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### Introduction and context
- Social unrest (civil disorder score from ICRG, sign flipped so higher = more unrest) increased by about 10 percent (or one standard deviation) since 2009.
- Popular protests in 2019 occurred in France, Greece, Hong Kong, India, Chile, Colombia, Bolivia, Iran, and Iraq; common drivers include stagnating living standards and inequality.
- COVID-19 produced a "Great Lockdown" and triggered the worst recession since the Great Depression; lockdowns caused surging unemployment and plunging labor force participation with losses concentrated in lower-wage industries and among women and youth.
- Research questions: Do pandemics lead to more social unrest? If so, through what channels?

### Methodology
- Two complementary econometric approaches applied to a sample of 133 countries from 2001 to 2018:
  - Local projection method (Jordà, 2005) with monthly data; model estimated for each horizon k = 0,...,60 with monthly data. Baseline monthly specification: y_{i,t+k} − y_{i,t−1} = α_{i,k} + β_k D_{i,t} + θ_k X_{i,t} + ε_{i,t+k}, where X includes 1 to 24-month lags of the dependent variable. Standard errors clustered at the country level.
  - Panel VAR (annual) with 133 countries, annual data 2001-2018. Baseline panel VAR equation: Y_{it} = Y_{it−1} A1 + Y_{it−2} A2 + Y_{it−3} A3 + D_{it} B + u_i + e_{it}, where Y_{it} is the (1×3) vector: real GDP growth, change in inequality (net Gini), and civil disorder. Pandemic dummy treated as exogenous. Baseline Cholesky ordering (most to least exogenous): real GDP growth, disposable income Gini, civil disorder. Confidence bands: 90 percent, Gaussian approximation with 200 Monte Carlo draws.

### Main empirical findings
- Timing and magnitude:
  - Local projections: social unrest increases about 14 months after pandemics on average; direct effect peaks in about 24 months post-pandemic.
  - Local projections: five years after the pandemic, civil disorder is about 0.1 point higher than pre-shock level (civil disorder scores range 0 to 4). The 0.1 increase corresponds to approximately ¼ standard deviation of the average change of civil disorder score and represents a seven percent increase from the mean of the sample.
  - Panel VAR: confirms social unrest increases significantly one year after the pandemic event. Five years after the pandemic, civil disorder score goes up by 0.5 (out of 4), which is about one standard deviation of the average change of the civil disorder score and represents an 18 percent increase from the mean of the sample.
- Output effects:
  - Immediate: output lowers by 4.6 percent in the pandemic year.
  - Five years after the pandemic, cumulative output loss is still about 10 percent.
- Inequality effects:
  - Net Gini increases significantly two years after pandemic events; persistent effect.
  - Five years after the pandemic, net Gini goes up by 0.3, about one standard deviation of the average change of net Gini in the sample.
  - Contextual comparison: Furceri et al (2020) reported net Gini increases by about 1.2 percent after pandemics over the medium term.
- Feedback and persistence:
  - Panel VAR shows a reinforcing cycle: lower growth and higher inequality contribute to elevated social unrest, which in turn is followed by higher inequality and lower output, making impacts persistent beyond two years. Local projections estimate the direct effect flattens after two years.

### Transmission channels and dynamics
- Identified channels:
  - Lower economic growth and greater inequality (net Gini) are the primary channels through which pandemics increase social unrest.
- Shock responses:
  - A one standard deviation increase in net Gini leads to a significant increase in civil disorder.
  - A negative growth shock leads to a substantial and persistent increase in civil disorder.
- Vicious cycle:
  - Higher social unrest → higher inequality and lower output.
  - Higher growth → lower net Gini (persistent effect).
  - Weaker growth and higher inequality can be associated with more social unrest, creating potential self-reinforcing dynamics.

### Robustness checks and alternative specifications
- Cholesky ordering: impulse responses of net Gini and growth on civil disorder robust to alternative orderings; responses lie within baseline 90 percent confidence bands for most reorderings.
- Time fixed effects and time trend: baseline excludes year fixed effects because a pandemic dummy would be absorbed when pandemics are widely spread (e.g., H1N1); results with year fixed effects or time trend are broadly similar but less precise and lie within baseline confidence bands.
- Controls and exclusions: results robust when controlling for global conditions (US growth rate and oil prices), excluding H1N1, or including unemployment as an additional channel.
- Pandemic intensity proxy: using log(deaths) as intensity proxy yields that a one unit increase in log(deaths) is associated with a 0.2 increase in civil disorder five years after a pandemic (about ½ standard deviation). Magnitude not directly comparable to baseline pandemic-dummy impulse responses.

