  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.
