The trade-off between economic growth and coronavirus containment

By Kent R. Kroeger (Source: NuQum.com; October 19, 2020)

A Russian woman in a medical mask during the 2020 coronavirus epidemic (Photo by: https://www.vperemen.com; Used under the CC-BY-SA license)

Are strict containment policies (e.g., lockdowns) the key to containing the coronavirus and saving the economy?

In the U.S. case, how long did those strict lockdown measures need to be maintained during the first wave in order to minimize the second wave? Until ‘zero new infections’ were recorded for a specific amount of time? Until hospital ICU utilization rates fell below a certain threshold? Until there was a vaccine?

An Analysis of Economic Growth and Coronavirus Containment in 38 Countries

Figure 1 lists the 38 countries OWID used in the following trade-off analysis of coronavirus containment policies and economic growth for the period from January 1st to June 30th, 2020. Each country was placed into one of four quadrants based upon their relationship to the sample average for COVID-19 cumulative death rates and the strictness of coronavirus containment policies. For example, Japan and Latvia have (so far) experienced below average COVID-19 death rates while implementing some of the least stringent coronavirus policies. In contrast, Belgium and Portugal have seen above average COVID-19 death rates while pursuing some of the strictest coronavirus policies.

Final Thoughts

Sweden may have opted for the wrong strategy in controlling the coronavirus, but the net result, economically, has been similar to other European countries that adopted much stricter policies.

Research Postscript:

Along with estimating a linear model for GDP growth among the 38 selected countries, I also estimated a similar model for the 50 U.S. states (plus District of Columbia). That regression model is shown in the Appendix (Figure A.2). Compared to the world model and its two predictors of GDP growth (Figure A.2), the U.S. model was not a particularly good fit of the data, despite having five predictors. Surprisingly, the strictness of state coronavirus policies (as measured by Oxford’s Coronavirus Government Response Tracker [OxCGRT]) did not come close to statistical significance. Instead, three significant correlates with state-level GDP growth in 2020-Q2 were (in order of relative effect): (1) The state’s number of COVID-19 cases (per 1 million people), (2) the state’s number of COVID-19 deaths (per 1 million people), and (3) the average annual number of flu deaths in the state (per 1 million people).

APPENDIX: Linear model output

Figure A.1: Linear Model of Q2 GDP Growth % (n = 38 countries)

I am a survey and statistical consultant with over 30 -years experience measuring and analyzing public opinion (You can contact me at: kroeger98@yahoo.com)

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