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Scholars and legal practitioners of voting rights are concerned with estimating individual-level voting behavior from aggregate-level data. The most commonly used technique, King's ecological inference (EI), has been questioned for inflexibility in multiethnic settings or with multiple candidates. One method for estimating vote support for multiple candidates in the same election is called ecological inference: row by columns (R×C). While some simulations suggest that R×C may produce more precise estimates than the iterative EI technique, there has not been a comprehensive side-by-side comparison of the two methods using real election data that analysts and legal practitioners often rely upon in courts. We fill this void by comparing iterative EI and R×C models with a new statistical package--eiCompare--in a variety of R×C combinations including 2 candidates and 2 groups, 3 candidates and 3 groups, and up to 12 candidates and three groups and multiple candidates and four groups. Additionally, we examine the two methods with 500 simulated data sets that differ in combinations of heterogeneity, polarization, and correlation. Finally, we introduce a new model congruence score to aid scholars and voting rights analysts in the substantive interpretation of the estimates. Across all of our analyses, we find that both methods produce substantively similar results. This suggests that iterative EI and R×C can be used interchangeably when assessing precinct-level voting patterns in Voting Rights Act cases and that neither method produces bias in favor or against finding racially polarized voting patterns.

Various polls suggest that Donald Trump has enjoyed the support of a sizable minority of the Latinx electorate despite his racially offensive rhetoric and support for some of the most restrictive immigration policies in recent memory. Building on Social Identity Theory and Self-Categorization Theory, we contend that some Latinxs harbor negative stereotypes about immigrants, blame them for the status devaluation of the Latinx community, and cognitively distinguish themselves from Latinx immigrants. Rather than viewing anti-immigrant policies, rhetoric, and politicians as a direct status threat, those exhibiting this "Latinx Immigrant Resentment (LIR)" may regard them as a means to enhance the status and interests of "prototypical" Latinxs by signaling their distinction from "atypical" Latinxs. To evaluate this theory, we use the 2020 American National Election Study (ANES) and 2016 Collaborative MultiRacial Post-Election Survey (CMPS) as a proof-of-concept to first confirm that negative immigrant stereotypes and cognitive intragroup distinctions are associated with increased support for Donald Trump and restrictive immigration policies. We then introduce a more refined measure of LIR by fielding online surveys of US Latinxs administered through Lucid in 2020–2021 (N = 1,164) and 2021/22 (N = 1,017). We demonstrate the validity of this measure and its predictive power for attitudes toward Donald Trump, Ron DeSantis, and restrictive immigration policies after accounting for a range of rival explanations. [ABSTRACT FROM AUTHOR]

Despite the increased scholarship on sanctuary localities in the United States, there is little research analyzing the factors that lead to the adoption of sanctuary resolutions at the municipal level. Drawing on a new dataset of sanctuary and nonsanctuary cities, we theorize that policy adoption is driven primarily by two factors and their interaction: the size of the foreign-born population and local partisanship. We examine cities that passed sanctuary policies between 2000 and 2018 and compare these localities to nonsanctuaries. Using a novel time series cross-section dataset (TSCS) of all cities and designated places and a Cox proportional hazard model, we find that Democratic-leaning cities with high foreign-born populations predict sanctuary passage, whereas Republican-leaning cities with larger foreign-born populations are unlikely to adopt these policies. We thus find that while partisanship motivates sanctuary policy adoption, at the same time, the size of the foreign-born population also increases the likelihood of passage. [ABSTRACT FROM AUTHOR]

To what extent do national partisan cues exert influence over local voting behavior? Despite being an "immigrant welcoming city," in November, 2019, Tucson, Arizona, voters rejected Prop. 205—the Tucson Families Free and Together Initiative. We leverage theories of elite partisan cues to explain why voters in a progressive city voted against such an initiative. In contrast to Democratic support for sanctuary cities at the national level, we argue that mixed cues from local Democratic elites contributed significantly to a surprising rejection of the initiative. Using aggregate-level data and a framing experiment, we find that the local political environment split Democratic votes (50% favored, 50% opposed) while keeping Republican voters—who received consistent elite cues of opposition—uniformly against the proposition. This study illustrates how local partisan elite cues can shape ballot initiative voting outcomes, even to the point of overriding negative partisanship and national co-partisan consensus on the same issue. [ABSTRACT FROM AUTHOR]

