Investigating the Applicability of Macro-Level Criminology Theory to Terrorism: A County-Level Analysis

Abstract

Objectives

This exploratory study examines if causal mechanisms highlighted by criminology theories work in the same way to explain both ideologically motivated violence (i.e., terrorism) and regular (non-political) homicides. We study if macro-level hypotheses drawn from deprivation, backlash, and social disorganization frameworks are associated with the likelihood that a far-right extremist who committed an ideologically motivated homicide inside the contiguous US resides in a particular county. To aid in the assessment of whether criminology theories speak to both terrorism and regular violence we also apply these hypotheses to far-right homicide and regular homicide incident location and compare the results.

Material and methods

We use data from the US Extremist Crime Database (ECDB) and the FBI’s SHR to create our dependent variables for the 1990–2012 period and estimated a series of logistic regression models.

Conclusions

The findings are complex. On the one hand, the models we estimated to account for the odds of a far-right perpetrator residing in a county found that some hypotheses were significant in all, or almost all, models. These findings challenge the view that terrorism is completely different from regular crime and argues for separate causal models to explain each. On the other hand, we estimated models that applied these same hypotheses to account for the odds that a far-right homicide incident occurred in a county, and that a county had very high regular homicide rate. Our comparison of the results found a few similarities, but also demonstrated that different variables were generally significant for each outcome variable. In other words, although criminology theory accounts for some of the odds for both outcomes, different causal mechanisms also appear to be at play in each instance. We elaborate on both of these points and highlight a number of important issues for future research to address.

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Notes

  1. 1.

    This paper compares the characteristics of counties where FRPs resided to counties where they did not reside. It does not compare the counties where FRPs resided to counties where far-left or Al Qaeda affiliated perpetrators who also committed ideologically motivated violence resided. Please see Chermak et al. (2015) for an in depth investigation of the latter issue.

  2. 2.

    As outlined in prior work (Freilich et al. 2014; see also Freilich et al. 2009), the far-right is operationalized as individuals or groups that subscribe to aspects of the following ideals: “… [far-rightists are] fiercely nationalistic (as opposed to universal and international in orientation), anti-global, suspicious of centralized federal authority, reverent of individual liberty (especially their right to own guns, be free of taxes), believe in conspiracy theories that involve a grave threat to national sovereignty and/or personal liberty and a belief that one’s personal and/or national “way of life” is under attack and is either already lost or that the threat is imminent (sometimes such beliefs are amorphous and vague, but for some the threat is from a specific ethnic, racial, or religious group), and a belief in the need to be prepared for an attack either by participating in or supporting the need for paramilitary preparations and training or survivalism. Importantly, the mainstream conservative movement and the mainstream Christian right are not included.”

  3. 3.

    Hamm (2007; see also Smith and Damphousse 2009) found that the far-right and Islamic jihadists commit different crime types in the United States. Far-right terrorists have been found on average to be older, and more likely to be male, religious, poorer and less educated and to operate in rural areas in the US compared to far-left terrorists (Gruenewald et al. 2013a, b; Handler 1990; Hewitt 2003; Smith 1994; Smith and Morgan 1994).

  4. 4.

    We recognize the critique that FRPs may be transients who recently moved into their residence and were not influenced by the characteristics of the county to which they just moved. We discuss how we addressed this important issue in FN 12 in the data and methods section.

  5. 5.

    Subsequently, other structural factors were added such as mixed land use, population density, dilapidation, the changing economy and employment opportunities, etc (Sampson 2012; Stark 1987; Wilson 1996).

  6. 6.

    Lyons (2007) Chicago hate crimes’ study found that anti-white hate crimes were more common in socially disorganized neighborhoods, while anti-black hate crimes occurred in organized areas. Although, almost all the FRPs in our study are white, we expect our findings will differ from Lyons results. First, we are focusing on homicides, while Lyons study excluded homicides. Lyons concludes that anti-black hate crimes occurred because white youths were “defending” their neighborhoods that were changing demographically (from white to more diverse). But, we expect that whites in changing neighborhoods would encourage actions like “routine” assaults or vandalism to “defend” their area (Freilich et al. 1999). Many whites, we suspect, would oppose the commission of exceptionally brutal assaults and homicides that may generate (negative) media coverage that could undermine their local support. Second, in addition to anti-minority hate homicides, our study also included other large categories of incidents such as anti-government homicides. Lyons did not study these types of crimes. Third, most hate crimes are committed by non-extremist youths acting in groups. Thus, few of the perpetrators in Lyons study were far-rightists, while all of the perpetrators in our study are far-rightists. Fourth, far-right racists are often stigmatized by mainstream society and many retreat to “free spaces” that allow them to act upon their beliefs away from society’s negative gaze (Simi and Futrell 2010). Such individuals are unlikely to be integrated into a county’s political and social elite or communal life (Beyerlein and Hipp 2005: 995). Finally, we examine county-level variation while Lyons focused on neighborhoods.

