Jailing Immigrant Detainees: A National Study of County Participation in Immigration Detention, 1983–2013

Abstract

Hundreds of county jails detain immigrants facing removal proceedings, a civil process. In exchange, local jails receive per diem payments from Immigration and Customs Enforcement. Immigration detention thus presents a striking case of commodification of penal institutions for civil confinement purposes. Yet we know very little about the counties participating in this arrangement and the predictors of their participation over time. Our study offers the first systematic analysis of immigration detention in county jails using new and comprehensive panel data on jails across the United States. First, we find that the number of counties confining immigrant detainees steadily increased between 1983 and 2013, with the largest growth concentrated in small- to medium-sized, rural, and Republican counties located in the South. Second, our regression analyses point to a number of significant predictors of county participation in immigration detention: (a) worsening labor market conditions, combined with growing excess bed space for the criminal inmate population; (b) an increasing Latino population up to a certain threshold level; and (c) increasing Republican Party strength. These findings have important implications for current debates raging across the United States about the proper role of local communities in detaining immigrants.

“You've got to go out and get a contract with ICE. That's your salvation.” Hazel McCraine, President of Ocilla-Irwin Chamber of Commerce, Georgia (Rappleye and Seville 2012)

The local jail population in the United States grew from 184,000 in 1980 to 740,700 in 2016, an increase of over 300 percent (Bureau of Justice Statistics [BJS] 2018). Such trends have sparked a growing interest in local jails as a critical site of analysis for the study of criminal incarceration in the United States (May et al. 2014; Turney and Conner 2019; Wildeman et al. 2018). Local jails, however, also provide a unique window into the world of immigration detention—a vast civil confinement system facing mounting public scrutiny and litigation over due process violations and human rights abuses (see, e.g., Office of Inspector General 2017). As the sole explicit aim of immigration detention is to facilitate the deportation process of individuals accused of immigration law violations, immigration authorities are not authorized to detain individuals for the purposes of punishment (Ryo 2019). Nonetheless, the U.S. federal government contracts with hundreds of local authorities across the country to hold immigrant detainees in jails on a per diem basis.

According to data compiled by U.S. Immigration and Customs Enforcement (ICE) in November 2017, the federal government entered into approximately 850 contracts with local authorities to detain immigrants in 669 counties (Misra 2018).¹ At the same time, many local communities are now embroiled in open debates about whether to participate in immigration detention (Romero 2018). Yet we know very little about what types of counties have come to participate in this arrangement and the predictors of their participation. This study provides the first national study of the role of local jails in confining immigrant detainees using diverse sources of panel data that span multiple decades. We analyze the proliferation of immigration detention in local jails throughout the United States beginning in 1983, which is the first year that county-level data on immigration detention in local jails is available. The 1980s also provide an important starting point for our analysis given that the modern expansion of immigration detention began with a series of laws that Congress started to enact in the mid-1980s as part of the war on drugs (García Hernández 2014; Stumpf 2014; for an earlier history of U.S. immigration detention, see, e.g., Hernández 2017; Lee 2003; Wilsher 2012).

A key data source for our analysis is the Vera Institute of Justice's In Our Backyards Data (IOB Data). The IOB Data, which draw on the BJS Census of Jails and Annual Survey of Jails, contain county-level information on jail and prison populations across the United States between 1970 and 2015 (the IOB Data contain information on ICE detainees starting in 1983).² In addition, we collected and merged with the IOB Data a variety of records on local immigration-related enforcement policies, local labor market conditions, and county-level election results to offer a more detailed and comprehensive analysis of changing county characteristics.

Drawing on this rich data set, our study addresses the following two questions: What are the temporal and spatial patterns of change in the involvement of counties in immigration detention? What factors predict whether counties will participate in immigration detention? To address the first question, we conducted a series of descriptive analyses of our national county-level data for the time period 1983–2013. To address the second question, we conducted a series of regression analyses that focus on the time period 1990–2013. As we explain in greater detail below, our regression analyses cover a shorter time period than our descriptive analyses due to data availability on the main covariates included in the regression models.

Addressing these key questions is important for a number of reasons. As with criminal incarceration, deep gender and racial inequalities characterize immigration detention. According to a recent national study of immigration detention, the bulk of the detainee population consists of Latino men,³ many of whom are often shuttled across a network of facilities scattered throughout the United States during their detention stay (Ryo and Peacock 2018). These detention facilities are situated in many different types of localities with varying access to legal counsel, community resources, and immigrant advocacy networks. This means that local community contexts in which immigrant detainees are confined can play an important role in shaping their detention experiences and outcomes (Ryo and Peacock 2019). From this standpoint, understanding which counties are likely to contract with ICE will provide new insights into whether and to what extent growing local movements to end immigration detention might have unintended negative consequences for certain immigrant detainees.

This study also advances our understanding of the far-reaching impacts of immigration detention on local criminal justice systems. Jails are penal institutions that exist to confine sentenced criminal offenders and individuals awaiting their criminal trials. Renting out bed spaces to hold individuals whose confinement has little to no connection to traditional justifications for criminal incarceration thus raises important questions about the shifting aims and the future of the criminal justice system. Existing research suggests that the moral authority and credibility of criminal law depend to a large degree on its conformity with widely shared beliefs regarding just punishment (Robinson 2000). Does immigration detention in local jails diminish or increase the legitimacy of the criminal justice system? Are there unintended consequences for immigrant communities of criminal justice reform efforts focused on criminal incarceration in isolation from immigration detention? These and related questions underscore the importance of elucidating the role of immigration detention in changing the nature and operation of the criminal justice system at the local level.

Finally, immigration enforcement in general and immigration detention in particular rely heavily on the cooperation of local public officials, law enforcement, and criminal justice systems. Investigating the predictors of such cooperation will advance the emerging scholarship on the role of place and subnational contexts in shaping the implementation and impact of federal immigration policy (see, e.g., Gulasekaram and Ramakrishnan 2015; Moinester 2018; Williamson 2018).

