The Brookings Papers on Economic Activity mini-conference on COVID-19 happened today as a webinar. I am reading several of the paper drafts that were discussed (but did not have the chance to tune in to the webinar). I may write more here about these papers, but for now I want to emphasize this graph from the paper by Baqaee et al., Policies for a Second Wave:
The graph is self-contained, so I don’t feel the need to explain it more.
I may indeed post again, in more detail, about this and other papers presented in this conference.
How to open a University campus relatively safely and conduct instruction with minimal numbers of infection and the maximal possible effectiveness of instruction is keeping University and College administrators up at night, not to mention faculty members like myself, who dread the possibility of being forced to teach in a classroom at a risk to their health they deem too high. Papers like the Gressman and Peck paper are valuable contributions to administrators’ decision-making and I hope they are taken seriously by them.
The main conclusions of the paper can be summarized simply. I do so now and I discuss the assumptions of the paper at the end of this blog post. The two outcomes the simulations focus on to evaluate the effectiveness of various infection control measures are (1) the total number of infections and (2) peak quarantine population.
Disclaimer: I read with reasonable care the main body of the paper and glanced at the part of the appendix where the results of robustness testing are reported. I did not read the analytical model presented at the end of the appendix, Section 5.3.
Main conclusions of the simulations:
The rate that testing for COVID-19 yields false positive results is unexpectedly and massively important for the results. This is in the context of the regime that several Universities (including mine) have announced for Fall 2020, where there would be extensive testing of community members and tracing and isolation of contacts that test positive. Such tracing would likely result in quarantining 10 to 20 students for every student who tests positive, which imposes a high cost in terms of the number quarantined.
An almost certain way to guarantee widespread infection is to allow classes larger than 120 students to meet face to face. As part of the main intervention the authors consider, classes with over 30 students would meet online only.
It matters a lot that students refrain from “all contact outside of academic and residential settings” (page 2).
Instructors have to prepare for online delivery of instruction to quarantined students, expecting at least 10% of students in any class to be quarantined on a given week. The experience of these students would not be the same as those attending class in person.
I found the paper convincing and its conclusions credible. I do want to emphasize some limitations of the analysis of the paper stemming from its assumptions. These are mostly made clear by the authors but do tend to push in the direction of making the conclusions overoptimistic. I offer these critical comments as a caution for readers, especially should they be University administrators, and not in order to diminish the contribution of the authors; obviously, all analyses have their limitations.
The first limitation is prominent in my own calculations for my personal safety: exposure to infection from using public transportation to commute to campus is not considered in the paper. Numerous faculty and students can be expected to use public transportation and import infections to campus in this way.
Compliance of individuals with regulations is assumed throughout. What are the chances individuals aged 18 to 22, to speak of the traditional age students who are still a large proportion of most campuses, will act responsibly outside of class and dorm, not going to multiple parties without any physical distancing in place? It is one thing to require mask-wearing in the classroom and another thing to expect mature behavior outside the campus setting by young people who also realize that their personal risks for a serious and possible fatal infection are small.
I could offer more minor nitpicking comments on the assumptions of the analysis, but I am stopping here, after having listed my main thoughts about the limitations of the paper. I view it as a very good and interesting paper and I look forward to additional simulations along the lines of those it offers that expand the reach of the model with assumptions amended along the lines I outlined in my critique.
The paper “How Did COVID-19 and Stabilization Policies Affect Spending and Employment? A New Real-Time Economic Tracker Based on Private Sector Data”, by Raj Chetty, John N. Friedman, Nathaniel Hendren, Michael Steiner, and the Opportunity Insights Team, was released on June 17, 2020. The authors have created a new freely accessible online tracker that allows you to see the evolution over time of many data series that give an idea of how the US economy and society are recovering from the COVID-19 pandemic. Both the paper and the tracker are highly recommended. Visit the tracker here: https://tracktherecovery.org/. To give you a flavor of what data the tracker contains and the visualizations it makes available, here are two screenshots from the home page. There is a wealth of data in the tracker and the visitor can create and almost endless variety of graphical representations of selected data sets.
I’ve been neglecting this blog badly. The least I can do is post a few of the photos I’ve made lately in an attempt to stay somewhat artistically creative in the pandemic. Here are some tulips in our dining room, from April 30th.