Some basic economics of COVID-19 policy

A look at the trade-offs we face in regulating behavior during the pandemic

Casey B. Mulligan, Kevin M. Murphy, and Robert H. Topel | Apr 27, 2020

Sections Economics Public Policy

Collections COVID-19 Crisis

The costs of the COVID-19 crisis come in two primary forms. The first is the direct impact in terms of health and lives lost. The second is the indirect impact that comes from efforts by individuals, private institutions, and governments to mitigate those health impacts, such as social distancing, stay-at-home orders, and mandatory business closures. It is imperative that we keep in mind that both are costs, and that less of one typically means more of the other. Like it or not, the first lesson of economics is that there are trade-offs, and choices are inevitable. 

Regardless of how we choose to bear them, the costs of the pandemic will be large. Some very rough estimates provide perspective. Based on our earlier work on the value of mortality reductions and improved health, we estimate that an unrestricted pandemic infecting 60 percent of the US population and with an infection fatality rate (IFR) below 1 percent would result in roughly 1.4 million deaths, heavily concentrated among the elderly, with a total value of lost lives of about $6 trillion.[1] For comparison, that is equivalent to about 30 percent of annual US GDP, suggesting that even small progress against the spread of the disease can be quite valuable. 

Against this, we estimate that efforts to slow the pandemic via a nationwide shutdown of “non-essential” economic activities would carry a cost approaching $7 trillion per year (roughly $20 billion per day), even ignoring other long-run costs from reduced values of human and physical capital and any intrinsic value of reduced civil liberties.[2] Of course, an unrestricted pandemic is implausible even in the absence of government interventions, as individuals have powerful incentives to engage in self-protection once the risks are even partially known. Even so, these are big numbers.

If we fully understood the trade-offs, the economic problem would be difficult but entirely standard. While the required choices would not be pleasant or easy, they amount to a far simpler problem than the one we actually face. If an extensive shutdown of economic activity costs $7 trillion, largely in terms of economic hardship, and a limited response would lead to a $6 trillion loss of life, then an intermediate solution could, in principle, achieve a great deal. The value of an intermediate solution is that by focusing on those actions that provide the largest benefits in terms of lives saved while imposing the least economic costs, we will be able to do better, maybe much better, than the trade-off of $7 trillion in costs for $6 trillion in benefits would imply. 

Take another policy arena involving externalities and decisions of life or death: highway safety. At one extreme we could consider the lives lost from fully unregulated driving behavior. At the other extreme we could take the costs of shutting down all highways. Neither extreme is among the serious alternatives, which include setting speed limits and requiring special licenses for operating the most dangerous types of vehicles. While it is helpful to know what it would cost to do nothing or to have a complete shutdown, the real policy work is on the intermediate solutions.[3]

In the current pandemic the benefits of reducing exposure vary substantially across individuals, while the costs of disease transmission differ substantially across both individuals and circumstances. These conditions imply that targeted policies are highly productive. For example, there is strong evidence that the risks of serious illness and death vary by an order of magnitude between the young and the old. At all ages, individuals with comorbidities, such as heart disease and diabetes, are much more vulnerable. Likewise, some activities (such as an individual driving alone in a long-haul truck, or working with familiar and careful colleagues) involve far less risk of infection than individuals greeting guests as they enter a hotel or interacting with strangers in a bar or at a sporting event.

The magnitude of the costs likely to be imposed by the pandemic, the gaps in the world's collective knowledge about COVID-19, and the dearth of experience in confronting similar events may contribute to the impression of a disorienting landscape for making policy decisions. This paper lays out the trade-offs involved with regulating the behavior of the general population during the COVID-19 pandemic.  It summarizes some of what we already know and highlights some key unknowns that remain. It compares relative advantages and disadvantages of large-scale social distancing (LSSD) regulation to a policy of screen, test, trace and quarantine (STTQ).  A key feature of LSSD is high fixed cost—the impact on economic activity is roughly independent of the level of infection—while many of the the costs of STTQ scale with the level of infection. While some aspects of an optimal strategy are fairly general, we find that the optimal type and timing of strategies depends critically on whether we expect to contain the pandemic long-term or until a vaccine or cure arises or whether we focus on minimizing the long-run costs of a pandemic that will run its course. In particular we examine strategies for (1) limiting the impact of the disease for a given level of infection—a policy that makes sense regardless of the end-game scenario; (2) buying time to limit congestion of the health-care system or find a vaccine or effective treatment; (3) limiting the long-run impact of a disease that runs its course; and (4) adopting a long-run solution to contain infections indefinitely. It concludes with robust regulatory principles, including favoring decentralized mechanisms over direct control, distinguishing marginal from average effects, treating information as a public good, and ensuring that the chosen regulations deliver more net benefits than less-stringent alternatives.

