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Read these 12 moving essays about life during coronavirus

Artists, novelists, critics, and essayists are writing the first draft of history.

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The world is grappling with an invisible, deadly enemy, trying to understand how to live with the threat posed by a virus . For some writers, the only way forward is to put pen to paper, trying to conceptualize and document what it feels like to continue living as countries are under lockdown and regular life seems to have ground to a halt.

So as the coronavirus pandemic has stretched around the world, it’s sparked a crop of diary entries and essays that describe how life has changed. Novelists, critics, artists, and journalists have put words to the feelings many are experiencing. The result is a first draft of how we’ll someday remember this time, filled with uncertainty and pain and fear as well as small moments of hope and humanity.

At the New York Review of Books, Ali Bhutto writes that in Karachi, Pakistan, the government-imposed curfew due to the virus is “eerily reminiscent of past military clampdowns”:

Beneath the quiet calm lies a sense that society has been unhinged and that the usual rules no longer apply. Small groups of pedestrians look on from the shadows, like an audience watching a spectacle slowly unfolding. People pause on street corners and in the shade of trees, under the watchful gaze of the paramilitary forces and the police.

His essay concludes with the sobering note that “in the minds of many, Covid-19 is just another life-threatening hazard in a city that stumbles from one crisis to another.”

Writing from Chattanooga, novelist Jamie Quatro documents the mixed ways her neighbors have been responding to the threat, and the frustration of conflicting direction, or no direction at all, from local, state, and federal leaders:

Whiplash, trying to keep up with who’s ordering what. We’re already experiencing enough chaos without this back-and-forth. Why didn’t the federal government issue a nationwide shelter-in-place at the get-go, the way other countries did? What happens when one state’s shelter-in-place ends, while others continue? Do states still under quarantine close their borders? We are still one nation, not fifty individual countries. Right?

Award-winning photojournalist Alessio Mamo, quarantined with his partner Marta in Sicily after she tested positive for the virus, accompanies his photographs in the Guardian of their confinement with a reflection on being confined :

The doctors asked me to take a second test, but again I tested negative. Perhaps I’m immune? The days dragged on in my apartment, in black and white, like my photos. Sometimes we tried to smile, imagining that I was asymptomatic, because I was the virus. Our smiles seemed to bring good news. My mother left hospital, but I won’t be able to see her for weeks. Marta started breathing well again, and so did I. I would have liked to photograph my country in the midst of this emergency, the battles that the doctors wage on the frontline, the hospitals pushed to their limits, Italy on its knees fighting an invisible enemy. That enemy, a day in March, knocked on my door instead.

In the New York Times Magazine, deputy editor Jessica Lustig writes with devastating clarity about her family’s life in Brooklyn while her husband battled the virus, weeks before most people began taking the threat seriously:

At the door of the clinic, we stand looking out at two older women chatting outside the doorway, oblivious. Do I wave them away? Call out that they should get far away, go home, wash their hands, stay inside? Instead we just stand there, awkwardly, until they move on. Only then do we step outside to begin the long three-block walk home. I point out the early magnolia, the forsythia. T says he is cold. The untrimmed hairs on his neck, under his beard, are white. The few people walking past us on the sidewalk don’t know that we are visitors from the future. A vision, a premonition, a walking visitation. This will be them: Either T, in the mask, or — if they’re lucky — me, tending to him.

Essayist Leslie Jamison writes in the New York Review of Books about being shut away alone in her New York City apartment with her 2-year-old daughter since she became sick:

The virus. Its sinewy, intimate name. What does it feel like in my body today? Shivering under blankets. A hot itch behind the eyes. Three sweatshirts in the middle of the day. My daughter trying to pull another blanket over my body with her tiny arms. An ache in the muscles that somehow makes it hard to lie still. This loss of taste has become a kind of sensory quarantine. It’s as if the quarantine keeps inching closer and closer to my insides. First I lost the touch of other bodies; then I lost the air; now I’ve lost the taste of bananas. Nothing about any of these losses is particularly unique. I’ve made a schedule so I won’t go insane with the toddler. Five days ago, I wrote Walk/Adventure! on it, next to a cut-out illustration of a tiger—as if we’d see tigers on our walks. It was good to keep possibility alive.

At Literary Hub, novelist Heidi Pitlor writes about the elastic nature of time during her family’s quarantine in Massachusetts:

During a shutdown, the things that mark our days—commuting to work, sending our kids to school, having a drink with friends—vanish and time takes on a flat, seamless quality. Without some self-imposed structure, it’s easy to feel a little untethered. A friend recently posted on Facebook: “For those who have lost track, today is Blursday the fortyteenth of Maprilay.” ... Giving shape to time is especially important now, when the future is so shapeless. We do not know whether the virus will continue to rage for weeks or months or, lord help us, on and off for years. We do not know when we will feel safe again. And so many of us, minus those who are gifted at compartmentalization or denial, remain largely captive to fear. We may stay this way if we do not create at least the illusion of movement in our lives, our long days spent with ourselves or partners or families.

Novelist Lauren Groff writes at the New York Review of Books about trying to escape the prison of her fears while sequestered at home in Gainesville, Florida:

Some people have imaginations sparked only by what they can see; I blame this blinkered empiricism for the parks overwhelmed with people, the bars, until a few nights ago, thickly thronged. My imagination is the opposite. I fear everything invisible to me. From the enclosure of my house, I am afraid of the suffering that isn’t present before me, the people running out of money and food or drowning in the fluid in their lungs, the deaths of health-care workers now growing ill while performing their duties. I fear the federal government, which the right wing has so—intentionally—weakened that not only is it insufficient to help its people, it is actively standing in help’s way. I fear we won’t sufficiently punish the right. I fear leaving the house and spreading the disease. I fear what this time of fear is doing to my children, their imaginations, and their souls.

At ArtForum , Berlin-based critic and writer Kristian Vistrup Madsen reflects on martinis, melancholia, and Finnish artist Jaakko Pallasvuo’s 2018 graphic novel Retreat , in which three young people exile themselves in the woods:

In melancholia, the shape of what is ending, and its temporality, is sprawling and incomprehensible. The ambivalence makes it hard to bear. The world of Retreat is rendered in lush pink and purple watercolors, which dissolve into wild and messy abstractions. In apocalypse, the divisions established in genesis bleed back out. My own Corona-retreat is similarly soft, color-field like, each day a blurred succession of quarantinis, YouTube–yoga, and televized press conferences. As restrictions mount, so does abstraction. For now, I’m still rooting for love to save the world.

At the Paris Review , Matt Levin writes about reading Virginia Woolf’s novel The Waves during quarantine:

A retreat, a quarantine, a sickness—they simultaneously distort and clarify, curtail and expand. It is an ideal state in which to read literature with a reputation for difficulty and inaccessibility, those hermetic books shorn of the handholds of conventional plot or characterization or description. A novel like Virginia Woolf’s The Waves is perfect for the state of interiority induced by quarantine—a story of three men and three women, meeting after the death of a mutual friend, told entirely in the overlapping internal monologues of the six, interspersed only with sections of pure, achingly beautiful descriptions of the natural world, a day’s procession and recession of light and waves. The novel is, in my mind’s eye, a perfectly spherical object. It is translucent and shimmering and infinitely fragile, prone to shatter at the slightest disturbance. It is not a book that can be read in snatches on the subway—it demands total absorption. Though it revels in a stark emotional nakedness, the book remains aloof, remote in its own deep self-absorption.

In an essay for the Financial Times, novelist Arundhati Roy writes with anger about Indian Prime Minister Narendra Modi’s anemic response to the threat, but also offers a glimmer of hope for the future:

Historically, pandemics have forced humans to break with the past and imagine their world anew. This one is no different. It is a portal, a gateway between one world and the next. We can choose to walk through it, dragging the carcasses of our prejudice and hatred, our avarice, our data banks and dead ideas, our dead rivers and smoky skies behind us. Or we can walk through lightly, with little luggage, ready to imagine another world. And ready to fight for it.

From Boston, Nora Caplan-Bricker writes in The Point about the strange contraction of space under quarantine, in which a friend in Beirut is as close as the one around the corner in the same city:

It’s a nice illusion—nice to feel like we’re in it together, even if my real world has shrunk to one person, my husband, who sits with his laptop in the other room. It’s nice in the same way as reading those essays that reframe social distancing as solidarity. “We must begin to see the negative space as clearly as the positive, to know what we don’t do is also brilliant and full of love,” the poet Anne Boyer wrote on March 10th, the day that Massachusetts declared a state of emergency. If you squint, you could almost make sense of this quarantine as an effort to flatten, along with the curve, the distinctions we make between our bonds with others. Right now, I care for my neighbor in the same way I demonstrate love for my mother: in all instances, I stay away. And in moments this month, I have loved strangers with an intensity that is new to me. On March 14th, the Saturday night after the end of life as we knew it, I went out with my dog and found the street silent: no lines for restaurants, no children on bicycles, no couples strolling with little cups of ice cream. It had taken the combined will of thousands of people to deliver such a sudden and complete emptiness. I felt so grateful, and so bereft.

And on his own website, musician and artist David Byrne writes about rediscovering the value of working for collective good , saying that “what is happening now is an opportunity to learn how to change our behavior”:

In emergencies, citizens can suddenly cooperate and collaborate. Change can happen. We’re going to need to work together as the effects of climate change ramp up. In order for capitalism to survive in any form, we will have to be a little more socialist. Here is an opportunity for us to see things differently — to see that we really are all connected — and adjust our behavior accordingly. Are we willing to do this? Is this moment an opportunity to see how truly interdependent we all are? To live in a world that is different and better than the one we live in now? We might be too far down the road to test every asymptomatic person, but a change in our mindsets, in how we view our neighbors, could lay the groundwork for the collective action we’ll need to deal with other global crises. The time to see how connected we all are is now.

The portrait these writers paint of a world under quarantine is multifaceted. Our worlds have contracted to the confines of our homes, and yet in some ways we’re more connected than ever to one another. We feel fear and boredom, anger and gratitude, frustration and strange peace. Uncertainty drives us to find metaphors and images that will let us wrap our minds around what is happening.

Yet there’s no single “what” that is happening. Everyone is contending with the pandemic and its effects from different places and in different ways. Reading others’ experiences — even the most frightening ones — can help alleviate the loneliness and dread, a little, and remind us that what we’re going through is both unique and shared by all.

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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.

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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.

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• Office for National Statistics (ONS)
• Ford T , et al
• Riordan R ,
• Ford J , et al
• Glonti K , et al
• McPherson JM ,
• Smith-Lovin L
• Granovetter MS
• Fancourt D et al
• Stadtfeld C
• Office for Civil Society
• Cook J et al
• Rodriguez-Llanes JM ,
• Guha-Sapir D
• Patulny R et al
• Granovetter M
• Winkeler M ,
• Filipp S-H ,
• Kaniasty K ,
• de Terte I ,
• Guilaran J , et al
• Wright KB ,
• Martin J et al
• Gabbiadini A ,
• Baldissarri C ,
• Durante F , et al
• Sommerlad A ,
• Marston L ,
• Huntley J , et al
• Turner RJ ,
• Bicchieri C
• Brennan G et al
• Watson-Jones RE ,
• Amichai-Hamburger Y ,
• McKenna KYA
• Page-Gould E ,
• Aron A , et al
• Pietromonaco PR ,
• Timmerman GM
• Bradbury-Jones C ,
• Mikocka-Walus A ,
• Klas A , et al
• Marshall L ,
• Steptoe A ,
• Stanley SM ,
• Campbell AM
• ↵ (ONS), O.f.N.S., Domestic abuse during the coronavirus (COVID-19) pandemic, England and Wales . Available: https://www.ons.gov.uk/peoplepopulationandcommunity/crimeandjustice/articles/domesticabuseduringthecoronaviruscovid19pandemicenglandandwales/november2020
• Rosenberg M ,
• Hensel D , et al
• Banerjee D ,
• Bruner DW , et al
• Bavel JJV ,
• Baicker K ,
• Boggio PS , et al
• van Barneveld K ,
• Quinlan M ,
• Kriesler P , et al
• Mitchell R ,
• de Vries S , et al

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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What were the immediate effects of life in lockdown on children?

10 October 2021 – Building on the  first report  in a series on child and adolescent mental health and timed with the launch of  UNICEF’s State of the World’s Children 2021: On My Mind: Promoting, protecting, and caring for children’s mental health , a new report released for World Mental Health Day in 2021 looks at how the early stages of the global COVID-19 pandemic in 2020 affected the mental health of children and adolescents. 

A rapid evidence review was conducted to understand two key research questions:

• What has been the immediate impact of COVID-19 and associated containment measures on the mental health and psychosocial well-being of children and adolescents?  
• Which risk and protective factors have affected the mental health of children and adolescents during the COVID-19 pandemic, and how have these factors varied across subgroups of children and adolescents?  

Building on a framework established for  UNICEF Innocenti’s Worlds of Influence  research report, the conceptual framework guiding the report explored the ramifications of COVID-19 on the child’s intimate world, the world around them and the outer worlds of influence while also assessing pre-existing and on-going risks. Uniquely the report also looked at positive mental health outcomes of the pandemic and found evidence on the impact of the pandemic on special populations and vulnerable groups including but not limited to migrant children, children in humanitarian settings, and children with disabilities.  

" Life in Lockdown emphasizes the importance of focusing on child and adolescent mental health during the pandemic, a group that was considered to have been largely spared from the physical effects of the virus, " said Manasi Sharma, a research consultant with UNICEF Innocenti who worked on the report. "Government-imposed lockdowns, school closures, and disruption of services (including mental health care) have led to increased reports of fear and stress, anxiety, depression, anger, irritability, inattention, alcohol/substance, along with irregular physical activity and sleep patterns," she added.

The report demonstrates that "although there were negative impacts, especially due to social isolation and loss of learning and networks, there were many positive outcomes and perceived opportunities, especially related to quality time with family, online learning, and time for recreation," Sharma said. "The use of digital technology during the pandemic provided social connectedness, remote learning opportunities, and a way to cope with isolation and stress. Engaging in positive coping strategies, prosocial behaviours and online learning opportunities have been key factors in building children’s resilience during this time, and these need to be highlighted and harnessed through greater investment in mental health promotion and prevention interventions."

Life in Lockdown’s  rapid research review included more than 130,000 children from 22 different countries. Most studies were from high- and upper-middle-income countries that were immediately affected by high infection and death rates in the early part of the pandemic, namely: China, the United States, and Italy. 

Key findings: 

Females reported greater depressive symptoms, anxiety and externalizing behaviout while males reported greater alcohol and substance abuse during COVID-19.

Older children and adolescents reported higher and more severe rates of depressive symptoms.

Children living in more affected areas, rural areas, or near the epi-centres of COVID-19 outbreaks were associated with higher stress and depressive symptoms including anxiety and substance abuse. 

Children living in poverty or in lower socio-economic status were found to be at greater risk of stress and depressive symptoms, whereas higher socio-economic status was found to be a protective factor. 

Children with pre-existing conditions were more significantly affected by pandemic-related changes. 

Children in lower socio-economic settings or humanitarian settings experienced more depression and trouble adapting to online education. 

Children who were exposed to pre-existing childhood abuse and neglect were at increased risk of stress. 

Family conflict increased the risk of mental distress among children and adolescents. 

Separation from families and parental depression were also risk factors for stress and adjustment during the Pandemic. 

Stigma based on ethnicity and all forms of racial discrimination were associated with greater anxiety among adolescents. 

Social isolation and loneliness during lockdowns contributed to a range of outcomes including depression, irritability, anxiety, stress, alcohol use and sedentary behaviours.  

However, in some studies, children reported benefits of confinement including spending time with family, relief from academic stressors, which correlated with more life satisfaction. 

Experience or fear of exposure to COVID-19 predicted stress and depressive symptoms but also positive outcomes of health promotion and infection prevention, great social distancing and news monitoring. 

Children and adolescents who spent more time on physical activities and maintaining routines were better protected from depressive symptoms. Stress management, leisure activities and regular communication with loved ones proved to be protective coping strategies to deal with the lockdown stressors. 

Engaging in recreational activities, using technology to communicate with loved ones, having more time for oneself and one’s family, protected against anxiety and contributed to overall wellbeing during the pandemic. 

Recommendations from the research for policy and programming: 

It’s never too early to start building a foundation for positive mental health in children.  

Foster family-friendly policies to support parents and quality family time during pandemics. 

Invest in age- and gender-sensitive child and adolescent mental health care interventions and services. 

Promote physical activity and good nutrition for young people. 

Make schools a safe space for positive mental health. 

Focus on at-risk young populations. 

Address stigma and discrimination in mental health. 

Support digital technologies as a force for change. 

Download the report to read about the recommendations to close the gaps in research on effects of COVID-19 on mental health. 

Life in Lockdown: Child and adolescent mental health and well-being in the time of COVID-19

Mind Matters: Lessons from past crises for child and adolescent mental health during COVID-19

Rapid Review Protocol - Life in Lockdown: Child and adolescent mental health and well-being in the time of COVID-19

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Brief research report article, consequences of lockdown during covid-19 pandemic in lifestyle and emotional state of children in argentina.

• 1 Instituto de Desarrollo e Investigaciones Pediátricas, Hospital de Niños de La Plata - Comisión de Investigaciones Científicas-Provincia de Buenos Aires, Buenos Aires, Argentina
• 2 Centro de Matemática La Plata, Facultad de Ciencias Exactas, Universidad Nacional de La Plata, Buenos Aires, Argentina
• 3 Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina

The implications of the coronavirus disease (COVID-19) lockdown measurements and social isolation in children and their parents are still unknown. The aims of this study were to examine the impact of COVID-19 lockdown on emotional state, feelings and lifestyle of children and their parents, to explore the association between parental characteristics and child well-being and to examine whether the impact of lockdown depends on socio-economic status. Parents completed an online survey including data about socio-demographic information, parent and child feelings and lifestyle during lockdown. Logistic regression and correlation analysis were used to establish associations between variables. In total, 814 parents with children between 4 and 11 were included in the study. According to parents, 69.5% of the children showed changes in their emotional state, 55.3% altered their routine and 62.6% showed sleep disorders. Families with lower socio-economic status were more worried about health, shortage of food and household income ( p < 0.01). Parent and children concern about food/essential items were highly associated [OR (CI 95%) 13.0 (6.81, 26.5), p < 0.01]. Adverse children's emotional state was associated with parental feeling of loneliness ( r = 0.35) and inversely associated with keeping a routine ( r = −0.11). Sleep changes were inversely associated with keeping a routine and having a balcony/garden ( r = −0.53 and −0.16). We conclude that lockdown affected emotional state and lifestyle of children and parents, which were strongly related. Routine and positive parental attitude supported children's well-being. Economic issues were an important concern in families with lower socio-economic status. Our findings can help to promote child health during lockdown.

