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Publicly Available Published by De Gruyter May 21, 2021

Supply-side Effects of Pandemic Mortality: Insights from an Overlapping-generations Model

Etienne Gagnon, Benjamin K. Johannsen and David López-Salido

Abstract

We use an overlapping-generations model to explore the implications of mortality during pandemics for the economy’s productive capacity. Under current epidemiological projections for the progression of COVID-19, our model suggests that mortality will have, in itself, only small effects on output and factor prices because projected mortality is small in proportion to the population and skewed toward individuals who are retired from the labor force. That said, we show that if the spread of COVID-19 is not contained, or if the ongoing pandemic were to follow a mortality pattern similar to the 1918–1920 Great Influenza pandemic, then the effects on the productive capacity would be economically significant and persist for decades.

JEL Classification: E21; E27; E43

1 Introduction

As the year 2021 begins, there are over 20 million confirmed cases and about 350,000 deaths attributed to COVID-19 in the United States, with both counts rising rapidly.[1] The Institute for Health Metrics and Evaluation (IHME) at the University of Washington projects about 570,000 pandemic-related deaths by April 2021 under its baseline scenario. Although treatments are improving and a number of vaccine candidates have shown promising results, health experts warn that the number of deaths and cases will continue to rise until a significant majority of the population is immune to the virus. On the economic front, the mandatory and voluntary social distancing measures enacted at the onset of the pandemic led to a deep recession, with the unemployment rate soaring by a record-shattering 10% points in April 2020, to about 15%. Although GDP rebounded in the second half of the year and most, though not all, job losses were erased, economic activity remains depressed in many sectors.

In this paper, we use a general-equilibrium, overlapping-generations model to explore some implications of COVID-19 mortality for the economy’s productive capacity. Our analysis is motivated by economic research suggesting that past pandemics had significant macroeconomic effects—notably by depressing output, lowering interest rates, and raising real wages—that sometimes persisted for decades.[2] Our model and its calibration are the same as in Gagnon, Johannsen, and López-Salido (Forthcoming). This model allows us to target mortality rates by age that are consistent with epidemiological estimates showing heavy mortality from COVID-19 among older individuals. The model also accounts for the fact that employment varies by age and birth cohort, allowing us to track the effects of mortality on the aggregate labor supply. Furthermore, the model’s life-cycle elements allow us to investigate how the stock of capital endogenously adjusts, through individual consumption/saving decisions, when worker mortality causes an unexpected fall in the labor supply. In the decentralized equilibrium of our model, this adjustment operates through a rise in real wages and a fall in real interest rates, as suggested in empirical studies of past pandemics. It bears emphasis that our model only speaks to the direct effects of pandemic-related mortality on the economy’s productive capacity and, thus, is not designed to address the cyclical downturn brought by the pandemic or the effectiveness of the economic policy response.[3]

Overall, our model simulations suggest that, under current epidemiological projections, the direct effects of COVID-19 mortality on the economy’s productive capacity are small. This conclusion reflects both the limited number of projected fatalities in proportion to the U.S. population and their concentration among senior citizens who have already exited the labor force. Our finding that the effects on the productive capacity of the economy are small may be useful to modelers who study the cyclical implications of COVID-19 because it implies that the longer-run values of economic variables around which models are often linearized may be little affected by the pandemic. We want to emphasize that our analysis in no way implies that the economic and social effects of COVID-19 are small. The human toll that COVID-19 has already taken is heartbreaking. Additionally, the cyclical economic effects of COVID-19 have already caused dramatic disruptions in economic activity.

For comparison, and to highlight the economic transmission channels of pandemic mortality, we simulate the model under two alternative scenarios that lead to significantly greater mortality overall. In the first alternative scenario, we assume that COVID-19 infections soar until the number of cases is sufficient to ensure herd immunity, which many experts estimate to be around 70% of the population. In the second alternative scenario, we assume that the overall deadliness of the virus and the age distribution of fatalities are consistent with mortality during the Great Influenza pandemic of 1918–1920. In contrast to COVID-19, the Great Influenza was most lethal for young adults. Both alternative scenarios lead to economically significant and persistent declines in output in the model, along with increases in real wages and declines in real interest rates. Under the herd immunity scenario, the size of the population and labor supply eventually return to their pre-pandemic trends because the fatalities occur almost entirely among older individuals who are past their reproductive period. By contrast, under the Great Influenza scenario, for which mortality affects young adults during their reproductive years, the model predicts that output would be on a permanently lower trajectory and that the adjustment of factor prices would drag out beyond the next couple of decades.

