In response to media reports of increased teasing and bullying in schools following the 2016 U.S. presidential election, we investigated its prevalence with a Virginia school climate survey completed by approximately 155,000 seventh- and eighth-grade students in 2013, 2015, and 2017. Survey results were mapped onto presidential election results for each school division’s locality. In localities favoring the Republican candidate, there were higher adjusted rates of students reporting that (a) they had experienced some form of bullying in the past year (18% higher) and (b) “students in this school are teased or put down because of their race or ethnicity” (9% higher). For these two outcomes, there were no meaningful differences prior to the election. These results provide modest support for educator concerns about increased teasing and bullying since the 2016 presidential election in some schools and warrant further investigation.

Bullying is recognized as a serious concern that affects approximately 21% of students ages 12 through 18 in the United States (Musu-Gillette, Zhang, Wang, Zhang, & Oudekerk, 2017). There is abundant evidence that peer victimization, especially bullying, is associated with a decline in student engagement and academic achievement (Eisenberg, Neumark-Sztainer, & Perry, 2003; Juvonen, Graham, & Schuster, 2003; Juvonen, Wang, & Espinoza, 2011; Nansel et al., 2001). Student victims of bullying also exhibit emotional adjustment problems and are at increased risk for long-term mental health problems such as depression (Benedict, Vivier, & Gjelsvik, 2014; Hong & Espelage, 2012; McDougall & Vaillancourt, 2015). Furthermore, pervasive bullying and teasing has a general effect on school climate that affects the student body as a whole. Studies have found that the perceived prevalence of bullying and teasing reported by students in a school, beyond the effects of personal victimization, has a negative association with student engagement, schoolwide academic achievement, and school graduation rates (Cornell, Gregory, Huang, & Fan, 2013; Lacey & Cornell, 2013; Mehta, Cornell, Fan, & Gregory, 2013).

How the Elections Could Influence Bullying Behavior The hypothesis that a presidential election or presidential rhetoric might influence student bullying behavior deserves examination. Bullying has been conceptualized as a social-ecological phenomenon produced by the interaction of individual and systems factors (Hong & Espelage, 2012; Hymel & Swearer, 2015). Most bullying research has concentrated on family and school influences, with less emphasis on community and mass media exosystems. Hong and Garbarino (2012) identified homophobic bullying as a problem especially vulnerable to exosystemic factors such as mass media and the community environment and by macro factors such as cultural norms and beliefs about sexuality. They asserted that tolerance for homophobic bullying and aggression against sexual minority youth is perpetuated by media stereotypes and cultural norms. However, there is little research investigating how national events such as a presidential election, or behavior by role models such as the president of the United States, could influence student interactions. There is some potentially relevant research on workplace bullying that examines the effects of the boss on employees (Mawritz, Mayer, Hoobler, Wayne, & Marinova, 2012). This research has found evidence of a “trickle-down” effect by which abusive behavior by the manager is associated with abusive behavior by subordinate supervisors, which in turn is associated with employee aggression such as making fun of others and acting rudely toward someone at work. Nevertheless, it remains unclear how behavior perceived as bullying by a presidential candidate or office holder would induce similar behavior among the nation’s adolescents. There might be multiple pathways of influence, and youth unfamiliar with presidential statements nevertheless might emulate family members and other adults in the community who have been influenced by presidential behavior. Furthermore, it seems likely that persons who share the president’s views and supported his election would be most likely to echo his statements and attitudes in their own behavior. The presidential election triggered a multitude of media activities. For example, after the presidential election, a prominent hate website, Daily Stormer, encouraged its audience to make foreigners and persons wearing Islamic clothing feel unwanted and frightened (Williams & Medlock, 2017). There is evidence that Russian operatives used Facebook ads to amplify political divisions and engender conflict on controversial topics including race, immigration, and sexual minority rights (Byers, 2017). It is plausible that some of these efforts affected adolescents or adults who had influence on adolescents, especially their parents. Social learning theory (Bandura, 1971) suggests that individuals learn behaviors by imitating others and behaviors are likely to persist when rewarded. Although statements by President Trump might directly encourage bullying attitudes and behaviors among students (Samaha et al., 2017), it does not seem plausible that large numbers of school-age youth were closely following the president’s statements. It seems more likely that there are multiple indirect effects and intermediate steps in the causal chain from campaign rhetoric and presidential statements to the activities of supporting organizations and others who share the president’s views. These effects are mediated by social and news media attention and, in turn, lead to the behavior of parents and other adults who have direct contact with youth. Children may be using coarse, racially charged language (learned from televised campaign rallies) to taunt their classmates (Fisher, 2016); and “if the president can say those things, why can’t they?” (Samaha et al., 2017). However, the acceptability of such language used in the campaigns differed by political party, with 66% of Democrats finding the rhetoric used in the campaigns as “repulsive” compared to 37% of Republicans (George Washington University Battleground Poll [GWUBP], 2016). In addition, 29% of Republicans felt that the language used during the presidential campaign was “not a big deal,” compared to 18% of Democrats (GWUBP, 2016). This suggests that the influence of the President’s statements might differ substantially based on the student’s immediate environment. Students residing in areas that are predominantly Republican might be expected to hear more support and emulation of the president. In addition to the complex network of effects that might encourage bullying, there were many who were critical of the president’s statements and their actions in support of targeted groups such as immigrants or minorities might reduce his impact. Consequently, we anticipated that students residing in localities where presidential candidate Trump was supported would have the most exposure to statements and behaviors that encouraged bullying compared to areas where he was not supported. Furthermore, we expected that the election of President Trump would incite celebratory activity and more active emulation of his rhetoric in localities where he won compared to localities where he did not win. For these reasons, we hypothesized that there would be higher rates of bullying among students attending schools in localities that supported candidate Trump than in localities that did not support candidate Trump.

