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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]

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]

One goal of the California Voting Rights Act (CVRA) is to broaden representation in largely White-controlled city councils state-wide by incentivizing cities to shift council electoral jurisdictions from at-large to single-member districts. However, little research has investigated whether the CVRA helped contribute to increased minority representation at the city level. This article employs matching and difference-in-difference methods to determine whether cities that switched to district elections as a result of the CVRA enhanced city council diversification. By comparing matched treatment and control group's racial composition of city councils before and after fully switching from at-large to district election jurisdictions, we estimate the average treatment effect (ATE) of city switching on minority city council representation at 10% to 12%. Further analysis reveals treatment effects are larger among cities with larger shares of Latinos (21%). Thus, states seeking to increase local-level minority representation should consider policies similar to those found in the CVRA. [ABSTRACT FROM AUTHOR]

Objective: We theorize that anxiety (fear) related to mass shootings and social violence increases support for gun control among the American public. Methods: We support our theory with a regression discontinuity analysis based on an actual mass shooting, observational analyses from the same data set testing the relationship between fear and support for gun control, and two survey experiments that prime anxiety in the context of mass shootings and social violence. Findings: We show that support for gun control increased on the day after an actual mass shooting. Observational analysis shows a positive correlation between fear of crime and support for gun control. One priming experiment shows that inducing anxiety about mass shootings increases support for gun control. A second priming experiment shows that exposure to a story about social violence activates anxiety and also increases support for gun control. Conclusions: Our analyses show that anxiety related to mass shootings and mass violence increases support for gun control. [ABSTRACT FROM AUTHOR]

Significant research indicates that attitude change is often a product of partisan learning. However, as the party system continues to rearrange around issues of race and immigration, and as new racial policy issues thrust onto the agenda, it is unclear whether voters learn to adopt racial policy attitudes more based on race/ethnicity or on party identification. We evaluate the partisan-learning model versus a racial-learning model with regards to public opinion on sanctuary cities/policies among survey respondents in CA and TX. Given President Trump's public antipathy toward sanctuary cities, we argue and show that negative partisanship is the most plausible vehicle for sanctuary city attitude change between 2015 and 2017. In this particular case, we find no support for a racial/ethnic-learning model. [ABSTRACT FROM AUTHOR]

Building upon existing literature, we offer a particular model of network policy diffusion—which we call sustained organizational influence. Sustained organizational influence necessitates an institutional focus across a broad range of issues and across a long period of time. Sustaining organizations are well‐financed, and exert their influence on legislators through benefits, shared ideological interests, and time‐saving opportunities. Sustaining organizations' centralized nature makes legislators' jobs easier by providing legislators with ready‐made model legislation. We argue that sustaining organizations uniquely contribute to policy diffusion in the U.S. states. We evaluate this model with a case study of state‐level immigration sanctuary policy making and the role that the American Legislative Exchange Council (ALEC) played in disseminating model legislation. Through quantitative text analysis and several negative binomial state‐level regression models, we demonstrate that ALEC has exerted an overwhelming influence on the introduction of anti‐sanctuary legislative proposals in the U.S. states over the past 7 years consistent with our particular model of network policy diffusion. Implications are discussed. [ABSTRACT FROM AUTHOR]

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]

Objectives: To assess predictors of sanctuary city attitudes in Texas. Specifically, to assess whether a criminal threat or immigration threat hypothesis better explains attitudes toward sanctuary cities. Methods: Pooled representative sample surveys of Texas respondents conducted during the first half of 2017, combined with county‐level Census and crime data. Regression analysis and Monte Carlo simulations. Results: Compared to the criminal threat hypothesis (as measured by county change in crime rates), the immigration threat hypothesis (as measured by Latino growth and Latino population) better explains Texans' attitudes toward sanctuary cities. Conclusions: Despite elite and media narratives linking sanctuary cities to crime, respondents' attitudes toward sanctuary cities are unrelated to physical crime threat and are structured by localized experience with immigration—specifically—Latino growth. [ABSTRACT FROM AUTHOR]

This article assesses the claim that sanctuary cities—defined as cities that expressly forbid city officials or police departments from inquiring into an individual's immigration status—are associated with post hoc increases in crime. We employ a causal inference matching strategy to compare similarly situated cities where key variables are the same across the cities except the sanctuary status of the city. We find no statistically discernible difference in violent crime, rape, or property crime rates across the cities. Our findings provide evidence that sanctuary policies have no effect on crime rates, despite narratives to the contrary. The potential benefits of sanctuary cities, such as better incorporation of the undocumented community and cooperation with police, thus have little cost for the cities in question in terms of crime. [ABSTRACT FROM AUTHOR]