The paper is organized as follows. Section 2 reviews how we constructed mortality rates by age for COVID-19 and the Great Influenza pandemics. Section 3 discusses the key transmission channels in our model, presents our main simulation results, and discusses some limitations. Section 4 summarizes our key findings and conclusions.

2 Mortality Assumptions

The key driving factor in our model simulation is the excess mortality associated with pandemics and how it is distributed across age groups. The unconditional probability that a person will die because of a pandemic (henceforth the “excess mortality rate”) can be expressed as the product of the probability that this person will be infected by the virus (known as the “attack rate” in epidemiological studies) and the deadliness of the virus conditional on being infected (known as the “infection fatality rate”). Because of data and testing limitations, along with a large number of asymptomatic cases that may never be diagnosed, estimates of the attack rate and infection fatality rate of COVID-19 remain somewhat imprecise even though millions of cases have been identified in the United States and abroad. Nonetheless, it is apparent that the virus is much more lethal for older individuals than for younger ones, in contrast to mortality rates during the Great Influenza, which, as we show below, were highest among young adults.

2.1 Mortality from COVID-19

The left panel of Figure 1 shows the baseline infection fatality rate, by age, that we use in our simulations. This rate is an interpolation of estimates by age deciles from Verity et al. (2020), who control for under-reporting (the possibility that mild cases are often not diagnosed), censoring (the possibility that infections and deaths are reported with a lag), and how these factors may vary with age.[4] The infection fatality rate is almost nil for children and young adults but rises rapidly with age as individuals reach their 50s and beyond. Among individuals aged 80 years or more, the overall infection fatality rate estimated by Verity et al. (2020) is 7.8%. The risk of dying from a COVID-19 infection is arguably declining as the pandemic continues because existing treatments are being deployed with greater efficiency and new treatment options have become available. In any event, the skewness of COVID-19 mortality toward the elderly population remains a defining feature of this pandemic.

Figure 1: 
Baseline mortality assumptions for COVID-19 pandemic.

Source: Authors’ interpolation of estimates from Verity et al. (2020); Institute for Health Metrics and Evaluation (IHME) – University of Washington. The IHME projections for total U.S. deaths attributable to COVID-19 are dated December 23, 2020, run through April 1, 2021, and use smoothed data. The “baseline” and “universal masks” projections embed the assumption that about 70 and 100% of the population wears a mask over the projection period.

Figure 1:

Baseline mortality assumptions for COVID-19 pandemic.

Source: Authors’ interpolation of estimates from Verity et al. (2020); Institute for Health Metrics and Evaluation (IHME) – University of Washington. The IHME projections for total U.S. deaths attributable to COVID-19 are dated December 23, 2020, run through April 1, 2021, and use smoothed data. The “baseline” and “universal masks” projections embed the assumption that about 70 and 100% of the population wears a mask over the projection period.

In our simulations, we posit that all individuals face the same risk of becoming infected, which Verity et al. (2020) argue is consistent with past studies showing that respiratory infections do not vary substantially by age. We target an attack rate that is consistent with COVID-19 causing 500,000 deaths across all age groups. As the right panel of Figure 1 shows, this illustrative target mortality is close to the total mortality predicted by the IHME by the spring of 2021. The mean (“baseline”) projection shown in the figure is conditioned on an assumed infection fatality rate by age that is similar to that estimated by Verity et al. (2020), along with assumptions about social distancing measures, mask mandates, other health policies, and their effects on the transmission of the virus and mortality. The “universal masks” projection embeds the alternative assumption that 100% of the population wears a mask in compliance with a universal mask mandate, compared with about 70% of the population under the IHME baseline assumptions.

