impact of covid 19 on globalization essay

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The impact of COVID-19 on globalization

Affiliations.

1 Department of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, TX, USA.
2 Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistan.
3 Department of Public Health & Prevention Sciences, Baldwin Wallace University, Berea, OH, USA.
4 Center for Natural Resources Studies, Dhaka, Bangladesh.
5 Department of Sociology and Anthropology, St Louis University, St. Louis, MO, USA.
6 Data analytics division, Zarrin Jam Marina, Tehran, Iran.
7 Centre for Epidemiology and Evidence-based Practice, Department of Social and Preventive Medicine, University of Malaya Kuala Lumpur, Malaysia.
8 Faculty of Applied Science, School of Engineering, The University of British Columbia (UBC), Okanagan, BC V1V 1V7, Canada.
PMID: 33072836
PMCID: PMC7553059
DOI: 10.1016/j.onehlt.2020.100180

Globalization has altered the way we live and earn a livelihood. Consequently, trade and travel have been recognized as significant determinants of the spread of disease. Additionally, the rise in urbanization and the closer integration of the world economy have facilitated global interconnectedness. Therefore, globalization has emerged as an essential mechanism of disease transmission. This paper aims to examine the potential impact of COVID-19 on globalization and global health in terms of mobility, trade, travel, and countries most impacted. The effect of globalization were operationalized in terms of mobility, economy, and healthcare systems. The mobility of individuals and its magnitude was assessed using airline and seaport trade data and travel information. The economic impact was measured based on the workforce, event cancellations, food and agriculture, academic institutions, and supply chain. The healthcare capacity was assessed by considering healthcare system indicators and preparedness of countries. Utilizing a technique for order of preference by similarity to ideal solution (TOPSIS), we calculated a pandemic vulnerability index (PVI) by creating a quantitative measure of the potential global health. The pandemic has placed an unprecedented burden on the world economy, healthcare, and globalization through travel, events cancellation, employment workforce, food chain, academia, and healthcare capacity. Based on PVI results, certain countries were more vulnerable than others. In Africa, more vulnerable countries included South Africa and Egypt; in Europe, they were Russia, Germany, and Italy; in Asia and Oceania, they were India, Iran, Pakistan, Saudi Arabia, and Turkey; and for the Americas, they were Brazil, USA, Chile, Mexico, and Peru. The impact on mobility, economy, and healthcare systems has only started to manifest. The findings of this study may help in the planning and implementation of strategies at the country level to help ease this emerging burden.

Keywords: COVID-19; Economic impact; GDP, Gross Domestic Product; GHI, Global Health Index; GLM, Generalized Linear Model; Global Health; Globalization; IMF, International Monetary Fund; Infectious diseases; LMIC, Low-and-middle-income countries; PVI, Pandemic Vulnerability Index; Pandemic; SARS-CoV-2; TEU, Twenty-foot Equivalent Unit; TOPSIS; TOPSIS, Technique for Order of Preference by Similarity to Ideal Solution; WHO, World Health Organization.

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COVID-19 has shone a light on how globalization can tackle inequality

impact of covid 19 on globalization essay

Assistant Professor, Agri-Food Trade and Policy, University of Guelph

Postdoctoral research fellow, International Institute of Social Studies

Professor of International Economics and Macroeconomics, International Institute of Social Studies

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Sylvanus Kwaku Afesorgbor receives funding from OMAFRA

Binyam Afewerk Demena and Peter A.G. van Bergeijk do not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.

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Globalization is a multifaceted concept that describes the process of creating networks of connections around the world. It involves the interdependence of national economies and the integration of information, goods, labour and capital, to name a few.

In recent years, globalization has been the subject of growing discontent and criticism , particularly after the election of former U.S. president Donald Trump , Brexit and the American refusal to appoint members to the World Trade Organization’s Appellate Body.

The backlash represents a major setback to the pace of globalization and sets the stage for growing protectionism and nationalism around the world. Many criticisms have been political, but the ongoing COVID-19 pandemic has introduced new health threats to globalization .

In a sense, the pandemic has illuminated both globalization (a virus went global in a few weeks thanks to globalization and interconnectedness) and deglobalization (the breakdown of international co-operation and the re-emergence of nationalism when it came to personal protective gear, medical devices and vaccines).

Read more: Canada's 'me first' COVID-19 vaccine strategy may come at the cost of global health

COVID-19 and globalization

In our recent research , we detail the pandemic’s impact on the world economy via three components of globalization: economic, social and political. The pandemic and the economic policy response to the crisis have had an impact on these three aspects to varying degrees.

1) Economic globalization involves the flow of goods, services, capital and information through long-distance market transactions. Although the pandemic is global, regions and countries have experienced it differently based on various economic indicators.

Merchandise trade contracted for the global economy , but the rate of decline was more pronounced in advanced economies than in developing and emerging economies. Not only were trade flows affected, but the the impact of COVID-19 on foreign direct investment (FDI) was immediate as global FDI flows declined by nearly half in 2020 .

2) Social globalization was also significantly impacted by COVID-19. It pertains to interactions with people abroad including via migration, international phone calls and international remittances paid or received by citizens.

Social globalization has been heavily affected by the COVID-19 pandemic because many countries have imposed travel restrictions on both residents and foreign travellers. Border closures hinder migration, especially the movement of tourists and international students. Migrant remittances were also affected , not because of any formal restrictions on remittances, but mainly because of the impact the pandemic had on immigrant employment.

Read more: Canada's Emergency Response Benefit does nothing for migrant workers

3) Political globalization involves the ability of countries to engage in international political co-operation and diplomacy, as well as implementing government policy.

The initial outbreak of the COVID-19 pandemic affected international co-operation negatively, in part because of the blame game between the two largest economies in the world, the United States and China .

Later, many nations worked together to fight the pandemic. China, for example, supported countries like Italy, which became the epicentre of the COVID-19 pandemic in Europe.

Politically, the outbreak of COVID-19 could be used as a building block in the future to reinforce international co-operation and strengthen the pillars of political globalization.

COVID-19 and previous economic crises

Because of well-established and interdependent global production and supply chains, economic forecasts were pessimistic in the early months of the pandemic due to international border closures and business shutdowns.

The prospect of the world plunging into another major and long-term economic recession similar to the Great Depression in the 1930s and the 2008 recession was top of mind for economists, governments and citizens.

But predictions about the death of globalization were, in hindsight, grossly exaggerated. Recovery efforts took hold early compared to those two major economic crises, suggesting global trade is much more resilient than anticipated.

In fact, there’s reason to be optimistic about the COVID-19 economic recovery as well as the future of globalization.

Multinational enterprises already had their stress test during the 2008-2009 collapse of world trade . That collapse kickstarted a process of deglobalization, but global merchandise trade and industrial production recovered to previous highs quickly — and they’ve done so even more swiftly during the COVID-19 crisis. The shock was sharp and immediate, but so was the recovery.

The so-called invisible flows (FDI, remittances, tourism, official development co-operation) have been hit harder, and full recovery is not to be expected until vaccination rollouts are sufficiently global in scope. Nonetheless, it’s not unrealistic to expect a speedy economic recovery once the pandemic has passed.

The disease of inequality

Ironically, the attacks on globalization were a symptom of an underlying disease — inequality — that have been illuminated by the pandemic.

Globalization lacked a trickling down of benefits to those who most needed them. The pandemic taught us that inequalities are the breeding ground for the spreading of literal diseases and the suffering that follows. Reducing vulnerabilities to future epidemics requires tackling those inequalities.

But the fight against future crises cannot be limited to domestic developments only, because inequality is global. Adhering to the United Nations Sustainable Development Goals is therefore a high-return investment project.

The push towards deglobalization certainly still exists. But economies are now digitally connected in ways they’ve never been before.

Medical staff wearing masks look out of a hospital window.

That’s a positive development, because ending the COVID-19 pandemic and preventing future crises requires international co-operation and a global effort to ensure no single country is left behind. Vaccines must be made available and affordable to all countries, as just reiterated by the leaders of G7 nations in their promise to supply one billion doses of the COVID-19 vaccine to poorer nations .

Just as globalization has ramifications for all countries, the health of one nation affects the health of all nations. It requires a global approach to ensure equality for all the world’s citizens.

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The State of Globalization in 2021

Steven A. Altman
Caroline R. Bastian

Trade, capital, and information flows have stabilized, recovered, and even grown in the past year.

As the coronavirus swept the world, closing borders and halting international trade and capital flows, there were questions about the pandemic’s lasting impact on globalization. But a close look at the recent data paints a much more optimistic picture. While international travel remains significantly down and is not expected to rebound until 2023, cross-border trade, capital, and information flows have largely stabilized, recovered, or even grown over the last year. The bottom line for business is that Covid-19 has not knocked globalization down to anywhere close to what would be required for strategists to narrow their focus to their home countries or regions.

Cross-border flows plummeted in 2020 as the Covid-19 pandemic swept the world, reinforcing doubts about the future of globalization. As we move into 2021, the latest data paint a clearer — and more hopeful — picture. Global business is not going away, but the landscape is shifting, with important implications for strategy and management.

Steven A. Altman is a senior research scholar, adjunct assistant professor, and director of the DHL Initiative on Globalization at the NYU Stern Center for the Future of Management .
CB Caroline R. Bastian is a research scholar at the DHL Initiative on Globalization.

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Chapter 1. The economic impacts of the COVID-19 crisis

The COVID-19 pandemic sent shock waves through the world economy and triggered the largest global economic crisis in more than a century. The crisis led to a dramatic increase in inequality within and across countries. Preliminary evidence suggests that the recovery from the crisis will be as uneven as its initial economic impacts, with emerging economies and economically disadvantaged groups needing much more time to recover pandemic-induced losses of income and livelihoods . 1

In contrast to many earlier crises, the onset of the pandemic was met with a large, decisive economic policy response that was generally successful in mitigating its worst human costs in the short run. However, the emergency response also created new risks—such as dramatically increased levels of private and public debt in the world economy—that may threaten an equitable recovery from the crisis if they are not addressed decisively.

Worsening inequality within and across countries

The economic impacts of the pandemic were especially severe in emerging economies where income losses caused by the pandemic revealed and worsened some preexisting economic fragilities. As the pandemic unfolded in 2020, it became clear that many households and firms were ill-prepared to withstand an income shock of that scale and duration. Studies based on precrisis data suggest, for example, that more than 50 percent of households in emerging and advanced economies were not able to sustain basic consumption for more than three months in the event of income losses . 2 Similarly, the average business could cover fewer than 55 days of expenses with cash reserves . 3 Many households and firms in emerging economies were already burdened with unsustainable debt levels prior to the crisis and struggled to service this debt once the pandemic and associated public health measures led to a sharp decline in income and business revenue.

The crisis had a dramatic impact on global poverty and inequality. Global poverty increased for the first time in a generation, and disproportionate income losses among disadvantaged populations led to a dramatic rise in inequality within and across countries. According to survey data, in 2020 temporary unemployment was higher in 70 percent of all countries for workers who had completed only a primary education. 4 Income losses were also larger among youth, women, the self-employed, and casual workers with lower levels of formal education . 5 Women, in particular, were affected by income and employment losses because they were likelier to be employed in sectors more affected by lockdown and social distancing measures . 6

Similar patterns emerge among businesses. Smaller firms, informal businesses, and enterprises with limited access to formal credit were hit more severely by income losses stemming from the pandemic. Larger firms entered the crisis with the ability to cover expenses for up to 65 days, compared with 59 days for medium-size firms and 53 and 50 days for small and microenterprises, respectively. Moreover, micro-, small, and medium enterprises are overrepresented in the sectors most severely affected by the crisis, such as accommodation and food services, retail, and personal services.

The short-term government responses to the crisis

The short-term government responses to the pandemic were extraordinarily swift and encompassing. Governments embraced many policy tools that were either entirely unprecedented or had never been used on this scale in emerging economies. Examples are large direct income support measures, debt moratoria, and asset purchase programs by central banks. These programs varied widely in size and scope (figure 1.1), in part because many low-income countries were struggling to mobilize resources given limited access to credit markets and high precrisis levels of government debt. As a result, the size of the fiscal response to the crisis as a share of the gross domestic product (GDP) was almost uniformly large in high-income countries and uniformly small or nonexistent in low-income countries. In middle-income countries, the fiscal response varied substantially, reflecting marked differences in the ability and willingness of governments to spend on support programs.

Figure 1.1 Fiscal response to the COVID-19 crisis, selected countries, by income group

: WDR 2022 team, based on IMF (2021). Data from International Monetary Fund, “Fiscal Monitor Update,” .

: The figure reports, as a percentage of gross domestic product (GDP), the total fiscal support, calculated as the sum of “above-the-line measures” that affect government revenue and expenditures and the subtotal of liquidity support measures. Data are as of September 27, 2021.

Similarly, the combination of policies chosen to confront the short-term impacts differed significantly across countries, depending on the availability of resources and the specific nature of risks the countries faced (figure 1.2). In addition to direct income support programs, governments and central banks made unprecedented use of policies intended to provide temporary debt relief, including debt moratoria for households and businesses. Although these programs mitigated the short-term liquidity problems faced by households and businesses, they also had the unintended consequence of obscuring the true financial condition of borrowers, thereby creating a new problem: lack of transparency about the true extent of credit risk in the economy.

Figure 1.2 Fiscal, monetary, and financial sector policy responses to the COVID-19 crisis, by country income group

: WDR 2022 team, based on Erik H. B. Feyen, Tatiana Alonso Gispert, Tatsiana Kliatskova, and Davide S. Mare, “Taking Stock of the Financial Sector Policy Response to COVID-19 around the World,” Policy Research Working Paper 9497, World Bank, Washington, DC, 2020; Eric Lacey, Joseph Massad, and Robert Utz, “A Review of Fiscal Policy Responses to COVID-19,” Macroeconomics, Trade, and Investment Insight 7, Equitable Growth, Finance, and Institutions Insight Series, World Bank, Washington, DC, 2021; World Bank, COVID-19 Crisis Response Survey, 2021, .

: The figure shows the percentage of countries in which each of the listed policies was implemented in response to the pandemic. Data for the financial sector measures are as of June 30, 2021.

The large crisis response, while necessary and effective in mitigating the worst impacts of the crisis, led to a global increase in government debt that gave rise to renewed concerns about debt sustainability and added to the widening disparity between emerging and advanced economies. In 2020, 51 countries—including 44 emerging economies—experienced a downgrade in their government debt risk rating (that is, the assessment of a country’s creditworthiness) . 7

Emerging threats to an equitable recovery

Although households and businesses have been most directly affected by income losses stemming from the pandemic, the resulting financial risks have repercussions for the wider economy through mutually reinforcing channels that connect the financial health of households, firms, financial institutions, and governments (figure 1.3). Because of this interconnection, elevated financial risk in one sector can spill over and destabilize the economy as a whole. For example, if households and firms are under financial stress, the financial sector faces a higher risk of loan defaults and is less able to provide credit. Similarly, if the financial position of the public sector deteriorates (for example, as a result of higher government debt and lower tax revenue), the ability of the public sector to support the rest of the economy is weakened.

Figure 1.3 Conceptual framework: Interconnected balance sheet risks

WDR 2022 team.

The figure shows the links between the main sectors of an economy through which risks in one sector can affect the wider economy.

This relationship is, however, not predetermined. Well-designed fiscal, monetary, and financial sector policies can counteract and reduce these intertwined risks and can help transform the links between sectors of the economy from a vicious doom loop into a virtuous cycle.

One example of policies that can make a critical difference are those targeting the links between the financial health of households, businesses, and the financial sector. In response to the first lockdowns and mobility restrictions, for example, many governments supported households and businesses using cash transfers and financial policy tools such as debt moratoria. These programs provided much-needed support to households and small businesses and helped avert a wave of insolvencies that could have threatened the stability of the financial sector.

Similarly, governments, central banks, and regulators used various policy tools to assist financial institutions and prevent risks from spilling over from the financial sector to other parts of the economy. Central banks lowered interest rates and eased liquidity conditions, making it easier for commercial banks and nonbank financial institutions such as microfinance lenders to refinance themselves, thereby allowing them to continue to supply credit to households and businesses.

The crisis response will also need to include policies that address the risks arising from high levels of government debt to ensure that governments preserve their ability to effectively support the recovery. This is an important policy priority because high levels of government debt reduce the government’s ability to invest in social safety nets that can counteract the impact of the crisis on poverty and inequality and provide support to households and firms in the event of setbacks during the recovery.

By 2021, after the collapse in per capita incomes across the globe in 2020, 40 percent of advanced economies had recovered and, in some cases, exceeded their 2019 output levels. The comparable share of countries achieving per capita income in 2021 that surpassed 2019 output is far lower among middle-income countries, at 27 percent, and lower still among low-income countries, at only 21 percent.