  7. 7.

    We are unable to make a comparison of FRP county residency to the perpetrators of regular homicides county residency because the latter information is unavailable. The FBI’s SHR only includes information on homicide incident location and does not report perpetrator residency location.

  8. 8.

    We only focused on FRHI that were committed by far-rightists in the contiguous (48 of the 50) US. Due to data limitations and methodological concerns, it is common for criminological research to sample from geographic regions located solely within the contiguous, continental US (Kaminski 2008; Lester 1996; Loftin and Hill 1974; Rosenfeld et al. 2007).

  9. 9.

    Recent studies have relied on the ECDB to examine the evolution of domestic extremist groups (Freilich et al. 2009), differences between violent and non-violent extremist groups (Chermak et al. 2013), comparisons between far-right homicides and “regular” non-extremist homicides (Gruenewald and Pridemore 2012), fatal far-right attacks against the police (Freilich and Chermak 2009; Suttmoeller et al. 2013) and lone wolf attacks (Gruenewald et al. 2013a, b).

  10. 10.

    The ECDB’s incident identification and coding is a multi-stage process (Freilich et al 2014). First, open-source publications (e.g. the FBI, GTD, and Southern Poverty Law Center’s Intelligence Report) and databases are used to identify cases that could fit the inclusion criteria. Additional incidents are identified in online newspaper articles. After potential incidents are identified, we systematically search more than 30 open-source search engines and databases to collect all publically available information on the homicide events. A coder then reads the documents, verifies that the incident met the inclusion criteria, conducts additional open-source searches, and codes each incident. Variables coded relate to the incident, the offenders, the victims, and the reliability of the open-source documentation. This coding process was iterative and reliability was increased through coder training and multiple coders examining each incident (Freilich et al. 2014).

  11. 11.

    The vast majority of the FRPs in this study were arrested. However, 14 of the perpetrators were technically not arrested.  Six of these perpetrators committed suicide before their arrest, and 5 were killed by law enforcement before they could be arrested. Three others were involved in a prison murder. For all 14 perpetrators the open source information that we collected specifically discussed their linkage to the homicide and described their extremist activities.

  12. 12.

    The ECDB only includes the FRP’s county of residence at the time of an incident and does not include the length of time the perpetrator resided in the county before engaging in an attack. To assess the likelihood that a FRP lived in the county for at least a year before committing a homicide, and would, therefore, be susceptible to county-level influences (in that they were exposed to the county’s characteristics for an extended period), we randomly selected 20 % of the FRPs (N = 59). We searched these FRPs in the web-engines BeenVerified.com and Ancestry.com to locate information about them. We also reviewed the open-source information collected on these FRPs in the ECDB to see if other sources, such as the media or court documents, specified how long the FRP had resided at the location. No conclusive information was found for 57 % of these FRPs, but we verified the residency for 25 FRPs (43 % of the sample). Twenty-two FRPs (88 %) were determined to live in their county of residency for at least one year prior to the incident, while only 3 FRPs were deemed known transients. Thus, close to 90 % of the FRPs we found information on had resided in the county for at least one year prior to committing a far-right homicide. While some (American) terrorists may engage in conduct to evade capture and take steps (e.g. using aliases) to “stay off the radar” and thus little information exists on their whereabouts (Cothren et al. 2008), FRPs do not fall into this category. The overwhelming number of FRPs in the ECDB was prosecuted for homicides that though they were ideologically motivated were the outcome of (situational) presented opportunities, as opposed to carefully planned strikes.

  13. 13.

    We also conducted the analysis using the undichotomized variable and the direction and significance are the same as when this measure is used.

  14. 14.

    We considered calculating the percentage of Jews in each county, but they constitute only about 2 % of the US religious landscape and only 4 % of counties have a Jewish population that exceeds 5 %.

  15. 15.

    In a separate analysis we looked at the relationship between having any Jews in the county and a county having a Jewish congregation. In 1990 over 62 % of counties that had any Jews also had a congregation, and in 2000 the overlap was 93 %. The results of our multivariate analyses were very similar (significant and in the same direction), regardless of what measure (any Jews present or a Jewish congregation in the county) was used.

  16. 16.

