Ensuring 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.
We describe the creation and quality assurance of a dataset containing nearly all available precinct-level election results from the 2016, 2018, and 2020 American elections. Precincts are the smallest level of election administration, and election results at this granularity are needed to address many important questions. However, election results are individually reported by each state with little standardization or data quality assurance. We have collected, cleaned, and standardized precinct-level election results from every available race above the very local level in almost every state across the last three national election years. Our data include nearly every candidate for president, US Congress, governor, or state legislator, and hundreds of thousands of precinct-level results for judicial races, other statewide races, and even local races and ballot initiatives. In this article we describe the process of finding this information and standardizing it. Then we aggregate the precinct-level results up to geographies that have official totals, and show that our totals never differ from the official nationwide data by more than 0.457%.
I construct a new measure of candidate quality using political endorsements made by local newspapers. Similar to expert opinions, newspaper editorial board endorsements are highly-informed judgements that reflect quality differences between candidates, once accounting for the partisan preferences of the newspapers. Using a new data set of over 22,000 local newspaper endorsements, I simultaneously estimate the quality differences between candidates in thousands of elections between 1950-2022 along with a dynamic measure of the partisan slant of hundreds of local newspapers across the United States. After validating the endorsement-based measures of quality and slant, I use the quality differential measure to assess the effect that candidate quality has on election results and governing performance. I conclude by discussing how the newspaper endorsement-based measures have a strong potential to help advance our understanding of the impact of candidate quality and media bias on political representation.
This paper investigates the direct effects of political endorsements on election results. I analyze county-level vote shares in statewide elections for Governor and U.S. Senate, where voters across the state participate in the same election but are "treated" with different local newspaper exposure. I find that credible endorsements have a small but statistically significant effect on vote shares of about 1.3 percentage points. I also demonstrate that county-level newspaper bias and vote shares are positively correlated, and that candidate quality can explain about three-fourths of the raw correlation between newspaper endorsements and electoral performance. In aggregate, endorsement effects only change election outcomes in a small subset of very close elections, but in those cases endorsements almost always help the higher quality candidate win. The results suggest that local newspapers can help improve political outcomes and that some voters may rely on newspaper endorsements to inform their perceptions of candidate quality.
Spatial models of vote choice predict that valence factors like candidate quality matter less to voters as differences between policy platforms increase. However, this and related claims are hard to test because it is difficult to measure candidate quality. I construct a measure of candidate quality differences using over 23,000 endorsements from newspapers around the United States. I estimate candidate quality differences in elections between 1950-2020 to evaluate the effects of quality on election outcomes. I find that the higher quality candidate wins in a large majority of elections in the United States. A one-standard-deviation increase in relative quality increases two-party vote share by 4 percentage points. Contrary to popular beliefs, the effect of quality differences on vote shares has actually increased slightly over time. However, the decrease in competitive elections over the same period has reduced the share of elections where candidate quality can plausibly alter the election’s outcome.
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