metadata: authors: 0: author: Calderon,Cesar
1: author: Kubota,Megumi
count: World
docna: 0: docna: Exploring the Growth Effects of COVID-19
            across Developing Countries
docty: Policy Research Working Paper
subtopic: Health Care Services Industry,International Trade and Trade Rules,Economic Growth,Industrial Economics,Economic Theory & Research,Financial Sector Policy
teratopic: International Economics and Trade,Industry,Macroeconomics and Economic Growth
abstracts: cdata!: This paper investigates the spread of
            the COVID-19 pandemic and its impact on economic growth
            across developing countries. It documents the evolution and
            co-movement of COVID-19 infections with government responses
            (including health containment measures) across developing
            countries. It then estimates the impact of the different
            channels of transmission of COVID-19 on economic
            growth—thus, identifying factors that contribute to the
            economic resilience of countries during the pandemic shocks.
            The findings show that the pandemic’s impact on the decline
            in growth was substantive across the different developing
            country groups—although at different rates. The estimates
            show that a deeper downturn in economic activity due to the
            pandemic can be averted in countries with higher levels of
            human capital, well-targeted containment measures, and
            improved global health security. Diversifying trade patterns
            (across products and markets) is also crucial, and so is
            strengthening intraregional trade, as higher commerce across
            borders within the different developing regions may help
            secure the supply chains of essential goods in times of
            crisis—and particularly during pandemics. Finally, having
            fiscal space and a less risky public debt profile can make
            these economies more resilient against crisis.
repnme: repnme: Exploring the Growth Effects of COVID-19
            across Developing Countries
display_title: Exploring the Growth Effects of
            COVID-19 across Developing Countries
listing_relative_url: /research/2021/12/33686517/exploring-growth-effects-covid-19-across-developing-countries
topicv3: Economic Growth m1326241 471,Trade m1326924 698,Financial Sector Development m1327681 1920,Health m1504661 3480,Coronavirus (COVID-19) m1939478 4828
publishtoextweb_dt: 2021-12-20T00:00:00Z
historic_topic: International Economics and Trade,Industry,Macroeconomics and Economic Growth
origu: Office of the Chief Economist (AFECE)
no_of_pages: 28
author_keys: 0: author_key: 166739
1: author_key: 566355
model_extractions: Policy Research Working Paper 9889 Exploring the Growth Effects of COVID-19 across Developing Countries _Cesar Calderon Megumi Kubota_ Africa Region Office of the Chief Economist December 2021
Policy Research Working Paper 9889

# **Abstract**

This paper investigates the spread of the COVID-19 pandemic and its impact on economic growth across developing countries. It documents the evolution and co-movement of COVID-19 infections with government responses (including health containment measures) across developing countries. It then estimates the impact of the different channels of transmission of COVID-19 on economic growth—thus, identifying factors that contribute to the economic resilience of countries during the pandemic shocks. The findings show that the pandemic’s impact on the decline in growth was substantive across the different developing country groups—although at different rates.

The estimates show that a deeper downturn in economic activity due to the pandemic can be averted in countries with higher levels of human capital, well-targeted containment measures, and improved global health security. Diversifying trade patterns (across products and markets) is also crucial, and so is strengthening intraregional trade, as higher commerce across borders within the different developing regions may help secure the supply chains of essential goods in times of crisis—and particularly during pandemics. Finally, having fiscal space and a less risky public debt profile can make these economies more resilient against crisis.

This paper is a product of the Office of the Chief Economist, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at ccalderon@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Exploring the Growth Effects of COVID-19 across Developing Countries**<sup>*</sup>

Cesar Calderon and Megumi Kubota _The World Bank_

**Key Words:** COVID-19, containment, fiscal space, Sub-Saharan Africa, drivers of growth **JEL Codes:** F43, F63, C21

> * Kubota, The World Bank, Office of the Chief Economist of the Africa Region (AFRCE). Email: <u>mkubota@worldbank.org. Cesar Calderon: The World Bank, AFRCE. Email: ccalderon@worldbank.org. This paper</u> was prepared as background research for Section 2 of the Africa’s Pulse October 2020. The views expressed in this paper are those of the authors, and do not necessarily reflect those of the World Bank or its Boards of Directors.
# **1. Introduction**

The COVID-19 pandemic has generated the deepest and most synchronized decline in economic growth rates across countries in the world since World War II. Current forecasts suggest that more than 90 percent of the countries in the world will experience a contraction in gross domestic product (GDP) per capita in 2020 –surpassing the 85 percent of countries in recession at the height of the Great Depression (World Bank 2020a). Compared with the 2008-09 global financial crisis, this ongoing pandemic crisis is not a liquidity crisis but a health crisis. Global liquidity was not an issue —as central banks in advanced economies already had an accommodative monetary policy prior to the pandemic. However, global markets were volatile due to uncertainty about the spread of COVID-19 and the duration of the pandemic. Uncertainty still continues as the health crisis is not over yet. Therefore, all countries cannot let their guard down.

Despite the synchronization of the global pandemic crisis in the world, there is still heterogeneity in the depth of the impact of policy responses across countries and regions. Those cross-country differences, reflected by different structural characteristics and policy responses, may have shaped the growth impact differences. This paper compares different developing country regions across the world and finds that Sub-Saharan Africa may appear to have suffered a lesser blow from the pandemic than other developing regions. For instance, the median decline in 2020 growth across Sub-Saharan African countries (5.4 percent) is smaller than that of East Asia and the Pacific (6 percent), the Middle East and North Africa (6.9 percent), Eastern Europe and Central Asia (7.6 percent), and Latin America and the Caribbean (7.7 percent).

The COVID-19 shock that has hit the world economy and resounded throughout nations is the combination of demand and supply shocks, which tend to covary. Theoretically, the COVID-19 shock has both aggregate supply and aggregate demand implications.<sup>1</sup> From the supply side, the virus can affect workers’ participation in economic activities and then reduce the labor supply. However, the reduction in labor supply goes beyond the number of infected people as a result of strict lockdown measures (such as travel bans, workplace and school closings, and so on). From the demand side, individuals going to the market to purchase goods are exposed to the virus. the virus exposes individuals who purchase goods to infection. Therefore, private consumption declines. The access to goods is even more restricted as stores shut down or operate at reduced hours. At the same time, the uncertainty about the duration and depth of the virus may delay the spending of consumers and firms (Knightian uncertainty).<sup>2</sup> The lockdown measures of the COVID-19 pandemic led to the temporary closure or slowdown in operations of many factories and businesses. Consequently, these operations demanded fewer inputs, including less energy and reduced cargo shipping. In turn, the demand for energy, such as LNG used in shipping, also decreased.<sup>3</sup> The decreased demand for energy (i.e. oil) had an impact

> 1 See Guerrieri, Lorenzoni, Straub and Werning (2020).

> 2 Knightian uncertainty refers to the absence of any quantifiable knowledge about the occurrence of an event. Agents cannot have the information needed to set the likelihood of such event.  In the presence of Knightian uncertainty, economic agents keep in their portfolio their safest assets and defer future consumption and investment plans.

> 3 Restrictions on domestic and international travel also added to the lower demand for energy commodities.

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on international energy prices (i.e. crude oil prices), and as a result of this global shock, stock markets plunged, and oil prices fell to even lower levels.

The COVID-19 crisis led to disruptions in global supply chains. Wuhan, the Chinese city in Hubei province that was the initial epicenter of the epidemic, is a major transportation hub with railways, roads and expressways passing through the city and connecting to other major cities. This city is also a traditional manufacturing hub and promotes modern industrial activities in China such as the automobile industry (e.g. Peugeot Citroen, Renault, Honda, Dongfeng, among others). The lockdown imposed in Wuhan by the Chinese government led to the temporary suspension or slowdown of business in manufacturing sectors and services and disrupted global supply chains. As the COVID-19 virus spread throughout different countries in Asia, Europe, and the Americas, some governments implemented containment measures including international travel bans —thus, disrupting the transportation and tourism industries. Global and local businesses slowed down significantly, leading many workers to be laid off.

This paper first compares the evolution and spread of the COVID-19 pandemic across the different developing regions. Consequently, we examine trends in COVID-19 infection and death rates, the public sector’s responses to the pandemic —in terms of economic support and health, containment and closure measures— as well as the trends in community mobility resulting from lockdown (and other restrictive) measures. Trends of the COVID-19 pandemic in developing countries are depicted by the evolution of total and new daily infections as well as death rates. We then explore the significance of alternative transmission mechanisms through which COVID-19 affects economic activity, using a cross-country regression framework.

The number of infections and fatalities due to the COVID-19 pandemic varies widely across regions with the highest number of cases among the developing world in Latin America and the Caribbean, followed by South Asia as well as Europe and Central Asia. In contrast, East Asia and the Pacific as well as Sub-Saharan Africa registered the lowest numbers of infections and deaths. We document the deployment of strict containment measures across developing countries (in terms of workplace and school closures, temporary limitations on public gatherings, restrictions on internal movements and international travel bans, among others) amid the rise in COVID-19 confirmed cases. The strength of government policy responses, consequently, demonstrates a pattern of co-movement with the evolution of new daily COVID-19 cases.

The main message is that the economic effects of the COVID-19 shock are transmitted through three main channels, including health, trade, and financing. Most importantly, what matters for the transmission mechanisms is not the extent of openness but the structure of products and markets. On the domestic front, higher levels of human capital (i.e. better health outcomes and higher education) help improve economic growth. Well-targeted containment measures matter for economic growth. Plausible macroeconomic policies are crucial. For instance, creating a cushion by building up a fiscal space can make an economy more resilient against crises, and better crisis management policies can also mitigate negative impacts on economic growth. Fiscal space for appropriate pandemic policies would be helpful particularly if a country has a higher degree of exposure to the global markets and their economic structure is less flexible. Maintaining primary balance and current account surpluses helps the economy cushion the impact of the crisis.

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On the external front, global health security is important for economic growth. Diversifying trade patterns (across products and markets) is crucial because the global pandemic disrupted global value chains. And so is strengthening intra-regional trade because higher commerce across borders within the region may help secure the supply chains of essential goods in times of crisis —and particularly during pandemics. Capital flows help finance during the pandemic but the composition of capital flows matters. For instance, portfolio investment flows and other investment flows help increase economic activities rather than FDI flows. The composition of debt profiles also matters because accumulating external debts by official creditors is less risky than those by private creditors. Especially a shift in the composition of debt from public creditors to private creditors can render the economy more vulnerable to greater currency, interest rate, maturity (i.e. shorter intervals) and refinancing risks. Principally, transparency and accountability are essential for debt management.

In our empirical analysis, we investigate the impacts of COVID-19 on economic growth in developing countries and identify factors that contribute to the economic resilience of countries during the COVID-19 pandemic shock. We present least squares and instrumental variables estimations of growth on a wide array of variables (i.e. human capital, macroeconomic, trade, and financial variables, among others). The dependent variable is the change in growth rate as measured by the difference of the 2020 GDP growth forecast undertaken by the World Bank’s Global Economic Prospects in June 2020 vis-à-vis January 2020. The regression analysis is conducted for a sample of 135 developing countries.<sup>4</sup> Our main findings are the following: first, the composition of government debt matters for alleviating the adverse growth impact associated to the COVID-19 pandemic. External borrowing from official creditors entails lower risks (in terms of interest rates, maturity, and refinancing) than from private creditors. In one of the striking findings, countries with less risky debt profiles (i.e. countries with a greater propensity to borrow from official creditors –including multilateral organizations) tended to exhibit a lower decline in their growth rate. This result may imply that managing debt profiles (i.e. reducing the government debt risk profile) may help tackle vulnerabilities, and hence increase resilience.

Next, fiscal space plays a role in mitigating the impact or averting a deeper decline in growth in response to the pandemic. Softening the blow of future deleterious shocks will require African countries to strengthen the institutions that support a transparent, efficient, and accountable fiscal policy. Governments will need to create the pre-conditions to foster domestic resource mobilization and improve their efficiency of public spending. Introducing digital technologies to broaden the tax base and strengthening tax administration are beneficial. Fostering public investment management systems has been found to boost the public investment multiplier.

Third, human capital is one of the key components for the engine of economic growth; consequently, higher education and better health are the key to recover and grow further. Especially, the global health condition matters for growing the economy.

> 4 When accounting for the full information of the variables involved in the regression analysis, the number of countries in the effective sample is up to 101.