Things we know with some confidence

In thinking about trade-offs, it is useful to begin with a few premises in which we, as economists, have a fair degree of confidence. 

  • The costs of disease and premature death are high. Living longer is a good thing, and empirical evidence shows life and health are valued highly, but they are not the only thing. People’s behavior reveals that they are willing to bear greater risks to life and health in order to have more of other goods and services. This willingness to substitute leads to what economists call the Value of a Statistical Life (VSL)—what individuals would be willing to pay for a small reduction in their own mortality risk that would save one life in a large population. Estimates vary, but the current VSL used by US government agencies for cost-benefit analysis is around $13 million. That’s an average across all ages. The VSL for very old individuals is lower because they have fewer years of remaining life to lose, and because they are in generally poorer health than younger people. The value of a statistical life is a powerful tool because it allows us to assess some fundamental trade-offs between health and other aspects of people’s lives. It is critical to remember that the trade-off here is not between “lives” and GDP—it is the trade-off between two things that people themselves value: health and other aspects of their lives.
  • The costs of a widespread shutdown of economic activity are also high, and unlikely to be sustainable over long periods. In recent research, one of the authors of this analysis, Casey B. Mulligan, puts the costs of a widespread shutdown like the current one in the US at $7 trillion per year—e.g. $1.75 trillion for a three-month shutdown. This includes some long-run costs but ignores others—such as those due to business failures (lost value of capital) and worker displacement (lost value of firm-specific human capital)—that will occur. It does not include any costs of deaths from the disease, some of which occur even with a shutdown. “Shutting off” the economy is not like shutting off the lights in your house—the economy will not return to its pre-pandemic productivity for some time, and many individuals and businesses will experience a permanent loss, so some of these costs represent future losses generated by the current economic distress.[4]  
  • The fact that individuals put great value on their own health and longevity means that there are strong individual incentives to engage in self-protection. These incentives are particularly strong for the elderly and individuals with comorbidities, who are much more vulnerable to the disease. Similar to what is observed with seasonal influenza, we should then expect much lower incidence of infection in vulnerable populations, but higher rates of hospitalization and death for those infected. Early evidence indicates that is the case with COVID-19. Absent an end to the pandemic, self-protection via social distancing and other efforts would exist in the absence of government-imposed restrictions, and they will persist after those restrictions are relaxed. 
  • Transmission of the virus is a two-sided affair involving an infected individual transmitting the virus to someone who is not yet infected. These interactions can be reduced by affecting the incentives of either side, but the costs of such policies will vary greatly across situations. When there are few infected individuals circulating in the population, quarantining the uninfected is not cost effective—a broad quarantine involves restricting the activity of many individuals to prevent relatively few infections. Under such conditions it is far more cost effective to work on the other side of the transmission mechanism, with a regime focused on testing and quarantining the infected and thereby limiting situations where one infected individual can infect many others. Broad-based lockdowns have a strong element of economies of scale—the high costs of implementing them is essentially unrelated to the level of infection in the population, while the benefits in terms of infections prevented are roughly proportional to the infection rate. This means that broad lockdowns make the most sense when the level of infection is high. In the language of economists, the marginal product of mandatory social distancing is greatest when there are many infected individuals circulating. 
  • If recovered individuals have immunity (which is not yet established), then recovered individuals represent a substantial resource. They can interact with both the uninfected and the infected with no direct costs to themselves or follow-on costs to others. Identifying these individuals is important for helping them make good individual decisions as well as for recruiting them for tasks that would be not suitable for others. Conscription of the immune is a national security possibility, but in our view conscription would be inappropriate and create poor incentives for voluntary testing.  Compensating immune individuals for the valuable services they provide would create far better incentives.
  • The nature of transmission via person-to-person proximity creates various “externalities”—individuals’ decisions to interact do not fully incorporate the costs imposed on others. In general, people with the disease (symptomatic or not) will have too many infection-causing contacts, and those without the disease will not have the proper incentive to avoid getting it. These “negative externalities” are the main reason for government-imposed social-distancing measures of the type now in effect. 
  • An infected individual can also generate externalities on the treatment side. Existing capacity of intensive-care facilities and other health infrastructure is inadequate to effectively deal with massive illness within a narrow window of time. “Flattening the curve” (spreading a given incidence of disease over time) will improve health outcomes and save lives when congestion reduces the effectiveness of care. Individuals will internalize some of these costs because they anticipate their own care will be of lower quality if they become infected when hospitals are highly congested, but they will not internalize the costs they impose on others.
  • The gain from reducing the incidence of infections in the presence of externalities may seem obvious, but there are important caveats. First, the externality created by an infected individual can actually benefit others (a positive externality), which would call for less social distancing. Suppression of the disease delays the development of “herd immunity”—if an individual’s recovery from the disease creates immunity and so the inability to transmit the disease to others, it might be better if those at low risk (such as the young) were quickly infected, while vulnerable groups were isolated. Moreover, when the pandemic will persist and the chance of future infection is large, a reduction in current contacts and infections can actually increase future infections. This can reverse the direction of the externality—individuals may have too much incentive to avoid infection early on if the primary impact of that avoidance is to increase infections later, when the externalities are larger. For example, if the highly vulnerable are isolated now but are difficult to isolate indefinitely, or if the externality from hospital congestion is less today than in the future, reducing infections today might make things worse.
  • A vaccine that will stop the disease is possible, but it is unlikely to be widely available for a year or more. This implies that any plan to buy time in anticipation of a vaccine or cure will require a cost profile that makes a multiyear investment in mitigation worthwhile.
  • Finally, we know that we will know more in a month or in six months than we do now. As we argue below, this is a key fact for designing policy responses, which can evolve from a more restrictive approach while the enemy is poorly understood, to a managed pandemic strategy characterized by more targeted restrictions as knowledge and information accumulate.

