 1.13 0.07 0.059 1.24 0.09 0.004 Gender (Female) 0.82 0.04 <0.001 0.80 0.04 <0.001 Age 1.21 0.01 <0.001 1.21 0.01 <0.001 1.22 0.01 <0.001 1.20 0.01 <0.001 Education 1.44 0.02 <0.001 1.44 0.02 <0.001 1.46 0.03 <0.001 1.42 0.03 <0.001 Gender x Sports participation 1.13 0.11 0.218 Observations 13286 13286 6416 6870 R 2 Tjur 0.215 0.215 0.225 0.207 AIC 13799.397 13799.879 6576.052 7223.317 Lastly, we used data from Waves 3 and 4 of the Add Health survey. For models nine through eleven, we used the binary measure of political rally attendance from Wave 3 as the dependent variable, and thus we used binomial logistic regression. For models 12 through 14, we used the ordinal measure of whether a participant usually votes in local or statewide elections from Wave 4 as our dependent variable, and thus we conducted ordinal logistic regression. Finally, in models 15 through 18, we looked at how sports participation in Wave 3, time t-1, affected whether a participant usually votes in local or statewide elections in Wave 4, or time t , using ordinal logistics regression. We controlled for gender, age, and income in all of our models. We also controlled for political rally attendance reported in Wave 3, or time t-1, in the case of using behavior reported in Wave 3 to predict behavior reported in Wave 4. For the series of models for Wave 3, we use the full sample of 3,320 participants with complete data, and then run models for the male (1,508) and female (1,812) sub-samples. For the series of models for Wave 4, we use the full sample of 3,917 participants with complete data, and then run models for the male (1,732) and female (2,185) sub-samples. Finally, for the series of models using measures from Wave 3 to predict behavior reported in Wave 4, we use the full sample of 3,901 participants with complete data and then run models for the male (1,724) and female (2,177) samples. In the first series of Add Health models, shown in Table 3, using data from Wave 3, we find that individual sport participation is predictive of political rally attendance. In particular, women who played individual sports are more likely to have attended a political rally, and these results are statistically significant ( p < <0.001 ). Overall, women who participated in individual sports are also slightly more likely to have attended a political rally than men who participated in individual sports. Whether a participant played team sports is not a statistically significant predictor of political rally attendance, nor is gender in the full model using only Wave 3 measures. However, using only Wave 4 measures, we do not find great support for the effect of sports participation on voting behavior. In Table 4, our models using only measures from Wave 4, we find that women and wealthier respondents are significantly more likely to have voted in local or statewide elections than men ( p < <0.001 ). Those who participated in team or individual sports were marginally more likely to have voted, but these results were not statistically significant. Table 3: Add Health Wave 3, political participation regressed on sport participation by gender Model 10 Model 11 - Men only Model 12 - Women only Predictors Odds Ratios std. Error p Odds Ratios std. Error p Odds Ratios std. Error p Team sport 1.00 0.06 0.979 0.93 0.12 0.581 1.02 0.07 0.732 Individual sport 1.23 0.05 <0.001 1.30 0.08 <0.001 1.15 0.07 0.025 Gender (Female) 1.14 0.20 0.439 Age 0.24 0.25 0.167 0.44 0.62 0.559 0.10 0.16 0.142 Personal Income 1.00 0.00 0.948 1.00 0.00 0.380 1.00 0.00 0.496 Observations 3820 2025 1795 R 2 Tjur 0.007 0.012 0.006 AIC 1272.847 691.363 585.206 Table 4: Add Health Wave 4, political participation regressed on sport participation by genders Model 13 Model 14 - Men only Model 15 - Women only Predictors Odds Ratios std. Error p Odds Ratios std. Error p Odds Ratios std. Error p Team sport 1.02 0.03 0.636 1.00 0.07 0.948 1.02 0.04 0.659 Individual sport 1.03 0.02 0.090 1.03 0.03 0.378 1.04 0.03 0.115 Gender (Female) 1.51 0.09 <0.001 Age 3.07 1.06 0.001 1.72 0.80 0.246 6.64 3.48 <0.001 Household Income 1.08 0.01 <0.001 1.07 0.02 <0.001 1.10 0.02 <0.001 Observations 3917 2185 1732 R 2 Nagelkerke 0.027 0.012 0.028 AIC 10691.968 6001.921 4695.538 Finally, Table 5 shows our models using measures from Wave 3 to predict voting behavior reported in Wave 4. The main independent variables were team and individual sport participation reported in Wave 3. We then controlled for team and individual sport participation reported in Wave 4 and then participant gender, household income, and age. For the models using measures from Wave 3 to predict behavior reported in Wave 4, we found that women, wealthier respondents and older respondents, and especially older men, were more likely to have voted. We did not find statistically significant evidence that individual or team sport participation reported in Waves 3 or 4 were related to a greater likelihood of voting. This was consistent in men and women, despite women being overall more likely to vote according to this data. Finally, unsurprisingly, those who reported attending a political rally at time t – 1 were more likely to vote at time t across all of the models using measures from Wave 3 to predict behavior reported in Wave 4. Table 5: Add Health Waves 3 and 4, political participation regressed on sport participation by gender Model 16 Model 17 - Men only Model 18 - Women only Predictors Odds Ratios std. Error p Odds Ratios std. Error p Odds Ratios std. Error p Team sport , t-1 1.03 0.03 0.334 0.99 0.05 0.784 1.04 0.03 0.164 Individual sport, t-1 1.03 0.02 0.121 1.04 0.03 0.198 1.03 0.03 0.338 Team sport 1.00 0.04 0.906 1.01 0.07 0.916 1.00 0.04 0.903 Individual sport 1.02 0.02 0.240 1.02 0.03 0.559 1.03 0.03 0.245 Gender (Female) 1.54 0.09 <0.001 Age 3.39 1.19 0.001 1.79 0.84 0.218 7.78 4.12 <0.001 Household Income 1.08 0.01 <0.001 1.07 0.02 <0.001 1.09 0.02 <0.001 Observations 3907 2179 1728 R 2 Nagelkerke 0.029 0.013 0.030 AIC 10663.929 5986.632 4685.673 Conclusion We have provided evidence that playing youth sports may matter for political participation, even when accounting for socioeconomic status. Sports matter for boys and girls, but there appears to be a slightly larger effect for girls, supporting research from sociology and psychology that sport can build self-esteem, pro-social behaviors, comfortability with challenges and conflict. All of these are often described as reasons for the gender gap in political interest and activity; thus, sport could be one avenue of intervention for building civic capacity in girls and eventually women. The Sharrow et al. (2024) report provides further evidence of women leaders citing their sport participation as key to their leadership development. We also find that sports participation has more of an effect on political participation for girls when political participation takes the form of voting and party membership and not in the case of political rally attendance. This is consistent with work on the gender gap in political participation that find that women and men participate in politics differently (Coffé & Bolzendahl, 2021). These benefits of sport make an even stronger case for why exclusion of trans kids from sport participation causes harm beyond just “getting to play.” But much more work needs to be done to uncover through which mechanism. That is, can we look to something like grit (Holbein et al. 2018) or competitiveness (Izatt & Fuller 2025) as a connecting factor from sports to politics? Do sports represent parental investment beyond SES, that we can uncover when we look to motivations for youth to enter and stay in sports for many years? What of the embodiment aspect of sport – is it the competition, such that an activity like debate would generate the same sort of capital? Or is there something about the physical challenges and body image self-esteem that lead to overcoming participation barriers? These are important and open questions. Future research should incorporate a broader set of measures, given a number of confounding factors in the secondary data around access, interest, motivation, and persistence in athletics. We also do not have a broad range of other extracurricular activities with which to compare the effect of sports; future work should look to a variety of youth activities and their accompanying motivations to better understand these potential pathways. Declarations Ethics Statement: All data is secondary, has been de-identified, and publicly available online. Funding Funding was provided by [redacted for peer review.] Author Contribution A.F. conceived the research question, developed the theory, and designed the hypothesis tests. N.V. and L.F. conducted the statistical analyses. N.V. ran the multivariate models and built the tables. L.S. conducted the literature review and identification of data sources, and all authors contributed to writing the manuscript. Acknowledgement Acknowledgements are provided in the cover letter, per the new submission instructions. Data Availability All of the data is publicly available. Links provided in the manuscript. References Braddock, J. 