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Fourth, diversifying trade patterns across markets is crucial as intra-regional trade integration may help secure the supply chains of essential goods –particularly, during pandemics. Consequently, Sub-Saharan Africa needs policies to foster greater intra-regional trade integration. This will help increase export market diversification and build resilience against shocks such as a pandemic crisis. The evidence in this paper shows that countries that are more exposed to trade with advanced countries have experienced a greater decline in economic growth (e.g. through global value chains). At the same time, countries that have greater intra-regional trade appear to have been relatively shielded.

Fifth, diversifying trade across products is important for the road to recovery. Countries with greater commodity trade exposure –more specifically, a larger share of commodity exports in GDP –tend to have a larger downfall in economic growth. During the pandemic period, more diversified value-added exporters have also experienced a larger decline in economic growth due to the disruption in global value chains. On the other hand, some less diversified oil exporters were able to mitigate a larger decrease in growth due to saving prior to the pandemic. Regardless of this result, as the global economy recovers, African countries still have to advance their export product agenda –especially away from commodities and towards goods and services with value addition. Designing strategies to diversify exports or create value addition in commodities (e.g. agribusiness and direct marketing) can improve the ability of countries to develop resilience against shocks.

Finally, the quality of institutions is essential to kick in the effects of policies: stronger policies along with better institutional quality enabled countries to record a lower decline in economic activity in the pandemic period. Enhancing institutions, consequently, help design and implement effective policy responses to recover rapidly and thrive in the aftermath of the pandemic (World Bank 2020b).

What would it take for countries to recover faster and more sustainably? A recovery from the disruption in global supply chains may take time (as well as other external divers of growth). Therefore, it is indispensable to find ways to reactivate the domestic and regional engine of growth. Fostering intra-regional trade will help boost the competitiveness of each region and promote the development of reginal value chains as global trade recovers (World Bank 2020b). To engineer a recovery that surpass pre-pandemic levels, key elements of the roadmap include building up institutions and generating fiscal space—including measures to mobilize domestic resources, promote public investment, and implement debt transparency and management.

The remainder of the paper is organized as follows. Section 2 presents an overview of the literature on the economic effects of pandemics. Section 3 examines spread of the COVID-19 pandemic in countries worldwide. Section 4 describes the data and their sources for our empirical analysis. Section 5 investigates the impacts of COVID-19 on economic growth across developing countries and identifies factors that contribute to the economic resilience of countries during the COVID-19 pandemic shock. Section 6 concludes.

# **2. The Economic Effects of Pandemics: A Brief Review of the Literature**

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The theoretical and empirical literature on the impacts of pandemics originates from empirical work on global pandemics episodes such as the Spanish flu in 1918-20, SARS in 2003, MERS in 2012, among others. Varieties of this strand of economic research have recently expanded due to the ongoing COVID-19 pandemic. This section focuses on a brief literature review of the growth effects from COVID-19 across developing countries.

An assessment of the economic contraction and mortality during the Great Influenza Pandemic of 1918-20 is provided by Barro, Ursúa, and Weng (2020). Using data for 48 countries, the authors estimate that about 2.1 percent of the total population may have died due to the Spanish influenza. Contractions in economic activity and consumption for the median country were estimated at 6 and 8 percent, respectively. Additional evidence shows that the economic disruption associated with the 1918-20 Great Influenza pandemic could be mitigated by the implementation of nonpharmaceutical interventions (NPI) such as social distancing.<sup>5</sup> Recent evidence documents the impact on economic activity of five recent global pandemic and epidemic episodes in the twenty-first century; namely, Severe Acute Respiratory Syndrome (SARS) in 2003, Swine Flu (H1N1) in 2009, Middle East Respiratory Syndrome (MERS) in 2012, Ebola in 2014, and Zika in 2016.<sup>6</sup> When looking at the distribution of growth rates among two groups of countries (disease at the onset year of all affected countries vis-à-vis countries in nondisease years and unaffected countries in disease years), the evidence shows that: (i) the average growth rate of economic activity in country-year pairs unaffected by the crisis is 3.96 percent, and it slows to an average rate of 0.77 percent during pandemic episode years; (ii) the distribution of growth rates is left-skewed during pandemic years (reflecting negative outcomes) while that of non-pandemic country years is right-skewed.<sup>7</sup> Panel evidence from 210 countries from 1960 to 2018 shows that the global impact of a pandemic on economic activity is negative and persistent.<sup>8</sup> For instance, the average output loss is about 2.6 percent for all countries at the onset of the pandemic, and it is still 3 percent below the prepandemic level five years later. Among the five previously identified epidemic episodes, the global output loss from H1N1 is somewhat larger (4 percent in the year of the outbreak, and 4.3 percent after five years).<sup>9</sup> Note that these estimates tend to be surrounded by uncertainty from the unknown timing, spread, and severity of the virus.

McKibbin et al. (2020) quantify the potential global economic costs of COVID-19 under different possible scenarios. They also show the economic benefits of globally-coordinated policy responses to tame the virus using the global macroeconomic model (G-Cubed) based on McKibbin and Triggs (2018). The model has six sectors and 24 countries, which is extended from McKibbin and Wilcoxen (1999, 2013). There are short-term and long-term policy responses for the COVID-19 pandemic. In the short term, central banks need to support the economies during the ongoing

> 5 See Correia, Luck and Verner (2020).

> 6 The five episodes were identified by Jamison et al. (2017).

> 7 See Ma, Rogers, and Zhou (2020a).

> 8 Furceri et al. (2020) show that modern pandemics—although smaller in the magnitude of infections and deaths than the COVID-19 pandemic—have led to greater income inequality with higher Gini coefficients, and lower employment-to-population ratio for people with basic education as opposed to those with higher education. 9 See Ma, Rogers, and Zhou (2020b).

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disease outbreaks. The shock of this pandemic needs not only demand management but also the combination of monetary, fiscal and health policy responses. Therefore, there is a critical role for the government to ease real and financial stress. Quarantine and social distance measures also work to reduce the spread of viruses. Maintaining better hygiene practices is highly effective with a low cost (Levine and McKibbin 2020). In the long term, global cooperation is necessary to strengthen public health and economic development as the pandemic can kill many people in any society if those diseases are generated in poor countries due to overcrowding, poor public health and interaction with wild animals. Consequently, public investment in health is crucial to improve the quality of life as a driver of economic growth.

New firm-level evidence on the impact of COVID-19 on sales and employment by the World Bank’s Business Pulse Surveys (BPS)<sup>10</sup> shows that firm sales dropped by 32 percent among highincome countries and by 48 percent among low- and middle-income countries. The decline in sales amid the pandemic varied widely across countries, sectors of economic activity and firm size. For instance, South Africa, Bangladesh, Nepal, Honduras, India and Jordan registered an average drop in sales that exceed 60 percent. Across sectors, firm activity in tourism as well as businesses that require face-to-face interactions were the most affected by the pandemic.<sup>11</sup>

# **3. COVID-19 in Developing Countries**

The COVID-19 pandemic has spread rapidly across the world, taking a human toll of nearly five million fatalities among the more than 240 million confirmed cases across 210 countries and territories.<sup>12</sup> As of mid-October, North America and South America have the largest number of infections by the COVID-19 pandemic, with 46 million and 45.5 million cases, followed by Western Europe and South Asia (38.4 million and 38.3 million cases), and Eastern Europe and Central Asia (33 million). Surprisingly, Sub-Saharan Africa has registered a low number of COVID-19 cases relative to most developing areas in the world since the pandemic started —about 5.9 million cases, which represent 2.5 percent of the infections worldwide.

After a rapid increase in the number of COVID-19 confirmed cases across the world during the second quarter of 2020, a series of containment measures (including restrictions on domestic and international movement, lockdowns, and social distancing) were rapidly implemented across countries —although with different degrees of stringency and effectiveness. These measures were aimed at limiting the spread of the virus and alleviating the pressure on weak health systems. Lockdown measures have led to a sharp drop in economic activity in all countries/regions of the world —particularly, in the second quarter of 2020. The stringency of the containment measures reached their peak when the infection and death rates were rapidly increasing across developing

> 10 The Business Pulse Surveys (BPS) is a globally harmonized questionnaire that evaluates the impact of COVID-19 on firms’ operating status, sales, labor adjustments, and responses to the shock, among others. It was collected by the World Bank Group and several partner institutions in 51 countries  covering more than 100,000 businesses. For more details, see Apedo-Amah et al. (2020) and Cirera, Cruz, et al. (2021).

> 11 See Apedo-Amah et al. (2020).

> 12 The estimated number of cases and fatalities presented in this section are as of mid-October 2021.

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countries. Some governments started to gradually reopen by 2020Q3 before the number of new daily cases peaked while others did so as they started to decline.

A resurgence of the coronavirus has taken place in 2020Q4 across North America, Western Europe and most developing areas —except for South Asia. In most of these regions, the second (or third) wave of COVID-19 infections and deaths has been worse than the first wave. New variants of the virus (first identified in the United Kingdom and South Africa), the general relaxation of protective measures (as reflected by the looser adherence of the population to basic health protocols) explain the resurgence by the end of 2020. This prompted governments to tighten restrictions again. While containment measures have helped to slow the new wave of infections in some countries, others are still facing an upward trend in infections and fatalities.

Finally, countries worldwide experienced another wave of the coronavirus pandemic due to the spread of the Delta variant, first identified in India. This variant grew to be more contagious than previous variants of the coronavirus. A priority for all countries is to accelerate the distribution and uptake of vaccines. This would provide a boost in economic activity. The success of COVID19 vaccination rollouts throughout the world will depend on reducing vaccine inequity worldwide, improving the logistics associated with vaccination campaigns (especially in low- and lowermiddle income countries), and bringing down vaccine hesitancy.

_Population structure and low incidence of COVID-19 in Sub-Saharan Africa_ The COVID-19 pandemic has threatened the lives and livelihoods of the lower income segments of the population more than those who are better paid —many of whom can continue working from home. The pandemic has stressed social and spatial disparities within cities: a large burden of the pandemic on lives and livelihoods has been borne by the urban poor. Several factors put the urban poor, especially those living in slums, at high risk for contracting infectious diseases such as COVID-19 (Baker, Cira and Lall 2020). These risks are particularly linked to: (i) overcrowded living conditions (both within slums and within households); (ii) limited access to basic services, particularly water, sanitation, and health services; (iii) reliance on crowded transport services; and (iv) specific aspects of working in the informal sector (often in crowded places, with no social protection to fall back on).

Despite the fact that lower-income countries have poorer health systems, are less prepared to deal with the pandemic, and people living in conditions that make them vulnerable, they have so far avoided the unprecedented health consequences from the COVID-19 pandemic affecting the globe.<sup>13</sup> For instance, Sub-Saharan Africa —the world region with the largest number of poor people— has nearly 14 percent of the population but the number of infections and deaths related to COVID-19 represent only 2.1 and 2.6 percent of the world’s cases.<sup>14</sup> In contrast, the population of Latin America and the Caribbean is nearly 8.5 percent of the world population and the number

> 13 Recent research shows that rich countries have experienced more COVID-19 deaths (per million people) than have poor countries (Deaton 2021).

> 14 In contrast, the population of Latin America and the Caribbean is nearly 8.5 percent of the world population while the number of infections and deaths amount to 26 and 34 percent of the world’s cases.

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of infections and deaths amount to 19 and 29 percent of the world’s cases.<sup>15</sup> Still, the low rates of infections, and especially death rates, in low-income countries relative to richer countries are arguably attributed to the younger age structure of the population in the region.

The age structure of the population plays a role in the health consequences of COVID-19. Nations with a large share of older people have typically registered high rates of COVID-19 infections and deaths. Figure 1 plots the percentage of the population 65 years or older vis-à-vis the COVID-19 confirmed cases per million people, while Figure 2 depicts the relationship between the percentage of the population 65 years or older and the COVID-19 deaths per million people. The scatterplots clearly illustrate the incidence of COVID-19 cases and fatalities across the different stages of the demographic dividend —as captured by the cross-country distribution of the population 65+ years for industrial countries, Sub-Saharan Africa (SSA), and non-SSA developing countries. The figures show that developing countries with lower shares of older population tend to have a lower incidence of infections and, particularly, deaths. Consequently, countries with greater shares of older populations tend to have higher infection rates and death rates (Figures 1 and 2).