1. BACKGROUND

Although immigration law is federal, local law enforcement agencies have become entangled in immigration enforcement through a variety of federal-local arrangements.⁴ We briefly describe a number of key programs that exemplify these arrangements to situate the use of local jails for immigration detention within the broader legal and policy landscape of interlocked immigration enforcement and criminal justice.

1.1 Federal-Local Cooperation on Immigration Enforcement

The Criminal Alien Program (CAP) has been referred to as a “jail check” program with predecessor programs that date back to 1988 (see Cantor et al. 2015; Macías-Rojas 2016). Under CAP, federal immigration officers screen inmates in federal, state, and local prisons and jails to identify noncitizens who may be removable. After the screening and interview process, CAP officers may issue a request for notification to state or local law enforcement agencies asking to be notified prior to the identified individual's release from the facility so that ICE may assume custody. CAP officers may also issue an “immigration detainer” to have the local facility hold the noncitizen for an additional 48 hours beyond his or her scheduled release date, which facilitates the custody transfer process (see Kandel 2016).

Another program known as Secure Communities moved CAP to a universal and automated screening system. Secure Communities achieved this goal by electronically linking local jails to databases maintained by the Department of Homeland Security (DHS) and the Federal Bureau of Investigations (FBI), respectively. When a state or local law enforcement agency arrests and books individuals, the agency sends their fingerprints to the FBI to check against FBI's criminal databases. Secure Communities requires the FBI to send those fingerprints to the DHS to be compared against DHS' biometric identification database, which contains information on removable and potentially removable noncitizens (see Miles and Cox 2014). Secure Communities began its rollout in 2008 and was activated across all counties in the United States in 2013. However, the Obama administration terminated the program in 2014 and replaced it with the Priority Enforcement Program, which set out enforcement priorities that focused on the removal of serious criminal offenders and recent border crossers (U.S. DHS 2016). According to the Migration Policy Institute, the 2014 enforcement priorities effectively protected 87 percent of unauthorized immigrants nationwide from removal (Capps et al. 2018). In 2017, President Trump reactivated Secure Communities through an executive order.

Another key federal-local arrangement that implicates local jails in immigration enforcement is known as the 287(g) program. The program name refers to section 287(g) of the Immigration and Nationality Act. Congress enacted section 287(g) to allow federal immigration enforcement to directly delegate certain immigration enforcement functions to state and local government agencies (U.S. DHS 2010). The law requires that this delegation be executed through a Memorandum of Agreement (MOA) between ICE and the local law enforcement agency. A local jurisdiction that has entered into a 287(g) agreement may employ a jail enforcement model, a task force model, or a combination of the two.

Under the jail enforcement model, deputized local law enforcement officers may work in state and local correctional facilities to identify and process charged or convicted individuals for removal. Under the task force model, deputized local law enforcement officers identify and process removable aliens in community settings during the course of their regular duties as patrol officers, detectives, or criminal investigators (U.S. DHS 2010: 20). The 287(g) program was curtailed during the Obama administration in response to concerns over inconsistent practices and racial profiling, but the program has been revitalized under the Trump administration (Rosenberg and Levinson 2017).

1.2 Intergovernmental Service Agreements

There is yet another way that local law enforcement agencies have become deeply intertwined with federal immigration enforcement. Under the Immigration and Nationality Act (8 U.S.C. § 1103(a)(11)(A)), the DHS has authority to contract with state or local government agencies “for necessary clothing, medical care, necessary guard hire, and the housing, care, and security of persons detained by [ICE]….” Over the years, a substantial number of local jurisdictions have entered into contracts with ICE to confine individuals who have been apprehended by immigration authorities.

According to a government report, in fiscal year 2016, ICE owned and operated only five out of hundreds of detention facilities used to hold immigrant detainees (Office of Inspector General 2018). ICE secures the remainder by contracting with private prison companies, the U.S. Marshal Service, or state and local governments. These agreements with state and local governments, known as Intergovernmental Service Agreements (IGSAs), require ICE to pay the local government a per diem for each bed space that it rents out. Some local governments further contract with private prison companies to operate their jails.⁵ The IGSAs have come under scrutiny and criticism for allowing ICE to avoid the public bidding process and to operate without standard operating procedures required of other federal awards (Office of Inspector General 2018: 5).

As shown in Figure 1, our analysis of the data on immigration detention released by ICE in November of 2017 (see National Immigrant Justice Center 2017) suggests that the estimated average daily population (ADP) of detainees in local jails started to increase in fiscal year 2010 before beginning to decline in fiscal year 2012. Figure 1 also illustrates a sharp downturn between fiscal years 2014 and 2015 when arrests and removals declined substantially with implementation of the Obama administration's new enforcement priorities in 2014 (see Capps et al. 2018: 4). However, Figure 1 shows a rapid uptick after fiscal year 2015. Given that the data do not allow us to identify local jails with precision, Figure 1 represents only a rough approximation—and an underestimate—of the actual ADP of detainees in local jails.⁶ Nonetheless, the general trend depicted in Figure 1 underscores the continuing and renewed importance of local jails in maintaining the immigration detention system as it exists today.

Figure 1. Estimated Total ADP of ICE Detainees in Jails in the United States, Fiscal Years 2009–2018. Source: Authors' calculations using detention data obtained by the Immigrant Legal Resource Center from ICE (National Immigrant Justice Center 2017). Note: A fiscal year runs from October 1st of a given year to September 30th of the next year. Fiscal year 2018 estimate is based on partial-year data provided by ICE that ended on November 6, 2017.

What factors predict whether a county will hold immigrant detainees in its local jails? We turn to the longstanding literature on carceral expansion in the United States to develop a theoretical framework for addressing this question.