Cristian Badarinza, Vimal Balasubramaniam, and Tarun Ramadorai, “The Household Finance Landscape in Emerging Economies,” 11 (December 2019): 109–29, .

Data from World Bank, COVID-19 Business Pulse Surveys Dashboard, .

The difference in the rate of work stoppage between less well-educated and more well-educated workers was statistically significant in 23 percent of the countries. See Maurice Kugler, Mariana Viollaz, Daniel Vasconcellos Archer Duque, Isis Gaddis, David Locke Newhouse, Amparo Palacios-López, and Michael Weber, “How Did the COVID-19 Crisis Affect Different Types of Workers in the Developing World?” Policy Research Working Paper 9703, World Bank, Washington, DC, 2021, .

Tom Bundervoet, María Eugenia Dávalos, and Natalia Garcia, “The Short-Term Impacts of COVID-19 on Households in Developing Countries: An Overview Based on a Harmonized Data Set of High-Frequency Surveys,” Policy Research Working Paper 9582, World Bank, Washington, DC, 2021, .

Markus P. Goldstein, Paula Lorena Gonzalez Martinez, Sreelakshmi Papineni, and Joshua Wimpey, “The Global State of Small Business during COVID-19: Gender Inequalities,” (blog), September 8, 2020, .

Carmen M. Reinhart, “From Health Crisis to Financial Distress,” Policy Research Working Paper 9616, World Bank, Washington, DC, 2021, https://openknowledge.worldbank.org/handle/10986/35411. Data from Trading Economics, Credit Rating (database), .

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In light of the global pandemic of COVID-19 that caused economic recession worldwide on top of the great number of lives lost to the virus there has been a lot of coverage of the risks of globalization. The increased interconnectedness of today’s world is seen not only in the increased flow of people, goods, and information but also viruses that sweep throughout the globe like a wildfire. Response to the economic devastation has included protectionist policies that will undoubtedly cause reversal of some aspects of globalization. Sources in this section discuss the role of globalization in spreading pandemics as well as consequences of pandemics for globalization.

The following titles link to fuller bibliographic information in the Library of Congress Online Catalog . Links to additional online content are included when available.

Internet Resources

The following online resources provide additional information on globalization and pandemics.

COVID-19 and Trade Policy: Why Turning Inward Won’t Work [e-book] External Richard Baldwin, Simon Evenett, Vox, 29 April 2020. The COVID-19 pandemic sparked broad-ranging resort to export restrictions on medical supplies and food. This eBook asks: Should governments react to the health, economic, and trade crises by turning inward? The authors provide an unequivocal answer: No. Turning inward won’t help today’s fight against COVID-19. National trade barriers in a world of internationalised manufacturing processes will make it harder for every nation to produce vital medical supplies. Insular policies will also fail to foster economic recovery, and they are a threat to the collaborative spirit that the human race will need to defeat this threat.
COVID-19: Turning Point for Globalization? External By Hans Yue Zhu, Yale Global, April 21, 2020. (Archived copy)
Economic Globalization and the COVID-19 Pandemic: Global Spread and Inequalities Jeanne L, Bourdin S, Nadou F, Noiret G. Economic globalization and the COVID-19 pandemic: global spread and inequalities. GeoJournal. 2023;88(1):1181-1188. doi: 10.1007/s10708-022-10607-6. Epub 2022 Mar 11. PMID: 35309019; PMCID: PMC8916502. The authors conclude: "Globalization and the geography of economic relations were the main drivers of the spatial structuring and speed of the international spread of the COVID-19."
Globalization and Infectious Diseases: A Review of the Linkages (PDF, 478 KB) External Lance Saker, et al. Special Programme for Research and Training in Tropical Diseases. World Health Organization, 2004
Globalization and Pandemics: Global Problems Require Global Responses External By Arthur E. Appleton, The Globalist, March 3, 2020
Globalization and Pandemics: The Case of COVID-19 External Mario Arturo Ruiz Estrada, University of Malaya (UM) - Social Security Research Centre (SSRC); University of Malaya (UM) and Alam Khan, KUST. March 26, 2020
Globalization Will Look Very Different After the Coronavirus Pandemic External By Richard Fontaine, Foreign Policy, April 17, 2020
How to Pandemic-Proof Globalization External By Shannon K. O'Neil, Foreign Affairs, April 1, 2020
What You Need to Know About Infectious Diseases: Global Challenges: Globalization External The National Academies
Will Covid-19 Have a Lasting Impact on Globalization? External by Steven A. Altman, Harvard Business Review, May 20, 2020
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Published: 04 February 2022

Analysis of the COVID-19 pandemic: lessons towards a more effective response to public health emergencies

Yibeltal Assefa ORCID: orcid.org/0000-0003-2393-1492 1 ,
Charles F. Gilks 1 ,
Simon Reid 1 ,
Remco van de Pas 2 ,
Dereje Gedle Gete 1 &
Wim Van Damme 2

Globalization and Health volume 18 , Article number: 10 ( 2022 ) Cite this article

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The pandemic of Coronavirus Disease 2019 (COVID-19) is a timely reminder of the nature and impact of Public Health Emergencies of International Concern. As of 12 January 2022, there were over 314 million cases and over 5.5 million deaths notified since the start of the pandemic. The COVID-19 pandemic takes variable shapes and forms, in terms of cases and deaths, in different regions and countries of the world. The objective of this study is to analyse the variable expression of COVID-19 pandemic so that lessons can be learned towards an effective public health emergency response.

We conducted a mixed-methods study to understand the heterogeneity of cases and deaths due to the COVID-19 pandemic. Correlation analysis and scatter plot were employed for the quantitative data. We used Spearman’s correlation analysis to determine relationship strength between cases and deaths and socio-economic and health systems. We organized qualitative information from the literature and conducted a thematic analysis to recognize patterns of cases and deaths and explain the findings from the quantitative data.

We have found that regions and countries with high human development index have higher cases and deaths per million population due to COVID-19. This is due to international connectedness and mobility of their population related to trade and tourism, and their vulnerability related to older populations and higher rates of non-communicable diseases. We have also identified that the burden of the pandemic is also variable among high- and middle-income countries due to differences in the governance of the pandemic, fragmentation of health systems, and socio-economic inequities.

The COVID-19 pandemic demonstrates that every country remains vulnerable to public health emergencies. The aspiration towards a healthier and safer society requires that countries develop and implement a coherent and context-specific national strategy, improve governance of public health emergencies, build the capacity of their (public) health systems, minimize fragmentation, and tackle upstream structural issues, including socio-economic inequities. This is possible through a primary health care approach, which ensures provision of universal and equitable promotive, preventive and curative services, through whole-of-government and whole-of-society approaches.

The pandemic of Coronavirus Disease 2019 (COVID-19) is a timely reminder of the nature and impact of emerging infectious diseases that become Public Health Emergency of International Concern (PHEIC) [ 1 ]. The COVID-19 pandemic takes variable shapes and forms in how it affects communities in different regions and countries [ 2 , 3 ]. As of 12 January, 2022, there were over 314 million cases and over 5.5 million deaths notified around the globe since the start of the pandemic. The number of cases per million population ranged from 7410 in Africa to 131,730 in Europe while the number of deaths per million population ranged from 110 in Oceania to 2740 in South America. Case-fatality rates (CFRs) ranged from 0.3% in Oceania to 2.9% in South America [ 4 , 5 ]. Regions and countries with high human development index (HDI), which is a composite index of life expectancy, education, and per capita income indicators [ 6 ], are affected by COVID-19 more than regions with low HDI. North America and Europe together account for 55 and 51% of cases and deaths, respectively. Regions with high HDI are affected by COVID-19 despite their high universal health coverage index (UHCI) and Global Health Security index (GHSI) [ 7 ].

This seems to be a paradox (against the established knowledge that countries with weak (public) health systems capacity will have worse health outcomes) in that the countries with higher UHCI and GHSI have experienced higher burdens of COVID-19 [ 7 ]. The paradox can partially be explained by variations in testing algorithms, capacity for testing, and reporting across different countries. Countries with high HDI have health systems with a high testing capacity; the average testing rate per million population is less than 32, 000 in Africa and 160,000 in Asia while it is more than 800, 000 in HICs (Europe and North America). This enables HICs to identify more confirmed cases that will ostensibly increase the number of reported cases [ 3 ]. Nevertheless, these are insufficient to explain the stark differences between countries with high HDI and those with low HDI. Many countries with high HDI have a high testing rate and a higher proportion of symptomatic and severe cases, which are also associated with higher deaths and CFRs [ 7 ]. On the other hand, there are countries with high HDI that sustain a lower level of the epidemic than others with a similar high HDI. It is, therefore, vital to analyse the heterogeneity of the COVID-19 pandemic and explain why some countries with high HDI, UHCI and GHSI have the highest burden of COVID-19 while others are able to suppress their epidemics and mitigate its impacts.

The objective of this study was to analyse the COVID-19 pandemic and understand its variable expression with the intention to learn lessons for an effective and sustainable response to public health emergencies. We hypothesised that high levels of HDI, UHCI and GHSI are essential but not sufficient to prevent and control COVID-19.

We conducted an explanatory mixed-methods study to understand and explain the heterogeneity of the pandemic around the world. The study integrated quantitative and qualitative secondary data. The following steps were included in the research process: (i) collecting and analysing quantitative epidemiological data, (ii) conducting literature review of qualitative secondary data and (iii) evaluating countries’ pandemic responses to explain the variability in the COVID-19 epidemiological outcomes. The study then illuminated specific factors that were vital towards an effective and sustainable epidemic response.

We used the publicly available secondary data sources from Johns Hopkins University ( https://coronavirus.jhu.edu/data/new-cases ) for COVID-19 and UNDP 2020 HDI report ( http://hdr.undp.org/en/2019-report ) for HDI, demographic and epidemiologic variables. These are open data sources which are regularly updated and utilized by researchers, policy makers and funders. We performed a correlation analysis of the COVID-19 pandemic. We determined the association between COVID-19 cases, severity, deaths and CFRs at the 0.01 and 0.05 levels (2-tailed). We used Spearman’s correlation analysis, as there is no normal distribution of the variables [ 8 ].

The UHCI is calculated as the geometric mean of the coverage of essential services based on 17 tracer indicators from: (1) reproductive, maternal, newborn and child health; (2) infectious diseases; (3) non-communicable diseases; and, (4) service capacity and access and health security [ 9 ]. The GHSI is a composite measure to assess a country’s capability to prevent, detect, and respond to epidemics and pandemics [ 10 ].

We then conducted a document review to explain the epidemic patterns in different countries. Secondary data was obtained from peer-reviewed journals, reputable online news outlets, government reports and publications by public health-related associations, such as the WHO. To explain the variability of COVID-19 across countries, a list of 14 indicators was established to systematically assess country’s preparedness, actual pandemic response, and overall socioeconomic and demographic profile in the context of COVID-19. The indicators used in this study include: 1) Universal Health Coverage Index, 2) public health capacity, 3) Global Health Security Index, 4) International Health Regulation, 5) leadership, governance and coordination of response, 6) community mobilization and engagement, 7) communication, 8) testing, quarantines and social distancing, 9) medical services at primary health care facilities and hospitals, 10) multisectoral actions, 11) social protection services, 12) absolute and relative poverty status, 13) demography, and 14) burden of communicable and non-communicable diseases. These indicators are based on our previous studies and recommendation from the World Health Organization [ 3 , 4 ]. We conducted thematic analysis and synthesis to identify the factors that may explain the heterogeneity of the pandemic.

Heterogeneity of COVID-19 cases and deaths around the world: what can explain it?

Table 1 indicates that the pandemic of COVID-19 is heterogeneous around regions of the world. Figure 1 also shows that there is a strong and significant correlation between HDI and globalisation (with an increase in trade and tourism as proxy indicators) and a corresponding strong and significant correlation with COVID-19 burden.

Human development index and its correlates associated with COVID-19 in 189 countries*

Globalisation and pandemics interact in various ways, including through international trade and mobility, which can lead to multiple waves of infections [ 11 ]. In at least the first waves of the pandemic, countries with high import and export of consumer goods, food products and tourism have high number of cases, severe cases, deaths and CFRs. Countries with high HDI are at a higher risk of importing (and exporting) COVID-19 due to high mobility linked to trade and tourism, which are drivers of the economy. These may have led to multiple introductions of COVID-19 into these countries before border closures.

The COVID-19 pandemic was first identified in China, which is central to the global network of trade, from where it spread to all parts of the world, especially those countries with strong links with China [ 12 ]. The epidemic then spread to Europe. There is very strong regional dimension to manufacturing and trading, which could be facilitate the spread of the virus. China is the heart of ‘Factory Asia’; Italy is in the heart of ‘Factory Europe’; the United States is the heart of ‘Factory North America’; and Brazil is the heart of ‘Factory Latin America’ [ 13 ]. These are the countries most affected by COVID-19 during the first wave of the pandemic [ 2 , 3 , 14 ].

It is also important to note that two-third of the countries currently reporting more than a million cases are middle-income countries (MICs), which are not only major emerging market economies but also regional political powers, including the BRICS countries (Brazil, Russia, India and South Africa) [ 3 , 15 ]. These countries participate in the global economy, with business travellers and tourists. They also have good domestic transportation networks that facilitate the internal spread of the virus. The strategies that helped these countries to become emerging markets also put them at greater risk for importing and spreading COVID-19 due to their connectivity to the rest of the world.

In addition, countries with high HDI may be more significantly impacted by COVID-19 due to the higher proportion of the elderly and higher rates of non-communicable diseases. Figure 1 shows that there is a strong and significant correlation between HDI and demographic transition (high proportion of old-age population) and epidemiologic transition (high proportion of the population with non-communicable diseases). Countries with a higher proportion of people older than 65 years and NCDs (compared to communicable diseases) have higher burden of COVID-19 [ 16 , 17 , 18 , 19 , 20 ]. Evidence has consistently shown a higher risk of severe COVID-19 in older individuals and those with underlying health conditions [ 21 , 22 , 23 , 24 , 25 ]. CFR is age-dependent; it is highest in persons aged ≥85 years (10 to 27%), followed by those among persons aged 65–84 years (3 to 11%), and those among persons aged 55-64 years (1 to 3%) [ 26 ].

On the other hand, regions and countries with low HDI have, to date, experienced less severe epidemics. For instance, as of January 12, 2022, the African region has recorded about 10.3 million cases and 233,000 deaths– far lower than other regions of the world (Table 1 ) [ 27 ]. These might be due to lower testing rates in Africa, where only 6.5% of the population has been tested for the virus [ 14 , 28 ], and a greater proportion of infections may remain asymptomatic [ 29 ]. Indeed, the results from sero-surveys in Africa show that more than 80% of people infected with the virus were asymptomatic compared to an estimated 40-50% asymptomatic infections in HICs [ 30 , 31 ]. Moreover, there is a weak vital registration system in the region indicating that reports might be underestimating and underreporting the disease burden [ 32 ]. However, does this fully explain the differences observed between Africa and Europe or the Americas?

Other possible factors that may explain the lower rates of cases and deaths in Africa include: (1) Africa is less internationally connected than other regions; (2) the imposition of early strict lockdowns in many African countries, at a time when case numbers were relatively small, limited the number of imported cases further [ 2 , 33 , 34 ]; (3) relatively poor road network has also limited the transmission of the virus to and in rural areas [ 35 ]; (4) a significant proportion of the population resides in rural areas while those in urban areas spend a lot of their time mostly outdoors; (5) only about 3% of Africans are over the age of 65 (so only a small proportion are at risk of severe COVID-19) [ 36 ]; (6) lower prevalence of NCDs, as disease burden in Africa comes from infectious causes, including coronaviruses, which may also have cross-immunity that may reduce the risk of developing symptomatic cases [ 37 ]; and (7) relative high temperature (a major source of vitamin D which influences COVID-19 infection and mortality) in the region may limit the spread of the virus [ 38 , 39 ]. We argue that a combination of all these factors might explain the lower COVID-19 burden in Africa.

The early and timely efforts by African leaders should not be underestimated. The African Union, African CDC, and WHO convened an emergency meeting of all African ministers of health to establish an African taskforce to develop and implement a coordinated continent-wide strategy focusing on: laboratory; surveillance; infection prevention and control; clinical treatment of people with severe COVID-19; risk communication; and supply chain management [ 40 ]. In April 2021, African Union and Africa CDC launched the Partnerships for African Vaccine Manufacturing (PAVM), framework to expanding Africa’s vaccine manufacturing capacity for health security [ 41 ].

