Abstract
Unauthorized immigration, already a divisive and controversial subject in American society, was reframed as a grave national security threat after the terrorist attacks on Sept. 11, 2001. Yet, despite substantial public, political and policy attention to the issue of undocumented immigration and terrorism, there has been relatively little empirical assessment of the relationship between unauthorized immigration flows and terrorist activity. We attempt to fill this gap by combining newly developed estimates of the unauthorized population, a novel use of sentencing and prosecutorial data to measure terrorism-related activity, and multiple data sources on the criminological, socioeconomic, and demographic context from all 50 states from 1990 to 2014. We then leverage this unique dataset to examine the longitudinal, macro-level relationship between undocumented immigration and various measures of terrorism. Results from fixed effects negative binomial models suggest that increased undocumented immigration over this period is not associated with terrorist attacks, radicalization, or terrorism prosecutions.
Keywords: Undocumented immigration, terrorism, radicalization
Undocumented immigration has been one the most socially and politically divisive issues in American society for decades. The discourse surrounding undocumented immigration, however, has changed markedly over time, particularly in the wake of the terrorist attacks on September 11, 2001. Prior to 2001, calls to stem unauthorized immigration1 through increased border enforcement were often framed as either economic or crime control matters (Jacobson 2008). Since 9/11, however, “national security” has provided the primary justification for stringent immigration enforcement (Chacón 2008). As a result, the narrative on immigration policy became inextricable from counterterrorism policy across much of the political spectrum (Waslin 2009). For example, in debating the “SAFE Border Act of 2003,” Republican Congressman Richard Baker of Louisiana stated “One of the many lessons of September 11th is that we cannot be too careful when it comes to our national immigration policy” (Congressional Record 2004a). A sentiment shared by both Democratic Senator Edward Kennedy, “Since the terrorist attacks on September 11th, we can no longer tolerate policies that fail to protect and control our borders,” (Congressional Record 2004b), as well as the then-Border Patrol Chief David V. Aguilar, “The nexus between our post-Sept. 11 mission and our traditional mission is clear…Terrorists and violent criminals may exploit smuggling routes used by immigrants to enter the United States illegally and do us harm” (Archibold 2006: 26).
Yet, despite the salience of terrorism concerns regarding the border, there is a surprising lack of systematic investigation into the undocumented immigration-terrorism nexus. This is largely due to data constraints. Owing to the lack of estimates that account for the unauthorized population separately, previous work in this area has been unable examine whether patterns (either temporal or geographic) of terrorist activity in the U.S. are linked to trends in undocumented migration flows. Addressing this gap is a paramount sociological and public policy concern for several reasons.
First, as immigration control became a key tool in the nation’s counterterrorism strategy, the amount of resources dedicated to border enforcement skyrocketed. Since 1990, the U.S. Border Patrol budget increased nearly 18-fold, from $263 million to nearly $4.7 billion. And since the creation of the Department of Homeland Security (DHS) in the aftermath of 9/11, the budgets for U.S. Customs and Border Patrol (CBP) and Immigration and Customs Enforcement (ICE) have more than doubled (American Immigration Council 2019). As a result, the federal government now spends more on immigration enforcement agencies than all other criminal enforcement agencies combined, including the FBI, DEA, Secret Service, and Marshal’s Service (Meissner et al. 2013). Within this context, understanding the relationship between terrorism and undocumented immigration is essential for evaluating the efficacy of these dramatic developments as well as the need for continued calls for further restrictions and investment at the border. For instance, in justifying the demand for a southwest border wall, the Trump administration has repeatedly claimed that terrorists have entered the U.S. via Mexico. In the President’s words, “We have terrorists coming through the southern border because they find that’s probably the easiest place to come through. They drive right in and they make a left” (LaPorta 2019).
Second, undocumented immigration represents one of the most significant demographic trends over the past several decades. From 1990 to 2014 the number of unauthorized migrants more than tripled, from 3.5 million to 11.3 million (Krogstad, Passel and Cohn 2016). These trends are historically unprecedented. Were unauthorized immigrants counted separately, they would be the fastest growing minority group in the U.S. since 1990 by far (compare Humes, Jones, and Ramirez 2011 and Grieco and Cassidy 2001). Thus, given the scale of unauthorized immigration over this period, the substantial amount of public angst associated with the border after 9/11, and the resulting investment in immigration enforcement, the time has come for a thorough investigation into the unauthorized immigration-terrorism link. Such an inquiry takes on added significance in light of competing schools of thought on this relationship. One body of work suggests that immigrant networks may be critical to spreading both terrorism and radicalization (Bove and Böhmelt 2016), while others suggest that the purported links between immigration and terrorism are largely socially constructed out of “moral panic” (Saux 2007).
Against this backdrop, this article provides the first macro-level assessment into whether undocumented immigration flows are related to terrorism in the United States. We begin by first detailing the conflicting theoretical predictions on the links between unauthorized migration and terrorism. From there we detail our data and methods. Combining longitudinal estimates of the unauthorized population with multiple key data sources on terrorism and the socio-demographic context of all 50 states from 1990 to 2014, we use fixed-effects negative binomial regression models to estimate the effect of undocumented immigration on terrorist activity. We then conclude with a discussion of the sociological implications of our findings and avenues for further research.
Unauthorized Immigration and Terrorism
Though undocumented immigration remains a conspicuous omission in the terrorism literature, the broader international body of work on migration and terrorism offers critical insights to motivate hypotheses on this nexus. Interestingly, however, these insights provide conflicting guidance. Based on one line of work, we might expect undocumented immigration to be associated with increased terrorism for several reasons. First, and most directly, undocumented immigrants may increase the number of potential terrorists via the importation of individuals with terroristic intent. As Leiken and Brooke (2006) note in their biographical analysis of nearly 400 global jihadist terrorists in Europe and North America between 1993 and 2004, while most immigrants are not terrorists, the majority of terrorists are immigrants. Additionally, Bove and Böhmelt (2016), for example, found that migrants function as a vehicle for terrorism diffusion. More specifically, they find that while overall immigration decreases the risk of terrorism, immigration from countries with substantial terrorist activity increases the risk of terror attacks for host societies. In a similar vein, Milton and colleagues (2013) found that receiving countries face a higher risk of terrorism from refugee flows.
