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Staff Reports

Clustering in Natural Disaster Damages

Previous title: “Clustering in Natural Disaster Losses”

Number 1135
November 2024 Revised January 2025

JEL classification: Q50, Q54

Authors: Jacob Kim-Sherman and Lee Seltzer

Empirical research in climate economics often relies on panel regressions of different outcomes on disaster damages. Interpreting these regressions requires an assumption that error terms are uncorrelated across counties and time, which climate science research suggests is unlikely to hold. We introduce a methodology to identify spatial and temporal clusters in natural disaster damages datasets, and show that accounting for clustering affects observed economic effects of disasters. Specifically, counties tend to experience 0.45% more disaster damage for every 1% increase in damage across other intra-cluster counties. Moreover, accounting for clustering makes some hazard types, such as droughts, appear more damaging.

Full Article

Author Disclosure Statement(s)

Jacob Kim-Sherman
Jacob Kim-Sherman declares that (s)he has no relevant or material financial interests that relate to the research described in this paper. Prior to circulation, this paper was reviewed in accordance with the Federal Reserve Bank of New York review policy, available at https://www.newyorkfed.org/research/staff_reports/index.html.

Lee Seltzer
Lee Seltzer declares that (s)he has no relevant or material financial interests that relate to the research described in this paper. Prior to circulation, this paper was reviewed in accordance with the Federal Reserve Bank of New York review policy, available at https://www.newyorkfed.org/research/staff_reports/index.html.

Suggested Citation:
Kim-Sherman, Jacob, and Lee Seltzer. 2024. “Clustering in Natural Disaster Damages.” Federal Reserve Bank of New York Staff Reports, no. 1135, November. https://doi.org/10.59576/sr.1135

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