Heterogeneity of the pandemic among countries with high HDI: what can explain it?

Figures 2 and 3 illustrate the variability of cases and deaths due to the COVID-19 pandemic across high-income countries (HICs). Contrary to the overall positive correlation between high HDI and cases, deaths and fatality rates due to COVID-19, there are outlier HICs, which have been able to control the epidemic. Several HICs, such as New Zealand, Australia, South Korea, Japan, Denmark, Iceland, and Norway, managed to contain their epidemics (Figs. 2 and 3 ) [ 15 , 42 , 43 ]. It is important to note that most of these countries (especially the island states) have far less cross-border mobility than other HICs.

Scatter plot of COVID-19 cases per million population in countries with high human development index (> 0.70)

Scatter plot of COVID-19 deaths per million population in countries with high human development index (> 0.70)

HICs that have been successful at controlling their epidemics have similar characteristics, which are related to governance of the response [ 44 ], synergy between UHC and GHS, and existing relative socio-economic equity in the country. Governance and leadership is a crucial factor to explain the heterogeneity of the epidemic among countries with high HDI [ 45 ]. There has been substantial variation in the nature and timing of the public health responses implemented [ 46 ]. Adaptable and agile governments seem better able to respond to their epidemics [ 47 , 48 ]. Countries that have fared the best are the ones with good governance and public support [ 49 ]. Countries with an absence of coherent leadership and social trust have worse outcomes than countries with collective action, whether in a democracy or autocracy, and rapid mobilisation of resources [ 50 ]. The erosion of trust in the United States government has hurt the country’s ability to respond to the COVID-19 crisis [ 51 , 52 ]. The editors of the New England Journal of Medicine argued that the COVID-19 crisis has produced a test of leadership; but, the leaders in the United States had failed that test [ 47 ].

COVID-19 has exposed the fragility of health systems, not only in the public health and primary care, but also in acute and long-term care systems [ 49 ]. Fragmentation of health systems, defined here to mean inadequate synergy and/ or integration between GHS and UHC, is typical of countries most affected by the COVID-19 pandemic. Even though GHS and UHC agendas are convergent and interdependent, they tend to have different policies and practices [ 53 ]. The United States has the highest index for GHS preparedness; however, it has reported the world’s highest number of COVID-19 cases and deaths due to its greatly fragmented health system [ 54 , 55 ]. Countries with health systems and policies that are able to integrate International Health Regulations (IHR) core capacities with primary health care (PHC) services have been effective at mitigating the effects of COVID-19 [ 50 , 53 ]. Australia has been able to control its COVID-19 epidemic through a comprehensive primary care response, including protection of vulnerable people, provision of treatment and support services to affected people, continuity of regular healthcare services, protection and support of PHC workers and primary care services, and provision of mental health services to the community and the primary healthcare workforce [ 56 ]. Strict implementation of public health and social intervention together with UHC systems have ensured swift control of the epidemics in Singapore, South Korea, and Thailand [ 57 ].

The heterogeneity of cases and deaths, due to COVID-19, is also explained by differences in levels of socio-economic inequalities, which increase susceptibility to acquiring the infection and disease progression as well as worsening of health outcomes [ 58 ]. COVID-19 has been a stress test for public services and social protection systems. There is a higher burden of COVID-19 in Black, Asian and Minority Ethnic individuals due to socio-economic inequities in HICs [ 59 , 60 ]. Poor people are more likely to live in overcrowded accommodation, are more likely to have unstable work conditions and incomes, have comorbidities associated with poverty and precarious living conditions, and reduced access to health care [ 59 ].

The epidemiology of COVID-19 is also variable across MICs, with HDI between 0.70 and 0.85, around the world. Overall, the epidemic in MICs is exacerbated by the rapid demographic and epidemiologic transitions as well as high prevalence of obesity. While India and Brazil witnessed rapidly increasing rates of cases and deaths, China, Thailand, Vietnam have experienced a relatively lower disease burden [ 15 ]. This heterogeneity may be attributed to a number of factors, including governance, communication and service delivery. Thailand, China and Vietnam have implemented a national harmonized strategic response with decentralized implementation through provincial and district authorities [ 61 ]. Thailand increased its testing capacity from two to over 200 certified facilities that could process between 10,000 to 100,000 tests per day; moreover, over a million village health volunteers in Thailand supported primary health services [ 62 , 63 ]. China’s swift and decisive actions enabled the country to contain its epidemic though there was an initial delay in detecting the disease. China has been able to contain its epidemic through community-based measures, very high public cooperation and social mobilization, strategic lockdown and isolation, multi-sector action [ 64 ]. Overall, multi-level governance (effective and decisive leadership and accountability) of the response, together with coordination of public health and socio-economic services, and high levels of citizen adherence to personal protection, have enabled these countries to successfully contain their epidemics [ 61 , 65 , 66 ].

On the other hand, the Brazilian leadership was denounced for its failure to establish a national surveillance network early in the pandemic. In March 2020, the health minister was reported to have stated that mass testing was a waste of public funding, and to have advised against it [ 67 ]. This was considered as a sign of a collapse of public health leadership, characterized by ignorance, neoliberal authoritarianism [ 68 ]. There were also gaps in the public health capacity in different municipalities, which varied greatly, with a considerable number of Brazilian regions receiving less funding from the federal government due to political tension [ 69 ]. The epidemic has a disproportionate adverse burden on states and municipalities with high socio-economic vulnerability, exacerbated by the deep social and economic inequalities in Brazil [ 70 ].

India is another middle-income country with a high burden of COVID-19. It was one of the countries to institute strict measures in the early phase of the pandemic [ 71 , 72 ]. However, the government eased restrictions after the claim that India had beaten the pandemic, which lead to a rapid increase in disease incidence. Indeed, on 12 January 2022, India reported 36 million cumulative cases and almost 485,000 total deaths [ 15 ]. The second wave of the epidemic in India exposed weaknesses in governance and inadequacies in the country’s health and other social systems [ 73 ]. The nature of the Indian federation, which is highly centripetal, has prevented state and local governments from tailoring a policy response to suit local needs. A centralized one-size-fits-all strategy has been imposed despite high variations in resources, health systems capacity, and COVID-19 epidemics across states [ 74 ]. There were also loose social distancing and mask wearing, mass political rallies and religious events [ 75 ]. Rapid community transmission driven by high population density and multigenerational households has been a feature of the current wave in India [ 76 ]. In addition, several new variants of the virus, including the UK (B.1.1.7), the South Africa (20H/501Y or B.1.351), and Brazil (P.1), alongside a newly identified Indian variant (B.1.617), are circulating in India and have been implicated as factors in the second wave of the pandemic [ 75 , 76 ].

Heterogeneity of case-fatality rates around the world: what can explain it?

The pandemic is characterized by variable CFRs across regions and countries that are negatively associated with HDI (Fig. 1 ). The results presented in Fig. 4 show that the proportion of elderly population and rate of obesity are important factors which are positively associated with CFR. On the other hand, UHC, IHR capacity and other indicators of health systems capacity (health workforce density and hospital beds) are negatively associated with the CFR (Figs. 1 and 4 ).

Correlates of COVID-19 cases, deaths and case-fatality rates in 189 countries

The evidence from several research indicates that heterogeneity can be explained by several factors, including differences in age-pyramid, socio-economic status, access to health services, or rates of undiagnosed infections. Differences in age-pyramid may explain some of the observed variation in epidemic severity and CFR between countries [ 77 ]. CFRs across countries look similar when taking age into account [ 78 ]. The elderly and other vulnerable populations in Africa and Asia are at a similar risk as populations in Europe and Americas [ 79 ]. Data from European countries suggest that as high as 57% of all deaths have happened in care homes and many deaths in the US have also occurred in nursing homes. On the other hand, in countries such as Mexico and India, individuals < 65 years contributed the majority of deaths [ 80 ].

Nevertheless, CFR also depends on the quality of hospital care, which can be used to judge the health system capacity, including the availability of healthcare workers, resources, and facilities, which affects outcomes [ 81 ]. The CFR can increase if there is a surge of infected patients, which adds to the strain on the health system [ 82 ]. COVID-19 fatality rates are affected by numerous health systems factors, including bed capacity, existence and capacity of intensive care unit (ICU), and critical care resources (such as oxygen and dexamethasone) in a hospital. Regions and countries with high HDI have a greater number of acute care facilities, ICU, and hospital bed capacities compared to lower HDI regions and countries [ 83 ]. Differences in health systems capacity could explain why North America and Europe, which have experienced much greater number of cases and deaths per million population, reported lower CFRs than the Southern American and the African regions, partly also due to limited testing capacity in these regions (Table 1 ) [ 84 , 85 , 86 ]. The higher CFR in Southern America can be explained by the relatively lower health systems surge capacity that could not adequately respond to the huge demand for health services [ 69 , 86 ]. The COVID-19 pandemic has highlighted existing health systems’ weaknesses, which are not able to effectively prepare for and respond to PHEs [ 87 ]. The high CFRs in the region are also exacerbated by the high social inequalities [ 69 ].

On the other hand, countries in Asia recorded lower CFRs (~ 1.4%) despite sharing many common risk factors (including overcrowding and poverty, weak health system capacity etc) with Africa. The Asian region shares many similar protective factors to the African region. They have been able to minimize their CFR by suppressing the transmission of the virus and flattening the epidemic curve of COVID-19 cases and deaths. Nevertheless, the epidemic in India is likely to be different because it has exceeded the health system capacity to respond and provide basic medical care and medical supplies such as oxygen [ 88 ]. Overall, many Asian countries were able to withstand the transmission of the virus and its effect due to swift action by governments in the early days of the pandemic despite the frequency of travel between China and neighbouring countries such as Hong Kong, Taiwan and Singapore [ 89 ]. This has helped them to contain the pandemic to ensure case numbers remain within their health systems capacity. These countries have benefited from their experience in the past in the prevention and control of epidemics [ 90 ].

There are a number of issues with the use of the CFR to compare the management of the pandemic between countries and regions [ 91 ], as it does not depict the true picture of the mortality burden of the pandemic. A major challenge with accurate calculation of the CFR is the denominator on number of identified cases, as asymptomatic infections and patients with mild symptoms are frequently left untested, and therefore omitted from CFR calculations. Testing might not be widely available, and proactive contact tracing and containment might not be employed, resulting in a smaller denominator, and skewing to a higher CFR [ 82 ]. It is, therefore, far more relevant to estimate infection fatality rate (IFR), the proportion of all infected individuals who have died due to the infection [ 91 ], which is central to understanding the public health impact of the pandemic and the required policies for its prevention and control [ 92 ].

Estimates of prevalence based on sero-surveys, which includes asymptomatic and mildly symptomatic infections, can be used to estimate IFR [ 93 ]. In a systematic review of 17 studies, seroprevalence rates ranged from 0.22% in Brazil to 53% in Argentina [ 94 ]. The review also identified that the seroprevalence estimate was higher than the cumulative reported case incidence, by a factor between 1.5 times in Germany to 717 times in Iran, in all but two studies (0.56 times in Brazil and 0.88 times in Denmark) [ 94 , 95 ]. The difference between seroprevalence and cumulative reported cases might be due to asymptomatic cases, atypical or pauci-symptomatic cases, or the lack of access to and uptake of testing [ 94 ]. There is only a modest gap between the estimated number of infections from seroprevalence surveys and the cumulative reported cases in regions with relatively thorough symptom-based testing. Much of the gap between reported cases and seroprevalence is likely to be due to undiagnosed symptomatic or asymptomatic infections [ 94 ].

Collateral effects of the COVID-19 pandemic

It is important to note that the pandemic has significant collateral effects on the provision of essential health services, in addition to the direct health effects [ 96 ]. Disruptions in the provision of essential health services, due to COVID-19, were reported by nearly all countries, though it is more so in lower-income than higher-income countries [ 97 , 98 ]. The biggest impact reported is on provision of day-to-day primary care to prevent and manage some of the most common health problems [ 99 ].

The causes of disruptions in service delivery were a mix of demand and supply factors [ 100 ]. Countries reported that just over one-third of services were disrupted due to health workforce-related reasons (the most common causes of service disruptions), supply chains, community mistrust and fears of becoming infected, and financial challenge s[ 101 ]. Cognizant of the disruptive effects of the pandemic, countries have reorganized their health system.

Countries with better response to COVID-19 have mobilized, trained and reallocated their health workforce in addition to hiring new staff, using volunteers and medical trainees and mobilizing retirees [ 102 ]. Several strategies have also been implemented to mitigate disruptions in service delivery and utilization, including: triaging to identify the most urgent patient needs, and postponing elective medical procedures; switching to alternative models of care, such as providing more home-based care and telemedicine [ 101 ].

This study identifies that the COVID-19 pandemic, in terms f cases and deaths, is heterogeneous around the world. This variability is explained by differences in vulnerability, preparedness, and response. It confirms that a high level of HDI, UHCI and GHSI are essential but not sufficient to control epidemics [ 103 ]. An effective response to public health emergencies requires a joint and reinforcing implementation of UHC, health emergency and disease control priorities [ 104 , 105 ], as well as good governance and social protection systems [ 106 ]. Important lessons have been learned to cope better with the COVID-19 pandemic and future emerging or re-emerging pandemics. Countries should strengthen health systems, minimize fragmentation of public health, primary care and secondary care, and improve coordination with other sectors. The pandemic has exposed the health effects of longstanding social inequities, which should be addressed through policies and actions to tackle vulnerability in living and working conditions [ 106 ].

The shift in the pandemic epicentre from high-income to MICs was observed in the second global wave of the pandemic. This is due to in part to the large-scale provision of vaccines in HICs [ 15 ] as well as the limitations in the response in LMICs, including inadequate testing, quarantine and isolation, contact tracing, and social distancing. The second wave of the pandemic in low- and middle-income countries spread more rapidly than the first wave and affected younger and healthier populations due to factors, including poor government decision making, citizen behaviour, and the emergence of highly transmissible SARS-CoV-2 variants [ 107 ]. It has become catastrophic in some MICs to prematurely relax key public health measures, such as mask wearing, physical distancing, and hand hygiene [ 108 ].

There is consensus that global vaccination is essential to ending the pandemic. Universal and equitable vaccine delivery, implemented with high volume, speed and quality, is vital for an effective and sustainable response to the current pandemic and future public health emergencies. There is, however, ongoing concern regarding access to COVID-19 vaccines in low-income countries [ 109 ]. Moreover, there is shortage of essential supplies, including oxygen, which has had a major impact on the prevention and control of the pandemic. It is, therefore, vital to transform (through good governance and financing mechanisms) the ACT-A platform to deliver vaccines, therapeutics, diagnostics, and other essential supplies [ 109 , 110 ]. The global health community has the responsibility to address these inequalities so that we can collectively end the pandemic [ 107 ].

The Omicron variant has a huge role in the current wave around the world despite high vaccine coverage [ 111 ]. Omicron appears to spread rapidly around the world ever since it was identified in November 2021 [ 112 ]. It becomes obvious that vaccination alone is inadequate for controlling the infection. This has changed our understanding of the COVID-19 pandemic endgame. The emergence of new variants of concern and their spread around the world has highlighted the importance of combination prevention, including high vaccination coverage in combination with other public health prevention measures [ 112 ].

Overall, the COVID-19 pandemic and the response to it emphasise valuable lessons towards an effective and sustainable response to public health emergencies. We argue that the PHC approach captures the different preparedness and response strategies required towards ensuring health security and UHC [ 113 ]. The PHC approach enables countries to progressively realize universal access to good-quality health services (including essential public health functions) and equity, empower people and communities, strengthen multi-sectoral policy and action for health, and enhance good governance [ 114 ]. These are essential in the prevention and control of public health emergencies, to suppress transmission, and reduce morbidity and mortality [ 115 ]. Access to high-quality primary care is at the foundation of any strong health system [ 116 ], which will, in turn, have effect on containing the epidemic, and reducing mortality and CFR [ 117 ]. Australia is a good example in this regard because it has implemented a comprehensive PHC approach in combination with border restrictions to ensure health system capacity is not exceeded [ 56 ]. The PHC approach will enable countries to develop and implement a context-specific health strategy, enhance governance, strengthen their (public) health systems, minimize segmentation and fragmentation, and tackle upstream structural issues, including discrimination and socio-economic inequities [ 118 ]. This is the type of public health approach (comprehensive, equity-focused and participatory) that will be effective and sustainable to tackle public health emergencies in the twenty-first century [ 119 , 120 ]. In addition, it is vital to transform the global and regional health systems, with a strong IHR and an empowered WHO at the apex [ 121 ]. We contend that this is the way towards a healthier and safer country, region and world.

