Job Market Papers

Extreme Weather Events and the Performance of Critical Utility Infrastructures: A Case Study of Hurricane Harvey


Natural disasters have considerable economic and social ramifications by disrupting public utility services, such as power outages, disconnecting phone service, and transportation interruptions. 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 five hundred residents of the Houston Metropolitan Statistical Area after Hurricane Harvey's landfall. Our initial data analysis concentrated on the number of homes that reported interruptions in electricity, water, phone/cellphone, Internet, public transportation, places of employment, and grocery stores. We also estimate the duration of each type of disruption. Around 69% of the respondents reported electricity disruption, while half (49%) had no water supply for up to six days. Two-thirds of the surveyed households did not have internet access, and 47% had their phone services disconnected. Finally, around 70% of the respondents could not commute to their workplace, while 71% could not buy groceries for their families. We incorporated the household survey responses into the Dynamic Inoperability Input-Output Model (DIIM) to estimate inoperability and economic losses in multiple linked sectors. The total economic loss (GDP) was over $6 billion, and workforce disruption is the major challenge that policymakers must consider. Understanding the resilience of each sector and the inherent interdependencies across the sectors can provide helpful input to policymakers for disaster risk management, notably preparedness and recovery planning for future events.

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.