COVID-19 vs The Economy: Today

With more stringent policy measures in place, COVID-19 infections should be lower, even as economic performance is degraded. Thus, the correlation should be firmly negative between measures of economic performance and COVID social safety. But that correlation actually varies across economies that experience different shocks to their rates of growth. Why?

It is straightforward logic that policy interventions that seek to break the chain of COVID infection also lower economic activity. Economic prosperity is obviously not well-served by shutting down schools; limiting the hours of operation for bars, restaurants, and other entertainment outlets; reducing vacation and business travel, or in general making mobility more stringent.

In this reasoning, there is a tradeoff between social safety and aggregate economic performance. If a society seeks more of one of these, it ends up having less of the other. Increasing social safety, say, by imposing greater stringency measures will raise wasteful frictions in economic activity, and thus lower economic performance. Call this the stringency effect. Data should show a negative relationship between social safety and economic growth due to the stringency effect.

In observed statistics, however, this tradeoff is offset by a countervailing force. That opposing impetus stems from COVID-19 being itself already a negative supply shock and a negative demand shock simultaneously. All else equal, output will fall from ill health and mortality in the workforce. Consumption will decline from reduced job security and consumer confidence; from a desire for increased savings; and from lowered propensity to take vacations, eat restaurant meals, or purchase leisurewear. Increased risk aversion will depress entrepreneurial activity and reduce investment. Call this the shock effect. All these latter effects imply a positive relationship between social safety and economic growth. The shock effect implies that higher is social safety, the greater is confidence that life will return to normal, the higher will be economic growth.

Simply looking at correlations between COVID-19 and economic growth conflates these two effects as they press in opposing directions. A more nuanced econometric analysis is needed to disentangle these different dynamics in the data.

(What follows below draw on calculations in my Github-hosted R script covid-base-2021.01.R, which updates from daily-refreshed online data at Our World in Data and the IMF.)

COVID-19 vs The Economy: Not a Straightforward Correlation

Preliminary analysis shows the following:

  1. Measure COVID-19 performance by 1 over the number of deaths per thousand population. Measure economic performance by the difference between economic growth over 2020-2021 and economic growth over 2016-2019.
  2. The former calibrates how well policy measures and social behaviour have been able to keep COVID-19 mortality low. The higher is this index, the more successful has that society been in combating COVID-19. On the other hand, the latter measures both the size and direction of the economic shock over the COVID-19 period. The higher this measure is algebraically, the more successful has that economy been in keeping economic performance high.
  3. Other things equal, this measure of economic performance penalizes those economies that have historically been fast-growing. Thus, that China’s economic growth rate has, during the pandemic, been high relative to other large nations does not by itself imply economic success. The question instead, is how well China has done, compared to its performance pre-COVID.
  4. The graph uses a log scale on the vertical axis (as indicated in the values graphed), to highlight better key features in the data.
  5. The best-fitting line through all the datapoints can be viewed as average behaviour: Given the shock in economic performance, how well did that economy do in maintaining social safety?
  6. The most distinctive feature of the graph is that the best-fitting line across the datapoints is neither positively- nor negatively-sloped. Towards positive growth end of the horizontal axis, however, the best-fitting line is positively-sloped. In other words, those economies that have done well are those that have kept the stringency effect minimal; instead these economies show more prominently COVID’s shock effect.
  7. Given the hit that each has taken on its economic performance, Singapore and China are well above average in social safety. The US and Brazil, in contrast, well below.
  8. This graph will, obviously, continue to change as numbers still unfolding continue to come in. Chances are India will move from where it sits today, right on average, to further below. Similarly, Brazil could well continue to move downwards in this graph.

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