Public opinion research suggests that rapid and significant individual-level fluctuations in opinions toward various policies is fairly unexpected absent methodological artifacts. While this may generally be the case, some political actions can and do face tremendous backlash, potentially impacting public evaluations. Leveraging broadcast and newspaper transcripts as well as a unique two-wave panel study we demonstrate that a non-random, rapid shift in opinions occurred shortly after President Donald Trump signed executive order 13769 into law, which barred individuals from seven predominantly Muslim countries from entering the United States for 90 days. The ban set off a fury of protests across U.S. cities and airports, garnering tremendous media attention and discussion. Drawing insights from literature on priming, we claim that an influx of new information portraying the “Muslim Ban” at odds with inclusive elements of American identity prompted some citizens to shift their attitudes. Our study highlights the potential broad political effects of mass movements and protests as it pertains to policies that impact racialized minority groups, and suggests that preferences can shift quickly in response to changing political circumstances. [ABSTRACT FROM AUTHOR]

Background: In 2012, Washington and Colorado became the first U.S. states to legalise recreational marijuana. By 2016, eight states and the District of Columbia had legalised recreational marijuana, with more expected to consider it in 2018. Despite this trend, little academic research explains what drives ballot-initiative vote choice on marijuana legalisation.Methods: This paper uses a pre-election random sample voter survey to examine the individual characteristics that correlated with Washington voters' support for legal recreational marijuana.Results: We find that voting on marijuana ballot initiatives largely reflects public opinion about marijuana and is particularly shaped voters' political ideology, party affiliation, religious affiliation and practice, and education. Notably, we find that those reporting experiences (i.e., someone they know) with the criminal justice system are more supportive of legalisation than those who do not.Conclusion: We conclude that marijuana legalisation voting behavior generally aligns with public opinion on the issue. However, one key aspect of Washington's legalisation campaign-the criminal injustices of marijuana illegality-helped shape Washington state voting behavior. Further research is needed to examine if, when, and in what contexts criminal justice campaign themes are likely to strengthen or undermine future states' marijuana legalisation efforts. [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]

Traditional vote-choice models include variables such as party identification, assessments of the economy, as well as other demographic characteristics. We argue that variables that tap shared racial/ethnic identity or some such similar dimension can enhance Latino vote-choice models beyond the traditional model. We evaluate Barack Obama and Mitt Romney's cross-racial mobilization of Latino voters during the 2012 Presidential election. Using a survey of several thousand Latino voters, we find that these candidates' policy stances vis-a-vis immigration and their ability to convey care and concern to the Latino community are important variables that guide Latino vote choice. Implications are discussed. [ABSTRACT FROM AUTHOR]

Social scientists have long hand-labeled texts to create datasets useful for studying topics from congressional policymaking to media reporting. Many social scientists have begun to incorporate machine learning into their toolkits. RTextTools was designed to make machine learning accessible by providing a start-to-finish product in less than 10 steps. After installing RTextTools, the initial step is to generate a document term matrix. Second, a container object is created, which holds all the objects needed for further analysis. Third, users can use up to nine algorithms to train their data. Fourth, the data are classified. Fifth, the classification is summarized. Sixth, functions are available for performance evaluation. Seventh, ensemble agreement is conducted. Eighth, users can cross-validate their data. Finally, users write their data to a spreadsheet, allowing for further manual coding if required. [ABSTRACT FROM AUTHOR]

Conventional wisdom based on public opinion surveys suggests that voters are generally supportive of direct democracy. Recent research has shown that perhaps public support for direct democracy may not be as strong as is traditionally thought. Given this, I hypothesize that voters without a college degree are less supportive of direct democracy once asked how they will vote on a series of ballot initiative questions in the context of a survey. Voters with a college degree will not be affected by these questions in their evaluations of direct democracy because they have more confidence in their ability to participate in politics. I conduct a split sample survey experiment to investigate opinions toward direct democracy. The results confirm that, when exposed to ballot initiative questions, voters without a college degree are less supportive of direct democracy compared to college graduates. Implications are discussed. [ABSTRACT FROM PUBLISHER]

Words are an increasingly important source of data for social science research. Automated classification methodologies hold the promise of substantially lowering the costs of analyzing large amounts of text. In this article, we consider a number of questions of interest to prospective users of supervised learning methods, which are used to automatically classify events based on a pre-existing classification system. Although information scientists devote considerable attention to assessing the performance of different supervised learning algorithms and feature representations, the questions asked are often less directly relevant to the more practical concerns of social scientists. The first question prospective social science users are likely to ask is, How well do such methods work? The second is, How much human labeling effort is required? The third is, How do we assess whether virgin cases have been automatically classified with sufficient accuracy? We address these questions in the context of a particular dataset—the Congressional Bills Project—which includes more than 400,000 bill titles that humans have classified into 20 policy topics. This corpus offers an unusual opportunity to assess the performance of different algorithms, the impact of sample size, and the benefits of ensemble learning as a means for estimating classification accuracy. [ABSTRACT FROM AUTHOR]

