Job Market Papers
*Abstract*
Extreme weather events have significant economic and social impacts, disrupting essential public services like electricity, phone communication, and transportation. This study seeks to understand the performance and resilience of critical infrastructure systems in Houston, Texas, using Hurricane Harvey (2017) as a case study. We surveyed 500 Houston Metropolitan Statistical Area residents after Hurricane Harvey's landfall about disruption experience in electricity, water, phone/cellphone, internet, public transportation, workplace, and grocery stores. Our household survey data revealed the proportion and duration of disruption in each system. Approximately 70% of respondents reported experiencing electricity outages, while half (51%) had no access to water for up to six days. Two-thirds of surveyed households lacked internet access, and 50% had their phone services disconnected. Additionally, around 71% of respondents were unable to commute to work, and 73% were unable to purchase groceries for their families during this period. We incorporated the household survey responses into the Dynamic Inoperability Input-Output Model (DIIM) to estimate inoperability and economic losses across interconnected sectors. The projected economic loss was estimated to be in the range of $6.7- $9.7 billion when sensitivity analysis is performed with respect to the number of working days. Understanding the resilience of each sector and the inherent interdependencies among them can provide beneficial insight to policymakers for disaster risk management, notably preparedness and recovery planning for future events.
Hurricane Maria and Housing Market in Puerto Rico
*Abstract*
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.