The Global Macroeconomic Impacts of covid-19: Seven Scenarios
b) Shocks to government expenditure
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- Table 8 – Shocks to government expenditure Region S04 S05 S06 S07
- Table 9 – Impact on populations under each scenario Country/Region Population (Thousands)
- Total 7,983,209 279 3,493 12,573 15,188 37,971 68,347 15,188
- Table 10 - GDP loss in 2020 (% deviation from baseline) Country/Region S01 S02 S03 S04
- Table 11 - GDP Loss in 2020 ($US billions) Country/Region S01 S02 S03 S04
- Total Change (USD Billion) (283) (720) (1,922) (2,330) (5,305) (9,170)
- 6. Conclusions and Policy Implications
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b) Shocks to government expenditure With the previous experience of pandemics, governments across the world have exercised a stronger caution towards the outbreak by taking measures, such as strengthening health screening at ports and investments in strengthening healthcare infrastructure, to prevent the outbreak reaching additional countries. They have also responded by increasing health expenditures to contain the spread. In modeling these interventions by governments, we use the change in Chinese government expenditure relative to GDP in 2003 during the SARS outbreak as a benchmark and use the average of Index of Governance and Index of Health Policy to obtain the potential increase in government expenditure by other countries. We then 18
scale the shock across scenarios using the mortality component of the labor shock. Table 8 demonstrates the magnitude of the government expenditure shocks for countries for Scenario 4 to 7.
Table 8 – Shocks to government expenditure Region S04 S05 S06 S07 Argentina 0.39 0.98
1.76 0.39
Australia 0.27
0.67 1.21
0.27 Brazil
0.39 0.98
1.76 0.39
Canada 0.26
0.66 1.19
0.26 China
0.50 1.25
2.25 0.50
France 0.30
0.74 1.34
0.30 Germany
0.27 0.68
1.22 0.27
India 0.52
1.30 2.34
0.52 Indonesia 0.47 1.18
2.12 0.47
Italy 0.34
0.84 1.51
0.34 Japan
0.30 0.74
1.33 0.30
Mexico 0.43
1.07 1.93
0.43 Republic of Korea 0.31 0.79
1.41 0.31
Russia 0.49
1.23 2.21
0.49 Saudi Arabia 0.38 0.95
1.71 0.38
South Africa 0.43
1.08 1.94
0.43 Turkey
0.47 1.17
2.11 0.47
United Kingdom 0.27
0.68 1.22
0.27 United States of America 0.22 0.54
0.98 0.22
Other Asia 0.39
0.99 1.77
0.39 Other oil producing countries 0.54 1.35
2.42 0.54
Rest of Euro Zone 0.33
0.81 1.46
0.33 Rest of OECD 0.28 0.70
1.26 0.28
Rest of the World 0.59
1.49 2.67
0.59 5. Simulation Results (a) Baseline scenario We first solve the model from 2016 to 2100 with 2015 as the base year. The key inputs into the baseline are the initial dynamics from 2015 to 2016 and subsequent projections from 2016 forward for labor-augmenting technological progress by sector and by country. The labor- augmenting technology projections follow the approach of Barro (1991, 2015). Over long periods, Barro estimates that the average catchup rate of individual countries to the world-wide
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productivity frontier is 2% per year. We use the Groningen Growth and Development database (2018) to estimate the initial level of productivity in each sector of each region in the model. Given this initial productivity, we then take the ratio of this to the equivalent sector in the US, which we assume is the frontier. Given this initial gap in sectoral productivity, we use the Barro catchup model to generate long term projections of the productivity growth rate of each sector within each country. Where we expect that regions will catch up more quickly to the frontier due to economic reforms (e.g., China) or more slowly to the frontier due to institutional rigidities (e.g., Russia), we vary the catchup rate over time. The calibration of the catchup rate attempts to replicate recent growth experiences of each country and region in the model. The exogenous sectoral productivity growth rate, together with the economy-wide growth in labor supply, are the exogenous drivers of sector growth for each country. The growth in the capital stock in each sector in each region is determined endogenously within the model. In the alternative COVID-19 scenarios, we incorporate the range of shocks discussed above to model the economic consequences of different epidemiological assumptions. All results below are the difference between the COVID-19 scenario and the baseline of the model.
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Table 9 contains the impact on populations in different regions. These are the core shocks that are combined with the various indicators above to create the seven scenarios. The mortality rates for each country under each scenario are contained in Table B-1 in Appendix B. Note that the mortality rates in Table B-1 are much lower in advanced economies compared to China.
