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The Global Macroeconomic Impacts of COVID-19:
Seven Scenarios
*
Warwick McKibbin
†
and Roshen Fernando
‡
2 March 2020
Abstract
The outbreak of coronavirus named COVID-19 has disrupted the Chinese economy and is
spreading globally. The evolution of the disease and its economic impact is highly uncertain,
which makes it difficult for policymakers to formulate an appropriate macroeconomic policy
response. In order to better understand possible economic outcomes, this paper explores seven
different scenarios of how COVID-19 might evolve in the coming year using a modelling
technique developed by Lee and McKibbin (2003) and extended by McKibbin and Sidorenko
(2006). It examines the impacts of different scenarios on macroeconomic outcomes and
financial markets in a global hybrid DSGE/CGE general equilibrium model.
The scenarios in this paper demonstrate that even a contained outbreak could significantly
impact the global economy in the short run. These scenarios demonstrate the scale of costs that
might be avoided by greater investment in public health systems in all economies but
particularly in less developed economies where health care systems are less developed and
popultion density is high.
Keywords: Pandemics, infectious diseases, risk, macroeconomics, DSGE, CGE, G-Cubed
JEL Codes:
*
We gratefully acknowledge financial support from the Australia Research Council Centre of Excellence in
Population Ageing Research (CE170100005). We thank Renee Fry-McKibbin, Will Martin, Louise Sheiner,
Barry Bosworth and David Wessel for comment and Peter Wilcoxen and Larry Weifeng Liu for their research
collaboration on the G-Cubed model used in this paper. We also acknowledge the contributions to earlier
research on modelling of pandemics undertaken with Jong-Wha Lee and Alexandra Sidorenko.
†
Australian National University; the Brookings Institution; and Centre of Excellence in Population Ageing
Research (CEPAR)
‡
Australian National University and Centre of Excellence in Population Ageing Research (CEPAR)
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1. Introduction
The COVID-19 outbreak (previously 2019-nCoV) was caused by the SARS-CoV-2 virus. This
outbreak was triggered in December 2019 in Wuhan city in Hubei province of China. COVID-
19 continues to spread across the world. Initially the epicenter of the outbreak was China with
reported cases either in China or being travelers from China. At the time of writing this paper,
at least four further epicenters have been identified: Iran, Italy, Japan and South Korea. Even
though the cases reported from China are expected to have peaked and are now falling (WHO
2020), cases reported from countries previously thought to be resilient to the outbreak, due to
stronger medical standards and practices, have recently increased. While some countries have
been able to effectively treat reported cases, it is uncertain where and when new cases will
emerge. Amidst the significant public health risk COVID-19 poses to the world, the World
Health Organization (WHO) has declared a public health emergency of international concern
to coordinate international responses to the disease. It is, however, currently debated whether
COVID-19 could potentially escalate to a global pandemic.
In a strongly connected and integrated world, the impacts of the disease beyond mortality (those
who die) and morbidity (those who are incapacitated or caring for the incapacitated and unable
to work for a period) has become apparent since the outbreak. Amidst the slowing down of the
Chinese economy with interruptions to production, the functioning of global supply chains has
been disrupted. Companies across the world, irrespective of size, dependent upon inputs from
China have started experiencing contractions in production. Transport being limited and even
restricted among countries has further slowed down global economic activities. Most
importantly, some panic among consumers and firms has distorted usual consumption patterns
and created market anomalies. Global financial markets have also been responsive to the
changes and global stock indices have plunged. Amidst the global turbulence, in an initial
assessment, the International Monetary Fund expects China to slow down by 0.4 percentage
points compared to its initial growth target to 5.6 percent, also slowing down global growth by
0.1 percentage points. This is likely to be revised in coming weeks
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.
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See OECD(2020) for an updated announcement
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This paper attempts to quantify the potential global economic costs of COVID-19 under
different possible scenarios. The goal is to provide guidance to policy makers to the economic
benefits of globally-coordinated policy responses to tame the virus. The paper builds upon the
experience gained from evaluating the economics of SARS (Lee & McKibbin 2003) and
Pandemic Influenza (McKibbin & Sidorenko 2006). The paper first summarizes the existing
literature on the macroeconomic costs of diseases. Section 3 outlines the global macroeconomic
model (G-Cubed) used for the study, highlighting its strengths to assess the macroeconomics
of diseases. Section 4 describes how epidemiological information is adjusted to formulate a
series of economic shocks that are input into the global economic model. Section 5 discusses
the results of the seven scenarios simulated using the model. Section 6 concludes the paper
summarizing the main findings and discusses some policy implications.
