Business and Economics Growth Related Questions

Online Supplement to

‘Modern Pandemics: Recession and Recovery’

by C. Ma, and J. Rogers, and S. Zhou

June 2020

S.1 Figures

Figure S.1 Effects of Health Crises on GDP by Severity

-4 2

P er

ce nt

0 1 2 3 4 5 Years

High Case Rate Medium Case Rate Low Case Rate

-4 2

P er

ce nt

0 1 2 3 4 5 Years

High Mortality Rate Medium Mortality Rate Low Mortality Rate

NOTE: Impulse response functions (IRF) are estimated based on the local projection method as in Jordà (2005) git+H = αHi + ∑

4 j=1 β

H j git− j + ∑

4 s=0 δ

H s D

H it−s + ∑

4 s=0 γ

H s D

M it−s + ∑

4 s=0 µ

H s D

L it−s + Xit + εit ,with H = 0,1,··· ,5, where git is the annual real GDP growth

rate for country i at year t, DHit ( DMit ,D

L it )

is a dummy variable indicating a high (medium, low) mortality rate or cases per population rate for an affected country i in year t, with Xit including country-level controls such as Trade/GDP, Domestic Credit/GDP, population and log GDP per capita. We also include a decade dummy, US recession dummy, a banking crisis dummy and country fixed effects. Standard errors are corrected using Driscoll and Kraay (1998). The blue line represents low, the green dash-dotted line represents medium and the red dashed line represents high. One standard error bands are shown.

S.1

Figure S.2 Effect on GDP Growth Conditional on Immediate Fiscal Response: Results for General Expenditures and Tax Revenues

Panel A: High Expenditure Response Panel B: Low Expenditure Response

-4 -3

-2 -1

0 1

2 3

P er ce nt

0 1 2 3 4 5 Years

-4 -3

-2 -1

0 1

2 3

P er ce nt

0 1 2 3 4 5 Years

Panel C: High Tax Response Panel D: Low Tax Response

-4 -3

-2 -1

0 1

2 3

P er ce nt

0 1 2 3 4 5 Years

-4 -3

-2 -1

0 1

2 3

P er ce nt

0 1 2 3 4 5 Years

NOTE: Impulse response functions (IRF) are estimated based on the local projection method as in Jordà (2005): git+H = αHi + ∑

4 s=1 β

H s git−s + ∑

4 s=0 δ

H s Dit−s + Xit + εit ,with H = 0,1,··· ,5, where git is the annual real GDP growth rate for country i at year t, Dit

is a dummy variable indicating a disease event hitting country i in year t, with Xit including country-level controls such as Trade/GDP, Domestic Credit/GDP, population and log GDP per capita. We also include a decade dummy, U.S. recession dummy, a banking crisis dummy and country fixed effects. Standard errors are corrected using Driscoll and Kraay (1998). One standard error bands are shown. Each row divides countries based on the average of Zit−Zit−1GDPit−1 across all six health episodes where t is the onset year of each episode. Z refers to fiscal spending in Panel A and B, and tax revenue in Panel C and D. High refers to countries in the 75 percentile and above while low refers to countries in the 25 percentile and below.

S.2

Figure S.3 Effect on Government Budget

Panel A: Central Government Debt (% GDP) Panel B: Fiscal Surplus (% GDP)

0 2

4 6

8 P er ce nt

0 1 2 3 4 5 Years

-3 -2

-1 0

1 P er ce nt

0 1 2 3 4 5 Years

Panel C: Government Spending (% GDP) Panel D: Government Revenue (% GDP)

-.5 0

.5 1

1. 5

P er ce nt

0 1 2 3 4 5 Years

-1 -.5

0 .5

P er ce nt

0 1 2 3 4 5 Years

NOTE: Impulse response functions (IRF) are estimated based on the local projection method as in Jordà (2005): yit+H = αHi + ∑

4 s=1 β

H s yit−s + ∑

4 s=0 δ

H s Dit−s + Xit + εit ,with H = 0,1,··· ,5, where yit is the annual central government debt (% GDP), fiscal surplus

(% GDP), government spending (% GDP) or government revenue (% GDP) for country i at year t, Dit is a dummy variable indicating a disease event hitting country i in year t, with Xit including country-level controls such as Trade/GDP, Domestic Credit/GDP, population and log GDP per capita. We also include a decade dummy, U.S. recession dummy, a banking crisis dummy and country fixed effects. Standard errors are corrected using Driscoll and Kraay (1998). One standard error bands are shown.

S.3

Figure S.4 Health Spending and Crisis Severity

Panel A: Health Spending Adjustment and Mortality Rate

-.0 1

0 .0

1 .0

2 .0

3 .0

4 H

ea lth

S pe

nd in

g A

dj us

tm en

t ( %

G D

P )

0 20 40 60 80 100 Mortality Rate (%)

Panel B: Health Spending Adjustment and Case Rate

-.0 1

0 .0

1 .0

2 .0

3 .0

4 H

ea lth

S pe

nd in

g A

dj us

tm en

t ( %

G D

P )

0 1 2 3 Case/Pop Rate

NOTE: Panel A plots the relationship between health spending adjustment (defined as the change of health spending in the onset year normalized by the previous year’s GDP) and the mortality rate, for all episodes in affected countries. The regression line has a slope of -0.000012 with t-stat at -0.59. Panel B plots the relationship between health spending adjustment and the case rate for all the episodes in affected countries. The regression line has a slope of 0.0009 with t-stat at 0.55.

S.4

S.2 Data Sources

Table S.1 List of Global Pandemic and Epidemic Events

Announcement Time Event Name Affected Countries (Economies) # of Affected Countries (in matched sample) Total Deaths Total Cases Average Mortality Rate 1968/07 Hongkong Flu ARG, AUS, CHL, DNK, FIN, FRA, GBR, GRC, HKG,

ITA, JAM, JPN, NLD, NOR, PRT, SWE, USA, ZAF 18 N.A. N.A. N.A.

2003/02 SARS AUS, CAN, CHE, CHN, DEU, ESP, FRA, GBR, HKG, IDN, IND, IRL, ITA, KOR, KWT, MAC, MNG, MYS, NZL, PHL, ROU, RUS, SGP, SWE, THA, USA, VNM, ZAF

28 737 7750 9.51%

2009/04 H1N1 AGO, ALB, AND, ARE, ARG, ASM, AUS, AUT, AZE, BDI, BEL, BGD, BGR, BHR, BHS, BIH, BLR, BLZ, BMU, BOL, BRA, BRB, BRN, BTN, BWA, CAN, CHE, CHL, CHN, CIV, CMR, COD, COG, COL, CPV, CRI, CUB, CYM, CYP, CZE, DEU, DMA, DNK, DOM, DZA, ECU, EGY, ESP, ETH, FIN, FJI, FRA, FSM, GAB, GBR, GEO, GHA, GRC, GRD, GTM, GUM, GUY, HND, HRV, HTI, HUN, IDN, IND, IRL, IRN, IRQ, ISL, ISR, ITA, JAM, JOR, JPN, KAZ, KEN, KHM, KIR, KNA, KOR, KWT, LAO, LBN, LBY, LCA, LIE, LKA, LSO, LUX, MAR, MDA, MDG, MDV, MEX, MHL, MKD, MLI, MLT, MMR, MNE, MNG, MOZ, MUS, MWI, MYS, NAM, NGA, NIC, NLD, NOR, NPL, NRU, NZL, OMN, PAK, PAN, PER, PHL, PLW, PNG, POL, PRI, PRT, PRY, PSE, QAT, ROU, RUS, RWA, SAU, SDN, SGP, SLB, SLV, SRB, STP, SUR, SVK, SVN, SWE, SWZ, SYC, TCD, THA, TJK, TLS, TON, TTO, TUN, TUR, TUV, TZA, UGA, URY, USA, VCT, VEN, VNM, VUT, WSM, YEM, ZAF, ZMB, ZWE

167 14390a 526353 2.73%

2012/03 MERS ARE, AUT, CHN, DEU, DZA, EGY, FRA, GBR, GRC, IRN, ITA, JOR, KOR, KWT, LBN, MYS, NLD, OMN, PHL, QAT, SAU, THA, TUN, TUR, USA, YEM

26 498 1289 38.63%

2014/08b Ebola ESP, GBR, GIN, ITA, LBR, MLI, NGA, SEN, SLE, USA 10 11323 28646 39.53% 2016/02c Zika ABW, ARG, ATG, BHS, BLZ, BOL, BRA, BRB, CAN,

CHL, COL, CRI, CUB, CYM, DMA, DOM, ECU, GRD, GTM, GUY, HND, HTI, JAM, KNA, LCA, NIC, PAN, PER, PRI, PRY, SLV, SUR, TCA, TTO, URY, USA, VCT, VIR

38 20 197689 0.01%

aThis estimates are from European Center for Disease Prevention and Controls (ECDC). We use their estimates since they provides detailed coverage and mortality rate for each country. Detailed information can be found here: https://en.wikipedia.org/wiki/2009_flu_pandemic_by_country. However, the estimate from US Centers for Disease Control and Prevention (CDC) for global death troll is 284,000, about 15 times more than the number of laboratory-confirmed cases. See details in http://www.cidrap.umn.edu/news-perspective/2012/06/ cdc-estimate-global-h1n1-pandemic-deaths-284000.

bThe West African Ebola outbreak began December 26, 2013 and was declared a PHEIC August 8, 2014. cThe Zika virus outbreak occurred at October, 2015 but was declared a PHEIC February 1, 2016

S .5

Table S.2 Details of Six Pandemic and Epidemic Events

Episodes Vaccine/Cure Government Response 1968 Flu “Split vaccine” developed in 1968 The 1968 Flu spread widely as a result of international air travel, but the effects surfaced differently in different regions

— the US and Canada experienced a severe initial wave with less a severe subsequent wave, while the reverse held true for Europe and Asia. In North America, where the burden of the flu was relatively small in comparison to in Europe and Asia, government relied on vaccination, hospitalization, and antibiotics to treat secondary pneumonia. Quarantines, closures, and other non-pharmaceutical means of intervention were not quite necessary to curb the disease.

