Moving into Risky Floodplains: The Spatial Implications of Federal Disaster Relief Policies (with Xinle Pang) (Sep, 2025) New Draft!
Journal of the European Economic Association, Reject and Resubmit
Abstract: This paper employs a quantitative dynamic spatial migration model to assess the welfare impacts of U.S. disaster relief policies in the context of flood risk. The model highlights the trade-off induced by mobility in the policy design. While relief policies provide insurance benefits to affected populations, they also inadvertently encourage sorting into flood-prone areas, generating a fiscal externality. Utilizing this model, we investigate the spatial economic consequences of relief efforts following Hurricane Harvey in Texas. To address the computational challenges posed by incorporating uncertainty into spatial framework, which results in a curse of dimensionality, we propose a novel solution method that leverages neural network. Our findings indicate that the current post-Harvey relief efforts have a positive effect on overall U.S. welfare when compared to a hypothetical scenario without such interventions. Furthermore, the paper explores alternative policies that yield even greater welfare benefits, such as floodplain taxation and moving subsidies.
The Effect of Flood Zoning Policies on Housing Markets: Evidence from Texas (with D. Noonan and L. Richardson) (May, 2024) Urban Studies, Revise and Resubmit
Abstract: Floods pose significant threats in the United States, prompting the National Flood Insurance Program to delineate Special Flood Hazard Areas (SFHA) through flood maps. Properties within SFHAs are subject to stringent regulations, yet the precise impact of these policies on the housing market remains elusive due to confounding factors such as flood risk and amenities. This study employs a Regression Discontinuity (RD) design with two parcel-level datasets to investigate this issue and analyze the effects of SFHA boundaries on the Texas housing market. Our findings reveal nuanced outcomes: while a conventional hedonic model suggests a price discount for properties within floodplains in coastal counties, a more robust analysis, accounting for confounding factors, reveals no significant effect in coastal counties. Interestingly, in inland counties, properties within floodplains exhibit higher housing values under SFHA regulations. These findings underscore the importance of addressing the influence of confounding factors in policy analysis, highlighting the complex relationship between flood zoning policies and housing market dynamics.
The Philosopher’s Stone for Science – The Catalyst Change of AI for Scientific Creativity (with Qian Chen, Yi-Jen (Ian) Ho, and Dashun Wang) (Mar, 2024)
Abstract: Limited research studies the impact of AI on scientific creativity. The work investigates whether AI is a catalyst for scientific creativity and what theoretical explanations behind observed AI-supported creativity are. Employing the Logical Creative Thinking (LCT) framework, we conjecture that AI enhances scientific creativity by providing faster search algorithms and offering possibilities to explore new search paths for uncovered knowledge. AI is expected to facilitate creative knowledge hybridization (i.e., recombination in LCT) across fields and serve as a stimulus for knowledge mutation (i.e., replacement in LCT) within a field. Accordingly, we consider two measures of scientific creativity: novelty (as hybridization) and disruption (as mutation). To quantify the AI impact, we analyze the publications from 2000 to 2021 and their citation networks. Our findings first inform that AI increases the novelty of mediocre (medium-level) and the top (90th-percentile) papers while enhancing the disruption of the mediocre papers only. Second, we identify nuanced variations in the impact on creativity across fields. Specifically, AI has the strongest and least impacts on basic science and humanity, respectively. Third, our citation-network analyses further uncover the direct and indirect effects of AI. Citing AI-related papers from other fields fosters novelty due to the hybridization of diverse techniques. Yet, citation concentration within specific or own fields leads to an indirect negative impact on novelty. As for disruption, we observe a similar pattern applied to mediocre papers only. For the most disruptive papers, over-emphasizing specific fields and references still hurts scientific mutation, whereas the increase in within-field citations deepens the understanding of prior work and illuminates new reasoning paths. Overall, the theoretical mapping and comprehensive analyses shed light on the intricate dynamics between AI and the evolution of creative research landscapes.