The Current Study The state of Virginia administered a statewide school climate survey to seventh- and eighth-grade students in the spring of 2013, 2015, and 2017 (with Grades 9–12 surveyed in alternate years). Of particular interest were the items that measured the prevalence of teasing and bullying. The fortuitous timing of the survey made it possible to examine differences in teasing and bullying rates before and after the 2016 presidential election. The phrase “teasing and bullying” is used broadly in recognition that there are various forms of peer aggression and that student reports do not necessarily conform to an academic definition of bullying. The items covered teasing about clothing or physical appearance, race or ethnicity, sexual topics, sexual orientation, and bullying (Konold et al., 2014). We had particular interest in items concerned with teasing about race/ethnicity and sexual orientation because campaign rhetoric raised concern about presidential attitudes toward racial/ethnic and gender minorities. In order to investigate possible campaign/election effects, we tested whether differences in student reports of teasing and bullying in 2017 were associated with voter preference for the Republican candidate in the school locality. Virginia public school divisions are organized to serve entire counties or cities, which can be matched with voting results. Virginia provides a good sample for analysis because voter preferences varied widely across the state, ranging from 10.6% to 82.3% in support of the Republican presidential candidate. In 2016, Virginia election poll results were 49.9% (1.92 million votes) for the Democratic candidate and 45.0% (1.73 million votes) for the Republican candidate. We hypothesized that, in areas favoring the Republican candidate, teasing and bullying would be higher in 2017 while controlling for prior prevalence rates, socioeconomic status (SES), population density, and the percentage of White student enrollment.

Results Using a Continuous Predictor Variable Results were first analyzed using the percentage of voters9 supporting the Republican candidate as the predictor of interest. Model fit indices for the path analyses were inspected and all χ2 values were not statistically significant (all ps > .05), indicating good model fit (see Table 2). As part of regression diagnostics, the variance inflation factor (VIF) of the predictor variable was inspected for multicollinearity. Results indicated that VIFs for the models with the continuous predictor of interest were extremely high and ranged from 8.1 to 9.9. The high VIFs seriously decrease the power of the model to detect effects and make finding statistically significant results difficult due to highly inflated standard errors (Allison, 2012). Table 2 Path Model Fit Statistics View larger version Nevertheless, for the outcome, “Students in this school are teased or put down because of their race or ethnicity” in 2017 (see Table 3), the percentage voting for the Republican candidate was a statistically significant predictor (B = .005, p = .01) while controlling for all other variables in the model. A 10 percentage point increase in voters supporting the Republican candidate was associated with a 5% change in teasing because of race/ethnicity (i.e., exp[10 × .005]–1). In addition, for being bullied in school this year, the coefficient for the percentage voting for the Republican candidate was also notable (B = .008, p = .08). A 10 percentage point increase in voters supporting the Republican candidate was associated with an 8% change in bullying victimization. Table 3 Path Analyses Results Using 2017 and 2015 Outcomes (n = 132 School Divisions) With a Continuous Predictor View larger version Using a Dichotomous Predictor Using a dichotomous predictor is also meaningful (i.e., the Republican candidate won or lost in the locality) because elections are determined on a binary basis: The individual with the most votes, by any margin, is declared the winner. As a result of some covariates being highly correlated, VIFs were high (i.e., the percentage of students eligible for FRPM and the percentage of parents with a bachelor’s degree or higher both had VIFs ~ 5.0). However, the higher VIFs were for the covariates but not for the predictor variable of interest (VIF REPUBLICAN ~ 2 to 3) and, thus, was not as problematic (Allison, 2012) in contrast to when the predictor was a continuous variable (VIF REPUBLICAN ~ 8 to 10). Model fit indices for the six path analyses using the dichotomous predictor are presented in Table 2. All alternative fit indices were reasonable (Fan et al., 1999), and all models (except for one) had χ2 values that were not statistically significant (ps > .05), which is indicative of good model fit. Results for the six path analyses are presented in Table 4. The 2017 prevalence rate differences for four of the six outcomes, while controlling for 2015 measures—the percentage of White students enrolled, level of parental education, population density, and the percentage of students eligible for FRPM—were statistically significant (ps < .05). Specifically, covariate adjusted rates were higher in areas where the Republican candidate won for the following outcomes: bullied in school this year (p < .001), teasing or put down because of their race/ethnicity (p < .01), teasing about sexual topics (p = .027), and teasing about sexual orientation (p = .035). The coefficients can be interpreted as the percentage point difference of teasing and bullying in the localities where the Republican candidate won versus lost (i.e., .031 indicates that teasing due to race was 3 percentage points higher in school divisions where the Republican candidate won). Table 4 Path Analyses Results Using 2017 and 2015 Outcomes (n = 132 School Divisions) With a Dichotomous Predictor View larger version For all of the 2015 outcomes, the differences in adjusted prevalence rates were, for the most part, not practically meaningful between the school divisions based on the two groups of interest. However, adjusted rates of teasing about sexual orientation were approximately 3.4% higher already in 2015 in the localities where the Republican candidate won (p = .06). In addition, when analyzed using the continuous predictor, the percentage voting for the Republican candidate was statistically significant (p < .01) for teasing due to sexual orientation. Results indicate that differences in 2017 for teasing due to sexual orientation were already present in 2015 and may not be a direct result of the elections. Comparing Both Sets of Analyses Using both the continuous and the dichotomous forms of the predictor, teasing due to race/ethnicity and being bullied in school this year showed more consistent results in both sets of analyses. The effect sizes, using the dichotomous predictor and based on standardized mean differences of the 2017 prevalence rates, can be considered moderate to large in size (i.e., d bullying = .72, d race = .52). However, to place this in context, the adjusted bullying prevalence rates in 2017 where the Republican candidate won was 19.9% compared to 16.8% where the Democratic candidate won (higher by approximately 18% or [19.9/16.8] – 1). For teasing because of race/ethnicity (M rep = 37.1 vs. M dem = 33.9), adjusted rates were higher by approximately 9%. The difference in adjusted prevalence rates can also be considered in the context that antibullying programs have been shown to reduce bullying victimization by approximately 20% (Ttofi & Farrington, 2011). A visual display of the adjusted differences for each of the outcomes by year is shown in Figure 3. Although teasing about sexual topics was higher in areas where the Republican candidate won in 2017, the relationship was negligible when using the continuous predictor. Download Open in new tab Download in PowerPoint