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The Journal of Politics , 82 (4), 1490-1501. Wolak, J., & McDevitt, M. (2011). The roots of the gender gap in political knowledge in adolescence. Political Behavior, 33(3), 505-533. Additional Declarations No competing interests reported. Supplementary Files Appendix.docx 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. 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For women, politics and sports are often connected, with women athletes leading in activism around racial justice and LGBTQ+ rights in addition to their efforts to achieve parity with their male counterparts in sport. And all of this begins from a young age, making youth sports a fruitful place to look for pathways to political involvement.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGenerally, young adults participate less in politics, and of course, for younger folks in particular countries, do not have the right to vote until 18 or even 21. A vast literature has emerged to better understand political socialization from parents, peers, and schools. Our present focus is on the possibility of youth extracurricular activities\u0026ndash; particularly organized sports \u0026ndash; that could provide psychological resources and pathways to political interest and engagement. Politics is competitive and full of conflict so the socialization of sport competition and working as a team in the face of opposition could have an effect, specifically for girls. Though boys enjoy the competitive nature of politics, this can be an obstacle for girls who tend to stay engaged if decisions are more consensus-based (Wolak \u0026amp; McDevitt, 2011). Of course, sport and other forms of extracurricular activities may simply be proxies for socioeconomic resource and parental/community support but we argue that organized sports exert a different effect because of the ensuing psychological and physical benefits. In fact, a recent large-scale report commissioned by the Women\u0026rsquo;s Sport Foundation in the U.S. found that 69% of women who played sports held a leadership position and a majority indicate through sport they learned \u0026ldquo;teamwork,\u0026rdquo; \u0026ldquo;learning from mistakes,\u0026rdquo; and \u0026ldquo;handling pressure\u0026rdquo; (Sharrow, Staurowsky \u0026amp; Davis, 2024). Importantly, as organized sport involvement persisted across years, this increased the odds women would hold leadership roles. We are interested in how youth sport involvement translates into everyday political interest and participation. To test these claims, we identified several secondary data sources collected across dozens of countries that include both sport and political variables. \u003cem\u003eYouth Political Socialisation and Gender\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eExtensive research has tied aspects of youth socialisation to civic and political participation in adulthood; however, the specific nature of this socialisation is still uncertain. \u0026nbsp;One important aspect of political engagement is the gender gap, which emerges after high school (Lawless and Fox 2014). The specific origins of this gap is debated, with explanations varying from socialisation and social role theory to conflict avoidance. Gendered socialisation theory argues that, \u0026ldquo;[gender] roles originated based on biological differences: Men\u0026rsquo;s strength and women\u0026rsquo;s childbearing meant that men occupied roles associated with hunting and laboring in the public sphere while women reared children and maintained home life.\u0026rdquo; (Schneider and Bos 2019, 175). These differences persisted as society became more industrial and meant that men were the primary economic actor and women the primary homemaker, establishing quite rigid gender norms that were difficult to shift (Wood \u0026amp; Eagly, 2012). In turn, men and women developed different traits and perceived norms of what a feminine woman and masculine man entailed. For women, this involved the development of communal traits (e.g., empathy, caring, kindness) that aid in caring tasks, whereas men prioritized agentic traits (e.g., aggressive, ambitious) that fit with their roles as hunter, farmer, worker, and eventually, leader (Wood \u0026amp; Eagly, 2012).\u003c/p\u003e\n\u003cp\u003eAdherence to the women-as-homemaker and men-as-provider archetypes declined over time, with physical strength mattering less in post-industrialized countries and women making advances in their educational attainment; however, occupational segregation (e.g., women as nurses, men as mechanics) persists, perpetuating perceptions and selective pressures that women and men ought to adhere to communal and agentic traits, respectively (Cejka \u0026amp; Eagly, 1999, Wood \u0026amp; Eagly, 2012, Koenig \u0026amp; Eagly, 2014, Fox, 2017). Furthermore, direct conflict between the communal/agentic social norms of gender roles and individual/communal political norms persists. Conroy and Green (2020) find that political candidates who successfully launched a campaign after expressing preliminary interest utilised more masculine-coded agentic language, whereas candidates who failed to launch a campaign after expressing preliminary interest tended to use more communal language. Therefore, Conroy and Green (2020) argue that masculine communication norms are better suited for politics, which presents a conflict between traditional gender norms and political norms when considering political participation. As the expectations of a feminine behavior are seen as incompatible with the masculine norms of politics. The masculinity of politics is further supported by Schneider, Holman, Diekman, and McAndrew (2016) who found, in a study of high school students in the United States, that people perceive politics as more masculine because they believe it delivers masculine-coded power goals as opposed to feminine-coded communal goals.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe frames that are applied to the goals of political tasks are also important. Kahalon, Shnabel, and Becker (2020) analyse the gender gap in math performance in a game setting, through role conflict and the influence of stereotypes. When performance in a math game was linked with a shared reward, women performed just as well as men (Kahalon et al., 2020). Yet, when the payoff was strictly for the individual, women performed worse than men on a math task, due to fear of a backlash from individual gains (Kahalon et al., 2020).\u003c/p\u003e\n\u003cp\u003eWith politics framed as conflict-heavy and women\u0026rsquo;s decreased confidence in their capacity to manage conflict in comparison with men, there is an apparent role-conflict here. This role conflict is demonstrated by Wolak and McDevitt (2011), where women are shown to have less confidence in their political knowledge, demonstrating an avenue for political exit. This is again affirmed by Wolak\u0026rsquo;s later work (2020), who writes that women may shy away from competitive settings due to, \u0026ldquo;reservations about taking risks\u0026rdquo; (1492), and, \u0026ldquo;Self-confidence rivals the effects of education and household politicization as explanations of psychological engagement in politics. Moreover, these are patterns that hold true for both young men and young women in the sample\u0026rdquo; (1495). Within the context of Wolak\u0026rsquo;s (2020) paper, self-confidence is used as a mediating effect which explains, \u0026ldquo;15% of gender\u0026rsquo;s effect on political interest\u0026rdquo; (1497). It thus appears that conflict-aversion is a piece of the gender gap in politics, acting in conjunction with socialisation to reduce women\u0026rsquo;s engagement with politics. Within the context of our study, the gap in self-confidence is theorised to be at least partially mediated by differing sports participation\u0026ndash;where youth sports participation offers a means of socialising an individual to be more comfortable with conflict and prepare them for future civic involvement through relevant skills like confidence and leadership.\u003c/p\u003e\n\u003cp\u003eSimilarly, Coff\u0026eacute; and Bolzendahl (2021) investigate the masculinity of politics using the 2012-13 Dutch Longitudinal Internet Studies for the Social Sciences panel survey data, where they determined communal traits are negatively associated with activist-type political activity while agentic traits are positively associated with political participation. However, women still participate in politics as much as men, just in the form of less resource-intensive private activism. This difference was theorised to be due to the communal/agentic gendered socialisation of women and men, with women favouring less resource-intensive private activism, while men favour more conflictive institutional politics (Coff\u0026eacute; and Bolzendahl 2021). They also found no significant difference between men and women in their participation in collective activism (when controlling for political interest), suggesting that perceived masculinity is connected to traditional political activity, but not of informal political avenues.