impact of covid 19 on employment essay

• Inside SC Johnson

COVID-19’s impact on work, workers, and the workplace of the future

What will the world of work look like, post COVID-19? A paper co-authored by Dyson School faculty member Kevin Kniffin along with 28 other researchers and scholars from around the world — “ COVID-19 and the Workplace: Implications, Issues, and Insights for Future Research and Action ” ( American Psychologist ) — includes a preview of how COVID-19 may change work practices in the long term and offers projections about the workplace of the future.

Kniffin and his co-authors took a broad view of the pandemic’s many impacts on the workplace, encapsulating existing research, predicting a few likely outcomes, and pointing to new questions worthy of study. “By organizing our experiences as researchers in a wide array of topical areas,” they wrote, “we present a review of relevant literatures along with an evidence-based preview of changes that we expect in the wake of COVID-19 for both research and practice.”

“‘Sensemaking’ was the first value generated by this extraordinary collaboration, which we undertook because of the extraordinary impacts associated with the emergence of COVID-19,” says Kniffin. “With so many dimensions of work and life changing rapidly in relation to COVID-19, a clear and succinct assessment was our first task—and a foundation for charting roadmaps for future research and action.”

A new normal: Working from home

When the pandemic hit the U.S. hard in March, millions of workers began working from home – an unprecedented and ongoing phenomenon “facilitated by the rise of connectivity and communication technologies,” Kniffin and his co-authors note in the paper.

The authors project that working from home will not only continue for many workers, but that “COVID-19 will accelerate trends towards working from home past the immediate impacts of the pandemic.” This will be driven, in part, as organizations recognize the health risks of open-plan offices. “As we now live and work in globally interdependent communities, infectious disease threats such as COVID-19 need to be recognized as part of the workscape,” write Kniffin et al. “To continue to reap the benefits from global cooperation, we must find smarter and safer ways of working together.” Organizations will also appreciate the cost-savings of replacing full-time employees with contractors who can stay connected digitally, note the authors.

In light of this anticipated shift, one goal of the paper is to guide future research to “examine whether and how the COVID-19 quarantines that required millions to work from home affected work productivity, creativity, and innovation.”

Best practices for high-functioning virtual teams

Virtual teams were already growing in number and importance pre-COVID-19, as noted in the paper. Now, many workers participate in a variety of remote teams, via synchronous and asynchronous digital communication. Since virtual teams are here to stay for many workers even post-pandemic, it’s important to recognize the challenges and adopt best practices. For example, the authors point out that “traditional teamwork problems such as conflict and coordination can escalate quickly in virtual teams” and offer recommendations based on prior research, including:

• Build structural scaffolds to mitigate conflicts, align teams, and ensure safe and thorough information processing.
• Formalize team processes, clarify team goals, and build-in structural solutions to foster psychologically safe discussions.
• Provide opportunities for non-task interactions among employees to allow emotional connections and bonding to continue among team members.

Greater appreciation for woman leaders?

“A feminine style of leadership might become recognized as optimal for dealing with crises in the future,” write Kniffin et al. They point to high-profile woman leaders who have grappled with COVID-19 effectively, including Angela Merkel, chancellor of Germany, and Tsai Ing-wen, president of Taiwan. And they list several feminine values and traits that can be effective in crisis management (pointing to the relevant research regarding each trait), including:

• a communal orientation in moral decision-making,
• higher sensitivity to risk, particularly about health issues,
• higher conscientiousness, and
• more attentive communication styles.

Creating roadmaps for new patterns of work

In addition to the sudden shift in working from home, “COVID-19 and the Workplace” touches on many other aspects of the pandemic’s impact on workers and organizations. They point to the economic, social, and psychological challenges and risks for workers deemed “essential” as well as for furloughed and laid-off workers. They touch on fundamental changes brought about in some industries, and new opportunities in others. Regarding impacts on workers, they discuss increases in economic inequality, social distancing and loneliness, stress and burnout, and addiction. The authors also refer to factors that moderate the impacts of workplace changes brought about by the pandemic, including age, race and ethnicity, gender, family status, personality, and cultural differences.

By drawing on existing research to help make sense of the crisis and highlighting topics ripe for new research, the authors hope to clear a path to guide studies focused on building positive, productive interactions that will aid in the ongoing transition to new patterns of work. “We hope that our effort will help researchers and practitioners take steps to manage and mitigate the negative effects of COVID-19 and start designing evidence-based roadmaps for moving forward.”

“When we started this project,” Kniffin added, “it wasn’t clear how long COVID-19 would persist as a force of disruption and destruction. As the pandemic has persisted, though, it’s increasingly clear that COVID-19 should be considered for its impact in relation to almost any work-related practice. On top of that, the many ways in which COVID-19 has variably and disparately impacted people and work around the world warrants close attention, concern, and action.”

• Organizational Behavior
• Thought Leadership

Tim Iorio, Ph.D.

I am working on a book concerning survival in Corporate America: Lessons Learned (my memoirs), including chapters on how COVID-19 has changed the landscape. Your research is needed and invaluable, and I look forward to following it. I will more than likely do some Qualitative Research myself on the subject. Thank you.

Rachel Frampton

From my point of view, businesses must invest in workplace covid management software that will protect their employees. Well, I agree with you that they must provide smarter and safer ways of working together. We also share the same opinion about the importance of providing virtual consultations and meetings.

Comments are closed.

The future of work after COVID-19

The COVID-19 pandemic disrupted labor markets globally during 2020. The short-term consequences were sudden and often severe: Millions of people were furloughed or lost jobs, and others rapidly adjusted to working from home as offices closed. Many other workers were deemed essential and continued to work in hospitals and grocery stores, on garbage trucks and in warehouses, yet under new protocols to reduce the spread of the novel coronavirus.

This report on the future of work after COVID-19 is the first of three MGI reports that examine aspects of the postpandemic economy. The others look at the pandemic’s long-term influence on consumption and the potential for a broad recovery led by enhanced productivity and innovation. Here, we assess the lasting impact of the pandemic on labor demand, the mix of occupations, and the workforce skills required in eight countries with diverse economic and labor market models: China, France, Germany, India, Japan, Spain, the United Kingdom, and the United States. Together, these eight countries account for almost half the global population and 62 percent of GDP.

Jobs with the highest physical proximity are likely to be most disrupted

Before COVID-19, the largest disruptions to work involved new technologies and growing trade links. COVID-19 has, for the first time, elevated the importance of the physical dimension of work. In this research, we develop a novel way to quantify the proximity required in more than 800 occupations by grouping them into ten work arenas according to their proximity to coworkers and customers, the number of interpersonal interactions involved, and their on-site and indoor nature.

This offers a different view of work than traditional sector definitions. For instance, our medical care arena includes only caregiving roles requiring close interaction with patients, such as doctors and nurses. Hospital and medical office administrative staff fall into the computer-based office work arena, where more work can be done remotely. Lab technicians and pharmacists work in the indoor production work arena because those jobs require use of specialized equipment on-site but have little exposure to other people (Exhibit 1).

We find that jobs in work arenas with higher levels of physical proximity are likely to see greater transformation after the pandemic, triggering knock-on effects in other work arenas as business models shift in response.

The short- and potential long-term disruptions to these arenas from COVID-19 vary. During the pandemic, the virus most severely disturbed arenas with the highest overall physical proximity scores: medical care, personal care, on-site customer service, and leisure and travel. In the longer term, work arenas with higher physical proximity scores are also likely to be more unsettled, although proximity is not the only explanation. For example:

• The on-site customer interaction arena includes frontline workers who interact with customers in retail stores, banks, and post offices, among other places. Work in this arena is defined by frequent interaction with strangers and requires on-site presence. Some work in this arena migrated to e-commerce and other digital transactions, a behavioral change that is likely to stick.
• The leisure and travel arena is home to customer-facing workers in hotels, restaurants, airports, and entertainment venues. Workers in this arena interact daily with crowds of new people. COVID-19 forced most leisure venues to close in 2020 and airports and airlines to operate on a severely limited basis. In the longer term, the shift to remote work  and related reduction in business travel, as well as automation of some occupations, such as food service roles, may curtail labor demand in this arena.
• The computer-based office work arena includes offices of all sizes and administrative workspaces in hospitals, courts, and factories. Work in this arena requires only moderate physical proximity to others and a moderate number of human interactions. This is the largest arena in advanced economies, accounting for roughly one-third of employment. Nearly all potential remote work is within this arena.
• The outdoor production and maintenance arena includes construction sites, farms, residential and commercial grounds, and other outdoor spaces. COVID-19 had little impact here as work in this arena requires low proximity and few interactions with others and takes place fully outdoors. This is the largest arena in China and India, accounting for 35 to 55 percent of their workforces.

COVID-19 has accelerated three broad trends that may reshape work after the pandemic recedes

The pandemic pushed companies and consumers to rapidly adopt new behaviors that are likely to stick, changing the trajectory of three groups of trends. We consequently see sharp discontinuity between their impact on labor markets before and after the pandemic.

Remote work and virtual meetings are likely to continue, albeit less intensely than at the pandemic’s peak

Perhaps the most obvious impact of COVID-19 on the labor force is the dramatic increase in employees working remotely. To determine how extensively remote work might persist after the pandemic, we analyzed its potential  across more than 2,000 tasks used in some 800 occupations in the eight focus countries. Considering only remote work that can be done without a loss of productivity, we find that about 20 to 25 percent of the workforces in advanced economies could work from home between three and five days a week. This represents four to five times more remote work than before the pandemic and could prompt a large change in the geography of work, as individuals and companies shift out of large cities into suburbs and small cities. We found that some work that technically can be done remotely is best done in person. Negotiations, critical business decisions, brainstorming sessions, providing sensitive feedback, and onboarding new employees are examples of activities that may lose some effectiveness when done remotely.

Some companies are already planning to shift to flexible workspaces after positive experiences with remote work during the pandemic, a move that will reduce the overall space they need and bring fewer workers into offices each day. A survey of 278 executives by McKinsey in August 2020 found that on average, they planned to reduce office space by 30 percent. Demand for restaurants and retail in downtown areas and for public transportation may decline as a result.

Remote work may also put a dent in business travel as its extensive use of videoconferencing during the pandemic has ushered in a new acceptance of virtual meetings and other aspects of work. While leisure travel and tourism are likely to rebound after the crisis, McKinsey’s travel practice estimates that about 20 percent of business travel, the most lucrative segment for airlines, may not return. This would have significant knock-on effects on employment in commercial aerospace, airports, hospitality, and food service. E-commerce and other virtual transactions are booming.

Many consumers discovered the convenience of e-commerce and other online activities during the pandemic. In 2020, the share of e-commerce grew at two to five times the rate before COVID-19 (Exhibit 2). Roughly three-quarters of people using digital channels for the first time during the pandemic say they will continue using them when things return to “normal,” according to McKinsey Consumer Pulse  surveys conducted around the world.

Other kinds of virtual transactions such as telemedicine, online banking, and streaming entertainment have also taken off. Online doctor consultations through Practo, a telehealth company in India, grew more than tenfold between April and November 2020 . These virtual practices may decline somewhat as economies reopen but are likely to continue well above levels seen before the pandemic.

This shift to digital transactions has propelled growth in delivery, transportation, and warehouse jobs. In China, e-commerce, delivery, and social media jobs grew by more than 5.1 million during the first half of 2020.

COVID-19 may propel faster adoption of automation and AI, especially in work arenas with high physical proximity

Two ways businesses historically have controlled cost and mitigated uncertainty during recessions are by adopting automation and redesigning work processes, which reduce the share of jobs involving mainly routine tasks. In our global survey of 800 senior executives  in July 2020, two-thirds said they were stepping up investment in automation and AI either somewhat or significantly. Production figures for robotics in China exceeded prepandemic levels by June 2020.

Many companies deployed automation and AI in warehouses, grocery stores, call centers, and manufacturing plants to reduce workplace density and cope with surges in demand. The common feature of these automation use cases is their correlation with high scores on physical proximity, and our research finds the work arenas with high levels of human interaction are likely to see the greatest acceleration in adoption of automation and AI.

The mix of occupations may shift, with little job growth in low-wage occupations

The trends accelerated by COVID-19 may spur greater changes in the mix of jobs within economies than we estimated before the pandemic.

We find that a markedly different mix of occupations may emerge after the pandemic across the eight economies. Compared to our pre-COVID-19 estimates, we expect the largest negative impact of the pandemic to fall on workers in food service and customer sales and service roles, as well as less-skilled office support roles. Jobs in warehousing and transportation may increase as a result of the growth in e-commerce and the delivery economy, but those increases are unlikely to offset the disruption of many low-wage jobs. In the United States, for instance, customer service and food service jobs could fall by 4.3 million, while transportation jobs could grow by nearly 800,000. Demand for workers in the healthcare and STEM occupations may grow more than before the pandemic, reflecting increased attention to health as populations age and incomes rise as well as the growing need for people who can create, deploy, and maintain new technologies (Exhibit 3).

Before the pandemic, net job losses were concentrated in middle-wage occupations in manufacturing and some office work, reflecting automation, and low- and high-wage jobs continued to grow. Nearly all low-wage workers who lost jobs could move into other low-wage occupations—for instance, a data entry worker could move into retail or home healthcare. Because of the pandemic’s impact on low-wage jobs, we now estimate that almost all growth in labor demand will occur in high-wage jobs. Going forward, more than half of displaced low-wage workers may need to shift to occupations in higher wage brackets and requiring different skills to remain employed.

As many as 25 percent more workers may need to switch occupations than before the pandemic

Given the expected concentration of job growth in high-wage occupations and declines in low-wage occupations, the scale and nature of workforce transitions required in the years ahead will be challenging, according to our research. Across the eight focus countries, more than 100 million workers, or 1 in 16, will need to find a different occupation by 2030 in our post-COVID-19 scenario, as shown in Exhibit 4. This is 12 percent more than we estimated before the pandemic, and up to 25 percent more in advanced economies (Exhibit 4).

Before the pandemic, we estimated that just 6 percent of workers would need to find jobs in higher wage occupations. In our post-COVID-19 research, we find not only that a larger share of workers will likely need to transition out of the bottom two wage brackets but also that roughly half of them overall will need new, more advanced skills to move to occupations one or even two wage brackets higher.

The skill mix required among workers who need to shift occupations has changed. The share of time German workers spend using basic cognitive skills, for example, may shrink by 3.4 percentage points, while time spend using social and emotional skills will increase by 3.2 percentage points. In India, the share of total work hours expended using physical and manual skills will decline by 2.2 percentage points, while time devoted to technological skills will rise 3.3 percentage points. Workers in occupations in the lowest wage bracket use basic cognitive skills and physical and manual skills 68 percent of the time, while in the middle wage bracket, use of these skills occupies 48 percent of time spent. In the highest two brackets, those skills account for less than 20 percent of time spent. The most disadvantaged workers may have the biggest job transitions ahead, in part because of their disproportionate employment in the arenas most affected by COVID-19. In Europe and the United States, workers with less than a college degree, members of ethnic minority groups, and women are more likely to need to change occupations after COVID-19 than before. In the United States, people without a college degree are 1.3 times more likely to need to make transitions compared to those with a college degree, and Black and Hispanic workers are 1.1 times more likely to have to transition between occupations than white workers. In France, Germany, and Spain, the increase in job transitions required due to trends influenced by COVID-19 is 3.9 times higher for women than for men. Similarly, the need for occupational changes will hit younger workers more than older workers, and individuals not born in the European Union more than native-born workers.

Companies and policymakers can help facilitate workforce transitions

The scale of workforce transitions set off by COVID-19’s influence on labor trends increases the urgency for businesses and policymakers to take steps to support additional training and education programs for workers. Companies and governments exhibited extraordinary flexibility and adaptability in responding to the pandemic with purpose and innovation that they might also harness to retool the workforce in ways that point to a brighter future of work.

Businesses can start with a granular analysis of what work can be done remotely by focusing on the tasks involved rather than whole jobs. They can also play a larger role in retraining workers, as Walmart, Amazon, and IBM have done. Others have facilitated occupational shifts by focusing on the skills they need, rather than on academic degrees. Remote work also offers companies the opportunity to enrich their diversity by tapping workers who, for family and other reasons, were unable to relocate to the superstar cities where talent, capital, and opportunities concentrated before the pandemic.

Policymakers could support businesses by expanding and enhancing the digital infrastructure. Even in advanced economies, almost 20 percent of workers in rural households lack access to the internet. Governments could also consider extending benefits and protections to independent workers and to workers working to build their skills and knowledge mid-career.

Both businesses and policymakers could collaborate to support workers migrating between occupations. Under the Pact for Skills established in the European Union during the pandemic, companies and public authorities have dedicated €7 billion to enhancing the skills of some 700,000 automotive workers, while in the United States, Merck and other large companies have put up more than $100 million to burnish the skills of Black workers without a college education and create jobs that they can fill.

The reward of such efforts would be a more resilient, more talented, and better-paid workforce—and a more robust and equitable society.

Go behind the scenes and get more insights with “ Where the jobs are: An inside look at our new Future of Work research ” from our New at McKinsey blog.

Susan Lund and Anu Madgavkar are partners of the McKinsey Global Institute, where James Manyika and Sven Smit are co-chairs and directors. Kweilin Ellingrud is a senior partner in McKinsey’s Minneapolis office. Mary Meaney is a senior partner in the Paris office. Olivia Robinson is a consultant in the London office.

This report was edited by Stephanie Strom, a senior editor with the McKinsey Global Institute, and Peter Gumbel, MGI editorial director.

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• Open access
• Published: 17 April 2021

Impact of the COVID-19 crisis on work and private life, mental well-being and self-rated health in German and Swiss employees: a cross-sectional online survey

• Martin Tušl 1 ,
• Rebecca Brauchli 1 ,
• Philipp Kerksieck 1 &
• Georg Friedrich Bauer 1  

BMC Public Health volume  21 , Article number:  741 ( 2021 ) Cite this article

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The COVID-19 crisis has radically changed the way people live and work. While most studies have focused on prevailing negative consequences, potential positive shifts in everyday life have received less attention. Thus, we examined the actual and perceived overall impact of the COVID-19 crisis on work and private life, and the consequences for mental well-being (MWB), and self-rated health (SRH) in German and Swiss employees.

Cross-sectional data were collected via an online questionnaire from 2118 German and Swiss employees recruited through an online panel service (18–65 years, working at least 20 h/week, various occupations). The sample provides a good representation of the working population in both countries. Using logistic regression, we analyzed how sociodemographic factors and self-reported changes in work and private life routines were associated with participants’ perceived overall impact of the COVID-19 crisis on work and private life. Moreover, we explored how the perceived impact and self-reported changes were associated with MWB and SRH.

About 30% of employees reported that their work and private life had worsened, whereas about 10% reported improvements in work and 13% in private life. Mandatory short-time work was strongly associated with perceived negative impact on work life, while work from home, particularly if experienced for the first time, was strongly associated with a perceived positive impact on work life. Concerning private life, younger age, living alone, reduction in leisure time, and changes in quantity of caring duties were strongly associated with perceived negative impact. In contrast, living with a partner or family, short-time work, and increases in leisure time and caring duties were associated with perceived positive impact on private life. Perceived negative impact of the crisis on work and private life and mandatory short-time work were associated with lower MWB and SRH. Moreover, perceived positive impact on private life and an increase in leisure time were associated with higher MWB.

The results of this study show the differential impact of the COVID-19 crisis on people’s work and private life as well as the consequences for MWB and SRH. This may inform target groups and situation-specific interventions to ameliorate the crisis.

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Key findings

31% of employees perceived a negative impact of the crisis on their work life. Mandatory short-time workers and those who lost their job felt the negative impact the most.

10% of employees perceived a positive impact of the crisis on their work life. Those working in home-office, particularly if experienced for the first time, felt the positive impact the most.

30% of employees perceived a negative impact of the crisis on their private life. Living in a single household, reduction in leisure time, and changes in quantity of caring duties (i.e., increase or decrease) were strongly associated with the negative impact.

13% of employees perceived a positive impact on their private life. Living with a partner or family, mandatory short-time work, increases in leisure time and caring duties were strongly associated with the positive impact.

Perceived negative impact of the crisis on work and private life and mandatory short-time work were strongly associated with lower mental well-being and self-rated health.

Perceived positive impact of the crisis on private life and an increase in leisure time were strongly associated with higher mental well-being and, for leisure time, also with higher self-rated health.

Targeted interventions for vulnerable groups should be established on a company/governmental levels such as psychological first aid accessible online or rapid financial aids for those who have lost their income partially or completely.

Companies may consider offering positive psychology trainings to employees to help them purposefully focus on and make use of the beneficial consequences of the crisis. Such trainings may also include workshops on optimal crafting of their work and leisure time during the pandemic.

On January 30, 2020, the World Health Organization (WHO) declared the outbreak of COVID-19 a Public Health Emergency of International Concern (PHEIC) [ 1 ]. In the following weeks, the virus quickly spread worldwide, forcing the governments of affected countries to implement lockdown measures to decrease transmission rates and prevent the overload of hospital emergency rooms. Switzerland entered full lockdown on March 16th, Germany followed 6 days later on March 22nd. Restrictive measures in both countries were comparable and included border controls, closing of schools, markets, restaurants, nonessential shops, bars, entertainment and leisure facilities, as well as ban on all public and private events and gatherings [ 2 , 3 ]. Such strict measures were in place until the end of April when both governments started to gradually ease the measures [ 4 , 5 ]. Consequently, much of the working population suddenly faced drastic changes to everyday life. People who commuted to work and had rich social lives outside their homes found themselves in a mandatory work from home (WFH) situation, many employees were furloughed or laid off as various businesses and industries had to shut down, and health workers in emergency rooms as well as supermarket staff and other essential employees were faced with a dramatic increase in workload and job strain [ 6 , 7 ].

Regarding the public health impact of the COVID-19 crisis, several studies suggest that working conditions have deteriorated and that employees are more likely to experience mental health problems, such as stress, depression, and anxiety [ 8 , 9 , 10 , 11 ]. In particular, women, young adults, people with chronic diseases, and those who have lost their jobs as a result of the crisis seem to be the most affected [ 11 , 12 , 13 , 14 ]. One of the common stressors that research has highlighted is the fear of losing one’s job and, consequently, one’s income [ 7 ]. Moreover, social isolation, conflicting messages from authorities, and an ongoing state of uncertainty have been described as some of the main factors contributing to emotional distress and negatively affecting mental health and well-being [ 8 , 14 , 15 , 16 , 17 , 18 ].

In the European context, Eurofound [ 12 ] released a report on research in April 2020 involving 85,000 participants across 27 EU member countries. The data indicate that the EU population experienced high levels of loneliness, low levels of optimism, insecurity regarding their jobs and financial future, as well as a decrease in well-being. Germany scored slightly below the EU27 average in well-being, and there is further evidence that it decreased significantly in the early stages of the COVID-19 pandemic, between March 2020 and May 2020 [ 19 ]. The Eurofound report does not discuss Switzerland; however, other studies suggest that there has been an increase in emotional distress in Swiss young adults [ 20 ] and that undergraduate students have experienced higher levels of stress, depression, anxiety, and loneliness compared to the time before the COVID-19 outbreak [ 14 ]. A Swiss social monitor study reports that over 40% of Swiss adults perceive a worsened quality of life compared to before the pandemic, 10% experience feelings of loneliness, 10% report fear of losing their job, and about 1% lost their job as a result of the pandemic. The report also indicates an increase in WFH by 29% compared to before the pandemic [ 21 ].

Accordingly, the data from Eurofound [ 12 ] also suggest that European employees have experienced a dramatic increase in WFH. About 37% of the EU working population transitioned to WFH as a result of the pandemic, and 24% WFH for the first time. Before the pandemic, employees had considered remote working a benefit when it followed their preferences. However, the COVID-19 lockdown changed this by forcing many employees into mandatory WFH [ 6 ]. This posed various challenges for employees without prior WFH experience, such as organizing the workspace, establishing new communication channels with colleagues, coping with work isolation, or managing boundaries between work and non-work [ 22 , 23 , 24 ]. Without proper support from the employer or insufficient resources to manage these challenges, mandatory WFH may become a burden that negatively affects employees’ well-being [ 8 ] and, in turn, their performance [ 22 ]. Furthermore, the increase in WFH has been highlighted as a potential threat to parents with small children at home, as this group is likely to experience difficulties in combining work duties with home schooling and household chores [ 12 , 23 ].

