Scholarly Articles (in print and working)
I research a variety of political behavior projects (although increasingly policy-related), generally invoking campaigns, voting, public opinion, race/ethnicity, but do have broad interests. Below you can find most of my published work and drafts currently in the working process.
Voting, Public Opinion, Survey research
Voting, Public Opinion, Survey research
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.
| Collingwood Lajevardi Oskooii (Political Behavior) |
A prominent feature of Donald Trump’s campaign for president was the use of racially inflammatory rhetoric and fear over immigration—specifically from Mexico—to galvanize the electorate. Despite the commonly accepted assertion that hostility toward Mexican immigrants was an important attractor of core supporters to his base, analysts and academics alike have failed to explore the role that environmental indicators of perceived threat from immigration, such as residing in an area with a growing Latino population, played in generating support for Trump early in his campaign. We demonstrate that residing in a highLatino-growth area is predictive of support for Trump following, but not before, his utterance of inflammatory and bellicose comments about Mexican immigrants. Our results suggest that, in addition to the importance of racial resentment and economic frustration, support for Trump in the early campaign period represented an adversarial reaction among Americans to Latino-led diversity.
| Newman Shah Collingwood (POQ) |
We evaluate the relationship between race of interviewer (ROI) and racial attitudes, using original telephone survey data that includes a response to the question: “What is my race?” A large percentage of our respondents answer, “don’t know.” Traditional racial attitude models tend to exclude ROI altogether, whereas alternative racial attitude models include perceived ROI but drop “don’t know” respondents. We propose a new modeling strategy that includes “don’t know” respondents and find that in general this modeling strategy is preferred because it leads to better model fit and fewer type II errors. We suggest that researchers control for “don’t know” ROI responses in any analysis of racial attitudes.
| Barreto et al (2015).pdf |
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.
| Collingwood Barreto Donovan (2012) |
Charter schools enjoy support among Republican and Democratic lawmakers in states and Congress but little research has examined their support among the electorate. We take advantage of Washington’s 2012 charter school ballot initiative— the first voter-approved charter initiative in the U.S.—to understand the politics of school choice at the mass level. Since in-depth, individual-level voter data is often unavailable in state-level elections, we leverage extensive precinct- and district level data to examine patterns of support and opposition toward the charter school initiative, focusing on how partisanship, ideology, and demographic factors serve to unify or divide voters. Our analysis reveals that the coalition of supporters cut across usual partisan and demographic cleavages, producing somewhat strange bedfellows. This finding has important implications for the strategies advocacy groups may consider as they seek to expand or limit school choice programs via ballot initiatives as opposed to the statehouse and provides suggestive evidence regarding the evolving shapers of voter support for school choice and ballot initiatives more generally.
| Collingwood Jochim Oskooii (2018) |
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.
| Collingwood (2012) |
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.
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.
| Collingwood Gonzalez-O'Brien Dreier (2018) |
In the aftermath of Donald Trump’s 2016 electoral college victory, journalists focused heavily on the white working class (WWC) and the relationship between economic anxiety, racial attitudes, immigration attitudes, and support for Trump. One hypothesized but untested proposition for Donald Trump’s success is that his unorthodox candidacy, particularly his rhetoric surrounding economic marginalization and immigration, shifted WWC voters who did not vote Republican in 2012 into his coalition. Using a large national survey we examine 1) whether racial and immigration attitudes or economic dislocation and marginality were the main correlates of vote switching, and; 2) whether this phenomenon was isolated among the white working class. We find a non-trivial number of white voters switched their votes in the 2016 election to Trump or Clinton, that this vote switching was more associated with racial and immigration attitudes than economic factors, and that the phenomena occurred among both working class and non-working class whites, though many more working class whites switched than non-working class whites. Our findings suggest that racial and immigration attitudes may be continuing to sort white voters into new partisan camps and further polarize the parties.
| Reny et al (POQ, forthcoming) |
Drop Boxes and Voting Technology
Recent developments in how and when voters cast their ballots have led analysts and scholars to inquire whether these developments hinder or help voter turnout. For example, on the one hand, in the United States, voters in the state of Washington cast their ballots via mail, although increasingly voters are depositing their ballots into drop boxes as opposed to mailing them in. On the other hand, in 2016, states like North Carolina reduced the number of early voting hours available at polling locations. Rvoterdistance is a new package that helps scholars and analysts assess whether these changes influence turnout by calculating the distance between each individual voter and their closest drop box, early voting location, or other such similar voting location. Users can then perform standard statistical operations (e.g. difference in difference; conditional logit), to assess changes in voting propensity pre/post the new implementation.