2. Related Literature
Many studies have found that population health, as measured by life expectancy, infant and
child mortality and maternal mortality, is positively related to economic welfare and growth
(Pritchett and Summers, 1996; Bloom and Sachs, 1998; Bhargava and et al., 2001; Cuddington
et al., 1994; Cuddington and Hancock, 1994; Robalino et al., 2002a; Robalino et al., 2002b;
WHO Commission on Macroeconomics and Health, 2001; Haacker, 2004).
There are many channels through which an infectious disease outbreak influences the economy.
Direct and indirect economic costs of illness are often the subject of the health economics
studies on the burden of disease. The conventional approach uses information on deaths
(mortality) and illness that prevents work (morbidity) to estimate the loss of future income due
to death and disability. Losses of time and income by carers and direct expenditure on medical
care and supporting services are added to obtain the estimate of the economic costs associated
with the disease. This conventional approach underestimates the true economic costs of
infectious diseases of epidemic proportions which are highly transmissible and for which there
is no vaccine (e.g. HIV/AIDS, SARS and pandemic influenza). The experience from these
previous disease outbreaks provides valuable information on how to think about the
implications of COVID-19
The HIV/AIDS virus affects households, businesses and governments - through changed labor
supply decisions; efficiency of labor and household incomes; increased business costs and
foregone investment in staff training by firms; and increased public expenditure on health care
and support of disabled and children orphaned by AIDS, by the public sector (Haacker, 2004).
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The effects of AIDS are long-term but there are clear prevention measures that minimize the
risks of acquiring HIV, and there are documented successes in implementing prevention and
education programs, both in developed and in the developing world. Treatment is also available,
with modern antiretroviral therapies extending the life expectancy and improving the quality
of life of HIV patients by many years if not decades. Studies of the macroeconomic impact of
HIV/AIDS include (Cuddington, 1993a; Cuddington, 1993b; Cuddington et al., 1994;
Cuddington and Hancock, 1994; Haacker, 2002a; Haacker, 2002b; Over, 2002; Freire, 2004;
The World Bank, 2006). Several computable general equilibrium (CGE) macroeconomic
models have been applied to study the impact of AIDS (Arndt and Lewis, 2001; Bell et al.,
2004).
The influenza virus is by far more contagious than HIV, and the onset of an epidemic can be
sudden and unexpected. It appears that the COVID-19 virus is also very contagious. The fear
of 1918-19 Spanish influenza, the “deadliest plague in history,” with its extreme severity and
gravity of clinical symptoms, is still present in the research and general community (Barry,
2004). The fear factor was influential in the world’s response to SARS – a coronavirus not
previously detected in humans (Shannon and Willoughby, 2004; Peiris et al., 2004). It is also
reflected in the response to COVID-19. Entire cities in China have closed and travel restrictions
placed by countries on people entering from infected countries. The fear of an unknown deadly
virus is similar in its psychological effects to the reaction to biological and other terrorism
threats and causes a high level of stress, often with longer-term consequences (Hyams et al.,
2002). A large number of people would feel at risk at the onset of a pandemic, even if their
actual risk of dying from the disease is low.
Individual assessment of the risks of death depends on the probability of death, years of life
lost, and the subjective discounting factor. Viscusi et al. (1997) rank pneumonia and influenza
as the third leading cause of the probability of death (following cardiovascular disease and
cancer). Sunstein (1997) discusses the evidence that an individual’s willingness to pay to avoid
death increases for causes perceived as “bad deaths” – especially dreaded, uncontrollable,
involuntary deaths and deaths associated with high externalities and producing distributional
inequity. Based on this literature, it is not unreasonable to assume that individual perception of
the risks associated with the new influenza pandemic virus similar to Spanish influenza in its
virulence and the severity of clinical symptoms can be very high, especially during the early
stage of the pandemic when no vaccine is available and antivirals are in short supply. This is
exactly the reaction revealed in two surveys conducted in Taiwan during the SARS outbreak
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in 2003 (Liu et al., 2005), with the novelty, salience and public concern about SARS
contributing to the higher than expected willingness to pay to prevent the risk of infection.