# **4. Data Description**

This section describes the sources of variables used in the regression analysis to assess the growth impact of: (a) the different channels of health and economic transmission of COVID-19, and (b) the policy responses to combat the pandemic. We collected data for a cross-section of 135 developing countries —of which 47 countries are in Sub-Saharan Africa. Given that the real effects of the pandemic are still evolving, we approximate the dependent variable as the differences between 2020 GDP growth forecasts conducted by the World Bank‘s Global Economic Prospects before and after the pandemic which is January 2020 and July 2020, respectively.<sup>16</sup> We also conduct our regression analysis with alternative measures of our dependent variable: (i) the differences between 2020 GDP growth forecasts conducted by the IMF in October 2020 vis-à-vis October 2019; and (ii) the differences between 2020 GDP growth forecasts between October 2020 and October 2019 as undertaken by the World Bank. The explanatory variables have been classified in five different groups: (a) international trade, (b) international financing flows, (c) health and human capital, (d) macroeconomic space, and (e) policy responses.

_International Trade._ Exposure to foreign trade is measured by the value of exports and imports as percentage of GDP. In our prior, the countries that are more exposed to international trade experience a greater decline. The impact of trade on growth is further explored to the trade structure by product and by partner. When looking at trade by product, we collect data on natural

> 15 Note that the relatively low incidence of COVID-19 cases (as well as fatalities) in poorer countries—and, notably, Sub-Saharan Africa—should be taken with caution because the spread of the pandemic in the region may be understated due to the low rates of testing—as cases could exist undetected. Some low-income countries have been unable to acquire the much-needed number of testing kits. Testing in these countries is low—especially when normalized by their entire population.

> 16 In a similar spirit, Berkmen, Gelos, Rennhack and Walsh (2012) use the difference between 2009 actual growth and growth forecasts made before the crisis to provide an initial exploration into the growth effects of the 2008-09 global financial crisis. The authors argue that the use of these forecasting errors enables them to avoid issues such as controlling for variations in growth rates that are due to differences in levels of development or cyclical positions, or other factors unrelated to the impact of the crisis.

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resource exports (as percentage of GDP), and manufacturing imports (as percentage of GDP). These variables will broadly capture the decline in commodity prices and the disruption of global value chains as a result of the pandemic. Finally, we gather data on exports to advanced countries, exports to developing countries outside the region, and intra-regional exports. All these variables are expressed as a percentage of GDP. Therefore, we explore whether exposure to inter- or intraregional trade exacerbates or mitigates the impact of the pandemic. All the trade data was collected from the World Bank’s World Development Indicators (WDI).

_International Financing Flows_ . Gross capital inflows as a percentage of GDP is used as the measure of exposure to or dependence on international financing flows. We assume that countries with greater dependence on foreign financing flows will exhibit a greater decline in growth. We also look at the impact of the composition of gross inflows to investigate whether certain types of financing flows either exacerbate or mitigate the growth impact of the pandemic. Consequently, we run additional regressions that include gross foreign direct investment (FDI) inflows, portfolio investment inflows, and other investment inflows. The different types of gross inflows are expressed as a percentage of GDP, and the data has been collected from the IMF’s Balance of Payments Statistics (BOPS6).

The responsiveness of growth to the pandemic through its exposure to other international financing flows such as remittances and tourism revenues is also examined. We gather data on workers’ remittances as a percentage of GDP from the World Bank (2020c).<sup>17</sup> The pandemic led to a series of containment measures that included international travel restrictions and there is evidence that economic activity is collapsing in countries that are highly dependent from tourism revenues. The data on international tourism receipts (as a percentage of GDP) is collected from the WDI.<sup>18</sup> Finally, the extent of international capital flows across developing countries is alternatively measured by the Chinn-Ito index of financial openness (Chinn and Ito 2006 2008). This indicator is based on the presence (or lack thereof) of restrictions on cross-border financial transactions reported in the IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions; namely, the existence of multiple exchange rates; the presence of restrictions on current account transactions; restrictions on capital account transactions, and regulatory requirements of the surrender of export proceeds.

_Health and Human Capital._ Our proxy of human capital is the human capital index (HCI), which reflects the distance of the human capital of the country relative to a benchmark of complete

> 17 Workers’ remittances are defined as the sum of: (1) workers’ remittances recorded under the heading “current transfers” in the current account of the balance of payments; (b) compensation of employees which includes wages, salaries, and other benefits of border, seasonal, and other nonresident workers (such as local staff of embassies) and which are recorded under the “income” subcategory of the current account; and (c) migrants’ transfers which are reported under “capital transfers” in the capital account of the IMF’s Balance of Payments Statistics. The calculations are based on data from the IMF Balance of Payments Statistics database and data releases from central banks, national statistical agencies, and World Bank country desks.

> 18 International tourism receipts are expenditures by international inbound visitors, including payments to national carriers for international transport. These receipts include any other prepayment made for goods or services received in the destination country. They may also include receipts from same-day visitors, except when these are important enough to justify separate classification. For some countries they do not include receipts for passenger transport items.

10
education and full health. Prior to the COVID-19 pandemic, a child born in a typical country in Sub-Saharan Africa could expect to achieve just 40 percent of her potential human capital, relative to a benchmark of complete education and full health. The data is collected from the World Bank (2018, 2020d). Another indicator in this dimension is the global health security index, an index that takes values from 0 to 100, that captures the prevention of the emergence or release of pathogens, early detection and reporting for epidemics, rapid response to and mitigation of the spread of an epidemic, sufficient and robust health systems to treat the sick and protect health workers, compliance with international norms, and risk environment and vulnerability to biological threats. The data is taken from NTI and Johns Hopkins University. Finally, we gathered information on health indicators such as hospital beds (per 1,000 people), physicians (per 1,000 people) and nurses (per 1,000 people). These indicators are gathered from WDI.

_Macroeconomic Space_ . The variables of macroeconomic space provide information on whether countries had some extent of macroeconomic resilience prior to the pandemic. These variables aim to capture whether the countries had fiscal space, monetary policy space, less risky debt profiles and external savings. We specifically examine the impact of having (liquidity or policy) space on growth amid the pandemic. Fiscal space is proxied by the general government primary balance and the general government gross debt —and both variables are expressed as a percentage of GDP. Monetary policy space is indirectly measured by the rate of annual CPI inflation. External savings are proxied by the current account balance. Finally, the risk profile of public debt is captured by including the composition of public debt by creditor. The regression analysis includes public and publicly guaranteed external debt owed to official creditors (bilateral and multilateral lenders), and to private creditors (bonds, commercial banks, and other private creditors). The data is collected from the IMF’s World Economic Outlook, and the World Bank’s International Debt Statistics.

_Policy Response._ To capture the policy responses to the pandemic, we use the Oxford COVID-19 government response tracker (OxCGRT) and the different components in this index. The overall government response index summarizes information on 17 indicators of government responses (8 indicators on containment and closure policies, 4 indicators on economic policies and 5 indicators on health system policies). We further look at two sub-indices: (a) a containment and health measure, which includes lockdown restrictions and closures), and (b) an economic support index, which captures measures such as income support and debt relief. Finally, we use the OxCGRT stringency index that captures the strictness of lockdown measures affecting people’s behavior — including school closures, workplace closures, and travel bans, among others. The data is obtained from Hale et al. (2020).<sup>19</sup>

> 19 Note that these indexes record the number and strictness of government policies rather than their effectiveness.

11
# **5. COVID-19 Empirical Evidence: An Exploratory Analysis of the Output Effects across Countries**

# **5.1 Estimation Technique**

We regress the change in growth rate on a wide array of variables (i.e. macroeconomic, trade, and financial variables). We conduct linear regression analysis and two-stage least-squares instrumental variables (IV) estimations to examine the impacts of COVID-19 on economic growth in developing countries. Therefore, we identify factors that drive the economic resilience of countries in the COVID-19 pandemic shock. The dependent variable is measured as the difference of the 2020 GDP growth forecast undertaken by the World Bank’s Global Economic Prospects in June 2020 minus the forecast of January 2020. Our sample comprises 135 developing countries, and the effective sample is up to 101 countries. Table 1 shows the baseline linear regressions while Tables 2 and 3 present the sensitivity analysis to the baseline regressions: Table 2 tests the responsiveness of our estimates to changes in the dependent variables (IMF projections vs World Bank projections), while Table 3 examines the robustness of our results to changes in the set of explanatory variables. Table 4 presents the results from our baseline regressions using two-stage IV estimations, while Tables 5 and 6 report our sensitivity analysis using two-stage IV estimation methods. Table 5 tests the robustness of our IV estimates to changes in the dependent variable (IMF projections vs World Bank projections), while Table 6 tests the changes in the set of explanatory variables.

Overall, the results from both least squares and IV estimations are qualitatively in line with what is expected in the literature. The least squares estimates may be biased and inconsistent, while IV techniques could produce consistent estimates. The challenges that arise from simple linear regression can be addressed using IV estimations because at least one of the explanatory variables could be endogenous or jointly determined with the dependent variable in our specifications. Therefore, we control for the endogeneity of the drivers of economic growth with instrumental variables estimations. Our baseline regression equation of the difference of economic growth presents the following specification: ′ YY where the dependent variable is the growth rate of country ggCC = αα+ ββ XXcc + εεcc _c_ in the year 2020 (as measured by the changes in the growth forecast for that year). The matrix YY contains information on the different drivers CC gg of growth as classified in Section 3. Most of the explanatory variables in are lagged (relative to the 2020 cc XX year growth forecast change) to alleviate problems of reverse causality. Finally, captures the residuals. cc XX **5.2 Least Squares Analysis** cc εε

## **5.2 Least Squares Analysis**

Economies with greater exposure to global markets may be more susceptible to shocks from the COVID-19 pandemic although having reserves and fiscal space could prevent the economies from entering into much deeper recessions. The economic effects of the COVID-19 shock can be transmitted through three main channels, including health, trade, and financing. Regarding the health channel, Tables 1 to 3 show that the estimated coefficients of government response and, especially containment and health measures (as captured by the stringency index), are negative 12
and significant.<sup>20</sup> Especially, the stringency measure has a robust negative coefficient. Containment and health and government economic support are negative and insignificant. Therefore, stringency measures and containment measures implemented by the government that target any specific segment of population or geographical area rather than generalized containment may help prevent the domestic economy from falling into a further slump. If the government fails to deliver any effective pandemic policies, then the economy suffers and growth declines further.<sup>21</sup> Our empirical results on the effect of health indicators are mixed. Therefore, they fail to show any significant impacts of health indicators in the regressions. This could be attributed to the fact that measuring impacts via the health channel is challenging as COVID-19 is still a novel disease.<sup>22</sup> Empirical evidence supports the existence of a negative relationship between trade exposure and growth. The deleterious effects of trade exposure can be partly explained by vulnerabilities to commodity price volatility (e.g. international oil prices). The estimated coefficients of international trade openness have a negative and insignificant impact on economic growth (-0.0116 points in regression [1] of Table 1).

The regression analysis finds that countries that are more dependent on external financing could experience deeper declines in economic growth. For instance, in regression [1] in Table 1, the estimated adverse impact of (gross) capital flows on economic growth at -0.0832 is significantly deeper than the one of trade openness. Therefore, countries with greater dependence on external financing tend to have larger declines in growth. When looking at the composition of capital inflows, the regression analysis finds that both portfolio investment inflows and other investment inflows are significantly positive at 0.264 and 0.228, respectively in regression [7] of Table 1. Consequently, portfolio investment inflows and other investment inflows help prevent further declines in GDP growth. The coefficients of FDI flows have insignificantly negative impacts on economic growth (i.e. at -0.0378 in regression [7] of Table 1). FDI is one of the major capital flows into the Sub-Saharan Africa region. Most likely, FDI activity is more dynamic in commodityabundant countries because those commodities attract massive foreign investments.

The cushion created by the fiscal space matters even if the economy’s structure tends to be driven by primary goods/commodities. For instance, empirical evidence suggests that natural resource/commodity exporters<sup>23</sup> experience larger negative and significant impacts on economic 20 The government response indicator (from Oxford Policy Tracker) captures a series of containment and health measures as well as economic support measures implemented by governments in response to COVID-19. The containment and health measures include school closings, workplace closings, cancelation of public events, restrictions on gathering, closure of public transportation, stay-at-home requirements, restrictions on internal movement, and international travel controls. The economic support measures record if the government is providing direct cash payments to people who lose their jobs or cannot work or if it is freezing households’ financial obligations. 21 The negative co-movement between the stringency of containment measures and economic activity is also found with other performance indicators such as consumption, investment, and industrial production, among others (IMF 2020).

