Statistical Assessments of Representational Reforms: A Case Study from Los Angeles
本文开发了一种统计框架，通过多种分析方法评估选举改革对代表性的影响，发现增加市议会席位效果有限，而取消低投票率初选和采用多成员选区能显著改善代表性。
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Representative Papers
Bayesian Uncertainty Quantification for Ranked Choice Voting Polls
Traditional polling methods struggle to quantify uncertainty in ranked-choice voting (RCV) elections, as outcomes depend on complex multi-round elimination paths that cannot be captured by a single parameter or simple margin of error. This study proposes the first Bayesian framework for RCV polling analysis, centering on win probabilities as the core metric. By leveraging conjugate priors, the method efficiently models the path-dependent structure inherent in RCV, overcoming limitations of frequentist approaches. Validated through posterior simulations combined with real ballot data from the 2021 New York City Democratic mayoral primary and the 2022 Alaska at-large congressional special election, the approach reveals significant shortcomings in conventional uncertainty assessments and demonstrates practical utility in real-world polling, as illustrated in Data for Progress surveys.
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Estimating the Impact of Case Management in MDLs: Lone Pine Orders and Bellwether Trials
This study examines how judicial case management affects outcomes in multidistrict litigation (MDL), focusing on Lone Pine orders and bellwether trials as mechanisms to mitigate coercive settlement pressures arising from information asymmetry and non-meritorious claims. Leveraging a comprehensive, longitudinal MDL panel dataset spanning 1992–2017, we employ an event-study design combined with a difference-in-differences (DID) strategy—integrating judicial opinions and case disposition records—to causally identify the impact of Lone Pine orders on MDL resolution rates. Results show that Lone Pine orders significantly increase the number of cases resolved within MDL proceedings, enhancing both procedural efficiency and claim-screening quality. Our contribution is the first nationally representative, long-term, causally identified empirical evidence on judicial management tools in MDLs, providing critical policy insights for improving class-action governance and federal judicial administration.
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Statistical Assessments of Representational Reforms: A Case Study from Los Angeles
本文开发了一种统计框架，通过多种分析方法评估选举改革对代表性的影响，发现增加市议会席位效果有限，而取消低投票率初选和采用多成员选区能显著改善代表性。
0 citationsRead paper
Bayesian Uncertainty Quantification for Ranked Choice Voting Polls
Traditional polling methods struggle to quantify uncertainty in ranked-choice voting (RCV) elections, as outcomes depend on complex multi-round elimination paths that cannot be captured by a single parameter or simple margin of error. This study proposes the first Bayesian framework for RCV polling analysis, centering on win probabilities as the core metric. By leveraging conjugate priors, the method efficiently models the path-dependent structure inherent in RCV, overcoming limitations of frequentist approaches. Validated through posterior simulations combined with real ballot data from the 2021 New York City Democratic mayoral primary and the 2022 Alaska at-large congressional special election, the approach reveals significant shortcomings in conventional uncertainty assessments and demonstrates practical utility in real-world polling, as illustrated in Data for Progress surveys.
0 citationsRead paper
Estimating the Impact of Case Management in MDLs: Lone Pine Orders and Bellwether Trials
This study examines how judicial case management affects outcomes in multidistrict litigation (MDL), focusing on Lone Pine orders and bellwether trials as mechanisms to mitigate coercive settlement pressures arising from information asymmetry and non-meritorious claims. Leveraging a comprehensive, longitudinal MDL panel dataset spanning 1992–2017, we employ an event-study design combined with a difference-in-differences (DID) strategy—integrating judicial opinions and case disposition records—to causally identify the impact of Lone Pine orders on MDL resolution rates. Results show that Lone Pine orders significantly increase the number of cases resolved within MDL proceedings, enhancing both procedural efficiency and claim-screening quality. Our contribution is the first nationally representative, long-term, causally identified empirical evidence on judicial management tools in MDLs, providing critical policy insights for improving class-action governance and federal judicial administration.
0 citationsRead paper