essay on economic impact of covid 19

The Global Economic Outlook During the COVID-19 Pandemic: A Changed World

An empty highway in Dubai during the coronavirus pandemic. Above the highway, a sign reads "Stay Safe, Stay Home."  © Mo Azizi/Shutterstock

The COVID-19 pandemic has spread with alarming speed, infecting millions and bringing economic activity to a near-standstill as countries imposed tight restrictions on movement to halt the spread of the virus. As the health and human toll grows, the economic damage is already evident and represents the largest economic shock the world has experienced in decades.

The June 2020 Global Economic Prospects  describes both the immediate and near-term outlook for the impact of the pandemic and the long-term damage it has dealt to prospects for growth. The baseline forecast envisions a 5.2 percent contraction in global GDP in 2020, using market exchange rate weights—the deepest global recession in decades, despite the extraordinary efforts of governments to counter the downturn with fiscal and monetary policy support. Over the longer horizon, the deep recessions triggered by the pandemic are expected to leave lasting scars through lower investment, an erosion of human capital through lost work and schooling, and fragmentation of global trade and supply linkages.

The crisis highlights the need for urgent action to cushion the pandemic’s health and economic consequences, protect vulnerable populations, and set the stage for a lasting recovery. For emerging market and developing countries, many of which face daunting vulnerabilities, it is critical to strengthen public health systems, address the challenges posed by informality, and implement reforms that will support strong and sustainable growth once the health crisis abates.

Historic contraction of per capita income

The pandemic is expected to plunge most countries into recession in 2020, with per capita income contracting in the largest fraction of countries globally since 1870. Advanced economies are projected to shrink 7 percent. That weakness will spill over to the outlook for emerging market and developing economies, who are forecast to contract by 2.5 percent as they cope with their own domestic outbreaks of the virus. This would represent the weakest showing by this group of economies in at least sixty years.

Every region is subject to substantial growth downgrades. East Asia and the Pacific will grow by a scant 0.5%. South Asia will contract by 2.7%, Sub-Saharan Africa by 2.8%, Middle East and North Africa by 4.2%, Europe and Central Asia by 4.7%, and Latin America by 7.2%.  These downturns are expected to reverse years of progress toward development goals and tip tens of millions of people back into extreme poverty.

Emerging market and developing economies will be buffeted by economic headwinds from multiple quarters: pressure on weak health care systems, loss of trade and tourism, dwindling remittances, subdued capital flows, and tight financial conditions amid mounting debt. Exporters of energy or industrial commodities will be particularly hard hit. The pandemic and efforts to contain it have triggered an unprecedented collapse in oil demand and a crash in oil prices. Demand for metals and transport-related commodities such as rubber and platinum used for vehicle parts has also tumbled. While agriculture markets are well supplied globally, trade restrictions and supply chain disruptions could yet raise food security issues in some places.

A possibility of even worse outcomes

Even this bleak outlook is subject to great uncertainty and significant downside risks. The forecast assumes that the pandemic recedes in such a way that domestic mitigation measures can be lifted by mid-year in advanced economies and later in developing countries, that adverse global spillovers ease during the second half of 2020, and that widespread financial crises are avoided. This scenario would envision global growth reviving, albeit modestly, to 4.2% in 2021.

However, this view may be optimistic. Should COVID-19 outbreaks persist, should restrictions on movement be extended or reintroduced, or should disruptions to economic activity be prolonged, the recession could be deeper. Businesses might find it hard to service debt, heightened risk aversion could lead to climbing borrowing costs, and bankruptcies and defaults could result in financial crises in many countries. Under this downside scenario, global growth could shrink by almost 8% in 2020.

Looking at the speed with which the crisis has overtaken the global economy may provide a clue to how deep the recession will be. The sharp pace of global growth forecast downgrades points to the possibility of yet further downward revisions and the need for additional action by policymakers in coming months to support economic activity.

A particularly concerning aspect of the outlook is the humanitarian and economic toll the global recession will take on economies with extensive informal sectors that make up an estimated one-third of the GDP and about 70% of total employment in emerging market and developing economies. Policymakers must consider innovative measures to deliver income support to these workers and credit support to these businesses.

Long-term damage to potential output, productivity growth

The June 2020 Global Economic Prospects looks beyond the near-term outlook to what may be lingering repercussions of the deep global recession: setbacks to potential output⁠—the level of output an economy can achieve at full capacity and full employment⁠—and labor productivity.  Efforts to contain COVID-19 in emerging and developing economies, including low-income economies with limited health care capacity, could precipitate deeper and longer recessions⁠—exacerbating a multi-decade trend of slowing potential growth and productivity growth. Many emerging and developing economies were already experiencing weaker growth before this crisis; the shock of COVID-19 now makes the challenges these economies face even harder. 

Another important feature of the current landscape is the historic collapse in oil demand and oil prices. Low oil prices are likely to provide, at best, temporary initial support to growth once restrictions to economic activity are lifted. However, even after demand recovers, adverse impacts on energy exporters may outweigh any benefits to activity in energy importers. Low oil prices offer an opportunity to oil producers to diversify their economies. In addition, the recent oil price plunge may provide further momentum to undertake energy subsidy reforms and deepen them once the immediate health crisis subsides.

In the face of this disquieting outlook, the immediate priority for policymakers is to address the health crisis and contain the short-term economic damage. Over the longer term, authorities need to undertake comprehensive reform programs to improve the fundamental drivers of economic growth once the crisis lifts.

Policies to rebuild both in the short and long-term entail strengthening health services and putting in place targeted stimulus measures to help reignite growth , including support for the private sector and getting money directly to people. During the mitigation period, countries should focus on sustaining economic activity with support for households, firms and essential services.

Global coordination and cooperation—of the measures needed to slow the spread of the pandemic, and of the economic actions needed to alleviate the economic damage, including international support—provide the greatest chance of achieving public health goals and enabling a robust global recovery.

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The Economic Impact of COVID-19 around the World

Working Paper 2022-030A by Fernando M. Martin, Juan M. Sánchez, and Olivia Wilkinson

For over two years, the world has been battling the health and economic consequences of the COVID‐19 pandemic. This paper provides an account of the worldwide economic impact of the COVID‐19 shock, measured by GDP growth, employment, government spending, monetary policy, and trade. We find that the COVID‐19 shock severely impacted output growth and employment in 2020, particularly in middle‐income countries. The government response, mainly consisting of increased expenditure, implied a rise in debt levels. Advanced countries, having easier access to credit markets, experienced the highest increase in indebtedness. All regions also relied on monetary policy to support the fiscal expansion. The specific circumstances surrounding the COVID‐19 shock implied that the expansionary fiscal and monetary policies did not put upward pressure on prices until 2021. We also find that the adverse effects of the COVID‐19 shock on output and prices have been significant and persistent, especially in emerging and developing countries.

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https://doi.org/10.20955/wp.2022.030

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PERSPECTIVE article

Economic consequences of the covid-19 outbreak: the need for epidemic preparedness.

• 1 Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, QLD, Australia
• 2 North Coast Public Health Unit, New South Wales Health, Lismore, NSW, Australia
• 3 The University of Sydney, University Centre for Rural Health, Lismore, NSW, Australia

COVID-19 is not only a global pandemic and public health crisis; it has also severely affected the global economy and financial markets. Significant reductions in income, a rise in unemployment, and disruptions in the transportation, service, and manufacturing industries are among the consequences of the disease mitigation measures that have been implemented in many countries. It has become clear that most governments in the world underestimated the risks of rapid COVID-19 spread and were mostly reactive in their crisis response. As disease outbreaks are not likely to disappear in the near future, proactive international actions are required to not only save lives but also protect economic prosperity.

Covid-19 and the Economy

On March 11, 2020, the World Health Organization (WHO) characterized COVID-19 as a pandemic, pointing to over 3 million cases and 207,973 deaths in 213 countries and territories ( 1 ). The infection has not only become a public health crisis but has also affected the global economy. Significant economic impact has already occurred across the globe due to reduced productivity, loss of life, business closures, trade disruption, and decimation of the tourism industry. COVID-19 may be that a “wake-up” call for global leaders to intensify cooperation on epidemic preparedness and provide the necessary financing for international collective action. There has been ample information on the expected economic and health costs of infectious disease outbreaks ( 2 , 3 ), but the world has failed to adequately invest in preventive and preparedness measures to mitigate the risks of large epidemics.

With globalization, urbanization, and environmental change, infectious disease outbreaks and epidemics have become global threats requiring a collective response. Although the majority of developed countries, predominantly European and North American, have strong real-time surveillance and health systems to manage infectious disease spread, improvements in public health capacity in low-income and high-risk countries—including human and animal surveillance, workforce preparedness, and strengthening laboratory resources—need to be supported by using national resources supplemented with international donor funding. International collective action among governments, non-government organizations, and private companies has been advocated in building and financing technological platforms to accelerate the research on and development response to new pathogens with epidemic potential ( 2 , 4 ). In the case of COVID-19, such cooperation is critical, especially for the development and production of a vaccine. The Coalition for Epidemic Preparedness Innovations (CEPI), a global partnership launched in 2017, has tracked global efforts in COVID-19 vaccine development activity and is advocating for strong international cooperation to ensure that vaccine, when developed, will be manufactured in sufficient quantities and that equitable access will be provided to all nations regardless of ability to pay ( 5 ). Furthermore, affected countries may benefit from exchanging technological innovations in contact tracing, such as health Quick Response (QR) codes, to manage the outbreak more effectively. However, there are important privacy implications that need to be considered ( 6 ). In the case of COVID-19, the collective response and adoption of preventive measures to stop the global spread were implemented too late, after COVID-19 had already penetrated other regions through international travel. Figure 1A presents the dynamics of confirmed COVID-19 cases and shows that large countries in Europe (e.g., Italy, Germany, and the UK) and the U.S. have already outnumbered China, the origin of epidemic, in the number of confirmed COVID-19 cases.

Figure 1. (A) Cumulative number of confirmed cases in emerging epicenters. Data sources: WHO Coronavirus disease (COVID-2019) situation reports ( 14 ). (B) Dynamics of the value of stock indices and oil futures relative to January 2, 2020. Data sources: historical data for stock indices and Brent oil futures were extracted from Yahoo Finance ( www.finance.yahoo.com ). Closing prices are used in the calculations. Daily values are calculated relative to an index value (100) on January 2, 2020.

In addition to the substantial burden on healthcare systems, COVID-19 has had major economic consequences for the affected countries. The COVID-19 pandemic has caused direct impacts on income due to premature deaths, workplace absenteeism, and reduction in productivity and has created a negative supply shock, with manufacturing productive activity slowing down due to global supply chain disruptions and closures of factories. For example, in China, the production index in February declined by more than 54% from the preceding month's value ( 7 ). In addition to the impact on productive economic activities, consumers typically changed their spending behavior, mainly due to decreased income and household finances, as well as the fear and panic that accompany the epidemic. Service industries such as tourism, hospitality, and transportation have suffered significant losses due to reduction in travel. The International Air Transport Association projects a loss in airline revenue solely from passenger carriage of up to $314 billion ( 8 ). Restaurants and bars, travel and transportation, entertainment, and sensitive manufacturing are among the sectors in the U.S. that are the worst affected by the COVID-19 quarantine measures ( 9 ). The advance seasonally adjusted insured unemployment rate in the U.S. has already reached a record level of 11% for the week ending April 11, 2020 ( 10 ).

In addition to marked health inequalities, especially in countries without universal healthcare coverage, the economic impact of the COVID-19 pandemic will be heterogeneous across the country's income distribution. For example, office workers are more likely to transition to flexible working arrangements during the restrictions, while many industrial, tourism, retail, and transport workers will suffer a significant reduction in work due to community restrictions and low demand for their goods and services.

Global financial markets have been heavily impacted by the effects of COVID-19 spread. As the numbers of cases started to increase globally, mainly through the US, Italy, Spain, Germany, France, Iran, and South Korea, the world financial and oil markets significantly declined. Since the start of the year, leading U.S. and European stock market indices (the S&P 500, FTSE 100, CAC 40, and DAX) have lost a quarter of their value, with oil prices declining by more than 65% as of April 24, 2020 ( Figure 1B ). Daily data on stock market volatility and price movements are good indicators of consumer and business confidence in the economy. There were significant negative relationships between the daily number of COVID-19 cases and various stock indices ( Figure 2 ). The correlation ranges from −0.34 to −0.80.

Figure 2 . Correlation between the number of COVID-19 cases and stock markets in selected countries.

Larger economic problems are associated with the current and potential future demand for oil translating into fluctuations in oil prices due to reduced economic activities driven by the COVID-19 pandemic. Expected excess supply was also responsible for significant price reductions. If lower than expected oil prices continue, many oil-dependent economies may contract following reductions in trade and investment. Shocks to the labor markets will be severe, especially for countries dependent on migration. Globally, migrant workers make important contributions to the labor markets, addressing imbalances in both high- and low-skilled occupations ( 11 , 12 ). As international travel restrictions and quarantine are likely to remain for the foreseeable future as countries try to halt the spread of COVID-19, migration flows will be limited, hindering global economic growth, and development ( 13 ).

As the spread of the virus is likely to continue disrupting economic activity and negatively impact manufacturing and service industries, especially in developed countries, we expect that financial markets will continue to be volatile. There is still a question as to whether this unfolding crisis will have a lasting structural impact on the global economy or largely short-term financial and economic consequences. In either case, it is evident that communicable diseases such as COVID-19 have the potential to inflict severe economic and financial costs on regional and global economies. Because of high transportation connectivity, globalization, and economic interconnectedness, it has been extremely difficult and costly to contain the virus and mitigate the importation risks once the disease started to spread in multiple locations. This warrants international collective action and global investment in vaccine development and distribution, as well as preventive measures including capacity building in real-time surveillance and the development of contact tracing capabilities at the national and international levels. As outbreaks of novel infections are not likely to disappear in the near future, proactive international actions are required not only to save lives but also to protect economic prosperity.

Author Contributions

AP and OA conceived and designed the study. AP and OA analyzed the data. AP, OA, AA, KR, EM, and DE contributed to the writing of the manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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6. Cho H, Ippolito D, Yu YW. Contact tracing mobile apps for COVID-19: privacy considerations and related trade-offs. arXiv [preprint] . arXiv:2003.11511 (020).

7. National Bureau of Statistics of China. Purchasing Managers Index for February 2020 National Bureau of Statistics of China [Press Release]. Beijing: Department of Service Statistics of NBS. (2020). Available online at: http://www.stats.gov.cn/english/PressRelease/202003/t20200302_1729254.html (accessed 30 March 2020).

8. International Air Transport Association. IATA Ecoznomics' Chart of the Week: return to air travel expected to be slow. In: IATA Economics Report (2020). Available online at: https://www.iata.org/en/iata-repository/publications/economic-reports/return-to-air-travel-expected-to-be-slow/ (accessed 23 April 2020).

9. Dey M, Loewenstein M. How many workers are employed in sectors directly affected by COVID-19 shutdowns, where do they work, and how much do they earn? Monthly Labor Rev. (2020). doi: 10.21916/mlr.2020.6

10. U.S.Department of Labour. COVID-19 Impact The COVID-19 virus continues to impact the number of initial claims and insured unemployment. In: Employment and Training Administration. Washington, DC (2020).

11. Green A. The role of migration in labour-market adjustment: the British experience in the 1980s. Environ Plann A . (1994) 26:1563–77. doi: 10.1068/a261563

12. Castles S. Migration, crisis, and the global labour market. Globalizations . (2011) 8:311–24. doi: 10.1080/14747731.2011.576847

13. Food and Agriculture Organisation of the United Nations. Migrant workers and the COVID-19 Pandemic . Rome (2020).

14. World Health Organization. Coronavirus Disease (COVID-2019) Situation Reports . Geneva (2020).

Keywords: SARS-CoV-2, COVID-19, global markets, economy, Coronavirus, pandemic

Citation: Pak A, Adegboye OA, Adekunle AI, Rahman KM, McBryde ES and Eisen DP (2020) Economic Consequences of the COVID-19 Outbreak: the Need for Epidemic Preparedness. Front. Public Health 8:241. doi: 10.3389/fpubh.2020.00241

Received: 30 March 2020; Accepted: 18 May 2020; Published: 29 May 2020.

Reviewed by:

Copyright © 2020 Pak, Adegboye, Adekunle, Rahman, McBryde and Eisen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Oyelola A. Adegboye, oyelola.adegboye@jcu.edu.au

† These authors share first authorship

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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The Human, Economic, Social, and Political Costs of COVID-19

Matteo bonotti.

Politics and International Relations, Monash University, Melbourne, Australia

Steven T. Zech

This chapter provides an overview of the human, economic, social, and political costs of COVID-19. The pandemic has had immediate negative health effects and is likely to also cause long-term health problems. In addition to economic repercussions across numerous sectors, COVID-19 has also had significant social and political effects. The chapter focuses on the strains that the pandemic has imposed on relationships between family members, friends, and romantic partners. It shows how COVID-19 has changed social practices in various everyday environments (e.g. restaurants, cafes, public transport), as the public has been forced to reimagine spaces and how to interact within them in ways that comply with new social distancing norms. The chapter also illustrates many of the political implications of COVID-19, including the way it has exacerbated ongoing political conflicts within and between states, compounded pre-existing international problems related to the movement of people, and affected levels of trust and political participation.

Introduction

The origins and evolution of the novel coronavirus (COVID-19) can be traced back to the Chinese province of Hubei, where the first cases were identified in December 2019. 1 The World Health Organization Director-General officially declared a global pandemic on 11 March 2020 as the virus spread rapidly around the world. 2 The virus initially took hold in Western Europe and the United States, with disproportionate spread in specific high-traffic cities that serve as transportation hubs for international travel. Some studies suggest that SARS-CoV-2 genomes might have already been present in Spanish wastewater from as far back as March 2019 3 and in Italy from December 2019. 4 This chapter offers an overview of the human, economic, social, and political costs of the pandemic in order to prepare the ground for our analysis of the implications of COVID-19 for civility in the remainder of the book.

The Human Cost

The human cost of COVID-19 is significant, yet its true scale is still uncertain. In addition to its immediate negative health effects, it is likely that the pandemic will also lead to a number of long-term health problems such as persistent pulmonary damage, post-viral fatigue, and chronic cardiac complications. 5 Furthermore, researchers have already connected policies aimed at reducing the spread of COVID-19 through social isolation to other negative outcomes, such as a spike in suicide rates. 6 Moreover, it has become apparent that COVID-19 is having disproportionate effects on specific subsections of the population in many of the countries affected. Factors may include age, race and ethnicity, class, and gender, among others. It is well known, for example, that older people face a higher risk of experiencing severe illness from COVID-19. 7 In many countries, aged care facilities have become hotspots of infection and residents experienced higher-than-average death rates. 8 Gender also seems to be a factor in mortality rates. There is growing evidence that men are more likely to die from COVID-19 than women, although the reasons are not yet clear. 9 Furthermore, public health experts estimate disproportionate effects on maternal and child mortality rates in lower- and middle-income countries as a direct result of the virus, the subsequent strain on health systems, and reduced access to food. 10

When it comes to race and ethnicity, some groups have been affected more than others. For example, black Americans have been disproportionately susceptible to infection and died at higher rates early on in the pandemic. 11 The Center for Disease Control (CDC) in the US found that racial and ethnic minority groups have been particularly affected by COVID-19 due to such diverse factors as discrimination; low levels of health insurance, access, and service utilization; disproportionate representation in occupations with greater exposure to COVID-19; educational, income, and wealth inequalities; and housing conditions that render prevention strategies more difficult to implement. 12 Other categories of vulnerable people who have been especially affected by COVID-19 include prisoners, 13 as well as asylum seekers and refugees in camps and detention centres. 14

Social class has also had a profound effect on the ability of people to protect themselves or recover from the virus. Data suggest, for example, that wealthier people have the resources to better adhere to social distancing policies and norms; are less likely to suffer from pre-existing health conditions; can more easily afford to stock up on food, medical, hygiene, and cleaning supplies; and are more likely to perform higher-skilled jobs that allow them to work from home. 15 At the extreme end of the wealth spectrum, global elites have been able to stockpile supplies and make use of remote properties 16 or yachts 17 to isolate from broader society during the pandemic.