Indisputably, the COVID-19 pandemic has had a strong impact on many aspects of our lives and will continue to do so for months and years to come. However, the consequences of the crisis and societal reactions to the challenges posed by the virus are not deemed solely negative. The new situation also holds opportunities for positive shifts in our work and private lives that were impossible before the COVID-19 crisis. Many may see this crisis as an opportunity to learn how to cope with profound changes in everyday life and even to adopt new pro-active behaviors. For instance, some employees may discover that the new ways of working (e.g., WFH) facilitate more productivity and are more satisfying compared to working in an office [ 25 ]. Data collected from employees in Denmark and Germany between March and May 2020 [ 26 ] suggest that 71% of respondents felt informed and well prepared for the changing work situation and WFH. Participants also reported several advantages of working from home, such as perceived control over the workday, working more efficiently, or saving time previously spent commuting. In contrast, some reported disadvantages of WFH included social isolation, loss of the value of work, and a lack of important work equipment. Nonetheless, respondents reported overall relatively more positive experiences of WFH than negative ones. Thus, we argue that more balanced studies are needed that examine both the negative and positive impact of the COVID-19 crisis on peoples’ lives, health, and well-being, considering differential effects in diverse subgroups. Such studies have the potential to conclude how to diminish the negative and enhance the positive outcomes of the current and future pandemic-related crises in the working population.

Aim and objectives

The overall aim of the present study was to examine the actual and perceived overall impact of the COVID-19 crisis on employees’ work and private life, along with its consequences for mental well-being (MWB) and self-rated health (SRH) in the German and Swiss working populations. Specifically, we pursued the following objectives:

To investigate the perceived positive and negative impact of the COVID-19 crisis on work and private life as well as to assess the self-reported changes in work and private life routines induced by the crisis.

To examine which sociodemographic variables and which self-reported changes in work and private life routines are associated with perceived positive and negative impact of the COVID-19 crisis on work and private life.

To investigate how the self-reported changes and perceived overall impact of the COVID-19 crisis on work and private life are associated with MWB and SRH as relevant health outcomes.

Although SRH has been identified as a relevant predictor of mental distress during the COVID-19 pandemic [ 10 , 27 ], to our knowledge, it has not been studied as an outcome variable in combination with MWB indicators as in our study.

The present study used a cross-sectional online survey design. We report our study following the STROBE guidelines for cross-sectional studies [ 28 ], and the checklist for reporting results of internet e-surveys (CHERRIES) [ 29 ], see ‘Additional file  1 .pdf’ in supplementary material.

Participants were recruited through a panel data service Respondi ( respondi.com ). Cross-sectional data were collected from employees in Germany and Switzerland via an online questionnaire using a web-based survey provider SurveyGizmo. The questionnaire was tested and checked by senior researchers from the field for face validity prior to the administration. The period of data collection was from 9th to 22nd April 2020, when both countries were in full lockdown as part of the control measures relating to COVID-19. Participants received a minimal incentive for completing the survey (i.e., points which could be redeemed towards a given service after participating in several surveys). Participation was voluntary and participant anonymity and confidentiality of their data were assured and emphasized. Each participant in the online panel service database had a unique code which ensured anonymity and prevented multiple submissions from one participant. Important items in the survey were mandatory and participants were informed if they accidently skipped an item. Further, the questionnaire used a logic to avoid asking redundant or non-applicable questions (e.g., participants who indicated that they lost their job were not asked about the change in working time or home-office). Moreover, we included several disqualifying items (i.e., “Please choose number three as an answer to this item”) as a quality check to exclude participants who would give random answers. Participants were able to go back in the survey and review or change their answers.

The eligibility criteria were: being employed (not self-employed), working more than 20 h per week, and being within the age range of 18 to 65 years. The final sample included 2118 participants. Figure  1 shows a flow diagram describing how the final sample was achieved.

Sample flow diagram

Sociodemographic characteristics of the sample are shown in Table  1 : the mean age was 46.51 years ( SD  = 11.28), 5% completed primary, 58% secondary, and 37% tertiary education, Footnote 1 55% were male, 77% were from Germany, and 72% were living with a partner, family, or in a shared housing.

Overall, in terms of age, education, and living situation (i.e., single households), the study sample seems to be a good representation of the target of the working population in Germany ( www.destatis.de ) and Switzerland ( www.bfs.admin.ch ). In general, males were slightly overrepresented in our sample (56%) compared to the general population (52%); however, the proportion of males in both countries did not differ significantly (56% from Germany, 52% from Switzerland), χ 2 (1) = 1.63, p  = 0.201.

Perceived overall impact of COVID-19 on work and private life

Assuming that both improvements and deteriorations can simultaneously occur due to COVID-19, we designed four separate items (see ‘Additional file  2 .pdf’ in supplementary material) to assess participants’ subjective evaluation of the overall impact of the COVID-19 crisis on their work and private lives: “The Corona-crisis has (a) worsened my work life; (b) improved my work life; (c) worsened my private life; (d) improved my private life.” The response scale ranged from 1 =  strongly disagree to 5 =  strongly agree . As a primer to this question, we defined the Corona-crisis as follows:

“The following questions deal directly with the current COVID-19 (Corona) pandemic and the consequent regulations from the government (i.e., business closures, school closures, event bans, contact reduction in public spaces, etc.). Hereafter, we refer to this collectively as the Corona-crisis. Please compare your current situation with the situation as it was before the government regulations.”

Changes in work and private life routines

The following items examined qualitative and quantitative changes in participants’ work and private life routines resulting from the COVID-19 crisis: (a) change in employment contract ( no change ; short-time work Footnote 2 with a reduced contract ; short-time work with a contract reduced to 0 h ; job loss ); (b) proportion of WFH before and after COVID-19 ( 0 to 100% ; participants were grouped into three categories according to their answers: None , Experienced , New Footnote 3 ); (c) changes in quantity of working time,; (d) changes in quantity of leisure time; and (e) changes in quantity of caring duties. The response scale for items c, d, and e ranged from 1 =  strongly decreased to 5 =  strongly increased . For the statistical analysis, responses were grouped into three categories: decreased (1 + 2), unchanged (3), increased (4 + 5).

• Mental well-being

MWB was assessed with the Warwick-Edinburgh Mental Well-Being Scale (WEMWBS) [ 30 ]. Specifically, we used the German translation of the 7-item short version of the WEMWBS [ 31 ]. WEMWBS is a measure of MWB capturing the positive aspects of mental health, namely, positive affect (feelings of optimism, relaxation), satisfying interpersonal relationships, and positive functioning (clear thinking, self-acceptance, competence, autonomy). The response scale ranged from 1 =  never to 5 =  all the time . For the statistical analysis (i.e., ordinal logistic regression model), we grouped participants into six categories according to their overall score in percentiles (10, 25, 50, 75, 90, 99%).

• Self-rated health

SRH was assessed with a single item: “In general, how would you evaluate your health?” [ 32 ]. The response scale ranged from 1 =  very bad to 5 =  very good . The application of single-item measures for self-evaluated health is a gold standard in public health research [ 33 ].

Statistical analysis

Data analysis was carried out using R version 4.0.2. In the first step, four ordinal logistic regression models using polr from the MASS R package [ 34 ] were fitted to assess associations of the perceived overall impact of COVID-19 on work and private life as outcome variables with sociodemographic factors (gender, age, country, living situation) and factors related to changes in work and private life routines (changes in employment contract, WFH, work time, leisure time, caring duties) as independent variables. To verify that there was no multicollinearity, the variables were tested a priori using the variance inflation factor tested vif from the car R package [ 35 ] (VIF < 2). The results are presented as adjusted odds ratio (OR) with 95% confidence intervals (95% CI) interpreted as the OR of reporting a higher level of the impact compared to the reference category.

Further, two additional ordinal logistic regression models were fitted to investigate the association between the perceived overall impact of COVID-19 on work and private life Footnote 4 and the self-reported changes in work and private life routines as independent variables and MWB with SRH as outcome variables. In both models, we also controlled for possible confounders (gender, age, country, living situation). The results are presented as adjusted OR with 95% CI interpreted as the OR of reporting a higher level of MWB/SRH compared to the reference category.

Figure  2 displays the correlations between the analyzed variables. Education was not included in the regression models due to missing data (see details in the Methods section).

Correlation matrix of the analyzed variables. Note: Only correlations with p  < 0.01 displayed; Gender (1 = Female, 2 = Male); Country (1 = Germany, 2 = Switzerland); Education (1 = Primary, 2 = Secondary, 3 = Tertiary); Living situation (1 = Alone, 2 = With partner/family); Contract change (1 = No change, 2 = Short-time reduced, 3 = Short-time 0, 4 = Job loss); Home-office (1 = None, 2 = Experienced, 3 = New)

Perceived overall impact of COVID-19 crisis and self-reported changes in work and private life routines

Figure  3 shows the results for the four items related to the perceived overall impact of the COVID-19 crisis on work and private life. Thirty-one percent of participants (strongly) agreed that their work life had worsened and 30% (strongly) agreed that their private life had worsened. In contrast, 10% (strongly) agreed that their work life had improved and 13% (strongly) agreed that their private life had improved as a result of the COVID-19 crisis.

Perceived impact on work and private life and self-reported changes in work time, leisure time, and caring duties. Note: Total percentage does not always equal 100% due to rounding error

Further, Fig.  3 shows self-reported changes with regard to the quantity of time actually spent in work and private life. Work time decreased for 38%, leisure time increased for 36%, while the amount of caring duties changed for 26% of participants.

Figures  4 and 5 show self-reported changes with regard to contracted working hours and home-office. Twenty-eight percent of participants experienced a change in their employment contract, while 27% were affected by mandatory short-time work, 1% lost their job as a result of the COVID-19 crisis. Fifty-one percent reported to WFH and of those, 20% reported doing so for the first time.

Self-reported changes in home-office. Note: None = 0% WFH before COVID-19, 0% after; Experienced = at least 10% WFH before and at least 10% after COVID-19; New = 0% WFH before and at least 10% after COVID-19

Self-reported changes in contracted working hours. Note: Short-time reduced = work hours temporarily partly reduced by employer; Short time 0 = work hours temporarily reduced to 0 by employer

Factors associated with perceived impact on work life

Table  2 shows OR comparisons between different subgroups concerning their evaluation of the degree to which their work life had worsened or improved due to the COVID-19 crisis, assessed by two separate dependent variables. Regarding perceived negative impact on work life, change in employment contract demonstrated the highest OR of reporting a deterioration of work life. The association was particularly strong in participants who had their contract reduced to mandatory short-time work with 0 working hours (OR = 9.72) and in those who had lost their job (OR = 35.07). Further, participants who reported a change in their work time had a significantly higher OR of reporting a deterioration of work life (OR = 2.95; 2.06). Finally, changes in leisure time and increased caring duties were significantly associated with perceived deterioration of work life. This association was particularly strong for a decrease in leisure time (OR = 1.62) and an increase in caring duties (OR = 1.58).

Regarding perceived positive impact of COVID-19 on work life, WFH had the highest OR of reporting an improvement in work life. The association was particularly strong in those who had started to WFH for the first time (OR = 2.77). Increase in leisure time was also significantly associated with a positive impact on work life. Further, older employees in the 51–60 and 61–65 age groups had significantly lower odds of reporting a positive impact of COVID-19 on work life (OR = 0.71; 0.61), as well as short-time employees, in particular those with a contract reduced to 0 working hours (OR = 0.53), and those who reported a decrease in work time (OR = 0.61).

Factors associated with perceived impact on private life

Table 2 further shows OR comparisons within different subgroups concerning their evaluation of the degree to which their private life had worsened or improved due to the COVID-19 crisis, assessed by two separate dependent variables. Regarding perceived negative impact on private life, the subgroup of participants living with a partner, family, or in a shared housing had significantly lower odds of reporting the deterioration of their private life compared to those living alone (OR = 0.41). The odds of reporting deterioration of private life were lower also for the 61–65 age group (OR = 0.58). Finally, changes in the quantity of leisure time and quantity of caring duties were associated with perceived deterioration of private life, and this association was particularly strong for a decrease in leisure time (OR = 2.62) and a decrease in caring duties (OR = 1.62).

Regarding perceived positive impact on private life, the strongest association was with an increase in leisure time (OR = 2.25), followed by living with a partner, family, or in a shared housing (OR = 1.74); WFH, particularly among those with prior WFH experience (OR = 1.72); and with an increase in caring duties (OR = 1.33). Short-time workers had significantly higher odds of reporting a positive impact on their private life compared to workers without any change, especially those with a contract reduced to 0 working hours (OR = 1.57).

Association between the perceived impact, self-reported changes, mental well-being and self-rated health

Table  3 shows the results of the associations between perceived overall impact, the self-reported changes in work and private life routines, and relevant health outcomes in terms of MWB and SRH, controlled for various sociodemographic variables. Regarding the perceived overall impact, participants who (strongly) agreed that COVID-19 had worsened their work life reported significantly lower MWB (OR = 0.61) compared to those who (strongly) disagreed. In addition, participants who neither agreed nor disagreed that their work life had worsened reported lower MWB (OR = 0.71) compared to those who (strongly) disagreed. A strong negative association could also be seen regarding perceived negative impact on private life: participants who (strongly) agreed that their private life had worsened reported lower MWB (OR = 0.62) and SRH scores (OR = 0.67) compared to those who (strongly) disagreed. Both outcomes were also negatively associated with employees who neither agreed nor disagreed that their private life had worsened (OR = 0.80; 0.66) compared to those who (strongly) disagreed. Finally, participants who (strongly) agreed that their private life had improved as a result of the COVID-19 crisis had higher odds of reporting a higher MWB score (OR = 1.39) compared to those who (strongly) disagreed.

Regarding the impact of the self-reported changes in work and private life routines, mandatory short-time workers with a contract reduced to 0 working hours reported significantly lower MWB (OR = 0.57) and SRH (OR = 0.49) compared to participants without any change in their employment contract. In contrast, an increase in leisure time was positively associated with both better MWB (OR = 1.23) and SRH (OR = 1.45).

The present study aimed to examine the impact of the COVID-19 crisis on employees’ work and private life and the consequences for MWB and SRH in German and Swiss employees. The first objective of the study was to assess the perceived impact and self-reported changes related to COVID-19. Although the research has thus far mostly emphasized the negative impact of the COVID-19 crisis [ 9 , 10 , 11 , 12 , 36 ], our data show that more than 40% of participants perceived no negative changes and over 10% even positive shifts in both life domains. This can be partly explained by the experienced changes in daily routines: 28% of participants were affected by a change in their employment contract and 49% by changes in the quantity of work time, confirming almost identical findings for Germany in the Eurofound report [ 12 ]. Also, quantity of leisure time and of caring duties changed for 58 and 26% respectively. The finding that about half WFH at least part of their working time, and 20% for the first time is also in line with Eurofound’s data where 24% reported WFH for the first time [ 12 ]. Overall, the proportion of people affected by changes in work and private life is comparable but hardly exceeds 50%, similar to the proportion of participants who reported a deterioration in their work and private life.

The second objective was to explore the factors associated with perceived impact on work and private life. A change in contracted work hours (i.e., mandatory short-time work, job loss), and changes in work time were strongly associated with reporting deterioration of work life. Those affected by short-time work experienced a significant disruption in their work routine as well as fear of losing the job, factors associated with increased level of distress and low MWB [ 7 ]. In consequence, employees whose contract had been reduced or terminated due to the lockdown measures are particularly vulnerable to developing mental health problems [ 11 , 13 ]. Further, an increase in caring duties, and, perhaps more surprisingly, increase and decrease in leisure time were strongly associated with perceived deterioration of work life. Such changes in private life routines may require efforts for readjustments that can interfere with work and work-life balance. These readjustments may be particularly difficult for older employees (i.e., age group 61–65) who were more likely to report deterioration of their work life. They may be particularly sensitive to changes in daily structure and less flexible in adapting to a new situation, such as mandatory WFH, less personal contact with colleagues, and an increase in the use of digital technology.

WFH was most strongly associated with perceived positive impact of the COVID-19 crisis on work life, particularly in those reporting WFH for the first time, supporting evidence from Ipsen and colleagues [ 26 ]. This positive impact of WFH may be explained by a reduction or absence of commute time, more job autonomy, more flexible workdays, and ultimately, extra time for leisure. In fact, increased leisure time was another important factor associated with perceived positive impact of the COVID-19 crisis on work life. More time for leisure may allow for better recovery from work and rebuilding of personal resources [ 37 , 38 ], which can then help an individual deal with work demands. In contrast, a change in contracted working hours and a decrease in work time were negatively associated with perceived positive impact on work life. A reduction in work time may not only cause financial problems, but also reduces important daily routines and social interactions at work, and may trigger fear of losing one’s job. Again, older employees may struggle more with the new situation and may be less successful in transforming it to their benefit, explaining why the oldest age groups, 54–60 and 61–65 years, were less likely to report an improvement in their work life.

Regarding the perceived impact on private life, participants living alone were more likely to report a deterioration and less likely to report an improvement of their private life compared to those living with a partner, family, or in a shared housing. The COVID-19 lockdown substantially restricted possibilities for social interactions beyond one’s own household, particularly affecting people living alone. For individuals who live alone, this may lead to feelings of loneliness [ 12 ], which in turn, threatens their MWB [ 39 ], highlighting the importance of having opportunities for direct exchange in such a crisis situation. This could also explain that an increase in caring duties, allowing for more exchange with family members, was associated with perceived positive shifts in private life. Further, an increase in WFH showed to be beneficial also to the private life, particularly to those experienced in WFH who did not need to first establish their workspace and new routines. Increase in leisure time and, more surprisingly, mandatory short-time work were also associated with positive impact on private life, as employees can engage more freely in activities they value. Interestingly, participants over 60 years old were less likely to report a deterioration of their private life. Older employees may be less dependent on the number of social contacts beyond their household, and they may have more mature emotion regulation strategies than the younger generations [ 40 ]. Indeed, mental well-being of the German elderly population (65+) remained largely unaltered during the early COVID-19 lockdown [ 41 ].

Finally, our third objective was to investigate how the perceived overall impact and self-reported changes induced by the crisis were associated with MWB and SRH. Low SRH has been associated with increased odds of depression [ 27 ], displaying the relevance of SRH for psychologically demanding situations, such as the COVID-19 pandemic. Our results suggest a strong negative association between the perceived negative impact on work and private life, MWB and SRH, indicating that this perception by itself is of relevance. It is of note that the perceived negative impact, particularly in private life, had such a strong association with SRH, which is more stable over time than MWB. In contrast, perceived positive impact on private life was associated with higher MWB. It seems that those who were able to cope with the COVID-19 crisis and translate the lockdown measures into some positive shifts in their private life, also benefited in terms of increased MWB.

Looking at the impact of the self-reported changes on MWB and SRH, mandatory short-time work with 0 contracted working hours was strongly associated with a lower MWB and SRH. Short-time work leads to significant losses of financial security and of daily structure and routines. Conversely, an increase in leisure time was positively associated with MWB, and the link was even stronger with SRH. More time for leisure gives extra opportunities for individuals to engage in meaningful activities that provide them with important resources that benefit their MWB and SRH. The overall strength of the associations indicates that MBW may be more affected by the perceived impact, as both are cognitive-emotional domains and are more dependent on the cognitive appraisal of one’s situation and emotional experience. SRH, on the other hand, may be more affected by actual changes in work and private life that increase or decrease opportunities to engage in activities that are perceived as beneficial to health.

Limitations and strengths

A major limitation is the cross-sectional design, which allowed only to infer associations between variables but did not provide evidence of the directions of the associations or potential causality. Furthermore, the online survey created timely data on the immediate impact of the COVID-19 crisis situation. However, the self-reported data may be influenced by common method biases [ 42 ], such as social desirability bias [ 43 ] or self-selection bias, posing potential threats to the validity of our findings. Thus, we hired a professional panel data service that guarantees collection of high quality data. Moreover, we implemented various strategies in the questionnaire such as using disqualifying items to prevent invalid answers. The sociodemographic characteristics of our sample indicate a good representation of the target population. Finally, we did not control for all variables that might have affected the results. For instance, coping with a crisis and MWB differ individually and may be influenced by variables such as personality traits, resilience, or coping style [ 44 , 45 , 46 , 47 ]. However, our study aimed to provide a broad picture of both the negative and positive impacts of the COVID-19 crisis on a large, diverse sample of the working population. Thus, it was beyond the scope of this study to investigate individual differences and characteristics. In addition, a more complete, lengthy survey would have likely reduced the participation rate.

A strength of the present study is the relatively large and heterogeneous sample size that allowed us to conduct a detailed analysis and explore different subgroups within the sample. Another strength is the time point of the data collection launched at the beginning of April 2020, close to the first peak of the COVID-19 outbreak in Germany and Switzerland and onset of the related lockdown measures. This enabled us to capture a valid picture of the immediate impact of the lockdown measures. Moreover, the survey assessed the present situation, adding to the validity compared to a retrospective survey design. Finally, the combination of a subjective evaluation of the impact of the crisis with relevant, standardized public health indicators of MWB and SRH increases the relevance of the results to public health research and for policymaking.

Conclusion and policy recommendations

The present study contributes to our understanding of the impact of the COVID-19 crisis on work and private life. It provides evidence on the covariates of a more negative/positive perceived impact and on the associations with MWB and SRH in the German and Swiss working populations. Employees whose employment contract was affected by the crisis seem to have felt the greatest negative impact on their work life. This highlights the crucial role of (un−/under-)employment in a crisis, as employment is associated with several health-promoting factors that cannot be substituted in any other way [ 48 ]. Moreover, the private life of employees living alone has been affected most negatively due to social isolation. Thus, psychological first aid also accessible online should be established particularly for these vulnerable groups [ 49 ]. Employers need to assure that they keep close social ties with and emotionally support employees with reduced contract or working hours. Moreover, rapid financial aids are needed to those who have lost their income partially or completely.

Nevertheless, we should also foster positive consequences of the crisis. In general, it seems that an increase in WFH was positive for work life. Learning from the beneficial effects of WFH in a crisis can inform future organizational and legislative policies to support this form of working. As employees experienced with WFH had a stronger positive impact on private life than first-timers, future WFH policies should include offering training and exchange of experience between employees on how to establish positive routines compatible with their private life. This will help employees to proactively identify their preferences and craft their work environment accordingly [ 50 ]. Further, an increase in leisure time was particularly positive for private life. More leisure time allows for dedicating extra time to activities one enjoys, and this may be beneficial also for recovery and detachment from work [ 51 ] and for mental health in general [ 52 ]. Thus, employees could also be trained in optimal crafting of their leisure time to strengthen these beneficial effects [ 53 , 54 ].

Finally, we saw that besides the reported actual changes in work and private life, also the perception of the overall positive or negative impact is related to the health outcomes. This suggests to offer positive psychology trainings to employees helping them to purposefully focus on and make use of potential positive consequences of the crisis [ 55 , 56 , 57 ]. From a longitudinal research perspective, it would be interesting to further examine how the actual and perceived impact of the ongoing crisis as well as the associated health outcomes change over time and whether some of the new routines developed during the pandemic will be maintained in the long term.

To conclude, our study adds to recent evidence [ 58 ] that the Covid-19 crisis and related lockdown measures do not have solely negative impact. Rather, it affects vulnerable groups of individuals who need targeted support, while the majority of the population remain healthy or even experience positive shifts in their daily life.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. The R code used for the statistical analysis is available in the GitHub repository: https://github.com/jesuismartin/covid

Education estimates are based on data from n  = 1194 participants who took part in a subsequent wave of data collection (December 2020), missing values ( n  = 924) were imputed using mice R package (for details see supplementary material). Education was not included in the regression models as the imputed data could potentially threaten the validity of our conclusions.

Short-time work is defined as “public programs that allow firms experiencing economic difficulties to temporarily reduce the hours worked while providing their employees with income support from the State for the hours not worked” (European Commission, 2020, Retrieved from: https://eur-lex.europa.eu/legal-content/EN/TXT/?qid=1587138033761&uri=CELEX%3A52020PC0139 ).

None  = 0% WFH before COVID-19, 0% after; Experienced  = at least 10% WFH before and at least 10% after COVID-19; New  = 0% WFH before and at least 10% after COVID-19.

Participants were grouped into three categories according to their answers: disagree (1 + 2), neither/nor (3), agree (4 + 5).

Abbreviations

World Health Organization

Public Health Emergency of International Concern

Work from home

European Union

Confidence interval

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Acknowledgements

The authors would like to thank to Roald Pijpker from Wageningen University for his helpful comments during the final editing of the manuscript.

MT received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 801076, through the SSPH+ Global PhD Fellowship Programme in Public Health Sciences (GlobalP3HS) of the Swiss School of Public Health. RB, PK, and GB received funding from the University of Zurich Foundation. Beyond providing the funding, these funding bodies were not involved at any stage of the study.

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MT planned and carried out data collection and analysis, interpretation of the results, writing and reviewing the manuscript in collaboration with the co-authors. RB contributed to the research concept, data collection, data analysis, and review of the manuscript. PK was involved with the conceptualization of the research, interpretation of the results, writing, and review of the manuscript. GB contributed to the conceptualization of the research, interpretation of results, writing, and review of the manuscript. All authors read and approved the final manuscript before submission.

