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Article

Extreme Hatred: Revisiting the Hate Crime and Terrorism Relationship to Determine Whether They Are “Close Cousins” or “Distant Relatives”

Colleen E. Mills1, Joshua D. Freilich1, and Steven M. Chermak2

Abstract Existing literature demonstrates disagreement over the relationship between hate crime and terrorism with some calling them “close cousins,” whereas others declare them “distant relatives.” We extend previous research by capturing a middle ground between hate crime and terrorism: extremist hate crime. We conduct negative binomial regressions to examine hate crime by non-extremists, fatal hate crime by far-rightists, and terrorism in U.S. counties (1992-2012). Results show that counties experiencing increases in general hate crime, far-right hate crime, and non-right-wing terrorism see associated increases in far-right hate crime, far-right terrorism, and far-right hate crime, respectively. We conclude that hate crime and terrorism may be more akin to close cousins than distant relatives.

1John Jay College of Criminal Justice; City University of New York, The Graduate Center New York City, USA 2Michigan State University, East Lansing, USA

Corresponding Author: Colleen E. Mills, John Jay College of Criminal Justice; City University of New York, The Graduate Center, 524 West 59th St., 2103 North Hall, New York, NY 10019, USA. Email: [email protected]

620626CADXXX10.1177/0011128715620626Crime & DelinquencyMills et al. research-article2015

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Keywords terrorism, violence, minorities, hate crime

Introduction

In June 2015, Dylann Storm Roof opened fire on Black congregants in the Emmanuel AME church in Charleston, South Carolina. Roof previously posted a manifesto, detailing his hatred for non-White races and confirming the racial motivation behind the shooting (Robles, 2015). Three years prior, Wade Michael Page stormed a Sikh Temple in Wisconsin, executing a mass shooting that was widely recognized as bias-motivated against the Sikh con- gregants. The authorities later revealed that Page was a White supremacist, active in the neo-Nazi music scene, and often spoke of the racial holy war (Elias, 2012). After both of these attacks, many designated the attack as domestic terrorism, lone wolf terrorism, as well as a hate crime (Elias, 2012; Gladstone & Zraick, 2015; Goode & Kovaleski, 2012; B. Levin, 2012; Murphy, 2012; Robles, 2015; “Unprosecuted Hate Crimes,” 2012). Incidents such as Page’s and Storm’s rampages blur the line between certain hate crimes and terrorism. Such confusion extends beyond the media to the schol- arly community. Existing literature recognizes the parallels between hate crime and terrorism (Deloughery, King, & Asal, 2012; Green, McFalls, & Smith, 2001; Hamm, 1993; Herek, Cogan, & Gillis, 2002; Krueger & Malečková, 2002, 2003), but some scholars disagree over the nature of the relationship. Krueger and Malečková (2002, 2003) deem them “close cous- ins” with their similarities outweighing their differences, whereas Deloughery et al. (2012) characterize them as “distant relatives,” finding their differences set them apart.

Although debate exists over the hate crime–terrorism relationship, only a limited body of research has empirically examined this relationship (Byers & Jones, 2007; Deloughery et al. 2012; Disha, Cavendish, & King, 2011; R. D. King & Sutton, 2013). Much of this work centers on the impact of the September 11 attack on hate crime offending (Disha et al., 2011; R. D. King & Sutton, 2013). To date, Deloughery et al.’s (2012) temporal analysis is the only study that examines the effects of the full range of anti-U.S. terrorist attacks on hate crimes. In addition, it is the only known study that tests for escalation from hate crimes to right-wing terrorism.

The current study extends Deloughery et al.’s (2012) important work by testing the spatial relationship between hate crime and terrorism on the county level. We unpack the relationships among (a) non-fatal hate crimes committed by non-extremists, (b) fatal far-right hate crime, and (c) terrorist

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attacks from 1992 to 2012. The current study fills another gap by utilizing bias-motivated homicides by the far-right catalogued in the Extremist Crime Database (ECDB). The use of the ECDB addresses a limitation acknowl- edged by Deloughery et al. in their study as they used Hate Crime Statistics Act (HCSA) data, which fails to account for perpetrator ideological strength or affiliation. The current study seeks to answer the following research question:

Research Question 1: Are hate crimes and terrorism more interrelated than prior research has demonstrated?

Literature Review

The Similarities and Differences Between Terrorism and Hate Crime

Some past research has highlighted the similarities between terrorism and hate crime. For example, one of the earliest forms of terrorism in the United States was racially and politically motivated violence of the postbellum Ku Klux Klan, which ushered in early legislative attempts to address the com- mon phenomenon of racially motivated terrorism. Examining the Klan’s use of violence to block African Americans’ political involvement, Law (2009) calls the Klan the “terrorist wing of the Democratic Party” (p. 132), high- lighting political motivations of the Klan’s reign of terror. Arguing that early efforts to combat political violence coincided with tackling racially moti- vated violence, Shimamoto (2004) remarks that the Enforcement Act of 1870 and the Ku Klux Klan Act of 1871 were the first measures taken by the United States to handle terroristic racially motivated violence, so as to preserve the rights of targeted citizens much like hate crime legislation. Thus, the line between hate crime and terrorism proves blurred historically as early American terrorism was both politically and racially motivated.

Hamm (1993) notes the similarities between the language of terrorism and hate crime definitions as stated by the U.S. government. Hamm (1993) cites the Federal Bureau of Investigation’s (FBI) definition of a terrorist incident as a “violent act or an act dangerous to human life in violation of the criminal laws . . . to intimidate or coerce a government, the civilian population, or any segment thereof, in the furtherance of political or social objectives” (pp. 106-107). The most recent language of federal hate crime legislation defines hate crimes as offenses motivated by “prejudice based on race, gender and gender identity, religion, disability, sexual orientation, or ethnicity” (U.S. Department of Justice, FBI, 2011b). Given the statutory

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language, Hamm (1993) argues that skinhead violence can be classified as hate crimes or terrorist acts, evidencing the similar nature of such acts.

Definitions of hate crime and terrorist acts reveal a number of shared traits. Both involve acts of violence against persons and property. Hate crime and terrorism definitions both focus on classifying civilian populations, or subgroups thereof, as victims (Shimamoto, 2004). Many definitions of ter- rorism rely on the political, social, and/or religious nature of the goals of terrorist perpetrators (Hoffman, 1998). Like terrorism, hate crimes express a number of socio-political objectives by targeting individuals based on their perceived group membership. Biases often prove intricately related to socio- political and/or religious views. Both acts serve as tactics in the arsenal of hate groups, a number of which are also labeled as terrorist organizations such as the Ku Klux Klan (Atkins, 2006; J. Levin, 2013). Similarly, McDevitt, Levin, and Bennet’s (2002) typology of hate crime offenders includes the “mission” category made up of members or supporters of orga- nized hate groups. “Mission” offenders are often racist White supremacist extremists who believe that they must purge the world of evil by eliminating the “other” group that threatens their group. B. Levin (2012) notes that these “hard core hatemongers are believed to be responsible for about 33%-40% of hate motivated homicides” (para. 7).

