{'fraction_non_alphanumeric': 0.029753471238311135, 'fraction_numerical': 0.019957090231955632, 'mean_word_length': 4.829164700330344, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Will the Economic Impact of COVID-19 Persist? Prognosis from 21st Century Pandemics

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. Keywords: impulse response, effects of pandemic, list of pandemic, pandemic event, baseline estimate, impact of COVID-19, COVID-19, Greenhouse gas emissions, Income inequality, Income distribution, Global

IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.   Tables  Table 1.

I. INTRODUCTION
The COVID-19 pandemic has had devastating impacts on economic activity in 2020. How long these impacts will persist remains unclear. Private sector forecasters and public agencies such as the IMF forecast a return to growth in 2021. The IMF's forecast is for global growth of 5.2 percent in 2021, erasing the effects of a projected 4.4 decline in 2020. For the United States, Consensus Forecasts-an average of several mostly private forecasts-predicts that after a 3.5% decline this year, GDP will grow 4 % in 2021 and 3% in 2022, thus raising incomes well above their pre-COVID level; forecasts for other major economies follow a similar pattern.
How credible are such forecasts that the adverse economic impacts of the pandemic will largely be contained to 2020, with little medium-term impact? And, looking beyond GDP, will the impact of the pandemic on other economic variables such as poverty and inequality-and on energy and environmental systems-be similarly short-lived? One approach to answering these questions has been to look to the medium-and long-term impacts of historical episodes such as the Black Death and the Spanish Flu of 1918. However, while these studies yield valuable insights into the effects of pandemics (Alfani 2020), their usefulness in predicting the medium-term impacts of COVID-19 may be limited. Despite its devastating death toll thus far, COVID-19 is expected to have mortality rates far below the magnitudes of the Black Death or the Spanish Flu; hence, as argued by Dosi, Fanti, Virgillito (2020), the nature and persistence of the economic impacts of COVID-19 is likely to be quite different from those historical episodes, particularly because of the much lower adverse impact on labor supply.
In this paper, we suggest that evidence on the impacts of more recent epidemics-SARS, H1N1, MERS, Ebola and Zika-might be a more useful guide for projections of the lingering impacts of COVID-19 on the economy. We go beyond existing work by examining the impact on a wide range of economic measures-including economic growth, unemployment and public debt; income inequality and poverty; and energy use and intensity and emissions-using a common econometric framework. 1 Our results show that previous pandemics have had significant and persistent negative repercussions for the macroeconomic outcomes we study: per capita income declines, unemployment goes up and income inequality increases. We also observe small declines 1 Other recent studies have focused on one of these variables, such as income (Ma et al. (2020) and Jorda et al. (2020)), inequality (Furceri et al. (2020a)), and poverty (Valensisi (2020) and Sumner et al. (2020)).
in energy and emissions intensity, suggesting that without deliberate policy actions, initial environmental gains will not remain entrenched. Applying the historical estimates to project the impact of COVID-19 until 2025, we forecast a persistent decline in the level of economic activity and an increase in poverty. Our estimates are likely a lower bound since COVID-19 is more widespread than the average health crisis in our sample. Indeed, containment measures (travel restrictions, lockdowns, social distancing measures) are without precedents in terms of speed and severity, and the global nature of the COVID-19 shock is likely to affect global value chains more than in the past.
The paper is organized as follows. Section 2 describes the data, the construction of our measure of the incidence of pandemics, and the empirical strategy to investigate the impact of past pandemics. Section 3 provides the impulse response functions that trace out the impacts of pandemics on economic outcomes. Section 4 uses the empirical estimates to simulate the repercussions of COVID-19 for the next five years. Section 5 concludes with a discussion of policy options to mitigate the projected impacts.

Data
We use data on the incidence of five major For the forecast analysis of the medium-term impact of COVID-19, we combine these data with GDP and population projections from the IMF World Economic Outlook of October 2019 and October 2020 (IMF, 2019, 2020). We use inequality Gini projections from Rao et al. (2018) for SSP2 as our near-term baseline for the Gini index. For the energy and environmental variables, we use the International Energy Agency's World Economic Outlook 2019 and 2020 forecasts (IEA, 2019(IEA, , 2020. While the IMF and Rao et al. (2018) forecasts are available at the country level, the IEA projections are available only for 14 large countries and macro-regions and in five-year time steps. Therefore, we disaggregate these values across countries using the primary energy shares of each country in 2015 and interpolate linearly for years that are not available.
We measure the effects of pandemics on several outcome variables using the number of cases in each pandemic episode. Specifically, we compute the logarithm of base 10 of the number of confirmed cases per 1000 inhabitants in each country-we add 1 to the cases-population ratio so that the new variable takes the value of zero in years without pandemics 2 : (1) Figure 1 shows the distribution of this variable for the various pandemics considered in this paper.