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Informed consent was obtained from all participants, the study included adult participants (18+ years) only. Participants voluntarily completed the questionnaires, guaranteeing their anonymity. For anonymous surveys on working/living conditions and self-reported mental well-being and health no ethical review was necessary under national, university, or departmental rules (Department of Data Protection at the University of Zurich, www.dsd.uzh.ch/en/ ). The study was conducted under strict observation of ethical and professional guidelines. The study was not registered prior to the start of the data collection as this is not common in the field of occupational health psychology where this study originated. The study is part of a larger longitudinal data collection on occupational health and individual strategies employee use to craft their work, started already before the Covid-19 pandemic. When the pandemic started, we decided to add the study aim to explore the immediate impact of the Covid-19 crisis on Swiss and German working population presented in this paper. The manuscript is an accurate and transparent account of the study, and no important aspects of the study or any analyses conducted have been omitted.

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Tušl, M., Brauchli, R., Kerksieck, P. et al. Impact of the COVID-19 crisis on work and private life, mental well-being and self-rated health in German and Swiss employees: a cross-sectional online survey. BMC Public Health 21 , 741 (2021). https://doi.org/10.1186/s12889-021-10788-8

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Remote Work and the Heterogeneous Impact of COVID-19 on Employment and Health

This paper examines the impact of the COVID-19 pandemic on employment and respiratory health for remote workers (i.e. those who can work from home) and non-remote workers in the United States. Using a large, nationally-representative, high-frequency panel dataset from March through July of 2020, we show that job losses were up to three times as large for non-remote workers. This gap is larger than the differential job losses for women, African Americans, Hispanics, or workers without college degrees. Non-remote workers also experienced relatively worse respiratory health, which likely occurred because it was more difficult for non-remote workers to protect themselves. Grouping workers by pre-pandemic household income shows that job losses and, to a lesser extent, health losses were highest among non-remote workers from low-income households, exacerbating existing disparities. Finally, we show that lifting non-essential business closures did not substantially increase employment.

The project described in this paper relies on data from surveys administered by the Understanding America Study (UAS), which is maintained by the Center for Economic and Social Research (CESR) at the University of Southern California. The content of this paper is solely the responsibility of the authors and does not necessarily represent the official views of USC, the UAS or the National Bureau of Economic Research. The collection of the UAS COVID-19 tracking data is supported in part by the Bill and Melinda Gates Foundation and by Grant U01AG054580 from the National Institute on Aging. Natalie Theys provided excellent research assistance.

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Impact of COVID‐19 on Employment: Exploring the Perspectives of Job Loss and Mental Health of Individuals From Minimal‐Resource Communities

Galaxina g. wright.

1 Department of Counselor Education and School Psychology, University of Central Florida

Lea Herbert

Breahannah hilaire, laurie o. campbell.

2 Department of Learning Science and Educational Research, University of Central Florida

This qualitative study examines the experiences of COVID‐19 job loss by individuals from minimal‐resource communities. Six participants were interviewed regarding their experience with becoming unemployed during the global pandemic. In general, participants described experiences that aligned with the core tenets of Gowan and Gatewood's (1997) model of response to job loss, as well as additional subthemes, including (a) internal support, (b) external support/resources, (c) survival, (d) mind‐set, (e) emotion regulation, and (f) mental health effects. Implications are provided to career practitioners with consideration of these experiences when working with unemployed individuals who have limited resources.

The U.S. economy has experienced detrimental economic effects as a result of the COVID‐19 pandemic, directly impacting the national unemployment rate. In April 2021, there were 9.8 million unemployed persons because of the global pandemic (Bureau of Labor Statistics, 2021 ). There is increasing concern that the unemployment crisis has disproportionately affected individuals from minimal‐resource communities. Although research shows COVID‐19 has exacerbated inequalities, populations of color and communities from lower socioeconomic status have been severely impacted and underserved (Flores et al., 2019 ; Kantamneni, 2020 ). Within the occupational realm, workplaces faced great demands to convert some in‐person jobs to remote work via online platforms (Dingel & Neiman, 2020 ). Missing from the literature is a vivid illustration of the unemployment experienced by individuals from minimal‐resource communities. In response to the unemployment crisis caused by the COVID‐19, the focus of this study was to explore the narratives of individuals who have lost jobs because of the pandemic to better understand how career development interventions can be tailored toward diverse populations. In addition, these narratives (a) provide implications for career practitioners, (b) add to the dearth of literature addressing vocational hardships among communities from low socioeconomic classes, and (c) indicate directions for future areas of research.

Immediate Impact of Job Loss for Individuals

For some individuals who have experienced job loss as a result of COVID‐19 and who identify as being from minimal‐resource communities, the unplanned and unprecedented disruption in employment has created a ripple effect of negative consequences related to unemployment status. The shock of being unemployed during COVID‐19 has been identified as traumatic for many individuals, with initial emotions including sadness, injustice, and anger (Akkermans et al., 2020 ; Drosos et al., 2021 ). In addition to the loss of financial stability, job loss from the pandemic has led to unequal access of power and privilege for historically marginalized populations, creating power differentials that include a lack of credit, status, and mobility (Flores et al., 2019 ). Researchers have found that individuals who are able to convert their jobs to remote platforms are less likely to become unemployed (Dey et al., 2020 ). However, Dingel and Neiman ( 2020 ) estimated after a review of 1,000 job descriptions that only 37% of jobs can be converted to online platforms; therefore, many nonconvertible jobs were lost as a result of the pandemic. Despite a partial recovery of the unemployment rate, overall recovery has slowed, and many temporary layoffs have become permanent (Handwerker et al., 2020 ). The transition from short‐term to long‐term job loss creates the need to evaluate the long‐term effects of the unemployment crisis.

Long‐Term Effects of Job Loss on Job Seeking

Potential long‐term consequences for individuals affected by the COVID‐19 unemployment crisis include an increased risk of mental and physical health and an increase in relational problems, both of which pose challenges for future employment (Blustein et al., 2019 ). In response to COVID‐19, mandates to quarantine and socially distance have created an increase in mental health disorders not limited to posttraumatic stress disorder, depression, and anxiety. These conditions create an added barrier for individuals who are looking for new employment (Brooks et al., 2020 ; Nelson et al., 2020 ). Quarantine mandates have influenced and disrupted relational processes that are important to job‐seeking behaviors. For example, researchers have found that there is a close association between relational processes and career‐associated tasks such as interviewing and networking (Blacker et al., 2020 ; Motulsky, 2010 ). Furthermore, social isolation has been found to affect work productivity and decision‐making, both vital processes that aid in obtaining future employment (Blacker et al., 2020 ). The aforementioned circumstances resulting from job loss during the pandemic have created complex dilemmas for job seekers. Therefore, it is important for career practitioners and others to better understand the COVID‐19 pandemic work phenomenon to devise best practices for career practitioners.

Theoretical Frameworks Related to Job Loss

The model of response to job loss (Gowan & Gatewood, 1997 ), a theoretical framework implemented for this study, explored four tenets of job loss: (a) coping resources (including both individual and situational), (b) mediating processes (i.e., cognitive appraisal and coping strategies), (c) immediate effects (i.e., surrounding affective state and reemployment status), and (d) long‐term effects on psychological functioning. This model has long been recognized as a model to explain coping with job loss, yet this is the first time the theory has been explored within qualitative research. The first tenet, coping resources, refers to the comparison between available resources and one's ability or inability to meet the demands of job loss (Gowan & Gatewood, 1997 ). Coping resources can include both individual factors such as upholding positive beliefs about oneself and situational factors such as social support and availability of financial resources. The second tenet, mediating processes, includes cognitive appraisal and coping strategies. Gowan and Gatewood ( 1997 ) believed that mediating processes serve as mediators between coping resources and immediate effects, as well as between cognitive appraisal and immediate effects. The third tenet, immediate effects, includes psychological affect and reemployment status, which the authors believed helped in better understanding the role between coping strategies and distress. The final tenet, long‐term effects, refers to outcomes relating to the job loss, including psychological, social, and physiological well‐being.

In addition, this study is supported by the psychology of working theory (PWT) developed by Blustein et al. ( 2019 ). The current study aligns with the many core assumptions of PWT, including the necessity of access to decent and stable work for an individual to survive, and the inseparable nature of an individual's cultural background, family context, and social identities in influencing their work environment (Blustein et al., 2019 ). In their most recent approach of PWT, Blustein et al. ( 2019 ) hypothesized that individuals who have access to decent work also experience general well‐being in terms of survival, social connection, and self‐determination. Similarly, we anticipated that job loss and lack of employment would result in participants facing many difficulties and adversities in obtaining well‐being, particularly mental health.

Purpose and Research Questions of the Current Study

Much of the current research surrounding the COVID‐19 pandemic has focused primarily on the specific number of cases and the overarching economic health of the United States. However, there is minimal literature on personal narratives of the impact of job loss among working‐class communities. The current study used a phenomenological research design to obtain a better understanding of the unique experiences of job loss during the pandemic. Interviews were analyzed following Gowan and Gatewood's ( 1997 ) model of response to job loss, based on the theoretical assumptions that those who experienced job loss during the COVID‐19 pandemic will face unique challenges related to coping resources, mediating process, and both short‐ and long‐term effects. The research questions included the following:

• Research Question 1 : How do individuals who identify with working‐class communities perceive their experience of job loss during the COVID‐19 pandemic?
• Research Question 2 : What challenges and needs are associated with unemployment during the COVID‐19 pandemic as described by individuals who identify with working‐class communities?

Research Team

The research team consisted of three female doctoral‐level students working toward their degrees in counselor education and supervision at a large southeastern U.S. academic institution and one female faculty adviser specializing in educational and human sciences at the same institution. All the researchers have varying experience working with underserved and underrepresented populations, and two of the researchers have backgrounds in career counseling and assisting clients with career‐related issues. The research team aided the primary researcher (the first author) with key components of the study, including development of interview protocol, data collection, transcription, and data analysis. Furthermore, the collaborative nature of the data analysis eliminated the potential for researcher bias while increasing the trustworthiness of the analysis. Two of the researchers were particularly mindful that their career counseling experiences and backgrounds could influence their approach to the study.

Multiple strategies for trustworthiness were incorporated to ensure the external and internal validity of our study, including peer debriefing of transcripts and reflexivity toward observed interviews. To ensure the credibility and transparency of the study findings, we endorsed bracketing of preexisting bias prior to data collection and agreed‐upon positionality inclusion (Morrow, 2005 ). In addition, we had weekly meetings to collaborate on logistical dynamics of the study and its design and aided with transcription coding and devising themes. Furthermore, the primary investigator kept a journal following each conducted interview, identifying and addressing thought processes throughout the study.

Participants

The primary investigator ensured that each participant met the inclusionary criteria of (a) involuntary job loss, (b) job loss as a result of the pandemic, and (c) an expression of willingness to participate in the study. The participant pool included adults who identified as being from minimal‐resource communities. To be eligible for the study, participants must have met the following criteria: (a) at least 18 years of age, (b) currently unemployed, and (c) lost their most recent employment due to reasons related to COVID‐19. The rationale for participants falling under these criteria was to fully understand individuals' current experiences, challenges, and needs as they pertained to being unemployed following the global pandemic. Participants were recruited from various avenues of outreach based on networks, affiliation, and word of mouth. Most participants were sampled from a local nonprofit employment assistance organization. Interested participants were provided an initial email containing information about the study, an informed consent form, and a link to the preliminary questionnaire to ensure eligibility.

Ten individuals expressed interest in participating in an interview, and six interviews were conducted of eligible participants. Interviews with four individuals could not be arranged. Demographic information of the participants includes five women and one man; four participants were between 25 and 34 years of age, one participant was between 35 and 44 years of age, and one participant was between 55 and 64 years of age. Two participants identified as White, one participant identified as Black/African American, and three participants identified as “other.” All the interviewees were located in the southeastern United States. Table ​ Table1 1 provides additional demographic and employment information on the participants.

Descriptions of the Six Study Participants

Interview Protocol

We developed semistructured interview questions to provide initial talking points during the interviews. Development of the interview questions considered the four major tenets of the response to job loss model (Gowan & Gatewood, 1997 ). Sample questions are as follows:

• What initial emotions did you portray toward your changed employment status? (immediate effects)
• What actions have you taken toward your socioemotional health following your job loss? (long‐term effects)
• What actions have you taken to alter/improve/change your unemployment status? (mediating processes)
• Since COVID‐19, how have your interactions with others been affected by your unemployment status? (coping resources)

Because of the unique nature and context of each participants' narrative, additional follow‐up questions were asked as new information presented during the interview. The primary researcher conducted all but one of the interviews, with the remaining interview conducted by another member of the research team.

After the development of the interview instrument, we obtained institutional review board permission to conduct the study. Each interviewee completed an electronic informed consent form prior to being interviewed. Interviews were conducted through electronic platforms and lasted 30 to 60 minutes, based on the individual narrative of each participant. Because of the pandemic and the potential risks of meeting in person, the interviews took place virtually via an electronic platform. Individual meetings, protected with a unique password, were arranged to ensure participants' confidentiality. Each meeting was set up in a virtual waiting room, requiring the interviewer to verify participants' identification prior to physically admitting them into the interview meetings.

Data Analysis

All interviews were recorded and then transcribed using Otter AI, a web‐based transcription service. Next, all the transcriptions were reviewed to ensure accuracy. Following the transcription, we conducted preliminary coding of themes based on the model of response to job loss (Gowan & Gatewood, 1997 ). To better understand the extent and nature of participant experiences, we applied an interpretative phenomenological analysis (IPA). The goal of IPA is to develop a deeper understanding of lived experiences while factoring in how individuals make meaning of an event (Biggerstaff & Thompson, 2008 ). Researchers using IPA seek answers to questions that focus on the experience and that include contexts of an experience, such as beliefs, feelings, and motives (van Manen, 2017 ). In our study, coding took place following the four critical stages of IPA: (a) familiarizing ourselves with the reading and rereading the devised transcripts, (b) developing themes that captured and conceptualized the experiences of the participants, (c) grouping themes into clusters/concepts of overarching categories or potential relationships utilizing the categories from the model of response to job loss, and (d) creating a table summary of themes (Gowan & Gatewood, 1997 ; Smith, 2018 ).

We divided the participants' transcripts, working in pairs for consensus coding. Each researcher coded three assigned transcripts individually, then met with their assigned partner to review their coding. Next, all the researchers came together to collectively reevaluate and discuss the rationale behind all coding decisions. In total, we reached approximately .75 interrater reliability using Miles and Huberman's ( 1994 ) reliability formula. Most of the disagreements were associated with determining whether participants' experiences aligned with individual coping resources or mediating coping strategies, two concepts of Gowan and Gatewood's ( 1997 ) model that are similar in nature. We settled this disagreement by associating tangible aspects with individual coping resources (e.g., finding a hobby) and internal aspects with mediating processes (i.e., belief in a higher being). To develop themes, we used bracketing to identify key words or phrases. Collectively, these procedures aided in the holistic visualization and conceptualization of the key characteristics of COVID‐19 job loss phenomenon and participants' experiences.

Qualitative research involves studies that are informed by existing literature and questions that help to guide the research process (Ritchie et al., 2018 ). Our study was supported by existing research exploring job loss, including mental health impacts that were both long and short term. We used an existing framework to devise interview questions and initial coding based on the theory of job loss (Gowan & Gatewood, 1997 ).

Our study represents the first time that Gowan and Gatewood's ( 1997 ) model of coping responses has been used to explain the phenomenology of involuntary job loss. The model aided in explaining participants' responses; however, additional themes appeared in our findings that were not a part of the original model. From the theoretical framework of the model of response to job loss, six subthemes emerged: (a) internal support, (b) external support/resources, (c) survival, (d) mind‐set, (e) emotion regulation, and (f) mental health effects. For the first tenet, coping strategies, the first two subthemes were confirmed, including internal support (individual) and external support (situational). Internal support refers to family and friends, and external support refers to community, government, and organizational support. For the tenet of mediating processes, two new subthemes emerged outside of the original framework, including survival and mind‐set (both positive and negative). Because the impacts of COVID‐19 were still ongoing and all interviewees were still unemployed, all effects were considered as immediate effects and included subthemes of emotion regulation and mental health effects.

Coping Resources: Internal and External Support

Internal support . The transcripts demonstrated internal support as a resource for coping, finding strategies based on participants' own accessibilities and interests. Areas of internal support included individual factors relating to positive beliefs about themselves relating to their job loss, ranging from actively participating in volunteer work to finding a hobby. Participant 2, a married woman without children, described revisiting old hobbies as an outlet to cope with her job loss:

Um, so I actually, just about 2 weeks ago, now I bought an old keyboard off of somebody. And so, whenever I'm getting—feeling that like overwhelming feeling again, I've been, you know, trying to play the old songs I used to remember, and it's definitely an emotional release, even though I'm not good at it anymore.

External support/resources . The transcripts revealed participants' use of external coping resources as a way to mitigate the impact of the pandemic. Within the category of coping resources, individuals utilized family and community support. In direct response to the pandemic and the mandate and/or encouragement to quarantine, most participants looked to their family members and their community to provide relief. Participant 1, a single mother with two children, expressed optimism and hope knowing that they were not alone: “Yes, it looks like everything's around us shutting down, but we have community, we have each other.”

In addition, participants coped by finding support through external resources from various institutions and organizations. To cope with the pandemic, many participants found monetary assistance through the support of organizations that helped with filing for unemployment benefits, food assistance, and job placement. Participant 6, who was in the highest age range of all the participants, stated,

So I, when I saw the jobs [from the career development program] come across, I said, “You know, I'm going to join. I need this—I need to get some type of help, so that I can maneuver through all of this.” Um, I can't do it alone. And, and it's OK to ask for help.

Many participants demonstrated initial reluctance to utilize the resources and programs, but they were able to persevere because of these organizations' ability to reduce the negative consequences of the pandemic.

Mediating Processes: Survival and Mind‐Set

Survival . Within the theme of mediating processes, a key subtheme addressed was behaviors relating to survival. Several participants identified prioritizing basic need for housing and groceries when adapting to job loss, as well as abandoning supplemental necessities that they could no longer afford. Specifically, as pressures for survival heightened, participants acknowledged major job loss adjustments that were nested in lifestyle changes. For example, Participant 5, a father coparenting three children, commented, “So ended up losing the place I was at, well, I didn't lose it, when the lease came to renew, I wasn't able to renew it. So I was forced to move in with my ex‐wife.”

Participants appeared to view their job loss experience as directly related to the social and political impacts of COVID‐19. Specifically, sociocultural impacts of COVID‐19 seemed to expose participants to events such as transitory remote work demands and stagnation of retail and direct‐to‐consumer sales. Participants expressed feeling disillusioned with the media reports of the pandemic. Some participants found COVID‐19‐related constraints modified their engagement with survival needs, making them more aware of the existence of savings, work‐life balance, and social support.

Mind‐set . Although the responsibilities and pressure of survival presented as a pronounced theme, a surprising theme in our findings was a sense of renewed mind‐set in some participants. A reframed mind‐set is revealed by participants' comments that included maintaining a positive attitude and the existence of meaning making, alongside pragmatic cognitive assessment. Participants recognized the vulnerability of acting on negative feelings during the onset of the pandemic and the rising unemployment nationwide. For many participants, their progression of emotion regulation was closely associated with aspects of hope despite being overwhelmed by their circumstances. Participant 5 articulated this clearly: “Biggest thing is just finding your happy place. Like that's the biggest thing that I can tell anybody, like you gotta find something that works for you and your family.” Participant 1 mirrored a similar positive mind‐set, stating, “But I'm like, there's nothing I can control. I can't control my circumstances—the only thing I can control is how I look at it.”

Immediate Effects: Psychological Effects and Reemployment Status

Consistent with the existing research regarding involuntary job loss, all participants expressed experiencing immediate effects (Akkermans et al., 2020 ; Blacker et al., 2020 ). Gowan and Gatewood's ( 1997 ) model demonstrated a clear delineation between immediate and long‐term effects; however, the themes observed surrounding these tenets varied across individuals depending on their situation, such as difficulty finding a job in the midst of the pandemic; constant fear and stress; and losing necessities such as housing, insurance, or childcare. Subthemes that existed across all participants included emotion regulation and mental health.

Emotion regulation . Although all participants varied in their emotional expression, each participant either shared the actual emotion that was attached to their job loss or described the reactions and thoughts they experienced in becoming unemployed. The range of emotions relating to the job loss included anger, shock, and fear—emotions used to describe not only the job loss but also the ambiguous information surrounding COVID‐19 during its initial emergence. Participant 6, a woman close to retirement prior to her job loss, shared, “I would say initially, I was very angry. So angry that I couldn't feel any other type of emotions, but anger, I couldn't cry. I couldn't, I was angry.”

Participant 3, who identified as a single mother and homeowner, stated,

What do I do? Now I'm scared, even though they say you'll be in forbearance for 6 months, it's still a fear embedded in you to be like, “Well, what if things never go back to normal, and I don't have the funding to pay my mortgage? Then, what [if] I haven't retired, I'm not gonna be homeless, you know, then my car, you know, loan modification on that.”

As demonstrated by Participant 3's statement, another common aspect that was revealed from the participants' interviews was a pattern of ruminating and interrupted thoughts in attempting to describe their emotions following job loss. This was evidenced by participants grouping several questions and thoughts surrounding uncertainty one after another, attempting to process and explain their initial emotions.

Mental health . Three of the participants expressed wanting to see a mental health counselor to address their job loss and COVID‐19 anxieties, but they indicated they were not able to access a counselor either because they lost their health insurance or because of their inability to pay for the cost of mental health care given their new limited budget. Participant 4, a former business owner who lost her business during the pandemic, described the challenge of masking her emotions for the sake of her family:

Yes, I mean, I'm living with my parents right now. I'm, you know, 44 years old. My husband and I, we've always been entrepreneurs, and it's like, you're kind of having a midlife crisis, like, like all this work. And it's like, you know, and trying to keep a brave face, you know, for your children as well, you know.

Among some of the participants, there was a pattern of reluctantly adjusting to the transition of becoming a help seeker. They discussed feeling like they were a burden to those they relied on for help, including spouses and other family members.

The transcripts revealed additional ripple effects of job loss that contributed to participants' emotional and psychological affect and influenced their mental health, including feelings of hopelessness, loneliness, and being stagnant in their career development. Participant 2 shared the following relating to her anxiety: “That's—that's another big thing is like, without working, it feels like you're not moving towards anything; you're not moving forward.”

This study was designed to explore the phenomenon of job loss during the global COVID‐19 pandemic and to gain a better understanding of the experiences and commonalities of those affected to inform the field of career development and career interventions. Six interviews with people who lost their jobs as a result of the COVID‐19 pandemic were analyzed. Job loss was exacerbated by the complexities of the pandemic. The following discussion situates the results of these interviews in three bodies of literature: (a) career development, (b) job loss, and (c) mental health.

Career Development

This study demonstrated the vast number of factors to consider when working with individuals who are unemployed, including access to internal and external resources. Unemployment affects not only individuals but also family members who are supported by those individuals (Blustein & Guarino, 2020 ). In response to their job loss, several participants discussed social support from their family and community as they faced various obstacles regarding housing and emotional support. Regarding job replacement, a few participants recognized they needed additional support from outside organizations relating to job search and future career development. Research has shown that efforts to find new employment are often unsuccessful because of the lack of job‐hunting skills; however, career interventions have been found to increase one's self‐efficacy and overall confidence toward job search (Drosos et al., 2021 ).

Participants in our study demonstrated personal lifestyle adjustments to persevere through challenging circumstances via internal coping mechanisms. Existing literature has shown that self‐regulation processes are a part of resilience, and both serve as protective factors while individuals are recovering from adversities such as job loss (McLarnon et al., 2020 ; Prescod & Zeligman, 2018 ). In addition, participants adjusted their internal coping strategies because of the new financial constraints, personal and familial needs, and quarantine mandates relating to COVID‐19. The findings of both individual and situational resources align with existing literature, serving as forms of coping with job loss (Gowan & Gatewood, 1997 ).

Results of this study indicated subthemes relating to mediating processes and emotion regulation. Researchers have found that, initially, job loss leads to strong negative emotions, with unemployed individuals needing to adjust (Drosos et al., 2021 ). One surprising factor was the participants' ability to carry a positive mind‐set despite their difficult circumstances. Job loss can be viewed by individuals as a time for self‐evaluation as they reframe lifestyle decision‐making and confront long‐term aftereffects of personal stagnation and loneliness. The results of this study resonate with emerging literature suggesting that COVID‐19 unemployment contributes to people feeling sad and unjustly treated (Akkermans et al., 2020 ; Drosos et al., 2021 ). Despite the complexities associated with recovery from unemployment, people from economically marginalized environments found reconnections with family, moments of gratitude, and eventual acceptance of a collective new norm.