\u003c/p\u003e\n\u003cp\u003eSimilarly, for girls and boys, Cicogani, Zani, Fournier, Gavray, and Born (2012) theorize that the gender gap is due to differences in which activities they value, not necessarily because girls value politics less. In a comparison of two studies, they found that female youth are more likely to participate in social groups and are also more likely to join structured clubs, both of which are less suited to political activity than the clubs boys typically join. In terms of volunteering, Riggers-Piehl, Lucchi, and Lim (2023) found that girls did a similar level or more of volunteering as boys. These considerations are also important in studying political participation, as different political activities involve different levels of conflict\u0026ndash;such as signing a petition or volunteering in comparison with campaigning and protesting.\u003c/p\u003e\n\u003cp\u003eThe importance of the role conflict theory is highlighted by Lawless and Fox (2014), who write that people are more likely to pursue political careers if they show an interest in them at an early age, but the political gap itself does not appear until college begins. The gap in sports participation also appears in a similar way, thus alluding to a possible connection between conflict and gender role socialisation both contributing to the future gap in politics. While not explicitly connecting sports and politics, Jenkin et al. (2017) affirm a connection between enjoyment of competition and sports, especially with respect to continuing sport participation. It is possible that extended sport participation socialises individuals to be more favourable to conflict, which by proxy could have knock-on effects for enjoyment of politics. If the role-conflict theory and sport-competition socialisation are correct, we expect to see extended sports activity leading to an increase in enjoyment of conflict and increased political engagement.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSports and Gender\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAlthough much of sport development literature is focused on the health benefits of sports, youth sport participation has also been tied to numerous social benefits. First and foremost, an important aspect of sports to childhood social development is in its influence on pro-social behavior. Youth sports can play an important role in the development of social skills, positive socialisation to conflict, and an increase in determination and self-worth (Super, Hermens, Verkooijen, and Koelen 2018). Youth development sport programs also may improve self-worth and body esteem in girls (Rauscher and Cooky 2016). While civic clubs may have more direct effects on one\u0026rsquo;s capacity to engage future politics, sports also develop pro-social skills like teamwork, self-reliance, or grit; all of which help in future organisational participation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere are several psychological pathways sport could operate in translating to other forms of community, societal, or political participation. As mentioned, pro social behavior, working as a team to achieve a goal, and managing conflict and competition all may contribute to building confidence in other areas of life. But all sports are not equal avenues for these experiences and development, particularly as some sports closely involve teammates and others are undertaken solo. Nia and Besharat (2010) examine the different benefits generated by team versus individual sports, finding that team sports have better environment for developing trust, altruism, and compliance (sociotropy and agreeableness) while individual sports develop conscientiousness and autonomy. This may be due to self-selection, but this has not been sufficiently investigated as a causal mechanism, and Jakobsen (2014) offers some evidence that there are not significant differences between reasons for team versus individual sport participation. Turnidge, C\u0026ocirc;t\u0026eacute;, and Hancock (2014) also investigated sport\u0026rsquo;s impact on positive youth development, finding that it can occur through sports, but they are not a unique activity in this regard.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGrit is also a potentially important but under-researched factor in both sports and political engagement. Grit is described by Cormier et al. as a, \u0026ldquo;person\u0026rsquo;s dispositional tendency towards passion and perseverance for long-term goals\u0026rdquo; (2024, p.2). Those with high grit tend to pursue goals amidst strife better than those with lower grit scores. In their literature review, they found that numerous studies show a positive relationship between sports participation and grit, with higher level athletes having increased grit. However, only one of 11 studies demonstrated a sex-based difference in grit values (with that single study showing women scoring significantly higher on grit scales). They also outline grit as a slightly problematic variable, as it is a composite of two subscales, which tend to have better prediction power if used individually. However, grit was still shown to be a useful measurement. In the context of sports, grit could potentially determine one\u0026rsquo;s tolerance for conflict and, by proxy, one\u0026rsquo;s enjoyment of it. Grit may therefore be useful as a mediating variable between sports and political engagement, although this has yet to be tested.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere is some evidence of a connection between grit, school engagement, and civic attitude/behavior (Holbein et al. 2018). Examining the theory that education later influences civic engagement, Holbein et al. (2018) suggest most studies neglect to test what part of the education socialization experience leads to this participation. Potentially, it is due to an increase of psychosocial attributes such as prosocial behavior through school involvement. As evidenced by previous studies, grit has been shown to positively contribute to success outcomes, such as higher scores in math and reading, increased volunteering, increased political efficacy, and increased likelihood of planning to vote (Holbein et al. 2018). By focusing on grit as resilience and self-efficacy, they argue grit can predict one\u0026rsquo;s ability to overcome the difficult and/or costly behaviors accompanying civic activity, in addition to supporting one\u0026rsquo;s self-confidence. Thus, if sport helps develop grit and grit provides a foundation for political involvement, this may be an additional pathway from sport to politics. Another such pathway is through leadership development, a skill set tightly connected to both sport and politics as well. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLeadership\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn the midst of building the current project and identifying key data sources to test our hypotheses, the Women\u0026rsquo;s Sport Foundation released a comprehensive and likely the most complete study of sport and women\u0026rsquo;s leadership to date (Sharrow, Staurowsky, and Davis 2024). Although the report does not meaningfully disentangle economic status from leadership and access to sports, they, nonetheless, illustrate a relationship between extensive sport involvement and future leadership positions (both formal and informal) for women. The key takeaways from their report were a connection between fundamental skills of leadership and sport development, as evidenced by survey responses linking sport activity and leadership skills as well as a correlation between named leadership roles in adulthood and youth sport participation. While the report has the depth and coverage currently missing from sport and politics literature, the survey questions are aimed at current women leaders so we are not able to compare the leadership of nonathletes or those who may answer about their sport experiences differently. Although the authors acknowledge socioeconomic barriers to sport, we cannot untangle how family income or other characteristics may lead to both sports and politics. Overall, the report is an important addition to investigations on sport and politics, and we intend to replicate several of their measures in our new data collection. For now, we see this set of findings as proof of concept about the different motivations, personality and skill development, and other accompanying benefits that translate youth athletic participation to leadership in adulthood.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAthlete Activism\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThough our present study is concerned with a variety of political activities, focusing on particular forms of activism might also uncover the sport-to-politics pipeline. Professional athletes have the unique position of celebrity status due to their achievement within a physical field, leading to the organic development of a platform via social media. Many athletes have utilized their platforms and status to spread awareness for causes, such Black National Football League players kneeling during national anthems in protest of police brutality, or the strong support of the Black Lives Matter movement by both WNBA and NBA athletes (Thomas and Wright 2022).