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

Abstract

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-indifferences 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 average, we estimate that lockdowns reduce the costs of the pandemic by 0.8% of annual GDP per capita, compared to purely voluntary responses.

The effect of COVID-19 on the economy: evidence from an early adopter of localized lockdowns

“…Our results show that lockdowns explain half of the effect, both in the raw time series ( Figure 3) and in the main regressions (Table 2). Thus, we offer a qualification to Brzezinksi et al [50], who found that not imposing lockdowns barely improves economic performance, while drastically increasing medical costs. This baseline drop probably includes threat or risk perception and includes other economic channels, like lower spending [25].…”

Decision Making and Risk Management in Biopharmaceutical Engineering—Opportunities in the Age of Covid-19 and Digitalization

“…In the absence of long-standing experience in the problem field, such dynamic progression can be also simulated based on simple, yet effective dynamic interrelationships or based on stochastic approaches, which are both also being utilized in modeling the Covid-19 pandemic. 1 , 24 , 25 …”

Predicting the cumulative medical load of COVID-19 outbreaks after the peak in daily fatalities

“…8, together with the rescaling factor f F and the accuracy S F of fatalities to case-count scaling, defined by Eq. (6). Also listed is the timescale T τ used for evaluating F before /F after (the number of fatalities per time before/after the peak) in Fig.…”

“…However, in spite of these limitations, it is safe to assume that both individuals and governments will react to a spread of a new infectious disease. Given the severity of the COVID-19 outbreak [4], it is unsurprising that the raising case and fatality numbers not only forced governments to impose lock-down measures [5,6], but also motivated people to avoid travelling and mass gatherings [7]. Hence, to understand the dynamics of COVID-19 outbreak, we propose to model the feedback of spontaneous societal and imposed governmental restrictions using a standard epidemic model that is modified in one key point: the reproduction rate of the virus is not constant, but evolves over time alongside with the disease in a way that leads to a 'flattening of the curve' [8].…”