Schoolyard Politics

Abstract

Bullying is a vexing social and policy problem in the United States. Education scholars consistently advocate for comprehensive antibullying policies; however, the forty-nine states that have adopted antibullying programs vary in their embrace of this approach. This article addresses the question of why this is the case. First, it provides a new measure of bullying policy comprehensiveness using item response theory. Second, it examines how social and demographic characteristics, as well as neighbor-state policies, relate to this new measure. I find that a state’s support for enumerated groups and the availability of slack financial resources are the strongest explanations for variation in antibullying measures. There is also weak evidence consistent with a backlash effect, whereby states whose neighbors have more comprehensive policies adopt less comprehensive legislation. Thus, bullying policies are driven, in part, by state responsiveness to vulnerable populations but are also constrained by the realities of finite resources.

Keywords

bullying student victimization education policy diffusion

Bullying is a vexing social problem facing students in the United States and abroad (Swearer et al. 2010). A substantial body of evidence demonstrates that bullying affects both victims and bullies physically (Fisher et al. 2012), psychologically (Arseneault, Bowes, and Shakoor 2010), socially (Smith et al. 2012), and educationally (Nakamoto and Schwartz 2010). Furthermore, while these effects might diminish with time, they do not disappear (Vaillancourt, Hymel, and McDougall 2013; Wolke et al. 2013). In response to these findings, scholars and practitioners have developed numerous ameliorative policy proposals (e.g., Nickerson et al. 2013; Olweus 1993; Rigby 2012; Smith 2014). Scholars generally call for a comprehensive approach to bullying prevention, including recognition of and support for targeted groups, counseling for bullies and victims, training for school personnel, mandatory reporting, and more. There is, however, a notable gap between comprehensive programs advanced by scholars and the antibullying legislation that has been adopted by forty-nine states (Kowalski, Limber, and Agatston 2008; Limber and Small 2003; Stuart-Cassell, Bell, and Springer 2011).

The politics of bullying in the United States are multifaceted. It is not simply a straightforward public health and education issue, but bullying also encapsulates debates over the freedom of speech (Hudson 2005; Stein 2003), the culture wars (Meyer and Stader 2009), and the rights of lesbian, gay, bisexual, transgendered, and questioning (LGBTQ) students (Rivero and Trump 2011; Vanga 2014). Scholars in education, law, and political science have touched on different explanations for the variation in antibullying measures in the United States, but they have not yet been formally tested together in order to better understand the potential sources of this variation. The agenda-setting effects of the media were important in the timing of the initial adoption of state antibullying policies (Winburn, Winburn, and Niemeyer 2014), but there is a substantial gap in understanding the content of these statutes. This study examines the extent to which policy diffusion theory explains not only the timing of adoption but also the content of state laws. Specifically, it seeks to answer the question of how internal state characteristics and influence by the policy approaches employed by neighbors explain systematic variation in state antibullying measures. This contributes not only to understanding this important policy area, but also how the predictors of a policy’s content differ from the timing of its adoption.

Using a new measure of antibullying policy comprehensiveness, there is consistent evidence that the availability of slack financial resources and variation in state responsiveness to vulnerable populations (i.e., disabled, LGBTQ, and minority students) relate positively to the comprehensiveness of a state’s antibullying strategy. Furthermore, there is weak evidence that is consistent with an interstate backlash effect and no evidence that a state’s free speech environment systematically shapes bullying policies. Additional research is necessary to establish a causal relationship, but this evidence is contrary to the typical expectation that neighboring states spur each other on in the adoption of innovations through policy learning or competition. In sum, the extent to which states adopt and implement comprehensive antibullying strategies appears to depend on the availability of financial resources and supportiveness of the state in regard to the needs of vulnerable citizens.

A Brief Background of Antibullying Policy in the States

Bullying has long been a problem in schools (Hughes 1857), though it was sometimes considered a passing phase of childhood (Urbanski and Permuth 2009). There are, however, serious long-term consequences of bullying (Vaillancourt, Hymel, and McDougall 2013; Wolke et al. 2013), suggesting that this “phase” is not only unpleasant but also harmful. While bullying activity increases, peaks, and fades through middle and high school (Hong and Espelage 2012), deeper psychosocial problems persist in the long term (Juvonen, Graham, and Schuster 2003; Nansel et al. 2004).

