w6 : drugs and crimes

Citizenship Status and Arrest Patterns for Violent and Narcotic-Related Offenses in Federal Judicial Districts along the U.S./Mexico Border

Deborah Sibila1 & Wendi Pollock2 & Scott Menard3

Received: 8 September 2016 /Accepted: 30 October 2016 / Published online: 10 November 2016 # Southern Criminal Justice Association 2016

Abstract Media reports routinely reference the drug-related violence in Mexico, linking crime in communities along the Southwest U.S. Border to illegal immigrants. The primary purpose of the current research is to examine whether the media assertions can be supported. Logistic regression models were run to determine the impact of citizenship on the likelihood of disproportionate arrest for federal drug and violent crimes, along the U.S./Mexico border. In arrests for homicide, assault, robbery, and weapons offenses, U.S. citizens were disproportionately more likely than non-citizens to be arrested. The only federal crime where non-citizens were disproportionately more likely to be arrested than were U.S. citizens was for marijuana offenses. Results of the current study challenge the myth of the criminal immigrant.

Keywords Citizenship . Arrest . Criminal immigrant . Gender

The myth of the criminal immigrant is perhaps one of the single most controversial factors contributing to America’s present day anti-immigrant fervor. In their book, The

Am J Crim Just (2017) 42:469–488 DOI 10.1007/s12103-016-9375-1

* Wendi Pollock [email protected]

Deborah Sibila [email protected]

Scott Menard [email protected]

1 Department of Government, Stephen F. Austin State University, Box 13045 SFA Station, Nacogdoches, TX 75962, USA

2 Department of Social Sciences, Texas A&M University, 6300 Ocean Drive, Corpus Christi, TX 78412, USA

3 Institute of Behavioral Science, University of Colorado, Boulder, USA

Immigration Time Bomb, authors Richard D. Lamm and Gary Imhoff contend that the issue of immigration and crime is a critically divisive topic easily subject to misinter- pretation (1985, p. 21). The belief that immigrants are more crime-prone than native- born is not a twentieth century development. Debates on this controversy date back more than 100 years (Hagan & Palloni, 1998; Martinez & Lee, 2000). Hagan and Pallon believed that the nexus between immigration and crime is so misleading that it constitutes a mythology (1999, p. 630). In a special report for the Immigration Policy Center, professors Ruben Rumbaut and Walter Ewing wrote B[The] misperception that the foreign-born, especially illegal, immigrants are responsible for higher crime rates is deeply rooted in American public opinion and sustained by media anecdote and popular myth^ (2007, p. 3). Lee (2013) similarly argues that immigrants have a long history of serving as scapegoats for a vast array of America’s societal problems including crime.

Public opinion surveys suggest that a significant number of Americans believe that immigrants, particularly illegal immigrants, are associated with higher crime rates (Kohut et al., 2006; Muste, 2013; Sohoni & Sohoni, 2014). Media sources routinely associate immigration, especially Hispanic immigrants, with crime (Bender, 2003; Martinez, 2002). Politicians also play a key role in perpetuating the belief that immigrants and crime are interrelated. Arizona Governor Jan. Brewer, Senator John McCain and former presidential hopeful Patrick Buchanan are just a few political figures that have gone on record linking immigration directly with high crime rates (Butcher & Piehl, 1998; USA Today, 2011). On May 15, 2006, during a presidential address to the nation on immigration reform, former President George W. Bush asserted that BIllegal immigration puts pressure on public schools and hospitals, it strains state and local budgets and brings crime to our communities.^ According to Rumbaut and Ewing (2007), regardless of the much-publicized media stereotyping and harsh political rhetoric, empirical evidence simply does not support the popular misperception that immigration is the cause of higher crime rates in America.

History of Immigration and Crime Theory and Research

Explanations of the link between immigration and crime have been offered from the perspectives of culture conflict, acculturation, social disorganization and the immigration revitalization perspective. From the culture conflict perspective, Sellin (1938) suggested that the conflict between the norms of behavior for divergent cultures, as represented by native born Americans versus immigrants, was one source of crime. Sutherland (1924, 1934) posited that it was not immigration itself, but rather acculturation, that led to the association between immigration and crime, and noted that second-generation immigrants had higher crime rates than first-generation immi- grants. From the social disorganization perspective, researchers from the Chicago School linked immigration to a number of social issues, including not only crime but also poverty, unemployment, poor housing, and substandard schools (Park et al., 1925; Shaw, 1929; Shaw & McKay, 1931, 1942; Thomas & Znaniecki, 1920, 1958). Shaw and McKay, in particular, suggested that it was not the characteristics of the immigrants themselves, but the characteristics of the urban neighborhoods in which they resided, that led to the apparent link between immigration and crime (see

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also Taylor, 1931). In contrast to the culture conflict, acculturation, and social disorganization perspectives, the immigration revitalization perspective (Lee, 2013; Martinez, 2006) suggests that immigrants tend to be less criminal than native-born Americans, and that the informal social controls that are an integral part of the culture in predominantly immigrant neighborhoods result in higher levels of social organiza- tion and lower rates of crime. The present paper is informed by, but not a direct test of, these theories, which disagree about whether immigrants should have dispropor- tionately higher (culture conflict, social disorganization; acculturation for the second generation), or lower (immigrant revitalization; acculturation for the first generation) involvement in illegal behavior.

