Socioeconomic differences and persistent segregation of Italian territories during COVID-19 pandemic

Giovanni Bonaccorsi,Francesco Manaresi,Stefano Ceri,Francesco Pierri,Francesco Scotti,Fabio Pammolli,Andrea Flori

doi:10.1038/s41598-021-99548-7

Abstract

Lockdowns implemented to address the COVID-19 pandemic have disrupted human mobility flows around the globe to an unprecedented extent and with economic consequences which are unevenly distributed across territories, firms and individuals. Here we study socioeconomic determinants of mobility disruption during both the lockdown and the recovery phases in Italy. For this purpose, we analyze a massive data set on Italian mobility from February to October 2020 and we combine it with detailed data on pre-existing local socioeconomic features of Italian administrative units. Using a set of unsupervised and supervised learning techniques, we reliably show that the least and the most affected areas persistently belong to two different clusters. Notably, the former cluster features significantly higher income per capita and lower income inequality than the latter. This distinction persists once the lockdown is lifted. The least affected areas display a swift (V-shaped) recovery in mobility patterns, while poorer, most affected areas experience a much slower (U-shaped) recovery: as of October 2020, their mobility was still significantly lower than pre-lockdown levels. These results are then detailed and confirmed with a quantile regression analysis. Our findings show that economic segregation has, thus, strengthened during the pandemic.

Highlights

IntroductionUsing a set of unsupervised and supervised learning techniques, we reliably show that the least and the most affected areas persistently belong to two different clusters
Lockdowns implemented to address the COVID-19 pandemic have disrupted human mobility flows around the globe to an unprecedented extent and with economic consequences which are unevenly distributed across territories, firms and individuals
Public health strategies extensively adopted by governments at the outbreak of the COVID-19 pandemic required the implementation of non-pharmaceutical interventions (NPIs) to reduce the transmission rate of the virus[1,2,3,4,5,6]

Summary

Introduction

Using a set of unsupervised and supervised learning techniques, we reliably show that the least and the most affected areas persistently belong to two different clusters The former cluster features significantly higher income per capita and lower income inequality than the latter. The least affected areas display a swift (V-shaped) recovery in mobility patterns, while poorer, most affected areas experience a much slower (U-shaped) recovery: as of October 2020, their mobility was still significantly lower than pre-lockdown levels These results are detailed and confirmed with a quantile regression analysis. We focus on Italian territories and we rely on the assumption that a reduction in mobility flows represents a first-order proxy for the contraction of economic activities This is plausible since a wide share of the population relies on mobility to reach workplaces, and a significant portion of productive polimi.it

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Conclusion