On this point, undocumented immigrants may pose a distinctive threat compared to other types of migrants. This is because the unregulated and clandestine nature of unauthorized migration may present an ideal route of ingress for potential terrorists. That is, individuals with extensive criminal histories or links to terrorist organizations have a strong incentive to remain undetected, and thus may be more likely to attempt to enter the U.S. using unofficial migration channels.
A second and less direct mechanism is the radicalization of immigrants once they are in the country. Several studies suggest that adverse socio-political and economic conditions faced by immigrants and their children could make them vulnerable to radicalization. Milton and colleagues (2013: 628), for example, contend “the dismal conditions within refugee camps and the treatment of the refugees by host states can contribute to the radicalization of the refugees.” Additionally, Piazza (2011) found that countries where minority groups faced economic discrimination, such as unequal access to healthcare, jobs, and educational opportunities, were at a greater risk for experiencing domestic terrorism. Finally, a survey of the literature on “homegrown” jihadist terrorism suggests that “socio-political alienation” is a major driver of radicalization of immigrants and their children (Wilner and Dubouloz 2010: 38). Given that undocumented immigrants and their descendants face marked social disdain and limited access to economic and social resources in the United States, which has led to the formation of a “new underclass” (Massey 2008), it is possible that unauthorized migrants in the United States are at increased risk of being radicalized. Related to this point, scholars have noted the ability of terror groups to exploit dense immigrant networks in host countries to spread radicalization. As Bove and Böhmelt (2016: 573) contend, “ties among a group’s members could potentially be exploited by terrorist organizations that then fuel migrants’ radicalization…Eventually, this may lead to a higher level of terrorism [in the host country].” Combined, there are multiple reasons to expect unauthorized immigration to increase the risk of terrorism.
A second body of work, however, provides counter-evidence to this view and suggests that undocumented immigration and terrorism are largely orthogonal. Regarding the terrorism consequences of immigration flows, Forrester and colleagues (2019) examined 170 countries over two and half decades (1990–2015), focusing on immigration from countries in the midst of armed conflicts and countries with majority Muslim populations, and found virtually no relationship between immigration and terrorism for receiving countries.
This null relationship may be particularly relevant in the case of the United States, as the vast majority of undocumented immigrants to the U.S. do not come from terror-prone countries. Indeed, seventy-one percent of unauthorized migrants to the United States come from Mexico, Guatemala, Honduras, or El Salvador (Rosenblum and Ruiz Soto 2015), none of which rank even in the top 50 countries globally for terrorist activity (Institute for Economics & Peace 2018).2 Thus, in line with the findings of Bove and Böhmelt (2016), who found that only immigration from terror prone countries is associated with increased terrorist activity, we might expect undocumented immigration flows to have little impact on terrorism in the U.S.
Moreover, while Leiken and Brooke (2006) found that most global jihadist terrorists are immigrants, they also note that only a minute percentage of terrorists were unauthorized. Only six percent of terrorists that conducted attacks in Europe and North America entered illegally, and not a single terrorist entered the United States from Mexico. The authors conclude that “the Mexican border appears to constitute a less serious national security danger than the Canadian border or, for that matter, [the United States’] air and sea boarders (Leiken and Brooke 2006: 513 [emphasis in original]).” A more recent biographical analysis of nearly 500 jihadist terrorists in the United States by the New American Foundation buttresses this point, finding that only 1 percent (5 total cases) were undocumented immigrants (Bergen et al. 2019). It also merits noting that an exclusive focus on jihadist terrorism omits the increasing number of terror attacks motivated by far-right ideology (Zapotosky 2019).
This lack of terrorist activity along the U.S./Mexico border helps explain the trivially small mortality risk unauthorized immigrant terrorists have posed to Americans in recent decades. A 2019 report by the CATO Institute calculated the risk of an American dying at the hands of a foreign-born terrorist between 1975 to 2017, including the deaths that occurred during the 9/11 attacks. It found that the annual murder rate among foreign-born terrorists was 0.026 per 100,000 (Nowrasteh 2019). To put that in perspective, the annual chance of an American being murdered in a normal criminal homicide was 264 times greater over this same period. Regarding undocumented immigrants specifically, the report found that the annual chance that an American was killed or injured by an unauthorized immigrant terrorist was zero. Taken together, these findings cast doubt that undocumented immigration would be associated with an increase in terrorism in the United States.
Data and Methods
Our analysis draws on multiple data sources collected annually at the state-level from 1990 to 2014. The terrorism data come from three distinct sources. The first is the Global Terrorism Database (GTD) collected by the National Consortium for the Study of Terrorism and Responses to Terrorism. The GTD is an open-source database of terrorist attacks (both domestic and international) from around the world and is one of the most widely used resources for information on terrorism (LaFree and Dugan 2007). It is, however, subject to a well-known critique: it relies entirely on public sources, such as media reports. Thus, if attacks are not reported and/or thwarted in the planning stages with little attention, they are unlikely to be recorded in the GTD data. Neither are cases involving terrorist networks that are not linked to a specific event (e.g. providing financial support to terrorist organizations).
To address these concerns, we draw from two additional data sources, the Profiles of Individual Radicalization in the United States (PIRUS) database and the U.S. Sentencing Commission (USSC). Like the GTD, the PIRUS data are collected by the National Consortium for the Study of Terrorism and Responses to Terrorism and are derived from publically available records. The key strength of PIRUS is a detailed look into those radicalized and a broader definition of “terrorists.” While the GTD includes terrorism events, PIRUS includes individuals “radicalized within the United States to the point of committing ideologically motivated illegal violent or non-violent acts, joining a designated terrorist organization, or associating with an extremist organization whose leader(s) has/have been indicted of an ideologically motivated violent offense” (PIRUS 2018: 3).3 It thus captures both terrorist activity and support for terrorist ideologies.