As a point of public policy, bullying was traditionally considered a dimension of the larger issue of school violence; however, it received discrete attention following the high-profile school shooting at Columbine High School in 1999. At the time, stories about Eric Harris and Dylan Klebold focused on the topics of social marginalization, bullying, and the effect of violent images on young minds (Lawrence and Birkland 2004). Contributing to this narrative, the 2002 Safe Schools Initiative report on school shootings found that 71 percent of attackers felt “persecuted, bullied, threatened, attacked or injured by others prior to the incident” (Vossekuil et al. 2002). While the release of more information over the next decade discounted the role of bullying at Columbine (Cullen 2009), the topic nonetheless attained national salience (Winburn, Winburn, and Niemeyer 2014). Other smaller-scale school shootings, acts of peer violence, and student suicides kept the issue on the national agenda (Marr and Field 2001). Columbine may have been a “blurry focusing event” (Birkland and Lawrence 2009), but Georgia’s 1999 antibully statute—whose entry into law preceded Columbine by less than one month—and national media coverage of Columbine, prompted the rapid spread of state policies that either mandated or encouraged school district antibullying efforts (Winburn, Winburn, and Niemeyer 2014). Since 1999, every state, except for Montana, has adopted some statewide antibullying policy.

Not only have new states acted, but existing adopters continue to expand and refine their statutes. In fact, the states passed over 120 pieces of antibullying legislation from 1999 through 2010 (Stuart-Cassell, Bell, and Springer 2011). Alas, the state-by-state approach to adopting antibullying strategies resulted in some laws that are ambiguous, lack clear direction for school districts, and vary in the protections provided to vulnerable groups (Cascardi et al. 2013; Greene and Ross 2005; Stuart-Cassell, Bell, and Springer 2011; Weaver et al. 2013).

Theory and Testable Implications

Education scholars are clear that comprehensive, multifaceted approaches to tackling the complex problem of bullying are the most effective (e.g., Olweus 1993). So why do state statutes vary so greatly when comprehensive legislation is necessary to effectively address the problem? Previous research demonstrated that the timing of state antibullying policy adoptions related to increased levels of national media attention (Winburn, Winburn, and Niemeyer 2014); however, it seems unlikely that increases in coverage shaped what was written into these laws. That being said, policy diffusion theory provides a useful starting point for understanding not only the when of policy adoption but also the scope of a state’s policy (Daley and Garand 2005; Kellough and Selden 2003). The traditional theory of policy diffusion suggests that the adoption of a policy innovation is a function of legislator motivations, resources, and obstacles, as well as external influences, such as the federal government or other states (Berry and Berry 1990, 2007). Scholars have also identified a set of possible causal mechanisms underlying the spread of innovative policies (Karch 2007; Maggetti and Gilardi 2016; Shipan and Volden 2008). The following includes five potential explanations for the variation in state antibullying laws that are drawn from the policy’s history and policy diffusion theory: (1) state support for free speech, (2) support for LGBTQ rights, (3) responsiveness to vulnerable populations, (4) slack financial and legislative resources, and (5) policy learning.

Support for Free Speech and the Rights of Enumerated Groups

Recognition of the complex narrative that frames the debate over bullying is necessary for understanding why resulting policies diverge. Bullying is not neatly categorized under education policy, but it is a crosscutting issue that also touches on free speech (Hudson 2005; Stein 2003), public health (David-Ferdon and Hertz 2007; Srabstein and Merrick 2013), human rights (Brewer and Harlin 2008; Greene 2006), and LGBTQ rights (Connolly 2012; Russell et al. 2010; Vanga 2014). More recently, the LGBTQ and freedom of speech frames have served as a proxy campaign in the broader culture wars (Kersten 2014; Rivero and Trump 2011; Vanga 2014). National campaigns such as the “The Trevor Project,” “It Gets Better,” and the Matthew Shepard Foundation increased the national salience of bullying and tied it more closely with LGBTQ rights (Kersten 2014). Some states have also included LGBTQ students in their explicit enumeration of protected groups (Vanga 2014). Concomitantly, legal scholars and parents have raised concerns about threats to free speech, particularly religious speech, due to vague statewide antibullying laws which provide schools broad and arbitrary authority to abridge students’ First Amendment rights (Hinduja and Patchin 2011; Kersten 2014; King 2010; Servance 2003; Stein 2003). This is not strictly a religious speech concern, however. Vanga (2014) argues that the inclusion of sexual orientation as an enumerated group raises concerns among conservative parents as to the potential promotion of “homosexual-themed curricula.”

