Bayesian Analysis of State Voter Registration Database Integrity

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.

Patterns of handgun divestment among handgun owners in California

Background Little is known about voluntary divestment of firearms among US firearm owners. Here, we aim to estimate the proportion of handgun owners who divest their handguns in the years following their initial acquisition; examine the timing, duration, and dynamics of those divestments; and describe characteristics of those who divest. Methods We use data from the Longitudinal Study of Handgun Ownership and Transfer, a cohort of registered voters in California with detailed information on 626,756 adults who became handgun owners during the 12-year study period, 2004–2016. For the current study, persons were followed from the time of their initial handgun acquisition until divestment, loss to follow-up, death, or the end of the study period. We describe the cumulative proportion who divest overall and by personal and area-level characteristics. We also estimate the proportion who reacquired handguns among persons who divested. Results Overall, 4.5% (95% CI 4.5–4.6) of handgun owners divested within 5 years of their first acquisition, with divestment relatively more common among women and among younger adults. Among those who divested, 36.6% (95% CI 35.8–37.5) reacquired a handgun within 5 years. Conclusions Handgun divestment is rare, with the vast majority of new handgun owners retaining them for years.

Universal Mail Ballot Delivery Boosts Turnout: The Causal Effects of Sending Mail Ballots to All Registered Voters

Some American states have transitioned to universal voting-by-mail, where all registered voters receive a ballot in the mail. While this practice was growing in popularity prior to the 2020 general election, universal voting-by-mail was suddenly used in a larger number of states due to the COVID-19 pandemic. In this paper, we utilize a unique situation in which registered voters in some legislative districts in Los Angeles County were subjected to universal voting-by-mail in the March 2020 primary, while most of the rest of the Los Angeles County electorate was not. Using difference-in-difference and regression discontinuity designs, we estimate the causal effects of universal voting-by-mail on voter turnout and on who votes. Our results indicate that voter turnout increased by around 3\% for voters who do not automatically receive a ballot in the mail otherwise, and the increase is larger for registered partisan voters than those without a party affiliation.

Driving turnout: the effect of car ownership on electoral participation

Inequalities in voter participation between groups of the population pose a problem for democratic representation. We use administrative data on 6.7 million registered voters to show that a previously-ignored characteristic of voters—access to a personal automobile—creates large disparities in in-person voting rates. Lack of access to a car depresses election day voter turnout by substantively large amounts across a variety of fixed-effects models that account for other environmental and voter characteristics. Car access creates the largest hindrance to voting for those people who live farther from the polls. These effects do not appear for absentee voting, suggesting a simple policy solution to solve large disparities in political participation. This study contributes to the theoretic understanding of political participation as well as the impact of potential policy reforms to solve participatory gaps.

Bayesian Analysis of State Voter Registration Database Integrity

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--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.

The Politics of Vaccine Hesitancy in the United States

ObjectiveWhy are Americans COVID-19 vaccine hesitant? We test social science hypotheses for vaccine hesitancy, focusing on partisanship, trust in institutions, and social-demographic characteristics of registered voters.MethodsWe use survey data from a representative sample of American registered voters collected in November 2020 to study vaccine hesitancy, and the reasons for vaccine hesitancy, at a point in time before the vaccine was available and hence show underlying responses based on beliefs and not on clinical trial data. We use multivariate logistic regression models to test hypotheses on vaccine hesitancy.ResultsWe find that consistently similar groups of people tend to be vaccine hesitant. Specifically, Black voters, those between the ages of 45 and 64, female voters, voters without college degrees, voters not worried about the spread of COVID-19, and voters who are concerned about government and the CDC’s handling of the COVID-19 pandemic, were vaccine hesitant. We also provide intriguing results showing the nuanced reasons that the vaccine hesitant provide.ConclusionsOur analysis allows us to establish important baseline information from a social science perspective on vaccine hesitancy at a crucial time, right before COVID-19 vaccines were beginning to be made available to adult Americans. What emerges from our analysis is a nuanced perspective on vaccine hesitancy in the United States, from this important point in the history of the COVID-19 pandemic.