Introduction

The health, social and economic implications of the coronavirus disease 2019 (COVID-19) pandemic are still difficult to estimate. To contain and mitigate the spread of COVID-19, in March 2020 the Argentinean Government decided for strong lockdown measurements such as the cessation of school programs for children who consequently needed to remain at home. Although some restrictions started to ease over time, by November 2020 most of the schools still remained closed ( 1 ).

COVID-19 pandemic and lockdown measurements led to social isolation that affected severely the mental health of the general population all over the world, causing an increase in mental distress ( 2 ), depression and anxiety through the lockdown ( 3 – 7 ), sometimes associated with changes in feelings and lifestyle that include reduced physical activity, unhealthy eating habits, inadequate sleep quality and feeling of loneliness ( 2 , 4 , 7 , 8 ). Family lifestyle was also drastically affected: parents suffered psychological distress due to unstable financial circumstances, school closures, and suspended educational services ( 9 , 10 ). Children and adolescents also started to experience adverse emotional responses (stress, worry, helplessness, social and risky behavioral problems, anxiety, and depression) ( 11 – 16 ) and changes in lifestyle such as sleeping problems, increased screen exposure, reduced physical activity and unhealthy eating habits ( 17 – 20 ).

The lockdown measurements affected household finances with stronger implications for families with children living in poverty and/or crowded housing conditions ( 21 – 23 ). In the first half of 2020 Argentina's poverty rate rose to 40.9%, as reported by the country's official INDEC statistics agency ( 24 ), underscoring the devastating impact of the pandemic on the country's population. The lockdown presumably affected to a different extent people from different socio-economic status, and precise estimation of such impact is extremely valuable to decide future Government measures to address the consequences of the unprecedented crisis.

As mentioned above, several studies reported the effects of pandemic lockdown in adults ( 3 , 4 , 8 , 25 ) and children ( 18 , 19 , 26 – 29 ) mainly in Asia and Europe, to our knowledge only one of these studies was conducted in toddlers and pre-schoolers from Latin America ( 26 ). In addition, few studies focused on parent-child dyads ( 10 , 21 , 22 , 30 ). Thus, this study aimed to examine the impact of COVID-19 pandemic lockdown on emotional state, feelings and lifestyle of children and their parents in Argentina, focusing on their emotions, emotional stability, worries, routine, sleep, and daily activities. Also, the study explored the association between parental feelings and worries and child well-being. Furthermore, the study examined whether the impact of social isolation during the pandemics depends on the socio-economic status of the family.

Materials and Methods

Sample selection.

Parents filled out an anonymous online survey, after reading the written consent form and explicitly agreeing to take part of the study. The survey was conducted from May 26th to June 17th 2020, targeting parents of children aged 4–11 years-old. This age range was chosen to include children receiving pre-school and primary education. In case of multiple children, parents were asked to report on one child only. All questions were answered by the parent. The survey was conducted using an online platform, accessible through any device with an Internet connection. The survey was disseminated through institutional and private social networks (Twitter, Facebook, and Instagram), and institutional mailing lists. This method of administration provides a sample whose population parameters cannot be controlled as it is the case for probabilistic sampling. Such strategy was effective for the research objectives, because it facilitated the wide dissemination of the survey during a period with territorial restrictions due to the pandemic. The final sample included 814 families because respondents with missing or implausible data ( n = 302, e.g., child age out of range) were excluded from the analyses. Inclusion criteria: adult (>18 years old) mothers and fathers with children 4–11 years-old. Exclusion criteria: adults who did not have children or whose children were out of the age range.

The survey was specifically built using Google Form by the Institute of Development and Paediatric Research (IDIP), La Plata's Children Hospital, Buenos Aires, Argentina. For this, scientific literature related to the impact of lockdown on emotional state, feelings and lifestyle was reviewed ( 3 , 4 , 8 , 12 , 18 , 28 ) and questionnaires applied in previous studies were considered for creating our survey. The survey was first tested in a small number of parents who were asked whether the questions were clear. The survey ( Supplementary Table 1 ) included 43 closed questions, for each a list of acceptable responses was provided. Questions were divided into three different sections: (1) parent and family socio-demographic data (age, educational level, hometown, employment, telework, public health assistance, social welfare benefits, number of rooms in the house, number of persons living in the house, having a balcony/garden in the house, presence of pets), (2) children's data, feelings and lifestyle during lockdown (gender, age, worries about COVID-19, feelings and worries during lockdown, emotional state, routine, time spent in different activities, sleep, virtual contact with family/friends), (3) parent's feelings and worries during lockdown (worries about COVID-19 and feelings and worries during lockdown).

Statistical Analyses

Statistical analyses were performed using the R software version 3.6.0. Quantitative variables are presented as median (interquartile range, IQR) and categorical data are summarized as frequency counts and percentages. Chi-squared test or Fisher's exact test were used to test for associations between categorical variables. Pairwise comparisons between multiple groups were adjusted by the Benjamini and Hochberg (BH) method ( 31 ). Logistic regression models were used to estimate the odds ratio (OR) and 95% confidence interval (CI) between children's and parent's feelings and worries. Associations between children's emotional state, sleep and daily activities and parent's feelings and socio-demographic factors were assessed by polyserial or polychoric correlations according to the nature of the variables. To identify possible socioeconomic status (SES) subgroups, we conducted a cluster analysis on the educational levels of parents, social welfare benefits, public health assistance, number of rooms in the house and number of persons living in the household. All statistical tests were two tailed/bilateral, and the significance level was set at p < 0.01.

Ethics Approval

The study was approved by the Institutional Committee for the Revision of Research Protocols (CIRPI) of the Institute of Development and Paediatric Research (IDIP), La Plata Children's Hospital, and conducted according to the Declaration of Helsinki guidelines and Argentinian legal provisions governing clinical research on humans. We obtained informed consent from the participants included in this study.

Family Features and Clustering Based on the Socio-Economic Status (SES)

Respondents were between 21 and 56 years-old (median: 39), primarily college or university students/graduates (87.17%), employees (62.9%) that during lockdown were working part-time (42.8%) and from home (68.9%). More than 13% reported receiving social welfare benefits and 12.3% were assisted in the public health system. Home residences predominantly had 2–3 rooms and 88.8% housed between 3 and 5 people. Based on this, the median for the ratio between the number of persons living in the household and the number of rooms in the house was 1.5. Children were uniformly distributed by gender and age ( Supplementary Table 2 ). To assess if the impact of the lockdown depended on SES, we generated a 2-group partitioning of the families by conducting a cluster analysis including the following categorical variables: (1) public health assistance, (2) employment, (3) education, (4) reception of social welfare benefits, and (5) ratio between the number of persons living in the household and the number of rooms in the house (above or below 1.5). As a result, 378 parents (46.4%) were attributed to a high SES cluster, which included parents with high educational levels (university), low reception of social welfare benefits, low use of public health assistance and a number of persons living in the household/number of rooms in the house ratio <1.5, while 436 parents (53.6%) were attributed to a low SES cluster.

Impact of COVID-19 Pandemic Lockdown on Children

Emotional state.

According to their parents, 69.5% of the children showed changes in their emotional state. More than half of the children had adverse consequences on their emotional state: 46.1% of the parents reported mood instability in their children, 4.1% reported a nervous or aggressive mood and 3.8% sadness or crying. On the other hand, 10.7% of the children were happy during lockdown. Also 4.8% of the parents reported another type of emotional change in their children. The percentage of children who were happier under lockdown was higher between 4 and 6 years-old (14.4%) than children between 9 and 11 (7.4%, p < 0.001) ( Supplementary Figure 1 ). The percentage of children with no changes in their emotional state was higher between 9 and 11 (41%, p < 0.001). No differences were observed between the low and high SES clusters ( p = 0.574) or between boys and girls ( p = 0.039).

Feelings of children during lockdown are shown in Table 1 . According to the parent's opinion, 27% of the children were worried about getting/transmitting the COVID-19, older children (9–11 years old) being more worried than younger children. More than 16% were afraid to leave the house. Most of the children missed visiting their relatives (90.4%) and attending to school (64.6%), independently of their age but more often among girls than boys (93.6 vs. 87.2%, p = 0.002 and 71.2 vs. 57.3%, p < 0.001, respectively). Children mainly between 6 and 7 years-old missed practicing sports (75.9%, p < 0.001) and their friends (89.1%, p = 0.002), with no significant gender differences. Regarding SES, children belonging to families in the low cluster were more worried about food or money shortage than children in the high SES cluster.

Table 1 . Children's feelings and lifestyle during lockdown [ n (%)].

Lifestyle Changes

As shown in Table 1 , 55.3% of the parents reported that their children altered their routine during lockdown, independently of their age. Around 62% showed sleep disorders, mainly going to sleep at late hours, and this percentage increased with age. Girls showed more sleep disorders than boys (67 vs. 56.7%, p = 0.004). Most of the children communicated with their friends/family outside of the household at least once a day via WhatsApp (65.5%), social media (32.2%), or online gaming (38.1%), and this percentage increased with age. Social media was used by 14.1% of the children and 19.5% played online games constantly or on-and-off throughout the day, especially boys ( p < 0.001 vs. girls). SES did not affect routine, sleep or virtual contact with friends/family.

Daily Activities

As shown in Table 2 , 31.8% of the children spent <30 min/day being outside, and 36.2% spent <30 min doing physical activity (inside or outside), without gender differences. Concerning indoor activities, 57.7% of the children spent 2 h or more playing inside and 36.2% spent <30 min doing handicrafts. Most of the children (62.4%) spent <30 min a day reading. Regarding screen time, 28.1% spent more than 2 h playing screen games and 33.9% spent more than 2 h watching videos and/or TV. Also 21.6% spent more than 2 h playing screen games plus more than 2 h watching videos and/or TV.

Table 2 . Time spent in different activities during lockdown [ n (%)].

Children spent time in different activities depending on their age and gender. Younger children (4–6 years-old) spent less time doing school homework than older children. Besides, younger children spent more time outside, doing physical activity, playing, doing handicrafts and reading than older children (9–11 years-old). On the other hand, older children spent more time playing screen games. Significant gender differences were observed in screen games and handicrafts: boys spent more time playing screen games (36.1% spent more than 2 h/day vs. 22.2% in girls, p < 0.001) and girls spent more time doing handicrafts (73.3% spent more than 30 min/day vs. 53.3% in boys, p < 0.001). Time spent in different activities was not affected by SES.

Impact of COVID-19 Pandemic Lockdown on Families and Association to Children's Lifestyle and Emotional State

Parent's feelings.

Almost half (47.1%) of the parents were worried about getting/transmitting COVID-19 and 27.9% were afraid to leave the house for essential activities such as work or essential shopping. Besides, 59.1% reported being worried about their children's use of screen, and 68.4% found it stressful to keep children entertained during lockdown. Also, 16.6% of the parents felt lonely, 18.8% did not feel capable to help their child with school homework and 45.1% did not have time to play with their children. These worries and feelings were not affected by SES. In contrast, preoccupations about health (physical or mental), shortage of food/essential items, total household income and children's future were higher in families in the low SES cluster ( Supplementary Table 3 ).

Associations Between Child and Parent Feelings and Worries During Lockdown

As shown in Table 3 , parental fright to leave the house and concern about accessibility to food/essential items, household income and children's future were highly associated with similar worries in the children. Particularly, children whose parents were concerned about having enough food were more likely to be worried about food shortage during lockdown [OR (CI 95%) 13.0 (6.81, 26.5)].

Table 3 . Odds ratio (CI 95%) between children's and parent's feelings and worries.

Associations Between Children's Emotional State, Sleep and Activities and Routine, Parent's Feelings, and Socio-Demographic Factors

Only selected associations (| r |>0.1, p < 0.01) ( 32 ) were highlighted here. As shown in Table 4 , parental age and part-time working were positively associated with time spent doing school homework. The presence of balcony/garden was inversely associated with changes in child's sleep and positively associated with the amount of time spent outside. Keeping a routine similar to how things were before COVID-19 was inversely associated with adverse emotional state and with changes in sleep, and positively associated with the time spent doing school homework. The feeling of loneliness of parents was associated with adverse emotional state and sleep changes in children, while the feeling of being able to help with school homework was inversely associated with adverse emotional state. Having time to play with children was inversely associated with adverse emotional state and with changes in sleep. Time spent in physical activity, reading, playing videogames or watching a screen did not present association with routine, socio-demographic factors or parent's feelings (data not shown).

Table 4 . Associations between children's emotional state, sleep and daily activities and routine, parent's feelings, and socio-demographic factors.

To our knowledge, this is the first study investigating how COVID-19 pandemic lockdown affects lifestyle and emotional state of children and the links between child and parent well-being in the context of pandemic-associated lockdown in families from Latin America. Our study found that the socio-economic status of the family, the alteration of the routine as a consequence of the pandemic, the parental feelings and the access to a balcony/garden strongly affect children's emotional state and lifestyle.

Current results indicate that 69.5% of the parents reported changes in the emotional state of their children after 2 months of lockdown. Younger children showed more dramatic mood changes. The most frequent feature was mood instability. Feelings of worry, fear and longing for their relatives and friends and school were also frequently reported by most parents. Younger children were also happier to staying at home, which may reflect their interest in spending more time with their parents/caregivers. No gender differences were observed, in agreement with other studies in children ( 20 , 27 ), although some studies in adolescents show higher depression and anxiety levels in females ( 16 ). On the whole, our results are in line with the observations of other authors. In Italian children, a self-reported study ( 20 ) showed frequent mood swings in nearly half of the responders along with anxiety and depression symptoms. Other study in Spanish and Italian children reported that 85.7% of the parents perceived changes in their children's emotional state and behavior during lockdown, mainly difficulty concentrating and boredom ( 15 ). Studies in Chinese children show behavioral and emotional problems (17.6%) ( 29 ) and anxiety and depression rates between 18% ( 27 ) and 24% ( 12 ). Although our study did not assess depressive or anxiety symptoms, the observed changes in the emotional state could precede mental health decline and further evolve into such anxiety, depression, and posttraumatic stress symptoms.

Our study found important changes in the lifestyle of children, mainly loss of routine and changes in sleep. One of the most reported stressors of parents under pandemic lockdown was the disruption of children's routine ( 33 ), which can be detrimental because routines help children feel safe and contribute to healthy habits ( 33 ). In line with our results, other studies also reported changes in sleep. Sleep time increased in Canadian and Spanish children ( 17 , 18 ), and behavioral health was impaired in American children ( 22 ). Other authors ( 20 ) also showed alterations in routine and sleep in Italian children aged 6 to 14 in a self-reported study conducted through video-calls. Communication with relatives/friends outside the household was mainly sustained on a digital level, which increases screen time, but may be beneficial in lockdown circumstances. Regarding daily activities, we observed commonly found gender and age-related differences ( 18 ): older children were less active than younger children and spent more time with screens, and boys spent more time playing screen games ( 34 ). Sedentary behaviors (<30 min of physical activity, more than 2 h playing screen games and/or watching videos and/or TV) were observed in more than 35% of the children. The high rate of sedentary behaviors is in line with the above referred Spanish and Canadian studies ( 17 , 18 ) that reported decreased physical activity and increased screen exposure during pandemic lockdown. Interestingly, a study performed in 2014 found that 24.5% of Argentinean children between 5 and 10 years-old did not meet the international requirements of physical activity and showed a sedentary behavior in front of screen ( 35 ). Therefore, the proportion of sedentary children during the COVID-19 outbreak was increased in Argentina, exacerbated by a decline in outdoor time. Spending time outdoors has already been associated with more physical activity, less sedentary time and improved sleep ( 18 ). Thus, children should be encouraged to play and be active, engaging in activities compatible with lockdown measures, to minimize the negative consequences of lockdown.

The COVID-19 crisis has particularly affected vulnerable populations, including families with young children, who face dual caregiver and/or breadwinner demands ( 21 ) in a context of increasing poverty rate in Argentina. Although emotional state, lifestyle and activities of the children during lockdown did not depend on the SES, parents with lower SES were more worried about their health and economic issues (income, food/essential items), and these worries were also evident in their children. These findings indicate that 2 months of lockdown have an unfavorable impact on the emotional well-being mostly of vulnerable families, in line with reports from other authors ( 21 ).

Our study also aimed to identify factors that helped to support children's well-being. The key features for children's well-being unmasked by the current study were keeping a routine, a positive attitude from the parents and having a balcony/garden. The latter favors outdoor time and sleep, but does not increase physical activity. Keeping a routine similar to how things were before COVID-19 improves sleep, emotional state and dedication to school homework. These results agree with other authors who reported that mood state is more strongly related to life changes than specific COVID worries ( 36 ). Being an older parent and part-time working also favor dedication to school homework, and parents with positive attitudes such as playing with their children or helping them with school homework have a favorable impact on their emotional state. On the other hand, parents feeling lonely negatively affect the child's emotional state and sleep quality, and parents who feel worried or afraid highly condition the children's fears and worries, especially about shortage of food and money. Other authors reported that the parents' difficulty to deal with lockdown is associated with parent's stress, which impacts on children's behavioral and emotional problems ( 30 ), and distress levels are also mediated by child's behavioral and emotional difficulties ( 10 ). This is in line with our findings that parents feel stressed to keep children entertained and do not find time to play with them, though spending a lot of time in the house. Our results and results from other authors ( 21 , 30 ) highlight the strong links between parental psychological well-being and the well-being of their children. When children do not have a predictable routine and do not have emotional support from their parents, they may show distress evidenced by emotional and behavioral problems.

It was recently reported that school closure due to COVID-19 has adverse consequences on children's physical and mental well-being ( 37 ), and similar disruptions are evident in our study. School closure isolates and socially deprives children from contact with their peers and teachers and is an important element in routine changes. School closure also plays a key role in the increase of sedentary behaviors since schools, and particularly physical education classes, provide an adequate environment to promote active behaviors among children and adolescents ( 17 ). Finally, parents are left alone dealing with children's education and having children at home 24 h/7 d, while also have to manage home-working and childcare ( 30 ). Therefore, the relevance of school closure on children's well-being should be taken into account when adopting preventive COVID-19 measures.

One of the strengths of our study lies in the fact that it was conducted 2 months after the beginning of the lockdown measurements, a very critical moment of the pandemic in Argentina. However, some features of the present study should be considered. First, this is a cross-sectional correlational study, therefore we cannot reach a conclusion about the long-term impact of lockdown or determine a causal relationship between the variables studied, a longitudinal analysis of the effects of lockdown on children and their parents over time would help to better understand the long-lasting consequences of lockdown. Moreover, the answers of the survey were exclusively provided by the parents. This data collection method may provide less information than child reports or direct evaluation by experts. However, it should be kept in mind that self-reports are not adequate for young children and pandemic restrictions limits direct evaluation by experts. Despite these limitations, this study is the first to provide data on the repercussions of COVID-19 lockdown on Argentinean children.