SARS No cure Efforts to suppress SARS included isolation of symptomatic patients and rigid hospital infection control practices. The latter proved to be particularly effective in the 2003 SARS pandemic in hospitals in Hong Kong SAR, China, in which none of the health care workers wearing proper PPE ever contracted SARS. Governments mainly utilized containment measures which mirrored those used to rid of bubonic plagues — case tracking, quarantining those infected, bans on large gatherings, examination of travelers, improved PPE and barrier protection. These measures, working in tandem with travel restrictions, successfully curbed SARS likely because SARS is characterized by an insignificant asymptomatic carrier state and relatively shorter incubation periods.

H1N1 Vaccine released in October of 2009 In response to the outbreak of the Swine Flu, several countries’ governments focused on restricting travel amongst infected regions. Additionally, private and public sector workers were advised to implement preventative measures, and schools were closed in areas of outbreak. China reverted to using the same measures it used to fight SARS, notably quarantining any and all persons who were possibly infected by H1N1. Moreover, many countries placed embargos on imports of pork from Mexico and the US. Airport screening was also implemented during this time. However, it has been shown that travel restrictions with regards to curbing influenza are only effective in delaying the spread and peak of the disease. Extensive travel restrictions are required to have significant impact on curbing influenza.

Mers No available vaccine or specific treatment The CDC collaborated with the World Health Organization, and began responding to the Mers crisis before it reached the US. Key areas of focus included epidemiology, laboratory science, travelers’ health, and infection control. Another was collaboration within countries and between countries. The CDC brought about data-sharing agreements between countries and promoted global sharing of specimens and reagents to deliver an effective response to the disease.

Ebola No known vaccine/treatment The hardest-hit countries imposed certain measures to curb the devastation of Ebola. In general, health agencies and hospitals relied on isolation of symptomatic patients, quarantining, and bolstering of hospital infection control practices to combat Ebola. Some countries were better equipped than others to execute disease prevention –— Nigeria had experience running an emergency operations center and utilizing global positioning systems for contact tracing during previous polio eradication efforts. Ultimately, putting an end to Ebola required a multinational effort, with the World Bank’s Pandemic Emergency Financing Facility (PEF) contributing US$3.8 billion to help with the costs of Ebola, and the World Bank Group pooling US$1.6 billion from the International Development Association and the International Finance Corporation to put towards economic recovery in Guinea, Liberia, and Sierra Leone.

Zika No vaccine/specific treatment In response to the outbreak, governments including those of the US and the UK declared travel precautions, advising pregnant women, in particular, to avoid travelling to countries affected by Zika. Control measures such as insect bite precautions and removal of possible breeding grounds for mosquitos were implemented, as well as regulatory reporting on recommendations regarding Zika and pharmaceutical intervention.

NOTE: The note relies on information mainly from Jamison et al. (2017), Mateus et al. (2014), Chang et al. (2016), Williams et al. (2015), Saunders-Hastings and Krewski (2016) and online information from https://graduateinstitute.ch/communications/news/brief-international-history-pandemics.

S .6

Table S.3 Quarterly GDP Country Coverage

Country Code Country Name Start Quarter End Quarter Country Code Country Name Start Quarter End Quarter

ARG Argentina 1994Q1 2018Q4 ISL Iceland 1961Q1 2018Q4 AUS Australia 1961Q1 2018Q4 ISR Israel 1996Q1 2018Q4 AUT Austria 1961Q1 2018Q4 ITA Italy 1961Q1 2018Q4 BEL Belgium 1961Q1 2018Q4 JPN Japan 1961Q1 2018Q4 BGR Bulgaria 1996Q1 2018Q4 KOR Korea, Rep. 1961Q1 2018Q4 BRA Brazil 1997Q1 2018Q4 LTU Lithuania 1996Q1 2018Q4 CAN Canada 1962Q1 2018Q4 LUX Luxembourg 1961Q1 2018Q4 CHE Switzerland 1961Q1 2018Q4 LVA Latvia 1996Q1 2018Q4 CHL Chile 1996Q1 2018Q4 MEX Mexico 1961Q1 2018Q4 CHN China 2011Q1 2018Q4 NLD Netherlands 1961Q1 2018Q4 COL Colombia 2006Q1 2018Q4 NOR Norway 1961Q1 2018Q4 CZE Czech Republ 1995Q1 2018Q4 NZL New Zealand 1988Q2 2018Q4 DEU Germany 1961Q1 2018Q4 POL Poland 1996Q1 2018Q4 DNK Denmark 1961Q1 2018Q4 PRT Portugal 1961Q1 2018Q4 ESP Spain 1961Q1 2018Q4 ROU Romania 1996Q1 2018Q4 EST Estonia 1996Q1 2018Q4 RUS Russian Fede 2004Q1 2018Q4 FIN Finland 1961Q1 2018Q4 SAU Saudi Arabia 2010Q1 2018Q4 FRA France 1961Q1 2018Q4 SVK Slovak Repub 1994Q1 2018Q4 GBR United Kingd 1960Q1 2018Q4 SVN Slovenia 1996Q1 2018Q4 GRC Greece 1961Q1 2018Q4 SWE Sweden 1961Q1 2018Q4 HUN Hungary 1996Q1 2018Q4 TUR Turkey 1999Q1 2018Q4 IDN Indonesia 1991Q1 2018Q4 USA United State 1960Q1 2018Q4 IND India 1997Q2 2018Q4 ZAF South Africa 1961Q1 2018Q4 IRL Ireland 1961Q1 2018Q4

Table S.4 Country-level Data: Summary Statistics

Variables N mean p50 sd p75 p25

GDP growth rate % 8,991 3.76 3.80 4.11 1.00 6.00 Unemployment rate % 5208 8.19 6.65 6.32 11.16 3.59 GDP Consensus Forecast % 612 2.57 2.44 2.02 1.55 3.38 Quarterly GDP growth rate % 7,876 3.33 3.24 3.51 1.49 5.22 Quarterly GDP Consensus Forecast % 1,552 2.93 2.61 1.78 1.93 3.42 Trade/GDP % 8,261 67.43 59.00 49.72 36.96 87.77 Domestic Credit/GDP % 7,605 33.78 23.00 39.23 12.00 45.00 Log(Population) 12,202 8.26 4.29 5.95 3.37 15.06 Log(GDP per capita) 9,172 5.97 5.51 2.82 3.40 8.46 Recession Dummy 12,272 0.27 0.00 0.44 0.00 1.00 Banking Crisis Dummy 12,272 0.01 0.00 0.11 0.00 0.00 Tax Change % 3,680 8.06 1.80 16.79 0.54 5.08 Expenditure Change % 3,464 8.58 2.36 16.95 0.84 5.92 Health Change % 2,947 0.63 0.50 0.61 0.24 0.90

S.7

Table S.5 Pre-trend Analysis

GDP growth rate %

(1) (2) (3)

Sample Period: 1960-2018 1990-2018 1960-2018

Shock (-1) -0.18 -0.12 -0.17 (0.37) (0.43) (0.49)

Shock -2.56** -2.55* -2.60* (1.22) (1.27) (1.30)

Shock (+1) 0.49* 0.47* 0.63* (0.25) (0.25) (0.31)

Shock (+2) 0.55*** 0.59*** 0.59*** (0.14) (0.13) (0.20)

Health Expenditure (Lagged) 0.16 (0.11)

Trade/GDP 0.17 0.54 2.82*** (0.22) (0.46) (0.48)

Domestic Credit/GDP -0.66* -0.70 -0.50 (0.38) (0.46) (0.43)

Log(Population) 0.19*** 0.15*** 0.97 (0.04) (0.05) (2.57)

Log(GDP per capita) -0.36*** -0.30*** 2.72** (0.08) (0.10) (1.24)

Recession -0.35 -0.50 -1.08** (0.25) (0.44) (0.48)

Banking Crisis -1.28*** -1.38*** -2.22** (0.32) (0.37) (1.02)

Constant 4.80*** 4.71*** -36.28 (0.49) (0.52) (45.87)

Observations 6348 4158 2708 Within R-square 0.058 0.067 0.131 Decade FE Yes Yes Yes Country FE Yes Yes Yes

NOTE: This table estimates a panel regression with four dummy variables that flags one year before the health crises, the onset year, one year after and two years after the health crises. We also add a lagged health expenditure (% GDP ) as a control in column (3). ∗, ∗∗ and ∗∗∗ indicate statistical significance at the 10%, 5%, and 1% level, respectively.