Socio-economic inequalities have had an impact on the effects of COVID-19 not only within individual countries, but also on a global scale. Many lower- and middle-income countries face significant economic contractions in terms of growth and income levels as a result of the pandemic. 18 However, the challenges go beyond economic production and outputs. In some cases, healthcare systems already under stress have faced additional pressure. Furthermore, in the case of India, to cite just one example, the government has had a limited capacity to reach rural areas and experienced political pressure to limit testing to keep official case tallies low. 19 The effects have been disastrous, with infection rates and death tolls well beyond the reported numbers. This will have carry-on effects on social welfare services aimed at those in need as resources are diverted to help combat the pandemic. But there are also success stories. Cuba, for example, has been able to respond to the pandemic promptly and efficiently, at least compared to other countries in the Caribbean and their Central and South American neighbours. Its free universal healthcare system proved crucial, combined with the highest doctor-to-population ratio in the world and the presence of an efficient national emergency planning structure. 20

The Economic Cost

In addition to the human costs, COVID-19 has also taken a significant toll on the global economy, particularly due to severe travel restrictions and lockdown measures aimed at reducing its spread. A significant number of workers across various sectors have lost their jobs, and this trend is likely to continue for the foreseeable future. 21 From early on, the World Bank predicted the worst global recession since WWII, with the global economy expected to shrink drastically. 22

The pandemic has also disrupted international trade relationships. For example, it rendered post-Brexit trade negotiations between the United Kingdom (UK) and the European Union (EU) more challenging 23 and has exacerbated existing tensions in the trade relationship between the United States (US) and China. 24 Furthermore, some regional markets (e.g. in Latin America) are experiencing significant economic downturns as a result of the pandemic. 25 Moreover, financial markets have become increasingly volatile 26 and the pandemic has also significantly disrupted global supply chains. 27 Economic downturns in states like Victoria, Australia may be especially strong compared to other parts of the country. Waves of coronavirus cases have resulted in border closures that make interstate and international migration nearly impossible in Australia. The tourism, hospitality, and education sectors that rely heavily on migrant labour and international travel have been the most affected. 28

Some sectors of the economy have been particularly affected by the pandemic and are experiencing significant contraction, as is the case with higher education. In some countries, such as the US, the UK, and Australia, many universities rely heavily (from a financial point of view) on the recruitment of international students, who generally pay higher enrolment fees than their domestic counterparts. Travel and visa restrictions during the pandemic have resulted in withdrawals and lower enrolment numbers among international students, with significant financial implications for the universities most affected. 29

The creative arts sector has also struggled to cope with and adapt to the pandemic. Many music venues, theatres, and cinemas around the world were forced to keep their doors closed in the first half of 2020 due to social distancing rules and to reduce risks associated with the spread of COVID-19 in indoor environments. Some places reintroduced these measures during subsequent waves of the pandemic. The music industry has been significantly affected, with shows and festivals cancelled and album releases postponed. 30 Unable to perform in person, production companies have had to reimagine theatrical performances for online audiences that still want to see a live show. 31 Furthermore, many major theatres and opera houses across the world have made some of their performances freely available to the public via online streaming platforms. 32 The global film industry has also been severely hit by the pandemic, with many cinemas closed, film festivals cancelled or moved online, and significant delays in the release of major motion pictures for fears that studios would be unable to recoup investments. 33 The movie industry has been forced to evolve as movie theatre chains have responded to these challenges by negotiating agreements with movie studios on how to release films and charge audiences for access. 34 In some cases, cinemas have tried to adapt to the new social distancing rules by rearranging their spaces and implementing strict health and safety checks. 35 In other cases, we have witnessed unexpected changes, such as the revival of drive-in cinemas. 36

The transportation sector will also feel the economic impact of the pandemic for the foreseeable future. Industry experts forecast a record-breaking financial loss for the commercial aviation sector. International flights in and out of many countries have been severely restricted, demand for air travel has plummeted, and airlines must take costly safety precautions to limit proximity to other passengers such as leaving middle seats empty. Cruise ship operators have not been immune to the pandemic either, especially due to people’s concerns regarding difficulties in abiding by social distancing rules in confined spaces. 37 Likewise, those working in the ‘gig economy’ as drivers for rideshare services like Uber or Lyft face restrictions and lowered demand for service. 38 Yet, some industry operators have benefitted from changing travel patterns and preferences among the general public. For example, sleeper trains have regained popularity among European travellers who are reluctant to fly between different cities and countries. 39

Relatedly, the tourism industry faces unprecedented economic challenges due to travel restrictions and lower levels of disposable income among consumers who have been financially hit by the pandemic, resulting in hundreds of billions US$ in losses across the sector. 40 The hotel industry has suffered similar hardships due to a sharp decline in hotel bookings. 41 COVID-19 will force the entire tourism industry to rethink its focus and priorities to reduce susceptibility to shocks related to the pandemic and looming crises tied to global warming. 42 The public may be forced to reimagine how it travels and start to prioritize local destinations, transforming the economic outlook of the sector. 43

The restaurant and food services sectors have faced significant obstacles to profitability, and many businesses have been forced to shutter their doors. In some cases, the government has stepped in to force temporary closures or implement measures that require significant adjustments to a standard restaurant business model. Restaurants have had to contend with a severe reduction in consumer demand, a lower capacity to seat patrons, and unexpected expenditures to address safety concerns like adding plastic partitions to protect staff or redesigning seating arrangements, sometimes by prioritizing outdoors spaces. 44 Many of them have adapted to this new environment by finding new ways to reach their customers. For example, an employee at one restaurant in Melbourne explained:

We knew that our restaurants would be very quiet so we immediately pushed our online orders when COVID-19 restrictions came into play. We’re lucky that we manufacture all our own pasta, sauces, pizzas and other products so pushing [these products] through clever marketing worked well for us. We introduced our ‘Door-to-Door Service’ which saw us visiting various suburbs on various days and this was very well received… It’s something our customers love and therefore something we’ll continue even when restrictions lift… We also decided to hold an online event. Like a dinner dance, but streamed online where customers purchase tickets to watch the entertainment and then they also have the option of purchasing a dinner pack that’s delivered to them before the event. This has also been well received. 45

Following the easing of restrictions after the first wave of the pandemic, some governments stepped in to provide financial support for the industry by encouraging people to dine out. 46 Cafes and the coffee industry have also been negatively impacted from an economic perspective. 47 Conversely, grocery stores have generally benefited from changes in consumer behaviour as more people eat at home. However, they have also had to adapt their business model to the changing retail environment, prioritizing online shopping, expanding delivery services, and even exploring the potential to introduce mobile stores to replace brick-and-mortar markets. 48

The sports industry has also been significantly affected by the pandemic, with major sporting events cancelled or postponed all over the world. Mass gatherings and large-scale events generate crowds where the risk of COVID-19 transmission rises exponentially. In March 2020, Japan’s Prime Minister and the president of the International Olympic Committee announced the postponement of the 2020 Tokyo Games, marking the first time the Olympics have been rescheduled for a reason other than war. 49 The pandemic has forced diverse forms of professional and college sports leagues to halt play or devise alternative ways to reach audiences if they are to weather the storm. Many leagues and franchises have been unable to generate previous levels of advertising revenue because of postponements and find themselves in dire financial straits. Players themselves have been apprehensive about resuming play and moving forward with seasons. Sports like Major League Baseball could suffer losses in the billions of dollars, leading to tense negotiations between team owners and players regarding compensation and risk, with some asking whether athletes should be seen as ‘exploited workers or greedy millionaires’. 50 US and Australian rules football leagues have faced similar challenges, with some players simply deciding to opt out. 51 Football (soccer) leagues throughout Europe made the decision to simply suspend or cancel their seasons in 2020. 52 When play resumed, it generally occurred behind closed doors. As sports teams and players suffer the financial costs of these disruptions, the public should be aware of the disparate capacity elite men’s clubs may have to contend with the financial challenges compared to women’s clubs, as is the case of English football. 53

The agricultural sector has also faced considerable challenges. At the early stages of the pandemic, the prices of agricultural goods fell significantly, particularly due to lower demand from hotels and restaurants. 54 While growing demand from grocery stores seems to have gradually offset those initial losses, farmers face new difficulties resulting from labour shortages and from the need to adapt to new social distancing rules. 55 Labour shortages may also result in higher prices for fruit and vegetables. 56 Furthermore, the meat industry has been particularly hard-hit by the pandemic. Many meat-processing plants have been the epicentres of COVID-19 outbreaks, resulting in shutdowns and meat shortages in the food supply chain. 57

In addition to the areas examined in the foregoing analysis, other sectors that have been affected by COVID-19 include the manufacturing industry, the financial sector, the healthcare and pharmaceutical industry, and the real estate and housing sector. 58 Other businesses on the economic fringes have also been hard-hit, especially those related to vice. For example, gambling hotspots in Las Vegas had to shutter their doors for some time, but gambling online has thrived even while sports betting has declined. 59 Illicit drug trafficking and local distribution markets have faced novel challenges in supply chain and consumer demand too. 60

Sex workers have also been affected by the social distancing restrictions implemented during the pandemic, given the central role that physical contact plays in the industry. Moreover, the stigma and discrimination that those working in this industry already experience has increased during the pandemic, further contributing to economic hardship across the sector. Some have responded by either demanding government support or by adapting their business model to emphasize other online services. 61 The Australian Sex Workers Association clearly articulated the difficulties for sex workers who have been ‘placed in the impossible position of having to balance the need to protect [them]selves and the community against the prospect of no income and no access to financial relief’. 62

The Social Cost

In addition to its extensive economic implications, COVID-19 has also had a drastic effect on social life around the globe. Government measures related to social distancing rules, stay-at-home orders, business lockdowns, and curfews have in many cases eroded community relationships by drastically reducing opportunities for physical face-to-face interaction. These measures have significantly affected family life, both by increasing proximity among those forced to shared confined spaces during lockdowns 63 and by keeping families apart to prevent risk of infection. For example, one grandmother we spoke with who lives in California described what interactions with her granddaughter looked like during the pandemic:

[My husband and I] were both looking at her and she’s looking at us and she’s hugging a dolly. And they’re through the glass. It was her birthday. And she came up to the glass, she puts her hand up [to ours] and she kissed the glass and I kissed the glass. We kissed each other through the glass and it was just heart-wrenching… I said, ‘I wish I could hug you, I miss you, I’m gonna throw you kisses’. We would go out into the yard and we stayed far away. We kind of did it all. [At first] we just did FaceTime. Then we started between the windows—we could at least see her there. 64

Likewise, a grandmother in Italy whom we also interviewed explained:

The pandemic has taken away spontaneity from normal gestures of affection. There is fear but, at the same time, there is also a desire to hug grandchildren, children, and friends during the lockdown. [The pandemic] has taken away physical contact and people have had to replace this with video calls or messages, both with relatives and friends, in an attempt to exorcise fear. 65

The pandemic has clearly rendered relationships among family and friends more difficult for many. 66 However, it has also brought some people closer together thanks to greater flexibility in time schedules, alternative working arrangements, and reduced opportunities for other social activities. 67 Relationships with both family and friends have also been sustained by the use of communication technologies during the pandemic. 68 Furthermore, social media have played a key role in reducing isolation for both older 69 and younger 70 people, even though these platforms have also contributed to spreading rumours and misinformation. 71 Romantic relationships and dating have also had to adapt to the new social distancing and travel restrictions. Some dating apps, for example, have altered user guidelines and introduced new video technology options so users can continue to interact with others while minimizing risks and adhering to social distancing guidelines. 72 More generally, COVID-19 has had an impact on romantic love, 73 and in some cases contributed to increasing stress among romantic partners, compounding factors that may lead to greater infidelity. 74 Big social events like weddings had to be postponed in places like metropolitan Melbourne, Australia during its strict Stage 4 lockdown. 75

Relationships between humans and non-human animals, and social practices surrounding them, have also been impacted. For example, data show that there has been a significant increase in pet ownership and adoption, as pets help reduce stress and loneliness, or encourage healthier and more active lifestyles. 76 There has also been contention around the implications of the pandemic for certain animals. For example, the dog racing industry in Victoria, Australia saw an exemption from strict Stage 4 lockdown measures amid debates about potential animal welfare issues. 77

The pandemic has also resulted in a housing crisis, as many people can no longer afford their rent or mortgage payments, thus risking eviction and homelessness. 78 This has sometimes generated extreme and violent responses. 79 In other cases, it has compounded pre-existing social harms like increased violence between intimate partners and other forms of abuse. Early indicators show that households in Brazil, Spain, the UK, and Cyprus saw spikes in domestic and family violence. 80 A study in Dallas, Texas found a spike in domestic violence during the first two weeks of the stay-at-home order that subsided afterwards. 81 The long-term isolation, stress, and uncertainty during the pandemic may also exacerbate alcohol and drug consumption. Furthermore, these conditions can increase the likelihood of relapse among recovering alcoholics and drug addicts too. 82 There has also been a rise in online gambling. 83

COVID-19 has also changed social practices in various everyday environments due to the need to re-imagine spaces and people’s interaction within them in ways that comply with social distancing norms. 84 There are obvious logistical challenges to in-person education and how to manage students on school campuses. Options have included a combination of closures and social distancing practices. 85 Educational institutions now rely on online learning to a greater degree, raising new challenges. 86 For example, we spoke with a school teacher in Italy who explained that the transition to distance learning had several advantages, but suffered from a number of shortcomings. The new teaching format was not always suitable for younger pupils or students with disabilities. Furthermore, online teaching tended to sharpen the ‘digital divide’ between families with different levels of access to suitable spaces in the home, tablets, and highspeed Internet connections. He also described ongoing unruly behaviour and cheating among students, then added:

When our school reopened… the space was reorganized with single-seat desks… pupils always had to wear surgical masks and could only remove them in ‘static’ moments, sitting at their desks. They could not move nor could they pass materials among themselves… The interaction between teachers also profoundly changed. Teachers used to gather in the faculty lounge, which could no longer be used due to COVID-19. Opportunities for meetings and interactions with colleagues were clearly reduced; at the same time, teachers began to meet in online spaces like Google Meet, especially to share teaching practices. Yet, the ability to interact was decidedly reduced. 87

Universities have also been forced to adjust courses and curricula for online delivery. While this is practically feasible, students may have fewer opportunities to participate in the off-line social networking that is crucial for career development. 88 Furthermore, many universities may not survive the financial hit resulting from the pandemic. 89

The pandemic has also affected the way people eat and drink. Restaurants, for example, have had to undergo several changes, including redesigning their spaces, accommodating lower numbers of customers in order to respect social distancing rules, making greater use of smart technologies (e.g. for menus and meal orders), and expanding their takeaway and delivery services. 90 Some of them have adopted creative strategies in order to guarantee social distancing between patrons. 91

Likewise, government restrictions have forced some bars to close for extended periods of time in many locations. Those that have reopened or remained open had to reimagine how they serve customers and manage interactions between staff and patrons. Complex rules around indoor and outdoor spaces, as well as food service as it relates to the sale of alcohol, affect whether we visit these establishments and our experiences while there. 92 An array of ‘multi-touch’ items like menus, salt and pepper shakers, cutlery, and coasters are now kept away from customers. 93 One Irish pub in Spain’s Canary Islands used humour to communicate some of the real dangers associated with social practices in bars, putting up a notice that patrons should avoid singing the Neil Diamond hit ‘Sweet Caroline’ at all costs. Employees wrote some lyrics on a chalkboard explaining that, as a health precaution under COVID-19, ‘[t]here will be no: touching hands, reaching out, touching me, touching you’. 94

Cafes have been forced to respond to the pandemic in creative ways as well, with some selling their stock as groceries and expanding their takeaway and delivery services. 95 Furthermore, in many countries the pandemic has undermined the role of cafes as ‘third spaces’ between home and work, crucial for socializing and networking. 96 The pandemic may have long-term effects on coffee culture around the globe.

Barbershops and hairdressers have also been at the epicentre of public debate concerning lockdown measures during the pandemic, with disagreement as to whether they constitute ‘essential’ businesses that should be exempt from lockdown restrictions. Barbershops traditionally serve important social functions for some cultural groups as spaces for community building, leisure and entertainment, gossip and local political engagement, 97 as well as local education initiatives. 98 They can also be important for men’s mental health. 99 Likewise, hair salons can provide ‘a comforting source of self-care and community’ 100 and serve as ‘an important channel between members of the community and services such as family violence shelters’. 101 This partly explains why many customers opposed and, in some cases, managed to revert government decisions to close down these businesses during the pandemic. 102 In one extreme case, an armed militia group helped keep a barbershop open in a small US town in the state of Michigan. 103

Beyond its direct effect on people’s health, COVID-19 has also indirectly affected people’s ability to stay healthy. For example, lockdown and social distancing restrictions aimed at reducing its spread have changed the way people exercise, 104 with online streaming classes and programmes becoming a popular way for people to connect and participate in workout activities. 105 Furthermore, when they have not been forced to close down, gyms have had to comply with strict health and safety measures, including the introduction of ‘hygiene marshals’. 106 Partly due to risks associated with exercising in closed spaces, outdoor exercise has become increasingly popular. 107 However, research shows that overeating and other unhealthy eating behaviours have also increased, thus posing additional challenges to individual and public health. 108

The pandemic has affected other areas of social life related to leisure and recreation. Event-based social networks like Meetup, for example, have been forced to transition to virtual platforms in order to interact. 109 A recent study in Australia found that activities within Meetup decreased by 86% during the pandemic. The researcher explains:

Participants in this study mentioned that Meetup was one of the main avenues in which they were exposed to new, potential relationships and that, due to lockdown measures, they had no way of expanding their social networks and thus making new friends. COVID-19 also had an amplifying effect on existing relationships within Meetup groups in the sense that close relationships became closer, and weak ones, weaker. Where relationships were strong enough, participants often used other social networking sites such as Facebook, WhatsApp and Instagram to maintain contact during lockdown, which highlights the importance of polymedia use. 110

The way people travel for holidays and tourism has also changed. 111 For example, both customers and business owners at beach destinations face unprecedented challenges that include new social distancing rules as well as stigma and public shaming for those who fail to respect them. 112 People’s ability to access and experience national 113 and local 114 parks, as well as public spaces more generally, 115 has also been deeply affected by the pandemic.

Travelling by public transport now includes additional demands to maintain social distance on crowded buses and subways. Passengers must also take new precautions to avoid handles and other surfaces that could spread the virus. Forward-thinking researchers will need to develop safer public transport infrastructure 116 and new transport technologies to prevent an unsustainable shift back to a car-driven transport system. 117 When it comes to pedestrians, proposed measures to contain the spread of the virus include touchless pedestrian crossings 118 and crowd simulation technology to encourage social distancing. 119 Rideshare services such as Uber have had to find ways of responding to reduced customer demand. For example, at times they have emphasized food delivery rather than taxi service to help keep drivers working and to mitigate issues of food insecurity. 120 However, disruptions to their business model have had important social implications for sectors of the population with disabilities who normally depend on rideshare transportation services. 121

The broader social effects of COVID-19 also concern the tensions that may arise between different individuals and social groups. Instances of social hoarding were particularly common at the onset of the pandemic, with people fighting over such products as toilet paper, hand sanitizer, flour, and pasta in shops and supermarkets. 122 There have also been incidents of extreme rage over facemask policies, leading to the death of innocent bystanders and fatal confrontations with law enforcement. 123 Furthermore, ageism and intergenerational tensions are on the rise in online spaces, especially between the ‘millennial’ and ‘baby boomer’ generations. 124 Social stigma targeting infected people and those who have recovered from the illness, as well as doctors and health workers, has also become a widespread phenomenon. 125 COVID-19 has also fuelled racism and xenophobia. 126 Hate speech, hate crimes, and discriminatory practices targeting people with Chinese and East Asian backgrounds, 127 Muslims, 128 Jews, 129 and Romani communities 130 have been especially common. At the international level, the pandemic has generated negative attitudes towards countries with high levels of infections. 131 One study, for example, revealed spikes in incivility directed at China on South Korean social media. 132

The Political Cost

The global pandemic has generated a range of international and domestic political problems. The COVID-19 health crisis constitutes an exogenous shock to the broader international system, disrupting international politics and creating new tensions between adversaries and allies alike. It will undoubtedly have profound implications for and lasting effects on geopolitics for years to come. 133 Political leaders from major powers like the US and China may seek to use the crisis to find advantage in an ongoing contest for hegemony in the global political order. 134 In many contexts, states have been left scrambling to secure sufficient supplies and resources to effectively contend with the virus, prioritizing national interest and the well-being of their own citizens. The US, for example, requested that the firm 3M refrain from selling protective masks to Canada and countries in Latin America to keep them for domestic use. 135 A form of ‘vaccine nationalism’ took hold in a race to develop a vaccine for the virus that created barriers to cooperation and prioritized domestic delivery when mass production got underway. 136

The pandemic has the potential to exacerbate ongoing political conflicts between states. For example, COVID-19 risks inflaming tensions between India and Pakistan over Kashmir. As political leaders in both countries focus on fighting the virus, we could see further entrenchment of the militarized status quo, as well as local efforts to highlight the inadequacy of Indian governance in Kashmir. There is the potential that hardline Indian nationalist policies might be used to divert public attention from the COVID-19 crisis. However, the scale of the pandemic threat will most likely shift attention in India and Pakistan to the immediate demands of public health services and the need to alleviate economic hardship domestically. 137

Polities with supranational governance structures like the European Union have experienced discord over new policies. EU member states eventually managed to compromise on an economic recovery plan in July 2020, despite tensions during the negotiation process, especially due to concerns of so-called ‘frugal’ countries about the cost of the plan. 138 However, tensions within the EU have also been driven by disputes concerning seasonal migrant labour, with some business, especially farmers, demanding access to foreign workers, and some populist leaders calling instead for tighter restrictions on immigration. 139

The pandemic has also compounded pre-existing international problems related to the movement of people. Asylum seekers and refugees have been particularly affected, 140 especially since the pandemic risks exacerbating existing humanitarian crises. 141 The pandemic has also had an impact on temporary economic migrants, particularly as a result of the economic downturn that has forced many companies to lay off employees. Even when governments have introduced economic measures to support businesses, temporary migrants have often been excluded from these programmes. 142 Some governments are also considering changes to migration rules 143 and taking drastic steps in modifying the way they address asylum claims, including limitations to face-to-face interviews, introducing new physical barriers, or even encouraging applicants to ‘bring [their] own black or blue ink pens’. 144 Internal migration has also been affected by the pandemic, as many governments have imposed restrictions on internal travel. 145

The public health crisis is also affecting domestic political divisions in multiple contexts. For example, during post-Brexit negotiations between the UK and the EU, some politicians exploited the pandemic for partisan political gain. 146 In some cases, politicians have challenged the authority of experts, undermining citizens’ trust in evidence-based knowledge. 147 They have also mischaracterized or appropriated scientific expertise around issues like mask wearing to advance their positions. 148 Debate about the pandemic in some countries has been driven by and exacerbated pre-existing political polarization, stoking tensions between regional/state and national/federal political authorities. However, calls for unity and coordinated action has sometimes also helped to reduce ideological and partisan divides. 149

The pandemic poses unique challenges to state stability and could compound risks of political violence, internal armed conflict, and incidents of state failure. Rebel groups and other militant actors have seized opportunities to expand control, advance political objectives, and demonstrate a capacity to govern and enforce rules. For example, armed actors operating along the southwest coast of Colombia made public declarations that curfew violators would be treated as ‘military targets’. 150 COVID-19 has provided a chance for armed opposition groups to scale up attacks and target government opponents in some cases, while in others groups have seized on the opportunity to improve claims of legitimacy and demonstrate their capacity to provide public services and govern. For example, the Islamic State, the Taliban, and al-Qaeda affiliates have all provided guidance and local support to contend with the pandemic. 151

Political participation has also been affected by the pandemic. Protest politics, for example, has been at the epicentre of public debate. On the one hand, citizens in some countries have taken to the streets to protest against government restrictions to contain the virus, such as lockdown and stay-at-home orders. 152 On the other hand, protests such as those organized by Black Lives Matter activists around the world became a topic of contention as citizens and political leaders disagreed as to whether those gatherings may have contributed to new COVID-19 outbreaks. 153

The effects on political participation also extend to electoral politics. For example, in some countries local and national political authorities decided to postpone elections 154 or reimagine electoral procedures and practices. Governments have taken steps like increasing the use of postal voting 155 or introducing measures to guarantee social distancing, health, and safety during the voting process. 156 There has also been an impact on campaign practices due to the need to restrict traditional rituals and habits like shaking hands. 157 Furthermore, political rallies constitute extreme health risks for the spread of the virus. 158 This point became especially prominent after former US President Donald Trump resumed large political campaign events shortly after his hospitalisation from COVID-19 treatment. 159 Other politicians experimented with virtual rallies and events to mark important milestones in campaigns like the Democratic Party’s announcement of a presidential candidate in August 2020. 160 The content of political campaigns and party politics has also evolved as a result of COVID-19. Issues such as public health and socio-economic and racial inequality, for example, have become more salient, 161 and parties traditionally divided over fiscal responsibility and public spending have sometimes converged on more similar positions. 162

Trust is an important aspect of political life as it relates to politicians, law enforcement, and the media, among others. High-profile incidents of politicians who ignore their own stay-at-home orders 163 or who publicly contradict or undermine health experts 164 can lead to general confusion and the erosion of trust in public officials. The politicization of issues like mandatory mask wearing illustrates how a lack of consensus and divergent policies can frustrate public health measures and lead to greater distrust not only towards politicians but also towards law enforcement officials tasked with ensuring compliance. In extreme cases, law violators have lashed out in violence against police officers enforcing these new laws. 165 In a particularly sensational case, members of an extremist militia were arrested in relation to alleged plans to kidnap Michigan’s Governor and put her on ‘trial’ for restrictive pandemic policies. 166 Furthermore, the media can have a compounding effect on public trust (or lack thereof), by employing framing techniques 167 or prioritising specific content as they deliver information to the public. 168 Social media can further complicate political trust, as they are a popular channel for politicians to spread misinformation about COVID-19 and related policies. 169

This chapter provided an overview of the human, economic, social, and political costs of the pandemic. The world faces unprecedented challenges related to COVID-19, including an immense strain on relationships and the way people interact with one another in different aspects of their lives. Uncertainty and disruptions to social and political life will require a better understanding as to how the broader public needs to prepare and respond. Politicians and other decision-makers will face increasing pressure to come up with policies that are effective at containing the pandemic, limiting its economic impact, and minimising harmful social and political consequences. They face the difficult task of balancing diverse interests, values, and demands, while also having to ensure that they rely on sound scientific evidence. In the remainder of this book, we will examine all these challenges through the lens of civility.