Argentina 43,418 -
- 50
126 226
50 Australia 23,800 -
- 21
53 96
21 Brazil
205,962 - - - 257
641 1,154
257 Canada
35,950 - - - 30
74 133
30 China
1,397,029 279 3,493 12,573 2,794
6,985 12,573 2,794
France 64,457
- - - 60 149
268 60
Germany 81,708
- - - 79 198
357 79
India 1,309,054 - -
3,693 9,232 16,617 3,693 Indonesia 258,162 - - - 647
1,616 2,909
647 Italy
59,504 - - - 59
147 265
59 Japan
127,975 - - - 127
317 570
127 Mexico
125,891 - - - 184
460 828
184 Republic of Korea 50,594 -
- 61
151 272
61 Russia
143,888 - - - 186
465 837
186 Saudi Arabia 31,557 -
- 29
71 128
29 South Africa 55,291 -
- 75
187 337
75 Turkey
78,271 - - - 116
290 522
116 United Kingdom 65,397 -
- 64
161 290
64 United States of America 319,929 - - - 236
589 1,060
236 Other Asia 330,935 - - - 530
1,324 2,384
530 Other oil producing countries 517,452 - - - 774
1,936 3,485
774 Rest of Euro Zone 117,427 - - - 106
265 478
106 Rest of OECD 33,954 -
- 27
67 121
27 Rest of the World 2,505,604 - - - 4,986 12,464 22,435 4,986
Table 9 shows that for even the lowest of the pandemic scenarios (S04), there are estimated to be around 15 million deaths. In the United States, the estimate is 236,000 deaths. These 21
estimated deaths from COVID-19 can be compared to a regular influenza season in the United States, where around 55,000 people die each year.
Country/Region S01 S02 S03 S04 S05 S06 S07 AUS
-0.3 -0.4
-0.7 -2.1
-4.6 -7.9
-2.0 BRA
-0.3 -0.3
-0.5 -2.1
-4.7 -8.0
-1.9 CHI
-0.4 -1.9
-6.0 -1.6
-3.6 -6.2
-2.2 IND
-0.2 -0.2
-0.4 -1.4
-3.1 -5.3
-1.3 EUZ
-0.2 -0.2
-0.4 -2.1
-4.8 -8.4
-1.9 FRA
-0.2 -0.3
-0.3 -2.0
-4.6 -8.0
-1.5 DEU
-0.2 -0.3
-0.5 -2.2
-5.0 -8.7
-1.7 ZAF
-0.2 -0.2
-0.4 -1.8
-4.0 -7.0
-1.5 ITA
-0.2 -0.3
-0.4 -2.1
-4.8 -8.3
-2.2 JPN
-0.3 -0.4
-0.5 -2.5
-5.7 -9.9
-2.0 GBR
-0.2 -0.2
-0.3 -1.5
-3.5 -6.0
-1.2 ROW
-0.2 -0.2
-0.3 -1.5
-3.5 -5.9
-1.5 MEX
-0.1 -0.1
-0.1 -0.9
-2.2 -3.8
-0.9 CAN
-0.2 -0.2
-0.4 -1.8
-4.1 -7.1
-1.6 OEC
-0.3 -0.3
-0.5 -2.0
-4.4 -7.7
-1.8 OPC
-0.2 -0.2
-0.4 -1.4
-3.2 -5.5
-1.3 ARG
-0.2 -0.3
-0.5 -1.6
-3.5 -6.0
-1.2 RUS
-0.2 -0.3
-0.5 -2.0
-4.6 -8.0
-1.9 SAU
-0.2 -0.2
-0.3 -0.7
-1.4 -2.4
-1.3 TUR
-0.1 -0.2
-0.2 -1.4
-3.2 -5.5
-1.2 USA
-0.1 -0.1
-0.2 -2.0
-4.8 -8.4
-1.5 OAS
-0.1 -0.2
-0.4 -1.6
-3.6 -6.3
-1.5 INO
-0.2 -0.2
-0.3 -1.3
-2.8 -4.7
-1.3 KOR
-0.1 -0.2
-0.3 -1.4
-3.3 -5.8
-1.3
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Tables 10 and 11 provide a summary of the overall GDP loss for each country/region under the seven scenarios. The results in Table 10 are the Change in GDP in 2020 expressed as a percentage change from the baseline. The results in Table 11 are the results from Table 10 converted into billions of $2020US.
