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<p>Empirical evidence on which structural factors reliably shape community resilience during pandemics remains fragmented: existing studies often focus on single dimensions, small indicator sets, or do not test the robustness of identified drivers. Using 401 German administrative districts and a large pool of 266 district-level indicators, we identify structural factors associated with regional COVID-19 impacts across four resilience dimensions (technical, organizational, social, and economic). We estimate principal component regression models to address multicollinearity and systematically vary model size to evaluate the stability of statistical significance. Across outcomes, we find that the set of significant correlates changes markedly with model size, underscoring the necessity of robustness checks and causal considerations when deriving policy-relevant conclusions. Nevertheless, several associations remain stable within the indicator set under consideration, including variables related to socio-economic resources, demographics, and care-related structures. We further compare data-driven findings to expert-judgment expectations, revealing both alignments and notable mismatches that are indicative of context-dependent mechanisms and measurement choices. Overall, our approach contributes (i) a multi-dimensional outcome mapping of resilience, (ii) a large-scale, district-level screening of structural drivers, and (iii) a stability-based robustness assessment to support more reliable evidence for resilience-oriented risk management and future research.</p>

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Assessing the statistical significance of structural factors on community resilience during the early phase of the COVID-19 pandemic

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月16日

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  • Hagen Dreßler,
  • Stefan G. Fischer,
  • Alexander Stolz

摘要

Empirical evidence on which structural factors reliably shape community resilience during pandemics remains fragmented: existing studies often focus on single dimensions, small indicator sets, or do not test the robustness of identified drivers. Using 401 German administrative districts and a large pool of 266 district-level indicators, we identify structural factors associated with regional COVID-19 impacts across four resilience dimensions (technical, organizational, social, and economic). We estimate principal component regression models to address multicollinearity and systematically vary model size to evaluate the stability of statistical significance. Across outcomes, we find that the set of significant correlates changes markedly with model size, underscoring the necessity of robustness checks and causal considerations when deriving policy-relevant conclusions. Nevertheless, several associations remain stable within the indicator set under consideration, including variables related to socio-economic resources, demographics, and care-related structures. We further compare data-driven findings to expert-judgment expectations, revealing both alignments and notable mismatches that are indicative of context-dependent mechanisms and measurement choices. Overall, our approach contributes (i) a multi-dimensional outcome mapping of resilience, (ii) a large-scale, district-level screening of structural drivers, and (iii) a stability-based robustness assessment to support more reliable evidence for resilience-oriented risk management and future research.