22 Medical researchers’ work is still on going and in progress, and they are still finding new evidence. Accordingly, there have not been always definite treatments of COVID-19 yet.

23 Although countries with more concentrated economic structures are vulnerable to the crisis, accumulated savings can work as a cushion against shocks. Oil abundant countries, for instance, experience relatively lower declines relative to non-oil abundant countries in Sub-Saharan Africa (-5.5 vis-à-vis -6.3 percent, respectively). Prior 13
growth (i.e. [5] in Table 1). It has been often suggested that commodity trade exposure can be reduced if a country diversifies its economic structure by developing greater value addition capabilities and exporting more value-added products. Sub-Saharan Africa has indeed increased its exposure to global value chains over the past 20 years. Global value chains nearly collapsed during the COVID-19 crisis, and the trade disruptions have resulted in temporary suspensions and/or slowdowns of production processes within global value chains. Table 1 confirms this finding, for example, with the negative and significant growth effects of countries with greater export linkages to advanced countries and the positive (although not significant) impact of intraregional trade. Greater dependence on global value chains has deeper negative impacts on economic growth. Therefore, the adverse economic effects of the pandemic have been larger in countries that are inserted in global value chains rather than those with greater intra-regional trade.

The regression analysis also examines the impact on growth of debt management and having (fiscal and/or external) savings. The empirical results show that 1) accumulating external debts by official creditors are less risky than ones by private creditors, therefore, the composition of debt profiles matters, and 2) maintaining primary balance and current account surpluses helps the economy cushion the impact of the crisis. Changes in a country’s borrowing pattern will alter the risk profile and may vary the transmission mechanisms of the pandemic shock. Especially a shift in the composition of debt from public creditors to private creditors can render the economy more vulnerable due to greater currency, interest rate, maturity (i.e. shorter intervals) and refinancing risks.

Enhanced debt management requires greater debt transparency, particularly in low-income countries. Poor recording, monitoring and reporting systems have increased opacity in the debt profiles of low-income countries.<sup>24</sup> This has resulted in greater risks associated with massive hidden debt operations and greater (than expected) interest payments, which leads to heavier government burdens (World Bank 2020b). Empirical evidence shows that countries with greater public debt exposure to private creditors tend to experience a larger decline in economic growth as the coefficients of PPG external debt to private creditors are negative and, in some cases, significant in Table 1 (i.e. -0.019 in [5] of Table1), while the ones of PPG external debt to public creditors are robustly positive and significant in Table 1 (i.e. 0.0935 points in regression [5] of Table 1).

Reducing general government gross debt also helps mitigate macroeconomic vulnerability in the event of economic shocks. For instance, the coefficients of the government primary balance and to the COVID-19 shock, oil exporters excluding Nigeria accumulated some reserves from oil export proceeds, and consequently, the resulting fiscal space from these revenues may have helped finance policy actions and reduce the adverse impact from the pandemic even if their economic structure was not as diversified. Oil abundant countries registered more favorable fiscal and current account balances in 2019, therefore, those countries had some resources in their coffers prior to the COVID-19 crisis. For instance, oil exporting countries in Sub-Saharan Africa recorded a fiscal surplus that exceeded 1 percent of GDP in 2019 and exhibited a current account deficit of around 2 percent—the smallest among commodity exporting groups in the region.

> 24 The recently published debt transparency heatmap by the World Bank shows that although there has been some progress in terms of the coverage of debt instruments reported as well as its publication, there is still a lot of work to do in terms of making available information to the public on annual borrowing plans and contingent liabilities (World Bank 2020b).

14
current account balance are positive and significant in regression [2] of Table 1, therefore, both fiscal and external savings play a role in limiting the adverse impacts of the pandemic on economic growth. Consequently, countries with smaller primary deficits or primary surpluses have space to finance policies that could help avoid deeper declines in economic growth. The same can be said about countries that have accumulated external savings (i.e. current account surpluses). Finally, the estimated coefficients of general government gross debt are negative but insignificant in most cases, and hence a country with larger general government gross debts could still face a significant decrease in economic growth.

The pandemic has adversely affected economic activity in all regions. Although economic growth declined significantly in Sub-Saharan Africa, this decline is smaller than the developing country average after controlling for an array of growth drivers (see Tables 1 to 3). Consequently, we test whether the different drivers of growth have a different impact in Sub-Saharan Africa vis-à-vis other developing countries in Table 3. We include interaction terms between the Sub-Saharan Africa dummy and selected explanatory variables —including, trade openness (level and composition by partner and product), gross capital inflows (level and composition by type of inflow), government response indicators, and health and human capital indicators. For most of the drivers, we are unable to find a significant coefficient for the interaction with SSA. Therefore, the average impact on growth for developing countries is similar to that for SSA countries. The only exception is gross capital inflows (as percentage of GDP), with a positive and significant interaction coefficient. This finding supports that the financing flow channel may be weaker in Sub-Saharan Africa when compared with other developing areas.

# **5.3 Instrumental Variables Estimation**

We conduct an in IV regression analysis to overcome endogeneity caused from a simple linear regression. The results from the 2020 human capital index (HCI) are positive and significant (i.e. 20.61 points in [2] in Table 4). Consequently, countries with higher levels of human capital (i.e. improved higher education and better health outcomes) have a higher rate of economic growth. Global health security shows a positive and insignificant coefficient, therefore, global health security matters on economic growth. The coefficient estimates of government response of both containment and health measures as well as stringency measures are robustly negative and significant. Therefore, governments measures of stringency and containment may not be providing efficient support or adequately targeting the lives and livelihoods that they want to protect. Ineffective pandemic policies can exacerbate the economy decline.

The results from IV estimations support the findings from the least squares regressions: they confirm the existence of a negative relationship between trade exposure and growth. The deleterious effects of trade exposure can be partly due to vulnerabilities to commodity price volatility. The results from trade openness and export to advanced countries are mostly negative and significant. Natural resource exports and gross capital inflows are negative and statistically not significant. The plunge in global trade as a result of the pandemic has affected more countries participating in global value chains compared with those that resort to intra-regional trade.

15
Countries that are more dependent on external financing could experience deeper declines in economic growth. For instance, in regression [1] in Table 4, the estimated adverse impact of (gross) capital flows on economic growth at -0.098 is significantly deeper than the one of trade openness. Therefore, countries with greater dependence on external financing tend to have larger declines in growth. Portfolio investment inflows are significantly positive at 0.387, other investment inflows are insignificantly positive at 0.161, and FDI inflows are insignificantly positive at 0.0466 in regression [7] of Table 4. Consequently, those inflows help prevent further declines in GDP growth.

Our results confirm the growth effects of recent changes in debt compositions in developing countries. For instance, accumulating external debts by official creditors are less risky than ones by private creditors, therefore, the composition of debt profiles matters. Maintaining primary balance and current account surpluses helps the economy cushion the impact of the crisis. Changes in a country’s borrowing pattern will alter the risk profile and may vary the transmission mechanisms of the pandemic shock. Especially a shift in the composition of debt from public creditors to private creditors can render the economy more vulnerable to greater currency, interest rate, maturity (i.e. shorter intervals) and refinancing risks. Table 4 support that countries with greater public debt exposure to private creditors tend to experience a larger decline in economic growth as the coefficients of PPG external debt to private creditors are mostly negative and significant while the ones of PPG external debt to public creditors are robustly positive and significant.

# **6. Conclusion**

The COVID-19 pandemic has pushed the global economy into the deepest and most synchronized downturn since World War II. The impact of the pandemic on economic activity is heterogeneous across countries and regions due to differences in structural characteristics and policy responses to the pandemic.

This paper explores the significance of alternative transmission mechanisms through which COVID-19 affects economic activity, using a cross-country regression framework. Our evidence shows that the economic impacts caused by the COVID-19 shock are transmitted through three main channels, including health, trade, and financing. The structures of products and markets are more important characteristics in the economy than the extent of openness.

We find evidence that higher levels of human capital, well-targeted containment measures, sound macroeconomic policies, better global health security, diversified trade patterns, deeper intraregional trade, and a less risky composition of debt profiles matter for adverting a deeper downturn in economic activity during the pandemic. Transparency and accountability with solid institutions are an integral part of an enhancement of economic growth.

16
# **Table 1**

# **Determinants of growth amid the COVID-19 pandemic: Baseline Regressions**

_Dependent variable: Change in the predicted 2020 growth rate (June 2020 vs. October 2019) Effective sample of 110 countries across the world Least Squares_

