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

Selected Working Papers

Keep Winning with WinRed? Online Fundraising Platform as the Party’s Public Good (with Zhao Li.)

We show that WinRed’s emergence as Republicans’ leading online fundraising platform proves how parties can evolve to help members achieve their ambitions (Aldrich 2011). We document that despite mounting fundraising pressures, Republicans’ adaptation to online fundraising had been slow and disjointed until 2019, while Democrats already had a coordinated fundraising platform (ActBlue). We theorize that the Republican Party, internalizing the collective benefits of coordinating members onto a single fundraising platform, created WinRed to rival ActBlue and implemented a top-down approach to enforce candidate adoption of this platform. We find that, in contrast to ActBlue, WinRed’s public rhetoric extols its value to the party’s shared fortunes, and that Republicans coordinated their online solicitation strategies on WinRed. Furthermore, a panel matching design shows the promise delivered: candidates, especially women and those reliant on small-dollar donations, reaped significant fundraising benefits upon joining WinRed. We discuss how this centralization may transform the GOP’s future.

Donate to Help Us Fight Back: Persuasion Rhetoric in Political Fundraising (with Jan Zilinsky and Brian Brew).

Political candidates utilize social media to mobilize supporters, persuade voters, and raise money. However, little is known about the structure of mass electoral appeals when donors are the primary target instead of voters. Because candidates’ donors and voters can differ significantly, with donors more partisan and ideologically extreme on average, we theorize that candidates use strategic rhetoric tailored to specific audiences. To analyze how campaigns perceive and target their “financial electorate,” we leverage data from the Facebook Ad Library for 2020 U.S. congressional candidates and distinguish political ads by their persuasion targets. Using text analysis, we test the hypotheses that donor-targeting messages are more toxic, negative, and likely to reference a polarizing president than voter-targeting messages. The results support our hypotheses, and Republican candidates, on average, used more toxic language than their Democratic counterparts. As campaigns’ scramble for donations intensifies, these characteristics of fundraising appeals may further polarize the electorate.

When Do Voter Files Accurately Measure Turnout? How Transitory Voter File Snapshots Impact Research and Representation (with Bernard Fraga.)

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.