Regarding our third source, the U.S. Sentencing Commission is an independent agency inside the federal judiciary charged with drafting the federal sentencing guidelines and collecting information for all felony and Class A misdemeanor cases sentenced in a federal court. Critical for our purposes, the USSC collects detailed information on cases involving terrorism and national defense related charges, even when the individual was not planning an immediate attack or was never involved in an act of violence. To our knowledge, no research has leveraged the USSC data to examine the etiology of terrorism. Thus, our USSC analysis represents a novel compliment to the GTD and PIRUS data and to the study of terrorism generally by answering calls for “systematic comparisons between event data on terrorism and terrorism data drawn from other sources” (LaFree and Dugan 2009: 462). As we show in the Appendix, our terrorism measures are not highly correlated with each other, suggesting that they may be tapping somewhat different processes. Radicalizations are correlated at about .30 (p < .05) with both terrorism events and prosecutions. Terrorism events and prosecutions, however, only correlate at .14 (p < .05).
Our primary measure of the undocumented population comes from the Center for Migration Studies. To ensure the robustness of our results, we also replicate our analysis in the appendix using estimates of the undocumented population produced by the Pew Research Center and find substantively similar conclusions. In addition, data on multiple political, religious, socioeconomic, demographic and criminogenic characteristics were collectively derived from Congressional voting data, the U.S. Census, the Bureau of Labor Statistics, the Uniform Crime Reports, the Religious Congregations and Membership Study (RCMS), and the Centers for Disease Control (CDC).
Taken together, these data provide a comprehensive resource for examining the impact of unauthorized immigration on terrorism. Table 1 displays the sources, measurement properties, and descriptive statistics for all variables in the analysis.4 Our full sample includes 1248 state-years after adjusting for missing data.5
Table 1.
Descriptive Statistics for Dependent and Explanatory Variables, 1990–2014 (N = 1,248)
Measures | Coding and Description | Mean | Source |
---|---|---|---|
Dependent Variables | |||
Terrorism Events | Number of terrorism incidents | 0.50 (1.25) | Global Terrorism Database (GTD) |
Radicalizations | Number of known radicalizations | 0.86 (1.67) | Profiles of Individual Radicalization in the United States (PIRUS) |
Terrorism/National Defense Casesa | Number of National Defense convictions and terrorism enhancements (§3A1.4) in federal courts | 1.00 (3.26) | U.S. Sentencing Commission |
Immigration Measures | |||
Undocumented Immigrants | Estimated proportion of population that is undocumented | 1.92 (1.75) | Center for Migration Studies |
Lawful Immigrants | Proportion of population that are lawful foreign-born residents | 6.43 (4.83) | IPUMS |
Covariates | |||
Political Ideology | NOMINATE measure of state government ideology | 50.53 (24.39) | Berry, Rinquist, Fording, and Hanson (1998) |
Evangelical Protestant | Number of Evangelical Protestants (per 1,000) | 158.61 (118.20) | Religious Congregations and Membership Study (RCMS) |
Poverty | Proportion of population that is in poverty | 13.12 (3.64) | U.S. Census Bureau / American Community Survey |
Unemployment | Proportion of civilian population over 16 that is unemployed | 5.75 (1.88) | U.S. Bureau of Labor Statistics |
Low Educational Attainment | Proportion of the population over 25 without a high school degree | 15.29 (5.15) | U.S. Census Bureau / American Community Survey |
Violent Crime | Violent crimes known to the police (per 100,000) - logged | 5.96 (0.54) | FBI Uniform Crime Reports |
Gun Availability | Proportion of suicides perpetrated with a gun | 55.62 (13.10) | CDC WONDER Underlying Cause-of-Death Mortality Files |
Police per Capita | Number of police officers (per 100,000 in the population) | 211.33 (47.61) | UCR Police Employee Data |
Population Density | Population per square mile | 184.88 (250.92) | U.S. Census Bureau / American Community Survey |
Crime Prone Ages | Percent of the population ages 18–24 | 9.93 (0.86) | U.S. Census Bureau / American Community Survey |
Note : Std. deviations reported in parentheses.
Data available from 1992–2014 (1148 state-years)
Dependent Variables
Our first measure of terrorism comes from the GTD, which records detailed information on each terror incident in the U.S. (regardless of motivation), including the date and location (or intended location). We use these geographic identifiers to aggregate the number of terror incidents, yielding an annual count of terror events for each state. Similarly, we create an annual count of individuals radicalized in each state based on the date of “exposure” – the date at which the individual’s activity first came to public attention, often involving an arrest or incident. The third measure comes from the number of terrorism related convictions taken from the standardized research files produced by the U.S. Sentencing Commission.6 We use two distinct offense types from the USSC data, those involving “National Defense Offenses” and those that received a terrorism enhancement at sentencing.7 Following the initial attack on the World Trade Center in 1993, the USSC created a special sentencing enhancement – section 3A1.4 – which allowed for a substantially enhanced penalty if the court deems that “the offense is a felony that involved, or was intended to promote, a federal crime of terrorism” (USSC Guidelines Manual 2010).8 Like our GTD and PIRUS measures, we aggregate the number of national defense cases and 3A1.4 enhancements at the state level for each year. Three points are noteworthy about this measure. First, the USSC helps address concerns about underreporting in the GTD and PIRUS by capturing inchoate and less publicized terrorism convictions. Second, the 3A1.4 terrorism enhancement applies under the preponderance of evidence standard, and thus the defendant need not have been actually found guilty of an act of terror. Third, both the 3A1.4 and national defense definitions are remarkably broad and include cases that would likely never be included in the GTD. For instance, a number of 3A1.4 enhancements involved neither terror events nor terror plots, but rather were applied for providing material support for foreign terror organizations (see Said 2015, Ch. 5). In the same vein, a common offense classified as a “national defense” violation involved the illegal exportation of munitions. Thus, the USSC data helps capture a distinct form of terrorism related activities typically omitted in analyses of terrorism. And it is important to note that both the national defense violations and the 3A1.4 enhancements are applicable to citizens and non-citizens. Of the 1,192 national defense and terrorism cases in our data, nearly half (47 percent) involve non-U.S. citizens, and 19 percent involve undocumented immigrants.
There is variation in the data coverage for our terrorism variables. While the GTD and PIRUS data are available over the entire study period (1990 to 2014), the USSC data is available starting in 1992, and 3A1.4 terrorism enhancement only began in 1996. Thus, our analysis of the USSC data includes state-years from 1992 to 2014, with 1992–1995 including only national defense violations.