Hate crimes and terrorist incidents act as message crimes, instilling fear and psychological harm, as well as behavioral modification. Noting the close relationship between hate crime and terrorism, Krueger and Malečková (2002, 2003) describe the goal of hate crimes to terrorize a larger group beyond the immediate victim, who is selected on the basis of her or his group identity. Hate crimes constitute not only an attack on a single person, but also they send an anti-“other” message to the target’s larger community. Hate crimes thus present unique harms that distinguish them from ordinary crimes as they align more closely with terrorism. Several studies (Barnes & Ephross, 1994; Iganski & Lagou, 2009; Lim, 2009; McDevitt, Balboni, Garcia, & Gu, 2001) also show that victims of hate crime suffer greater psychological and emotional harms, including depression, increased fear of victimization, anger, and stress. For example, Iganski and Lagou (2009) find that both racial minority (and the larger minority communities) and White victims of racially motivated crimes avoid certain places and are more likely to have moved (i.e., changed residences). Increased avoidance behaviors and other behavioral changes also follow in the aftermath of ter- rorist attacks, such as those of 9/11 and the 2005 London bombings (Gigerenzer, 2004, 2006; McArdle, Rosoff, & John, 2012; Prager, Beeler Assay, Lee, & von Winterfeldt, 2011; Rubin, Brewin, Greenberg, Simpson, & Wessely, 2005).

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Hate crimes and terrorist acts can be defensive or retaliatory. Defensive hate crimes are those in which offenders “defend their turf” and send a message to the larger community to which the victim belongs (Green, Glaser, & Rich, 1998; McDevitt et al., 2002). Interviewing White youth in Brooklyn, Pinderhughes (1993) finds that youth committed racially motivated attacks to defend their turf as they believed that the govern- ment was taking their jobs and giving them to racial minorities while Whites suffered unemployment and homelessness. Retaliatory hate crimes occur in response to some precipitating event, specifically a perceived or actual hate crime against a member of the offender’s ingroup (McDevitt et al., 2002). One study of hate crimes in New York City, for example, found that “cross-sectionally, antiwhite incidents correlate with the num- ber of antiblack incidents, and temporally these two monthly time series seem to follow a tit-for-tat pattern” (Green, Glaser, & Rich, 1998 in Green, Strolovitch, & Wong, 1998, p. 399). Terrorist acts can also be con- ceptualized as defensive or retaliatory. For example, the Troubles in Ireland exemplify both models with republican dissidents “defending” Ireland from the British or acting in retaliation with tit-for-tat attacks by republican dissidents and loyalists or British forces (LaFree, Dugan, & Korte, 2009).

Although many scholars argue for the similarities between the two, oth- ers note how each are unique. In investigating the association between hate crimes and terrorism, Deloughery et al. (2012) address the claim that hate crime acts as a “poor man’s terrorist attack” that eventually escalates to more serious acts of terrorism (p. 665). Unlike most terrorist attacks that require some level of planning and resources, hate crimes are usually com- mitted on the spur of the moment.1 Therefore, hate crimes present an ave- nue for extremists to pursue their socio-political objectives without the necessity of planning. Hate crimes also pose less danger of arrest. Hate crimes are underreported and under-investigated and prosecuted (Freilich & Chermak, 2013; R. D. King, 2007; R. D. King, Messner, & Baller, 2009). Terrorist attacks garner media, government, and law enforcement attention and pose a greater threat of apprehension. Hate crimes thus present an effective route for upholding ideological beliefs while minimizing the costs of resources and risks.

Hate crime and terrorism further differ in certain ways. Hamm (1993) argues that the distinction between hate crime and terrorism is nuanced, remarking that only extreme hate crimes driven by socio-political goals should be considered terrorism. Deloughery et al. (2012) find that hate crimes constitute more of a “downward” offense with a majority party attacking a member of a minority, whereas terrorism proves to be an

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“upward” crime with a less powerful group attacking a more powerful one. Although this may be a valid distinction, it fails to acknowledge the nature of a terrorist group’s social, political, or religious objectives. Michael (2003) comments that “terrorism is almost always linked to a wider social move- ment. . . . Klan terrorism in the South was part of a broader pattern of white resistance to the civil rights struggle” (p. 105). Shimamoto (2004) also argues that both terrorism and hate crime attack fundamental notions of democracy and the state. Therefore, the argument remains that hate crimes attack society at large by attacking its norms, targeting dearly held values of equality, liberty, and basic human rights. Such a conception of hate crimes aligns them with the “upward” nature of terrorism, refuting that hate crimes are only a downward crime.

Other differences between hate crime and terrorism pertain to offender and incident characteristics. J. Levin and McDevitt (2002; see also Phillips, 2009) find that the majority of hate crimes are actually committed by groups of thrill-seeking youth who lack firm ideological beliefs or hate group affiliation.2 In addition to this thrill nature and the peer dynamics, these incidents usually involve alcohol or drug consumption and are unplanned (J. Levin & McDevitt, 2002; McDevitt et al., 2002; Messner, McHugh, & Felson, 2004). It must be noted though, that thrill-seeking hate crimes still send a message to the targeted group and often are an out- growth of societal cultural norms. Byers, Crider, & Biggers (1999) shows that many in their sample of thrill offenders expressed negative views of their Amish victims. These thrill hate crime offenders also thought that the larger community agreed with them that Amish persons were inferior and not a part of society. Although thrill-seeking hate crime offenders are not political extremists and are far from being firmly committed terrorists, they still may be motivated by quasi-political motives. Thrill-seeking offenders, in other words, often commit these attacks to send a message that reflects both their personal biases and what they believe to be their society’s cultural norms.

Another difference is that offenders typically do not claim responsibil- ity for the attack or publicize it as terrorists often do, but they do not necessarily need to publicize their crimes themselves (LaFree & Dugan, 2004). As message crimes, hate crimes themselves issue a warning to the victim’s larger group. Such crimes often garner enough media attention to get their objective publicized. Despite important differences between hate crime and terrorism, their similarities provide the groundwork for further investigation of the relationship between the two phenomena. See Figure 1 for summary of similarities and differences between hate crime and terrorism.

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The Theoretical Context of the Hate Crime and Terrorism Relationship

General Hate Crime Escalating to Extremist Hate Crime

The theoretical basis for investigating the relationships between hate crime, extremist hate crimes, and terrorism relies on intergroup conflict and related theories, including normative support and social identity theory. Several studies (Grattet, 2009; Green, Glaser, & Rich, 1998; Green, Strolovitch, & Wong, 1998; Jacobs & Wood, 1999; R. D. King & Brustein, 2006; R. D. King & Sutton, 2013; C. J. Lyons, 2007) investigate the role of intergroup conflict and hate crime. Green, Strolovitch, and Wong’s (1998) “defended neighbor- hoods thesis” draws on realist group conflicts theories. In brief, realist group conflict theories posit that White intolerance manifests when racial and

Similarities Differences

•• Early U.S. terrorism linked to KKK, a notorious hate organization

•• Hate crimes are often committed on the spur of the moment; and usually require less planning and resources

•• Similar language in statutes (violence, civilian populations, socio-political objectives)

•• Hate crimes are less likely to result in an arrest; are under-reported, -investigated, -prosecuted

•• Biases linked to socio-political and religious ideologies

•• Hate crimes can be downward (powerful subgroup attacking a minority subgroup)

•• Overlap between hate groups and terrorist groups

•• Many hate crimes are committed by offenders fueled by alcohol, and drugs.