S.8

Table S.6 The Effect of Health Crises on Real GDP Growth: Weighted by Disease Severity

GDP growth rate %

(1) (2) (3) (4) (5) (6)

Sample Period: 1960-2018 1990-2018 1960-2018 1990-2018

Mortality Rate -3.62* -3.42* -5.85*** (1.94) (1.86) (1.42)

Cases/Pop -3.36*** -3.20*** -5.46*** (1.11) (1.13) (0.98)

Consensus Forecast 0.52*** 0.57*** (0.14) (0.16)

Trade/GDP 0.19 0.52 0.83 0.18 0.50 0.75 (0.22) (0.38) (0.67) (0.21) (0.38) (0.63)

Domestic Credit/GDP -0.71* -0.75 -1.64 -0.71* -0.75 -1.45 (0.42) (0.50) (1.10) (0.40) (0.48) (1.04)

Log(Population) 0.17*** 0.12* 0.06 0.17*** 0.11* 0.05 (0.03) (0.06) (0.05) (0.03) (0.06) (0.05)

Log(GDP per capita) -0.33*** -0.23* -0.05 -0.32*** -0.22* -0.03 (0.08) (0.13) (0.14) (0.07) (0.12) (0.14)

Recession -0.51* -0.83* -0.75 -0.48* -0.77* -0.53 (0.27) (0.45) (0.66) (0.25) (0.41) (0.58)

Banking Crisis -1.26*** -1.30*** 1.12 -1.27*** -1.31*** 0.97 (0.37) (0.47) (0.97) (0.36) (0.46) (0.92)

Constant 4.62*** 4.58*** 1.98*** 4.61*** 4.54*** 1.80*** (0.45) (0.51) (0.51) (0.45) (0.50) (0.49)

Observations 6522 4296 530 6525 4299 530 Within R2 0.042 0.039 0.134 0.045 0.044 0.156 Decade FE Yes Yes Yes Yes Yes Yes Country FE Yes Yes Yes Yes Yes Yes

NOTE: The dependent variable is real annual GDP growth rate. The sample period for columns (1) and (4) is 1960-2018 while the sample period for columns (2)-(3) and (5)-(6) is 1990-2018. Country and decade fixed effects are included. All standard errors are corrected using Driscoll and Kraay (1998) and reported in parentheses. ∗, ∗∗ and ∗∗∗ indicate statistical significance at the 10%, 5%, and 1% level, respectively.

S.9

Table S.7 Disease Severity and Health Expenditure Response Dummy

Panel A: Disease Severity and Health Expenditure Response Dummy

1968Flu SARS H1N1 MERS Ebola Zika

Country Name Country Code

Mortality Rate

Case/Pop Health Expendi- ture

Mortality Rate

Case/Pop Health Expendi- ture

Mortality Rate

Case/Pop Health Expenditure

Mortality Rate

Case/Pop Health Expenditure

Mortality Rate

Case/Pop Health Expenditure

Mortality Rate

Case/Pop Health Expenditure

Aruba ABW 0 0 N.A. 0 0 N.A. 1 3 N.A. 0 0 N.A. 0 0 N.A. 1 2 N.A. Afghanistan AFG 0 0 N.A. 0 0 2 2 2 2 0 0 2 0 0 2 0 0 2 Angola AGO 0 0 N.A. 0 0 2 1 1 1 0 0 1 0 0 1 0 0 2 Albania ALB 0 0 N.A. 0 0 2 3 1 1 0 0 1 0 0 2 0 0 1 Andorra AND 0 0 N.A. 0 0 2 0 1 1 0 0 1 0 0 1 0 0 2 United Arab ARE 0 0 N.A. 0 0 1 3 1 1 2 3 1 0 0 1 0 0 1 Argentina ARG 1 1 N.A. 0 0 2 3 3 2 0 0 2 0 0 2 1 1 2 Armenia ARM 0 0 N.A. 0 0 2 0 0 1 0 0 2 0 0 2 0 0 1 American Sam ASM 0 0 N.A. 0 0 N.A. 1 3 N.A. 0 0 N.A. 0 0 N.A. 0 0 N.A. Antigua and ATG 0 0 N.A. 0 0 1 1 2 1 0 0 1 0 0 2 1 2 1 Australia AUS 3 3 N.A. 1 2 1 2 3 2 0 0 2 0 0 2 0 0 1 Austria AUT 0 0 N.A. 0 0 1 1 2 1 1 2 2 0 0 1 0 0 2 Azerbaijan AZE 0 0 N.A. 0 0 2 3 1 2 0 0 2 0 0 2 0 0 2 Burundi BDI 0 0 N.A. 0 0 1 1 1 2 0 0 1 0 0 1 0 0 2 Belgium BEL 0 0 N.A. 0 0 2 1 1 2 0 0 1 0 0 1 0 0 1 Benin BEN 0 0 N.A. 0 0 1 0 0 1 0 0 2 0 0 1 0 0 1 Burkina Faso BFA 0 0 N.A. 0 0 1 0 0 2 0 0 1 0 0 1 0 0 2 Bangladesh BGD 0 0 N.A. 0 0 1 2 1 1 0 0 1 0 0 1 0 0 1 Bulgaria BGR 0 0 N.A. 0 0 2 3 1 1 0 0 2 0 0 2 0 0 2 Bahrain BHR 0 0 N.A. 0 0 2 2 3 1 0 0 2 0 0 1 0 0 1 Bahamas, The BHS 0 0 N.A. 0 0 1 3 2 1 0 0 1 0 0 1 1 2 2 Bosnia and H BIH 0 0 N.A. 0 0 2 3 2 1 0 0 1 0 0 1 0 0 1 Belarus BLR 0 0 N.A. 0 0 2 0 0 1 0 0 2 0 0 2 0 0 2 Belize BLZ 0 0 N.A. 0 0 2 1 2 2 0 0 1 0 0 1 1 2 2 Bermuda BMU 0 0 N.A. 0 0 N.A. 3 2 N.A. 0 0 N.A. 0 0 N.A. 1 2 N.A. Bolivia BOL 0 0 N.A. 0 0 2 2 3 2 0 0 2 0 0 2 1 1 2 Brazil BRA 0 0 N.A. 0 0 2 3 2 2 0 0 2 0 0 2 3 3 2 Barbados BRB 0 0 N.A. 0 0 2 2 3 1 0 0 2 0 0 1 1 2 1 Brunei Darus BRN 0 0 N.A. 0 0 1 2 3 1 0 0 1 0 0 1 0 0 1 Bhutan BTN 0 0 N.A. 0 0 1 1 1 2 0 0 2 0 0 1 0 0 1 Botswana BWA 0 0 N.A. 0 0 2 1 1 2 0 0 2 0 0 2 0 0 2 Central Afri CAF 0 0 N.A. 0 0 1 0 0 2 0 0 2 0 0 2 0 0 1 Canada CAN 0 0 N.A. 3 3 2 3 3 2 0 0 1 0 0 1 1 1 1 Switzerland CHE 3 3 N.A. 1 2 1 2 2 1 0 0 1 0 0 1 0 0 2 Chile CHL 3 3 N.A. 0 0 2 2 3 2 0 0 2 0 0 2 1 1 2 China CHN 0 0 N.A. 2 3 2 2 2 2 1 1 2 0 0 2 0 0 2 Cote d’Ivoir CIV 0 0 N.A. 0 0 1 1 1 1 0 0 2 0 0 2 0 0 1 Cameroon CMR 0 0 N.A. 0 0 1 1 1 1 0 0 2 0 0 2 0 0 1 Congo, Dem. COD 0 0 N.A. 0 0 2 1 1 2 0 0 2 0 0 2 0 0 1 Congo, Rep. COG 0 0 N.A. 0 0 1 1 1 1 0 0 2 0 0 1 0 0 2 Colombia COL 0 0 N.A. 0 0 2 3 2 2 0 0 1 0 0 1 1 2 1 Comoros COM 0 0 N.A. 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 Cabo Verde CPV 0 0 N.A. 0 0 1 1 2 1 0 0 2 0 0 1 0 0 1 Costa Rica CRI 0 0 N.A. 0 0 2 2 3 2 0 0 1 0 0 2 1 2 1 Cuba CUB 0 0 N.A. 0 0 1 3 2 2 0 0 1 0 0 2 1 1 1 Cayman Islan CYM 0 0 N.A. 0 0 N.A. 2 3 N.A. 0 0 N.A. 0 0 N.A. 1 3 N.A. Cyprus CYP 0 0 N.A. 0 0 1 1 3 1 0 0 1 0 0 1 0 0 2 Czech Republ CZE 0 0 N.A. 0 0 2 3 2 2 0 0 1 0 0 1 0 0 1 Germany DEU 3 3 N.A. 1 2 1 2 3 2 2 2 1 0 0 2 0 0 2 Djibouti DJI 0 0 N.A. 0 0 N.A. 1 1 N.A. 0 0 N.A. 0 0 1 0 0 1 Dominica DMA 0 0 N.A. 0 0 1 1 3 2 0 0 1 0 0 1 1 3 1 Denmark DNK 3 3 N.A. 0 0 1 1 2 2 0 0 1 0 0 1 0 0 2 Dominican Re DOM 0 0 N.A. 0 0 2 3 2 2 0 0 2 0 0 2 1 1 2

