Alvarez, R. Michael, Nicholas J. Adams-Cohen, Seo-young Silvia Kim, Yimeng Li. 2020. “Securing Elections: How Data-Driven Election Monitoring Can Improve Democracy.” Cambridge University Press.
Peer Reviewed Journal Articles
- Lopez, Jennifer, R. Michael Alvarez, and Seo-young Silvia Kim. 2022. “Latinos, Group Identity, and Equal Opportunity on the 2020 California Ballot.” Social Science Quarterly. Published online Oct 2022.
- Kim, Seo-young Silvia. 2022. “Automatic Voter Re-registration as a Housewarming Gift: Quantifying Causal Effects on Turnout Using Movers.” American Political Science Review. Published online Oct 2022.
- Kim, Seo-young Silvia, and Jan Zilinsky. 2022. “Division Does Not Imply Predictability: Demographics Continue to Reveal Little About Voting and Partisanship.” Political Behavior. Published online Aug 2022.
- Cao, Jian, Seo-young Silvia Kim, and R. Michael Alvarez. 2022. “Bayesian Analysis of State Voter Registration Database Integrity.” Statistics, Politics and Policy 13(1): 19–40.
- Kim, Seo-young Silvia, Hannah Lebovits, and Sarah Shugars. 2022. “Networking 101 for Graduate Students: Building a Bigger Table.” PS: Political Science & Politics 55(2): 307–12.
- Kim, Seo-young Silvia, R. Michael Alvarez, and Christina M. Ramirez. 2020. “Who Voted in 2016? Using Fuzzy Forests to Understand Voter Turnout.” Social Science Quarterly 101(2): 978–88.
- Alvarez, R. Michael, Jonathan N. Katz, and Seo-young Silvia Kim. 2020. “Hidden Donors: The Censoring Problem in U.S. Federal Campaign Finance Data.” Election Law Journal: Rules, Politics, and Policy 19(1): 1–18.
- Kim, Seo-young Silvia, Spencer Schneider, and R. Michael Alvarez. 2020. “Evaluating the Quality of Changes in Voter Registration Databases.” American Politics Research 48(6): 670–676.
Selected Working Papers
We argue that WinRed, the newly emergent online campaign fundraising platform of the Republican Party, supports theories of parties as endogenous institutions (Aldrich 2011), in which parties evolve to help their members achieve their ambitions. We show that given historical contexts of coordination failures and higher fundraising pressures, the Republican Party implemented a top-down centralization in 2019 to enforce member contributions to a “public good,” i.e., coordinating on a single platform. After analyzing the party’s theoretical motivations, we investigate the characteristics that explain which congressional candidates in the 2020 general election complied with the party’s efforts. Finally, we examine whether joining the platform was beneficial to individual candidates in fundraising outcomes using a matching approach. Although superior fundraisers did self-select into WinRed, joining the centralized platform had short-term benefits, especially for non-incumbents and House candidates, and more small-dollar donations. We discuss the implications of this new centralized structure of the party.
Donate to Help Us Fight Back: Persuasion Rhetoric in Political Fundraising (with Jan Zilinsky and Brian Brew).
How do campaigns differentially target donors and voters? We show that fundraising messages are an important class of electoral persuasion that reveals how campaigns perceive and target their “financial electorate.” Because candidates’ voters and donors can differ significantly, we theorize that rhetoric is chosen strategically for the target audience. Using data from the Facebook Ad Library for U.S. congressional candidates in the 2020 general election, we distinguish ads by persuasion targets. Then we use text analysis to test whether donor-targeting messages are, on average, more toxic, negative, and likely to reference a polarizing political figurehead (Donald Trump). While these expectations were largely borne out, there was significant variation by party and chamber. For example, Republican House candidates’ appeals were more toxic than Democrats’ and even more so when soliciting money. As the scramble for donations intensifies, these characteristics of appeals for cash may further polarize the electorate.
Voter files are an essential tool for both election research and campaigns, but relatively little work has established best practices for using these data. We focus on how the timing of voter file snapshots affects the most commonly cited advantage of voter file data: accurate measures of who votes. Outlining the panel structure inherent in voter file data, we demonstrate that opposing patterns of accretion and attrition in the voter registration list result in temporally-dependent bias in estimates of voter turnout for a given election. This bias impacts samples for surveys, experiments, or campaign activities by skewing estimates of the potential and actual voter populations; low-propensity voters are particularly impacted. We provide an approach that allows researchers to measure the impact of this bias on their inferences. We then outline methods that measurably reduce this bias, including combining multiple snapshots to preserve the turnout histories of dropped voters.
Why Vote in Person in a Pandemic? Using Machine Learning to Predict Voting Methods (with Akhil Bandreddi and R. Michael Alvarez.)
What spurs voters to vote in person, despite an established universal vote-by-mail (VBM) system and a once-in-a-century pandemic? We explore this question with official voter data from Colorado, a vote-by-mail state since 2013, but where 6% of voters still vote in person. Using multiclass classification, we analyze (1) the choice between voting by mail (VBM), voting in person, and not voting in the 2020 general election, and (2) the choice to switch to in-person voting despite having used VBM in previous cycles. The results suggest that the choice of voting modes is mainly habitual; variables such as registration date, active status, and past choices of turnout and voting mode are among the top most important variables. Local variations of COVID-19 and demographics hardly mattered. Notably, Republican partisanship plays an important role in predicting “switchers” to in-person voting; indeed, the probability of switching to in-person voting was 5.2% conditional on being a Republican as opposed to 1.9% conditional on being a Democrat.