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• Darren Gray, ‘Coronavirus: Horticulture Giants Warn Fruit and Vegetable Prices Could Rise Due to Labour Shortage’, The Sydney Morning Herald , 30 July 2020. https://www.smh.com.au/business/companies/horticulture-giants-warn-fruit-and-vegetable-prices-could-rise-due-to-labour-shortage-20200729-p55gll.html .
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• Anonymous, ‘COVID-19 Impact and Response for Sex Workers’, Scarlet Alliance , 2020. http://www.scarletalliance.org.au/COVID-19/ .
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• A grandmother in California, video interview, 17 October 2020.
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• Jessie Davies, ‘Dating Apps Notice Traffic Bump Amid Coronavirus Social-Distancing’, ABC News , 2 April 2020. https://www.abc.net.au/news/2020-04-03/online-dating-in-the-age-of-coronavirus/12102282 .
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• Katy Ramsey Mason, ‘Landlord-Leaning Eviction Courts Are About to Make the Coronavirus Housing Crisis a Lot Worse’, The Conversation , 29 July 2020. http://theconversation.com/landlord-leaning-eviction-courts-are-about-to-make-the-coronavirus-housing-crisis-a-lot-worse-142803 .
• Ganesh Setty, ‘Tenant Decapitated Landlord with a Sword over a Rent Dispute, Hartford Police Say’, CNN , 31 July 2020. https://edition.cnn.com/2020/07/31/us/hartford-sword-homicide-trnd/index.html .
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• Patrick Hatch, ‘Illegal Online Casinos Boom During COVID-19 Lockdown’, The Sydney Morning Herald , 17 June 2020. https://www.smh.com.au/business/companies/illegal-online-casinos-boom-during-covid-19-lockdown-20200616-p552yl.html .
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• Chris McGonigal, ‘10 Photos Show How Restaurants Around The World Are Social Distancing’, Huffington Post , 13 May 2020. https://www.huffpost.com/entry/photos-restaurants-social-distancing-coronavirus_l_5ebc0589c5b6ac90c5bafb67 .
• Lora Jones, ‘Coronavirus: When and How Are Pubs Allowed to Open?’, BBC News , 14 October 2020. https://www.bbc.com/news/business-52977388 .
• Jessica Hinchliffe, ‘Did Coronavirus Kill the Humble Beer Coaster?’, ABC News , 18 July 2020. https://www.abc.net.au/news/2020-07-19/covid19-spells-the-end-of-the-beer-coaster-pubc/12467108 .
• Summer Woolley, ‘Pub “bans” Neil Diamond’s Hit Song Due to COVID Risk’, 7NEWS , 4 August 2020. https://7news.com.au/lifestyle/health-wellbeing/pub-bans-neil-diamonds-sweet-caroline-during-covid-pandemic-c-1214745 .
• Matt Bungard and Laura Chung, ‘Coronavirus Australia: Sydney Cafes Innovate Amid COVID-19 Lockdown Restrictions’, The Sydney Morning Herald , 6 May 2020. https://www.smh.com.au/business/consumer-affairs/from-coffees-to-groceries-sydney-cafes-innovate-amid-covid-19-lockdown-restrictions-20200424-p54n0t.html .
• Connor McCallum, ‘COVID Has Wounded America’s Coffee Culture, and We Could Be Next’, The Big Smoke , 4 July 2020. https://www.thebigsmoke.com.au/2020/07/04/covid-has-wounded-americas-coffee-culture-and-we-could-be-next/ .
• Anonymous, ‘The Community Roles of the Barber Shop and Beauty Salon’, National Museum of African American History and Culture , n.d. https://nmaahc.si.edu/blog/community-roles-barber-shop-and-beauty-salon ; Patricia Burke Wood and Rod K. Brunson, ‘Geographies of Resilient Social Networks: The Role of African American Barbershops’, Urban Geography , Vol. 32, No. 2 (2013): 228–243. https://doi.org/10.2747/0272-3638.31.2.228 .
• Kevin Armstrong, ‘A South Bronx Barber Takes on a New Role in His Pandemic-Ravaged Community’, The Washington Post , 18 July 2020. https://www.washingtonpost.com/national/a-south-bronx-barber-takes-on-a-new-role-in-his-pandemic-ravaged-community/2020/07/17/dcd645ac-c2b0-11ea-b178-bb7b05b94af1_story.html .
• Nkayla Afshariyan, ‘Barbers Want Their Shops to Be Safe Spaces for Men’s Mental Health’, Triple J , 24 April 2020. https://www.abc.net.au/triplej/programs/hack/might-and-mane-mental-health-training-for-barbers/11022744 .
• Lauren Valenti, ‘How Hair Salons Will Be Transformed by the Global Pandemic’, Vogue , 2 May 2020. https://www.vogue.com/article/hair-salons-covid-19-global-pandemic .
• Hannah McCann, ‘Coronavirus Shutdowns: What Makes Hairdressing “Essential”? Even the Hairdressers Want to Close’, The Conversation , 15 April 2020. https://theconversation.com/coronavirus-shutdowns-what-makes-hairdressing-essential-even-the-hairdressers-want-to-close-135811 .
• Brett Worthington, ‘“Just Bonkers”: Hairdressers Slam Reversal of 30-Minute Time Limit and Lack of Shutdown’, ABC News , 25 March 2020. https://www.abc.net.au/news/2020-03-26/coronavirus-hairdresser-limit-to-be-scrapped/12091700 .
• Moriah Balingit, ‘Armed Militia Helped a Michigan Barbershop Open, a Coronavirus Defiance That Puts Republican Lawmakers in a Bind’, The Washington Post , 13 May 2020. https://www.washingtonpost.com/national/coronavirus-michigan-republicans-whitmer/2020/05/12/54975e1a-9466-11ea-82b4-c8db161ff6e5_story.html .
• Australian Government Department of Health, ‘Exercising and Staying Active During Coronavirus (COVID-19) Restrictions’, Australian Government Department of Health , 11 May 2020. https://www.health.gov.au/news/health-alerts/novel-coronavirus-2019-ncov-health-alert/ongoing-support-during-coronavirus-covid-19/exercising-and-staying-active-during-coronavirus-covid-19-restrictions ; Anonymous, ‘#HealthyAtHome—Physical Activity’, World Health Organisation , 2020. https://www.who.int/news-room/campaigns/connecting-the-world-to-combat-coronavirus/healthyathome/healthyathome---physical-activity .
• Francesca Gillett, ‘Coronavirus: How Joe Wicks’ Fitness Workouts “Changed My Life”’, BBC News , 22 July 2020. https://www.bbc.com/news/uk-53487312 .
• Adriane Reardon and Timothy Fernandez, ‘“It’s Been Very Full on”: 24/7 Gyms Feel the Pinch as New COVID-19 Restrictions Take Effect’, ABC News , 1 August 2020. https://www.abc.net.au/news/2020-08-01/nsw-gyms-require-coronavirus-marshal/12514014 ; Mimi Becker, ‘Timeline for Gyms Reopening Across Australia’, 9Now , 20 May 2020. https://9now.nine.com.au/a-current-affair/coronavirus-gyms-reopening-across-australia-in-may-and-june-how-they-plan-to-minimise-covid19-risk/66a89335-d5f8-4f6f-bfc8-0ce2b520b2be ; Jacob McMaster and Ferguson Claudia, ‘COVID-19 Rules Tighten for Gyms’, Narooma News , 7 August 2020. https://www.naroomanewsonline.com.au/story/6866646/covid-19-rules-tighten-for-gyms/ .
• Anonymous, ‘Massive Boost to Outdoor Exercise in Sydney’s Green Spaces During COVID-19, Survey Finds’, ABC News , 28 June 2020. https://www.abc.net.au/news/2020-06-28/sydneys-green-space-use-booms-during-covid-19/12400104 .
• Timothy Walsh and Deborah Glasofer, ‘Raising a Teenager with an Eating Disorder in a Pandemic’, OUPblog , 7 August 2020. https://blog.oup.com/2020/08/raising-a-teenager-with-an-eating-disorder-in-a-pandemic/ ; Achraf Ammar et al., ‘Effects of COVID-19 Home Confinement on Eating Behaviour and Physical Activity: Results of the ECLB-COVID19 International Online Survey’, Nutrients , Vol. 12, No. 6 (2020): 1583. 10.3390/nu12061583; Andrea Phillipou et al., ‘Eating and Exercise Behaviors in Eating Disorders and the General Population During the COVID-19 Pandemic in Australia: Initial Results from the COLLATE Project’, International Journal of Eating Disorders , Vol. 53, No. 7 (2020): 1158–1165. 10.1002/eat.23317.
• Liffy Thomas, ‘How Meetup Groups Are Staying in Touch Through Virtual Platforms’, The Hindu , 3 August 2020. https://www.thehindu.com/news/cities/chennai/how-meetup-groups-are-staying-in-touch-through-virtual-platforms/article32258295.ece .
• Lourdes Zamanillo Tamborrel, interview questions via personal correspondence, 2 November 2020. For additional details see Lourdes Zamanillo Tamborrel, ‘Meetup.com: A Cohesive Tool for Multicultural Societies?’, PhD thesis in progress, Monash University, Melbourne.
• Kenneth Kiesnoski, ‘Travel Changed After 9/11; Here’s How It Will Look After the Covid-19 Pandemic Finally Recedes’, CNBC , 10 May 2020. https://www.cnbc.com/2020/05/10/heres-how-travel-will-change-after-the-covid-19-pandemic-recedes.html .
• Ben Smee, ‘Bondi Beach Closed After Crowds Defy Ban on Gatherings of 500-Plus’, The Guardian , 21 March 2020. https://www.theguardian.com/australia-news/2020/mar/21/bondi-beach-closed-down-after-crowds-defy-ban-on-gatherings-of-more-than-500-people ; Angela Giuffrida, ‘Coronavirus Brings Tension and Prejudice to Italy’s Beaches’, The Guardian , 12 July 2020. https://www.theguardian.com/world/2020/jul/12/coronavirus-brings-tension-prejudice-italy-beaches ; Anonymous, ‘War, Algae, Virus: The Beach That Won’t Be Beaten’, BBC News , 15 June 2020. https://www.bbc.com/news/av/world-europe-53054161 .
• Peter Hannam, ‘Visits to NSW National Parks Soar During COVID-19’, The Sydney Morning Herald , 5 August 2020. https://www.smh.com.au/environment/conservation/good-for-the-soul-visits-to-nsw-national-parks-soar-amid-covid-19-20200805-p55iu8.html ; Lauren Roberts and Henry Zwartz, ‘Uluru-Kata Tjuta National Park Closure Extended Out of “Utmost Respect” Amid Coronavirus Concerns’, ABC News , 4 August 2020. https://www.abc.net.au/news/2020-08-04/coronavirus-fears-force-closure-of-uluru/12521324 .
• Vikas Mehta, ‘The New Proxemics: COVID-19, Social Distancing, and Sociable Space’, Journal of Urban Design , published online on 2 July 2020. https://doi.org/10.1080/13574809.2020.1785283 .
• Setha Low and Alan Smart, ‘Thoughts About Public Space During Covid-19 Pandemic’, City & Society , Vol. 32, No. 1 (2020). 10.1111/ciso.12260.
• Selby Coxon et al., ‘Rethinking Public Transport Post-Pandemic’, Monash Lens , 3 June 2020. https://lens.monash.edu/@politics-society/2020/06/03/1380582?slug=returning-confidence-in-public-transport-in-a-post-covid-19-pandemic-world .
• Peter Sweatman and Majid Sarvi, ‘Covid-19: A Challenge and an Opportunity for Public Transport’, Pursuit , 3 August 2020. https://pursuit.unimelb.edu.au/articles/covid-19-a-challenge-and-an-opportunity-for-public-transport .
• Aimee Chanthadavong, ‘Victorian Government Trials Touchless Pedestrian Crossing Technology in Melbourne’, ZDNet , 30 July 2020. https://www.zdnet.com/article/victorian-government-trials-touchless-pedestrian-crossing-technology-in-melbourne/ .
• Roland Geraerts, ‘Coronavirus: Using Crowd Simulation to Encourage Social Distancing’, The Conversation , 4 August 2020. http://theconversation.com/coronavirus-using-crowd-simulation-to-encourage-social-distancing-143121 .
• Eliza W. Kinsey, Dirk Kinsey, and Andrew G. Rundle, ‘COVID-19 and Food Insecurity: An Uneven Patchwork of Responses’, Journal of Urban Health: Bulletin of the New York Academy of Medicine , Vol. 97, No. 3 (2020): 332–335. 10.1007/s11524-020-00455-5.
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• Nguyen Ngoc Long and Bui Huy Khoi, ‘An Empirical Study About the Intention to Hoard Food During COVID-19 Pandemic’, EURASIA Journal of Mathematics , Science and Technology Education , Vol. 16, No. 7 (2020): em1857. 10.29333/ejmste/8207; Benjamin Oosterhoff and Cara Palmer, ‘Psychological Correlates of News Monitoring, Social Distancing, Disinfecting, and Hoarding Behaviors Among US Adolescents During the COVID-19 Pandemic’, PsyArXiv , published online on 23 March 2020. 10.31234/osf.io/rpcy4; Stephanie Preston, ‘Your Brain Evolved to Hoard Supplies and Shame Others for Doing the Same’, The Conversation , 27 March 2020. http://theconversation.com/your-brain-evolved-to-hoard-supplies-and-shame-others-for-doing-the-same-134634 ; Conor Wynn, ‘Understanding Personality May Explain Why Some of Us Are More Prone to Toilet Paper Panic’, ABC News , 24 March 2020. https://www.abc.net.au/news/2020-03-25/coronavirus-personality-types-toilet-paper/12085776 ; Eric Dolan, ‘Narcissistic Personalities Linked to Defiance of Coronavirus Prevention Guidelines and Hoarding’, PsyPost , 21 July 2020. https://www.psypost.org/2020/07/narcissistic-personalities-linked-to-defiance-of-coronavirus-prevention-guidelines-and-hoarding-57230 .
• Kate Berg, ‘Lansing Man Dies After Being Stabbed at Quality Dairy in Mask Dispute’, Lansing State Journal , 10 August 2020. https://www.lansingstatejournal.com/story/news/2020/08/10/lansing-man-dies-after-being-stabbed-quality-dairy-mask-dispute/3333509001/ .
• Brad A. Meisner, ‘Are You OK, Boomer? Intensification of Ageism and Intergenerational Tensions on Social Media Amid COVID-19’, Leisure Sciences , published online on 24 June 2020. https://doi.org/10.1080/01490400.2020.1773983 .
• Soutik Biswass, ‘India Coronavirus: “Our Neighbours Made Us Covid-19 Pariahs”’, BBC News , 19 July 2020. https://www.bbc.com/news/world-asia-india-53418762 .
• Chin Tan, ‘Of All the Coronavirus Racist Attacks We’ve Seen, One Story Struck Me the Most’, ABC News , 9 May 2020. https://www.abc.net.au/news/2020-05-09/coronavirus-covid-19-racist-attacks-data-collection-strategy/12229162 .
• Anonymous, ‘Covid-19 Fueling Anti-Asian Racism and Xenophobia Worldwide’, Human Rights Watch , 12 May 2020. https://www.hrw.org/news/2020/05/12/covid-19-fueling-anti-asian-racism-and-xenophobia-worldwide ; Asian Australian Alliance and Osmond Chiu, ‘COVID-19 Coronavirus Racism Incident Report’, Australian Asian Alliance , 24 April 2020. http://diversityarts.org.au/app/uploads/COVID19-racism-incident-report-Preliminary-Official.pdf .
• Imran Awan, ‘Coronavirus: Conspiracy Theories and Fake Videos Fuel Rise in Islamophobia’, The Conversation , 25 April 2020. http://theconversation.com/coronavirus-conspiracy-theories-and-fake-videos-fuel-rise-in-islamophobia-137107 ; Anonymous, ‘Coronavirus, Fear and How Islamophobia Spreads on Social Media’, Anti - Muslim Hatred Working Group , 20 April 2020. https://anti-muslim-hatred-working-group.home.blog/2020/04/20/coronavirus-fear-and-how-islamophobia-spreads-on-social-media/ .
• Stuart Winer, ‘COVID-19 Fueling Worldwide Wave of Anti-Semitism, Researchers Find’, The Times of Israel , 23 June 2020. https://www.timesofisrael.com/covid-19-fueling-worldwide-wave-of-anti-semitism-researchers-find/ .
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• François Heisbourg, ‘From Wuhan to the World: How the Pandemic Will Reshape Geopolitics’, Survival , Vol. 62, No. 3 (2020): 7–24. https://doi.org/10.1080/00396338.2020.1763608 .
• Anonymous, ‘COVID-19 and Conflict: Seven Trends to Watch’, Crisis Group , 24 March 2020. https://www.crisisgroup.org/global/sb4-covid-19-and-conflict-seven-trends-watch ; Colum Lynch and Robbie Gramer, ‘U.S. and China Turn Coronavirus Into a Geopolitical Football’, Foreign Policy , 11 March 2020. https://foreignpolicy.com/2020/03/11/coronavirus-geopolitics-china-united-states-trump-administration-competing-global-health-response/ .
• Anonymous, ‘Coronavirus: US “Wants 3M to End Mask Exports to Canada and Latin America”’, BBC News , 2 April 2020. https://www.bbc.com/news/world-us-canada-52161032 .
• Thomas J. Bollyky and Chad P. Bown, ‘The Tragedy of Vaccine Nationalism’, Foreign Affairs , 6 October 2020. https://www.foreignaffairs.com/articles/united-states/2020-07-27/vaccine-nationalism-pandemic .
• Paul Staniland, ‘Kashmir, India, and Pakistan and Coronavirus—Coronavirus in Conflict Zones: A Sobering Landscape’, Carnegie Endowment for International Peace , 14 April 2020. https://carnegieendowment.org/2020/04/14/kashmir-india-and-pakistan-and-coronavirus-pub-81529 .
• Gavin Lee, ‘Coronavirus: EU Leaders Reach Recovery Deal After Marathon Summit’, BBC News , 21 July 2020. https://www.bbc.com/news/world-europe-53481542 .
• Rory Carroll Sam Jones in Madrid et al., ‘Covid-19 Crisis Stokes European Tensions over Migrant Labour’, The Guardian , 11 May 2020. https://www.theguardian.com/world/2020/may/11/covid-19-crisis-stokes-european-tensions-over-migrant-labour .
• Anonymous, ‘Migrants and Refugees Are Being Forgotten in the COVID-19 Response. This Has to Change’, Europeansting , 13 August 2020. https://europeansting.com/2020/08/13/migrants-and-refugees-are-being-forgotten-in-the-covid-19-response-this-has-to-change/ ; Nassim Khadem, ‘Coronavirus Recession Puts Thousands of Refugees and Asylum Seekers at Risk of Job Loss, Homelessness’, ABC News , 30 July 2020. https://www.abc.net.au/news/2020-07-30/coronavirus-recession-refugees-asylum-seekers-at-risk-homeless/12503874 .
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• Anonymous, ‘Trump Considers Banning Re-Entry by Citizens Who May Have Coronavirus’, The Indian Express , 11 August 2020. https://indianexpress.com/article/coronavirus/trump-considers-banning-re-entry-by-citizens-who-may-have-coronavirus-6549731/ .
• Anonymous, ‘USCIS Response to COVID-19’, U.S. Citizenship and Immigration Services , 7 October 2020. https://www.uscis.gov/about-us/uscis-response-to-covid-19 .
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• Mark Leonard, ‘The Brexit Parenthesis: Three Ways the Pandemic Is Changing UK Politics’, European Council of Foreign Relations , 12 August 2020. https://www.ecfr.eu/publications/summary/the_brexit_parenthesis_three_ways_the_pandemic_is_changing_uk_politics .
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• Lauren Aratani, ‘How Did Face Masks Become a Political Issue in America?’, The Guardian , 29 June 2020. http://www.theguardian.com/world/2020/jun/29/face-masks-us-politics-coronavirus .
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• Megan Janetsky and Anthony Faiola, ‘Colombian Guerrillas Are Using Coronavirus Curfews to Expand Their Control. Violators Have Been Killed’, The Washington Post , 26 July 2020. https://www.washingtonpost.com/world/the_americas/colombia-coronavirus-farc-eln-guerrillas/2020/07/25/927d3c06-cb64-11ea-bc6a-6841b28d9093_story.html .
• Caleb Weiss, ‘Jihadists Discuss Coronavirus, Offer Advice’, FDD’s Long War Journal , published online on 13 March 2020. https://www.longwarjournal.org/archives/2020/03/jihadists-discuss-coronavirus-offer-advice.php ; Gregoire Phillips, ‘As Governments Dither on COVID-19, Jihadists and Gang Leaders Step In’, Political Violence at a Glance , 15 April 2020. https://politicalviolenceataglance.org/2020/04/15/as-governments-dither-on-covid-19-jihadists-and-gang-leaders-step-in/ .
• Kate Connolly, ‘Berlin Protests Against Coronavirus Rules Divide German Leaders’, The Guardian , 3 August 2020. http://www.theguardian.com/world/2020/aug/03/berlin-protests-against-coronavirus-rules-divide-german-leaders .
• Anonymous, ‘CoronaCheck: Is Victoria’s COVID-19 Increase Linked to the Black Lives Matter Protests?’, ABC News , 25 June 2020. https://www.abc.net.au/news/2020-06-26/coronacheck-victoria-black-lives-matter-protests-family-spike/12391628 .
• Laignee Barron, ‘Lessons From Hong Kong’s Decision to Postpone Elections’, Time , 7 August 2020. https://time.com/5877242/coronavirus-elections-postpone-delay-hong-kong-covid19/ .
• Kate Rabinowitz and Brittany Renee Mayes, ‘At Least 84% of American Voters Can Cast Ballots By Mail In the Fall’, The Washington Post , 25 September 2020. https://www.washingtonpost.com/graphics/2020/politics/vote-by-mail-states/ ; Anonymous, ‘Postal Voting for Local Government Elections on October 24’, The Wimmera Mail - Times , 15 May 2020. https://www.mailtimes.com.au/story/6758651/postal-voting-for-local-government-elections-on-october-24/ .
• Anonymous, ‘Singapore to Hold General Elections Amid Covid-19: What Is at Stake?’, The Indian Express , 25 June 2020. https://indianexpress.com/article/explained/singapore-general-elections-coronavirus-6473772/ ; Christopher Thomas, ‘Elections and COVID-19: Ensuring Safety in the Voting Process’, Esri , 21 April 2020. https://www.esri.com/about/newsroom/blog/covid-19-social-distancing-elections-process/ .
• Yvette Tan, ‘Coronavirus in Singapore: Election Campaigning Without the Handshakes’, BBC News , 5 July 2020. https://www.bbc.com/news/world-asia-53216390 .
• Sean Murphy, ‘Health Official: Trump Rally “Likely” Source of Virus Surge’, AP NEWS , 8 July 2020, https://apnews.com/article/ad96548245e186382225818d8dc416eb ; Anonymous, ‘Trump Rally a “Dangerous Move” as Coronavirus Cases Spike in US’, The Sydney Morning Herald , 14 June 2020. https://www.smh.com.au/world/north-america/trump-rally-a-dangerous-move-as-coronavirus-cases-spike-in-us-20200615-p552ke.html .
• Anonymous, ‘White House Rally: Trump Holds First Public Event Since Covid Diagnosis’, BBC News , 10 October 2020. https://www.bbc.com/news/election-us-2020-54493575 .
• Barbara Sprunt, ‘Harris, as Biden’s Running Mate, Says Case Against Trump Is “Open And Shut”’, NPR , 12 August 2020. https://www.npr.org/2020/08/12/901462712/biden-and-harris-to-introduce-their-presidential-ticket-in-delaware .
• Molly Ball, ‘Donald Trump’s COVID-19 Diagnosis Is Forcing Him to Face His Personal—and Political—Vulnerability’, Time , 8 October 2020. https://time.com/5897886/trump-coronavirus/ .
• Peter McCutcheon, ‘How COVID-19 Is Changing the Political Debate on Queensland’s Debt’, ABC News , 25 July 2020. https://www.abc.net.au/news/2020-07-25/coronavirus-queensland-debt-analysis-election-alp-lnp/12488184 .
• Daisy Fancourt, Andrew Steptoe, and Liam Wright, ‘The Cummings Effect: Politics, Trust, and Behaviours During the COVID-19 Pandemic’, The Lancet , Vol. 396, No. 10249 (2020): 464–465. 10.1016/S0140-6736(20)31690-1.
• Simone Fox Koob and Jewel Topsfield, ‘Mask-Dodging Woman Allegedly Smashed Female Cop’s Head into Concrete’, The Age , 4 August 2020. https://www.theage.com.au/national/victoria/mask-dodging-woman-allegedly-smashed-female-cop-s-head-into-concrete-20200804-p55ica.html .
• Hannah Allam, ‘Michigan Domestic Terror Plot Sends Shockwaves Through Militia World’, NPR , 9 October 2020. https://www.npr.org/2020/10/09/922319136/michigan-domestic-terror-plot-sends-shockwaves-through-militia-world .
• Denis Muller, ‘Tensions Rise on Coronavirus Handling as the Media Take Control of the Accountability Narrative’, The Conversation , 11 August 2020 http://theconversation.com/tensions-rise-on-coronavirus-handling-as-the-media-take-control-of-the-accountability-narrative-144195 .
• Mark Hertsgaard, ‘The Media’s Covid-19 Coverage Proves It Could Also Spotlight the Climate Crisis’, The Nation , 25 March 2020. https://www.thenation.com/article/environment/coronavirus-media-climate/ .
• Heather Kelly, ‘Facebook Removes a Coronavirus Misinformation Post from Trump for the First Time Ever’, The Washington Post , 8 August 2020. https://www.washingtonpost.com/technology/2020/08/05/trump-post-removed-facebook/ .

