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Political Behavior
Erschienen in: Political Behavior 2/2016

15.09.2015 | Original Paper

When Threat Mobilizes: Immigration Enforcement and Latino Voter Turnout

verfasst von: Ariel White

Erschienen in: Political Behavior | Ausgabe 2/2016

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Abstract

Immigration enforcement, and deportation in particular, has been shown to have social and psychological effects on the non-deported as well, but its political effects have gone largely unexamined. I use the staggered implementation of Secure Communities, an information-sharing program between the federal government and local law enforcement, to estimate the short-term effects of stricter immigration enforcement on Latino voter turnout. A difference-in-differences analysis indicates that enrollment in Secure Communities led to an increase in county-level Latino voter turnout of 2–3 percentage points. This relatively large effect appears due to greater Latino activism in the wake of program implementation, rather than individuals responding to particular police interactions. These results extend the existing literature on mobilization in response to threat, demonstrate that policies can have far-reaching and unexpected political implications, and suggest that the current immigration debate may have major consequences for the future makeup of the American electorate.
Vorheriger Artikel Sorting and the Split-Ticket: Evidence from Presidential and Subpresidential Elections
Nächster Artikel Partisanship and Preference Formation: Competing Motivations, Elite Polarization, and Issue Importance

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Fußnoten
1
Latino citizens are not the only ones who could be exposed to fears of deportation second-hand. But it seems like a fairly common experience for Latinos, and focusing on a smaller group rather than all voters makes it easier to see small changes in turnout. Further, Latinos (including registered voters) report highly unfavorable views of deportation, which should make the immigration enforcement “treatment” more straightforward for this group of voters than for others with more mixed views (Lopez et al. 2011).
 
2
There is some work linking policy and treatment of Latinos to voter behavior, such as Bowler et al. (2006) and Barreto et al. (2005). But my contention is that immigration policies could be shaping political behavior even in the absence of political rhetoric about one party’s hostility toward Latinos, simply because of actual government actions.
 
3
Having family members face deportation, for example, could mean that voters have less time, energy, or money available for electoral activities. Further, having negative interactions with an uncaring and bureaucratic government could turn off voters (Soss 1999; Bruch et al. 2010).
 
4
Another such program was the 287(g) program, which trained and deputized local police to perform immigration enforcement duties. Having such a program in place neither prevented nor guaranteed Secure Communities implementation in a city or county.
 
5
Other related work focuses on the mobilizing effects of threat on other ethnic groups, such as Cho et al. (2006)’s discussion of high-SES Arab-Americans’ political mobilization after 9/11.
 
6
SB1070’s requirement that police determine the immigration status of anyone arrested or detained is broader than the Secure Communities program, but Latino voters could feel targeted nonetheless.
 
7
This could shape their vote choice as well as their turnout, which is beyond the scope of this paper. See Bowler et al. (2006) for consideration of partisanship in the face of anti-Latino policies.
 
8
Also see Pérez (2015b) for a discussion of how elite rhetoric shapes political trust among Latinos.
 
9
These efforts join the GOTV activities of groups focused solely on Latino civic engagement, such as Mi Familia Vota.
 
10
As an example: some places might select into the program as a response to growing Latino turnout rates or the expectation of future turnout increases, perhaps because existing political elites felt threatened by growing Latino political power. If this were the case, a simple comparison of turnout rates in treated and untreated places could show a positive “treatment” effect on turnout even if the Secure Communities program did nothing.
 
11
I focus here on the 2010 general election because it was the only federal election for which this research design is possible. At the time of the 2008 presidential election, only a handful of jurisdictions had been enrolled in the program as a pilot. By the 2012 presidential election, nearly the entire country was enrolled. Only in 2010 was there useful variation in enrollment.
 
12
I also omit about 120 jurisdictions nationwide for which there is not reliable turnout data, due to a combination of incomplete population estimates and missing or unreliable vote data from Catalist. About 80 of these jurisdictions are dropped due to implausible Latino turnout estimates when the two data sources are combined (i.e., over 100 %); the results presented are robust to simply including these places and their estimated turnout. This is a very small proportion of all units in the analysis, and represents places with extremely small Latino populations.
 
13
For the purpose of Secure Communities implementation, “jurisdictions” are generally counties, but in some states they may also include county-equivalents, such as the independent cities of Virginia.
 
14
Using ACS data provides intercensal estimates, so population estimates can change across the two election years.
 
15
One other approach that might otherwise be desirable, adding in state fixed effects, is not possible in this study because there is no within-state variation in treatment in the dataset.
 
16
This may seem quite low. Note that this is based on all eligible voters, not just those who have registered. It is also a midterm election, and Latino turnout has been observed to be quite low during midterm elections (Cassel 2002). Validated vote studies that are not prone to the over-reporting problems of survey self-reports find low Latino turnout in both presidential and midterm elections. (Shaw et al. 2000; Cassel 2002)
 
17
Regression tables available upon request.
 
18
States besides my 5 “treated” states signed MOAs; however, not all units in these other states became enrolled in the program by the 2010 election. This seems to have been due to differences in agreement timing, the structure of state criminal justice information systems, and possibly ICE field office resources.
 
19
Arizona does not appear in this dataset because all of its counties were enrolled individually in the SC program before the 2010 election. This does not seem to have occurred as a result of any state action, as in the “treated” states, but simply as a gradual voluntary enrollment.
 
20
Source Democracia Ahora’s 2010 election-day liveblog, accessed December 2014 through the Internet Archive: https://​web.​archive.​org/​web/​20101123133606/​http://​democracia-ahora.​org/​blog/​election_​day_​live_​blog/​
 
22
For this analysis, I omit responses from jurisdictions that may have selected into the SC program, so my geographic coverage is comparable to the main analysis. That is, responses are included from “reluctant enrollee” counties and unenrolled counties as of the 2010 election.
 
23
See Appendix for regression tables.
 
24
These estimates are available for about half of the jurisdictions in the main dataset.
 
25
For the purposes of this test, I focus on full states, rather than on “state” clusters that omit jurisdictions that selected into the SC program. I think this is more realistic, as treatment was determined at the state level. However, the results do not change substantively if I omit the jurisdictions that voluntarily enrolled in SC, as in the dataset used for the main analysis; there is still no significant relationship between 2002 and 2006 change in turnout and treatment at the state level. Similarly, no significant relationship emerges if I weight the regression by the number of units in the state pre-collapse, or by the cluster’s 2002 Latino population. Finally, no significant relationship emerges if I run the same analysis at the county level rather than the state.
 
26
I perform this matching using the “Synth” package for R (Abadie et al. 2011).
 
27
It may seem that Texas, the only cluster with a negative point estimate, should be weighted more heavily. But recall that the population used is the Latino population in the cluster after having dropped places that voluntarily selected into the program. Texas’ major population centers were enrolled into the SC program quite early.
 
28
This may not be an accurate assumption, but there was no more precise data available on the timing of fingerprint submissions. Still, I use this assumption only to divide the sample into above- and below-median jurisdictions on submissions, so even a rough measure should provide a reasonable division. Further, this analysis is used only to ensure that Secure Communities enrollment is not driving the main results, and this assumption should, if anything, overestimate the number of people who had direct experience with the program prior to the election.
 
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Metadaten
Titel
When Threat Mobilizes: Immigration Enforcement and Latino Voter Turnout
verfasst von
Ariel White
Publikationsdatum
15.09.2015
Verlag
Springer US
Erschienen in
Political Behavior / Ausgabe 2/2016
Print ISSN: 0190-9320
Elektronische ISSN: 1573-6687
DOI
https://doi.org/10.1007/s11109-015-9317-5

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