|Variables|**[1]**|**[2]**|**[3]**|**[4]**|**[5]**|**[6]**|**[7]**|**[8]**|**[9]**|**[10]**|**[11]**|**[12]**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|_International Trade_|||||||||||||
|Trade Openness<br>_(% GDP)_|-0.0116<br>(0.0157)|-0.00985<br>(0.0150)|-0.0147<br>(0.0156)|-0.00967<br>(0.0116)|..|..|-0.00173<br>(0.00899)|-0.00991<br>(0.0151)|..|..|-0.00361<br>(0.00893)|-0.0125<br>(0.0148)|
|<br>Natural resource exorts|||||-0124*||||-0126*||||
|p<br>_(% GDP)_|..|..|..|..|.<br>(0.0701)|..|..|..|.<br>(0.0690)|..|..|..|
|Manufacturing imports<br>|..|..|..|..|0.00389<br>|..|..|..|-0.00368<br>|..|..|..|
|_(% GDP)_<br>Exports to Advanced|..|..|..|..|(0.0435)<br>..|-0.0832**|..|..|(0.0430)<br>..|-0.0852**|..|..|
|<br>Countries_(% GDP)_<br>||||||(0.0402)<br>||||(0.0396)<br>|||
|Exports to Developing Countries<br>Outside the region_(% GDP)_|..|..|..|..|..|-0.154<br>(0.128)|..|..|..|-0.162<br>(0.126)|..|..|
|<br>Intra-regional exports|..|..|..|..|..|0.0744|..|..|..|0.0677|..|..|
|_(% GDP)_||||||(0.0601)||||(0.0592)|||
|_International Finance_|||||||||||||
|Gross Capital Inflows<br>|-0.0832<br>|-0.109<br>|-0.0837<br>|-0.100<br>|-0.103<br>|-0.0991<br>||-0.109<br>|-0.104<br>|-0.101<br>||-0.109<br>|
|_(% GDP)_<br>Gross FDI Inflows|(0.0702)<br>..|(0.0673)<br>..|(0.0693)<br>..|(0.0674)<br>..|(0.0812)<br>..|(0.0666)<br>..|-0.0378|(0.0677)<br>..|(0.0800)<br>..|(0.0656)<br>..|-0.0391|(0.0663)<br>..|
|_(% GDP)_|||||||(0.0771)||||(0.0759)||
|<br>Gross Portfolio Investment|..|..|..|..|..|..|0.264**|..|..|..|0.255**|..|
|Inflows_(% GDP)_|||||||(0.112)||||(0.110)||
|Gross Other Investment|||||||0228***||||0222**||
|<br>Inflows_(% GDP)_|..|..|..|..|..|..|.<br>(0.0856)|..|..|..|.<br>(0.0842)|..|
|Remittances|0.0254|0.0328|0.0410|0.0389|0.0128|0.0176|-0.0315|0.0340|0.0372|0.0306|-0.0207|0.0487|
|_(% GDP, received)_<br>International Tourism|(0.0599)<br>0.0230|(0.0570)<br>0.0398|(0.0599)<br>0.0237|(0.0569)<br>0.0221|(0.0816)<br>0.00789|(0.0559)<br>0.0285|(0.0393)<br>0.0143|(0.0579)<br>0.0402|(0.0822)<br>0.00979|(0.0555)<br>0.0275|(0.0393)<br>0.0145|(0.0570)<br>0.0404|
|Revenues_(% GDP)_|(0.0304)|(0.0294)|(0.0300)|(0.0286)|(0.0416)|(0.0320)|(0.0203)|(0.0297)|(0.0409)|(0.0315)|(0.0200)|(0.0290)|
|_Health and Human Capital_|||||||||||||
|Human Capital<br>|-2.235<br>|0.666<br>|-2.994<br>|..|1.096<br>|2.110<br>|-1.867<br>|0.538<br>|-0.263<br>|1.035<br>|-2.523<br>|-0.405<br>|
|2020 Index_(0-1)_|(6.570)|(6.317)|(6.354)||(6.970)|(6.034)|(3.812)|(6.403)|(6.729)|(5.838)|(3.644)|(6.095)|
|Global Health Security|..|..|..|-0.0102|..|..|..|..|..|..|..|..|
|<br>_(0-100 index)_||||(0.0480)|||||||||
|_Macroeconomic Space_|||||||||||||
|<br>Primary Balance|0.149|0.164*|0.171*|0.143|0.341*|0.194**|0.135**|0.168*|0.347**|0.217**|0.153**|0.188**|
|_(% GDP)_<br>General Govt Gross|(0.0969)<br>00146|(0.0922)<br>000301|(0.0960)<br>00136|(0.0922)<br>-000699|(0.172)<br>-000793|(0.0908)<br>000328|(0.0585)<br>-000258|(0.0958)<br>000276|(0.169)<br>-000934|(0.0899)<br>000237|(0.0581)<br>-000395|(0.0913)<br>000201|
|<br>Debt_(% GDP)_|.<br>(0.0171)|.<br>(0.0167)|.<br>(0.0169)|.<br>(0.0164)|.<br>(0.0203)|.<br>(0.0162)|.<br>(0.0110)|.<br>(0.0169)|.<br>(0.0200)|.<br>(0.0159)|.<br>(0.0108)|.<br>(0.0165)|
|PPG External Debt<br>_% GDP_|0.00294<br>000888|..|0.00409<br>000876|..|..|..|..|..|..|..|..|..|
|_( )_<br>|(.)||(.)||||||||||
|PPG Ext.Dbt. Official<br>Creditors_(% GDP)_|..|0.0949***<br>(00316)|..|0.0994***<br>(00323)|0.0935**<br>(00366)|0.105***<br>(00309)|0.0694*** <br>(00190)|0.0952***<br>(00318)|0.0926**<br>(00360)|0.105***<br>(00304)|0.0701*** <br>(00187)|0.0950***<br>(00311)|
|<br>b i||.<br>||.<br>|.<br>*|.<br>|.<br>*|.<br>|.<br>|.<br>|.<br>*|.<br>|
|PPG Ext.Dt. Prvate<br>Creditors_(% GDP)_|..|-0.0168<br>(0.0107)|..|-0.0160<br>(0.0109)|-0.0190<br>(0.0113)|-0.0150<br>(0.0103)|-0.0117<br>(0.00622)|-0.0164<br>(0.0109)|-0.0171<br>(0.0111)|-0.0135<br>(0.0101)|-0.0108<br>(0.00610)|-0.0153<br>(0.0105)|
|Inflation Rate|00569|00579|00641|00804|00547|00583|00244|00588|00606|00659|00271|00653|
|<br>_(% end of period)_|.<br>(0.0532)|.<br>(0.0505)|.<br>(0.0527)|.<br>(0.0505)|.<br>(0.0610)|.<br>(0.0487)|.<br>(0.0358)|.<br>(0.0511)|.<br>(0.0602)|.<br>(0.0483)|.<br>(0.0353)|.<br>(0.0500)|
|Current Account|0.142**|0.117**|0.140**|0.110**|0.131*|0.131**|0.0392|0.116**|0.131*|0.130**|0.0353|0.115**|
|Balance_(% GDP)_|(0.0566)|(0.0545)|(0.0559)|(0.0505)|(0.0756)|(0.0535)|(0.0363)|(0.0549)|(0.0744)|(0.0527)|(0.0358)|(0.0537)|
|_Policy Response_|||||||||||||
|<br>Government Response (GR)|-0.0488|-0.0576*|..|-0.0598*|-0.0570*|-0.0523*|-0.0340*|..|..|..|..|..|
|_(Oxford tracker)_|(0.0328)|(0.0313)||(0.0312)|(0.0337)|(0.0301)|(0.0193)||||||
|Stringency Index<br>|..|..|-0.0602**<br>|..|..|..|..|..|-0.0659**<br>|-0.0598**<br>|-0.0380**<br>|-0.0649**<br>|
|_(Oxford tracker)_<br>GR: Containment & Health|..|..|(0.0286)<br>..|..|..|..|..|-0.0520|(0.0297)<br>..|(0.0260)<br>..|(0.0168)<br>..|(0.0273)<br>..|
|_(Oxford tracker)_||||||||(0.0330)|||||
|GR: Economic Support|..|..|..|..|..|..|..|-0.00541|..|..|..|..|
|_(Oxford tracker)_||||||||(0.0208)|||||
|_Regional Dummies_|||||||||||||
|Dummy: Sub-Saharan<br>|2.170*<br>|3.049**<br>|2.052*<br>|2.762***<br>|3.585**<br>|3.465***<br>|1.543**<br>|3.069**<br>|3.370**<br>|3.366***<br>|1.456*<br>|2.932**<br>|
|Africa (SSA)|(1.243)|(1.216)|(1.228)|(0.978)|(1.382)|(1.226)|(0.757)|(1.230)|(1.368)|(1.209)|(0.747)|(1.202)|
|Observations|89|89|89|93|78|89|79|89|78|89|79|89|
|R-squared<br>Standard errors in parentheses|0.306|0.381|0.325|0.363|0.441|0.438|0.491|0.381|0.458|0.454|0.506|0.399|
|*** p<0.01, ** p<0.05, * p<0.1|||||||||||||

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# **Table 2**

**Determinants of growth amid the COVID-19 pandemic: Sensitivity Analysis I Changes in the dependent variable: IMF vs. WBG Projection Changes** _Effective sample of 110 countries across the world Least Squares_

|||**IMF**|**Growth Fore**|**cast Change**|**s **_1/_|||**WBG**|**Growth Fo**|**recast Chang**|**es **_2/_||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Variables|**[1]**|**[2]**|**[3]**|**[4]**|**[5]**|**[6]**|**[7]**|**[8]**|**[9]**|**[10]**|**[11]**|**[12]**|
|_International Trade_|||||||||||||
|<br>Trade Openness<br>_% GDP_|-0.0248<br>00254|-0.0228<br>00249|..|..|-0.0185<br>00163|-0.0250<br>00248|-0.0286<br>00266|-0.0266<br>00261|..|..|-0.0169<br>00149|-0.0278<br><br>00262|
|_( )_<br>|(.)|(.)|||(.)|(.)|(.)|(.)|||(.|(.)|
|Natural resource exports<br>|..|..|-0.247**<br>|..|..|..|..|..|-0.297**<br>|..|..|..|
|_(% GDP)_|||(0.113)||||||(0.119)||||
|Manufacturing imports<br>|..|..|0.00464<br>|..|..|..|..|..|0.000422<br>|..|..|..|
|_(% GDP)_<br>Exports to Advanced|..|..|(0.0705)<br>..|-0.154**|..|..|..|..|(0.0741)<br>..|-0.170**|..|..|
|<br>Countries_(% GDP)_||||(0.0665)||||||(0.0706)|||
|Exports to Developing Countries<br>Outside the region_(% GDP)_|..|..|..|-0.134<br>(0.212)|..|..|..|..|..|-0.0937<br>(0.225)|..|..|
|Intra-regional exports<br>|..|..|..|0.138<br>|..|..|..|..|..|0.110<br>|..|..|
|_(% GDP)_||||(0.0995)||||||(0.106)|||
|_International Finance_|||||||||||||
|Gross Capital Inflows|-0.246**|-0.274**|-0.209|-0.251**|..|-0.275**|-0.286**|-0.315***|-0.266*|-0.284**|..|-0.316***|
|_(% GDP)_|(0.113)|(0.112)|(0.131)|(0.110)||(0.111)|(0.119)|(0.117)|(0.138)|(0.117)||(0.117)|
|Gross FDI Inflows<br>_(% GDP)_|..|..|..|..|-0.155<br>(0.139)|..|..|..|..|..|-0.163<br>(0.127)|..|
|Gross Portfolio Investment|..|..|..|..|0.233|..|..|..|..|..|0.142|..|
|Inflows_(% GDP)_|||||(0.202)||||||(0.184)||
|Gross Other Investment|||||0232||||||0169||
|<br>Inflows_(% GDP)_|..|..|..|..|.<br>(0.154)|..|..|..|..|..|.<br>(0.141)|..|
|<br>Remittances|0.0617|0.0699|0.0580|0.0783|0.0544|0.0987|0.0874|0.0958|0.0658|0.103|0.0673|0.121|
|<br>_(% GDP, received)_|(0.0965)|(0.0946)|(0.135)|(0.0933)|(0.0720)|(0.0954)|(0.101)|(0.0994)|(0.142)|(0.0990)|(0.0657)|(0.101)|
|International Tourism|-0.0273|-0.00853|-0.0823|-0.0183|-0.0604|-0.00701|-0.0157|0.00351|-0.0827|-0.0116|-0.0630*|0.00498|
|Revenues_(% GDP)_|(0.0489)|(0.0488)|(0.0671)|(0.0529)|(0.0366)|(0.0485)|(0.0513)|(0.0513)|(0.0706)|(0.0562)|(0.0334)|(0.0512)|
|_Health and Human Capital_|||||||||||||
|<br>Human Capital|7.365|10.61|2.977|8.196|3.477|7.256|11.72|15.04|9.288|12.85|5.449|11.64|
|2020 Index_(0-1)_|(10.58)|(10.49)|(11.03)|(9.807)|(6.672)|(10.20)|(11.09)|(11.02)|(11.60)|(10.41)|(6.089)|(10.77)|
|_Macroeconomic Space_|||||||||||||
|<br>Primary Balance|0.159|0.176|0.588**|0.257*|0.145|0.227|0.154|0.171|0.660**|0.244|0.109|0.220|
|_(% GDP)_|(0.156)|(0.153)|(0.277)|(0.151)|(0.106)|(0.153)|(0.164)|(0.161)|(0.291)|(0.160)|(0.0970)|(0.161)|
|<br>General Govt Gross|-0.00720|-0.0202|-0.0349|-0.0237|-0.0359*|-0.0226|-0.00139|-0.0146|-0.0295|-0.0179|-0.0315*|-0.0169|
|Debt_(% GDP)_|(0.0276)|(0.0277)|(0.0327)|(0.0268)|(0.0198)|(0.0276)|(0.0289)|(0.0291)|(0.0344)|(0.0284)|(0.0181)|(0.0291)|
|PPG External Debt|-0.00307|..|..|..|..|..|-0.00411|..|..|..|..|..|
|_(% GDP)_|(0.0143)||||||(0.0150)||||||
|<br>PPG Ext.Dbt. Official|..|0.0999*|0.0725|0.117**|0.0541|0.0982*|..|0.101*|0.0734|0.114**|0.0499|0.0988*|
|Creditors_(% GDP)_||(0.0524)|(0.0591)|(0.0511)|(0.0343)|(0.0520)||(0.0550)|(0.0621)|(0.0543)|(0.0313)|(0.0549)|
|PPG Ext.Dbt. Private|..|-0.0252|-0.0217|-0.0157|-0.0127|-0.0213|..|-0.0267|-0.0266|-0.0177|-0.0137|-0.0229|
|Creditors_(% GDP)_||(0.0177)|(0.0182)|(0.0170)|(0.0112)|(0.0175)||(0.0186)|(0.0191)|(0.0181)|(0.0102)|(0.0185)|
|Inflation Rate|0.0921|0.0932|0.116|0.109|0.0609|0.106|0.0624|0.0636|0.0811|0.0747|0.0357|0.0746|
|_(% end of eriod)_|(00856)|(00839)|(00986)|(00811)|(00646)|(00837)|(00898)|(00881)|(0104)|(00861|(00590|(00884)|
|_p_<br>Current Account|.<br>0.239**|.<br>0.210**|.<br>0.311**|.<br>0.236***|.<br>0.0446|.<br>0.207**|.<br>0.200**|.<br>0.171*|.<br>0.245*|.<br>0.204**|.<br><br>-0.0261|.<br>0.168*|
|Balance_(% GDP)_|(0.0912)|(0.0904)|(0.122)|(0.0885)|(0.0655)|(0.0899)|(0.0956)|(0.0949)|(0.128)|(0.0940)|(0.0598)|(0.0949)|
|_Policy Response_|||||||||||||
|Government Response (GR)|-0.133**|-0.143***|..|..|..|..|-0.129**|-0.139**|..|..|..|..|
|<br>_(Oxford tracker)_|(00528)|(00520)|||||(00554)|(00546)|||||
|<br>Stringency Index|.<br>..|.<br>..|-0.126**|-0.121***|-0.108***|-0.133***|.<br>..|.<br>..|-0.115**|-0.114**|-0.0828**|*<br>-0.124**|
|<br>_(Oxford tracker)_|||(0.0488)|(0.0437)|(0.0307)|(0.0456)|||(0.0513)|(0.0464)|(0.0280)|(0.0482)|
|_Regional Dummies_<br>Dummy: Sub-Saharan|3.911*|4.896**|5.132**|5.172**|1.906|4.734**|4.838**|5.843***|6.192**|5.958***|2.393*|5.713***|
|<br> Africa (SSA)|(2.001)|(2.019)|(2.243)|(2.032)|(1.369)|(2.012)|(2.099)|(2.121)|(2.358)|(2.157)|(1.249)|(2.124)|
|Observations|89|89|78|89|79|89|89|89|78|89|79|89|
|R-squared|0.390|0.422|0.513|0.478|0.478|0.429|0.357|0.389|0.485|0.436|0.455|0.389|

