This study adopts a spatial dynamic panel data model with common factors and a connectivity matrix based on cross-province population flows to help explain the spread of COVID-19 infections across Italian provinces during the period 2020–21. We find that an increase in the infections in a province has a positive and statistically significant effect on neighbours’ infections, which highlights the relevance of spatial spillover effects. This finding is robust to several robustness checks. Furthermore, we investigate cross-provincial transmission heterogeneity using a heterogeneous spatial dynamic panel, which provides novel insights into the diffusion patterns of the disease.

The diffusion of COVID-19 across Italian provinces: a spatial dynamic panel data approach with common factors

Lisa Gianmoena
Co-primo
;
Vicente Rios
Co-primo
2023-01-01

Abstract

This study adopts a spatial dynamic panel data model with common factors and a connectivity matrix based on cross-province population flows to help explain the spread of COVID-19 infections across Italian provinces during the period 2020–21. We find that an increase in the infections in a province has a positive and statistically significant effect on neighbours’ infections, which highlights the relevance of spatial spillover effects. This finding is robust to several robustness checks. Furthermore, we investigate cross-provincial transmission heterogeneity using a heterogeneous spatial dynamic panel, which provides novel insights into the diffusion patterns of the disease.
2023
Gianmoena, Lisa; Rios, Vicente
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1205689
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