Early empirical investigations into the link between immigration and crime were limited and were focused at the individual level (Abbott, 1915; Hourwich, 1912; Lind, 1930; Taft, 1933, 1936; Van Vechten, 1941). These studies found little evidence of a causal relationship between immigration and crime. Three major government commis- sions (the Industrial Commission of 1901, the [Dillingham] Immigration Commission of 1911, and the [Wickersham] National Commission on Law Observance and Enforcement of 1931) similarly explored the issue of whether immigration increases crime. Each of the commissions found that immigrants were less likely to commit crime than were their native-born counterparts.

More recent studies investigating the association between immigration and crime tend to agree with earlier research, specifically that immigrants are not disproportion- ately involved in crime and are oftentimes significantly less involved than native-born Americans (Hagan & Palloni, 1998; Martinez & Lee, 2000; Mears, 2002; Rumbaut & Ewing, 2007). Contemporary researchers examining the immigrant-violent crime nexus have concentrated their efforts on macro-level studies, conducting both neigh- borhood and city-level studies. Findings from macro-level research surrounding the immigration-violent crime question have been more inconsistent than the individual- level studies. There are a handful recent studies that find some positive relationships when examining the impact of immigration and certain crime variables under specific conditions (Lee, et al., 2000; Martinez, 2000, 2003; Sampson & Raudenbush, 1999). A significant portion of contemporary research examining the relationship between immigration and crime has focused on ethnic gangs and violent crime, especially homicide. More recently, researchers have explored the idea that immigration has actually contributed to the decline in U.S. crime rates since the 1990s (Sampson, 2006, 2008). In addition to recent academic studies, at least one government com- mission was tasked with examining the issue of immigration and crime during the last 20 years. The U.S. Commission on Immigration Reform (1994) examined the impact of Mexican immigration on crime rates in metropolitan areas along the Southwest border as compared to those in non-border cities. The commission found that crimes rates were typically lower in cities with large Mexican populations along the border than for non-border cities.

The Southwest Border

Because of the current study’s focus on the impact of immigrant status on violent crime and narcotic-related offenses in federal jurisdictions along the United States-

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Mexico border during a time of unprecedented drug violence in Mexico, it is important to provide some background information regarding the location and timeframe for the research. The U.S.-Mexico border commonly referred to, as the Southwest border is an international boundary separating the United States and Mexico. The border was created in 1848 after the end of the Mexican-American War under the Treaty of Guadalupe-Hidalgo. The Southwest border is one of the longest borders in the world (1954 miles) and runs along the U.S. states of California, Arizona, New Mexico and Texas on the northern side and the Mexican states of Baja California, Sonora, Chihuahua, Coahuila, Nuevo Leon and Tamaulipas on the southern side. It is also considered to be the busiest international boundary in the world (Andreas, 2000). The La Paz Agreement signed by the United States and Mexico in 1983 specifies that the Bborder region^ between the U.S.-Mexico en- compass the band of land that stretches 100 km (approximately 62.5 miles) on either side of the boundary line. There were approximately 70.9 million residents living in the four border states in 2010, with approximately 7.5 million people residing in 37 counties that comprise the border region (Rex, 2014). According to the U.S. Census Bureau, the border region contains the largest concentration of Hispanic population in the country — from 25 % to more than 50 % of the population (Ennis et al., 2011). The Pew Research Center estimates that in 2010 approximately 4.7 million illegal immigrants lived in the four states along the Southwest border (Passel & Cohn, 2011).

The movement of illegal drugs and movement of unauthorized immigrants from Mexico into the United States via Southwest border have long been areas of contention in U.S.-Mexico relations (Andrews, 2012; Domínguez & De Castro, 2009; Payan, 2006; Seelke, 2010). For decades, marijuana and heroin (primarily Mexican brown and black tar heroin) were smuggled across the border without significant interference by law enforcement in either country (Andreas, 2000). During the 1980s, Mexico became an important transshipment center for drugs entering the United States following the crackdown on the importation of Colombian cocaine and marijuana through South Florida. The crackdown led to the redirection of federal drug interdiction efforts from South Florida to the Southwest border. The passage of the Immigration Reform and Control Act of 1986, or IRCA, which granted amnesty to millions of undocumented immigrants living in the United States, further contributed to public scrutiny of Southwest border issues and the problem of undocumented immigration (Immigration Reform and Control Act of 1986, n.d.). Efforts by law enforcement agencies to curb further illegal immigration across the Southwest border following the passage of the IRCA legislation proved essentially futile (Payan, 2006). During the 1990s, the U.S. federal government increased the number and scope of interdiction operations targeting both drugs and illegal immigrants attempting to cross the United States- Mexico border (e.g., Operations Gate Keeper, Hold the Line, Safeguard, and Rio Grande). These operations made little inroad into the drug and immigration prob- lems plaguing the border region (Andrews, 2012; Domínguez & De Castro, 2009; Payan, 2006). Meanwhile, Mexico’s efforts to curb the expansion of drug cartel influence during the 1990s were largely ineffective. The cartels expanded their influence throughout Mexico, and drug-related corruption was rampant throughout the country’s government, police organizations and military (Andreas, 2000;

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Carpenter, 2009). The U.S. federal government dramatically escalated border polic- ing following the 9/11 terrorist attacks in New York and Washington. Perhaps the most affected area in the U.S. after the attacks was the Southwest border (Payan, 2006, p. 13; see also Domínguez & De Castro, 2009). National security concerns following 9/11 led to the tightening of border security checks, hardening of land and sea points of entry, crackdowns on unauthorized immigration and increased militarization of the border. Despite all these safeguards, public perception that government and law enforcement efforts to secure the Southwest border are substantially ineffectual persists. Cornelius (2005) suggests that the government’s efforts to secure the border are not only inadequate, but have been more effective keeping illegal immigrants inside the U.S. than acting as an deterrent to others attempting to enter the country (p. 12). Americans remain divided over how to stem the flow of illegal immigrants across the Southwest border.