2. THEORETICAL FRAMEWORK

Between 1980 and 2013, both the prison and jail incarceration rates in the United States increased dramatically, and the United States became the nation with the highest incarceration rate in the world (Lofstrom and Raphael 2016). This growth has generated rich and voluminous research on the causes and consequences of carceral expansion in the United States (see, e.g., Morenoff and Harding 2014; Travis et al. 2014; Western and Muller 2013). We do not know whether the subnational dynamics that shape criminal incarceration apply equally to, or operate in the same way, for local law enforcement agencies' involvement in immigration detention.⁷ However, given the scarcity of empirical research on temporal and spatial variations in immigration detention, criminal incarceration research serves as a useful starting point and a baseline for developing a conceptual framework for our analysis.

Our study draws on two closely related bodies of research on carceral expansion. The first set of studies on carceral expansion examines the increases in incarcerated populations (see Kang-Brown et al. 2018 for varying metrics of incarcerated population). These studies analyze, for example, predictors of prison admission rates or incarceration rates. The second set of studies on carceral expansion examines increases in the construction of prison facilities throughout the United States. These studies focus on understanding the causes and consequences of prison openings, sitings, or placement (see, e.g., Eason 2017; King et al. 2004).

A key insight from this research is that trends in carceral expansion vary greatly across states and localities. We narrow the focus of our discussion here to studies that examine correlates of carceral expansion at the state or local level. Given that incarceration rates climbed and states continued to build prisons even as crime rates began to decline in the 1990s (Travis et al. 2014), scholars have looked beyond crime rates to explore other possible explanations of carceral expansion.⁸ In particular, a large body of research has focused on understanding the effect of economic, racial, and political factors on incarceration. We discuss each of these perspectives and apply them to develop our empirical expectations about the predictors of immigration detention in local jails.

2.1 Economic Perspective

Studies show that incarceration and prison facilities are concentrated in economically disadvantaged communities (Sampson and Loeffler 2010; Thorpe 2015). Much research has been devoted to understanding how economic conditions might be linked to incarceration beyond their contributions to criminal offending. Under one prominent strand of social control theory, incarceration is the dominant group's way of controlling the poor or “surplus labor” (for a review, see Western et al. 2006). Accordingly, many studies on the economic predictors of incarceration have examined the effects of unemployment (and other related measures such as poverty, wages, and economic inequality). Much of this research has focused on the relationship between labor market conditions in urban areas and mass incarceration. More recently, however, a growing number of scholars have advanced compelling theoretical and empirical reasons to explore this relationship in nonmetropolitan settings as well (Eason et al. 2017; Simes 2018).

Prison siting studies have also examined how economic factors shape prison sitings. The “demand” for prisons in small, rural towns may be fueled by the assumption that prisons can revitalize local economies and promote job growth (Lobao and Adua 2011; Mauer 2006; Schlosser 1998). For example, according to Mona Lynch's (2009: 133) case study of prison siting decisions in Arizona, in the eyes of local officials, “prisons have foremost become economic development opportunities in both the private and public sectors.” Consistent with this account, studies find that prisons built since the 1970s are disproportionately concentrated in rural, economically disadvantaged areas (Eason 2010, 2017). By contrast, findings are mixed on the question of whether prison building does in fact produce economic benefits to the host communities (Eason 2017; Glasmeier and Farrigan 2007; Hooks et al. 2010; Hoyman and Weinberg 2006).

Economic factors could play a role in county decisions to hold ICE detainees in two ways. First, pervasive in media reports are accounts of small towns in financial trouble looking to revitalize their local economy by signing contracts with the federal government to hold ICE detainees. The following report about counties in Arizona succinctly summarizes this common narrative: “Rural towns and counties have eagerly embraced the arrival of immigrant prisoners for the attendant economic benefits, including tax revenues and jobs…. The expanding prison populations have allowed small towns to carry budget surpluses in a state that has otherwise been pummeled by the recession” (Kirkham 2017). Such reports are neither new nor limited to communities in Arizona; rather, they date back at least a decade and focus on many different localities experiencing economic downturns or financial distress (see, e.g., Bernstein 2008).

Second, economic incentives to contract with ICE may not be sufficient to participate in immigration detention for counties that lack the basic infrastructure or bed space. At least in some jurisdictions, criminal justice reforms intended to reduce the incarcerated population had the effect of reducing both the prison and jail populations (Garland et al. 2014). Media accounts have noted that the opening up of jail bed spaces produced new pressures or opportunities that enabled struggling counties to become involved in immigration detention (Associated Press 2017; Pickoff-White and Small 2017). Immigrant detainees could function as a reserve supply of confinable people for struggling facilities looking to fill their jail space.

Taken together, the foregoing discussion suggests that worsening local economic conditions in times of growing bed space in local jails will increase the likelihood that counties participate in immigration detention. We evaluate this expectation in our regression analysis by examining the interaction effect between county unemployment rates and measures of unused or “excess” bed space associated with the criminal jail population.

2.2 Race and Politics Perspective

Another stream of criminal incarceration research investigates the importance of racial dynamics in explaining carceral expansion. John Eason (2010), in his study of rural towns, finds that towns with higher percentages of Black and Hispanic populations are more likely to receive a new prison. Other studies suggest that relative size of a community's Black population predicts incarceration rates (Campbell et al. 2015; Smith 2004). The racial threat hypothesis offers one possible explanation for the relationship between the size of the minority population and incarceration rate.

The central claim of the racial threat hypothesis is that whites view growing minority populations as a threat to their political and cultural dominance, which in turns leads to the community's increasing reliance on coercive social control mechanisms, including criminal punishment and incarceration (Blalock 1967). The relationship between minority population size and criminal incarceration may not be linear, however. Keen and Jacobs (2009) show that after the relative size of the minority population reaches a critical threshold, the positive association between minority presence and incarceration reverses. Increasing minority population from already-high levels affords the minority group enough political and economic clout to weaken punitive policies that affect them disproportionately (see also Arvanites 2014).