| Collingwood Rvoterdistance (R Package) |
Considerable interest among academics and practitioners alike centers around identifying ways to improve voter turnout and voting parity across various subgroups in the U.S. population. Many scholars have investigated convenience voting and found mixed results in terms of its effects on turnout and the composition thereof. A relatively new, but unstudied method of voting is via ballot drop box, a method states and voters have increasingly turned to. We exploit the placement of over 30 new drop boxes in King County, WA, the home of Seattle, during the 2016 election. We find that distance to the closest ballot drop box increases one's probability of voting but primarily in off-year elections and primaries. We find mixed results for heterogeneous treatment effects. Implications are discussed.
| Collingwood et al (ELJ, 2018) |
Immigration politics
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.
| oskooii_et_al_2018.pdf |
On the night of November 8, 2016, once election results showed an almost certain presidential victory for Donald Trump, private prison stock values increased. Trump’s harsh anti-immigrant campaign rhetoric, followed by his attempted crackdown on sanctuary cities (and immigrants more generally), had the potential to expand the carceral market to greater shares of undocumented immigrants. We develop a theory of carceral market expansion, arguing that private actors seek to expand carceral markets – for profit – just as in any other market. This paper examines whether private companies, like CCA and GEO, that contract with Immigration Customs and Enforcement (ICE) to operate detention facilities exert influence over federal anti-sanctuary legislation in the 113th and 114th Congresses. Specifically, we examine 1) Whether campaign donations made by private prison companies and other contractors to state legislators (carceral lobbying hypothesis), and 2) Having a privately owned or managed ICE detention facility in a legislator’s district (carceral representation hypothesis) increases the probability that legislators will co-sponsor more harsh immigration legislation in the U.S. states. We find strong support for the carceral representation hypothesis but limited to no support for the carceral lobbying hypothesis. Implications are discussed.
| Collingwood Morin & El-Khatib (2018) |
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.
| Collingwood El-Khatib Gonzalez (2018) |
Significant research indicates that attitude change is often a product of partisan learning (Green, Palmquist, Schickler 2004; Lenz 2011). As the party system continues to rearrange around issues of race and immigration, it may be that (on race-related issues) voters are learning more based on race/ethnicity and not on party. We evaluate the partisan learning model versus a racial-learning model with regards to public opinion on sanctuary cities among survey respondents in California and Texas – two states that have experienced extensive recent debate on the issue. After the rise of Trump and his connection with opposition to sanctuary cities, we show that partisanship is much more predictive of attitudes on sanctuary cities in 2017 versus 2015, whereas findings for racial/ethnic learning are not so forthcoming. Implications are discussed.
| Collingwood Gonzalez O'Brien & Tafoya (working) |
Sanctuary city policies forbid local officials and law enforcement from inquiring into residents’ immigration status. Opponents of sanctuary cities, such as President Donald Trump, Attorney General Jeff Sessions, and Texas Governor Gregg Abbott, frequently claim that sanctuary policies lead to higher crime rates, despite evidence to the contrary (Gonzalez et al., 2017; Lyons et al., 2013; Wong, 2017). Given this crime narrative, we might expect public opinion about sanctuary cities to be driven primarily by concern of and contextual experience with crime. However, given that sanctuary cities are associated with undocumented immigration, and that undocumented immigration is inextricably linked to Latino – and specifically Mexican – immigration, public opinion on sanctuary cities may instead be driven by experiences with racial and cultural threat embodied by rapid Latino growth. We analyze two polls from Texas, a state where the sanctuary debate is highly salient – not only because of its long border with Mexico, but because its governor has fought against such cities, signing highly controversial legislation. We find that opinion on sanctuary cities is unrelated to respondents’ county crime rates but is strongly related to county Latino growth and Latino population size. We find some evidence that sanctuary city opinion is related to individual concerns about both immigration and crime. Overall, beyond partisan and ideological staples, we conclude that opinion on sanctuary cities is driven primarily by racial threat and not by actual crime exposure.
| Collingwood & Gonzalez O'Brien (2018, Forthcoming) |
What explains legislators’ behavior when they are uncertain whether they will be rewarded or punished at re-election? Typically, politicians are incentivized to deliver policies preferred by the majority. Less well understood is what happens when legislators face decisions on issues on which their traditional supporters disagree. Owing to the unavailability of public opinion data for congressional districts, however, studies evaluating competing theories of representation on such issues are scarce. We examine this question by evaluating leading theories of representation on the 1986 Immigration Reform and Control Act (IRCA), a complex, historically significant, highly salient, and controversial bill that gave citizenship to millions of undocumented immigrants – precisely the type of group that past research suggests should struggle to obtain representation. We employ recent advances in estimating public opinion using multilevel regression and post-stratification to estimate district level public opinion on IRCA. Contrary to traditional conceptions of subconstituency politics, the results suggest that, under at least some circumstances, traditionally marginalized groups are able to make important policy advances in the face of negative opinion, particularly when they are able to build coalitions that cross party lines and divide their opposition.