Whether the IHME projections understate or overstate the ultimate number of casualties is highly uncertain at this stage. Many U.S. states (such as New York and New Jersey) and countries (such as China, South Korea, and Western European countries) that faced initially large caseloads showed that aggressive containment measures can greatly reduce the number of new infections and accompanying deaths. Indeed, as of this writing, a number of countries and U.S. states have announced or are considering the reintroduction of strict confinement measures to reduce the propagation of the virus. Moreover, understanding of how the virus propagates and of which treatments are effective is steadily improving, with encouraging news about vaccines pointing to a reasonable chance of building immunity in a large segment of the population—or, at least, in the at-risk population—by the spring or summer of 2021. Even so, resistance by part of the public to remediation measures (including vaccination), the daunting logistical issues posed by vaccination on an unprecedented scale, the unknown duration of the protection conferred by vaccines, possible virus mutations, and continued U.S. and global transmission suggest a material possibility that the number of infections could rise substantially more than currently projected before the pandemic is over. Because of this possibility, and to illustrate the economic mechanisms of pandemics, we will consider the alternative extreme assumption that 70% of the U.S. population ultimately contracts the virus. This proportion is a common estimate of the immunization rate needed to ensure herd immunity.[5] Finally, we note that little is known regarding the medium-run to long-run health consequences of COVID-19 infections for survivors because the virus is so new. However, evidence is mounting that a small proportion of those infected may suffer from various chronic ailments.[6] In our simulations, we assume that survivors have the same labor supply and mortality risk over the remainder of their lives as those of the same birth cohort who did not become infected. To the extent that long-term complications emerge, our model would understate the negative effects of COVID-19 on the labor supply for a given mortality rate.

2.2 Mortality from the Great Influenza

The Great Influenza afflicted the U.S. population in waves between the spring of 1918 and the spring of 1920, with the wave in the fall of 1918 being the deadliest and most widely spread geographically.[7] Retrospective estimates of this pandemic’s attack rate and infection fatality rate are highly imprecise due to the absence of testing methods at the time. However, information on the number of deaths related to the pandemic is sufficiently detailed to yield a reasonable approximation of the associated excess mortality by age, which is the key ingredient in our simulations. In particular, we estimate the excess mortality rate of the Great Influenza by assembling a data set of annual mortality statistics, by age bins and causes of death, using the annual mortality tables published by the U.S. Bureau of the Census. We calculate the number of deaths attributed to influenza as well as to all forms of pneumonia and bronchopneumonia, which were frequent complications of the flu.[8] We use cubic splines to interpolate the statistics by age bin to the model’s quarterly frequency. We then calculate the mortality rates due to these causes that are in excess of the corresponding average mortality rate for the 1913–1917 reference period. Our Appendix A and replication materials contain the data sources and methodology.

The left panel of Figure 2 displays the fraction of the population that died from influenza and related pulmonary causes in 1918, 1919, and 1920. In 1918 alone, almost one percent of individuals in their late 20s and early 30s died from these causes—an order of magnitude more than had been observed, on average, in the previous five years. Mortality was also unusually elevated among other individuals aged 70 years or less, with significant excess mortality among newborns and young children. By contrast, the mortality of individuals older than 70 in 1918, while large in comparison to that of most other age groups, was less than had been observed in the previous five years.[9] Why the Great Influenza affected young adults most severely, and why it spared the elderly to some degree, remains debated. Researchers have notably suggested that high mortality rates among young adults might reflect overactive immune responses, that the elderly had acquired protective immunity from past exposures to similar virus strains, or that exposure to other diseases, such as tuberculosis and the 1889–1890 “Russian Flu” pandemic, could have led to health complications among young adults.[10] As we discuss below, irrespective of its causes, the fact that the Great Influenza affected young adults most severely meant that it led to a significant and permanent reduction in the aggregate labor supply.

Figure 2: 
Estimated mortality from the Great Influenza.

Source: Authors’ calculations using data from the annual publication “Mortality Statistics” of the U.S. Bureau of the Census for the years 1913–1920. The mortality rates and excess mortality rates encompass all death categorized under “influenza” and related pulmonary causes. See Appendix A for details.

Figure 2:

Estimated mortality from the Great Influenza.

Source: Authors’ calculations using data from the annual publication “Mortality Statistics” of the U.S. Bureau of the Census for the years 1913–1920. The mortality rates and excess mortality rates encompass all death categorized under “influenza” and related pulmonary causes. See Appendix A for details.

