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Natural Disasters and the Nonprofit Sector

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  • Mayleen Cortez-Rodriguez

Abstract

When natural disasters strike, individuals, communities, and even entire countries can suffer. Researchers have studied the impacts of disasters on various factors of interest, from mental health, to poverty, to economic activity. However, the impact of disasters on the nonprofit sector is understudied despite the nonprofit sector's perhaps surprising role in local or national economies as well as its role in disaster response and recovery. Thus, we study the effect of natural disaster damage on different county-level nonprofit outcomes using a panel dataset spanning 1991 to 2021 and causal inference methods tailored to panel data. Contrary to prior work, which found small but positive associations between disaster damage and nonprofit revenue or assets, we find no evidence of a causal effect.

Suggested Citation

  • Mayleen Cortez-Rodriguez, 2026. "Natural Disasters and the Nonprofit Sector," Papers 2609.04136, arXiv.org.
  • Handle: RePEc:arx:papers:2609.04136
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    References listed on IDEAS

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    1. Naim Kapucu & Farhod Yuldashev & Mary Ann Feldheim, 2018. "Nonprofit Organizations in Disaster Response and Management: A Network Analysis," Journal of Economics and Financial Analysis, Tripal Publishing House, vol. 2(1), pages 69-98.
    2. Anita A. Pena & Sammy Zahran & Anthony Underwood & Stephan Weiler, 2014. "Effect of Natural Disasters on Local Nonprofit Activity," Growth and Change, Wiley Blackwell, vol. 45(4), pages 590-610, December.
    3. Charles F. Manski, 2013. "Identification of treatment response with social interactions," Econometrics Journal, Royal Economic Society, vol. 16(1), pages 1-23, February.
    4. Valentinov, Vladislav & Hielscher, Stefan & Pies, Ingo, 2015. "Nonprofit organizations, institutional economics, and systems thinking," Economic Systems, Elsevier, vol. 39(3), pages 491-501.
    5. Susan Athey & Dean Eckles & Guido W. Imbens, 2018. "Exact p-Values for Network Interference," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(521), pages 230-240, January.
    6. Mark Skidmore & Hideki Toya, 2002. "Do Natural Disasters Promote Long-Run Growth?," Economic Inquiry, Western Economic Association International, vol. 40(4), pages 664-687, October.
    7. Ho, Daniel E. & Imai, Kosuke & King, Gary & Stuart, Elizabeth A., 2007. "Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference," Political Analysis, Cambridge University Press, vol. 15(3), pages 199-236, July.
    8. Daniel A. Griffith, 2003. "Spatial Autocorrelation and Spatial Filtering," Advances in Spatial Science, Springer, number 978-3-540-24806-4.
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