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe advent of Title IX in the USA also provided another avenue for the development of advocacy skills, through which young women advocated for equality in both professionalised post-secondary sports and lower-level access. Title IX is a part of a series of amendments to the Education Act in 1972, and after a period of legal challenges, it established that college athletes in the United States are guaranteed the right to equal resources regardless of sex (Carrick et al., 2021, Druckman and Sharrow, 2023). In the fallout of Title IX, it was theorised women were provided with a framework through which they could advocate for equality in a traditionally male-dominated field, thus enhancing their civic skills. More recently, in the United States and other parts of the world, debates around the inclusion or exclusion of trans athletes in various competitions have highlighted the very political nature of who gets to play (Sharrow, 2025).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSports and civic involvement\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe have argued that there is a sport and politics overlap, both in motivations and selection into participating as well as the kinds of skills and dispositions associated with both realms. We are not the first to connect athletic endeavours to political participation. In a study of Japanese adults, Hayashida and Shimizu (2022) found continuous sport participation from youth to young adulthood is linked to increasing adult civic participation. Perks (2007) affirms this finding using Canadian data, \u0026ldquo;that the effects of youth sport participation on adult participation in community activities lasted throughout the lifecycle\u0026rdquo; (378), though the correlation is quite modest. In a longitudinal study of third graders, there is further evidence of this connection between sports and civic participation, even when accounting for socioeconomic status (Rotolo et al. 2020). Moreover, Donovan, Bowler, and Hanneman (2004) found a strong connection between athletics and passive political participation (e.g. voting), and individual sport participation was significantly related to voting, for Black young adults, even when accounting for family socioeconomic status and individual-level efficacy (Braddock and Dawkins 2007). They did not find these same increases for those participating in varsity \u003cem\u003eteam\u0026nbsp;\u003c/em\u003esports.\u003c/p\u003e\n\u003cp\u003eBut the evidence overall is definitely mixed. MacFarland and Thomas (2006) aggregated NELS and Add Health datasets to determine that while youth voluntary associations increase adult political participation, sports do not when controls for socioeconomic status were applied to their models. Moreover, Kirlin (2003) argues sports and hobby clubs have limited impact on later civic activity, while debate clubs and other community groups increase the likelihood of later civic activity. Rotolo, Johnson, and McCall (2020) also find that high school sport participation is not linked with civic engagement and countered the assumption that youth club activity increases adult civic behavior and prosocial behavior. Additionally, MacFarland and Thomas (2006) found non-school and school sports are not correlated with long-term political activity once background characteristics are considered. This data was collected at various time points, with a mix of measures, depending on data availability, and generally, scholars have not considered the specific focus on gender.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIs it sports or is it socioeconomics?\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSocioeconomic status is a vital qualifying consideration within sports and politics literature. This is known as the selection hypothesis, which predicts that socioeconomic or background effects explain the predicted effect of organised/formal youth sports on civic engagement (Rotolo et al. 2020, p. 180). This theory was tested by Rotolo et al. (2020) utilising exclusively American, longitudinal data, which they note is unique from previous studies in the field which garnered mixed results (MacFarland and Thomas 2006; Perks 2007). Importantly, Rotolo et al. (2020) found that once background characteristics were taken into account, the sports activity of surveyed American youth did not have a significant impact on civic or political participation (p. 193). Moreover, aspects such as parental expectation of the youth\u0026rsquo;s academic performance, their \u0026ldquo;academic self-concept,\u0026rdquo; and grades, all also rendered sports insignificant to their future voting likelihood.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere is no discounting the value of Rotolo et al.\u0026rsquo;s (2020) article to the sport-politics research paradigm, with its well-developed measures of sports, types of civic and political engagement, and background variables. However, it is a small study, at only 703 participants, which lies in stark contrast of Perks\u0026rsquo; (2007) survey of 13,000 Canadians. Yet, Rotolo et al. (2020) perform longitudinal analysis while Perks (2007) does not. It is possible that the consequences of socioeconomic factors are not fully apparent without a longitudinal study, given it is challenging to accurately identify someone\u0026rsquo;s childhood wealth and current familial economic support; parental education can only approximate so much. Moreover, both Rotolo et al. (2020) and Perks (2007) leave the gender gap of both sports and politics unexplored, diminishing either study\u0026rsquo;s full support or disapproval of future research into the specific relationship between girls and women in sports and politics.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese studies also neglect to analyse sport motivations, which may or may not align with the educational expectation Rotolo et al. (2020) performed. There is simply no data here exploring a connection between dedication to athletics correlating with a dedication to academia, which may both correlate with political and/or civic engagement. Though we cannot discern the nature of these relationships from current extant surveys, we do a broad survey of the field of sport and politics data, identifying key datasets with which we test the relationships between sport and politics, by gender, and eventually build multivariate models with a subset of this data. Finally, we propose a new study to better understand the mechanisms involved in the selection into sport, the skills developed by athletes, how this compares to other kinds of activities, and what this means for political engagement. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypotheses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH1: There is a positive relationship between youth sport participation and political engagement, even when controlling for parental socioeconomics.\u003c/p\u003e\n\u003cp\u003eH2: There is an interaction with gender such that girls experience a higher positive relationship\u003c/p\u003e\n\u003cp\u003ebetween sport and political engagement, as compared to boys. \u0026nbsp;\u003c/p\u003e"},{"header":"Data and Methods","content":"\u003cp\u003eIn an extensive review of the youth sports and civics literature, we uncovered a variety of data sources, which we narrowed to eight key datasets (see Table A.1 in the appendix for details on samples, measures, and bivariate relationships). The eight remaining datasets were chosen because their survey questions were most pertinent to our hypothesis, with relevant measures for socioeconomic status and sufficiently large sample sizes for generalisability and significance testing. Below we detail the significant, bivariate correlations, noted in Table A.1.\u003c/p\u003e\u003cp\u003e\u003cem\u003eFlash Eurobarometer 408 (2014).\u003c/em\u003e We utilised its measure of participating in activities of a political party in the past year compared with membership in a sports club. For girls, there was a significant positive correlation between sports and politics. (0.077, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Sample size: 13,454; ages, 15\u0026ndash;30.\u003c/p\u003e\u003cp\u003e\u003cem\u003eEurobarometer 62 (2004).\u003c/em\u003e We compared frequency of political discussion and frequency of sport participation. Sports and politics were significantly correlated for both girls/women (0.147, p\u0026thinsp;=\u0026thinsp;0) and boys/men (0.036, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Sample size: 24,603; ages 15\u0026ndash;97.