What we don’t know (yet):

We know much more about the nature of the pandemic today than we did a month or two ago. Many key features remain unknown, but they are knowable. 

  • The extent of infection in the general population remains mysterious. Commonly discussed mortality rates are mainly based on “cases” of the disease, which occur when a person with more-or-less severe symptoms seeks medical care and tests positive. Limited testing capacity in the short run typically means that only those with severe symptoms and those known to have been exposed are tested. Evidence from several sources indicates that many or even most infected individuals are either asymptomatic or experience only mild symptoms—like a cold—that do not result in medical treatment or a test. Rough estimates of the share of unrecorded infections range from about half up to nearly 90 percent. Thus we have little knowledge of how lethal the disease really is, and how lethality differs across groups. Nor do we know how many carriers are in the population, the flow of new carriers, and the stock of individuals who have recovered from the disease and are possibly immune. All of these uncertainties can and hopefully will be resolved via random testing, both for the presence of disease and for antibodies that indicate recovery from it.
  • On top of our ignorance of the extent of infection, we have limited evidence on the effectiveness of screening and testing at mitigating transmission. This is especially true at the beginning of the pandemic. As discussed below, this is a key reason for initially blunt policies such as mandatory social distancing and the forced closure of businesses. Yet limited evidence suggests that targeted policies informed by widespread testing can be very effective in arresting the spread of disease. As this is written, Iceland has (mostly non-randomly) tested more than 6 percent of its population and, using this information, has quarantined infected individuals and their contacts, and isolated the elderly and other vulnerable groups, all while imposing fewer society-wide restrictions than most other countries. Perhaps as a result, Iceland’s case mortality rate (currently below 0.4 percent) is among the lowest in the world, and its stock of known active infections has steadily declined by 60 percent from its early-April peak. Importantly, less than 5 percent of known infections in Iceland are among individuals aged 70 or above, compared to nearly 40 percent in Italy and Spain and over 20 percent in the US.[5] Random testing in Iceland has started in early April, and results indicate that less than 1 percent of the population is currently infected.[6]
  • We are largely ignorant of the relative transmission rates of symptomatic and non-symptomatic individuals, and how many of each type there are. Evidence indicates that virtually all infected individuals are asymptomatic for at least a number of days, but they are nevertheless infectious to some greater or lesser degree. Some may never experience symptoms, and many have only mild symptoms that do not require medical attention. These individuals have little or no private incentive to be tested, and in most places limited testing resources have confined tests to those exhibiting severe symptoms and/or those known to be exposed. And because test results and symptoms are recorded at a point in time, we have very little information on how many asymptomatic and mildly symptomatic remain so.