### Policy recommendations
- Avoid scarring effects on the livelihoods of the least advantaged to reduce post-pandemic social unrest risk.
- Recommended measures include:
  - Unemployment benefits.
  - Improved health benefits, such as sick leaves.
  - Cash transfers where informality is pervasive.
  - Address longstanding inequalities in access to health and basic services, finance, and the digital economy.
  - Enhance social protection for informal workers.
- Without swift and bold policies, there is a heightened risk of social unrest post COVID-19.

### Directions for future research
- Heterogeneity: effects likely vary across countries depending on development level, institutional quality, and response capacity.
- Nonlinearities: relationships among pandemics, growth, inequality, and unrest could be nonlinear; effects of inequality on unrest may be stronger when initial income inequality is high and when redistributive transfers are low.

### Key statistics and sample characteristics
- Sample: 133 countries, 2001-2018.
- Civil (Dis)order (ICRG): Obs 2030, Mean 2.7, Std. Dev. 0.59.
- Real GDP growth (WEO (2020)): Obs 2030, Mean 3.90, Std. Dev. 4.10.
- Net gini (SWIID 8.2): Obs 1904, Mean 43.8, Std. Dev. 8.47.
- Number of Confirmed Cases (Furceri et al. (2020)): Obs 1971, Mean 341282638.
- Number of deaths (Furceri et al. (2020)): Obs 1971, Mean 13157.
- Total Pandemic and Epidemic Events: 220.
  - 2003 SARS — Number of countries: 27.
  - 2009 H1N1 — Number of countries: 148.
  - 2012 MERS — Number of countries: 22.
  - 2014 Ebola — Number of countries: 5.
  - 2016 Zika — Number of countries: 18.

*Source: wpiea2020216-print-pdf*

### References .............................................................................................................

### I. INTRODUCTION

### Social unrest trends and recent events
- Social unrest, measured by the civil disorder score from International Country Risk Guide (ICRG), increased by about 10 percent (or one standard deviation) since 2009.
- Popular protests in 2019 occurred in France, Greece, Hong Kong, India, Chile, Colombia, Bolivia, Iran, and Iraq; triggers ranged from rising transport costs to higher fuel prices, with a common theme of stagnating living standards and inequality.
- Social unrest has decreased in recent months as mobility declined during COVID-19, with notable exceptions including recent protests in the United States, worldwide protests against police brutality and systemic racism, and protests in Lebanon.

### COVID-19 and socioeconomic impacts
- The COVID-19 pandemic produced a "Great Lockdown" and triggered the worst recession since the Great Depression (IMF, 2020a; Deb et al. 2020).
- Lockdown measures caused surging unemployment and plunging labor force participation, with job losses concentrated in lower-wage industries and among women and youth—indicating early signs of worsening distributional outcomes.

### Research questions and empirical approach
- Two main questions: Do pandemics lead to more social unrest? If so, what are the key channels?
- Two complementary econometric approaches applied to a sample of 133 countries from 2001 to 2018:
  - Local projection method (Jordà, 2005) using monthly data to estimate the impulse response of social unrest to pandemics.
  - Panel vector autoregressions (panel VAR) using annual data to explore channels, focusing on inequality (Gini coefficient) and economic growth.

### Key empirical findings
- Local projections: social unrest increases about 14 months after pandemics on average; the direct effect peaks in about 24 months post-pandemic.
- Panel VAR: confirms social unrest increases a year after pandemics; suggests pandemics contribute to social unrest by lowering economic growth and increasing inequality (Gini coefficient).
- Persistence: the effect of pandemics on social unrest is more persistent in the panel VAR than in local projections, driven by feedback among lower growth, higher inequality, and elevated social unrest forming a reinforcing cycle.

