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Demography
Published in: Demography 6/2017

03-11-2017

Interior Immigration Enforcement and Political Participation of U.S. Citizens in Mixed-Status Households

Published in: Demography | Issue 6/2017

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Abstract

The 2000s have witnessed an expansion of interior immigration enforcement in the United States. At the same time, the country has experienced a major demographic transformation, with the number of U.S. citizens living in mixed-status households—that is, households where at least one family member is an unauthorized migrant—reaching 16 million. U.S. citizens living in mixed-status households are personally connected to the struggles experienced by their unauthorized family members. For them, immigration policy is likely to shape their current and future voting behavior. Using data from the 2002–2014 Current Population Survey Voting and Registration Supplements, we examine whether intensified immigration enforcement has affected the political engagement of U.S. citizens living in mixed-status households. We find that immigration enforcement has chilled their electoral participation by lowering their propensity to register by 5 %; however, it has not visibly affected their voting propensity among those registered. Importantly, their lower voting registration likelihood does not seem to reflect indifference for community and public matters, given that it has been accompanied by greater involvement in civic forms of political participation, such as volunteering. Understanding how immigration policy affects the political participation of a fast-growing segment of the electorate is imperative because they will inevitably constitute a rapidly rising political force in future elections.
previous article Cohort Measures of Internal Migration: Understanding Long-Term Trends
next article Second-Generation Outcomes of the Great Migration

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Appendix
Available only for authorised users
Footnotes
1
See U.S. Department of Homeland Security, Budget in Brief, fiscal years 2003–2013 (http://​www.​dhs.​gov/​dhs-budget). Data on apprehensions can be found in Table 33 at http://​www.​dhs.​gov/​yearbook-immigration-statistics-2013-enforcement-actions. Data on interior removals can be found at http://​www.​migrationpolicy.​org/​research/​deportation-and-discretion-reviewing-record-and-options-change.
 
2
These are households whose members have different citizenship and immigration statuses. For the purpose of this study, it will include households with, at least, one likely unauthorized immigrant.
 
3
DAPA, a proposed program, would temporarily defer the deportation of undocumented parents of U.S. citizens and lawful permanent residents. On June 15, 2017, the Department of Homeland Security Secretary signed a memorandum rescinding the November 20, 2014 memorandum that created the DAPA program.
 
4
See section A of Online Resource 1 for a summary of the enforcement policies adopted in the 2000s.
 
5
Secure Communities is an information-sharing program used in the apprehension and deportation of unauthorized immigrants. Under the program, local law enforcement agencies can submit information obtained during arrests, such as fingerprints, to an integrated database with ICE that allows for the identification of the immigration status and criminal activity of any individual. See https://​www.​ice.​gov/​secure-communities.
 
6
The college-age population has been particularly targeted during the past presidential elections, starting with President Obama’s first campaign for the presidency in 2008.
 
7
We also experiment with more-restrictive proxies that, in addition to the prior attributes, also require that they be in occupations typically classified as low-skilled—namely, food preparation and serving; building and grounds cleaning and maintenance; personal care and service; sales and related; office and administrative support; farming; fishing; forestry; construction; extraction; installation, maintenance, and repair; and production. Results can be found in Online Resource 1, Table S1.
 
8
Data restrictions inhibit us from properly implementing more sophisticated statistical imputation methods. As Van Hook et al. (2015) noted, the precision of the estimates derived using statistical imputation methods depends on (1) the joint observation of every pair of variables and (2) both target and donor samples being drawn from the same universe. In our case, where unauthorized status is not observed in the CPS supplement (target data set), we would need to have information on the civic outcomes in a donor sample that has information on the unauthorized status and originates from the same sample universe. In addition, the donor sample would have to be representative of smaller geographic units, such as MSAs in our case, and informative over the 2002–2014 period. It is unclear whether such a data set exists.
 
9
The CPS allows us to group household members using a unique household identification number. Using our definition for likely unauthorized migrants, we are able to identify how many members of the household are likely unauthorized. We then classify the household as mixed-status if the household has at least one likely unauthorized member. This approach reveals only whether a likely unauthorized immigrant resides in the same household as a U.S. citizen. It does not allow us to identify the relationship of the unauthorized immigrant to the U.S. citizen. To gain insight into relationship status of these unauthorized migrants, we also followed a second approach, which is to use parent location and spouse location in the CPS to link U.S. citizens to unauthorized parents or spouses. We then compare the number of U.S. citizens living with an unauthorized parent or spouse to the number of U.S. citizens living in a mixed-family household to find that the unauthorized household members are predominantly parents or spouses.
 