What is in the toolbox?

There are two primary types of tools that have been proposed to fight the COVID-19 virus in the near-term:

  1. Social distancing policies that attempt that mitigate the spread of the disease by limiting the extent to which individuals interact. Social distancing comes in many sizes. In our discussion we will focus mostly on large scale social distancing (LSSD) policies that are applied to the general population and cover a wide range of “non-essential” activities; examples of these policies include the stay-at-home orders currently in place in many countries. LSSD policies will typically have large costs that are independent of the level of infection because they restrict the activities of the general population rather than the activities of the much-smaller sub-group of the currently infected. Mulligan’s estimates imply current lockdown costs in the US of roughly $20 billion per day. Costs aside, early evidence indicates that LSSD has been successful at slowing the spread of the disease. Social distancing does not have to be uniformly applied nationwide; it can be applied to specific areas or populations where disease is most prevalent. We focus on LSSD policies due to their current prominence, but we also discuss the role that more targeted and more limited distancing policies can play.
  2. The second widely proposed tool is a system we refer to as screen, test, trace, and quarantine (STTQ). Under STTQ individuals are screened on observable characteristics such as health condition (fever and other symptoms) and, based on screening criteria, tested for the presence of the disease. Those found to be infected are quarantined. Known contacts of the infected are traced, monitored, and quarantined as needed. When coupled with large-scale testing—such as random testing of the general population or testing of sub-populations suspected of having high levels of infection—these methods attempt to limit the spread of the disease by focusing on the potentially infected rather than those not infected. Importantly, unlike broad lockdown policies, the costs of STTQ are not purely fixed; instead they vary directly with the number of infected. The first component, screening, is an important part of limiting the costs of such a protocol since an effective screening policy reduces the population that must be tested.

Each method has a cost advantage. Their relative advantages will vary with the level of infection in the population. The LSSD “lockdown” policy is relatively cost effective (though still quite costly) when the level of infection is high. This is because the main component of costs—lost income from economic activity—do not increase with the level of infection. Further, LSSD reduces the chance of infection for a large number of individuals when many or all are at risk. In contrast, the STTQ policy is more cost effective, in relative terms, when the level of infection is low, because the main components of cost are only incurred when infections are found or when individuals pass some initial screen such as the existence of symptoms. Basic economics tells us that we should focus more on the broad-quarantine LSSD policy when infection rates are high and on STTQ when infection rates are low.

What are the objectives?

The broad policy objective is to save lives at the least cost. How that is achieved depends on circumstances and the nature of the disease, so it can be helpful to focus on more specific objectives as a means to reaching that overall goal. There are several specific objectives that a mitigation strategy might seek to achieve. It is helpful to outline these first since, as we argue below, different strategies will have different levels of effectiveness under each. As explained below, we believe specific objectives fall into four main categories:

  1. Limit the impact of the disease for a given level of infection
  2. Buy time
  3. Limit the long-run impact of the disease while achieving population immunity
  4. Adopt a long-run solution to contain infections indefinitely

We note up front that these objectives need not be mutually exclusive. For example, the first, limiting the impact of the disease for a given level of infection, is surely efficient regardless of other objectives. In contrast, achieving population immunity may be impossible, or simply unacceptable if the case mortality rate is very high, which means that long-run containment is the only viable strategy. For example, the CMR for the Ebola virus is around 50 percent—public policy is unlikely to accept that level of risk in order to achieve population immunity. 

For each objective we ask which of the two general strategies—LSSD or STTQ—would be most effective. What follows is a brief analysis of policies applied to each specific objective.