We found it difficult to delineate immediate and long‐term effects of job loss, which could potentially be a result of participants still experiencing the effects of unemployment during their interview. Although all participants experienced job loss several months prior to their interview and were still unemployed, it was hard to determine whether the emotions and reactions shared in the interview were experienced immediately following their termination or were still reoccurring. Job loss has been described as traumatic and has been compared with grief, both of which vary in length of recovery depending on the individual (Akkermans et al., 2020 ; Tang et al., 2021 ). Although many researchers agree that unemployment affects individuals at various stages, time frame is generally not specified, similar to the model used in this study.

Mental Health

This study's findings demonstrate the emotional and psychological burden of job loss on participants. As stated previously, we found varying emotions across participants related to their unique situations. Research has demonstrated the effects of involuntary job loss on mental health, as well as increased risks of anxiety and depression from COVID‐19 quarantine mandates (Brooks et al., 2020 ; Nelson et al., 2020 ). All participants detailed their experiences of coping with extensive stress, angst about the future, and feelings of hopelessness at some point during their unemployment.

Prior research has explored the intersection between employment status and mental health. Those who are unemployed are more likely to experience increased stress, negative mental and physical health symptoms, and lower life satisfaction (Tang et al., 2021 ). Much of the reactionary feelings that participants felt reflected similar symptoms of mental health disorders that have resulted from quarantine mandates (Blustein et al., 2019 ). These findings support the need for career development agencies to communicate possible mental health resources to those who have lost their jobs because of the pandemic.

Implications for Practice

Career practitioners play a vital role in providing resources and career interventions to job seekers, and findings from this study should be considered when working with diverse clients. Furthermore, according to the National Career Development Association's ( 2015 ) Code of Ethics, career practitioners carry an ethical obligation to expand their multicultural competence and ensure they stay abreast of the evolving culture and background of diverse clients.

The results of our study support the need for career practitioners to implement culturally sensitive career interventions to meet the unique needs of minimal‐resource communities. As theorized within PWT, each participant in this study shared various experiences in which cultural and family aspects influenced their job loss (Blustein et al., 2019 ). In addition, increases in family and community support were noted among participants; therefore, career practitioners are encouraged to identify relational supports because job‐seeking efforts and decisions will more than likely affect these networks. Furthermore, prior research has identified the correlation between relational processes and their influence on career decision‐making and career development tasks (Blacker et al., 2020 ; Tang et al., 2021 ). According to Motulsky ( 2010 ), having clients identify significant relationships in their personal lives will allow them to assess both positive and negative aspects that may influence career development. Because of the importance of relationship building, career practitioners may want to consider group career counseling interventions, which can assist with an added level of support and aid the development of self‐esteem during the job search process (Drosos et al., 2021 ). Moreover, group counseling may assist clients with development of social skills in response to extensive quarantine mandates.

In combination with recommending social support and connection, career practitioners are encouraged to utilize hope as a motivation in their work with clients, mirroring a strengths‐based perspective. Many of the participants demonstrated positive mind‐sets despite having a lack of control in their circumstances. Prescod and Zeligman ( 2018 ) elaborated on how a combination of hope and resilience building can allow career practitioners to help clients develop meaning and posttraumatic growth after their unemployment. In addition, Akkermans et al. ( 2015 ) found that career competencies can help job seekers to increase engagement and resilience in the face of employment setbacks. However, career practitioners should also consider realistic expectations and outcomes for clients, particularly when working with eager job seekers (Chiesa et al., 2020 ). Expectations regarding reemployment should especially be considered given the high demand of employment from a national standpoint.

To adequately address mental health concerns to help clients recover from this crisis, career practitioners need to be knowledgeable about the psychological, cognitive, and affective impacts on unemployed individuals and their families during the pandemic, particularly those from minimal‐resources communities. Participants discussed the emotional distress they felt about becoming unemployed, which can potentially affect their ability to find new employment. Career development agencies can provide information to clients about free or low‐cost access to mental health resources in the community or through online providers. These community resources can be delivered through text messaging, video updates, chatrooms, helplines, or other social media outlets. Furthermore, when providing career interventions, career practitioners should view clients holistically from a trauma‐informed lens, recognizing the influence that potential trauma from job loss can have on clients' career development.

Limitations and Research Directions

One limitation of this study was the selection of participants, with involuntary job loss serving as a primary criterion. Findings might have been different had we examined those who actively made a decision to terminate their employment because of issues surrounding COVID‐19. In addition, we interviewed participants while they were still unemployed and actively looking for work, which might have influenced their answers differently than if they were reflecting on unemployment as a past event. As the rate of employment rises, future research should examine reemployed participants to see whether their perspectives have changed. Furthermore, researchers should examine our findings via quantitative studies to see if our themes are generalizable to a larger population. Another limitation is that most of our participants were from a local nonprofit employment assistance organization. It is possible that participants' responses were similar because of their involvement with the assistance program, and this has the potential for bias. Future research should consider random sampling from diverse populations.

Counselor educators and counseling researchers have an obligation to understand the extent to which job loss from the pandemic has affected various aspects of individuals' lives to better serve in areas surrounding advocacy, career counseling services, and additional outreach. This article serves to expand the limited literature of job loss among communities with minimal resources by specifically sampling from this population and incorporating qualitative findings about job loss. Future research should include further qualitative research that explores the narratives of those who have experienced job loss. Additional research could explore stigma related to help‐seeking behaviors as a response to COVID‐19, because this served as an underlying theme regarding external resources. Future research should also focus on other factors that are part of job loss during the COVID‐19 pandemic, such as geographic location, cultural background, and specific roles of family systems.

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• Covid-19, unemployment...

Covid-19, unemployment, and health: time for deeper solutions?

Read our latest coverage of the coronavirus outbreak.

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• Peer review
• Martin Hensher , associate professor of health systems financing and organisation 1 2
• 1 Deakin Health Economics, Deakin University, 221 Burwood Highway, Burwood, VIC 3125, Australia
• 2 Menzies Institute for Medical Research, University of Tasmania
• Correspondence to: martin.hensher{at}deakin.edu.au

As covid-19 drives unemployment rates around the world to levels unseen in generations, once radical economic policy proposals are rapidly gaining a hearing. Martin Hensher examines how job guarantee or universal basic income schemes might support better health and better economics

Covid-19 has been a dramatic global health and economic shock. As SARS-CoV-2 spread across nations, economic activity plummeted, first as individuals changed their behaviour and then as government “lockdowns” took effect. 1 Macroeconomic forecasters foresee a major recession continuing through 2020 and into 2021. 2 Although the governments of many nations have taken novel steps to protect workers, unemployment has risen dramatically in many countries ( box 1 , fig 1 ); poverty and hunger are on the rise in low and middle income countries. 5 Covid-19 has directly caused illness and death at a large scale, and further threatens health through disruption of access to health services for other conditions.

Covid-19 and unemployment

Although unemployment soared in response to covid-19 in some nations, the policy measures undertaken by others have prevented many workers from becoming technically unemployed. In the United Kingdom, the headline rate of unemployment for April-June 2020 was 3.9%—only slightly higher than the 3.89% rate in April-June 2019. Yet in June 2020 9.3 million people were in the coronavirus job retention scheme (“furlough”) and another 2.7 million had claimed a self-employment income support scheme grant; there had been the largest ever decrease in weekly hours worked; 650 000 fewer workers were reported on payrolls in June than in March; and the benefit claimant count had more than doubled from 1.24 million to 2.63 million people. 3 The Australian Bureau of Statistics has produced an adjusted estimate of Australian unemployment that includes all those temporarily stood down or laid off, to allow a closer comparison with US and Canadian statistics ( fig 1 ). As emergency support measures are wound back, concern is growing that the downwards trend from the April peak might not be maintained in coming months.

Unemployment rates in Australia, Canada, and the United States from March to July 2020. 4

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The pandemic continues to spread, and hopes for a rapid “return to normal” look increasingly unfounded. The economic consequences of covid-19 have the potential to further damage human health if not managed effectively—even after the pandemic has faded. Even with the most rose tinted views of recovery, the effects of covid-19 on unemployment are likely to be substantial and long lived. Ambitious responses to the imminent scourge of mass unemployment are being discussed. Two such proposals—a job guarantee and universal basic income—might protect and promote health as well as prosperity. Governments around the world should consider radical plans to safeguard their citizens’ livelihoods and wellbeing.

Unemployment and health in the time of covid-19

Decades of accumulated evidence show a strong and consistent association between unemployment and a range of adverse health outcomes, including all cause mortality, death from cardiovascular disease and suicide, and higher rates of mental distress, substance abuse, depression, and anxiety. 6 7 8 Job insecurity is similarly associated with poorer self-assessed health status, mental distress, depression, and anxiety. 9 Unemployment and economic adversity are intimately related with despair and lack of hope, which have increasingly been linked with mortality and the rise and severity of the US opioid epidemic. 10 11 Whether recessions and mass unemployment increase aggregate mortality is less clear; historical studies indicated improvements in mortality during the Great Depression in the 1930s, 7 but more recent US research found that older workers (aged 45-66) who lose their jobs in a recession have higher mortality than those who lose their jobs in boom times. 12 Insecurity, precariousness, and austerity harmed both unemployed and employed people during the protracted economic crisis in Greece after 2008-09. 13 Meanwhile, differing welfare state institutions and unemployment insurance arrangements directly limit or amplify health inequalities in a society. 7 14

These factors could adversely affect the health of growing numbers of unemployed workers after covid-19. 15 16 Governments, business lobbyists, and civil society advocates around the world are debating how economies might best recover from the covid recession. Although governments currently acknowledge the need to spend freely during the crisis, experience suggests that pressure to pursue misguided austerity policies might grow, threatening subsequent recovery. Options on the table range from “green new deal” programmes to build a post-carbon economy and national industrial strategies to bring globalised manufacturing back onshore through to calls for reducing wages and labour protections to “free up” labour markets. Yet these are all indirect approaches to the effects of unemployment. Proposals for a job guarantee or a universal basic income seek to act more directly to support individual citizens.

The job guarantee

The idea of a right to employment can be traced back to the US New Deal in the 1930s, and to Article 23 of the 1948 United Nations Universal Declaration of Human Rights. More recently, in the contest for the Democratic Party’s 2020 candidate for US president, senators Bernie Sanders, Kirsten Gillibrand, and Cory Booker all included a job guarantee in their platforms, as did Alexandria Ocasio-Cortez’s green new deal resolution. More than one detailed proposal for a Federal Job Guarantee has been published in the US 17 18 and in Australia. 19 In one US proposal, 18 a federally funded public service employment programme would provide a standing offer of work at a living wage ($15 (£12; €13) an hour), along with key benefits including healthcare coverage. Employees of this programme would be deployed on a wide range of public works and community development activities, delivered through federal, state, local, and non-profit agencies. The proposal argues that this would effectively eliminate unwanted joblessness and underemployment and would rapidly force the private sector to increase wages to match this “living wage” alternative, lifting millions out of poverty and greatly improving the incomes of working poor people. 18 Proponents argue that the job guarantee is the most efficient “automatic stabiliser” for the economy throughout the business cycle, able to adjust up and down to reflect the changing economic health of the private sector. In economic downturns, it would provide guaranteed employment to stop people falling into poverty and losing “employability,” while also supporting aggregate demand to lift the economy out of recession. In boom times, workers will simply exit the programme for the private sector, as firms offer higher wages to secure the additional labour they need.

In the US, the job guarantee has been proposed as not only a key tool for recovery from covid-19, 20 but also a mechanism to ensure that this recovery breaks down historically entrenched racial inequalities in wealth. 21 Similarly, an emerging job guarantee proposal for Australia could rectify decades of welfare policy failures that have disproportionately affected indigenous Australians. 22 Proponents point to successful past or present international experiences with full or targeted employment guarantee programmes, including Argentina’s Plan Jefes, South Africa’s Expanded Public Works Programme, India’s National Rural Employment Guarantee Act, Belgium’s Youth Job Guarantee, the US Youth Incentive Entitlement Pilot Projects, and the UK’s Future Jobs Fund. 20

Universal basic income

Over the past few years, there has been a global explosion of interest in the concept of universal basic income. 23 24 25 Andrew Yang, another former contender for the 2020 Democrat presidential nomination, made universal basic income a central plank of his platform. Such proposals share key characteristics: they are a transfer of income (from the state to individuals) that is provided universally (to everyone, with no targeting), unconditionally (with no requirements, for example to work), and in cash (with no controls on what the money can be spent on). 25 Proposals also typically specify an income that is sufficiently generous that it can fully cover a basic level of living expenses. 23 Universal basic income is a direct means of reducing poverty, by ensuring that all in society receive enough to live with dignity; it could reduce income inequality; it could radically simplify current social welfare systems and remove poverty traps and disincentives to move from welfare into work; it could improve the ability of workers to refuse poorly paid, insecure, exploitative or unsafe jobs, through a reduced fear of loss of income; and it could be a buffer against technological unemployment, as automation and artificial intelligence replace human labour. 23 25 Universality is the key difference from today’s welfare systems; everyone should receive universal basic income as a right of citizenship, and its receipt by all should build the solidarity and legitimacy that will sustain this right. Universal basic income could improve health and reduce health inequities through direct action on various social determinants of health. 26 27 This variety of aims leads to the concept being simultaneously supported by those on the left as a radical, anti-capitalist policy, often viewed as an essential component of the ecological degrowth agenda, and by libertarian, tech capitalists as an efficient solution to the risk that ever expanding digital automation will destroy more jobs than it creates, and as a vital measure to help capitalism survive mass technological unemployment in the future. 28

In the wake of the covid-19 economic shock, universal basic income has been discussed as a potentially powerful policy solution to unprecedented economic dislocation. It has specifically been suggested as a tool for limiting the economic, social, and psychological trauma of covid-19. 29 The Spanish government has just introduced a nationwide, means tested minimum income programme (not universal) as a direct response to covid related unemployment. 30 The US government has made unconditional, one-off economic impact payments to most (but not all) American households. Near universal and unconditional universal basic income programmes have only operated at nationwide scale in two countries, Mongolia and Iran. The Mongolian programme has since ceased, and the Iranian programme is no longer strictly universal (the richest people are no longer eligible). Partial schemes and regional pilots, however, have been run successfully in a wide range of nations. 25 A recent trial that provided universal basic income to 2000 recipients in Finland found that employment outcomes, health, and wellbeing measures were better in the universal basic income group than in the comparison group, 31 and the Scottish government has been contemplating a three year trial of universal basic income in an experimental group of recipients. 32

Potential health benefits

Given the substantial evidence linking unemployment to poor health, proponents of both job guarantee and universal basic income schemes point to their potential health benefits as major arguments in their favour ( table 1 ). 20 26 These measures could be expected to positively affect health through four main pathways: direct effects for individual beneficiaries; knock-on effects improving labour market conditions for all workers; the macroeconomic and distributive benefits of more widespread prosperity; and more localised community effects unlocked by these programmes.

Health effects of job guarantee (JG) and universal basic income (UBI) programmes

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Multiple mechanisms would work through these four pathways to deliver potential health benefits, including reduced mortality and improved physical and mental health status. Key mechanisms include reducing poverty, improving economic security, improving the quality of jobs and work, and rebuilding stronger local communities. Unsurprisingly, pathways that link unemployment with poorer health will be more reliably affected by job guarantee programmes than by universal basic income. But universal basic income offers alternative pathways for better health through informal caring and non-market activities. Both types of programme could help resolve one of the problems that the covid-19 pandemic has brought into sharp focus—that low paid, insecure, and casualised workforces cannot afford to self-isolate or stay at home when sick or potentially infected because they lack access to paid sick leave. This problem has proved especially disastrous for those who care for elderly people.

Controversies and choices

Supporters of job guarantee or universal basic income programmes typically have different priorities and view them as two alternative options, not as complementary programmes that could co-exist. Most job guarantee proposals see it as not only a means to fight unemployment, but also an explicit instrument of macroeconomic policy 38 ; universal basic income would not function as an “automatic stabiliser” in the same way. Critics of job guarantee and universal basic income schemes primarily question their affordability and potential macroeconomic consequences ( box 2 ).

Economic controversies

Implementing a job guarantee or universal basic income programme would be a major economic reform in any nation and a decisive break with the economic orthodoxy that has prevailed since the Thatcher-Reagan revolution of the 1980s. It would undoubtedly be controversial. Most obviously, some would question them on cost and affordability grounds. A job guarantee programme would incur a substantial net cost to governments—modelling of proposed programmes indicates a net cost to the federal budget equivalent to 1.5% of annual general domestic product (GDP) in the US 18 and 2.6% in Australia (based on a net budgetary cost of A$51.7bn). 19 By comparison, the Australian government is spending A$70bn, or 3.6% of its GDP, on its emergency JobKeeper employment protection programme this year—budget costs of these magnitudes are not unheard of. The gross costs of a universal basic income programme would be substantially larger: income of $12 000 (close to the 2017 US poverty line) for every US adult would cost the federal budget about $3tn, or nearly 14% of GDP. 23 Yet this gross cost estimate is arguably misleading, 39 not only because universal basic income would be partially offset by large savings from current welfare programmes, but because so many recipients would return much or all of it in the form of tax payments. One estimate of the net cost of such a programme indicates that it could be as low as 2.95% of US GDP. 39 These proposals emerge as a growing number of economists are saying that the governments of countries in possession of their own sovereign currency can never “run out of money” and can always purchase whatever goods and services are for sale in the currency they issue. 38 40 They also suggest that inflation—the other risk often pointed to by critics of job guarantee or universal basic income—is currently highly unlikely, with a general fear that the covid-19 recession will prove to be deflationary rather than inflationary.

For those concerned with health, however, philosophical differences might be of more interest. Social determinants and socioeconomic inequalities are well understood to be powerful forces driving health outcomes at both individual and population levels. Universal basic income seeks to reduce poverty and inequality by putting in place an absolute floor—a minimum income provided to everyone in society. A job guarantee seeks to affect poverty by ensuring that anyone who wants to work can work, for a living wage in a decent job. But in so doing, a job guarantee also explicitly increases the relative power of workers, ensuring that a larger share of national income flows to labour, rather than to the owners of capital—potentially reducing some of the extreme inequalities in income and wealth distribution that have arisen over the past four decades. One criticism of universal basic income is that it might (whether inadvertently or by design) become a “plutocratic, philanthropic” programme 28 —scraps from the table of the ultra wealthy, which might cement dependence and powerlessness in a future of technological unemployment. Equally, a job guarantee might be criticised as being a mid 20th century solution to a 21st century problem, which will reinforce social hierarchies by insisting on participation in paid employment as the solution to poverty.

The unemployment triggered by covid-19 in so many countries is a clear and present danger to individual and population health. Tinkering around the margins of current welfare systems, exhortations for yet more labour market “flexibility,” or an unwillingness to maintain public spending through a potentially long and drawn out downturn all offer a fast track to poor outcomes. The scale of the covid economic shock demands more radical action. The substantial health harms of unemployment might be mitigated by a universal basic income programme, but if unemployment is the problem, then employment seems likely to deliver more effective mitigation along the many and complex pathways by which these harms are transmitted. If so, implementing national job guarantee programmes should be a more urgent priority for governments in the immediate aftermath of covid-19. A successful job guarantee scheme would avert the harms of unemployment, strengthen the position of ordinary working people, and deliver a more broadly distributed prosperity in the short to medium term. This would be a much better position from which to then debate and trial universal basic income, allowing it to be correctly framed as a strategic, long term solution to the changing future of work, rather than simply as a response to the current economic crisis.

Key messages

Covid-19 has triggered economic recession and unprecedented rapid rises in unemployment in many countries

Mass unemployment has the potential to cause grave harm to individual and population health if not effectively mitigated

The scale of the crisis means that radical solutions might need to be considered, such as a job guarantee or universal basic income programmes

These policies have the potential to protect human health and dignity, but would mark a significant break with economic orthodoxy

Acknowledgments

I acknowledge the Wurundjeri people of the Kulin Nation as the traditional owners of the land on which this work was undertaken.

Contributors and sources: MH has worked on health financing, planning, and economics as a senior policy maker and researcher in the UK, South Africa, and Australia and as a consultant for the World Bank, World Health Organization and the European Commission. His research on the ecological and economic sustainability of healthcare systems has included examining a number of emerging heterodox economic approaches, two of which are gaining in significance: ecological economics and modern monetary theory. Members of these schools have promoted universal basic income and a job guarantee, respectively, over many years. This article builds on the existing academic literature to consider very recent policy proposals that are emerging in response to the threat of mass unemployment in the wake of covid-19.

Patient involvement: No patients were involved.

Competing interests: I have read and understood BMJ policy on declaration of interests and have the following interests to declare: this research was supported by an Australian Government Research Training Scholarship.

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• ↵ Office for National Statistics. Labour market overview, UK: August 2020. 2020. https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/bulletins/uklabourmarket/august2020
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Employment impact of Covid-19 crisis: from short term effects to long terms prospects

• Published: 15 July 2020
• Volume 47 , pages 391–410, ( 2020 )

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• Marta Fana 1 ,
• Sergio Torrejón Pérez 1 &
• Enrique Fernández-Macías 1  

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We contribute to the assessment of the employment implications of the COVID crisis by classifying economic sectors according to the confinement decrees of three European countries (Germany, Spain and Italy). The analysis of these decrees can be used to make a first assessment of the implications of the COVID crisis on labour markets, and also to speculate on mid and long-term developments, since the most and least affected sectors are probably going to continue to operate differently until a vaccine or other long-term solution is found. Using an ad-hoc extraction of EU-LFS data, we apply this classification to the analysis of employment in Germany, Italy and Spain but also UK, Poland and Sweden, in order to cover the whole spectrum of institutional labour market settings within Europe. Our results, in line with recent literature, show that the employment impact is asymmetric within and between countries. In particular, the countries that are being hardest hit by the pandemic itself (Spain and Italy, and also the UK) are the countries more likely to suffer the worst employment implications of the confinement, because of their productive specialisation and labour market institutions. Indeed, these were also the labour markets that were more vulnerable before the crisis: characterised by high unemployment and precarious work (especially temporary contracts).

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1 Introduction to the debate

The COVID-19 crisis hit Europe in the first quarter of 2020. Since January, when the first cases were notified, the number of contagions and deaths has been continuously increasing, and confinement measures and restrictions on economic activities have been implemented in most countries from late February to halt the spread of the virus. After periods of several weeks in which the most restrictive measures were implemented (from late February to April), these measures started to be softened progressively in most countries in May. Although the pandemic is still present and continues evolving, the first available studies on its economic and employment impact seem to converge around similar conclusions: the impact of the crisis is being clearly asymmetric, with the most vulnerable countries and segments of the workforce being hardest hit by the pandemic.

Beland et al. ( 2020 ) examine the short-term consequences of COVID-19 on employment and wages in the US. Their findings suggest that COVID-19 increased the unemployment rate, decreased hours of work and labour force participation and had no significant impacts on wages. The negative impacts on labour market outcomes are larger for men, younger workers, Hispanics and less educated workers, indicating that the COVID-19 crisis increases labour market inequalities. They also construct three indices (using ACS and O*NET data) in order to classify jobs according to their exposure to disease, proximity to co-workers and the ability to do remote work, and find that the occupations that depend on physical proximity to others are the ones that are being more affected economically, in contrast to occupations that can be performed remotely. Similar results have been found for the case of Europe. According to Pouliakas and Branka ( 2020 ) and Fana et al. ( 2020 ), the segments of the workforce most likely to be impacted by social distancing measures and practices due to the COVID-19 pandemic are the most vulnerable groups, such as women, non-natives, those with non-standard contracts (self-employed and temporary workers), the lower educated, those employed in micro-sized workplaces and low-wage workers. In line with these findings, Palomino et al. ( 2020 ) find that the crisis is producing in all European countries increases in the levels of inequality and poverty. However, these differences among workers with different employment status and conditions are related to some degree to the segregation of different types of workers across economic sectors. In particular, precarious and vulnerable workers are over-represented in activities related to entertainment, hospitality and tourism, and more generally low productivity services which are facing the hardest short-term impact in the COVID crisis due to both the economic lockdown and the confinement measures (Fana et al. 2020 ). Barrot et al. ( 2020 ), using data from France, show that the decrease in employment caused by social distancing measures is the highest in hotel and restaurants; arts and leisure; agriculture; service activities; food; wholesale and retail and construction, and the lowest in computer services; telecommunications and consulting and scientific and technical activities. They also analyse the effects of social distancing on value added growth for each sector, and find that the sectors experiencing greater losses are mining; arts and leisure; technical activities; food, hotel and restaurants. At the bottom of the figure, with the smallest losses on value added growth, are real estate activities; computer services; scientific research and service activities.