### Data and variables
- Social unrest measure: civil disorder score from ICRG (original range 0 to 4, higher = less disorder); authors flip the sign so an increase indicates higher social unrest. Civil disorder variable available for 133 countries from 2001 to the present.
- Inequality: Gini coefficient from the Standardized World Income Inequality Database (SWIID 8.2).
- Other macro variables (including real GDP growth): World Development Indicators and IMF’s World Economic Outlook database.
- Pandemics studied (following Ma et al. 2020 and Furceri et al. 2020): SARS in 2003, H1N1 in 2009, MERS in 2012, Ebola in 2014, and Zika in 2016.
  - H1N1: more than 6,000,000 confirmed cases across 148 countries and about 19,000 fatalities.
  - Ebola and MERS had the highest average mortality rates (deaths/confirmed cases), followed by SARS, Zika, and H1N1.
- Pandemic event variable: dummy = 1 when WHO declares a pandemic for the country, constructed at monthly and yearly frequency.

### Econometric specification (local projection)
- Baseline local projection equation (Jordà, 2005):
  - y_{i,t+k} - y_{i,t-1} = α_{i,k} + β_k D_{i,t} + θ_k X_{i,t} + ε_{i,t+k}
  - where y_{i,t} is social unrest (negative of ICRG civil disorder rating, sign flipped), α_{i,k} are country fixed effects, D_{i,t} is pandemic dummy, X_{i,t} includes 1 to 24-month lags of the dependent variable.
- Identification: baseline does not control for other month-varying factors because pandemic events are treated as unpredictable and exogenous; high-frequency data reduces omitted variable bias risk.
- Equation estimated for an unbalanced panel of 133 countries over 2001–2018.

### Relation to existing literature
- Contributes to literature on socio-economic effects of pandemics showing large and persistent effects on economic activity (Atkeson 2020; Barro et al. 2020; Deb et al. 2020; Eichenbaum et al. 2020; Gonzalez-Torres and Esposito, 2020; IMF 2020c; Jordà et al. 2020; Ma et al. 2020; Furceri et al. 2020).
  - Ma et al. (2020): real GDP significantly lower in the year outbreak is declared and remains below pre-shock level five years later.
  - Furceri et al. (2020): major epidemics led to persistent increases in the Gini coefficient, raised income share of higher-income deciles, and lowered employment-to-population ratio for those with basic education relative to higher education.
  - Gonzalez-Torres and Esposito (2020): Ebola led to >40 percent increase in civil violence one year later in Western Africa.
  - IMF (2020d) finds no significant short-term effects of pandemics on social unrest; Li and Coppo (2020) find severe epidemics (mortality above 75 percentile) increase risks of riots and anti-government demonstration in the medium term.
- Connects to literature on economic drivers of social unrest (Miguel, Satyanath, and Sergenti 2004; Vassallo 2019) and debates on inequality:
  - Grievance theory predicts higher inequality → more social unrest.
  - Relative power theory predicts higher inequality → less participation in non-violent protest by the poor.
  - Empirical result here: greater inequality is associated with more social unrest on average; related work (Saadi Sedik and Xu, forthcoming) shows nonlinear effect—stronger when initial income inequality is high.

*Source: wpiea2020216-print-pdf - References*

### 2018. We estimate the model for each horizon k=0,...,60 with monthly data. The database

### wpiea2020216-print-pdf - 2018

### Methodology: Local Projections and Monthly Data
- Model estimated for each horizon k = 0,...,60 with monthly data.
- Database comprises monthly civil disorder scores from ICRG and pandemic events identified on a monthly basis.
- Impulse response functions computed using estimated coefficients 훽훽 푘푘; confidence bands obtained from estimated standard errors of 훽훽 푘푘 using robust standard errors clustered at the country level.
- Local projection specification (monthly) used: 푦푦 푖푖,푡푡+푘푘 − 푦푦 푖푖,푡푡−1 = 훼훼 푖푖 푘푘 + 훽훽 푘푘 퐷퐷 푖푖,푡푡 + 휃휃 푘푘 푋푋 푖푖,푡푡 + 휀휀 푖푖,푡푡+푘푘
  - 푦푦 푖푖,푡푡 is the civil disorder rating (high score = more civil disorder).
  - 퐷퐷 푖푖,푡푡 is a pandemic-event dummy for country i in month t.
  - 푋푋 푖푖,푡푡 includes 1 to 24-month lags of the dependent variable.
  - Standard errors clustered at the country level.
- Local projections estimate direct effect of pandemics on social unrest but do not identify channels or feedback; hence a panel VAR (annual) is used to explore channels.