In conclusion, current results show that 2 months of pandemic lockdown in Argentina affected emotional state and lifestyle of children and their parents. During the COVID-19 crisis, strong links between parental psychological status and the well-being of children were observed. Lockdown especially affected the emotional well-being of more vulnerable families. Although the impact of the pandemic lockdown seems inevitable, our results show the importance of keeping a consistent routine during school closure, with enough opportunities to play, read, rest, and engage in physical activity, trying to avoid spending too much time in front of a screen. Besides, support for parents who are facing a stressful experience should also be provided. Our findings can guide efforts to preserve and promote child well-being during lockdown, helping governments to decide the confinement rules to apply to children, especially regarding school closing. Confinement rules should be accompanied by recommendations and guidelines for parents and caregivers to help children (and adults) to cope with the COVID-19 crisis.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

The studies involving human participants were reviewed and approved by Institutional Committee for the Revision of Research Protocols (CIRPI) of the Institute of Development and Paediatric Research (IDIP), La Plata Children's Hospital. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

MFA, MF, and MP designed the study and wrote the manuscript. MF, MP, MÁA, AA, MS, and MFA collected, analyzed, and interpreted the data. MF performed the statistical analysis. All authors approved the submitted versions.

This work was supported by grants from Fondo para la Investigación Científica y Tecnológica (FONCyT) and Gran Logia de la Argentina (PICTO2017-0086), from Fundación Alberto Roemmers and from Fundación Florencio Fiorini to MFA.

Conflict of Interest

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

Acknowledgments

We thank the participants for their contribution to the study. We are grateful to Dr. Mario Perello for the supportive comments and careful reading of the manuscript and all the staff of the institute for their help in the dissemination of the survey.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fped.2021.660033/full#supplementary-material

Supplementary Figure 1. Emotional state of children during lockdown. Percentage of children displaying different emotional states during lockdown according to parental report.

Supplementary Table 1. Survey.

Supplementary Table 2. Description of parent and child characteristics ( N = 814).

Supplementary Table 3. Parent's feelings and worries during lockdown [n (%)].

1. Hale T, Angrist N, Cameron-Blake E, Hallas L, Kira B, Majumdar S, et al. Oxford COVID-19 Government Response Tracker . Blavatnik School of Government (2020).

Google Scholar

2. Novotny JS, Gonzalez-Rivas JP, Kunzova S, Skladana M, Pospisilova A, Polcrova A, et al. Risk factors underlying COVID-19 lockdown-induced mental distress. Front Psychiatry. (2020) 11:603014. doi: 10.3389/fpsyt.2020.603014

CrossRef Full Text | Google Scholar

3. Canet-Juric L, Andres ML, Del Valle M, Lopez-Morales H, Poo F, Galli JI, et al. A longitudinal study on the emotional impact cause by the COVID-19 pandemic quarantine on general population. Front Psychol. (2020) 11:2431. doi: 10.3389/fpsyg.2020.565688

PubMed Abstract | CrossRef Full Text | Google Scholar

4. Di Renzo L, Gualtieri P, Cinelli G, Bigioni G, Soldati L, Attina A, et al. Psychological aspects and eating habits during COVID-19 home confinement: results of EHLC-COVID-19 Italian online survey. Nutrients . (2020) 12:2152. doi: 10.3390/nu12072152

5. Gullo S, Misici I, Teti A, Liuzzi M, Chiara E. Going through the lockdown: a longitudinal study on the psychological consequences of the coronavirus pandemic. Res Psychother. (2020) 23:494. doi: 10.4081/ripppo.2020.494

6. Roma P, Monaro M, Colasanti M, Ricci E, Biondi S, Di Domenico A, et al. A 2-month follow-up study of psychological distress among Italian people during the COVID-19 Lockdown. Int J Environ Res Public Health. (2020) 17:8180. doi: 10.3390/ijerph17218180

7. Gierc M, Riazi NA, Fagan MJ, Di Sebastiano KM, Kandola M, Priebe CS, et al. Strange days: adult physical activity and mental health in the first two months of the COVID-19 pandemic. Front Public Health. (2021) 9:325. doi: 10.3389/fpubh.2021.567552

8. Di Renzo L, Gualtieri P, Pivari F, Soldati L, Attina A, Cinelli G, et al. Eating habits and lifestyle changes during COVID-19 lockdown: an Italian survey. J Transl Med . (2020) 18:229. doi: 10.1186/s12967-020-02399-5

9. Fontanesi L, Marchetti D, Mazza C, Di Giandomenico S, Roma P, Verrocchio MC. The effect of the COVID-19 lockdown on parents: a call to adopt urgent measures. Psychol Trauma. (2020) 12:S79–81. doi: 10.1037/tra0000672

10. Mazza C, Ricci E, Marchetti D, Fontanesi L, Di Giandomenico S, Verrocchio MC, et al. How personality relates to distress in parents during the Covid-19 lockdown: the mediating role of child's emotional and behavioral difficulties and the moderating effect of living with other people. Int J Environ Res Public Health. (2020) 17:6236. doi: 10.3390/ijerph17176236

11. Chen F, Zheng D, Liu J, Gong Y, Guan Z, Lou D. Depression and anxiety among adolescents during COVID-19: a cross-sectional study. Brain Behav Immun. (2020) 88:36–8. doi: 10.1016/j.bbi.2020.05.061

12. Duan L, Shao X, Wang Y, Huang Y, Miao J, Yang X, et al. An investigation of mental health status of children and adolescents in china during the outbreak of COVID-19. J Affect Disord. (2020) 275:112–8. doi: 10.1016/j.jad.2020.06.029

13. Golberstein E, Wen H, Miller BF. Coronavirus disease 2019 (COVID-19) and mental health for children and adolescents. JAMA Pediatr. (2020) 174:819–20. doi: 10.1001/jamapediatrics.2020.1456

14. Liu JJ, Bao Y, Huang X, Shi J, Lu L. Mental health considerations for children quarantined because of COVID-19. Lancet Child Adolesc Health. (2020) 4:347–9. doi: 10.1016/S2352-4642(20)30096-1

15. Orgiles M, Morales A, Delvecchio E, Mazzeschi C, Espada JP. Immediate psychological effects of the COVID-19 quarantine in youth from Italy and Spain. Front Psychol. (2020) 11:2986. doi: 10.3389/fpsyg.2020.579038

16. Meherali S, Punjani N, Louie-Poon S, Abdul Rahim K, Das JK, Salam RA, et al. Mental health of children and adolescents amidst COVID-19 and past pandemics: a rapid systematic review. Int J Environ Res Public Health. (2021) 18:3432. doi: 10.3390/ijerph18073432

17. Lopez-Bueno R, Lopez-Sanchez GF, Casajus JA, Calatayud J, Gil-Salmeron A, Grabovac I, et al. Health-related behaviors among school-aged children and adolescents during the Spanish Covid-19 confinement. Front Pediatr. (2020) 8:573. doi: 10.3389/fped.2020.00573

18. Moore SA, Faulkner G, Rhodes RE, Brussoni M, Chulak-Bozzer T, Ferguson LJ, et al. Impact of the COVID-19 virus outbreak on movement and play behaviours of Canadian children and youth: a national survey. Int J Behav Nutr Phys Act. (2020) 17:85. doi: 10.1186/s12966-020-00987-8

19. Pietrobelli A, Pecoraro L, Ferruzzi A, Heo M, Faith M, Zoller T, et al. Effects of COVID-19 lockdown on lifestyle behaviors in children with obesity living in Verona, Italy: a longitudinal study. Obesity. (2020) 28:1382–5. doi: 10.1002/oby.22861

20. Segre G, Campi R, Scarpellini F, Clavenna A, Zanetti M, Cartabia M, et al. Interviewing children: the impact of the COVID-19 quarantine on children's perceived psychological distress and changes in routine. BMC Pediatr. (2021) 21:231. doi: 10.1186/s12887-021-02704-1

21. Gassman-Pines A, Ananat EO, Fitz-Henley J II. COVID-19 and parent-child psychological well-being. Pediatrics. (2020) 146:e202007294. doi: 10.1542/peds.2020-007294

22. Patrick SW, Henkhaus LE, Zickafoose JS, Lovell K, Halvorson A, Loch S, et al. Well-being of parents and children during the COVID-19 pandemic: a national survey. Pediatrics . (2020) 146:e2020016824. doi: 10.1542/peds.2020-016824

23. Paz J. Child Poverty and Inequality in Argentina. COVID-19 Effects . UNICEF (2020).

24. INDEC. Incidencia de la Pobreza y la Indigencia en 31 Aglomerados Urbanos. Primer Semestre de 2020 (2020).

25. Wang C, Pan R, Wan X, Tan Y, Xu L, Ho CS, et al. Immediate psychological responses and associated factors during the initial stage of the 2019 coronavirus disease (COVID-19) epidemic among the general population in China. Int J Environ Res Public Health. (2020) 17:1729. doi: 10.3390/ijerph17051729

26. Aguilar-Farias N, Toledo-Vargas M, Miranda-Marquez S, Cortinez-O'ryan A, Cristi-Montero C, Rodriguez-Rodriguez F, et al. Sociodemographic predictors of changes in physical activity, screen time, and sleep among toddlers and preschoolers in Chile during the COVID-19 pandemic. Int J Environ Res Public Health. (2020) 18:176. doi: 10.3390/ijerph18010176

27. Xie X, Xue Q, Zhou Y, Zhu K, Liu Q, Zhang J, et al. Mental health status among children in home confinement during the coronavirus disease 2019 outbreak in Hubei province, China. JAMA Pediatr. (2020) 174:898–900. doi: 10.1001/jamapediatrics.2020.1619

28. Zhang X, Zhu W, Kang S, Qiu L, Lu Z, Sun Y. Association between physical activity and mood states of children and adolescents in social isolation during the COVID-19 epidemic. Int J Environ Res Public Health. (2020) 17:7666. doi: 10.3390/ijerph17207666

29. Cui Y, Li F, Leckman JF, Guo L, Ke X, Liu J, et al. The prevalence of behavioral and emotional problems among Chinese school children and adolescents aged 6-16: a national survey. Eur Child Adolesc Psychiatry. (2021) 30:233–41. doi: 10.1007/s00787-020-01507-6

30. Spinelli M, Lionetti F, Pastore M, Fasolo M. Parents' stress and children's psychological problems in families facing the COVID-19 outbreak in Italy. Front Psychol. (2020) 11:1713. doi: 10.3389/fpsyg.2020.01713

31. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B. (1995) 57:289–300. doi: 10.1111/j.2517-6161.1995.tb02031.x

32. Cohen J. A power primer. Psychol Bull. (1992) 112:155–9. doi: 10.1037/0033-2909.112.1.155

33. Adams EL, Smith D, Caccavale LJ, Bean MK. Parents are stressed! patterns of parent stress across COVID-19. Front Psychiatry. (2021) 12:300. doi: 10.3389/fpsyt.2021.626456

34. Leblanc AG, Broyles ST, Chaput JP, Leduc G, Boyer C, Borghese MM, et al. Correlates of objectively measured sedentary time and self-reported screen time in Canadian children. Int J Behav Nutr Phys Act. (2015) 12:38. doi: 10.1186/s12966-015-0197-1

35. Tuñon I, Laíño F. Insuficiente actividad física en la infancia: niños, niñas y adolescentes entre 5 y 17 años en la Argentina urbana . Buenos Aires: Observatorio de la Deuda Social Argentina, Barómetro de la Deuda Social de la Infancia, Universidad Católica Argentina (2014).

36. Nikolaidis A, Paksarian D, Alexander L, Derosa J, Dunn J, Nielson DM, et al. The Coronavirus Health and Impact Survey (CRISIS) reveals reproducible correlates of pandemic-related mood states across the Atlantic. Sci Rep. (2021) 11:8139. doi: 10.1038/s41598-021-87270-3

37. Hoffman JA, Miller EA. Addressing the consequences of school closure due to COVID-19 on children's physical and mental well-being. World Med Health Policy. (2020) 12:300–10. doi: 10.1002/wmh3.365

Keywords: COVID-19, pandemic, lockdown, children, emotional state

Citation: Fasano MV, Padula M, Azrak MÁ, Avico AJ, Sala M and Andreoli MF (2021) Consequences of Lockdown During COVID-19 Pandemic in Lifestyle and Emotional State of Children in Argentina. Front. Pediatr. 9:660033. doi: 10.3389/fped.2021.660033

Received: 28 January 2021; Accepted: 15 June 2021; Published: 14 July 2021.

Reviewed by:

Copyright © 2021 Fasano, Padula, Azrak, Avico, Sala and Andreoli. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: María F. Andreoli, mfandreoli@fbcb.unl.edu.ar

This article is part of the Research Topic

The Consequences of COVID-19 on the Mental Well-being of Parents, Children and Adolescents

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• Published: 27 September 2021

Why lockdown and distance learning during the COVID-19 pandemic are likely to increase the social class achievement gap

• Sébastien Goudeau   ORCID: orcid.org/0000-0001-7293-0977 1 ,
• Camille Sanrey   ORCID: orcid.org/0000-0003-3158-1306 1 ,
• Arnaud Stanczak   ORCID: orcid.org/0000-0002-2596-1516 2 ,
• Antony Manstead   ORCID: orcid.org/0000-0001-7540-2096 3 &
• Céline Darnon   ORCID: orcid.org/0000-0003-2613-689X 2  

Nature Human Behaviour volume  5 ,  pages 1273–1281 ( 2021 ) Cite this article

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The COVID-19 pandemic has forced teachers and parents to quickly adapt to a new educational context: distance learning. Teachers developed online academic material while parents taught the exercises and lessons provided by teachers to their children at home. Considering that the use of digital tools in education has dramatically increased during this crisis, and it is set to continue, there is a pressing need to understand the impact of distance learning. Taking a multidisciplinary view, we argue that by making the learning process rely more than ever on families, rather than on teachers, and by getting students to work predominantly via digital resources, school closures exacerbate social class academic disparities. To address this burning issue, we propose an agenda for future research and outline recommendations to help parents, teachers and policymakers to limit the impact of the lockdown on social-class-based academic inequality.

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The widespread effects of the COVID-19 pandemic that emerged in 2019–2020 have drastically increased health, social and economic inequalities 1 , 2 . For more than 900 million learners around the world, the pandemic led to the closure of schools and universities 3 . This exceptional situation forced teachers, parents and students to quickly adapt to a new educational context: distance learning. Teachers had to develop online academic materials that could be used at home to ensure educational continuity while ensuring the necessary physical distancing. Primary and secondary school students suddenly had to work with various kinds of support, which were usually provided online by their teachers. For college students, lockdown often entailed returning to their hometowns while staying connected with their teachers and classmates via video conferences, email and other digital tools. Despite the best efforts of educational institutions, parents and teachers to keep all children and students engaged in learning activities, ensuring educational continuity during school closure—something that is difficult for everyone—may pose unique material and psychological challenges for working-class families and students.

Not only did the pandemic lead to the closure of schools in many countries, often for several weeks, it also accelerated the digitalization of education and amplified the role of parental involvement in supporting the schoolwork of their children. Thus, beyond the specific circumstances of the COVID-19 lockdown, we believe that studying the effects of the pandemic on academic inequalities provides a way to more broadly examine the consequences of school closure and related effects (for example, digitalization of education) on social class inequalities. Indeed, bearing in mind that (1) the risk of further pandemics is higher than ever (that is, we are in a ‘pandemic era’ 4 , 5 ) and (2) beyond pandemics, the use of digital tools in education (and therefore the influence of parental involvement) has dramatically increased during this crisis, and is set to continue, there is a pressing need for an integrative and comprehensive model that examines the consequences of distance learning. Here, we propose such an integrative model that helps us to understand the extent to which the school closures associated with the pandemic amplify economic, digital and cultural divides that in turn affect the psychological functioning of parents, students and teachers in a way that amplifies academic inequalities. Bringing together research in social sciences, ranging from economics and sociology to social, cultural, cognitive and educational psychology, we argue that by getting students to work predominantly via digital resources rather than direct interactions with their teachers, and by making the learning process rely more than ever on families rather than teachers, school closures exacerbate social class academic disparities.

First, we review research showing that social class is associated with unequal access to digital tools, unequal familiarity with digital skills and unequal uses of such tools for learning purposes 6 , 7 . We then review research documenting how unequal familiarity with school culture, knowledge and skills can also contribute to the accentuation of academic inequalities 8 , 9 . Next, we present the results of surveys conducted during the 2020 lockdown showing that the quality and quantity of pedagogical support received from schools varied according to the social class of families (for examples, see refs. 10 , 11 , 12 ). We then argue that these digital, cultural and structural divides represent barriers to the ability of parents to provide appropriate support for children during distance learning (Fig. 1 ). These divides also alter the levels of self-efficacy of parents and children, thereby affecting their engagement in learning activities 13 , 14 . In the final section, we review preliminary evidence for the hypothesis that distance learning widens the social class achievement gap and we propose an agenda for future research. In addition, we outline recommendations that should help parents, teachers and policymakers to use social science research to limit the impact of school closure and distance learning on the social class achievement gap.

Economic, structural, digital and cultural divides influence the psychological functioning of parents and students in a way that amplify inequalities.

The digital divide

Unequal access to digital resources.

Although the use of digital technologies is almost ubiquitous in developed nations, there is a digital divide such that some people are more likely than others to be numerically excluded 15 (Fig. 1 ). Social class is a strong predictor of digital disparities, including the quality of hardware, software and Internet access 16 , 17 , 18 . For example, in 2019, in France, around 1 in 5 working-class families did not have personal access to the Internet compared with less than 1 in 20 of the most privileged families 19 . Similarly, in 2020, in the United Kingdom, 20% of children who were eligible for free school meals did not have access to a computer at home compared with 7% of other children 20 . In 2021, in the United States, 41% of working-class families do not own a laptop or desktop computer and 43% do not have broadband compared with 8% and 7%, respectively, of upper/middle-class Americans 21 . A similar digital gap is also evident between lower-income and higher-income countries 22 .

Second, simply having access to a computer and an Internet connection does not ensure effective distance learning. For example, many of the educational resources sent by teachers need to be printed, thereby requiring access to printers. Moreover, distance learning is more difficult in households with only one shared computer compared with those where each family member has their own 23 . Furthermore, upper/middle-class families are more likely to be able to guarantee a suitable workspace for each child than their working-class counterparts 24 .

