Bayesian Analysis of State Voter Registration Database Integrity
Bibliographic Data
| ID | 13036664 |
|---|---|
| Authors | Jian Cao (0000-0001-9266-1970, California Institute of Technology, corresponding author), Seo-Young Silvia Kim (0000-0002-8801-9210, American University), R Michael Alvarez (0000-0002-8113-4451, California Institute of Technology) |
| Year | 2022 |
| Volume | 13 |
| Issue | 1 |
| Pages | 19-40 |
| Publication date | 2022-01-14 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Statistics Politics and Policy (JOURNAL) |
| Journal identifiers | ISSN: 2151-7509 • E-ISSN: 2194-6299 |
| Publisher | De Gruyter (PUBLISHER • DE) |
| DOI | 10.1515/spp-2021-0016 |
| OpenAlex | W4226161555 |
| Language | EN |
| Citations received | 2 |
| References cited | 31 |
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
Bayesian probability · Database · Econometrics · Interquartile range · Political science · Range (aeronautics · Statistics · Voter registration · Voting · Census and Population Estimation · Computer Science · Electoral Systems and Political Participation · Internet Traffic Analysis and Secure E-voting · Law · Mathematics · Artificial Intelligence
Beta Regression for Modelling Rates and Proportions
Monte Carlo sampling methods using Markov chains and their applications
BRMS
The Changing Nature … and Costs … of Election Administration
Election Administration Finance in California Counties
The Effect of Administrative Burden on Bureaucratic Perception of Policies
One Person, One Vote
Access Denied? Investigating Voter Registration Rejections in Florida
Hava and the States
Measuring Voter Registration and Turnout in Surveys
Can Registration-Based Sampling Improve the Accuracy of Midterm Election Forecasts
Making Every Vote Count
Detecting Election Fraud from Irregularities in Vote-Share Distributions
Our Voter Rolls Are Cleaner Than Yours
Evaluating the Quality of Changes in Voter Registration Databases
Verifying Voter Registration Records
Election administration and perceptions of fair elections
| Unique citing works | 2 |
|---|---|
| Citations per year | 0,67 |
| Citation span | 2023 - 2024 (2) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 2 |