Book

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

Addressing Measurement Errors in Ranking Questions for Social Sciences (with Yuki Atsusaka.)

We introduce a statistical framework to improve ranking data analysis by addressing measurement errors in ranking questions. First, we propose a formal framework to define measurement errors arising from random responses—arbitrary and meaningless responses based on a wide range of random patterns. We then quantify bias due to random responses, show that the bias may change our conclusion in any direction, and clarify why item order randomization alone does not solve the statistical issue. Next, we introduce our methodology based on two key design-based considerations. Using item order randomization, researchers can learn about the direction of the bias due to random responses. Leveraging anchor questions—auxiliary questions whose correct answers are known, scholars can estimate the proportion of random responses, which enables our bias-corrected estimator for ranking data. We illustrate our methods by studying people’s relative importance of party identification compared to their gender, religion, and race/ethnicity.

Support and Preference for Grassroots Fundraising (with Yimeng Li.)

Do Americans support small individual donations over other sources of political fundraising? Small online contributions are becoming more prevalent, and political elites and the media often idealize them as leveling the playing field in the American political ecosystem. However, we have little understanding of whether and, if any, how much the public supports small donations as a campaign funding source over others and whether such preferences translate into tangible changes in political behavior. Using a conjoint experiment via a nationally representative survey of U.S. citizens, we test whether candidates with higher dependence on small individual donors are more likely to be chosen. Surprisingly, candidates relying more on small donors attract a higher likelihood of vote choice and candidate ratings, not just within primaries or for Democrats, but across primaries, general elections, and all partisan affiliations. Moreover, the public believes that there should be more small donations in American elections and that, compared to the current baseline, the ideal composition of campaign funding should rely less on PACs and large individual donations and more on small donations and other sources such as candidate self-financing. Such beliefs are unshaken when presented with information about lawmakers with the highest reliance on small donors, who are generally perceived as outsiders or ideologically extreme.

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.

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.


Book Reviews