The Global Macroeconomic Impacts of covid-19: Seven Scenarios
Epidemiological assumptions
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- Table 2 – Epidemiological Assumptions for China Scenario Attack Rate for China Case-fatality Rate for
- Table 3 – Scenario Assumptions
- Nature of Shocks Shocks Activated Shocks Activated
- Table 4 – Shocks to labor supply
- Table 5 – Shock to equity risk premium for scenario 4-7
- Table 6 – Shocks to cost of production
- Table 7 – Shocks to consumption demand
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Epidemiological assumptions The attack rates (proportion of the entire population who become infected) and case-fatality rates (proportion of those infected who die) and the implied mortality rate (proportion of total population who die) assumed for China under seven different scenarios are contained in Table 2 below. Each scenario is given a name. S01 is scenario 1.
S01
1% 2.0%
0.02% S02
10% 2.5%
0.25% S03
30% 3.0%
0.90% S04
10% 2.0%
0.20% S05
20% 2.5%
0.50% S06
30% 3.0%
0.90% S07
10% 2.0%
0.20%
We explore seven scenarios based on the survey of historical pandemics in McKibbin and Sidorenko (2006) and the most recent data on the COVID-19 virus. Table 3 summarizes the scenarios for the disease outbreak. The scenarios vary by attack rate, mortality rate and the countries experiencing the epidemiological shocks.. Scenarios 1-3 assume the epidemiological events are isolated to China. The economic impact on China and the spillovers to other countries are through trade, capital flows and the impacts of changes in risk premia in global financial markets – as determined by the model. Scenarios 4-6 are the pandemic scenarios where the epidemiological shocks occur in all countries to differing degrees. Scenarios 1-6 assume the shocks are temporary. Scenario 7 is a case where a mild pandemic is expected to be recurring each year for the indefinite future. 10
The shock to labor supply in each country includes three components: mortality due to infection, morbidity due to infection and morbidity arising from caregiving for affected family members. For the mortality component, a mortality rate is initially calculated using different attack rates and case-fatality rates for China. These attack rates and case-fatality rates are based on observations during SARS and following McKibbin and Sidorenko (2006) on pandemic influenza, as well as currently publicly available epidemiological data for COVID-19. We take the Chinese epidemiological assumptions and scale these for different countries. The scaling is done by calculating an Index of Vulnerability. This index is then applied to the Chinese mortality rates to generate country specific mortality rates. Countries that are more vulnerable than China will have higher rate of mortality and morbidity and countries who are less vulnerable with lower epidemiological outcomes, The Index of Vulnerability is constructed by aggregating an Index of Geography and an Index of Health Policy, following McKibbin and Sidorenko (2006). 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 (see GHSIndex 2020). The Index of Geography and Index of Health Policy for different countries are presented in Figures 1 and 2,
Countries Affected Seve rity Attack Rate for China Case fatality rate China Nature of Shocks Shocks Activated Shocks Activated China
Other countries 1 China
Low 1.0%
2.0% Temporary All Risk
2 China
Mid 10.0%
2.5% Temporary All Risk
3 China
High 30.0%
3.0% Temporary All Risk
4 Global
Low 10.0%
2.0% Temporary All All
5 Global
Mid 20.0%
2.5% Temporary All All
6 Global
High 30.0%
3.0% Temporary All All
7 Global
Low 10.0%
2.0% Permanent All All
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respectively. The lower the value of the Index of Health Policy, the better would be a given country’s health standards. However, a lower value for the Index of Geography represents a lower risk to a given country. When calculating the second component of the labor shock we need to adjust for the problem that the model is an annual model. Days lost therefore must be annualized. The current recommended incubation period for COVID-19 is 14 days 5 , so we assume an average employee in a country would have to be absent from work for 14 days, if infected. Absence from work indicates a loss of productive capacity for 14 days out of working days for a year. Hence, we calculate an effective attack rate for China using the attack rate assumed for a given scenario, and the proportion of days absent from work and scale them across other countries using the Index of Vulnerability. The third component of the labor shock accounts for absenteeism from work due to caregiving family members who are infected. We assume the same effective attack rate as before and that around 70 percent of the female workers would be care givers to family members. We adjust the effective attack rate using the Index of Vulnerability and the proportion of labor force who have to care for school-aged children (70 percent of female labor force participation). This does account for school closures.