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Economic Development

Understanding the economic consequences of the covid-19 pandemic.

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Emi is a Global Health Manager in the Health Policy and Insights team at Economist Impact. Emi is a global health equity specialist with a focus on the social determinants of health and has a wealth of experience in global health research, policy and programming. Her expertise in global health advisory, program design and healthcare communications means that Emi brings a breadth of experience to the team across technical areas. Her current role involves exploratory research using economic models, rapid reviews of scientific papers and the development of a global index on health inclusivity. Emi also designs and works on longer-term research assignments across the international development sphere, including related to Education and WASH. Emi has experience working across sectors, having held various roles across the health and social care industry, serving as a Health Inequalities Manager within the UK Department of Health and Social Care, Consultant Epidemiologist with the World Health Organisation under the Health Securities and Preparedness Division and as a Technical Delivery Officer with UNICEF. Emi has an undergraduate degree in Biomedical Science from the University of Warwick, a Master's in Public Health from Imperial College London and is currently completing a PhD in Health Equity at the University of Exeter.

The covid-19 pandemic cannot be seen solely as a global health crisis; the impact on the health, livelihoods and functioning of individuals and global economies deems it a humanitarian and economic crisis. It is estimated that an additional half a billion people have fallen into poverty due to the pandemic [1] . In addition to the significant loss of life—the number of deaths has reached over 6.7m—the destruction of industries and broadscale impacts on healthcare systems globally demonstrates the extensive impact of the pandemic at all levels of society [2] .

Transmission of SARS-CoV-2 (the virus which causes covid-19) across communities persists despite significant efforts and investment to stop the virus in its tracks. By the end of November 2022, over US$4trn had been invested in response and recovery packages in the US alone, through the Coronavirus Aid, Relief and Economic Security (CARES) Act, supplemental legislation and the American Rescue Plan Act [3] . Alongside direct medical costs, indirect costs attributed to the spread of the virus include disruption to millions of children’s education, unemployment, lost earnings and lost economic output [4] . The pandemic has resulted in global economic shifts, responsible for one of the largest global recessions since the second world war. In addition to the 2020 stock market crash (the largest stock market decline since the financial crisis of 2007-08), economies faced a global supply-chain crisis, global panic buying and price gouging [5] .

While many reports have highlighted the current and historic economic consequences of the pandemic to date, fewer studies have explored potential future impacts of covid-19 from a global perspective. Estimating the potential future impact of persistent covid-19 in a global context will enable governments, multilateral organisations, individuals and civil society to better prepare and take action to minimise the consequences of ongoing covid-19 challenges and other future health emergencies. The aim of this study is to quantify the future economic implications of ongoing covid-19 transmission by considering the following research questions:

• What is the future economic impact of persistent transmission of SARS-CoV-2 as a result of mortality and morbidity within the working-age population?
• How does sustained covid-19 infection impact different labour markets?
• How do labour market disruptions as a result of covid-19 feed into broader economic impacts (for example, economic output and gross domestic product—GDP)?
• What factors influence the magnitude of covid-19 at a country level?

Through an evidence review, model and series of in-depth interviews, this study explores the estimated economic impact of covid-19 in a future where the virus persists globally. It focuses on the impact of covid-induced mortality or morbidity to the working-age population. Recognising that the virus has varying effects on countries driven by a series of country-specific factors, Economist Impact has identified four distinct country archetypes to assess the potential impacts across a range of countries. The model forecasts impacts for each archetype under three hypothetical scenarios: a baseline scenario which assumes that 2022 infection rates will continue through 2025, and optimistic and pessimistic scenarios where 2022 covid-19 infection rates decrease or increase, respectively, by 10% in 2023 and remain at that level through 2025.* 

The research gives rise to several key findings:

• Countries characterised by both high infection rates and high productivity levels are likely to experience the greatest economic losses. For a reference country characterised by high infection rates and high productivity levels, this analysis forecasts potential GDP losses in 2025 between 0.76% in the base case scenario and almost 1% in a pessimistic scenario. In a country the size of the UK, this could imply a loss of up to US$ 25bn. Comparatively, a reference country with low infection rates and productivity levels might lose between 0.019% to 0.023% of its GDP (around US$ 1bn for an economy the size of the UK).
• Without measures in place to suppress infection rates, SARS-CoV-2 infection could continue to have substantial impacts on economies. Even in an optimistic scenario, some groups of countries (those with high infection rates and high productivity rates) could see GDP loss of over 0.75% in 2025, suppressing economic growth and development. This analysis sheds light on these potential impacts highlighting the need for continued action and efforts by governments and policymakers. In addition, country-specific factors, such as productivity rate, adherence to control measures, extent of mitigation efforts and the implementation of fiscal support programmes, strongly influence the economic impact of sustained covid-19 infection rates.
• Measures to reduce the severity of illness caused by the infection can play an important role in minimising the economic consequences across all countries, but particularly those with higher infection rates. The model forecasts the loss to GDP based on the productive hours of work that are lost due to covid-19 infection. The findings from the literature review suggest that the majority of work hours lost to covid-19 are associated with acute symptomatic covid-19 infection and the post-acute sequelae of SARS-CoV-2 infection (long covid) rather than covid-related deaths and exit from the workforce. Therefore, global and national measures to reduce the severity of illness from levels that force people infected by covid-19 temporarily out of work can help to mitigate against some of the potential economic impacts of persistent infection.

This study seeks to quantify how the virus may continue to impact global economies, and explores how actions to mitigate economic impact, control infection alter the overall economic impact of sustained infection rates. The report offers considerations for governments and policymakers to reduce the economic and societal impact of future health emergencies by considering actions to boost resilience and reduce the vulnerabilities of economic systems, all critical components for stronger responses to future global emergencies.

* 2022 Infection rates were sourced from covid-19 estimates modelled by the Institute for Health Metrics and Evaluation (IHME). IHME forecasts country infection rates, among other indicators, using a hybrid model that is “grounded in real-time data.”

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[1]  World Health Organization. More than half a billion people pushed or pushed further into extreme poverty due to health care costs. Available from: https://www.who.int/news/item/12-12-2021-more-than-half-a-billion-people-pushed-or-pushed-further-into-extreme-poverty-due-to-health-care-costs [2]  Mathieu E, Ritchie H, Rodés-Guirao L, et al. Coronavirus pandemic (covid-19). Available from: https://ourworldindata.org/coronavirus [3]  USASpending. The federal response to covid-19. Available from: https://www.usaspending.gov/disaster/covid-19?publicLaw=all [4]  Appleby J. The public finance cost of covid-19. BMJ 2022; 376 :o490. [5]  World Bank. Covid-19 to plunge global economy into worst recession since World War II. Press release. 2020 Jun 8. Available from: https://www.worldbank.org/en/news/press-release/2020/06/08/covid-19-to-plunge-global-economy-into-worst-recession-since-world-war-ii

Related content

Championing Inclusivity: progress towards good health for all

In the UK for example, black women are four times more likely than white women to die in childbirth. Furthermore, babies that are black or black-British, Asian or Asian-British have a more than 50% higher risk of perinatal mortality, compared to white-British babies.

Typically, people with the highest incomes from dominant or majority groups enjoy the best health and the most years of their lives in good health—while people with lower incomes from marginalised groups are most vulnerable to morbidity and mortality. There is a need to think dynamically about the role of structural barriers and sociocultural influences and how they impact holistic health:this is where inclusivity in health comes in. It’s about challenging us to think differently about health: exploring new partnerships, better understanding of what good health means to the different communities within our societies, engaging with the public and thinking outside the box to bring new stakeholder groups into action.

In October we launched the Health Inclusivity Index, developed by Economist Impact and supported by Haleon. The first edition of a three-year research program assessing the state of health inclusivity in an initial 40 countries, based on three domains: health in society,  inclusive health systems, and community and individual empowerment. I had the pleasure of sharing a stage with influential opinion leaders during the launch event–organised by Haleon at the Wellcome Collection—where we discussed how inclusivity is essential to better health for all. We explored the role of policy in facilitating collaboration to improve health and removing structural barriers to accessing care, and  the critical need to match policy with structured implementation mechanisms.

The majority (93%) of countries in our index recognise health as a human right; only Jordan, the UAE, and the US fail to do so. All but seven countries also recognise that health (as a human right) means more than access to healthcare and includes access to safe drinking water, sanitation, food, housing and other requirements for a health-promoting environment. Despite this one in five countries have exclusionary policies or practices that explicitly restrict access to healthcare for certain groups or individuals. Six of these eight countries are also countries who claim to  recognise health as a human right. Professor David Napier, professor of medical anthropology at University College London, introduced the concept of defining “who we, (the population) are”. He highlighted that governments must define “we” and this is often  narrowly focused   on the majority, leaving those who fall outside of this definition of ‘we’ without access to social services.

According to our findings there is a clear role for inclusivity in improving health and plugging the inequitable gap in outcomes for the most vulnerable. While life expectancy has improved globally, healthy life expectancy has not, meaning we are living more of our life in poor health. Countries with a higher inclusivity index have populations that live for longer in better health.

We know that inclusivity goes beyond the provision of services. Barriers to health prevent individuals within a population from accessing services, even when they are readily available. Services that are free at the point of use are not inclusive if they are under-resourced, low in quality,  have limited hours of service, do not cater to language differences and require long-distance  travel. Baroness Tanni Grey-Thompson, a member of House of Lords, detailed how under-resourced they are and therefore lack the capacity to effectively respond to the overwhelming number of public requests.

People need the capacity to engage with and influence their health, recognising that many barriers are outside of their control. Examples include being time poor - lacking  the time to exercise or prepare healthy food and having a job that does not pay for time off to seek healthcare. Language barriers and limited literacy skills,—particularly health literacy and the ability to understand health information. Domain 3 of our index, “Community, and Individual Empowerment”, emerged as the strongest driver of inclusivity. Countries that prioritised empowering local communities—removing these socio-cultural barriers—and placing individuals at the centre of service delivery, were among the highest-scoring for health inclusivity.  Eight of the top ten scoring countries achieve their highest score in this domain. Beyond this, Domain 3 has the strongest correlation with overall inclusivity score, indicating that it is the best predictor of a country’s overall score in the index.

What should be done?

Don’t stop campaigning for universal health coverage and the social determinants of wellbeing —they are critical to expanding access to healthcare particularly for the most vulnerable

Empower communities and enable self-agency:an effective approach to expanding access to whole health. During our discussions, Katy Jon Went, head of methodology at the Human Library, reminded us at the event of the “need to humanise the data” recognising that there are individuals, communities and societies behind the numbers

Work from the outside in. By deliberately supporting vulnerable groups, you will help improve health for all and remove structural barriers that mostly impact the minority

Pull in the same direction: elevate the importance of coordination to achieve common goals

Advocate for high-quality data collection, and “real-world evidence” for inclusivity

Nations must tackle all three domains of the Health Inclusivity Index to achieve an inclusive system that promotes universal wellbeing . The Health Inclusivity Index provides the first ever quantitative measure of inclusivity, but also provides a framework for countries to pull levers that drive inclusivity and improve health for all.  For more information, explore the Health Inclusivity Index Hub and white paper.

Personalised healthcare for billions: Communication challenges in the post...

Personalised healthcare for billions: Communication challenges in the post covid-19 age is a report written by Economist Impact and commissioned by WhatsApp. 

Understanding the healthcare communications methods that worked during the covid-19 pandemic, and the new and innovative approaches and digital tools that facilitated this, can help guide the development of an improved approach to healthcare communications in the future. The experience of governments in managing complex healthcare challenges, such as mass vaccinations, while combating misinformation and ensuring data privacy, also provide key insights to guide the development of further digitalisation of healthcare communications and services.

Key findings from this project include:

Economist Impact would like to thank the interviewees who generously offered their time and insights, including:

The findings and views expressed in this report are those of Economist Impact and do not necessarily reflect the views of survey respondents, interviewees or the project sponsor.

Five important questions for health in a post-pandemic world

How will digital health evolve? Will cost containment come back? Are we prepared for the next pandemic? Will mental health remain as a priority? These are common questions Economist Impact gets from stakeholders in health, nearly two-and-a-half years since covid-19 first dominated the world’s agenda. While it’s challenging to separate passing fads from long-term drivers, there are clear themes that will rightly shape the future of health. However, the path each takes is not predetermined—at least not yet. 

Here are five important trends we are tracking in a post-pandemic world of health:

Is covid-19 really over?

In most of the world, the pendulum has already swung from one end to the other and back again with responses to covid-19. Long periods of strict mask adherence, widespread testing and restrictions on social interaction have given way to activities that are nearing pre-pandemic levels. A reason for this shift is due to human nature, where the combination of exhaustion and desire for normalcy drive current behaviors. However, another factor stems from changing perceptions about the virus, levels of risk posed and the anticipated movement to endemic status. 

There are positive signs,  such as the ratio of cases to hospitalisations and the effectiveness of vaccines, indicating a different stage in the covid-19 evolution, but it’s also clear the path forward will be both uneven and unpredictable. Countries employed varying tactics during the pandemic, from “zero-covid” strategies in China and New Zealand to a mixed-policy approach in America and the UK, but all have experienced similar or worse metrics this month, than a year before. Also, with mounting evidence about long-term health concerns for those with prior infections, we are likely to see more—not fewer— risks in the near future.

In this sense, there is a need for a balanced approach moving forward. Instead of “learning to live”with the virus, affected stakeholders—health, economic, societal—can seek out nuanced policies and integrated actions to mitigate future threats. The scars of the recent past should also spur proactive monitoring and preparation as frantic, reactive efforts across the world have already proven too costly. As covid-19 maintains an active presence, these actions allow for a greater chance of success and will  also foster an environment better placed to deal with future pandemics. 

What to do with the silent pandemic? 

Many health experts argue that another major crisis had been prevalent before covid-19, but its slow-building nature ensured it did not attract nearly as much attention. The “silent pandemic”of non-communicable diseases (NCDs)—diabetes, cancer, respiratory and cardiovascular conditions—had plagued advanced and emerging economies for decades. This stems from a combination of underlying lifestyle choices and ageing populations. While progress had been made, countries were still falling behind targets such as Sustainable Development Goal (SDG) 3.4 and the reduction of premature deaths from NCDs.

The pandemic not only halted progress but led to regression: postponement of public health screenings, disruptions in quality treatments, lower patient engagement, worsening healthy behaviors and overstretched healthcare workforce. In the past year, as much of the world has attempted to return to past care dynamics, these factors have led to a “double burden” with NCDs, where the backlog of cases weighing down fragile health systems is putting the “silent pandemic” on an even more precarious path.    

Tackling this will be an ongoing effort for years to come. However, positive ramifications from the pandemic—new tools in health, better understanding of wellbeing, active support from outside of health systems—can lead to improved interventions and outcomes. A pertinent example is the current dialogue and action around mental health—in the workplace, in communities and the mainstream media— raising awareness and promoting openness to combat a critical issue. Sustaining that trend across different NCDs could lead to lasting change.

What will technology’s role be in the future health ecosystem?

Technology has long offered great potential for health; the challenge has not been generating innovative ideas, but translating them into real-world solutions. The pandemic experience—either through necessity or real progress—has in part bridged the existing gap, providing a clear roadmap for the application of tools such as “augmented intelligence” in proactive decision-making. This type of problem-solving goes beyond health, intersecting with societal challenges such as ensuring the important principle of medical neutrality in conflict zones. 

The question of who will lead the way in generating impactful solutions remains. For years, expectations have been high for technology firms increasing their health presence, yet measured impact has been inconsistent at best. However, the pandemic has accelerated this movement with Alphabet’s growing investment in health and Amazon’s recent acquisition of a US primary care entity.. This trend is expected to continue, especially as the technology industry applies lessons from its role in the pandemic response towards more mainstream healthcare needs.

Where is health’s voice in the sustainability movement?

Health is intertwined with one of the world’s most important movements: the urgent need for global action towards a more sustainable planet. The recent heatwave across many parts of the world is another reminder of the importance of sustainability efforts and its relationship with health. As Natalia Kanem from the United Nations Population Fund (UNFPA) aptly stated at last year’s World Health Summit, “climate change affects poverty, affects hunger, certainly affects health”.

Acting upon that clear and logical connection will be a critical area of focus for health. From more eco-friendly healthcare supply chains, to access to sustainable food systems for balanced diets, a multitude of opportunities exist for stakeholders to assume greater leadership. Not only will health further strengthen the need for increased investment and attention on this issue, a “health in all policies” approach will also ensure a holistic, societal view around sustainability goals. 

Will the pandemic foster a new age or will we revert to past norms?

One of the most critical lessons from the pandemic is found throughout history—the power of collective action and singular focus on a shared goal. In the case of covid-19, this was manifested through numerous collaborations: vaccine development and distribution, research and public health communication and societal interventions to slow the spread of a dangerous new virus. Actors that embraced a dedication to the “common good” instead of individual objectives, generated clear results: findings from an Economist Impact study on pandemic response is one example of many that identified stakeholder collaboration as a vital element of success.

The opportunity exists to employ the same tactic for the biggest issues that rose in importance following the pandemic: health equity, sustainable innovation and holistic wellness. Underpinning this window for seismic change is a greater recognition from actors in health and society that known problems in health require new approaches. That recognition, along with existing models of success, such as a cross-sectoral group of actors working together for healthy ageing, offer a roadmap to replicate in the future. 

Tackling these issues requires the same collaborative spirit and long-run view; two dynamics that are difficult to maintain beyond moments of crisis. Indeed, a return to short-term focused, incentive-driven and siloed activity in health is likely. To ensure the window is not lost, it is vital to reframe the benefits of wellness in a way that aligns shared goals between a wider group of actors. The vision laid out by business leaders, who increasingly see health as a strategic imperative, is a signal of a larger paradigm shift in how we can collectively work towards a world of better health for all. 

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Impact of COVID-19 on people's livelihoods, their health and our food systems

Joint statement by ilo, fao, ifad and who.

The COVID-19 pandemic has led to a dramatic loss of human life worldwide and presents an unprecedented challenge to public health, food systems and the world of work. The economic and social disruption caused by the pandemic is devastating: tens of millions of people are at risk of falling into extreme poverty, while the number of undernourished people, currently estimated at nearly 690 million, could increase by up to 132 million by the end of the year.

Millions of enterprises face an existential threat. Nearly half of the world’s 3.3 billion global workforce are at risk of losing their livelihoods. Informal economy workers are particularly vulnerable because the majority lack social protection and access to quality health care and have lost access to productive assets. Without the means to earn an income during lockdowns, many are unable to feed themselves and their families. For most, no income means no food, or, at best, less food and less nutritious food. 