In the context of school closures, such disparities are likely to have important consequences for educational continuity. In line with this idea, a survey of approximately 4,000 parents in the United Kingdom confirmed that during lockdown, more than half of primary school children from the poorest families did not have access to their own study space and were less well equipped for distance learning than higher-income families 10 . Similarly, a survey of around 1,300 parents in the Netherlands found that during lockdown, children from working-class families had fewer computers at home and less room to study than upper/middle-class children 11 .

Data from non-Western countries highlight a more general digital divide, showing that developing countries have poorer access to digital equipment. For example, in India in 2018, only 10.7% of households possessed a digital device 25 , while in Pakistan in 2020, 31% of higher-education teachers did not have Internet access and 68.4% did not have a laptop 26 . In general, developing countries lack access to digital technologies 27 , 28 , and these difficulties of access are even greater in rural areas (for example, see ref. 29 ). Consequently, school closures have huge repercussions for the continuity of learning in these countries. For example, in India in 2018, only 11% of the rural and 40% of the urban population above 14 years old could use a computer and access the Internet 25 . Time spent on education during school closure decreased by 80% in Bangladesh 30 . A similar trend was observed in other countries 31 , with only 22% of children engaging in remote learning in Kenya 32 and 50% in Burkina Faso 33 . In Ghana, 26–32% of children spent no time at all on learning during the pandemic 34 . Beyond the overall digital divide, social class disparities are also evident in developing countries, with lower access to digital resources among households in which parental educational levels were low (versus households in which parental educational levels were high; for example, see ref. 35 for Nigeria and ref. 31 for Ecuador).

Unequal digital skills

In addition to unequal access to digital tools, there are also systematic variations in digital skills 36 , 37 (Fig. 1 ). Upper/middle-class families are more familiar with digital tools and resources and are therefore more likely to have the digital skills needed for distance learning 38 , 39 , 40 . These digital skills are particularly useful during school closures, both for students and for parents, for organizing, retrieving and correctly using the resources provided by the teachers (for example, sending or receiving documents by email, printing documents or using word processors).

Social class disparities in digital skills can be explained in part by the fact that children from upper/middle-class families have the opportunity to develop digital skills earlier than working-class families 41 . In member countries of the OECD (Organisation for Economic Co-operation and Development), only 23% of working-class children had started using a computer at the age of 6 years or earlier compared with 43% of upper/middle-class children 42 . Moreover, because working-class people tend to persist less than upper/middle-class people when confronted with digital difficulties 23 , the use of digital tools and resources for distance learning may interfere with the ability of parents to help children with their schoolwork.

Unequal use of digital tools

A third level of digital divide concerns variations in digital tool use 18 , 43 (Fig. 1 ). Upper/middle-class families are more likely to use digital resources for work and education 6 , 41 , 44 , whereas working-class families are more likely to use these resources for entertainment, such as electronic games or social media 6 , 45 . This divide is also observed among students, whereby working-class students tend to use digital technologies for leisure activities, whereas their upper/middle-class peers are more likely to use them for academic activities 46 and to consider that computers and the Internet provide an opportunity for education and training 23 . Furthermore, working-class families appear to regulate the digital practices of their children less 47 and are more likely to allow screens in the bedrooms of children and teenagers without setting limits on times or practices 48 .

In sum, inequalities in terms of digital resources, skills and use have strong implications for distance learning. This is because they make working-class students and parents particularly vulnerable when learning relies on extensive use of digital devices rather than on face-to-face interaction with teachers.

The cultural divide

Even if all three levels of digital divide were closed, upper/middle-class families would still be better prepared than working-class families to ensure educational continuity for their children. Upper/middle-class families are more familiar with the academic knowledge and skills that are expected and valued in educational settings, as well as with the independent, autonomous way of learning that is valued in the school culture and becomes even more important during school closure (Fig. 1 ).

Unequal familiarity with academic knowledge and skills

According to classical social reproduction theory 8 , 49 , school is not a neutral place in which all forms of language and knowledge are equally valued. Academic contexts expect and value culture-specific and taken-for-granted forms of knowledge, skills and ways of being, thinking and speaking that are more in tune with those developed through upper/middle-class socialization (that is, ‘cultural capital’ 8 , 50 , 51 , 52 , 53 ). For instance, academic contexts value interest in the arts, museums and literature 54 , 55 , a type of interest that is more likely to develop through socialization in upper/middle-class families than in working-class socialization 54 , 56 . Indeed, upper/middle-class parents are more likely than working-class parents to engage in activities that develop this cultural capital. For example, they possess more books and cultural objects at home, read more stories to their children and visit museums and libraries more often (for examples, see refs. 51 , 54 , 55 ). Upper/middle-class children are also more involved in extra-curricular activities (for example, playing a musical instrument) than working-class children 55 , 56 , 57 .

Beyond this implicit familiarization with the school curriculum, upper/middle-class parents more often organize educational activities that are explicitly designed to develop academic skills of their children 57 , 58 , 59 . For example, they are more likely to monitor and re-explain lessons or use games and textbooks to develop and reinforce academic skills (for example, labelling numbers, letters or colours 57 , 60 ). Upper/middle-class parents also provide higher levels of support and spend more time helping children with homework than working-class parents (for examples, see refs. 61 , 62 ). Thus, even if all parents are committed to the academic success of their children, working-class parents have fewer chances to provide the help that children need to complete homework 63 , and homework is more beneficial for children from upper-middle class families than for children from working-class families 64 , 65 .

School closures amplify the impact of cultural inequalities

The trends described above have been observed in ‘normal’ times when schools are open. School closures, by making learning rely more strongly on practices implemented at home (rather than at school), are likely to amplify the impact of these disparities. Consistent with this idea, research has shown that the social class achievement gap usually greatly widens during school breaks—a phenomenon described as ‘summer learning loss’ or ‘summer setback’ 66 , 67 , 68 . During holidays, the learning by children tends to decline, and this is particularly pronounced in children from working-class families. Consequently, the social class achievement gap grows more rapidly during the summer months than it does in the rest of the year. This phenomenon is partly explained by the fact that during the break from school, social class disparities in investment in activities that are beneficial for academic achievement (for example, reading, travelling to a foreign country or museum visits) are more pronounced.

Therefore, when they are out of school, children from upper/middle-class backgrounds may continue to develop academic skills unlike their working-class counterparts, who may stagnate or even regress. Research also indicates that learning loss during school breaks tends to be cumulative 66 . Thus, repeated episodes of school closure are likely to have profound consequences for the social class achievement gap. Consistent with the idea that school closures could lead to similar processes as those identified during summer breaks, a recent survey indicated that during the COVID-19 lockdown in the United Kingdom, children from upper/middle-class families spent more time on educational activities (5.8 h per day) than those from working-class families (4.5 h per day) 7 , 69 .

Unequal dispositions for autonomy and self-regulation

School closures have encouraged autonomous work among students. This ‘independent’ way of studying is compatible with the family socialization of upper/middle-class students, but does not match the interdependent norms more commonly associated with working-class contexts 9 . Upper/middle-class contexts tend to promote cultural norms of independence whereby individuals perceive themselves as autonomous actors, independent of other individuals and of the social context, able to pursue their own goals 70 . For example, upper/middle-class parents tend to invite children to express their interests, preferences and opinions during the various activities of everyday life 54 , 55 . Conversely, in working-class contexts characterized by low economic resources and where life is more uncertain, individuals tend to perceive themselves as interdependent, connected to others and members of social groups 53 , 70 , 71 . This interdependent self-construal fits less well with the independent culture of academic contexts. This cultural mismatch between interdependent self-construal common in working-class students and the independent norms of the educational institution has negative consequences for academic performance 9 .

Once again, the impact of these differences is likely to be amplified during school closures, when being able to work alone and autonomously is especially useful. The requirement to work alone is more likely to match the independent self-construal of upper/middle-class students than the interdependent self-construal of working-class students. In the case of working-class students, this mismatch is likely to increase their difficulties in working alone at home. Supporting our argument, recent research has shown that working-class students tend to underachieve in contexts where students work individually compared with contexts where students work with others 72 . Similarly, during school closures, high self-regulation skills (for example, setting goals, selecting appropriate learning strategies and maintaining motivation 73 ) are required to maintain study activities and are likely to be especially useful for using digital resources efficiently. Research has shown that students from working-class backgrounds typically develop their self-regulation skills to a lesser extent than those from upper/middle-class backgrounds 74 , 75 , 76 .

Interestingly, some authors have suggested that independent (versus interdependent) self-construal may also affect communication with teachers 77 . Indeed, in the context of distance learning, working-class families are less likely to respond to the communication of teachers because their ‘interdependent’ self leads them to respect hierarchies, and thus perceive teachers as an expert who ‘can be trusted to make the right decisions for learning’. Upper/middle class families, relying on ‘independent’ self-construal, are more inclined to seek individualized feedback, and therefore tend to participate to a greater extent in exchanges with teachers. Such cultural differences are important because they can also contribute to the difficulties encountered by working-class families.

The structural divide: unequal support from schools

The issues reviewed thus far all increase the vulnerability of children and students from underprivileged backgrounds when schools are closed. To offset these disadvantages, it might be expected that the school should increase its support by providing additional resources for working-class students. However, recent data suggest that differences in the material and human resources invested in providing educational support for children during periods of school closure were—paradoxically—in favour of upper/middle-class students (Fig. 1 ). In England, for example, upper/middle-class parents reported benefiting from online classes and video-conferencing with teachers more often than working-class parents 10 . Furthermore, active help from school (for example, online teaching, private tutoring or chats with teachers) occurred more frequently in the richest households (64% of the richest households declared having received help from school) than in the poorest households (47%). Another survey found that in the United Kingdom, upper/middle-class children were more likely to take online lessons every day (30%) than working-class students (16%) 12 . This substantial difference might be due, at least in part, to the fact that private schools are better equipped in terms of online platforms (60% of schools have at least one online platform) than state schools (37%, and 23% in the most deprived schools) and were more likely to organize daily online lessons. Similarly, in the United Kingdom, in schools with a high proportion of students eligible for free school meals, teachers were less inclined to broadcast an online lesson for their pupils 78 . Interestingly, 58% of teachers in the wealthiest areas reported having messaged their students or their students’ parents during lockdown compared with 47% in the most deprived schools. In addition, the probability of children receiving technical support from the school (for example, by providing pupils with laptops or other devices) is, surprisingly, higher in the most advantaged schools than in the most deprived 78 .

In addition to social class disparities, there has been less support from schools for African-American and Latinx students. During school closures in the United States, 40% of African-American students and 30% of Latinx students received no online teaching compared with 10% of white students 79 . Another source of inequality is that the probability of school closure was correlated with social class and race. In the United States, for example, school closures from September to December 2020 were more common in schools with a high proportion of racial/ethnic minority students, who experience homelessness and are eligible for free/discounted school meals 80 .

Similarly, access to educational resources and support was lower in poorer (compared with richer) countries 81 . In sub-Saharan Africa, during lockdown, 45% of children had no exposure at all to any type of remote learning. Of those who did, the medium was mostly radio, television or paper rather than digital. In African countries, at most 10% of children received some material through the Internet. In Latin America, 90% of children received some remote learning, but less than half of that was through the internet—the remainder being via radio and television 81 . In Ecuador, high-school students from the lowest wealth quartile had fewer remote-learning opportunities, such as Google class/Zoom, than students from the highest wealth quartile 31 .

Thus, the achievement gap and its accentuation during lockdown are due not only to the cultural and digital disadvantages of working-class families but also to unequal support from schools. This inequality in school support is not due to teachers being indifferent to or even supportive of social stratification. Rather, we believe that these effects are fundamentally structural. In many countries, schools located in upper/middle-class neighbourhoods have more money than those in the poorest neighbourhoods. Moreover, upper/middle-class parents invest more in the schools of their children than working-class parents (for example, see ref. 82 ), and schools have an interest in catering more for upper/middle-class families than for working-class families 83 . Additionally, the expectation of teachers may be lower for working-class children 84 . For example, they tend to estimate that working-class students invest less effort in learning than their upper/middle-class counterparts 85 . These differences in perception may have influenced the behaviour of teachers during school closure, such that teachers in privileged neighbourhoods provided more information to students because they expected more from them in term of effort and achievement. The fact that upper/middle-class parents are better able than working-class parents to comply with the expectations of teachers (for examples, see refs. 55 , 86 ) may have reinforced this phenomenon. These discrepancies echo data showing that working-class students tend to request less help in their schoolwork than upper/middle-class ones 87 , and they may even avoid asking for help because they believe that such requests could lead to reprimands 88 . During school closures, these students (and their families) may in consequence have been less likely to ask for help and resources. Jointly, these phenomena have resulted in upper/middle-class families receiving more support from schools during lockdown than their working-class counterparts.

Psychological effects of digital, cultural and structural divides

Despite being strongly influenced by social class, differences in academic achievement are often interpreted by parents, teachers and students as reflecting differences in ability 89 . As a result, upper/middle-class students are usually perceived—and perceive themselves—as smarter than working-class students, who are perceived—and perceive themselves—as less intelligent 90 , 91 , 92 or less able to succeed 93 . Working-class students also worry more about the fact that they might perform more poorly than upper/middle-class students 94 , 95 . These fears influence academic learning in important ways. In particular, they can consume cognitive resources when children and students work on academic tasks 96 , 97 . Self-efficacy also plays a key role in engaging in learning and perseverance in the face of difficulties 13 , 98 . In addition, working-class students are those for whom the fear of being outperformed by others is the most negatively related to academic performance 99 .

The fact that working-class children and students are less familiar with the tasks set by teachers, and less well equipped and supported, makes them more likely to experience feelings of incompetence (Fig. 1 ). Working-class parents are also more likely than their upper/middle-class counterparts to feel unable to help their children with schoolwork. Consistent with this, research has shown that both working-class students and parents have lower feelings of academic self-efficacy than their upper/middle-class counterparts 100 , 101 . These differences have been documented under ‘normal’ conditions but are likely to be exacerbated during distance learning. Recent surveys conducted during the school closures have confirmed that upper/middle-class families felt better able to support their children in distance learning than did working-class families 10 and that upper/middle-class parents helped their children more and felt more capable to do so 11 , 12 .

Pandemic disparity, future directions and recommendations

The research reviewed thus far suggests that children and their families are highly unequal with respect to digital access, skills and use. It also shows that upper/middle-class students are more likely to be supported in their homework (by their parents and teachers) than working-class students, and that upper/middle-class students and parents will probably feel better able than working-class ones to adapt to the context of distance learning. For all these reasons, we anticipate that as a result of school closures, the COVID-19 pandemic will substantially increase the social class achievement gap. Because school closures are a recent occurrence, it is too early to measure with precision their effects on the widening of the achievement gap. However, some recent data are consistent with this idea.

Evidence for a widening gap during the pandemic

Comparing academic achievement in 2020 with previous years provides an early indication of the effects of school closures during the pandemic. In France, for example, first and second graders take national evaluations at the beginning of the school year. Initial comparisons of the results for 2020 with those from previous years revealed that the gap between schools classified as ‘priority schools’ (those in low-income urban areas) and schools in higher-income neighbourhoods—a gap observed every year—was particularly pronounced in 2020 in both French and mathematics 102 .

Similarly, in the Netherlands, national assessments take place twice a year. In 2020, they took place both before and after school closures. A recent analysis compared progress during this period in 2020 in mathematics/arithmetic, spelling and reading comprehension for 7–11-year-old students within the same period in the three previous years 103 . Results indicated a general learning loss in 2020. More importantly, for the 8% of working-class children, the losses were 40% greater than they were for upper/middle-class children.

Similar results were observed in Belgium among students attending the final year of primary school. Compared with students from previous cohorts, students affected by school closures experienced a substantial decrease in their mathematics and language scores, with children from more disadvantaged backgrounds experiencing greater learning losses 104 . Likewise, oral reading assessments in more than 100 school districts in the United States showed that the development of this skill among children in second and third grade significantly slowed between Spring and Autumn 2020, but this slowdown was more pronounced in schools from lower-achieving districts 105 .

It is likely that school closures have also amplified racial disparities in learning and achievement. For example, in the United States, after the first lockdown, students of colour lost the equivalent of 3–5 months of learning, whereas white students were about 1–3 months behind. Moreover, in the Autumn, when some students started to return to classrooms, African-American and Latinx students were more likely to continue distance learning, despite being less likely to have access to the digital tools, Internet access and live contact with teachers 106 .

In some African countries (for example, Ethiopia, Kenya, Liberia, Tanzania and Uganda), the COVID-19 crisis has resulted in learning loss ranging from 6 months to more 1 year 107 , and this learning loss appears to be greater for working-class children (that is, those attending no-fee schools) than for upper/middle-class children 108 .

These findings show that school closures have exacerbated achievement gaps linked to social class and ethnicity. However, more research is needed to address the question of whether school closures differentially affect the learning of students from working- and upper/middle-class families.

Future directions

First, to assess the specific and unique impact of school closures on student learning, longitudinal research should compare student achievement at different times of the year, before, during and after school closures, as has been done to document the summer learning loss 66 , 109 . In the coming months, alternating periods of school closure and opening may occur, thereby presenting opportunities to do such research. This would also make it possible to examine whether the gap diminishes a few weeks after children return to in-school learning or whether, conversely, it increases with time because the foundations have not been sufficiently acquired to facilitate further learning 110 .

Second, the mechanisms underlying the increase in social class disparities during school closures should be examined. As discussed above, school closures result in situations for which students are unevenly prepared and supported. It would be appropriate to seek to quantify the contribution of each of the factors that might be responsible for accentuating the social class achievement gap. In particular, distinguishing between factors that are relatively ‘controllable’ (for example, resources made available to pupils) and those that are more difficult to control (for example, the self-efficacy of parents in supporting the schoolwork of their children) is essential to inform public policy and teaching practices.

Third, existing studies are based on general comparisons and very few provide insights into the actual practices that took place in families during school closure and how these practices affected the achievement gap. For example, research has documented that parents from working-class backgrounds are likely to find it more difficult to help their children to complete homework and to provide constructive feedback 63 , 111 , something that could in turn have a negative impact on the continuity of learning of their children. In addition, it seems reasonable to assume that during lockdown, parents from upper/middle-class backgrounds encouraged their children to engage in practices that, even if not explicitly requested by teachers, would be beneficial to learning (for example, creative activities or reading). Identifying the practices that best predict the maintenance or decline of educational achievement during school closures would help identify levers for intervention.

Finally, it would be interesting to investigate teaching practices during school closures. The lockdown in the spring of 2020 was sudden and unexpected. Within a few days, teachers had to find a way to compensate for the school closure, which led to highly variable practices. Some teachers posted schoolwork on platforms, others sent it by email, some set work on a weekly basis while others set it day by day. Some teachers also set up live sessions in large or small groups, providing remote meetings for questions and support. There have also been variations in the type of feedback given to students, notably through the monitoring and correcting of work. Future studies should examine in more detail what practices schools and teachers used to compensate for the school closures and their effects on widening, maintaining or even reducing the gap, as has been done for certain specific literacy programmes 112 as well as specific instruction topics (for example, ecology and evolution 113 ).

