All things equal? Heterogeneity in policy effectiveness against COVID-19 spread in chile

Abstract

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 characteristics. Leveraging a natural experiment in the determination of early quarantines, 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 municipalities, 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.

Keywords: Augmented Synthetic Control; COVID-19; Causal inference; Observational study.

Fig. 1

Number of total confirmed cases for different weeks during the COVID-19 pandemic in Santiago, Chile.

Fig. 2

Municipalities that were in lockdown at different times during March and April in the Metropolitan Region (First lockdowns: started March 26th; Second lockdowns: started April 9th-16th; Third lockdowns: started April 23rd-30th).

Fig. 3

Estimated difference in number of new cases between treated municipalities and synthetic control pre- and post-quarantine using Augmented Synthetic Control Method (90% CI in shaded region).

Fig. 4

Estimated difference in number of new cases between treated municipalities and synthetic control pre- and post-quarantine by income using Augmented Synthetic Control (90% CI in shaded region).

Fig. 5

Estimated difference in number of new cases between early-entrance, lower-income treated municipalities and synthetic control pre- and post-quarantine using Augmented Synthetic Control Method (90% CI in shaded region).

Fig. 6

Percent change in transit station mobility measure with respect to baseline for the Metropolitan Region (Google, 2020), with weekends highlighted in shaded regions.

Fig. 7

Difference in Mobility Index using ASCM for municipalities subjected to lockdowns by income level.

Fig. 8

Year-over-year percentage change in subway validations for different types of municipalities.

Fig. 9

Estimated positivity rate and proportion of private testing by date.

Fig. 10

Cumulative proportion of new cases between reports by days since first symptoms to confirmed diagnosis, for high and lower income municipalities that were in quarantine in the Metropolitan Region.

Fig. 11

Map for high and lower-income municipalities that had lockdowns before May 5th, including buffer zones used for robustness test.