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.
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. 2019. “Evaluating the Quality of Changes in Voter Registration Databases.” American Politics Research 48(6): 670-676.
Selected Working Papers
Getting Settled in Your New Home: The Costs of Moving on Voter Turnout
(job market paper)
What is the dynamic impact of moving on turnout? Moving depresses turnout by imposing various costs on voters. However, movers eventually settle down, and such detrimental effects can disappear over time. I measure these dynamics using United States Postal Services (USPS) data and detailed voter panel data from Orange County, California. Using a generalized additive model, I show that previously registered voters who move close to the election are significantly less likely to vote (at most -16.2 percentage points), and it takes at least six months on average for turnout to recover. This dip and recovery is not observed for within-precinct moves, suggesting that costs of moving matter only when the voter’s environment has sufficiently changed. Given this, can we accelerate the recovery of movers’ turnout? I evaluate an election administration policy that resolves their re-registration burden. This policy proactively tracks movers, updates their registration records for them, and notifies them by mailings. Using a natural experiment, I find that it is extremely effective in boosting turnout (+5.9 percentage points). This success of a simple, pre-existing, and non-partisan safety net is promising, and I conclude by discussing policy implications.
Kim, Seo-young Silvia, and Jan Zilinsky. 2021. The Divided (But Not More Predictable) Electorate: A Machine Learning Analysis of Voting in American Presidential Elections
Political parties are increasingly homogeneous both ideologically and demographically. With increased party-line voting, a natural corollary of sorting is that membership in demographic groups should be increasingly prognostic of vote choice. We argue that predictability of voting decisions is a useful quantity of interest for testing hypotheses from the literature on partisan and demographic sorting. Contrary to expectations, we find that demographic sorting has not produced a very predictable electorate. Tree-based machine learning models, trained on demographic labels from public opinion surveys between 1952 and 2020, predict only 63.5% of out-of-sample vote choices correctly on average. Moreover, demographics have not grown more predictive over time, while partisanship has. Partisanship’s diagnosticity has risen in absolute terms, and its relative dominance over ideology has been stable for the last seven decades. Additional data about voters can still yield superior predictions, but its added value decreases over time as partisanship’s predictive power grows.