The current image of the Southwest border as a region plagued by violence and crime is intrinsically linked to the rising tide of drug-related violence in Mexico during the last ten years (Beittel, 2009, 2011; Carpenter, 2012; Payan, 2006). Violence in Mexico significantly worsened following Mexico’s President Felipe Calderon’s declaration of war against his country’s drug cartels shortly after his inauguration in December 2006. The level of drug-related crimes (i.e., homicides, kidnappings, home invasions, drive-by shootings) increased throughout Mexico, particularly in the northern territories along the United States border (Beittel, 2009, 2011; Domínguez & De Castro, 2009). According to Human Rights Watch (2013), approximately 60,000 Mexicans lost their lives as a result of drug-related violence during President Calderon’s tenure as president (2006–2012).

Since 2007, the media in United States, especially those along the Southwest border have been preoccupied with the drug violence and bloodshed in Mexico (Andrews, 2012; del Bosque, 2009). Media sources routinely sensationalize re- ports of U.S. citizens dying and being kidnapped in suspected drug-related incidents in cities along the Southwest border. The media coverage has persuaded Americans that the turmoil in Mexico is no longer confined to that country (Correa-Cabrera, 2012). Many are convinced that Mexico’s drug-related violence has Bspilled over^ into communities along the Southwest border. ‘Spillover’ has become a new media buzzword (del Bosque, 2009). While there is no firm definition for the term spillover violence it is generally understood to be violence that occurs as a result of drug trafficking. Civilians, law enforcement officers and other criminals or criminal organizations can all be the targets of spillover violence (Lee & Olson, 2013, p. 100). There is some debate as to whether spillover violence is an actual phenomenon (Correa-Cabrera, 2012; Lee & Olson, 2013). Without a concise definition, the ability of law enforcement to identify and measure spillover violence is problematic. In a report to Congress regarding the issue of Southwest border violence, Finklea and colleagues note that Bno comprehensive, publicly available data exists that can definitively answer the question of whether there has been a significant spillover of drug trafficking- related violence into the United States^ (2010, p. 19). Further complicating the issue of spillover violence has been the media linkage of the problem to the Bflood^ of unauthorized immigrants moving across the Southwest border (del Bosque, 2009). Proponents of stricter immigration controls argue that increased

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border security will significantly impede the flow of undocumented immigrants thereby reducing rates of violent and property crimes in border communities (Nevins, 2002; Payan, 2006).

The Federal Criminal Justice System and the Southwest Border

Criminal offenses occurring along the Southwest border can be prosecuted in either state or federal court. The primary trial court in the federal judiciary is the U.S. district court. There are currently 94 federal judicial districts in the United States, each with its own district court. Each district has different judges, court- house cultures and specialized local needs (Abrams et al., 2010). There are five federal districts along the Southwest border: The District of Arizona, the District of New Mexico, the Southern District of California, the Southern District of Texas, and the Western District of Texas (Fig. 1).

Each district has its own U.S. Attorney. The U.S. Attorneys’ Office (USAO) represents the federal government in all cases where the United States is a party. It has the discretion to decide whether federal charges will be filed in a case or if the case should be referred to the state system. Some factors that may affect the USAO’s choice between federal or state prosecution include: (1) primary investi- gative jurisdiction; (2) custody of the suspect; (3) possibility of a duplicative prosecution; (4) caseload and resources; (5) legal advantage; and (6) inter- agency relationships and relations among agents (Abrams et al., 2010). Federal crimes prosecuted by the USAO are listed under various titles of the United States Code (USC) including Title 18 (the primary criminal and penal code of the U.S. federal government) and Title 26 (the Internal Revenue Code).

Fig. 1 Counties in southwestern judicial districts

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Current Study

The following research questions will be addressed: Research question 1. What are the characteristics (age, gender, ethnicity, marital

status and citizenship) of individuals arrested for the following federal offenses along the U.S.-Mexico border?

& 1a.Homicide & 1b. Assault & 1c. Robbery & 1d. Marijuana & 1e. Hard Drugs & 1f. Weapons

Research question 2. Are non-citizen arrestees disproportionately more likely to be arrested for any of the following offenses, when compared to U.S. citizen arrestees?

