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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]