The right panel of Figure 2 shows the excess mortality rate, by age, that we use in our counterfactual simulations of a pandemic matching the mortality risk observed during the Great Influenza. The excess mortality rate sums the mortality rates from influenza and related pulmonary causes between 1918 and 1920 that are in excess of what was observed, on average, during the 1913–1917 period. Thus, if all waves of the pandemic had occurred at once, it would have added as much as 1¼% points to the mortality rate of individuals in their late 20s and early 30s, whereas individuals older than 70 would have benefited from reduced mortality risk.

3 Model Simulations

To explore the possible supply-side effects of pandemic mortality, we simulate the general-equilibrium, overlapping-generations model from Gagnon, Johannsen, and López-Salido (Forthcoming).[11] We consider three mortality scenarios: a “baseline” COVID-19 scenario in which 500,000 individuals die; a “herd immunity” COVID-19 scenario in which 70% of the population is infected; and a “Great Influenza” scenario in which the excess mortality rate by age matches our corresponding estimates of excess mortality for all waves of the Great Influenza.

3.1 Summary of the Model

The production side of the model is neoclassical, with a representative firm operating a Cobb-Douglas production function. Households are representative of their birth cohorts in terms of demographic characteristics. They must allocate their period income between consumption and savings, a decision that is made with knowledge that households will rely primarily on savings to sustain consumption late in life. Households’ labor endowments vary according to the observed trends in employment rates by age and birth cohort. By aggregating across households’ life-cycle decisions, the model generates predictions for the paths of the aggregate stock of capital and the aggregate labor supply, whose ratio plays a central role in our analysis.

The model is calibrated to observed and projected trends in mortality risk, labor supply, and fertility choices of American households. Therefore, the model can be used to explore the macroeconomic implications of changes in cohort sizes over time, including changes caused by excess mortality due to pandemics. Of note, the model is not designed to address near-term to medium-term disruptions to economic activity caused by factors such as social distancing measures, business bankruptcies, policy interventions, the effects of uncertainty on economic decisions, or demand-driven cyclical disturbances more generally. Thus, the results should be interpreted as an exploration of the direct effects of pandemic mortality on the productive capacity of the economy.

We simulate pandemic mortality as a single wave of fatalities that hits in the second quarter of 2020—that is, in the period when mortality associated with COVID-19 first soared in the United States. Obviously, pandemic mortality can occur over several quarters or even years—indeed, past pandemics have generally featured more than one wave of infection. That said, given our interest in the consequences over extended periods, the lumping of fatalities into a single quarterly period is largely inconsequential for our key conclusions.

3.2 The Importance of the Aggregate Capital-labor Ratio

The aggregate capital-labor ratio plays a central role in our simulation results and thus warrants some discussion. The representative firm’s production function can be expressed as

Y t = A t ( K t ) α ( L t ) 1 α = A t K t ( K t L t ) α 1 ,

where Y t is real output, A t is total factor productivity, K t is the aggregate capital stock, L t is aggregate labor, and α ( 0 , 1 ) is a parameter. Because the aggregate stock of capital is fixed within the period, current output depends solely on, and is inversely related to, the capital-labor ratio (equivalently, current output depends solely on, and is positively related to, aggregate labor supply).

We assume that the firm rents its capital and labor inputs in competitive markets such that the real wage, W t , and the real rental rate of capital, R t , can also be expressed as functions of the aggregate capital-labor ratio,

W t = ( 1 α ) A t ( K t L t ) α ; R t = α A t ( K t L t ) α 1 .

Under these assumptions, sudden mortality among workers causes output to fall by depressing the labor input. The increased scarcity of labor relative to capital, in turn, causes real wages to rise and the rental rate of capital to fall. The relative scarcity of labor more generally causes interest rates to fall, with the real short-term interest rate in the model being expressed as r t = R t δ , where δ is the depreciation rate of capital. Because the capital stock adjusts slowly through individual consumption/saving decisions—and because the path of the aggregate labor supply is determined by exogenous demographic variables—the effects of pandemic-related mortality on the capital-labor ratio and on other variables can persist for some time. Jordà, Singh, and Taylor (2020) highlight these channels to explain their findings that real wages rose and interest rates declined in the wake of past pandemics.