& 1a.Homicide & 1b. Assault & 1c. Robbery & 1d. Marijuana & 1e. Hard Drugs & 1f. Weapons

Data

Data for the current study comes from the Federal Justice Statistics Program (FJSP): Arrests and Bookings for Federal Offenses (2007–2010) research series. The datasets contain comprehensive information about suspects and defendants processed in the federal criminal justice system during fiscal years 2007 to 2010. Offenders arrested for federal offenses are transferred to the custody of the United States Marshals Service (USMS) for processing, transportation, and detention. The current dataset was con- structed from the USMS Prisoner Tracking System (PTS) database. Records include arrests made by federal law enforcement agencies (including the USMS), state and local agencies, and self-surrenders. The USMS uses the PTS application to maintain tracking information for all federal prisoners in USMS custody. The PTS was imple- mented by the USMS in March 1993 to maintain tracking information for federal prisoners and to monitor federal prisoners in state and local detention facilities under contract to the USMS. The PTS replaced the Prisoner Population Management System. The PTS contains information that is specific to each individual prisoner, including the prisoner’s personal data (e.g., gender, ethnicity, age, marital status and citizenship status), property, medical information, criminal information, and location. Prisoners’ records are created using information derived from key source documents (e.g., USMS and other agency arrest sheets), and this information is entered into the PTS.

The rationale for the location and time setting of this research warrants some discussion. While USMS arrest statistics are collected in all federal judicial districts

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nationwide, the current research will only utilize data from the five federal districts on the U.S.-Mexico border (Arizona, New Mexico, California Southern, the Texas Western and Southern Texas). The five Southwest border districts accounted for 56 % of all federal suspects arrested and booked in the U.S. and 90 % of all immigration arrests in 2010 (Motivans, 2012). This study departs from most existing research on immigration and crime, in that the focus is on a variety of federal offenses committed in five federal judicial districts whose jurisdiction extends to multiple states along the Southwest border. Previous research has almost been exclusively limited to examining a limited number of offenses (i.e. homicide) at either the city or neigh- borhood level (Martinez, 2006). The time period covered by this research (2007– 2010) is significant because it covers a period of unprecedented drug violence in Mexico that was thought to contribute to crime levels in states along the Southwest border. Media reports routinely referenced the drug-related violence in Mexico, linking crime in communities along the Southwest border to drug cartel members and illegal immigrants.

Variables

The dependent variables in this study are six different measurements of arrests. In each case the prevalence of arrest was used, where 1 = arrested and 0 = not arrested in the respective year (2007–2010). The six forms of arrest include marijuana, hard drugs (heroin, cocaine, hallucinogens, other opiates, amphetamines, barbiturates, and syn- thetic drugs), homicide, robbery, assault, and weapon offenses. Prevalence was used instead of frequency of arrest due to the fact that it is extremely rare that an individual would be arrested twice in the same fiscal year for a federal offense.

Independent variables in the current study include age, sex, race, citizenship status, and marital status.

The arrestee’s sex is measured with a dichotomous variable that is coded B1^ if the offender is male and B0^ if the offender is female. Ethnicity of the arrestee is broken down into three categories: White/White Hispanic, Black/Black Hispanic, and Other. Ethnicity is broken into three dummy coded variables where 1 = being a member of that ethnic category and 0 = non-membership in that category. In each model White/ White Hispanic was used as the reference category because it is the largest category. This predictor variable was coded using the same ethnicity categories provided in FJSP datasets, the source of information used in this analysis. As is common in many datasets, Hispanic defendants in the FJSP research series are categorized as either white or black. The lack of ethnicity and citizenship information in criminal justice datasets is a significant handicap to researchers examining the interaction between citizenship status and race/ethnicity.

Citizenship status is broken into three dummy coded variables: (1) U.S. citizen, (2) Non-citizen, and (3) Unknown status. For each citizenship status variable, 1 = being a member of that citizenship category and 0 = non-membership in that category. For the purpose of this study, U.S. citizens include individuals born in the U.S.; individuals who were born outside the U.S., but who have at least one parent who is a U.S. citizen; and individuals who were born alien but who have lawfully become citizens of the United States (i.e., Bnaturalized citizens^). Non-citizens are individuals who are legal (resident) aliens, unauthorized immigrants or individuals without U.S. citizenship

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whose immigration status is unknown. An unauthorized immigrant is a person who resides in the United States but who is not a U.S. citizen, has not been admitted for permanent residence, and is not in a set of specific authorized temporary statuses permitting longer-term residence and work (see Passel et al., 2004).

In each model U.S. citizenship was used as the reference category because it is the largest category. Descriptive analysis revealed that approximately 3178 cases (9.1 %) of total cases were missing citizenship status. Rather than excluding those cases it was decided to recode them as unknown in order to identify possible patterns in the missing values and to determine if those patterns were consistent with or different from other categories. One author of this study has 22 years of personal experience as a federal criminal investigator and supervisory special agent. Based on that experience, the following are possible explanations for missing demographic information in the PTS database (source of information for FJSP datasets used in this analysis). Reasons include the inaccurate completion of investigative reports due to human error and a lack of quality review of those reports by supervisory personnel. Additionally, the U.S. Department of Justice Office of the Inspector General (2004) determined that the USMS (proprietor of the PTS database) lacked proper internal controls to identify and correct erroneous or missing information contained within the computer database.

Marital status is also broken into three dummy variables, (1) married, (2) Unmarried (including divorced, single, and widowed individuals), and (3) un- known marital status. Like other dummy coded variables in this study, 1 = being a member of that marital category and 0 = non-membership in that category. In each model Unmarried was used as the reference category because it is the largest category. Descriptive analysis revealed that approximately 15,166 cases (43.5 %) of total cases were missing marital status. Rather than excluding those cases it was decided to recode them as unknown in order to identify possible patterns in the missing values and to determine if those patterns were consistent with or different from other categories. Possible explanations for the missing values are discussed above. Age, the final independent variable, was measured as age in years, at the time that the individual was arrested. Prior to any analysis being run, these variables were checked for collinearity, and none was found. VIF scores ranged from 1.023 (Tolerance = .977) to 1.415 (Tolerance = .707).