Limit the impact of the disease for a given level of infection

Both LSSD and STTQ limit the impact of the disease, though with different costs depending on the scale of infections at any point in time. Like STTQ—which is selectively applied by definition—more refined versions of LSSD can be targeted at segments of the at-risk population where the costs of infection are highest. Thus LSSD is likely to be an effective strategy in a densely populated area, such as New York City, when the disease is rampant and spreads easily via typical person-to-person contact, but it is less productive in a low-density area where infection rates remain low. And the two strategies can be applied in combination. Evidence shows that elderly individuals and those with existing comorbidities are far more vulnerable to the disease, so targeted LSSD for them is likely to be an element of any rational strategy, both because it mitigates infection and reduces health-care system congestion. STTQ can be applied to less-vulnerable groups and groups for which the cost of LSSD are high. Preliminary evidence from countries that followed forms of this mixed strategy—such as Iceland and South Korea--indicates large benefits in terms of limiting overall mortality, protecting vulnerable groups, and reducing strain on the health-care system. Since minimizing impact can be done for any level of overall infection, this objective can be combined with the others listed below.

Buy time

Potential discovery of a vaccine or cure implies that slowing the progression of the disease can have high value if doing so pushes the time of widespread infection past the date where a vaccine or cure arrives. This delay would be valuable even if it has no effect on the overall level of infection if a vaccine or cure fails to arrive. The key “output” we need to produce in this end-game scenario is time. In most models of disease progression the disease is slowed by reducing either the number of infected individuals at a given point in time—so interactions result in fewer future infections—or by limiting the rate at which a given number of infected can infect others. Both LSSD and STTQ can do this, but with much different costs. 

Consider a strategy that is applied for a length of time T, say a month, reducing the rate of transmission by X percent, say 50 percent. During the early exponential growth phase of the disease for given T and X, it doesn’t matter whether the strategy is LSSD or STTQ—the intervention will postpone the date at which any level of infection occurs by the same amount, which can be larger than T if the infected population declines during the quarantine.[7] We can then ask: when should the delay be applied, and by which method, so as to achieve delay at the lowest cost?  

Figures 1a and 1b illustrate the effects of “earlier” and “later” interventions during the disease’s exponential growth phase. Using a standard Susceptible-Exposed-Infected-Recovered (SEIR) model of disease progression, the figures compare the impact of a T=30 and X=50 percent intervention that begins at either day 30 or day 60 of the pandemic. By day 90, the point at which the later intervention ends, both interventions yield the same level of infections. But active infections under the early intervention are lower from day 30 to day 90. Further, cumulative infections under early intervention remain lower for even longer (Figure 1b)—the early intervention “buys more time”.[8] Another advantage of acting early, when active infections are low, is that the costs of achieving any delay will be lower under the more scalable STTQ method. 

Scenarios in which a policy intervention cuts the transmission rate in half for a period of time

Figure 1a shows the evolution of active infections during the exponential-growth phase for two 30-day interventions that reduce transmission rates by 50 percent.

Figure 1b shows the evolution of cumulative infections during the exponential-growth phase for two 30-day interventions that reduce transmission rates by 50 percent.

Mulligan et al., 2020

Limit the long-run impact of the disease 

When a vaccine or long-term containment (discussed below) are unlikely, managing the progression of the disease to the point where the population gains natural immunity may be the only feasible objective. Then widespread infection may be inevitable, and buying time simply postpones the date at which “herd immunity” occurs. Assuming any postponement would be short (say under a few years), the value of delay would be low at reasonable discount rates. This shifts the policy focus to limiting the costs of widespread infection—the fact of widespread infection is determined, but the costs imposed are not. Recall from above that a targeted policy can affect who gets infected by selectively applying the infection-limiting strategies to populations where the costs of infections are highest. Further, policy can slow the progression of the disease and so reduce mortality by reducing congestion in the health-care system—"flattening the curve.” Both of these would likely be elements of a sound policy under this objective. Finally, we can affect the long-run level of cumulative infection by limiting the amount by which the terminal level of infection exceeds the minimum level of infection needed to achieve population-level immunity.

Under this scenario, the goal switches from shifting infections through time to limiting the peak rates of infection and limiting the cumulative infection rate at the end of the pandemic, including shifting the composition of the infected population away from vulnerable groups. Very early intervention accomplishes little. Given the relatively low cost of STTQ at low infection rates it may pay to do some of it early on. But an effective strategy may also include lockdowns, though these likely will come later. Given the high cost of LSSD per unit time, broad shutdowns should be applied when their marginal product is greatest, which is when infection rates are high. There are two reasons. First, lockdowns have roughly the same costs in terms of foregone economic activity regardless of the level of infection, but have their greatest effect on the number of new infections when the flow of new infections is high. Second, the impact of any reduction in current infections on the terminal number of infections will be highest late in the process, when those reduced infections will be less likely to be offset by higher levels of future infections. 