### Panel VAR Model (Annual)
- Panel VAR estimated with annual data because Gini and economic growth not available monthly.
- Equation (2) specification: 푌푌 푖푖푡푡 = 푌푌 푖푖푡푡−1 퐴퐴 1 + 푌푌 푖푖푡푡−2 퐴퐴 2 + 푌푌 푖푖푡푡−3 퐴퐴 3 + 퐷퐷 푖푖푡푡 퐵퐵 + 푢푢 푖푖 + 푒푒 푖푖푡푡
  - 푌푌 푖푖푡푡 is a (1×3) vector: real GDP growth, change in inequality (net Gini), and civil disorder score.
  - All three dependent variables pass the unit-root test for stationarity.
  - Panel: 133 countries, annual data from 2001-2018.
  - 퐷퐷 푖푖푡푡 is the pandemic-event dummy; 푢푢 푖푖 denotes country fixed effect.
- Identification and inference:
  - Cumulative orthogonalized impulse responses plotted.
  - Confidence bands (90 percent) estimated using Gaussian approximation based on 200 Monte Carlo draws from fitted panel VAR.
  - Baseline Cholesky ordering (most to least exogenous): real GDP growth, disposable income Gini, civil disorder.
  - December civil disorder score used to reduce contemporaneous impact on growth and inequality.
  - Pandemic dummy assumed exogenous.
  - Robustness to alternative orderings examined.

### Main Finding: Social Unrest Increases after Pandemics
- From monthly local projections (Figure 2):
  - Pandemic events lead to significantly higher risk of civil disorder after 14 months.
  - Five years after the pandemic, civil disorder is about 0.1 point higher than the pre-shock level.
  - Civil disorder scores range between 0 and 4.
  - The 0.1 increase corresponds to approximately ¼ standard deviation of the average change of civil disorder score in the sample.
  - The impact represents a seven percent increase from the mean of the sample.
- Panel VAR confirms monthly result:
  - Risk of civil disorder increases significantly one year after the pandemic event.
  - Five years after the pandemic, civil disorder score goes up by 0.5 (out of 4), which is about one standard deviation of the average change of the civil disorder score.
  - The 0.5 increase represents 18 percent increase from the mean of the sample.

### Transmission Channels: Growth and Inequality
- Inequality and output responses to pandemics (panel VAR, Figure 4):
  - Net Gini increases significantly two years after pandemic events; effect is persistent.
  - Five years after the pandemic, net Gini goes up by 0.3, about one standard deviation of the average change of net Gini in the sample.
  - The marginal impact on net Gini remains significant after five years.
  - Furceri et al (2020) reported net Gini increases by about 1.2 percent after pandemics over the medium term (contextual comparison).
- Output:
  - Immediate effect: output lowers by 4.6 percent in the pandemic year.
  - Five years after the pandemic, cumulative output loss is still about 10 percent.
- Causal channel interpretation:
  - Greater inequality and lower growth are identified as two channels through which pandemics increase social unrest.
  - Panel VAR captures feedback loops that make impacts persistent beyond two years, unlike local projections where direct impact flattens.

### Additional Dynamics: Shock Effects on Civil Disorder
- Effects of shocks on civil disorder (Figure 5):
  - A one standard deviation increase in net Gini leads to a significant increase in civil disorder.
  - A negative growth shock leads to a substantial and persistent increase in civil disorder.
- Vicious cycle (Figures 6 and 7):
  - Increase in social unrest is followed by higher inequality and lower output.
  - Higher growth is associated with lower net Gini; the impact is persistent.
  - Weaker growth and higher inequality can be associated with more social unrest, creating a potential vicious cycle.

### Robustness Checks
- Ordering: Impulse responses of net Gini and growth on civil disorder robust to alternative Cholesky orderings.
- Year fixed effects:
  - Baseline excludes time-fixed effects because pandemic dummy would be absorbed when pandemics are widely spread (e.g., H1N1).
  - Results with time fixed effects broadly similar but less precise.
- Controls and exclusions:
  - Results robust when controlling for time trend, global conditions proxied by US growth rate and oil prices, or excluding H1N1.
  - Including unemployment as additional channel yields consistent results.
- Pandemic intensity proxy:
  - Using log(deaths) at each pandemic event (per Furceri et al. (2020)) as intensity proxy: a one unit increase in log(deaths) is associated with a 0.2 increase in civil disorder five years after a pandemic (about ½ standard deviation).
  - Magnitude not directly comparable to baseline pandemic-dummy impulse responses.