S .10

Disease Severity and Health Expenditure Response Dummy (Cont.)

1968Flu SARS H1N1 MERS Ebola Zika

Country Name Country Code

Mortality Rate

Case/Pop Health Expendi- ture

Mortality Rate

Case/Pop Health Expendi- ture

Mortality Rate

Case/Pop Health Expenditure

Mortality Rate

Case/Pop Health Expenditure

Mortality Rate

Case/Pop Health Expenditure

Mortality Rate

Case/Pop Health Expenditure

Algeria DZA 0 0 N.A. 0 0 1 3 1 2 2 2 2 0 0 2 0 0 1 Ecuador ECU 0 0 N.A. 0 0 2 3 2 2 0 0 2 0 0 2 1 2 1 Egypt, Arab EGY 0 0 N.A. 0 0 1 2 2 2 1 1 2 0 0 2 0 0 2 Eritrea ERI 0 0 N.A. 0 0 2 0 0 2 0 0 N.A. 0 0 N.A. 0 0 N.A. Spain ESP 0 0 N.A. 1 1 2 2 2 1 0 0 1 1 2 1 0 0 1 Estonia EST 0 0 N.A. 0 0 2 3 2 1 0 0 1 0 0 2 0 0 2 Ethiopia ETH 0 0 N.A. 0 0 2 1 1 2 0 0 2 0 0 2 0 0 2 Finland FIN 1 1 N.A. 0 0 1 1 2 1 0 0 2 0 0 1 0 0 1 Fiji FJI 0 0 N.A. 0 0 1 1 3 1 0 0 1 0 0 2 0 0 1 France FRA 2 2 N.A. 3 2 1 3 1 1 2 1 1 0 0 1 0 0 1 Faroe Island FRO 0 0 N.A. 0 0 N.A. 0 0 N.A. 0 0 N.A. 0 0 N.A. 0 0 N.A. Micronesia, FSM 0 0 N.A. 0 0 2 1 3 2 0 0 1 0 0 1 0 0 2 Gabon GAB 0 0 N.A. 0 0 1 1 1 1 0 0 1 0 0 1 0 0 2 United Kingd GBR 3 3 N.A. 1 1 2 2 3 2 3 2 1 1 1 1 0 0 1 Georgia GEO 0 0 N.A. 0 0 2 0 0 2 0 0 2 0 0 2 0 0 2 Ghana GHA 0 0 N.A. 0 0 2 2 1 2 0 0 2 0 0 2 0 0 1 Gibraltar GIB 0 0 N.A. 0 0 N.A. 1 3 N.A. 0 0 N.A. 0 0 N.A. 0 0 N.A. Guinea GIN 0 0 N.A. 0 0 1 0 0 1 0 0 1 3 3 2 0 0 2 Gambia, The GMB 0 0 N.A. 0 0 2 0 0 2 0 0 2 0 0 1 0 0 1 Guinea-Bissa GNB 0 0 N.A. 0 0 1 0 0 1 0 0 1 0 0 2 0 0 1 Equatorial G GNQ 0 0 N.A. 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 Greece GRC 2 2 N.A. 0 0 2 3 2 1 3 2 1 0 0 1 0 0 1 Grenada GRD 0 0 N.A. 0 0 1 1 2 1 0 0 1 0 0 1 1 3 2 Greenland GRL 0 0 N.A. 0 0 N.A. 0 0 N.A. 0 0 N.A. 0 0 N.A. 0 0 N.A. Guatemala GTM 0 0 N.A. 0 0 2 2 2 1 0 0 1 0 0 2 1 2 2 Guam GUM 0 0 N.A. 0 0 N.A. 2 3 N.A. 0 0 N.A. 0 0 N.A. 0 0 N.A. Guyana GUY 0 0 N.A. 0 0 1 1 2 2 0 0 2 0 0 1 1 2 1 Hong Kong SA HKG 1 1 N.A. 3 3 N.A. 0 0 N.A. 0 0 N.A. 0 0 N.A. 0 0 N.A. Honduras HND 0 0 N.A. 0 0 2 2 2 2 0 0 2 0 0 2 1 1 2 Croatia HRV 0 0 N.A. 0 0 2 2 3 1 0 0 1 0 0 1 0 0 1 Haiti HTI 0 0 N.A. 0 0 2 1 1 2 0 0 1 0 0 2 1 1 2 Hungary HUN 3 3 N.A. 0 0 2 3 2 1 0 0 1 0 0 1 0 0 2 Indonesia IDN 0 0 N.A. 1 1 1 2 1 1 0 0 1 0 0 2 0 0 1 India IND 0 0 N.A. 1 1 1 3 2 2 0 0 2 0 0 1 0 0 2 Ireland IRL 0 0 N.A. 1 2 2 2 3 1 0 0 1 0 0 1 0 0 1 Iran, Islami IRN 0 0 N.A. 0 0 2 2 2 2 2 2 2 0 0 N.A. 0 0 N.A. Iraq IRQ 0 0 N.A. 0 0 N.A. 2 2 1 0 0 1 0 0 1 0 0 1 Iceland ISL 0 0 N.A. 0 0 2 2 3 2 0 0 1 0 0 2 0 0 2 Israel ISR 0 0 N.A. 0 0 1 3 3 1 0 0 2 0 0 2 0 0 1 Italy ITA 2 2 N.A. 1 1 1 2 2 1 1 1 1 1 1 1 0 0 1 Jamaica JAM 1 1 N.A. 0 0 1 3 2 1 0 0 1 0 0 1 1 2 2 Jordan JOR 0 0 N.A. 0 0 1 2 3 2 2 3 1 0 0 2 0 0 1 Japan JPN 3 3 N.A. 0 0 1 2 2 1 0 0 1 0 0 1 0 0 1 Kazakhstan KAZ 0 0 N.A. 0 0 2 0 1 2 0 0 2 0 0 1 0 0 2 Kenya KEN 0 0 N.A. 0 0 2 1 1 2 0 0 2 0 0 2 0 0 1 Kyrgyz Repub KGZ 0 0 N.A. 0 0 2 0 0 2 0 0 2 0 0 2 0 0 1 Cambodia KHM 0 0 N.A. 0 0 1 3 1 2 0 0 1 0 0 1 0 0 2 Kiribati KIR 0 0 N.A. 0 0 1 1 2 1 0 0 1 0 0 2 0 0 2 St. Kitts an KNA 0 0 N.A. 0 0 1 3 2 1 0 0 1 0 0 2 1 3 2 Korea, Rep. KOR 0 0 N.A. 1 1 2 3 2 2 2 3 1 0 0 2 0 0 2 Kuwait KWT 0 0 N.A. 1 2 1 2 3 2 2 3 1 0 0 2 0 0 1 Lao PDR LAO 0 0 N.A. 0 0 2 2 2 2 0 0 2 0 0 1 0 0 1 Lebanon LBN 0 0 N.A. 0 0 1 2 3 2 1 2 2 0 0 2 0 0 2

S .11

Disease Severity and Health Expenditure Response Dummy (Cont.)