Focal Independent Measure
The lack of analytical focus on the undocumented population in prior terrorism research is a function of data limitations, in that only recently have researchers had reliable estimates of the unauthorized population. Our key independent variable – the proportion of the population that is unauthorized – comes from the Center for Migration Studies. CMS estimates are derived using a variant of the residual methodology based on Census Bureau data. Stated briefly, the residential method involves subtracting the number of authorized immigrants from the total foreign-born population. The remainder, or residual, is then the estimated number of potentially unauthorized immigrants. This approach is generally accepted as the best current methodology for estimating the size of the unauthorized population (Passel and Cohn 2009).
The CMS methodology involves four steps. First, the raw residual count likely overestimates the actual number of undocumented immigrants. Thus, the CMS applies logical edits based on demographic, social, economic and geographic characteristics that are unlikely to apply to an unauthorized immigrant (e.g. occupations that require legal status, those that receive public benefits that are restricted to legal residents).9 Second, the CMS calculates independent population controls by country of origin for unauthorized residents; an important feature given that the likelihood of being authorized varies substantially by national origin. Third, utilizing the population controls from step two, final selections are made of individual respondents to be classified as either legal or unauthorized. Finally, annual estimates are adjusted by the factors that influence population fluctuations: emigration rates, under-count rates, removals, adjustments to lawful statuses, and mortality rates (Warren and Warren 2013).
It bears mentioning that both the CMS and Pew estimates are highly correlated with those produced by the Department of Homeland Security (Bachmeier, Van Hook, and Bean 2014), the federal agency charged with both counterterrorism policy and immigration enforcement. As such, they are highly relevant for informing policy discussions on the unauthorized migration-terrorism nexus.
Control Measures
We control for an array of macro-level factors known or suspected to influence the incidence of terrorism. Prior research suggests that political ideology is an important consideration given the increasing emphasis among law enforcement on the pernicious consequences of right-wing terrorism (Johnson 2012). Political context, however, is often measured using rather crude proxies, such as whether a state voted for a Republican or Democratic presidential candidate in the previous election or currently has a Republican or Democratic governor. We go considerably beyond this approach by using the NOMINATE measure of state government ideology developed by Berry et al. (2010), a widely used measure in political science but underutilized in sociology.10 Though this methodology has been detailed elsewhere, we briefly review the measurement properties and merits of this approach for our research. This approach begins by rating each legislative vote cast by congressional representatives along a conservative-liberal dimension and creating a scaled measure for each representative for every term (known as the NOMINATE Common space scores developed by political scientists Poole and Rosenthal 1997). The NOMINATE scores are then combined with measures that tap the outcomes of congressional elections, the partisan division of state legislatures, the party of the governor, and changes in public opinion and elite views (see Berry et al. 1998 for details). The result is a measure of state ideology that has significantly greater variation (ranging from 0 to 100, where greater values correspond to more liberal ideologies), is dynamic over time, captures greater nuance and ideological complexity beyond dichotomous labels (i.e. some Democratic legislators are more liberal than others, just as some Republican legislators are more conservative), and statistically outperforms alternative measures of state ideology (Berry et al. 2010).
Religious context may also be salient given the affinity between political conservatism and Christian fundamentalism (Wald, Owen, and Hill 1988). We thus include a measure for the number of individuals who identify as Evangelical Protestant (per 1,000 in the population). This data comes from the Religious Congregations and Membership Study (RCMS), the decennial census of religion in the U.S. We use the 1990, 2000, and 2010 data and linearly interpolate the non-census years for each state.
It is widely speculated that terrorism is born out of economic anxiety and despair, though there is substantial debate on this issue (see Meierrieks and Gries 2013). We gauge deleterious economic conditions by including variables for the poverty and unemployment rates, as well as the proportion of the population with low educational attainment (the percentage of the population above 25 without high school degrees). Criminological factors may also help explain terrorism. For instance, it is plausible that extremist ideology is more likely to manifest into terror attacks in areas where crime and violence are already prevalent. In line with this idea, we include a measure for the violent crime rate from the Uniform Crime Reports (per 100,000 and logged to reduce positive skewness).11 Along similar lines, it is conceivable that terrorist attacks are more likely to occur in places where weapons are easily attainable. Correspondingly, we include a proxy measure of gun availability, calculated from CDC death records as the percentage of suicides committed by firearm12 (c.f. Kubrin and Wadsworth 2009). We also include the police force size (per 100,000), as it is tenable that smaller police forces may be unable to effectively monitor or respond to terror threats.
We capture multiple features of states’ demographic composition. Critical among these is the lawful immigrant population, calculated as the proportion of the population that are lawful immigrants.13 Both lawful and unauthorized immigration increased substantially since 1990, and thus the inclusion of this measure helps isolate the effects of unauthorized immigration from general migration trends. We also include a measure for population density, as more populous areas may be targeted. Lastly, we measure the percentage of the population between the ages of 18 and 24 because younger age groups commit a disproportionate amount of serious crime and terrorism (Rosenfeld 2002; Hudson 1999).14
Analytical Strategy
Despite widespread attention to threat of terrorism, it remains a relatively rare event in the U.S. Between 1990 and 2014, there were no terrorist events in 75 percent of state-years. Radicalization is more prevalent, but still infrequent, with 63 percent of state-years without a known radicalization. Terrorism-related convictions are as rare as events, with 71 percent of state-years having no federal criminal cases involving a national defense violation or terrorism enhancement. As shown in the histogram plots in the appendix, the result is that our dependent variables are characterized by considerable positive skew with evidence of over dispersion, as the variance for each measure is substantially larger than its mean. These non-normal distributions render standard OLS regression potentially problematic (Long 1997). Under such conditions, negative binomial regression is more appropriate for count data such as ours (c.f. Disha et al. 2011; Deloughery et al. 2012).