•• Communicative nature •• Many hate crimes are committed by non-extremist youths, acting with others, for the “thrill” of it

•• Instill psychological harms, fear, and behavior modification

•• Hate crimes can lack a publicity aspect

•• Both can be upward (terrorism and hate crime attack notions of democracy, equality, human rights)

Figure 1. The Similiarities and Differences Between Hate Crime and Terrorism (Barnes & Ephross, 1994; Deloughery, King, & Asal, 2012; Hamm, 1993; Iganski & Lagou, 2009; LaFree & Dugan, 2004; J. Levin & McDevitt, 2002; Law, 2009; Lim, 2009; Messner et al., 2004; McDevitt et al., 2001; McDevitt et al., 2002; Michael, 2003; Phillips, 2009; Shimamoto, 2004).

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ethnic minority groups move into their areas, thereby representing a threat to their political and economic interests as a competing force for resources (Green, Strolovitch, & Wong, 1998). Several studies (Grattet, 2009; Green, Strolovitch, & Wong, 1998; C. J. Lyons, 2007) support the “defended neigh- borhoods” thesis, evidencing that racially/ethnically based hate crimes are highly correlated with the influx of minorities into former almost all-White areas.

The following section examines how the presence of general anti-“other” hate crimes can result in fatal hate crimes by far-rightists. Normative support proves to be a very significant factor in influencing the use of violence in intergroup conflict. Louis and Taylor (2002) explain how group norms shape individual members’ perceptions, specifically perceptions of intergroup con- flict. Senechal de la Roche (1996) explains that solidarity is integral for collective violence, permitting violent expression of group grievance. As such, lynching varied with local solidarity in the American South with close- knit communities seeing increased lynchings. Gurr (1968) explains that experimental evidence demonstrates that highly cohesive groups are much more likely to express hostility against “outsiders” (p. 272). Regarding the importance of normative support for violence in enabling terrorists, M. King, Noor, and Taylor (2011) note how Milgram’s experiments demon- strated that individuals are susceptible to accepting violence when sur- rounded by others who were compliant with engaging in violence. M. King et al. (2011) find that jihadi terrorists receive normative support from their families, as well as the larger community. Ingroup identification provides a mechanism for individuals to positively see themselves. Social identity the- ory dictates that people derive self-esteem through their group membership and by viewing their group positively compared with other groups; further- more, such group identification strengthens individual conformity to group norms (Cohrs & Kessler, 2013; Federico, 2013; Louis & Taylor, 2002; P. A. Lyons, Kenworthy, & Popan, 2010). P. A. Lyons et al. (2010) find that the interaction of ingroup identification and mean and high-level group narcis- sism among U.S. citizens was associated with negative attitudes and behav- iors toward Arab immigrants.

The research demonstrates that extremists are more likely to resort to violence against a perceived threat when they receive normative support from their ingroup. Hamm (1993) finds that skinheads are synanomic, which he defines as “hyperactively bonded to the dominant social order and to one another” (p. 212). As a result, far-right extremists should be more likely to not only be more aggressively bonded to their goals of car- rying out their socio-political objectives in sustaining “traditional” values, but also their ingroup (White, heterosexual, working-class men).

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Furthermore, far-right extremists should prove to feed off of the normative support of their ingroup in exercising violence against outgroups.

In sum, there are two causal mechanisms that could explain how regu- lar hate crimes committed by non-extremists lead to fatal ideologically motivated attacks committed by far-right extremists. First, regular hate crimes often attract attention from the media, the larger community, and committed far-rightists. These regular hate crimes may encourage far- rightists to conclude that “regular” persons in the general community share their racial and extremist grievances. For example, Green and Rich (1998) investigated the association between White supremacist rallies and demonstrations and cross burnings on the county level in North Carolina. They found that in counties where White supremacist rallies occurred, the likelihood of subsequent cross burnings increased. The authors concluded that White supremacist rallies could encourage individuals traveling to the event by drawing attention to racial grievances, and therefore facili- tating action in the form of racial intimidation. Deloughery et al. (2012) similarly explain that anti-minority hate crimes can highlight growing anti-minority sentiment to which extremists may respond with more seri- ous violence. Our argument is that regular hate crimes committed by non- extremists could (perhaps unintentionally) highlight these same racial grievances that then encourage far-right extremists to commit fatal acts of ideologically motivated hate crimes.

Second, these regular hate crimes are often fiercely denounced by gov- ernment officials, minority communities, and advocacy groups (Jenness & Grattet, 2004; J. Levin & McDevitt, 2002). Simi and Futrell (2010) have explained that far-rightists commonly feel stigmatized by mainstream soci- ety. Many therefore retreat to “free places” where they are better able to subscribe to and act upon their extremist beliefs, and interact with others who think like them. It is possible that these denunciations of regular hate crimes aggravate the feelings of persecution held by many far-rightists that is reinforced by others who share their views. This in turn could create a backlash effect that results in some far-rightists committing fatal hate crimes. As the far-right movement often attracts violent individuals (see, for example, Ezekiel, 1995; Freilich, Adamczyk, Chermak, Boyd, & Parkin, 2015), we wonder, in other words, whether some far-rightists engage in fatal bias-motivated violence in response to the condemnation of regular anti-minority hate crime, which presents an attack on their grievances and ideology.

Based upon both of these possible causal mechanisms, we hypothesize that places experiencing hate crimes in general are more likely to experience fatal hate crimes by far-right extremists.

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Extremist Hate Crime Escalating to Extremist Terrorism

In terms of extremist hate crime escalating to terrorism in the intergroup context, Michael (2003) discusses Sprinzak’s theory of “split-delegimitization” as applied to right-wing terrorism, which asserts that “outsiders” as well as the state simultaneously come under attack (p. 95). Michael (2003) contends that this theoretically supports the evolution of right-wing terrorism with attacks escalating from those against the “outsiders” to the state due to the state’s per- ceived alliance with the “outsiders” (p. 95). Supporting the theoretical escala- tion, Michael (2003) looks to Hewitt’s (2000) descriptive data on American domestic right-wing terrorism, which evidences a demonstrable escalation in violence against the state. Hewitt’s (2000) data show that the majority of the first wave of far-right attacks from the 1950s to the 1970s was against people based on their race or ethnicity, followed by civil rights workers. The second wave from the 1970s to the present shows that the far-right has increasingly targeted the government, including attacks against law enforcement, politicians, and government facilities. Examining the life course of American far-right groups, Kerodal, Freilich, Chermak, and Suttmoeller (2015) empirically test and find support for Sprinzak’s theory, uncovering that the far-right initially attacked non-government targets but began to equally strike both non-government and government targets after becoming disillusioned with the government. Such findings support the idea that the far-right may move from simply engaging in hate crimes against minorities to anti-government attacks as well, signaling an escalation in their activities. Deloughery et al.’s (2012) case study of Timothy McVeigh’s horrific anti-government bombing attack of the Federal building in Oklahoma City in 1995 demonstrated that increases in anti-minority hate crimes were a way to express right-wing grievances and can act as a warning or a signal that some extremists will subsequently potentially “upgrade” to (anti-govern- ment or American society at large) terrorism (p. 668). As a result, we hypothe- size that counties experiencing fatal hate crimes by far-rightists would also see far-right terrorism with these extremists employing violence against both minor- ity and government targets and American society at large.