\u003c/p\u003e\u003cp\u003e\u003cem\u003eAdd Health, Waves 3 and 4 (2001\u0026ndash;2002; 2007\u0026ndash;2008)\u003c/em\u003e. This is a longitudinal dataset tracking primarily health outcomes of Americans, but during Waves 3 and 4, both sport and political questions were asked. The waves were considered both separately and together as there were some variables which were not consistent across waves. In Wave 3, participants were aged 18\u0026ndash;26, while Wave 4 they were aged 24\u0026ndash;32. In Wave 3, individual sport participation and political rally support saw positive correlations for both women (0.09, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and men (0.078, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Wave 4 uncovered a positive correlation for men between voting in local elections and both team (0.0316, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and individual (0.047, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) sport participation. We also considered a longitudinal comparison, where sport participation in Wave 3 was compared with the voting in a location election variable from Wave 4. Again, there was only a positive correlation for men for both team (0.038, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and individual (0.041, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) participation. Samples size: 4,856, ages 18\u0026ndash;32.\u003c/p\u003e\u003cp\u003e\u003cem\u003eInternational Social Survey Programme 2007, Leisure time and sports\u003c/em\u003e. We compared participation in sport, civic, and cultural groups with political participation. For all measures, there was a significant correlation (p\u0026thinsp;\u0026lt;\u0026thinsp;.001) between club participation and political participation (girls/women r\u0026thinsp;=\u0026thinsp;.178; boys/men r\u0026thinsp;=\u0026thinsp;.16). Civic group participation and politics were more strongly related (girls/women r\u0026thinsp;=\u0026thinsp;.331; boys/men r\u0026thinsp;=\u0026thinsp;.356) as were cultural groups and politics (r\u0026thinsp;=\u0026thinsp;0.263 for girls/women and r\u0026thinsp;=\u0026thinsp;0.299 for boys/men). Sample size: 27,599; ages 15\u0026ndash;97.\u003c/p\u003e\u003cp\u003e\u003cem\u003eUniversity of Washington (2000\u0026ndash;2008), Beyond Highschool (ICPSR 33321) (2000\u0026ndash;2010)\u003c/em\u003e. We compared participation in sports, debate, and drama clubs of Washington grade 12s with voting in the presidential election of 2008. The only significant correlation was a negative relationship between sports and voting for both girls/women (-0.136, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and boys/men (-0.154, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). There was no significant correlation between debate or drama clubs and presidential voting. Sample size: 831; ages 16\u0026ndash;18 (First survey) and 26\u0026ndash;28 (second survey, 10 years later).\u003c/p\u003e\u003cp\u003e\u003cem\u003eThe Civic and Political Health of the Nation survey (2002).\u003c/em\u003e We compared voter registration and various activities both inside and outside high school. For boys/men, there were only significant correlations between non-high school sports participation (0.235, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and non-high school political groups (-0.25, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For girls/women, there were only significant correlations with voter registration between student council (-0.100, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), debate club (0.035, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and non-high school political group participation (-0.287, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Sample size: 209\u0026ndash;910 (varies for each activity); ages 15\u0026ndash;25.\u003c/p\u003e\u003cp\u003e\u003cem\u003eEurobarometer 88.4 (2017)\u003c/em\u003e. We compared a measure of political interest with sport club membership, finding a significant correlation for both girls/women (0.082, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and boys/men (0.028, p\u0026thinsp;\u0026le;\u0026thinsp;0.05). Sample size: 27,933; ages 15\u0026ndash;99.\u003c/p\u003e\u003cp\u003e\u003cem\u003eEurobarometer 97.3 (2022)\u003c/em\u003e. We compared a measure of political interest with sports participation frequency, finding a significant correlation for both girls/women (0.123, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and boys/men (0.038, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Sample size 26,569; ages 15\u0026ndash;99.\u003c/p\u003e\u003cp\u003eIn summary, for girls/women, there were nine significant positive correlations between extracurricular activities and political engagement, with five for sports and four for non-sports; and three significant negative correlations (one sport, two nonsports). For boys/men, there were 12 significant positive correlations with politics, with 10 for sports and two for non-sports and two negative correlations (one each for sports and nonsports). If we compare the significant correlations across all datasets, with both girls/women and boys/men, 6/8 show girls and/or women having a higher correlation between an activity and political participation. Of these, all six relate to sports measures. Importantly, for one of these, girls/women have a smaller negative correlation than boys/men by a small margin (The University of Washington Longitudinal study). Of course, there is also evidence of no relationship between politics and extracurricular activities. For girls/women, this included the Add Health measures of sport and local voting and the Civic and Political Health of the Nation measure of non-high school sports and voter registration. The only nonsignificant correlation between political discussion and sport for boys/men were Flash 408 and the non-sport measures from the Civic and Political Health of the Nation data set (student council, debate, and a non-high school political group). Overall, the significant correlations are small but are comparable in size to those found by Perks (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThere is a severe lack of long-term data on the influence of sports on political activity. Of what is measured, there are limited activities considered. Most importantly, the majority of correlations across all datasets considered were found to have no statistical significance. This perhaps explains some of the diversity in results for previous studies. Moreover, the year the study took place appears to be irrelevant for determining correlation level, as the datasets with significant results collected data from across the 2000s (2000\u0026ndash;2022). There was heavy variation in the type of political measure utilised by the surveys, but it is uncertain what impact this had on the connection between activity and outcome; it ranged from political interest, support, and actual participation\u0026ndash;and there were significant correlations for all of these in at least one dataset. The potential differential impact of team versus individual sport is also uncertain, with few datasets differentiating sport type. Add Health saw a slight increase with respect to individual over team sport for local voting in both 2008 and the longitudinal measure, but this increase is quite small (+\u0026thinsp;0.016 for W4, +\u0026thinsp;0.003 for longitudinal). The women\u0026rsquo;s result also showed no significance, so a gendered comparison is not possible.\u003c/p\u003e\u003cp\u003eVery few datasets cover both in-depth sport and political questions. Generally, they fail in one of three areas: (1) the political or sport measure is too simplistic or only tracks one aspect like voter registration or frequency of activity, (2) lack of longitudinal data, (3) small sample size within an otherwise large dataset. Importantly, even some longitudinal datasets like the Add Health survey from 1994\u0026ndash;2018 lack repeated questions between years, meaning both sports activity and political participation were measured only during a single wave. Because of these additional shortcomings, we further reduce our corpus of data to the three best surveys for our purposes and test these further below.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMultivariate Analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTaking all of this into account, we identified three surveys with sufficient measures and sample sizes to analyze using various regression models to further unpack the gendered relationship between sports participation and political participation. We chose these three datasets because we felt they had the best combinations of the highest quality measures of sports club or team participation and political participation: Waves 3 and 4 of Add Health, ISSP 2007 Sports and Leisure wave, and the Flash Eurobarometer 408 (2014). For the measures of sports participation, the main exclusion criteria when we were identifying datasets for this paper were whether the measure used a direct question about sports club participation of membership. If the dataset has a proxy for sport participation, like time spent doing other leisure activities, we excluded it from our paper and appendix. If the sports participation measure is about explicit sport participation as an extracurricular activity, then we feel it is a strong measure for our purposes and included it in our correlation analysis. The other dimension is the kind of political measures, divided between participation and political interest measures. Our research question for this paper concerns the relationship between youth sports and political participation, whether that be participating in discussion groups, political clubs, political acts/behaviors, voting and/or eventual candidacy.