As states start to open up, economic activity will undoubtedly increase. The S&P 500 is up approximately 26% from its March 23rd low, perhaps suggesting the worst is behind us for now. Of course, the question on everyone’s mind is whether there will be a second wave as more people congregate at work, in public, via travel, and in retail and leisure establishments. And even if there is no second wave how many businesses will stay closed because they don’t have the financial wherewithal to open or demand is just not there to make it through the new abnormal? And if the dreaded second wave occurs and sheltering in place is reinstated then what will be the economic impact of that?
I read an interesting article written by researchers who are Ph.D. candidates at the University of California, Berkeley. Their goal was to disaggregate how much of the economic contraction that we are experiencing is from sheltering in place, which is more of a temporary phenomenon, and how much of it is due to the pandemic, which could be considered more long-lasting if there are future waves of it. The article is entitled Unemployment Effect of Stay-at-Home Orders.
Before I go into the analysis and conclusions, I thought I would show one headline and some key graphs from the week as I like to use this blog as a partial diary that I can look back on to remember what was going on at particular times and what I was thinking. Another very ugly week, although less ugly than the previous few weeks.
Hopefully, that didn’t get you too nauseous.
Between March 16th and April 4th, the percentage of the population under stay at home orders exploded from 0% to approximately 95%.
From the authors:
These orders have recently come under public criticism for exacerbating the economic disruption. However, it is unclear how much of the disruption was due to the stay-at-home orders themselves and how much is due to factors that would have occurred in their absence.
In Baek et al. (2020), we take a first step in disentangling the direct effect of stay-at-home orders from the general economic disruption brought on by the coronavirus pandemic. We do so by studying the impact of such policies on initial claims for unemployment insurance, a high-frequency, regionally disaggregated indicator of real economic activity in the US.
The authors try to do this by showing how unemployment claims, scaled for state employment, differed for early adopters of stay at home orders versus later ones. One can see from the following chart that early adopters had a much higher level of unemployment claims than late ones but they tended to converge as virtually the entire population became subjected to such orders.
So what did they conclude? The bold sentence is my emphasis.
We calculate that the direct effect of stay-at-home orders is accountable for 4 million unemployment insurance claims between 14 March and 4 April, which accounts for approximately a quarter of the overall rise in unemployment claims in that period. The direct effect of stay-at-home orders on unemployment is, therefore, small relative to the aggregate increase in unemployment insurance claims, suggesting that a large majority of the increase in unemployment may have occurred in the absence of such orders.
Although 25% is not an insignificant percentage of the tremendous increase in unemployment claims, the vast majority would have occurred even without Stay-at-Home orders according to the authors.
To corroborate this 25% figure, the authors used Google data to calculate retail mobility between early adopter counties and late adopter counties. The following graph shows how retail mobility dropped more for early adopters than late adopters which are not surprising.
This is what the authors concluded.
Before stay-at-home orders were implemented, retail mobility evolved similarly across counties, as evidenced by the flat line. The day they were announced, the orders reduced retail mobility by 5%. For the following two weeks, retail mobility stayed close to 10% lower relative to other counties as a result of the stay-at-home orders. Considering that during this period mobility fell by 40%, this data suggests a similar share of economic decline attributable to stay-at-home orders as the unemployment insurance data.
Putting all of this together leads the authors to conclude that lifting stay-at-home orders, while helpful, is not the magic bullet to completely lift the economy out of the doldrums. Unless the pandemic is alleviated and people believe this is the case and return to past behaviors, assuming they have the purchasing power to do so, then lifting the stay at home orders will bring limited economic relief.
This suggests that any economic recovery that arises from undoing stay-at-home orders will be limited if the underlying pandemic is not resolved. Weak consumer confidence, supply-chain disruptions, and self-imposed social distancing are just a few examples of economic headwinds that could persist even in the absence of stay-at-home orders. At the same time, the orders are likely to have public-health benefits from slowing the spread of the coronavirus.1 Taken together, our results caution policymakers not to expect the reopening of the economy to be an economic panacea.
Time will tell and the next month or so will be very interesting to see how economic activity rebounds as more people are able to leave their homes and frequent non-essential businesses. This analysis suggests that a quarter of the contraction should be recouped fairly quickly but only time will tell how much of the 75% is recaptured.