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.
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.
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.
There are two primary types of tools that have been proposed to fight the COVID-19 virus in the near-term:
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.
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:
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.
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.
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.
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
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.
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.
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
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.
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.
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.
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.”
Chicago Booth’s Matthew J. Notowidigdo discusses what it would take for the US health system to produce better outcomes.
How Can We Fix US Healthcare?
One idea for helping consumers avoid debt traps didn’t work in a UK experiment, partly because people didn’t have the funds.
Paying Off Credit-Card Debt May Take More Than a Nudge
Chicago Booth’s Sendhil Mullainathan visits the podcast to discuss if AI is really “intelligent” and whether a profit motive is always bad.
Capitalisn’t: Who Controls AI?Your Privacy
We want to demonstrate our commitment to your privacy. Please review Chicago Booth's privacy notice, which provides information explaining how and why we collect particular information when you visit our website.