- _1/ IMF 2020 Growth Forecast Changes (WEO October 2020 vs. WEO October 2019). 2/ WBG 2020 Growth Forecast Changes (MPO October 2020 vs. MPO October 2019). Standard errors in parentheses.  *** p<0.01, ** p<0.05, * p<0.1_

18
# **Table 3**

# **Determinants of growth amid the COVID-19 pandemic: Sensitivity Analysis II Changes in the set of explanatory variables**

_Dependent variable: Change in the predicted 2020 growth rate (June 2020 vs. October 2019) Least Squares, Effective sample of 110 countries across the world_

|||**Financial O**|**enness inde**||**201 **|**Human Ca**|**ital Index**||**Sens**|**tivitto Tr**|**de Variable**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Variables|**[1]**|<br>**[2]**|<br>**[3]**|**[4]**|<br>**[5]**|<br>**[6]**|<br>**[7]**|**[8]**|<br>**[9]**|**y **<br>**[10]**|<br>**[11]**|**[12]**|
|_International Trade_|||||||||||||
|Trade Openness<br>_(% GDP)_|-0.0168<br>(00148)|..|..|-0.0186<br>(00148)|-0.0164<br>(00115)|..|..|-0.0117<br>(00112)|..|..|..|..|
|<br>Natural resource exports|.<br>..|-0.174**|..|.<br>..|.<br>..|-0.134**|..|.<br>..|..|..|..|..|
|_(% GDP)_||(0.0709)||||(0.0659)|||||||
|<br>Manufacturing imports||-000935||||-000142|||||||
|<br>_(% GDP)_|..|.<br>(0.0420)|..|..|..|.<br>(0.0420)|..|..|..|..|..|..|
|<br>Exports to Advanced|..|..|-0.0966**|..|..|..|-0.0755*|..|..|..|..|..|
|<br>Countries_(% GDP)_|||(0.0416)||||(0.0386)||||||
|Exports to Developing Countries|..|..|-0.151|..|..|..|-0.0761|..|..|..|..|..|
|Outside the region_(% GDP)_<br>|||(0.138)<br>||||(0.128)<br>*||||||
|Intra-regional exports|..|..|0.0236|..|..|..|0.0986|..|..|..|..|..|
|_(% GDP)_|||(0.0614)||||(0.0585)||||||
|Natural resource trade|||||||||-00884||-0114**||
|<br>_(% GDP, exports and imports)_|..|..|..|..|..|..|..|..|.<br>(0.0540)|..|.<br>(0.0527)|..|
|Manufacturing trade|..|..|..|..|..|..|..|..|-0.00302|..|-0.00451|..|
|_(% GDP, exports and imports)_<br>|||||||||(0.0211)||(0.0207)||
|Trade to Advanced Countries|..|..|..|..|..|..|..|..|..|-0.0535**|..|-0.0614**|
|_(% GDP, exports and imports)_||||||||||(0.0229)||(0.0237)|
|Trade to Developing Countries<br>|..|..|..|..|..|..|..|..|..|-0.0308<br>|..|-0.00329<br>|
|Outside the region_(% GDP)_||||||||||(0.0759)||(0.0919)|
|Intra-regional Trade|..|..|..|..|..|..|..|..|..|0.0333|..|0.0252|
|<br>_(% GDP, exports and imports)_||||||||||(0.0252)||(0.0259)|
|_International Finance_|||||||||||||
|Gross Capital Inflows|..|..|..|..|-0.0730|-0.0719|-0.0998|-0.0935|-0.0976|-0.103|..|..|
|_(% GDP)_<br>Financial Openness Index|-0591**|-0610**|-0419|-0556*|(0.0667)<br>|(0.0783)<br>|(0.0602)<br>|(0.0646)<br>|(0.0805)<br>|(0.0643)<br>|-0609**|-0440|
|<br>_(Chinn-Ito index)_|.<br>(0.296)|.<br>(0.299)|.<br>(0.307)|.<br>(0.296)|..|..|..|..|..|..|.<br>(0.302)|.<br>(0.294)|
|Remittances<br>_(% GDP received)_|0.0423<br>(00577)|-0.00331<br>(00805)|0.0294<br>(00572)|0.0540<br>(00581)|0.0146<br>(00593)|0.00263<br>(00841)|0.0190<br>(00565)|0.0234<br>(00571)|0.0934<br>(00766)|0.0281<br>(00587)|0.0759<br>(00741)|0.0259<br>(00626)|
|_,_<br>International Tourism|.<br>0.0419|.<br>-0.0119|.<br>0.0144|.<br>0.0415|.<br>0.0205|.<br>-0.00815|.<br>0.0336|.<br>0.0336|.<br>0.0188|.<br>0.0455|.<br>0.00696|.<br>0.0441|
|Revenues_(% GDP)_|(0.0306)|(0.0402)|(0.0336)|(0.0304)|(0.0288)|(0.0394)|(0.0313)|(0.0281)|(0.0390)|(0.0307)|(0.0372)|(0.0328)|
|_Health and Human Capital_|||||||||||||
|Human Capital|-1.726|-4.408|-1.507|-2.738|..|..|..|..|0.653|0.679|-2.014|-2.730|
|<br>2020 Index_(0-1)_<br>|(6.639)|(6.605)|(6.203)|(6.469)|||||(6.813)|(5.957)|(6.681)|(6.710)|
|Human Capital<br>2017 Index_(0-1)_|..|..|..|..|-11.80**<br>(4.544)|-9.733*<br>(5.788)|-7.803<br>(4.705)|-9.547**<br>(4.447)|||||
|_Macroeconomic Space_|||||||||||||
|Primary Balance|0.0808|0.326*|0.138|0.0899|0.167*|0.303*|0.197**|0.181**|0.299*|0.219**|0.254|0.111|
|<br>_(% GDP)_|(0.130)|(0.165)|(0.133)|(0.129)|(0.0918)|(0.162)|(0.0879)|(0.0884)|(0.164)|(0.0904)|(0.158)|(0.131)|
|General Govt Gross|0.0180|0.00163|0.0186|0.0180|0.00591|-0.00923|-0.00816|-0.00433|-0.00728|0.00559|0.00345|0.0184|
|Debt_(% GDP)_|(00146)|(00187)|(00143)|(00145)|(00159)|(00197)|(00151)|(00158)|(00201)|(00162)|(00188)|(00146)|
|<br>PPG External Debt<br> _(% GDP)_|.|.|.|.|.<br>0.00779<br>(0.00824)|.|.|.|.|.|.|.|
|<br>PPG Ext.Dbt. Official|0.0705**|0.0550|0.0791**|0.0705**||0.0764**|0.107***|0.0897***|0.0965***|0.102***|0.0656*|0.0777**|
|<br>Creditors_(% GDP)_|(0.0323)|(0.0359)|(0.0321)|(0.0321)||(0.0371)|(0.0300)|(0.0310)|(0.0359)|(0.0303)|(0.0355)|(0.0315)|
|PPG Ext.Dbt. Private|-0.0124|-0.0162|-0.0109|-0.0111||-0.0132|-0.00698|-0.00975|-0.0176|-0.0128|-0.0163|-0.00868|
|Creditors_(% GDP)_|(0.0108)|(0.0109)|(0.0104)|(0.0107)||(0.0111)|(0.0102)|(0.0102)|(0.0112)|(0.0102)|(0.0109)|(0.0105)|
|Inflation Rate|-0.0140**|-0.0137*|-0.0163**|-0.0132*|0.0803|0.0785|0.0882*|0.0820*|0.0511|0.0529|-0.0156**|-0.0152**|
|_(% end of period)_|(0.00695)|(0.00700)|(0.00692)|(0.00692)|(0.0506)|(0.0593)|(0.0474)|(0.0487)|(0.0606)|(0.0489)|(0.00699)|(0.00721)|
|<br>Current Account|0.192***|0.216***|0.205***|0.190***|0.137***|0.166**|0.109**|0.118**|0.134*|0.119**|0.210***|0.190***|
|Balance_(% GDP)_|(0.0495)|(0.0579)|(0.0476)|(0.0493)|(0.0501)|(0.0764)|(0.0454)|(0.0486)|(0.0750)|(0.0520)|(0.0582)|(0.0475)|
|_Policy Response_|||||||||||||
|Government Response (GR)<br>_(Oxford tracker)_|-0.0526<br>(00316)||||||||||||
|<br>Stringency Index|.|-0.0505*|-0.0502*|-0.0539*|-0.0531*|-0.0556*|-0.0509*|-0.0571**|-0.0788**|-0.0570**|-0.0686**|-0.0470*|
|_(Oxford tracker)_||(0.0291)|(0.0271)|(0.0280)|(0.0276)|(0.0300)|(0.0265)|(0.0266)|(0.0302)|(0.0265)|(0.0296)|(0.0274)|
|_Regional Dummies_|||||||||||||
|Dummy: Sub-Saharan|2.287*|1.967|2.463*|2.208*|0.412|1.435|1.857|1.274|3.520**|3.104**|2.477*|2.029|
|<br> Africa (SSA)|(1.261)|(1.377)|(1.291)|(1.256)|(1.154)|(1.403)|(1.144)|(1.154)|(1.363)|(1.285)|(1.357)|(1.497)|
|Observations|88|79|88|88|93|81|93|93|78|89|79|88|
|R-squared|0.39|0.483|0.440|0.397|0.358|0.471|0.455|0.413|0.454|0.450|0.476|0.444|

_Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1_ 19
# **Table 4**

# **Determinants of growth amid the COVID-19 pandemic: Baseline Regressions**

_Dependent variable: Change in the predicted 2020 growth rate (June 2020 vs. October 2019) Effective sample of 110 countries across the world_ **_Instrumental Variables 1/_**

