What Vietnam’s localized lockdown policy showed: it did not work and was too late

Figures & data

Figure 1. Differences in (a) cases per capita and (b) lockdown days among the 63 provinces of Vietnam.

Note: Lighter colours represent fewer infected cases and lockdown days; dark colours represent the opposite. The number of lockdown days was calculated from the difference between the implementation of Directive No. 16/CT-TTg and 29 July 2021 (i.e., last day of non-pharmaceutical intervention).

Figure 2. Effects of localized lockdown on the number of infected cases.

Note: Results from the panel event study are shown. Point estimates are represented along with their 95% confidence intervals as described in the model equation in specific time intervals and controlling for residential mobility and national holidays. The baseline (omitted) base period (solid vertical line) was one day before the adoption of a localized lockdown by each province. The event date was when each province applied Directive No. 16/CT-TTg (i.e., strict social distancing measures) to contain the spread of COVID-19.

Figure 3. Effects of localized lockdown on the number of infected cases considering fixed effects.

Note: Results from the panel event study are shown considering multiple levels of fixed effects. Point estimates are represented along with their 95% confidence intervals as described in the model equation in specific time intervals and controlling for residential mobility (estimated by principal component analysis) and national holidays.

Figure 4. Effects of localized lockdown on the cases per population considering fixed effects.

Note: Results from the panel event study are shown considering multiple levels of fixed effects with cases per population. Point estimates are represented along with their 95% confidence intervals as described in the model equation in specific time intervals and controlling for residential mobility (estimated by principal component analysis – PCA) and national holidays.

Table 1. Post-estimation test for the joint significance of leads, lags and event terms.

Test	Leads	Lags	Both leads and lags
Chi-square	68.973***	1850.581***	68.973***
d.f.	58	58	58

Note: * < 0.1; ** < 0.05; *** < 0.01.

Table 2. Estimation results of mobility and socio-economic factors on the number of infected cases.

Variables	Model 1	Model 2
Mobility 1	−53.47 [−1.39]	−137.71 [−1.51]
Mobility 2	−131.29** [−2.45]	−207.20** [−3.05]
Holiday	−61.75 [−0.70]
Holiday(t+7)	24.18 [0.83]
Holiday(t+14)	2.62 [0.06]
Number of hospital beds per capita	−5545.05 [−0.21]
Population density	0.11*** [3.09]
Provincial Competitiveness Index	−8.70 [−0.73]
Lead-lag terms	Yes	Yes
Province fixed effect	No	Yes
Time fixed effect	No	Yes
Observation	1407	1407
Adjusted R^2	0.58	0.65

Note: *< 0.1; **< 0.05; ***< 0.01. The dependent variable is the number of infected cases. Our method for constructing Mobility 1 and Mobility 2 is the first principal-component factors, and the rotation is based on orthogonal varimax (Kaiser on) based on six variables from the Google Mobility data, capturing the time spent by their users each day in specific places (see Table A2 in Appendix A in the supplemental data online). The loadings parameter is chosen as < 0.5 omitted for our study. Factor rotation matrix between Mobility 1 and Mobility 2 is 0.29. There is no correlation between the two variables. Accordingly, Mobility 1 consists of Retail, Grocery, Parks and Transit, while Mobility 2 has Workplaces and Residential.

DATA AVAILABILITY

The data are available from the author upon request.