“The Cost of Staying Open: Voluntary Social Distancing and Lockdowns in the US”

We just put out our new working paper: “The Cost of Staying Open: Voluntary Social Distancing and Lockdowns in the US”

In combating the spread of COVID-19, some governments have been reluctant to adopt lockdown policies due to their perceived economic costs. Such costs can, however, arise even in the absence of restrictive policies, if individuals’ independent reaction to the virus slows down the economy. This paper finds that imposing lockdowns leads to lower overall costs to the economy than staying open. We combine detailed location trace data from 40 million mobile devices with difference-in-differences estimations and a modification of the epidemiological SIR model that allows for societal and political response to the virus. In that way, we show that voluntary reaction incurs substantial economic costs, while the additional economic costs arising from lockdown policies are small compared to their large benefits in terms of reduced medical costs. Our results hold for practically all realistic estimates of lockdown efficiency and voluntary response strength. We quantify the counterfactual costs of voluntary social distancing for various US states that implemented lockdowns. For the US as a whole, we estimate that lockdowns reduce the costs of the pandemic by 1.7% of annual GDP per capita, compared to purely voluntary responses.

Comments much appreciated!


@David_Van_Dijcke_University_of_Michigan Interesting paper. My initial response would be - no way. But maybe you’re right. The 1.7% GDP per capita must be a huge number for the non-lockdown areas, around $340 billion if it was everybody. Still big if it was just 50%. I’d be surprised if that played out. I think it would be useful to look at an industry breakdown, because GDP costs for each state vary by industry. It’d be nice to see raw scatterplots of the data to see, outside of the SIR/econometric modeling framework, what the raw correlations look like.

@Thomas_Young_Econometric_Studios_Utah_Legislature obviously our particular results depend quite a bit on our SIR model assumptions. But there’s quite some other research backing up the idea that consumption takes a large dip even when staying open (see f.e. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3609814). I agree that it will depend a lot on the industry, the goal of our cost calculations is more to provide a sort of elaborate back-of-the-envelope calculation on the basis of the empirical findings without developing a full-fledged model, with all the parameter uncertainty that that introduces. There’s a nice paper by some of my colleagues here at INET Oxford that has detailed industry breakdowns though: https://www.inet.ox.ac.uk/publications/supply-and-demand-shocks-in-the-covid-19-pandemic/. Regardless of industry heterogeneity, Figure B.4 does suggest a fairly stable relation between the Rt and the regional unemployment rate – suggesting that regional industry differences aren’t hugely important for determining how a state fares. Thanks for your comments!

Oops, wrong paper by the right authors: https://arxiv.org/abs/2005.04630.
A lot of people have been very productive… :laughing:

@David_Van_Dijcke_University_of_Michigan I’m trying to think about the logical policy conclusion. If more places keep the lockdown in places, does your paper imply that economic growth would be stronger vs. starting to ease restrictions?

@Thomas_Young_Econometric_Studios_Utah_Legislatureno, we don’t have much to say about easing of restrictions – we just assume that at some point, you hit a low enough level of new infections that you can switch to test and trace, and then the simulation ends – but there’s lockdown until that point. The policy conclusion would be about whether and when to impose lockdown: yes, and early on. The main point is that, if you want to stay open because you think it will spare the economy, that’s not gonna be the case – in fact, the costs are probably gonna be larger.

@David_Van_Dijcke_University_of_Michigan I have a question about how you derive the potential medical costs. It seems that you don’t have empirical data on that, is there any previous evidence to support your model assumptions on that?

Hi Ruowei. Thanks for the question. The medical cost parameter comes from https://arxiv.org/abs/2004.00493, whose model we use. It’s based on estimates of hospitalization costs and the COVID-19 hospitalization rate, among others (see their Appendix).

Can also be found here: https://nature-research-under-consideration.nature.com/users/37265-nature-communications/posts/66689-containment-efficiency-and-control-strategies-for-the-corona-pandemic-costs