Practical recommendations

We are aware of the debate about whether social science research on COVID-19 is suitable for making policy decisions 114 , and we draw attention to the fact that some of our recommendations (Table 1 ) are based on evidence from experiments or interventions carried out pre-COVID while others are more speculative. In any case, we emphasize that these suggestions should be viewed with caution and be tested in future research. Some of our recommendations could be implemented in the event of new school closures, others only when schools re-open. We also acknowledge that while these recommendations are intended for parents and teachers, their implementation largely depends on the adoption of structural policies. Importantly, given all the issues discussed above, we emphasize the importance of prioritizing, wherever possible, in-person learning over remote learning 115 and where this is not possible, of implementing strong policies to support distance learning, especially for disadvantaged families.

Where face-to face teaching is not possible and teachers are responsible for implementing distance learning, it will be important to make them aware of the factors that can exacerbate inequalities during lockdown and to provide them with guidance about practices that would reduce these inequalities. Thus, there is an urgent need for interventions aimed at making teachers aware of the impact of the social class of children and families on the following factors: (1) access to, familiarity with and use of digital devices; (2) familiarity with academic knowledge and skills; and (3) preparedness to work autonomously. Increasing awareness of the material, cultural and psychological barriers that working-class children and families face during lockdown should increase the quality and quantity of the support provided by teachers and thereby positively affect the achievements of working-class students.

In addition to increasing the awareness of teachers of these barriers, teachers should be encouraged to adjust the way they communicate with working-class families due to differences in self-construal compared with upper/middle-class families 77 . For example, questions about family (rather than personal) well-being would be congruent with interdependent self-construals. This should contribute to better communication and help keep a better track of the progress of students during distance learning.

It is also necessary to help teachers to engage in practices that have a chance of reducing inequalities 53 , 116 . Particularly important is that teachers and schools ensure that homework can be done by all children, for example, by setting up organizations that would help children whose parents are not in a position to monitor or assist with the homework of their children. Options include homework help groups and tutoring by teachers after class. When schools are open, the growing tendency to set homework through digital media should be resisted as far as possible given the evidence we have reviewed above. Moreover, previous research has underscored the importance of homework feedback provided by teachers, which is positively related to the amount of homework completed and predictive of academic performance 117 . Where homework is web-based, it has also been shown that feedback on web-based homework enhances the learning of students 118 . It therefore seems reasonable to predict that the social class achievement gap will increase more slowly (or even remain constant or be reversed) in schools that establish individualized monitoring of students, by means of regular calls and feedback on homework, compared with schools where the support provided to pupils is more generic.

Given that learning during lockdown has increasingly taken place in family settings, we believe that interventions involving the family are also likely to be effective 119 , 120 , 121 . Simply providing families with suitable material equipment may be insufficient. Families should be given training in the efficient use of digital technology and pedagogical support. This would increase the self-efficacy of parents and students, with positive consequences for achievement. Ideally, such training would be delivered in person to avoid problems arising from the digital divide. Where this is not possible, individualized online tutoring should be provided. For example, studies conducted during the lockdown in Botswana and Italy have shown that individual online tutoring directly targeting either parents or students in middle school has a positive impact on the achievement of students, particularly for working-class students 122 , 123 .

Interventions targeting families should also address the psychological barriers faced by working-class families and children. Some interventions have already been designed and been shown to be effective in reducing the social class achievement gap, particularly in mathematics and language 124 , 125 , 126 . For example, research showed that an intervention designed to train low-income parents in how to support the mathematical development of their pre-kindergarten children (including classes and access to a library of kits to use at home) increased the quality of support provided by the parents, with a corresponding impact on the development of mathematical knowledge of their children. Such interventions should be particularly beneficial in the context of school closure.

Beyond its impact on academic performance and inequalities, the COVID-19 crisis has shaken the economies of countries around the world, casting millions of families around the world into poverty 127 , 128 , 129 . As noted earlier, there has been a marked increase in economic inequalities, bringing with it all the psychological and social problems that such inequalities create 130 , 131 , especially for people who live in scarcity 132 . The increase in educational inequalities is just one facet of the many difficulties that working-class families will encounter in the coming years, but it is one that could seriously limit the chances of their children escaping from poverty by reducing their opportunities for upward mobility. In this context, it should be a priority to concentrate resources on the most deprived students. A large proportion of the poorest households do not own a computer and do not have personal access to the Internet, which has important consequences for distance learning. During school closures, it is therefore imperative to provide such families with adequate equipment and Internet service, as was done in some countries in spring 2020. Even if the provision of such equipment is not in itself sufficient, it is a necessary condition for ensuring pedagogical continuity during lockdown.

Finally, after prolonged periods of school closure, many students may not have acquired the skills needed to pursue their education. A possible consequence would be an increase in the number of students for whom teachers recommend class repetitions. Class repetitions are contentious. On the one hand, class repetition more frequently affects working-class children and is not efficient in terms of learning improvement 133 . On the other hand, accepting lower standards of academic achievement or even suspending the practice of repeating a class could lead to pupils pursuing their education without mastering the key abilities needed at higher grades. This could create difficulties in subsequent years and, in this sense, be counterproductive. We therefore believe that the most appropriate way to limit the damage of the pandemic would be to help children catch up rather than allowing them to continue without mastering the necessary skills. As is being done in some countries, systematic remedial courses (for example, summer learning programmes) should be organized and financially supported following periods of school closure, with priority given to pupils from working-class families. Such interventions have genuine potential in that research has shown that participation in remedial summer programmes is effective in reducing learning loss during the summer break 134 , 135 , 136 . For example, in one study 137 , 438 students from high-poverty schools were offered a multiyear summer school programme that included various pedagogical and enrichment activities (for example, science investigation and music) and were compared with a ‘no-treatment’ control group. Students who participated in the summer programme progressed more than students in the control group. A meta-analysis 138 of 41 summer learning programmes (that is, classroom- and home-based summer interventions) involving children from kindergarten to grade 8 showed that these programmes had significantly larger benefits for children from working-class families. Although such measures are costly, the cost is small compared to the price of failing to fulfil the academic potential of many students simply because they were not born into upper/middle-class families.

The unprecedented nature of the current pandemic means that we lack strong data on what the school closure period is likely to produce in terms of learning deficits and the reproduction of social inequalities. However, the research discussed in this article suggests that there are good reasons to predict that this period of school closures will accelerate the reproduction of social inequalities in educational achievement.

By making school learning less dependent on teachers and more dependent on families and digital tools and resources, school closures are likely to greatly amplify social class inequalities. At a time when many countries are experiencing second, third or fourth waves of the pandemic, resulting in fresh periods of local or general lockdowns, systematic efforts to test these predictions are urgently needed along with steps to reduce the impact of school closures on the social class achievement gap.

Bambra, C., Riordan, R., Ford, J. & Matthews, F. The COVID-19 pandemic and health inequalities. J. Epidemiol. Commun. Health 74 , 964–968 (2020).

Google Scholar  

Johnson, P, Joyce, R & Platt, L. The IFS Deaton Review of Inequalities: A New Year’s Message (Institute for Fiscal Studies, 2021).

Education: from disruption to recovery. https://en.unesco.org/covid19/educationresponse (UNESCO, 2020).

Daszak, P. We are entering an era of pandemics—it will end only when we protect the rainforest. The Guardian (28 July 2020); https://www.theguardian.com/commentisfree/2020/jul/28/pandemic-era-rainforest-deforestation-exploitation-wildlife-disease

Dobson, A. P. et al. Ecology and economics for pandemic prevention. Science 369 , 379–381 (2020).

Article   CAS   PubMed   Google Scholar  

Harris, C., Straker, L. & Pollock, C. A socioeconomic related ‘digital divide’ exists in how, not if, young people use computers. PLoS ONE 12 , e0175011 (2017).

Article   PubMed   PubMed Central   Google Scholar  

Zhang, M. Internet use that reproduces educational inequalities: evidence from big data. Comput. Educ. 86 , 212–223 (2015).

Article   Google Scholar  

Bourdieu, P. & Passeron, J. C. Reproduction in Education, Society and Culture (Sage, 1990).

Stephens, N. M., Fryberg, S. A., Markus, H. R., Johnson, C. S. & Covarrubias, R. Unseen disadvantage: how American universities’ focus on independence undermines the academic performance of first-generation college students. J. Pers. Soc. Psychol. 102 , 1178–1197 (2012).

Article   PubMed   Google Scholar  

Andrew, A. et al. Inequalities in children’s experiences of home learning during the COVID-19 lockdown in England. Fisc. Stud. 41 , 653–683 (2020).

Bol, T. Inequality in homeschooling during the Corona crisis in the Netherlands. First results from the LISS Panel. Preprint at SocArXiv https://doi.org/10.31235/osf.io/hf32q (2020).

Cullinane, C. & Montacute, R. COVID-19 and Social Mobility. Impact Brief #1: School Shutdown (The Sutton Trust, 2020).

Bandura, A. Self-efficacy: toward a unifying theory of behavioral change. Psychol. Rev. 84 , 191–215 (1977).

Prior, D. D., Mazanov, J., Meacheam, D., Heaslip, G. & Hanson, J. Attitude, digital literacy and self efficacy: low-on effects for online learning behavior. Internet High. Educ. 29 , 91–97 (2016).

Robinson, L. et al. Digital inequalities 2.0: legacy inequalities in the information age. First Monday https://doi.org/10.5210/fm.v25i7.10842 (2020).

Cruz-Jesus, F., Vicente, M. R., Bacao, F. & Oliveira, T. The education-related digital divide: an analysis for the EU-28. Comput. Hum. Behav. 56 , 72–82 (2016).

Rice, R. E. & Haythornthwaite, C. In The Handbook of New Media (eds Lievrouw, L. A. & Livingstone S. M.), 92–113 (Sage, 2006).

Yates, S., Kirby, J. & Lockley, E. Digital media use: differences and inequalities in relation to class and age. Sociol. Res. Online 20 , 71–91 (2015).

Legleye, S. & Rolland, A. Une personne sur six n’utilise pas Internet, plus d’un usager sur trois manques de compétences numériques de base [One in six people do not use the Internet, more than one in three users lack basic digital skills] (INSEE Première, 2019).

Green, F. Schoolwork in lockdown: new evidence on the epidemic of educational poverty (LLAKES Centre, 2020); https://www.llakes.ac.uk/wp-content/uploads/2021/03/RP-67-Francis-Green-Research-Paper-combined-file.pdf

Vogels, E. Digital divide persists even as americans with lower incomes make gains in tech adoption (Pew Research Center, 2021); https://www.pewresearch.org/fact-tank/2021/06/22/digital-divide-persists-even-as-americans-with-lower-incomes-make-gains-in-tech-adoption/

McBurnie, C., Adam, T. & Kaye, T. Is there learning continuity during the COVID-19 pandemic? A synthesis of the emerging evidence. J. Learn. Develop. http://dspace.col.org/handle/11599/3720 (2020).

Baillet, J., Croutte, P. & Prieur, V. Baromètre du numérique 2019 [Digital barometer 2019] (Sourcing Crédoc, 2019).

Giraud, F., Bertrand, J., Court, M. & Nicaise, S. In Enfances de Classes. De l’inégalité Parmi les Enfants (ed. Lahire, B.) 933–952 (Seuil, 2019).

Ahamed, S. & Siddiqui, Z. Disparity in access to quality education and the digital divide (Ideas for India, 2020); https://www.ideasforindia.in/topics/macroeconomics/disparity-in-access-to-quality-education-and-the-digital-divide.html

Soomro, K. A., Kale, U., Curtis, R., Akcaoglu, M. & Bernstein, M. Digital divide among higher education faculty. Int. J. Educ. Tech. High. Ed. 17 , 21 (2020).

Meng, Q. & Li, M. New economy and ICT development in China. Inf. Econ. Policy 14 , 275–295 (2002).

Chinn, M. D. & Fairlie, R. W. The determinants of the global digital divide: a cross-country analysis of computer and internet penetration. Oxf. Econ. Pap. 59 , 16–44 (2006).

Lembani, R., Gunter, A., Breines, M. & Dalu, M. T. B. The same course, different access: the digital divide between urban and rural distance education students in South Africa. J. Geogr. High. Educ. 44 , 70–84 (2020).

Asadullah, N., Bhattacharjee, A., Tasnim, M. & Mumtahena, F. COVID-19, schooling, and learning (BRAC Institute of Governance & Development, 2020); https://bigd.bracu.ac.bd/wp-content/uploads/2020/06/COVID-19-Schooling-and-Learning_June-25-2020.pdf

Asanov, I., Flores, F., McKenzie, D., Mensmann, M. & Schulte, M. Remote-learning, time-use, and mental health of Ecuadorian high-school students during the COVID-19 quarantine. World Dev. 138 , 105225 (2021).

Kihui, N. Kenya: 80% of students missing virtual learning amid school closures—study. AllAfrica (18 May 2020); https://allafrica.com/stories/202005180774.html

Debenedetti, L., Hirji, S., Chabi, M. O. & Swigart, T. Prioritizing evidence-based responses in Burkina Faso to mitigate the economic effects of COVID-19: lessons from RECOVR (Innovations for Poverty Action, 2020); https://www.poverty-action.org/blog/prioritizing-evidence-based-responses-burkina-faso-mitigate-economic-effects-covid-19-lessons

Bosumtwi-Sam, C. & Kabay, S. Using data and evidence to inform school reopening in Ghana (Innovations for Poverty Action, 2020); https://www.poverty-action.org/blog/using-data-and-evidence-inform-school-reopening-ghana

Azubuike, O. B., Adegboye, O. & Quadri, H. Who gets to learn in a pandemic? Exploring the digital divide in remote learning during the COVID-19 pandemic in Nigeria. Int. J. Educ. Res. Open 2 , 100022 (2021).

Attewell, P. Comment: the first and second digital divides. Sociol. Educ. 74 , 252–259 (2001).

DiMaggio, P., Hargittai, E., Neuman, W. R. & Robinson, J. P. Social implications of the Internet. Annu. Rev. Sociol. 27 , 307–336 (2001).

Hargittai, E. Digital na(t)ives? Variation in Internet skills and uses among members of the ‘Net Generation’. Sociol. Inq. 80 , 92–113 (2010).

Iivari, N., Sharma, S. & Ventä-Olkkonen, L. Digital transformation of everyday life—how COVID-19 pandemic transformed the basic education of the young generation and why information management research should care? Int. J. Inform. Manag. 55 , 102183 (2020).

Wei, L. & Hindman, D. B. Does the digital divide matter more? Comparing the effects of new media and old media use on the education-based knowledge gap. Mass Commun. Soc. 14 , 216–235 (2011).

Octobre, S. & Berthomier, N. L’enfance des loisirs [The childhood of leisure]. Cult. Études 6 , 1–12 (2011).

Education at a glance 2015: OECD indicators (OECD, 2015); https://doi.org/10.1787/eag-2015-en

North, S., Snyder, I. & Bulfin, S. Digital tastes: social class and young people’s technology use. Inform. Commun. Soc. 11 , 895–911 (2008).

Robinson, L. & Schulz, J. Net time negotiations within the family. Inform. Commun. Soc. 16 , 542–560 (2013).

Bonfadelli, H. The Internet and knowledge gaps: a theoretical and empirical investigation. Eur. J. Commun. 17 , 65–84 (2002).

Drabowicz, T. Social theory of Internet use: corroboration or rejection among the digital natives? Correspondence analysis of adolescents in two societies. Comput. Educ. 105 , 57–67 (2017).

Nikken, P. & Jansz, J. Developing scales to measure parental mediation of young children’s Internet use. Learn. Media Technol. 39 , 250–266 (2014).

Danic, I., Fontar, B., Grimault-Leprince, A., Le Mentec, M. & David, O. Les espaces de construction des inégalités éducatives [The areas of construction of educational inequalities] (Presses Univ. de Rennes, 2019).

Goudeau, S. Comment l'école reproduit-elle les inégalités? [How does school reproduce inequalities?] (Univ. Grenoble Alpes Editions/Presses Univ. de Grenoble, 2020).

Bernstein, B. Class, Codes, and Control (Routledge, 1975).

Gaddis, S. M. The influence of habitus in the relationship between cultural capital and academic achievement. Soc. Sci. Res. 42 , 1–13 (2013).

Lamont, M. & Lareau, A. Cultural capital: allusions, gaps and glissandos in recent theoretical developments. Sociol. Theory 6 , 153–168 (1988).

Stephens, N. M., Markus, H. R. & Phillips, L. T. Social class culture cycles: how three gateway contexts shape selves and fuel inequality. Annu. Rev. Psychol. 65 , 611–634 (2014).

Lahire, B. Enfances de classe. De l’inégalité parmi les enfants [Social class childhood. Inequality among children] (Le Seuil, 2019).

Lareau, A. Unequal Childhoods: Class, Race, and Family Life (Univ. of California Press, 2003).

Bourdieu, P. La distinction. Critique sociale du jugement [Distinction: a social critique of the judgement of taste] (Éditions de Minuit, 1979).

Bradley, R. H., Corwyn, R. F., McAdoo, H. P. & Garcia Coll, C. The home environments of children in the United States part I: variations by age, ethnicity, and poverty status. Child Dev. 72 , 1844–1867 (2001).

Blevins‐Knabe, B. & Musun‐Miller, L. Number use at home by children and their parents and its relationship to early mathematical performance. Early Dev. Parent. 5 , 35–45 (1996).

LeFevre, J. A. et al. Pathays to mathematics: longitudinal predictors of performance. Child Dev. 81 , 1753–1767 (2010).

Lareau, A. Home Advantage. Social Class and Parental Intervention in Elementary Education (Falmer Press, 1989).

Guryan, J., Hurst, E. & Kearney, M. Parental education and parental time with children. J. Econ. Perspect. 22 , 23–46 (2008).

Hill, C. R. & Stafford, F. P. Allocation of time to preschool children and educational opportunity. J. Hum. Resour. 9 , 323–341 (1974).

Calarco, J. M. A Field Guide to Grad School: Uncovering the Hidden Curriculum (Princeton Univ. Press, 2020).

Daw, J. Parental income and the fruits of labor: variability in homework efficacy in secondary school. Res. Soc. Strat. Mobil. 30 , 246–264 (2012).

Rønning, M. Who benefits from homework assignments? Econ. Educ. Rev. 30 , 55–64 (2011).

Alexander, K. L., Entwisle, D. R. & Olson, L. S. Lasting consequences of the summer learning gap. Am. Sociol. Rev. 72 , 167–180 (2007).