Analytic Strategy

Logistic regression was chosen to analyze the preceding research questions, because it is appropriate for use when the dependent variable or variables (in this case the prevalence of arrest for each type of offense) are dichotomous, and it is appropriate with all types of independent variables (Menard, 2010). Model statistical significance will be determined using the p-value associated with the model chi-squared statistic, and the model substantive significance will be determined using the likelihood ratio R2, also known as McFadden R2 or R2L. This measure of model substantive significance was chosen because it is conceptually the closest to R2 in OLS regression, in that it reflects the proportional reduction in the quantity actually being minimized (Menard, 2010). Predictor statistical significance will be determined using p-values computed

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from the Wald statistic, while predictor substantive significance will be determined using fully standardized regression coefficients. These coefficients have the same interpretation in logistic regression as in ordinary least squares regression: A one standard deviation change in the predictor is associated with a b* standard deviation change in the outcome, where b* is the fully standardized regression coefficient. For a full description of how these coefficients are calculated, see Menard, 2010. While it is the fully standardized regression coefficients that will be interpreted by the authors, odds ratios have also been inserted into the tables, for readers who prefer that measure of predictor substantive significance. Table 1 illustrates the descriptive statistics for the variables in the current study.

Results

Table 2 illustrates the results of the logistic regression arrest models, for each offense type. On this table we can see that the federal homicide arrest model is statistically (p = .000) and substantively (R2L=.276) significant. Accordingly, the model explains 27.6 % of the variation between federal homicide arrests and federal arrests for other offenses. According to Wald statistics, individuals who are disproportionately arrested for a federal homicide offense are more likely to be male (p = .002), White/White Hispanic versus Black or Black/Hispanic (p = .016), a member of an Bother^ racial

Table 1 Descriptive statistics

N Mean Standard deviation

Dependent variables

Homicide Arrests 34829 .0088 .09362

Assault Arrests 34829 .0313 .17412

Robbery Arrests 34829 .0146 .11989

Marijuana Arrests 34829 .5671 .49548

Hard Drug Arrests 34829 .2804 .44919

Weapons Arrests 34829 .0978 .29704

Independent variables

Age 34099 31.36 10.412

Sex 34828 .8410 .36566

White/White Hispanic 34828 .8984 .30207

Black/ Black Hispanic 34828 .0596 .23671

Other Race 34828 .0420 .20054

Married 34829 .2545 .43557

Unmarried 34829 .3101 .46254

Unknown Marriage 34829 .4354 .49582

US Citizen 34829 .5472 .49778

Non-Citizen 34829 .3616 .48046

Unknown Citizen Status 34829 .0912 .28796

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category (p = .000), a U.S. citizen versus a non-citizen (p = .000), and/or a U.S. citizen versus a member of an unknown citizen classification (p = .000). Examination of the standardized coefficients reveals that being a member of an Bother^ or unknown race is the strongest predictor of federal homicide arrests (b*M = .198) followed by non-citizen status (b*M = −.175), unknown citizenship status (b*M = −.139), race (White/White Hispanic versus Black or Black/Hispanic; b*M = −.111), and finally by gender (b*M = .058). Note that the typical cutoff for substantive significance is a standardized regression coefficient of .100 or higher. Using that standard, gender (being male) is not substantively significant, while all other statistically significant predictors are.

Table 2 also shows that the model for federal assault arrests is statistically (p = .000) and substantively (R2L= .221) significant. Accordingly, it explains 22.1 % of the variation between federal arrests for assault and federal arrests for other offenses. The statistical significance of the predictors indicates that individuals who were dispropor- tionately arrested for federal assault, were more likely to be male (p = .010), Black or Black/Hispanic (p = .001), a member of an Bother^ racial category (p = .000), a member of an unknown marriage status category (p = .000), a U.S. citizen versus a non-citizen (p = .000), and/or a U.S. citizen versus a member of an unknown citizen classification (p = .000). Examination of the standardized coefficients reveals that being a member of an Bother^ or unknown race is the strongest predictor of federal assault arrests (b*M = .305) followed by citizenship status (non-citizen, b*M = −.210 and unknown citizenship, b*M = −.138), having an unknown marital status (b*M = .070), being Black/Black Hispanic (b*M = .049), and finally by gender (b*M = .043). As previously noted, the typical cutoff for substantive significance is a standardized regression coefficient of .100 or higher. Accordingly, the predictors of gender (being male), being Black or Black/Hispanic and having an unknown marriage status while statistically significant are not substantively significant.

The federal robbery arrest model is also statistically significant (p = .000), but not substantively significant (R2L= .008). The model only explains .8 % of the variation in the dependent variable. Federal robbery arrests have a statistically significant relation- ship with age (being older; p = .008), being male (p = .000), being Black or Black/ Hispanic (p = .000), being a member of an Bother^ racial category (p = .000), being unmarried (versus being married or having an unknown marital status (p = .000 for both comparisons), and/or with being a U.S. citizen versus a non-citizen (p = .000) or being in an unknown citizen classification (p = .000). Examination of the standardized coefficients reveals that citizenship status is the strongest predictor of federal robbery arrests (non-citizen, b*M = −.103 and unknown citizenship status, b*M = -.054), followed by gender (b*M = .036), being unmarried versus married (b*M = −.028), being Black/Black Hispanic (b*M = .027), being a member of an Bother^ or unknown race (b*M = .019), being unmarried versus having an unknown marital status (b*M = −.016), and finally age (b*M = .013). Being a U.S. citizen (versus being a non-citizen) is the only predictor that is both statistically and substantively significant.