In all negative binomial regressions we include state and year fixed effects. The inclusion of state effects is useful for our inquiry because they account for all time-invariant causes of terrorism. In reference to our focal independent variable, the inclusion of state effects focuses analytical attention on annual changes in the stock of undocumented immigrants. This emphasis on within-state change not only elimnates the effects of cross-state variation in reporting and data collection methods but also obviates concerns regarding any unique challenges to estimating the undocumented population in each state, provided that any measurement error is stable over time. In addition, the year effects adjust the model parameters for all unmeasured trends that affected states equally, including any national patterns of terrorist activity and systematic under or over-counting the undocumented population. Moreover, with the inclusion of year effects we need make no assumptions about the functional form of terrorist activity over time (e.g. linear, quadratic, etc.). To ensure proper time ordering, we lag the independent variables by one year so that changes in the predictors precede changes in terrorism. Finally, we report robust standard errors clustered by state to account for non-independence in the underlying error variance–covariance matrix.15
Results
We begin by first examining the bivariate associations among our principal measures. Because the focus is on changes over time, Figure 1 displays correlations between mean deviations in the proportion undocumented on the x-axis and mean deviations in terrorism/radicalization (panels A-C) on the y-axis.16 The results in Figure 1 provide little consistent evidence that links undocumented immigration to terrorist activity. In panel A, the correlation between undocumented immigration and terrorism events is negative, meaning that increased unauthorized populations corresponds with fewer terror incidents. In panel B, however, the correlation between undocumented immigration and radicalizations is positive, suggesting more radicalizations as unauthorized populations increase. In Panel C, the relationship between undocumented immigration and terrorism convictions is positive but weak. In fact, in each case the correlations are rather slight, ranging from r = −.13 (p < .05) to r = .19 (p < .05), suggesting little connection between undocumented flows and terrorism. However, the descriptive nature of these findings cautions against strong conclusions. We thus turn to our multivariate analyses to see if a more stable pattern surfaces.
Figure 1.
Bivariate Longitudinal Associations Between Undocumented Immigration and Terrorism
Table 2 reports fixed-effects negative binomial regressions examining the link between unauthorized immigration and terrorism events from the Global Terrorism Database. Model 1 reports a baseline regression in which terrorism is predicted only by the unauthorized population and state and year fixed effects. From there we add lawful immigration in model 2, and then the full complement of covariates in model 3.
Table 2.
Negative Binomial Estimates of Undocumented Immigration on Terrorism Events (GTD), 1990–2014
Model 1 |
Model 2 |
Model 3 |
|
---|---|---|---|
Measures | b | b | b |
Immigration Measures | |||
Undocumented Immigration | 0.23 (0.17) | 0.17 (0.18) | 0.21 (0.21) |
Lawful Immigration | 0.18 (0.15) | 0.18 (0.16) | |
Political / Religious Context | |||
Political Ideology | −0.00 (0.00) | ||
Evangelical Protestant | 0.01 (0.01) | ||
Economic Conditions | |||
Poverty | −0.07 (0.05) | ||
Unemployment | 0.04 (0.07) | ||
Low Educational Attainment | −0.05 (0.05) | ||
Criminological Context | |||
Violent Crime | 0.20 (0.48) | ||
Gun Availability | 0.01 (0.02) | ||
Police per Capita | −0.00 (0.00) | ||
Demographic Context | |||
Population Density | −0.00 (0.01) | ||
Crime Prone Ages | −0.12 (0.16) | ||
Constant | −1.20* (0.29) | −1.36* (0.34) | −3.34 (4.27) |
Summary Information | |||
Year Effects? | Yes | Yes | Yes |
State Effects? | Yes | Yes | Yes |
AIC | 1836.72 | 1832.63 | 1820.37 |
Pseudo R2 | 0.21 | 0.21 | 0.21 |
N | 1198 | 1198 | 1199 |
Note :
p < 0.05 (two-tailed tests).
All predictors are lagged by one year. Robust clustered Std. Errors reported in parentheses.
Across all three models, a consistent pattern emerges: undocumented immigration has virtually no effect on terrorist incidents. More specifically, since 1990 we find that increases in the unauthorized population has not significantly affected the likelihood of terror events, either positively or negatively. This is true for lawful immigration as well. As seen in models 2 and 3, the effects of legal immigration are inconsequential. And it bears mentioning that the immigration effects, both lawful and unauthorized, to not approach conventional levels of statistical significance. Rather than increasing terrorism, these results align more with the body of work suggesting that the processes of terrorism and undocumented immigration are independent of one another in the United States.
In fact, we find few significant predictors of terrorist events, a general pattern we uncover for radicalizations and terrorism convictions as well (see Tables 3 and 4). We think this likely stems from two sources. First, given the episodic nature of terrorist behavior and the evolving nature of terrorist threats, it is inherently difficult to predict future attacks absent information on an organizations’ prior terrorist activity (Yang, Pah, and Uzzi 2019). Second, most of the explanatory power in our models comes from the state and year effects. Indeed, in supplemental models we compared our most demanding specification (model 3) to a model that includes only the state and year measures. While the Akaike information criterion (AIC) showed an improved model fit in model 3, this resulted in virtually no change in terms of explained variance, as measured by the pseudo R2 (both were .21). In line with this view, we replicate our models in the appendix with all covariates but omit the state and year effects. In these models, we find more predictors that are significant, including among our immigration measures (the estimate for undocumented immigration is positive and significant for radicalizations and convictions). In defense of state and year effects, however, we believe they should be included for several reasons. First, they provide greater analytical rigor by removing all time-stable confounding influences, regardless if they are measured or even known. Second, the inclusion of state effects is what allows us to engage the most critical question motivating our inquiry: how do changes in undocumented immigration affect changes in terrorism? As Ousey and Kubrin (2018: 75) note in their recent meta-analysis of immigration and crime, “because immigration is a process of social and demographic transition, longitudinal research that measures within-place change in the immigrant base is a better representation of the phenomena of interest than are cross-sectional studies that measure between-place differences in the immigrant population share” (emphasis added). Finally, much of the panel data research on the impacts of immigration include fixed effects. Hence, we believe our preferred estimates are more in line with current empirical practice.
Table 3.