Extremist Hate Crime as Response to Terrorism

Regarding extremist hate as a response to anti-American terrorism, a review of the literature on group grievance, social control, and retaliation is useful. Black (1983) posits a theory of crime as social control, in which individuals use crime as “self-help” to express their group’s grievance against a particular subgroup to maintain social control. McCauley and Moskalenko (2011) define group (or political) grievance as a mechanism for radicalization and as the “threat or harm

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to a group or cause the individual cares about can move the individual to hostil- ity and violence toward perpetrators” (p. 21). Terrorist attacks perceived to attack “traditional” or “American” values thus present extremists with a group grievance that manifests in violent retribution. Vicarious retribution occurs when an ingroup member views an entire outgroup responsible for a harm against a fellow ingroup member and thus attacks an outgroup member for ret- ribution (Lickel, Miller, Stenstrom, Denson, & Schmader, 2006). Several schol- ars (Lickel et al., 2006; McCauley & Moskalenko, 2008, 2011) explain that a popular mechanism for both radicalization and vicarious retribution is dehu- manization of the “enemy.” Lickel et al. (2006) comment that intergroup con- flict sees dehumanization of the outgroup, which facilitates vicarious retribution as outgroup members are seen “as being interchangeable and therefore equally deserving of retaliation” (p. 378). Retaliatory hate crimes involve individuals who seek revenge by targeting innocent bystanders whom they perceive as rep- resentative of a larger enemy. Several studies (Byers & Jones, 2007; Deloughery et al., 2012; Disha et al., 2011; R. D. King & Sutton, 2013; McDevitt et al., 2002) demonstrate the prevalence of hate crimes following terrorist attacks. Hate crimes targeting perceived Middle Eastern victims occurred not only after 9/11 and the Boston Marathon, but also immediately at the start of the Iran hos- tage crisis in 1979 (J. Levin & McDevitt, 2002). Retaliatory hate crimes thus act as micro-level manifestation of broader conflicts on the international scale.

The synanomic nature of far-right extremists thus explains why they are likely to respond to terrorist attacks against “traditional” or “American” val- ues with hate crimes against outgroups they perceive as a threat or as respon- sible for precipitating terrorist attacks. As a result, extremists prove more likely to exercise hate crime as a form of social control. Furthermore, norma- tive support exists for retributive violence in the course of intergroup conflict (Lickel et al., 2006). Therefore, extremists feed off of normative support to not only engage in hate crimes in general, but also specifically as a form of vicarious retribution. Retaliatory hate crimes following terrorist attacks thus express group grievance, as well as social control, by those ultra-committed to upholding the dominant social order. We hypothesize that counties that experience terrorist attacks by non-right-wing groups would be more likely to see an increase in fatal hate crimes by far-right extremists.

Revisiting Deloughery et al. (2012): Are Hate Crimes Only Distant Relatives?

Using HCSA and Global Terrorism Database (GTD) data, Deloughery et al. (2012) examine the temporal proximity of hate crimes and terrorism and find that (a) hate crimes do not necessarily lead to future right-wing terrorism, (b)

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hate crimes are more often a response to terrorism, and (c) anti-minority hate crimes prove especially prevalent after non-right-wing terrorist attacks that seem to attack traditional “American” values. As a result, they conclude that hate crimes and terrorism are more akin to “distant relatives” as hate crimes are not indicative of future terrorist attacks. One limitation acknowledged by the authors is the absence of a measure for ideological strength or offender affiliation. The majority of hate crime offenders consist of thrill offenders, who lack firmly committed extremist ideological beliefs (J. Levin & McDevitt, 2002; McDevitt et al., 2002). Therefore, HCSA data does not allow research- ers to identify which offenders subscribe to extremist views which undermines our ability to study this phenomenon. Offenders who subscribe to extremist right-wing ideology, however, prove more likely to resort to both ideologi- cally motivated hate crimes and terrorist acts than non-ideological offenders. This study extends Deloughery’s important work in two ways: by utilizing fatal bias-motivated homicides committed by far-rightists as contained in the ECDB and by examining the county-level association of hate crime and terrorism.

Finally, based upon the prior literature we also examine four additional hypotheses (for a total of seven). Research establishes that intergroup conflict occurs when minorities pose a threat to the interests of the dominant group (Blalock, 1967; Green, 1998, Green, Strolovitch, & Wong, 1998, R. D. King & Brustein, 2006). Greater rates of minority presence are said to lead to White intolerance, and in turn violence against minorities, as their presence poses a threat to White economic interests (Green, Strolovitch, & Wong, 1998). Previous studies (Disha et al., 2011; C. J. Lyons, 2007) find that greater racial/ethnic minority presence explains interracial violence. Intergroup conflict theories also posit that ethnic heterogeneity can also lead to greater conflict (Olzak, Shanahan, & McEneaney, 1996; Shanahan & Olzak, 1999). Another important predictor in studies examining intergroup conflict is demographic change. Green, Strolovitch, and Wong’s (1998) defended neighborhoods thesis holds that demographic change over time with minority growth in areas contributes to White violence against “invad- ing” minorities. We hypothesize that those counties with greater minority presence as well as greater ethnic heterogeneity will be more likely to see far-right activity. We further hypothesize that demographic change (i.e., minority presence increasing over time) will be associated with far-right activity as well.

The literature on intergroup conflict often relies on measures of eco- nomic competition. Theoretically, poor economic conditions foster increased racial competition for resources, which, in turn, fosters increased intergroup conflict leading to violent outcomes such as hate crimes and

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terrorist incidents (Corzine, Huff-Corzine, & Creech, 1988; C. J. Lyons, 2007; Olzak, 1989, 1990; Soule, 1992; Tolnay & Beck, 1995; Tolnay, Deane, & Beck, 1996). Relatedly, deprivation frameworks in criminology such as the classic strain theories maintain that poorer locations usually provide fewer opportunities for success. Some persons use crime as an alternative way to achieve financial success as the legal opportunities are closed to them (Merton, 1938). Often, persons in these areas are socially isolated from mainstream society. These areas may also attract offenders from other locations who exacerbate this locale’s crime problem (Messner & Rosenfeld, 2007). Significantly, economic deprivation has also been seen as linked to far-right extremism (Lipset & Raab, 1977). Freilich et al. (2015; see also Pridemore & Freilich, 2006), for example, discuss how far- right extremists residing in poorer locations might conclude that their ide- ological opponents are responsible for their economic deprivation. These far-rightists may therefore then attack these opponents. We hypothesize that counties experiencing both higher rates of unemployment and poverty, as well as increased rates in both of these domains over time, will see higher numbers of far-right activity.