### Conclusions and Policy Recommendations
- Summary conclusions:
  - Past pandemics have significantly contributed to social unrest via depressed economic growth and increased inequality.
  - Pandemics tend to depress economic growth and increase inequality; both lower growth and greater inequality are important drivers of social unrest.
  - Social unrest is associated with output loss and higher inequality, implying a potential vicious cycle.
  - Without swift and bold policies, there is a heightened risk of social unrest post COVID-19.
- Policy recommendations:
  - Policymakers should focus on preventing scarring effects on the livelihoods of the least advantaged.
  - Recommended measures include:
    - Unemployment benefits.
    - Improved health benefits, such as sick leaves.
    - Cash transfers where informality is pervasive.
    - Addressing longstanding inequalities in access to health and basic services, finance, and the digital economy.
    - Enhancing social protection for informal workers.
- Directions for future research:
  - Heterogeneity: effects likely vary across countries depending on development level, institutional quality, and response capacity.
  - Nonlinearities: relationships among pandemics, growth, inequality, and unrest could be nonlinear; effects of inequality on unrest may be stronger when initial inequality is high and when redistributive transfers are low.

*Source: wpiea2020216-print-pdf - 2018 (IMF working paper excerpt).*

### REFERENCES

### REFERENCES

### Key cited works
- Ahir, Hites; Nicholas Bloom; Davide Furceri (2020), “Global Uncertainty Related to Coronavirus at Record High”, IMF Blog, April 2020.
- Alesina, A., G., Tabellini, F. Trebbi (2017). “Is Europe an Optimal Political Area?”, NBER Working Paper 23325.
- Atkeson, Andrew (2020), “What Will Be the Economic Impact of COVID-19 in the US? Rough Estimates of Disease Scenarios”, NBER Working Paper 26867.
- Barro, Robert J.; José F. Ursua; Joanna Weng (2020), “The Coronavirus and the Great Influenza Pandemic: Lessons from the "Spanish Flu" for the Coronavirus's Potential Effects on Mortality and Economic Activity”, NBER Working Paper 26866.
- Boix, Carles (2008), “Economic Roots of Civil Wars and Revolutions in the Contemporary World”, World Politics, 60(3):390-437.
- Buhaug, Halvard; Lars-Erik Cederman; Kristian Skrede Gleditsch (2014), “Square Pegs in Round Holes: Inequalities, Grievances, and Civil War”, International Studies Quarterly, 58(2):418-431.
- Dabla-Norris and Rhee (2020), “A “New Deal” for Informal Workers in Asia”, IMF Blog, April 30.
- Deb, P.; Furceri, D.; Ostry, J.D.; Tawk, N. (2020b). The Economic Effects of COVID-19 Containment Measures. IMF Working Paper No. 20/158.
- Furceri, D.; P. Loungani; J. D. Ostry (2020), “How Pandemics Leave the Poor Even Farther Behind”. IMF Blog, May 11.