Political factors are distinct, but related, predictors of incarceration. Republicans have traditionally campaigned on law-and-order platforms, which generally favor criminal justice policies that lengthen sentences and expand prison capacity. Likewise, Republican voters are generally more likely to support punitive responses to crime. Accordingly, most studies find that measures of Republican Party strength in communities are significantly and positively associated with those communities' prison incarceration or admission rates (Beckett and Western 2001; Jacobs and Carmichael 2001; Jacobs and Helms 1996; Smith 2004). Other scholars have argued that the effects of Republican Party strength on incarceration vary over time and by area of the country (Campbell et al. 2015).

Studies tend to investigate racial dynamics alongside political ideology on the theory that incarceration is driven by politics that racialize crime and target minorities for punishment. Keen and Jacobs (2009), for example, explore the hypothesis that African Americans should face higher incarceration rates than whites in areas where Republican attempts to link crime with concerns about a “dangerous racial underclass” have been successful.

Although an emerging empirical literature has begun to explore the nature and consequences of immigration detention (see, e.g., Eagly et al. 2018; Ryo and Peacock 2018, 2019), no empirical study to date has examined how racial and political dynamics might be affecting decisions of local officials to participate in immigration detention. Local officials are responsible for decisions about whether to sign an IGSA and whether to continue confining ICE detainees in local jails. These local officials are likely influenced by antiimmigrant or proimmigrant sentiments that prevail in their communities and among their constituents.

More concretely, we might expect that the greater the anti-immigrant sentiment, the greater a community's willingness to contract with ICE. Further, studies show that anti-immigrant sentiments are more prevalent in places undergoing influxes of immigrants that trigger heightened perceptions of threat, and when salient political rhetoric and nativism reinforce that sense of threat (Campbell et al. 2006; Hopkins 2010). Most relevant to the current study, Creek and Yoder (2012) find that a growing Latino population and Republican governorship are significant and positive predictors of whether a state signs a 287(g) agreement (see also Wong 2012). Moreover, Provine et al. (2016) find in their study of local law enforcement agencies that a key predictor of agency practices relating to immigrants is the political ideology of local voters. These findings reflect the increasing political polarization on immigration issues, with Democrats generally favoring integrationist approaches and Republicans favoring restrictionist approaches (Hajnal and Rivera 2014).⁹

We expect that increases in the county-level Latino population (up to a certain level) and Republican Party strength will increase the likelihood of counties participating in immigration detention. We focus on the Latino population, as existing research suggests that growth and geographic dispersion of the Latino population played a key role in prompting militarized and restrictive enforcement measures in the United States (see, e.g., Massey and Pren 2012). The majority of the population at risk of immigration apprehension, detention, and removal are Mexicans and Central Americans. For example, in 2001, the first year for which the Yearbook of Immigration Statistics published information on immigration detention by nationality, individuals from Mexico constituted 50 percent of the detainee population, followed by individuals from Guatemala, Honduras, and El Salvador (together, the Northern Triangle region, 16 percent) (U.S. Department of Justice 2003: 235). In 2017, 47 percent of the detainee population originated from the Northern Triangle region, followed by Mexico at 38 percent (U.S. DHS 2019: 9).

3. DATA AND METHODS

3.1 Data

This study draws on several major datasets. The primary data set, the IOB Data compiled by the Vera Institute of Justice, contains county-level information on prison and jail populations over time across the United States. The IOB Data also contain county-level demographic variables such as the county population size, race, and gender of the resident population, and whether a county is urban or rural. As detailed documentation on the IOB Data is available elsewhere (Vera Institute of Justice 2018), we focus our description only on those components of the data directly relevant to the current study.

The IOB Data rely on a number of different sources, including the BJS Census of Jails and the BJS Annual Survey of Jails. The Census of Jails is conducted every five to eight years and contains information on all jails in the United States. The Annual Survey of Jails, fielded almost every year since 1985 except for the census years, is used to estimate the annual total U.S. jail population. The Annual Survey of Jails contains information on approximately one-third of all jails across the country, including nearly all the largest jails and a representative sample of hundreds of others. Together, these two sources aim to provide a consistent annual series of the total number of individuals incarcerated in local jails. Over 3000 counties or county equivalents are included in the IOB Data.¹⁰

We compiled and merged into the IOB Data two data sets that relate to major federal-local immigration enforcement partnerships. First, we collected and hand-coded information on whether and when a county entered into a 287(g) agreement with ICE (287g Data). We relied on two primary sources in compiling the 287g Data: (1) archived ICE web pages that show individual 287(g) agreements across all local jurisdictions in the United States, and (2) a list that ICE produced under the Freedom of Information Act on “all 287(g) memoranda of agreements/understanding (MOA) established with ICE since the program was established.”¹¹ Our 287g Data contain local jurisdiction names, as well as the start and end dates, if applicable, of individual MOAs signed. Second, we compiled information on Secure Communities (Secure Communities Data) from the ICE “FOIA Library” available on ICE's website (U.S. DHS 2014). The Secure Communities Data contain information on the specific date on which Secure Communities was activated in each county in the United States.

The analysis draws on three additional data sources. First, we used the Bureau of Labor Statistics (BLS) Local Area Unemployment Statistics (https://www.bls.gov/lau/) to capture annual average percent unemployed for each county (BLS Data). The BLS makes the data available on its website for the years 1990 through 2017 (BLS 2018).¹² Second, we used data from the Voting and Elections Collection (CQ Press 2018) to create measures of political partisanship (Election Data). The Election Data contain county-level voting results on presidential elections held between 1980 and 2016. Third, to complete the missing information on the crime variables found in the IOB Data (specifically, the IOB Data are missing the years 1993 and 2015 on the crime variables), we used the Uniform Crime Reporting data (UCR Data) found on the Inter-university Consortium for Political and Social Science database (Kaplan 2018).