3.3 Implications for the Population and Labor Supply

Figure 3 shows, for each of the three scenarios, the effects of pandemic mortality on the population and aggregate labor supply (both expressed in percent deviations from their no-pandemic trends). For COVID-19, the death of 500,000 persons in the baseline scenario reduces the population by 0.15% (left panel). Under an infection rate consistent with achieving herd immunity, a little over 2.13 million persons would die, which is equivalent to 0.64% of the U.S. population. For both COVID-19 scenarios, the model predicts that the effects of pandemic mortality on the size of the population will be essentially undone in the coming few decades because almost all deaths occur among individuals who are past their reproductive period, leaving population growth through births essentially unchanged.[12]

Figure 3: 
Effect of pandemic mortality on population and aggregate employment.

Source: Authors’ calculations. The attack rate is calibrated to yield 500,000 total deaths in the “COVID-19 (baseline)” simulation and set to 70% of the population in the “COVID-19 (herd immunity)” simulation. The “Great Influenza” simulation is calibrated to our estimates of excess mortality rates during the 1918–1920 flu pandemic.

Figure 3:

Effect of pandemic mortality on population and aggregate employment.

Source: Authors’ calculations. The attack rate is calibrated to yield 500,000 total deaths in the “COVID-19 (baseline)” simulation and set to 70% of the population in the “COVID-19 (herd immunity)” simulation. The “Great Influenza” simulation is calibrated to our estimates of excess mortality rates during the 1918–1920 flu pandemic.

The initial reductions in aggregate employment in the two COVID-19 scenarios (right panel) are half as small as the corresponding reductions in the population. The relatively small initial employment declines reflect the fact that COVID-19 mortality is strongly skewed toward the elderly, many of whom had already exited the labor force. The effect on aggregate employment also dissipates faster than the effect on the population because the workers who die are closer to retirement than to death. As with the decline in the population, the decline in aggregate employment ultimately proves transitory because COVID-19 mortality has essentially no influence on fertility.

Figure 3 also reports responses under our counterfactual “Great Influenza” scenario. When the mortality risk matches our estimates for the 1918–1920 flu pandemic, the number of fatalities totals 1.41 million (0.43% of the population), which is many times larger than under our baseline COVID-19 scenario and roughly half as much as under the “herd immunity” scenario. The initial fall in aggregate employment, at 0.61%, is a bit larger than the effect on the population, reflecting the fact that young workers were unusually likely to die from the virus. Importantly, and in contrast to the two COVID-19 scenarios, the effects of Great-Influenza-like mortality are largely permanent because the death of young adults leads to fewer births. In fact, the model predicts that the decline in the population relative to its pre-pandemic trend should accentuate over the remainder of the decade, reflecting the assumption that elderly mortality diminishes thanks to some protection to the specific virus strain.

In sum, the simulations illustrate that both the total mortality rate and the age distribution of fatalities are important determinants of the evolution of the population and workforce.

3.4 Implications for Output, Real Wages, and Interest Rates

Figure 4 shows the implications of pandemic mortality for some key macroeconomic variables. Because of the sudden and persistent drop in labor supply, along with the slow adjustment of the capital stock, all three scenarios feature a sudden and persistent rise in the capital-labor ratio (top-left panel). Pandemic mortality also leads to a reduction in output (top-right panel); under our assumptions about the production function, this initial drop equals the contraction in the labor supply adjusted by the labor share in production. For the baseline COVID-19 scenario, this initial drop in employment is small, leading to a decline in output of only 0.06%. By contrast, under the herd immunity scenario and the Great Influenza scenario, the declines in output due to increased mortality are more economically significant, at 0.24 and 0.39%, respectively.

Figure 4: 
Macroeconomic implications of pandemic mortality.

Source: Authors’ calculations. The attack rate is calibrated to 500,000 total deaths in the “COVID-19 (baseline)” simulation and set to 70% of the population in the “COVID-19 (herd immunity) simulation.” The “Great Influenza” simulation is calibrated to our estimates of excess mortality rates during the 1918–1920 flu pandemic.

Figure 4:

Macroeconomic implications of pandemic mortality.

Source: Authors’ calculations. The attack rate is calibrated to 500,000 total deaths in the “COVID-19 (baseline)” simulation and set to 70% of the population in the “COVID-19 (herd immunity) simulation.” The “Great Influenza” simulation is calibrated to our estimates of excess mortality rates during the 1918–1920 flu pandemic.

