The Social Divide of Social Distancing: Shelter-in-Place Behavior in Santiago during the Covid-19 Pandemic

TL;DR

This study uses detailed mobile phone data to analyze how socioeconomic inequality affects social distancing and Covid-19 infection rates in Santiago, Chile, revealing significant disparities in mobility reduction and the importance of targeted policies.

Contribution

It provides granular evidence on socioeconomic disparities in shelter-in-place behavior and links mobility patterns to infection rates using innovative geolocated mobile data.

Findings

High-income areas reduced mobility by 50-90%, lower-income areas by 20-50%.

Mobility increases by 10% are associated with a 5% rise in infection rates.

Mobility disparities contribute to infection rate differences across socioeconomic groups.

Abstract

Voluntary shelter-in-place directives and lockdowns are the main non-pharmaceutical interventions that governments around the globe have used to contain the Covid-19 pandemic. In this paper we study the impact of such interventions in the capital of a developing country, Santiago, Chile, that exhibits large socioeconomic inequality. A distinctive feature of our study is that we use granular geolocated mobile phone data to construct mobility measures that capture (1) shelter-in-place behavior, and (2) trips within the city to destinations with potentially different risk profiles. Using panel data linear regression models we first show that the impact of social distancing measures and lockdowns on mobility is highly heterogeneous and dependent on socioeconomic levels. More specifically, our estimates indicate that while zones of high socioeconomic levels can exhibit reductions in mobility of around 50\% to 90\% depending on the specific mobility metric used, these reductions are only 20\% to 50\% for lower-income communities. The large reductions in higher-income communities are significantly driven by voluntary shelter-in-place behavior. Second, also using panel data methods we show that our mobility measures are important predictors of infections: roughly, a 10\% increase in mobility correlates with a 5\% increase in the rate of infection. Our results suggest that mobility is an important factor explaining differences in infections rates between high and low incomes areas within the city. Further, they confirm the challenges of reducing mobility in lower-income communities, where people generate their income from their daily work. To be effective, shelter-in-place restrictions in municipalities of low socioeconomic levels may need to be complemented by other supporting measures that enable their inhabitants to increase compliance.