This is not only a debate in law reviews and the courts, but it affects state legislation. In 2014, lawmakers in Tennessee passed the Religious Viewpoints Antidiscrimination Act (RVAA), which protects religious speech in schools that can be otherwise interpreted as bullying behavior under the state’s antibullying statute. Similar bills have been introduced in Oklahoma and Michigan. Some states have engaged in an odd juxtaposition of free speech rights that provides direct evidence of education policy serving as a proxy battle in the culture wars. For example, Texas enacted an RVAA, but also enacted a law that restricts the inclusion of literature and speech that positively presents LGBTQ topics in the classroom. Related laws have been passed in nine states and are colloquially referred to as “no promo homo” laws (Russell et al. 2010). These laws serve as an obstacle for adopting more expansive antibullying measures. Thus, some states are moving to simultaneously protect religious speech that may violate antibullying statues while banning speech that positively portrays the LGBTQ community. Therefore, states that broadly support free speech rights, particularly those connected with religious speech, are more likely to have weak antibullying protections. Further, states that undermine LGBTQ speech are more likely to maintain perfunctory antibullying laws.

Free Speech Hypothesis: States that prioritize free speech rights have less comprehensive antibullying measures.

LGBTQ Speech Hypothesis: States that have adopted other measures restricting the positive portrayal of homosexuality in schools also have less comprehensive antibullying policies, on average.

Responsiveness to Enumerated Groups

While a stronger free speech environment may constrain antibullying efforts, it is important to also test the impact of a state’s social climate as either an obstacle or asset for adopting comprehensive antibullying programs. Namely, states with more supportive environments for vulnerable groups—LGBTQ students, disabled students, and racial minorities—should also have stronger antibullying protections. While the true distribution of LGBTQ and disabled students in schools across America is likely closer to uniform, there is variation in the willingness of group members to identify themselves (Herrick 2008). For this reason, demographers have struggled with reliably quantifying the LGBTQ community (Gates 2012, 2014). The willingness to self-identify as LGBTQ is likely tied to the supportiveness of a state’s culture and politics. Gates and Newport (2013) point out that “states with proportionally larger LGBT populations generally have supportive LGBT legal climates” and that while variation in quantification of the LGBTQ community across the states is small, it provides “interesting information about LGBT identification and its possible relationship to the ideological and legal climate in different states.” Additionally, a systematic reduction in the willingness to report an LGBTQ identity within a less-supportive environment decreases the political cohesion of that group (Egan 2012), which may consequently reduce the level of political responsiveness in a state and affect policies related to the group.

Variation in self- and school-based identification of students across a range of disabilities is likewise prevalent (Kavale and Forness 1998; Lester and Kelman 1997; Singer et al. 1989; Wery and Cullinan 2013). For example, there is a stronger correlation between sociopolitical and demographic factors and learning disability diagnoses in the states than with biological prevalence (Lester and Kelman 1997). Additionally, the disabled community is traditionally a less-cohesive group that participates less in politics (Schur and Adya 2013; Schur et al. 2002; Shields, Schriner, and Schriner 1998), which reduces its effectiveness as a social movement (Scotch 1988). Therefore, states with larger identified LGBTQ and disabled student populations are more supportive of their demands, including comprehensive antibullying policies that explicitly enumerate vulnerable groups.

LGBTQ Support Hypothesis: States with larger identified LGBTQ populations have more comprehensive antibullying policies.

Disabled Student Support Hypothesis: States with more identified disabled students have more comprehensive antibullying policies.

