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Davide Furceri, Mr. Prakash Loungani, Mr. Jonathan David Ostry, and Pietro Pizzuto

and recessions on E/P ratio – basic education Table A1. Data Sources and Descriptive Statistics Table A2. List of Pandemic and Epidemic Episodes Table A3. List of Pandemic and Epidemic Episodes Table A4. Impact of pandemics on market Gini and net Gini coefficients Table A5. Pandemic Dummy Regression, Pooled Probit Estimator (average marginal effects) Table A6. Average Treatment Effect of Pandemics, AIPW Estimates Table A7. F-tests difference Table A8. Pandemics and inequality – IV first stage ANNEX B

Johannes Emmerling, Davide Furceri, Francisco Líbano Monteiro, Mr. Prakash Loungani, Mr. Jonathan David Ostry, Pietro Pizzuto, and Massimo Tavoni
COVID-19 has had a disruptive economic impact in 2020, but how long its impact will persist remains unclear. We offer a prognosis based on an analysis of the effects of five previous major epidemics in this century. We find that these pandemics led to significant and persistent reductions in disposable income, along with increases in unemployment, income inequality and public debt-to-GDP ratios. Energy use and CO2 emissions dropped, but mostly because of the persistent decline in the level of economic activity rather than structural changes in the energy sector. Applying our empirical estimates to project the impact of COVID-19, we foresee significant scarring in economic performance and income distribution through 2025, which be associated with an increase in poverty of about 75 million people. Policy responses more effective than those in the past would be required to forestall these outcomes.
Johannes Emmerling, Davide Furceri, Francisco Líbano Monteiro, Mr. Prakash Loungani, Mr. Jonathan David Ostry, Pietro Pizzuto, and Massimo Tavoni

Table A2. List of Pandemic and Epidemic Episodes Table A3. The social, economic and environmental effects of pandemics – Baseline estimates Table A4. Instrumental Variable results.

Davide Furceri, Mr. Prakash Loungani, Mr. Jonathan David Ostry, and Pietro Pizzuto
This paper provides evidence on the impact of major epidemics from the past two decades on income distribution. The pandemics in our sample, even though much smaller in scale than COVID-19, have led to increases in the Gini coefficient, raised the income share of higher-income deciles, and lowered the employment-to-population ratio for those with basic education compared to those with higher education. We provide some evidence that the distributional consequences from the current pandemic may be larger than those flowing from the historical pandemics in our sample, and larger than those following typical recessions and financial crises.
Davide Furceri, Mr. Prakash Loungani, Mr. Jonathan David Ostry, and Pietro Pizzuto

affects country i in year t . X i,t is a vector that includes two la gs of the dependent variable and two lags of the pandemic dummy. See Table A2 - A3 in the Online Appendix for the full list of pandemic events. The Online Appendix reports our robustness checks. First, we check the sensitivity of our results to alternative measures of inequality, such as the market Gini from SWIID and the Ginis from the World Bank POVCAL database—which covers the period 1978–2017 and includes 171 countries (1711 observations)—and the World Institute for Development Research

Johannes Emmerling, Davide Furceri, Francisco Líbano Monteiro, Mr. Prakash Loungani, Mr. Jonathan David Ostry, Pietro Pizzuto, and Massimo Tavoni

average pandemic (0.80 cases per 1000). Shaded areas indicate 90% (95% lighter) confidence intervals. See Table A2 in the Appendix for the full list of pandemic events Figure 3. Impulse response functions of past pandemics on four energy and climate macro variables Notes: Estimates based on equation (2). The x-axis shows years ( k ) after pandemic events; t = 0 is the year of the pandemic event. Dotted lines indicate the dynamic effect of an increase in the number of infections equivalent to the size of an average pandemic (0.80 cases per 1000). Shaded

Tahsin Saadi Sedik and Rui Xu

indicates more civil disorder; α t are country fixed effects; D i,t is a dummy variable indicating a pandemic event that affects country i in month t. X i,t is a vector that includes 1 to 24-month lags of the dependent variable. Standard errors are clustered at the country level. See Table A2 for the full list of pandemic events. B. Transmission Channels: Growth and Inequality Estimates from the panel VAR model shed light on the channels through which pandemics lead to social unrest. We present the cumulative impulse response functions with 90 percent

Tahsin Saadi Sedik and Rui Xu
In this paper we analyze the dynamics among past major pandemics, economic growth, inequality, and social unrest. We provide evidence that past major pandemics, even though much smaller in scale than COVID-19, have led to a significant increase in social unrest by reducing output and increasing inequality. We also find that higher social unrest, in turn, is associated with lower ourput and higher inequality, pointing to a vicious cycle. Our results suggest that without policy measures, the COVID-19 pandemic will likely increase inequality, trigger social unrest, and lower future output in the years to come.
Tahsin Saadi Sedik

et al. (2020) 4320 0.0 35.9 255.3 Real GDP growth WEO (2020) 4320 1.8 1.5 3.0 Net Gini SIID 8.2 2119 29.4 29.4 4.9 Pandemic events Furceri et al. (2020) 4320 0.0 0.2 0.4 N.Obs with Pand. Dumm. = 1 SARS (2003) 78 H1N1 (2009) 392 MERS (2012) 164 Ebola (2014) 72 IFR = International Federation of Robotics WIOD = World Input-Output Data, Socio Economic Accounts Table A.2. List of Pandemics and Epidemic

Tahsin Saadi Sedik
COVID-19 has exacerbated concerns about the rise of the robots and other automation technologies. This paper analyzes empirically the impact of past major pandemics on robot adoption and inequality. First, we find that pandemic events accelerate robot adoption, especially when the health impact is severe and is associated with a significant economic downturn. Second, while robots may raise productivity, they could also increase inequality by displacing low-skilled workers. We find that following a pandemic, the increase in inequality over the medium term is larger for economies with higher robot density and where new robot adoption has increased more. Our results suggest that the concerns about the rise of the robots amid the COVID-19 pandemic seem justified.