The data for our regression analysis contain 29,941 county years pertaining to 1252 unique counties.¹³ A total of 33 counties had missing data on one or more predictor variables. Because these counties comprised less than 1 percent of all counties, we excluded them using listwise deletion.

3.2 Measures

Table A1 contains detailed descriptions of all of the measures that we discuss below. Our descriptive analysis focuses on Counties with ICE Detainees, which is the total number of counties that held ICE detainees in local jails in any given year. We examine these annual total counts by a number of basic, and relatively stable, demographic, geographic, and political characteristics at the county level. Region indicates the area of the country where the counties are located: (1) Midwest, (2) Northeast, (3) South, and (4) West. Population Size is the total number of county residents (of all ages). Urbanicity distinguishes the counties along an urban–rural spectrum and has four categories: (1) urban, (2) suburban, (3) medium and small metro, and (4) rural (for details on the urban–rural classification scheme, see Kang-Brown and Subramanian 2017: 8).

Political Partisanship captures the counties' voting results in presidential elections for the time period included in our descriptive analysis, 1983–2013. During this time period, some counties remained Republican strongholds, others remained Democrat strongholds, and yet others shifted between Republican and Democrat. To capture these variations categorically in a parsimonious way, we constructed Political Partisanship by first dividing the time period into two groups (1983–1997 and 1998–2013) using 1997 as the midpoint.¹⁴ For any given county, if the plurality vote was Republican (Democrat) for the majority of the years in a given time period, we classified that county as Republican (Democrat) for that time period. Using this coding process, we classified each county into the following four categories: (1) Democrat–Democrat, (2) Democrat–Republican, (3) Republican–Democrat, and (4) Republican–Republican.

Our regression analysis uses the following time-varying measures. The dependent variable, Held ICE Detainees, indicates whether a county held any ICE detainee in any given year. Our regression analysis includes the following key predictor variables. Unemployment Rate is the percent unemployed in a given county's labor force. Excess Bed Space is a measure of unused bed space for jail, pretrial, and other jail populations, respectively. As coded in the IOB Data, these three populations do not overlap and are defined as follows: Jail population refers to convicted criminal offenders serving time in local jails. Pretrial population refers to criminal defendants held pretrial in local jails. Other jail population refers to individuals held in local jails for all other federal, state or other local law enforcement and corrections agencies excluding ICE.

For each of these jail populations, we separately measured the annual total number of unused or “excess” bed space in each county, z _it, by applying the following formula:

(1) $z_{\mathrm{it}}=1-\left(\frac{x_{\mathrm{it}}-\min \left(x_i\right)}{\max \left(x_i\right)-\min \left(x_i\right)}\right),$

where x _it is ADP (of jail population, pretrial population, and other jail population, respectively)¹⁵ of county i at time t; min(x _i) and max(x _i) are the observed minimum and maximum ADPs, respectively, across all years within county i. Subtracting the observed minimum ADP from a given year's ADP and dividing the result by the difference between the observed maximum ADP and the observed minimum ADP normalizes the ADP values to a range of 0 and 1. Subtracting these normalized values from 1 produces an estimate of how many bed spaces could have been filled by the jail population, pretrial population, and other jail population, respectively, but were not. Each of the three Excess Bed Space variables associated with each type of jail population is thus a relative measure (relative to the observed maximum within each county) of unused bed space.¹⁶

Percent Latino is the percent of the county population aged between 15 and 64 that is Latino. Following previous studies on racial threat, we include a quadratic term for the Latino population in our regression model to test whether the effect of Latino population on counties participating in immigration detention is curvilinear; if so, the Percent Latino measure should have a significant positive effect, whereas the quadratic term should have a significant negative effect.¹⁷

Republican indicates whether the plurality vote of a given county was Republican in any given presidential election. This coding stays constant for all intervening years between presidential elections.

We use the following control variables. Secure Communities indicates whether Secure Communities was activated in a given county for any given year. To be coded as 1 on Secure Communities, the program had to have been active in the community for at least one day during a given year. Active 287g indicates whether a given county had an active 287(g) agreement with ICE during a given year. We coded this variable as 1 if the county had an active 287(g) agreement for at least one day during a given year. If only a city within a county had an active 287(g) agreement, we did not code the county in which the city is located as having an active 287(g) agreement, as our unit of analysis is county years.

In addition, our regression models also include two key crime-related control variables. Violent Crime Rate is the total number of violent offenses per 1000 county residents between the ages of 15 and 64. Property Crime Rate is the total number of property offenses per 1000 county residents between the ages of 15 and 64.

3.3 Analytical Strategy

Our analysis unfolds in two stages. First, to understand temporal and spatial patterns of detention proliferation, we consider changes over time (1983–2013) in the total number of counties across the United States that held immigrant detainees in their local jails. We conduct this analysis descriptively by examining a series of graphs that show temporal changes by a key set of basic county characteristics. For this descriptive analysis, we focused on county characteristics that are more or less constant over time in order to ensure the relative stability of counties' membership in various categories shown in each of the graphs.

Second, to identify the factors associated with the likelihood of a county holding immigrant detainees in its local jails, we conduct a series of logistic regressions using conditional maximum likelihood. We examine the effects of various independent variables of interest on the log-odds of counties holding immigrant detainees using year and county fixed-effects models. Fixed-effects models cannot estimate the effect of predictors that are constant over time. However, fixed-effects models allow us to address omitted variable bias. Specifically, our two-way fixed-effects models control for (1) any changes over time that impact all counties, and (2) any observed and unobserved time-invariant heterogeneity (e.g., region, rural–urban distinction) between counties. For example, the year-specific fixed effect absorbs any changes in law, policies, or political environment at the national level that might affect the likelihood of counties getting involved in immigration detention. Additionally, our county-specific fixed effect absorbs all stable observable or unobservable differences across counties not represented by the variables in our model.