The pandemic has been affecting the entire food system and has laid bare its fragility. Border closures, trade restrictions and confinement measures have been preventing farmers from accessing markets, including for buying inputs and selling their produce, and agricultural workers from harvesting crops, thus disrupting domestic and international food supply chains and reducing access to healthy, safe and diverse diets. The pandemic has decimated jobs and placed millions of livelihoods at risk. As breadwinners lose jobs, fall ill and die, the food security and nutrition of millions of women and men are under threat, with those in low-income countries, particularly the most marginalized populations, which include small-scale farmers and indigenous peoples, being hardest hit.

Millions of agricultural workers – waged and self-employed – while feeding the world, regularly face high levels of working poverty, malnutrition and poor health, and suffer from a lack of safety and labour protection as well as other types of abuse. With low and irregular incomes and a lack of social support, many of them are spurred to continue working, often in unsafe conditions, thus exposing themselves and their families to additional risks. Further, when experiencing income losses, they may resort to negative coping strategies, such as distress sale of assets, predatory loans or child labour. Migrant agricultural workers are particularly vulnerable, because they face risks in their transport, working and living conditions and struggle to access support measures put in place by governments. Guaranteeing the safety and health of all agri-food workers – from primary producers to those involved in food processing, transport and retail, including street food vendors – as well as better incomes and protection, will be critical to saving lives and protecting public health, people’s livelihoods and food security.

In the COVID-19 crisis food security, public health, and employment and labour issues, in particular workers’ health and safety, converge. Adhering to workplace safety and health practices and ensuring access to decent work and the protection of labour rights in all industries will be crucial in addressing the human dimension of the crisis. Immediate and purposeful action to save lives and livelihoods should include extending social protection towards universal health coverage and income support for those most affected. These include workers in the informal economy and in poorly protected and low-paid jobs, including youth, older workers, and migrants. Particular attention must be paid to the situation of women, who are over-represented in low-paid jobs and care roles. Different forms of support are key, including cash transfers, child allowances and healthy school meals, shelter and food relief initiatives, support for employment retention and recovery, and financial relief for businesses, including micro, small and medium-sized enterprises. In designing and implementing such measures it is essential that governments work closely with employers and workers.

Countries dealing with existing humanitarian crises or emergencies are particularly exposed to the effects of COVID-19. Responding swiftly to the pandemic, while ensuring that humanitarian and recovery assistance reaches those most in need, is critical.

Now is the time for global solidarity and support, especially with the most vulnerable in our societies, particularly in the emerging and developing world. Only together can we overcome the intertwined health and social and economic impacts of the pandemic and prevent its escalation into a protracted humanitarian and food security catastrophe, with the potential loss of already achieved development gains.

We must recognize this opportunity to build back better, as noted in the Policy Brief issued by the United Nations Secretary-General. We are committed to pooling our expertise and experience to support countries in their crisis response measures and efforts to achieve the Sustainable Development Goals. We need to develop long-term sustainable strategies to address the challenges facing the health and agri-food sectors. Priority should be given to addressing underlying food security and malnutrition challenges, tackling rural poverty, in particular through more and better jobs in the rural economy, extending social protection to all, facilitating safe migration pathways and promoting the formalization of the informal economy.

We must rethink the future of our environment and tackle climate change and environmental degradation with ambition and urgency. Only then can we protect the health, livelihoods, food security and nutrition of all people, and ensure that our ‘new normal’ is a better one.

Media Contacts

Kimberly Chriscaden

Communications Officer World Health Organization

Nutrition and Food Safety (NFS) and COVID-19

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Impact of the coronavirus pandemic on the global economy - Statistics & Facts

Affected industries, country and regional comparison: uk economy hit hard, china less affected, key insights.

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Global economic impact.

• Basic Statistic Cumulative cases of COVID-19 worldwide from Jan. 22, 2020 to Jun. 13, 2023, by day
• Basic Statistic COVID-19 cases worldwide as of May 2, 2023, by country or territory
• Premium Statistic GDP loss due to COVID-19, by economy 2020
• Premium Statistic Forecasted global real GDP growth 2019-2024
• Premium Statistic Value of COVID-19 stimulus packages in the G20 as share of GDP 2021
• Basic Statistic Number of unemployed persons worldwide 1991-2024
• Premium Statistic COVID-19: effect on income groups globally 2020
• Premium Statistic Central bank policy rates in advanced and emerging economies 2019-2024

Cumulative cases of COVID-19 worldwide from Jan. 22, 2020 to Jun. 13, 2023, by day

Number of cumulative cases of coronavirus (COVID-19) worldwide from January 22, 2020 to June 13, 2023, by day

COVID-19 cases worldwide as of May 2, 2023, by country or territory

Number of coronavirus (COVID-19) cases worldwide as of May 2, 2023, by country or territory

GDP loss due to COVID-19, by economy 2020

Share of Gross Domestic Product (GDP) lost as a result of the coronavirus pandemic (COVID-19) in 2020, by economy

Global real Gross Domestic Product (GDP) growth after the coronavirus (COVID-19) from 2019 with a forecast until 2024

Value of COVID-19 stimulus packages in the G20 as share of GDP 2021

Value of COVID-19 fiscal stimulus packages in G20 countries as of May 2021, as a share of GDP

Number of unemployed persons worldwide 1991-2024

Number of unemployed persons worldwide from 1991 to 2024 (in millions)

COVID-19: effect on income groups globally 2020

Change in number of people in selected income tiers due to the coronavirus (COVID-19) pandemic worldwide in 2020 (in millions)

Central bank policy rates in advanced and emerging economies 2019-2024

Central bank policy rates in major advanced and emerging economies from September 2019 to July 2024

Stock markets and COVID-19

• Premium Statistic Change in global stock index values during coronavirus outbreak 2020
• Basic Statistic Share price index in major developed and emerging economies 2019-2023
• Basic Statistic Monthly Shanghai Stock Exchange Composite Index performance 2018-2024
• Premium Statistic Coronavirus impact on the CAC 40 index in France 2020-2023
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Change in global stock index values during coronavirus outbreak 2020

Change in value during coronavirus outbreak of selected stock market indices worldwide from January 1 to March 18, 2020

Share price index in major developed and emerging economies 2019-2023

Share price index in major developed and emerging economies from January 2019 to June 2023

Monthly Shanghai Stock Exchange Composite Index performance 2018-2024

Monthly development of the Shanghai Stock Exchange Composite Index from July 2018 to July 2024

Coronavirus impact on the CAC 40 index in France 2020-2023

Impact of the coronavirus (COVID-19) outbreak on the CAC 40 index in France from January 24, 2020 to April 17, 2023

Weekly development DAX Index 2024

Weekly development of the DAX Index from January 2020 to July 2024

Weekly development Dow Jones Industrial Average Index 2020-2024

Weekly development of the Dow Jones Industrial Average Index from January 2020 to July 2024

Impact on major industries

• Basic Statistic Weekly flights change of global airlines due to COVID-19 as of January 2021
• Basic Statistic Weekly oil prices in Brent, OPEC basket, and WTI futures 2022-2024
• Premium Statistic Global PMI for manufacturing and new export orders 2018-2024
• Premium Statistic Global merchandise imports index 2019-2023, by region
• Premium Statistic Global merchandise exports index 2019-2023, by region
• Premium Statistic Industrial production growth worldwide 2019-2024, by region

Weekly flights change of global airlines due to COVID-19 as of January 2021

Year-on-year change of weekly flight frequency of global airlines from January 6, 2020 to January 4, 2021, by country

Weekly oil prices in Brent, OPEC basket, and WTI futures 2022-2024

Closing price of Brent, OPEC basket, and WTI crude oil at the beginning of each week from September 12, 2022 to September 9, 2024 (in U.S. dollars per barrel)

Global PMI for manufacturing and new export orders 2018-2024

Global Purchasing Manager Index (PMI) for manufacturing and new export orders from January 2018 to April 2024

Global merchandise imports index 2019-2023, by region

Global merchandise imports index between January 2019 to November 2023, by region

Global merchandise exports index 2019-2023, by region

Global merchandise exports index from January 2019 to November 2023, by region

Industrial production growth worldwide 2019-2024, by region

Global industrial production growth from January 2019 to April 2024, by region

Impact on trade and world's largest economies

• Premium Statistic GDP growth rate of the world's seven largest economies 2021, by country
• Premium Statistic Business confidence index among the world's seven largest economies 2020-2023
• Premium Statistic Change in GDP and trade volume globally 2007-2025
• Premium Statistic Monthly change in goods trade globally 2018-2024

GDP growth rate of the world's seven largest economies 2021, by country

GDP growth rate of the world's seven largest economies as of 3rd quarter of 2021, by country (compared to growth rate in 2020)

Business confidence index among the world's seven largest economies 2020-2023

Business confidence index (BCI) among the world's seven largest economies from January 2020 to August 2023*

Change in GDP and trade volume globally 2007-2025

Growth in GDP and trade volume worldwide from 2007 to 2025

Change in global goods trade volume from January 2018 to May 2024

Impact on Asia

• Basic Statistic GDP growth APAC 2018-2022, by sub-region
• Premium Statistic Projected GDP growth in China 2024
• Premium Statistic Cumulative number of workers to be fired due to COVID-19 Japan 2023, by industry
• Basic Statistic Estimated quarterly impact from COVID-19 on India's GDP FY 2020-2022
• Premium Statistic COVID-19 impact on unemployment rate in India 2020-2022
• Basic Statistic Estimated economic impact from COVID-19 in India 2020-21, by sector

GDP growth APAC 2018-2022, by sub-region

Gross domestic product (GDP) growth in Asia Pacific from 2018 to 2020 with forecasts to 2022, by sub-region

Median forecast for China's GDP growth rates in 2024 and 2025 as of July 2024

Cumulative number of workers to be fired due to COVID-19 Japan 2023, by industry

Cumulative number of employees who are planned to be dismissed due to the coronavirus disease (COVID-19) impact in Japan as of March 2023

Estimated quarterly impact from COVID-19 on India's GDP FY 2020-2022

Estimated quarterly impact from the coronavirus (COVID-19) on India's GDP growth in financial year 2020 to 2022

COVID-19 impact on unemployment rate in India 2020-2022

Impact on unemployment rate due to the coronavirus (COVID-19) lockdown in India from January 2020 to May 2022

Estimated economic impact from COVID-19 in India 2020-21, by sector

Estimated impact from the coronavirus (COVID-19) on India from April 2020 to September 2021, by sector

Impact on Europe

• Premium Statistic GDP growth rate forecasts in European Union 2024
• Basic Statistic Market capital value of Europe's largest banks since the coronavirus 2019-2024
• Basic Statistic Market capitalizatio of European stock exchanges since Coronavirus outbreak 2019-2024
• Premium Statistic Impact of coronavirus (COVID-19) on real GDP in Italy 2021-2022
• Basic Statistic German export expectations for manufacturing 1991-2023
• Premium Statistic Coronavirus (COVID-19) impact on GDP growth in France 2020, by scenario
• Basic Statistic Impact of COVID-19 on GDP dynamics in CEE region 2020

GDP growth rate forecasts in European Union 2024

Gross domestic product growth rate forecasts in the European Union in 2024, by member state (percentage increase on previous period)

Market capital value of Europe's largest banks since the coronavirus 2019-2024

Monthly market capitalization of Europe's largest banks since the coronavirus from December 2019 to March 2024 (in billion U.S. dollars)

Market capitalizatio of European stock exchanges since Coronavirus outbreak 2019-2024

Monthly market capitalization of European stock exchanges since the Coronavirus outbreak between December 2019 and March 2024 (in billion U.S. dollars)

Impact of coronavirus (COVID-19) on real GDP in Italy 2021-2022

Impact of coronavirus (COVID-19) on real gross domestic product (GDP) growth in Italy in 2021 and 2022

German export expectations for manufacturing 1991-2023

Monthly balance values of the ifo export expectations for the German manufacturing sector from January 1991 to November 2023 (seasonally adjusted)

Coronavirus (COVID-19) impact on GDP growth in France 2020, by scenario

Forecasted impact of the novel coronavirus COVID-19 on real gross domestic product (GDP) growth in France from 2020, by scenario

Impact of COVID-19 on GDP dynamics in CEE region 2020

Negative impact of the coronavirus (COVID-19) epidemic on GDP dynamics in Central and Eastern European countries in 2020

Impact on the United States

• Basic Statistic U.S. real GDP growth by quarter Q2 2013- Q2 2024
• Basic Statistic U.S. unemployment insurance claims per week December 2022
• Basic Statistic U.S. seasonally adjusted unemployment rate 2022-2024
• Basic Statistic U.S. unemployment rate 2024, by industry and class of worker
• Basic Statistic U.S. monthly change in nonfarm payroll employment 2024, by industry
• Basic Statistic U.S. monthly change in chained inflation 2024-2024
• Premium Statistic Share of workers and businesses impacted by COVID-19 2020

U.S. real GDP growth by quarter Q2 2013- Q2 2024

Annualized growth of real GDP in the United States from the second quarter of 2013 to the second quarter of 2024

U.S. unemployment insurance claims per week December 2022

Number of initial unemployment insurance claims made per week in the United States from the beginning of the pandemic to December 2022 (in 1,000s)

U.S. seasonally adjusted unemployment rate 2022-2024

Monthly unemployment rate in the United States from July 2022 to August 2024 (seasonally-adjusted)

U.S. unemployment rate 2024, by industry and class of worker

Unemployment rate in the United States in August 2024, by industry and class of worker

U.S. monthly change in nonfarm payroll employment 2024, by industry

Monthly change in nonfarm payroll employment in the United States in August 2024, by industry sector (in 1,000s)

U.S. monthly change in chained inflation 2024-2024

Monthly change in the chained inflation rate in the United States from July 2024 to July 2024

Share of workers and businesses impacted by COVID-19 2020

Share of workers and businesses impacted by the COVID-19 outbreak on in the United States as of March 24, 2020, by effect

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Explaining the economic impact of COVID-19: Core industries and the Hispanic workforce

Subscribe to the center for economic security and opportunity newsletter, aaron klein and aaron klein miriam k. carliner chair - economic studies , senior fellow - center on regulation and markets ember smith ember smith former research analyst - center on children and families.

February 5, 2021

• 31 min read

This report was developed for our partners at Brookings Mountain West; the original version was published on their site on February 4, 2021.

As the United States prepares for a COVID-19 recovery, policymakers need to understand why some cities and communities were more vulnerable to the pandemic’s economic consequences than others. In this paper, we consider the association between a city’s core industry, its economic susceptibility to the pandemic, and the recession’s racially disparate impact across six select metropolitan areas. We find that areas with economies that rely on the movement of people—like Las Vegas with tourism—faced substantially higher unemployment at the end of 2020 than cities with core industries based on the movement of information. Further, we find the hardest-hit areas have larger Hispanic or Latino communities, reflecting the demographic composition of workers in heavily impacted industries and susceptible areas. We conclude by recommending targeted federal policy to address the regions and communities most impacted by the COVID-19 recession.

Introduction

• City and metro economies before COVID-19
• The economic impact of COVID-19

Policy implications

Back to top ⇑

More so than any prior economic downturn, the COVID-19 recession has crushed certain industries—those that depend on the movement of people—while leaving others relatively unscathed—those that depend on the movement of information. City economies are concentrated in different industries: Las Vegas and Orlando in travel and tourism, Seattle and San Francisco in technology, and Washington D.C. in government. Thus, the COVID-19 recession’s economic geography is uniquely impacted by the pandemic’s effect on a city’s primary industry. Overlaying geography with race reveals another under-appreciated impact of this recession: an increase in the economic hardship faced by Hispanic or Latino communities.

This piece explores the economic implications of the COVID-19 recession using select metropolitan areas (often referred to by the name of the metro’s primary city), identifying problems and offering policy responses. We examine six metropolitan areas: three with heavy concentration in industries negatively impacted by COVID-19 (Las Vegas, Orlando, and Reno) and three with economies heavily concentrated in industries less negatively, or even positively, impacted by COVID-19 (Seattle, San Francisco, and Washington, D.C.). We find that the cities with industries more acutely impacted have a higher concentration of Hispanic or Latino residents.

City and metro economies before Covid-19

Cities and metropolitan areas often specialize in select industries, creating agglomeration economies. Put simply, there is an economic benefit when firms producing similar goods are located near each other. For example, the auto industry is headquartered in Detroit, finance in New York, entertainment in Los Angeles, information technology in Seattle, and so on. The performance of core industries spills over to supporting industries and affects the entire regional economy; restaurants and retail stores do better when the core industry is booming and struggle when it is not. In this section, we discuss the primary industries in each metropolitan area of interest prior to COVID-19.

Before COVID-19, Orlando had the largest tourism industry in the nation, producing $26 billion per year, while Las Vegas came in second at over $19 billion. 1 However, Las Vegas’ total GDP is smaller than Orlando’s, so the impact of tourism is relatively larger—hospitality and leisure employed more than a quarter of Las Vegas workers in 2019. 2 There are a larger share of leisure and hospitality workers in Las Vegas than government workers in D.C. Orlando and Reno have similarly high employment concentrations in hospitality and leisure, although production as a portion of their economy is sizably smaller than in Las Vegas. Figure 2 shows that roughly one in five workers in Orlando (21%) worked directly in hospitality and leisure in 2019, as did 16% (roughly one in seven) of Reno’s workforce. 3 In these cities, many secondary industries—like the professional or business sector—are driven by their primary economic engines.

Seattle and San Francisco, on the other hand, specialize in technology, an industry that may have benefitted from COVID-19. Seattle is the well-known birthplace of Microsoft and the home of Amazon. San Francisco is the modern-day home of enormous tech conglomerates like Salesforce and Adobe and features major corporate offices for many of the Silicon Valley giants located nearby. Anchor industries employ different types of workers; employment in Seattle and San Francisco are both over two times (2.36 and 2.14 respectively) more concentrated in their largest occupational group, computer and mathematical occupations, than the national average. 4 Orlando, by contrast, has slightly less than the national rate of employment in computer and mathematical occupations, while that figure plummets in Las Vegas (50%) and Reno (54%). 5  Put another way, San Francisco and Seattle have more than four times as many employees in computers and math than Las Vegas and Reno, proportionate to the total number of workers in each metro.

Moving beyond the technology versus tourism binary, we add the nation’s capital and government hotspot, Washington, D.C., where one in five workers are employed directly by the government. The corresponding army of lawyers is a good indicator of how the primary industry of a city drives secondary workforces; D.C. has almost three times (2.76) as many legal service workers per capita as the national average. With governing also comes a demand for research (military and civilian) and, as a result, D.C. has an even greater share of employees in computer and mathematics than Seattle or San Francisco (2.46 times the national average), approaching five times as many as Las Vegas and Reno, as a proportion of each metro’s workers. 6

The economic impact of Covid-19

COVID-19, which devastated some industries like leisure and hospitality, barely impacted others. Table 1 shows the change in the unemployment rate among our comparison metros; Las Vegas’ unemployment increased by nearly eight percentage points from November 2019 to November 2020—almost five percentage points more than the nation as a whole. Las Vegas and Orlando are among the metros with the current highest unemployment rates in the country; Las Vegas had the fourth highest unemployment rate of all metropolitan areas, over five points higher than the national rate in November 2020. 7 Las Vegas and Orlando also had among the top 10% highest employment declines of all metro areas from November 2019 to November 2020 (the most recent data available at the metro level 8 ). Meanwhile, the technology- and government-based metros tend to have lower unemployment than the national average, even if they started with rates similar to (or even slightly higher than) Orlando.

In this section, we examine the impact of the coronavirus pandemic on the leisure and hospitality sector (the hardest-hit industry and core sector of Las Vegas, Reno, and Orlando), the pandemic’s effect on COVID-19-resilient industries (like technology in Seattle and San Francisco or government in Washington, D.C.), and discuss economic outcomes for the Hispanic or Latino population in each city.

Table 1: Metropolitan area unemployment, November 2019-20

The Most COVID-19-vulnerable Industry: Leisure and Hospitality

Cities with core industries that have been negatively impacted by the COVID-19 recession have broader spillover effects (e.g., an unemployed casino worker in Las Vegas is less likely to buy new clothes). In the aggregate, the devastation of a core industry can mean the decline of others nearby, like with manufacturing in the Rust Belt in the second half of the twentieth century. As a result, metropolitan areas concentrated in hard-hit industries are likely to see negative ripple effects throughout their economy (lower tax revenue, less spending, etc.). As we will explore, the metropolitan areas concentrated in industries susceptible to COVID-19 tend to have larger Hispanic or Latino populations as well. Thus, the pandemic’s economic geography magnifies existing disparities, exacerbating the racial wealth gap for Hispanic or Latino families. This is particularly concerning given that the federal government’s initial COVID-19 relief policies failed to appreciate the economic and geographic realities of this recession and were implemented in a way that reduced benefits for many Hispanic or Latino families. 9

Ten months since the initial wave of closures due to COVID-19, leisure and hospitality workers continue to face the highest unemployment rate amidst the pandemic; over 16% of the sector’s labor force is unemployed. 10 While every metropolitan area has hotels, only a few stake their economies on them. Being a destination city for travel includes the economic benefit of both personal tourism and corporate conferences; COVID-19 devastated both as people stopped travelling altogether. The $100 billion a year U.S. conference industry, which fills hotels during the week for conferences in cities that become hotspots for vacationers on the weekends, is at a near standstill. 11 In November 2019, 88% of Las Vegas’s hotel or motel rooms were occupied; in November 2020, that figure was just 47%. 12 Similarly, 59% fewer passengers passed through Las Vegas’ McCarran International Airport in November 2020 than a year earlier, and 52% fewer tourists visited the city. Orlando is suffering a similar fate; 44% fewer flights were serviced at Orlando’s airport in October 2020 compared to a year before. 13

We see the spillover effect in force; cities that depend on hospitality and leisure also had higher overall unemployment, suggesting that the performance of the core industry impacted the performance of a metro area’s overall economy. Las Vegas, for example, has the second highest concentration of jobs in hospitality and faced the second largest increase in unemployment (behind Atlantic City). Orlando also stands out with a particularly large hospitality workforce and substantial increase in overall unemployment; both rank among the top 50 metros in November 2020 unemployment. Seattle and Washington D.C., by contrast, are below average in both concentration in hospitality and leisure and change in unemployment, demonstrating again how COVID-19-resilient industry concentrations have helped temper overall job loss.

Figure 3 also overlays the size of a metro’s Hispanic or Latino population: the bigger the circle, the larger the Hispanic or Latino share of the metro’s population. Tourism-dependent cities like Las Vegas and Orlando also tend to have larger Hispanic or Latino populations, while cities with below-average changes in unemployment like Seattle and Washington D.C. tend to have smaller Hispanic or Latino populations.