Timing a policy intervention

Figure 2 shows the impact on cumulative infections of a 30-day, 50 percent reduction in the transmission rate that begins on the indicated dates. The largest impacts occur when the infection rate is largest.

Mulligan et al., 2020

For commonly used models of disease dynamics, the benefits of any given percentage reduction in the infection rate is highest near (actually slightly after) the peak rate of infection. The relative effectiveness compared to early interventions can be enormous. Figure 2 plots the reduction in the terminal infection rate from a lockdown that reduces the transmission rate of the disease by 50 percent for 30 days, starting from any date, for a disease that would eventually infect 90 percent of the population in the absence of containment. This is greater than the infection rate that would achieve herd immunity (62.5 percent) because an unconstrained pandemic will “overshoot” the herd immunity level. The figure demonstrates that a fixed-duration lockdown has a negligible impact on long-run cumulative infections if applied early, but a substantial impact if applied when many infected individuals would be circulating. Figures 3a and 3b further illustrate this by comparing the cumulative percent infected for two alternative 30-day lockdown policies: one that starts at day 30 and one that starts around the peak rate of infection (roughly day 125). The early lockdown substantially delays infections and so the date when the ultimate level of infection occurs, but it has virtually no impact on that ultimate level of infection—it’s still about 90 percent. In contrast, the later intervention substantially reduces the long-run level of infection—there is less overshooting of herd immunity because the later intervention slows the pandemic’s momentum. This difference is why the choice between these two policies depends critically on the scenario we expect to play out. The early lockdown buys a great deal of time but accomplishes little in terms of reducing long-term infections. The later strategy does more to limit long-term infections but does not buy time up front.

A strategic decision: delay the rise in infections or reduce the overall number?

Figure 3a shows the impact on cumulative infections of a 60-day intervention that reduces the infection rate by 50 percent starting at day 30. In this simulation, the pandemic would infect 92 percent of the population if left unregulated, well above the herd immunity rate of 62.5 percent. Cumulative infections are essentially unchanged by the policy.

Figure 3b shows the impact on cumulative infections of a 60-day intervention that reduces the infection rate by 50 percent starting at day 120. The intervention reduces the long-run infection rate to about 78.8 percent, which is substantially closer to the herd immunity rate of 62.5 percent.

Mulligan et al., 2020

Adopt a long-run solution to contain infections indefinitely

When a vaccine is estimated to be very far off, simply buying time is of little value. Barring eradication, the disease must be more-or-less permanently contained. This has been the strategy applied for many years to the Ebola virus, for which, until very recently, there was no vaccine. Given the high costs of a broad lockdown, a widely applied LSSD policy is unlikely to be a sustainable long-run solution. In contrast, a highly effective STTQ strategy could be viable long-term if the infection rate can be maintained at a low enough level to prevent the pandemic at reasonable continuing cost, because the costs of STTQ tend to scale with the level of infection.  Whether such a solution is worthwhile is essentially a question of whether the flow costs of the STTQ policy exceed the benefit of delaying the pandemic’s progress until the arrival of a vaccine or mutation of the virus that ends the threat, or indefinitely if the threat persists.[9] Again, if such a strategy is optimal, it should be applied early. Since these long-run containment strategies are similar to those adopted in the “buy time” scenario, they are relatively robust to cases where it is uncertain when or if a vaccine or effective treatment will arrive. Acting early is valuable here since that reduces the stock of infections at the time the strategy is implemented. This reduces both the variable costs of the strategy and the number of infections resulting from the stock of initially infected.

Summary

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 rather than LSSD 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.[10] 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.

Robust policy principles 

Based on the analysis above, how should public policy proceed when faced with a new but poorly understood pandemic? Some simple economic principles provide a basis. 

