This paper presents a large-scale analysis of the impact of lockdown measures introduced in response to the spread of novel coronavirus disease 2019 (COVID-19) on socioeconomic conditions of Italian citizens. We leverage a massive near–real-time dataset of human mobility and we model mobility restrictions as an exogenous shock to the economy, similar to a natural disaster. We find that lockdown measures have a twofold effect: First, their impact on mobility is stronger in municipalities with higher fiscal capacity; second, they induce a segregation effect: mobility contraction is stronger in municipalities where inequality is higher and income per capita is lower. We highlight the necessity of fiscal measures that account for these effects, targeting poverty and inequality mitigation.
In response to the coronavirus disease 2019 (COVID-19) pandemic, several national governments have applied lockdown restrictions to reduce the infection rate. Here we perform a massive analysis on near–real-time Italian mobility data provided by Facebook to investigate how lockdown strategies affect economic conditions of individuals and local governments. We model the change in mobility as an exogenous shock similar to a natural disaster. We identify two ways through which mobility restrictions affect Italian citizens. First, we find that the impact of lockdown is stronger in municipalities with higher fiscal capacity. Second, we find evidence of a segregation effect, since mobility contraction is stronger in municipalities in which inequality is higher and for those where individuals have lower income per capita. Our results highlight both the social costs of lockdown and a challenge of unprecedented intensity: On the one hand, the crisis is inducing a sharp reduction of fiscal revenues for both national and local governments; on the other hand, a significant fiscal effort is needed to sustain the most fragile individuals and to mitigate the increase in poverty and inequality induced by the lockdown.