5 There is evidence that this figure could be close to 21 days. This would increase the scale of the shock. 12
Table 4 contains the labor shocks for countries for different scenarios. Table 4 – Shocks to labor supply Region
S01 S02
S03 S04
S05 S06
S07 Argentina 0 0
- 0.65 - 1.37 - 2.14 - 0.65
Australia 0 0 0 - 0.48 - 1.01 - 1.58 - 0.48 Brazil
0 0 0 - 0.66 - 1.37 - 2.15 - 0.66
Canada 0 0 0 - 0.43 - 0.89 - 1.40 - 0.43 China
- 0.10 - 1.10
- 3.44 - 1.05 - 2.19 - 3.44 - 1.05 France
0 0 0 - 0.52 - 1.08 - 1.69 - 0.52
Germany 0 0 0 - 0.51 - 1.06 - 1.66 - 0.51 India
0 0 0 - 1.34 - 2.82 - 4.44 - 1.34
Indonesia 0 0 0 - 1.39 - 2.91 - 4.56 - 1.39 Italy
0 0 0 - 0.48 - 1.02 - 1.60 - 0.48
Japan 0 0 0 - 0.50 - 1.04 - 1.64 - 0.50 Mexico
0 0 0 - 0.78 - 1.64 - 2.57 - 0.78
Republic of Korea 0 0 0 - 0.56 - 1.17 - 1.85 - 0.56 Russia
0 0 0 - 0.71 - 1.48 - 2.31 - 0.71
Saudi Arabia 0 0 0 - 0.41 - 0.87 - 1.37 - 0.41 South Africa 0 0
- 0.80 - 1.67 - 2.61 - 0.80
Turkey 0 0 0 - 0.76 - 1.59 - 2.50 - 0.76 United Kingdom 0 0
- 0.53 - 1.12 - 1.75 - 0.53
United States of America 0 0 0 - 0.40 - 0.83 - 1.30 - 0.40 Other Asia 0 0
- 0.88 - 1.84 - 2.89 - 0.88
Other oil producing countries 0 0 0 - 0.97
- 2.01 - 3.13 - 0.97 Rest of Euro Zone 0 0
- 0.46 - 0.97 - 1.52 - 0.46
Rest of OECD 0 0 0 - 0.43 - 0.89 - 1.39 - 0.43 Rest of the World 0 0
- 1.29 - 2.67 - 4.16 - 1.29
b) Shocks to the equity risk premium of economic sectors We assume that the announcement of the virus will cause risk premia through the world to change. We create risk premia in the United States to approximate the observed initial response to scenario 1. We then adjust the equity risk shock to all countries across a given scenario by applying the indexes outlined next. We also scale the shock across scenarios by applying the different mortality rate assumptions across countries. The Equity Risk Premium shock is the aggregation of the mortality component of the labor shock and a Country Risk Index. The Country Risk Index is the average of three indices: Index of Governance Risk, Index of Financial Risk and Index of Health Policy. In developing these indices, we use the US as a benchmark due to the prevalence of well-developed financial markets there (Fisman and Love 2004). The Index of Governance Risk is based on the International Country Risk Guide, which assigns countries scores based on performance in 22 variables across three categories: political, economic, and financial (see PRSGroup 2020). The political variables include government 13
stability, as well as the prevalence of conflicts, corruption and the rule of law. GDP per capita, real GDP growth and inflation are some of the economic variables considered in the Index. Financial variables contained in the Index account for exchange rate stability and international liquidity among others. Figure 3 summarizes the scores for countries for the governance risk relative to the United States. One of the most easily available indicators of the expected global economic impacts of COVID-19 has been movements in financial market indices. Since the commencement of the outbreak, financial markets continue to respond to daily developments regarding the outbreak across the world. Particularly, stock markets have been demonstrating investor awareness of industry-specific (unsystematic) impacts. Hence, when developing the Equity Risk Premium Shocks for sectors, we include an Index of Financial Risk, even though it is already partially accounted for within the Index of Governance Risk. This higher weight on financial risk enables us to reproduce the prevailing turbulence in financial markets. The Index of Financial Risk uses the current account balance of the countries as a proportion of GDP in 2015. Figure 4 contains the scores for the countries relative to the United States Even though construction of the Index of Health Policy follows the procedure described for developing the mortality component of the labor shock, the US has been used as the base- country instead of China, when developing the shock on equity risk premium since the US is the center of the global financial system and in the model, all risks are defined relative to the US. Figure 5 contains the scores for the countries for the Index of Health Policy relative to the United States. The Net Risk Index for countries is presented in Figure 6 and Shock on Equity Risk Premia for Scenario 4-7 are presented in Table 5.