Studies of the macroeconomic effects of the SARS epidemic in 2003 found significant effects
on economies through large reductions in consumption of various goods and services, an
increase in business operating costs, and re-evaluation of country risks reflected in increased
risk premiums. Shocks to other economies were transmitted according to the degree of the
countries’ exposure, or susceptibility, to the disease. Despite a relatively small number of cases
and deaths, the global costs were significant and not limited to the directly affected countries
(Lee and McKibbin, 2003). Other studies of SARS include (Chou et al., 2004) for Taiwan, (Hai
et al., 2004) for China and (Sui and Wong, 2004) for Hong Kong.
There are only a few studies of economic costs of large-scale outbreaks of infectious diseases
to date: Schoenbaum (1987) is an example of an early analysis of the economic impact of
influenza. Meltzer et al. (1999) examine the likely economic effects of the influenza pandemic
in the US and evaluate several vaccine-based interventions. At a gross attack rate (i.e. the
number of people contracting the virus out of the total population) of 15-35%, the number of
influenza deaths is 89 – 207 thousand, and an estimated mean total economic impact for the
US economy is $73.1- $166.5 billion.
Bloom et al. (2005) use the Oxford economic forecasting model to estimate the potential
economic impact of a pandemic resulting from the mutation of avian influenza strain. They
assume a mild pandemic with a 20% attack rate and a 0.5 percent case-fatality rate, and a
consumption shock of 3%. Scenarios include two-quarters of demand contraction only in Asia
(combined effect 2.6% Asian GDP or US$113.2 billion); a longer-term shock with a longer
outbreak and larger shock to consumption and export yields a loss of 6.5% of GDP (US$282.7
billion). Global GDP is reduced by 0.6%, global trade of goods and services contracts by $2.5
trillion (14%). Open economies are more vulnerable to international shocks.
Another study by the US Congressional Budget Office (2005) examined two scenarios of
pandemic influenza for the United States. A mild scenario with an attack rate of 20% and a
case fatality rate (.i.e. the number who die relative to the number infected) of 0.1% and a more
severe scenario with an attack rate of 30% and a case fatality rate of 2.5%. The CBO (2005)
study finds a GDP contraction for the United States of 1.5% for the mild scenario and 5% of
GDP for the severe scenario.
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McKibbin and Sidorenko (2006) used an earlier vintage of the model used in the current paper
to explore four different pandemic influenza scenarios. They considered a “mild” scenario in
which the pandemic is similar to the 1968-69 Hong Kong Flu; a “moderate” scenario which is
similar to the Asian flu of 1957; a “severe” scenario based on the Spanish flu of 1918-1919
((lower estimate of the case fatality rate), and an “ultra” scenario similar to Spanish flu 1918-
19 but with upper-middle estimates of the case fatality rate. They found costs to the global
economy of between $US300 million and $US4.4trillion dollars for the scenarios considered.
The current paper modifies and extends that earlier papers by Lee and McKibbin (2003) and
McKibbin and Sidorenko (2006) to a larger group of countries, using updated data that captures
the greater interdependence in the world economy and in particular, the rise of China’s
importance in the world economy today.
3. The Hybrid DSGE/CGE Global Model
For this paper, we apply a global intertemporal general equilibrium model with heterogeneous
agents called the G-Cubed Multi-Country Model. This model is a hybrid of Dynamic Stochastic
General Equilibrium (DSGE) Models and Computable General Equilibrium (CGE) Models
developed by McKibbin and Wilcoxen (1999, 2013)
(9)
The G-Cubed Model
The version of the G-Cubed (G20) model used in this paper can be found in McKibbin and
Triggs (2018) who extended the original model documented in McKibbin and Wilcoxen (1999,
2013). The model has 6 sectors and 24 countries and regions. Table 1 presents all the regions
and sectors in the model. Some of the data inputs include the I/O tables found in the Global
Trade Analysis Project (GTAP) database (Aguiar et al. 2019), which enables us to differentiate
sectors by country of production within a DSGE framework. Each sector in each country has a
KLEM technology in production which captures the primary factor inputs of capital (K) and
labor (L) as well as the intermediate or production chains of inputs in energy (E) and materials
inputs (M). These linkages are both within a country and across countries.