Data and Methods

This study investigates the research question, “Are hate crimes and terrorism more interrelated than prior research has demonstrated?” Using incident data aggregated to the county level, we seek to address whether hate crime and terrorism prove more similar to each other than not by studying the spatial association between the two phenomena. We test the following seven hypotheses:

Hypothesis 1: An increase in counties’ non-fatal anti-minority/anti- “other” hate crimes committed by all type of perpetrators is associated with an increase in counties’ fatal hate crimes committed by far-rightists. Hypothesis 2: An increase in counties’ fatal hate crimes committed by far-rightists is associated with an increase in counties’ far-right terrorist attacks. Hypothesis 3: An increase in counties’ terrorist attacks by non-right-wing groups that attack “traditional/American values” is associated with an increase in counties’ fatal hate crimes committed by far-rightists. Hypothesis 4: An increase in counties’ levels of minority presence and diversity is associated with increases in counties’ far-right activity. Hypothesis 5: Growing minority presence and diversity over time is asso- ciated with increases in counties’ far-right activity.

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Hypothesis 6: An increase in counties’ levels of poor economic condi- tions (poverty and unemployment) is associated with increases in coun- ties’ far-right activity. Hypothesis 7: Worsening economic conditions (poverty and unemploy- ment) over time is associated with increases in counties’ far-right activity.

To address these hypotheses, this study conducts a county analysis using pooled event counts by county over a 20-year period (1992-2012) from three different databases: the U.S. ECDB, the HCSA from the Uniform Crime Report (UCR), and U.S. cases from the GTD.

The GTD is a terrorist event database that includes all terrorist attacks that occur around the globe using open-source data (see LaFree & Dugan, 2007, for information on incident inclusion criteria). This study uses incident-level data from the GTD to examine pooled counts of 223 right-wing3 and 225 non-right-wing/anti-“American” (primarily far-left animal/environmental terrorists and radical Islamists) terrorist attacks in the United States over the 20-year period from 1992 to 2012 (excluding 1993, which is missing from the GTD) (data from National Consortium for the Study of Terrorism & Responses to Terrorism, 2014). As Deloughery et al. (2012) do in their study, this study identifies the perpetrator type (i.e., far-right vs. far-left) by using data from the Terrorist Organization Profiles (TOPS), which codes and orga- nizes groups by ideology (Deloughery et al., 2012). The current study also evaluates each potential individual or unknown perpetrator attack to discern and classify terrorist attacks according to evidence indicating far-right or non-right-wing/anti-“American” perpetrators or motivations.4

The HCSA of 1990 provides for the collection of data on hate crime inci- dents in the United States with law enforcement agencies recording and sub- mitting counts and other possible descriptive information of hate crime incidents in their jurisdictions to the FBI for inclusion in the UCR (U.S. Department of Justice, FBI, 2011a). Currently, the federal hate crimes act charges the Attorney General with collecting data on designated crimes moti- vated by “prejudice based on race, gender and gender identity, religion, dis- ability, sexual orientation, or ethnicity” (U.S. Department of Justice, FBI, 2011b). Similar to other official crime databases, limitations exist with the HCSA data as hate crimes suffer from underreporting as well as differential compliance with recording and reporting hate crimes by location (R. D. King, 2007; R. D. King et al., 2009).

Importantly though, despite its limitations the FBI’s HCSA is recognized as one of the most reliable sources available for county-level hate crime data. The HCSA includes more participating police agencies and covers more of

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the nation’s population than the FBI’s National Incident Based Recording System’s (NIBRS) bias crime data. In addition, watch-groups do not publish annual listings of all hate crimes for the entire nation in any systematic fash- ion (Freilich & Chermak, 2013). Regarding the National Crime Victimization Survey, there could be significant variation in the respondents’ understanding of hate crime victimization. In sum, the HCSA is one of the more reliable sources for hate crimes data. Indeed, a series of studies have used HCSA data to investigate a variety of important issues (see, for example, Byers & Jones, 2007; Deloughery et al., 2012; Disha et al., 2011; R. D. King et al., 2009; R. D. King & Sutton, 2013).

The current study uses a pooled count of 130,289 non-fatal anti-“other” or anti-minority (including all minority groups protected by the federal legisla- tion listed above) hate crimes from 1992 to 2012 (excluding 1993). Data on only non-fatal acts ensure there is no overlap with any of the fatal far-right bias crimes obtained by the ECDB (data obtained from U.S. Department of Justice, Federal Bureau of Investigation, 2014).

The U.S. ECDB provides a rich source of data on violent and financial crimes committed by extremists, specifically far-rightists, Al-Qaeda inspired, as well as extremist animal or environmental rights advocates (Freilich, Chermak, Belli, Gruenewald, & Parkin, 2014). Unlike other databases, the ECDB includes only incidents, plots, or schemes in which at least one extrem- ist was involved. In addition to non-ideological violence, fatal incidents cap- tured by the ECDB include ideologically motivated homicides against government targets as well as other ideological targets based on biases (i.e., against racial groups). In addition to ideologically motivated violence most closely matching common definitions of terrorism, the specification of bias- motivated violence by extremists most closely approaches the phenomenon of interest for this study. The ECDB has proved to be a valid source of data on fatal far-right ideologically motivated attacks (Chermak, Freilich, Parkin, & Lynch, 2012). Recent studies have relied on the ECDB to examine the evolution of domestic extremist groups (Freilich, Chermak, & Caspi, 2009), differences between violent and non-violent extremist groups (Chermak, Freilich, & Suttmoeller, 2013; Suttmoeller, Chermak, & Freilich, 2015), comparisons between far-right homicides and “regular” non-extremist homi- cides (Gruenewald & Pridemore, 2012), fatal far-right attacks against the police (Freilich & Chermak, 2009; Suttmoeller, Gruenewald, Chermak, & Freilich, 2013), lone wolf attacks (J. Gruenewald, Chermak, & Freilich, 2013a, 2013b), and county-level variation in extremist violence (Chermak & Gruenewald, 2015; Freilich et al., 2015).

The use of the ECDB improves upon the use of HCSA data in Deloughery et al.’s (2012) study because it provides data on bias-motivated violent

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incidents in which at least one perpetrator is a far-rightist who committed a fatal attack to further their extremist ideology. This study uses 118 bias- motivated fatal attacks committed by far-rightists pooled over a 20-year period (1992-2012, excluding 1993).5 The attacks include those commit- ted because of the suspects’ bias against persons based on sexual orienta- tion, homelessness, or membership in minority racial/ethnic or religious groups.

Importantly, although we are examining two measures of far-right extrem- ist criminal activity, they are distinct universes and no case was double- counted. The ECDB’s data on bias-motivated homicides include far-right fatal attacks that targeted racial/ethnic and religious minorities, gay, bisexual, and homeless persons. All of these attacks are ideologically motivated but many (though by no means all) are also the outcome of “presented opportuni- ties” (Freilich et al., 2015). In these cases, the offenders’ paths crossed with the victim at which time the perpetrators seized the opportunity to attack. These incidents are also often labeled as “hate crimes” and not “terrorism.” All ECDB anti-government and anti-abortion attacks were excluded as they already appeared in the GTD, and we thus insured that they were not double- counted. However, the GTD far-right cases include mostly planned attacks against the government or American society at large, abortion-related targets as well as anti-minority (i.e., bias/hate) cases. Importantly though, the 11 fatal far-right attacks in the GTD that targeted a minority, gay, or homeless person were removed as they were already included in the ECDB universe just discussed. This allowed us to better capture this middle ground of extrem- ist hate crime and insured that no case was double-counted.