Negative Binomial Estimates of Undocumented Immigration on Radicalizations (PIRUS), 1990–2014
Model 1 |
Model 2 |
Model 3 |
|
---|---|---|---|
Measures | b | b | b |
Immigration Measures | |||
Undocumented Immigration | 0.16 (0.14) | 0.13 (0.13) | 0.08 (0.11) |
Lawful Immigration | 0.09 (0.10) | −0.00 (0.10) | |
Political / Religious Context | |||
Political Ideology | 0.00 (0.00) | ||
Evangelical Protestant | −0.00 (0.00) | ||
Economic Conditions | |||
Poverty | 0.01 (0.04) | ||
Unemployment | 0.06 (0.04) | ||
Low Educational Attainment | −0.02 (0.03) | ||
Criminological Context | |||
Violent Crime | −0.58 (0.36) | ||
Gun Availability | 0.01 (0.02) | ||
Police per Capita | 0.00 (0.00) | ||
Demographic Context | |||
Population Density | 0.00 (0.00) | ||
Crime Prone Ages | 0.11 (0.10) | ||
Constant | −0.79* (0.32) | −0.85* (0.31) | 0.53 (3.09) |
Summary Information | |||
Year Effects? | Yes | Yes | Yes |
State Effects? | Yes | Yes | Yes |
AIC | 2542.32 | 2541.25 | 2534.66 |
Pseudo R2 | 0.20 | 0.20 | 0.20 |
N | 1198 | 1198 | 1199 |
Note :
p < 0.05 (two-tailed tests).
All predictors are lagged by one year. Robust clustered Std. Errors reported in parentheses.
Table 4.
Negative Binomial Estimates of Undocumented Immigration on Terrorism Cases (USSC), 1992–2014
Model 1 |
Model 2 |
Model 3 |
|
---|---|---|---|
Measures | b | b | b |
Immigration Measures | |||
Undocumented Immigration | −0.01 (0.18) | 0.02 (0.18) | 0.09 (0.21) |
Lawful Immigration | −0.16 (0.18) | −0.08 (0.21) | |
Political / Religious Context | |||
Political Ideology | 0.00 (0.00) | ||
Evangelical Protestant | 0.01 (0.01) | ||
Economic Conditions | |||
Poverty | 0.03 (0.04) | ||
Unemployment | −0.04 (0.06) | ||
Low Educational Attainment | −0.06 (0.05) | ||
Criminological Context | |||
Violent Crime | −0.05 (0.52) | ||
Gun Availability | −0.03 (0.02) | ||
Police per Capita | 0.00 (0.00) | ||
Demographic Context | |||
Population Density | −0.00 (0.01) | ||
Crime Prone Ages | −0.16 (0.18) | ||
Constant | −1.34* (0.28) | −1.31* (0.29) | 1.10 (5.65) |
Summary Information | |||
Year Effects? | Yes | Yes | Yes |
State Effects? | Yes | Yes | Yes |
AIC | 1927.45 | 1906.12 | 1920.04 |
Pseudo R2 | 0.31 | 0.31 | 0.31 |
N | 1148 | 1148 | 1149 |
Note :
p < 0.05 (two-tailed tests).
All predictors are lagged by one year. Robust clustered Std. Errors reported in parentheses.
Our next set of estimates examines radicalizations. While the effects of undocumented immigration on terrorism events appears to be null, perhaps unauthorized immigration is associated with inchoate terrorist activity or to the support of terrorist organizations and ideologies. We test this possibility in Table 3 where the analytical setup is identical to Table 2 except that the dependent variable is the number of individuals radicalized in the United States.
Using this alternative measure of terrorist activity, we find little evidence of a relationship between unauthorized migration flows and terrorism. Across all three models, the effects of undocumented immigration are null, largely aligning with the bivariate findings.
Table 4 switches focus to terrorism/national defense convictions. In each model, the effects of undocumented immigration are small in both substantive terms and relative to their standard errors. Combined, the results in Tables 2–4 generally buttress the view that terrorist activity and undocumented immigration are largely orthogonal to one other. This appears to be the case for lawful immigration also, as evidenced by the statistically insignificant effects shown in each table.
Alternative Tests
Taken together, the results thus far suggest that the timing and location of terrorism in the U.S. is not driven by unauthorized migration flows. However, given the salience of terrorism and the controversy surrounding this topic, it is important to consider the robustness of this finding. We do so in panels A-C in Table 5 using multiple alternative measures of terrorism. We begin in panel A using the number of terrorism and terrorism-related convictions between 2001 and 2014 provided by the Department of Justice.17 This DOJ list has been both highly publicized and remarkably controversial. In linking the issue of terrorism to immigration, President Trump referenced this list to a joint session of Congress in 2017 claiming “the vast majority of individuals convicted of terrorism and terrorism-related offense since 9/11 came here from outside of our country” (United States Office of White House Communications 2017). Since that time, the list has been subject to substantial criticism on varied fronts (Davis and Nixon 2018). It focuses exclusively on “international terrorism investigations,” and therefore omits the more numerous class of “domestic terrorism” convictions. In this regard, the list necessarily focuses on cases that have international connections and thus those more likely to involve foreign-born individuals. As law professor Shiran Sinnar (2017) highlights, “If you exclude all convictions for ‘domestic terrorism’ at the outset, how can you draw any overall conclusions on the citizenship status or national origin of those convicted of terrorism?” Second, the list contains over a 100 convictions for crimes that “arose from the nationwide investigation conducted after September 11, 2001” but “regardless of whether investigators developed or identified evidence that they had any connection to international terrorism” (Sinnar 2017). For instance, many of the terrorism-related convictions involved citizenship fraud, passport fraud, or making false statements to an immigration officer (Nowrasteh 2019). They count as “terrorism-related” only by the fact that they originated from a terrorism investigation.
Table 5.