ORCID iD

Francis L. Huang https://orcid.org/0000-0002-5900-7763

Notes 1

The survey, in its current format, was first administered statewide in 2013. In 2013 and 2015, the survey only included seventh- and eighth-grade students; but in 2017, sixth-grade students were added to the survey. In order to maintain consistency, this study used only surveys from seventh- and eighth-grade students. 2

See http://www.politico.com/2016-election/results/map/president/virginia/. The largest percentage of votes from any school division for an independent party candidate was 4.7%. 3

This was recommended by a reviewer. 4

This is also referred to as the “diploma divide,” where non-college-educated White voters were more likely to support the Republican candidate (Harris, 2018). 5

See https://demographics.coopercenter.org/sites/demographics/files/2018-01/VA-Intercensal-Estimates_2010-2017_UVA-CooperCenter.xls (published January 29, 2018). 6

See https://www.census.gov/prod/cen2010/cph-2-48.pdf 7

Separate polling data were available for Emporia City, Fairfax City, and James City, which were part of the school divisions of Greensville County, Fairfax County, and Williamsburg City, respectively. As a result, the electoral data were combined to match the appropriate school division. In addition, two small school divisions (Colonial Beach and West Point) did not have disaggregated polling results, so the polling data from their surrounding counties (Westmoreland County and King William County, respectively) were used. Colonial Beach had a population of 3,500 people with a total 570 K–12 students (http://www.cbschools.net/). In 2013, Westmoreland County Public Schools enrolled 761 K–12 students (http://www.wpschools.net/business-and-finance-6b81a708). A middle school in one school division (Nelson County) with only one school with seventh and eighth graders did not participate in the 2015 survey; so in the current analysis, 2013 data were used as a proxy for the 2015 data. In 2017, Southampton County, which had only one middle school, had only seven students in total respond to the school climate survey, all of whom indicated that they were not bullied. Instead, the 2015 bullying prevalence rate of 10.5% was used. Population density estimates were based on a ratio of the population in the given year and the land area in square miles. Similar to the polling data, data for Emporia City, Fairfax City, and James City were combined with the corresponding city/county. Data for the Colonial Beach and West Point districts were downloaded from their respective school division websites and had one constant figure for the 3 years of analysis (because the year-by-year breakdown was not available). 8

Models were also estimated using the lavaan 0.6-3 (Rosseel, 2012/2018) package in R, which showed the same coefficients and standard errors. Model fit indices, however, differed slightly. 9

For interpretability of the regression coefficients, the variable was scaled from 0 to 100 instead of using 0.00 to 1.00.