Empirical strategy
To estimate the medium-term impact of pandemics, we follow the method proposed by Jordà (2005) and estimate impulse response functions directly from local projections: 2 That is, for zero cases, ( , ) takes on the value of zero, for 9 cases per 1000 inhabitants (around 1% of the population), it takes on the value of 1, and for 99 cases per 1000 inhabitants it takes on the value of 2.
where , + is our variable of interest for country i in year t, are country fixed effects, included to control for unobservable country-specific factors. The terms are time fixed effects, included to take account of global shocks such as shifts in oil prices or the global business cycle. Finally, , is a vector of controls that includes two lags of the dependent variable and the pandemic shock variable. Equation (2)  We also evaluate the consequences for economic inequality. Figure 2 shows that an average pandemic episode leads to an increase of the Gini index of around 0.1 percentage points above pre-pandemic levels after five years. This implies that for the maximum incidence observed historically (52 cases per 1000 people), the Gini index would increase by 0.7 percentage point.
These are sizable effects for an indicator that moves very slowly over time. In addition to aggregate inequality measures, we also looked, where data are available, at the income by percentile of the distribution (e.g., bottom 10 th percentile, bottom 20 th percentile). We find that at the lowest part of the income distribution, the negative impact of pandemics is much more substantial compared to the top of the distribution; the poor are more severely hit while the rich are resilient in terms of their income share. The regressive impact of the pandemic also persists over time, even after five years (see Figure A1 in the Appendix -Panels A and B). The evidence is similar to the one presented by Furceri et al. (2020a), who also find that pandemics lowered the employment-topopulation ratio for those with basic education compared to those with higher education.
Another consequence of the pandemics is that they lead to an increase in the government debt-to-GDP ratio. This is accounted for both by lower economic activity (which lowers government revenues) and the expansionary fiscal response typically taken by governments to limit the negative economic effects of pandemics (Furceri et al. 2020b). Our analysis suggests that past pandemics have led to a rise in public debt of 2 to 5 per cent of GDP after one and five years, respectively ( Figure 2).
As economic activity plummets in response to pandemic events, energy consumption declines too. Figure 3 shows a considerable contraction of final energy demand. An increase of cases to average pandemic levels is associated with a medium-term reduction in energy demand of about 5 percent. A smaller energy system translates into lower emissions of greenhouse gases and air pollutants. CO2 emissions drop by as much as 11% and we find a similar drop in NOx, an air pollutant closely related to transportation ( Figure A1 in the Appendix -Panel F). We also see a significant reduction in oil consumption ( Figure A1 -Panel C), while the decrease in electricity consumption is smaller ( Figure A1 -Panel D). Among generation technologies, Solar Photovoltaics significantly increases ( Figure A1 -Panel E).
The reduction of greenhouse gases can be interpreted as a sign of environmental progress.
However, it is essential to differentiate the consequences of reduced economic activity from structural transformation towards a low carbon energy system. To disentangle structural and cyclical effects, we compute energy and emission intensities-respectively defined as the ratio of primary energy to GDP and emissions to primary energy. The results show that energy intensity declines only marginally in the medium term ( Figure 3). Emission intensity decreases by up to 4 percent after five years. A shift to electricity demand and more substantial reduction in oil consumption, and an increase in solar photovoltaic generation explain this (small) emission intensity gain. However, compared to the 11 percent drop in total CO2 emissions from fossil fuel combustion and industry, this indicates that around one-third of the reduction is due to carbon intensity improvements while the remaining two-thirds is due to the contraction of energy demand.
From the observed changes in electricity consumption and in particular Solar PV generation, a slight shift towards (residential) electricity consumption leads to shifts from higher carbon-content sources of energy. The reduction in oil demand is the most significant. Moreover, in terms of power generation mix, there is evidence for an increase in Solar PV generation leading to a reduction in carbon intensity of electricity. These factors are however dominated by the reduction in energy demand, which ultimately explains two thirds of the overall CO2 emissions reduction.
Overall, these efficiency gains are too small to make a significant contribution to efforts to combat climate change, unless policy actions are taken to lock in these declines when demand recovers.