Negative Binomial Estimates of Undocumented Immigration on Alternative Measures of Terrorism
Model 1 |
Model 2 |
Model 3 |
|
Panel A: DOJ Terrorism Convictions | b | b | b |
Immigration Measures | |||
Undocumented Immigration | 0.22 (0.36) | 0.32 (0.38) | −0.01 (0.40) |
Lawful Immigration | 0.28 (0.23) | 0.01 (0.29) | |
Covariates? | No | No | Yes |
Year Effects? | Yes | Yes | Yes |
State Effects? | Yes | Yes | Yes |
Pseudo R2 | 0.28 | 0.28 | 0.29 |
N | 698 | 698 | 699 |
Model 1 |
Model 2 |
Model 3 |
|
Panel B: International Terror Events (GTD) | b | b | b |
Immigration Measures | |||
Undocumented Immigration | −0.26 (0.33) | −0.25 (0.33) | −0.10 (0.32) |
Lawful Immigration | −0.14 (0.35) | 0.12 (0.21) | |
Covariates? | No | No | Yes |
Year Effects? | Yes | Yes | Yes |
State Effects? | Yes | Yes | Yes |
Pseudo R2 | 0.35 | 0.35 | 0.37 |
N | 1198 | 1198 | 1199 |
Model 1 |
Model 2 |
Model 3 |
|
Panel C: Immigrant Radicalization (PIRUS) | b | b | b |
Immigration Measures | |||
Undocumented Immigration | −0.37 (0.32) | −0.39 (0.33) | −0.19 (0.33) |
Lawful Immigration | −0.13 (0.20) | −0.19 (0.23) | |
Covariates? | No | No | Yes |
Year Effects? | Yes | Yes | Yes |
State Effects? | Yes | Yes | Yes |
Pseudo R2 | 0.36 | 0.36 | 0.36 |
N | 1198 | 1198 | 1199 |
Note :
p < 0.05 (two-tailed tests).
All predictors are lagged by one year. Robust clustered Std. Errors reported in parentheses.
Cognizant of these issues, we utilize this data source for two primary reasons. First, it is clear that these data are motivating national security and immigration policy. For instance, in response to a DOJ report based on these convictions, the White House issued a fact sheet declaring “this report shows, once again, that our current immigration system jeopardizes our national security” and called for an end to “chain migration and the visa lottery” (United States Office of White House Communications 2018). For this reason alone, it is an important data source to consider. Second, because these data focus on “international terrorism,” they provide a very conservative test of our results. That is, if there is a connection between undocumented immigration and terrorist activity, it should be evident using the narrow range of DOJ terrorism cases that disproportionately involve foreign-born individuals.
The results from this more conservative test are shown in Table 5, panel A. Like the USSC data, we use the federal district to aggregate the number of DOJ terrorism convictions to the state-year.18 For parsimony, we report only the immigration results but note that the independent variable specifications across all models are identical to those in Tables 2–4. Despite the substantially different definition of terrorism in the DOJ data, there is virtually no difference in the magnitude or overall pattern of results. Across all models in Table 5, there is no effect of undocumented immigration on terrorist activity, nor is there evidence to suggest a link between lawful immigration and terror convictions. Given that the DOJ measure purposefully focuses more on foreign-born individuals, this is a powerful null finding.
Next, we examine a more specific subset of terrorism events in the GTD data. Our main analysis examined terrorist events regardless of the underlying motivation. It thus may paint with too broad a brush by lumping together both domestic and international forms of terrorism, especially if the emphasis is on understanding the role of undocumented immigration in these events. We investigate the extent to which this distinction changes our results in panel B by examining only those terrorism events that were logistically or ideologically international in character. In the GTD data, an event is logistically international if the perpetrator group crossed a border to carry out the attack. It is ideologically international if the perpetrator group attacked a target of a different nationality. Over our study period, 186 terror events met these criteria. In 91 percent of state-years, there were no international-related terror events. Focusing on the models panel B, we uncover no evidence linking undocumented immigration flows to this more limited group of terror events.
Our final analysis examines the specific issue of immigrant radicalization. That is, while undocumented immigration may not be linked to radicalization generally, it may be linked to the radicalization of immigrants. We test this possibility in panel C by examining only those in the PIRUS data that were either first or second-generation immigrants. Of the 1083 radicalized individuals over our study, 195 were either first or second-generation immigrants. Based on the results in panel C though, the processes that led to their radicalization appear to have little to do with undocumented immigration flows. In fact, in each model the effect of unauthorized immigration is negative, although not significant in any specification.
We also direct interested readers to the online appendix where we replicate our analysis using undocumented figures from the Pew Research Center, examine only states with large undocumented population, inspect the role outliers, and reproduce our results using OLS regression.
Discussion
Whether undocumented immigration is related to terrorism remains at the fore of contemporary public and political dialogue. Yet, despite its salience, this question has received little empirical scrutiny. This is a significant sociological blind spot given there are competing theoretical predictions concerning the unauthorized immigration-terrorism nexus and the U.S. government has apportioned substantial resources to combat terrorism via border enforcement. In this study, we fill this gap using a unique combination of immigration and terrorism data to address empirical issues that have hindered previous work in this area. Most notably, rather than relying on trends in overall immigration, we leverage the availability of recently developed estimates of the unauthorized population to provide the first longitudinal assessment as to whether undocumented immigration increases the prevalence of terrorism. We find little consistent evidence, descriptive or otherwise, to suggest that it does. Indeed, we reliably fail to reject the null hypothesis that the effect of undocumented immigration on terrorism is zero, even in models that rely on the Department of Justice’s list of “international terrorism” convictions and focusing on immigrant radicalizations specifically. Collectively, these results suggest that despite voluminous political pronouncements to the contrary, unauthorized immigration has not been a central driver of terrorist activity in recent decades.
Our body of results make sense in light of the fact that many terrorists over this period were U.S. citizens or lawful immigrants and that an increasing number of terrorists are driven by far-right ideology (e.g. anti-government, white supremacist, and anti-abortion ) (Zapotosky 2019). In the post 9/11 era, for instance, Bergen and colleagues (2019) found that even among jihadist terrorists in the U.S., 84 percent were either U.S. citizens or permanent residents. Moreover, between 2002 and 2014, terror attacks motivated by far-right wing beliefs accounted for more homicides than all other ideologies combined.19 The PIRUS data are also instructive in this regard. Of the 1083 recorded radicalizations over our study period, only 13 (1 percent) involved undocumented immigrants. Taken together, our analysis calls into question the purported links between unauthorized immigration and terrorism, at least in the United States.
