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Published in: Demography 3/2018

21-05-2018

Beyond the Border and Into the Heartland: Spatial Patterning of U.S. Immigration Detention

Author: Margot Moinester

Published in: Demography | Issue 3/2018

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Abstract

The expansion of U.S. immigration enforcement from the borders into the interior of the country and the fivefold increase in immigration detentions and deportations since 1995 raise important questions about how the enforcement of immigration law is spatially patterned across American communities. Focusing on the practice of immigration detention, the present study analyzes the records of all 717,160 noncitizens detained by Immigration and Customs Enforcement (ICE) in 2008 and 2009—a period when interior enforcement was at its peak—to estimate states’ detention rates and examine geographic variation in detention outcomes, net of individual characteristics. Findings reveal substantial state heterogeneity in immigration detention rates, which range from approximately 350 detentions per 100,000 noncitizens in Connecticut to more than 6,700 detentions per 100,000 noncitizens in Wyoming. After detainment, individuals’ detention outcomes are geographically stratified, especially for detainees eligible for pretrial release. These disparities indicate the important role that geography plays in shaping individuals’ chances of experiencing immigration detention and deportation.
Appendix
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Footnotes
1
Removals include deportations and voluntary departures.
 
2
Removals from the U.S. interior began to increase in 2017 for the first time in a number of years, but have not reached the levels seen in 2008 and 2009 (ICE 2017).
 
3
Legally present noncitizens with certain criminal convictions are also removable.
 
4
These offices are located across the country and do not map onto a coherent spatial unit. Although some states have multiple field offices, others have only one or share an office with neighboring states.
 
5
Here, I draw on the legal scholar Hiroshi Motomura’s definition of discretion as including “not only decisions to proceed against identified individuals, but also systemic choices to commit resources and set priorities” (Motomura 2011:174).
 
6
Through the 287(g) program, state and local law enforcement agencies can choose to partner with ICE and designate specific officers to perform immigration enforcement functions (https://​www.​ice.​gov/​287g). With the Secure Communities program, all fingerprints collected by local and state law enforcement are automatically shared with ICE to determine whether the individual is potentially removable (https://​www.​ice.​gov/​secure-communities). Although now activated in all jurisdictions, Secure Communities was initially piloted in select places.
 
7
Like ICE’s field offices, immigration courts are distributed across the country and do not map onto a coherent spatial unit. Some states have multiple courts, and other states have only one court or share a court with neighboring states.
 
8
The version of the Benchbook that includes a discussion of these factors was discontinued in 2017.
 
9
See the appendix for details of this process.
 
10
Potential differences between legal status at the time of entry and legal status at the time of arrest are not captured in the analysis. For example, an individual who was granted a visa and overstayed would have a legal status at time of entry of “present with admission” despite being present in the country without authorization at the time of arrest. Most individuals in the data entered the country without admission and are not able to adjust their legal status from within the country. As such, in discussing results, I refer to individuals who entered the country without admission as unauthorized.
 
11
The legal status at time of entry variable accounts for 99.8 % of missingness. When the full data set is compared with records without missing data, the distribution of detention outcomes and place of origin change slightly. Percentage Mexican drops from 62.5 % to 59.8 %; percentage El Salvadorian, Guatemalan, and Honduran each increase slightly. Percentage voluntary departure drops from 20.1 % to 14.5 %, and percentage deported increases from 63.7 % to 68.1 %. Records missing information on legal status also have missing data on entry date into the United States.
 
12
In some states, however, the vast majority of apprehensions leading to detention were of undocumented immigrants. Online Resource 1 includes estimates of states’ detention rates using data on states’ undocumented population from Warren and Warren (2013) and information on the share of detentions from each state that are of undocumented immigrants.
 
13
State of first detention is likely a strong proxy for apprehension state given that only 5.2 % of geocoded apprehensions occurred in a state other than the state of first detention and apprehension landmark appeared missing at random.
 
14
I discuss results using the higher-bound estimates because they provide a more complete picture of enforcement and because the relative position of states’ detention rates remains fairly stable across the estimates. The key exception is Arizona, where border crossers are likely included in the higher-bound estimate, indicating that this estimate is biased upward.
 
15
See the appendix for details on how all measures were constructed.
 
16
Terminations occur most often because a judge dismisses the case or awards some kind of relief, but they can also occur if ICE finds a mistake in seeking to deport the individual.
 
17
For the 4 % of detention outcomes not examined, see the appendix.
 
18
Due to litigation in the First, Second, and Ninth Circuits and to INA 236(c)(2), which pertains to cooperating witnesses or informants in criminal investigations, there are extenuating circumstances in which mandatorily detained individuals can receive bond.
 
19
Episodes ending with detainees winning their case are excluded because this outcome occurs too infrequently to provide reliable estimates after I condition on nonmandatory detention.
 
20
U.S. citizens sometimes get caught up in enforcement activities (Rosenbloom 2013; Stevens 2010).
 
21
It is unclear what legal statuses are included in the “other” category.
 
22
This drop may be due in part to the state losing its contract with ICE in February 2009 (Bernstein 2009).
 
23
These patterns hold when looking at unauthorized men from other countries and regions who exit detention as nonmandatorily detained.
 
24
Results did not substantively change when states’ detention rates were logged or when Lowess smoothing was used.
 
25
Because we do not know how selection into being defined as mandatorily detained might differ across states in a way that is correlated with states’ detention rates, we have to be cautious in interpreting these results.
 
26
Section 134 of the Illegal Immigration Reform and Immigrant Responsibility Act mandates the Attorney General to assign at least 10 full-time immigration officers in each state. In states with a small undocumented immigrant population, 10 officers may constitute substantial personnel capacity.
 
27
When Arizona is dropped from the analysis, the correlation coefficient reduces to .093.
 
28
See Online Resource 1 for a visualization of these results.
 
29
See Online Resource 1 for a visual display of this relationship. Additionally, because we do not know how selection into being defined as mandatorily detained might differ across states in a way that is correlated with the distribution of detention outcomes across states, we have to be cautious in interpreting these results.
 
30
More than 98 % of the cases heard in this court in 2008 and 2009 ended in deportation or voluntary departure (TRAC 2017b).
 
31
Approximately 18 % of the records were missing information on the location of apprehension. In this case, the state of first detention was used as a proxy for the state of apprehension given that more than 95 % of geocodable records were apprehended in the same state where they were first detained.
 
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Metadata
Title
Beyond the Border and Into the Heartland: Spatial Patterning of U.S. Immigration Detention
Author
Margot Moinester
Publication date
21-05-2018
Publisher
Springer US
Published in
Demography / Issue 3/2018
Print ISSN: 0070-3370
Electronic ISSN: 1533-7790
DOI
https://doi.org/10.1007/s13524-018-0679-2

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