Table 2 illustrates that the model for federal marijuana arrests is statistically (p = .000) significant and explains 7.8 % of variation in the dependent variable (R2L=.078). Federal marijuana arrests have a statistically significant relationship with age (being younger; p = .000), being female (p = .000), being White/White Hispanic versus Black or Black/Hispanic (p = .000) or being a member of an Bother^ racial

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Table 2 Logistic regression arrest models

Dependent Variable

Model Statistics

Independent Variables

Unstandardized Coefficients (b)

Standardized Coefficients (b*)

Significance (Wald Statistic)

Federal Homicide Arrests

R2L=.276 Age .010 .028 .087

p = .000 Male .584 .058 .002

Black/Black Hispanic -1.728 -.111 .016

Other Race 3.615 .198 .000

Unknown Marriage -.205 -.028 .142

Married .156 .019 .396

Non-Citizen -1.334 -.175 .000

Unknown Citizenship -1.770 -.139 .000

Federal Assault Arrests

R2L= .221 Age -.004 -.020 .266

p = .000 Male .245 .043 .010

Black/Black Hispanic .443 .049 .001

Other Race 3.190 .305 .000

Unknown Marriage .297 .070 .000

Married -.121 -.025 .257

Non-Citizen -.915 -.210 .000

Unknown Citizenship -1.002 -.138 .000

Federal Robbery Arrests

R2L= .008 Age .011 .013 .008

p = .000 Male .890 .036 .000

Black/Black Hispanic 1.006 .027 .000

Other Race .829 .019 .000

Unknown Marriage -.282 -.016 .000

Married -.583 -.028 .000

Non-Citizen -1.910 -.103 .000

Unknown Citizenship -1.664 -.054 .000

Federal Marijuana Arrests

R2L= .078 Age -.020 -.094 .000

p = .000 Male -.188 -.031 .000

Black/Black Hispanic -1.460 -.156 .000

Other Race -1.382 -.125 .000

Unknown Marriage .531 .119 .000

Married .157 .031 .000

Non-Citizen .686 .149 .000

Unknown Citizenship .630 .082 .000

Federal Hard Drug Arrests

R2L= .037 Age .021 .093 .000

p = .000 Male -.380 -.059 .000

Black/Black Hispanic .742 .075 .000

Other Race -.933 -.080 .000

Unknown Marriage -.465 -.098 .000

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category (p = .000), having an unknown marriage status (p = .000), being married (p = .000), being a non-citizen versus a U.S. citizen (p = .000), and being an unknown citizen classification versus a U.S. citizen (p = .000). Examination of the standardized coefficients reveals that being White/White Hispanic versus Black/Black Hispanic is the strongest predictor of federal marijuana arrests (b*M = −.156), followed by being a non-citizen (b*M = .149), being White/White Hispanic versus a member of an Bother^ or unknown race (b*M = −.125), having an unknown marital status (b*M = .119), being younger (b*M = −.094), having an unknown citizenship status (b*M = .082), and finally gender (being female, b*M = −.031) and being married (b*M = .031). Age, gender (being female), being married and having an unknown citizenship status are not substantively significant, while all other statistically significant predictors are.

As with other models, the model for federal hard drug arrests is statistically (p = .000) and substantively (R2L=.037) significant. Accordingly, the model explains 3.7 % of the variation between federal hard drug arrests and federal arrests for other offenses. Federal hard drug arrests have a statistically significant relationship with age (being older; p = .000), being female (p = .000), being Black or Black/Hispanic (p = .000), being White/White Hispanic versus being a member of an Bother^ racial category (p = .000), being unmarried versus having an unknown marriage status (p = .000), and being a U.S. citizen versus being a non-citizen (p = .000). Examination of the standardized coeffi- cients reveals that being unmarried versus having an unknown marital status is the strongest predictor of federal hard drug arrests (b*M = −.098), followed by age (b*M = −.093), not being a member of an Bother^ or unknown race (b*M = −.080), being Black/Black Hispanic (b*M = .075), being female (b*M = −.059) and finally being a U.S. Citizen versus a non-citizen (b*M = −.044), however, none of the predictors reached the standard cutoff for substantive significance (b*M ≥ .100).

