3rd Workshop on COVID-19 Modelling in Chile, Universidad de Chile

Date: 09/14/2020


Several variables and practices affect the evolution and geographic spread of COVID-19. Some of these variables pertain to policy measures such as social distancing, quarantines for specific areas, and testing availability. In this paper, I analyze the effect that lockdown and testing policies had on new contagions in Chile, especially focusing on potential heterogeneity given by population char- acteristics. Leveraging a natural experiment in the determination of early quar- antines, I use an Augmented Synthetic Control Method to build counterfactuals for high and lower-income areas that experienced a lockdown during the first two months of the pandemic. I find substantial differences in the impact that quarantine policies had for different populations: While lockdowns were effective in containing and reducing new cases of COVID-19 in higher-income municipali- ties, I find no significant effect of this measure for lower-income areas. To further explain these results, I test for difference in mobility during quarantine for high and lower-income municipalities, as well as delays in test results and testing availability. These findings are consistent with previous results, showing that differences in the effectiveness of lockdowns could be partially attributed to heterogeneity in quarantine compliance in terms of mobility, as well as differential testing availability for higher and lower-income areas.

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