Racial 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.

No abstract

The electoral dominance of “quality” candidates—political insiders with a history of holding office—is well‐established. However, research on the recent rise in successful political neophytes is less studied. Despite longstanding trends in the predominance of experienced candidates in primary elections, nearly half of all quality candidates who ran in non‐incumbent races lost to a candidate without prior electoral experience in 2018. In this article, we investigate the success of political newcomers in elections for the U.S. House of Representatives by examining a topic often overlooked in the growing literature on primaries: campaign finance. We show that, from 2016 to 2020, political newcomers saw (1) greater success in future fundraising, and (2) an increased likelihood of primary election victory when they garnered more early contributions from outside their district. This contrasts with prior elections, where early money from inside a candidate's own congressional district served as the strongest predictor of future fundraising and electoral success.

How do citizens interpret contentious symbols that pervade their community? And what downstream effects does state protection of these symbols have on how citizens of different backgrounds feel they belong in their community? We approach these questions through the lens of race and Confederate monuments in the American South. We rely on two original surveys to illustrate 1) the symbolic meanings Americans attach to these monuments and 2) how state protection of them impacts residents’ feelings of belonging. We find that perceptions of Confederate monuments vary by race: White U.S. residents are drastically less likely to perceive them as symbolic of racial injustice than are Black U.S. residents. Further, state protection of Confederate monuments leads to a diminished sense of belonging among Blacks, while leaving Whites unaffected. This research moves beyond scholarship examining simple support for or opposition toward contentious symbols, developing a deeper understanding of what meaning those symbols can hold for individuals and what their impacts are on individuals’ feelings of belonging and engagement in their communities.

Identifying 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.