One other potential demographic difference warrants mention and inclusion: race. While there is evidence that minority students are overrepresented in school disciplinary actions (Skiba et al. 2011), it is less clear whether ethnic heterogeneity contributes to bullying in the classroom (Felix and You 2011; Vervoort, Scholte, and Overbeek 2010) and whether there are disparities in the prevalence and consequences of bullying victimization for African American and Latino students (Peguero 2011; Wang, Iannotti, and Nansel 2009; Williams and Peguero 2013). While bullying and school violence are not limited to intergroup conflict (Mendez, Bauman, and Guillory 2012), it is possible that in-group biases result in less attention to in-group peer victimization and more to out-group peer victimization (Gini 2006). Thus, the analysis explores whether greater racial diversity correlates with more comprehensive antibullying policy.

Resource Constraints

There is, perhaps, a more basic explanation for divergence in state antibullying strategies: slack resources. Policy diffusion theory points to the importance of available slack resources in the adoption of an innovation (Berry and Berry 1990, 1992; Boehmke and Skinner 2012) as well as in the choice of its specific provisions (Daley and Garand 2005; Kellough and Selden 2003). In fact, Winburn, Winburn, and Niemeyer (2014) found that a greater availability of slack financial resources (per pupil spending) related to an increased likelihood of antibullying policy adoption.

Antibullying measures are not costless. While the state may not directly spend its funds, school districts that implement state mandated provisions such as counseling and teacher training must have the resources to do so. Therefore, legislators may take the availability of financial resources into account when deciding whether to burden districts with more costly provisions. Simply put, states have finite resources to draw from when adopting and implementing new policies and thus differences in resource constraints among the states may help explain why their antibullying provisions vary.

Resource constraints are not only financial, they are also institutional. The length of a state’s legislative calendar and resources provided to its legislators (e.g., salary, staff, among other) particularly constrain the amount of attention available for competing priorities and, as a result, the breadth and complexity of adopted laws (Kousser 2006). It stands to reason that states with more professionalized legislatures have the capacity to adopt more comprehensive legislation.

Financial Resource Hypothesis: States with greater financial resources adopt more comprehensive antibullying strategies.

Legislative Professionalism Hypothesis: States with more professionalized legislatures adopt more comprehensive antibullying strategies.

Regional Policy Learning

With over 120 antibullying bills passed between 1999 and 2011, there has been ample opportunity for policy learning and reinvention. Many states have strengthened their protections; however, more recent efforts in Tennessee, Texas, Oklahoma, and Michigan suggest that some are also rolling them back. Such restrictions on policies can be due to antidiffusion or negative backlash within later adopting states (Nelson and Mason 2007). In the case of learning and reinvention, laggard states may adopt more comprehensive policies than early adopters, while early adopters are themselves updating and expanding their original laws (Bouché and Volden 2011; Glick and Hays 1991; Mooney and Lee 1995). Learning is often predicted to occur regionally, as states are more likely to learn and copy from their contiguous neighbors (Berry and Berry 1990; Walker 1969). If states are learning from their neighbors, then a state’s policy comprehensiveness should be positively related to that of its neighboring states. However, declining comprehensiveness over time and a negative regional effect would suggest that some degree of backlash is occurring.

Regional Learning Hypothesis: States whose contiguous neighbors have more comprehensive antibullying policies also adopt more comprehensive provisions.

Data and Methods

Measuring Policy Comprehensiveness

A measure of antibullying comprehensiveness is needed in order to test the hypotheses presented above. That measure should capture the multiple items that states can include as requirements or recommendations for school district implementation. Fortunately, the Department of Education tracks sixteen major items included in state antibullying laws (Stuart-Cassell, Bell, and Springer 2011). Table 1 displays each item, its description, and the number of adopting states.¹ Items range from a simple statutory definition of bullying as distinct from harassment to more complex and resource-intensive programs such as providing mental health counseling for victims and bullies. There is not only substantial variation in the extent to which states include these items in their antibullying policies but also substantive variation in the types of items included.

Table 1.

Key Legislative Items and Total Adopting States.