Given the fluid context of primaries and observed swings in national polls, many Democratic voters likely switched candidate support over the course of the 2008 primary campaign. We examine how perceptions of early caucus and primary outcomes subsequently affected voter choice and candidate momentum. Although the 2008 calendar left many voters with a brief window to assess candidates, it nonetheless allowed a non-front-runner to benefit from momentum and win the Democratic nomination. This article employs a panel study of voters surveyed at two time points during the nomination contest to assess individual-level change in candidate support. Results from the earlier states sent signals about candidate viability to people who had not yet voted. We find that voters deciding after results were in from early states changed their perceptions of candidate viability and that this changed whom they intended to support. We conclude that momentum remains an important factor in presidential nominations. [ABSTRACT FROM AUTHOR]

The importance of the Latino electorate has been the subject of both academic inquiry and media discourses. The question of Latino influence is frequently limited by an approach that focuses on single variable considerations (e.g., voter turnout or ethnic-targeted campaign spending) that are often contest-specific idiosyncrasies. Relying on theoretically appropriate concepts, the authors measure Latino political influence as a function of three factors: in-group population traits, electoral volatility, and mobilization. Using the 2008 presidential election, the authors demonstrate the utility of incorporating a multifaceted measure that accounts for the contemporary complexity within the electoral environment. Because this framework is rooted in theoretical concepts, as opposed to discrete group or contest characteristics, it may be applied to any “influence group” in different electoral settings. Data are culled from several publicly available outlets, making it possible for scholars to replicate these measures and further investigate questions associated with group influence in American politics. [ABSTRACT FROM PUBLISHER]

On January 27, 2017, President Trump signed executive order 13769, which denied citizens of seven Muslim-majority countries entry into the United States. Opposition to what was termed the "Muslim ban" quickly amassed, producing sudden shifts to the information environment and to individual-level preferences. The present study examines whether within-subject shifts against the ban lasted over an extended period of time. Evidence from a three-wave panel study indicates that individual-level opinions, once they shifted against the ban, remained fairly stable one year later. Analysis of a large corpus of cable broadcast transcripts and newspaper articles further demonstrates that coverage of the ban from February 2017 to January 2018 did not dissipate, remained largely critical, and lacked any significant counter-narratives to potentially alter citizens' preferences once again. Our study underscores the potential of capturing the dynamics of rapid attitudinal shifts with timely panel data, and of assessing the durability of such changes over time. It also highlights how mass movements and political communication may alter and stabilize citizens' policy preferences, even those that target stigmatized groups. [ABSTRACT FROM AUTHOR]

Despite a sizable literature on racial priming, scholars have failed to account for the shifting nature of racial appeals. First, theories of racial priming have not yet been widely applied to increasingly common anti‐immigrant and anti‐Latino political appeals. Second, theories of racial priming have not adequately accounted for both an increasingly racialized political climate and increased tolerance for explicit anti‐minority appeals. In two survey experiments fielded both before Trump's rise and after his presidential victory, we find the Implicit‐Explicit (IE) model always fails for anti‐black appeals, sometimes fails for anti‐immigrant appeals, but consistently holds for anti‐Latino appeals. While we find the null effects of implicit versus explicit anti‐black and anti‐immigrant appeals are partly driven by tolerance for the explicit appeals, we also find evidence that white Americans are adept at recognizing the racial content of appeals featuring widely used, congruent issue‐group pairs. Our findings shed light on conditions under which the IE model does and does not hold in the current political era. [ABSTRACT FROM AUTHOR]

We consider the cascading effects of researcher decisions throughout the process of quantifying racially polarized voting (RPV). We contrast three methods of estimating precinct racial composition, Bayesian Improved Surname Geocoding (BISG), fully Bayesian BISG, and Citizen Voting Age Population (CVAP), and two algorithms for performing ecological inference (EI), King’s EI and EI:RxC using eiCompare. Using data from two different elections we identify circumstances in which different combinations of methods produce divergent results, comparing against ground-truth data where available. We first find that BISG outperforms CVAP at estimating racial composition, though fully Bayesian BISG does not yield further improvements. Next, in a statewide election, we find that all combinations of methods yield similarly reliable estimates of RPV. However, county-level analyses and results from a non-partisan school board election reveal that BISG and CVAP produce divergent estimates of Black preferences in elections with low turnout and few precincts. Our results suggest that methodological choices can meaningfully alter conclusions about RPV, particularly in smaller, low-turnout elections. [ABSTRACT FROM AUTHOR]