Demographic indicators come from the Decennial Census from the years 1990, 2000, and 2010.6 To account for the county racial/ethnic minority presence, we use the average percentage of the non-White/non- Hispanic population from 1990 to 2010. Given far-right’s general preju- dice against all non-White racial and ethnic groups, this analysis considers the entire non-(non-Hispanic) White population that we label as minority presence. The average diversity index, as well as the accompanying change in the index over the 20-year period, is another predictor of interest, accounting for ethnic heterogeneity.7 As the average minority presence and change predictors are both highly correlated with the average diversity index and change variable, respectively, we use them in separate models. We also include a measure of demographic change, specifically account- ing for the absolute change in the non-White population from 1990 to 2010. Using data from the U.S. Bureau of Labor Statistics for 1990, 2000, 2010 (U.S. Bureau of Labor Statistics, 1990; 2000; 2010 and the Census Bureau for1989, 1999, and 2009 (U.S. Census Bureau, 1990; 2000; 2010),

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we use two oft-used economic indicators: unemployment and percent below the poverty level. We calculate the average unemployment and pov- erty and their absolute changes in these rates, respectively, over the 20-year period.

Analytical Approach

The current study investigates “spaces of hate” (Disha et al., 2011, p. 40), testing the association between hate crimes and terrorist acts at the county level. We focus on the dependent variables of fatal hate crimes committed by far-rightists (ECDB) and far-right terrorist acts (GTD). We use the total counts at the county level over the 20-year period (1992-2012) for these two types of events in 3,137 U.S. counties. Given the rare event nature of extrem- ist activity (specifically homicides and terrorist acts by far-rightists), both dependent variables are skewed with many counties failing to experience either type of extremist activity. With the skewed distribution and overdisper- sion, we conduct a series of negative binomial regressions to test the associa- tions between hate crime and terrorism, as well as county-level demographic and economic characteristics.8

Results

Descriptive statistics are presented in Table 1. They show that extremist activity is very rare with county-level means close to zero for fatal far-right hate crime, far-right and non-right-wing terrorist acts. General hate crimes average about 42 in 3,132 U.S. counties9 over 1992 to 2012.

The first set of analyses investigates the association between general hate crime and fatal hate crime by far-rightists with the results presented in Table 2. The results show a significant, yet weak, positive relationship between general hate crime and bias-motivated homicides by extremists. Model 1 presents the baseline model regressing general hate crimes on fatal hate crimes by far-rightists. Regarding the economic and demo- graphic predictors relevant to intergroup conflict, the full models (Models 2 and 3) present a number of interesting findings. Model 2 includes the average minority presence, and Model 3 includes the average diversity index. Inspecting demographic predictors, both minority presence and ethnic heterogeneity explain increases in far-right hate crime. Minority presence is a much weaker predictor accounting for only a 2% increase in such events. The diversity index shows that increased ethnic heterogene- ity is associated with an increase in far-right hate crime at a rate of approximately 93 times greater. This is most likely due to the

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ratio measure of the diversity index. Similarly, demographic change is associated with increases in far-right hate crime. Although changes in the unemployment rate over time fails to achieve significance, increases in average unemployment see a 10% (p < .1) and 15% increase in extremist hate crime in Models 2 and 3, respectively. Contrary to expectations, pov- erty is negatively associated with far-right hate crime with approximately an 8% decrease in such events. Growth in poverty over time, however, accounts for about an 11% increase in far-right hate crime. Counties expe- riencing a growth in poverty see greater numbers of far-right hate crime, which is in line with the predicted relationship. All in all, there is weak evidence to support the first hypothesis.

The second hypothesis, however, receives much more support. Table 3 shows strong, positive associations between fatal far-right hate crimes and far-right terrorist attacks. Model 1 presents the baseline model with an increase in fatal far-right hate crimes seeing 9 times more far-right ter- rorist acts. Models 2 and 3 show that counties seeing increased far-right hate crime are about 4 times more likely to see far-right terrorist acts, respectively. As in Table 2, minority presence and ethnic heterogeneity significantly increase far-right terrorism while the average poverty pres- ence observes significant declines. In Models 2 and 3, the change in pov- erty is significant with increased poverty over time seeing an 11% and 9% increase in far-right terrorism. In addition to change in minority presence

Table 1. Descriptive Statistics.

M SD

No. of general hate crimes (HCSA) 41.59 264.64 No. of fatal FR hate crimes (ECDB) 0.04 0.26 No. of FR terrorist acts (GTD) 0.07 0.44 No. of non-right-wing/anti-“American” terrorist acts (GTD) 0.07 0.54 Average % minority presence 18.59 18.86 Change in minority presence (1990-2010) 6.28 6.13 Average unemployment rate 6.58 2.28 Change in unemployment rate (1990-2010) 3.07 2.66 Average % below poverty level 15.72 6.74 Change in % below poverty level (1989-2009) −0.36 4.05 Average diversity index 0.25 0.18 Change in diversity index (1990-2010) 0.08 0.07

Note. N = 3,137 U.S. counties from 1992 to 2012 (excluding 1993). HCSA = Hate Crime Statistics Act; FR = far-right; ECDB = Extremist Crime Database; GTD = Global Terrorism Database.

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and ethnic heterogeneity, the average unemployment rate and its change over time, however, fail to achieve significance.

Finally, the third set of analyses tests the association between non-right- wing/anti-“American” terrorism and extremist hate crime. Once again, the results in Table 4 demonstrate a significant, positive association between such terrorist acts and far-right hate crime. The results in the full Models 2 and 3 show that increases in non-right-wing/anti-“American” terrorist acts see a 78% and a 64% increase in far-right hate crime, respectively. Regarding the demographic and economic predictors, the results remain largely the same as Table 2 as the main predictors are just alternated in Table 4. The three sets of analyses confirm our original hypotheses regard- ing the associations between hate crime and extremist activities.

Table 2. Negative Binomial Regression Models: Fatal Hate Crimes by Far- Rightists.

Independent variables

Model 1 Model 2 Model 3

I.R.R. (SE) I.R.R. (SE) I.R.R. (SE)

General hate crimes 1.00*** 1.00* 1.00* (0.00) (0.00) (0.00) Average % minority presence 1.02** (0.01) Change in minority presence

(1990-2010) 1.07***

(0.02) Average diversity index 93.03*** (69.55) Change in diversity index

(1990-2010) 54.40*

(91.79) Average unemployment rate 1.10† 1.15** (0.05) (0.06) Change in unemployment rate

(1990-2010) 0.97 0.95

(0.05) (0.05) Average % below poverty level 0.92*** 0.91*** (0.02) (0.02) Change in % below poverty level

(1989-2009) 1.11*** 1.11***

(0.03) (0.03) Wald χ2 26.50*** 156.42*** 160.27*** Log pseudolikelihood −415.39 −390.55 −387.91

Note. N = 3,132 U.S. counties from 1992 to 2012 (excluding 1993). †p <≤.1. *p ≤ .05. **p ≤ .01. ***p ≤ .001. I.R.R. = Incident Rate Ratio.