Item	Content	States
Definitions
Purpose	Purpose of law and prohibitions against bullying	44
Scope	Scope of law (e.g., where law applies)	47
Prohibited behavior	Specific behavior defined as bullying	46
Enumerated groups	Group characteristics as target of intention	18
District policy development and review
District policy	Development and implementation of district policy	47
District policy review	State review of school district policy	23
District policy items
Definitions	Definition of prohibited behavior	32
Reporting	Responsibility for reporting bullying incidents	40
Investigations	Responsibility for investigating reports	38
Written records	Responsibility for keeping records of incidents	25
Consequences	Consequences or sanctions for bullying perpetrators	46
Mental health	Counseling, services for victims/perpetrators	16
Additional items
Communications	Communication of policy to students, parents, and school personnel	44
Training/prevention	Prevention and training for school personnel and students	39
Transparency/monitoring	Monitoring incidents and actions/public reporting	23
Legal remedies	Victim rights to pursue legal remedies	20

Note: Adapted from exhibit B of Stuart-Cassell, Bell, and Springer (2011) and updated using http://www.stopbullying.gov/laws/index.html.

Given the large variation in the items included in Table 1, a simpler additive score may not adequately capture the underlying dimension of comprehensiveness necessary for this analysis.² Furthermore, some policy items appear to discriminate better between states than others. An item response theory (IRT) model provides an estimate of the latent concept of policy comprehensiveness for each of the states by leveraging the variation in “difficulty” of the items (Baker and Kim 2004; van der Linden and Hambleton 1997). Given that the adoption of each item is dichotomized, a variant of the Rasch model (1960) was used to measure comprehensiveness. The Rasch model places an individual’s ability and the difficulty of an item on the same logit scale, and its purpose is to provide a unidimensional measure of a latent trait. In this case, the resulting measure of state antibullying policy comprehensiveness derived from the sixteen policy items displays good internal consistency (Cronbach’s α = .86) which, combined with the breadth of the items scored by the Department of Education, suggests that the measure also has good construct validity. Online Supplemental Text includes descriptive statistics and a further description of selecting this modeling approach.

Geographic and Temporal Variation in Comprehensiveness

Online Supplemental Figures 1 and 2 demonstrate the geographic and temporal distribution of the comprehensiveness scores from the IRT model. Online Supplemental Figure 1 is a map of the fifty states that is shaded based on the comprehensiveness of their antibullying policy. Online Supplemental Figure 2 plots each state’s score by the year in which it first adopted an antibullying policy. States that have “reinvented” their policies through the subsequent adoption of additional items, as of 2014, are marked as red triangles and states that have not are marked as black squares. There appears to be some regional clustering in Online Supplemental Figure 1, with states near either coast adopting more comprehensive antibullying statutes than states in the middle of the country. This is consistent with regional learning. Online Supplemental Figure 2, however, provides a mixed picture that suggests internal characteristics may be larger factors in the systematic differences in state antibullying policies. While nearly all of the earlier adopting states have expanded their antibullying measures to some extent, there is still wide variation in the comprehensiveness of their current laws. Some states, like Georgia and Maryland, greatly enhanced their statutes over time, while others, like Nebraska and Kansas, made fewer substantive changes and, consequently, still have less comprehensive policies. This suggests that the changes that they are making are driven more by internal constraints than by rising awareness of successes and failures in more comprehensive programs elsewhere. These initial intuitions must still be systematically tested.

Method and Data

An ordinary least squares model is used to test predictors of antibullying policy comprehensiveness associated with the hypotheses outlined above. Due to the lack of a good measure of free speech rights in elementary and secondary schools, two alternative measures are used to test the impact of a state’s free speech environment. The Foundation for Individual Rights in Education’s (FIRE) measure of red light institutions in higher education from its 2013 report is used as one proxy.³ Red light institutions are those that have “at least one policy that both clearly and substantially restricts freedom of speech, or that bars public access to its speech-related policies by requiring a university login and password for access” (FIRE 2013). Thus, states with higher FIRE scores are assumed to be more supportive of policies restricting free speech. While this is not a direct test of elementary or secondary education, three-quarters of the included universities and colleges are public, thus the proxy captures the support for free speech in public institutions, to some extent. A general measure of personal freedoms in the states from the Mercatus Center’s Freedom in the fifty states score of policies is also included (Ruger and Sorens 2015).