\u003c/p\u003e\u003cp\u003eThe Flash Eurobarometer 408 (2014) was a study of 28 countries within the European Union, surveying \u0026ldquo;education, job prospects and volunteering of the European Youth.\u0026rdquo; (European Commission, 2015). We utilised its measure of participating in activities of a political party in the past year compared with membership in a sports club. For Add Health we used Waves 3 and 4 (1994\u0026ndash;2018, with Wave 3 taking place from 2001\u0026ndash;2002 with participant age ranging from 18\u0026ndash;26 and Wave 4 taking place from 2008\u0026ndash;2009 with participant age ranging from 24\u0026ndash;32). This is a longitudinal dataset tracking primarily health outcomes of Americans, but during Waves 3 and 4, both sport and political questions were asked. More specifically, participants were asked in Wave 3 whether they attended a political rally in the past 12 months, if they participated in individual and/or team sports over the past 12 months, and for Wave 4, whether they voted in recent local elections or participated in individual and/or team sports over the past 12 months (Harris and Urdy, 1994\u0026ndash;2018). Finally, we used the ISSP 2007, Leisure Time and Sports module (ISSP Research Group, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). From this data, we compared participation in sport, civic, and cultural groups with political participation.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe first series of models used the data from ISSP 2007 Leisure Time and Sport module. We used ordinary least squared regression, as the measures of sports participation and political participation used continuous scales of the number of days during a week people participated in these activities on average. Our dependant variable is whether an individual participated in the political party or organization in the last twelve months, with responses ranging from never to at least once a week. Our independent variable concerns participation in sports clubs or teams and uses the same scale as our dependent variable. We also controlled for self-perceptions of social class, as respondents were asked to rate their place on a ten-point scale, where ten was the highest and one was the lowest, as access to organized sport clubs and sport equipment is mediated by wealth in many cases (Rotolo et al. 2020). Gender is included as we are interested in how sport may act as a form of gendered socialization that encourages girls to participate politically later in life, and age as another relevant sociodemographic factors. These models are shown in Table 1.\u003c/p\u003e\n\u003cp\u003eIn our first model, we use the entire sample of 42,790 respondents and find that sport participation is a statistically significant predictor of political participation at the 95% significance level. As people participate in sport clubs more, they are more likely to participate in political parties and organizations (\u003cem\u003ep \u0026lt;\u0026nbsp;\u003c/em\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e). We can also see from our second model that the interaction between gender and sports participation is statistically significant (\u003cem\u003ep \u0026lt;\u0026nbsp;\u003c/em\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e) though substantively small. As another way to look at these effects, we split the sample by men (19,390) and women (23,400) and find that women are slightly more likely than men to participate in political parties if they are greater participants in sports clubs, finding support for our hypotheses (\u003cem\u003ep \u0026lt;\u0026nbsp;\u003c/em\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e). As a robustness test, we also have ordinal logistic regression models in the appendix (Table A.2) that replicate our findings using OLS.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"700\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\" style=\"width: 700px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1: ISSP 2007, political participation regressed on sport participation by gender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3 - Men only\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 4 - Women only\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ePredictors\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eEstimates\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003estd. Error\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eEstimates\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003estd. Error\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eEstimates\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003estd. Error\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eEstimates\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003estd. Error\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSports participation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSocial Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGender (Female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGender x Sports participation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.030\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\" style=\"width: 700px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRandom Effects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026sigma;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026tau;\u003csub\u003e00\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.04 \u003csub\u003eV5\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.04 \u003csub\u003eV5\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.05 \u003csub\u003eV5\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.03 \u003csub\u003eV5\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eICC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e31 \u003csub\u003eV5\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e31 \u003csub\u003eV5\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e31 \u003csub\u003eV5\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e31 \u003csub\u003eV5\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e42790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e42790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e19390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e23400\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMarginal R\u003csup\u003e2\u003c/sup\u003e / Conditional R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.028 / 0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.028 / 0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.023 / 0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.029 / 0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e82320.848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e82327.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e40322.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e41506.390\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eOur next series of models rely on data from the Flash Eurobarometer 408 and use binomial logistic regression, as our dependent variable in this dataset concerned whether or not a participant had voted in a local, regional, or national election in the last year. Our independent variable was also binary and concerned whether or not a respondent had participated in a sports club in the last 12 months. Finally, we controlled for participant education as a measure of socioeconomic status, as access to organized sport clubs and sport equipment is mediated by wealth in many cases (Rotolo et al. 2020). We report odds ratios for our binomial regression models, where coefficients greater than one represent a variable that has a positive relationship with the dependent variable and variables with coefficients that are smaller than 1 representing a negative relationship (Persoskie and Ferrer, 2017). These results are shown in Table 2, which shows the coefficient for the effect of sports participation and education level on likelihood of voting in a local election from models 7 and 8.