10
The NCSL is a bipartisan nongovernmental organization that serves the members and staff of state legislatures of the United States. Information on immigration can be found online (http://​www.​ncsl.​org/​research/​immigration.​aspx).
 
11
\( Total Enforcment\ {Index}_{m,t}={\sum}_{k=1}^5{EI}_{m,t}^k \), where k = SC, MSA 287(g), State 287(g), OIL, and E-verify.
 
12
The index is a proxy of the intensity of immigration enforcement to which respondents in a particular MSA might be exposed. The true intensity of any enforcement measure will inevitably vary across jurisdictions because each one is different and might implement alike measures more or less strictly depending on who is in charge of its implementation or other unobserved local traits. To address that limitation, we include area fixed effects as well as area-specific time trends intended to capture such idiosyncrasies.
 
13
In the heterogeneity analysis, our five individual indices are separated into police- and employment-based measures. For \( police\underline{} based\ {enforcement}_{m,t}={\sum}_{k=1}^4{EI}_{m,t}^k \) or k = SC, MSA 287(g), State 287(g), and OIL. It is the sum of the four measures for each MSA for each year. The \( employment\underline{} based\ {enforcement}_{mt}=E\underline{}{verify}_{mt} \).
 
14
Many of the immigration enforcement measures, such as the Secure Communities and the 287(g) agreements, were designed to substitute, replace, or continue one another. In addition, they overlap, which has the potential to exponentially raise their effect given that each measure builds on the police infrastructure established by the other.
 
15
Because ethnicity (in particular, being Hispanic) is used as a predictive trait of being unauthorized, most eligible voters in households with a likely unauthorized immigrant are going to be of the same ethnicity. Exceptions would include, for example, non-Hispanic U.S. citizens married to a likely unauthorized immigrant.
 
16
An example would be the case with confounding changes in wealth among mixed-status households if they suffered greater income losses during the recession.
 
17
As shown in Table 1, the average level of enforcement is 0.790 with a standard deviation of 0.911. Additionally, the mean for registration among households is 0.669. The estimated effect is computed as (1 SD increase in enforcement × coefficient) / mean of the dependent variable. That is, (0.911 × 0.039) = 0.0355, or 3.6 percentage points. Or, if we want to use a unit-less measure, we can divide 0.0355 by 0.669, which gives us 0.0531, or 5.3 %.
 
18
To serve as a reference, this is a similar effect to that of being Hispanic—a trait that is associated with a 6 % lower likelihood of registering to vote, as we shall discuss later herein.
 
19
In separate analyses, we assess whether the registration and voting likelihood of eligible voters in mixed-status households who were exposed to intensified immigration enforcement significantly varies during a presidential election year. We do not find any statistically significant evidence of that being the case. Results are available from the authors.
 
20
Our finding proves robust to the use of an alternative definition of likely unauthorized that restricts that denomination to individuals who, in addition to being Hispanic, being noncitizens, having less than a high school diploma, and having spent five or more years in the United States, are employed in low-skill occupations. See Table S1 in Online Resource 1.
 
21
The standard deviation of police-based immigration enforcement is 0.669. Thus, we interpret the effect as 0.041 × 0.669 = 0.0274, or 2.7 percentage points; we then divide by the dependent variable mean, which is 0.669, to get 4.1 %.
 
22
Separate analyses reveal that most of this impact originates from the Secure Communities program. However, for the reasons noted earlier, that effect should be interpreted with caution. Results are available from the authors.
 
24
This may be more the case for Mexicans residing in California where there are stronger networks and political mobilization efforts among Mexicans in the state (Pantoja et al. 2001).
 
25
Albeit not statistically different from 0 at conventional levels, the coefficient on the interaction term in the registration equation for Hispanics of Mexican descent is close to being significant with a t statistic equal to 1.56.
 
26
We group these categories owing to the relative low incidence of volunteering in some of them. Using categories allows us to identify more specific ways (including more time-intensive) ways that people volunteer.
 
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Metadata
Title
Interior Immigration Enforcement and Political Participation of U.S. Citizens in Mixed-Status Households
Publication date
03-11-2017
Published in
Demography / Issue 6/2017
Print ISSN: 0070-3370
Electronic ISSN: 1533-7790
DOI
https://doi.org/10.1007/s13524-017-0627-6

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