| Skulley et al (2017) |
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.
| Gonzalez-Obrien Collingwood & El-Khatib (2017).pdf |
Latino Voting and cross-racial mobilization
In this paper, we challenge the existing framework which argues that ethnic cues essentially operate for Latino candidates but not for Anglo/non-Latino candidates. Instead, we suggest that Anglo candidates can tap into Latino shared-identity by employing social-identity bridging mobilization during their campaigns. Using measures that gauge policy, culture, and mobilization, we examine what drives Latino support for presidential candidate Jeb Bush. We find that support for Bush increases among Latino voters who prefer Anglo candidates who mobilize voters using social-identity bridging. We corroborate our results with an analysis of real-world voting behavior of recent senate and gubernatorial elections, finding that candidates who make more appeals to the Latino community by minimizing the social space do considerably better than candidates who do not do this. Implications are discussed.
| Alamillo and Collingwood (2016) |
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-à-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.
| Collingwood Barreto and Garcia-Rios (2014) |
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 e above and beyond party identification and other traditional vote-choice predictors.
| Barreto and Collingwood (2014) |
We examine correlates of Black political participation (voter registration) preVoting Rights Act (VRA) to determine whether White candidates’ covert pursuit of the Black vote had positive effects on Black political participation (covert cross-racial mobilization). Previous scholarship has discovered that both socio-economic and political variables account for variation in Black political participation, but scholars have not linked cross-racial mobilization to Black political participation during this time period, nor have they examined the covert ways that cross-racial mobilization occurred prior to the Voting Rights Act. We examine covert cross-racial mobilization in the context of a 1950 U.S. Senate campaign in Florida and show that some White candidates made covert appeals for the Black vote. We corroborate our qualitative findings with data suggesting that candidate covert appeals can possibly lead to increases in Black voter registration. We offer covert cross-racial mobilization as a new conceptualization that theoretically captures the reality White candidates faced when seeking to mobilize the Black vote during the pre-VRA time period
| Collingwood and Gonzalez O'Brien (Forthcoming) |
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.
| Barreto Collingwood Manzano (2010) |
Precinct voting and ecological inference
Social scientists and statisticians often use aggregate data to predict individual-level behavior because the latter are not always available. Various statistical techniques have been developed to make inferences from one level (e.g., precinct) to another level (e.g., individual voter) that minimize errors associated with ecological inference. While ecological inference has been shown to be highly problematic in a wide array of scientific fields, many political scientists and analysis employ the techniques when studying voting patterns. Indeed, federal voting rights lawsuits now require such an analysis, yet expert reports are not consistent in which type of ecological inference is used. This is especially the case in the analysis of racially polarized voting when there are multiple candidates and multiple racial groups. eiCompare was developed to easily assess two of the more common ecological inference methods: the EI method developed by King (1997), and the EI:R×C method developed by Rosen et al. (2001); Lau et al. (2006). The package facilitates a seamless comparison between these methods so that scholars and legal practitioners can easily assess the two methods and whether they produce similar or disparate findings.
| Collingwood et al eiCompare (2016) |
Scholars of race and politics are concerned with estimating individual level voting behavior from aggregate-level data. The most commonly used technique, King’s Ecological Inference (EI), has been criticized 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 (RxC). While some simulations show that RxC estimates are more accurate than the iterative EI technique, there has not been a side-by-side comparison of the two methods using real election data. We assess the two methods by comparing iterative EI and RxC models in a variety of RxC combinations including two candidates and two groups, three candidates and three groups, up to 12 candidates and three groups, and multiple candidates and four groups. Both methods produce similar results pointing to the presence of racially polarized voting, and very little differences emerge across the estimates.
| Barreto Collingwood Garcia-Rios Oskooii (RR) |
Quantitative Text Analysis
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.
| Jurka et al (2013) |
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.
| Collingwood and Wilkerson (2012) |
Book Project
Campaigning in a Racially Diversifying America: How candidates employ cross-racial mobilization to win elections
Currently under revise and resubmit at Oxford University Press, this book develops a theory of "Cross-Racial Mobilization" (CRM) that explains why, when, and how candidates for office make cross-racial electoral appeals. CRM is the process by which candidates of one race/ethnicity go about electorally mobilizing voters of another race/ethnicity. My main theoretical argument is that candidates weigh the potential costs and benefits of conducting cross-racial mobilization based on the size of the minority electorate (benefit) and the overall level of white racial hostility (cost) within their electoral jurisdiction. Taking into account a few other factors, if the conditions are favorable, the candidate is primed to conduct CRM; whereas if the conditions are unfavorable the candidate is much less likely to engage in CRM.