The decline in travel and hospitality employment was similar across the cities we analyze. The leisure and hospitality industry in Las Vegas suffered a 21.4 percentage point decline in employment since November 2019, but the leisure and hospitality industry in Orlando, D.C., San Francisco, and Seattle all declined by 30 percent or more. 15 Reno is the only city in our sample that faced a smaller unemployment decline in the sector (16%) than Las Vegas (21%). 16 In other words, there was nothing unique about working in the hospitality industry in Las Vegas, Orlando, or Reno as compared to Seattle, San Francisco, or Washington, D.C. except the portion of employment in the sector. If anything, employment held up better in cities’ core industries. However, the employment effects in non-core industries seemed to have been compounded or mitigated by core industry performance. Over a quarter of Las Vegas workers are in the hard-hit leisure and hospitality industry, and the metro’s information, financial activities, and professional business service industries also fared the worst of our comparison metros. Unsurprisingly, Las Vegas’ overall unemployment is also the highest among this group. By contrast, almost a quarter of Washington, D.C.’s employment is in government, a sector that performed better in November 2020 in the metro than in 2019; D.C. also faced the second smallest increase in unemployment among our comparison metropolitan areas.

COVID-19-Resilient Industries: Information and Government

While COVID-19 wreaked havoc on industries that depend on in-person contact, distancing restrictions caused a sharp increase in the usage of technology for remote work and business transactions. Businesses of all types invested more in technology, with one survey by McKinsey finding that, “about the impact of the crisis on a range of measures, [executives] say that funding for digital initiatives has increased more than anything else—more than increases in costs, the number of people in technology roles, and the number of customers.” 17 That survey also found a sharp increase in the share of North American consumers who interact digitally, rising by over 58% as a result of the crisis.

Relative to other industries, information technology and government have done well. Between February and April 2020, sales for non-store retailers (i.e., online shopping) increased by 15%—Amazon added 400,000 jobs this year, nearly doubling its workforce in response to the pandemic. 18 While these jobs are spread throughout the nation, Amazon’s corporate headquarter(s) will likely see disproportionate economic gain from the company’s growth. Facebook also announced plans to hire 10,000 additional workers in April 2020. 19 Meanwhile, the 12-month change in information and government industry unemployment is less than half that of leisure and hospitality. 20

As Table 2 indicates, job losses in information and technology were generally in-line with or slightly below total job loss rates for technology hub cities like Seattle and San Francisco, as well as for D.C., Orlando, and Reno. Interestingly, only in Las Vegas and D.C. were the proportion of job losses greater in information than overall job losses. This could be the result of classification, where information industry jobs that are part of hospitality and leisure or government are classified differently, although one might expect similar impacts in Orlando and Reno.

Table 2: 12-month percent change in employment by industry, November 2019-20

Acceleration of long-run trends towards increased technology use benefits technology firms and, consequently, the communities where technology firms are located. When Amazon and Facebook grow in both employment and value (see Amazon, Facebook stock prices), wealth is disproportionately created in their headquarter cities. As the growth of the auto industry powered Detroit’s rise in the 20th century, growth in technology is powering Seattle and San Francisco’s rise in the 21 st century. COVID-19, while a net loser for all of society, is a relative winner for technology firms and correspondingly, on a relative basis, for their main cities.

Likewise, COVID-19 has put the federal government to the test and Washington responded with money and new jobs. The federal government grew by over 50,000 jobs from the end of 2019 to the end of 2020 and the D.C. metro’s government employment grew by over two percent, one of the few positive figures in Table 2. 21 The old Washington adage that “the most secure job is a federal government job” held and, during the COVID-19 pandemic, secure employment is incredibly valuable. One caveat to our analysis is that while federal government hiring has remained strong, state and local government has not. State and local governments across the country lost over 1.1 million jobs during over the same period, more than offsetting the federal employment boost. 22  Thus, state capitals may not be experiencing similar government booms to Washington D.C.

Perhaps over the long run, structural changes allowing for increased remote work started by the response to COVID-19 will weaken the link between cities and their major industry. If so, this will likely be stronger in the IT sector, where a greater share of remote work is possible than in service sectors such as hospitality, leisure, and gaming. Put simply, the amenities that Las Vegas and Orlando offer cannot be as easily substituted by people sitting behind a computer a thousand miles away as may be the case for technology or government jobs.

The Impact on Hispanic or Latino Workers

Hispanic or Latino workers are particularly negatively impacted by the COVID-19 recession, as has been found in prior studies. In December 2020, the Hispanic or Latino unemployment rate was 9.3%, over three points higher than the white unemployment rate. 23  When COVID-19 initially struck, the Hispanic or Latino unemployment rate skyrocketed, surpassing the Black unemployment rate. By the end of 2020, the gap between Hispanic or Latino and white workers was still larger than when COVID-19 unemployment first struck around March.

Our metro-level analysis confirms the race gap in unemployment; metropolitan areas with above-average unemployment at the end of 2020 are 31% Hispanic or Latino, compared to 10.9% Hispanic or Latino in metro areas with below-average unemployment. Thus, the geographic spillovers in industry performance likely drive the increase in the racial disparity between the Hispanic or Latino and white unemployment rates.

Compounding the geographic effects are industrial concentration differences between racial or ethnic groups. Prior to COVID-19, nearly a quarter of the hospitality sector’s labor force was Hispanic or Latino. COVID-19 has decimated tourism, driving the hospitality and leisure industry to the highest unemployment rate among major industries. 24 Figure 5 shows select industries’ change in employment from November 2019 to November 2020 and the portion of Hispanic or Latino workers in each industry in 2019.

With unemployment also comes a number of other issues; employees often receive health benefits from their employer and losing a job may mean losing affordable health care. These impacts compound existing racial inequity in health care access as the Hispanic or Latino population is also disproportionately likely to contract COVID-19. Las Vegas coronavirus rates per 1,000 residents are much higher among Hispanic or Latino people than white people. This helps explain why data through mid-January 2021 indicate that one out of twelve Hispanic or Latino Las Vegans have had COVID-19, while only one in twenty white residents have. 25 On an age-adjusted basis, death rates for Hispanic residents in Nevada are nearly three times as great as that of white residents. 26

Federal aid has so far been suboptimal in allocating economic assistance to those who need it the most. Over half of coronavirus aid went directly to businesses, many of which were not compelled to keep their employees or prove that they were negatively impacted by the pandemic. 27 By contrast, only about a fifth went directly to workers and families, and the aid that did was not always well-targeted. For example, initial direct payments (stimulus checks) excluded children if they had one parent who was an undocumented immigrant. 28  Direct stimulus payments were also administered slowly, with millions of American families waiting months to receive their funds.

For the purpose of this analysis, the most well-targeted program was supplemental unemployment insurance. By tracking unemployment and incorporating a broader definition of unemployed workers, enhanced unemployment benefits should have flown disproportionately to those in more impacted industries such as leisure and hospitality. As a result, enhanced benefits did more to support the economies of Las Vegas and Orlando than their relative impact in San Francisco, Seattle, and Washington, D.C. Likewise, we would expect Hispanic or Latino workers to make up a disproportionate number of claims given that they faced disproportionately high unemployment. Herein lies one serious potential problem. Many states continue to struggle with significant difficulty in administering the new unemployment insurance aid.

Multiple factors are at play, including specific states’ difficulty modernizing their systems to accommodate the new federal rules and the sudden spike in demand. Florida, for example, had an archaic system that made it difficult for newly eligible workers to qualify. 29 Nevada’s difficulty in expanding eligibility and processing record levels of unemployment claims were also well-documented, leading to a class-action lawsuit against the state’s employment department. 30 Delays in processing claims and providing payments are particularly harmful for people with little savings and difficulty accessing short-term credit at a reasonable cost, burdens that apply disproportionately to Hispanic or Latino Nevadans. This could be one reason why enhanced unemployment insurance benefits were not equitably taken up by those who need it; about the same proportion of workers who filed for unemployment benefits are Hispanic or Latino as are in the workforce, even though Hispanic or Latino workers were disproportionately unemployed (see Figure 4). 31

The heralded Paycheck Protection Program (PPP), which offered affected businesses and workers forgivable loans (in effect grants), saved many fewer jobs than the lofty anticipated 30 million; in the first two months of the program, researchers estimate that only 2.3 million jobs were saved, at a price of $286,000 each. 32 The PPP grants that were distributed seemed mismatched with the unemployment rate in those sectors. According to a Washington Post analysis, 32% of jobs lost were in the lodging, restaurants, and bar industry (a core component of hospitality and leisure), but the industry only received 8% of PPP grants. Similarly, the arts, entertainment & recreation industry had a job loss rate three times higher than the portion of PPP grants it received. Correspondingly, finance and insurance companies that relatively prospered throughout the pandemic received over $8 billion in PPP funds. Put another way, finance and insurance received over $350,000 in PPP funding per job lost from February to April as compared to about $8,000 in arts, entertainment, and recreation, and $7,800 in accommodation and food services. 33

Table 3: Paycheck Protection Program (PPP) spending, by industry 34

Much of the Coronavirus Aid, Relief and Economic Security (CARES) Act money allocated directly to state and local governments was allocated by population, despite the demonstrated disparate geographic and economic effects of COVID-19. 35  Allocating by population rather than economic impact results in too little money going to states and local government suffering larger economic consequences. Because the economic geography of COVID-19 fell disproportionately Hispanic or Latino workers, this error will have consequences for racial equity; funding misallocation exacerbates existing racial income and wealth gaps.

Even if all unemployment benefits, PPP loans, and other COVID-19 aid were distributed in the most equitable way possible, people of color—especially Hispanic or Latino workers—are more likely to be unemployed in general and because of COVID-19, more likely to live in the key metro areas disproportionately hit by the recession, and are more likely to contract COVID-19. The impacts of the recession will also not disappear in the years to come. Hispanic or Latino workers who lost their job over the course of the pandemic may not be able to find work for months or years after the final COVID-19 aid has been distributed. There will also be a long lag in tourism’s recovery. Even if most Americans who want to be have been vaccinated, international tourism and close contact among people may take months or years to recover. Stimulus spending and temporary aid are a great starting point, but policymakers should pay attention to the industries and people who will face an uphill battle in the foreseeable future.

For government aid to maximize its assistance to vulnerable Americans, increased attention to actual need is necessary. Specific improvements include:

• Focus support to businesses in more impacted industries, such as hospitality and leisure as opposed to blanket first-come, first-served funding for all industries.
• Increase assistance to metro areas that depend on the hardest-hit industries such as Las Vegas, Orlando, and Reno.
• Reverse discriminatory rules that reduce access for negatively impacted populations, such as precluding parents from receiving child benefits based on one parent’s nationality. This is one area where the COVID-19 legislation passed by Congress in December showed improvement.
• Realize that geography interacts with economics and race as the nation turns to the aftermath of the COVID-19 recession, and that the recession’s impact will be larger and longer for some communities.

Full PDF version of this report available here .

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Special thanks to William E. Brown, Jr., UNLV Director of Brookings Mountain West, and Caitlin Saladino, Director of Strategic Development of Brookings Mountain West for their input. Thanks also to: Ashley LeClair for editing and formatting the final report; Mia Seymour for her research support; and to Becca Portman for her data visualization assistance.

• World Travel and Tourism Council, “Cities Economic Impact Report,” 2019 ( https://wttc.org/Research/Economic-Impact/Cities/ )
• U.S. Bureau of Labor Statistics, “Economy at a Glance: Las Vegas-Paradise, NV,” 2019 annual ( https://www.bls.gov/eag/eag.nv_lasvegas_msa.htm )
• U.S. Bureau of Labor Statistics, “Economy at a Glance: Orlando-Kissimmee-Sanford,FL,” 2019 ( https://www.bls.gov/eag/eag.fl_orlando_msa.htm );U.S. Bureau of Labor Statistics, “Economy at a glance: Reno-Sparks,” 2019 ( https://www.bls.gov/eag/eag.nv_reno_msa.htm )
• U.S. Bureau of Labor Statistics, “Occupation Employment Statistics Seattle,” May 2019 ( https://www.bls.gov/oes/current/oes_42660.htm ); U.S. Bureau of Labor Statistics, “Occupational Employment Statistics San Francisco,” May 2019 ( https://www.bls.gov/oes/current/oes_41860.htm )
• U.S. Bureau of Labor Statistics, “Occupational Employment Statistics Orlando,” May 2019 ( https://www.bls.gov/oes/current/oes_36740.htm ); U.S. Bureau of Labor Statistics, “Occupational Employment Statistics Las Vegas,” May 2019 ( https://www.bls.gov/oes/current/oes_29820.htm ); U.S. Bureau of Labor Statistics, “Occupational Employment Statistics Reno,” May 2019 ( https://www.bls.gov/oes/current/oes_39900.htm#otherlinks )
• U.S. Bureau of Labor Statistics, “Occupational Employment Statistics Orlando,” May 2019 ( https://www.bls.gov/oes/current/oes_36740.htm ); U.S. Bureau of Labor Statistics, “Occupational Employment Statistics Las Vegas,” May 2019 ( https://www.bls.gov/oes/current/oes_29820.htm ); U.S. Bureau of Labor Statistics, “Occupational Employment Statistics Reno,” May 2019 (https://www.bls.gov/oes/current/oes_39900.htm)
• U.S. Bureau of Labor Statistics, “Over-the-Year Change in Unemployment Rates for Metropolitan Areas,” November 2020 ( https://www.bls.gov/web/metro/laummtch.htm )
• Aaron Klein and Ariel Gelrud Shiro, “The Covid-19 recession hit Latino workers hard. Here’s what we need to do.,” October 2020 ( https://www.brookings.edu/blog/how-we-rise/2020/10/01/the-covid-19-recession-hit-latino-workers-hard-heres-what-we-need-to-do/ )
• U.S. Bureau of Labor Statistics, “Labor Force Statistics from the Current Population Survey,” December 2020 ( https://www.bls.gov/web/empsit/cpseea31.htm )
• Ben Popken, “America’s $100 billion convention industry is back, but changed forever’” June 2020 ( https://www.nbcnews.com/business/business-news/america-s-100-billion-convention-industry-back-changed-forever-n1231228 )
• UNLV Lee Business School, “Economic Data – Las Vegas/Clark County,” December 2020 ( https://cber.unlv.edu/CCEconData.html )
• Federal Aviation Administration, “Air Traffic Activity System” ( https://aspm.faa.gov/opsnet/sys/Main.asp?force=atads )
• U.S. Bureau of Labor Statistics, “Over-the-Year Change in Unemployment Rates for Metropolitan Areas,” November 2020 ( https://www.bls.gov/web/metro/laummtch.htm ); Nevada Department of Employment, Training & Rehabilitation, “Current Payroll Employment Estimates,” 2019 ( http://nevadaworkforce.com/Home/DS-Results-CES2 )
• U.S. Bureau of Labor Statistics, “Economy at a Glance,” November 2020. For Las Vegas-Paradise, Washington, D.C., Reno, San Francisco-Oakland-Hayward, Seattle-Tacoma-Bellevue, and Orlando-Kissimmee-Sanford. ( https://www.bls.gov/regions/home.htm )
• U.S. Bureau of Labor Statistics, “Economy at a Glance,” November 2020. For Las Vegas-Paradise, Washington, D.C., Reno, San Francisco-Oakland-Hayward, Seattle-Tacoma-Bellevue, and Orlando-Kissimmee-Sanford. (https://www.bls.gov/regions/home.htm)
• McKinsey and Company, “How COVID-19 has pushed companies over the technology tipping point—and transformed business forever,” October 2020 ( https://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/how-covid-19-has-pushed-companies-over-the-technology-tipping-point-and-transformed-business-forever )
• U.S. Bureau of Labor Statistics, “Employment by industry, monthly changes,” December 2020 ( https://www.bls.gov/charts/employment-situation/employment-by-industry-monthly-changes.htm ) ; OCED, “E-Commerce in the time of Covid-19,” October 2020, ( http://www.oecd.org/coronavirus/policy-responses/e-commerce-in-the-time-of-covid-19-3a2b78e8/ ) ; Jay Greene (The Washington Post), “Amazon now employs more than 1 million people,” October 2020 ( https://www.washingtonpost.com/technology/2020/10/29/amazon-hiring-pandemic-holidays/ )
• Todd Spangler (Variety), “Facebook to Hire 10,000 Workers this Year, Sets Plans for Doling out $100M in Aid to Small Businesses,” April 2020 ( https://variety.com/2020/digital/news/facebook-hiring-10000-workers-small-business-grants-1234570018/ )
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• Peter Whoriskey, Douglas MacMillan, and Jonathan O’Connell (Washington Post), “‘Doomed to fail’: Why a $4 trillion bailout couldn’t revive the American Economy,” October 2020 ( https://www.washingtonpost.com/graphics/2020/business/coronavirus-bailout-spending/ )
• Lauren Wamsley (NPR), “Gov. Says Florida’s Unemployment System Was Designed To Create ‘Pointless Roadblocks,” August 2020 ( https://www.npr.org/sections/coronavirus-live-updates/2020/08/06/899893368/gov-says-floridas-unemployment-system-was-designed-to-create-pointless-roadblock )
• Joyce Lupiani and Angelina Baray (KTNV), “TIMELINE: Nevada Dept. of Employment and handling of pandemic unemployment claims,” January 2021 ( https://www.ktnv.com/news/timeline-nevada-dept-of-employment-and-handling-of-pandemic-unemployment-claims ); Darcy Spears (KNTV), “Class-action lawsuit seeks to force Nevada’s unemployment office to do its job,” July 2020 ( https://www.ktnv.com/13-investigates/class-action-lawsuit-seeks-to-force-nevadas-unemployment-office-to-do-its-job )
• The Century Foundation, ”Who is Receiving Unemployment Insurance, and Who Just Lost $600 Per Week?,” August 2020, ( https://tcf.org/content/commentary/receiving-unemployment-insurance-just-lost-600-per-week/?session=1&session=1 )
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• Authors’ calculations based on: Bureau of Labor Statistics, “Employment, Hours, and Earnings from the Current Employment Statistics survey (National),” for the ‘Arts, entertainment, and recreation,’ ‘Accommodation and Food Services,” and ‘Finance and Insurance’ subsectors, February and April 2020 ( https://www.bls.gov/iag/ ); Peter Whoriskey, Douglas MacMillan, and Jonathan O’Connell (Washington Post), “ ‘Doomed to fail’: Why a $4 trillion bailout couldn’t revive the American Economy,” October 2020 ( https://www.washingtonpost.com/graphics/2020/business/coronavirus-bailout-spending/ )
• Jared Walczack (Tax Foundation), “State and Local Funding Totals Under the CARES Act,” April 2020 ( https://taxfoundation.org/federal-coronavirus-aid-to-states-under-cares-act/ )

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At the end of 2019, the world grappled with an unparalleled public health crisis due to the COVID-19 pandemic, which also precipitated a global economic downturn. Concurrently, material panic buying occurred frequently. To restore benign market order, the government instituted a series of interventions to stabilize the market. This scholarly exploration dives deep into evaluating the tangible impact of these governmental measures in Hubei, China, a region which found itself at the very epicenter of the epidemic in its onset phase. Existing papers often employ structured questionnaires and structural equation methods, with small samples and limited effective information. In contrast, we used a dataset of tens of thousands of entries and employed text analysis to maximize the extraction of valid information. Through a meticulous analysis of public feedback, our findings unveil several pivotal insights: (1) The news measures of materials have the best effect. Their effectiveness, in descending order, is ranked as: material sufficiency > authority effect > market supervision > appeal and guidance; (2) Government measures during the epidemic’s initial phase exhibited a delay. After the lockdown measures, the phenomenon of large-scale buying has been formed, and the relevant material news was released later; (3) A dual approach combining authority influence with material sufficiency yielded the most favorable results. In light of these findings, the paper concludes with tailored recommendations aimed at amplifying the efficacy of government-led public opinion interventions in future crises.

Introduction

Following the emergence of COVID-19, many newspapers from different countries published photos of barren supermarket shelves, underscoring the shortage of food and essentials (Lufkin, 2020 ; Nicholson, 2020 ). These individuals usually present this phenomenon as ‘panic buying’ (Nicholson, 2020 ). The social and psychological reactions of the general public to new outbreaks of infectious diseases, such as SARS, the H2N1 pandemic, and the Ebola virus, often provoke public sentiments such as fear, anxiety, and depression. (Maunder et al., 2003 ; Sim et al., 2010 ). In fact, research has shown that only a small number of people purchase a large amount of goods (for example, only 3% of people purchase an excessive amount of pasta (Kantar, 2020)). Such data suggest that while many people anticipate and act on potential shortages during crises, only a fraction engage in extreme purchasing, which is different from compulsive purchasing disorders (Sharma et al., 2020 ). However, after this purchasing behavior occurs, the resulting social problems are extremely serious, leading to supply chain collapses and market disarray. At this point, expedient government intervention is extremely important. The purpose of this paper is to explore how the government can swiftly intervene, thereby mitigating social impact after such panic buying behavior occurs.

At present, several scholars have found that government intervention plays a very important role in disease prevention and control (Zhao et al., 2020 ). However, there is a noticeable gap in the literature in regard to addressing panic buying during the COVID-19 outbreak, especially during its early stages. Such an investigation holds significant academic value, given that swift and appropriate government actions can effectively reduce its negative impact on society. Therefore, it is highly important to study the influence of government intervention on panic buying during the early outbreak of COVID-19. For example, Li and Dong ( 2022 ) developed a game theoretic supply chain model to assess the impact of government regulation on the shortage of life-saving materials and profits within the supply chain (Liu et al. 2022 ). Olowookere et al. ( 2022 ) emphasized that the government should comprehensively help people overcome the difficulties resulting from epidemics, particularly vulnerable populations. Cariappa et al. ( 2022 ) proposed a fundamental panic buying intervention, i.e., starting from agriculture to build public confidence. Taylor ( 2022 ) summarized the intervention experience. An examination of current intervention research reveals multifaceted analytical perspectives. However, many of these approaches lack the immediacy required for swift responses, rendering them more suitable for post crisis management rather than urgent interventions. In addition, public opinion cannot be accurately reflected, and most related research methods rely heavily on model simulation (Rajkumar and Arafat, 2021 ); thus, actual public data are lacking. Therefore, it is necessary to conduct mining and analysis based on netizens’ online comments on government measures to assess the effectiveness of implementing various government intervention measures. By identifying the most impactful factors on intervention outcomes, this approach aims to shape more effective strategies for future incidents.

Given this background, the cardinal objective of this research is to evaluate the impact of government intervention measures on panic buying during the COVID-19 pandemic by mining and analyzing online comments dispersed across cyberspace. In addition to merely assessing the tangible outcomes of these interventions, this study aims to determine the actual effectiveness of government intervention measures, gain deeper insights into public perceptions and attitudes toward these measures, analyze the emotional trends of the public under different government interventions, and provide valuable insights for formulating more effective strategies in the future. The research methodology will involve semantic network analysis, sentiment analysis, and LDA modeling for categorization and exploration of implementation effects. Additionally, a multiple regression model will be used to analyze the factors influencing the intervention effects. The findings of this study will contribute to enhancing intervention efficiency, mitigating the negative consequences of panic buying, and promoting the normalization of market order.