AUS
(4) (5)
(9) (27)
(60) (103)
(27) BRA
(9) (12)
(19) (72)
(161) (275)
(65) CHI
(95) (488) (1,564) (426) (946)
(1,618) (560)
IND (21)
(26) (40)
(152) (334)
(567) (142)
EUZ (11)
(13) (19)
(111) (256)
(446) (101)
FRA (7)
(8) (11)
(63) (144)
(250) (46)
DEU (11)
(14) (21)
(99) (225)
(390) (78)
ZAF (1)
(2) (3)
(14) (33)
(57) (12)
ITA (6)
(7) (9)
(54) (123)
(214) (56)
JPN (17)
(20) (28)
(140) (318)
(549) (113)
GBR (5)
(6) (9)
(48) (108)
(187) (39)
ROW (24)
(29) (43)
(234) (529)
(906) (227)
MEX (2)
(2) (3)
(24) (57)
(98) (24)
CAN (3)
(4) (6)
(32) (74)
(128) (28)
OEC (5)
(6) (10)
(40) (91)
(157) (36)
OPC (10)
(12) (18)
(73) (164)
(282) (69)
ARG (2)
(3) (5)
(15) (33)
(56) (11)
RUS (10)
(12) (19)
(84) (191)
(331) (81)
SAU (3)
(3) (5)
(12) (24)
(40) (22)
TUR (3)
(4) (6)
(33) (75)
(130) (30)
USA (16)
(22) (40)
(420) (1,004)
(1,769) (314)
OAS (6)
(10) (19)
(80) (186)
(324) (77)
INO (6)
(7) (11)
(45) (99)
(167) (46)
KOR (3)
(4) (7)
(31) (71)
(124) (29)
Total Change (USD Billion) (283) (720) (1,922) (2,330) (5,305) (9,170) (2,230)
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Tables 10 and 11 illustrate the scale of the various pandemic scenarios on reducing GDP in the global economy. Even a low-end pandemic modeled on the Hong Kong Flu is expected to reduce global GDP by around $SU2.4 trillion and a more serious outbreak similar to the Spanish flu reduces global GDP by over $US9trillion in 2020. Figures 9-11 provide the time profile of the results for several countries. The patterns in the figures represents the nature of the assumed shocks which for the first 6 scenarios are expected to disappear over time, Figure 9 contains results for China under each scenario. We present results for Real GDP, private investment, consumption, the trade balance and then the short real interest rate and the value of the equity market for sector 5 which is durable manufacturing. Figure 10 contains the results for the United States and Figure 11 for Australia. The shocks which make up the pandemic cause a sharp drop in consumption and investment. The decline in aggregate demand, together with the original risk shocks cause a sharp drop in equity markets. The funds from equity markets are partly shifted into bonds, partly into cash and partly overseas depending on which markets are most affected. Central banks respond by cutting interest rates which drive together with the increased demand for bonds from the portfolio shift drives down the real interest rate. Equity markets drop sharply both because of the rise in risk but also because of the expected economic slowdown and the fall in expected profits. For each scenario, there is a V shape recovery except for scenario 7. Recall that scenario 7 is the same as scenario 4 in year 1, but with the expectation that the pandemic will recur each year into the future. Similar patterns can be seen in the dynamic results for the United States and Australia shown in Figures 10 an 11. The quantitative magnitudes differ across countries, but the pattern of a sharp shock followed by a gradual recovery is common across countries. The improvement in the trade balance of China and deterioration in the US trade balance reflect the global reallocation of financial capital as a result of the shock. Capital flows out of severely affected economies like China and other developing and emerging economies and into safer advanced economies like the United States, Europe and Australia. This movement of capital tends to appreciate the exchange rate of countries that are receiving capital and depreciate the exchange rates of countries that are losing capital. The deprecation of the exchange rate increases exports and reduced imports in the countries losing capital and hence lead to the current account adjustment that is consistent with the capital account adjustment.
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These results are very sensitive to the assumptions in the model, to the shocks we feed in and to the assumed macroeconomic policy responses in each country. Central banks are assumed to respond according to a Henderson-Mckibbin-Taylor rule which differs across countries (see Mckibbin and Triggs (2018)). Fiscal authorities are allowing automatic stabilizers to increase budget deficits but cover addition debt servicing costs with a lump-sum tax levied on households over time. In addition, there is the fiscal spending increase assumed in the shock design outlined above. 25
This paper has presented some preliminary estimates of the cost of the COVID-19 outbreak under seven different scenarios of how the disease might evolve. The goal is not to be definitive about the virus outbreak, but rather to provide information about a range of possible economic costs of the disease. At the time of writing this paper, the probability of any of these scenarios and the range of plausible alternatives are highly uncertain. In the case where COVID-19 develops into a global pandemic, our results suggest that the cost can escalate quickly. A range of policy responses will be required both in the short term as well as in the coming years. In the short term, central banks and Treasuries need to make sure that disrupted economies continue to function while the disease outbreak continues. In the face of real and financial stress, there is a critical role for governments. While cutting interest rates is a possible response for central banks, the shock is not only a demand management problem but a multi- faceted crisis that will require monetary, fiscal and health policy responses. Quarantining affected people and reducing large scale social interaction is an effective response. Wide dissemination of good hygiene practices as outlined in Levine and McKibbin (2020) can be a low cost and highly effective response that can reduce the extent of contagion and therefore reduce the social and economic cost. The longer-term responses are even more important. Despite the potential loss of life and the possible large-scale disruption to a large number of people, many governments have been reluctant to invest sufficiently in their health care systems, let alone public health systems in less developed countries where many infectious diseases are likely to originate. Experts have warned and continue to warn that zoonotic diseases will continue to pose a threat to the lives of millions of people with potentially major disruption to an integrated world economy. The idea that any country can be an island in an integrated global economy is proven wrong by the latest outbreak of COVID-19. Global cooperation, especially in the sphere of public health and economic development, is essential. All major countries need to participate actively. It is too late to act once the disease has taken hold in many other countries and attempt to close borders once a pandemic has started. 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,
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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.
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