Cooper, H., Nye, B., Charlton, K., Lindsay, J. & Greathouse, S. The effects of summer vacation on achievement test scores: a narrative and meta-analytic review. Rev. Educ. Res. 66 , 227–268 (1996).

Stewart, H., Watson, N. & Campbell, M. The cost of school holidays for children from low income families. Childhood 25 , 516–529 (2018).

Pensiero, N., Kelly, A. & Bokhove, C. Learning inequalities during the Covid-19 pandemic: how families cope with home-schooling (University of Southampton, 2020); https://doi.org/10.5258/SOTON/P0025

Stephens, N. M., Markus, H. R. & Townsend, S. S. Choice as an act of meaning: the case of social class. J. Pers. Soc. Psychol. 93 , 814–830 (2007).

Kraus, M. W., Piff, P. K. & Keltner, D. Social class, sense of control, and social explanation. J. Pers. Soc. Psychol. 97 , 992–1004 (2009).

Dittmann, A. G., Stephens, N. M. & Townsend, S. S. Achievement is not class-neutral: working together benefits pople from working-class contexts. J. Pers. Soc. Psychol. 119 , 517–539 (2020).

Zimmerman, B. J. Investigating self-regulation and motivation: historical background, methodological developments, and future prospects. Am. Educ. Res. J. 45 , 166–183 (2008).

Backer-Grøndahl, A., Nærde, A., Ulleberg, P. & Janson, H. Measuring effortful control using the children’s behavior questionnaire–very short form: modeling matters. J. Pers. Assess. 98 , 100–109 (2016).

Johnson, S. E., Richeson, J. A. & Finkel, E. J. Middle class and marginal? Socioeconomic status, stigma, and self-regulation at an elite university. J. Pers. Soc. Psychol. 100 , 838–852 (2011).

Størksen, I., Ellingsen, I. T., Wanless, S. B. & McClelland, M. M. The influence of parental socioeconomic background and gender on self-regulation among 5-year-old children in Norway. Early Educ. Dev. 26 , 663–684 (2015).

Brady, L. et al. 7 ways for teachers to truly connect with parents. Education Week (31 December 2020); https://www.edweek.org/leadership/opinion-7-ways-for-teachers-to-truly-connect-with-parents/2020/12

Montacute, R. Social mobility and Covid-19: implications of the Covid-19 crisis for educational inequality (Sutton Trust, 2020); https://dera.ioe.ac.uk/35323/2/COVID-19-and-Social-Mobility-1.pdf

Dorn, E., Hancock, B., Sarakatsannis, J. & Viruleg, E. COVID-19 and student learning in the United States: the hurt could last a lifetime (McKinsey & Company, 2020); https://www.mckinsey.com/industries/public-and-social-sector/our-insights/covid-19-and-student-learning-in-the-united-states-the-hurt-could-last-a-lifetime

Parolin, Z. & Lee, E. K. Large socio-economic, geographic and demographic disparities exist in exposure to school closures. Nat. Hum. Behav. 5 , 522–528 (2021).

Saavedra, J. A silent and unequal education crisis. And the seeds for its solution (World Bank, 2021); https://blogs.worldbank.org/education/silent-and-unequal-education-crisis-and-seeds-its-solution

Murray, B., Domina, T., Renzulli, L. & Boylan, R. Civil society goes to school: parent–teacher associations and the equality of educational opportunity. Russell Sage Found. J. Soc. Sci. 5 , 41–63 (2019).

Calarco, J. M. Avoiding us versus them: how schools’ dependence on privileged ‘helicopter’ parents influences enforcement of rules. Am. Sociol. Rev. 85 , 223–246 (2020).

Rist, R. Student social class and teacher expectations: the self-fulfilling prophecy in ghetto education. Harv. Educ. Rev. 40 , 411–451 (1970).

Tobisch, A. & Dresel, M. Negatively or positively biased? Dependencies of teachers’ judgments and expectations based on students’ ethnic and social backgrounds. Soc. Psychol. Educ. 20 , 731–752 (2017).

Brantlinger, E. Dividing Classes: How the Middle-class Negotiates and Rationalizes School Advantage (Routledge, 2003).

Calarco, J. M. ‘I need help!’ Social class and children’s help-seeking in elementary school. Am. Sociol. Rev. 76 , 862–882 (2011).

Calarco, J. M. The inconsistent curriculum: cultural tool kits and student interpretations of ambiguous expectations. Soc. Psychol. Quart. 77 , 185–209 (2014).

Goudeau, S. & Cimpian, A. How do young children explain differences in the classroom? Implications for achievement, motivation, and educational equity. Perspect. Psychol. Sci. 16 , 533–552 (2021).

Croizet, J. C., Goudeau, S., Marot, M. & Millet, M. How do educational contexts contribute to the social class achievement gap: documenting symbolic violence from a social psychological point of view. Curr. Opin. Psychol. 18 , 105–110 (2017).

Goudeau, S. & Croizet, J.-C. Hidden advantages and disadvantages of social class: how classroom settings reproduce social inequality by staging unfair comparison. Psychol. Sci. 28 , 162–170 (2017).

Kudrna, L., Furnham, A. & Swami, V. The influence of social class salience on self-assessed intelligence. Soc. Behav. Personal. 38 , 859–864 (2010).

Wiederkehr, V., Darnon, C., Chazal, S., Guimond, S. & Martinot, D. From social class to self-efficacy: internalization of low social status pupils’ school performance. Soc. Psychol. Educ. 18 , 769–784 (2015).

Jury, M., Smeding, A., Court, M. & Darnon, C. When first-generation students succeed at university: on the link between social class, academic performance, and performance-avoidance goals. Contemp. Educ. Psychol. 41 , 25–36 (2015).

Jury, M., Quiamzade, A., Darnon, C. & Mugny, G. Higher and lower status individuals’ performance goals: the role of hierarchy stability. Motiv. Sci. 5 , 52–65 (2019).

Autin, F. & Croizet, J.-C. Improving working memory efficiency by reframing metacognitive interpretation of task difficulty. J. Exp. Psychol. Gen. 141 , 610–618 (2012).

Schmader, T., Johns, M. & Forbes, C. An integrated process model of stereotype threat effects on performance. Psychol. Rev. 115 , 336–356 (2008).

Usher, E. L. & Pajares, F. Self-efficacy for self-regulated learning: a validation study. Educ. Psychol. Meas. 68 , 443–463 (2008).

Bruno, A., Jury, M., Toczek-Capelle, M.-C. & Darnon, C. Are performance-avoidance goals always deleterious for academic achievement in college? The moderating role of social class. Soc. Psychol. Educ. 22 , 539–555 (2019).

Holloway, S. D. et al. Parenting self-efficacy and parental involvement: mediators or moderators between socioeconomic status and children’s academic competence in Japan and Korea? Res. Hum. Dev. 13 , 258–272 (2016).

Tazouti, Y. & Jarlégan, A. The mediating effects of parental self-efficacy and parental involvement on the link between family socioeconomic status and children’s academic achievement. J. Fam. Stud. 25 , 250–266 (2019).

Andreu, S. et al. Évaluations 2020, repères CP, CE1: premiers résultats [2020 assessments, first and second grades benchmarks: first results] (Ministère de l’Éducation nationale, de la Jeunesse et des Sports, 2020); https://www.education.gouv.fr/evaluations-2020-reperes-cp-ce1-premiers-resultats-307122

Engzell, P., Frey, A. & Verhagen, M. D. Learning loss due to school closures during the COVID-19 pandemic. Proc. Natl Acad. Sci. USA 118 , e2022376118 (2021).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Maldonado, J. E. & De Witte, K. The effect of school closures on standardized student test outcomes (KU Leuven—Faculty of Economics and Business, 2020); https://limo.libis.be/primo-explore/fulldisplay?docid=LIRIAS3189074&context=L&vid=Lirias&search_scope=Lirias&tab=default_tab&lang=en_US

Domingue, B., Hough, H. J., Lang, D. & Yeatman, J. Changing patterns of growth in oral reading fluency during the COVID-19 pandemic (PACE, 2021); https://edpolicyinca.org/publications/changing-patterns-growth-oral-reading-fluency-during-covid-19-pandemic

Dorn, E., Hancock, B., Sarakatsannis, J. & Viruleg, E. COVID-19 and learning loss—disparities grow and students need help (McKinsey & Company, 2020); https://www.mckinsey.com/industries/public-and-social-sector/our-insights/covid-19-and-learning-loss-disparities-grow-and-students-need-help

Angrist, N. et al. Building back better to avert a learning catastrophe: estimating learning loss from COVID-19 school shutdowns in Africa and facilitating short-term and long-term learning recovery. Int. J. Educ. Dev. 84 , 102397 (2021).

Reddy, V., Soudien, C. & Winnaar, L. Disrupted learning during COVID-19: the impact of school closures on education outcomes in South Africa (The Conversation, 2020); https://theconversation.com/impact-of-school-closures-on-education-outcomes-in-south-africa-136889

Entwisle, D. R. & Alexander, K. L. Summer setback: race, poverty, school composition, and mathematics achievement in the first two years of school. Am. Sociol. Rev. 57 , 72–84 (1992).

Kieffer, M. J. Catching up or falling behind? Initial English proficiency, concentrated poverty, and the reading growth of language minority learners in the United States. J. Educ. Psychol. 100 , 851–868 (2008).

Calarco, J. M., Horn, I. & Chen, G. A. ‘You need to be more responsible’: how math homework operates as a status-reinforcing process in school. Preprint at SocArXiv https://doi.org/10.31235/osf.io/xf96q (2020).

Kaiper-Marquez, A. et al. On the fly: adapting quickly to emergency remote instruction in a family literacy program. Int. Rev. Educ. 66 , 1–23 (2020).

Barton, D. C. Impacts of the COVID‐19 pandemic on field instruction and remote teaching alternatives: results from a survey of instructors. Ecol. Evol. 10 , 12499–12507 (2020).

Article   PubMed Central   Google Scholar  

IJzerman, H. et al. Use caution when applying behavioural science to policy. Nat. Hum. Behav. 4 , 1092–1094 (2020).

Taylor, J. & Mallery, J. In person and online learning go together (Stanford Institute for Economic Policy Research, 2020); https://siepr.stanford.edu/research/publications/person-and-online-learning-go-together

Dietrichson, J., Bøg, M., Filges, T. & Klint Jørgensen, A. M. Academic interventions for elementary and middle school students with low socioeconomic status: a systematic review and meta-analysis. Rev. Educ. Res. 87 , 243–282 (2017).

Núñez, J. C. et al. Teachers’ feedback on homework, homework-related behaviors, and academic achievement. J. Educ. Res. 108 , 204–216 (2015).

Singh, R. et al. In Artificial Intelligence in Education (eds Biswas, G.et al.) 328–336 (Springer Berlin Heidelberg, 2011).

Harackiewicz, J. M., Rozek, C. S., Hulleman, C. S. & Hyde, J. S. Helping parents to motivate adolescents in mathematics and science: an experimental test of a utility-value intervention. Psychol. Sci. 23 , 899–906 (2012).

Jeynes, W. A meta-analysis of the efficacy of different types of parental involvement programs for urban students. Urban Educ. 47 , 706–742 (2012).

Mol, S. E., Bus, A. G., De Jong, M. T. & Smeets, D. J. Added value of dialogic parent–child book readings: a meta-analysis. Early Educ. Dev. 19 , 7–26 (2008).

Angrist, N., Bergman, P. & Matsheng, M. School’s out: experimental evidence on limiting learning loss using “low-tech” in a pandemic (National Bureau of Economic Research, 2021); https://www.nber.org/papers/w28205

Carlana, M. & La Ferrara, E. Apart but connected: online tutoring and student outcomes during the COVID-19 pandemic (Institute of Labor Economics, 2021); http://hdl.handle.net/10419/232846

Pagan, S. & Sénéchal, M. Involving parents in a summer book reading program to promote reading comprehension, fluency, and vocabulary in grade 3 and grade 5 children. Can. J. Educ. 37 , 1–31 (2014).

Sénéchal, M. & LeFevre, J. A. Parental involvement in the development of children’s reading skill: a five‐year longitudinal study. Child Dev. 73 , 445–460 (2002).

Starkey, P. & Klein, A. Fostering parental support for children’s mathematical development: an intervention with Head Start families. Early Educ. Dev. 11 , 659–680 (2000).

Buheji, M. et al. The extent of Covid-19 pandemic socio-economic impact on global poverty: a global integrative multidisciplinary review. Am. J. Econ. 10 , 213–224 (2020).

The world economy on a tightrope (OECD, 2020); http://www.oecd.org/economic-outlook/june-2020/

Martin, A., Markhvida, M., Hallegatte, S. & Walsh, B. Socio-economic impacts of COVID-19 on household consumption and poverty. Econ. Disasters Clim. Change 4 , 453–479 (2020).

Jetten, J., Mols, F. & Selvanathan, H. P. How economic inequality fuels the rise and persistence of the Yellow Vest movement. Int. Rev. Soc. Psychol. 33 , 2 (2020).

Wilkinson, R. G. & Pickett, K. E. Income inequality and social dysfunction. Annu. Rev. Sociol. 35 , 493–511 (2009).

Sommet, N., Morselli, D. & Spini, D. Income inequality affects the psychological health of only the people facing scarcity. Psychol. Sci. 29 , 1911–1921 (2018).

Hattie, J. Visible Learning: A Synthesis of over 800 Meta-analyses Relating to Achievement (Routledge, 2008).

Cooper, H., Charlton, K., Valentine, J. C., Muhlenbruck, L. & Borman, G. D. Making the most of summer school: a meta-analytic and narrative review. Monogr. Soc. Res. Child 65 , 1–127 (2000).

Heyns, B. Schooling and cognitive development: is there a season for learning? Child Dev. 58 , 1151–1160 (1987).

McCombs, J. S., Augustine, C. H. & Schwartz, H. L. Making Summer Count: How Summer Programs can Boost Children’s Learning (Rand Education, 2011).

Borman, G. D. & Dowling, N. M. Longitudinal achievement effects of multiyear summer school: evidence from the teach Baltimore randomized field trial. Educ. Eval. Policy 28 , 25–48 (2006).

Kim, J. S. & Quinn, D. M. The effects of summer reading on low-income children’s literacy achievement from kindergarten to grade 8: a meta-analysis of classroom and home interventions. Rev. Educ. Res. 83 , 386–431 (2013).

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Acknowledgements

We thank G. Reis for editing the figure. The writing of this manuscript was supported by grant ANR-19-CE28-0007–PRESCHOOL from the French National Research Agency (S.G.).

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Goudeau, S., Sanrey, C., Stanczak, A. et al. Why lockdown and distance learning during the COVID-19 pandemic are likely to increase the social class achievement gap. Nat Hum Behav 5 , 1273–1281 (2021). https://doi.org/10.1038/s41562-021-01212-7

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Alexander d. arnon , alexander d. arnon penn wharton budget model john a. ricco , and john a. ricco penn wharton budget model kent a. smetters kent a. smetters the wharton school discussants: alessandra fogli alessandra fogli federal reserve bank of minneapolis.

September 23, 2020

This paper is part of the fall 2020 edition of the Brookings Papers on Economic Activity , the leading conference series and journal in economics for timely, cutting-edge research about real-world policy issues. Research findings are presented in a clear and accessible style to maximize their impact on economic understanding and policymaking. The editors are Brookings Nonresident Senior Fellow and Northwestern University Professor of Economics  Janice Eberly  and Brookings Nonresident Senior Fellow and Harvard University Professor of Economics  James Stock .  Read summaries of all the papers from the journal here.

Orders encouraging people to leave their homes for only their most essential needs during the early months of the COVID-19 pandemic reduced deaths at a lower economic cost than mandatory business shutdowns, suggests a paper discussed at the Brookings Papers on Economic Activity (BPEA) conference on September 24.

The paper’s authors—Alex Arnon, John Ricco, and Kent Smetters of the University of Pennsylvania—created an integrated framework that evaluated the health and economic effects of “non-pharmaceutical interventions” by state and local governments at the county level. They focused on three major interventions—stay-at-home orders, nonessential business closures, and school closures.

They used different types of cell phone data to estimate the effect of interventions on people’s contacts with people they did not live with. They then looked at COVID-19 deaths and estimated changes in employment at the county level, relying a range of nontraditional data, including from mobile devices, web searches, and business payrolls.

Interventions “that target individual behavior (such as stay-at-home orders) were more effective at reducing [virus] transmission at lower economic cost than those that target businesses (shutdowns),” the authors conclude in Epidemiological and economic effects of lockdown . School closures fell between stay-at-home orders and business closures in terms of the tradeoff between job losses and social distancing gains, they write.

The authors also find that state and local government interventions explain only nine percent of the reduction in social contacts through the second week of April. Voluntary social distancing explains most of the reduction. But, importantly, they estimate that the modest additional reduction in social contacts achieved by government interventions prevented about 33,000 deaths through May 31 (U.S. virus deaths at that point totaled nearly 115,000).

“It appears policy played a role in changing people’s behavior,” Smetters said in an interview with Brookings. “People took it much more seriously because there was a policy.”

At the same time, the authors estimate that non-pharmaceutical interventions reduced employment by about three million, nearly 15 percent of the total decline from the start of the pandemic through the end of May.

The paper also suggests that jurisdictions that imposed stay-at-home orders swiftly, as the virus was first spreading, were better able to avoid harsher business shutdowns later.

“If you impose a stay-at-home order at the first sign of an occurrence … the tradeoff will probably be better than if you wait until things get worse and then … close everything all at once,” Arnon said.

One of the paper’s objectives, the authors write, is “to provide useful insights to policymakers managing the current and any future infectious disease outbreaks.” But, they caution, “no analysis … can answer the question of whether the economic costs of a particular intervention are worth it” without explicitly considering the value of the years of life saved and other potential health benefits gained. “We hope that our analysis serves as a key part of helping to make that determination.”

David Skidmore authored the summary language for this paper.

Arnon , Alex ander D. , John Ricco, and Kent A. Smetters . 2020. “ Epidemiological and Economic Effects of Lockdown . ” Brookings Papers on Economic Activity , Fall, 61-108.

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Impacts of the Covid-19 lockdown and relevant vulnerabilities on capability well-being, mental health and social support: an Austrian survey study

• Judit Simon 1 , 2 ,
• Timea M. Helter 1 ,
• Ross G. White 3 ,
• Catharina van der Boor 3 &
• Agata Łaszewska 1  

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Impacts of the Covid-19 pandemic and its public health measures go beyond physical and mental health and incorporate wider well-being impacts in terms of what people are free to do or be. We explored the impacts of the Covid-19 lockdown and relevant vulnerabilities on capability well-being, mental health and social support in Austria.

