The Economics of COVID-19 Prevention

Ryan Bourne

My new book, Economics In One Virus, uses the pandemic to highlight a range of economic principles and ideas. But there has been plenty of interesting new applied economics on the pandemic since its publication. The latest is a fascinating paper by the University of Chicago’s Casey Mulligan on the economics of COVID-19 mitigation in the workplace.

Basic epidemiology suggests the risk of getting infected with COVID-19 was much higher in workplaces or other crowded gatherings than staying home, right? Well, not necessarily, at least on a per‐​hour basis. The risk balance is also affected by prevention measures taken at each venue and these are often far more significant at work than at home.

A pandemic brings new costs to workplaces: infection costs from the virus and then the prevention costs of any mitigation efforts, whether that be resources spent on screens, tests, and masks, or new inefficiencies introduced by desk spacing or new screening activities. Workplaces then will tend to invest in prevention measures only if the benefits of reduced infection costs exceed those costs of prevention.

We should expect economies of scale in delivering prevention though. Bigger organizations are likely better placed to spread the costs of monitoring compliance, gathering data, or purchasing certain equipment over larger numbers of employees. Of course, for very large gatherings of people we would probably eventually see diseconomies of scale too: concert venues, for example, are likely to find it incredibly costly to coordinate prevention.

Mulligan’s theoretical exposition therefore suggests that venues with very small numbers of people (like households or very small businesses) and very large numbers of people (like entertainment venues) likely wouldn’t engage in much prevention, but a wide range of workplaces in between would. And if these places are effective in mitigating COVID-19 transmission risks, then it might be worse for society for an individual to stay home than attend their safer workplace, even though the worker may decide to stay home anyway because they do not want to bear the costs of complying with their workplace’s new policies.

Are there examples to suggest that workplace prevention measures made large workplaces safer than time spent at home? Mulligan explains that we’d ideally need studies that traced worker or student infections to their sources to assess this. But these studies are limited in number, so must be supplemented with comparing worker infection rates in certain venues to community rates of infection, or case studies assessing “secondary attack rates”—i.e. how often an infected case at a location then infected others at the venue. When assessing this evidence, Mulligan suggests that, yes, certain venues with large gatherings of people appear to be safer on a per‐​hour basis than time spent outside of work.

Hospitals, businesses, and schools used a wide range of prevention measures to mitigate risks, including physical barriers (masking, eye protection, air flow filtering or shields), screening (testing, quarantining the sick, creating pods), or social distancing (physical distancing, closing common areas, or prohibiting touch). Some of these proved to be very effective.

Between March 15 and April 14^th, the Duke Health system in North Carolina, for example, had seen a ratio of hourly infection rates in its health system against the broader community of 1.67. In the next six weeks, after new mitigation measures including mask wearing were introduced, that number fell to 0.31. After the prevention measures, in other words, an hour worked at hospital went from more to less dangerous than an average hour in the community.

Meatpacking plants were notorious for being high risk environments for the virus. But Mulligan shows that prevention measures even within them had a big effect on risk mitigation: “Nebraska meat‐​processing employees were being infected with COVID-19 at 15 times the rate that other residents of the surrounding counties were. After the protocols, that ratio drops to about three.”

Though some other evidence is necessarily less clear‐​cut, not least because of the potential for asymptomatic cases in children, school data from North Carolina and Wisconsin suggests that schools were more than four times as safe as the places frequented by students and staff when not in school, on a per hour basis. Of course, given schools are places of large gatherings, this suggests that mitigation measures were, again, very effective.

University campuses are less distorted by individuals spending lots of time outside of the venue setting being investigated. The operation of a surveillance‐​testing system in the University of Chicago, however, coincided with per‐​capita student infection rates that were only nine percent of the city of Chicago’s more broadly. Though this is not the ideal counterfactual (we’d prefer to compare the outcomes to the impacts if they’d stayed home, which may be outside of Chicago), it at leasts suggest evidence that these prevention measures had big effects in easing transmission risks. Studies of secondary attack rates in hair stylists, offices, and schools, likewise find lower risks of transmission than at home.

Now, it is wrong to extrapolate from this evidence to assume that this proves stay‐​at‐​home orders or even private decisions to stay home worsened the spread of COVID-19. Mulligan’s evidence is derived from behavior given the disease patterns experienced under the existing policy regime. But if more people had been going to the office or work it’s still possible that at a community level prevalence would rise significantly, even with these successful private mitigation measures.

For many, particularly small households, returns to work would bring a net increase in contacts, and even if these raised risks marginally, household members would still be close enough to each other in non‐​work time that less time spent at home overall might not offset these new risks much. A lot more people going to work would bring more congestion on public transport, eateries around offices and after‐​work socializing, potentially bringing additional risks of transmission too. Evidence suggests too that while schools, for example, are not significant drivers of transmission when prevalence is generally low, they can be accelerants when the prevalence of the disease in the community is very high.

What Mulligan’s paper does show, however, is that many private interests had strong incentives to mitigate risks in workplaces and that those mitigation efforts were extremely powerful.

An economic analysis like this, in fact, is suggestive of the conditions under which private mitigation efforts are likely to occur. The flipside is that it exposes where private efforts are likely to be disincentivized by the costs and benefits: at home or in extremely large gatherings.

Governments banned mass gatherings including sports attendance and concerts, of course. But guidance on how aerosol transmission made ventilation important, or what to do if a household member became infected, has been sadly lacking in this pandemic, especially relative to the focus on shutdowns or guidance aimed at businesses.

In fact, the federal government has at times disincentivized or banned cheap, private at‐​home mitigation measures. At first, officials told people not to buy facemasks for use in the community. For months, the FDA then stalled on approving cheap, at‐​home rapid tests that could have been used as screening devices before socializing in these riskier home settings.

As the retrospectives on the pandemic are written, it’s crucial we try to understand the economic incentives at play in how people behaved and so where government action might have helped or hindered public health efforts. Clearly, too little economics has informed policy to date.

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For more on the Economics of COVID-19, you can order the book from Amazon U.S., Cato, Amazon UK, and many other outlets.