Suchergebnisse

Abstract
Identifying racial disparities in policy and politics is a pressing area of research within the United States. Where early work made use of identifying potentially noisy correlations between county or precinct demographics and election outcomes, the advent of Bayesian Improved Surname Geocoding (BISG) vastly improved estimation of race by employing voter lists. Machine Learning (ML)-modified BISG in turn offers accuracy gains over the static – and potentially outdated – surname dictionaries present in traditional BISG. However, the extent to which ML might substantively alter the policy and political implications of redistricting is unclear given its improvements in voter race estimation. Therefore, we ascertain the potential gains of ML-modified BISG in improving the estimation of race for the purpose of redistricting majority-minority districts. We evaluate an ML-modified BISG program against traditional BISG estimates in correctly estimating the race of voters for creating majority-minority congressional districts within North Carolina and Georgia, and in state assembly districts in Wisconsin. Our results demonstrate that ML-modified BISG offers substantive gains over traditional BISG, especially in diverse political geographic units. Further, we find meaningful improvements in accuracy when estimating majority-minority district racial composition. We conclude with recommendations on when and how to use the two methods, in addition how to ensure transparency and confidence in BISG-related research.

AbstractIdentifying the geographic constituencies of representatives is among the most crucial, yet challenging, aspects of state and local politics research. Regularly changing district lines, incomplete data, and computational obstacles can present barriers to matching individuals to their respective districts. Geocoding residential addresses is the ideal method for matching purposes. However, cost constraints can limit its applicability for many researchers, leading to geographic assignment methods that use polygonal units, such as ZIP codes, to estimate constituency membership. In this study, we quantify the trade-offs between three geographic assignment matching methods – centroid, geographic overlap, and population overlap matching – on the assignment of individual voters to state legislative districts. We confirm that population overlap matching produces the highest accuracy in assigning voters to their state legislative districts when polygonal location data are all that is available. We validate this finding by improving model estimates of lobbying influence through a replication analysis of Bishop and Dudley (2017), "The Role of Constituency, Party, and Industry in Pennsylvania's Act 13," State Politics and Policy Quarterly 17 (2): 154–79. Our replication suggests that distinguishing between out-of-district and in-district donations reveals a greater impact for in-district lobbying efforts. We make evident that population overlap assignment can confidently be used to identify constituencies when precise location data is not available.

AbstractEnsuring descriptive representation of racial minorities without packing minorities too heavily into districts is a perpetual difficulty, especially in states lacking voter file race data. One advance since the 2010 redistricting cycle is the advent of Bayesian Improved Surname Geocoding (BISG), which greatly improves upon previous ecological inference methods in identifying voter race. In this article, we test the viability of employing BISG to redistricting under two posterior allocation methods for race assignment: plurality versus probabilistic. We validate these methods through 10,000 redistricting simulations of North Carolina and Georgia's congressional districts and compare BISG estimates to actual voter file racial data. We find that probabilistic summing of the BISG posteriors significantly reduces error rates at the precinct and district level relative to plurality racial assignment, and therefore should be the preferred method when using BISG for redistricting. Our results suggest that BISG can aid in the construction of majority-minority districts during the redistricting process.

AbstractRedistricting poses a potential harm to American voters in limiting choice and accountability at the polls. Although voters still retain their right to contact their representatives, research shows that the confusion created when redistricting divides ZIP codes confounds the constituent–representative link. We build on existing research that shows splitting ZIP codes across multiple congressional districts leads to harms in representation. Specifically, we examine the role of splitting ZIP codes on the recognition of the racial group membership of one's Congressional representative, a foundational component of the descriptive representation of racial minority voters via minority–majority districts in the United States. We find that citizens living in split ZIP codes are significantly less likely to know the race of their member of Congress. This occurs even when controlling for a host of factors including the race and partisanship of the constituent, the tenure of the member, and the amount of time a constituent has lived in their congressional district. Our work provides further evidence of the democratic harms experienced by American citizens living in ZIP codes that are split between multiple congressional districts. This work also points to the representational harms produced by poor district design on the representation of American voters.

Citizens need to trust in the integrity of news reporting for the free press to fulfill its role as a democratic institution that enables citizens to hold representatives accountable. Growing research has shown that increased use of social media erodes trust in legacy news. However, trust-reducing social media messages are not contained to social media platforms; they are widely recirculated by the mainstream media. We argue that mainstream media corporations select social media messages to recirculate precisely because of their trust-reducing features in order to respond to short-term competitive market incentives. We turn to Donald Trump's Twitter posts as examples of trust-reducing messages and show that the media cites more trust-reducing messages more quickly and more frequently than less trust-reducing messages. These findings implicate mainstream media corporations alongside social media platforms in the systematic and ongoing degradation of trust in legacy news reporting.

AbstractRacial identification is a critical factor in understanding a multitude of important outcomes in many fields. However, inferring an individual's race from ecological data is prone to bias and error. This process was only recently improved via Bayesian improved surname geocoding (BISG). With surname and geographic-based demographic data, it is possible to more accurately estimate individual racial identification than ever before. However, the level of geography used in this process varies widely. Whereas some existing work makes use of geocoding to place individuals in precise census blocks, a substantial portion either skips geocoding altogether or relies on estimation using surname or county-level analyses. Presently, the trade-offs of such variation are unknown. In this letter, we quantify those trade-offs through a validation of BISG on Georgia's voter file using both geocoded and nongeocoded processes and introduce a new level of geography—ZIP codes—to this method. We find that when estimating the racial identification of White and Black voters, nongeocoded ZIP code-based estimates are acceptable alternatives. However, census blocks provide the most accurate estimations when imputing racial identification for Asian and Hispanic voters. Our results document the most efficient means to sequentially conduct BISG analysis to maximize racial identification estimation while simultaneously minimizing data missingness and bias.

Featuring cutting edge research from top elections scholars, Pandemic at the Polls examines the context under which the 2020 U.S. elections were buffeted by a global pandemic unfolding during a hotly contested presidential election, how states and voters responded, and how to fortify the administration of elections for the future.