In line with the previous findings, data from the US suggest that entertainment, restaurants and tourism face large supply and demand shocks (del Rio-Chanona et al. 2020 ). At the occupation level, the same authors show that high-wage occupations are relatively immune from adverse supply and demand side shocks, while low-wage occupations are much more vulnerable.

The intensity of the economic effect strongly depends on country specialisation. Countries relying more on low productive service activities and with a low share of public employment are the most hardly hit. A recent survey conducted by Eurofound ( 2020 ) shows that the share of people reporting that their working time during the COVID-19 pandemic decreased (a lot or a little) is above the EU average in all Mediterranean countries. These results are in line with the estimates of Fana et al. ( 2020 ), showing that the share of employment in sectors that are forcefully closed by confinement measures and therefore inactive during the COVID crisis is highest in some Mediterranean countries (Malta, Cyprus, Spain, Greece, Italy) and Ireland, while the proportion is below the average in most Nordic, Easter and central European regions. In summary, in Europe the Mediterranean countries are the ones that are being hardest hit by employment implications of the pandemic.

An important point to emphasize is that the employment and economic impact of the COVID crisis in each country will in the medium-long term be much less determined by the strictness of the confinement than by structural and institutional differences such as economic specialisation, social protection and labour market regulation. In fact, the economic effect of the pandemic occurs regardless of whether governments mandate an economic lock-down or not, as a recent experiment suggests (Andersen et al. 2020 ).

Additionally, the position of each country in the international division of labour as it results from the integration in complex value chains will play a pivotal role in the medium term. This is particularly important for European countries whose productive structures evolved asymmetrically in the last decades, with both Southern and Eastern periphery being more dependent on the Center, led by the German productive model (Simonazzi et al. 2013 ).

In this context, Barrot et al. 2020 suggest that a more severe contraction of GDP can be expected in both Eastern European countries (Bulgaria, Romania, Hungary and Lithuania) as well as some Mediterranean ones, although different confinement measures have been applied in both cases. The Nordic countries, on the other hand, appear as the ones facing the best scenario. Doerr and Gambacorta ( 2020 ) find that, in general, employment in regions in Southern Europe and France is more exposed to the negative effects of the pandemic than regions in northern Europe, with Eastern and central European regions in between.

In the context of the current crisis, telework has allowed to mitigate part of the negative consequences caused by social distancing and restrictions on activities. Working from home requires important changes in the lifestyle of workers and, as a consequence, creates new challenges for work-life balance, mental health issues and work organisation practices. But in terms of employment, it is a practice that allows people to maintain their activity (and income) even when the strictest restrictions are imposed, at least for those with standard employment arrangements. In this sense, telework helps those that are able to perform their professional activity remotely to dodge the economic impact of the crisis. But not all workers can benefit from this form of work.

According to Dingel and Neiman ( 2020 ), the share of jobs that can be done at home exceeds 40% in Sweden and the UK, while the proportion decreases in the cases of France (38%), Italy (35%) or Spain (32%). A similar divide between the north and the south of Europe has been documented by Palomino et al. ( 2020 ). Thus, the potential for telework seems to be lower in the countries that are being hardest hit by the COVID crisis. As a result, the Mediterranean countries are not only severely affected by the crisis, but also worse prepared than other EU countries for the large-scale transition to telework triggered by the crisis. Also, we have to consider that the expansion of telework has consequences in terms of inequality not only across countries, but also within countries and across groups of workers. The jobs that can more easily shift to telework have, on average, higher wages and qualifications. According to Dingel and Neiman ( 2020 ) estimates, among high-paid activities 83% of jobs can be done at home for educational services; 80% for professional, scientific and technical services; 79% for management of companies and enterprises, etc.; conversely, among low-paid activities only 14% for retail trade; 8% for agriculture, forestry, fishing and hunting and 4% for accommodation and food services can be performed remotely.

In this paper, we contribute to the assessment of the employment implications of the COVID crisis by classifying economic sectors according to the confinement decrees of three European countries (Germany, Spain and Italy). The analysis of these decrees, which explicitly classify economic sectors as essential or non-essential, and in some cases specify sectors that must be forcefully closed, can be used to make a first assessment of the implications of the COVID crisis on labour markets, and also to speculate on mid and long-term developments, since the most and least affected sectors are probably going to continue to operate differently until a vaccine or other long-term solution is found.

2 Methodology

The present study is based on a detailed comparative analysis of the sector lockdowns in three European countries: Germany, Spain and Italy, as they result from national confinement decrees approved in March 2020. The three countries analysed have regulated the productive lockdown by identifying essential and not essential activities, broadly related to the satisfaction of fundamental needs: health, food, security, education and administrative services. Moreover, in the three countries as well as in most other countries, the firms that are allowed to operate are instructed to meet stringent health and safety requirements for their employees. Footnote 1

After a detailed qualitative analysis of the confinement decrees summarized in Table 3 of the " Appendix ", for each specific sector (NACE at the 2-digit level) and country we provide an indicator that ranges from 0 to 1. A value of 1 indicates that the sector is explicitly defined as essential, and thus can continue to operate even in the strictest confinement. A value of 0 indicates that the sector is considered non-essential, which may mean that it is forcefully closed or that it can operate only under certain conditions. Values between 0 and 1 indicate that some sub-sectors (NACE 3 or even 4 digit codes) within a given sector (NACE at 2 digits, which is our baseline) are considered essential and some not: in these cases, the value of the indicator reflects the share of sub-sectors considered essential, when possible adjusted for relative employment shares using EU-LFS data. Then, for each NACE 2-digit sector the values of the three countries are averaged into an overall indicator, which can be interpreted as the average degree to which a given sector is considered essential in the three countries analysed. Then, this indicator has been used to rank the sectors, providing a first criterion to classify them according to the impact of the COVID confinement decrees. The values of the three country-specific indicators, and the aggregate index, can be found in Table 4 in the " Appendix ".

Two additional criteria have been established to complete the classification of economic activities according to the decrees. First, whether a given sector can operate via telework, which mostly depends on the nature of economic activity in the sector: in general, activities and services that do not involve direct physical interaction (either with things or with people) can be remotely provided making use of ICT equipment. All the confinement decrees analysed state explicitly that independently of whether a given sector is considered essential or not, whenever possible it should operate via telework. Second, there is also an implicit or explicit differentiation in the decrees of those (non-essential) activities that are forcefully closed because they require direct face-to-face interaction with clients and therefore, they are particularly risky in the context of the COVID pandemic. Thus, the activities which are fully or mostly non-essential (values below 0.3 in the indicator) are classified in two different categories: those that are forcefully closed (5), and those that are mostly non-essential but not forcefully closed (and thus at least partly active, code 4). These two additional criteria are indicated in the column “Notes” of Table 3 in the " Appendix ".

Following this procedure (and as shown in the column “Clasif.” of Table 4 in the " Appendix "), the five categories in which we classified economic sectors according to the impact of the COVID confinement measures are summarised in Table 1 .

Using an ad-hoc extraction of EU-LFS data, Footnote 2 we applied this classification to the analysis of employment in Germany, Italy and Spain but also UK, Poland and Sweden trying to cover a wide spectrum of institutional labour market settings within Europe (Esping-Andersen 1990 ; Gallie 2009 ; Hall and Soskice 2001 ). The analysis of the employment structure across the previously defined sector categories allows to discuss the potential socio-economic effects of the confinement measures in the short-run, but also to speculate on the medium-term prospects (from the end of the confinement to the return to full normality). In the following section we will briefly document the employment distribution across categories in each of the European countries analysed, as well as the age and gender profiles of workers in the sectors classified by the impact of the COVID crisis. Then, we will highlight the differences in terms of employment characteristics, focusing on employment status and duration of contracts, and in terms of average wage levels.

A first impression of the differences across countries is provided by Fig.  1 summarising employment shares in each category. As discussed in the previous section, the categories are sector specific and therefore these patterns are entirely the result of structural differences. In particular, Poland is characterized by the biggest share of employment in essential activities —even higher than the EU-28 average—reflecting the importance in Poland of the primary sector (considered essential in the three confinement decrees analysed) compared to other countries. Indeed, as shown in the European Jobs Monitor 2019 (Hurley et al. 2019 ), the Polish economy while shifting its productive system toward core manufacturing sectors mainly related to European value chains (anchored to German manufacture industries, Danninger and Joutz 2007 ), is still characterized by a strong primary sector. On the other hand, employment in sectors active via telework is higher than the EU28 average in Sweden and the UK, but for different reasons: the predominance of the Public Sector in Sweden contrasts with the higher share of financial and professional services in the UK. More heterogeneity emerges between countries when dealing with the manufacturing sector which is split between the categories of mostly essential or mostly non-essential (but in both cases partly active and “not teleworkable”). Spain, Germany and Italy are characterized by an employment share above average in the mostly essential sectors, driven by a relative specialisation in chemical manufacturing, wholesale and retail trade. Furthermore, employment in the mostly non-essential activities (which includes the rest of manufacturing and construction) ranges between 25% for Poland to 15% for the UK. However, the three countries with a strongest manufacturing sector, Germany, Poland and Italy, specialize in different industries and occupy different positions in the European value chain, which will probably lead to different outcomes in the economic crisis ahead (Simonazzi et al. 2013 ).

Employment distribution across sector categories and country, (%)

Finally, Southern European countries and the UK emerge as those with the highest share of employment in the forcefully closed sectors. These mainly involve accommodation, leisure and tourism as well as personal care activities, all belonging to the category of Less Knowledge Intensive (LTI) services activities. As highlighted by Esping-Andersen ( 1990 ), this form of specialisation, especially in the private provision of care services, can be linked to the liberalization of the health sector reinforced by an increase in the demand due to an aging population. At the same time, the high share of employment in tourism and related activities in Italy and Spain is part of a deindustrialization process strengthened by the structural reforms of recent years and the labour market reforms approved after the 2008 crisis, which may have shifted investment towards less innovative and more labour intensive sectors. This last evidence is consistent with the regional polarization analysis presented in the 2019 European Jobs Monitor report, according to which Italy and Spain suffered a downgrading dynamic of their employment structure, compared to the average European trend in the last two decades (Hurley et al. 2019 ).

Differences in institutional settings and labour market regulations not only result in different employment structures across economic activities but they may have a differential impact on different segments of the population. In other words, the impact of the COVID lockdown decrees (and the COVID-induced economic crisis) vary across population groups, as we will discuss now.

According to Table 2 , the two categories that are more gender-segregated (dominated by one gender) are the closed sectors and the mostly non-essential sectors. In the closed sectors, the proportion of women for the EU28 as a whole is 56%, with even higher values in Poland and Germany. On the other hand, the mostly non-essential sectors are very heavily dominated by men, with only 24% of women for the EU28 as a whole. This latter result can be explained by the sectoral composition of the category, mainly driven by manufacturing and construction, which are very male-dominated. The other categories ( essential , teleworkable and partly active ) are not characterised by a clear gender segregation at the European level, but show a lot of variation by country. For instance, in Germany, Sweden and the UK, women are significantly more prevalent in the essential and teleworkable sectors (Poland also has more women in the latter category). Conversely, for the mostly essential sectors, the share of women is above the EU average in Spain and Poland.

Overall, the asymmetry in the impact of the COVID lockdowns by gender is quite evident for the forcefully closed sectors, which are likely to suffer more also in the mid-long term because of the lockdown and a more than probable decline in final demand for this type of services. However, changes in aggregate demand will shape the overall economic crisis ahead and may particularly hit the most internationally integrated manufacturing sectors, with a potential stronger effect on male workers who dominate those economic activities. However, the differences by gender at the EU level do not seem particularly strong, but they are stronger in some countries such as Poland.

Turning to differences by age, we can observe that higher shares of young workers (those aged 15–29) are found in the closed and to a lesser extent in the mostly essential sectors. But again, differences across countries need to be highlighted. First, Italy and Spain are characterised by a generally low level of youth employment which results in a share below average in all categories. Still, in these two countries young workers are relatively underrepresented in essential , teleworkable and mostly non-essential sectors. This can be explained by the very high average age of public employees, but also the low level of employment in financial and professional activities.

More evident are the differences in labour market regulations, which are related to significant differences in terms of share of temporary (Fig.  2 ) and self-employed (Fig.  3 ) workers across categories and countries. For the EU28, temporary employees represent 14% of total employment, but in the forcefully closed sectors the share of temps increases up to 21.6%. As a result of labour market flexibilisation processes occurred in recent decades, in Southern and Eastern countries the proportion of workers with fixed term contracts is higher than elsewhere across all sector categories compared to the other selected countries. The only exception is Sweden, where temporary workers are overrepresented in the closed sectors converging to the Southern countries level. Spain even doubles the average share of temporary employment in all categories but the closed ones. A similar pattern applies to Poland for the most essential and partly active sectors, drawing attention to the precarious character of the impressive employment growth of Poland in the last two decades. A second proxy for precariousness in the labour market is the share of self-employed (without employees) in the total economy. While the self-employed are over-represented in closed activities almost everywhere – 21.6% compared to the 14% across all economic activities at the EU-28 level, suggesting that the self-employed have been hit particularly hard by the Covid-19 crisis (see also Blundell and Machin 2020 )—, there exists a remarkable variation across countries. As Fig.  3 underlines, the proportion of self-employed in Poland almost doubles the EU average, followed by Italy. The above figure also suggests that in the two Southern countries, self-employment is over-represented in the mostly essential sectors, where wholesale and retail trade dominate.

Share of temporary employment by sector category and country

Share of self-employment by sector category and country

To conclude our analysis of the different employment impact of the COVID confinements by country and sector category, we explore the job-wage distribution in each country-category pair. More precisely, following the approach adopted by Hurley et al. ( 2019 ) we rank each occupation-sector combination (jobs) by their average wages in each country, and then assign those jobs to the corresponding job-wage tercile. In other words, the employment structure is partitioned into three terciles—low, mid and high paid jobs—generated by the weighted wage ranking built as an ordinal measure. This way, we are able to compute the share of employment in each tercile by category and country. As underlined by Fig.  4 , the closed and mostly essential sectors are not only the more precarious but also those with the highest share of low-paid jobs (60% on average). This evidence thus reflects the vicious nexus between atypical employment and low wages (Raitano and Fana 2019 ), as well as the relationship between Less Knowledge Intensive Services and low wages. Although in most manufacturing activities mid-paid jobs dominate the wage distribution, the share of low-paid jobs reaches more than the 20% in the countries with a stronger manufacturing base (Germany, Italy and Poland), probably reflecting the effect of wage moderation policies adopted during the last decades.

Jobs-wage terciles by country and sector category

4 Conclusion and discussion

As expected, the previous analysis reveals very asymmetric effects of the COVID lockdown measures across different groups of workers within and between the selected European countries. In particular, it reveals that the most negative effects tend to concentrate on the most vulnerable and disadvantaged workers in low productivity services. It seems reasonable to assume that the workers more likely to lose their jobs because of the lockdown in the short run, and face a particularly high uncertainty in the mid-term, are the same categories identified in our analysis as the most negatively affected by the COVID confinement measures. These workers are overrepresented in countries where a downgrading dynamic of the economic structure toward low productive services has been recently observed (Hurley et al. 2019 ). At the same time, medium term effects may extend these negative effects also to countries with a higher share of manufacturing activities mainly dependent on European “core” value chains, as in the case of the automotive sector.

Thus, the negative consequences, unfortunately, tend to pile up. The countries that are being hardest hit by the pandemic itself (Spain and Italy, and also the UK) are the countries more likely to suffer the worst employment implications of the confinement, because of their specialisation in sectors which are more likely to be forcefully closed. In fact, these were also the countries that were most vulnerable before the crisis: characterised by high unemployment, precarious work (especially temporary contracts), inequality and relative poverty compared to the rest of the EU. Unfortunately, Spain and Italy were also the countries most affected by the financial crisis and both fiscal consolidation and structural reform packages. The current crisis, therefore, is likely to exacerbate ongoing economic asymmetries in Europe, as well as pre-existing inequalities in general, unless very drastic policy measures are implemented very quickly, with a decisive redistributive component also at the EU level.

A recent ad-hoc survey carried out by Eurofound ( 2020 ) paints a stark picture of people across the 27 EU Member States who have seen their economic situation worsen and are deeply concerned about their financial future. The same survey also showed a dramatic fall in trust in the EU and their national governments, an observation that warns about the possible political consequences of the crisis at all levels in the short and the mid-term. All these concerns and problems are likely to be intensified in the countries that are being hardest hit by the current crisis.

The COVID crisis is so deep that it will not only radically affect labour markets in the short and medium run, but it can also change substantially the way the work is organised. Telework may be here to stay, as recent data suggest, but this is not the only transformation. Early evidence from Italy suggests that industries employing more robots per worker in production tend to exhibit a lower risk of contagion due to Covid-19 (Caselli et al. 2020 ). As has already happened with telework, automation could be accelerated in the aftermath of the crisis since it can be used as a strategy to minimize risks for health while preserving production and economic activity.

The possibilities for economic recovery are very uncertain to say the least and strongly depend on the economic policies adopted both at the national and European level. As in any deep crisis, we will have to face sharp economic restructuring within and between countries as operating margins, income and demand fall sharply in the following months and years. Ten years after the last crisis, we are now aware that a narrow focus on fiscal consolidation and exports as the main exit strategy resulted in asymmetric weaknesses and vulnerabilities that are again surfacing in the last few months.

While it is imperative that European economies provide income support to the most affected groups as soon as possible, a longer term vision should be put in place for confronting the still severe effects of deindustrialisation in many European countries and for reversing the recent narrowing of social welfare: for instance, by fostering alternative sources of economic growth at a properly large scale (i.e. EU Green Deal) and by setting the foundations a future European Welfare State.

In particular, the comparative analysis is based on the Recommendations from the Minister of Health and the Agreement between the Chancellor and the heads of state for Germany, approved respectively on March 16th and 22nd. For the Italian case, we use the decree approved on March 10th containing urgent measures at the national level, and the one approved on March 25th, Urgent Measured to tackle the epidemiological emergency related to Covid-19 . Finally, the Spanish analysis is based on two main Royal decrees: the first one was approved on March 14th (Royal Decree 463/2020) and declared the State of Alarm in the country, while the second one (Royal Decree 10/2020) was approved on March 29th) and identified the activities considered essential. More info on the content of the main decrees regulating activities can be found in Table 3 .

The ad-hoc extraction is based on 2018 annual data and uses Nace rev.2 classification for economic sectors, therefore the analysis provided do not need any reclassification over time and across countries.

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Fana, M., Torrejón Pérez, S. & Fernández-Macías, E. Employment impact of Covid-19 crisis: from short term effects to long terms prospects. J. Ind. Bus. Econ. 47 , 391–410 (2020). https://doi.org/10.1007/s40812-020-00168-5

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Received : 29 May 2020

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Published : 15 July 2020

Issue Date : September 2020

DOI : https://doi.org/10.1007/s40812-020-00168-5

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

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

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

Worsening inequality within and across countries

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

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

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

The short-term government responses to the crisis

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

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

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

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

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

Emerging threats to an equitable recovery

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

Figure 1.3 Conceptual framework: Interconnected balance sheet risks

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

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

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

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

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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.

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The unemployment impacts of COVID-19: lessons from the Great Recession

Subscribe to the economic studies bulletin, stephanie aaronson and stephanie aaronson senior associate director, division of research and statistics - federal reserve board francisca alba francisca alba former research analyst - economic studies.

April 15, 2020

• 12 min read

Efforts to stop the spread of the novel coronavirus—particularly the closure of nonessential businesses—are having an unprecedented impact on the U.S. economy. Nearly 17 million people filed initial claims for unemployment insurance over the past three weeks, suggesting that the unemployment rate is already above 15 percent [1] —well above the rate at the height of the Great Recession.

However, these aggregate statistics mask substantial variation across the country. Some cities, such as New York, are already experiencing full blown pandemics and non-essential business activity has been substantially halted. In other areas economic activity has slowed less. This variation represents the degree of spread of the virus, the timing and extent of the state and local response, and the sectoral mix of economic activity. Work by our colleagues suggests that metropolitan areas dependent on energy, tourism, and leisure and hospitality are likely to suffer greater slowdowns, while those that depend more on industry, agriculture, or professional services will suffer less.

Figure 1 [2] displays the sum of initial claims for unemployment insurance filed during the weeks ending March 21, March 28, and April 4 for selected states as a share of the labor force [3] . As can be seen, in the hardest hit areas, the number of initial claims as a share of the labor force was double or triple that of the least affected areas. While some of the differential likely reflects variation in unemployment insurance systems across states, this explanation is unlikely to explain the entire differential. Since, as can be seen, the states with relatively more claims include those dependent on tourism (Nevada and Hawaii) and those which have been hard hit by the virus ( Rhode Island, Pennsylvania, and Michigan ), while those with few claims have low incidence of the virus. Hence, it does appear, at least to start, there has been an idiosyncratic aspect to how states, and implicitly metropolitan areas, are affected by the pandemic. Eventually, however, a shock of the magnitude of the novel coronavirus will certainly result in a national recession, affecting the entire country to a greater or lesser degree.

In this post, we examine how shocks to the economy, like the one we are experiencing now with the coronavirus, play out at the metropolitan level, with a specific focus on the unemployment rate. We use as our laboratory the Great Recession, which started in metropolitan areas that were most affected by the housing bubble and bust, but then spread nationally. In line with previous research, we find that there is persistence in the unemployment rate across metropolitan areas. Idiosyncratic shocks disrupt these persistent differentials, but over time local economies adjust, and metropolitan areas tend to re-sort back to their previous place in the distribution. Our results also suggest that negative macroeconomic shocks tend to affect high-unemployment rate areas most harshly, and that strong macroeconomic performance helps to ameliorate not only the aggregate shocks, but also the differences across metropolitan areas.

Metropolitan Areas Tend to Have Similar Unemployment Rates Over Time

As has been well documented, the economies of metropolitan areas vary in structural ways, for instance based on their industrial mix, geography , demographics , and infrastructure. These structural differences result in persistent differences in labor market outcomes, including unemployment rates [4] .

In Figure 2, we examine the persistence of the unemployment rate by metropolitan area. Each dot represents a metropolitan area, and dots are color coded according to their quartile in the distribution of unemployment rates in 2006. The x-axis denotes the metropolitan area’s unemployment rate in 2006 and the y-axis the area’s unemployment rate in 2018. These are both years at which the economy was near, but not at its peak.

Figure 2 shows a clear, positive relationship between unemployment rates in 2006 and 2018: lower unemployment rates in 2006 are associated with lower unemployment rates in 2018. Notably this relationship holds across the entire sample, and also within the unemployment rate quartiles. Our results suggest that a 1 percentage point higher unemployment rate in 2006 is associated with a 0.6 percentage point higher unemployment rate in 2018. Moreover, the unemployment rate in 2006 explains 44 percent of the variation in the unemployment rate in 2018.

Although Metropolitan Areas Experiencing Idiosyncratic Shocks Undergo Large Changes in Their Unemployment Rates, They Tend to Revert Back to Their Previous Place in the Distribution:

In addition to the persistent characteristics that shape the economies of metropolitan areas over long periods, idiosyncratic events specific to metropolitan areas can also have a significant impact. Examples of these types of shocks include storms, like Hurricane Katrina, which reshaped New Orleans, or technical changes such as hydraulic fracturing, which made it possible to extract oil and gas from areas where they were previously inaccessible. These idiosyncratic shocks may or may not have long-lasting impacts.

Figure 3 shows the distribution of metropolitan area unemployment rates over a fourteen-year period. The figure highlights five metropolitan areas. In 2006 these highlighted areas were in the first quartile of the distribution; meaning that these areas had lower levels of unemployment than 75 percent of the metropolitan areas displayed in the figure. By 2009, these five areas had unemployment rates that were in the top quartile of the distribution that year. While it is true that the unemployment rate on aggregate was also rising during this period (as can be seen by the fact that the unemployment rates of all the other metropolitan areas, represented by the light gray bars, move up), these areas were affected earlier and by more—a function of the fact that they were hit by a specific, negative idiosyncratic shock: the bursting of the housing bubble. These metropolitan areas are located in Florida and Nevada, states with large housing bubbles, and the specific metropolitan areas highlighted experienced large drops in local housing prices when the bubble burst in 2007 [5] .

Like the financial crisis, the current crisis also has an idiosyncratic component. As noted in the introduction, metropolitan areas first affected by the virus closed non-essential businesses earlier. Moreover, the economies of metropolitan areas reliant on tourism, leisure and hospitality, and energy slowed quickly as travel restrictions were imposed and global demand declined. Other areas with fewer cases of the virus and those with economies dependent on industry, agriculture, or professional services appear so far to have been less impacted.