The COVID-19 pandemic demonstrates that the world remains vulnerable to public health emergencies with significant health and other socio-economic impacts. The pandemic takes variable shapes and forms across regions and countries around the world. The pandemic has impacted countries with inadequate governance of the epidemic, fragmentation of their health systems and higher socio-economic inequities more than others. We argue that adequate response to public health emergencies requires that countries develop and implement a context-specific national strategy, enhance governance of public health emergency, build the capacity of their health systems, minimize fragmentation, and tackle socio-economic inequities. This is possible through a PHC approach that provides universal access to good-quality health services through empowered communities and multi-sectoral policy and action for health development. The pandemic has affected every corner of the world; it has demonstrated that “no country is safe unless other countries are safe”. This should be a call for a strong global health system based on the values of justice and capabilities for health.

Availability of data and materials

Data are available in a public, open access repository: Johns Hopkins University: https://coronavirus.jhu.edu/data/new-cases , and UNDP: http://hdr.undp.org/en/2019-report ; WHO: https://www.who.int/publications/m/item/weekly-epidemiological-update%2D%2D-22-december-2020

Abbreviations

Coronavirus Disease 2019

Case-fatality rates

Human development index

Universal health coverage index

Global Health Security index

High-income countries

Middle-income countries

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Assefa, Y., Gilks, C.F., Reid, S. et al. Analysis of the COVID-19 pandemic: lessons towards a more effective response to public health emergencies. Global Health 18 , 10 (2022). https://doi.org/10.1186/s12992-022-00805-9

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Reimagining the global economy: Building back better in a post-COVID-19 world

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November 17, 2020

The COVID-19 global pandemic has produced a human and economic crisis unlike any in recent memory. The global economy is experiencing its deepest recession since World War II, disrupting economic activity, travel, supply chains, and more. Governments have responded with lockdown measures and stimulus plans, but the extent of these actions has been unequal across countries. Within countries, the most vulnerable populations have been disproportionately affected, both in regard to job loss and the spread of the virus.

The implications of the crisis going forward are vast. Notwithstanding the recent announcement of vaccines, much is unknown about how the pandemic will spread in the short term and beyond, as well as what will be its lasting effects. What is clear, however, is that the time is ripe for change and policy reform. The hope is that decisionmakers can rise to the challenge in the medium term to tackle the COVID-19 virus and related challenges that the pandemic has exacerbated—be it the climate crisis, rising inequality, job insecurity, or international cooperation.

In this collection of 12 essays, leading scholars affiliated with the Global Economy and Development program at Brookings present new ideas that are forward-looking, policy-focused, and that will guide policies and shape debates in a post-COVID-19 world.

Sustainable Development Goals

Authors: Homi Kharas , John W. McArthur

Some have questioned whether the pandemic has put attaining the already ambitious 17 Sustainable Development Goals (SDGs) out of reach, and whether they should be scaled back and deprioritized. In this essay, Homi Kharas and John McArthur argue that the SDGs remain as relevant as ever and that the goals can in fact provide a handrail for recovery policy.

Leadership at the local level

Authors: Anthony F. Pipa , Max Bouchet

The pandemic has revealed the importance of good leadership at the local level. In this essay, Anthony F. Pipa and Max Bouchet explore the role that global cities can have in driving a sustainable recovery.

Multilateralism

Authors: Kemal Derviş

Given the global nature of the pandemic, there have been calls for greater international cooperation. In this essay, Kemal Derviş examines the state of multilateralism and presents lessons of caution as its future is reimagined.

Rebooting the climate agenda

Authors: Amar Bhattacharya

Shared recognition of the climate agenda is central to global cooperation. In this essay, Amar Bhattacharya explores how international action can pursue a recovery that produces sustainable, inclusive, and resilient growth.

The international monetary and financial system

Authors: Brahima Sangafowa Coulibaly , Eswar Prasad

The pandemic has exposed the weaknesses in the international financial system and the need to improve the financial safety net for emerging and developing countries. In this essay, Brahima Coulibaly and Eswar Prasad make the case for an international monetary and financial system that is fit for purpose to help countries better withstand shocks like a global pandemic.

The future of global supply chains

Authors: David Dollar

International trade has slowed, and existing trade challenges, including automation, new data flows, and the rise of protectionism, could accelerate post-COVID. In this essay, David Dollar discusses these challenges, the future of global supply chains, and the implications for international trade.

The global productivity slump

Authors: Alistair Dieppe , M. Ayhan Kose

COVID-19 could further accelerate the fall in global productivity, which has been slowing since the global financial crisis. Evidence from other recent pandemics such as SARS and Ebola show their negative impact on investment growth and productivity. In this essay, Alistair Dieppe and Ayhan Kose argue that policy approaches to boost productivity must be country-specific and well-targeted.

Dislocation of labor markets

Authors: Marcela Escobari , Eduardo Levy Yeyati

Throughout the world, the health and economic costs of the pandemic have been felt harder by less well-off populations. On the jobs front, the pandemic is affecting labor markets differently across and within advanced and developing countries as low-wage, high-contact jobs are disproportionally affected. In this essay, Marcela Escobari and Eduardo Levy Yeyati explore the future of work and policies for formalizing and broadening labor protections to bolster resiliency.

Tackling the inequality pandemic

Authors: Zia Qureshi

Technology, globalization, and weakening redistribution policies are leading to rising inequality in many countries. To tackle inequality, Zia Qureshi discusses policies to better harness technology for fostering inclusive economic growth.

The human costs of the pandemic

Authors: Carol Graham

Evidence suggests that the poor have been suffering higher emotional costs during the pandemic. In this essay, Carol Graham offers a look into well-being measurement and strategies to combat the effects of the lockdowns.

The complexity of managing COVID-19

Authors: Alaka M. Basu , Kaushik Basu , Jose Maria U. Tapia

From strict lockdowns to ensuring sufficient supplies of personal protective equipment to sending students home from school, governments around the world have enacted varying measures to respond to the virus. In this essay, Alaka M. Basu, Kaushik Basu, and Jose Maria U. Tapia examine how governments in emerging markets have managed the crisis so far, as they design governance strategies that both reduce the spread of infection and avoid prohibiting economic activity.

Global education

Authors: Emiliana Vegas , Rebecca Winthrop

COVID-19 disrupted education systems everywhere and has accelerated education inequality as seen through what service governments could provide: At one point during the pandemic, 1 in 4 low-income countries was able to provide remote education, while 9 in 10 high-income countries were able to. In this essay, Emiliana Vegas and Rebecca Winthrop present an aspirational vision for transforming education systems to better serve all children.

Global Trade Sustainable Development Goals

Global Economy and Development

Stephen Karingi, Jason McCormack

July 30, 2024

Robin Brooks, Peter R. Orszag, William E. Murdock III

July 18, 2024

Alan Hirsch

July 17, 2024

Globalization after COVID-19: what’s in store?

COVID-19 represents the epitome of globalization in its blind disrespect for borders, but might trigger the most significant reversal of globalization we have seen in decades.

COVID-19 has burst onto the scene at the end of a decade in which global integration has been constantly challenged by the rise of economic nationalism and protectionism.

The economic tailspin resulting from COVID-19 is expected to be at least as devastating if not more so than that of the financial crisis. With unemployment levels in the US taking two weeks to reach what took 6 months in the 2008 crisis, there is no doubt we are facing a global recession which some predict could reach 10% or more of GDP. It is still up for debate how quickly a V-shaped recovery could take place. But whatever the economic contraction, globalization in the form of the flows of goods and services will be negatively impacted.

The drop in global demand will be compounded by the effect of governments protecting their own and implementing policies to boost local employment at the expense of imports. Many of the government assistance programs being introduced are expected to have built-in conditions obliging the money to be spent domestically as was the case with the Recovery Act following the 2008 crisis. As such, trade in products, as well as services such as shipping which support them, are expected to suffer in the short- to medium-term, not only with regards to volumes but also prices.

It should not come as a surprise if as a result we see more consolidation on a global basis in certain industries. Sectors which will be particularly hard hit include the tourism and hospitality sectors as well as airlines. In their case and others that involve the movement of people, it is unclear whether a return to the previous status quo will ever take place, as the acceptance of and comfort with digital technologies may result in a new equilibrium. By contrast, consumer goods and durables are expected to enjoy a quicker recovery.

As with all forms of volatility, there are both losers and winners, and the case of COVID-19 is no different. While globalization may be negatively impacted in the form of the trade of goods and certain services such as travel, other sectors may experience heightened demand. More remote forms of work will only spur on the cross-border flow of data and of dispersed but easily exchanged professional services. As such, not only the suppliers of these services but also the enablers such as Zoom and broadband providers will be the beneficiaries.

“COVID-19 adds to those pressures on supply chains that have been mounting ever since the beginning of the US-China trade war.”

Humanity is indeed finding new and innovative ways of connecting and cooperating under COVID-19. These range from global hackathons to address COVID-19, to people connecting on LinkedIn in order to cooperate in designing and 3-D printing ventilator parts. In this sense, the world may well be becoming more globalized in terms of the flow of ideas and solutions, if not necessarily products.

So these are the macro changes we are seeing. But how about changes at the company level? When it comes to company operations, COVID-19 adds to those pressures on supply chains that have been mounting ever since the beginning of the US-China trade war, with firms pondering the need to avoid being dependent on single-source supply chains. Furthermore, when it comes to the healthcare sector, governments are trying to guarantee domestic supplies of critical products; as such, they may begin to treat them as strategic goods, turning to firms for their local production in the future.

Globally centralized supply chains in low labor-cost countries are also being challenged by the increased use of robotics and automation, allowing firms to keep production in relatively expensive countries. COVID-19 has highlighted the importance of automation, as the threat to operations posed by “non-essential” business closures is based on the need to keep people at home. As such, operations that leverage robotics will be less affected. Ironically, among those countries that have weathered this pandemic the best are many with high levels of robotics usage such as South Korea.

How will changes in supply chains impact the overarching strategies of firms? Whereas centralized globally efficient hubs have been the dominant approach of many large multinationals, the pressures of more dispersed supply chains, combined with increased protectionism, will have a fundamental impact on the global vs. local decision firms face. We will likely see a greater dispersion not only of assets but also of decision making, in order to accommodate increased local demands.

On the positive side, a greater dispersion of supply chains could potentially allow countries that have lost their productive capacities to China in the past 15 years to slowly recapture some of it, and allow purely domestic firms to be more viable.

While trade flows and FDI have grown over recent decades, the same cannot be said for the authority of the global institutions that govern them. As Pankaj Ghemawat says, we are living in a semi-global world, where markets are global but regulatory institutions are still, for the most part, national. Seen in this light, what will the impact of COVID-19 be on sentiments towards and the power of global institutions of cooperation such as the WHO, UN and OECD? Will it be a repeat of the era which followed WWI and the formation of weak institutions such as the League of Nations? Or, will there be a recognition that global problems require global solutions with credible institutions to support them?

The reality is that the rise of populism over the past decade has resulted in a weakening of institutions of global cooperation, fostered by an us-first approach to national politics. The direction we take will greatly depend on the leadership styles exhibited by dominant powers across the globe. Will such leaders strengthen nationalistic and, in some cases, xenophobic tendencies, playing the blame game and labeling COVID-19 a foreign virus?

As the UN Secretary General Antonio Guterres has implied, we have gone from a bipolar to a unipolar to a no-polar world, where global powers, be they traditional friends or foes, are unable to work together. This lack of hegemony currently threatens any chance of a global solution.

As a result of COVID-19, borders have been resurrected to keep foreigners out. They are so far temporary but will people come to prefer the new status quo? Will governments continue to block the exports of essential medical products? Or will we realize that global pandemics and other such challenges require global solutions and cooperation? Many questions remain.

One sign of optimism is the level of cross-border cooperation among the scientific community as they seek solutions to the current crisis. At the community level, further examples abound of assistance and cooperation, even when governmental actions lag. In the UK for instance, when the NHS asked for volunteers to help with the crisis, it hoped to receive 250,000 responses, but instead had over 500,000. It remains to be seen whether this sense of civic unity can be transposed to the international arena.

The world today finds itself at a fork in the road. Which path we take towards globalization has broad and long-lasting implications not only for business but for how we confront future challenges to humanity.

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Impact of COVID-19 on Global Health and Economic Sectors: A Comprehensive Analysis

8 Pages Posted: 6 Aug 2024

Ralph Leader

Independent Scholar

Date Written: August 03, 2024

This article provides a comprehensive analysis of the multifaceted impact of the COVID-19 pandemic on global health and economic sectors. Integrating findings from a range of studies, the article highlights methodologies and results from various research efforts, including predictive modeling, clustering analysis, and the development of data visualization tools. The synthesis of these studies offers insights into the intersection of public health, social behavior, and economic stability during the pandemic.

Keywords: Covid-19, Public Health, Vaccination, Health Data Science, Clinical Data Science

Suggested Citation: Suggested Citation

Ralph Leader (Contact Author)

Independent scholar ( email ).

Solumbia, SC United States

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The impact of COVID-19 on globalization

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Spotlight /

The impact of COVID-19 on global health goals

21 may 2021 | spotlight, covid-19 responsible for at least 3 million excess deaths in 2020.

As of 31 December 2020, COVID-19 had infected over 82 million people and killed more than 1.8 million worldwide. But preliminary estimates suggest the total number of global “excess deaths” directly and indirectly attributable to COVID-19 in 2020 amount to at least 3 million , 1.2 million higher than the official figures reported by countries to WHO.

With the latest COVID-19 deaths reported to WHO now exceeding 3.4 million, based on the estimates produced for 2020, we are likely facing a significant undercount of total deaths directly and indirectly attributed to COVID-19.

The term “excess deaths” describes deaths beyond what would have been expected under “normal” conditions. It captures not only confirmed deaths, but also COVID-19 deaths that were not correctly diagnosed and reported as well as deaths attributable to the overall crisis conditions. This provides a more comprehensive and accurate measure when compared with confirmed COVID-19 deaths alone.

For example, some countries only report COVID-19 deaths occurring in hospitals or the deaths of people who have tested positive for COVID-19. In addition, many countries cannot accurately measure or report cause of death due to inadequate or under-resourced health information systems.

The pandemic has likely increased deaths from other causes due to disruption to health service delivery and routine immunizations, fewer people seeking care, and shortages of funding for non-COVID-19 services. The second WHO “pulse survey” of 135 countries in March 2021 highlighted persistent disruptions at a considerable scale over one year into the pandemic, with 90% of countries reporting one or more disruptions to essential health services.

Real-time, quality data to track population health is critical for every country to improve health outcomes and eliminate health inequalities.

“Real-time, quality data to track population health is critical for every country to improve health outcomes and eliminate health inequalities", says Dr Tedros Adhanom Ghebreyesus, Director-General of the World Health Organization. "WHO is committed to work with countries and partners to strengthen health information systems and support data-driven policies and interventions."

COVID-19 disproportionately impacts vulnerable populations

COVID-19 has exposed persistent inequalities by income, age, race, sex and geographic location. Despite recent global health gains, across the world people continue to face complex, interconnected threats to their health and well-being rooted in social, economic, political and environmental determinants of health.

The pandemic has also revealed significant gaps in country health information systems. While high-resource settings have faced challenges related to overstretched capacity and fragmentation, weaker health systems risk jeopardizing hard-won health and development gains made in recent decades.

Data from the COVID-19 World Symptoms survey shows a decline in preventive behaviours such as physical distancing, mask wearing and hand washing as household overcrowding increases. Among people living in uncrowded households, 79% reported trying to physically distance themselves compared with 71% in moderately overcrowded and 65% in extremely overcrowded households. Similar trends were observed for hand washing and mask-wearing, underscoring vulnerabilities due to socioeconomic status.

Source: WHO calculations using COVID-19 World Symptoms Survey data led by Facebook and the University of Maryland for 35 high-income countries, May 2020 – February 2021.

Irrespective of the pandemic, inequalities and data gaps impede targeted interventions. For example, a recent WHO global assessment of health information systems capacity found that only half of countries include disaggregated data in their published national health statistical reports. Investing in strong health information systems is vital to ensure disaggregated data reaches decision-makers and achieve equitable health outcomes.

With stronger, more equitable health information systems we can more accurately measure progress towards the health-related Sustainable Development Goals and WHO’s Triple Billion targets. “We are now less than nine years away from 2030”, says Dr Samira Asma, Assistant Director-General for the Division of Data, Analytics and Delivery for Impact at WHO. “We know where the gaps are, and we have the solutions to address them. What we need now is commitment and investment to accelerate progress and reach our goals.”