Counts and estimates from the U.S. Census Bureau are used for measuring the three included enumerated groups: LGBTQ, disabled, and racial minorities. Specifically, the U.S. Census (2010) includes a measure of each state’s count of same-sex couples per 1,000 households and the total percentage of black and Hispanic residents in a state; and the American Community Survey measures the percentage of students (age 5 to 20) in a state with a reported disability (U.S. Census Bureau 2007). Two measures of a state’s slack resources are included: state per capita income, measured in thousands of dollars (U.S. Census Bureau 2012); and, alternatively, a narrow measure of a state’s relative investment in education using the percentage of total state expenditures devoted to education from the U.S. Census Bureau’s (2013) State Government Finances Survey. Legislative professionalism is measured using Squire (1992, 2007) and a measure of the average policy comprehensiveness of a state’s contiguous neighbors provides an intuition as to whether regional learning has occurred. Finally, a count of time since the first adoption year (1999) is included for each state to account for potential duration dependence. Online Supplemental Table 1 contains summary statistics and descriptions for each of the covariates.

Results

Table 2 presents the primary regression results (i.e., the base model). Additionally, Online Supplemental Table 2 contains the remaining models that serve as robustness checks. What becomes immediately apparent is the support for the financial resources and enumerated groups hypotheses across all six models. Meaning, the greater availability of slack financial resources associates positively with the scope of a state’s antibullying strategy and greater supportiveness of enumerated groups (i.e., disabled students and lesbian, gay, bisexual, or transgender households). Furthermore, the enumerated groups model demonstrates that these three predictors also help explain whether a state specifically enumerates protected groups in their antibullying policy. Additionally, racial diversity is positively associated with both policy comprehensiveness and the specific enumeration of protected groups. Meaning, racially diverse states are more likely to have comprehensive bullying policies that include group enumeration.

Table 2.

Results of Antibullying Policy Comprehensiveness Base Model.

Variable	Base Model
Neighbor comprehensiveness	−0.59** (0.27)
FIRE (2013)	−0.00 (0.00)
Per capita income (US$1,000)	0.07** (0.02)
Legislative professionalism	0.00 (0.01)
Percent disabled students	0.40** (0.12)
LGBT households	0.20** (0.08)
Percent African American and Hispanic	0.03** (0.01)
Time	−0.06 (0.04)
Intercept	−6.29** (1.50)
N	48
Adjusted R ²	.37

Note: Standard errors in parentheses. FIRE = Foundation for Individual Rights in Education’s; LGBT = lesbian, gay, bisexual, or transgender.

*Indicates significance at p < .10 (two-tailed).

**p < .05.

It is important to note that the support for the financial resources hypothesis is only apparent for the broader measure of a state’s slack resources (i.e., per capita income). The education budget model shows no relationship between the resources a state devotes to education and its bullying policy. Given the strong association for per capita income and lack of one for education’s share of the budget, it appears that the overall pools of slack resources are more important for this particular policy innovation than a state’s specific support for education funding. What is also apparent from the results is that while legal scholars have raised freedom of speech concerns, and some states may be starting to respond, a state’s orientation toward personal freedom and free speech does not associate with the comprehensiveness of their antibullying policies. This suggests that, at the present time, discourse over student speech rights has no systematic effect on the scope of a state’s antibullying policy. One must take appropriate caution in the veracity of the inferences drawn from these free speech findings, however, given the indirect measurement of support for free speech in secondary and primary schools.

The results regarding regional learning are consistent across most of the models, but inconsistent with the expectations of policy diffusion theory. Namely, they suggest that the comprehensiveness of a state’s antibullying policy is negatively related to that of its contiguous neighbors. Neighbor states did not increase the likelihood of bullying policy adoption (Winburn, Winburn, and Niemeyer 2014), but the evidence here is consistent with a negative backlash in terms of the types of provisions they adopt. Further exploration of the source of this negative relationship is necessary, as it has broader implications for policy diffusion theory. While it is important to not oversell this finding, given the weak statistical significance and the cross-sectional nature of the analysis, it does suggest that neighbors may not influence the when of adoption, but they may still influence the what.

In terms of the strength of the effects, Online Supplemental Figure 3 demonstrates how each of the five statistically significant predictors relate to the expected comprehensiveness of a state’s antibullying policy. The percentage of disabled students in a state exhibits the strongest effect. Going from the minimum (4.4 percent) to the maximum (9.4 percent), when all other variables are held at their means, results in a 2.23 standard deviation (SD) increase in the expected comprehensiveness score. The other four variables exhibit between 1.40 and 2.03 SD shifts when going from their minimum to maximum values, holding all other variables at their means.