Sanctuary city policies seek to protect undocumented community members from federal detention or deportation. Debates over sanctuary cities have become increasingly prominent and partisan in American politics. Republicans accuse sanctuary cities of enabling crime, while Democrats laud them for protecting communities from rights violations. Despite partisan salience, we have little information about peoples' substantive knowledge of sanctuary policies or how crucial that knowledge is in shaping partisan attitudes toward those policies. Drawing on a unique survey dataset of sanctuary attitudes, we demonstrate that an absence of political knowledge has asymmetrical effects on sanctuary attitudes along ideological and partisan lines. Knowledge about sanctuary policies increases support for sanctuary cities among liberals/Democrats, whereas conservatives/Republicans do not require substantive knowledge to align their attitudes on sanctuary cities with their ideological predispositions. This finding advances scholarship on the interplay between political knowledge and ideology, and has important immigration‐related policy and advocacy implications. Related Articles: Pearson‐Merkowitz, Shanna. 2012. "Aqui No Hay Oportunidades: Latino Segregation and the Keys to Political Participation." Politics & Policy40 (2): 258‐295. https://doi.org/10.1111/j.1747-1346.2012.00349.x Pearson‐Merkowitz, Shanna. 2012. "Aqui No Hay Oportunidades: Latino Segregation and the Keys to Political Participation." Politics & Policy40 (2): 258‐295. https://doi.org/10.1111/j.1747-1346.2012.00349.x Butz, Adam M, Jason E.Kehrberg. 2015. "Social Distrust and Immigrant Access to Welfare Programs in the American States." Politics & Policy43 (2): 256‐286. https://doi.org/10.1111/polp.12115 Turner, Robert C., WilliamSharry. 2012. "From Progressive Pioneer to Nativist Crackdown: The Transformation of Immigrant Policy in Oklahoma." Politics & Policy40 (6): 983‐1018. https://doi.org/10.1111/j.1747-1346.2012.00392.x [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]

Social Identity Theory suggests that individuals are motivated to support/oppose policies and politicians that benefit/harm members of their ingroup as a means of protecting their social status. Since the Republican Party's rhetoric against immigrants in recent decades has often been viewed as an assault upon those of Latinx descent, it is not surprising that strong majorities oppose restrictionist immigration policies and support the Democratic Party. However, the existing literature has overlooked why a sizeable minority of Latinx voters express support for restrictionist immigration policies and the politicians who espouse them. Our analysis of Latinx voters with the 2012 and 2016 American National Election Studies (ANES) demonstrates that the degree to which individuals prioritize their US American identity over their Latinx identity has a significant influence over support for conservative immigration policies and GOP candidates. This relationship emerges above and beyond partisanship, ideology, and other key explanatory factors. Such attitudes likely represent an individual social mobility strategy in which members of a social group attempt to "pass" as a member of a higher-status group. Prioritizing a US American identity, supporting the Republican Party, and expressing hostility toward the interests of undocumented immigrants are a means of distinguishing themselves from a social group that has become increasingly associated with negative stereotypes. In contrast, those who are unwilling or unable to make this transition are likely pursuing a collective social mobility strategy (e.g. linked fate) whereby they attempt to enhance their individual status by elevating that of the entire social group. [ABSTRACT FROM AUTHOR]

Objective: This article examines the impact that reducing the distance to a voter's nearest ballot drop box has on turnout. Methods: The placement of five new ballot drop boxes was randomized among six potential sites identified based on similar criteria. The randomization of the five boxes across the six sites created natural Treatment (those sites that received a new box) and Placebo (the site that did not receive a new box) groups. We then employed a difference‐in‐difference design to determine whether voters in the Treatment group were more likely to vote in the 2017 general election compared to those in the Placebo group. Results: We find that a decrease of one mile to the nearest drop box increased the probability of voting by 0.64 percent. Conclusion: Our finding indicates that drop boxes have a positive effect on voter turnout and that decreasing the distance to these boxes can lead to an increased likelihood of voting. [ABSTRACT FROM AUTHOR]

The refugee sanctuary movement in the United States has shifted to include undocumented immigrants fleeing violence and economic strife. Given the negative tenor of coverage of undocumented immigration, and ties between framing and policy views on immigration, how the media frames sanctuary cities is likely to impact public perceptions of these cities and their policies. To assess media coverage of sanctuary policies, we analyzed articles from five national newspapers from 1980 to 2017 with both human content analysis and dictionary-based computational analysis. We find that framing around religion/morality and conflict has decreased, while stories focusing on crime and partisanship have increased. We discuss implications for public opinion and the likelihood that the American public will take their cues from media framing and elite discourse when it comes to sanctuary policies. [ABSTRACT FROM AUTHOR]