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20 Crime & Delinquency

Discussion

Much of the prior literature disagrees over the nature of the hate crime–ter- rorism relationship with some calling the two phenomena “close cousins,” whereas others call them “distant relatives.” As previous studies focus on either general hate crime and terrorism data sources, they miss an important middle ground between the two phenomena, specifically fatal hate crimes committed by far-right extremists that are not included in the data on terror- ism. Data on extremist hate crime can provide a promising avenue for future examination of the hate crime–terrorism relationship. Our analysis runs counter to Deloughery et al.’s (2012) findings with positive associations between hate crime and terrorism at the county level. Whereas there is only a small positive association between general hate crime offending and fatal

Table 3. Negative Binomial Regression Models: Far-Right Terrorist Acts.

Independent variables

Model 1 Model 2 Model 3

I.R.R. (SE) I.R.R. (SE) I.R.R. (SE)

Fatal FR hate crimes 9.33*** (2.40)

4.33*** (1.16)

3.73*** (0.96)

Average % minority presence 1.05*** (0.01) Change in minority presence

(1990-2010) 1.02

(0.02) Average diversity index 258.01*** (222.17) Change in diversity index (1990-2010) 10.20 (14.67) Average unemployment rate 0.93 0.97 (0.04) (0.05) Change in unemployment rate

(1990-2010) 1.08 1.06

(0.05) (0.05) Average % below poverty level 0.89*** 0.91*** (0.02) (0.02) Change in % below poverty level

(1989-2009) 1.11*** 1.09**

(0.03) (0.03) Wald χ2 75.26*** 167.53*** 184.42*** Log pseudolikelihood −651.50 −596.42 −589.16

Note. N = 3,132 U.S. counties from 1992 to 2012 (excluding 1993). FR = far-right; I.R.R. = Incident Rate Ratio.

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far-right hate crime, there are much stronger positive associations between fatal far-right hate crime and far-right terrorism, as well as fatal hate crime and non-right-wing terrorism that targets “traditional” American values. The results show that counties experiencing increases in general hate crime, far- right hate crime, and non-right-wing terrorism see associated increases in far-right hate crime, far-right terrorism, and far-right hate crime, respectively. In summary, counties undergoing increases in one type of extremist activity are likely to see increases in other types of extremist activity.

In addition to supporting our main hypotheses, the results also corroborate hypotheses stemming from intergroup conflict theories. Regarding minority group threat, the analyses consistently show significant, positive associations between both measures of minority presence and ethnic heterogeneity and

Table 4. Negative Binomial Regression Models: Fatal Hate Crimes by Far- Rightists.

Independent variables

Model 1 Model 2 Model 3

I.R.R. (SE) I.R.R. (SE) I.R.R. (SE)

Anti-U.S. terror acts 2.55*** 1.78*** 1.64*** (0.44) (0.19) (0.15) Average % minority presence 1.03*** (0.01) Change in minority presence

(1990-2010) 1.08***

(0.02) Average diversity index 213.73*** (145.01) Change in diversity index (1990-2010) 34.15*

(54.56) Average unemployment rate 1.10† 1.16** (0.05) (0.06) Change in unemployment rate

(1990-2010) 0.96 0.94

(0.05) (0.05) Average % below poverty level 0.90*** 0.89*** (0.02) (0.02) Change in % below poverty level

(1989-2009) 1.13*** 1.12***

(0.04) (0.04) Wald χ2 29.21*** 153.78*** 175.97*** Log pseudolikelihood −436.58 −388.62 −386.76

Note. N = 3,132 U.S. counties from 1992 to 2012 (excluding 1993). †p ≤ .1. *p ≤ .05. **p ≤ .01. ***p ≤ .001. I.R.R. = Incident Rate Ratio.

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far-right hate crimes and terrorist acts. Furthermore, demographic change measures of increased minority presence and ethnic heterogeneity over time consistently prove positively associated with far-right bias-motivated homi- cides. Contrary to the theoretical framework, increased poverty proves sig- nificantly associated with fewer far-right hate crimes and terrorist acts. This, however, corresponds with previous research finding a negative relationship between poverty and extremist presence and activities (Freilich et al., 2015; LaFree & Bersani, 2014). As more far-right acts are committed in counties with less poverty, it could be that those with “more to lose” are committing these attacks (Freilich et al., 2015). In this sense, this finding would be con- sistent with backlash models that view hate crime offenders as reacting to perceived threats. However, increased poverty over time corresponds with the predicted relationships in all of the models. This finding may illustrate that the far-right is reacting to worsening poverty as more of perceived threat than the general level of poverty. The current findings corroborate the previ- ous research showing that counties experiencing higher levels of poverty are not at risk of extremist activities; however, the findings do demonstrate the importance of investigating change in poverty over time. Counties coping with higher unemployment rates also see increased far-right hate crimes. As a result, poor or worsening economic conditions over time are more strongly associated with far-right activities. For the most part, the analyses support the major tenets of minority group threat with growing minority presence and poor or worsening economic conditions being linked to intergroup violence, with White intolerance manifesting in far-right extremist acts. As a result, all levels of government should be concerned with the effects of worsening eco- nomic conditions and work to improve such conditions. Addressing such macro-level economic conditions can potentially reduce the appeal of the far-right and its ideology and thus reduce the threat of its violent activities.

Given the results, it appears that counties experiencing any type of extrem- ist activity are likely to be targets for other extremist activities. Such results have potential implications for law enforcement. Since the passage of hate crime legislation, law enforcement agencies across the country established specialized bias crime units to handle the unique threat of bias-motivated crimes. Due to the unique harms caused by hate crimes and terrorism, both require specialized attention by law enforcement. Such extremist acts inflict injury and death to both law enforcement as well as citizens, especially those targeted for their inherent characteristics (race, ethnicity, religion, etc.). Freilich, Chermak, and Simone (2009) present survey data that show that 85% of state police agencies reported the presence of right-wing groups. They also find that these state police agencies consider Islamic terrorism a greater threat on the national and state level than that of far-right terrorism. Given the

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variation in far-right groups and actions, Chermak, Freilich, and Shemtob (2009) emphasize the importance of understanding the distinctions between different groups, their beliefs, and how they inform their extremist activities. Greater attention to such details should figure into training for law enforce- ment in dealing with the far-right. The current study shows that law enforce- ment should be attentive to the gradations in far-right extremist crimes.

The results reinforce the need for government policy makers and practitio- ners, especially law enforcement to defuse tensions and strengthen community relations in counties seeing such extremist activities. The presence of far-right activities can reveal the underlying issues at work in the county. Demographic change and worsening economic conditions can exacerbate group tensions, dam- aging community relations; intervention, however, can defuse such tensions. For example, this study’s results would be useful for the U.S. government’s Community Relations Service (CRS). The CRS exists as a mediating agency, working with various types of institutions at the government and organizational levels, including community and civil rights groups. The CRS endeavors to address “community conflicts and tensions arising from differences of race, color, national origin, gender, gender identity, sexual orientation, religion, and disability” (U.S. Department of Justice, n.d.). Agencies such as the CRS would benefit from this study’s results by addressing what states, and more specifically what counties, need their services to address their local-level conflicts evidenced by the higher rates of extremist activities. Turning to specifically addressing counties’ extremist presence, past research uncovering the county-level processes facilitating extremist activities emphasizes the need for law enforcement to estab- lish communication with far-right groups (Adamczyk, Gruenewald, Chermak, & Freilich, 2014). In addition to recommending that police monitor hate groups and track bias-motivated incidents, Freilich and Chermak (2013) stress the impor- tance of law enforcement reaching out to the various community stakeholders invested in the problem of bias-motivated violence, including schools, academ- ics, victims services, as well as other community organizations. Further explora- tion of the relationship between hate crime and terrorism will contribute to the production of policies aimed at preventing the escalation of violence by far-right extremists, thus preventing harm to citizens and law enforcement.