|Variables|**[1]**|**[2]**|**[3]**|**[4]**|**[5]**|**[6]**|**[7]**|**[8]**|**[9]**|**[10]**|**[11]**|**[12]**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|_International Trade_|||||||||||||
|<br>Trade Openness|-0.0726*|-0.0540*|-0.0773*|-0.0342*|..|..|-0.0295|-0.0583|..|..|-0.0214|-0.0588*|
|_(% GDP)_<br>Natural resource exports|(0.0419)<br>..|(0.0296)<br>..|(0.0429)<br>..|(0.0191)<br>..|-0.150|..|(0.0220)<br>..|(0.0375)<br>..|-0.149|..|(0.0172)<br>..|(0.0316)<br>..|
|_(% GDP)_|||||(0.121)||||(0.127)||||
|Manufacturing imports|..|..|..|..|-0.161|..|..|..|-0.177|..|..|..|
|_(% GDP)_<br>Exports to Advanced|..|..|..|..|(0.121)<br>..|-0.126**|..|..|(0.130)<br>..|-0.126**|..|..|
|Countries_(% GDP)_||||||(0.0608)||||(0.0627)|||
|Exports to Developing Countries<br>Outside the region_(% GDP)_|..|..|..|..|..|-0.109<br>(0.172)|..|..|..|-0.160<br>(0.178)|..|..|
|Intra-regional exports|..|..|..|..|..|-0.135|..|..|..|-0.132|..|..|
|<br>_(% GDP)_||||||(0.132)||||(0.132)|||
|_International Finance_<br>|||||||||||||
|Gross Capital Inflows|-0.0980|-0.140|-0.101|-0.108|-0.0858|-0.137|..|-0.139|-0.100|-0.141|..|-0.138|
|_(% GDP)_<br>Gross FDI Inflows|(0.115)<br>..|(0.0903)<br>..|(0.116)<br>..|(0.0777)<br>..|(0.135)<br>..|(0.0910)<br>..|0.0466|(0.0969)<br>..|(0.141)<br>..|(0.0943)<br>..|0.0179|(0.0921)<br>..|
|_(% GDP)_|||||||(0.124)||||(0.0987)||
|Gross Portfolio Investment<br>|..|..|..|..|..|..|0.387**<br>|..|..|..|0.305**<br>|..|
|Inflows_(% GDP)_<br>Gross Other Investment|||||||(0.171)<br>0161||||(0.132)<br>0163||
|<br>Inflows_(% GDP)_|..|..|..|..|..|..|.<br>(0.142)|..|..|..|.<br>(0.115)|..|
|Remittances<br>_% GDP id_|0.150<br>0112|0.140<br>00855|0.226*<br>0134|0.113<br>00689|0.316<br>0219|0.107<br>00837|0.0299<br>00659|0.153<br>0111|0.413<br>0266|0.159<br>00996|0.0401<br>00598|0.199*<br>0102|
|_( , receve)_<br>International Tourism|(.)<br>-0.0105|(.)<br>0.0197|(.)<br>0.00448|(.)<br>0.0213|(.)<br>-0.0535|(.)<br>-0.0248|(.)<br>0.0155|(.)<br>0.0216|(.)<br>-0.0178|(.)<br>-0.0167|(.)<br>0.0173|(.)<br>0.0286|
|Revenues_(% GDP)_|(0.0530)|(0.0410)|(0.0519)|(0.0339)|(0.0760)|(0.0497)|(0.0300)|(0.0448)|(0.0762)|(0.0497)|(0.0243)|(0.0415)|
|_Health and Human Capital_|||||||||||||
|<br>Human Capital|21.56|20.61*|9.887|..|21.99|20.17*|9.213|21.26|8.266|10.44|0.708|11.32|
|2020 Index_(0-1)_<br>Glbl Hlh Si|(16.05)|(12.09)|(12.25)|00828|(16.52)|(11.64)|(8.829)|(13.30)|(13.05)|(9.218)|(5.181)|(9.742)|
|oa eat ecurty<br>_(0-100 index)_|..|..|..|.<br>(0.0709)|..|..|..|..|..|..|..|..|
|_Macroeconomic Space_|||||||||||||
|Primary Balance|0.0658|0.0962|0.245|0.115|0.163|0.112|0.122|0.119|0.225|0.259*|0.200***|0.234*|
|_(% GDP)_<br>General Govt Gross|(0.160)<br>0.0239|(0.124)<br>0.00386|(0.171)<br>0.0145|(0.105)<br>-0.00423|(0.318)<br>-0.00134|(0.126)<br>0.00309|(0.0846)<br>-0.00377|(0.171)<br>0.00237|(0.319)<br>-0.0128|(0.139)<br>-0.00266|(0.0732)<br>-0.0104|(0.134)<br>-0.00148|
|Debt_(% GDP)_|(0.0292)|(0.0230)|(0.0288)|(0.0196)|(0.0341)|(0.0227)|(0.0165)|(0.0256)|(0.0358)|(0.0238)|(0.0134)|(0.0234)|
|<br>PPG External Debt|0.000142|..|0.00900|..|..|..|..|..|..|..|..|..|
|_(% GDP)_|(0.0145)||(0.0148)||||||||||
|<br>PPG Ext.Dbt. Official|..|0.135***|..|0.120***|0.120*|0.135***|0.0950***|0.140***|0.110*|0.131***|0.0866***|0.128***|
|Creditors_(% GDP)_||(0.0448)||(0.0372)|(0.0616)|(0.0441)|(0.0300)|(0.0538)|(0.0629)|(0.0448)|(0.0235)|(0.0445)|
|PPG Ext.Dbt. Private<br>Creditors_(% GDP)_|..|-0.0276*<br>(0.0149)|..|-0.0223*<br>(0.0128)|-0.0279<br>(0.0189)|-0.0256*<br>(0.0147)|-0.0182*<br>(0.00969)|-0.0264<br>(0.0171)|-0.0128<br>(0.0196)|-0.0152<br>(0.0144)|-0.0114<br>(0.00714)|-0.0178<br>(0.0144)|
|Inflation Rate|0105|00934|0142|0106*|0152|00965|00646|0103|0172|0131|00583|0123|
|<br>_(% end of period)_|.<br>(0.0910)|.<br>(0.0702)|.<br>(0.0986)|.<br>(0.0597)|.<br>(0.115)|.<br>(0.0713)|.<br>(0.0541)|.<br>(0.0889)|.<br>(0.124)|.<br>(0.0815)|.<br>(0.0439)|.<br>(0.0763)|
|Current Account|0.154*|0.121*|0.135|0.127**|0.167|0.170**|0.0381|0.116|0.156|0.158**|0.0230|0.110|
|Balance_(% GDP)_|(0.0906)|(0.0716)|(0.0919)|(0.0613)|(0.124)|(0.0733)|(0.0507)|(0.0805)|(0.128)|(0.0750)|(0.0416)|(0.0736)|
|_Policy Response_<br>|||||||||||||
|Government Response (GR)|-0.461*|-0.353**|..|-0.278**|-0.462*|-0.351**|-0.223**|..|..|..|..|..|
|_(Oxford tracker)_|(0.242)|(0.163)||(0.123)|(0.245)|(0.179)|(0.111)||||||
|<br>Stringency Index|..|..|-0.421**|..|..|..|..|..|-0.444*|-0.330**|-0.145**|-0.328**|
|_(Oxford tracker)_|||(0.214)||||||(0.237)|(0.168)|(0.0716)|(0.152)|
|<br>GR: Containment & Health||||||||-0346|||||
|<br>_(Oxford tracker)_|..|..|..|..|..|..|..|.<br>(0.269)|||||
|GR: Economic Support||||||||-00392|||||
|<br>|..|..|..|..|..|..|..|.<br>|||||
|_(Oxford tracker)_||||||||(0.0777)|||||
|_Reional Dummies_|||||||||||||
|_g_<br>Dummy: Sub-Saharan|1.893|3.677**|1.259|2.101|2.906|3.659**|1.797|3.691**|1.459|3.090|1.310|3.014*|
|Africa (SSA)|(2.175)|(1.691)|(2.300)|(1.310)|(2.369)|(1.745)|(1.139)|(1.811)|(2.753)|(1.891)|(0.921)|(1.797)|
|Observations|85|85|85|88|75|85|75|85|75|85|75|85|
|R-squared||||0.103|||||||0.218||

- 1/ We control the likely endogeneity of the policy response with 2019 levels of real GDP per capita, government tax revenues (% GDP), voice and accountability, and political stability Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

20
# **Table 5**

**Determinants of growth amid the COVID-19 pandemic: Sensitivity Analysis I Changes in the dependent variable: IMF vs. WBG Projection Changes** _Effective sample of 110 countries across the world_

**_Instrumental Variables  1/_**

|||**IMFG**|**rowth Forec**|**astChanges **|**2**_/_|||**WBG**|**Growth Fore**|**castChange**|**s3**_/_||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Variables|**[1]**|<br>**[2]**|<br>**[3]**|<br>**[4]**|<br>**[5]**|**[6]**|**[7]**|<br>**[8]**|<br>**[9]**|<br>**[10]**|<br>**[11]**|**[12]**|
|_International Trade_|||||||||||||
|Trade Openness<br>|-0.111*<br>|-0.0795*<br>|..|..|-0.0572*<br>|-0.0934*<br>|-0.121*<br>|-0.0900*<br>|..|..|-0.0559*<br>|-0.0993*<br>|
|_(% GDP)_<br>|(0.0639)|(0.0448)|||(0.0339)|(0.0512)|(0.0662)|(0.0470)|||(0.0312)|(0.0520)|
|Natural resource exports|..|..|-0.213|..|..|..|..|..|-0.255|..|..|..|
|_(% GDP)_|||(0.178)||||||(0.182)||||
|Manufacturing imports<br>_(% GDP)_|..|..|-0.208<br>(0.184)|..|..|..|..|..|-0.225<br>(0.188)|..|..|..|
|<br>Exports to Advanced|..|..|..|-0.209**|..|..|..|..|..|-0.233**|..|..|
|Countries_(% GDP)_||||(0.0923)||||||(0.0948)|||
|Exports to Developing Countries|..|..|..|-0.115|..|..|..|..|..|-0.0751|..|..|
|<br>Outside the region_(% GDP)_||||(0.261)||||||(0.268)|||
|Intra-regional exports|..|..|..|-0.123|..|..|..|..|..|-0.161|..|..|
|<br>_(% GDP)_||||(0.195)||||||(0.200)|||
|_International Finance_|||||||||||||
|Gross Capital Inflows|-0.264|-0.315**|-0.220|-0.299**|..|-0.312**|-0.313*|-0.366**|-0.291|-0.344**|..|-0.363**|
|_(% GDP)_<br>Gross FDI Inflows|(0.175)|(0.137)|(0.199)|(0.139)|00181|(0.149)|(0.181)|(0.143)|(0.203)|(0.143)|-000921|(0.151)|
|<br>_(% GDP)_|..|..|..|..|.<br>(0.194)|..|..|..|..|..|.<br>(0.178)|..|
|Gross Portfolio Investment<br>|..|..|..|..|0.423<br>|..|..|..|..|..|0.322<br>|..|
|Inflows_(% GDP)_|||||(0.259)||||||(0.238)||
|Gross Other Investment|..|..|..|..|0.0856|..|..|..|..|..|0.0357|..|
|Inflows_(% GDP)_|||||(0.227)||||||(0.208)||
|<br>Remittances|0.223|0.198|0.513|0.230|0.167|0.306*|0.255|0.231*|0.531|0.258*|0.171|0.329**|
|_(% GDP, received)_|(0.171)|(0.130)|(0.375)|(0.147)|(0.118)|(0.165)|(0.177)|(0.136)|(0.384)|(0.150)|(0.108)|(0.168)|
|International Tourism|-0.0465|-0.00693|-0.0524|-0.0474|-0.0121|0.00621|-0.0362|0.00423|-0.0507|-0.0438|-0.0124|0.0181|
|Revenues_(% GDP)_|(0.0808)|(0.0622)|(0.108)|(0.0731)|(0.0478)|(0.0672)|(0.0838)|(0.0652)|(0.110)|(0.0751)|(0.0439)|(0.0682)|
|_Health and Human Capital_|||||||||||||
|Human Capital|40.36*|36.02**|10.77|19.11|8.820|22.92|47.12*|43.06**|18.77|25.42*|11.30|28.69*|
|2020 Index_(0-1)_|(24.50)|(18.32)|(18.40)|(13.56)|(10.20)|(15.78)|(25.39)|(19.21)|(18.80)|(13.93)|(9.365)|(16.02)|
|_Macroeconomic Space_|||||||||||||
|<br>Primary Balance|0.0621|0.106|0.323|0.331|0.300**|0.330|0.0479|0.0922|0.360|0.309|0.258*|0.313|
|_(% GDP)_|(0.244)|(0.188)|(0.450)|(0.205)|(0.144)|(0.218)|(0.253)|(0.197)|(0.460)|(0.210)|(0.132)|(0.221)|
|General Govt Gross|-0.000980|-0.0263|-0.0462|-0.0362|-0.0589**|-0.0348|0.00889|-0.0171|-0.0363|-0.0272|-0.0508**|-0.0257|
|Debt_(% GDP)_|(0.0445)|(0.0348)|(0.0505)|(0.0350)|(0.0263)|(0.0380)|(0.0461)|(0.0365)|(0.0516)|(0.0359)|(0.0242)|(0.0385)|
|PPG External Debt|-0.00568|..|..|..|..|..|-0.00821|..|..|..|..|..|
|_(% GDP)_|(0.0222)||||||(0.0230)||||||
|<br>PPG Ext.Dbt. Official|..|0.155**|0.101|0.148**|0.0936**|0.148**|..|0.159**|0.103|0.146**|0.0869**|0.148**|
|Creditors_(% GDP)_||(0.0679)|(0.0887)|(0.0660)|(0.0463)|(0.0721)||(0.0712)|(0.0906)|(0.0677)|(0.0426)|(0.0732)|
|PPG Ext.Dbt. Private|..|-0.0382*|-0.0115|-0.0166|-0.0127|-0.0232|..|-0.0419*|-0.0178|-0.0200|-0.0147|-0.0264|
|Crditr_(% GDP)_||(00227)|(00277)|(00211)|(00141)|(00233)||(00238)|(00283)|(00217)|(00129)|(00237)|
|eos <br>Inflation Rate|0.172|.<br>0.151|.<br>0.255|.<br>0.200*|.<br>0.139|.<br>0.205*|0.136|.<br>0.115|.<br>0.214|.<br>0.158|.<br>0.105|.<br>0.164|
|_(% end of period)_|(0.139)|(0.106)|(0.175)|(0.120)|(0.0864)|(0.124)|(0.144)|(0.112)|(0.179)|(0.123)|(0.0794)|(0.126)|
|Current Account|0.261*|0.222**|0.332*|0.277**|0.0246|0.202*|0.224|0.185|0.267|0.251**|-0.0418|0.166|
|Balance_(% GDP)_|(0.138)|(0.109)|(0.181)|(0.110)|(0.0819)|(0.119)|(0.143)|(0.114)|(0.185)|(0.113)|(0.0753)|(0.121)|
|_Policy Response_|||||||||||||
|Government Response (GR)|-0.736**|-0.543**|..|..|..|..|-0.750*|-0.559**|..|..|..|..|
|_(Oxford tracker)_|(0.370)|(0.247)|||||(0.383)|(0.259)|||||
|Stringency Index|..|..|-0.614*|-0.476*|-0.350**|-0.546**|..|..|-0.607*|-0.470*|-0.313**|-0.530**|
|<br>_(Oxford tracker)_|||(0.335)|(0.247)|(0.141)|(0.247)|||(0.342)|(0.254)|(0.129)|(0.250)|
|_Regional Dummies_|||||||||||||
|Dummy: Sub-Saharan|3.369|5.676**|2.161|4.609*|1.463|4.503|4.338|6.701**|3.285|5.547*|1.982|5.612*|
|<br> Africa (SSA)|(3.321)|(2.564)|(3.883)|(2.782)|(1.812)|(2.912)|(3.441)|(2.688)|(3.968)|(2.857)|(1.665)|(2.956)|
|Observations|85|85|75|85|75|85|85|85|75|85|75|85|
|R-squared||0.012||0.026||||||0.009|||