Before COVID-19 the world was making progress towards global health goals - but not fast enough

The World Health Statistics 2021 report presents the most up-to-date data and trends on more than 50 health-related indicators for the Sustainable Development Goal and WHO’s Triple Billion targets.

The data shows that global life expectancy at birth has increased from 66.8 years in 2000 to 73.3 years in 2019, and healthy life expectancy has increased from 58.3 years to 63.7 years. But even before the pandemic struck, progress was too slow and uneven to meet many targets including reduced premature mortality from noncommunicable diseases, tuberculosis and malaria incidence, and new HIV infections.

Chart showing where people living a higher proportion of years in good health

“Although we are living extended lives and more years in good health, these are no grounds for complacency”, says Dr Bochen Cao, Technical Officer in the Division of Data, Analytics and Delivery for Impact at WHO. “Many of the underlying health determinants still need critical improvements, and COVID-19 is yet another wake-up call to remind us that our health remains at risk unless urgent actions are taken to close the gaps.”

For instance, while global tobacco use has decreased by 33% since 2000 the prevalence of adult obesity is increasing, and in 2016 up to a quarter of the populations in high-income countries were obese. And although the prevalence of hypertension declined worldwide between 2000 and 2015, it is increasing slightly in low-income countries.

Children and women in low and lower-middle-income countries are also at higher risk of malnutrition including stunting, wasting, and anaemia during pregnancy, while people in upper-middle-income countries are more susceptible to being overweight.

Before COVID-19, many countries were making progress towards universal health coverage. Improvements in the coverage of essential health services were recorded in all income groups and across different types of services, despite persistent inequalities. But financial protection has been deteriorating. As of the latest figures, the proportion of the population spending more than 10% of their household budget on healthcare rose from 9% to 13% between 2000 and 2015, and almost 3% were spending more than 25% of their budget on health care.

Chart showing global progress towards triple billion targets

Health emergencies protection also requires urgent reform. Despite an increased focus on global health security, COVID-19 has revealed a critical need for a well-coordinated, multisectoral health emergency surge capacity and preparedness at all levels and within all countries. Continuing efforts are needed to improve and maintain early warning systems to mitigate and manage public health risks within the national context and to consider the worldwide pandemic contexts for national health emergency and operational preparedness planning.

World Health Data Hub to improve access to data

In addition to underscoring inequalities and data gaps, COVID-19 has highlighted the need for universal access to global health data. WHO’s new World Health Data Hub will provide an interactive digital platform and trusted source for all global health data, fulfilling WHO’s commitment to provide health data as a public good.

The Hub will provide easy access to powerful visualization tools that reveal trends, patterns and connections and draw insights. It will also allow Member States to upload and review their data in a secure environment, will be scalable to allow different varieties, volumes and velocities of data and will provide access to the latest predictive analytics technologies.

The Hub brings together all of WHO’s data assets including the Global Health Observatory , the GPW 13 Triple Billion dashboard , the health equity monitor , and the WHO Mortality Database .

Leveraging partnerships to get back on track

The World Health Data Hub is made possible through partnerships that combine digital technology and innovative solutions with the global convening capabilities of an organization like WHO. As key technology partners, Microsoft and Avanade are supporting WHO to deliver this ambitious end-to-end solution with a shared commitment to promote health data as a public good.

“This partnership was started to address a common goal not only to respond to the pandemic but to ensure that every person, every citizen and every country has a chance for a healthier life”, says Dr Samira Asma. “We have to be accountable for results, but that accountability can’t come if we don’t have underlying data systems and robust partnerships.”

Public-private partnerships like this one maximize the combined leadership, expertise, resources and reach of each organization to rapidly scale solutions and deliver measurable impact.

“It is our greatest ambition with the World Health Data Hub that we are more effective as a global community in making a difference in people’s lives because we have come together, building on our respective expertise, to bring to life that unified view that we've never had before,” says Justin Spelhaug, Vice President of Tech for Social Impact at Microsoft.

“At Avanade, our purpose is to make a genuine human impact,” adds Pam Maynard, CEO at Avanade. “The work our Tech for Social Good teams are doing to create scalable, repeatable and affordable solutions for the social sector is one way we bring that purpose to life every day.”

COVID-19 has underscored the need for efficient, multilateral cooperation that is responsive to country needs and reflects their unique priorities. WHO is committed to collaborating with all partners to support countries and get back on track towards the SDGs and Triple Billion targets. “There's no one organization, one nation or one group that's going to solve COVID-19”, says Spelhaug. “It requires full mobilization of the public and private sector at new levels of scale to empower countries, policymakers, and responders.”

“It’s been incredibly rewarding to see how the power of data and analytics can transform organizations, like the WHO, to accelerate from insights to action, allowing them to spend more time focusing on what matters most: improving the lives of people and communities around the world,” says Maynard.

With less than nine years to 2030, we have no time to lose.

Pandemic, policy, and markets: insights and learning from COVID-19’s impact on global stock behavior

Open access
Published: 01 August 2024

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Shuxin Yang ORCID: orcid.org/0000-0002-1579-5374 1 na1

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The COVID-19 pandemic has triggered an unprecedented shock to global stock markets, exceeding the economic impacts of prior pandemics. This paper examines the pandemic’s impact on global stock markets across 34 countries, focusing on the relationship between the pandemic’s severity, government policy responses, and economic stimuli. Panel data regressions reveal that increased daily COVID-19 cases initially negatively impacted stock returns and increased volatility. Stringent government measures positively influenced market returns but also heightened volatility. The research challenges previous assumptions about the influence of geographical and economic factors on market reactions. By segregating the sample period by investor sentiment, the study finds a consistent pattern of negative lagged returns, indicating stronger mean reversion during high VIX periods. During low market volatility, government stringency measures are perceived as harmful to economic activity, negatively impacting stock returns. The insights from the COVID-19 pandemic can inform responses to future market disruptions from health crises, geopolitical tensions, environmental disasters, or other systemic shocks.

Stock market reaction to the COVID-19 pandemic: an event study

Time-varying effects of the COVID-19 pandemic on stock markets and economic activity: evidence from the US and Europe

Impact of COVID-19 Pandemic on Financial Markets: a Global Perspective

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1 Introduction

Declared a pandemic by the WHO on March 12, 2020 (Hartman 2020 ), COVID-19 has significantly influenced global stock markets, leading to sharp declines in stock prices and suddenly increase in volatility (see e.g. Baker et al. 2020 ; Al-Awadhi et al. 2020 ; Cinelli et al. 2020 , etc.). This effect surpasses any previous infectious disease outbreak, marking COVID-19 as unique in its economic implications. On this day alone, the Dow Jones Industrial Index declined by 13.84%, the Standard and Poorś 500 (S &P 500) fell by 12.77%, and the FTSE MIB plunged by over 18%. In the US volatility levels were found to be even higher than those experienced during the 2007/08 Global Financial Crisis (Baker et al. 2020 ). Compared to earlier pandemics, the global response to COVID-19, including widespread lockdowns and intense media coverage, has created novel challenges for investors and policymakers. Therefore, as an aftermath, we find taking a further look at the impact of the COVID-19 on global stock behaviour can provide guidance on future pandemics or crisis.

Additionally, the pandemic has garnered unparalleled attention globally, both from governments and the public. Over a hundred countries and territories went into lockdown for at least a month; non-essential air traffic was suspended; and day-to-day travel was reduced to a fraction of that in previous years (Hale et al. 2020 ). In March 2020, the US imposed restrictions on air travel from Europe, China, and other high-risk areas; the EU implemented widespread travel restrictions, suspending non-essential travel from non-EU countries, which included a coordinated effort among member states to limit air traffic and reduce the risk of importing COVID-19 cases; India suspended all international and domestic commercial flights in March 2020 as part of a nationwide lockdown, which lasted until May 2020, when domestic flights were gradually resumed under strict health and safety guidelines; China significantly reduced international flights in early 2020, maintaining only essential air services to facilitate cargo and the repatriation of citizens. Domestic flights were also reduced, especially in and out of Wuhan, the epicenter of the outbreak. The scope, severity, and duration of government restrictions have been beyond those implemented during any previous infectious disease outbreak. At the same time, information is also spreading faster than ever and with the COVID-19 outbreak constantly making headlines across the media landscape, investors are facing substantial uncertainties (Cinelli et al. 2020 ).

This paper seeks to bridge a gap in existing literature by analyzing how the COVID-19 pandemic’s severity, government interventions, and their consequent effects shape stock market returns and volatility in various countries. This approach offers a broader perspective compared to previous studies, which have focused more narrowly on either the economic impact of the pandemic or governmental responses individually. This research contributes to a deeper understanding of how these factors interact and influence global equity markets, offering insights that could be valuable for policymakers, investors, and researchers in understanding the economic implications of large-scale health crises.

This research has the following contributions to the literature. Firstly, this paper extends beyond the initial stages of the pandemic, which most prior studies concentrate on. Secondly, it provides a comprehensive analysis throughout 2020, capturing the evolving nature of the pandemic’s impact. Thirdly, built on previous studies focusing on specific regions or markets, this paper investigates stock market responses across 34 countries, offering a more global view. Fourthly, we delve into how the severity of the pandemic and the strictness of government policies influenced market reactions, a dimension less explored in earlier studies. By examining the variations in market reactions based on the level of pandemic severity and policy strictness, this study provides nuanced insights that fill a notable gap in existing research. Finally, our inclusion of controls for seasonal effects and geographical locations to explain stock market changes adds a unique angle to the research. Our findings on the non-significant difference in market reactions between high-income and low-income economies offer a new perspective, as most studies have not explicitly focused on this comparison.

The volume of literature on the financial and economic impact of COVID-19 has also exploded as the number of confirmed cases rises. Research examining the relation between the COVID-19 pandemic and financial markets generally focuses on specific asset classes and geographical locations. The impact on equity markets has been one of the most researched topics (Al-Awadhi et al. 2020 ; Kowalewski and Śpiewanowski 2020 ; Just and Echaust 2020 ; Hartman 2020 ; Baker et al. 2020 ; Huo and Qiu 2020 ; Bai et al. 2020 ; Ashraf 2020 ; Sen et al. 2020 ; Chang et al. 2021 ; Liu et al. 2022 ), whilst the unexpected decline in oil prices has also prompted extensive research on commodity markets, including energy and precious metals (Mensi et al. 2020 ; Gil-Alana and Monge 2020 ; Narayan 2020 ; Nicole et al. 2020 ). The impact of COVID-19 on the debt market has also been investigated (De Sio et al. 2016 ; Ashton 2009 ; Aguiar-Conraria et al. 2012 ). Several studies have highlighted the significant increase in stock market volatility due to COVID-19. For instance, Scherf et al. ( 2022 ) using the Generalized Forecast Error Variance Decomposition (GFEVD) method demonstrated that the pandemic led to unprecedented volatility levels across global markets, particularly in higher-income countries which initially overreacted but recovered more quickly compared to lower-income countries. However, as the pandemic progressed, markets demonstrated learning effects, leading to more efficient responses to government interventions and policy measures (Scherf et al. 2022 ). Similarly, (Andrada-Félix et al. 2024 ) study noted that the COVID-19 crisis, compounded by the Ukraine war, resulted in more severe adverse effects on the US stock market than previous crises, such as the 2008 Global Financial Crisis. The efficiency of financial markets during the pandemic has been a subject of debate. Initial reactions to COVID-19 were marked by inefficiencies, with significant under- and overreactions. Tiwari et al. ( 2022 ) conduct causality analysis and show that there exist a causal relationship exists between the number of cases of COVID 19 infections and stock market liquidity.

Studies that demonstrate the increased volatility present in stock markets and their loss of value as the severity (often measured as confirmed cases and death tolls) of the pandemic increases in the short-term (Gormsen and Koijen 2020 ; Baker et al. 2020 ; Just and Echaust 2020 ; Ramelli and Wagner 2020 ; Liu et al. 2020 ; Huo and Qiu 2020 ; Bai et al. 2020 ). Similarly, Baker et al. ( 2020 ) review movements of the US stock market and find that restrictions on travel and business activities have significantly damaged the US economy. Adopting an alternative focus by analyzing government policies, Zhang et al. ( 2020 ) provide evidence that the impact of the pandemic on stock market movements has differed from market to market, but investors generally react positively to government actions that contain outbreaks. The psychological impact of the pandemic on investors has been substantial. Research has shown that negative sentiment and psychological pressure led to increased market volatility (Bai et al. 2023 ). Media coverage and financial news significantly influenced investor behavior, causing overreactions in stock markets (Ji et al. 2024 ).

Other than confirmed cases and death tolls from the pandemic, government interventions are also often considered as independent variables in analyses; for example, Rebucci et al. ( 2022 ) and Narayan et al. ( 2021 ) examine the impacts of governments’ restrictive policies, Zaremba et al. ( 2020 ) and Zhang et al. ( 2020 ) investigate the reactions of stock markets to control measures such as lockdowns, and school and workplace closures, Toda ( 2020 ) investigated the impact of weather. Notably, their results show that the strengthening of restrictions significantly increase stock market volatility. Ji et al. ( 2024 ) find that markets initially underreacted to lockdown announcements, followed by overreactions that were corrected over time. Their results indicate that the impact of COVID-19 on stock markets varied significantly across countries. Yu and Xiao ( 2023 ) reveal that government responses and societal trust levels played only partial roles in affecting market volatility, whereas Szczygielski et al. ( 2023 ) find the opposite.

This study contributes to the literature by offering a comprehensive analysis of stock market reactions across different phases of the pandemic, evaluating the impact of country-specific factors, and exploring the relationship between the severity of the pandemic, government policy strictness, and market responses. By comparing the effects of COVID-19 policies across markets and phases of the pandemic, this study highlight how national contexts, such as healthcare capacity and governmental trust, modulate the impact of these policies. We investigate feedback effects where rising case numbers lead to stricter controlling measures, which may in turn affect equity markets, can provide a more comprehensive picture of the pandemic’s impact.

Our analysis indicates that increases in daily confirmed cases led to decreased returns and increased volatility, as demonstrated by Al-Awadhi et al. ( 2020 ), Akhtaruzzaman et al. ( 2021 ), Andrada-Félix et al. ( 2024 ), Ji et al. ( 2024 ), especially in the first quarter of 2020. However, the reaction of stock markets varied depending on the severity of the pandemic and government responses. Notably, our findings challenge some of the early assumptions that the market responses to the pandemic strongly and long lasting Brodeur et al. ( 2021 ), Song et al. ( 2021 ), providing new insights into the interplay between public health crises and financial markets. We observe that stock market returns fall immediately following increase in daily new confirmed cases in the majority of countries but recover quickly in the second quarter of 2020. To address this issue, we introduce seasonal controls and examine the extent to which they explain the time-series variation of COVID-19 impact.

We then turn to the pandemic’s severity and government policies’ strictness in different countries to address the inconclusive results presented by previous research. Controls for severity and strictness groups are added to the baseline model. This way, we are the first to illustrate how stock markets in countries with different level of severity and strictness react to the changes in daily new confirmed cases and government policies. According to our observation, the equity market reacts more dramatically to increase in daily new confirmed cases in countries with higher severity level. Furthermore, increase in stringency leads to greater volatility especially for countries with low level of strictness.

Next, we evaluate whether markets reacted differently to the COVID-19 pandemic depending on their development status and dependence on domestic income. To test this, we run panel regressions with variables controlling for the income level of economies. We find no evidence of stock markets in high-income economies reacting differently than those in low-income economies.

Toda ( 2020 ) argues that the cold weather in the Northern Hemisphere may affect the spread of the virus and in return negatively affect the stock market. However, it should be noted that not only the Northern Hemisphere, the Southern Hemisphere was also affected by the pandemic during summer. Previous research was normally conducted at the early stage of the pandemic when patterns were not fully discovered. To address this issue, we include the whole 2020 as sample period and include controls for geographical locations. We discover that geographical locations cannot explain stock market changes, and the stock market recovered since the second quarter.

Previous research on the impact of the COVID-19 pandemic has concentrated mainly on the pandemic’s early stage and the impact caused by total cases or total death tolls (see e.g. Albulescu 2020 ; Baker et al. 2020 ; Baek et al. 2020 , etc.). However, changes in equity market returns and volatility based on the level of severity of the pandemic and government policies’ strictness still remain under-researched. As governments enact life-saving policies, this study provides empirical evidence on global stock market reactions to both the COVID-19 pandemic and the ensuing policy responses.

2 Data and methodology

COVID-19 data were sourced from the CSSE at Johns Hopkins University (Dong et al. 2020 ). To address potential time lags and inaccuracies in this data, we cross-referenced it with government-reported figures, ensuring alignment with stock market data timelines. Reported cases are sometimes subject to inaccuracy; hence, we modify the data in some cases according to government corrections. Claimant countries consider disputed territories differently, which also may cause mismatch between government-reported cases and those in the database.