Interestingly, Figure 3 also illustrates that by 2018 these metropolitan areas that faced a negative shock from the bursting of the housing bubble had largely recuperated, with unemployment rates returning to levels similar to 2005/2006. This finding is in line with Blanchard and Katz (1992) who show that state-level unemployment rates tend to recover approximately five to seven years after experiencing a negative shock to employment. Note, this isn’t to say that adjustment is automatic—indeed specific policies geared at addressing idiosyncratic shocks may be necessary to help local areas cope when they face a crisis.

A Strong National Economy Helps All Metropolitan Areas, Even Those with Persistently High Unemployment Rates

Figure 4 plots the distribution of the unemployment rate by metropolitan area from 2005 to 2018, with dots of different colors and sizes identifying the quartiles of the unemployment rate distribution in 2006, as in Figure 2. (We make the dots different sizes to make it possible to follow the movements in the unemployment rates of the metropolitan areas from year to year.)

There are several phenomena that can be observed in this graph. One is the central tendency of the metropolitan area unemployment rates—as a whole, are the unemployment rates relatively high or low in a given year—which reflects the state of the business cycle. The second is how disperse the unemployment rates are—are the unemployment rates across the metropolitan areas relatively similar (are they clumped together) or are they spread out, with some areas having high rates and others relatively low rates. And the third is the relative position of the unemployment rates of specific metropolitan areas—do metropolitan areas that have high or low unemployment rates to start remain in those positions over the entire time period. To help elucidate these points, we also show the mean, range, and variance of the unemployment rates for groups of years in Table 1.

The first thing to note in Figure 4 is the impact of the Great Recession across metropolitan areas. As the recession gained full force in 2009, metropolitan unemployment rates as a whole began to increase. Second, the differences in unemployment rates across metropolitan areas widened in years in which the economy was underperforming. And, metropolitan areas that started off relatively disadvantaged tended to experience the highest unemployment rates during the recession. This information is summarized in Table 1, where we can see that the mean, variance, and range of the unemployment rate all increase substantially during the recession from the pre-recession period.

Table 1: Spread of the Unemployment Rate

Of course, this aggregate phenomenon is being laid on top of the idiosyncratic shocks we discussed previously, in particular, the bursting of the housing bubble. For instance, the metropolitan areas that we identified as having been particularly hard hit by the bursting of the housing are among those metropolitan areas captured by the yellow dots, which rise much more than average during the financial crisis and recession. But, as the economy recovered, and the aggregate unemployment rate fell, metropolitan area unemployment rates began to converge again. Many areas that saw the largest deterioration in their unemployment rates during the financial crisis and the Great Recession experienced substantial improvement. This finding is consistent with prior research demonstrating that strong macroeconomic conditions are particularly beneficial for workers that are disadvantaged in the labor market.

Notably, the distribution of unemployment rates in 2018 looks fairly similar to that of 2005 and 2006. By this we mean that metropolitan areas with the lowest unemployment rates prior to the Great Recession (the yellow dots) tend to have lower unemployment rates in 2018 and metropolitan areas with the highest unemployment rates (the purple dots) tend to have higher unemployment rates. This is just another way of illustrating the result in Figure 2, showing the persistence of the unemployment rate across metropolitan areas over time, even in the face of significant idiosyncratic and macroeconomic shocks.

Policy Implications for COVID-19:

Metropolitan areas have high (or low) unemployment rates for different reasons. First, there are structural causes—such as average education levels or industry mix—which mean that some areas tend to have high or low unemployment rates over time. Second, there are local idiosyncratic shocks that might cause metropolitan areas to see large but typically transitory increases or decreases in their unemployment rates. Finally, metropolitan areas are buffeted by the business cycle—aggregate shocks that play out similarly, although not identically, across metropolitan areas.

The current crisis in which we find ourselves is no different. Before the pandemic reached our shores, metropolitan areas had distinct capacities to respond based on their structural differences. The impact of the virus will vary across metropolitan areas depending on their exposure and industrial mix. Finally, all metropolitan areas will experience the spillovers from the deep recession as economic activity is curtailed.

Policymakers should take into account these different types of shocks that are buffeting localities, because they suggest different policies. Our results indicate that policies aimed at ensuring liquidity in financial markets now and stimulating aggregate demand once it becomes safe to engage in non-essential economic activity will have a broad positive impact on economic outcomes across metropolitan areas and will reduce disparities between them. However, some localities will require more help, either because they face a particularly pernicious impact from the pandemic or because long-standing structural factors make it particularly difficult for them to weather the economic headwinds we face. Our colleagues Louise Sheiner and Sage Belz show that state tax revenues declined by about 9 percent during the Great Recession and argue that recently passed legislation—such as CARES Act and FFCRA—does not provide enough funding to prevent states and localities from cutting spending. Similarly, our colleague Matt Fiedler and Wilson Powell III make the case for increasing the federal match rate for Medicaid in proportion to the amount that the state’s unemployment rate exceeds some threshold. And the Metropolitan program discuss policies that would bolster metropolitan areas by supporting small businesses.

Becca Portman contributed to the graphics/data visualization for this blog.

[1] This is a back-of-the-envelope calculation which assumes all initial claims translate into spells of unemployment. We take the number of initial claims from the weeks ending in April 4, March 28, and March 21 (16,780 thousand); add the number of unemployed people in March 2020 (7140 thousand); and divide by the March 2020 labor force: (16,780 + 7140)/162913 = 14.68%. Although it is not always the case that initial claims translate into spells of unemployment, this calculation is, nonetheless, most likely an underestimate of the unemployment rate as not all people who become unemployed are eligible to receive benefits and not everyone who is eligible for unemployment insurance applies. Moreover, this estimate likely understates the number of people who have tried to file claims in recent weeks, due to limitations with the state unemployment insurance systems which have been overwhelmed. That said, there is currently less certainty about the relationship between insured unemployment and aggregate unemployment because of changes in the unemployment insurance eligibility rules. [2] Note that the ratios in this graph should be interpreted with caution. We choose the total labor force as the denominator because recent legislation has changed the types of workers covered by unemployment insurance. However, this denominator likely overstates the number of people covered by unemployment insurance. The numerator is not without issues either. As mentioned above, it is likely to understate the number of people who have attempted to file claims, due to limitations with the unemployment insurance systems. [3] Claims data by metropolitan area aren’t readily available. [4] Katheryn Russ and Jay Shambaugh show that the persistence of the unemployment rate is related to the average level of education in a county. They find that counties with lower levels of education have higher levels of persistence. In other words, areas with lower, average education are more likely to get “stuck” with a high unemployment rate over time. [5] We also examine metropolitan areas that were in the fourth quartile of the distribution in 2006 and subsequently moved to near the bottom of the distribution in 2009. We find that these areas are mostly located in places with positive energy shocks.

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This article provides an account of the worldwide economic impact of the COVID-19 shock. In 2020, it severely impacted output growth and employment, particularly in middle-income countries. Governments responded primarily by increasing expenditure, supported by an expansion of the supply of money and debt. These policies did not put upward pressure on prices until 2021. International trade was severely disrupted across all regions in 2020 but subsequently recovered. For 2021, we find that the adverse effects of the COVID-19 shock on output and prices were significant and persistent, especially in emerging and developing countries.

Fernando Martin is an assistant vice president and economist, Juan M. Sánchez is a vice president and economist, and Olivia Wilkinson is a senior research associate at the Federal Reserve Bank of St. Louis.

INTRODUCTION

For over two years, the world has been battling the health and economic consequences of the COVID-19 pandemic. As of the writing of this article, deaths attributed to COVID-19 have surpassed six-and-a-half million people.  Global economic growth was severely impacted: World output by the end of 2021 was more than 4 percentage points below its pre-pandemic trend.  International trade was also significantly disrupted at the onset of the pandemic. The pandemic also prompted a strong policy response, resulting in a rise of government deficits and debt as well as widespread increases in the money supply. Finally, after an initial decline, prices have soared, resulting in elevated inflation rates.

This article provides an account of the worldwide economic impact of the COVID-19 shock. This shock was not felt simultaneously around the world, and mitigation policies, both health related and economic, varied substantially across countries. Yet there are some significant similarities in outcomes, especially when considering the pandemic period as a whole. Our analysis focuses on the shock's effects on specific groups of countries, related by their level of development and geographical location.

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, and hence the money supply increased everywhere. The specific circumstances surrounding the shock implied that the expansionary fiscal and monetary policies did not put upward pressure on prices until 2021. International trade was severely disrupted across all regions in 2020 but subsequently recovered. When extending the analysis to 2021, we find that the adverse effects of the shock on output and prices have been significant and persistent, especially in emerging and developing countries.

Read the full article .

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Research Article

The varying impacts of COVID-19 and its related measures in the UK: A year in review

Roles Conceptualization, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft

* E-mail: [email protected]

Affiliation Department of Sociology, University of Oxford, Oxford, United Kingdom

Roles Funding acquisition, Writing – review & editing

• Muzhi Zhou, 
• Man-Yee Kan

• Published: September 29, 2021
• https://doi.org/10.1371/journal.pone.0257286
• Peer Review
• Reader Comments

We examine how the earnings, time use, and subjective wellbeing of different social groups changed at different stages/waves of the pandemic in the United Kingdom (UK). We analyze longitudinal data from the latest UK Household Longitudinal Survey (UKHLS) COVID study and the earlier waves of the UKHLS to investigate within-individual changes in labor income, paid work time, housework time, childcare time, and distress level during the three lockdown periods and the easing period between them (from April 2020 to late March 2021). We find that as the pandemic developed, COVID-19 and its related lockdown measures in the UK had unequal and varying impacts on people’s income, time use, and subjective well-being based on their gender, ethnicity, and educational level. In conclusion, the extent of the impacts of COVID-19 and COVID-induced measures as well as the speed at which these impacts developed, varied across social groups with different types of vulnerabilities.

Citation: Zhou M, Kan M-Y (2021) The varying impacts of COVID-19 and its related measures in the UK: A year in review. PLoS ONE 16(9): e0257286. https://doi.org/10.1371/journal.pone.0257286

Editor: Florian Fischer, Charite Universitatsmedizin Berlin, GERMANY

Received: October 13, 2020; Accepted: August 27, 2021; Published: September 29, 2021

Copyright: © 2021 Zhou, Kan. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All data files are available from the UK Data Service database (study number(s) 6641, 8644). Dat file URL: https://beta.ukdataservice.ac.uk/datacatalogue/studies/study?id=8644 https://beta.ukdataservice.ac.uk/datacatalogue/studies/study?id=6641 .

Funding: This work is supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (awardee: Man-Yee Kan, grant number 771736). Funding website: https://ec.europa.eu/programmes/horizon2020/en . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

More than one year has passed since the United Kingdom (UK) officially announced its first national lockdown on 23 March 2020 due to the rapid spread of COVID-19. The outbreak of COVID-19 and the massive lockdown measures have greatly changed people’s lives. When people were instructed to stay at home and maintain physical distancing, the lives of millions of people were affected. For months, many people were unable to go to work or school, nor could they meet friends and relatives. What was unexpected was that people in the UK experienced a total of three national lockdowns over the past year. Now, people’s lives are far from what they were before the first lockdown, and the pandemic is still not over.

Recent evidence has shown that the COVID-19 pandemic and related social and economic measures, such as physical distancing and business closure, have differential impacts on various social groups. In the UK, for example, women and parents are found to have experienced a larger reduction in subjective wellbeing [ 1 , 2 ]. Black, Asian, and minority ethnic (BAME) immigrants were more likely to experience economic hardship immediately after the first national lockdown [ 3 ]. In addition, among those who were known to have COVID-19, people of BAME background in the UK had a death rate that was higher than that of white people [ 4 ]. As Damian Barr said in his poem, “we are in the same storm, but we are not all in the same boat [ 5 ]”.

These earlier findings identified the existence of immediate unequal impacts for different social groups, but our understanding of the longer-term impacts of COVID-19 and related measures remains limited. We know little about how the impacts might have changed since the first lockdown. The COVID-19 pandemic has already lasted for more than one year, and the UK has experienced three national lockdowns. Early research was confined by data that covered only two time points—such as before and shortly after the announcement of the first lockdown. Little is known about to how unequal social impacts reveal themselves at different stages of the COVID-19 pandemic, especially with repeated lockdowns. This omission hinders our understanding of how COVID-19 and COVID-induced social policies, such as physical distancing measures, working from home, and the closure of certain businesses, which have been changing on a weekly or even daily basis, progressively affect people’s lives. Documenting the development of the impacts of COVID-19 and COVID-induced measures is important for us to understand the consequences of this rapidly developing pandemic and help policymakers plan for future waves and future pandemics.

We need more comprehensive and up-to-date research on how inequalities have changed as the COVID-19 pandemic develops with repeated waves and the various measures to contain it were implemented over the past year. We conducted analyses on a nationally representative population data from the latest UK Household Longitudinal Survey (UKHLS), which was conducted before the first lockdown in March 2020, during the first lockdown from April to June 2020, during the ease of the first lockdown (June to September 2020), and during the later two lockdowns (November 2020, and from January 2021 to March 2021). In this paper, we contribute to COVID-19 research by providing a dynamic picture of how people’s labor earnings, time use, and wellbeing changed across different stages of the pandemic. We further investigated whether and the extent to which the inequalities in these outcomes based on gender, ethnicity, and educational level have changed over the past year.

In what follows, we first review the latest works on the impact of COVID-19 and COVID-induced measures on people’s lives, focusing on three dimensions of social inequality: gender, race/ethnicity, and education. We then outline the development of the COVID-19 pandemic and the lockdown measures in the UK from March 2020 to April 2021. Next, we introduce the data and its longitudinal design, which enables us to compare the information of the same individuals before the start of this pandemic and at different time points over the past year. Finally, we will report the results of fixed-effect regression analyses and discuss our conclusions.

The impacts of COVID-19 and its related measures

The COVID-19 pandemic has developed for over one year. In many countries, repeated waves of COVID-19 have been observed. The primary aim of COVID-19 induced measures is to contain the virus by reducing physical contacts between people. Many of these measures immediately affect people’s behaviors, but others could have longer-term impacts. For example, the closure of businesses and work-from-home guidance tremendously altered people’s working patterns. Reductions in paid work time and earnings have been immediately recorded in countries that have introduced lockdown measures such as Australia [ 6 ], the UK [ 3 , 7 ], and the United States (US) [ 8 ]. When more people stayed at home and the option of outsourcing domestic work was reduced due to business closure or the fear of contracting COVID-19, it is not surprising to see that people spent substantially more time on unpaid domestic work than they had in the past [ 6 , 7 , 9 , 10 ].

People’s feelings also changed. The contraction of COVID-19 is associated with a series of symptoms such as a high temperature, continuous cough and a loss or change to the sense of smell or taste. Serious cases will result in hospital admission and death. In the UK, the case-fatality rate is estimated to be 2.1% [ 11 ]. Daily news reporting the surging number of new cases and deaths brings in a high level of worry about health and security [ 2 ]. In addition, loss of employment, financial strain, and social isolation are well-known factors that negatively affect mental health [ 12 – 14 ]. Not surprisingly, soon after the start of the pandemic, worsened subjective wellbeing was observed in Australia [ 6 , 15 ], the UK [ 2 , 16 , 17 ], and the US [ 18 ]. Once daily increase of COVID-19 cases declined and the lockdown restrictions began to be lifted, people’s subjective wellbeing started to recover. As Pierce et al. [ 2 ] noted by using the first five waves of the same UKHLS COVID study data as in this paper, “[b]etween April and October 2020, the mental health of most UK adults remained resilient or returned to pre-pandemic levels.” However, “[a]round one in nine individuals had deteriorating or consistently poor mental health.”

This COVID-19 pandemic and its related measures have raised increasing concerns of exacerbated social inequalities. Since long before the pandemic, gender inequalities have existed in the labor market. In the UK, the labor force participation rate for men is higher than that for women, and men are also much more likely to work full time [ 9 , 19 ]. Women are more likely to be at-home workers. Reasons for this inequality include inflexible workplace expectations, gender norms expecting men to be the primary earners and women the primary caregivers, and discrimination in the labor market. When people are required to work from home, the spatial boundary between market work and family life is blurred. Many studies have investigated whether the changes in time use due to lockdown measures are the same for women and men. Between March and May 2020 (UK 1st lockdown), British men were found to be more likely to be furloughed or dismissed from work than women [ 20 ]. However, studies focusing on the labor market performance of parents reveal a different pattern. In the UK, during the first lockdown period from April to May 2020, among parents with children aged between 4 and 15, mothers were found to be more likely to be laid off, furloughed, or quit their jobs [ 21 ]. Similarly, in Australia [ 6 ], Canada [ 22 ], and the US [ 23 ], mothers with young children experienced a larger change in their paid work time or were more likely to leave their jobs. On the other hand, several studies have reported improvements in the domestic division of labor: the increase in domestic work was larger for men than for women during the lockdown period in Australia [ 6 ], Canada [ 24 ], France [ 25 ], and the US [ 26 ]. However, contrary results were reported in Germany [ 27 ] and Spain [ 28 ]. The decline in subjective wellbeing also differs between women and men. In the UK and Australia, women were found to experience a larger reduction in subjective wellbeing than men [ 1 , 2 , 6 , 9 , 29 ].

In the UK, BAME immigrants were more likely to experience economic hardship just after the first lockdown [ 3 ]. Compared with their white counterparts, BAME immigrants were also found to suffer a larger decline in subjective wellbeing at the beginning of the March 2020 lockdown in the UK [ 3 , 30 ]. In the US state of Indiana, Black Americans were more than three times more likely to lose their jobs than whites [ 31 ]. In contrast, another study highlights that white Britons in middle-income jobs were more likely to experience job loss, primarily driven by the fact that many BAME people are employed in key sectors such as the health and social care services, which were exempt from the lockdown measures and instead had a surge in work demands, during the first UK lockdown [ 20 ]. Notably, in the UK, people of BAME backgrounds had a death rate that was higher than that of white people after they were confirmed to have COVID-19 [ 4 ].

People with less education and lower income suffered substantially during the pandemic. They were particularly hit hard with a higher chance of losing their jobs and earnings in countries such as Canada [ 32 ], the UK [ 20 ], and the US [ 31 ]. Many of the less educated are trapped in lower-skilled occupations with tight financial constraints. Consequently, the less educated group reported a heightened level of distress during the first lockdown in the UK [ 33 ]. However, one US study reports that the decline in subjective wellbeing up to April 2020 was larger among the more educated, possibly because the more educated might have felt a greater loss of control and wealth due to COVID-19-related uncertainties [ 18 ]. Another study conducted in the US between April 2020 and June 2021 pointed out that part of the reason for the deterioration of mental health results should be attributed to the concurrent presidential election and unrest in domestic politics [ 34 ].

Again, the current literature has focused extensively on the impacts of the relatively early stage of this pandemic. In particular, studies that have employed the same British data source as the present study have examined the changes in earnings, time use, and subjective wellbeing during the implementation of the first national lockdown in late March 2020 [ 3 , 7 , 9 , 10 , 20 ]. Pierce et al.’s work [ 2 ] on subjective wellbeing is an exception. Their work examined the recovery of subjective wellbeing when the first lockdown measures were eased from June to October 2020. However, their study did not cover the later lockdowns in November 2020 and January 2021. In this article, we will provide a first-year review of COVID-19 development in the UK and document how people have responded to the first lockdown, the ease of the first lockdown, and the later two lockdowns. This evaluation will reveal whether people responded similarly to repeated lockdowns and whether these changes in earnings, time use, and feelings are temporary or long-lasting.

Timeline of the lockdown measures in the UK

On 31 January 2020, the first two positive cases of COVID-19 were confirmed in the UK. On 5 March 2020, the first patient who tested positive for COVID-19 died. On 23 March 2020, the Prime Minister placed the UK on lockdown to slow down the outbreak of this pandemic. These measures included physical distancing, school closures, working from home, and closure of non-essential businesses, including pubs and cafes. Key sectors, including health and social care, education and childcare, and key public services, were allowed to operate.

To maintain employment and to protect individuals and businesses from economic hardship, a coronavirus job retention scheme was implemented for the period between late March and the end of October 2021 to cover 80 percent of the regular salary of furloughed employees, up to a maximum of £2,500 per month [ 35 ]. In April, the UK had more than 10,000 deaths related to COVID-19. In May, phased reopening of shops and schools was announced, and those who were unable to work from home were expected to return to the workplace.

Beginning on 1 June 2020, schools were open for all Reception, Year 1 and Year 6 pupils, but the summer holiday soon arrived. Nonessential businesses reopened gradually beginning on 15 June. Beginning on 4 July, pubs, cinemas, restaurants reopened. Physical distancing rules were relaxed from a “two-meter” to a “one-meter plus” rule. In August, restrictions were eased further, although the pandemic was far from over.

The UK variant of the coronavirus (scientific name B.1.1.7, WHO name Alpha) was first identified in September 2020 and was considered to be more transmissible and potentially deadlier. In late September, people were required to work from home with a 10 pm curfew for the hospitality sector. In October, England entered a 3-tier system where different regions were classified into different tiers depending on the level of the spread of the virus. Soon after, the second national lockdown came into force on 5 November and lasted until 2 December. People were told to stay at home. Other measures included the closure of the hospitality sector and nonessential shops, but schools were open, and people could leave their home for outdoor exercise. After 2 December, the UK then entered a stricter 3-tier restriction system.

However, this 3-tier system did not last long. After Scotland announced a lockdown, on 4 January 2021, a third national lockdown was announced. Schools were closed again, and people were urged to stay at home. This time, the measures were stricter than those in the second lockdown. They included “Stay at home at all times, wherever possible,” “Not allowed to meet others from outside your household (or support bubble),” “All retail and hospitality venues must close,” and “Personal care services have to close.” Schools were closed to most pupils, except for the children of critical workers and the most vulnerable children. Nurseries were kept open.

Since 8 March, schools in the UK have been completely reopened. Nonessential retail and personal care services have been reopened since 12 April. People have been allowed to meet outdoors, as a number of restrictive measures have been lifted since 17 May. A complete easing will occur on 19 July 2021. The Prime Minister has pledged that all adults in the UK will be offered their first dose of a COVID-19 vaccine by the end of July.

By 16 April 2021, the recorded number of deaths related to COVID-19 had reached over 127,000 in the UK. Fig 1 displays the spread of COVID-19 and related deaths in the UK during the research period. A more detailed timeline of the UK lockdowns can be found at [ https://www.instituteforgovernment.org.uk/sites/default/files/timeline-lockdown-web.pdf ]. Fig 1 shows the development of the COVID-19 pandemic in the UK based on data provided by the UK government.

• PPT PowerPoint slide
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Note: Data source: https://coronavirus.data.gov.uk/details . Crude death rate is new deaths within 28 days of a positive test per 100,000 population.

https://doi.org/10.1371/journal.pone.0257286.g001

Data and methods

Data and sample.

We use data from the first eight waves of the UKHLS COVID study data and the preceding two waves (2017/18 and 2018/19) of the UKHLS main survey [ 36 ]. The UKHLS is a household panel survey and started its first wave in 2009 with a nationally representative sample of 51,000 adults (aged 16 and above) from approximately 40,000 households. Individuals were followed up annually and were interviewed face-to-face. This research is based completely on the UKHLS data that are publicly available through the UK Data Service (Study numbers: 6614 and 8644) and are completely anonymous.

Regarding the COVID study, households who participated in previous UKHLS surveys were contacted to fill in a monthly online questionnaire beginning in April 2020. The complementary telephone survey started in May 2020. Participation in the survey was voluntary. Approximately 16,000 respondents (aged 16 and above) completed this first wave of the COVID survey with a response rate of 42%. Currently, data from the first eight waves of surveys conducted in the last week in April, May, June, July, September, November in 2020 and the last week in January and March in 2021 are available.

Our analytic sample contains individuals who have participated in the UKHLS main survey and at least one of the eight waves of the COVID study. The respondents all had access to the internet or telephone to participate in the surveys. This requirement might have caused a sample selection bias. In a supplementary analysis, the sample from the COVID study is found to be socioeconomically advantaged in terms of employment, occupation, education, and homeownership compared to the full UKHLS sample. If we assume that one’s socioeconomic status has a protective effect on the negative consequences of the COVID-19 and related lockdown measures, the reported results may underestimate the potential negative impacts of the COVID-19 and the related lockdown. Nonetheless, one paper discusses this issue of nonrandom sample selection and demonstrates that the bias due to sample selection is very limited once weight is considered [ 37 ]. In the following analysis, we apply the individual weights, which were adjusted for “unequal selection probabilities and differential nonresponse” and are supplied in the data [ 38 ]. Based on the User Guide for the data, these weights “scale respondents to the eligible population in the UKHLS wave 9 sample, adjusted for death, incapacity and emigration occurring between wave 9 and the start of the COVID-19 web survey.” [ 38 ] This approach has been used in previous work analyzing the same data [ 2 , 3 , 20 ].