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Table 1 – Overview of the G-Cubed (G20) model
Countries (20)
Regions (4)
Argentina
Rest of the OECD
Australia
Rest of Asia
Brazil
Other oil-producing countries
Canada
Rest of the world
China
Rest of Eurozone
Sectors (6)
France
Energy
Germany
Mining
Indonesia
Agriculture (including fishing and hunting)
India
Durable manufacturing
Italy
Non-durable manufacturing
Japan
Services
Korea
Mexico
Economic Agents in each Country (3)
Russia
A representative household
Saudi Arabia
A representative firm (in each of the 6 production sectors)
South Africa
Government
Turkey
United Kingdom
United States
The approach embodied in the G-Cubed model is documented in McKibbin and Wilcoxen
(1998, 2013). Several key features of the standard G-Cubed model are worth highlighting here.
First, the model completely accounts for stocks and flows of physical and financial assets. For
example, budget deficits accumulate into government debt, and current account deficits
accumulate into foreign debt. The model imposes an intertemporal budget constraint on all
households, firms, governments, and countries. Thus, a long-run stock equilibrium obtains
through the adjustment of asset prices, such as the interest rate for government fiscal positions
or real exchange rates for the balance of payments. However, the adjustment towards the long-
run equilibrium of each economy can be slow, occurring over much of a century.
Second, firms and households in G-Cubed must use money issued by central banks for all
transactions. Thus, central banks in the model set short term nominal interest rates to target
macroeconomic outcomes (such as inflation, unemployment, exchange rates, etc.) based on
Henderson-McKibbin-Taylor monetary rules. These rules are designed to approximate actual
monetary regimes in each country or region in the model. These monetary rules tie down the
long-run inflation rates in each country as well as allowing short term adjustment of policy to
smooth fluctuations in the real economy.
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Third, nominal wages are sticky and adjust over time based on country-specific labor
contracting assumptions. Firms hire labor in each sector up to the points that the marginal
product of labor equals the real wage defined in terms of the output price level of that sector.
Any excess labor enters the unemployed pool of workers. Unemployment or the presence of
excess demand for labor causes the nominal wage to adjust to clear the labor market in the long
run. In the short-run, unemployment can arise due to structural supply shocks or changes in
aggregate demand in the economy.
Fourth, rigidities prevent the economy from moving quickly from one equilibrium to another.
These rigidities include nominal stickiness caused by wage rigidities, lack of complete
foresight in the formation of expectations, cost of adjustment in investment by firms with
physical capital being sector-specific in the short run, monetary and fiscal authorities following
particular monetary and fiscal rules. Short term adjustment to economic shocks can be very
different from the long-run equilibrium outcomes. The focus on short-run rigidities is important
for assessing the impact over the initial decades of demographic change.
Fifth, we incorporate heterogeneous households and firms. Firms are modeled separately
within each sector. There is a mixture of two types of consumers and two types of firms within
each sector, within each country: one group which bases its decisions on forward-looking
expectations and the other group which follows simpler rules of thumb which are optimal in
the long run.
4. Modeling epidemiological scenarios in an economic model
We follow the approach in Lee and McKibbin (2003) and McKibbin and Sidorenko (2006) to
convert different assumptions about mortality rates and morbidity rates in the country where
the disease outbreak occurs (the epicenter country). Given the epidemiological assumptions
based on previous experience of pandemics, we create a set of filters that convert the shocks
into economic shocks to reduced labor supply in each country (mortality and morbidity); rising
cost of doing business in each sector including disruption of production networks in each
country; consumption reduction due to shifts in consumer preferences over each good from
each country (in addition to changes generated by the model based on change in income and
prices); rise in equity risk premia on companies in each sector in each country (based on
exposure to the disease); and increases in country risk premium based on exposure to the
disease as well as vulnerabilities to changing macroeconomic conditions.
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In the remainder of this section, we outline how the various indicators are constructed. The
approach follows McKibbin and Sidorenko (2006) with some improvements. There are, of
course, many assumptions in this exercise and the results are sensitive to these assumptions.
The goal of the paper is to provide policymakers with some idea of the costs of not intervening
and allowing the various scenarios to unfold.
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