Date of Award
2026
Degree Type
Open Access Dissertation
Degree Name
Economics, PhD
Program
School of Social Science, Politics, and Evaluation
Advisor/Supervisor/Committee Chair
Greg DeAngelo
Dissertation or Thesis Committee Member
Carlos Algara
Dissertation or Thesis Committee Member
Rainita Narender
Terms of Use & License Information
This work is licensed under a Creative Commons Attribution 4.0 License.
Rights Information
© 2026 Bianca Appel Kranzler
Keywords
Accountability, Crime, Election, Forecasting
Subject Categories
Economics
Abstract
This dissertation focuses on electoral accountability at the local and national levels. The first chapter focuses on judicial elections in Houston, while the last two chapters forecast the 2024 national election. This first chapter examines whether changes in local crime rates influence voter behavior in partisan judicial elections. Using precinct-level crime and election data from Houston, Texas, this study tests whether a quarterly increase in crime raises the probability that a voting precinct flips from a Democratic to a Republican majority in local district judge elections. A two-way fixed-effects model with precinct and year fixed effects is estimated across seven specifications, using both crime rate changes and media coverage of crime as measures of crime perception. The results indicate that neither variable is a statistically significant predictor of partisan vote flipping. These findings suggest that voters in low-information judicial elections do not systematically respond to local crime trends when selecting judges, and that partisan affiliation or name recognition are more likely to drive voting decisions. The results contribute to the broader discussion on the trade-off between judicial independence and democratic accountability in partisan electoral systems. The second chapter considers both presidential approval and party brand differentials, as measured by the generic ballot, to forecast the 2024 US presidential and congressional elections. Although both variables are leveraged to forecast collective partisan election outcomes, we consider the variables together as distinct determinants of partisan fortunes at both the executive and legislative levels. First, using a novel time series of mass national opinion since 1937, we show that presidential approval and generic brands are distinct conceptual and empirical measures of mass public assessments of collective institutions. Second, in a series of fully specified models validated with out-of-sample predictions, we show that presidential approval is the main predictor of presidential elections, yet, perhaps surprisingly, the vast bulk of the incumbent party’s performance in congressional elections is explained by partisan brands. Lastly, we forecast the 2024 U.S. national elections and find that Republicans are well positioned to win back the White House this November. By contrast, our model forecasts control of both chambers of the US Congress to be essentially a tied contest. The third chapter considers (1) presidential approval, (2) party brand differentials, as measured by the generic ballot, and (3) the presidential candidate polling differentials during the general election campaign to forecast the 2024 U.S. presidential and congressional elections. While all these three mass public opinion variables are leveraged to forecast collective partisan election outcomes, we consider the variables together as theoretically distinct determinants of partisan fortunes at both the executive and legislative levels and make the following contributions. First, using novel time-series data of mass opinion since 1937, we show that all three variables are weakly correlated and thus distinct conceptual and empirical measures of mass public assessments of partisan stimuli. Second, we use these three mass opinion variables to specify a unified model of U.S. national elections which better predicts variation in electoral outcomes compared to the standard forecasting approaches, finding that congressional election outcomes are predicted by party brands while presidential elections are predicted by presidential approval and the presidential candidate polling differentials heading into election day. Lastly, we validate our forecasting model using out-of-sample and 2024 forecasting predictions against other standard forecasting approaches.
ISBN
9798244862225
Recommended Citation
Kranzler, Bianca Appel. (2026). Essays on Election. CGU Theses & Dissertations, 1111. https://scholarship.claremont.edu/cgu_etd/1111.