To gain insights into government responses, we employed the Oxford COVID-19 Government Response Tracker (OxCGRT), focusing on stringency and economic support indices. This included examining the stringency and economic support indices, as well as specific containment measures. In particular, we focus on the stringency index, the economic support index, and procedures that aim to control COVID-19 outbreaks. This last category of policies includes school and workplace closures, prohibition of public events, restrictions on gatherings, public transport cancellations, stay-at-home orders, and national and international travel controls. The incorporation of daily COVID-19 case numbers and detailed government policy data provides a dynamic view of the pandemic’s progression and response measures. This approach allows for a more nuanced understanding of how these factors influence stock market volatility and returns. To investigate the trend shifting in investor sentiment, we collected the VIX index price in the same period and construct high and low VIX periods following (Smith et al. 2016 ).

Our study assesses the impact of the COVID-19 crisis by analyzing the returns and volatility of major stock market indices. Using data from 34 countries’ stock markets allows for a comprehensive and diverse analysis. This broad scope enables the capturing of a wide range of market reactions to the pandemic, providing a more global perspective. We gathered daily open, high, low, and close prices for stock indices from 34 countries Footnote 1 , Footnote 2 each representing their respective primary market portfolio. To ensure robustness, indices with high internal correlations (as shown in Table 3 ) were consolidated, resulting in a final sample of 34 indices. Our primary data sources were Yahoo Finance, supplemented and verified through Investing.com, forming the analytical foundation of our study. The statistical properties of these indices, detailed in Tables 1 and 2 , indicate varying impacts of the pandemic based on countries’ economic dependencies and government measures. Our results indicate that the average daily return is not solely determined by the number of new daily cases or geographical location. Countries whose governments employ strict rules to control the spread of the virus tend to experience tend to experience decline in their economies.

2.2 Methodology

In this research, two sets of stock market indicators are computed. The first indicator, the log return, is selected for its efficacy in representing proportional changes in financial data and is calculated as

where \(price_{i,t}\) is the daily closing price for index i on day t .

As the second indicator, we utilize the Yang and Zhang ( 2000 ) volatility model to measure the return volatility. This model is particularly apt for our study as it accounts for intra-day price movements and opening jumps, critical during the pandemic’s market upheavals (Shu and Zhang 2006 ). The model is expressed as

where \(\sigma _o^2\) is the overnight volatility, \(\sigma _c^2\) is the open-to-close volatility, \(\sigma _{rs}^2\) is the Rogers-Satchell volatility, Z is the number of trading days in a year, n is the time window in days for calculating the volatility (30 days in this research), and O , H , L , C are the opening, high, low, and closing index values, respectively. Finally, k is the weighting assigned to the open-to-close volatility.

To incorporate both temporal and individual country effects, this study employs panel data analysis with the Hausman test indicating a preference for a fixed effects model. This method is appropriate for examining the relationships between the variables of interest and is robust and widely accepted in empirical economics. Thus, the data are assumed to follow a fixed effects model. Our baseline model is structured as follows:

where \(X_{i,t}\) is the endogenous variable. In this case, \(X_{i,t}\) represents stock market return \(Ret_{i,t}\) or the first difference of Yang and Zhang volatility represented as \({\sigma _{YZ_{i,t}}^2}\) ; and \(X_{i,t-1}\) is the one time period lag of endogenous variables. \(Cases_{i,t}\) represents the number of daily new confirmed cases for country i on day t . The number of cases is scaled down by taking log of 1 plus the number of cases that are normalized by population. \(Gov_{i,t}\) is the stringency index value for country i on day t , and \(Econ_{i,t}\) is the economic stimulus index for country i on day t . \(X_{i,t-1}\) is included in the model as returns and volatility are commonly recognized to be self-dependent (Lewellen 2002 ; Schwartz and Whitcomb 1977 ; McQueen et al. 1996 ). Control variables ( \(Control_{k,i}\) ) are also included to account for other influential factors, such as economic indicators and regional differences.

We include control variables ( \(Control_{k,i}\) ) to account for additional influential factors. These include dummy variables representing hemispherical location and continent, which are expected to capture geographical differences in pandemic impact and market reactions. Furthermore, income level, based on the IMF’s classification, is also used as a control, distinguishing high-income economies from others. For example, countries located in the Southern Hemisphere are given a value of 1 and those in the Northern Hemisphere are given a value of 0. Similarly, countries are marked using dummy variables to indicate their continent.

Quarter controls are included in to capture the effect of time. Since an economy’s degree of development has also been shown to influence stock market reactions (Topcu and Gulal 2020 ; Anh and Gan 2020 ), dummy variables related to income are added. Countries marked with a value of 1 if it is classified as a high-income economy by the IMF ( 2020 ). Footnote 3 The quarterly controls are not used in the analysis of investor sentiment periods since these subperiods have already covered the time effect.

Including controls for seasonal effects and geographical locations helps isolate the impact of the pandemic from other variables that might influence stock market behavior, such as weather patterns or regional economic conditions. By running panel regressions with variables controlling for the income level of economies, we address an often-overlooked aspect of how economic status might influence market reactions to global crises.

The severity of the pandemic is quantified as the percentage of total confirmed cases relative to the population, allowing for cross-country comparability. We also consider the initial strictness of government responses, hypothesizing that stock market reactions may vary based on these factors (Bora and Basistha 2021 ).

Adding controls for severity and strictness groups allows for a differentiated analysis of how markets in countries with varying levels of pandemic severity and government response strictness react to changes in the pandemic and policy measures. This approach adds depth to this analysis. By focusing on how global stock markets have reacted to both the pandemic’s progression and the corresponding government policies, this study provides empirical evidence that can inform policy decisions in future crises.

To explore these dynamics, our main model integrates interaction terms, such as the product of new confirmed cases and severity level ( \(Cases_{i,t}\times SEV_{m,i}\) ) and government policy stringency and its strictness level ( \(Gov_{i,t}\times STRICT_{n,i}\) ). These interactions aim to shed light on how varying degrees of pandemic severity and policy strictness influence stock market behavior. Our model, structured as follows, seeks to capture the multifaceted nature of these relationships:

In this model, we expect that the coefficients will reveal the direction and magnitude of the impact of each factor on stock market returns and volatility. We acknowledge the assumptions inherent in our model, including the linearity of relationships and potential limitations due to data constraints and unobserved heterogeneity.

In this study, we analyze three distinct time intervals within 2020: the entire year, and each of high and low VIX periods. This division allows for a nuanced examination of the evolving impact of the pandemic and corresponding policy responses under different investor sentiments. We repeat the same regression analysis for each period, using a grouping method based on the severity of the pandemic ( SEV ) and the strictness of government interventions ( STRICT ).

For each period, percentile values (25th, 50th, and 75th) of SEV and STRICT at the end of the period are calculated to define the groups. This percentile-based grouping enables a balanced comparison across countries. For instance, a country in the first group for SEV at the end of the first quarter indicates it’s among those with the least severity. A statistically significant coefficient for \(Cases_{i,t}\times SEV_{1,i}\) in this group would suggest that changes in daily new cases have a notable impact on stock markets in these least affected countries.

For example, countries with severity between 0 and the 25th percentile value by the end of the first quarter are classified into the first group of the first quarter. Countries will be marked with a value of 1 for \(SEV_{1}\) , meaning that those countries have the least severity across all countries. A statistically significant estimated coefficient of parameter \(Cases_{i,t} * SEV_{1,i}\) means that, for countries with the least (less than the 25th percentile of the whole sample) percentage of population infected with the COVID-19, changes in daily new confirmed cases have significant impact on the endogenous variable. Similarly, countries with the lowest strictness across all countries will be marked with a value of 1 for \(STRICT_{1}\) . A statistically significant estimated coefficient of parameter \(Gov_{i,t}*STRICT_{1,i}\) means that, for countries with the least strict (less than the 25th percentile of the whole sample) policies implemented, changes in government policies have significant impact on the endogenous variable. For more detailed information on the grouping criteria and the definitions of severity and strictness levels, readers are referred to Table 8 .

We also employ a rolling window estimation technique to dynamically assess the impact of the COVID-19 pandemic on stock markets throughout the sample period. We estimate the regression parameters for a 30-day window of the data and then rolling this window forward in time, re-estimating the parameters at each step. This approach allows us to capture evolving market reactions to changes in pandemic-related indicators over time (Hamilton 1994 ).

3 Regression results

3.1 summary statistics.

The time series plot of Fig. 1 , which includes equity market data from June 2019 to December 2019 to aid comparison, demonstrates how return and volatility have changed over the course of the COVID-19 pandemic. Volatility rose steeply during the early stage of the pandemic. The equity market then became less volatile after the first quarter of 2020, but the volatility level remained higher than the pre-pandemic level.

Time series plot of the cross-sectional average of return (dash-dotted line line), volatility (solid line), and daily new confirmed cases (dotted line). The line grey area represents the period when the cross-sectional average volatility is higher than 0.15%. The first dark grey area is the period from March 11, 2020, the day when the WHO declared the COVID-19 as a global pandemic, to the end of April; and the second dark grey area is the 1 month since the US presidential election date (from November 3, 2020, to December 3, 2020)

The data from June to December 2019 serves as a baseline, showing the state of the equity markets before the onset of the COVID-19 pandemic. The level of volatility and returns during this period can be considered ’normal’ or ’typical’ market conditions, against which the pandemic’s impact is measured. The steep rise in volatility at the beginning of the pandemic (early 2020) likely reflects the market’s immediate reaction to the uncertainty and economic disruption caused by COVID-19. This increase in volatility is a common response in financial markets to unforeseen, high-impact events, as investors reassess risks and potential impacts on company earnings and economic growth.

The observation that the equity market became less volatile after the first quarter of 2020 suggests a period of market adjustment. This could be attributed to investors gradually digesting the initial shock of the pandemic and adapting to the ’new normal’. This period may also coincide with various government and central bank interventions aimed at stabilizing the markets and the economy. The fact that volatility levels remained higher than the pre-pandemic level throughout 2020, despite a decrease after the first quarter, indicates ongoing market sensitivity and uncertainty. This could be due to continuing concerns about the trajectory of the pandemic, its long-term economic impacts, and the effectiveness of policy responses.

The trend may reflect underlying shifts in investor sentiment and behavior in response to the pandemic. For instance, the initial spike in volatility might indicate panic selling or a rush to liquidity, while the subsequent moderation could suggest a period of cautious optimism or market stabilization.

In our analysis of the statistical properties of equity markets during various periods of the COVID-19 pandemic, we observe a marked shift in market dynamics compared to the pre-pandemic period. Results are presented in Table 4 . During low VIX period, the market exhibited a relatively stable pattern with average returns hovering close to zero and moderate volatility. However, with the onset of the pandemic, both returns and volatility underwent significant changes. The increased range and standard deviation pointed to more pronounced fluctuations, reflecting heightened market risk and investor uncertainty. The equity market have shown different landscape under different level of VIX, which reveals the necessity of investigating the impact of the pandemic and government responses to the pandemic in low and high VIX period separately.

3.2 Estimation results of the baseline model

Our analysis uncovers a significant interplay among pandemic developments, governmental responses, and global stock market dynamics during the COVID-19 pandemic. Our panel data regression results, detailed in Table 5 , provide insights into how these elements collectively influenced market behavior in 2020.

In Panel A, focusing on daily returns, we observe that increases in daily confirmed COVID-19 cases correlate with reduced stock market returns. Conversely, government policies designed to control the outbreak, such as stringency measures and stimulus packages, are positively associated with market returns. This suggests that proactive governmental responses not only helped to mitigate the pandemic’s adverse effects but also instilled investor confidence, thereby buoying market returns. Interestingly, the time controls for quarters following Q1 indicate a swift recovery of global stock markets from the initial pandemic shock, underscoring the resilience of financial markets amidst unprecedented challenges.

Panel B shifts the focus to stock market volatility. Here, both confirmed case numbers and the strictness of government policies exhibit a positive correlation with market volatility. The stringency of government responses, in particular, stands out as a significant factor in explaining the rise in volatility, affirming the notion that while such measures are necessary for public health, they can also contribute to heightened market uncertainty. Notably, declining coefficients for quarter controls post-Q1 underscore a gradual stabilization of market volatility as the year progressed.

The quarterly controls ( Q 2, Q 3, and Q 4) demonstrate varying impacts on returns and volatility, aligning with the evolving nature of the pandemic and market reactions over time. The results suggest that the global stock market gradually adapted to the pandemic, with returns increasing and volatility decreasing over time, particularly after the initial shock in Q 1. This finding is noteworthy for policymakers, as it suggests that governments can stabilize their stock markets by implementing consistently strict policies.

Our findings also challenge certain existing notions of Toda ( 2020 ). For instance, contrary to some previous studies, we find that country-specific characteristics like geographical location and income level do not significantly influence market returns or volatility. This counters the hypothesis that factors such as climate or development status might be determinants in the pandemic’s financial impact, as suggested by prior research.

In summary, our analysis delineates a complex landscape where increases in new COVID-19 cases are linked with decreases in stock market returns and increases in volatility, which agrees to previous research (see e.g. Baker et al. 2020 ; Zaremba et al. 2020 ; Zhang et al. 2020 ). Government interventions, while boosting returns, also contribute to greater market volatility as found by Zaremba et al. ( 2020 ). Economic supports show a positive effect on returns but their impact becomes less pronounced after accounting for country-specific variables. These insights underline the delicate balance policymakers must strike in navigating the pandemic’s economic repercussions, striving to bolster markets while managing inherent risks and uncertainties.

Table 6 illustrates that stock market reactions to various factors differ substantially between periods of low and high VIX. During high volatility periods, past returns have a more pronounced negative impact, and government stringency measures are perceived more positively. Regional effects also vary, with generally more negative impacts during stable periods for North America, South America, Asia, and EU.

The constant term in Table 6 reflects the baseline stock market return in the absence of the specified independent variables. Its positive value during low VIX periods suggests a generally stable and positive market environment, while its negative value during high VIX periods indicates a more volatile and negative market responds. The lagged return is negatively significant in both periods, but the magnitude of the coefficient is larger during high VIX periods ( \(-\) 0.2138 vs. \(-\) 0.0552). This suggests that past returns have a stronger negative impact on current returns during periods of high market volatility. This variable is not significant in either period, indicating that daily new confirmed cases do not have a direct statistically significant impact on stock market returns when controlled for investor sentiment.

The stringency index has a negative and significant effect during low VIX periods but a positive and significant effect during high VIX periods. This implies that stricter government measures are perceived negatively during stable periods but positively during volatile periods, potentially due to the market’s view of stringent measures as stabilizing during high uncertainty. Similarly to that presented in Table 5 , economic stimulates do not have a strong direct effect on stock returns.

3.3 Estimation results of the main model

We then analyze the relation between the severity of the COVID-19 pandemic and the strictness of government policies and stock market returns and volatility. The results from the main model, as presented in Table 7 , offer a detailed examination of the impact of the COVID-19 pandemic’s severity and the strictness of government policies on stock market reactions, specifically in terms of daily returns and volatility. The model takes into account varying levels of pandemic severity and policy strictness across different time periods of 2020.

In Panel A, the consistently negative relationship of lagged daily returns ( \(Ret_{t-1}\) ) across all models indicates a tendency for negative returns to persist, indicates a tendency for negative returns to be followed by further negative returns, signifying a persistence in market trends. During the low sentiment period, the interaction terms between daily new confirmed cases and different severity levels ( \(Cases_{t-1} \times SEV_{m,i}\) ) are negative and statistically significant when the severity level is low but insignificant when the severity level is high. Countries with higher percentage of COVID-19-confirmed population tend to have lower reactions when daily new confirmed cases increase by one. This indicates that the stock market may stop taking the increase of confirmed cases as new information as the severity level goes up. However, in periods when VIX is high, the increase in confirmed cases can hardly have significant impact on stock returns. Interaction terms between the stringency index and strictness levels ( \(Gov_{t} \times STRICT_{m,i}\) ) are positive and significant during high VIX period but negative during low VIX period. This result suggests that stricter government policies are associated with higher return when investor sentiment is high.

