Better politicians, fewer deaths? Local resilience in overcoming the pandemic crisis in Italy

The quality of institutions is widely recognized as a key determinant of public sector performance across various levels of governance. This paper investigates how institutional quality shaped the resilience of Italian Labour Market Areas during the COVID-19 pandemic. To this end, we introduce a localized, non-parametric Interrupted Time Series (ITS) approach, using long-run mortality data (2004-2023), to construct a data-driven, local-level resilience index. This index captures deviations from counterfactual mortality trajectories, reflecting the ability of local areas to withstand and recover from the pandemic. We then assess the determinants of this resilience index, with a particular focus on institutional quality. Our findings show that higher institutional quality - particularly the quality of local politicians - emerges as the most significant factor driving differences in performance at the local level. Multiple robustness checks, including alternative model specifications and pre-pandemic forecast accuracy benchmarks, confirm the reliability of our results.

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Publication Details

Journal
Social Science & Medicine
Published
2026-03-16
DOI
https://doi.org/10.1016/j.socscimed.2026.119198
Primary Topic
Regional resilience and development
Type
article
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article

Better politicians, fewer deaths? Local resilience in overcoming the pandemic crisis in Italy

Francesco Vidoli, Giacomo Pignataro, Calogero Guccio, Stefania Fontana
Social Science & Medicine
Regional resilience and development
article

Better politicians, fewer deaths? Local resilience in overcoming the pandemic crisis in Italy

Francesco Vidoli, Giacomo Pignataro, Calogero Guccio, Stefania Fontana
article en

Abstract

The quality of institutions is widely recognized as a key determinant of public sector performance across various levels of governance. This paper investigates how institutional quality shaped the resilience of Italian Labour Market Areas during the COVID-19 pandemic. To this end, we introduce a localized, non-parametric Interrupted Time Series (ITS) approach, using long-run mortality data (2004-2023), to construct a data-driven, local-level resilience index. This index captures deviations from counterfactual mortality trajectories, reflecting the ability of local areas to withstand and recover from the pandemic. We then assess the determinants of this resilience index, with a particular focus on institutional quality. Our findings show that higher institutional quality - particularly the quality of local politicians - emerges as the most significant factor driving differences in performance at the local level. Multiple robustness checks, including alternative model specifications and pre-pandemic forecast accuracy benchmarks, confirm the reliability of our results.

Social Science & MedicineVol. 398
University of Urbino (IT), University of Catania (IT), Politecnico di Milano (IT)
Openalex Percentile: Top 23%
Regional resilience and development
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