A difference of means tests was also performed to determine whether states with “no promo homo” laws also have, on average, less comprehensive antibullying policies. Online Supplemental Figure 4 visualizes the difference in average bullying policy comprehensiveness for states that have passed these laws. There is no statistically significant difference between the nine states⁴ that have adopted LGBTQ speech restrictions (M = 0.03, SD = 0.50) and the remaining states that have not (M = 0.02; SD = 0.95), t(50) = 0.07, p = .95. Furthermore, when included in the base model, there is no significant shift in the intercept between states that do and do not have no promo homo laws.

Discussion and Conclusions

Bullying remains a vexing social problem in America’s schools. Since 1999, forty-nine states have taken action to set, at the very least minimal, standards for antibullying policies. The substantial variation in these policies, however, reflects a federal system that allows policy experimentation as well as modification to suit local norms. The complexities of interstate sharing and external pressure meeting intrastate social, economic, and political dynamics can help us understand why some innovations spread and the patterns that they follow (Berry and Berry 1990; Walker 1969). This is especially important for an issue like bullying where it is evident that state standards do not match the comprehensive solutions tested and promoted by education scholars. The purpose of this article is to further our understanding of how internal and external influences result in a disjuncture between scholarly standards and adopted state standards.

While previous scholarship points to external media salience as a key driver of the timing of bullying policy adoption (Winburn, Winburn, and Niemeyer 2014), the content of those policies and subsequent reinventions relates more strongly to internal characteristics. States where more LGBTQ and disabled self-identify, as well those with greater racial diversity, adopt more comprehensive antibullying strategies. Given that the supportiveness of a state to each community is likely a factor in their willingness to identify (Gates 2012, 2014), this article finds evidence consistent with the theory that identification results in better group cohesion and, thus, more consistent political engagement (Egan 2012). Overcoming the gap between education research and political practice may be exceedingly difficult, given how challenging it is to change a state’s culture. That being said, it is clear that external pressure was useful for moving almost every state to adopt something (Winburn, Winburn, and Niemeyer 2014). Therefore, it is possible that a resurgence in issue salience, within specific states or nationally, could further open policy windows allowing for the expansion of weak state statutes (Kingdon 2003).

Internal social dynamics are not the entire story, however. Slack resources remain an important factor for more comprehensive policy solutions, and there may in fact be a backlash effect among adopting neighbors whereby states whose neighbors have highly comprehensive policies adopt less comprehensive policy. The explanation for a potential backlash or antidiffusion response requires additional research in order to make any causal claims. These findings are important for diffusion scholars, as there has been only limited testing of the spread of policies with multiple components (Boehmke 2009; Daley and Garand 2005; Kellough and Selden 2003) and it is not clear how well the general model of policy diffusion holds when it comes to the specific components of an innovation. Furthermore, the theory and empirical support for antidiffusion remain underdeveloped.

Finally, there is little evidence that First Amendment concerns shaped this first phase of antibullying policy adoption. Granted, there are limitations in the proxy measures used, but neither the broad or narrow rights tests yielded significant results. That being said, this long-standing concern among legal scholars is gaining ground in some states and may spread to others as time progresses. As long as this issue remains closely tied to enumerated groups, it is possible that opponents will use free speech claims to weaken rules that potentially threaten certain speech. It is important to understand the politics of bullying in light of the larger culture wars debates. In fact, future research examining the implementation of these laws by schools and individual teachers can help us better understand how larger social debates affect not only school curriculum (Berkman and Plutzer 2010) but also how we protect students from peer victimization.

Footnotes

Author’s Note

Reproduction files can be found at the author’s Dataverse ().

Acknowledgments

The author would like to thank Charles Crabtree, Erica Frankenberg, Marie Hojnacki, A. Lee Hannah, David Lowery, Chris Ojeda, Amanda Parks, and Jacqueline Stefkovich for their comments on earlier drafts of this article.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Supplemental Material

The online [appendices/data supplements/etc] are available at .

Notes

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