1968Flu SARS H1N1 MERS Ebola Zika

Country Name Country Code

Mortality Rate

Case/Pop Health Expendi- ture

Mortality Rate

Case/Pop Health Expendi- ture

Mortality Rate

Case/Pop Health Expenditure

Mortality Rate

Case/Pop Health Expenditure

Mortality Rate

Case/Pop Health Expenditure

Mortality Rate

Case/Pop Health Expenditure

Liberia LBR 0 0 N.A. 0 0 1 0 0 2 0 0 2 3 3 2 0 0 1 Libya LBY 0 0 N.A. 0 0 1 2 2 2 0 0 N.A. 0 0 N.A. 0 0 N.A. St. Lucia LCA 0 0 N.A. 0 0 1 2 3 1 0 0 1 0 0 1 1 2 1 Liechtenstei LIE 0 0 N.A. 0 0 N.A. 0 2 N.A. 0 0 N.A. 0 0 N.A. 0 0 N.A. Sri Lanka LKA 0 0 N.A. 0 0 1 3 2 2 0 0 1 0 0 1 0 0 2 Lesotho LSO 0 0 N.A. 0 0 1 1 2 2 0 0 2 0 0 2 0 0 1 Lithuania LTU 0 0 N.A. 0 0 2 3 1 1 0 0 1 0 0 1 0 0 2 Luxembourg LUX 0 0 N.A. 0 0 1 2 3 2 0 0 2 0 0 1 0 0 1 Latvia LVA 0 0 N.A. 0 0 1 0 0 1 0 0 1 0 0 1 0 0 2 Macao SAR, C MAC 0 0 N.A. 1 3 N.A. 0 0 N.A. 0 0 N.A. 0 0 N.A. 0 0 N.A. Morocco MAR 0 0 N.A. 0 0 1 2 2 2 0 0 1 0 0 1 0 0 2 Monaco MCO 0 0 N.A. 0 0 1 0 2 1 0 0 1 0 0 1 0 0 1 Moldova MDA 0 0 N.A. 0 0 2 0 0 2 0 0 2 0 0 2 0 0 1 Madagascar MDG 0 0 N.A. 0 0 1 2 2 1 0 0 1 0 0 2 0 0 2 Maldives MDV 0 0 N.A. 0 0 1 2 2 1 0 0 2 0 0 2 0 0 2 Mexico MEX 0 0 N.A. 0 0 2 2 3 1 0 0 2 0 0 1 0 0 1 Marshall Isl MHL 0 0 N.A. 0 0 1 2 3 1 0 0 2 0 0 1 0 0 2 North Macedo MKD 0 0 N.A. 0 0 1 3 1 1 0 0 1 0 0 1 0 0 2 Mali MLI 0 0 N.A. 0 0 1 1 1 1 0 0 2 3 2 1 0 0 1 Malta MLT 0 0 N.A. 0 0 1 2 3 1 0 0 2 0 0 2 0 0 2 Myanmar MMR 0 0 N.A. 0 0 2 1 1 2 0 0 2 0 0 2 0 0 2 Montenegro MNE 0 0 N.A. 0 0 N.A. 3 2 N.A. 0 0 N.A. 0 0 N.A. 0 0 N.A. Mongolia MNG 0 0 N.A. 1 3 1 2 3 1 0 0 2 0 0 2 0 0 1 Mozambique MOZ 0 0 N.A. 0 0 2 2 1 2 0 0 2 0 0 2 0 0 2 Mauritania MRT 0 0 N.A. 0 0 2 0 0 1 0 0 1 0 0 2 0 0 1 Mauritius MUS 0 0 N.A. 0 0 1 3 2 1 0 0 1 0 0 2 0 0 1 Malawi MWI 0 0 N.A. 0 0 2 1 1 2 0 0 2 0 0 2 0 0 2 Malaysia MYS 0 0 N.A. 3 2 1 3 2 1 3 2 1 0 0 2 0 0 1 Namibia NAM 0 0 N.A. 0 0 2 2 2 1 0 0 2 0 0 2 0 0 1 New Caledoni NCL 0 0 N.A. 0 0 N.A. 2 3 N.A. 0 0 N.A. 0 0 N.A. 0 0 N.A. Niger NER 0 0 N.A. 0 0 1 0 0 2 0 0 1 0 0 1 0 0 1 Nigeria NGA 0 0 N.A. 0 0 2 3 1 1 0 0 2 2 2 1 0 0 1 Nicaragua NIC 0 0 N.A. 0 0 1 2 3 2 0 0 2 0 0 2 1 2 2 Netherlands NLD 3 3 N.A. 0 0 2 3 2 1 1 2 1 0 0 1 0 0 1 Norway NOR 3 3 N.A. 0 0 2 2 3 2 0 0 2 0 0 2 0 0 1 Nepal NPL 0 0 N.A. 0 0 1 2 1 2 0 0 2 0 0 2 0 0 2 Nauru NRU 0 0 N.A. 0 0 N.A. 1 3 1 0 0 2 0 0 2 0 0 2 New Zealand NZL 0 0 N.A. 1 2 1 2 3 2 0 0 1 0 0 2 0 0 2 Oman OMN 0 0 N.A. 0 0 1 2 3 1 2 3 1 0 0 2 0 0 1 Pakistan PAK 0 0 N.A. 0 0 1 0 0 1 0 0 1 0 0 2 0 0 1 Panama PAN 0 0 N.A. 0 0 1 2 2 2 0 0 2 0 0 2 1 2 2 Peru PER 0 0 N.A. 0 0 1 2 3 2 0 0 2 0 0 1 1 1 2 Philippines PHL 0 0 N.A. 3 2 2 2 2 2 1 1 2 0 0 1 0 0 2 Palau PLW 0 0 N.A. 0 0 1 1 3 1 0 0 2 0 0 2 0 0 2 Papua New Gu PNG 0 0 N.A. 0 0 1 1 1 1 0 0 2 0 0 2 0 0 1 Poland POL 0 0 N.A. 0 0 1 3 1 2 0 0 1 0 0 1 0 0 2 Puerto Rico PRI 0 0 N.A. 0 0 N.A. 0 1 N.A. 0 0 N.A. 0 0 N.A. 3 3 N.A. Korea, Dem. PRK 0 0 N.A. 0 0 N.A. 1 1 N.A. 0 0 N.A. 0 0 N.A. 0 0 N.A. Portugal PRT 2 2 N.A. 0 0 1 1 3 1 0 0 1 0 0 1 0 0 2 Paraguay PRY 0 0 N.A. 0 0 2 3 2 2 0 0 2 0 0 2 1 1 2 West Bank an PSE 0 0 N.A. 0 0 N.A. 2 3 N.A. 0 0 N.A. 0 0 N.A. 0 0 N.A. French Polyn PYF 0 0 N.A. 0 0 N.A. 2 3 N.A. 0 0 N.A. 0 0 N.A. 0 0 N.A. Qatar QAT 0 0 N.A. 0 0 2 3 1 1 2 3 1 0 0 2 0 0 1

S .12

Disease Severity and Health Expenditure Response Dummy (Cont.)

