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Hurricane Sandy: Damages, Disruptions and Pathways to Recovery


Critical infrastructure and public utility systems are often severely damaged by natural disasters like hurricanes. Based on a framework of household disaster resilience, this paper focuses on the role of utility disruption on household-level recovery in the context of Hurricane Sandy. Using data collected through a two-stage household survey, it first confirms that the sample selection bias is not present, thus the responses can be estimated sequentially. Second, it quantitatively examines factors contributing to hurricane-induced property damages and household-level recovery. The finding suggests that respondents who suffered from a longer period of utility disruptions (e.g., electricity, water, gas, phone/cell phone, public transportation) are more likely to incur monetary losses and have more difficulty in recovering. Effective preparedness activities (e.g., installing window protections, having an electric generator) can have positive results in reducing adverse shocks. Respondents with past hurricane experiences and higher educational attainments are found to be more resilient compared to others. Finally, the paper discusses the implications of the findings on effective preparation and mitigation strategies for future disasters.

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Data Availability

(data transparency): data will be made available upon reasonable request subject to compliance with IRB guidelines.

Code availability

(software application or custom code): code will be made available upon request.


  1. 1.

    The idea of using the area under \(W(t)\) to present household resilience is adapted from Zobel (2011) and inspired by other later works with his co-authors (Zobel and Khansa 2014; Davis et al. 2019), which utilized the area of triangle to indicate the loss of disaster resilience.

  2. 2.

    The states include New Jersey (NJ), New York (NY), Connecticut (CT), Maryland (MD), Massachusetts (MA), Virginia (VA), Delaware (DE), Pennsylvania (PA), Rhode Island (RI), and West Virginia (WV).

  3. 3.

    No significant differences in the estimation results were found when using the original levels instead of the reclassified three categories. Therefore, we used the three-category setting for simplicity in the analysis.

  4. 4.

    Figure 4 is prepared by using ArcMap 10.2 software. The location of each respondent is Geo-coded based on the longitude and latitude. Coordinates are in GCS North American 1983.

  5. 5.

    We did not present the disruption rate of each utility service in Fig. 5. In fact, 73.32% of respondents have reported electricity disruption, 12.76% have reported water disruption, 15.26% have reported gas disruption, and 46.93% and 33.97% have reported phone/cell phone and public transportation disruption, respectively.

  6. 6.

    Hurricane Irene (2011) marked one of the most damaging hurricanes to make landfall prior to Hurricane Sandy in the New York and New Jersey areas.

  7. 7.

    HAZUS is a geographic information system (GIS) -based natural hazard developed and freely distributed by the Federal Emergency Management Agency (FEMA). For technical details, see:

  8. 8.

    Wind speed is likely to positively correlate with utility disruption, and omitting it will potentially overestimate the impact of utility disruption. We acknowledge that using Census tract provides only a rough estimation of the wind speed, and errors may be present from the HAZUS estimation. However, we believe the wind speed from HAZUS estimation can be used as a good control variable to enrich the survey data.

  9. 9.

    We use a generalized linear latent and mixed model (GLLAMM) with the ssm command in STATA to estimate the ordered logit model with sample selection.

  10. 10.

    Note that there are no substantial differences on the estimation results between ordered logit regression with and without sample selection method.


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We acknowledge support from the National Science Foundation (Award #0838683, #1204762, #1832693), Florida Division of Emergency Management (DEM), and International Hurricane Research Center at the Florida International University (FIU), Miami, Florida. We also acknowledge the support from FIU’s University Graduate School through the Dissertation Year Fellowship (awarded to Sisi Meng). Nadia Seeteram, Eric Van Vleet, Subrina Tahsin, Fan Jiang and Chiradip Chatterjee have provided excellent research support. We thank Hugh Gladwin, Douglass Shaw and William Vasquez for their comments and feedback at various stages in pursuing this research. We are also thankful to all those respondents who participated in hurricane Sandy survey and GFK (formerly Knowledge Networks) staff members who implemented the survey. However, the opinions expressed here are solely of the authors.


(information that explains whether and by whom the research was supported): We acknowledge support from the National Science Foundation (Award #0838683, #1204762, #1832693), Florida Division of Emergency Management (DEM), and International Hurricane Research Center at the Florida International University (FIU), Miami, Florida. We also acknowledge the support from University Graduate School (FIU) through the Dissertation Year Fellowship (awarded to Sisi Meng).

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Correspondence to Sisi Meng.

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Meng, S., Mozumder, P. Hurricane Sandy: Damages, Disruptions and Pathways to Recovery. EconDisCliCha 5, 223–247 (2021).

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  • Household recovery
  • Utility disruption
  • Hurricane sandy
  • Hurricane preparation