- Eichenbaum, Martin; Sergio Rebelo; Mathias Trabandt (2020), “The Macroeconomics of Epidemics”, NBER Working Paper 26882.
- Furceri, Davide; Prakash Loungani; Jonathan D. Ostry; Pietro Pizzuto (2020), “Will Covid-19 affect inequality? Evidence from past pandemics”, CEPR Covid Economics, Issue 12, May 1.
- Georgieva, K (2020), “Reduce inequality to create opportunity,” IMF Blog, January 7.
- Global Peace Index (2020), Institute for Economics and Peace.
- Gonzalez-Torres, A.; Esposito, E. (2020). Epidemics and conflict: Evidence from the Ebola outbreak in Western Africa. Working paper.
- Gottlieb, C.; J. Grobovsek; M. Poschke (2020), “Working from Home across Countries,” CEPR Covid Economics 8.
- Gurr, Ted Robert (1970), “Why Men Rebel”. Princeton: Princeton University Press.
- Hadzi-Vaskov, Metodij; Samuel Pienknagura Loor; Luca Antonio Ricci (forthcoming), “The Macroeconomic Impact of Social Unrest”, IMF Working Paper (forthcoming), Washington, DC.
- IMF, (2020a), “The Great Lockdown”, World Economic Outlook, April.
- IMF, (2020b), Chapter 1, World Economic Outlook, October.
- IMF, 2020c. Chapter 2. The Great Lockdown: Dissecting the Economic Effects. World Economic Outlook, October.
- IMF, 2020d. October 2020 World Economic Outlook, Box 1.4.
- Jordà, O. (2005), “Estimation and inference of impulse responses by local projections”, American Economic Review, 95, 161–182.
- Jordà, Òscar; Sanjay R. Singh; Alan Taylor (2020), “Longer-Run Economic Consequences of Pandemics”, Federal Reserve Bank of San Francisco Working Paper.
- Justino, Patricia; Bruno Martorano (2016), “Inequality, Distributive Beliefs and Protests: A Recent Story from Latin America”, IDS Working Paper 467.
- Li Nan; Mattia Coppo (2020). Severe Epidemics in Modern History: Growth, Debt and Civil Unrest. IMF’s Special Series on COVID-19.
- Lipsky, Michael (1968), “Protest as a Political Resource.” American Political Science Review, 62(4):1144–58.
- Ma, C.; J. Rogers; S. Zhou (2020) “Global Financial Effects”. CEPR Covid Economics 5.
- Miguel, Edward; Shanker Satyanath; Ernest Sergenti (2004), “Economic Shocks and Civil Conflict: An Instrumental Variables Approach”, Journal of Political Economy, 112(4):725-753.
- Rodrik, Dani (1998) “Where Did All The Growth Go? External Shocks, Social Conflict, and Growth Collapses”, Journal of Economic Growth, Vol 4: 358-412.
- Saadi Sedik Tahsin; Rui Xu. “Pandemics and Social Unrest: When Inequality Becomes Intolerable”. IMF Working Paper (Forthcoming).
- Solt, Frederick (2008), “Economic Inequality and Democratic Political Engagement”, American Journal of Political Science, 52(1):48-60.
- Vassallo, Francesca (2019), “After the crisis: political protest in the aftermath of the economic recession”, Comparative European Politics (2020) 18:45-72.

