Influencing factors for potential bike-sharing users: an empirical analysis during the COVID-19 pandemic

Significance 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.