A Literature Review and Meta Analysis of the Effects of Lockdowns on COVID 19 Mortality

Weight (1/SE)
Figure 7 shows a funnel plot for all estimates in Table 7, except Chaudhry et al. (2020) <strong>and</strong>
Bonardi et al. (2020), where common st<strong>and</strong>ard errors cannot be derived. Two estimates from
Toya <strong>and</strong> Skidmore (2020) st<strong>and</strong>s out with a precision far higher than those <strong>of</strong> o<strong>the</strong>r studies, <strong>and</strong>
estimates are placed with some ‘tail’ to <strong>the</strong> left, which could indicate some publication bias, i.e.
reluctance to publish results that show large positive (more deaths) effects <strong>of</strong> lockdowns. The
most precise estimates are ga<strong>the</strong>red around 0%, while less precise studies are spread out between
-58% <strong>and</strong> 36%. The precision-weighted average <strong>of</strong> all estimates across all NPIs is -0.6%.
Figure 7: Funnel plot for estimates from studies <strong>of</strong> specific NPIs
4,000
3,500
3,000
2,500
2,000
1,500
1,000
Border closure (/quarantine)
Business closure (/bars & restaurants)
Facemasks/Employee face masks
Limiting ga<strong>the</strong>rings (