Scholars and legal practitioners of voting rights are concerned with estimating individual-level voting behavior from aggregate-level data. The most commonly used technique, King's ecological inference (EI), has been questioned for inflexibility in multiethnic settings or with multiple candidates. One method for estimating vote support for multiple candidates in the same election is called ecological inference: row by columns (R×C). While some simulations suggest that R×C may produce more precise estimates than the iterative EI technique, there has not been a comprehensive side-by-side comparison of the two methods using real election data that analysts and legal practitioners often rely upon in courts. We fill this void by comparing iterative EI and R×C models with a new statistical package—eiCompare—in a variety of R×C combinations including 2 candidates and 2 groups, 3 candidates and 3 groups, and up to 12 candidates and three groups and multiple candidates and four groups. Additionally, we examine the two methods with 500 simulated data sets that differ in combinations of heterogeneity, polarization, and correlation. Finally, we introduce a new model congruence score to aid scholars and voting rights analysts in the substantive interpretation of the estimates. Across all of our analyses, we find that both methods produce substantively similar results. This suggests that iterative EI and R×C can be used interchangeably when assessing precinct-level voting patterns in Voting Rights Act cases and that neither method produces bias in favor or against finding racially polarized voting patterns. [ABSTRACT FROM AUTHOR]

Section 2 of the Voting Rights Act is one of the most important tools for litigants challenging discriminatory voting procedures. The Supreme Court outlined the test governing vote dilution claims--which are claims that an electoral system, process, or procedure weakens a minority group's ability to elect candidates of their choice--under Section 2 of the Voting Rights Act in Thornburg v. Gingles. The Court held, in part, that Section 2 requires plaintiffs to prove that voting patterns within the challenged jurisdiction are polarized by race, which is called racially polarized voting. Put simply, racially polarized voting means that voters of different races vote cohesively with voters of their race and have opposite electoral preferences of other races. Because vote choice is private and most states do not track the race of voters, social scientists have developed statistical methods to make the evidentiary showing that Gingles requires. These methods are decades old and are often the subject of intense scrutiny in vote dilution trials. In some cases, the size of the jurisdiction and the quality of the voter file and voting records prevent plaintiffs from meeting their burden of proof. Analyzing the presence of racially polarized voting will be one of the most important issues during and after the redistricting cycle currently underway following the 2020 Census. Within the last year, an innovative method adapted from other fields of study called Bayesian Improved Surname Geocoding (BISG) has been applied to racially polarized voting analysis in vote dilution cases and has been approved by a federal district court in New York and the U.S. Court of Appeals for the Second Circuit. BISG has received little scholarly attention in voting rights legal scholarship, but it promises to be a critical advancement in detecting vote dilution. This Article seeks to showcase this method, equipping voting rights advocates, social scientists and governments alike with additional tools to secure equal voting rights nationwide. This Article argues that BISG can be used by voting rights advocates to bolster racially polarized voting analysis when the necessary data is available and of sufficient quality. Further, BISG can be helpful to smaller jurisdictions which might have smaller sample sizes in American Community Survey data or a smaller number of precincts. [ABSTRACT FROM AUTHOR]

In the United States, drop box mail-in voting has increased, particularly in the all vote by mail (VBM) states of Washington, Colorado, Utah, and Oregon. To assess if drop boxes improve voter turnout, research proxies box treatment by voters' residence distance to nearest drop box. However, no research has tested the assumption that voters use drop boxes nearest their residence more so than they do other drop boxes. Using individual-level voter data from a 2020 Washington State election, we show that voters are more likely to use the nearest drop box to their residence relative to other drop boxes. In Washington's 2020 August primary, 52% of drop box voters in our data used their nearest drop box. Moreover, those who either (1) vote by mail, or (2) used a different drop box from the one closest to their residence live further away from their closest drop box. Implications are discussed. [ABSTRACT FROM AUTHOR]

This article explores the effect of explicitly racial and inflammatory speech by political elites on mass citizens in a societal context where equality norms are widespread and generally heeded yet a subset of citizens nonetheless possesses deeply ingrained racial prejudices. The authors argue that such speech should have an 'emboldening effect' among the prejudiced, particularly where it is not clearly and strongly condemned by other elite political actors. To test this argument, the study focuses on the case of the Trump campaign for president in the United States, and utilizes a survey experiment embedded within an online panel study. The results demonstrate that in the absence of prejudiced elite speech, prejudiced citizens constrain the expression of their prejudice. However, in the presence of prejudiced elite speech – particularly when it is tacitly condoned by other elites – the study finds that the prejudiced are emboldened to both express and act upon their prejudices. [ABSTRACT FROM AUTHOR]