Robustness checks
We performed several robustness checks of all regressions to validate our results. First, we repeat our analysis using an alternative identification strategy previously adopted by Furceri et al.
(2020a) and Ma et al. (2020). We construct a dummy variable, _ , , which takes the value of one in the year when WHO declares a pandemic for the country and 0 otherwise. This approach addresses the concern of possible measurement errors in cases detection due to different testing strategies and/or different efficiency of the health sectors of the countries considered. Moreover, the date of the pandemic event is likely to be more exogenous to the economy than the number of cases. The drawback of this approach, however, is that it considers all episodes as equal. In particular, we estimate the following model: with impulse response functions computed as follows: Figure A2 in the Appendix shows that the results of this exercise are very similar to, and not statistically different from, those presented in the text. The main difference with respect to the baseline results concerns the demand of final energy and CO2 emissions for which the estimations based on pandemic dummy do not point to significant effects. These results may be related to the fact that the effect on emissions crucially depends on the severity of the pandemics, which is not taken into account in this dummy variable approach.
Second, to address the issue of potential omitted variable bias, we add several control variables in the baseline regression, such as proxies for the level of economic development, demographics, and measures of trade and financial globalization. The results for these exercises, reported in Figure A3, are also very similar to, and not statistically different from, those presented in the text.
Third, we run a placebo test through the estimation of impulse response functions obtained by attributing randomly the values of our measure of shock across the whole sample. Figure A4 shows that the results of this exercise do not point to significant results, thus, confirming the validity of the parallel trend assumption in the evolution of our outcome variables before the pandemic.
Finally, to address possible concerns of estimating equation (2) by OLS, we employ an instrumental variable (IV) approach. At the outset, we should note that our econometric methods already tackle to a great extent many concerns that arise with the use of OLS. The first concern is omitted variable bias. This is attenuated in our case since we include in our baseline estimations both country and time fixed effects of accounting for unobserved cross-country and yearly heterogeneity and provide results for robustness checks using several control variables. The second concern is endogeneity since our empirical strategy may suffer from reverse causality. For example, pandemics may increase income inequality and, conversely, higher existing income inequalities may increase the probability to experience a pandemic due to the higher vulnerability of marginalized people that usually have lower economic resources, lack of access to health care and live in less healthy places. Finally, measurement errors related to the total cases detected could be an issue due to different testing strategies or different health sectors' efficiency of the countries variable, these measures seem to be suitable since it can reasonably be assumed that these indicators are randomly distributed across countries and do not affect our outcome variables. Our IV estimation is as follows: with where S is the instrument. The analysis also controls for country and time fixed effects and can therefore be seen as a differences-in-differences approach. As baseline instrument, we use the interaction between the dummy indicating the year of the pandemics and the average country temperature, supplementing it with the interaction between the dummy and the average degree of urbanization when the latter exhibits a stronger F-test in the first stage regression.

IV. PROJECTING THE REPERCUSSIONS OF COVID-19
The empirical evidence we found using past pandemic episodes allows us to perform a simulation exercise to estimate the impact of COVID-19 into the future. Specifically, we construct our out-of-sample forecast of the short-to medium-term effect of the COVID-19 pandemic based on the estimated impacts of past pandemics. In the second step, we compute the COVID-19 incidence as follows: To obtain the global values, we aggregate the country-level forecasts: where we use the appropriate weights based on the latest available historical data point (2019 for economic and 2017 for environmental variables) where applicable-quantities including energy, emissions, and GDP are just aggregated ( ,2019 = 1), for the debt ratio and energy intensity we use GDP weights, for unemployment rate population weights, and for emission intensity we use primary energy as weights.
We perform this simulation at the country level, and aggregate results up to the global values, that are shown in Figure 4. In Table 3, we summarize the estimated global impacts in relative terms to the reference scenario. In the analysis we also report the updated projections by the IMF and IEA as of October 2020 for the years 2020-2024 and compare these official projections to our empirically-based forecasts from effects of past pandemics.
We foresee sizable medium-term output losses of COVID-19, larger than those currently projected by the IMF and in the order of magnitude of past severe financial crises (Romer and Romer 2017). The increase in the Gini index (average across countries) is also to expected to be significant-of about 0.4 point. Energy demand is expected to drop significantly but the decline in energy intensity of GDP is likely to be small. In terms of CO2 emissions, we project a reduction of about 7% in 2020-within the range of estimates (5-9%) of Le Quéré et al. (2020) and of about 23% in 2024-while the emission intensity of energy decreases by 1.7% in the short term and 8.0% in the medium term. 5 Finally, we use the GDP per capita across countries and our estimates on the impact on the Gini index to estimate the impact of COVID-19 on absolute poverty. Previous estimates of the impacts on poverty range widely, from 37 million (Gates Foundation/IHME), 68 million (Valensisi 2020), up to 85-420 million (Sumner, Hoy, and Ortiz-Juarez 2020). We improve upon these studies by using a robust econometric model including linear, quadratic, and interactions of GDP and inequality, which explain about 98 percent of the variance in poverty rates. 6 We estimate the following fixed effects regression, including country and year fixed effects: The regression yields an overall adjusted R 2 of 0.963, implying an in-sample correlation of 0.981 with the observed poverty variable. Table 4 shows the estimated coefficients of interest.
Evaluating all the transformed variables at the mean, we can compute the marginal effect of a onepoint increase in the Gini index. We find that it increases the poverty rate by about 0.8 percentage point. Based on this model and the projections on GDP and inequality, we predict the poverty rate across countries from 2020 onward ( Figure 5). Our results suggest an increase of global poverty numbers-defined as those people with income below 1.9$ per day-from 662 to 738 million people in 2020 or an increase of 75 million people. This effect is projected to be persistent, declining only slightly in 2021 to 61 million above the pre-COVID scenario. 7 Looking at the regional level, we find that the increase in global poverty is concentrated in Southern Asia (India, Afghanistan) and Sub-Saharan Africa (Democratic Republic of Congo, Niger, Chad, Nigeria, Zimbabwe, and Madagascar). 5 Figure A5 in the Appendix presents the projections for some additional variables previously discussed (i.e. Total Oil consumption, Electricity Consumption, Solar Photovoltaics electricity generation). 6 The strong link of poverty and inequality has already been made in Lakner et al. (2020). 7 For the projections up to 2024 see Figure A6 in the Appendix.