  • Buy some time upfront, but use that time wisely. Now and in the near future any actions we take will be based on less information than we will have in the future, hopefully much less. If we think there is likely value in buying time, it makes sense to buy it now since that puts off the need to take further action until we can make a more informed decision. That decision could include deciding which of the potential end-game objectives make the most sense.
  • Use the time so purchased to gather information and build capacity to deal with the various scenarios that might play out. On the information front, learning about the transmission properties of the disease and potential screening options would be of primary importance. Given the high costs of a broad LSSD policy or a broad pandemic that runs its course, a workable STTQ policy could be very effective. 
  • Given the high stakes, the out-of-pocket costs for most policy responses are likely to be round-off error in the calculations. For example, spending $10 million (or even $100 million) to gather data on infection rates and probabilities would only need to improve our policies slightly to imply a large return on investment. Similarly a $20,000 ventilator would only need a 1/50 probability of saving a single life (with even a very low $1,000,000 VSL) to be worth the investment. At $100 per test, a regime that on average tested 10 percent of the US population every month would cost roughly $40 billion per year, or roughly the cost of “closing” the economy for 2 days. 
  • Design policies and disseminate information so as to leverage, rather than thwart, individual incentives and local information. Individual incentives don’t have to be perfect to get most of the way to efficiency, while poor targeting can be worse than doing nothing, even if the targeting is correct on average.[11]
  • Apply scarce resources, such as mandatory social distancing and the shutdown of economic activities, where they have the greatest marginal impact. Targeted policies are generally superior to blunt ones, but as shown above we need to know who and what to target, and when to target them. 
  • Isolate the most vulnerable—primarily the elderly and those with serious preconditions. This makes sense under any scenario. With accurate information about risks, their private incentives to avoid what would be for them a very costly infection will make them cooperative in such efforts and willing recipients of advice. The primary externality flowing from these individuals is the increased strain on the health-care system if they become infected. Also, shortening the duration of the time they need to be isolated will increase the return on their individual efforts, so those efforts will be more vigilant and effective. Such policies have proven very effective in protecting the most vulnerable in Iceland, Austria, and South Korea while reducing the overall cost of policy restrictions.
  • Evidence indicates we are still in the early stages of the pandemic, when the level of overall infection is still low. In this phase, policies that identify those likely to be infected and trace individuals with whom the infected have had contact—STTQ—have a substantial comparative advantage over broad ex-ante restrictions (LSSD) that require economies of scale. Such targeted policies are preferable to broad ex-ante restrictions where the costs are high regardless of the level of infection.
  • Enlist employers as a productive source of population testing, with the goal of allowing the economy to function while still reducing the transmission of the disease. Employers could be required to continually test employees working in at-risk situations, such as in an open office or factory floor, and to require quarantine (non-work at a minimum) for the infected. Assuming acquired immunity, previously infected individuals could return to work and would not require further testing. Employees have a stronger incentive to cooperate in testing if that is a condition of employment. Further, employees will have greater interest in participating and engaging in distancing and testing if that helps monitor and reduce infection among their coworkers—with whom they are familiar—than if it benefits complete strangers. This workplace-based approach is likely to be particularly advantageous because we are far from more permanent solutions like a vaccine or herd immunity. In contrast, maintaining draconian restrictions while waiting for those solutions turns the mere prospect of high costs into a virtual certainty. And if a vaccine does not arise such draconian policies incur immense costs with little return—we would be left in a position with regard to the disease that is only marginally better than where we are today.
  • Provide guidelines for individuals and businesses to follow.[12] Information is a public good. Markets work well when they aggregate information and allow those with a comparative advantage to specialize in providing a service. In this case most market participants have little or no experience on how best to deal with the pandemic, and the normal processes of learning by doing and natural selection of better methods take substantial time. Under such conditions guidelines can provide important input to individual decision making. Guidelines also provide market participants with an authoritative basis for actions that might otherwise get less cooperation from employees, employers, or customers.
  • Any “buy-time” or long-term containment strategy will have to be based on an effective STTQ policy. Since the costs of those policies depend on the number of individuals infected they can have low costs when infection rates are kept low. Further, the cost of the strategy falls if infections can be reduced over time. This cost-reducing complementarity makes a STTQ strategy particularly attractive for long-term application.
  • Social distancing policies can have their greatest benefit in limiting interactions where an infected individual can infect many others. This calls for limiting large interactions where one person may come in contact with many others, such as sporting events or concerts, or in densely populated areas. This is particularly true under a containment strategy based on STTQ. Tracing contacts when an individual has a 1/10 chance of infecting 10 people is much easier than tracing contacts when that person has a 1/1000 chance of infecting 1000 people, even though both result in the same aggregate transmission rate.
  • Given the great heterogeneity in the level of infection and the conditions generating new infections (such as density and interaction rates), the optimal extent and timing of policies are likely to differ substantially across time and space. This argues strongly for letting local actors have flexibility in how and when to impose restrictions. However, since areas do not represent closed systems and infection can leak out to other areas—an externality—there is a social benefit in pushing localities to adopt more aggressive strategies than they would on their own.