The subsequent evolution of output largely depends on whether the drop in employment is temporary or permanent. Under the two COVID-19 scenarios, aggregate employment ultimately recovers, which helps output return to its trend level within a couple of decades. Under the Great Influenza scenario, output never returns to its original trend because of the permanent step down in aggregate employment. Moreover, in that scenario, the model predicts that output would slide further below its pre-pandemic trend this decade and next because sudden mortality leaves the capital stock above its new normal level (as evidenced by the jump in the capital-labor ratio). Over time, households increase their capital holdings by less than they would otherwise, thus contributing to a further fall in aggregate output. The slow decumulation of capital also cushions the fall in output under the two COVID-19 scenarios, which can be seen from the fact that output returns to its trend level sooner than aggregate employment.

The bottom panels show the effects of pandemic mortality on real wages and the real short-term interest rate. Again, the effects in the baseline COVID-19 scenario are tiny, reflecting the correspondingly tiny rise in the capital-labor ratio. By comparison, mortality in the herd immunity and Great Influenza scenarios causes real wages to rise 0.13 and 0.21%, respectively, relative to the no-pandemic case. The corresponding effect on the (annualized) real short-term interest rate is always small, however, never exceeding a few basis points. Intuitively, the change in the level of interest rates corresponds to the change in the level of the real rental rate of capital ( R t ). Because the value of R t is small to begin with, R t does not fall much, even when a swing in the capital-labor ratio raises R t by a significant amount in percentage terms.

3.5 Discussion

Our simulations suggest that, unless mortality from COVID-19 turns out to be much larger than currently projected, the economy’s productive capacity will be largely intact. In that sense, past pandemics may be misleading guides for the possible longer-run effects of COVID-19. This conclusion and our quantitative results are subject to several considerations and caveats.

Importantly, by design, the model does not capture cyclical influences and thus cannot address, say, how social distancing measures, policy stimulus, and business bankruptcies affect the economy over the near to medium terms. In addition, the model abstracts from channels other than pandemic mortality that could affect the economy’s productive capacity. For example, the pandemic-induced recession could lead to a permanently lower output trajectory by hindering investment (see Barlevy (2007)). Higher public and private borrowing as fiscal authorities, businesses, and households seek to lessen the blow from the crisis could eventually apply upward pressure on equilibrium interest rates and persistently crowd out some investment (see Engen and Hubbard (2005) and Laubach (2009) for evidence of such an effect). Similarly, the pandemic could alter the behavior of consumers and firms persistently, for example by limiting participation in group activities or altering global supply chains.

In addition, the direct macroeconomic effects of pandemic mortality could differ from those predicted by the model for a number of reasons. For instance, the effects could be smaller than we estimate because, for a given age, individuals with pre-existing health conditions both are more likely to die from COVID-19 and tend to have lower labor force participation than healthy individuals. Other non-modeled elements could instead magnify some of the macroeconomic effects. Because labor supply is exogenous in our model, the normalization of the capital-labor ratio following the death of workers operates through a reduction in household savings over time. Under an endogenous labor supply, bequests from deceased individuals might create an income effect that leads to a reduction in the labor supply of survivors, thus accentuating the fall in output and swings in factor prices.

Finally, even under our most extreme mortality scenarios, our model predicts that the direct effect of COVID-19 mortality on the level of interest rates is small. This conclusion contrasts with the empirical findings of Barro, Ursúa, and Weng (2020) and Jordà, Singh, and Taylor (2020), who report that the Great Influenza and other historic pandemics led to economically significant declines in real interest rates. The multi-country regression of Barro, Ursúa, and Weng (2020) notably suggests that a mortality rate of the kind observed in the United States during the Great Influenza lowered the real return on government bills by about 3.5% points. They trace back this decline to both a fall in the expected real safe interest rate and an increase in inflation at the time. The extent to which the predicted decline reflects cyclical aspects not present in our paper, the concurrent effects of World War I (which the authors attempt to capture), other factors, or statistical noise is unclear.[13] Using multi-country panel data going back centuries, Jordà, Singh, and Taylor (2020) estimate that pandemics are associated with a peak decline in the real interest rate of nearly 2%. However, the structure of the economy has changed dramatically—even in the past century—and the deadliness of pandemics such as the Black Plague dwarfed that of COVID-19.[14] Under our assumptions about the production function, roughly one-fourth of the workforce would need to die to lower the real interest rate by 2% points.