According to Table 2, the federal weapons arrest model is statistically significant (p = .000) and substantively significant (R2L = .075). The model explains 7.5 % of the variation in the dependent variable. Federal weapons arrests have a statistically

Table 2 (continued)

Dependent Variable

Model Statistics

Independent Variables

Unstandardized Coefficients (b)

Standardized Coefficients (b*)

Significance (Wald Statistic)

Married .022 .004 .482

Non-Citizen -.214 -.044 .000

Unknown Citizenship .046 .006 .352

Federal Weapons Arrests

R2L= .075 Age .001 .003 .450

p = .000 Male 1.709 .162 .000

Black/Black Hispanic .705 .043 .000

Other Race -.270 -.014 .000

Unknown Marriage -.339 -.043 .000

Married -.306 -.034 .000

Non-Citizen -.964 -.120 .000

Unknown Citizenship -1.537 -.114 .000

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significant relationship with being male (p = .000), being Black or Black/Hispanic (p = .000), being White/White Hispanic versus being a member of an Bother^ racial category (p = .000), being unmarried versus being married (p = .000) or having an unknown marriage status (p = .000), and with being a U.S. citizen versus being a non- citizen (p = .000) or being an unknown citizen classification (p = .000). Examination of the standardized coefficients reveals that gender (being male) is the strongest predictor of federal weapon offenses (b*M = .162), followed by being a U.S. citizen (versus being a non-citizen, b*M = −.120, and being of an unknown citizenship status, b*M = −.114), being Black/Black Hispanic (b*M = .043) being married (versus being in an unknown marital category, b*M = −.043, or being married, b*M = −.034), and finally, being White/White Hispanic as opposed to being a member of an Bother^ or unknown race (b*M = −.014). Only the predictors of gender (being male), being a U.S. citizen are both statistically and substantively significant.

Discussion

This study analyzed the impact of citizenship status on disproportionate federal arrests for six violent and narcotic-related offenses (homicide, assault, robbery, marijuana, hard drug and weapons) along the U.S./Mexico border, in the interest of answering two specific research questions. The answers to those questions require some attention here. Recall that the first research question asked about the characteristics of individuals who were disproportionately arrested for each of the federal offenses. Table 3 contains a summary of the multivariate findings that are both statistically and substantively significant. From this table, we can see that individuals arrested for a federal homicide offense are disproportionately individuals whose ethnicity is either White/White Hispanic or Bother^, and who are U.S. citizens. Individuals arrested for a federal assault

Table 3 Summary of multivariate results

Homicide Arrests

Assault Arrests

Robbery Arrests

Marijuana Arrests

Hard Drug Arrests

Weapons Arrests

Age

Male +

Black/Black Hispanic

− −

Other Race + + −

Unknown Marriage

+

Married

Non-Citizen − − − + −

Unknown Citizenship

− − −

A positive sign (+) indicates a statistically and substantively significant positive relationship, while a negative sign (−) indicates a significant negative relationship

482 Am J Crim Just (2017) 42:469–488

are disproportionately U.S. citizens and individuals whose ethnicity is of an unknown classification. Individuals who are arrested for a federal level robbery offense, as opposed to some other federal offense, are disproportionately U.S. citizens. Individuals arrested on federal marijuana charges are disproportionately White/White Hispanic, have an unknown marital status, and are non-citizens. None of the examined independent variables had a statistically and substantively significant impact on dis- proportionate federal arrests for hard drug use and therefore, our research does not lend any insight into the characteristics of these offenders. Finally, individuals arrested on federal weapons charges are disproportionately male and are U.S. citizens.

Though not substantively significant, one interesting findings from our multivariate analyses is that females were statistically significantly, disproportionately, more likely than males to be arrested for marijuana and hard drug offenses. This is consistent with previous research that shows that women are more likely to be arrested for drug and property related offenses rather than violent crime (Chesney-Lind, 1997; Bloom et al., 2004). According to the Bureau of Justice statistics, females accounted for 15 % of all DEA drug arrests and 20 % of all methamphetamine arrests in 2010 (Motivans, 2010). Therefore, the results from this study add further support to the differences in arrest by type of crime and gender.

The second research question specifically asked if noncitizens were disproportion- ately more likely than U.S. citizens to be arrested for each of the six federal offenses. With only one exception, the answer to these research questions was no – noncitizens were not disproportionately more likely to be arrested than U.S. citizens in the federal districts that line the U.S./Mexico border. Recall that this region contains the largest concentration of Hispanic citizens in the United States (Ennis et al., 2011), and an estimated 4.7 million illegal immigrants (Passel & Cohn, 2011). The only federal crime where noncitizens were disproportionately more likely to be arrested than were U.S. citizens was for marijuana offenses. Overall, these findings are consistent with recent research that shows that noncitizens are less likely to be arrested than U.S. citizens for violent and drug-related offenses. The only exception, marijuana contra- dicts recent studies that suggest that U.S. citizens are more likely to be arrested for drug related offenses than are noncitizens (Becker et al., 2013; Hagan & Palloni, 1999; Kposowa et al., 2009). Marcelli (2004) did find that noncitizens who were arrested were more likely to be apprehended for a drug-related offense than any other type of crime.

Cultural, opportunity structure and social disorganization are three traditional theo- retical frameworks that are used to explain immigrant criminality. These theories all generally postulate that immigration promotes criminal activity. More recently, the immigration revitalization perspective argues that protective factors of immigrant communities actually decrease (rather than increase) the likelihood of criminal activity. While this research did not directly test any of the theories, they did offer a perspective for why one might expect to see differences in arrest rates relative to citizenship status. Overall, this research showed that with the exception of federal marijuana arrests, that noncitizens were disproportionately less likely to be arrested for violent and narcotic- related federal offenses than were U.S. citizens. Results of this study tend to support the immigration revitalization perspective rather than traditional criminological theories (i.e., cultural, opportunity structure and social disorganization) that suggest that non- citizens are more likely than U.S. citizens to be arrested.