Plots of estimated coefficients for COVID-19 indicators estimated through a rolling window model with index return as the endogenous variable. The dashed lines are the lower and upper bounds of the 95% confidence interval

Plots of estimated coefficients for COVID-19 indicators estimated through a rolling window model with stock market volatility as the endogenous variable. The dashed lines are the lower and upper bounds of the 95% confidence interval

In Panel B, the positive coefficients for the lagged first difference of Yang and Zhang volatility ( \({\sigma _{YZ_{t-1}}^2}\) ) across all models indicate that higher volatility on one day tends to be followed by higher volatility the next day, showing a continuation of market uncertainty. Stock markets are found to be more volatile for countries with low severity level. Stock markets in those countries react more drastically to the increase in daily new confirmed cases than countries with higher severity level. The interaction terms between confirmed cases and severity levels show a positive and significant relationship with volatility over the whole 2020, suggesting that higher pandemic severity leads to increased market volatility. However, separating the year according to VIX level, we find that the increase of confirmed cases does not further lift volatility in the market. The government policy stringency and its interaction with strictness levels show mixed results. During low VIX period, stock market volatility increase as rules getting stricter, whereas the changes in rules can hardly affect market volatility during high VIX period.

3.4 Results of the rolling window estimation

Figures 2 and 3 show the estimated coefficients for daily new confirmed COVID-19 cases and the stringency of government policies, examining their relationship with stock market returns and volatility, respectively. The dashed lines representing the 95% confidence intervals in both figures underscore the statistical significance of these findings. The results show that changes in daily new confirmed cases gradually ceased to influence stock market returns and volatility. Furthermore, stock market returns and volatility tend to rise in case of strict restrictions.

In Fig. 2 , the rolling estimates for the impact of new cases on market returns initially show significant variability, indicating a strong market reaction to the pandemic’s early developments. However, over time, these effects appear to diminish, suggesting that markets gradually adapted to the ongoing pandemic, reducing the influence of new case numbers on returns. This observation aligns with the notion that financial markets can absorb and adjust to persistent risks over time. In contrast, stringency index is found to load more on return. which indicates that returns tend to be higher when policies are strict. However, such relation is not statistically significant.

Figure 3 demonstrates a less significant impact of new cases on market volatility. Furthermore, the analysis reveals that stricter government restrictions tend to increase both market volatility. This dual effect of volatility and return can be attributed to the confidence instilled by decisive government action, which may boost returns, alongside the inherent uncertainty associated with such interventions, contributing to increased volatility.

The rolling window analysis highlights the dynamic nature of market responses to the COVID-19 pandemic. The gradual decrease in the influence of new cases on returns suggests market adaptation, while the impact on volatility shows the persistent uncertainty surrounding the pandemic. These findings offer insights into how global financial markets navigate periods of crisis and uncertainty, contributing to a deeper understanding of market behavior in the face of global health emergencies.

3.5 Robustness check

To affirm the reliability of our findings, we conducted robustness checks using two alternative methods for calculating stock market volatility: the historical close-to-close volatility and the Garman-Klass volatility model. These methods were chosen for their distinct approaches to volatility estimation, thereby providing a comprehensive validation of our results.

Historical close-to-close volatility : We recalculated the stock market volatility using the historical close-to-close method, defined by the formula:

where \(\sigma \) denotes volatility, Z the number of closing prices in a year, n the number of historical prices, \(C_i\) the closing price on day i , and \(r_i\) the log return on day i . This method focuses on the variation in closing prices, offering a straightforward measure of market fluctuations.

In this model, the estimated coefficient for lagged volatility ( \(\sigma _{t-1}\) ) is 0.2635, significant at the 1% level. Daily new confirmed cases ( \(Cases_{t-1}\) ) have a coefficient of 0.0002, and government stringency ( \(Gov_t\) ) is significant at the 10% level with a coefficient of 0.0019. Economic support index ( \(Econ_{t}\) ) shows a coefficient of 0.0006. These results align with our initial findings using Yang and Zhang volatility, confirming the robustness of our analysis.

Garman-Klass volatility : We further validate our results using the Garman-Klass volatility model, which is calculated as:

where \(\sigma _{GK}\) is the Garman-Klass volatility, Z is the number of closing prices in a year, n is the number of historical prices used for the volatility estimate. This model incorporates both the range of high to low prices and the closing-to-opening prices, providing a comprehensive view of intra-day price movements.

Using the Garman-Klass model, we find the coefficient for \(\sigma _{GK_{t-1}}\) to be 0.3242, significant at the 1% level. The coefficients for \(Cases_{t-1}\) and \(Gov_t\) are 0.0001 and 0.0021, respectively, both significant at the 1% level. The coefficient for \(Econ_{t}\) is 0.0009, though not statistically significant. This robustness check with the Garman-Klass volatility further corroborates our initial results, underscoring the consistency of our findings across different volatility measurement techniques.

4 Conclusion

This paper investigates the stock market’s response to the COVID-19 pandemic, revealing multifaceted impacts influenced by the pandemic’s severity, governmental policy responses, and economic stimuli. Our findings indicate that, initially, increases in daily new confirmed COVID-19 cases negatively impacted stock market returns and led to increased market volatility. Furthermore, our analysis reveals that stricter government policies, while beneficial for market returns, are associated with higher market volatility. Economic stimulus measures, aimed at fostering economic growth, are associated with elevated returns, though they introduce additional volatility, albeit not significantly.

Our investigation of investor sentiment periods show that new COVID-19 cases do not have a direct impact on stock market returns during low or high VIX periods. However, higher government stringency measures are associated with lower stock returns during low VIX periods but higher stock returns during high VIX periods. Regional controls show significant effects primarily during low VIX periods, while economic support measures show limited influence in both periods.

Interestingly, our analysis suggests a quickly reduced sensitivity of the stock market to pandemic developments after the first quarter of 2020. This could imply that investors have acclimatized to the ongoing pandemic, perceiving further increases in confirmed cases as less informative for investment decisions. This adaptation reflects a new normal in investor behavior, where pandemic-related statistics are no longer novel or actionable information.

This study pioneers in exploring the intricate relationship between the pandemic’s severity, policy rigor, and their impacts on stock markets. The observed trends indicate that countries grappling with higher pandemic severity experience reduced market returns and elevated volatility. These findings underscore the critical role of effective governmental interventions in managing the pandemic’s spread and its economic fallout. Particularly noteworthy is the significant impact of non-pharmaceutical interventions on market returns and volatility.

While our study yields significant insights, we recognize certain limitations that must be acknowledged. The rapidly evolving nature of the pandemic and the varying degrees of data accuracy across countries present challenges in fully capturing the global stock market’s response. Additionally, our analysis primarily focuses on the pandemic’s direct impact, leaving room for exploring indirect effects such as changes in consumer behavior, supply chain disruptions, and long-term economic shifts.

Future research could build upon our findings by examining the long-term impacts of the pandemic on different sectors within the stock market. Additionally, studies could explore the role of vaccines, and global supply chain adjustments in shaping market responses to future global crises. A deeper understanding of these aspects would enrich our comprehension of the pandemic’s enduring impact on financial markets and guide strategic decision-making for investors and policymakers.

Countries included in this study are listed below with country code in parentheses: Australia (AUS), Austria (AUT), Belgium (BEL), Brazil (BRA), Canada (CAN), China (CHN), Denmark (DNK), Egypt (EGY), France (FRA), Germany (DEU), Hong Kong (HKG), India (IND), Indonesia (IDN), Israel (ISR), Italy (ITA), Japan(JPN), Korea (KOR), Mexico (MEX), Netherlands (NLD), New Zealand (NZL), Philippine (PHL), Portugal (PRT), Russia (RUS), Saudi Arabia (SAU), Singapore (SGP), South Africa (ZAF), Spain (ESP), Sweden (SWE), Switzerland (CHE), Taiwan (TWN), Thailand (THA), United Kingdom (GBR), United States (USA), and Vietnam (VNM)

This paper recognizes regions with independently developed stock exchanges as separate markets. Taiwan and Hong Kong, for example, are considered as separate markets from mainland China.

According to the IMF, high income economies in our sample are: Australia, Austria, Belgium, Denmark, France, Germany, Greece, Hong Kong SAR, Israel, Italy, Japan, Korea, Netherlands, New Zealand, Portugal, Singapore, Spain, Sweden, Switzerland, Taiwan Province of China, United Kingdom, and United States.

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Appendix A: Countries included in strictness and severity groups

Table 8 presents countries in each strictness and severity group. Groups of 2020 are determined based on the year end severity and annual average of strictness.

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Yang, S. Pandemic, policy, and markets: insights and learning from COVID-19’s impact on global stock behavior. Empir Econ (2024). https://doi.org/10.1007/s00181-024-02648-2

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DOI : https://doi.org/10.1007/s00181-024-02648-2

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Economic globalization and the COVID-19 pandemic: global spread and inequalities

Ludovic jeanne.

EM Normandie Business School Metis Lab, Le Havre, France

Sébastien Bourdin

Fabien nadou, gabriel noiret.

In just a few weeks, COVID-19 has become a global crisis and there is no longer any question of it being a major pandemic. The spread of the disease and the speed of transmission need to be squared with the forms and characteristics of economic globalization, disparities in development between the world’s different regions and the highly divergent degree of their interconnectedness. Combining a geographic approach based on mapping the global spread of the virus with the collection of data and socio-economic variables, we drew up an OLS model to identify the impact of certain socio-economic factors on the number of cases observed worldwide. Globalization and the geography of economic relations were the main drivers of the spatial structuring and speed of the international spread of the COVID-19.

Introduction

The increasing integration of the global economy has facilitated the interconnection between the world's territories. And globalization, characterized by the increase in human mobility and the exchange of goods throughout the world, can be considered a vector for the spread of epidemics and even pandemics (Berlinguer, 1999 ). The history of pandemics is a long one, and it is certainly not the first time that an infectious agent has spread across the globe. However, the most recent pandemics in historic terms appear to have been wiped from the collective memories. This helps to explain the widespread impression in the West that the current pandemic is exceptional, leading public and media reports to compare it with the “Spanish flu” of 1918–1919. Different factors account for the “forgotten” or otherwise overlooked pandemics: an available vaccination (Grippa A H1N1, 2009), initially linking the disease to what was believed to be a well-defined social group (SIDA, 1981), silent or almost silent media (Hong-Kong Flu in 1968–1970, Asian Flu 1956–1958), inadequate national resources to detect and record cases, the impression of a distant spatial threat (SARS-CoV, 2003) or an anthropological evolution that is often difficult to objectify (e.g., a shift in the relationship with death, lethal risks and mortality). It is nonetheless understandable that the danger represented by the SARS-COV-2 (WHO, 2020 ) has been seen as exceptional since, where almost a year was needed for the Spanish Flu to become a global pandemic, only three months was needed for Covid-19 to go global, and only two months for the main centers of globalization to be affected.

Indeed, as everyone feared from the moment the virus was first flagged in Wuhan (China) on 31 December 2019, the SARS-CoV-2 virus has now been transmitted worldwide (Al Hasan, 2020 ). While the global data available suffers from insurmountable problems (disparity in national institutions’ recording conditions, political agendas, unequal socio-economic effects in the identification and treatment of cases, etc.), it is nonetheless unquestionable that the virus has now escalated worldwide (Fig. 1 ). By 15 April 2020, 1,914,916 cases had been reported in over 180 countries or territories (194 member states of the WHO), with 123,010 deaths.

An external file that holds a picture, illustration, etc.
Object name is 10708_2022_10607_Fig1_HTML.jpg

Number of deaths due to Covid-19 (7 Avril 2020)

Several previous studies have used mapping to analyze the spread of epidemics by highlighting “spatial patterns”. These include tuberculosis (Roth et al., 2016), cholera (Adesina, 1984 ; Ali et al., 2002 ), SARS-CoV (Lai et al., 2004 ; Meade, 2014 ; Shannon & Willoughby, 2004 ; Wang et al., 2008 ), MERS-CoV (Cotten et al., 2014 ), H1N1 influenza (Smallman-Raynor & Cliff, 2008 ; Souris et al., 2010 ), HIV (Wallace and Wallace, 1995 ; Wood et al., 2000 ) and dengue (Acharya et al., 2021 ; Atique et al., 2018 ; Zhu et al., 2019 ). In line with the approach of earlier studies, this paper attempts to analyze how the virus was transmitted across the globe and the underlying causes of its spread. We specifically interrogate how globalization has been a driver of the spatial diffusion of Covid-19.

In the rest of the paper, we present the methodology used. Then, we highlight our results before concluding and discussing the implications of our findings.

Mapping epidemics and pandemics is a widely acknowledged method for understanding how they are transmitted and the factors that influence the spread (WHO, 2016 ). As Koch and Koch ( 2005 ) explain, using such techniques helps us to understand how to respond by being more prepared for health crises. In recent studies, it has been demonstrated that COVID-19 has primarly hit more developed regions (Bourdin et al., 2021 ; Paez et al., 2020 ). Therefore we have added in our model the GDP/capita which measures in a comparable way the levels of wealth of the States. In the same vein, in a context of globalisation where territories are interconnected (Michie, 2019 ), previous studies have shown that highly interconnected countries tend to be highly exposed in the event of an epidemic or pandemic (Hufnagel et al., 2004 ; Zou, 2016). Consequently, to understand the extent to which the spread of COVID-19 is due to economic globalization, we added a measure of trade intensity (intensity of commercial exchanges) for each country as an explanatory variable. Moreover, in medical geography (Meade, 2014 ; Dobis, 2020), health infrastructures have also been shown to play a role in the number of cases recorded in the epidemics observed. Therefore, we added two covariates relatives to health infrastructure: the density of beds and doctors .

At the global level, we only have access to data relative to the number of cases recorded, the number of deaths recorded and the number of recovered cases. We used the official data released by the WHO to inform our study, and we built a linear regression model (OLS) in order to complete our mapping analysis. Our model can be written as follow:

where Y i represents the number of cases or the number of deaths; β 0 , β 1 , β 2 ⋯ β n are the parameters of the model (in italic in Table Table1); 1 ); and ε represents the error term.

Description of the variables

Variable	Date of data	Source of data
Number of cases on 7/4/2020	7/4/2020	World Health Organization
Number of deaths on 7/4/2020	7/4/2020	World Health Organization
GDP/capita	2018	World Bank
Intensity of commercial exchanges (exports of goods and services in constant dollars)	The most recent value between 2016 and 2019	World Bank
Number of doctors per 1000 inhabitants	The most recent value between 2016 and 2019	World Bank
Number of beds per 1000 inhabitants	The most recent value between 2016 and 2019	World Bank

Understanding the spread of COVID-19: Between geographical and functional proximities

Mapping deaths due to Covid-19 worldwide until 7 Avril 2020 showed that the SARS-CoV-2 virus is active across the globe and, potentially, in all societies and human groups, with the possible exception of the most isolated regions (notably Africa, Asia and Amazonia). The pandemic situation is thus indisputable, with some notable variations. In effect, Fig. 1 indicates that on 7 April 2020, the most severely affected regions in the world were the Extreme Orient, Europe and North America, with major infra-regional variations (especially between Western and Eastern Europe, and between the USA and Canada). Given what we now know about the exceptionally high degree of contagion, the average length of the incubation period (5/6 days and up to 14 days) and the very widespread potential of asymptomatic cases (Read et al., 2020 ; Ren et al., 2020 ; Wang et al., 2020 ), the global scale of diffusion makes it particularly challenging to eradicate the virus. This underscores the strategic importance of developing a vaccine in the fight against Covid-19 and the very high likelihood of resurgence (Table (Table2 2 ).

OLS model (7 April 2020)

	Model cases					Model deaths
	(1)	(2)	(3)	(4)	(5)	(1)	(2)	(3)	(4)	(5)
Bed	− 0.182**	− 0125**				− 0.158	− 0.112
Doctor	0.219***		0.195***			0.237**		0.206**
Exchanges	0.080**			0.118**		0.115**			0.156**
GDP	0.784***				0.768***	0.417***				0.485***
R	0.742	0.63	0.59	0.48	0.61	0.31	0.36	0.48	0.46	0.58
Log likehood	− 72.258	− 74.71	− 77.16	− 74.17	− 76.62	− 59.55	− 59.34	− 59.13	− 58.84	− 58.63
AIC	3284.7	3272.22	3259.74	3247.26	3234.78	2516.48	2391.46	2266.44	2394.49	2269.47

Overexposure to the epidemic of countries most deeply embedded in economic globalization

The pandemic spread across the globe in the space of 4 months. The main stages of the spatial spread of the virus closely follow the economic geography of today’s economic world. Thus, the spread of SARS-CoV-2 over space and time appears to provide considerable information about the main mechanisms at work (Fig. 2 ).