Materials and methods

Literature materials.

In Google Scholar’s academic research, our document retrieval was carried out with “panic buying” and “intervention” as the main fields, yielding a wealth of studies addressing various facets of panic buying, encompassing its reasons for formation, prevention methods and functional evaluation. Many studies have analyzed the reasons for the formation of these rocks via various methods. Prevention methods and effect evaluation are also used, but the methods are relatively simple. At present, the analysis of intervention effects has been conducted mainly by constructing regression models (Si et al., 2020 ) and PMC index models (Chen et al., 2021 ). Billore and Anisimova ( 2021 ) summarized the relevant research of the past 20 years, upon which we have also based our considerations. This section provides a comprehensive literature review, discusses the trajectory from the root causes of panic buying to preventive methods, and explores the evaluation of intervention outcomes. This approach ensures a thorough understanding of the complexities surrounding panic buying.

The research literature elucidates the genesis of panic buying by examining it predominantly through the lens of psychological triggers and the sway of external media. Psychological factors such as perceived scarcity and fear of the unknown contribute to anxiety and panic buying behavior (Yuen et al., 2020 ; Omar et al., 2021 ; Taylor, 2021 ). This behavior can create a cycle of increased anxiety (Prentice et al., 2022 ). Social influences such as norms and observational learning also play a role in amplifying perceptions of scarcity and triggering panic buying (Yuen et al., 2022 ). External media, particularly social media, also significantly influences panic buying. Expert opinions and official communications during public health crises can trigger panic buying (Naeem, 2021 ). Excessive exposure to information on social media intensifies perceived scarcity and purchase intentions (Islam et al., 2021 ). Government and corporate interventions can mitigate panic buying while influencing social dynamics (Prentice et al., 2021 ). Research methods include correlation analysis (Lins and Aquino, 2020 ; Bentall et al., 2021 ), qualitative analysis (Taylor, 2021 ), and statistical simulations (Fu et al., 2021 ) to explore the causes and dynamics of panic buying during crises. Communication patterns during disasters also impact panic buying behaviors (Arafat et al., 2022 ). De Brito Junior et al. (2023) found some results that may assist policymakers in introducing public policies and managing resources during a crisis that requires social distancing and lockdowns. In general, the examination of panic buying’s root causes is characterized by diverse methodologies and well-substantiated conclusions, suggesting a mature body of research in this domain.

The intervention strategies outlined in the literature can be broadly categorized into three types: psychological, market-based, and network monitoring. Lei et al. ( 2020 ) used the SAS and SDS to increase anxiety and depression rates in affected populations, stressing the need for government initiatives in economic aid, medical support, and psychological intervention. Bermes ( 2021 ) used structural equation modeling to suggest improving consumer resilience and modifying consumers’ information environment. Ho et al. ( 2020 ) emphasized psychological support, highlighting the vulnerability of people exposed to epidemics. Mukhtar ( 2020 ) reviewed past epidemics to develop crisis intervention plans. Roy et al. ( 2020 ) and Zheng et al. ( 2020 ) stressed mental health care and psychological panic reduction, respectively. Wu ( 2009 ) focused on network emergency management. Tsao et al. ( 2019 ) advocated retail strategy changes to ease market pressure. Ling et al. ( 2020 ) and Sahin et al. ( 2020 ) proposed social and economic system adjustments. Boyacι-Gündüz et al. ( 2021 ) underscored food system resilience amid population growth. This review reveals diverse intervention approaches, highlighting gaps in multidimensional studies combining psychology and market dynamics.

With regard to the assessment of the outcomes of diverse interventions for panic buying, several studies have examined this topic further. Prentice et al. ( 2020 ) noted that countries typically employ regular interventions during such events. Using Twitter data for Australia, they found that government measures aligned with panic buying periods through semantic analysis. Arafat et al. ( 2021a ) discussed historical perspectives and crisis prevention planning, integrating sociology, marketing, and industrial purchasing. Prentice et al. ( 2021 ) studied the impacts of government, business, and social groups on panic buying, highlighting the roles of government and business over social groups. Mao et al. ( 2022 ) developed a dynamic game model showing that government interventions could control panic buying duration. Rajkumar ( 2021 ) proposed a biological-psychological-social model, suggesting measured punitive actions, responsible media reporting, and social contact to mitigate panic buying. Niu et al. ( 2021 ) emphasized targeted interventions based on survey data. Fast ( 2014 ) used network analysis to predict social responses to disease outbreaks. In a series of papers (2020a; 2020b; 2020c), Arafat et al. analyzed media reports on panic buying. However, these studies did not adequately capture the public’s reactions to these events. In fact, public comments imply a great deal of valuable information. In addition, Arafat et al. ( 2021b ) introduced panic buying intervention measures in their book, but most of them used summary words and did not analyze changes in public sentiment. The above literature analysis shows that existing studies mostly analyze structural equations, questionnaires and mathematical modeling but rarely mine information from people’s online comments.

In addition, empirical investigations into panic buying in China have produced insightful findings. Prentice et al. ( 2021 ) and Islam et al. ( 2021 ) partially used samples from China in their analysis. However, Wang and Na ( 2020 ) applied a multivariate statistical model to study rational and irrational motives for food hoarding by aggregating online samples from three Chinese cities. The results confirmed the occurrence of rational and irrational food hoarding. Fu et al. ( 2022 ) utilized mathematical modeling techniques to analyze the efficacy of panic buying interventions and validated the model using Chinese panic buying data. In addition, Yang et al. ( 2022 ) conducted a survey of 517 participants who experienced panic buying during the Omicron pandemic in China. Their findings revealed connections between media exposure, perceived emotional risk, stakeholder perception, protective awareness, and panic buying behavior. Research on panic buying in China has yielded promising results, but in-depth research is still strongly needed.

In summary, structural questionnaires and structural equation methods may inadvertently overlook much valid information. In contrast, online comments are relatively free and can multidimensionally and immediately capture emotional changes and actual needs. Therefore, more effective information can be obtained by mining people’s online comments on government interventions. As such, this paper carries out data analysis by crawling online comments under different government interventions and further uses the LDA topic extraction model to determine the effects of the intervention, which has strong practical significance. The LDA review by Jelodar et al. ( 2019 ) covers research on LDA from 2003 to 2016, revealing its application across various fields such as software engineering, political science, medicine, and linguistics. Moreover, it has been studied in fields such as communication research (Maier et al., 2021 ) and artificial intelligence (Yu and Xiang, 2023 ), highlighting its broad applicability.

LDA (latent Dirichlet allocation) model

The LDA model, introduced by (Blei et al., 2003 ), addresses certain limitations inherent in traditional text analysis mechanisms. While classical methods, such as TF-IDF, gauge the correlation between two documents by counting shared words and employing metrics such as term frequency (TF) and term frequency-inverse document frequency (TF-IDF), they often overlook deeper semantic connections. Such methods merely scratch the surface, focusing on word frequency without diving into the underlying themes and associations. Therefore, the LDA method can better determine the relationships among comments. This section analyses the effect of government intervention measures on preventing panic buying through the use of the powerful topic extraction function of LDA.

The similarity adaptive method is used to find the optimal LDA topic number; this method does not require manual debugging of the topic number and has a small number of iterations, high operational efficiency, fast speed, and high accuracy. The specific operation steps are as follows.

Randomly select the initial topic number K to obtain the initial model and calculate the similarity between the topics, i.e., the average cosine value cosθ ( i represents the dimension, K represents the number of topics). The specific calculation formula is as follows:

where θ represents the angle between X and Y , and X and Y represent two n -dimensional vectors, i.e., X is represented by \(({X}_{1},\,{X}_{2,}\,\ldots {X}_{n})\) , and Y is represented by \(({Y}_{1},{Y}_{2,}\ldots {Y}_{n})\) . A larger cosine value indicates that the texts are more similar and are grouped into the same category during topic extraction.

The model is trained again by increasing or decreasing the value of K , and the average cosine between subjects is calculated.

Perform step (2) again, and the cycle is repeated until the optimal K value is obtained, i.e., termination of the iteration. At this time, K is the optimal number of topics extracted.

Text sentiment analysis methods

Semantic networks primarily analyze the relationships between sentences. By plotting the relationships between evaluation targets and their respective opinions, they aid in visually analyzing the attributes among evaluation targets. ROSTCM5.8 software is used to generate semantic network graphs for the four intervention categories. By constructing semantic networks, potential connections and hidden information between evaluation targets and evaluation opinions can be uncovered; these are primarily represented through directed edges and nodes. Edges represent connections between nodes, while nodes represent individuals or events.

Sentiment analysis involves analyzing the sentiment of each sentence. A paragraph of text is input, processed with Python code, and automatically received feedback on the emotional orientation of the text, along with a score indicating whether it is a positive endorsement or a negative critique. This magical functionality is known as text sentiment analysis, also referred to as opinion mining. It involves the collection, processing, analysis, summarization, and inference of subjective texts with emotional tones, spanning multiple research fields such as artificial intelligence, machine learning, data mining, and natural language processing. Text sentiment analysis plays a crucial role in today’s information industry era: in sentiment analysis, it dissects hot events emotionally, identifies emotional reasons, aids governments in understanding public sentiment, and prevents harmful events from occurring.

Multiple regression analysis

Toward the end of the article, we employed multiple regression analysis to further explore the factors influencing people’s emotional tendencies. Multiple regression is a statistical analysis method used to study the relationship between a dependent variable and multiple independent variables. This approach helps us understand the impact of multiple independent variables on the dependent variable and quantifies the magnitude of these impacts. In multiple regression, we initially assume a linear relationship between the dependent variable and the independent variables and establish a mathematical model to describe this relationship. Then, we conduct further analysis using multiple regression to explore this relationship further.

Background material

The intervention of government departments can alleviate public panic and reduce adverse social impacts. However, there is a lack of action in sorting out intervention measures and evaluating effects, especially when the sorting of government intervention plans during the initial outbreak of the epidemic in Hubei Province is limited. Therefore, this section aims to delve into the backdrop of panic buying, examine official intervention mandates, categorize the government’s countermeasures against panic buying, and subsequently assess their effectiveness.

Event Background

At the onset of the epidemic, the government lacked corresponding management experience, resulting in various practical issues. By retrospectively analyzing the initial control plans and their shortcomings, our study can glean valuable lessons to enhance future management strategies and improve overall responsiveness. Therefore, this paper takes panic buying in Hubei, China, during the initial period of the epidemic as an example to evaluate the effectiveness of the intervention measures. First, we meticulously chart the progression of the epidemic in Hubei Province, aligning it with a chronological framework. Key milestones and pivotal events are itemized, providing a clear and sequential depiction of the unfolding situation. The trajectory of event progression is shown in Fig. 1 , which presents a flow chart capturing the sequence and development of events during the epidemic in the province.

Epidemic development timeline in Hubei Province.

Among them, “Shuanghuanglian” is a traditional Chinese patent medicine and a simple preprotection that was once said to inhibit COVID-19. The “One yuan dish” was a pound dish that was launched during the epidemic in Wuhan to stabilize the market and the public.

As illustrated in Fig. 1 , the epidemic in Hubei Province arose rapidly, prompting immediate and decisive actions from the government. Notably, the interval between the discovery of human-to-human transmission and the “closure of the city” was less than 3 days. Lockdown measures effectively stemmed the large-scale spread of the epidemic. However, in its aftermath, the public swiftly reacted with a spree of panic buying at supermarkets. In response, the government sought to assure the public with announcements regarding ample supplies while also enforcing market regulations. However, government intervention has a notable lag. Panic buying had already taken root by the time the city was closed, and there was a delay before official assurances regarding supply sufficiency were broadcasted. Such delays had lasting consequences. Ideally, news about sufficient supplies should be synchronized with, if not preempted, actions such as city closures to better manage public panic and reduce market strain.

Sorting out intervention measures for panic buying

Google’s search function is powerful and can directly show changes in item demand. However, this article analyzes drug demand based on Weibo data for three reasons: First, Weibo is a platform based on user relationships, used for information sharing, dissemination, and acquisition. The earliest and most famous microblogging platform is Twitter from the United States. According to official reports, as of the end of 2023, the daily active users of Weibo have reached 260 million, and Weibo has become an important channel for the public to express their opinions. The government also releases reports on relevant measures through Weibo. For drugs that are popular or have regional characteristics in China, the emotional mining of its comments can more accurately reflect changes in demand. Secondly, Weibo online comments contain rich and detailed personal experiences and emotional expressions of users, such as feelings, effects, and side effects of using drugs, providing more in-depth information for emotional mining. Google Trends data, on the other hand, is more macroscopic and general. Finally, the text of Weibo comments has strong social interactivity. The replies and discussions among users can form the collision and dissemination of viewpoints, revealing the deep-seated reasons and potential influencing factors for drug demand. Google Trends is based on search data and lacks the rich information brought by this social interaction. In addition, compared with structured data such as questionnaires and interviews, online comment data are more spontaneous and expansive, and their data volume is large, often dozens, hundreds, or even thousands of times that of the questionnaire, which offers a richer mine of actionable insights. There are many fields in which online review data are used for data mining to optimize management plans, such as through the use of online reviews to evaluate hotel management (Guo et al., 2022 ) and commodity reviews to optimize product functions (Song et al., 2021 , Lan et al., 2020 ); these fields have been proven to be able to draw effective conclusions and promote industrial development.

Therefore, to better understand public sentiment toward government interventions, this study uses “panic buying”, “snap purchase”, “masks”, “materials” and other search keywords from Weibo to screen out related intervention reports on the CCTV News, People’s Daily Online, People’s Daily and other official media.

Panic buying possesses several distinct characteristics. For example, it is typically triggered by factors like concerns regarding the shortage of supplies, panic over ineffective market regulation, and unease resulting from the absence of authoritative information. Research has found that adequate supplies have a major impact on panic buying (Fu et al., 2022 ; Lins and Aquino, 2020 ). During the COVID-19 pandemic, local governments disclosed supplies of materials like grains, vegetables, and masks. For instance, a city said its grain reserves could last over half a year. This avoided panic buying and stabilized the market. The authority effect also influences panic buying (Zhang et al., 2020 ; Naeem, 2021 ). When there were rumors, experts were invited to explain. For example, for the rumor of a drug treating COVID-19, experts clarified, eliminating doubts. The government’s regulatory measures can curb panic buying. Keane and Neal ( 2021 ) said they are crucial for stability. For comparison, there’s initiative guidance (Mao et al., 2022 ), a non-mandatory measure, guiding the public through information.

We divided the Weibo topics based on these characteristics and relevant research. We initially conducted keyword searches such as “authority effect”, “adequate supplies”, “market regulation”, and “active guidance” to filter relevant Weibo topics in Fig. 2 . For instance, in the market regulation category, we specifically searched for Weibo topics related to “panic buying” and “market regulation”, and manually selected those with a significant amount of data for analysis. The specific categories and classification descriptions are shown in Table 1 .

Proportion of each category.

By selecting representative Weibo topics with relatively hot discussions among the four categories, a total of 14 topics were screened out, and Python was used to crawl the corresponding public comment data to further analyze their perceptions. A total of 84,534 comments were crawled. The Weibo topics and comment volumes are summarized in Table 2 , where the Weibo topics are marked with “#”.

Utilizing data scraped from official Weibo comments allows us to understand in a timely manner the acceptance and emotional preferences of netizens toward governmental announcements, thereby enabling us to better grasp public opinion and determine the following work. In this section, the crawled data are cleaned, and semantic network analysis and sentiment analysis are used to further explore the netizens’ perceptions of government intervention measures. The semantic network can clearly show the connection between the topic and the subject, which is helpful for us to observe the basic information of the comment. Emotional analysis delves deeper, unearthing underlying emotional biases in the comments, aiding us in assessing the effectiveness and reception of the related governmental texts.

Selecting and cleaning comments

Given the vast internet penetration and extensive user base, netizens voice diverse opinions while maintaining a semblance of moral integrity, resulting in intricate and multifaceted feedback. To best harness these data, we employ Python for preliminary processing, ensuring that only valid comments are retained. This approach allows the subsequent analysis results to more closely reflect the real situation. The data cleansing rule in this section is to delete invalid content on the basis of retaining comment information to the greatest extent. The main steps are as follows:

Nonessential comments, such as punctuation, place names, personal names, modal particles, and advertisements, are eliminated.

Comments such as “xswl”, “hhh” and other character-based comments that cannot be accurately identified during sentiment analysis are eliminated in an abbreviated manner.

Use regular expressions to remove invalid comment content, including URLs, links, and “reply to @XXX”, from a comment, and retain the remaining valid content.

Translate the English expressions into Chinese.

After cleaning, a total of 80,280 comments remained, among which the proportion of each category is shown in Fig. 2 .

As depicted in Fig. 2 , the public is more enthusiastic about material news, with comments accounting for 44.27%, while the proportion of the authority effect is lower. The data were collected mainly because, regarding panic buying, authoritative figures, such as Zhong Nanshan, tend to focus more on the epidemic than on the social problems caused by the epidemic. (Zhong Nanshan is a respiratory disease expert and a key figure leading the fight against the COVID-19 pandemic.)

Semantic analysis based on online comments

The abovementioned cleaned comment data can be visually analyzed to obtain additional essential information.

Semantic network analysis of initiative guidance

Semantic network analysis is performed on the Weibo comments related to initiative guidance, and the results are shown in Fig. 3 . The statistics of the top 30 words and their frequencies are calculated to validate the results of the Semantic network analysis, as shown in Table 3 .

Semantic network of initiative guidance.

As shown in Fig. 3 , people’s attention was primarily focused on face masks. Despite government opposition to hoarding, the term “face mask” appeared to be staggered 4281 times, leading to a spike in prices. Both offline and online pharmacies experienced shortages of face masks. Additionally, “hoarding” appeared 781 times and “shortage” 676 times. People’s emotions were fragile due to the pandemic, making them prone to overinterpreting official statements. For instance, a Weibo repost by the People’s Daily claimed that Shuanghuanglian could suppress the novel coronavirus, triggering panic buying of this traditional Chinese medicine. Although officials later clarified that Shuanghuanglian is not a treatment and urged people to stop panic buying, the impact was limited, with cases reporting worsened conditions due to self-administration. Overall, government guidance was ineffective, and there were instances of careless communication. Officials should exercise greater caution to avoid unnecessary misunderstandings during crises. It is noteworthy that toilet paper appeared approximately 200 times, which aligns with reality. Research (Garbe et al., 2020 ) suggests that toilet paper can provide a sense of security, highlighting its unique prominence in panic-buying events triggered by this pandemic. This calls for reflection.

Semantic network analysis of market regulation

The comment data of market regulation are integrated to generate a semantic network, as shown in Fig. 4 , and the frequencies of the words are shown in Table 4 .

Semantic network of market regulation.

Figure 4 highlights a significant concern during the epidemic: rising prices, especially for face masks. The analysis revealed that masks are becoming more expensive and of lower quality. Public discourse centers mainly around mask issues, with “masks” mentioned 6058 times. Terms such as “secondary”, “second-hand”, “fake”, and “black heart” indicate the presence of recycled or counterfeit masks, with “second-hand” appearing 342 times. Reusing masks reduces their effectiveness and increases the risk of cross-infection, posing significant harm and negative social impacts. Additionally, terms such as “severe punishment”, “calling”, and “deserving it” show public support for government actions and active participation in reporting price gouging. Severe punishment was mentioned 380 times, and deserved punishment was mentioned 372 times, reflecting strong public sentiment. Moreover, alongside rising mask prices, living essentials also see price hikes. The term “price increase” was mentioned 1205 times, indicating widespread concern. In conclusion, the analysis combines word frequency and semantic networks to highlight robust public support and participation in regulatory measures. However, it also underscores significant regulatory loopholes, suggesting that regulations should not only focus on pricing but also consider public sentiment.

Semantic network analysis of the authority effect

The comment data of the authority effect are integrated to generate a semantic network, as shown in Fig. 5 , and the frequencies of the words are shown in Table 5 . (Li Lanjuan is an expert in infectious diseases and is one of the spokespersons representing the fight against the epidemic.)

Semantic network of the authority effect.

Figure 5 shows that when a person’s prestige is particularly high, the influence of his or her speech is greater than that of the general populace, and it is easier for him or her to gain trust. Therefore, rational use of the authority effect can effectively improve work efficiency. During the epidemic, Zhong Nanshan, Li Lanjuan, and other authoritative figures drew the public’s attention. The aged academicians Zhong Nanshan and Li Lanjuan who fought on the front line of the epidemic are knowledgeable and graceful, enjoying high public prestige. When officials release their speeches, the public is more willing to believe and obey. From the word frequencies in Table 4 , “Zhong Nanshan” and “Li Lanjuan” appeared 703 and 317 times, respectively, of which “Zhong Nanshan” ranked first, which shows the public’s attention given to public figures. It is not difficult for Semantic Networks that have experienced authority effects to find that people are more positive, including describing them as “cute”, which has appeared 451 times, hoping that they can do a good job in “protection”, which has appeared 378 times, and expressing understanding of their behavior. Most of the comments are positive and express their desire to return home from Hubei Province early. Overall, the effect of authority has a significant effect.

Semantic network analysis of sufficient materials

Figure 6 illustrates a semantic network derived from integrated comment data, with corresponding word frequencies detailed in Table 6 . During the epidemic, the assurance of adequate supplies provided significant comfort to affected populations. The analysis highlighted essential food items such as “cabbage”, “rice”, “beef and mutton”, and “potato”, which were frequently mentioned with 501, 332, 434, and 329 instances, respectively. Additionally, phrases such as “thank you” (461 mentions), “refueling” (321 mentions), and “waste” (308 mentions) were prominent. These findings indicate gratitude for material sources and concerns about food waste. People not only appreciate access to essential supplies but also demonstrate conscientiousness toward resource conservation. This mutual support fosters national cohesion. Overall, the impact of adequate supplies during the epidemic is evident, emphasizing positive sentiments without derogatory connotations.

Semantic network of sufficient materials.

Sentiment analysis based on online comments

Sentiment analysis can help netizens intuitively grasp their attitudes toward government intervention. The data collected in this paper are obtained from intervention plans for panic buying events, mainly in Hubei Province, the center of the epidemic, at the beginning of the COVID-19 pandemic. Comments belonging to Hubei were analyzed separately to observe the actual relationship between the effect of government intervention and the severity of the epidemic in the intervention area.