Our sample includes respondents of prime working age (between 20 and 65) in 2020. Two percent of the UKHLS COVID sample has missing values in the predictors to be used in regressions. The numbers of observations with no missing predictors are 10484, 9008, 8478, 8210, 7642, 7083, 7019, and 7525 in the first eight waves of the COVID study. The final sample for each regression is dependent on the outcome variables with nonmissing values (some outcome variables are not asked in certain waves) and the selection of subgroups (for example, people who had a job before the pandemic). Please refer to S1 Table for more details of the sample selection process. The focus on within-individual changes in the outcome variables indicates that the respondents should be followed up for more than one wave. Previous analyses using the same data and selecting the individuals interviewed for more than one wave do not find that this selection would bias the results [ 39 ].

Monthly labor income, weekly paid work hours, subjective wellbeing, weekly housework hours, and weekly childcare hours are the five dependent variables or outcomes of interest.

Monthly labor income.

Respondents’ labor income in January or February 2020 (before the lockdown) was collected retrospectively in the COVID survey. Respondents also provided their current labor income in each month thereafter. We calculate the natural log of the labor income. Those who had a job in January or February 2020 were selected to predict this outcome.

Weekly paid work hours.

Respondents retrospectively reported their current paid work hours per week and their usual working hours in January or February 2020. During the period of the COVID-19 pandemic, the question asked was “How many hours did you work, as an employee or self-employed, last week?” During the prepandemic period, the question was “During January and February 2020, how many hours did you usually work per week?” Those who had a job in January or February 2020 were selected to predict this outcome.

Subjective wellbeing.

Subjective wellbeing is the mental wellbeing reported by the respondents in a General Health Questionnaire (GHQ). The value is the sum of 12 items (GHQ-12) scored on a Likert scale from 0 to 3: “ability to concentrate,” “losing sleep,” “playing a useful role in life,” “capability of making decisions,” “feeling under stress,” “overcoming difficulties,” “ability to enjoy activities,” “ability to face problems,” “feeling unhappy or depressed,” “losing confidence,” “believing in self-worth,” and “feeling generally happy.” The overall scale ranges from 0 (least distressed) to 36 (most distressed). This measurement is a validated and widely used measure of nonspecific mental distress in surveys [ 40 ]. The same information was collected in earlier waves of the main survey of the UKHLS and in each wave of the COVID study. The full sample was used to predict this outcome.

Weekly housework hours.

Respondents’ weekly housework hours were collected by the question “Thinking about last week, how much time did you spend on housework, such as time spent cooking, cleaning and doing the laundry?” Information about housework hours before the COVID survey was derived from the earlier UKHLS waves (the latest one was collected in the years between 2018 and 2019). The full sample was used to predict this outcome.

Weekly childcare hours.

Respondents’ childcare hours were collected by the question “About how many hours did you spend on childcare or home-schooling last week?” This information is only available in the COVID survey. Only those who had a child younger than 16 years old in the household (referred to as parents in later analyses) were asked this question, and these respondents are used for analyses.

Independent variables.

We include the wave dummies, which represent the time point when information was collected to examine the dynamics in those outcome variables.

The key socioeconomic independent variables are constant for the same individual across the waves. These variables are gender (52.7% females), whether an individual is Black, Asian or another minority ethnic (10.1%) or not (reference group: whites), and educational level (university degree holders 32.2%). The underrepresentation of ethnic minority groups is common in a panel survey sample (the 2011 census reported that 85.6% of the working-age people were from white ethnic groups) because of the selection of people with repeated observations to satisfy the requirement of the fixed-effect models. People with disadvantaged backgrounds are known to be more likely to drop out in repeated surveys [ 41 ]. The later regression analysis has considered this sample selection issue using weights, as discussed above. Moreover, attrition in panel surveys is not found to have a significant impact on the estimations in predicting income [ 42 ], time use [ 43 ], or attitudes [ 44 ].

Whether the respondent had a positive COVID-19 test outcome was asked in each wave. We included this variable in the model to control for the impact of contracting COVID-19 so that the period indicators could better represent the spread of COVID-19 and COVID-19-related policy change at the macro-level. This variable has four categories: “having no test” (reference, 89.7%), “tested positive” (0.8%), “tested negative” (9.0%), and “result pending” (0.5%).

All models controlled for respondents’ partnership status (whether they live with a partner) and parenthood status (the presence of a child younger than age 16 in the household) to account for potential changes in the family status that are correlated with the outcomes [ 45 , 46 ].

Analytical strategies

We applied linear fixed-effect regressions to predict the five outcomes. By interacting the month indicator with gender, BAME group, and education levels, we examined how the change in income, time use, and wellbeing differed across individuals in the three different sociodemographic groups in different periods of the pandemic. The reference time point is January and February 2020 for earnings and weekly paid work hours outcomes. The reference time point is the year 2018/2019 for the subjective wellbeing (distress level) and weekly housework hours outcomes. For weekly childcare hours, the reference time point is April 2020, which was during the first national lockdown. The outcome variables compare the information reported by the same individuals at each time point and hence reveal within-person changes. This analytic approach enabled us to investigate trajectories of the outcome variables over the past year conditional on the same individual.

The fixed-effect regression method takes full account of the time-constant individual characteristics that are correlated with both the independent variable and the outcome variables. This is achieved by demeaning the dependent and independent variables using person-specific means [ 47 ].

The samples in the UKHLS main survey and the COVID survey are probability samples of postal addresses. The samples are clustered and stratified. Accordingly, clustered standard errors are used to consider this sampling design [ 48 ].

These analyses were conducted in Stata/SE 16.1. Replication codes are available at https://github.com/jomuzhi/ukcovidunderstandingsociety .

Descriptive results

We first report the weighted mean values of the key outcomes in Table 1 . Please note that the information was collected at the end of each survey month.

https://doi.org/10.1371/journal.pone.0257286.t001

First, among those who worked before this pandemic (between January and February 2020), there was a clear reduction in their average earnings when the pandemic started in the UK. Their income recovered by almost ten percent in May from the April level, which should have been mainly driven by the implementation of the job retention scheme . Some workers who could not work from home, such as those working on construction sites, also returned to the workplace in May. Since then, average monthly net earnings have remained at approximately the level of £1,550. Notably, since the first lockdown, people’s take-home earnings has never returned to their prepandemic level but never fell below 90% of the pre-pandemic level.

Before the pandemic, those who worked in January and February 2020 worked 34.7 hours per week on average. A record low of 21.9 hours per week was observed in April 2020. The persistent decline in paid work time over the past year is evident, although working hours have recovered gradually since May and reached a peak of approximately 30 hours per week in September 2020. The later two national lockdowns (November 2020 and January 2021) did not reduce the working hours as much as the first national lockdown. Weekly paid work hours were maintained at approximately 28 hours.

People felt more distressed beginning in March 2020. The worst number of 13.4 was recorded in the last two rounds of lockdown-November 2020 and January 2021, when new cases and deaths grew sharply at the beginning of these lockdowns.

People’s housework hours increased and reached the highest level of 12.3 hours per week in April and May 2020. Then, housework time declined gradually and was maintained at 10.5 hours per week. Compared with the figure recorded in September 2020 when most lockdown restrictions were eased, the figure in January 2021 did not change significantly, even though a stricter lockdown was in place. This finding concurs with the small reduction in paid work hours from September 2020 to January 2021.

The average childcare hours per week reached 16.7 hours for parents in April, but this figure gradually declined to approximately 13 hours per week before the third national lockdown. In January 2021, childcare hours only increased 0.5 hours per week over the September figure, even though schools were closed to most pupils during the third lockdown. Overall, people’s time use had become less responsive to repeated lockdowns.

Changes in earnings, paid work time, subjective wellbeing, housework and childcare time

Fig 2 reports within-individual changes in earnings, paid work hours, distress level, and housework hours across waves. The red lines indicate the time point when the national lockdowns started to enforce. Please note that the information was collected at the end of each survey month. Detailed coefficients are reported in S2 Table .

https://doi.org/10.1371/journal.pone.0257286.g002

Respondents’ earnings stayed lower than the pre-pandemic level over the entire year, with the largest decline (~9%) recorded in late April, the first month after the announcement of the first national lockdown. Earnings recovered slightly after the gradual relaxation of restrictive measures and the implementation of the job retention scheme. Following the third lockdown, when almost the same strict measures as the first lockdown were imposed, we found a similar level of decline in earnings (~8%) compared with the prepandemic period, as in the first lockdown. One year after the onset of the pandemic in the UK, our sample still experienced a 7.4% decline in earnings compared with the pre-pandemic level.

Paid work hours remained much lower than the prepandemic level over the entire year. The largest drop of nearly 13 hours was observed in the first month after the March 2020 lockdown. Then, paid work hours recovered and have never returned to the same lowest point. People worked the longest hours in September 2020, when restrictive measures were minimal. Interestingly, despite the implementation of the second and the stricter third national lockdowns, paid work hours dropped only slightly compared to the September figure and were even higher than the July 2020 figure, even though all shops were allowed to open back in July 2020. This observation suggests an increased adaptation to the work-from-home practice. After the first lockdown, more firms announced a long-term strategy to allow employees to work from home [ 49 ]. Accordingly, people have increased their paid work time even though they might still work from home.

In this pandemic, people’s subjective well-being has been damaged. The distress level (a higher score indicating more distress) stayed higher than the prepandemic level over the past year. In the three-month period after the first lockdown, a high level of distress was recorded. An improvement in subjective wellbeing was observed from July and before the enforcement of the second lockdown. The November lockdown brought a further decline in subjective wellbeing, which is consistent with the findings in one earlier study [ 2 ]. The distress level in November 2020 and January 2021 was even higher than that in the first lockdown period. It appears that people were much less optimistic and suffered tremendously as the pandemic dragged longer. People became slightly less negatively affected in their subjective wellbeing in March 2021, although the level was only similar to that in April 2020. One year after the onset of the pandemic in the UK, respondents’ subjective wellbeing returned to the level of April 2020, which was one month after the announcement of the first national lockdown.

The increase in housework hours was the highest during the first lockdown. Compared with the housework hours during the easing period in September 2020, the January 2021 lockdown was not associated with an increase in people’s housework time. This change echoes the relatively high level of paid work time in the later two lockdown periods.

Next, we examine childcare time since the first national lockdown. In Fig 3 , we can see that beginning in April 2020 (during the first lockdown period), childcare hours have been dropping. The lowest level was observed in September 2020, when schools completely reopened. Interestingly, childcare hours in January 2021 were similar to those in September 2020, despite the closure of schools to most children in January 2021.

https://doi.org/10.1371/journal.pone.0257286.g003

Differential impacts on women and men

Figs 4 and 5 report whether changes in the five indicators differ between women and men. For monthly net earnings and weekly paid work hours, we analyzed an additional sample that includes only non-key workers. We will examine whether a disproportionate number of female workers in certain key sectors, such as health and social care, drive the results.

https://doi.org/10.1371/journal.pone.0257286.g004

https://doi.org/10.1371/journal.pone.0257286.g005

First, the reduction in earnings for female workers (those who worked in Jan/Feb 2020) was smaller than that for male workers during the first lockdown in April 2020 (p = 0.011). Since then, there has been no difference between women and men in changes in earnings, reflecting the faster recovery of men’s earnings. Differential impacts on women and men were not found among non-keyworkers. Therefore, the higher proportion of women working in key sectors, which were operating much more actively than other sectors during the first lockdown period, should be the main reason for the gender difference in the earning decline during the first lockdown.

During the first lockdown, the decline in paid work hours was smaller for female workers than for male workers, disregarding their keyworker status (p<0.001). The gender difference in the reduction in paid work hours decreased as the first lockdown ended and became statistically insignificant at the 0.05 level from July to September 2020, indicating a faster recovery of paid work time for men than for women. The differential impacts of gender on paid work hours observed in the first lockdown were not observed in later lockdowns among non-keyworkers.

In Fig 5 , the growth in distress level was much higher for women than for men in the first month of the first lockdown (p<0.001). Then, women’s subjective wellbeing recovered, and men’s distress levels began to rise. These findings suggest that men’s response to this pandemic lagged behind that of women in terms of their subjective wellbeing in the first lockdown. The distress level of both women and men was reduced to the lowest level from July to September 2020, when life in general had returned to normal. Once the cases of COVID-19 surged and lockdown restrictions were reimposed in November 2020 (p = 0.056) and January 2021 (p = 0.061), women again suffered from a larger increase in distress levels than men. The distress level of women reached a similar high point across the three lockdowns. For men, their distress level was higher in the later lockdowns than in the first lockdown, when the cases of COVID-19 and its related deaths worsened.

We do not observe a gender-specific impact on housework time. The gender gap in housework time was maintained over the past year.

Differential impacts on BAME people and white people

Figs 6 and 7 report whether changes in the five indicators differ between BAME people and whites. For monthly net earnings and weekly paid work hours, we analyzed an additional sample that includes only non-key workers.

https://doi.org/10.1371/journal.pone.0257286.g006

https://doi.org/10.1371/journal.pone.0257286.g007

Compared with whites, the earnings of the BAME group were particularly negatively affected by the pandemic. The differential impacts on earnings persisted across almost all months over the past year, except during the third lockdown. The gap was large even when most lockdown restrictions were eased in September 2020 (p = 0.003). The earning gap between the BAME group and whites was even larger among non-key workers. Over the past year, the decline in market working time was similar for the BAME group and whites in both the full and the non-key worker samples. In March 2021, the reduction in paid work time decreased less for the BAME group than for the whites (p = 0.006).

Regarding the distress level ( Fig 7 ), the increase for the BAME group was larger than that for whites during the first lockdown, but the difference was not statistically significant at the 0.05 level due to the large standard error of the estimates of the BAME group. Beginning in September 2020, the changes in the distress levels were similar for the BAME group and whites. The increase in housework hours seems to be larger for the BAME group, but the large standard errors prevent us from drawing a reliable conclusion.

Differential impacts on degree and non-degree holders

Figs 8 and 9 report whether changes in the five indicators differ between degree and non-degree holders. For monthly net earnings and weekly paid work hours, we analyzed an additional sample that includes only non-key workers.

https://doi.org/10.1371/journal.pone.0257286.g008

https://doi.org/10.1371/journal.pone.0257286.g009

As expected, the decline in earnings and paid work hours was particularly acute among non-degree holders. These differential impacts were even larger among non-key workers. When the spread of the virus decreased and most of the restrictive measures eased from July to September 2020, the difference in the impacts on non-degree and degree holders became smaller but was sustained. For paid work hours, the difference was insignificant between July and September 2020 for both the full and the non-keyworker samples. Once restrictive measures were reimposed, the difference became substantial again (p<0.001).

As Fig 9 shows, there was no significant difference in the change in subjective wellbeing between degree and non-degree holders before January 2021. However, degree holders experienced a larger increase in distress level during the third national lockdown that started in January 2021 (p = 0.028), but the differential effect disappeared in March 2021.

We do not observe a statistically significant difference in the changes in housework time between the two groups.

Changes in weekly childcare hours since April 2020

Fig 10 reports whether changes in the weekly childcare hours differ across these groups.

https://doi.org/10.1371/journal.pone.0257286.g010

Our findings show that women and men, BAME people and whites, and degree and non-degree holders did not differ significantly in changes to their childcare time since April 2020. However, there is a tendency that the reduction in childcare time in September, which should be associated with pupils returning to schools after summer vacation, was larger for mothers and the more educated group, suggesting that women and the more educated might have spent more time taking care of children at home.

For more details of the results, please refer to S2 – S5 Tables. The within-individual R-squares are small when predicting earnings, subjective wellbeing, housework time, and childcare time. Small within-individual R-squares are not uncommon in fixed-effect regressions, especially when predicting housework time and subjective wellbeing [ 50 , 51 ]. These results suggest that a limited number of individuals have changed their partnership and parenthood status and COVID-test results, but their outcome variables—earnings, time use, and subjective wellbeing-have changed considerably over the past year. The inclusion of more time-varying variables might be able to improve the explanatory power. Those variables could be whether furloughed, whether participated in the job retention scheme, or whether went back to work/school. However, the purpose of this paper is to provide an overall net impact of COVID-19 and its related measures on an individual instead of focusing on a specific policy or the spread of COVID-19. Given the focus on the trajectories of earnings, time use, and subjective wellbeing at different stages of the pandemic, we do not include those time-varying variables suggested above.

Discussion and conclusion

In this article, we have utilized the latest UK COVID panel data to provide a comprehensive analysis of the dynamics of earnings, time use, and subjective wellbeing at different stages of the pandemic over the past year. Our research, with a much extended time scope, surpasses past UK studies that only followed a short period after the first lockdown imposed in March 2020 [for example, 3, 7, 9, 20]. Our analysis has incorporated multiple domains of outcomes across several social groups. We aim to examine how the spread of COVID-19 and COVID-induced policies have had unequal and dynamic impacts on different social groups in the UK. Our findings offer important insights into whether inequalities in changes in income, time use and wellbeing are likely to be long lasting or temporary.

Overall, the initial outbreak of COVID-19 and the first national lockdown brought the largest change in earnings and time use. The later two lockdowns together with the repeated new highs of the COVID-19 cases and deaths impacted people’s subjective wellbeing the most. Although strict measures that aimed to reduce people’s physical contact were imposed in the later two lockdowns, people’s time use did not respond as strongly as they did during the first lockdown. Among the five indicators, none had returned to their prepandemic level until late March 2021. It remains uncertain when and whether earnings, working patterns, family life, and subjective wellbeing will return to the prepandemic level.

Female workers experienced less reduction in their earnings than male workers, which is largely due to the relatively high proportion of women working in key sectors, especially in the health and social care industry. Women have made an important contribution to the fight against COVID-19 by working in key sectors. However, even among non-key workers, the decline in paid work hours was smaller for women but only during the first lockdown period. These findings concur with earlier research that reported that men in the UK were more likely than women to be laid off or furloughed during the first lockdown [ 20 ]. Once lockdown measures were gradually lifted beginning in June 2020, men’s paid work time recovered faster than that of women. This finding is similar to previous work on the gendered impact of natural disasters on market labor [ 52 ]. In summary, our analysis has shown that in the UK, men’s paid work time was more responsive to the restrictive measures of the first lockdown, but women’s and men’s paid work time responded similarly in the later two lockdowns.

The subjective wellbeing of women was more sensitive to the outbreak of COVID-19 and related lockdown measures than that of men. For example, the increase in women’s distress level was substantial in April, but it then gradually improved until the next lockdown. Men’s responses lagged behind of those of women. Past COVID-19 research has highlighted the gender difference in social networks, where women tend to have more friends [ 29 ]. The larger exposure to news related to COVID-19 for those with more close friends might be the factor that explains the diverging trajectories of women’s and men’s subjective wellbeing [ 53 , 54 ]. Theses differential impacts became smaller in later two lockdowns, as the pandemic had developed for a certain period. At the beginning of the pandemic, women and men seemed to have perceived the danger of this infectious disease differently.

The gender gap in housework time was maintained over the past year. Overall, the gender-specific changes in earnings, paid work time, and subjective wellbeing were mainly observed when strict restrictions were in place, and the gender gap returned to its prepandemic level once those measures were lifted.

People of a BAME background experienced a larger loss in earnings than whites. This finding is consistent with an earlier finding on BAME immigrants in the UK [ 3 ]. We have further shown that the enlarged earning gaps between BAME and white people persisted almost over the entire year.

Persistently enlarged earning gaps were observed between non-degree and degree holders. The gap was even larger among non-key workers. Non-degree holders suffered from a larger reduction in earnings across all months over the past year. This gap was particularly large during the national lockdown periods. A similar observation was found for weekly paid work hours. The spread of COVID-19 and lockdown restrictions are associated with an enlarged gap in paid work time between non-degree and degree holders. This effect on paid work time is likely to be temporary because differential impacts were not observed from July to September 2020, when lockdown measures were mostly lifted.

One limitation of this study is that some changes could be brought by seasonal fluctuations beyond COVID-19 and its related restrictions. For example, people’s paid work time in winter may differ from that in summer. General psychological health was usually worse in winter than in summer [ 55 ]. The ideal solution is to compare information collected in the same month before the pandemic and in 2020. However, this approach is not possible with the current data. If the current survey retains the current monthly or bimonthly data collection frequency, future work can compare the same month in 2020 and the years after to examine pandemic and post-pandemic differences. We have also included the measure of the spread of COVID-19 (daily new cases or daily new death rates, as shown in Fig 1 ) to examine whether the outcomes are affected by the macrolevel development of the COVID-19 pandemic in the UK. We do not find strong evidence showing that those measures are associated with the outcomes. Our results reveal the trajectories of earnings, time use, and subjective wellbeing at different time points over the past year but cannot identify the exact impact of a specific lockdown restrictive policy. There could be other non-COVID-19-related policy updates that occurred in parallel over the past year that may have had an impact on the same outcomes. Nonetheless, the trends of the observed changes in income, time use, and subjective wellbeing corresponded closely to the different waves of the pandemic and the lockdown timeline. Therefore, the major sources of those changes should be related to the spread of COVID-19 and its related lockdown measures.

In conclusion, our findings suggest that the long-lasting pandemic and the related restrictions to contain the virus over the past year have produced persistent negative consequences for earnings, work patterns, and subjective wellbeing. The spread of COVID-19 and the national lockdowns at different stages had distinct patterns and measures, and their impacts on labor earnings, time use and subjective well-being varied. Time use patterns became less sensitive to the later lockdowns, but the distress levels reached a new high with repeated lockdowns in multiple waves of the pandemic. The differential impacts of the lockdown measures based on gender became insignificant once lockdown measures were lifted. However, some social groups, including BAME and white people and non-degree holders and degree holders, experienced persistently enlarged gaps in earnings. The negative impacts of the spread of COVID-19 and its related measures vary not only in their extent but also in their speed among different social groups. Further research should be conducted to understand factors that have driven these social inequalities and to monitor how inequalities based on gender, educational level, and ethnic minority status might be persistent or even exacerbated in the long term.

Supporting information

S1 table. samples and sample selection..

https://doi.org/10.1371/journal.pone.0257286.s001

S2 Table. Baseline model: Changes in the five indicators across waves.

https://doi.org/10.1371/journal.pone.0257286.s002

S3 Table. Gender and period interaction model results.

https://doi.org/10.1371/journal.pone.0257286.s003

S4 Table. Ethnicity and period interaction models.

https://doi.org/10.1371/journal.pone.0257286.s004

S5 Table. Education and period interaction model results.

https://doi.org/10.1371/journal.pone.0257286.s005

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• Volume 76, Issue 2
• COVID-19 pandemic and its impact on social relationships and health
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• http://orcid.org/0000-0003-1512-4471 Emily Long 1 ,
• Susan Patterson 1 ,
• Karen Maxwell 1 ,
• Carolyn Blake 1 ,
• http://orcid.org/0000-0001-7342-4566 Raquel Bosó Pérez 1 ,
• Ruth Lewis 1 ,
• Mark McCann 1 ,
• Julie Riddell 1 ,
• Kathryn Skivington 1 ,
• Rachel Wilson-Lowe 1 ,
• http://orcid.org/0000-0002-4409-6601 Kirstin R Mitchell 2
• 1 MRC/CSO Social and Public Health Sciences Unit , University of Glasgow , Glasgow , UK
• 2 MRC/CSO Social and Public Health Sciences Unit, Institute of Health & Wellbeing , University of Glasgow , Glasgow , UK
• Correspondence to Dr Emily Long, MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow G3 7HR, UK; emily.long{at}glasgow.ac.uk

This essay examines key aspects of social relationships that were disrupted by the COVID-19 pandemic. It focuses explicitly on relational mechanisms of health and brings together theory and emerging evidence on the effects of the COVID-19 pandemic to make recommendations for future public health policy and recovery. We first provide an overview of the pandemic in the UK context, outlining the nature of the public health response. We then introduce four distinct domains of social relationships: social networks, social support, social interaction and intimacy, highlighting the mechanisms through which the pandemic and associated public health response drastically altered social interactions in each domain. Throughout the essay, the lens of health inequalities, and perspective of relationships as interconnecting elements in a broader system, is used to explore the varying impact of these disruptions. The essay concludes by providing recommendations for longer term recovery ensuring that the social relational cost of COVID-19 is adequately considered in efforts to rebuild.

• inequalities

Data availability statement

Data sharing not applicable as no data sets generated and/or analysed for this study. Data sharing not applicable as no data sets generated or analysed for this essay.

