The economics of COVID-19 policy

I've spent dozens of hours reading up various analyses of the COVID-19 pandemic, economic impact, public health responses, and policy options. Most of what I've read is pretty interesting. However, a lot of it suffers either from being very lightweight in terms of considering the trade-offs (economic cost vs. human health cost). I'll talk a little bit more about that in my next post. However in the meantime, this article by Casey Mulligan, Kevin Murphy, and Robert Topel (all from the University of Chicago) is by far the best that I have read so far.

Mulligan et al. do a great job of laying out the policy trade-offs in a clear way, outlining what we know and what we don't know, and working through the implications of the main policy alternatives. The article is quite details and difficult to excerpt from, so here is their own summary from near the end of the article (emphasis is mine):

Our analysis indicates that the features of a cost-effective strategy will depend on both current circumstances and how we expect the pandemic to play out. Some elements are common, such as the desire to use STTQ [Screen, Test, Trace and Quarantine] rather than LSSD [Large-Scale Social Distancing] when infection rates are low, and shifting the incidence of disease away from the most vulnerable. These apply whether the objective is to buy time, manage the progression of the disease, or limit the long-run impact of a pandemic that will run its course. The key difference in terms of the optimal strategy is whether our focus is on keeping the disease contained. If the objective is to buy time, then our analysis favors early and aggressive intervention. This minimizes the overall impact and allows for strong but scalable measures via STTQ. In contrast, limiting the cumulative cost of a pandemic that will ultimately run its course argues for aggressive policies later, when they will have the biggest impact on the peak load problem for the health-care system and when they will have the greatest impact on the ultimate number infected. Given the desire to protect the most vulnerable, this objective can even argue for allowing faster transmission to those that are less vulnerable, which further limits the burden on the vulnerable and also reduces the burden on the health-care system... Finally, the objective of long-run containment calls for an effective STTQ strategy applied early to keep the overall infection level low. Starting early lowers overall costs and lowers cumulative infections under the long-term containment strategy.

The bolded bits demonstrate that the optimal approach depends on the policy-makers' objective: to buy time, or to limit the cumulative cost of the pandemic. It isn't at all clear which approach is better, and even after the pandemic is over we probably still won't know. One of the important parts of this article is the acknowledgement that buying time allows us to wait until we have better information on which to base decisions.

I encourage you to read the whole article, which as I said is by far the best that I have read so far. Having said that, I wouldn't take their modelling too seriously - I really don't think that there is any model as yet doing a good enough job while incorporating sufficient heterogeneity in the population in terms of infection risk and behavioural response. However, the way that Mulligan et al. frame the issues and work through them is important and to my mind this provides a model for how we should be thinking about these issues.

[HT: Marginal Revolution]