V. CONCLUSIONS
We empirically estimate how past pandemics affected economic activity, unemployment, poverty, income inequality, public debt and energy use and emissions. We find significant and persistent reductions of GDP, along with increases in unemployment and income inequality and public debt-to-GDP ratios. Moreover, energy demand and CO2 emissions drop significantly during a pandemic event. However, in terms of systemic change, we find only a small effect: energy intensity declines only about 2 percent five years after the pandemic and only about one-third of the emission reduction is due to improvements in the carbon emission intensity of energy.
Using these historical estimates to trace the likely impact of the COVID-19 pandemic, our results indicate a persistent decline in the level of per capita GDP, with long-lasting effects on income inequality and an increase in the number of people living in absolute poverty of some 75 million people. It is worth noting that our resultslikely provide lower-bound estimates of the effects of COVID-19 since it is more widespread than the average health crisis in our sample and containment measures undertaken to limit the contagion are without precedents.
While energy demand and emissions decrease substantially, we find that this effect is mostly driven by the cyclical changes in economic activity rather than by gains in energy and carbon efficiency. For CO2 emissions, about one-third of the overall reduction can be attributed to the decarbonization of energy, notably due to a reduction in oil demand and shifting to electricity.
However, these gains are not sufficient to contribute significantly to a greener economy.
These projections point to the need for a strong policy response to counter the lingering adverse effects of COVID-19. The projected increase in public debt-to-GDP ratios may prompt concerns about debt sustainability in many countries. However, a hasty turn toward austerity is likely to further add to declines in per capita GDP, thereby frustrating the attempt to lower the debt-to-GDP ratio (Furceri et al., 2020b). Instead, fiscal and other macro policies should be calibrated to achieve equitable and sustainable growth. Moreover, there is a need for a "green" design of stimulus packages, to not only address economic and social impacts, but also to ensure medium-and long-term improvement in energy and emission intensity, including alleviating the costs of future climate mitigation action (Hanna et al., 2020).

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 areas indicate 90% (95% lighter) confidence intervals. See Table A2 in the Appendix for the full list of pandemic events

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 areas indicate 90% (95% lighter) confidence intervals. See Table A2 in the Appendix for the full list of pandemic events. .

FIGURE 4. GLOBAL PROJECTION OF INDICATORS WITH AND WITHOUT COVID-19
Notes: Estimates based on equation (8). The x-axis shows years. Solid lines show the projections from IMF and IEA before the pandemic (2019); dotted lines show the projection as of October 2020; dashed lines portray our empirical forecasts of the COVID-19 impact (90% confidence intervals shaded).  (1) for k=5. Robust t-statistics based on standard errors clustered at the country level in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Country and time fixed effects as well as control variables included but not reported.  (1) for k=5. Robust t-statistics based on standard errors clustered at the country level in parentheses. Instrument for columns 1 and 5-8: Interaction of year of pandemic with average country temperature. Instrument for columns 2-4: Interaction of year of pandemic with average country urbanizations. *** p<0.01, ** p<0.05, * p<0.1. Country and time fixed effects as well as control variables included but not reported.           (1). Robust t-statistics based on standard errors clustered at the country level in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Country and time fixed effects as well as control variables included but not reported.