Economic and social consequences of human mobility restrictions under COVID-19

Abstract

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.

Keywords: COVID-19; economic segregation; human mobility; national lockdown.

Fig. 1.

Connectivity of the Italian mobility network during COVID-19 epidemic. (A and B) Snapshots of the mobility network on two Mondays before and after national lockdown (March 9), that is, on (A) February 24 and (B) March 30. Nodes represent municipalities aggregated at the province level, and they all have equal size, whereas thickness of edges is proportional to their weight. Insets provide an outlook on different regions, where node size is instead proportional to the population of the province. (C) The temporal evolution of the network connectivity in terms of number of weakly connected components (No. WCC, red) and size of the giant connected component (Size LWCC, blue), measured on daily snapshots of the mobility network from February 23 to April 4; trends are significantly increasing (M-K: $P\approx 0$; K-T: $P\approx 0\hspace{0.17em}R=0.64$; T-S: $R=30.52$) and decreasing (M-K: $P\approx 0$; K-T: $P\approx 0$, $R=-0.67$; T-S: $R=-58.58$), respectively. (D) The temporal evolution of the global efficiency for the Italian mobility network from February 23 to April 4. Efficiency is computed according to ref. . We use the reciprocal of weights to model distances between nodes. The trend is significantly decreasing (M-K: $P\approx 0$; K-T: $P\approx 0$, $R=-0.75$; T-S: $R=-0.00003$). To visualize trends in C and D, we show a locally estimated scatterplot smoothing (LOESS) regression (dashed line) with 95% CI (shaded area), and highlight lockdown and weekdays with a solid and dotted vertical red lines, respectively.

Fig. 2.

Characteristics of the (A) most affected and (B) the least affected municipalities aggregated at the province level. (Left) Geographic distributions with colors corresponding to median income per capita in every province. (Right) Position of each province in the distribution of income inequality with respect to the average inequality in the sample (gray dotted line). Italian regions with no available data have been grayed out.