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Limitations

It is important to acknowledge some limitations to the present study. One limitation of this research is that the utilization of citizenship status in the assessment of immigrant criminality has some important drawbacks. The primary issue is that citizenship data cannot distinguish between noncitizens who are in the U.S. legally (resident aliens) and those who are unauthorized. It is, of course, the unauthorized noncitizens that are the focus of media attention and who members of the general public appear to be concerned with in terms of criminality, among other things. This limitation is not unique to this research (Heckathorn, 2006). Despite the inability to distinguish between authorized and unauthorized noncitizens, researchers often use citizenship information when examining immigrant criminality. This is due to the inherent difficulty in gaining information from individuals who are in the United States illegally. Still, the lack of information regarding immigrant status continues to be a significant impediment to researchers studying immigrant criminality.

A second limitation of the current study was that the dataset used in this research (the FJSP research series) like many formal crime data sources (e.g., Uniform Crime Report) failed to provide an ethnic breakdown of defendants. As is common in many datasets, Hispanic defendants were categorized as either white or black. The lack of ethnicity in criminal justice datasets continues to impede research efforts examining the link between ethnicity, race and crime. Another limitation of this data set is the use of arrests as an indication of criminal behavior. As has been amply documented elsewhere (for example, O’Brien, 1985; Pollock et al., 2015), arrest data reflect not only illegal behavior but also policies with regard to enforcement and the recording of police actions, and are less consistent and reliable indicators of illegal behavior and victim- ization data. While the use of arrestees does allow us to examine whether arrests for different types of crime are disproportionately concentrated within immigrant or non- immigrant populations, it is not possible from these data to compute rates of illegal behavior relative to the general population of immigrants and non-immigrants, partic- ularly because estimates of the numbers of the immigrant (including but not limited to illegal immigrant) vary widely, by as much as 10 %, or between 10.7 and 11.7 million in 2010 (Passel & Cohn, 2011; Warren & Warren, 2013).

Stated differently, it is virtually impossible to get information on actual criminal behavior from immigrants in general and even more so from illegal immigrants. This is because illegal immigrants are, understandably, difficult to locate and interview regard- ing their criminal behavior or their victimization. Arrest records are admittedly flawed indicators of criminal behavior, for the above stated reasons, but they are the best measure presently available for examining the current issue on a large scale. In addition, we know from previous research (see Pollock, 2014), that there is a statisti- cally significant, positive, correlation between criminal behavior and arrest. Therefore, some knowledge can be gained regarding criminal behavior through arrest records, though again, self-report or victimization data would be preferable.

Despite the above limitations, the current study has some important implications for policy, as well as future research. The goal of the current study was to contribute to the body of research providing a non-discriminative understanding of both crime and immigration. Overall, the results of the current research challenge the stereotypical linkage of non-citizens to crime. This research like other recent studies investigating the

484 Am J Crim Just (2017) 42:469–488

association between immigration and crime found that noncitizens were not dispropor- tionately arrested for federal crime on the U.S. / Mexico border. This is important because it challenges the claims of politicians and media sources that promote the fear of immigrants in order to further their own agendas. The myth of the criminal immigrant is perhaps one of the single most controversial factors contributing to America’s present day anti-immigrant fervor. Results of the current study and other research provide hard evidence challenging the mythology of the criminal immigrant and will hopefully contribute to a more coherent and meaningful national dialogue on immigration policy.

Additional research is necessary to explore the intricacies of the relationship be- tween citizenship and crime in the United States. Investigation into the consistency of the impact of citizenship on federal arrests over time (i.e., before and after President Calderon’s presidency) was not addressed in the current study. Second, the findings of this study were confined to the federal arrest process. Future studies are needed to determine whether similar results hold true for state agencies. Third, inter-district variation was not explored in the current study. Investigating differences in arrest predictors between federal districts has not been done previously and would certainly contribute to the body of research exploring the role of citizenship and arrest. Finally, examination of the role of citizenship status as a predictor of federal arrests for other types of offenses (e.g., property) is also essential to gain a more complete picture of the interaction between citizenship and crime.

Acknowledgments The authors would like to thank the Inter-university Consortium for Political and Social Research (ICPSR) for allowing the use of the data in this study.

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Deborah A. Sibila is an Assistant Professor of Criminal Justice at Stephen F. Austin State University in Nacogdoches, Texas. She received her B.A. in Law Enforcement/Police Science from Sam Houston State University (SHSU), her MPA from Jacksonville State University and her Ph.D. from SHSU. Her current research focus includes female and elderly offending, immigrant criminality and drug policy.

Wendi Pollock is an assistant professor of Criminal Justice at Texas A&M University – Corpus Christi. She received her B.S. and M.S. in criminal justice from Sul Ross State University, and her Ph.D. in criminal justice from Sam Houston State University. Her current research focus includes the correlates of disproportionate police contact both longitudinally and across generations, perceptions of police fairness, the impact of criminal justice system policies that center on arrest, quantitative methods, and methodological concerns in self- reported data.

Scott Menard is a retired Professor of Criminal Justice (Sam Houston State University) and a Research Associate in the Institute of Behavioral Science at the University of Colorado, Boulder. He received his Ph.D. in Sociology from the University of Colorado, Boulder. His publications include work in statistics, particularly logistic regression analysis and longitudinal research, plus criminological theory testing and research on crime, delinquency, and victimization intergenerationally and over the life course.

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