Government mandated lockdowns do not reduce Covid-19 deaths: implications for evaluating the stringent New Zealand response

Abstract

The New Zealand policy response to Coronavirus was the most stringent in the world during the Level 4 lockdown. Up to 10 billion dollars of output (≈3.3% of GDP) was lost in moving to Level 4 rather than staying at Level 2, according to Treasury calculations. For lockdown to be optimal requires large health benefits to offset this output loss. Forecast deaths from epidemiological models are not valid counterfactuals, due to poor identification. Instead, I use empirical data, based on variation amongst United States counties, over one-fifth of which just had social distancing rather than lockdown. Political drivers of lockdown provide identification. Lockdowns do not reduce Covid-19 deaths. This pattern is visible on each date that key lockdown decisions were made in New Zealand. The apparent ineffectiveness of lockdowns suggests that New Zealand suffered large economic costs for little benefit in terms of lives saved.

KEYWORDS:

• Covid-19
• deaths
• impact evaluation
• lockdown
• response stringency

JEL CODES:

• C21
• I18

Acknowledgements

Helpful comments from the editor and an anonymous referee and assistance with the mapping from Geua Boe-Gibson are acknowledged. These are the views of the author.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 One cost-benefit analysis subsequently published by a government agency concerns the April 20 decision to extend Level 4 by five days. Heatley (2020) calculates that this extension provided a benefit of 239 quality-adjusted life years (QALYs), at a cost of 22,453 QALYs. In other words, the costs were over 90 times larger than the benefits. In line with the current analysis, Heatley (2020) restricts attention to using data that were available to decision makers at the time of making decisions about lockdowns.

2 https://www.rnz.co.nz/news/political/412403/all-of-new-zealand-must-prepare-to-go-in-self-isolation-now-prime-minister

3 Nevertheless, deaths data have some problems of over-counting especially when positive Covid-19 tests are linked with subsequent deaths from any cause (Loke & Heneghan, 2020; Williams et al., 2020). For the county level data used here, reporting is mostly from state public health agencies who all follow the same set of CDC guidelines for Covid deaths which are available here: https://www.cdc.gov/nchs/data/nvss/vsrg/vsrg03-508.pdf

4 Even U.S. state-level data may mislead; 75% of the variance in death rates is within rather than between states.

5 One issue with using U.S. data is whether lockdown means the same thing as in New Zealand. Fortunately, the OxCGRT stringency index based on containment and closure regulations has been extended to cover U.S. states (Hale, et al., 2020). While not as stringent as New Zealand, which is inherent in New Zealand’s response being the most stringent in the world, several states (e.g. Maryland) had stringency index values that exceeded those recorded for the United Kingdom (GBR) in Figure 1.

6 Many counties have zero deaths so the inverse-hyperbolic-sine transformation is used. This is identical to using logarithms for non-zero observations, but let zeros be used without resorting to crude adjustments like adding one to all values before logging (Gibson, Datt, Murgai, & Ravallion, 2017).

7 The 10 Standard Federal Administrative Regions (SFARs). With some instrumental variables defined at state level, using state fixed effects introduces a collinearity problem.

8 In other words, the treatment of lockdown is potentially endogenous. Some evidence for this is from Lurie, Silva, Yorlets, Tao, and Chan (2020) who show that Covid-19 case numbers were increasing faster during March, with shorter doubling times, in states that subsequently had a lockdown compared to those states that did not subsequently have a lockdown.

9 The descriptive statistics on the instruments and other variables are in Appendix 2.

10 Statistical insignificance of the coefficient on the first-stage residuals implies (via the added-variable form of the Hausman test) that potential selection on unobservables (in terms of which counties have lockdown) may not cause significant bias in OLS results.

11 Deaths after June 1 likely reflect changed treatment since the lockdowns in early April, including restrictions being relaxed from early May in many states and the large Black Lives Matter protests from 26 May. The falling trend in 7-day averages of cases, and in deaths (lagged 23 days, following Homburg, 2020) both reversed in early June, suggesting that factors from early April that caused differences in Covid-19 deaths by county were being supplanted by more recent driving forces. Perhaps for this reason, over-identification tests are statistically significant in June even though they were not in March and April so less weight should be placed on the control function results from later dates.

12 Only 7% of Texas counties with (or soon to) lockdown had a Covid-19 death by 23 March (6% nationally) so it was not deaths driving lockdown. Two months later, by 18 May, the risk a county had any Covid-19 deaths, conditional on having no deaths by 23 March, had increased by significantly more for lockdown counties compared to those that did not lockdown, further suggesting ineffectiveness of lockdowns.