4 Conclusion

Our simulations suggest that, under current epidemiological projections, mortality from COVID-19 may have, in itself, small effects on the productive capacity of the U.S. economy and production factor prices. This conclusion contrasts with evidence that past pandemics had longer-lasting macroeconomic effects and speaks to the fact that mortality from COVID-19 is heavily concentrated among older individuals. Of course, this finding should not be understood as diminishing in any way the pandemic’s heavy human cost and economic disruptions over the short to medium terms, aspects that our model does not address by design. In addition, if the spread of COVID-19 is not contained, then the effects on the productive capacity of the U.S. economy could be significant and felt for decades.


Corresponding author: Etienne Gagnon, Federal Reserve Board, 20th Street and Constitution Avenue NW, Washington, DC, 20551, USA, E-mail:

The analysis and conclusions set forth are those of the authors and do not indicate concurrence by other members of the research staff or the Board of Governors. We thank Ed Herbst, Rahul Kasar, and seminar participants at the Federal Reserve Board for their helpful comments and suggestions.


Appendix

A. Estimating the Mortality Rates during the Great Influenza

This appendix derives estimates of mortality rates and excess mortality rates, by age, attributable to the Great Influenza of 1918–1920. The main source of information is a series of annual reports titled Mortality Statistics published by the Bureau of the Census on the number of deaths and their main causes. PDF copies of the reports are accessible through the Centers for Disease Control and Prevention at the following link: https://www.cdc.gov/nchs/products/vsus/vsus_1890_1938.htm.

A.1. Mortality Rates

Let M R a , t U S be the mortality rate of individuals aged a in period t in the United States that is due to influenza. We measure the mortality rate as

M R a , t U S = 100 × N u m b e r o f c i v i l i a n d e a t h s d u e t o i n f l u e n z a a , t U S P o p u l a t i o n a , t U S ,

where P o p u l a t i o n a , t U S is the U.S. civilian non-institutional population. The mortality rates in excess of normal yearly mortality due to influenza, which we use in the model simulations, are discussed below.

We focus on the civilian population because the Bureau of the Census reports deaths for the civilian population and the military population separately, with the latter subpopulation being much smaller and the information much less detailed. Moreover, the Great Influenza began as a large number of persons served in World War I; these persons faced mortality risk that arguably was not necessarily representative of the population. That said, many of those mobilized were young adults, a category that was hit severely by the disease.

There are a few methodological challenges in translating death and population data from the Bureau of the Census into mortality rate statistics. One challenge is that only a subset of states reported mortality statistics. As Table A.1 shows, the number of reporting areas grew steadily throughout the 1910s as new states joined the United States and as some existing states began to report data. Hawaii joined the United States as a territory in 1917 but the Bureau opted to report its deaths separately from those of other states “because of the distant location of Hawaii and the peculiar constitution of its population.”

Table A1:

Estimates of the civilian populations in the United States and reporting areas.

Year Population  ( 1922 ) t U S Population  ( 1922 ) t R A Coverage (percent)
1913 96,512,407 63,200,625 65.5
1914 97,927,516 65,813,315 67.2
1915 99,342,625 67,096,681 67.5
1916 100,757,735 71,349,162 70.8
1917 102,172,845 74,984,498 73.4
1918 103,587,955 81,333,675 78.5
1919 105,003,065 85,166,043 81.1
1920 106,418,175 87,486,713 82.2

  1. Source: Bureau of the Census, Mortality Statistics 1920 (published in 1922); authors’ calculations.

    The variables P o p u l a t i o n ( 1922 ) t U S and Population  ( 1922 ) t R A represent the total population in the United States and in the states and territories reporting mortality statistics at the time, respectively. The reporting-area population excludes Hawaii. Population statistics are as of July 1 of each year.

Estimates of the size of the U.S. and reporting-area populations are subject to some uncertainty, especially during intercensal periods. The population estimates produced late in the 1910s were unusually uncertain because WWI led to significant movements of the population. For these reasons, and to ensure consistency across mortality and population sources, the population statistics reported in Table A.1 come from the report Mortality Statistics 1920, which includes information from the 1920 census. In contrast to population estimates, death counts and death causes are generally not revised from one report to the next, even after new census information is published. Mortality statistics are published for 5-year age bins starting with the bin “0–4 years” and ending with the bin “95–99 years.” The yearly reports also include statistics per years of age through age 4 years (to focus on early childhood mortality) and for 100 years and over. Our interpolation to a quarterly age frequency uses the most disaggregated information by age where possible.

