Research

A full list of publications is available in my CV.

Selected Working Papers

  • Growing Up in Disability-Dense School Districts: Long-Term Impacts of Childhood Exposure on Educational Attainment (preprint available at EdWorkingPapers), R&R at Educational Evaluation and Policy Analysis

    • Sole author

    • This study examines whether exposure to higher disability density during childhood affects long-term educational attainment. Using administrative data on more than 170,000 children from six Texas kindergarten cohorts (1994-1999) who move across districts during K-12, I exploit variation in disability density across school districts to estimate causal effects on high school and college completion. Moving to districts with higher disability density improves both outcomes for children with disabilities, with effects nearly twice as large as those for children without disabilities. Effects on children without disabilities are positive or at least statistically indistinguishable from zero across specifications, providing no evidence of negative spillovers and suggesting that disability-diverse school districts may benefit all children.

  • A Geography of Administrative Burden: Accessibility to Social Security Field Offices and Benefit Receipt in the United States (preprint available at SSRN), R&R at Applied Spatial Analysis and Policy

    • First author

    • Geographic access to the offices where citizens apply for public benefits shapes the administrative burden of claiming them, yet few studies measure that access, and fewer still test whether it tracks benefit receipt. This study measures geographic accessibility to the Social Security Administration (SSA) field offices in the United States using the Enhanced Two-Step Floating Catchment Area method and asks whether the resulting index behaves as a measure of administrative burden should. Approximately 2.7 million residents live in 933 tracts where no SSA field office is reachable within the 120-minute catchment. Accessibility is positively and significantly associated with receipt of Supplemental Security Income and Social Security Disability Insurance, two programs that impose enough documentation and review burden to make an office visit likely, but not with receipt of retired-worker benefits, which the agency determines from a work record it already holds. That contrast holds under a spatial error specification and across four distance decay functions. Applying the measure to 16 announced field office closures would leave 167,317 residents with no reachable office.

  • Neighborhood Effects on the Educational Attainment of Children with Disabilities (preprint available at SSRN)

    • Sole author

    • This study examines how neighborhood environments shape long-term educational attainment for children with and without disabilities, using administrative records on approximately 1.2 million children in Texas from six kindergarten cohorts (1994-1999). I exploit two sources of variation, the educational opportunity gap between origin and destination school districts and the duration of childhood exposure to the destination, to estimate causal neighborhood effects by disability status. Exposure to higher-opportunity districts improves both high school and college completion for all children, with effects larger for children with disabilities than for those without. These effects scale with the duration of childhood exposure, such that children who move earlier accumulate larger gains. Effects also differ by disability type, with children with physical or sensory disabilities experiencing larger gains in high school completion but attenuation at the college level, and children with cognitive or emotional disabilities showing persistent gains across both outcomes. These results are robust to alternative fixed effects specifications and displacement shock identification. The findings suggest that disability inequality is deeply localized, as children with disabilities are disproportionately shaped by the neighborhoods where they grow up.

  • Mobile Health Intervention Integrating Personal PM2.5 Monitoring and Deep Learning to Reduce Pediatric Asthma Exacerbations: Pilot Study (preprint available at JMIR)

    • First author

    • Objective: This study aimed to evaluate the feasibility and effectiveness of a pilot mobile health (mHealth) intervention that integrates personal PM2.5 monitoring, deep learning (DL)-based prediction and tailored behavioral recommendations to mitigate exacerbation risks in children.

    • Methods: In this 3-year pilot study, 272 pediatric patients with asthma were enrolled across nine tertiary hospitals in Korea. Using asthma symptom reports and personal PM monitoring data collected via smartphones and portable devices, a 1D CNN-LSTM model was developed to identify PM-sensitive patients and predict exacerbations. After model construction, 109 participants entered the intervention phase and were allocated to three groups (model-based intervention, forecast-based intervention, or no intervention) through initial screening and model-based grouping. The model-based group received individualized alerts with behavioral recommendations based on DL predictions, while the forecast-based group received the same recommendations based on regional air quality forecasts. The primary outcome was change in asthma exacerbation rates (measured by Intervention Effectiveness Ratio, IER).

    • Results: The model-based group demonstrated significant reductions in exacerbation rates (median IER decrease: 6.6%; mean IER decrease: 11.5%; P < 0.05), whereas no significant changes were observed in the other groups. Odds ratio analysis indicated that the model-based group had 5.92- and 4.22-fold lower odds of PM2.5-related exacerbations compared with the no-intervention and forecast-based groups, respectively. Stratified and adjusted analyses confirmed that the benefit of model-based alerts remained robust despite baseline differences in asthma severity and control status.