Although we provide a novel explication of the links between undocumented immigration and terrorism, our inquiry is only a necessary first step. We thus highlight several fruitful avenues for further research. First, while we attempted to address concerns about underreporting in the GTD and PIRUS databases, the USSC data are not a panacea. The application of federal terrorism and national defense convictions are no doubt subject to the priorities of federal law enforcement, changing administrations, and considerable prosecutorial and judicial discretion, the use of which has not been subject to much empirical examination (but see Said 2015). Thus, future research would do well to study the decision-making practices involved in terrorism cases at various stages of criminal case processing (e.g. arrest, charging, sentencing, etc.) to better understand how terrorism cases track terrorist activity. Second, while state-level analyses have precedent in unauthorized immigration research (Durand, Massey, and Capoferro 2005) and immigration policies are increasingly formulated by state governments (Gulasekaram and Ramakrishnan 2012), states may still be an imperfect unit of analysis. One potential concern may be the issue of internal mobility. That is, there may be a mismatch between where terrorists reside and where they attack (or plan to attack). If this discrepancy were more pronounced for undocumented immigrants, our analysis may miss this important aspect of immigrant terrorism and radicalization. We find little evidence for this view in the PIRUS data. Indeed, the correlation between the state in which an undocumented immigrant resides and the state where they plot an attack is .80. For other radicals, the correlation is .82, suggesting that terrorists tend to attack/plot within the states they live, regardless of documentation status.
That said, states are necessarily heterogeneous units of analysis and for this reason, future research should examine the terrorism consequences of unauthorized immigration at more proximate units of analysis (e.g. counties, neighborhoods) (see Brodeur 2018 for an example). This is, however, a difficult empirical challenge given that sampling variability at more granular levels decreases the stability of unauthorized estimates. Lastly, a critical next step is to expand the international scope of our inquiry. One potential explanation for the mostly null results is the fact that few unauthorized immigrants in the U.S. come from terror-prone countries. By implication, it is plausible that the unauthorized immigration-terrorism relationship may be different in countries where there is a substantial undocumented population from regions with greater concentrations of terrorist networks.
We close with a discussion of the implications of our study. Our findings do not undervalue the very real threat posed by the global proliferation of terrorism (Institute for Economics and Peace 2018). Rather, they underscore that the judicious allocation of scarce counterterrorism resources requires an empirically grounded understanding of the risk factors associated with terrorism. On this point, our study provides supporting evidence for the view expressed by Karen Greenberg, the executive director at the Center on National Security at Fordham University, “If you are looking to create a fact-based policy for making the country secure against terrorism, focusing on immigrants will not provide the answer” (Hirschkorn 2017).
Supplementary Material
Acknowledgments
Support for this research was provided by the Office of the Vice Chancellor for Research and Graduate Education at the University of Wisconsin-Madison with funding from the Wisconsin Alumni Research Foundation and grant T32 HD007014, awarded to the Center for Demography and Ecology at the University of Wisconsin-Madison by the Eunice Kennedy Shriver National Institute of Child Health & Human Development.
Footnotes
We use the terms unauthorized and undocumented throughout the manuscript to avoid redundancy.
For reference, the rankings of terrorist activity for each country is as follows: Mexico (56th), Guatemala (120th), Honduras (81st), El Salvador (138th) (Institute for Economics & Peace 2018).
Information on the inclusion criteria is detailed in the PIRUS Codebook available at: https://www.start.umd.edu/sites/default/files/files/research/PIRUSCodebook.pdf
There is little evidence of problematic multicollinearity in our data, as the variance inflation factors (VIF) among the measured covariates are less than 5 in the full sample, well below the recommended cutoff of 10. Unsurprisingly, the largest VIF is for lawful immigration given that the legal and unauthorized immigrant population are correlated significantly. However, as we demonstrate throughout the analysis, our central findings are unaffected by the inclusion of the lawful immigration measure.
Police officer information is missing for West Virginia for 2008 and 2014. Data for Washington DC is available for most measures in our analysis, except for political ideology. In supplemental analyses, we re-estimated the main models excluding this measure and thus utilizing the DC data. The primary findings are substantively unchanged in these models (results shown in appendix).
These data files are available at https://www.ussc.gov/research/datafiles/commission-datafiles.
This definition is similar to the “Terrorism / National Security” designation used to examine terrorism-related prosecutions by the Transactional Records Access Clearinghouse (TRAC).
In addition to an array of specific statutes, a federal crime of terrorism is defined under 18 U.S.C. section 2332b(g)(5)(1) as “an offense that is calculated to influence or affect the conduct of government by intimidation or coercion, or to retaliate against government conduct.”
Details regarding these logical edits are available at Warren (2014).
These data are available at https://rcfording.wordpress.com/state-ideology-data/.
The UCR definitions include murder, rape, robbery and assault as violent crimes.
Previous research demonstrates that this measure is a valid proxy for firearm availability (Miller, Azrael, and Hemenway 2002) and empirically outperforms other commonly used measure of firearm availability, such as National Rifle Association membership (Azrael, Cook, and Miller 2004).
This number is calculated as the total foreign-born population minus the undocumented population.
In alternative specifications, we examined the proportion of young foreign-born men specifically. These results did not change the relationship between undocumented immigration and any of our terrorism measures (see appendix table 8).
Though we believe it is methodologically appropriate to cluster the standard errors, we also replicated our main results in the appendix using non-clustered standard errors.
We omit data points in the 1st and 99th percentiles from the graphs for presentation purposes.
This list includes nearly 580 convictions between 2001 and 2014. The raw data is available here: https://web.archive.org/web/20161109030252/http://www.sessions.senate.gov/public/_cache/files/6e9a95e6–3552-45f7-bb0c-4fd41f5a28ca/01.13.16-original-doj-nsd-list.pdf
It is common to see a significant lag between the charging and conviction dates. We utilize the charging data for this analysis because it is more temporally proximate to the underlying criminal behavior.
As of 2019, jihadist ideology had claimed 104 victims. Far right ideology has claimed 109 (Bergen et al. 2019).
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Contributor Information
Michael T. Light, University of Wisconsin-Madison.
Julia T. Thomas, University of Wisconsin-Madison
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