Mayleen Cortez-Rodriguez
arXiv 3 Sep 2026 · Statistics — Applications
arXiv:2609.04136 · PDF · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Kosuke Imai, In Song Kim, and Erik H Wang (2023) Matching methods for causal inference with time-series cross-sectional data | 0.974 | 13 | 4 | 92% |
| 2 | Anita A Pena, Sammy Zahran, Anthony Underwood, and Stephan Weiler (2014) Effect of natural disasters on local nonprofit activity | 0.830 | 7 | 5 | 57% |
| 3 | Kevin T Smiley, Junia Howell, and James R Elliott (2018) Disasters, local organizations, and poverty in the usa, 1998 to 2015 | 0.737 | 4 | 3 | 50% |
| 4 | Peter M Aronow and Cyrus Samii (2017) Estimating average causal effects under general interference, with application to a social network experiment | 0.737 | 3 | 3 | 67% |
| 5 | Adam Rauh, In Song Kim, and Kosuke Imai (2025) Panelmatch: Matching methods for causal inference with time-series cross-section data | 0.737 | 3 | 2 | 100% |
| 6 | U.S Government Accountability Office (2012) Federal disaster assistance: Improved criteria needed to assess a jurisdiction's capability to respond and recover on its own | 0.644 | 3 | 2 | 67% |
| 7 | Dmitry Arkhangelsky and Guido Imbens (2024) Causal models for longitudinal and panel data: a survey | 0.511 | 3 | 2 | 33% |
| 8 | Yuhao Ba, Jessica Berrett, and Jason Coupet (2023) Panel data analysis: A guide for nonprofit studies | 0.511 | 3 | 2 | 33% |
| 9 | Jean C Digitale, Jeffrey N Martin, and Medellena Maria Glymour (2022) Tutorial on directed acyclic graphs | 0.511 | 2 | 2 | 50% |
| 10 | A Miguel, ROBINS HERNAN, and M James (2023) Causal inference: what if | 0.511 | 2 | 2 | 50% |
Showing the top 10 of 62 scored citations.