 

Casey B. Mulligan is Professor in Economics and the College at the University of Chicago.

Kevin M. Murphy is George J. Stigler Distinguished Service Professor in Economics, Booth School of Business and the Law School.

Robert H. Topel is Isidore Brown and Gladys J. Brown Distinguished Service Professor of Economics at Chicago Booth.

 

[1] As this is written in mid-April, confirmed cases per capita in highly impacted countries such as Italy and Spain have risen to about .0025, or one-quarter of one percentage point. Doubling this to roughly account for unrecorded infections implies an infection rate of about half a percentage point. For the US a similar calculation implies an infection rate of about one-seventh of a percentage point. We know that the stock of total infections will continue to rise, and that actual cases may significantly exceed confirmed cases, but the key point is that we are a long way from 60 percent. 

[2] Using the same value of a life and the current projection of 68,841 US deaths from COVID-19, the value of lives lost with the shutdown would be about $0.3 trillion.

[3] Indeed, longstanding principles of regulatory policy state that "It is not adequate simply to report a comparison of the agency's preferred option to the chosen baseline [do nothing]." (Office of Management and Budget Circular A-4). A "less-stringent" alternative pursuing the same goals should also be considered.

[4] For example, one such cost that is easy to overlook is the cost of closing schools. While students in K-12 produce little in the way of tangible output they are engaged in the process of human capital production, which has enormous long-run value. Economic evidence suggests that a lost year of schooling is roughly equivalent to a 7-10 percent loss of lifetime earnings, and there are currently over 70 million young people enrolled in school. If we assume an 8 percent return per year of schooling, the present value of lost lifetime earnings from a complete shutdown of school for one year could exceed $1 trillion.

[5] Daily updates on Iceland’s testing regime and outcomes are available on the web at covid.is. 

[6] As economists, we cannot rule out the possibility that countries like Iceland eventually “lose control” of the number of infections.

[7] For a simple SIR model and an intervention during the early exponential phase, the delay in time is approximately equal to a(1-x)/(a-δ) T, where the parameter a is the rate of new infections per infected individual and δ is the rate at which those currently infected recover. 

[8] In fact, the path of cumulative infections for the earlier intervention remains lower until day 185, when the two paths actually cross. This implies that while the early intervention delays the time of infection more over this interval, it actually does less to reduce infection in the longer run. We discuss this more below.

[9] Specifically, the benefit of delay is the product of the full cost of additional infections and a interest rate factor, which is the sum of the hazard of otherwise ending the pandemic and a time value of money.

[10] If infection and recovery lead to immunity then we should look to obtain immunity at the least cost in terms of economics and lives. Younger, less-vulnerable individuals are likely to be active spreaders of the disease if they are not immune. Achieving immunity through them has several important benefits: 1) it allows us to increase immunity at low cost since they are less likely to have adverse consequences; 2) making them immune does the most to reduce transmission since they are the primary transmitters absent immunity; 3) given they are less likely to develop complications, they place less strain on the health-care system for a given level of immunity achieved; 4) these individuals are likely to have the highest costs of quarantine since they are economically active at either work or school. For all of these reasons, keeping them inactive for too long is likely a bad idea.

[11] This is a longstanding principle of regulatory policy. The Office of Management and Budget’s Circular A-4, which is its guidance to federal agencies on the development of regulations, acknowledges the desirability of "market-oriented approaches rather than direct controls. Market-oriented approaches that use economic incentives should be explored. ... alternatives that rely on incentives and offer increased flexibility are often more cost-effective than more prescriptive approaches." Using an excise tax rather than a prohibition is a practical example of a policy that makes use of local information about the costs and benefits of protective actions.

[12] This is another longstanding principle of regulatory policy. As Circular A-4 describes, "If intervention is contemplated to address a market failure that arises from inadequate or asymmetric information, informational remedies will often be preferred.... A regulatory measure to improve the availability of information, particularly about the concealed characteristics of products, provides consumers a greater choice than a mandatory product standard or ban."