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Table 5 – Shock to equity risk premium for scenario 4-7 Region
S04 S05
S06 S07
Argentina 1.90
2.07 2.30
1.90 Australia 1.23 1.37
1.54 1.23
Brazil 1.59
1.78 2.03
1.59 Canada
1.23 1.36
1.52 1.23
China 1.97
2.27 2.67
1.97 France
1.27 1.40
1.59 1.27
Germany 1.07
1.21 1.41
1.07 India
2.20 2.62
3.18 2.20
Indonesia 2.06
2.43 2.93
2.06 Italy
1.32 1.47
1.66 1.32
Japan 1.18
1.33 1.53
1.18 Mexico
1.76 1.98
2.27 1.76
Republic of Korea 1.25
1.43 1.67
1.25 Russia
1.77 1.96
2.22 1.77
Saudi Arabia 1.38
1.52 1.70
1.38 South Africa 1.85 2.06
2.33 1.85
Turkey 1.98
2.20 2.50
1.98 United Kingdom 1.35 1.50
1.70 1.35
United States of America 1.07
1.18 1.33
1.07 Other Asia 1.51 1.75
2.07 1.51
Other oil-producing countries 2.03
2.25 2.55
2.03 Rest of Euro Zone 1.29 1.42
1.60 1.29
Rest of OECD 1.11
1.22 1.38
1.11 Rest of the World 2.21 2.51
2.91 2.21
As well as the shock to labor inputs, we identify that other inputs such as Trade, Land Transport, Air Transport and Sea Transport have been significantly affected by the outbreak. Thus, we calculate the share of inputs from these exposed sectors to the six aggregated sectors of the model and compare the contribution relative to China. We then benchmark the percentage increase in the cost of production in Chinese production sectors during SARS to the first scenario and scale the percentage across scenarios to match the changes in the mortality component of the labor shock. Variable shares of inputs from exposed sectors to aggregated economic sectors also allow us to vary the shock across sectors in the countries. Table 6 contains the shocks to the cost of production in each sector in each country due to the share of inputs from exposed sectors. a) Shocks to consumption demand 15
The G-Cubed model endogenously changes spending patterns in response to changes in income, wealth, and relative price changes. However, independent of these variables, during an outbreak, it is likely that preferences for certain activities will change with the outbreak. Following McKibbin and Sidorenko (2006), we assume that the reduction in spending on those activities will reduce the overall spending, hence saving money for future expenditure. In modeling this behavior, we employ a Sector Exposure Index. The Index is calculated as the share of exposed sectors: Trade, Land, Air & Sea Transport and Recreation, within the GDP of a country relative to China. The reduction in consumption expenditure during the SARS outbreak in China is used as the benchmark for the first scenario. The advantage is that this response was observed. The disadvantage is that other countries could behave differently. Given we don’t have observations of other epicenters start with this assumption and then adjust it as follows. This benchmark is then scaled across other scenarios relative to the mortality component of the labor shock and adjusted across countries through the different sectoral exposure. Figure 7 contains the Sector Exposure Indices for the countries and the shock to consumption demand is presented in Table 7. Note that CBO (2005) uses a shock of 3% to US consumption from an H5N1 influenza pandemic which is between S05 and S06 in Table 7.