1968Flu SARS H1N1 MERS Ebola Zika

Country Name Country Code

Mortality Rate

Case/Pop Health Expendi- ture

Mortality Rate

Case/Pop Health Expendi- ture

Mortality Rate

Case/Pop Health Expenditure

Mortality Rate

Case/Pop Health Expenditure

Mortality Rate

Case/Pop Health Expenditure

Mortality Rate

Case/Pop Health Expenditure

Romania ROU 1 1 N.A. 1 1 2 2 2 1 0 0 1 0 0 1 0 0 2 Russian Fede RUS 0 0 N.A. 1 1 2 2 1 1 0 0 2 0 0 2 0 0 1 Rwanda RWA 0 0 N.A. 0 0 2 1 2 2 0 0 2 0 0 1 0 0 2 Saudi Arabia SAU 0 0 N.A. 0 0 1 2 3 2 2 3 2 0 0 2 0 0 1 Sudan SDN 0 0 N.A. 0 0 2 3 1 2 0 0 2 0 0 2 0 0 1 Senegal SEN 0 0 N.A. 0 0 2 0 0 1 0 0 2 1 2 1 0 0 1 Singapore SGP 0 0 N.A. 3 3 1 2 3 1 0 0 1 0 0 1 0 0 2 Solomon Isla SLB 0 0 N.A. 0 0 2 3 1 1 0 0 1 0 0 2 0 0 1 Sierra Leone SLE 0 0 N.A. 0 0 2 0 0 2 0 0 1 2 3 2 0 0 1 El Salvador SLV 0 0 N.A. 0 0 1 3 2 1 0 0 1 0 0 1 1 1 1 San Marino SMR 0 0 N.A. 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 Somalia SOM 0 0 N.A. 0 0 N.A. 1 1 N.A. 0 0 N.A. 0 0 N.A. 0 0 N.A. Serbia SRB 2 2 N.A. 0 0 2 3 2 1 0 0 2 0 0 1 0 0 1 South Sudan SSD 0 0 N.A. 0 0 N.A. 0 0 N.A. 0 0 N.A. 0 0 N.A. 0 0 N.A. Sao Tome and STP 0 0 N.A. 0 0 2 3 3 2 0 0 2 0 0 1 0 0 2 Suriname SUR 0 0 N.A. 0 0 2 2 2 2 0 0 2 0 0 1 3 3 2 Slovak Repub SVK 0 0 N.A. 0 0 1 3 2 1 0 0 2 0 0 1 0 0 1 Slovenia SVN 0 0 N.A. 0 0 2 3 2 1 0 0 1 0 0 1 0 0 1 Sweden SWE 1 1 N.A. 1 3 1 2 2 1 0 0 1 0 0 2 0 0 1 Eswatini SWZ 0 0 N.A. 0 0 2 1 1 2 0 0 1 0 0 2 0 0 2 Seychelles SYC 0 0 N.A. 0 0 1 1 3 2 0 0 2 0 0 1 0 0 2 Syrian Arab SYR 0 0 N.A. 0 0 2 3 2 N.A. 0 0 N.A. 0 0 N.A. 0 0 N.A. Turks and Ca TCA 0 0 N.A. 0 0 N.A. 0 3 N.A. 0 0 N.A. 0 0 N.A. 1 3 N.A. Chad TCD 0 0 N.A. 0 0 1 1 1 1 0 0 1 0 0 1 0 0 1 Togo TGO 0 0 N.A. 0 0 1 0 0 2 0 0 2 0 0 2 0 0 2 Thailand THA 0 0 N.A. 3 2 1 2 3 1 1 1 1 0 0 1 0 0 1 Tajikistan TJK 0 0 N.A. 0 0 2 0 1 2 0 0 2 0 0 2 0 0 2 Turkmenistan TKM 0 0 N.A. 0 0 2 0 0 1 0 0 2 0 0 2 0 0 2 Timor-Leste TLS 0 0 N.A. 0 0 1 0 1 1 0 0 1 0 0 1 0 0 1 Tonga TON 0 0 N.A. 0 0 1 3 2 1 0 0 2 0 0 2 0 0 2 Trinidad and TTO 0 0 N.A. 0 0 2 2 2 1 0 0 1 0 0 1 1 3 1 Tunisia TUN 0 0 N.A. 0 0 1 2 2 2 2 2 2 0 0 2 0 0 1 Turkey TUR 0 0 N.A. 0 0 2 3 1 1 3 1 1 0 0 2 0 0 2 Tuvalu TUV 0 0 N.A. 0 0 2 1 3 2 0 0 1 0 0 2 0 0 2 Tanzania TZA 0 0 N.A. 0 0 2 2 1 1 0 0 2 0 0 1 0 0 2 Uganda UGA 0 0 N.A. 0 0 2 1 1 2 0 0 2 0 0 1 0 0 1 Ukraine UKR 0 0 N.A. 0 0 2 0 0 2 0 0 2 0 0 1 0 0 2 Uruguay URY 0 0 N.A. 0 0 2 3 2 2 0 0 2 0 0 2 1 1 2 United State USA 3 3 N.A. 1 2 2 3 2 2 1 1 2 2 1 2 1 1 2 Uzbekistan UZB 0 0 N.A. 0 0 2 0 0 2 0 0 2 0 0 2 0 0 2 St. Vincent VCT 0 0 N.A. 0 0 1 1 2 1 0 0 1 0 0 1 1 3 1 Venezuela, R VEN 0 0 N.A. 0 0 2 3 2 2 0 0 1 0 0 2 1 2 2 British Virg VGB 0 0 N.A. 0 0 N.A. 1 2 N.A. 0 0 N.A. 0 0 N.A. 1 3 N.A. Virgin Islan VIR 0 0 N.A. 0 0 N.A. 0 0 N.A. 0 0 N.A. 0 0 N.A. 1 3 N.A. Vietnam VNM 0 0 N.A. 2 3 2 3 1 2 0 0 2 0 0 1 0 0 2 Vanuatu VUT 0 0 N.A. 0 0 1 1 1 1 0 0 1 0 0 1 0 0 1 Samoa WSM 0 0 N.A. 0 0 1 2 3 2 0 0 1 0 0 1 0 0 2 Yemen, Rep. YEM 0 0 N.A. 0 0 2 2 2 1 3 2 2 0 0 1 0 0 N.A. South Africa ZAF 3 3 N.A. 3 1 2 2 3 2 0 0 2 0 0 2 0 0 2 Zambia ZMB 0 0 N.A. 0 0 2 1 1 2 0 0 2 0 0 1 0 0 2 Zimbabwe ZWE 0 0 N.A. 0 0 N.A. 1 1 N.A. 0 0 1 0 0 2 0 0 2

Panel B: Correlation between Disease Severity and Health Expenditure Adjustment

1968Flu SARS H1N1 MERS Ebola Zika

Mortality Rate Case/Pop Mortality Rate Case/Pop Mortality Rate Case/Pop Mortality Rate Case/Pop Mortality Rate Case/Pop Mortality Rate Case/Pop

Health Spending Adjustment N.A. N.A. -0.0003 -0.1219 -0.0893 -0.0502 -0.119 -0.0282 0.1036 0.6779 -0.0128 -0.1313 Significance N.A. N.A. 0.9986 0.5529 0.2706 0.5297 0.5626 0.8911 0.7757 0.0312 0.9425 0.459 Obs N.A. N.A. 26 26 154 159 26 26 10 10 34 34

NOTE: Panel A depicts the severity dummy and health expenditures adjustment dummy, by country and within each disease episode. For the former, we use either mortality rate or case-to- population rate. 0 means unaffected. For the 1968 Flu, 1, 2 and 3 means isolated, regional and widespread. For the health expenditures adjustment dummy, we divide countries into three groups based on the change in health expenditure in the crisis onset year, normalized by the previous year’s GDP. Panel B reports the cross-country correlation between health spending adjustment and the severity measure (mortality rate or cases rate) for each episode in affected countries.

S .13

Table S.8 The Effect of Health Crises on GDP Growth: Trade Linkages (Severity of Crises)

GDP growth rate %

(1) (2) (3) (4) (5) (6)

Sample Period: 1988-2018

Shock -2.22** -1.98** (1.03) (0.97)

Mortality Rate -2.07** -2.40* (0.86) (1.22)

Cases/Pop -2.50*** -1.54*** (0.62) (0.55)

Shock to Trade Partner -0.52** -1.11 -1.04 (0.23) (0.71) (0.65)

Trade Weighted by Indirect Shock -1.00** (0.38)

Trade Weighted by Mortality Rates -0.10 (0.07)

Trade Weighted by Cases/Pop -0.14*** (0.02)

Trade/GDP 0.19 0.17 0.24 0.32 0.23 0.21 (0.33) (0.33) (0.35) (0.38) (0.35) (0.34)

Domestic Credit/GDP -0.73 -0.73 -0.76 -0.76 -0.76 -0.73 (0.46) (0.46) (0.49) (0.49) (0.48) (0.46)

Log(Population) 0.12** 0.11** 0.11** 0.12** 0.11** 0.12** (0.05) (0.05) (0.05) (0.05) (0.05) (0.05)

Log(GDP per capita) -0.20** -0.21** -0.23** -0.22** -0.22** -0.19* (0.09) (0.09) (0.10) (0.10) (0.09) (0.10)

Recession -0.56 -0.57 -0.85* -0.83* -0.79* -0.47 (0.38) (0.38) (0.42) (0.44) (0.39) (0.32)

Banking Crisis -1.54*** -1.54*** -1.45*** -1.44*** -1.46*** -1.52*** (0.37) (0.36) (0.41) (0.43) (0.40) (0.40)

Constant 4.76*** 4.99*** 5.08*** 4.64*** 5.02*** 4.51*** (0.46) (0.51) (0.59) (0.50) (0.56) (0.45)

Observations 4502 4502 4502 4502 4502 4502 Within R2 0.065 0.066 0.051 0.045 0.055 0.061 Decade FE Yes Yes Yes Yes Yes Yes Country FE Yes Yes Yes Yes Yes Yes

NOTE: The dependent variable is the real annual GDP growth rate. Shock dummy equals one for country i at onset year t, and zero otherwise. Shock to trade partner equals to 1 if one of the country’s trading partner is hit by a health crisis, and 0 otherwise. The weight trade network in column (2) is constructed by multiplying the shock to a country’s trading partner dummy by the share of bilateral trade between these two countries in the country’s total trade (Trade weighted by indirect shock). The weight trade network in column column (4) and (6) is constructed by multiplying the trading partner’s ex post mortality rate or cases number per population by the trade share (trade weighted by morality rate and cases to population). Standard errors are corrected using Driscoll and Kraay (1998) and reported in parentheses. ∗, ∗∗ and ∗∗∗ indicate statistical significance at the 10%, 5%, and 1% level, respectively.

S.14

S.3 Consumption and Investment

We first estimate how the consumption and investment components of GDP were affected by past health crises. There are many reasons why a health crisis might lower consumption and investment.S32 For example, with an increase in uncertainty in the economy (see Baker et al. (2020)), people might increase precautionary savings and thus reduce consumption and investment plans. These effects will be even stronger if people expect a negative impact of health crises on future income. The decline in spending could further strengthen the negative impact of crises on the production side and slow down the recovery phase.