\u003c/p\u003e\n\u003cp\u003eIn our fifth model, we use the complete sample of 13,286 participants. We find that respondents who participated in sports clubs were more likely to have voted in the last 12 months than those who did not participate in sports clubs. However, we find that women were less likely to have voted than men. Both results were statistically significant at the 95% significance level (\u003cem\u003ep\u003cstrong\u003e\u0026nbsp;\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/strong\u003e\u003c/em\u003e); however, the interaction effect between gender and sports participation in model six was not statistically significant (\u003cem\u003ep = 0.218\u003c/em\u003e). Nonetheless, we ran models by splitting the sample into men (6,416) and women (6,870). Here, we only found evidence in the women-only sample that those who participated in sports clubs were more likely to have voted, and these results were statistically significant (\u003cem\u003ep\u003cstrong\u003e\u0026nbsp;\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/strong\u003e\u003c/em\u003e). Overall, we found evidence in support of our hypothesis as despite women being less likely to political participate than men, women who participated in sports clubs were more likely to have voted than women who did not participate in sports clubs.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2: Eurobarometer 408, political participation regressed on sport participation by gender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 7 - Men only\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 8 - Women only\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ePredictors\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eOdds Ratios\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003estd. Error\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eOdds Ratios\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003estd. Error\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eOdds Ratios\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003estd. Error\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eOdds Ratios\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003estd. Error\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSports participation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGender (Female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGender x Sports participation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e13286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e13286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e6416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e6870\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e Tjur\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e13799.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e13799.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e6576.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e7223.317\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLastly, we used data from Waves 3 and 4 of the Add Health survey. For models nine through eleven, we used the binary measure of political rally attendance from Wave 3 as the dependent variable, and thus we used binomial logistic regression. For models 12 through 14, we used the ordinal measure of whether a participant usually votes in local or statewide elections from Wave 4 as our dependent variable, and thus we conducted ordinal logistic regression. \u0026nbsp; \u0026nbsp;Finally, in models 15 through 18, we looked at how sports participation in Wave 3, time \u003cem\u003et-1,\u003c/em\u003e affected whether a participant usually votes in local or statewide elections in Wave 4, or time \u003cem\u003et\u003c/em\u003e, using ordinal logistics regression. We controlled for gender, age, and income in all of our models. We also controlled for political rally attendance reported in Wave 3, or time \u003cem\u003et-1,\u003c/em\u003e in the case of using behavior reported in Wave 3 to predict behavior reported in Wave 4. For the series of models for Wave 3, we use the full sample of 3,320 participants with complete data, and then run models for the male (1,508) and female (1,812) sub-samples. For the series of models for Wave 4, we use the full sample of 3,917 participants with complete data, and then run models for the male (1,732) and female (2,185) sub-samples. Finally, for the series of models using measures from Wave 3 to predict behavior reported in Wave 4, we use the full sample of 3,901 participants with complete data and then run models for the male (1,724) and female (2,177) samples.\u003c/p\u003e\n\u003cp\u003eIn the first series of Add Health models, shown in Table 3, using data from Wave 3, we find that individual sport participation is predictive of political rally attendance. In particular, women who played individual sports are more likely to have attended a political rally, and these results are statistically significant (\u003cem\u003ep \u0026lt; \u0026lt;0.001\u003c/em\u003e). Overall, women who participated in individual sports are also slightly more likely to have attended a political rally than men who participated in individual sports. Whether a participant played team sports is not a statistically significant predictor of political rally attendance, nor is gender in the full model using only Wave 3 measures. However, using only Wave 4 measures, we do not find great support for the effect of sports participation on voting behavior. In Table 4, our models using only measures from Wave 4, we find that women and wealthier respondents are significantly more likely to have voted in local or statewide elections than men (\u003cem\u003ep \u0026lt; \u0026lt;0.001\u003c/em\u003e). Those who participated in team or individual sports were marginally more likely to have voted, but these results were not statistically significant. \u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3: Add Health Wave 3, political participation regressed on sport participation by gender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 11 - Men only\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 12 - Women only\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ePredictors\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eOdds Ratios\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003estd. Error\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eOdds Ratios\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003estd. Error\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eOdds Ratios\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003estd. Error\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTeam sport\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIndividual sport\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.025\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGender (Female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePersonal Income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.496\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e3820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e1795\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e Tjur\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e1272.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e691.363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e585.206\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4: Add Health Wave 4, political participation regressed on sport participation by genders\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 14 - Men only\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 15 - Women only\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ePredictors\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eOdds Ratios\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003estd. Error\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eOdds Ratios\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003estd. Error\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eOdds Ratios\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003estd. Error\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTeam sport\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIndividual sport\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGender (Female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHousehold Income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e3917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e2185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e1732\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e Nagelkerke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e10691.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e6001.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e4695.538\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, Table 5 shows our models using measures from Wave 3 to predict voting behavior reported in Wave 4. The main independent variables were team and individual sport participation reported in Wave 3. We then controlled for team and individual sport participation reported in Wave 4 and then participant gender, household income, and age. For the models using measures from Wave 3 to predict behavior reported in Wave 4, we found that women, wealthier respondents and older respondents, and especially older men, were more likely to have voted. We did not find statistically significant evidence that individual or team sport participation reported in Waves 3 or 4 were related to a greater likelihood of voting. This was consistent in men and women, despite women being overall more likely to vote according to this data. Finally, unsurprisingly, those who reported attending a political rally at time \u003cem\u003et \u0026ndash; 1\u003c/em\u003e were more likely to vote at time\u0026nbsp;\u003cem\u003et\u0026nbsp;\u003c/em\u003eacross all of the models using measures from Wave 3 to predict behavior reported in Wave 4.