Introduction
The outbreak of coronavirus, or COVID-19, has spread globally. The evolution of the disease and its economic impact are highly uncertain which makes it difficult for policymakers to formulate an appropriate macroeconomic policy response.
The scenarios in this research demonstrate that even a contained outbreak could significantly impact the global economy in the short run.
Poverty kills poor people, but the outbreak of COVID-19 shows that if diseases are generated in poor countries due to overcrowding, poor public health and interaction with wild animals, these diseases can kill people of any socioeconomic group in any society. There needs to be vastly more investment in public health and development in the richest but also, and especially, in the poorest countries. This study indicates the possible costs that can be avoided through global cooperative investment in public health in all countries. We have known this critical policy intervention for decades, yet politicians continue to ignore the scientific evidence on the role of public health in improving the quality of life and as a driver of economic growth.
Warwick McKibbin maintains a large economic model of the world economy, known as G-Cubed, that is widely used by governments and companies. He has estimated the economic effects of the COVID-19 virus under seven scenarios. His analysis, “Global Macroeconomic Implications of COVID-19: Seven Scenarios,” is posted HERE.
Here is a Q&A with him about his research.
Q: How does COVID-19 differ from past episodes, such as SARS in 2003 and the Avian flu in 1997? How do the economic risks differ?
SARS was also a coronavirus but had a much higher case-mortality rate (10%) compared with COVID-19 (between 2%-4%) and a much lower case-mortality rate than the Avian flu (60%); on the other hand, COVID-19 may be more contagious than SARS and more similar in contagion to Avian flu. There is still a great deal of uncertainty about COVID-19 which is what makes it very concerning.
Another similarity is that these outbreaks all began in China. China, of course, is a much bigger part of and much more integrated in the world economy than it was 15 years ago, so economic disruption there has much larger spillover effect than it used to.
Q: You have seven scenarios for the COVID-19 virus. In the first three, the virus is contained to China; in the most extreme, a mild pandemic recurs each year for the foreseeable future. Let’s focus on your fourth scenario. Describe that.
In scenario four, we assume that COVID-19 eventually affects all countries, but this pandemic is a one-time event and its severity is low. For China, we assume a 10% attack rate (10% of all people fall ill) and a 2% case fatality rate (2% of those who are ill subsequently die). We then use an Index of Vulnerability for each country, basically calibrating how much it is like or different from China, to estimate the effects of the virus in other countries. These assumptions are fed into the model to determine the severity of the outbreak in each country.
Q: In this scenario, you assume that 10% of the Chinese population get the virus, and 2% of those Chinese die. What are the comparable numbers for the U.S.?
The total mortality rate from the virus is the product of the attack rate and the case-fatality rate. In the fourth scenario, 0.2% of the population in China dies as a result of the virus; the assumed mortality rate for the US is 0.07%. (Editor's note: that is about 230,000 people in the US).
Q: What are the global economic costs of that scenario? What are the costs to the U.S. economy?
The loss of real GDP, relative to what would have been the model prediction in 2020 without the virus, is approximately $US2.3 trillion for the world, which is 2% lower than the baseline. Of that, the US economy loses $US420 billion in 2020, or about 2% less than the pre-virus baseline. Of course, if the virus spreads more widely or turns out to be more severe, the costs would be larger.
Q: The direct costs of a pandemic are, of course, deaths and sickness that prevent people from working. Your model adds indirect effects. What are they, and how do you estimate their effects on the economy?
In the modeling exercise, we reduce the labor supply by the number of people who die, the hours lost due to sickness, and the hours lost due to people caring for family members who are sick. We also make assumptions about the rising cost of doing business in each sector, including disruption of production networks in each country; shifts in consumption as a result of changes in household preferences; and the expected rise in equity risk premia on companies in each sector in each country (based on exposure to the disease).
Q: When you make your projections, what do you assume that fiscal and monetary policymakers will do in reaction to the virus? How potent do you think fiscal and monetary policy are in ameliorating the economic damage of COVID-19?
We model the policy response of central banks in each economy. Some central banks, such as the US Federal Reserve, adjust nominal interest rates in order to target an inflation outcome, while attempting to minimize the loss of output across the economy. For other countries, like China, central banks also target the exchange rates. Fiscal authorities are assumed to change government spending by an amount related to the health and other intervention costs associated with the virus outbreak, and budget deficits increase automatically in response to the economic downturn.
Both monetary and fiscal policies help, but because a significant part of the shock is a disruption to supply, demand management policies, such as fiscal and monetary policies, go only part of the way to stabilizing the economy. In countries that follow a monetary policy rule which does not only focus on output, the response of monetary policy can make the outcome worse. This would be the case for China, for example; by continuing to try to prevent an exchange rate depreciation relative to the US dollar, the Chinese central bank would need to tighten monetary policy or change the exchange rate target.
We construct a series of indexes that attempt to quantify how one would adjust the Chinese numbers (or US numbers where that is relevant to calculating changes in financial variables such as risk). For example, the Index of Vulnerability is constructed by aggregating an Index of Geography and an Index of Health Policy. The Index of Geography is the average of two indexes. The first is the urban population density of countries divided by the share of urban in total population. This is expressed relative to China. The second sub index is an index of openness to tourism relative to China. The Index of Health Policy also consists of two components: the Global Health Security Index and Health Expenditure per Capita relative to China. The Global Health Security Index assigns scores to countries according to six criteria, which includes the ability to prevent, detect, and respond to epidemics.
Q: In part, your projections are based on what we learned from the SARS episode in 2003 and on what we’ve seen in China so far. You then build projections based on how countries resemble or differ from China. How do you do that? What’s the rationale for this approach?
Ideally, from a modeling perspective, we would have enough pandemics and long enough data samples to estimate, using statistical techniques, how important each index has been in explaining past disease outbreaks. Fortunately, we haven’t had enough pandemics, so we have to use what is really an informed guess on what we think is important. This is all that we can do given the reality of the data that is available. The alternative of not attempting any quantification at all is less information for policymakers.
Q: What lessons do you want policymakers to draw from your scenarios?
The cost of a pandemic can be very large. The response should be large enough to reduce the effects of the pandemic once it emerges. Low-cost actions such as promoting good hygiene practices are a good place to start, but other actions such as quarantine and other interventions as advocated by epidemiologists, although disruptive, are probably cost effective.
It is far better to invest substantial amounts to reduce the likelihood of a pandemic emerging. This requires investment not only in developed countries’ public health systems, but, more importantly, in the public health systems of poor countries. Even with a low and uncertain probability of a serious pandemic with very large costs, the scenarios in the paper imply much larger expenditure than is currently planned by national governments.
It may well be that, despite the scientifically-based warnings of epidemiologists, a future pandemic does not emerge. Even in that case, the money invested in improving public health systems would not have been wasted; it would improve the quality of life for all citizens.
Professor Warwick J. McKibbin is a Non-Resident Senior Fellow at The Brookings Institution, Director of the Centre for Applied Macroeconomic Analysis in the ANU Crawford School of Public Policy, and Director of Policy Engagement in the ARC Centre of Excellence in Population Aging Research (CEPAR).
This article is republished with permission from The Brookings Institution, a non-profit public policy organization based in Washington, DC, and Warwick McKibbin.