Data preprocessing

First, Python is used to reprocess the cleaned 80,280 comments, and the Jieba word segmentation package is adopted to segment the Weibo comments. To avoid redundant words and retain effective content, stopped words, including “on the other hand”, “here” and other conjunctions without actual emotional meaning, are removed, increasing the accuracy of the results of the sentiment analysis. For a bird’s-eye view comparison, out of an original cache of 1878 comments emanating from Hubei, a streamlined set of 1301 comments emerges postpurification.

Subsequently, the geographical origin of user comments becomes the focal point of our analysis, resulting in the illustrative Fig. 7 , in which the circle represents the user’s region. A darker color indicates more corresponding people. This indicates that, except for western regions such as Qinghai, Tibet, and Ningxia, comment volume is relatively harmonized across the vast expansion of other regions. Thus, even when narrowed down to a specific 1301 comments from Hubei, the analytical value of these comments remains undiminished and profoundly significant.

User distribution (dark color represents more users).

Sentiment analysis

Sentiment analysis in Python involves two dictionaries-a sentiment word dictionary and a degree word dictionary-primarily based on the HowNet Chinese sentiment dictionary. The sentiment dictionary is divided into positive and negative emotion words, while the degree word dictionary categorizes words such as “most”, “very”, “more”, “ish”, “insufficiently”, and “inverse”, each assigned specific weights for degree distinctions (Li et al., 2015 ). For instance, “most” carries a weight of 2, “very” is weighted at 1.5, and intriguingly, “inverse” is marked with −1.

The results of the sentiment analysis are summarized separately for non-Hubei areas in Fig. 8 and for the Hubei region in Fig. 9 . Positive sentiment is notably highest in the sufficient materials category, constituting 47.55% of favorable reactions. Conversely, initiative guidance shows the lowest positive sentiment at 25.26%. Conversely, initiative guidance evokes the highest negative sentiment at 38.81%, whereas sufficient materials record the lowest negative sentiment at 19.53%. This pattern indicates a public preference for sufficient material interventions over initiative guidance. In terms of neutral emotions, initiative guidance is the most common, suggesting that it polarizes public sentiment, while the authority effect elicits the least neutral response. Notably, negative emotions under initiative guidance significantly outweigh positive emotions, contrasting with better public perceptions in the other intervention categories.

The sentiment analysis results for the non-Hubei areas.

The sentiment analysis results for the Hubei region.

The average sentiment values further clarify the dataset’s collective sentiment. The sufficient materials category scores highest for positive sentiment, with a notable score of 2.3. Conversely, the authority effect and market regulation categories score lowest for negative sentiment, both at −2.2, indicating stronger negative public sentiment despite positive aspects. This discrepancy may stem from a perceived lack of market regulation or overly technical authoritative statements.

In sum, the sufficient materials category clearly garners the most public appreciation among the interventions. When these interventions are ranked based on positive public sentiment, the hierarchy is as follows: sufficient materials > authority effect > market regulation > initiative guidance.

As illustrated in Fig. 9 , the favorability of people in Hubei, the center of the epidemic, is also sorted as follows: sufficient materials > authority effect > market regulation > initiative guidance, which is consistent with the situation outside Hubei. However, it is obvious that the percentage of positive emotions for all types of interventions is greater than that in non-Hubei areas, indicating that people in high-risk areas are more eager for government control and have greater support for it than people in low-risk areas. A total of 56.48% of the participants reported having positive emotions toward such interventions, which indicates that more than half of the participants had positive emotions toward such interventions, and these positive emotions are significant.

In terms of the average emotion score, the highest positive score is 2.6 for both sufficient materials and initiative guidance, which is 0.3 higher than the highest score for non-Hubei areas, and the negative score is significantly lower. Conversely, the negative sentiment peaks at −2.6 for Hubei, which is a decrease of 0.4 from the non-Hubei areas, because people in the center of the epidemic have more intense emotional expressions and are more likely to experience extreme emotions. Given these insights, the government should fully consider the impact of risk level on panic rush behavior when controlling and developing intervention measures in conjunction with risk level.

Analyzing government intervention effects on panic buying using the LDA model

In our preceding analysis, we delved into the fundamental connections within the comment data and discerned emotional inclinations. To gain a deeper understanding of the impact of government intervention measures on panic buying, this section adopts the LDA topic model for semantic mining, further explores the correlation between texts, extracts the topic of the four intervention categories, and further analyzes the implementation effect under each category. At the same time, relevant variables are selected, and regression models are used to explore the factors that affect people’s perceptions of government intervention. Regression models are often applied for correlation analysis among multiple factors and are more appropriate for this section.

LDA topic number optimization

The analysis divides comments into four types, extracting positive and negative emotions based on emotion scores, resulting in 8 distinct comment sets. For each category, the average cosine similarity of positive and negative emotions is calculated. Figures 10 – 13 illustrate the findings:

Average cosine similarity change in the initiative guidance category.

Average cosine similarity change in the market regulation category.

Average cosine similarity change in the authority effect category.

Average change in the cosine similarity for the sufficient materials category.

Figure 10 shows that for initiative guidance, setting the LDA topic number ( K ) to 2 achieves the lowest average cosine similarity for positive comments. For negative comments, K values of 2 or 6 yield the lowest average cosine similarity.

Figure 11 reveals that in market regulation, ( K  = 3) results in the lowest average cosine similarity for positive comments, while ( K  = 4) achieves this for negative comments.

Figure 12 indicates that for the authority effect, K  = 2 yields the lowest average cosine similarity for both positive and negative comments.

Figure 13 demonstrates that in sufficient materials, ( K  = 3) or ( K  = 7) achieves the lowest average cosine similarity for positive comments, and ( K  = 3) for negative comments.

These insights guide the optimal selection of ( K) values for each category, facilitating more nuanced topic extraction and sentiment analysis from the comments.

Topic extraction and analysis

The LDA model, also known as the three-tiered Bayesian probability framework, encompasses documents, topics, and words. The model introduces Dirichlet’s principle, which has a strong generalization ability and is not prone to overfitting.

Taking the authoritative effect class (constituting comment set D , with d representing a comment in set D , hereinafter referred to as document d ) as an example, the main steps of topic extraction using LDA are as follows:

Step 1: Select the document that has been divided into words, and use the word sequence to represent \(d=({w}_{1},{w}_{2},\ldots {w}_{n})\) , where w represents the word, 1,2… n represents the word sequence number, and select a document with a prior probability \(P({w}_{i})\) . The Dirichlet distribution is used to create the topic distribution \({\varphi }_{d}\) , which is realized by using the hyperparameter α in the distribution function ( i represents the dimension, K represents the number of topics). The distribution formula used is:

where \(\varGamma \left(\cdot \right)\) represents the gamma function and K represents the number of topics.

Step 2: Each topic obeys a polynomial distribution, and the topic word z of document d is generated by sampling from the distribution, corresponding to the polynomial distribution of topics as follows.

Step 3: The word distributions for each topic are based on the Dirichlet distribution.

Step 4: Each word also obeys a polynomial distribution, and the keyword w under the topic is generated from the word polynomial distribution.

Using Python’s Gensim module, we applied LDA topic modeling to both the positive and negative comment datasets. After determining the optimal topic numbers, we conducted LDA analysis to identify recurrent words per topic. For negative comments in the “initiative guidance” category, given K -values of 2 and 6, we tested 2 and 6 topics, respectively, noting excessive word repetition at 6 topics. There were 2 topics with positive comments, as detailed in Table 7 .

Positive comments under “initiative guidance” highlighted two main dimensions: (1) trust in official statements and (2) reduced public anxiety about rational cognition. Negative comments indicated poor independent thinking, judgment, and misleading official information.

For “market regulation”, 3 topics were considered positive, and 4 were considered negative. The LDA analysis results for the keywords are detailed in Table 8 . Positives focused on timely government interventions (“response”, “timely”, “fast”), robust regulatory measures, and prompt departmental actions. Negatives cited inadequate regulation prone to repetition, network flaws (e.g., order cancellations), material safety concerns, and limited regulatory effectiveness. Market regulation remains a challenging, ongoing effort.

Utilizing LDA topic modeling, we analyzed the “authority effect” category with 2 topics each for positive and negative sentiments. Table 9 summarizes the key themes: positive sentiments emphasize trust in authority figures (“know”, “thank you”, “hard work”), and the social influence of authorities, notably “fans”. Negative comments critique those who challenge authority, spread misinformation, and highlight gaps in public understanding of disease, stressing the need for transparent and credible epidemic information.

In the “sufficient materials” category, after comparing K  = 3 and K  = 7 for positive comments, we settled on 3 topics due to lower cosine values indicating distinct topics. Table 10 reveals positive comments focusing on national unity in epidemic response, material security reducing panic, and increased patriotism. Negative sentiments highlight regional food disparities affecting disaster preparedness, excessive hoarding, and panic buying. Effective government supervision is crucial for managing hoarding and ensuring the equitable distribution of resources tailored to regional needs.

Analysis of factors affecting intervention effects based on multiple regression

Government interventions aim to create an environment where people can thrive, with public feedback guiding their refinement. Understanding public perception postimplementation is crucial for optimizing these measures. Emotions serve as a key indicator of public perception, and a regression analysis will explore factors influencing these emotions and inform intervention adjustments. Stepwise regression will help manage independent variables, ensuring that the analysis avoids collinearity issues.

Construction of the multiple regression model

The comprehensive dataset used in this section encapsulates various dimensions, from user-specific parameters such as ID; the number of followers, fans, posts and likes; contextual data such as follow-up comments; the timestamp of the original Weibo post; comment time; geographical attribution; and the core content of the comment, as illustrated in Fig. 14 . An evaluation system for government interventions under COVID-19 based on the literature (Chen et al., 2020 ) is constructed to reflect the effects of different types of interventions. The evaluation system includes 4 first-level indicators and 8 second-level indicators. These 8 indicators are obtained by directly crawling Weibo data or by expanding crawling data. The indicators at all levels and their meanings are summarized in Table 11 .

Crawled comments.

The dependent variable Y is obtained from emotion analysis to represent people’s perception of government intervention, and a multiple regression model is constructed as follows.

where \(\varepsilon\) represents the error term, \({b}_{0}\) represents the constant term, and \({b}_{i}\) ( i  = 1, 2, 3…,8) represents the respective variable coefficients.

Analysis of factors influencing the effect of intervention

Based on the regression model analyzed using SPSS 26, collinearity among independent variables was checked and found to be within acceptable limits (VIF < 10). The significance level for variable inclusion or exclusion was set at 0.1. The initial results in Table 12 indicate that variables X_1 and X_6 have p values less than 0.05, suggesting that their parameters are statistically significant and should be retained in the regression equation. The final regression results in Table 13 show that epidemic severity (X_1) alone explains 60.1% of the variance in public perception (Y), while together with follow-up comments (X_6), they explain 70% of the variance.

The findings indicate that as epidemic severity increases, there is a stronger public inclination toward robust government intervention and a heightened demand for transparent intervention strategies. This aligns with findings by Chen et al. ( 2022 ) that during severe epidemics, public behavior such as panic buying is significantly influenced by government actions. Negative correlations imply that greater public engagement on the topic increases susceptibility to negative emotions, which diminishes gradually over time.

Certain variables, such as the location of the epidemic center, number of user followers, number of user blogs, timeliness of commenting, and number of user likes, do not significantly influence the dependent variable-the emotional value of the public. This suggests that despite various indicators of user attention and engagement, most individuals maintain their own perspectives unaffected by these factors. The location of the epidemic center also does not appear to significantly impact public emotional responses, possibly due to a perceived similarity of experience among those affected Fig. 15 .

Regression normalization residuals.

The regression model requires that residuals follow a normal distribution for the analysis to be valid. SPSS tests confirm this requirement, ensuring the effectiveness of the regression analysis.

Figure 16 illustrates the temporal dynamics of online public discourse, specifically focusing on fluctuations in the number of comments over time. This reveals that there is a notable increase in public engagement within the first 3 h after the release of relevant reports, peaking between the 4th and 5th hours. Subsequently, the volume of public discussions gradually declines, stabilizing after approximately 8 h. This pattern indicates that governmental interventions typically occur preemptively within 4–5 h of report release, aiming to guide public opinion and mitigate potential consequences before discussions peak.

Variation in comment volume with time interval for each category.

Result analysis

This section provides a summary of the key findings of the paper, compares them with the conclusions of the literature, and proposes relevant recommendations. The paper concludes by highlighting the main contributions of this article.

Government prevention and control lag

From the background analysis of 2.3.1, it can be seen that when sorting the timeline of the epidemic development events in Hubei Province, a clear pattern appears, that is, the measures taken by the government lag behind. After the announcement of the lockdown, panic buying has taken root and spread, and relevant news has been released to assure the public of material abundance. At that time, large-scale panic buying had already occurred and the social impact was uncontrollable.

The problem of insufficient breadth and depth of market supervision urgently needs to be solved

In the analysis of 3.2 and 3.4, we deeply perceive that there are many shortcomings to be remedied in the aspects of breadth and depth of market supervision. Due to the limitations of the categories of regulated materials and the places, thorny problems such as repeated price increases frequently occur, seriously disrupting the normal order of the market.

The in-depth research conducted by Shan and Pi ( 2023 ) on government supervision shows that when the government chooses the “active supervision” strategy, its decision-making basis mainly depends on supervision costs and government credibility, rather than simply the “amount of fines”. This finding fully demonstrates that the starting point of government management is based on rationality and always puts the people’s interests first.

Market supervision plays a crucial and indispensable role in curbing panic buying. Herbon and Kogan ( 2022 ) emphasizes that market intervention can start from subsidizing enterprises and consumers, but it is restricted by the government’s financial pressure. Relevant departments should increase management efforts, optimize the system, refine the supervision plan, effectively intervene in the market, and deal with similar problems. The core lies in that market supervision should not only focus on solving current problems but also pay attention to the establishment of long-term mechanisms to enhance the self-regulatory ability and risk-resistance ability of the market and ensure that the market can still operate stably in the face of various impacts.

The effects of the adequate supplies category and the authority effect category are more remarkable

It can be clearly seen from the results of the 3.3 sentiment analysis that among various intervention measures, the effect of the adequate supplies category is the most outstanding, followed by the authority effect category. If the two can be ingeniously integrated and implemented, that is, when authoritative figures come forward to declare the sufficiency of supplies, it undoubtedly can arouse stronger and more positive public sentiments, and thus is expected to achieve more powerful and effective intervention results.

During the difficult period when the epidemic was rampant, the influence of those prestigious authoritative public figures far exceeded that of ordinary people (Ding, 2009 ). Renowned figures like Zhong Nanshan and Li Lanjuan not only have highly authoritative teams as support but also, with their rich practical experience and professional and precise interpretation abilities, can easily win high recognition and full trust from the public.

This phenomenon is not limited to the epidemic itself. Even in the face of a series of social phenomena such as panic buying caused by the epidemic, releasing news about the adequacy of supplies through influential figures at this special stage is highly likely to achieve unexpectedly good effects. The underlying principle is that when the public faces uncertainties and potential risks, they tend to seek guidance and security from authorities. When authoritative figures convey a clear signal of adequate supplies, it can greatly alleviate the anxiety and unease in the public’s hearts, thereby effectively suppressing the spread of panic sentiments and the irrational purchasing behaviors caused thereby.

There was a significant positive correlation between epidemic severity and public perception

In the analysis of 3.5, the severity of the epidemic can independently explain 60.1% of the changes in public perception. The correlation between the severity of the epidemic and the changes in public perception is the strongest, indicating that the public’s perception of government intervention is strongly affected by the severity of the epidemic. The more severe the epidemic is, the more eager the public is for government control, and the greater their perception of government intervention is. Prentice et al. ( 2021 ) found that government intervention and support influenced panic-buying participation.

When the government intervenes in response to panic buying, it can start with the initial break of the epidemic. On the one hand, timely disclosure of epidemic trends can alleviate public panic caused by unknown factors; on the other hand, it can actively promote the implementation of epidemic prevention and control measures and release relevant prevention and control news. In addition, the government can appropriately intervene and guide the internet, remove rumors, cut off sources of dissemination, correct the direction of public opinion in a timely manner, and guide the public to think rationally.

Practical implications

The practical significance of this paper lies in that it provides in-depth analysis and possible strategic directions for understanding and responding to the phenomenon of panic buying.

Firstly, it helps the government formulate and adjust intervention policies more precisely. By evaluating the effects of intervention measures and understanding the public attitude, the government can promptly identify the deficiencies of existing policies and thus formulate more targeted and effective strategies to better cope with similar emergencies. For example, at the beginning of 2020, a sudden COVID-19 outbreak occurred in a certain city, leading to the public’s panic buying of masks and disinfectants. Through monitoring the market situation and collecting public opinions, the government found that the previous measure of simply appealing to the public to buy rationally was ineffective. Based on the viewpoint proposed in this article that the intervention measures of the adequate supply category are effective, the government promptly coordinated local related enterprises to increase the production of masks and disinfectants and allocated materials from other places to increase market supply. At the same time, based on the viewpoint that the intervention measures of the authority effect category are effective, medical experts were invited to explain the scientific usage methods of masks and disinfectants and the material reserves of the city to the public through TV and online live broadcasts. As a result, within a short period of time, the public’s rush-buying behavior was alleviated, the prices of masks and disinfectants in the market gradually stabilized, and the supply was sufficient.

Secondly, the revelation that the severity of the epidemic is positively correlated with public perception provides strong theoretical support for the government’s crisis communication and public opinion guidance strategies. According to the dynamic changes of the epidemic, the government needs to formulate a phased and hierarchical information release and communication plan to meet the information needs of the public at different stages of the epidemic. For example, in the early days of the COVID-19 epidemic, the public had limited understanding of the epidemic, lacked a clear judgment on the direction of the situation, and the herd mentality was obvious. According to the development of the epidemic, the government will disclose the real data of the epidemic, prevention and control measures, and the allocation of medical resources to the public in stages. During the severe phase of the outbreak, the number of new cases increases significantly. At this time, the government’s medical resources are facing great pressure, and the people also have greater psychological pressure. The government updated the relevant information of the number of cases and medical resources in a timely and accurate manner, and explained the government’s active efforts to make the public clearly aware of the severe situation of the epidemic, so as to encourage the public to more consciously comply with the epidemic prevention and control measures and actively cooperate with the government’s prevention and control work; At the stage when the epidemic is under control, the trend of reducing new cases and related policies for resuming work and production will be announced in a timely manner. For example, some restaurants are gradually opening their dining services to the extent permitted by the government, and customers are taking precautions to maintain social distancing in accordance with regulations. The clear announcement of this policy change enabled the gradual return of social and economic activities to normal under the premise of safety. Through transparent, timely and accurate information transmission, the public’s understanding of the epidemic has gradually become rational, avoiding excessive panic and blind optimism, and thus better pooling social consensus to fight the epidemic together.

Conclusions

In summary, the analysis highlights several key aspects of the government’s response to the epidemic in Hubei. First, there was a noticeable lag in the implementation of prevention and control measures, leading to panic buying and uncontrollable social impacts. Second, interventions by authoritative figures and assurances about material sufficiency had a positive effect on public sentiment, emphasizing their influential role during the crisis. Third, market regulation has shown insufficient breadth and depth, resulting in issues such as repeated price increases. Finally, there was a significant positive correlation between the severity of the epidemic and public perception of government intervention, indicating that the more severe the epidemic was, the stronger the public’s desire for government control and perception of government involvement were.

Based on the data of public comments, this paper evaluates government intervention measures and draws some conclusions. However, it is imperative to recognize certain inherent limitations, providing avenues for further exploration:

This paper uses online comment data to analyze the effect of government intervention, mainly from the public’s perspective. Future studies can pivot toward an economic lens, drawing insights from the sales metrics of prominent retail outlets before and after the rollout of government initiatives in epidemic areas. In addition, online comment data may have certain limitations, although we chose Weibo, a platform with 230 million daily active users, to obtain as many as 80,000 comment data points as possible. However, a segment of the population, particularly those less digitally inclined, remains unrepresented in the online discourse. Therefore, further optimization can be carried out in the future to solve this problem.

While this study primarily anchors its analysis to data from the early stages of the epidemic, it is crucial to understand that government measures should be dynamic and adaptable to the evolving nature of the epidemic. It is necessary to consider the characteristics of epidemic development, grasp people’s emotional situation, and formulate relevant measures. Consequently, the insights gained from this research might not seamlessly translate to the subsequent phases of epidemic management in China.

While the digital realm is often celebrated for its perceived freedom, the reality is that internet censorship is a ubiquitous phenomenon characterized by global implementation. A salient aspect of such censorship is internet filtering, which blocks some illegal or sensitive words. Worldwide renowned filtering tools such as PureSight PC, CYBERsitter, SafeEyes, and CyberPatrol have been employed worldwide for this purpose. Filtering and blocking are normally performed on ISP (Internet Service Provider) servers. This shows that although cyberspace is free, speech is not 100% open. In the context of the People’s Republic of China, ISPs are the main body responsible for internet censorship, as are most other countries. Consequently, it is acknowledged that a minority of the comments might have been expunged, rendering them inaccessible for our analysis. However, the volume of data we amassed in this study mitigates this limitation, ensuring the robustness of our findings despite such omissions. Moving forward, subsequent research endeavors could focus on quantifying this specific impact, paving the way for improving the study’s robustness and raising additional credibility to its findings.

Data availability

The author confirms that all data generated or analyzed during this study are included in this published article. Furthermore, secondary sources and data supporting the findings of this study were all publicly available at the time of submission. Additional data related to this study can be found in the Supplementary Information submitted with this article.

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Acknowledgements

This research is supported by the Major Program of National Philosophy and Social Science Foundation of China (Grant No. 22&ZD162), Zhejiang Provincial Natural Science Foundation of China (Grant No. LY22G010004), as well as Zhejiang Gongshang University “digital +” discipline construction key project (Grant No. SZJ2022B019).

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School of Artificial Intelligence and Electronic Commerce, Zhejiang Gongshang University Hangzhou College of Commerce, Hangzhou, China

Tinggui Chen & Bing Wang

School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, China

Tinggui Chen & Yumei Jin

Department of Computer Science and Information Systems, University of North Georgia, Oakwood, GA, USA

Jianjun Yang

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Conceptualization: Tinggui and Bing Wang; methodology: Yumei Jin and Tinggui Chen; software: Yumei Jin; validation: Jianjun Yang; formal analysis, Yumei Jin and Jianjun Yang; data curation: Bing Wang; writing-original draft: Tinggui Chen, Yumei Jin and Bing Wang. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Bing Wang .

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Chen, T., Jin, Y., Wang, B. et al. The government intervention effects on panic buying behavior based on online comment data mining: a case study of COVID-19 in Hubei Province, China. Humanit Soc Sci Commun 11 , 1200 (2024). https://doi.org/10.1057/s41599-024-03725-8

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Received : 24 October 2023

Accepted : 05 September 2024

Published : 13 September 2024

DOI : https://doi.org/10.1057/s41599-024-03725-8

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