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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.

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]

We evaluate a theory of campaign learning in the context of immigration and the 2012 Latino vote. Following events in Nevada and Arizona after the 2008 election and prior to the 2012 election, we argue and show that Obama's campaign team learned from several Democratic U.S. Senate campaigns in how best to mobilize the Latino vote on the issue of immigration. As a result, we argue, this campaign learning led to an increase in the Latino vote for Obama. To demonstrate this, we compare a group-based appeals model against a traditional vote-choice model, and show that variables measuring Latino Outreach had the greatest impact on the 2012 Latino vote – above and beyond party identification and other traditional vote-choice predictors. [ABSTRACT FROM AUTHOR]