Working Paper: NBER ID: w28541
Authors: Jaymee Sheng; Anup Malani; Ashish Goel; Purushotham Botla
Abstract: SARS-CoV-2 has had a greater burden, as measured by rate of infection, in poorer communities within cities. For example, 55% of Mumbai slums residents had antibodies to COVID-19, 3.2 times the seroprevalence in non-slum areas of the city according to a sero-survey done in July 2020. One explanation is that government suppression was less severe in poorer communities, either because the poor were more likely to be exempt or unable to comply. Another explanation is that effective suppression itself accelerated the epidemic in poor neighborhoods because households are more crowded and residents share toilet and water facilities. We show there is little evidence for the first hypothesis in the context of Mumbai. Using location data from smart phones, we find that slum residents had nominally but not significantly (economically or statistically) higher mobility than non-slums prior to the sero-survey. We also find little evidence that mobility in non-slums was lower than in slums during lockdown, a subset of the period before the survey.
Keywords: COVID-19; mobility; slums; Mumbai; public health
JEL Codes: I12; I14; I15; I18; R0
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Higher mobility in slums (J62) | Higher incidence of COVID-19 (H22) |
| Higher mobility in slums (J62) | Higher seroprevalence rates (I12) |
| Household crowding (R20) | Higher incidence of COVID-19 (H22) |
| Higher mobility in slums and household crowding (R23) | Higher incidence of COVID-19 (H22) |
| Lockdown measures (F38) | Mobility in slums comparable to nonslum residents (R23) |