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Region Ener
gy Mining Agriculture Durable Manufacturi ng Non-durable Manufacturi ng
Service s Argentina 0.37 0.24
0.37 0.35
0.40 0.38
Australia 0.43
0.43 0.42
0.39 0.41
0.45 Brazil
0.44 0.46
0.44 0.42
0.45 0.44
Canada 0.44
0.37 0.42
0.40 0.41
0.44 China
0.50 0.50
0.50 0.50
0.50 0.50
France 0.38
0.31 0.36
0.40 0.42
0.46 Germany
0.43 0.37
0.40 0.45
0.45 0.47
India 0.47
0.33 0.47
0.42 0.45
0.43 Indonesia 0.37 0.33
0.31 0.36
0.40 0.38
Italy 0.36
0.33 0.38
0.42 0.44
0.46 Japan
0.45 0.40
0.45 0.47
0.47 0.49
Mexico 0.41
0.38 0.39
0.42 0.42
0.41 Other Asia 0.44 0.39
0.44 0.45
0.45 0.47
Other oil producing countries 0.49 0.41
0.47 0.40
0.43 0.45
Republic of Korea 0.39
0.30 0.37
0.43 0.42
0.43 Rest of Euro Zone 0.42 0.41
0.43 0.43
0.46 0.48
Rest of OECD 0.42
0.38 0.41
0.41 0.43
0.46 Rest of the World 0.52 0.46
0.51 0.45
0.49 0.48
Russia 0.54
0.37 0.43
0.41 0.42
0.45 Saudi Arabia 0.32 0.25
0.29 0.29
0.25 0.35
South Africa 0.40
0.35 0.39
0.41 0.43
0.38 Turkey
0.37 0.36
0.39 0.39
0.42 0.42
United Kingdom 0.39
0.37 0.39
0.39 0.42
0.46 United States of America 0.53
0.40 0.51
0.50 0.51
0.53
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Region S04
S05 S06
S07 Argentina - 0.83 - 2.09
- 3.76 - 0.83
Australia - 0.90
- 2.26 - 4.07
- 0.90 Brazil
- 0.92 - 2.31
- 4.16 - 0.92
Canada - 0.90
- 2.26 - 4.07
- 0.90 China
- 1.00 - 2.50
- 4.50 - 1.00
France - 0.93
- 2.31 - 4.16
- 0.93 Germany
- 0.95 - 2.36
- 4.25 - 0.95
India - 0.91
- 2.29 - 4.11
- 0.91 Indonesia - 0.86 - 2.15
- 3.86 - 0.86
Italy - 0.93
- 2.32 - 4.18
- 0.93 Japan
- 1.01 - 2.51
- 4.52 - 1.01
Mexico - 0.89
- 2.22 - 4.00
- 0.89 Other Asia - 0.95 - 2.38
- 4.28 - 0.95
Other oil producing countries - 0.92
- 2.31 - 4.16
- 0.92 Republic of Korea - 0.89 - 2.23
- 4.01 - 0.89
Rest of Euro Zone - 0.98
- 2.45 - 4.40
- 0.98 Rest of OECD - 0.92 - 2.31
- 4.16 - 0.92
Rest of the World - 0.98
- 2.45 - 4.42
- 0.98 Russia
- 0.92 - 2.31
- 4.16 - 0.92
Saudi Arabia - 0.74
- 1.86 - 3.35
- 0.74 South Africa - 0.82 - 2.05
- 3.69 - 0.82
Turkey - 0.88
- 2.19 - 3.95
- 0.88 United Kingdom - 0.94 - 2.34
- 4.22 - 0.94
United States of America - 1.06
- 2.66 - 4.78
- 1.06
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