Adult Austrian residents ( n  = 560) provided responses to a cross-sectional online survey about their experiences during Covid-19 lockdown (15 March-15 April 2020). Instruments measuring capabilities (OxCAP-MH), depression and anxiety (HADS), social support (MSPSS) and mental well-being (WHO-5) were used in association with six pre-defined vulnerabilities using multivariable linear regression.

31% of the participants reported low mental well-being and only 30% of those with a history of mental health treatment received treatment during lockdown. Past mental health treatment had a significant negative effect across all outcome measures with an associated capability well-being score reduction of − 6.54 (95%CI, − 9.26, − 3.82). Direct Covid-19 experience and being ‘at risk’ due to age and/or physical health conditions were also associated with significant capability deprivations. When adjusted for vulnerabilities, significant capability reductions were observed in association with increased levels of depression (− 1.77) and anxiety (− 1.50), and significantly higher capability levels (+ 3.75) were associated with higher levels of social support. Compared to the cohort average, individual capability impacts varied between − 9% for those reporting past mental health treatment and + 5% for those reporting one score higher on the social support scale.

Conclusions

Our study is the first to assess the capability limiting aspects of lockdown and relevant vulnerabilities alongside their impacts on mental health and social support. The negative capability well-being, mental health and social support impacts of the Covid-19 lockdown were strongest for people with a history of mental health treatment. Future public health policies concerning lockdowns should pay special attention to improve social support levels in order to increase public resilience.

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Introduction

The recently discovered coronavirus, known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread globally within a span of a few months since December 2019 [ 1 ]. The Covid-19 disease caused by the virus was declared as a pandemic by the World Health Organisation (WHO) on 11 March 2020. Initial evidence suggested that the infection has a high effective reproduction rate with older populations and those with underlying health conditions being at high risk of severe disease and death, thereby forcing numerous countries into temporary lockdowns to limit the spread of the disease. Consequently, the Covid-19 pandemic went from a direct health emergency to a systemic crisis affecting people’s lives in multiple ways [ 2 ]. Covid-19 impacts have been unprecedented because of its evolution from a health shock to a global economic and social crisis [ 2 ].

Substantial evidence from the past studies of the impacts of Severe Acute Respiratory Syndrome, Middle East Respiratory Syndrome, and Ebola epidemics on the suffering individuals and the healthcare providers showed substantial neuropsychiatric linkage [ 3 ]. There is an increasing amount of research related to the impacts Covid-19 on people’s mental health and well-being [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. Beside the direct health impact, public health emergencies may also affect individuals and communities through isolation, stigma, job insecurity, or inadequate resources for medical response [ 15 ]. These effects generate a range of emotional reactions, and can be particularly prevalent among those individuals who contract the disease, or those who are at increased risk due to their age or pre-existing medical conditions [ 15 ]. Evidence from previous pandemics shows that individuals who contracted the disease experienced fear, anxiety, emotional distress, and post-trauma stress symptoms [ 3 ]. The mental health/well-being impacts of the Covid-19 pandemic have been shown to be even more significant for those who are prone to psychological problems [ 6 ].

Impacts of the Covid19 pandemic and its public health measures go beyond physical and mental health and incorporate wider well-being impacts in terms of what people are free to do or be. Due to these complexities, the assessment of personal consequences related to well-being is challenging and may be best addressed within the conceptual framework of the capability approach introduced by Amartya Sen in the early 1980s [ 23 ]. The core focus of the capability approach is on what individuals are able to be and do in their lives, in other words, what they are capable of [ 23 ]. The capability approach provides a richer evaluative space beyond health and proposes that well-being is determined by people’s freedom to engage in forms of being and doing that are of intrinsic value to the person [ 23 ]. Beside the recently proposed use of the capability framework in the understanding of policy challenges [ 24 ], this freedom aspect can be interpreted in the narrower mental health context as both the actual capabilities of a person, for instance, good mental health, and the processes that enable them, for instance, legal regulations [ 25 ]. Not only has the Covid-19 pandemic had a profound psychological impact, but it also affects personal freedoms to engage in behaviours that are consistent with subjectively held values beyond health, for instance, visiting loved ones, engaging in recreational activities, spending time outdoors. Despite these important links, the connection between pandemics and individual capabilities have not yet been researched.

In Europe, Austria stood out as a nation that adopted aggressive and early strategies and thereby saw a smaller proportion of deaths from Covid-19 compared to some other European countries [ 26 ]. The first Covid-19 case in Austria was reported on 25 February 2020 [ 27 ]. The Austrian government issued general laws to contain the epidemic by restricting social contacts and imposing strict lockdown measures from 16 March onwards [ 27 ] most of which have been lifted gradually since 15 April.

Early studies assessing the Covid-19 pandemic, and related public health measures, impacts found significant impact on the mental health of the Austrian population. The studies found that symptoms of moderate to severe anxiety and depression have tripled in Austria, and 8–13% of the population showed severe depression and 6–11% severe anxiety symptoms [ 28 , 29 ].

The capability approach embodies a range of interlinking concepts and several studies have indicated that capability outcomes are strongly associated with mental health and social outcomes [ 30 , 31 , 32 , 33 , 34 ]. However, despite the increasing number of studies exploring the Covid-19 impact on mental health/well-being, information is still missing on the broader capability impact of the pandemic. Hence, this study aimed to explore the impact the Covid-19 lockdown period on people’s capabilities in association with mental health/well-being and social support, especially in the case of specific vulnerable groups in Austria. Covid-19 lockdown vulnerable groups were pre-defined as: (i) being categorised as ‘at risk’ group based on age and/or pre-existing physical health conditions; (ii) self-reported mental health treatment prior to the coronavirus pandemic; (iii) direct exposure to Covid-19 (having symptoms or being tested positive); (iv) indirect exposure to Covid-19 through a family member/friend; (v) having employment status impacted by the lockdown; or (vi) being categorised as critical worker.

Study participants

Participants were recruited using convenience sampling, i.e. the study sample consisted of people who responded to our survey advert. The advert was distributed via multiple channels including social media platforms (including Facebook, Twitter, WhatsApp) and emails targeting a wide range of individuals and organisations (universities, non-profit organisation such as Red Cross, and local governments) throughout Austria. In order to be able to participate in the study, respondents had to be older than 18 years, have sufficient German knowledge, and be residents in Austria at the time of the Covid-19 outbreak.

Study design and data collection

Cross-sectional data were collected via an online survey in May/June 2020, with all questions, including standardised outcome instruments, referring to the one-month lockdown period in Austria between 15 March and 15 April 2020.

The survey was developed in the SoSci online survey platform (Version 2), which is a publicly available tool and is free of charge for academic research [ 35 ]. The weblink of the survey was included in an advert, along with a QR code, that was circulated via social media platforms (including Facebook, Twitter, WhatsApp, etc.) and emails targeting a wide range of individuals and organisations throughout Austria.

Respondents who provided sociodemographic and Covid-19-related information and completed at least one standardised outcome instrument were considered for analysis. Those participants who discontinued the survey before fully completing at least one standardised outcome instrument were excluded from the analyses. The current analysis is based on the questions that were completed as part of the survey as outlined in the supplementary file (Supplementary file  1 ).

Survey and instruments

The online survey consisted of the participant information and consent forms followed by a section on sociodemographics. Subsequent sections assessed people’s perceptions about the Covid-19 pandemic and the public health measures in place during the lockdown in Austria in response to the outbreak. The final part of the questionnaire consisted of four self-reported standardised and validated outcome instruments, which were used to assess capability well-being (OxCAP-MH), depression and anxiety levels (HADS), social support (MSPSS) and mental well-being (WHO-5) similar to a parallel linked survey in the UK [ 36 ]. The outcome instruments were adapted to the online survey including the adaptation of their introductory text reflecting the period of interest, i.e. the one-month lockdown period in Austria between 15 March and 15 April 2020.

The Oxford CAPabilities questionnaire-Mental Health (OxCAP-MH) instrument was developed by Simon et al. in 2013 [ 37 ]. It is specifically designed to capture different well-being dimensions within the capability framework in the area of mental health across 16 items. The OxCAP-MH is scored on a 0–100 scale, with higher scores indicating better capabilities. The OxCAP-MH has shown good psychometric properties including internal consistency (Cronbach’s alpha between 0.79 and 0.85), test-retest reliability (intra class correlation coefficient 0.80), construct validity and responsiveness in both English and German populations [ 33 , 34 ]. The German version of the OxCAP-MH [ 38 ] was obtained from the authors for the study.

The Hospital Anxiety and Depression Scale (HADS) was developed by Zigmond and Snaith in 1983 [ 39 ]. The questionnaire is divided into Anxiety (HADS-A) and Depression (HADS-D) subscales both containing seven items scored on a four-point scale from zero (not present) to three (considerable). Both the HADS-A and HADS-D subscales are scored from 0 to 21, with higher scores indicating higher anxiety or depression levels. Normal, borderline and abnormal anxiety/depression scores are defined as 0–7, 8–10 and 11–21, respectively [ 39 ]. The HADS was found to perform well in assessing the presence and severity of anxiety disorders and depression in both somatic and psychiatric cases, also beyond the hospital setting, including the primary care patients and general population (mean sensitivity 0.90 and mean specificity 0.78 for a cut-off score of 8+ for HADS-A; and mean sensitivity 0.83 and mean specificity 0.79 for a cut-off score of 8+ for HADS-D) [ 40 ]. Cronbach’s alpha coefficient of internal consistency reported in several studies varied from 0.68 to 0.93 for HADS-A and from 0.67 to 0.90 for HADS-D [ 40 ]. The German translation of HADS was obtained from Hogrefe Publishing Group.

The Multidimensional Scale of Perceived Social Support (MSPSS) is a self-reported measure of subjectively assessed social support developed by Zimet et al. in 1988 [ 41 ]. The questionnaire can be divided into three subscales, each addressing a different source of support: Family, Friends, and Significant Other. Low, moderate and high support are defined as < 3, 3–5 and > 5, respectively [ 41 ]. The instrument has good internal consistency (Cronbach’s alpha 0.88) and test-retest reliability (0.85) [ 41 ]. An official German translation of MSPSS was obtained from the developer of the original English version.

The World Health Organisation-Five Well-being Index (WHO-5) is a short self-reported measure of current mental well-being introduced in 1998 by the WHO Regional Office in Europe [ 42 ]. Respondents are asked to rate how well each of the five statements about positive well-being applied to them in the given period from 5 (all of the time) to 0 (none of the time). The WHO-5 is scored 0–25, with higher scores representing higher well-being [ 43 ]. The WHO-5 has been used in multiple studies across countries and disease areas [ 43 ]. A review of 213 articles using the WHO-5 as an outcome measure confirmed that the instrument has satisfactory construct validity, responsiveness and it can be used as a screening tool for depression [ 43 ]. The German translation of the WHO-5 is available in the public domain without registration.

Definition of vulnerabilities

In the current study, six hypothesised associations between increased levels of mental health symptoms and decreased levels of well-being were tested according to pre-defined vulnerabilities identified as relevant to Covid-19: 1) “At risk” group; 2) Past mental health treatment; 3) Direct Covid-19 experience; 4) Indirect Covid-19 experience; 5) Employment status affected by Covid-19; and 6) Critical worker. Individuals were defined as ‘at risk’ if they were aged 65 years or over and/or had a self-reported underlying physical health condition including diabetes, heart/cardiovascular disease, stroke/cerebrovascular disease, lung disease, liver disease, or cancer. Participants who reported mental health service use prior to the period of interest were categorised as ‘having past mental health treatment’. Participants with ‘direct Covid-19 experience’ were those who tested positive for Covid-19 or experienced Covid-19 symptoms, but were not tested. ‘Indirect Covid-19 experience’ was defined as having a friend and/or family member infected or knowing someone who died of Covid-19. Participants with ‘employment status affected’ were those who reported losing their job due to the pandemic or being sent to short-time working (German ‘Kurzarbeit’). Finally, participants who reported having a job categorised by the government as critical worker, e.g. healthcare staff, police officer or food supply worker, were defined as ‘critical workers’.

Statistical analysis

Anonymous data were extracted from the online survey and checked for logical inconsistencies. Characteristics of the study cohort in comparison to the general Austrian population were presented.

Correlations between the different outcome measures were explored using Pearson’s correlations and interpreted as small < 0.3, moderate 0.3–0.49, or large ≥0.50 [ 44 ]. In order to explore the impacts of the Covid-19 lockdown and relevant pre-defined vulnerabilities on capabilities, mental health/well-being and social support, multivariable linear regression analyses were conducted using the OxCAP-MH, HADS-D, HADS-A, MSPSS and WHO-5 scores as dependent variables and six binary variables that defined vulnerable groups as independent variables. Analyses were adjusted for age, gender, having children, education level and initial employment status.

The potential impact of current depression, anxiety and social support on capabilities was investigated separately in a multivariable regression with OxCAP-MH capability score as the dependent variable and HADS-D, HADS-A, MSPSS scores as independent variables, adjusted for sociodemographic characteristics (age, gender, having children, education level and initial employment status) and the six relevant vulnerabilities as described above. Significance level of p  < 0.05 was considered in all analyses. Analyses were conducted on complete cases in STATA v.15.1 [ 45 ].

Participant characteristics

Of the 848 persons who accessed the survey, 560 respondents (74.1% female, mean age M  = 40.22 years, SD  = 11.60) completed it and were included in the analyses (Fig.  1 ). The average time needed to complete the survey was 17 min.

Survey flowchart

The majority of participants were Austrian citizens (87%) and employed at the beginning of the Covid-19 lockdown (73%). More than half of the survey participants (56%) had children, 52% were married or had a registered partnership. Data relating to the sociodemographic characteristics of the sample compared to official Austrian population statistics, with respect to age, gender, distribution of population across federal states [ 46 ], migration background [ 47 ], education level [ 48 ], and employment status [ 49 ], are shown in Table  1 .

Vulnerabilities

A total of 13% of the respondents ( N  = 72) were categorised as belonging to the ‘at risk’ group based on age and/or co-existing physical health conditions. While 17% of the participants ( N  = 97) reported that they received treatment for mental disorders before the period of interest, only 6% of the participants ( N  = 34) reported receiving mental health treatment during the pandemic. Overall, only 30% of those with a mental health service use history ( N  = 29) reported receiving treatment also during the lockdown.

A total of 1% of participants ( N  = 7) had been diagnosed with Covid-19, another 6% ( N  = 32) of the participants experienced Covid-19-like symptoms without being tested, and 20% of the respondents ( N  = 110) had indirect Covid-19 experience through an infected friend and/or family member, or knew someone who died of Covid-19. Employment status was affected for 15% ( N  = 84) of participants (job terminated: 3%, N  = 15; short-term work: 12%, N  = 69), and 38% of the respondents ( N  = 214) reported having a job categorised as ‘critical worker’ (Table  1 ).

The level of missing values for the standardised outcome instruments was low with a maximum of ten observations missing (1.8%) for MSPSS and WHO-5. The mean OxCAP-MH score was 74.10 ( SD  = 12.30). The mean WHO-5 score was 15.10 ( SD  = 4.80) with 31% ( N  = 174) of the respondents reporting a score below 13 indicating low mental well-being [ 42 ]. The mean scores on HADS-A and HADS-D subscales were 6.26 ( SD  = 4.19) and 4.72 ( SD  = 4.09), respectively, indicating that respondents on average reported higher levels of anxiety than depression symptoms. A total of 74% of participants ( N  = 416) reached the threshold of > 5 for high social support on the MPSS scale. Average scores for the MSPSS subscales were 5.41 for family support, 5.53 for support from friends and 5.96 for support from significant others.

Correlations between capability well-being, mental health/well-being and social support outcomes

Capability well-being (OxCAP-MH) was significantly strongly/moderately associated with all other outcome measures, the strongest correlation being with depression (HADS-D: r (557) = −.64, p  < .01; HADS-A: r (557) = −.56, p  < .01; WHO-5: r (448) = .58, p  < .01; MSPSS: r (448) = .42, p  < .01). In terms of social support, capabilities and depression had the same strength of correlations, but of opposite directions. (Table  2 ).

Outcome associations with different types of vulnerability

Outcome associations with different types of vulnerabilities adjusted for sociodemographics are shown in Table  3 . Past mental health treatment had a significant negative effect across all outcome measures with an associated capability well-being score reduction of − 6.54 ( b  = − 6.54, t (502) = − 4.73, p  < .01), while direct Covid-19 experience had the second most detrimental impact with an associated capability well-being score reduction of − 4.58 ( b  = − 4.58, t (502) = − 2.27, p  = 0.02). Capabilities were similarly negatively affected also for those who belonged to the category ‘at risk’ ( b  = − 4.45, t (502) = − 2.70, p  < .01). These correspond to capability deprivations of − 9% and − 6%, respectively, when compared to the average capability level of the study cohort.

Having employment status affected by the pandemic produced consistently lower capability and mental well-being scores as well as higher depression and anxiety scores, but these associations did not reach statistical significance. We did not observe any significant impacts for the category ‘critical worker’ either.

Associations between capability well-being and current depression, anxiety and social support levels

Additional associations between current levels of depression and anxiety as well as social support with capability well-being were investigated in a separate multivariable regression analysis adjusted for all vulnerabilities and sociodemographics (Table  4 ). Current levels of depression and anxiety separately showed a capability score reduction of − 1.77 ( b  = − 1.77, t (500) = − 16.89, p  < .01) and − 1.50 ( b  = − 1.50, t (500) = − 13.52, p  < .01), respectively, per one point difference in the relevant HADS scores. Social support on the other hand proved to be a major capability resilience factor. One point score improvement on the MSPSS scale was associated with an improvement of + 3.75 ( b  = 3.75, t (491) = 9.60, p  < .01) in the capability scores.

This is the first study to assess the impact of the Covid-19 lockdown and relevant vulnerabilities on capabilities well-being, mental health and social support and their associations as observed in Austria.

Our findings that Covid-19 direct experience is associated with intensified anxiety symptoms, lower mental well-being and lower capabilities are in line with other recent studies exploring the impact of the Covid-19 pandemic on mental health and well-being in Austria [ 28 , 29 , 50 , 51 , 52 ]. Our study showed that participants who reported mental health treatment before the Covid-19 pandemic reported worse outcomes on all measures, including the OxCAP-MH, HADS-D, HADS-A, MSPSS and WHO-5. However, only the OxCAP-MH capability questionnaire showed a significant negative impact for participants categorised as belonging to the ‘at risk’ group. It should be noted that it is likely that the Covid-19 lockdown restrictions accentuated levels of distress experienced by those with existing physical health conditions. This association has not been captured by any other outcome measure, suggesting an increased sensitivity of the OxCAP-MH in comparison to the other scales used in this study and confirming the advantage of its broader measurement scope when assessing the well-being impact of a pandemic and related public health measures. The study also confirmed that the capability approach, which provides an indication of people’s freedom to engage in forms of being and doing that are of intrinsic value to the person, has direct relevance to situations/policies that inherently limit personal freedoms, i.e. public health emergencies.