As a robustness check, we reestimated our fixed-effects models using lagged covariates (lagged by one year in relation to the dependent variable) to test the possibility that the relationships at issue operate on longer time gaps than what we have estimated. The results were substantively and statistically similar to the results obtained by models that do not use lagged variables, as shown in Table A2. Note that fixed-effects logistic regression analysis uses only within-county variation. For counties that did not hold any ICE detainees and counties that always held ICE detainees during our analysis period, there is no within-county variation in the outcome variable, and the fixed-effects logistic regression drops these counties. We replicated our logistic regression analysis that specifies random intercepts for counties (with year fixed effects), which allows us to include these dropped counties, and we found results that are similar to the results from the fixed-effects models. The results of the random-effects models are shown in Table A2. Our discussion focuses on the fixed-effects models, however, as Hausman tests comparing the fixed-effects models against the random-effects models favored fixed-effects models.¹⁸

Our analyses focus on the period 1983–2013 because this is the period for which necessary data are available. Because the IOB Data contain information on ICE detainees starting in 1983, our descriptive analysis begins with 1983. However, our regression analysis begins with the year 1990 because measurement on one of our key independent variables—the size of the Latino population at the county level—is available beginning only in 1990. The period of observation ends in 2013 because this is the most recent year for which the BJS Census of Jails—the census that covers all counties, as discussed above—has released its data.¹⁹

We do not analyze changes in the number of ICE detainees as an outcome variable in this study. The measure of ICE detainee population size, ADP, found in the IOB data is distinct from the number of detainees admitted into facilities in any given year. It is also distinct from the total number of detainees held in facilities in any given year. As noted earlier, ADP represents the sum of daily populations across all local jails in a given county, divided by 365 days. This means that changes in the ADP of ICE detainees in any given county reflect some combination of changes in the number of new admissions and changes in the composition of detainees who continued to be detained from the previous year. Insofar as these compositions change over time within any given county, it is difficult to reliably estimate the relationship between various predictors of interest and annual changes in the detainee population level using the ADP.

Additional challenges unique to the immigration detention context further complicate the use of ADP as an outcome variable. To make the ADP comparable across counties, it needs to be standardized by some underlying rate of individuals “at risk” of detention. However, we cannot reliably measure the relevant “at risk” population (e.g., noncitizens subject to mandatory detention, undocumented immigrants) because these types of data are not available, particularly at the county level for the range of years that we examine. Moreover, because many ICE detainees experience interfacility transfers during their detention stay, the population “at risk” of detention who reside in a given county ultimately may not be detained in that county.

4. RESULTS

4.1 Descriptive Analysis

Our descriptive analysis maps changes—over time and across various county characteristics—in the total number of counties that held immigrant detainees in their local jails. Figure 2 shows the total number of county jails holding immigrant detainees between 1983 and 2013. Figure 2 reveals an increasing number of counties over time, steadily rising from 128 counties in 1983 to 727 counties in 2013—nearly a sixfold increase.

Figure 2. Total Number of Counties with ICE Detainees by Year, 1983–2013. [Color figure can be viewed at wileyonlinelibrary.com] Source: IOB Data. Note: The line corresponds to a linear prediction plot. The years are calendar years.

The most substantial rise occurred in the South, followed by the Midwest and the West, as Figure 3 illustrates. Compared to these other regions, the Northeast remained relatively flat over the years. Within regions, a large share of the growth was concentrated within a relatively small number of states. By 2013, approximately half of all counties holding ICE detainees were in just ten states: Texas (107 counties holding ICE detainees), Georgia (45), North Carolina (35), California (34), Florida (28), Kentucky (25), Virginia (24), Wisconsin (22), Indiana (22), and Colorado (20). While states like Texas, California, and Florida have been longstanding destinations for international migrants, many of the states with high incidence of counties holding ICE detainees represent the so-called “new destinations” without the same tradition of immigration (see, e.g., Marrow 2005).

Figure 3. Total Number of Counties with ICE Detainees by Region, 1983–2013. [Color figure can be viewed at wileyonlinelibrary.com] Source: IOB Data. Note: The line corresponds to a linear prediction plot. The years are calendar years.

Figure 4 shows that the most notable increase in the number of counties participating in immigration detention took place in small counties with population sizes between 10,000 and 249,999. By contrast, the smallest counties (less than 10,000) and the largest counties (1 million or more) experienced the least growth. Mapping the trend by urban–rural spectrum provides us with an additional perspective. Figure 5 shows that while the number of counties holding immigrant detainees remained relatively flat in urban areas, rural, and medium/small metro areas experienced a steady growth, followed by suburban areas. These trends mirror the findings from research on criminal incarceration documenting the expanding use of jails in small counties (defined as counties with less than 250,000 people) and rural areas (nonmetropolitan areas remote from major population centers) since the 1970s (Kang-Brown and Subramanian 2017).

Figure 4. Total Number of Counties with ICE Detainees by Population Size, 1983–2013. [Color figure can be viewed at wileyonlinelibrary.com] Source: IOB Data. Note: The line corresponds to a linear prediction plot. The years are calendar years. For the population size categories, we adopted the National Center for Health Statistics' classification scheme (National Center for Health Statistics 2014).

Figure 5. Total Number of Counties with ICE Detainees by Urbanicity, 1983–2013. [Color figure can be viewed at wileyonlinelibrary.com] Source: IOB Data. Note: The line corresponds to a linear prediction plot. The years are calendar years. For details on how the urbanicity categories are coded, see Kang-Brown and Subramanian (2017).

Finally, we examine the relationship between political partisanship and involvement of counties in immigration detention. As discussed earlier, we divided the years between 1983 and 2013 into two periods, with 1997 as the midpoint. We then identified each county's modal plurality vote (Democrat or Republican) in presidential elections across the pre-1997 years, and across the years 1997 and after, respectively. Our coding produced four categories of Democrat–Democrat, Democrat–Republican, Republican–Democrat, and Republican–Republican. Republican–Republican counties, for example, are counties that voted Republican between 1983 1997 as well as between 1997 and 2013. Figure 6 shows a steep increase in the number of counties participating in immigration detention in the Republican–Republican category.

