Affiliation:
1. California Institute of Technology , Pasadena , CA , USA
2. American University , Washington , DC , USA
Abstract
Abstract
How do we ensure a statewide voter registration database’s accuracy and integrity, especially when the database depends on aggregating decentralized, sub-state data with different list maintenance practices? We develop a Bayesian multivariate multilevel model to account for correlated patterns of change over time in multiple response variables, and label statewide anomalies using deviations from model predictions. We apply our model to California’s 22 million registered voters, using 25 snapshots from the 2020 presidential election. We estimate countywide change rates for multiple response variables such as changes in voter’s partisan affiliation and jointly model these changes. The model outperforms a simple interquartile range (IQR) detection when tested with synthetic data. This is a proof-of-concept that demonstrates the utility of the Bayesian methodology, as despite the heterogeneity in list maintenance practices, a principled, statistical approach is useful. At the county level, the total numbers of anomalies are positively correlated with the average election cost per registered voter between 2017 and 2019. Given the recent efforts to modernize and secure voter list maintenance procedures in the For the People Act of 2021, we argue that checking whether counties or municipalities are behaving similarly at the state level is also an essential step in ensuring electoral integrity.
Subject
Sociology and Political Science,Statistics and Probability,Economics, Econometrics and Finance (miscellaneous)
Reference41 articles.
1. 117th Congress (2021–2022). 2021. For the People Act of 2021.
2. Alvarez, R. M. 2005. Potential Threats to Statewide Voter Registration Systems. Caltech/MIT Voting Technology Project Working Paper 40. Caltech/MIT Voting Technology Project.
3. Alvarez, R. M., J. Jonas, W. E. Winkler, and R. N. Wright. 2009. “Interstate Voter Registration Database Matching: The Oregon-Washington 2008 Pilot Project.” In Proceedings of the 2009 Electronic Voting Technology Workshop-Workshop on Trustworthy Elections.
4. Alvarez, R. M., N. Adams-Cohen, S.-Y. Silvia Kim, and Y. Li. 2020. Securing American Elections: How Data-Driven Election Monitoring Can Improve Our Democracy. New York, NY: Cambridge University Press.
5. Ansolabehere, S., and E. Hersh. 2010. The Quality of Voter Registration Records: A State-By-State Analysis. Report 6. Caltech/MIT Voting Technology Project.
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献