Research

Under Review

  • Are Neighborhood Effects Larger for Children with Disabilities? Evidence from Texas (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.

  • Growing Up in Disability-Dense School Districts: Long-Term Impacts of Childhood Exposure on Educational Attainment (preprintavailable at EdWorkingPapers)

    • 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: Social Security Field Office Accessibility and the Consequences of Closures (preprint available at SSRN)

    • First author (with Peter Kedron)

    • This study examines the geography of administrative burden by quantifying the spatial mismatch between Social Security Administration (SSA) field office supply and population demand and evaluating the implications of office closures. Using the Enhanced Two-Step Floating Catchment Area method, which captures not only physical proximity but also competition among residents for limited office capacity, we construct tract- and county-level accessibility indices, estimate their association with Supplemental Security Income (SSI) participation rates, and assess the distributional consequences of the Department of Government Efficiency’s (DOGE) proposed lease terminations. Our results show that more than 2.7 million residents already live in administrative deserts, defined as areas where no SSA field office is geographically reachable within a 120-minute drive, prior to any closures. We further find that this spatial mismatch constitutes a meaningful dimension of administrative burden: accessibility is positively and significantly associated with SSI participation rates even after controlling for a wide range of socioeconomic and demographic factors. Evaluating DOGE’s proposed closures of 12 offices shows that an additional 165,163 residents would lose access entirely and approximately 753,000 additional residents would be pushed into areas of severely constrained access, against $700,616 in projected annual lease savings reported by DOGE.

  • Does Carbon Pricing Outperform Command-and-Control Regulation? Firm-Level Evidence from Korea’s Dual Regulatory Framework (preprint available at SocArXiv)

    • Sole author

    • This study evaluates the comparative effects of carbon pricing and command-and-control regulation on firm-level environmental performance. Leveraging South Korea’s unique dual-policy framework, I employ a difference-in-differences design using firm-level panel data from 2011 to 2022 to compare outcomes between firms regulated under a traditional command-and-control program (Target Management System, TMS) and those subject to a market-based carbon pricing mechanism (Emissions Trading Scheme, ETS). The results show that ETS-regulated firms reduced energy use by approximately 5.8% to 8.8% and carbon emissions by 7.3% to 8.5% across model specifications. However, the effects on carbon intensity were inconsistent and sensitive to model specifications. Event-study analyses suggest that these differing effects are driven by the heterogeneous timing of firm responses: immediate but short-lived reductions in energy use, persistent declines in carbon emissions, and gradual improvements in emissions efficiency. Phase-specific estimates further indicate that more market-oriented ETS phases were associated with stronger reductions in carbon emissions and intensity, underscoring the role of incentive-based policy design in enhancing environmental outcomes.

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

    • First author

    • Background: Fine particulate matter (PM2.5) is a major trigger of pediatric asthma exacerbations, yet individual sensitivity varies considerably. Existing interventions often adopt a uniform approach, despite this heterogeneity.

    • 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.

  • Geographic Accessibility to Preventive Maternal and Child Oral Health in Kilifi County, Kenya: A Roadmap for Workforce Training

    • First author

    • Introduction: Preventive oral health services remain largely absent from maternal and child health (MCH) care in rural sub-Saharan Africa. This study quantified geographic accessibility to preventive maternal and child oral health (MCOH) services in Kilifi County, Kenya, and evaluated the potential impact of MCH nurse training scenarios on accessibility outcomes.

    • Methods: We applied the Enhanced Two-Step Floating Catchment Area (E2SFCA) method to ward-level women of reproductive age (WRA) population data and public health facility data from Kilifi County. Wards falling below the estimated Kenyan national public oral health workforce ratio of 4.3 oral health providers per 100,000 WRA were designated as priority areas and classified by disparity type. Three MCH workforce training scenarios (efficiency-centered, equity-centered, and hybrid) were evaluated against a baseline, each with a cumulative training constraint of 20 MCH nurses and midwives.

    • Results: Only 15 of 35 wards met the estimated national public oral health workforce ratio of 4.3 providers per 100,000 WRA at baseline (mean 3.55, SD 1.84). Below-average wards comprised 10 demand-driven and 10 provider shortage wards. All three intervention scenarios substantially improved accessibility. The equity-centered scenario extended accessibility above the national ratio to 33 of 35 wards (mean 7.19, SD 2.41), while the efficiency-centered scenario achieved the highest mean index with 28 wards above the national ratio (8.00, SD 4.50) but with greater spatial variability. The hybrid scenario yielded intermediate results with 30 wards above the national ratio (mean 7.43, SD 3.59).

    • Conclusions: Training MCH nurses and midwives to deliver MCOH services can substantially expand geographic accessibility without new infrastructure or specialist redistribution. The E2SFCA-based scenario framework developed here offers a spatially explicit tool for evidence-driven oral health workforce planning in resource-limited settings across Kenya and sub-Saharan Africa.

Publications

  • 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)

    • First author (with Sunghye Choi, Dohyeong Kim, and Chang-Kil Lee)

    • 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.

  • Geospatial Analysis of Community-Level Social and Environmental Barriers for Adult Burn Injury Survivors in North Texas (2025, Burns, 51(5), 107512)

    • First author (with Dohyeong Kim, Richard Scotch, Dohyo Jeong, and Karen Kowalske)

    • This preliminary study examines geographic differences in community integration among burn injury survivors in North Texas and identifies community factors that may shape their post-injury reintegration. Drawing on data from 153 adults in the Burn Model System between 2015 and 2022, we mapped county-level changes in Community Integration Questionnaire (CIQ) scores by comparing pre-injury levels with scores at six and twelve months. We then grouped counties based on whether survivors experienced consistent declines over the 12-month period and compared these counties to all others. Preliminary results reveal clear spatial disparities: counties with persistent decreases in CIQ scores tended to have higher poverty and unemployment, more crime, and poorer access to healthy food options. These patterns suggest that rural and disadvantaged communities may provide less supportive environments for reintegration. While exploratory, these findings indicate the importance of addressing local socioeconomic and environmental barriers to improve community integration outcomes for burn injury survivors.

  • Have Offender Demographics Changed Since the COVID- 19 Pandemic? Evidence from Money Mules in South Korea (2024, Journal of Criminal Justice, 91, 102156)

    • Co-author (with Sunmin Hong and Dohyo Jeong)

    • 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)

    • First author (with Hyunhoe Bae)

    • 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.