Economist at ERIA

Working Papers

ARCHIVE: A database of tabulated results from American ranked-choice voting elections   [link]

In the past two decades, a growing number of ranked-choice voting (RCV) elections have been conducted in various jurisdictions across the United States. However, tabulated results of RCV have been reported and stored in widely different styles across places and years, making it infeasible for researchers to perform systematic analyses of vote tabulations. We introduce ARCHIVE: a database of standardized tabulated results for over 7600 round-level candidate vote counts from 514 American RCV elections, 2004-2024. To construct the database, we develop a methodological procedure based on large-language models that semi-automatically collect, standardize, and store candidate vote counts while instantly validating the resulting information. Our database releases multiple levels of data, including election metadata, round-level attributes, and candidate-level information with consistent election identifiers, allowing users to address key questions in electoral competition under RCV. To illustrate, we show how users may estimate the effective number of candidates per round.

Peer Reviewed Publications

Evaluating Predictors of Participation in Telephone-Base Social Connectedness Interventions for Older Adults: A Dual Machine-Learning and Regression Approach Gerontology & Geriatric Medicine, 2023

Social isolation is a well-documented contributor to poor mental and physical health, and interventions promoting social connectedness have been associated with various health benefits. This study examined predictors of participation in a telephone-based social connectedness intervention for socially isolated older adults. Data were obtained from a social-connectedness intervention that paired college students with Houston-area, community-dwelling adults aged 65 years and older and enrolled in Medicare Advantage plans. We combined machine learning and regression techniques to identify significant predictors of program participation. The following machine-learning methods were implemented: (1) k-nearest neighbors, (2) decision tree and ensembles of decision trees, (3) gradient-boosted decision tree, and (4) random forest. The primary outcome was a binary flag indicating participation in the telephone-based social-connectedness intervention. The most predictive variables in the ML models, with scores corresponding to the 90th percentile or greater, were included in the regression analysis. The predictive ability of each model showed high discriminative power, with test accuracies greater than 95%. Our findings suggest that telephone-based social-connectedness interventions appeal to individuals with disabilities, depression, arthritis, and higher risk scores. scores. Recognizing features that predict participation in social-connectedness programs is the first step to increasing reach and fostering patient engagement.

Estimating the effect of focused donor registration efforts on the number of organ donors

Waiting times for organs in the United States are long and vary widely across regions. Donor registration can increase the number of potential donors, but its effect on the actual number of organ transplants depends upon several factors. First among these factors is that deceased donor organ donation requires both that death occur in a way making recovery possible and that authorization to recover organs is obtained. We estimate the potential donor death rate and donor authorization rate conditional on potential donor death by donor registration status for each state and for key demographic groups. With this information, we then develop a simple measure of the value of a new donor registration. This combined measure using information on donor authorization rates and potential death rates varies widely across states and groups, suggesting that focusing registration efforts on high-value groups and locations can significantly increase the overall number of donors. Targeting high-value states raises 26.7 percent more donors than a uniform, nationwide registration effort. Our estimates can also be used to assess alternative, but complemtary, policies such as protocols to improve authorization rates for non-registered potential donors.

Recommended citation: Cardon JH, Holbrook JC, Showalter MH (2020) Estimating the effect of focused donor registration efforts on the number of organ donors. PLoS ONE 15(11), PUBLIC LIBRARY OF SCIENCE: e0241672. https://doi.org/10.1371/journal.pone.0241672

Works in Progress

Trade and Wage Rigidity: Accessing the Role of Monetary Policy   [pdf]   [slides]