Figure S.5 reports the impulse response functions for the growth rates of private con- sumption expenditure and fixed investment. Private consumption growth in affected coun- tries is 2.8% less than for unaffected countries in the onset year, with a 0.1% bounce-back one year later. Perhaps not surprisingly, the drop in fixed investment growth is much larger: 8.3% relative decline in affected countries in the onset year, with a negative 1.0% one year later and a bounce-back only two years later. The sharp and persistent drop in investment and a larger bounce-back two years later is consistent with the observed greater volatility in investment, in this case likely due to the heightened uncertainty accompanying the health shock and recession (Baker et al. (2016)).

The dynamics of consumption and investment behavior during the health crises help us understand the output dynamics. When the outbreak occurs, the negative shock elicits cuts in both consumption and investment expenditures. The effect on consumption is relatively short-lived — when output starts to recover in the first year, consumption resumes. For investment, it takes one more year to recover from the negative shock. Furthermore, the bounce-back in investment is not sufficient to offset the negative impact the health crisis causes. As a result, the health crisis can have a persistent effect on output.

S32Malmendier and Shen (2018) show that personal experiences from negative economic shocks “scar” consumer behavior in the long run. The authors do not directly address health crises per se, but instead show that households who have lived through times of high unemployment spend significantly less on food and total consumption, after controlling for income, wealth, employment, demographics, and the current unemployment rate. Their model of experience-based learning is suggestive of a channel through which a shock like COVID could have persistent effects. Carroll et al. (2020) also study the negative impact of COVID on consumption spending.

S.15

Figure S.5 The Effect of Health Crises on Consumption and Investment

Panel A: Private Consumption Growth Panel B: Fixed Investment Growth -1 0

-7 -4

-1 2

5 P er ce nt

0 1 2 3 4 5 Years

-1 0

-7 -4

-1 2

5 P er ce nt

0 1 2 3 4 5 Years

NOTE: Impulse response functions (IRF) are estimated based on the local projection method as in Jordà (2005): git+H = αHi + ∑

4 s=1 β

H s git−s + ∑

4 s=0 δ

H s Dit−s + Xit + εit ,with H = 0,1,··· ,5, where git is the annual real growth rate of private consumption in Panel A

and fixed investment in Panel B for country i at year t, Dit is a dummy variable indicating a disease event hitting country i in year t, with Xit including country-level controls such as Trade/GDP, Domestic Credit/GDP, population and log GDP per capita. We also include a decade dummy, US recession dummy, a banking crisis dummy and country fixed effects. Standard errors are corrected using Driscoll and Kraay (1998). One standard error bands are shown.

S.4 Recovery in GDP growth: A Higher-frequency Look

Our analysis using annual data and a large sample of countries suggests that bounce-back occurs in the year after the health shock. It is interesting to investigate by how much and how quickly bounce-back occurs using higher frequency data. We have available quarterly GDP data from OECD, though only for 47 countries. See Table S.3 for details. Figure S.6 displays the quarterly GDP growth distribution of affected and unaffected countries side by side. We plot these distributions over three different intervals of three consecutive quarters: (1) from five quarters before to two quarters before onset, (2) centered in the onset quarter, and (3) from three quarters to six quarters after the onset quarter. We choose a three quarter window because the official declaration of a health crisis by WHO tends to be conservative (slow). This consideration does not affect identification in our annual sample nearly as much as it could affect the quarterly identification.S33

The average, annualized growth rate in the three quarter window centered on the health crisis onset is -0.4% for affected countries and 2.8% for unaffected countries. This is in line with our estimates using annual data above. In quarters 2 to 5 before the health crisis, the

S33In addition, note that all countries in the quarterly sample were affected by H1N1, also unlike the annual sample. This weakens identification.

S.16

average growth rate in affected countries is not much different than in unaffected countries, nor is it in quarters 3 to 6 after the health shock. This suggests that the bounce-back of GDP growth is quick. Examining the magnitudes of these comparative responses, however, we see that bounce-back is not sufficient to restore the level of GDP within this time interval, consistent with the results from the annual sample.

We also estimate panel regressions using quarterly GDP growth data. Table S.9 con- firms that our main results hold in the quarterly data. Health crises shocks lower GDP growth in affected countries compared to unaffected countries, with an impact magnitude that is slightly larger than in the annual data. Furthermore, each individual health crisis contributes to this negative effect, with the exception of Ebola (see Table S.10). We also use the high, medium or low severity dummy to replace the shock dummy in Table S.11 or directly weight the health shock by the severity of each health crisis in Table S.12. We find that a more severe health crisis is associated with larger declines in GDP growth. Our last exercise is a placebo test of randomly picking a country-quarter to replace our quar- terly shock dummy, as seen in Table S.13. The insignificant coefficient on the artificially constructed variable suggests that our identification is valid.

S.17

Figure S.6 Quarterly GDP Growth Distribution

0 .0

5 .1

.1 5

D en

si ty

-5 0 5 10

(-5) to (-2) Quarters(Affected Countries) Mean = 2.63

0 .0

5 .1

.1 5

D en

si ty

-5 0 5 10

Onset (-1) to (+1) Quarters(Affected Countries) Mean = -.44

0 .0

5 .1

.1 5

D en

si ty

-5 0 5 10

(+3) to (+6) Quarters(Affected Countries) Mean = 3.36

0 .0

5 .1

.1 5

D en

si ty

-5 0 5 10

(-5) to (-2) Quarters(Unaffected Countries) Mean = 2.91

0 .0

5 .1

.1 5

.2 D

en si

ty

-5 0 5 10

Onset (-1) to (+1) Quarters(Unaffected Countries) Mean = 2.83

0 .0

5 .1

.1 5

.2 D

en si

ty

-5 0 5 10

(+3) to (+6) Quarters(Unaffected Countries) Mean = 3.32

NOTE: The real quarterly year-over-year seasonally adjusted GDP growth rate distribution for the affected and unaffected country groups. 0 represents the quarter when WHO declares a health crisis hits a country.

S .18

Table S.9 The Effect of Health Crises on Real Quarterly GDP Growth

Quarterly GDP growth rate (YoY)%

(1) (2) (3) (4)

Sample Period: 1960-2018 1990-2018

All Events All Events All Events Without H1N1

Shock (Q) -3.73*** -3.80*** -2.32*** -0.98*** (1.23) (1.16) (0.52) (0.23)

Consensus Forecast (Q) 1.37*** 1.35*** (0.22) (0.21)

Trade/GDP 0.03 -0.03 0.57 0.48 (0.79) (0.80) (1.21) (1.16)

Domestic Credit/GDP -1.81*** -1.94*** -1.20 -1.20 (0.56) (0.68) (1.35) (1.33)

Log(Population) -0.25*** -0.31* -0.00 -0.01 (0.09) (0.17) (0.08) (0.08)

Log(GDP per capita) 0.59*** 0.71* 0.08 0.10 (0.18) (0.37) (0.23) (0.22)

Recession -1.48** -1.85* -1.36** -1.29** (0.70) (1.06) (0.61) (0.63)

Banking Crisis (Q) 0.29 0.52 -0.16 -0.26 (1.14) (1.25) (0.90) (0.90)

Constant 3.38*** 3.48*** -1.59 -1.48 (0.81) (1.05) (1.67) (1.63)

Observations 5218 3959 1240 1222 Adjusted R2 0.126 0.108 0.378 0.346 Decade FE Yes Yes Yes Yes Country FE Yes Yes Yes Yes

NOTE: The dependent variable is real quarterly GDP growth rate, annualized. The sample period for column (1) is 1960-2018 while the sample period for column (2)-(4) is 1990-2018. The shock dummy equals one for country i hit by a health crisis at onset year t, and zero otherwise. In columns (1)-(3), we include all six health crises while column (4) excludes H1N1 and the 1968 Flu. Country and decade fixed effects are included. All standard errors are corrected using Driscoll and Kraay (1998) and reported in parentheses. ∗, ∗∗ and ∗∗∗

indicate statistical significance at the 10%, 5%, and 1% level, respectively.