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 5: Add Health Waves 3 and 4, political participation regressed on sport participation by gender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 17 - Men only\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 18 - Women only\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ePredictors\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eOdds Ratios\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003estd. Error\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eOdds Ratios\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003estd. Error\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eOdds Ratios\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003estd. Error\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTeam sport\u003cem\u003e, t-1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIndividual sport, \u003cem\u003et-1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.338\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTeam sport\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.903\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIndividual sport\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGender (Female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHousehold Income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e3907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e2179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e1728\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e Nagelkerke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e10663.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e5986.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e4685.673\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe have provided evidence that playing youth sports may matter for political participation, even when accounting for socioeconomic status. Sports matter for boys and girls, but there appears to be a slightly larger effect for girls, supporting research from sociology and psychology that sport can build self-esteem, pro-social behaviors, comfortability with challenges and conflict. All of these are often described as reasons for the gender gap in political interest and activity; thus, sport could be one avenue of intervention for building civic capacity in girls and eventually women. The Sharrow et al. (2024) report provides further evidence of women leaders citing their sport participation as key to their leadership development. We also find that sports participation has more of an effect on political participation for girls when political participation takes the form of voting and party membership and not in the case of political rally attendance. This is consistent with work on the gender gap in political participation that find that women and men participate in politics differently (Coff\u0026eacute; \u0026amp; Bolzendahl, 2021). These benefits of sport make an even stronger case for why exclusion of trans kids from sport participation causes harm beyond just \u0026ldquo;getting to play.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003eBut much more work needs to be done to uncover through which mechanism. That is, can we look to something like grit (Holbein et al. 2018) or competitiveness (Izatt \u0026amp; Fuller 2025) as a connecting factor from sports to politics? Do sports represent parental investment beyond SES, that we can uncover when we look to motivations for youth to enter and stay in sports for many years? What of the embodiment aspect of sport \u0026ndash; is it the competition, such that an activity like debate would generate the same sort of capital? Or is there something about the physical challenges and body image self-esteem that lead to overcoming participation barriers? These are important and open questions. Future research should incorporate a broader set of measures, given a number of confounding factors in the secondary data around access, interest, motivation, and persistence in athletics. We also do not have a broad range of other extracurricular activities with which to compare the effect of sports; future work should look to a variety of youth activities and their accompanying motivations to better understand these potential pathways. \u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics Statement: \u003c/h2\u003e\n\u003cp\u003eAll data is secondary, has been de-identified, and publicly available online.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003e Funding was provided by [redacted for peer review.]\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eA.F. conceived the research question, developed the theory, and designed the hypothesis tests. N.V. and L.F. conducted the statistical analyses. N.V. ran the multivariate models and built the tables. L.S. conducted the literature review and identification of data sources, and all authors contributed to writing the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eAcknowledgements are provided in the cover letter, per the new submission instructions.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll of the data is publicly available. Links provided in the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBraddock, J. H., Hua, L., \u0026amp; Dawkins, M. P. (2007). Effects of participation in high school sports and non-sport extracurricular activities on political engagement among black young adults. \u003cem\u003eNegro Educational Review\u003c/em\u003e, \u003cem\u003e58\u003c/em\u003e(3/4), 201.\u003c/li\u003e\n \u003cli\u003eCejka, M. A., \u0026amp; Eagly, A. H. (1999). 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ZA4850 Data file Version\u0026nbsp;\u003c/em\u003e\u003cem\u003e2.0.0,\u0026nbsp;\u003c/em\u003e\u003cem\u003ehttps://doi.org/10.4232/1.10079\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eAttitudes Surrounding Fairness and Competition in Sports Predict Choices to Partisan Gerrymander. \u003cem\u003ePolitical Behavior\u003c/em\u003e, 1-27. https://doi.org/10.1007/s11109-025-10086-8.\u003c/li\u003e\n \u003cli\u003eJakobsen, A. M. (2014). Are there differences in motives between participants in individual sports compared to team sports. \u003cem\u003eLASE Journal of Sport Science\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(2), 30-40.\u003c/li\u003e\n \u003cli\u003eJenkin, C. R., Eime, R. M., Westerbeek, H., O\u0026rsquo;Sullivan, G., \u0026amp; Van Uffelen, J. G. (2017). 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Self-confidence and gender gaps in political interest, attention, and efficacy. \u003cem\u003eThe Journal of Politics\u003c/em\u003e, \u003cem\u003e82\u003c/em\u003e(4), 1490-1501.\u003c/li\u003e\n \u003cli\u003eWolak, J., \u0026amp; McDevitt, M. (2011). The roots of the gender gap in political knowledge in adolescence. Political Behavior, 33(3), 505-533.\u003c/li\u003e\n\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":"","lastPublishedDoi":"10.21203/rs.3.rs-8001537/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8001537/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePolitics and sports are historically men’s domains. Increasingly, women have made strides in participating in politics, running for office, and competing in professional sports. Psychologically, participating in sports facilitates comfortability with competition, solving collective action issues as a team, increases in self-esteem, and bolstering other psychological resources. We suggest that these resources could spillover into an interest and engagement in political and civic activities. Using 8 secondary datasets across dozens of countries, we investigate the relationships between sports and politics by gender. We also control for parental socioeconomic variables and find a pattern of results that suggest youth sport participation is associated with higher levels of political engagement, particularly for girls. This broad set of evidence suggests sport might be a potential pathway to prepare youth for political behavior and calls for more research to uncover potential longitudinal effects of this activity.\u003c/p\u003e","manuscriptTitle":"Youth Sports, Gender, and Pathways to Political Involvement","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-22 17:36:16","doi":"10.21203/rs.3.rs-8001537/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":"0bb0889f-005d-4656-949a-a035323976fe","owner":[],"postedDate":"November 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-07T19:38:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-22 17:36:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8001537","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8001537","identity":"rs-8001537","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}