    The ARDA stores the major source of data on religious groups in America. The Glenmary Research Center is the organization that publishes the data. We contacted them directly to see if they knew of a 1990 county-level measure. They did not think that a reliable measure exists for 1990.

  17. 17.

    The mainline Protestant groups that were included in both the 1990 and 2000 data collections were: American Baptist Churches in the USA, Christian Church (Disciples of Christ), Congregational Christian Churches, Episcopal Church, Moravian Church in America, National Association of Congregational Christian Churches, Presbyterian Church (USA), Reformed Church in America, United Church of Christ, and United Methodist Church.

  18. 18.

    The SPLC’s Hate Group Listing places groups into several categories. We combined racist skinheads, skinheads, identity, white nationalists, Ku Klux Klan, Neo-Confederates, and Neo-Nazis in the white hate group count and excluded Black hate groups since FRPs are almost 100 %.White.

  19. 19.

    The SPLC published both reports annually (since 1994 for militia/patriots and 1990 for hate groups). Although scholars have questioned the SPLC’s procedures (Chermak 2002; Freilich and Pridemore 2006), they have used the same strategies to identify organizations over time (SPLC, 2011).Unlike law enforcement agencies and others that compile intelligence information only on criminally active groups, the SPLC tracks violent and non-violent groups.

  20. 20.

    There were other variables from the ARDA, US Census, and Uniform Crime Report that we considered including as controls, such as, percentage Evangelical, female headed households, male, African American, white, foreign born, response to the US census, homeowner occupancy rate, unemployment rate, economic inequality index, number of police, per capita police pay, and the ratio of female to male wages. We also considered several GSS variables that were tied to our theoretical concepts. None of these variables contributed to any unique variation in our dependent variables (i.e. no significant effects) or meaningfully changed any of our key findings.

  21. 21.

    There is a higher number of perpetrator counties than incident counties because multiple perpetrators could have been involved with one incident.

  22. 22.

    To account for the rarity of the dependent variable, we considered using a procedure suggested by King and Zeng (2001) for generating approximately unbiased and lower-variance estimates of logit coefficients and their variance–covariance matrix. Unfortunately, we could not find a way to use this procedure while also estimating the three models simultaneously, and testing for significant differences in the coefficients across models. Additionally, for the most part the direction of the coefficients and significance levels differed minimally regardless of the modeling technique.

  23. 23.

    We considered only presenting an analysis for the entire 22 time-period using key predictors from the 1990s. Most of the independent and control variables from 1990 to 2000 are highly correlated at 0.90 and above. However, some characteristics (i.e. divorce rates, 9–11) about the US changed over the 22-year time span.

  24. 24.

    GeoDa allows one to run regression analysis to investigate this relationship, however to the best of our knowledge the program does not model binary outcome data.

  25. 25.

    In a separate analysis we found that poverty was significantly and highly (at least 0.66) correlated with a Gini index of economic inequality and the county’s unemployment rate. However, when entered with poverty, these two coefficients were in the opposite direction. Higher levels of unemployment and more economic inequality were associated with greater odds of a FRP. We considered including economic inequality and unemployment in the full analysis, but they were not significant when entered with the remaining variables in the final model. We also considered combining them with poverty to develop an overall measure of deprivation, but the three variables were operating in different directions with the dependent variable.

  26. 26.

    The mean 1990 income in a county with a FRP was $36,927, and in a county without a FRP it was $29,456. We would have used mean income instead of poverty, but poverty was a more robust indicator and remained significant when other variables were entered.

  27. 27.

    Other county-level research has found that poverty is associated with increased odds of a terrorist incident occurring in the county (see for e.g. LaFree and Bersani 2014); but these studies examined all terrorist incidents and did not disaggregate to only examine far-right attacks.

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Acknowledgments

This research was funded by a series of Grants from the U.S. Department of Homeland Security (DHS) Science and Technology Directorate’s University Program Division; and Resilient Systems Division both directly and through the National Consortium for the Study of Terrorism and Responses to Terrorism (START), and a John Jay College Collaborative Research Award. 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 DHS, START, or John Jay College.

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Correspondence to Joshua D. Freilich.

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Freilich, J.D., Adamczyk, A., Chermak, S.M. et al. Investigating the Applicability of Macro-Level Criminology Theory to Terrorism: A County-Level Analysis. J Quant Criminol 31, 383–411 (2015). https://doi.org/10.1007/s10940-014-9239-0

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Keywords

  • Social disorganization theory and terrorism
  • Macro-level criminology theories and terrorism
  • Domestic terrorism
  • Political violence