    • Conclusions: This pilot study demonstrates the feasibility and potential effectiveness of an mHealth intervention that integrates personal PM monitoring, DL-based prediction and tailored behavioral recommendations in pediatric asthma. This approach shows promise for reducing PM-related exacerbations and warrants validation in larger, longer-term studies

Selected Publications

  • Does Carbon Pricing Outperform Command-and-Control Regulation? Firm-Level Evidence from Korea’s Dual Regulatory Framework (conditionally accepted, Climate Policy, preprint available at SSRN) [Impact Factor = 5.5, JCR Top 5%]

    • Sole author

    • This study compares the effects of carbon pricing and command-and-control regulation on firms’ environmental performance. I leverage a unique dual-policy setting in South Korea, where a carbon pricing scheme (the Emissions Trading Scheme, ETS) and a command-and-control program (the Target Management System, TMS) operate in parallel. Because each firm is regulated under only one of the two, the setting allows a direct comparison of the two approaches. Using a difference-in-differences design on firm-level panel data from 2011 to 2022, I find that firms that transitioned from the TMS to the ETS reduced energy use by 5.8% to 8.8% and carbon emissions by 7.3% to 8.5% relative to firms that remained under the TMS, while effects on carbon intensity were not robust. Because TMS firms were themselves subject to binding emissions targets, these estimates reflect the additional effect of carbon pricing over command-and-control regulation. The absence of a robust intensity effect indicates that the ETS reduced emissions without a measurable improvement in emissions efficiency. A phase-specific analysis further shows that the reductions were strongest in the more market-oriented phases of the ETS, which featured allowance auctioning and benchmark-based allocation. This suggests that jurisdictions transitioning from command-and-control regulation to carbon pricing can strengthen its effectiveness by phasing in auctioning and benchmark-based allocation rather than relying on free allocation.

  • Bayesian Spatio-Temporal Modeling for Policy Evaluation: Sensitivity of Policy Effect Estimates in the Context of COVID-19 Stay-at-Home Orders (2026, PLOS One, 21(2). e0339196) [Impact Factor=2.8, JCR Q2]

    • First author

    • This study applies a Bayesian spatio-temporal model to demonstrate the sensitivity of policy effect estimates to spatial and temporal structure, using COVID-19 stay-at-home orders as a case study. Unlike conventional approaches, this framework accounts for geographic spillovers, temporal dependence, and space-time interaction, all of which are central to policy effect evaluation in heterogeneous settings. Implemented via Integrated Nested Laplace Approximation (INLA), the model also accommodates missing data and supports inference in high-dimensional contexts. Using Google mobility data and policy information from the Oxford COVID-19 Tracker, we estimate four models of increasing complexity: OLS, spatial, temporal, and spatio-temporal. While simpler models suggest substantial reductions in workplace and residential mobility, these effects become statistically insignificant once spatio-temporal interactions are incorporated. This pattern indicates that earlier studies may have overstated policy effects by overlooking spatio-temporal heterogeneity. Our findings demonstrate the importance of spatio-temporal modeling for policy evaluation, particularly when working with large-scale, incomplete, and unevenly distributed data.

  • Have offender demographics changed since the COVID-19 pandemic? Evidence from money mules in South Korea (2024, Journal of Criminal Justice, 91, 102156) [Impact Factor = 2.5, JCR Q1]

    • Co-author

    • This study aims to investigate how the demographic characteristics of offenders have changed after the COVID-19 pandemic. Specifically, our research focuses on shifts in the nationality, gender distribution, and age profiles of money mules during this period. We utilized arrest reports data provided by the Seoul Metropolitan Police Agency in South Korea, including all 1407 individuals arrested for money mules in Seoul from February 1, 2018, to December 31, 2021. Our findings, derived from interrupted time series analyses, show a decrease in the percentage of non-Korean money mules, an increase in the proportion of female individuals engaged in money mule activities, and a rise in the average age of money mules after the outbreak of the pandemic. These insights hold significant implications for developing targeted policy interventions to mitigate potential threats associated with money mule activities.

  • Do Firms Respond Differently to the Carbon Pricing by Industrial Sector? How and Why? A Comparison Between Manufacturing and Electricity Generation Sectors Using Firm-Level Panel Data in Korea (2022, Energy Policy, 162, 112773) [Impact Factor = 9.0, JCR Top 2%]

    • First author

    • With firm-level panel data for seven years, this study evaluates the effect of carbon pricing policy and analyzes how firms respond to the carbon price, focusing on Korea’s Emission Trading Scheme (ETS). Assuming that firms’ responses to the carbon price may differ across industries, this study compares the manufacturing and electricity generation sectors. Our panel regression analyses show that the ETS has significant impacts on firms’ carbon reduction. However, the mechanisms through which firms reduce emissions differ by industrial sector. Firms in the manufacturing sector reduce carbon emissions by improving the energy efficiency of their facilities, whereas those in the electricity generation sector reduce emissions by phasing out fossil fuels and increasing the use of low carbon-intensive energy sources. These findings imply that carbon pricing works as designed, sending economic signals for firms to decarbonize their activities, and that its effectiveness varies according to each industry’s characteristics.