Figure 6. Total Number of Counties with ICE Detainees by Political Partisanship, 1983–2013. [Color figure can be viewed at wileyonlinelibrary.com] Source: IOB Data and Election Data. Note: The line represents a linear prediction plot. The years are calendar years. Political partisanship is coded based on voting results from presidential elections. For details on how the political partisanship categories are coded, see Table A1.

In sum, the analyses show a steady rise in the number of counties engaged in immigration detention in the past three decades. Localities that became involved in immigration detention during this period are not randomly placed. Rather, the most notable growth occurred in relatively small, nonurban areas that are largely Republican and located in the South.

4.2 Multivariate Analysis

Our two-way fixed-effects logistic regression analysis investigates the predictors of counties holding ICE detainees in local jails. Table 1 presents basic descriptive statistics on the variables used in our logistic regression models. The results of the regression analysis are presented in Table 2. A positive (negative) coefficient indicates that an increase in a given predictor variable x increases (decreases) the log-odds of counties holding ICE detainees. We interpret the magnitude of the estimated coefficients by converting them to odds ratios, which represent the estimated factor by which an independent variable predicts the odds of a given outcome. We do not present marginal effects estimates because doing so would assume that all time-invariant county-level effects are equal to zero (see Norton and Dowd 2018: 875).

Table 1. Means/Proportions for Variables Used in Fixed-Effects Logistic Regression, 1990–2013

N = 29,941 county-years pertaining to 1252 unique counties.

Table 2. Fixed-Effects Logistic Regression Results Predicting Counties Holding ICE Detainees

Note: N = 29,941 county-years. All models include year and county fixed effects. Standard errors in parentheses.

^*p < .05; ^**p < .01; ^***p < .001 (two-tailed tests).

Because Violent Crime Rate and Property Crime Rate are highly correlated (r = .689), we first estimated the models using Violent Crime Rate. We then replaced Violent Crime Rate with Property Crime Rate in the same set of models. Our findings are robust to this alternative specification. To save space, we present the results from the models containing Violent Crime Rate (alternative model results are available upon request). The regression models are nested to first include only the key predictor variables of interest (baseline models), to which we add the control variables discussed earlier (full models). Given that the inclusion of these control variables to the baseline models (Models 1a, 2a, and 3a) leaves the estimated effects of the key predictor variables unchanged, our discussion focuses on the full model results (Models 1b, 2b, and 3b).

Model 1b includes Excess Bed Space—Jail Population, the measure of unused bed space for the criminal jail population, and its interaction with Unemployment Rate, along with Percent Latino, Percent Latino Squared, and Republican. The interaction term indicates that the positive effect of increasing Unemployment Rate on the likelihood of holding ICE detainees is greater when values of Excess Bed Space are high. This suggests that excess bed space positively moderates the relationship between local unemployment rate and the likelihood of counties holding ICE detainees. Akaike information criterion, Bayesian information criteria, and likelihood ratio test statistics consistently indicate that the model with the interaction term, compared to an alternate model without the interaction term, is a better fitting model.

Model 1b also shows that each percent increase in Latino population within a county predicts a 29 percent increase ((exp [0.255] – 1) x 100) in the odds of that county holding ICE detainees. However, the negative sign of the quadratic term for Percent Latino indicates that this relationship attenuates as Percent Latino increases. Finally, Model 1b shows that political partisanship of voters at the county level is a significant predictor. Specifically, changes in voting results that favor a Democrat candidate in one presidential election to a Republican candidate in the next presidential election increase the odds of counties holding ICE detainees by about 50 percent ((exp [0.401] – 1) x 100). Participation in the Secure Communities and 287(g) program increase the odds of counties holding ICE detainees, suggesting that immigration detention at the local level may be part and parcel of broader local-federal cooperative arrangements on immigration enforcement more generally.

Models 2a and 2b, respectively, are identical to Models 1a and 1b, except that the former set of models replace Excess Bed Space—Jail Population with Excess Bed Space—Pretrial Population. Similar to Model 1b, Model 2b shows that the interaction between Excess Bed Space—Pretrial Population and Unemployment Rate is positive and significant. Likewise, the results for Percent Latino, Percent Latino Squared, and Republican, are generally the same as in Model 1b. Similar to what we found in Models 1a and 2b, a variety of model fit tests indicate that the model with the interaction term is a better fitting model than the one without the interaction term.

Finally, Models 3a and 3b, respectively, are identical to Models 1a and 1b, except that the former set of models replace Excess Bed Space—Jail Population with Excess Bed Space—Other Population. Unlike in Models 1b and 2b, Model 3b shows that the interaction between Excess Bed Space—Other Population and Unemployment Rates is not statistically distinct from zero (p < .05). However, the results for Percent Latino, Percent Latino Squared, and Republican, are generally the same as those that we obtained in Models 1b and 2b.

Our regression analysis produced a notable set of patterns controlling for county fixed effects, year fixed effects, local policies related to immigration enforcement, and local crime rates. First, increases in excess bed space for the jail and pretrial populations appear to amplify the positive effect of unemployment on the likelihood of counties participating in immigration detention. Second, an increase in the Latino population consistently predicts an increase in the probability of counties participating in immigration detention, though this relationship attenuates as the Latino population continues to rise. Third, Republican Party strength in presidential elections also predicts a higher likelihood of participating in immigration detention. We conclude by considering the implications of these findings below.