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Table S.10 The Effect of Health Crisis on Real Quarterly GDP Growth, by Crisis

Quarterly GDP growth rate (YoY)%

(1) (2) (3) (4)

Sample Period: 1960-2018 1990-2018

All Events All Events All Events Without H1N1

EBOLA 0.40 0.30 -0.21 -0.21 (0.35) (0.35) (0.26) (0.27)

H1N1 -6.39*** -6.18*** -3.59*** (1.01) (1.24) (0.86)

MERS -0.86*** -0.79*** -0.87*** -0.85*** (0.27) (0.27) (0.24) (0.23)

SARS -1.34*** -1.55*** -1.45*** -1.46*** (0.39) (0.36) (0.28) (0.27)

Zika -2.62*** -2.62*** -0.93*** -0.94*** (0.41) (0.40) (0.27) (0.27)

Hkflu -0.77* (0.44)

Consensus Forecast (Q) 1.34*** 1.35*** (0.22) (0.22)

Trade/GDP 0.01 -0.06 0.53 0.48 (0.78) (0.79) (1.20) (1.16)

Domestic Credit/GDP -1.76*** -1.90*** -1.22 -1.20 (0.56) (0.68) (1.34) (1.33)

Log(Population) -0.25*** -0.32* -0.01 -0.01 (0.09) (0.17) (0.08) (0.08)

Log(GDP per capita) 0.60*** 0.72* 0.09 0.10 (0.18) (0.37) (0.23) (0.22)

Recession -1.36** -1.69 -1.29** -1.31** (0.68) (1.06) (0.61) (0.63)

Banking Crisis (Q) 0.21 0.42 -0.23 -0.26 (1.13) (1.25) (0.90) (0.90)

Constant 3.36*** 3.42*** -1.47 -1.46 (0.83) (1.08) (1.67) (1.63)

Observations 5218 3959 1240 1222 Adjusted R2 0.136 0.120 0.384 0.347 Decade FE Yes Yes Yes Yes Country FE Yes Yes Yes Yes

NOTE: The dependent variable is real quarterly GDP growth rate, annualized. The sample period for column (1) is 1960-2018 while the sample period for columns (2)-(4) is 1990-2018. Country and decade fixed effects are included. All standard errors are corrected using Driscoll and Kraay (1998) and reported in parentheses. ∗, ∗∗ and ∗∗∗ indicate statistical significance at the 10%, 5%, and 1% level, respectively.

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Table S.11 The Effect of Health Crises on Real Quarterly GDP Growth, by Severity

Quarterly GDP growth rate (YoY)%

(1) (2) (3) (4) (5) (6)

Sample Period: 1960-2018 1990-2018 1960-2018 1990-2018

High Mortality Rate -4.77*** -5.09*** -2.72*** (1.36) (1.25) (0.75)

Medium Mortality Rate -5.17*** -4.93*** -3.66*** (1.27) (1.31) (1.06)

Low Mortality Rate -2.45*** -2.60*** -1.24*** (0.88) (0.83) (0.27)

High Cases/Pop -3.65*** -3.82*** -2.56*** (1.20) (1.23) (0.90)

Medium Cases/Pop -4.43*** -4.40*** -2.57*** (1.28) (1.19) (0.47)

Low Cases/Pop -3.02** -3.09*** -1.72*** (1.23) (1.11) (0.40)

Consensus Forecast (Q) 1.36*** 1.37*** (0.22) (0.22)

Trade/GDP 0.05 -0.02 0.56 0.03 -0.03 0.57 (0.80) (0.81) (1.21) (0.79) (0.80) (1.22)

Domestic Credit/GDP -1.80*** -1.93*** -1.23 -1.81*** -1.93*** -1.19 (0.57) (0.68) (1.35) (0.56) (0.68) (1.35)

Log(Population) -0.25*** -0.31* -0.00 -0.25*** -0.31* -0.00 (0.09) (0.17) (0.08) (0.09) (0.17) (0.08)

Log(GDP per capita) 0.59*** 0.71* 0.09 0.60*** 0.72* 0.08 (0.18) (0.37) (0.23) (0.18) (0.37) (0.23)

Recession -1.45** -1.81* -1.33** -1.47** -1.85* -1.36** (0.69) (1.06) (0.60) (0.69) (1.06) (0.61)

Banking Crisis (Q) 0.28 0.50 -0.18 0.29 0.52 -0.16 (1.13) (1.25) (0.89) (1.14) (1.25) (0.90)

Constant 3.36*** 3.46*** -1.57 3.37*** 3.48*** -1.59 (0.81) (1.06) (1.67) (0.81) (1.05) (1.68)

Observations 5218 3959 1240 5218 3959 1240 Adjusted R2 0.128 0.111 0.382 0.126 0.109 0.378 Decade FE Yes Yes Yes Yes Yes Yes Country FE Yes Yes Yes Yes Yes Yes

NOTE: The dependent variable in column (1)-(6) is real quarterly GDP growth rate, annualized. The sample period for columns (1) and (4) is 1960-2018 while the sample period for columns (2)-(3) and (5)-(6) is 1990-2018. Country and decade fixed effects are included. All standard errors are clustered corrected using Driscoll and Kraay (1998) and reported in parentheses. ∗, ∗∗ and ∗∗∗ indicate statistical significance at the 10%, 5%, and 1% level, respectively.

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Table S.12 The Effect of Health Crises on Real Quarterly GDP Growth: Weighted by Severity of Crises

Quarterly GDP growth rate (YoY)%

(1) (2) (3) (4) (5) (6)

Sample Period: 1960-2018 1990-2018 1960-2018 1990-2018

Mortality Rate -4.67* -4.65* -4.33** (2.68) (2.46) (1.66)

Cases/Pop -8.36*** -8.18*** -2.29** (1.67) (2.01) (1.07)

Consensus Forecast (Q) 1.41*** 1.40*** (0.24) (0.24)

Trade/GDP 0.09 0.06 0.70 0.07 0.03 0.69 (0.83) (0.85) (1.30) (0.82) (0.84) (1.31)

Domestic Credit/GDP -1.84*** -1.98*** -1.13 -1.81*** -1.95*** -1.15 (0.59) (0.71) (1.36) (0.58) (0.70) (1.36)

Log(Population) -0.26*** -0.32* -0.01 -0.26*** -0.32* -0.01 (0.09) (0.18) (0.08) (0.09) (0.17) (0.08)

Log(GDP per capita) 0.60*** 0.71* 0.08 0.60*** 0.72* 0.09 (0.18) (0.37) (0.23) (0.18) (0.37) (0.23)

Recession -1.55** -1.98 -1.43** -1.50* -1.90 -1.40** (0.78) (1.20) (0.67) (0.77) (1.18) (0.67)

Banking Crisis (Q) 0.42 0.67 -0.04 0.38 0.62 -0.06 (1.18) (1.32) (0.96) (1.18) (1.31) (0.96)

Constant 3.32*** 3.46*** -1.86 3.31*** 3.43*** -1.82 (0.83) (1.09) (1.80) (0.84) (1.10) (1.79)

Observations 5214 3959 1240 5214 3959 1240 Adjusted R2 0.11 0.08 0.36 0.11 0.09 0.36 Decade FE Yes Yes Yes Yes Yes Yes Country FE Yes Yes Yes Yes Yes Yes

NOTE: The dependent variable in column (1)-(6) is real quarterly GDP growth rate, annualized. The sample period for columns (1) and (4) is 1960-2018 while the sample period for columns (2)-(3) and (5)-(6) is 1990-2018. Country and decade fixed effects are included. All standard errors are clustered corrected using Driscoll and Kraay (1998) and reported in parentheses. ∗, ∗∗ and ∗∗∗ indicate statistical significance at the 10%, 5%, and 1% level, respectively.

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Table S.13 The Effect of Health Crises on Real Quarterly GDP Growth: Placebo Test

Quarterly GDP growth rate (YoY)%

(1) (2) (3) (4)

Sample Period: 1960-2018 1990-2018

All Events All Events All Events Without H1N1

Shock (Q) -0.27 -0.64 0.02 -0.07 (0.46) (0.53) (0.35) (0.32)

Consensus Forecast (Q) 1.42*** 1.35*** (0.24) (0.21)

Trade/GDP 0.10 0.06 0.69 0.49 (0.83) (0.86) (1.30) (1.16)

Domestic Credit/GDP -1.85*** -1.99*** -1.15 -1.20 (0.60) (0.71) (1.37) (1.33)

Log(Population) -0.26*** -0.32* -0.01 -0.01 (0.09) (0.18) (0.08) (0.08)

Log(GDP per capita) 0.60*** 0.72* 0.09 0.10 (0.18) (0.37) (0.24) (0.23)

Recession -1.57* -2.00 -1.44** -1.28** (0.80) (1.22) (0.68) (0.64)

Banking Crisis (Q) 0.45 0.71 -0.03 -0.26 (1.19) (1.33) (0.97) (0.90)

Constant 3.33*** 3.47*** -1.87 -1.50 (0.84) (1.10) (1.81) (1.64)

Observations 5218 3959 1240 1222 Adjusted R2 0.105 0.082 0.358 0.344 Decade FE Yes Yes Yes Yes Country FE Yes Yes Yes Yes

NOTE: The dependent variable in column (1)-(4) is real quarterly GDP growth rate, annualized. The sample period for column (1) is 1960-2018 while the sample period for columns (2)-(4) is 1990-2018. The shock variable is randomly generated. Country and decade fixed effects are included. All standard errors are clustered corrected using Driscoll and Kraay (1998) and reported in parentheses. ∗, ∗∗ and ∗∗∗ indicate statistical significance at the 10%, 5%, and 1% level, respectively.

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