In our analysis, we treat deaths from non-reporting states and Hawaii as missing at random—that is, we assume that the individuals in reporting and non-reporting areas face the same mortality risk. Accordingly, we approximate the population-wide mortality rates using those for reporting areas (which use “RA” superscripts):

M R a , t U S M R a , t R A = 100 × N u m b e r o f c i v i l i a n d e a t h s d u e t o i n f l u e n z a a , t R A P o p u l a t i o n a , t R A .

Another complication is that we do not have population statistics by years of age in the reporting areas. We posit that the age composition in reporting areas is the same as for the overall U.S. population. Population counts by years of age are available for most age groups in the Bureau’s 2016 vintage of historical data. One exception is for people aged 75 years or more, which are aggregated into a single age category. As an alternative, we use the mid-year population estimates generated by our model; these estimates use population, life cycle, and migration information to populate all age periods and are consistent with the 2016 vintage. See the technical appendix to Gagnon, Johannsen, and López-Salido (Forthcoming) for our methodology. The 2016 historical total population estimates differ a little—by about 1% or less—from the 1922 total population estimates shown in Table A.1, in part because of small differences in the coverage of states and territories. We adjust the population estimates to account for these small differences. The imputed population by age in reporting areas is

P o p u l a t i o n a , t R A P o p u l a t i o n ( 1922 ) t R A × P o p u l a t i o n ( 2016 ) a , t U S P o p u l a t i o n ( 2016 ) t U S .

As a check on the mortality rates, we can look at estimates produced by Davis and Mitchell (1920) in a special Bureau report on the death counts in the final four months of 1918 for Indiana, Kansas, and Philadelphia, PA. These estimates use unpublished population counts by age in these areas. The mortality rates for Philadelphia, at around 4% for young adults, were among the highest recorded at the time and thus cannot be assumed to be representative of the rates for the U.S. population. That said, the broader age pattern of mortality is consistent with our estimates for all reporting areas.

A.2. Excess mortality rates

Influenza is a cause of death each year; therefore, its typical effects are incorporated into life-cycle mortality tables. For this reason, our modeling work focuses on the excess mortality rate, which is the difference between the mortality rates registered during the pandemic and the counterfactual mortality rates that would have been registered if the pandemic had not happened. To obtain the latter counterfactual, we compute the average mortality rate by years of age in the five-year period that preceded the Great Influenza (that is, for the 1913–1917 period). We then calculate the excess mortality rates by age as

E M R a , t U S = M R a , t U S M R a , 1913 1917 U S .

Because the death counts suggest that older generations enjoyed some protection from the virus strain that caused the Great Influenza, our excess mortality rate estimates for older generations are negative.

A final complication is that many deaths related to the Great Influenza are miscategorized. Although the yearly reports contain death counts specific to influenza, the Bureau of the Census warned, in its Mortality Statistics 1919 report, that “in studying the effects of the pandemic of influenza, it is not believed to be best to study separately influenza and the various forms of pneumonia, bronchitis, and the respiratory diseases, for doubtless many cases were returned as influenza when the deaths were caused by pneumonia, and vice versa. The best method, therefore, seems to be to study as one group deaths from influenza and pneumonia (all forms), disregarding deaths from the other respiratory diseases, which were comparatively few.” For this reason, we measure excess mortality due to the Great Influenza in terms of excess mortality in the categories “10: influenza,” “91: Bronchopneumonia,” and “92: Pneumonia (Lobarpneumonia and pneumonia (undefined)).”

In our implementation, we calculate excess mortality rates by age due to the Great Influenza separately for the years 1918, 1919, and 1920. We then calculate cumulative excess mortality rates during the Great Influenza epidemic by summing up the excess mortality rates for those three years, as if the virus had hit in a single wave. We make no adjustment for the fact that people had different ages during the three waves; doing so would have at most a tiny effect on the total death counts in proportion to the population and accompanying macroeconomic effects.

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Supplementary Material

The online version of this article offers supplementary material (https://doi.org/10.1515/bejm-2020-0196).


Received: 2020-09-10
Accepted: 2021-01-16
Published Online: 2021-05-21

© 2021 Walter de Gruyter GmbH, Berlin/Boston