Job Market Papers

Extreme Weather Events and Critical Infrastructure Resilience: Lessons from Hurricane Irma in Florida 


Severe weather events can have profound economic implications, disrupting vital public services such as electricity, communication, and transportation. This study examines the functionality and resilience of critical infrastructure systems within 14 Metropolitan Statistical Areas (MSAs) in Florida, specifically focusing on Hurricane Irma (2017). Following Hurricane Irma, a survey was given to 547 Florida residents to learn about their firsthand experiences with service disruptions. These sectors included disruptions in electricity, water, phone, internet, transportation, employment, and grocery access. Analysis of the survey data illuminated the extent and duration of disruptions within each system. Noteworthy findings include 73% of respondents experiencing electricity outages, 35% enduring up to two days without water access, 63% facing internet disruptions, and 39% encountering issues with phone services. Additionally, 51% struggled with commuting to work, and 55% could not purchase groceries for their families during the impacted period. The household survey responses were integrated into the Dynamic Inoperability Input-Output Model (DIIM). It assessed inoperability and economic losses across 71 interconnected Florida industries reliant on seven infrastructure systems. Simulated scenarios using the DIIM estimated the projected economic loss to range from $2.97 to $4.30 billion. The estimate considers the number of affected working days. This research underscores the importance of understanding specific sector resilience and acknowledging their inherent connections. Such insights offer invaluable guidance to policymakers involved in disaster risk management, especially in anticipating and formulating recovery strategies for future events. By recognizing the vulnerabilities of diverse infrastructure systems, authorities can better prepare for and mitigate the economic impact of extreme weather events. This identification ultimately enhances the overall resilience of communities.

Hurricane Maria and Housing Market in Puerto Rico


TIn 2017, Hurricane Maria made landfall in Puerto Rico, becoming the deadliest hurricane ever recorded on the island. The hurricane caused damage to hundreds of thousands of homes and left millions without power for days. This study seeks to investigate how that devastation affected the housing prices in Puerto Rico. We collected 1001 single-family house data from the Zillow website between 2018 to 2021. For the causal inference (treatment-effect), distance buffer from the track, location (house is on the right or left side of the hurricane path), and flood zone (house located in a flood zone or not), etc., were used as part of the primary identification strategy. We also combined the traditional hedonic price model with Regression Discontinuity Design (RDD) to measure the hurricane's causal (treatment) effect on housing prices. First, findings from the basic difference-in-difference hedonic price models indicated a downward trend/pattern of housing prices in post-hurricane years. We then used sharp and fuzzy RDD models with single and multiple cut-offs to estimate similar specifications. The RDD results also confirm the negative trend of falling prices. Identifying the best functional form of spatial hedonic models can help Puerto Rico policymakers and future researchers analyze housing price fluctuation in Puerto Rico in the aftermath of a major natural disaster.