There exist several limitations with the current analysis. The first limita- tion lies with the HCSA data from the UCR. Some counties have zero hate crimes due to either a lack of compliance with reporting requirements or the inability of their law enforcement agencies to recognize and investigate hate crimes as such. This shortcoming, however, is limited to only one predictor (general hate crime) in the analyses testing our first hypothesis. Second, the analysis is one that is concerned with what Disha et al. (2011) term “spaces of hate” (p. 40); as such, the data include the cumulative totals for each

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county over a 20-year period. As a result, the issue of time arises in our analy- ses. Tita and Cohen (2004) address how research often examines the effects of space and time on crime separately; however, they note it is important to consider space and time simultaneously in their analysis as phenomena such as the “mechanisms of crime . . . are interdependent both over time and across geographic space” (p. 171). Future analysis may want to control for the time period as well. It may prove necessary to further unpack these associations as it may be possible that these relationships are working in reverse. Regardless, the positive associations at the county level evidence that those counties that experience higher levels of various types of bias-motivated or extremist vio- lence are more likely to witness higher levels of other types of bias-motivated or extremist violence. In summary, the current study provides the ground- work for further analysis to more deeply investigate these interesting associa- tions between different types of extremist activities at the county level.

Conclusion: Extremist Hate Crime as Common Ground

Although hate crime and terrorism differ in important ways, their similarities warrant further investigation into the relationship between the two phenom- ena. In addition to both serving as tactics by hate and terrorist groups, hate crime and terrorism share common characteristics, including their socio- political, communicative aspects, as well as their use as defensive or retalia- tory tactics. Through the use of multiple data sources, this study uncovers the positive associations between hate crime and terrorism. In the context of intergroup conflict, there appears to be a continuum between the bias-moti- vated actions of non-extremists to the hate crimes and terrorist acts commit- ted by far-rightists, with the presence of one type of activity seeing an escalation in the next type. As a result, it appears that hate crime and terror- ism may be more akin to close cousins than distant relatives.

Acknowledgments

We thank Dr. Mike Maxfield for his invaluable feedback on earlier drafts of this article, Dr. Jeremy Porter for his helpful advice on methods, Dr. Ashmini Kerodal for all of her feedback on this project, as well as Maggie Schmuhl, M.A. for her help and for providing the Diversity Index for this project.

Authors’ Note

The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Department of Homeland Security, or START.

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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 authors disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This research was supported by the Office of University Programs Science and Technology Directorate of the U.S. Department of Homeland Security through the Center for the Study of Terrorism and Behavior (CSTAB–Center Lead) Grant made to the START Consortium (Grant 2012-ST-61-CS0001).

Notes

1. There are exceptions to both of these claims. A few hate crimes are planned (e.g., a group of youths plan to go “gay-bashing” later that night), and certain terror- ist attacks arise from “presented opportunities” (e.g., an anti-government patriot who is pulled over by the police and then spontaneously kills the officers due to his anti-government ideology).

2. Although Phillips’ (2009) research on a sample of hate crime offenders from a New Jersey county also found that thrill seekers were the most common type of hate crime offenders, it was only a plurality (43%) of the total. Significantly though, Phillips also found that ideologically motivated extremist mission offenders comprised a larger share of all hate crime offenders compared with Levin and McDevitt’s sample.

3. Right-wing traits include

“fiercely nationalistic, anti-global, suspicious of federal authority and reverent of individual liberties . . . believe in conspiracy theories involving imminent threats to national sovereignty or personal liberty and beliefs that their personal or national “way of life” is under attack . . . for some the threat also originates from specific racial or religious groups. They believe that they must be prepared to defend against this attack by participating in paramilitary training or survivalism.” (Freilich, Chermak, Belli, Gruenewald, & Parkin, 2014, p. 380). Anti-abortion attacks included.

4. For such individual/unknown cases in the GTD, we used the incident’s GTD incident description and follow-up open-source searching to evaluate and deter- mine whether the cases evidenced right-wing or non-right-wing perpetrators or motivations (especially in cases where there were no sources for the incident) for inclusion in the study.

5. The 118 incidents from the ECDB do not include all ideologically motivated homi- cides contained in the database. This analysis excludes attacks prior to 1992, as well as those that occurred in 1993 (anti-government and anti-abortion ideological

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homicides also excluded). The analysis, however, was more inclusive than the ECDB when classifying cases as bias-motivated when ECDB guidelines found incidents non-ideological as long as the attack was motivated as least “in part” by bias (based on statutory membership categories) as hate crime legislation dictates.

6. Census Bureau Data on Race/Hispanic Origin for 1990, 2000, and 2010 obtained from the Minnesota Population Center. National Historical Geographic Information System: Version 2.0. Minneapolis, MN: University of Minnesota 2011 (2011a;2011b;2011c).

7. The diversity index is a ratio measure, showing the likelihood that two randomly selected people would differ by race/ethnicity with the formula, “Square the per- cent for each group B. Sum the squares, and subtract the sum from 1” (U.S. Census Bureau, 2001). We calculate the average diversity index using the com- puted indices from the Census in 1990, 2000, and 2010.

8. Poisson regressions for our baseline models returned significant Pearson good- ness-of-fit statistics, so we proceeded with negative binomial regressions. We also use robust standard errors as they slightly reduced the standard errors in most cases in our models.

9. Due to Hate Crime Statistics Act (HCSA) reporting, this analysis used the bor- ough/county of New York for all New York City-based acts and dropped the four remaining boroughs; acts based in St. Louis were also consolidated into St. Louis County and St. Louis City was dropped from the analysis.

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Author Biographies

Colleen E. Mills is a doctoral student at John Jay College of Criminal Justice/The City University of New York, The Graduate Center. She is a Project Manager for the Extremist Crime Database (ECDB) at John Jay College. Her research focuses on hate crime, far-right extremism, racism, and group conflict.

Joshua D. Freilich is a member of the Criminal Justice Department and the Criminal Justice PhD Program at John Jay College. He is the Creator and co-Director of the United States Extremist Crime Database (ECDB), an open source relational database of violent and financial crimes committed by political extremists in the U.S. Professor Freilich’s research has been funded by the Department of Homeland Security (DHS) and the National Institute of Justice (NIJ). His research focuses on the causes of and responses to terrorism, measurement issues, and criminology theory, especially envi- ronmental criminology and crime prevention.

Steven M. Chermak is a Professor in the School of Criminal Justice at the Michigan State University. He studies domestic terrorism, media coverage of crime and justice issues, and the effectiveness of specific policing strategies. Recent publications have appeared in Terrorism and Political Violence, Crime and Delinquency, and the Journal of Quantitative Criminology.

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The author has requested enhancement of the downloaded file. All in-text references underlined in blue are linked to publications on ResearchGate.The author has requested enhancement of the downloaded file. All in-text references underlined in blue are linked to publications on ResearchGate.