Job Market Papers
*Abstract*
Natural disasters bring considerable economic and social ramifications by disrupting public utility services, such as power outages, phone service disconnections, and transportation interruptions. This study seeks to understand the performance and resilience of critical infrastructure systems using Hurricane Harvey (2017) as a case study. We collected the data from a household survey of 500 respondents residing in the Houston Metropolitan Statistical Area (MSA) during Hurricane Harvey's landfall. Based on the responses, we first investigated the number of households who experienced various types of utility services disruptions (electricity, water, waste disposal, phone/cellphone, Internet, public transport, educational institutions, workplace/business office, financial institutions, hospital/doctor's office, pharmacy/medical stores, medical test centers, and grocery stores). We also estimate the duration of each type of disruption. Around 69% of the respondents reported having electricity disruption, while half (49%) of the respondents 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 60% of the respondents could not commute, and 53% of the respondents could not visit hospitals for medical emergencies. We then 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 is over $4 billion and workforce disruption are the major challenge that policymaker has to take into account. Our estimate determines the total economic loss 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 Response in Puerto Rico
*Abstract*
The deadliest hurricane ever recorded in Puerto Rico, Hurricane Maria, made landfall in 2017. Hundreds of thousands of homes were damaged, and millions of people lost 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 tract, 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 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 dynamics in the hedonic model can help Puerto Rican authorities and scholars assess house price fluctuations after a natural disaster.