I also evaluate why some candidates are more or less successful at cross-racial mobilization. Given societal distrust between racial and ethnic groups, candidates must convince voters of different racial backgrounds that the candidate's appeals for support are authentic. Thus, candidates who can demonstrate cultural competence (e.g., D'Blasio's black son (and black wife) in an advertisement), and do so repeatedly (e.g., large number of appearances at race-related/cultural events over time), are much more likely to be successful with their CRM compared to candidates who do not have/do these things.
The book examines cross-racial mobilization trends over the last sixty years to gauge how politics in various places have changed as the American electorate has become increasingly diverse. Methodologically, the book employs a mixed method design. First, I use archival and case-study methods to test theory and make the work more accessible and interesting to lay readers. My in-depth research in over 30 archives in nine states over the course of four years in the U.S. South provides new insights on minority mobilization heretofore uncovered. Second, I compile large scale observational datasets (including candidate data and survey data) to statistically assess the book's theoretical arguments leading to generalizable conclusions. Finally, I use experimental approaches to assess causality in the context of voter response to candidate behavior in order to definitively demonstrate various aspects of the theory. In total, this mixed method design provides a rigorous assessment of cross-racial mobilization from a variety of angles. Cross-racial mobilization, is, indeed, the future of American politics.
Currently under revise and resubmit at Oxford University Press, this book develops a theory of "Cross-Racial Mobilization" (CRM) that explains why, when, and how candidates for office make cross-racial electoral appeals. CRM is the process by which candidates of one race/ethnicity go about electorally mobilizing voters of another race/ethnicity. My main theoretical argument is that candidates weigh the potential costs and benefits of conducting cross-racial mobilization based on the size of the minority electorate (benefit) and the overall level of white racial hostility (cost) within their electoral jurisdiction. Taking into account a few other factors, if the conditions are favorable, the candidate is primed to conduct CRM; whereas if the conditions are unfavorable the candidate is much less likely to engage in CRM.
I also evaluate why some candidates are more or less successful at cross-racial mobilization. Given societal distrust between racial and ethnic groups, candidates must convince voters of different racial backgrounds that the candidate's appeals for support are authentic. Thus, candidates who can demonstrate cultural competence (e.g., D'Blasio's black son (and black wife) in an advertisement), and do so repeatedly (e.g., large number of appearances at race-related/cultural events over time), are much more likely to be successful with their CRM compared to candidates who do not have/do these things.
The book examines cross-racial mobilization trends over the last sixty years to gauge how politics in various places have changed as the American electorate has become increasingly diverse. Methodologically, the book employs a mixed method design. First, I use archival and case-study methods to test theory and make the work more accessible and interesting to lay readers. My in-depth research in over 30 archives in nine states over the course of four years in the U.S. South provides new insights on minority mobilization heretofore uncovered. Second, I compile large scale observational datasets (including candidate data and survey data) to statistically assess the book's theoretical arguments leading to generalizable conclusions. Finally, I use experimental approaches to assess causality in the context of voter response to candidate behavior in order to definitively demonstrate various aspects of the theory. In total, this mixed method design provides a rigorous assessment of cross-racial mobilization from a variety of angles. Cross-racial mobilization, is, indeed, the future of American politics.
Statistics
To answer questions of political import, I am decidedly a mixed-methodologist. I conduct in-depth interviews with elite political actors, and have spent considerable time in the archives. I do however, love statistics, and writing code. I have therefore written several R packages, which are available on the CRAN repository, Github, and below.
eiCompare compares estimates from ecological inference routines (EI versus RxC). The package is available on CRAN, R's primary library repository, so within the R console users can type install.packages("eiCompare") to install the package. A github development version is also available. I am the author and maintainer, should you have any issues with the code or operability, please send me an email.
| eiCompare_1.9.tar.gz |
RTextTools is a one-stop-shop for supervised learning quantitative text analysis. I am one of the lead authors of the package, although Tim Jurka is the maintainer, and ultimately the lead author. The package is available on the CRAN.
Rweights was designed to iteratively automate survey post-stratification demographic weighting, using a standard raking routine. I developed this when I worked for the Washington Poll to make nightly weighting of data fast and easy, however the version never got past beta. If you are interested in using it, send me a line.
| Rweights3_1.0.tar.gz |
Rvoterdistance was designed primarily to calculate the distance between voters (usually from a voter file) and drop box locations, or all polling locations, then return the closest box to their residence. The code expedites the calculation process by porting the distance calculation to C++, improving speeds dramatically. The package is available on CRAN. Simply type install.packages("Rvoterdistance"). The development version is on github: install_github("lorenc5/Rvoterdistance"). Please send me a note if you have any questions or suggestions.
| Rvoterdistance_1.1.tar.gz |
| Rvoterdistance_1.0.zip |