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/ .

https://doi.org/10.1136/jech-2021-216690

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Introduction

Infectious disease pandemics, including SARS and COVID-19, demand intrapersonal behaviour change and present highly complex challenges for public health. 1 A pandemic of an airborne infection, spread easily through social contact, assails human relationships by drastically altering the ways through which humans interact. In this essay, we draw on theories of social relationships to examine specific ways in which relational mechanisms key to health and well-being were disrupted by the COVID-19 pandemic. Relational mechanisms refer to the processes between people that lead to change in health outcomes.

At the time of writing, the future surrounding COVID-19 was uncertain. Vaccine programmes were being rolled out in countries that could afford them, but new and more contagious variants of the virus were also being discovered. The recovery journey looked long, with continued disruption to social relationships. The social cost of COVID-19 was only just beginning to emerge, but the mental health impact was already considerable, 2 3 and the inequality of the health burden stark. 4 Knowledge of the epidemiology of COVID-19 accrued rapidly, but evidence of the most effective policy responses remained uncertain.

The initial response to COVID-19 in the UK was reactive and aimed at reducing mortality, with little time to consider the social implications, including for interpersonal and community relationships. The terminology of ‘social distancing’ quickly became entrenched both in public and policy discourse. This equation of physical distance with social distance was regrettable, since only physical proximity causes viral transmission, whereas many forms of social proximity (eg, conversations while walking outdoors) are minimal risk, and are crucial to maintaining relationships supportive of health and well-being.

The aim of this essay is to explore four key relational mechanisms that were impacted by the pandemic and associated restrictions: social networks, social support, social interaction and intimacy. We use relational theories and emerging research on the effects of the COVID-19 pandemic response to make three key recommendations: one regarding public health responses; and two regarding social recovery. Our understanding of these mechanisms stems from a ‘systems’ perspective which casts social relationships as interdependent elements within a connected whole. 5

Social networks

Social networks characterise the individuals and social connections that compose a system (such as a workplace, community or society). Social relationships range from spouses and partners, to coworkers, friends and acquaintances. They vary across many dimensions, including, for example, frequency of contact and emotional closeness. Social networks can be understood both in terms of the individuals and relationships that compose the network, as well as the overall network structure (eg, how many of your friends know each other).

Social networks show a tendency towards homophily, or a phenomenon of associating with individuals who are similar to self. 6 This is particularly true for ‘core’ network ties (eg, close friends), while more distant, sometimes called ‘weak’ ties tend to show more diversity. During the height of COVID-19 restrictions, face-to-face interactions were often reduced to core network members, such as partners, family members or, potentially, live-in roommates; some ‘weak’ ties were lost, and interactions became more limited to those closest. Given that peripheral, weaker social ties provide a diversity of resources, opinions and support, 7 COVID-19 likely resulted in networks that were smaller and more homogenous.

Such changes were not inevitable nor necessarily enduring, since social networks are also adaptive and responsive to change, in that a disruption to usual ways of interacting can be replaced by new ways of engaging (eg, Zoom). Yet, important inequalities exist, wherein networks and individual relationships within networks are not equally able to adapt to such changes. For example, individuals with a large number of newly established relationships (eg, university students) may have struggled to transfer these relationships online, resulting in lost contacts and a heightened risk of social isolation. This is consistent with research suggesting that young adults were the most likely to report a worsening of relationships during COVID-19, whereas older adults were the least likely to report a change. 8

Lastly, social connections give rise to emergent properties of social systems, 9 where a community-level phenomenon develops that cannot be attributed to any one member or portion of the network. For example, local area-based networks emerged due to geographic restrictions (eg, stay-at-home orders), resulting in increases in neighbourly support and local volunteering. 10 In fact, research suggests that relationships with neighbours displayed the largest net gain in ratings of relationship quality compared with a range of relationship types (eg, partner, colleague, friend). 8 Much of this was built from spontaneous individual interactions within local communities, which together contributed to the ‘community spirit’ that many experienced. 11 COVID-19 restrictions thus impacted the personal social networks and the structure of the larger networks within the society.

Social support

Social support, referring to the psychological and material resources provided through social interaction, is a critical mechanism through which social relationships benefit health. In fact, social support has been shown to be one of the most important resilience factors in the aftermath of stressful events. 12 In the context of COVID-19, the usual ways in which individuals interact and obtain social support have been severely disrupted.

One such disruption has been to opportunities for spontaneous social interactions. For example, conversations with colleagues in a break room offer an opportunity for socialising beyond one’s core social network, and these peripheral conversations can provide a form of social support. 13 14 A chance conversation may lead to advice helpful to coping with situations or seeking formal help. Thus, the absence of these spontaneous interactions may mean the reduction of indirect support-seeking opportunities. While direct support-seeking behaviour is more effective at eliciting support, it also requires significantly more effort and may be perceived as forceful and burdensome. 15 The shift to homeworking and closure of community venues reduced the number of opportunities for these spontaneous interactions to occur, and has, second, focused them locally. Consequently, individuals whose core networks are located elsewhere, or who live in communities where spontaneous interaction is less likely, have less opportunity to benefit from spontaneous in-person supportive interactions.

However, alongside this disruption, new opportunities to interact and obtain social support have arisen. The surge in community social support during the initial lockdown mirrored that often seen in response to adverse events (eg, natural disasters 16 ). COVID-19 restrictions that confined individuals to their local area also compelled them to focus their in-person efforts locally. Commentators on the initial lockdown in the UK remarked on extraordinary acts of generosity between individuals who belonged to the same community but were unknown to each other. However, research on adverse events also tells us that such community support is not necessarily maintained in the longer term. 16

Meanwhile, online forms of social support are not bound by geography, thus enabling interactions and social support to be received from a wider network of people. Formal online social support spaces (eg, support groups) existed well before COVID-19, but have vastly increased since. While online interactions can increase perceived social support, it is unclear whether remote communication technologies provide an effective substitute from in-person interaction during periods of social distancing. 17 18 It makes intuitive sense that the usefulness of online social support will vary by the type of support offered, degree of social interaction and ‘online communication skills’ of those taking part. Youth workers, for instance, have struggled to keep vulnerable youth engaged in online youth clubs, 19 despite others finding a positive association between amount of digital technology used by individuals during lockdown and perceived social support. 20 Other research has found that more frequent face-to-face contact and phone/video contact both related to lower levels of depression during the time period of March to August 2020, but the negative effect of a lack of contact was greater for those with higher levels of usual sociability. 21 Relatedly, important inequalities in social support exist, such that individuals who occupy more socially disadvantaged positions in society (eg, low socioeconomic status, older people) tend to have less access to social support, 22 potentially exacerbated by COVID-19.

Social and interactional norms

Interactional norms are key relational mechanisms which build trust, belonging and identity within and across groups in a system. Individuals in groups and societies apply meaning by ‘approving, arranging and redefining’ symbols of interaction. 23 A handshake, for instance, is a powerful symbol of trust and equality. Depending on context, not shaking hands may symbolise a failure to extend friendship, or a failure to reach agreement. The norms governing these symbols represent shared values and identity; and mutual understanding of these symbols enables individuals to achieve orderly interactions, establish supportive relationship accountability and connect socially. 24 25

Physical distancing measures to contain the spread of COVID-19 radically altered these norms of interaction, particularly those used to convey trust, affinity, empathy and respect (eg, hugging, physical comforting). 26 As epidemic waves rose and fell, the work to negotiate these norms required intense cognitive effort; previously taken-for-granted interactions were re-examined, factoring in current restriction levels, own and (assumed) others’ vulnerability and tolerance of risk. This created awkwardness, and uncertainty, for example, around how to bring closure to an in-person interaction or convey warmth. The instability in scripted ways of interacting created particular strain for individuals who already struggled to encode and decode interactions with others (eg, those who are deaf or have autism spectrum disorder); difficulties often intensified by mask wearing. 27

Large social gatherings—for example, weddings, school assemblies, sporting events—also present key opportunities for affirming and assimilating interactional norms, building cohesion and shared identity and facilitating cooperation across social groups. 28 Online ‘equivalents’ do not easily support ‘social-bonding’ activities such as singing and dancing, and rarely enable chance/spontaneous one-on-one conversations with peripheral/weaker network ties (see the Social networks section) which can help strengthen bonds across a larger network. The loss of large gatherings to celebrate rites of passage (eg, bar mitzvah, weddings) has additional relational costs since these events are performed by and for communities to reinforce belonging, and to assist in transitioning to new phases of life. 29 The loss of interaction with diverse others via community and large group gatherings also reduces intergroup contact, which may then tend towards more prejudiced outgroup attitudes. While online interaction can go some way to mimicking these interaction norms, there are key differences. A sense of anonymity, and lack of in-person emotional cues, tends to support norms of polarisation and aggression in expressing differences of opinion online. And while online platforms have potential to provide intergroup contact, the tendency of much social media to form homogeneous ‘echo chambers’ can serve to further reduce intergroup contact. 30 31

Intimacy relates to the feeling of emotional connection and closeness with other human beings. Emotional connection, through romantic, friendship or familial relationships, fulfils a basic human need 32 and strongly benefits health, including reduced stress levels, improved mental health, lowered blood pressure and reduced risk of heart disease. 32 33 Intimacy can be fostered through familiarity, feeling understood and feeling accepted by close others. 34

Intimacy via companionship and closeness is fundamental to mental well-being. Positively, the COVID-19 pandemic has offered opportunities for individuals to (re)connect and (re)strengthen close relationships within their household via quality time together, following closure of many usual external social activities. Research suggests that the first full UK lockdown period led to a net gain in the quality of steady relationships at a population level, 35 but amplified existing inequalities in relationship quality. 35 36 For some in single-person households, the absence of a companion became more conspicuous, leading to feelings of loneliness and lower mental well-being. 37 38 Additional pandemic-related relational strain 39 40 resulted, for some, in the initiation or intensification of domestic abuse. 41 42

Physical touch is another key aspect of intimacy, a fundamental human need crucial in maintaining and developing intimacy within close relationships. 34 Restrictions on social interactions severely restricted the number and range of people with whom physical affection was possible. The reduction in opportunity to give and receive affectionate physical touch was not experienced equally. Many of those living alone found themselves completely without physical contact for extended periods. The deprivation of physical touch is evidenced to take a heavy emotional toll. 43 Even in future, once physical expressions of affection can resume, new levels of anxiety over germs may introduce hesitancy into previously fluent blending of physical and verbal intimate social connections. 44

The pandemic also led to shifts in practices and norms around sexual relationship building and maintenance, as individuals adapted and sought alternative ways of enacting sexual intimacy. This too is important, given that intimate sexual activity has known benefits for health. 45 46 Given that social restrictions hinged on reducing household mixing, possibilities for partnered sexual activity were primarily guided by living arrangements. While those in cohabiting relationships could potentially continue as before, those who were single or in non-cohabiting relationships generally had restricted opportunities to maintain their sexual relationships. Pornography consumption and digital partners were reported to increase since lockdown. 47 However, online interactions are qualitatively different from in-person interactions and do not provide the same opportunities for physical intimacy.

Recommendations and conclusions

In the sections above we have outlined the ways in which COVID-19 has impacted social relationships, showing how relational mechanisms key to health have been undermined. While some of the damage might well self-repair after the pandemic, there are opportunities inherent in deliberative efforts to build back in ways that facilitate greater resilience in social and community relationships. We conclude by making three recommendations: one regarding public health responses to the pandemic; and two regarding social recovery.

Recommendation 1: explicitly count the relational cost of public health policies to control the pandemic

Effective handling of a pandemic recognises that social, economic and health concerns are intricately interwoven. It is clear that future research and policy attention must focus on the social consequences. As described above, policies which restrict physical mixing across households carry heavy and unequal relational costs. These include for individuals (eg, loss of intimate touch), dyads (eg, loss of warmth, comfort), networks (eg, restricted access to support) and communities (eg, loss of cohesion and identity). Such costs—and their unequal impact—should not be ignored in short-term efforts to control an epidemic. Some public health responses—restrictions on international holiday travel and highly efficient test and trace systems—have relatively small relational costs and should be prioritised. At a national level, an earlier move to proportionate restrictions, and investment in effective test and trace systems, may help prevent escalation of spread to the point where a national lockdown or tight restrictions became an inevitability. Where policies with relational costs are unavoidable, close attention should be paid to the unequal relational impact for those whose personal circumstances differ from normative assumptions of two adult families. This includes consideration of whether expectations are fair (eg, for those who live alone), whether restrictions on social events are equitable across age group, religious/ethnic groupings and social class, and also to ensure that the language promoted by such policies (eg, households; families) is not exclusionary. 48 49 Forethought to unequal impacts on social relationships should thus be integral to the work of epidemic preparedness teams.

Recommendation 2: intelligently balance online and offline ways of relating

A key ingredient for well-being is ‘getting together’ in a physical sense. This is fundamental to a human need for intimate touch, physical comfort, reinforcing interactional norms and providing practical support. Emerging evidence suggests that online ways of relating cannot simply replace physical interactions. But online interaction has many benefits and for some it offers connections that did not exist previously. In particular, online platforms provide new forms of support for those unable to access offline services because of mobility issues (eg, older people) or because they are geographically isolated from their support community (eg, lesbian, gay, bisexual, transgender and queer (LGBTQ) youth). Ultimately, multiple forms of online and offline social interactions are required to meet the needs of varying groups of people (eg, LGBTQ, older people). Future research and practice should aim to establish ways of using offline and online support in complementary and even synergistic ways, rather than veering between them as social restrictions expand and contract. Intelligent balancing of online and offline ways of relating also pertains to future policies on home and flexible working. A decision to switch to wholesale or obligatory homeworking should consider the risk to relational ‘group properties’ of the workplace community and their impact on employees’ well-being, focusing in particular on unequal impacts (eg, new vs established employees). Intelligent blending of online and in-person working is required to achieve flexibility while also nurturing supportive networks at work. Intelligent balance also implies strategies to build digital literacy and minimise digital exclusion, as well as coproducing solutions with intended beneficiaries.

Recommendation 3: build stronger and sustainable localised communities

In balancing offline and online ways of interacting, there is opportunity to capitalise on the potential for more localised, coherent communities due to scaled-down travel, homeworking and local focus that will ideally continue after restrictions end. There are potential economic benefits after the pandemic, such as increased trade as home workers use local resources (eg, coffee shops), but also relational benefits from stronger relationships around the orbit of the home and neighbourhood. Experience from previous crises shows that community volunteer efforts generated early on will wane over time in the absence of deliberate work to maintain them. Adequately funded partnerships between local government, third sector and community groups are required to sustain community assets that began as a direct response to the pandemic. Such partnerships could work to secure green spaces and indoor (non-commercial) meeting spaces that promote community interaction. Green spaces in particular provide a triple benefit in encouraging physical activity and mental health, as well as facilitating social bonding. 50 In building local communities, small community networks—that allow for diversity and break down ingroup/outgroup views—may be more helpful than the concept of ‘support bubbles’, which are exclusionary and less sustainable in the longer term. Rigorously designed intervention and evaluation—taking a systems approach—will be crucial in ensuring scale-up and sustainability.

The dramatic change to social interaction necessitated by efforts to control the spread of COVID-19 created stark challenges but also opportunities. Our essay highlights opportunities for learning, both to ensure the equity and humanity of physical restrictions, and to sustain the salutogenic effects of social relationships going forward. The starting point for capitalising on this learning is recognition of the disruption to relational mechanisms as a key part of the socioeconomic and health impact of the pandemic. In recovery planning, a general rule is that what is good for decreasing health inequalities (such as expanding social protection and public services and pursuing green inclusive growth strategies) 4 will also benefit relationships and safeguard relational mechanisms for future generations. Putting this into action will require political will.

Ethics statements

Patient consent for publication.

Not required.

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Twitter @karenmaxSPHSU, @Mark_McCann, @Rwilsonlowe, @KMitchinGlasgow

Contributors EL and KM led on the manuscript conceptualisation, review and editing. SP, KM, CB, RBP, RL, MM, JR, KS and RW-L contributed to drafting and revising the article. All authors assisted in revising the final draft.

Funding The research reported in this publication was supported by the Medical Research Council (MC_UU_00022/1, MC_UU_00022/3) and the Chief Scientist Office (SPHSU11, SPHSU14). EL is also supported by MRC Skills Development Fellowship Award (MR/S015078/1). KS and MM are also supported by a Medical Research Council Strategic Award (MC_PC_13027).

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed.

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Long COVID has affected millions. Here's what scientists now know.

July 19, 2024 / 5:19 PM EDT / The Conversation

Ziyad Al-Aly  is chief of research and development at VA St. Louis Health Care System and a clinical epidemiologist at  Washington University in St. Louis .

Since 2020, the condition known as long COVID-19 has become a widespread disability affecting the health and quality of life of millions of people across the globe and costing economies billions of dollars in reduced productivity of employees and an overall drop in the work force.

The intense scientific effort that long COVID sparked has resulted in more than 24,000 scientific publications , making it the most researched health condition in any four years of recorded human history.

Long COVID is a term that describes the constellation of long-term health effects caused by infection with the SARS-CoV-2 virus. These range from persistent respiratory symptoms, such as shortness of breath, to debilitating fatigue or brain fog that limits people's ability to work, and conditions such as heart failure and diabetes, which are known to last a lifetime.

I am a physician scientist, and I have been deeply immersed in studying long COVID since the early days of the pandemic. I have testified before the U.S. Senate as an expert witness on long COVID, have published extensively on it and was named as one of Time's 100 most influential people in health in 2024 for my research in this area.

Over the first half of 2024, a flurry of reports and scientific papers on long COVID added clarity to this complex condition. These include, in particular, insights into how COVID-19 can still wreak havoc in many organs years after the initial viral infection, as well as emerging evidence on viral persistence and immune dysfunction that last for months or years after initial infection.

How long COVID affects the body

A new study that my colleagues and I published in the New England Journal of Medicine on July 17, 2024, shows that the risk of long COVID declined over the course of the pandemic. In 2020, when the ancestral strain of SARS-CoV-2 was dominant and vaccines were not available, about 10.4% of adults who got COVID-19 developed long COVID. By early 2022, when the omicron family of variants predominated, that rate declined to 7.7% among unvaccinated adults and 3.5% of vaccinated adults. In other words, unvaccinated people were more than twice as likely to develop long COVID.

While researchers like me do not yet have concrete numbers for the current rate in mid-2024 due to the time it takes for long COVID cases to be reflected in the data, the flow of new patients into long COVID clinics has been on par with 2022.

We found that the decline was the result of two key drivers: availability of vaccines and changes in the characteristics of the virus — which made the virus less prone to cause severe acute infections and may have reduced its ability to persist in the human body long enough to cause chronic disease.

Despite the decline in risk of developing long COVID, even a 3.5% risk is substantial. New and repeat COVID-19 infections translate into millions of new long COVID cases that add to an already staggering number of people suffering from this condition.

Estimates for the first year of the pandemic suggests that at least 65 million people globally have had long COVID. Along with a group of other leading scientists, my team will soon publish updated estimates of the global burden of long COVID and its impact on the global economy through 2023.

In addition, a major new report by the National Academies of Sciences Engineering and Medicine details all the health effects that constitute long COVID . The report was commissioned by the Social Security Administration to understand the implications of long COVID on its disability benefits.

It concludes that long COVID is a complex chronic condition that can result in more than 200 health effects across multiple body systems. These include new onset or worsening:

• heart disease
• neurologic problems such as cognitive impairment , strokes and dysautonomia. This is a category of disorders that affect the body's autonomic nervous system — nerves that regulate most of the body's vital mechanisms such as blood pressure, heart rate and temperature.
• post-exertional malaise, a state of severe exhaustion that may happen after even minor activity — often leaving the patient unable to function for hours, days or weeks
• gastrointestinal disorders
• kidney disease
• metabolic disorders such as diabetes and hyperlipidemia, or a rise in bad cholesterol
• immune dysfunction

Long COVID can affect people across the lifespan from children to older adults and across race and ethnicity and baseline health status. Importantly, more than 90% of people with long COVID had mild COVID-19 infections.

The National Academies report also concluded that long COVID can result in the inability to return to work or school; poor quality of life; diminished ability to perform activities of daily living; and decreased physical and cognitive function for months or years after the initial infection.

The report points out that many health effects of long COVID, such as post-exertional malaise and chronic fatigue, cognitive impairment and autonomic dysfunction, are not currently captured in the Social Security Administration's Listing of Impairments , yet may significantly affect an individual's ability to participate in work or school.

A long road ahead

What's more, health problems resulting from COVID-19 can last years after the initial infection.

A large study published in early 2024 showed that even people who had a mild SARS-CoV-2 infection still experienced new health problems related to COVID-19 in the third year after the initial infection.

Such findings parallel other research showing that the virus persists in various organ systems for months or years after COVID-19 infection. And research is showing that immune responses to the infection are still evident two to three years after a mild infection. Together, these studies may explain why a SARS-CoV-2 infection years ago could still cause new health problems long after the initial infection.

Important progress is also being made in understanding the pathways by which long COVID wreaks havoc on the body. Two preliminary studies from the U.S. and the Netherlands show that when researchers transfer auto-antibodies – antibodies generated by a person's immune system that are directed at their own tissues and organs – from people with long COVID into healthy mice, the animals start to experience long COVID-like symptoms such as muscle weakness and poor balance.

These studies suggest that an abnormal immune response thought to be responsible for the generation of these auto-antibodies may underlie long COVID and that removing these auto-antibodies may hold promise as potential treatments.

An ongoing threat

Despite overwhelming evidence of the wide-ranging risks of COVID-19, a great deal of messaging suggests that it is no longer a threat to the public. Although there is no empirical evidence to back this up, this misinformation has permeated the public narrative.

The data, however, tells a different story.

COVID-19 infections continue to outnumber flu cases and lead to more hospitalization and death than the flu. COVID-19 also leads to more serious long-term health problems . Trivializing COVID-19 as an inconsequential cold or equating it with the flu does not align with reality.

This article is republished from The Conversation under a Creative Commons license.

• Coronavirus Disease 2019
• Coronavirus

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78 References

The impact of the russia–ukraine war on volatility spillovers, connectedness between monetary policy uncertainty and sectoral stock market returns: evidence from asymmetric tvp-var approach, covid-19 and extreme risk spillovers between oil and brics stock markets: a multiscale perspective, analyzing spillover effects of selected cryptocurrencies on gold and brent crude oil under covid-19 pandemic: evidence from gjr-garch and evt copula methods, correlations between the crude oil market and capital markets under the russia–ukraine conflict: a perspective of crude oil importing and exporting countries, time–frequency co-movement and risk connectedness among cryptocurrencies: new evidence from the higher-order moments before and during the covid-19 pandemic, time–frequency return co-movement among asset classes around the covid-19 outbreak: portfolio implications, gold or bitcoin, which is the safe haven during the covid-19 pandemic, extreme dependence and risk spillover across g7 and china stock markets before and during the covid-19 period, forecasting oil and gold volatilities with sentiment indicators under structural breaks, related papers.

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What We Know About the Global Microsoft Outage

Airlines to banks to retailers were affected in many countries. Businesses are struggling to recover.

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By Eshe Nelson and Danielle Kaye

Eshe Nelson reported from London and Danielle Kaye from New York.

Across the world, critical businesses and services including airlines, hospitals, train networks and TV stations, were disrupted on Friday by a global tech outage affecting Microsoft users.

In many countries, flights were grounded, workers could not get access to their systems and, in some cases, customers could not make card payments in stores. While some of the problems were resolved within hours, many businesses, websites and airlines continued to struggle to recover.

What happened?

A series of outages rippled across the globe as information displays, login systems and broadcasting networks went dark.

The problem affecting the majority of services was caused by a flawed update by CrowdStrike , an American cybersecurity firm, whose systems are intended to protect users from hackers. Microsoft said on Friday that it was aware of an issue affecting machines running “CrowdStrike Falcon.”

But Microsoft had also said there was an earlier outage affecting U.S. users of Azure, its cloud service system. Some users may have been affected by both. Even as CrowdStrike sent out a fix, some systems were still affected by midday in the United States as businesses needed to make manual updates to their systems to resolve the issue.

George Kurtz, the president and chief executive of CrowdStrike, said on Friday morning that it could take some time for some systems to recover.

How a Software Update Crashed Computers Around the World

Here’s a visual explanation for how a faulty software update crippled machines.

How the airline cancellations rippled around the world (and across time zones)

Share of canceled flights at 25 airports on Friday

50% of flights

Ai r po r t

Bengalu r u K empeg o wda

Dhaka Shahjalal

Minneapolis-Saint P aul

Stuttga r t

Melbou r ne

Be r lin B r anden b urg

London City

Amsterdam Schiphol

Chicago O'Hare

Raleigh−Durham

B r adl e y

Cha r lotte

Reagan National

Philadelphia

1:20 a.m. ET

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