An external file that holds a picture, illustration, etc.
Object name is 10708_2022_10607_Fig2_HTML.jpg

The global spread of Covid-19 from December 2019 (date when the threshold of ten cases was exceeded)

Three countries (Thailand, South Korea and Japan) reported their first cases quickly after the first case identified in Wuhan (16th of November 2019), at the end of January 2020. Taiwan followed suit on 1 February 2020. These are countries that, given their geographical proximity to the Chinese city where the epidemic first broke out, have frequent face-to-face interactions with Chinese interlocuters compared to more distant spatially countries where the cost of transport (both financial and temporal) to enact a physical encounter reduces the frequency of face-to-face interactions. Other countries in South-East Asia whose economies are linked to that of China and which have large Chinese diasporas were also affected (e.g., Vietnam, Malaysia).

When we analyse the Fig. 2 , the case of Iran and the United Arab Emirates in the Middle East is more curious. However, one reason the United Arab Emirates has been affected could be to do with its positioning as an intercontinental air hub, with many commercial flights making a stopover between Europe and Asia. Iran, on the other hand, has enjoyed commercial and industrial relations with China linked to the oil sector for many years. March 2020 witnessed the spatial expansion of the epidemic to Latin America, South Asia, Eastern Europe (affected later than Western Europe) and Russia, as well as several regions in Africa. Regarding Russia, the relative lateness of the epidemic, while the country has borders with China, could be related to the geography of the country—so wide that the face-to-face interactions between Russians people from West to East is not so frequent (Sardadvar & Vakulenko, 2020 ), and the location of the Wuhan area, quite far from the China-Russia border.

Besides, we can clearly see that the countries most directly linked to China economically are generally the richest and most developed nations, and these were the first to see a rapid rise in cases. In the table, we observe a positive and significant effect of the level of development on the number of cases. In addition, the extent of a country's participation in international trade as measured by trade intensity has a positive and significant effect on the number of cases. From this point of view, we can say that the very rapid planetary spread of the pandemic was driven by the reticular links of “functional proximity” woven by economic globalisation between territories that are often geographically very distant from one another but associated, and therefore articulated and interdependent. Countries that were heavily hit at the beginning of the crisis are countries where business and trade relations with China and between centers of economic globalization, of which they are part, lead to frequent mobilities of people engaged in Business activities: i.e., Western Europe, North America and Australia. For these countries, opportunities for interindividual exposure (mixing of people from different countries, transit, meetings, interconnection-supporting sites or simply co-presence) are far higher in globalized regions and cities than for populations in countries where economic globalization is less effective.

In our model, we also observe that there is a positive and significative effect of the level of healthcare system (proxied by the density of beds and doctors) on the number of cases. This result suggests that countries with the most cutting-edge healthcare systems present the highest number of infections. It may seem counterintuitive. But recent studies have shown that although the number of doctors and beds per capita was high, the scale of the epidemic meant that these high levels of health infrastructure were still insufficient. Furthermore, the overconfidence that developed countries had in their health systems had deleterious effects because they were ill-prepared for the coming wave (Rodríguez-Pose, & Burlina, 2021 ). They thought that the high level of health services would be sufficient to absorb the pandemic waves, but this was not the case (Gudi & Tiwari, 2020 ).

Thus, the spread of SARS-CoV-2 from the industrial city of Wuhan in China appears to be highly dependent on the spatial organization of economic globalization. What is generally considered as an (economic) advantage, in other words, being connected to the most intensive global economic flows, in this instance has become a component of direct and increased exposure to the risk of epidemic. In contrast, regions, economies and populations that are less exposed to economic globalization have been affected later and more slowly by the spatial spread of the Covid-19 epidemic. This illustrates the way economic globalization not only concerns the circulation of goods, but also an intense flow of people, the main factor in the transmission of the virus.

The speed of diffusion has led to the phenomenon that we began to observe at the end of March: the majority of regions affected include a large number of cases of infection requiring hospitalization and admission to intensive care units within a very short timeframe (a few weeks). Consequently, the hardest hit regions are all trying to obtain the same resources on the global marketplace more or less simultaneously: drugs and drug compounds, protective masks, protective medical gear, medical equipment (respirators, etc.), and so on. These directly concurrent demands, combined with their concentration due to the rapid onset of a large number of severe cases at the same time, inevitably leads to both economic and political tensions. The global production capacity for all this medical equipment cannot be increased with in such a pace to meet all the demands so quickly. The situation is made worse by the excessive concentration of production sites in China for much of the medical equipment needed, in addition to the fact that the country has had to compress a lot of this production which is based in the area where the epidemic first broke out, leading to many of its industrial activities slowing down or even stopping altogether (Ishida, 2020 ).

Increased risk and huge uncertainty for the least developed countries

These effects, linked to the speed of the spread of the epidemic, could be a major disadvantage for certain regions and countries that were less exposed in the initial stages of the global spread of SARS-CoV-2, but are also less well equipped and less able to ensure access to the medical equipment and drugs required. The present pandemic is thus likely to take a literally geo-economic turn of events as it leads to rivalry between national governments, themselves unequally able to deal with the issues affecting the health interests of their respective populations. The resulting economic and political tensions linked to access to medical resources between developed countries should be seen as a warning sign and a potentially aggravating factor with regard to the pandemic developing in Africa, Asia (Middle East, South Asia, Central Asia) and Latin America.

However, it is difficult to build a true picture as there is a lack of reliable epidemiological information on the different ways that populations respond to or will respond to the SARS-CoV-2 infection. Africa serves as a good illustration in this respect. While, on the one hand, the continent seems particularly lacking in equipment (hospitals, number of beds, amount of medical equipment available, etc.) and in political resources to deal with the risk of the spread of Covid-19, it is difficult to factor in other variables: relative youth of the population in face of a virus where the most severe cases appear to lead to death in patients over 65 years old (according to what has been observed in Europe); populations exposed to specific combinations of medical treatments and health-related environments; lessons learned from previous epidemics (Ebola, 2013–2016 in West Africa, for example), to name just a few.

Despite these reserves, temporality seems to be a fundamental and even decisive aspect of pandemics and their final impact healthwise. In effect, the rapid transmission of infection leading to numerous severe cases that require highly specific equipment in a very short space of time is bound to be an aggravating factor in view of the challenges involved in getting access to medical equipment. As we have seen, the rapidity of the spread is due to an epidemiological issue (the extreme contagiousness of the virus and the absence of an immune system barrier because of its novelty) combined to a specific geo-economic context (connectivity between major centers of economic globalization). Without these factors, the Covid-19 pandemic would not have had the same impact and would probably not have generated the same sense of urgency or such a major crisis. This is aggravated by the fact that the original outbreak just happened to be in one of the major production centers of goods that are now in global demand.

These considerations help us to understand that the kinetics of the spatial spread of SARS-CoV-2 across the globe need to be supplemented by other analytical frameworks to examine the issue on other scales. Thus, if we observe the process at European scale (excluding Russia), the different regional responses to the epidemic become much clearer. Not only do countries become affected at different times, as illustrated by the factors put forward above, but the national kinetics also appear to vary significantly. This suggests that within each country, the diffusion of SARS-CoV-2 does not occur at the same speed. This is all the more interesting in that Europe, compared to other regions of the world, is characterized by the existence of a regional integration institution, the European Union, and integrated, partner economies.

Finally, the temporal dimension of the pandemic has another consequence linked to the speed of its spread and so to multiple temporal coincidences between national crises: when the epidemic spreads to African countries, western and developed countries, including China, have been still in a crisis themselves and will not be able to help, support or provide additional resources which they can generally offer.

Covid-19, the first real pandemic of globalization?

The present Covid-19 pandemic could thus be considered as the first real pandemic of the age of globalization since it effectively combines certain underlying characteristics: global scale, extremely fast speed of transmission, cross effects of global interterritorial interdependencies, interdependence of nations in the management of their respective epidemics and growing complexity in the spatial organisation of economic globalization.

The speed of the spatial spread of SARS-CoV-2 appears to be largely due to the reciprocal economic integration of major economic globalization centers. The counterpart to this integration is the ever-growing rise in the circulation of goods and, above all, people for economic and tourism purposes over the last thirty years. The Covid-19 pandemic has now challenged this international spatial mobility of people. The interruption of the hypermobility inherent in globalization, and often even of mobility itself with confinements, has led to an unprecedented development of telework (Belzunegui-Eraso & Erro-Garcés, 2020 ), as if, under the constraint of Covid-19, the preference for proximity has overtaken that for mobility. The geography of mobility is reduced to its vital minimum. This is evidenced by the thousands of closed hotels (and restaurants) that have been closed with the lockdowns implemented in many countries around the world (Škare et al., 2021 ). And better still by the provision of thousands of unoccupied hotel rooms to caregivers whose homes are far from their hospitals or for people in precarious situations impacted by the crisis (Kirby, 2020 ).

In this respect, health risks and economic risks are dramatically interwoven, each having a major impact on the other in terms of public decision-making processes. The high economic cost of such measures may effectively give rise to neo-Darwinian approaches, in contrast to current thinking about public health and each individual’s right to health. And yet it is this right of each human being to healthcare which underpinned article 25 of the Universal Declaration of Human Rights.

Finally, the speed of the global spread of SARS-CoV-2 underpins and strengthens the need for a coordinated and far more integrated international response. However, this is not what we have observed to date, especially in the face of a “new” virus about which we lacked precise knowledge. It is therefore essential to drastically step up research in two directions:

First and foremost, medical and biological research in order to gain a better understanding of infectious agents liable to provoke such pandemics;
Second, Humanities and Social Science for a better understanding of social and organizational behaviors in the framework of a health crisis of this extent. Indeed, one thing the Covid-19 pandemic has shown is the extreme challenges of getting affected populations or those liable to be affected to adopt the new behaviors required in so little time and on such a scale.

For a pandemic like Covid-19, it seems that medical solutions need to closely tie in with organizational, behavioral and, no doubt, cultural solutions.

Acknowledgements

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Declarations

We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.

We confirm that the manuscript has been read and approved by all named authors. We confirm that the order of authors listed in the manuscript has been approved by all named authors.

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Move over, New York: China’s southern metropolis is the new global city of opportunity.

Guangzhou Shows Why China Is So Attractive to the Global South

According to United Nations projections , by 2100, eight out of 10 people will live in Asia or Africa. This demographic shift starkly contrasts with the trends in Europe and North America, where many countries are struggling with demographic decline. While numbers alone do not define the future, such a significant disparity between the populations of emerging economies and those of developed ones will inevitably reshape the global economic and political order. This means globalization too will be very different from the one we know.

These global changes are already tangible, and cities provide an ideal spot to observe them. New York has been the quintessential city of the current era of globalization, which has been shaped and dominated by the West. It is a city of opportunity that, in the 19th and 20th centuries, attracted people from all over the world seeking the American dream. In contrast, Guangzhou, the capital of China’s Guangdong province, offers a glimpse into the future of globalization.

It’s no coincidence that this city is in China, the country that best exemplifies the world’s ongoing transition. In roughly 50 years, China has transformed from one of the poorest countries to the world’s second-largest economy. It has become a land of opportunity too, drawing individuals from diverse regions, especially those left out of today’s globalization benefits.

Guangzhou, the urban center of the Pearl River Delta, is renowned for its multiculturalism – a stark contrast to the rest of China, which has a lower percentage of foreign residents than even North Korea . Entrepreneurs from Ethiopia, Kenya, Sudan, Pakistan, and Iraq can be found in the city.

An Arab supermarket in Xiaobei, Guangzhou, is testament to the Middle Eastern diaspora population in the Chinese city. Photo by Gabriele Manca.

Foreigners have long been part of Guangzhou’s history, dating back to when it was a major port on the ancient Silk Road. The maritime route connecting Guangzhou to the Persian Gulf via the South China Sea and the Indian Ocean was the world’s most important at that time. Today, Guangzhou is a key hub for purchasing low-cost goods, often counterfeit, exported to South and Southeast Asia, Latin America, and predominantly the Middle East and Africa.

“If you go to Shanghai, you’ll find more Europeans and Americans; big business happens there. Here, we do things on a smaller scale, buying some goods and reselling them back home,” Ahmed told me in an Arab restaurant in Xiaobei, a district in Guangzhou.

Ahmed, an Ethiopian, has been traveling between Addis Ababa and Guangzhou for about 20 years. He knows China well and loves it, particularly appreciating “the safety and the freedom to be who you want to be, thanks to the many opportunities China offers.” That phrase had a strong flavor of the old American dream but with a Chinese twist.

Over the years, Xiaobei has emerged as “Little Africa,” becoming the focal point of the African community in Guangzhou, which is the largest in Asia. Many Middle Eastern men and women also live there. Providing an exact number is nearly impossible, both because the government does not release such data and due to the often transient nature of foreigners’ stays in the city.

A woman walks down the street in Xiaobei, Guangzhou’s “Little Africa.” Photo by Gabriele Manca.

Ten years ago, there were an estimated 500,000 foreigners in Guangzhou. By 2018, this number had decreased to around 80,000. The headcount has fluctuated over time, but the pandemic drastically reduced it. During the two years of China’s zero-COVID policy, many foreigners faced extreme precariousness due to the lack of Chinese citizenship. The inability to conduct their businesses, the main reason for being in Guangzhou, forced them to return home.

Contributing to their departure was also a rise in racism reported after the COVID-19 pandemic began. Many Africans reported being targeted with suspicion and subjected to forced evictions and arbitrary quarantines. The pandemic was indeed a turning point.

I visited Guangzhou this July to see how the Arab and African presence in the city had changed. Two years after China reopened, Xiaobei remains the emblematic Arab-African district, but with a significant difference: few foreigners now live there year-round. Most individuals only remain for a few months, which is sufficient time to conduct their business.

Citizens from Arab and African countries primarily occupy hotels and hostels. In the hostel where I was staying, I met Hassam, a Sudanese man who has been coming to China for over a decade. He is fluent in Mandarin and has studied computer science in Beijing. After living in various Chinese cities, he now has an import-export business.

A road sign advertising shipping from China to Iraq in Arabic, English, and Chinese in Guangzhou, China. Photo by Gabriele Manca.

The ongoing vitality and dynamism of trade between Guangzhou and African and Middle Eastern countries are evident from the numerous street signs advertising shipping services, even door-to-door, from China to Iraq or Nigeria.

Among the remaining Africans residing in the city, the majority are affluent and may not be actively engaged in business. This is the case for Abdel, who came from Tanzania and is studying mechanical engineering. His parents moved to China five years ago for work-related reasons. However, he plans to graduate and move to “somewhere in Europe or Canada” because, he says, China now offers fewer opportunities than when his family first arrived. Additionally, it’s not easy being an African in China; he often faces discrimination, and the language barrier has limited his friendships to those in his international university program.

Even with the major changes brought about by the pandemic, Guangzhou still showcases a unique form of globalization. It reflects a grassroots globalization marked by independent traders who buy goods in large quantities and sell them in their home countries through official retail stores and informal street markets. The city on the Pearl River reflects how many emerging economies view China: abundant in opportunity and a paradigm of development and modernity, different from European or American models.

These micro-level dynamics mirror the macro-level, where China cultivates political and economic relationships with emerging economies. Today, Beijing is the primary trading partner for most emerging African and Middle Eastern economies. Its role as a central hub and driving force of an alternative form of globalization is well represented by the Belt and Road Initiative, the cornerstone of China’s economic and geopolitical strategy, which aims to promote its standards globally, both financial and political. It’s no coincidence that most countries involved in the Chinese project are emerging economies, with crucial nodes in the Middle East and Africa.

Over the past decade, through economic influence, soft power, political pressure, and diplomatic initiatives, China has advanced its worldview, offering it to countries where the West’s appeal has been undermined by years of exploitation and paternalism. In many cases, the Chinese charm offensive is working.

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DOI: 10.1016/j.eatbeh.2024.101907
Corpus ID: 271674919

How parent stress and COVID-19 impact on the family are associated with parental pressure to eat during COVID-19.

Annie Choi , Mara Z. Vitolins , +3 authors Callie L. Brown
Published in Eating Behaviors 1 August 2024
Medicine, Psychology, Sociology

30 References

Parental stress, food parenting practices and child snack intake during the covid-19 pandemic, associations between parenting stress, parent feeding practices, and perceptions of child eating behaviors during the covid-19 pandemic, a preliminary study of covid-19-related stressors, parenting stress, and parental psychological well-being among parents of school-age children, parents are stressed patterns of parent stress across covid-19, the relationship between covid-related parenting stress, nonresponsive feeding behaviors, and parent mental health, parental challenges during the covid-19 pandemic: psychological outcomes and risk and protective factors, associations between parental stress, parent feeding practices, and child eating behaviors within the context of food insecurity, real-time predictors of food parenting practices and child eating behaviors in racially/ethnically diverse families, changes to the home food environment and parent feeding practices during the covid-19 pandemic: a qualitative exploration, the social and economic impact of covid-19 on family functioning and well-being: where do we go from here, related papers.

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