### Appendix — Data Sources and Descriptive Statistics (Table A1)
- Main sample: 133 countries, 2001-2018
- Variables and summary statistics:
  - Civil (Dis)order: Source ICRG, Obs 2030, Mean 2.7, Std. Dev. 0.59
  - Real GDP growth: Source WEO (2020), Obs 2030, Mean 3.90, Std. Dev. 4.10
  - Net gini: Source SWIID 8.2, Obs 1904, Mean 43.8, Std. Dev. 8.47
  - Number of Confirmed Cases: Source Furceri et al. (2020), Obs 1971, Mean 341282638
  - Number of deaths: Source Furceri et al. (2020), Obs 1971, Mean 13157

### Appendix — List of Pandemic and Epidemic Episodes (Table A2)
- Total Pandemic and Epidemic Events: 220
- 2003 SARS — Number of countries: 27
  - Affected countries listed: AUS, CAN, CHE, CHN, DEU, ESP, FRA, GBR, HKG, IDN, IND, IRL, ITA, KOR, MNG, MYS, NZL, PHL, ROU, RUS, SGP, SWE, THA, TWN, USA, VNM, ZAF
- 2009 H1N1 — Number of countries: 148
  - Affected countries listed include AFG, AGO, ALB, ARG, ARM, AUS, AUT, BDI, BEL, BGD, BGR, BHS, BIH, BLR, BLZ, BOL, BRA, BRB, BTN, BWA, CAN, CHE, CHL, CHN, CIV, CMR, COD, COG, COL, CPV, CRI, CYP, CZE, DEU, DJI, DMA, DNK, DOM, DZA, ECU, EGY, ESP, EST, ETH, FIN, FJI, FRA, FSM, GAB, GBR, GEO, GHA, GRC, GTM, HND, HRV, HTI, HUN, IDN, IND, IRL, IRN, IRQ, ISL, ISR, ITA, JAM, JOR, JPN, KAZ, KEN, KHM, KNA, KOR, LAO, LBN, LCA, LKA, LSO, LTU, LUX, LVA, MAR, MDA, MDG, MDV, MEX, MKD, MLI, MLT, MNE, MNG, MOZ, MUS, MWI, MYS, NAM, NGA, NIC, NLD, NOR, NPL, NZL, PAK, PAN, PER, PHL, PLW, PNG, POL, PRI, PRT, PRY, QAT, ROU, RUS, RWA, SAU, SDN, SGP, SLB, SLV, STP, SVK, SVN, SWE, SWZ, SYC, TCD, THA, TJK, TON, TUN, TUR, TUV, TZA, UGA, UKR, URY, USA, VEN, VNM, VUT, WSM, YEM, ZAF, ZMB, ZWE
- 2012 MERS — Number of countries: 22
  - Affected countries listed: AUT, CHN, DEU, EGY, FRA, GBR, GRC, IRN, ITA, JOR, KOR, LBN, MYS, NLD, PHL, QAT, SAU, THA, TUN, TUR, USA, YEM
- 2014 Ebola — Number of countries: 5
  - Affected countries listed: ESP, GBR, ITA, LBR, USA
- 2016 Zika — Number of countries: 18
  - Affected countries listed: ARG, BOL, BRA, CAN, CHL, COL, CRI, DOM, ECU, HND, LCA, PAN, PER, PRI, PRY, SLV, URY, USA
- Source: Based on Ma and others (2020).

### Appendix — Impulse Response Figures (Figures A1–A7): Estimation notes and robustness checks
- General estimation setup (applies across figures):
  - Model: Panel VAR estimated from Equation (2).
  - Sample: 133 countries over 2001-2018.
  - Endogenous variables (baseline): real growth, change in net Gini, and civil disorder.
  - Pandemic dummy: exogenous covariate in the panel VAR.
  - Controls: Country fixed effects are controlled for and standard errors are clustered at the country level.
  - Confidence bands: 90 percent confidence bands estimated using Gaussian approximation based on 200 Monte Carlo draws from the fitted panel VAR model.
  - X-axis for all figures: years after pandemic events, with t=0 being the year of the pandemic event.
  - Estimates based on orthogonalized impulse response functions.

- Figure A1. Impulse Response Functions with Different Ordering of Variables
  - The black line and shaded area show baseline responses and 90 percent confidence bands.
  - Dashed lines show impulse responses for five alternative Cholesky orderings; they lie within the confidence bands of the baseline effect.
  - For growth, impulse responses are outside the confidence band when civil disorder is ordered first, but they are statistically significant with 90 percent confidence bands.
  - Comment: Different impulse response sizes may reflect use of December rating of civil disorder, which should not affect growth contemporaneously.

- Figure A2. Impact of Pandemics on Civil Disorder (with year FE)
  - Blue line: impulse response with year fixed effects; it lies within the baseline confidence bands.

- Figure A3. Impact of Pandemics on Civil Disorder (with time trend)
  - Blue line: impulse response with a time trend; it lies within the baseline confidence bands.

- Figure A4. Impact of Pandemics on Civil Disorder (without H1N1)
  - Blue line: impulse response excluding H1N1 from pandemic episodes; it lies within the baseline confidence bands.

- Figure A5. Impact of Pandemics on Civil Disorder (with global controls)
  - Blue line: impulse response including global controls (US growth rate and oil prices); it lies within the baseline confidence bands.

- Figure A6. Impact of Pandemics on Civil Disorder (with unemployment)
  - Model variant: four endogenous variables — real growth, change in unemployment rate, change in net Gini, and civil disorder.
  - Blue line: impulse response including changes in unemployment rate as an additional endogenous variable; it lies within the baseline confidence bands.

- Figure A7. Impact of Pandemic Fatality on Civil Disorder
  - Feature: logarithm of pandemic-related deaths used as an exogenous covariate in the panel VAR.
  - Responses shown with 90 percent confidence bands using Gaussian approximation based on 200 Monte Carlo draws.

*Source: wpiea2020216-print-pdf - REFERENCES*

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_Source: https://www.imf.org/-/media/files/publications/wp/2020/english/wpiea2020216-print-pdf.pdf_