_1/ We control the likely endogeneity of the policy response with 2019 levels of real GDP per capita, government tax revenues (% GDP), voice and accountability, and political stability_ _2/ IMF 2020 Growth Forecast Changes (WEO October 2020 vs. WEO October 2019). 3/ WBG 2020 Growth Forecast Changes (MPO October 2020 vs. MPO October 2019). Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1_ 21
# **Table 6**

# **Determinants of growth amid the COVID-19 pandemic: Sensitivity Analysis II Changes in the set of explanatory variables**

_Dependent variable: Change in the predicted 2020 growth rate (June 2020 vs. October 2019) Effective sample of 110 countries across the world_

# **_Instrumental Variables  1/_**

|||**Financial O**|**enness inde**|**x**|**201 **|**Human Ca**|**ital Index**||**Sen**|**itivitto Tr**|**de Variable**|**s**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Variables|**[1]**|<br>**[2]**|<br>**[3]**|**[4]**|<br>**[5]**|<br>**[6]**|<br>**[7]**|**[8]**|<br>**[9]**|**y **<br>**[10]**|<br>**[11]**|**[12]**|
|_International Trade_|||||||||||||
|Trade Openness<br>_(% GDP)_|-0.0675**<br>(00327)|..|..|-0.0807*<br>(00432)|-0.0728<br>(00509)|..|..|-0.0454<br>(00286)|..|..|..|..|
|<br>Natural resource exports<br>_(% GDP)_|.<br>..|-0.171<br>(0.157)|..|.<br>..|.<br>..|-0.143<br>(0.111)|..|.<br>..|..|..|..|..|
|<br>Manfactrin imorts||-0242||||-0153|||||||
|uug p<br>|..|.<br>|..|..|..|.<br>|..|..|..|..|..|..|
|_(% GDP)_<br>Exports to Advanced|..|(0.182)<br>..|-0.181*|..|..|(0.120)<br>..|-0.137**|..|..|..|..|..|
|<br>Countries_(% GDP)_|||(0.0932)||||(0.0634)||||||
|Exports to Developing Countries|..|..|-0.0150|..|..|..|-0.165|..|..|..|..|..|
|Outside the region_(% GDP)_<br>|||(0.259)<br>||||(0.166)<br>||||||
|Intra-regional exports<br>_(% GDP)_|..|..|-0.239<br>(0.201)|..|..|..|-0.000256<br>(0.0921)|..|..|..|..|..|
|Natural resource trade|..|..|..|..|..|..|..|..|-0.267*|..|-0.271**|..|
|<br>_(% GDP, exports and imports)_|||||||||(0.138)||(0.135)||
|Manufacturing trade|..|..|..|..|..|..|..|..|-0.0461|..|-0.0545|..|
|_(% GDP, exports and imports)_<br>|||||||||(0.0460)||(0.0482)||
|Trade to Advanced Countries|..|..|..|..|..|..|..|..|..|-0.0702*|..|-0.0925*|
|_(% GDP, exports and imports)_||||||||||(0.0380)||(0.0483)|
|Trade to Developing Countries<br>|..|..|..|..|..|..|..|..|..|-0.0737<br>|..|-0.0960<br>|
|Outside the region_(% GDP)_<br>Intra-regional Trade|..|..|..|..|..|..|..|..|..|(0.120)<br>-0.0800|..|(0.178)<br>-0.113|
|<br>_(% GDP, exports and imports)_||||||||||(0.0723)||(0.0997)|
|_International Finance_|||||||||||||
|Gross Capital Inflows<br>..|..|..|..|..|-0.158|-0.163|-0.183*|-0.191*|-0.0512|-0.138|..|..|
|_(% GDP)_|||||(0.173)|(0.126)|(0.104)|(0.114)|(0.134)|(0.0980)|||
|Financial Openness Index|-0.410|0.0331|-0.0658|-0.194|..|..|..|..|..|..|-0.186|-0.154|
|_(Chinn-Ito index)_|(0.451)|(0.778)|(0.640)|(0.558)<br>|||||||(0.564)|(0.589)|
|Remittances<br>_(% GDP received)_|0.153<br>(00939)|0.460<br>(0357)|0.198<br>(0145)|0.230*<br>(0133)|0.237<br>(0170)|0.358<br>(0243)|0.133<br>(00860)|0.185*<br>(0102)|0.409*<br>(0223)|0.240*<br>(0145)|0.365<br>(0222)|0.284<br>(0200)|
|_,_<br>International Tourism|.<br>0.0180|.<br>-0.0543|.<br>-0.0330|.<br>0.0217|.<br>-0.00229|.<br>-0.0335|.<br>-0.00291|.<br>0.0299|.<br>-0.0545|.<br>-0.00455|.<br>-0.0712|.<br>-0.0218|
|Revenues_(% GDP)_|(0.0464)|(0.0940)|(0.0682)|(0.0521)|(0.0725)|(0.0697)|(0.0482)|(0.0473)|(0.0737)|(0.0528)|(0.0758)|(0.0718)|
|_Health and Human Capital_|||||||||||||
|Human Capital|19.79|6.822|8.627|11.97|..|..|..|..|7.166|12.12|6.090|12.74|
|<br>2020 Index_(0-1)_<br>|(13.75)|(16.00)|(12.32)|(12.80)|||||(11.87)|(10.64)|(11.62)|(15.10)|
|Human Capital<br>2017 Index_(0-1)_|..|..|..|..|11.82<br>(15.29)|15.91<br>(13.80)|12.25<br>(8.510)|12.56<br>(9.935)|..|..|..|..|
|_Macroeconomic Space_|||||||||||||
|Primary Balance|-0.00688|0.155|-0.0299|0.0603|0.331|0.339|0.279**|0.262*|0.392|0.228|0.324|0.00518|
|<br>_(% GDP)_|(0.184)|(0.387)|(0.253)|(0.207)|(0.243)|(0.269)|(0.135)|(0.149)|(0.281)|(0.143)|(0.265)|(0.237)|
|General Govt Gross|0.0276|0.0104|0.0225|0.0275|0.0160|-0.0114|-0.000146|-0.00153|-0.00569|-0.00222|0.0128|0.0263|
|Debt_(% GDP)_<br>|(0.0215)|(0.0407)|(0.0262)|(0.0243)|(0.0374)<br>|(0.0318)|(0.0226)|(0.0250)|(0.0329)|(0.0256)|(0.0303)|(0.0266)|
|PPG External Debt<br> _(% GDP)_|..|..|..|..|0.0116<br>(0.0186)|..|..|..|..|..|..|..|
|<br>PPG Ext.Dbt. Official|0.115**|0.0998|0.112*|0.113**|..|0.137**|0.155***|0.151***|0.0930|0.130***|0.0784|0.115*|
|Creditors_(% GDP)_|(0.0507)|(0.0809)|(0.0615)|(0.0570)||(0.0618)|(0.0470)|(0.0521)|(0.0568)|(0.0475)|(0.0554)|(0.0611)|
|PPG Ext.Dbt. Private|-0.0250|-0.0130|-0.0114|-0.0160|..|-0.0255|-0.0188|-0.0217|-0.0202|-0.0168|-0.0216|-0.0149|
|Creditors_(% GDP)_<br>|(0.0164)<br>|(0.0232)<br>|(0.0184)<br>|(0.0172)<br>||(0.0180)<br>|(0.0136)<br>|(0.0154)<br>|(0.0178)<br>|(0.0152)<br>|(0.0172)<br>|(0.0185)<br>|
|Inflation Rate|-0.0170*|-0.0154|-0.00833|-0.0111|0.184|0.144|0.124|0.129|0.137|0.126|-0.0158|-0.0131|
|_(% end of period)_<br>C A|(0.00973)<br>0209***|(0.0148)<br>0238*|(0.0138)<br>0280***|(0.0113)<br>0192**|(0.145)<br>0116|(0.114)<br>0114|(0.0779)<br>0131|(0.0861)<br>00944|(0.111)<br>0178|(0.0875)<br>0130*|(0.0108)<br>0229**|(0.0125)<br>0225***|
|urrent ccount<br>Balance_(% GDP)_|.<br>(0.0705)|.<br>(0.123)|.<br>(0.0934)|.<br>(0.0809)|.<br>(0.139)|.<br>(0.112)|.<br>(0.0827)|.<br>(0.0919)|.<br>(0.121)|.<br>(0.0772)|.<br>(0.0933)|.<br>(0.0858)|
|_Policy Response_<br>|||||||||||||
|Government Response (GR)<br>_(Oxford tracker)_|-0.375**<br>(0185)|..|..|..|..|..|..|..|..|..|..|..|
|<br>Stringency Index<br>_(Oxford tracker)_|.<br>..|-0.528<br>(0.334)|-0.436<br>(0.283)|-0.393*<br>(0.222)|-0.587<br>(0.367)|-0.420*<br>(0.243)|-0.326**<br>(0.158)|-0.370**<br>(0.185)|-0.424*<br>(0.223)|-0.355*<br>(0.194)|-0.397*<br>(0.235)|-0.426<br>(0.279)|
|_Regional Dummies_<br>Dummy: Sub-Saharan|2.833|-0.259|0.987|1.979|0.0226|2.492|3.314*|2.975|1.653|3.136|0.948|2.103|
|<br> Africa (SSA)|(1.836)|(3.374)|(2.766)|(2.180)|(3.280)|(2.350)|(1.759)|(1.904)|(2.611)|(1.990)|(2.497)|(2.624)|
|Observations<br>R-squared|84|76|84|84|82|73|82|82|75|85|7|6<br>84|

1/ We control the likely endogeneity of the policy response with 2019 levels of real GDP per capita, government tax revenues (% GDP), voice and accountability, and political stability Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 22
**Figure 1. Population Structure and COVID-19 infection rates**

<!-- Start of picture text --> 14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>0 5 10 15 20 25 30<br>Percentage of the population 65 years and older<br>Sub-Saharan Africa Developing Countries Industrial Countries<br>COVID-19 infections (per million people, logs)<br><!-- End of picture text --> 23
**Figure 2. Population Structure and COVID-19 death rates**

<!-- Start of picture text --> 10<br>9<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>0 5 10 15 20 25 30<br>Percentage of the population 65 years and older<br>Sub-Saharan Africa Developing Countries Industrial Countries<br>COVID-19 deaths (per million people, logs)<br><!-- End of picture text --> _Source: Our World in Data COVID-19 database_ 24