COVID-19 policy analysis: labour structure dictates lockdown mobility behaviour

Abstract

Countries and cities around the world have resorted to unprecedented mobility restrictions to combat COVID-19 transmission. Here we exploit a natural experiment whereby Colombian cities implemented varied lockdown policies based on ID number and gender to analyse the impact of these policies on urban mobility. Using mobile phone data, we find that the restrictiveness of cities’ mobility quotas (the share of residents allowed out daily according to policy advice) does not correlate with mobility reduction. Instead, we find that larger, wealthier cities with more formalized and complex industrial structure experienced greater reductions in mobility. Within cities, wealthier residents are more likely to reduce mobility, and commuters are especially more likely to stay at home when their work is located in wealthy or commercially/industrially formalized neighbourhoods. Hence, our results indicate that cities’ employment characteristics and work-from-home capabilities are the primary determinants of mobility reduction. This finding underscores the need for mitigations aimed at lower income/informal workers, and sheds light on critical dependencies between socio-economic classes in Latin American cities.

The adoption of non-pharmaceutical interventions and the role of digital infrastructure during the COVID-19 Pandemic in Colombia, Ecuador, and El Salvador

“…Looking at sociodemographic features, we find that both population and density have a positive significant coefficient in all countries. This is in line with previous findings that have shown how individuals living in larger and denser areas had more pronounced mobility reductions [53,56]. An older population has been observed to be associated with higher mobility reductions [53,56].…”

“…The GDP per capita coefficient is also positive but is significant only for El Salvador. This finding confirms the critical role of socio-economic attributes in NPIs adherence reported in previous works [11,13,[53][54][55]. Looking at sociodemographic features, we find that both population and density have a positive significant coefficient in all countries.…”

Socio-economic determinants of mobility responses during the first wave of COVID-19 in Italy: from provinces to neighbourhoods

“…In conclusion, our work underscores the important role of socio-economic factors and in particular of the labour structure [ 29 ] in shaping behavioural responses during the full course of the pandemic cycle, from early interventions to the reopening. In particular, our approach highlighted the unequal impact of mobility restrictions in urban areas, where central districts experienced a much more prolonged reduction of mobility and social contacts than the periphery.…”

“…To understand the impact of the local economy [ 29 , 43 ], we include a number of economic variables: the average personal income, the unemployment rate, the fraction of commuters of the population, and the structure of the local workforce, described as the proportion of workers who belong to the three basic sectors: agriculture, industry and services. The latter three variables are compositional data, as they sum to the unit value, therefore in the mixed effects model, we consider only two of them (agriculture and industry) as regressors, computing their ratio with respect to the third one (services).…”

Aggravated social segregation during the COVID-19 pandemic: Evidence from crowdsourced mobility data in twelve most populated U.S. metropolitan areas

“…A notable finding from many studies is that the pandemic's impact on mobility is heterogenous over different income groups ( Hong et al, 2021 ). More reductions in mobility were observed from wealthier populations ( Hernando et al, 2020 ; Heroy et al, 2021 ; Weill et al, 2020 ). These studies reveal critical changes in collective travel behavior and structural changes in urban mobility.…”