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.
Table 2 – Epidemiological Assumptions for China
Scenario
Attack Rate for
China
Case-fatality Rate for
China
Mortality Rate for
China
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
Table 3 – Scenario Assumptions
a) Shocks to labor supply
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,
Scen
ario
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
11
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
- 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
- 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
- 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
- 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
- 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
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.
14
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
c) Shocks to the cost of production in each sector
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.
16
Table 6 – Shocks to cost of production
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
17
Table 7 – Shocks to consumption demand
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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