5. DISCUSSION AND CONCLUSIONS

This study advances research on immigration detention and criminal incarceration in a number of ways. Our regression analysis indicates that economic factors play an important role in local involvement in immigration detention, a finding that is generally consistent with prevailing popular accounts of why local jurisdictions engage in immigration detention. One report, for example, cites a Washington County commissioner in Oregon, who justified the county's decision to contract with ICE in this way: “We're not doing anything but providing a bed for them [ICE detainees] in the night and meals…. It's renting a bed, like a motel room” (Wilson 2018).

Our findings, however, also underscore the importance of considering economic factors in tandem with changes in the relative size of the local criminal inmate population to fully understand the emergence of immigration detention in local jails. More specifically, our regression analysis results suggest that county labor market conditions, together with relative fluctuations in the local criminal inmate population, may generate a policy environment that is particularly conducive to immigration detention.²⁰ Thus the fate of criminal mass incarceration and civil immigration detention may be tied in ways that pose fundamental challenges to the decarceration movement aimed at reducing the correctional population.

From the standpoint of counties, whether immigration detention is indeed an economic boon for them in the short and long term are open questions with important policy implications. The idea that prisons have become a profit-seeking economic enterprise has generated wide-ranging discussion of ethical and policy issues related to prison labor and privatization of prisons (Dolovich 2005; Zatz 2009). Our findings suggest that this conversation should be extended to include immigration detention as another important mechanism through which local penal institutions are becoming commodified and contributing to those institutions' legitimacy crises.

Our analysis also shows that race and politics are significant predictors of county involvement in immigration detention. Our findings are consistent with research that shows that political partisanship plays an important role in immigration policymaking. Wong (2017: 212), for example, finds in his study of congressional voting on immigration-related legislation that Republican representatives are 3.7 times more likely than Democrat representatives to vote for restrictive immigration-related legislation. Likewise, Farris and Holman (2017) find that immigration attitudes and political ideology of county sheriffs—officials who are accountable to local voters through elections—are predictive of county sheriffs' willingness to cooperate with federal immigration enforcement.²¹

In the current climate of increasingly polarized immigration politics, local debates over immigration detention present opportunities for community members and elected county officials to make important political statements. For example, as one Sacramento County supervisor declared in voting to end Sacramento's contract with ICE: “For me, it came down to an administration that is extremely hostile to immigrants. I didn't feel we should be part of that” (Romero 2018). But do proimmigrant attitudes and local immigrant rights activism that motivate cancelation of ICE contracts in certain counties result in transfers of detainees to local jurisdictions that are more remote and hostile to immigrant rights? This remains an important question for local communities and the future of antidetention movement.

While we have examined the significance of race and politics as a predictor of county involvement in immigration detention, also worth considering are potential ways that immigration detention in local jails might aggravate perceptions of racial threat and deepen existing partisan divides. In 2016, the Homeland Security Advisory Council recommended that ICE reduce its reliance on detention in county jails given that county jails are the “most problematic facilities for immigration detention.” The Advisory Council explained (2016: 7–8):

Because most [jails] are mixed-use facilities primarily handling county detainees in the criminal-justice process, such facilities often will not accept the full range of detailed detention standards that ICE has developed…. Moreover, the officials operating such county facilities can be resistant to changes in their practices in response to identified problems, in part because they do not wish to have sharp differences in treatment for different categories of detainees (ICE vs. local) held at the same facility.

Does the placement of immigrant detainees in county jails that render immigrant detainees indistinguishable from the inmate population harden punitive attitudes toward immigrants on the part of correctional officers, public officials, or the public at large? Do people's assumptions about the criminality of immigrants strengthen as the immigrant detainee population in local jails grows? If so, local jails might be serving not only an important instrumental role in federal immigration enforcement, but also a critical symbolic function that further polarizes racial and political divides across the United States.

We conclude by highlighting a number of other pressing issues that cannot be fully understood without considering the role of place and local context. Existing research shows that the presence of legal service providers and social support networks for immigrants in communities in which detention facilities are located predicts shorter detention length, controlling for a variety of individual characteristics and contextual factors (Ryo and Peacock 2019). Insofar as the type of counties that have become increasingly involved in immigration detention is also the type that might lack legal service providers and social support infrastructures for immigrants, our findings have significant policy implications for the protection of due process rights of detainees. Thus, an important next step for future research is a systematic investigation of whether and to what extent the counties participating in immigration detention differ from the nonparticipating counties along these and related dimensions.

Future research should also examine whether and how a county's decision to participate in immigration detention might spread or diffuse to its neighboring counties. Shipan and Volden (2008) suggest at least four mechanisms through which such a diffusion might occur: learning from prior adopters, economic competition, imitation, and coercion from higher levels of government. Diffusion might also occur through partisan actors or issue entrepreneurs who promote the policy in jurisdictions where it is politically feasible (see, e.g., Gulasekaram and Ramakrishnan 2015), or through social movements (Steil and Vasi 2014). Yet another mechanism of diffusion might involve public official associations that implicitly or explicitly promote the adoption of certain policies or practices. Adjudicating between these various diffusion mechanisms will require careful theorizing and innovative empirical strategies. Such a project promises to yield important insights into whether federal-local cooperation on immigration detention will persist in its current form, undergo innovation and transformation, or eventually disappear over time and across local jurisdictions.

Finally, there is a critical need to drill down even further—beyond counties—to consider yet more disaggregated and immediate community contexts in which immigration detention is situated. Scholars of criminal incarceration have long appreciated the centrality of neighborhood contexts and city-level characteristics in understanding punishment outcomes (Morenoff and Harding 2014; Sampson and Loeffler 2010; Simes 2018). Likewise, there is a growing recognition of the importance of cities in immigration regulation and integration (Varsanyi et al. 2011; Williamson 2018). By contrast, micro-unit analysis is conspicuously lacking in research on immigration detention due to persistent and pervasive problems of data scarcity. Investigations that overcome such challenges to attend to city-level economic, racial, and political dynamics will generate more nuanced understandings about temporal and spatial changes in immigration detention. After all, efforts to promote or resist immigration detention at the local level often originate and take form at the city level.