The vulnerabilities referred to in this study as ‘employment status affected’ by Covid-19 or being a ‘critical worker’ were not significantly associated with any of the outcomes. Besides the issue of sample size, this may also reflect the Austrian government’s employment support policy implemented in the early stages of the pandemic including the introduction of the short-term working scheme to help retain jobs [ 53 , 54 ].

When considering the average capability well-being score observed in our cohort, the relative impact of different vulnerabilities and other factors on capability levels were estimated between − 9% for those reporting past mental health treatment vs. + 5% for reporting one score higher on the social support scale. In future analyses, the outcome scores obtained in this study could also be compared to scores observed in studies prior to the Covid-19 pandemic to further asses the overall impact of this public health emergency and lockdown on the well-being of the Austrian population. Previous studies using the WHO-5 instrument found that 26–27% of the Austrian sample reported scores corresponding to low mental well-being [ 55 , 56 ]. This is lower than the 31% of respondents who were identified as having low mental well-being (WHO-5 score below 13) in our study. Furthermore, 19% of the participants in this study had borderline and 16% ‘abnormal’ anxiety levels according to HADS-A scoring system, somewhat higher than the levels reported in earlier Austrian studies [ 57 , 58 , 59 , 60 ]. These results seem to be confirmative of the expected negative impacts of the Covid-19 pandemic, including those of the lockdown, on mental well-being including increased levels of anxiety and stress. Previous studies using the MSPSS scale in Austrian populations reported comparable scores, indicating relatively high social support [ 61 , 62 ].

In addition to providing an indication of the Covid-19 and lockdown impacts on vulnerable groups, this study also highlighted the interactions between capability well-being levels and current mental health that indicate a strong negative impact of current depression and anxiety. On the other hand, social support was shown as a major capability resilience factor. Future (public health) policies should take the strong associations between capabilities and current mental health and social support levels directly into consideration to minimise the negative long-term health, social and economic issues related to future public health emergencies.

Furthermore, our results suggest that amongst all investigated vulnerabilities, people with past mental health treatment represent the most vulnerable group. A recent study from Austria found that the number of people treated with psychotherapy during lockdown (personal, phone or virtual contacts) decreased by one-third [ 63 ]. In our study, the proportion of people receiving mental health treatment during lockdown in comparison to the period before the pandemic was 6% vs. 17%, respectively. We found evidence of the continuation of treatment between the two periods for only 30% of those participants who received mental health treatment prior to the pandemic. Even under the most conservative assumptions, these results suggest a substantial level of under-utilisation of mental health services (due to whatever causes) during the lockdown period. For future strategic healthcare planning during next waves of the pandemic, policy makers and health and social care providers need to be aware of the exceptional vulnerability of this group and efforts should be focused on maintaining continuity in mental health service provision. Digital e-health treatment options provide potential solution to assure this continuity of treatment whilst simultaneously protecting the health of the service-users and professionals [ 64 , 65 ].

The results of this research also have major implications for government departments, social care services and community-based support initiatives in planning how best to support the population during future pandemics, and in terms of the special attention needed for those with pre-existing mental health service use. Findings also provide crucial evidence for policy makers and members of the public by indicating how important and protective social support networks can be in mitigating the mental health and (capability) well-being impacts of public health emergencies through increased resilience. The latter finding goes beyond the health sector with relevant implications also for the education sector when considering decisions about university openings and necessary support networks for students. Future research should explore whether the observed impacts on capabilities, mental health and social support levels remain, worsen or diminish (via adjustment) as the pandemic continues and how they develop in the long-term after the public health situation is resolved.

Furthermore, strategies that can help to alleviate the negative impacts of the Covid-19 lockdown in the Austrian population should be identified. Priority should be given to assuring the continuity of mental health services, as well as identifying new cases of mental disorders, mental distress and anxiety that might arise due to lockdowns. This will require the capacity of mental health support services to be increased, policy initiatives to be communicated in a clear and transparent way so that anxiety is reduced among the population, and the introduction of work arrangements that allow for home-schooling for both parents (reducing the burden posed on mothers) that prevent loss of household income [ 66 ]. Moreover, governments should implement economic measures and reinforce essential health, social and education services to identify population needs, reduce inequalities in health and protect most vulnerable citizens including people with pre-existing conditions, elderly, migrant population, children and those with lower socio-economic status [ 67 ].

The main limitation of our study is that the participants completed the survey retrospectively about 1 month after the lockdown (mid-May 2020). This time-lag may have introduced some recall bias considering the self-reported outcome measures. Since data were collected at the time when the number of new Covid-19 cases were relatively low and the Austrian epidemic curve has flattened, we assume that the presented estimates are more conservative and optimistic than if the survey questions would have been completed directly during the lockdown. Moreover, since the analysis is based on one measurement point, the study allows no causal conclusions. Our study is also prone to limitations of online survey; results are based fully on self-reporting with the potential to reporting bias [ 68 ] and some groups (females, younger ages, higher educated), were over-represented in the survey sample compared to the general population [ 69 , 70 ]. The survey on the other hand achieved satisfactory representation in terms of more than half of the Austrian provinces, migration background and employment status.

This research contributes to the understanding of the impact that pandemics and nationwide responses to pandemics can have on mental health and broader capability well-beings in light of their major policy relevance. Furthermore, the study confirms that the OxCAP-MH capability measure is a valid and relevant tool to understand the impacts of the Covid-19 pandemic and related public health measures, which due to the negative externalities of any infectious disease inherently limit individual freedoms to some extent. Future research is planned to compare cultural aspects of lockdown experiences across countries and explore long-term mental health/well-being impacts from the perspective of the capability approach.

Availability of data and materials

The datasets generated during the current study and the study protocol have been released in a scientific data repository and can be accessed under the link: https://doi.org/10.5281/zenodo.4271534 .

Abbreviations

The Hospital Anxiety and Depression Scale

The Multidimensional Scale of Perceived Social Support

The Oxford CAPabilities questionnaire-Mental Health

World Health Organisation

The World Health Organisation-Five Well-being Index

WHO. WHO Coronavirus Disease (COVID-19) Dashboard. https://covid19.who.int/ . Accessed 30 Oct 2020.

UNDP. COVID-19 and human development: Assessing the crisis, envisioning the recovery. http://hdr.undp.org/en/hdp-covid . Accessed 30 Oct 2020.

Shah K, Kamrai D, Mekala H, Mann B, Desai K, Patel RS. Focus on mental health during the coronavirus (COVID-19) pandemic: applying Learnings from the past outbreaks. Cureus. 2020;12(3):e7405. https://doi.org/10.7759/cureus.7405 .

Article   PubMed   PubMed Central   Google Scholar  

Caqueo-Urízar A, Urzúa A, Aragón-Caqueo D, Charles CH, El-Khatib Z, Otu A, et al. Mental health and the COVID-19 pandemic in Chile. Psychol Trauma. 2020;12(5):521–3. https://doi.org/10.1037/tra0000753 .

Article   PubMed   Google Scholar  

Chen S, Bonanno GA. Psychological adjustment during the global outbreak of COVID-19: a resilience perspective. Psychol Trauma. 2020;12(S1):S51–S4. https://doi.org/10.1037/tra0000685 .

Cullen W, Gulati G, Kelly BD. Mental health in the COVID-19 pandemic. QJM. 2020;113(5):311–2. https://doi.org/10.1093/qjmed/hcaa110 .

Article   CAS   PubMed   Google Scholar  

Dong L, Bouey J. Public mental health crisis during COVID-19 pandemic, China. Emerg Infect Dis. 2020;26(7):1616–8. https://doi.org/10.3201/eid2607.200407 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Javed B, Sarwer A, Soto EB, Mashwani ZU. The coronavirus (COVID-19) pandemic's impact on mental health. Int J Health Plann Manag. 2020;35:993–6. https://doi.org/10.1002/hpm.3008 .

Article   Google Scholar  

Kavčič T, Avsec A, Zager KG. Psychological functioning of Slovene adults during the COVID-19 pandemic: does resilience matter? Psychiatr Q. 2020:1–10. https://doi.org/10.1007/s11126-020-09789-4 .

Khoury B, El-Khoury J, Ammar J. Psychological needs and response during the COVID-19 pandemic in Lebanon. Psychol Trauma. 2020;12(5):497–8. https://doi.org/10.1037/tra0000757 .

Koushik NS. A population mental health perspective on the impact of COVID-19. Psychol Trauma. 2020;12(5):529–30. https://doi.org/10.1037/tra0000737 .

Lai J, Ma S, Wang Y, Cai Z, Hu J, Wei N, et al. Factors associated with mental health outcomes among health care workers exposed to coronavirus disease 2019. JAMA Netw Open. 2020;3(3):e203976. https://doi.org/10.1001/jamanetworkopen.2020.3976 .

Loades ME, Chatburn E, Higson-Sweeney N, Reynolds S, Shafran R, Brigden A, et al. Rapid Systematic Review: The Impact of Social Isolation and Loneliness on the Mental Health of Children and Adolescents in the Context of COVID-19. J Am Acad Child Adolesc Psychiatry. 2020;59(11):1218–1239.e3. https://doi.org/10.1016/j.jaac.2020.05.009 .

Mancini AD. Heterogeneous mental health consequences of COVID-19: costs and benefits. Psychol Trauma. 2020;12(S1):S15–S6. https://doi.org/10.1037/tra0000894 .

Pfefferbaum B, North CS. Mental health and the Covid-19 pandemic. N Engl J Med. 2020;383(6):510–2. https://doi.org/10.1056/NEJMp2008017 .

Rossi R, Socci V, Talevi D, Mensi S, Niolu C, Pacitti F, et al. COVID-19 pandemic and lockdown measures impact on mental health among the general population in Italy. Front Psychiatry. 2020;11:790. https://doi.org/10.3389/fpsyt.2020.00790 .

Seghi F, Barbini B, Franchini L, Colombo C. The challenge of mental health during Covid-19 outbreak: experience from metropolitan area of Milan. Eur Arch Psychiatry Clin Neurosci. 2020:1–2. https://doi.org/10.1007/s00406-020-01154-7 .

Serafini G, Parmigiani B, Amerio A, Aguglia A, Sher L, Amore M. The psychological impact of COVID-19 on the mental health in the general population. QJM. 2020;113(8):531–7. https://doi.org/10.1093/qjmed/hcaa201 .

Article   CAS   PubMed Central   Google Scholar  

Sher L. The impact of the COVID-19 pandemic on suicide rates. QJM. 2020;133(11):707–12. https://doi.org/10.1093/qjmed/hcaa202 .

Article   CAS   Google Scholar  

Talevi D, Socci V, Carai M, Carnaghi G, Faleri S, Trebbi E, et al. Mental health outcomes of the CoViD-19 pandemic. Riv Psichiatr. 2020;55(3):137–44. https://doi.org/10.1708/3382.33569 .

Tang F, Liang J, Zhang H, Kelifa MM, He Q, Wang P. COVID-19 related depression and anxiety among quarantined respondents. Psychol Health. 2020:1–15. https://doi.org/10.1080/08870446.2020.1782410 .

Torales J, O'Higgins M, Castaldelli-Maia JM, Ventriglio A. The outbreak of COVID-19 coronavirus and its impact on global mental health. Int J Soc Psychiatry. 2020;66(4):317–20. https://doi.org/10.1177/0020764020915212 .

Sen A. Commodities and capabilities: OUP catalogue; 1999.

Google Scholar  

Sen A. Capability and well-being. In: Nusbaum M, Sen A, editors. The Quality of Life: Oxford: Clarendon Press; 1993. p. 30–53.

Anand P, Ferrer B, Gao Q, Nogales R, Unterhalter E. COVID-19 as a capability crisis: using the capability framework to understand policy challenges. J Human Dev Capabil. 2020:293–9. https://doi.org/10.1080/19452829.2020.1789079 .

Gibney E. Whose coronavirus strategy worked best? Scientists hunt most effective policies. Nature. 2020;581(7806):15–6. https://doi.org/10.1038/d41586-020-01248-1 .

Moshammer H, Poteser M, Lemmerer K, Wallner P, Hutter HP. Time course of COVID-19 cases in Austria. Int J Environ Res Public Health. 2020;17(9):3270. https://doi.org/10.3390/ijerph17093270 .

Pieh C, Budimir S, Probst T. Mental health during COVID-19 lockdown: a comparison of Austria and the UK. SSRN. https://doi.org/10.2139/ssrn.3592372 .

Traunmüller C, Stefitz R, Gaisbachgrabner K, Schwerdtfeger A. Psychological correlates of COVID-19 pandemic in the Austrian population. BMC Public Health. 2020;20:1395. https://doi.org/10.1186/s12889-020-09489-5 .

Brunner R. Why do people with mental distress have poor social outcomes? Four lessons from the capabilities approach. Soc Sci Med. 2017;191:160–7.

Mitchell PM, Al-Janabi H, Byford S, Kuyken W, Richardson J, Iezzi A, et al. Assessing the validity of the ICECAP-A capability measure for adults with depression. BMC Psychiatry. 2017;17(1):46. https://doi.org/10.1186/s12888-017-1211-8 .

Hackert MQN, van Exel J, Brouwer WBF. Does the ICECAP-O cover the physical, mental and social functioning of older people in the UK? Qual Life Res. 2019;28(3):761–70. https://doi.org/10.1007/s11136-018-2042-x .

Łaszewska A, Schwab M, Leutner E, Oberrauter M, Spiel G, Simon J. Measuring broader wellbeing in mental health services: validity of the German language OxCAP-MH capability instrument. Qual Life Res. 2019;28(8):2311–23. https://doi.org/10.1007/s11136-019-02187-9 .

Vergunst F, Jenkinson C, Burns T, Anand P, Gray A, Rugkåsa J, et al. Psychometric validation of a multi-dimensional capability instrument for outcome measurement in mental health research (OxCAP-MH). Health Qual Life Outcomes. 2017;15(1):250. https://doi.org/10.1186/s12955-017-0825-3 .

Leiner DJ. SoSci Survey (Version 3.1.06). 2019. https://www.soscisurvey.de .

White RG, Van Der Boor C. Impact of the COVID-19 pandemic and initial period of lockdown on the mental health and well-being of adults in the UK. B J Psych Open. 2020;6(5):e90. https://doi.org/10.1192/bjo.2020.79 .

Simon J, Anand P, Gray A, Rugkåsa J, Yeeles K, Burns T. Operationalising the capability approach for outcome measurement in mental health research. Soc Sci Med. 2013;98:187–96. https://doi.org/10.1016/j.socscimed.2013.09.019 .

Simon J, Łaszewska A, Leutner E, Spiel G, Churchman D, Mayer S. Cultural and linguistic transferability of the multi-dimensional OxCAP-MH capability instrument for outcome measurement in mental health: the German language version. BMC Psychiatry. 2018;18(1):173. https://doi.org/10.1186/s12888-018-1762-3 .

Snaith RP, Zigmond AS. The hospital anxiety and depression scale. Br Med J. 1986;292(6516):344. https://doi.org/10.1136/bmj.292.6516.344 .

Bjelland I, Dahl AA, Haug TT, Neckelmann D. The validity of the hospital anxiety and depression scale. An updated literature review. J Psychosom Res. 2002;52(2):69–77. https://doi.org/10.1016/s0022-3999(01)00296-3 .

Zimet GD, Dahlem NW, Zimet SG, Farley GK. The multidimensional scale of perceived social support. J Pers Assess. 1988;52(1):30–41. https://doi.org/10.1207/s15327752jpa5201_2 .

WHO. Wellbeing Measures in Primary Health Care/The Depcare Project. Copenhagen: Report on a WHO Meeting; 1998. https://www.euro.who.int/__data/assets/pdf_file/0016/130750/E60246.pdf Accessed 30 Oct 2020

Topp CW, Østergaard SD, Søndergaard S, Bech P. The WHO-5 well-being index: a systematic review of the literature. Psychother Psychosom. 2015;84(3):167–76. https://doi.org/10.1159/000376585 .

Cohen J. Statistical power analysis for the behavioral sciences. Hillsdale: Erbaum Press; 1988.

StataCorp. Stata Statistical Software: Release 15. College Station: TX: StataCorp LLC; 2017.

Statistik Austria. Bevölkerung am 1.1.2019 nach Alter und Bundesland. 2019. http://www.statistik.at/web_de/statistiken/menschen_und_gesellschaft/bevoelkerung/bevoelkerungsstruktur/bevoelkerung_nach_alter_geschlecht/023470.html . Accessed 30 Oct 2020.

Statistik Austria. Bevölkerung 2017 nach detaillierter Staatsangehörigkeit, Geschlecht und Bundesland. 2017. http://www.statistik.at/web_de/statistiken/menschen_und_gesellschaft/bevoelkerung/volkszaehlungen_registerzaehlungen_abgestimmte_erwerbsstatistik/bevoelkerung_nach_demographischen_merkmalen/index.html . Accessed 30 Oct 2020.

Statistik Austria. Bildungsstand der Bevölkerung ab 15 Jahren 2017 nach Altersgruppen und Geschlecht. 2017. http://www.statistik.at/web_de/statistiken/menschen_und_gesellschaft/bildung/bildungsstand_der_bevoelkerung/121189.html . Accessed 30 Oct 2020.

Statistik Austria. Austria: Data. Figures Facts http://www.statistik.at/wcm/idc/idcplg?IdcService=GET_NATIVE_FILE&dDocName=029252 . Accessed 30 Oct 2020.

Braun M, Niederkronenthaler T, Till B. SARS CoV-2: Mental Health in Österreich Ausgewählte Ergebnisse zur ersten Befragungswelle, 23.04.-05.05.2020: Unit Suizidforschung und Mental Health Promotion, Abteilung für Sozial- und Präventivmedizin, Zentrum für Public Health, Medizinische Universität Wien; 2020.

Nitschke JP, Forbes P, Ali N, Cutler J, Apps MAJ, Lockwood P, Lamm C. Resilience during uncertainty? Greater social connectedness during COVID-19 lockdown is associated with reduced distress and fatigue. Br J Health Psychol. 2020. https://doi.org/10.1111/bjhp.12485 .

Stieger S, Lewetz D, Swami V. Psychological well-being under conditions of lockdown: an experience sampling study in Austria during the COVID-19 pandemic. PsyArXiv Preprints. 2020. https://doi.org/10.31234/osf.io/qjhfp .

Hofer H, Titelbach G, Fink M. Die österreichische Arbeitsmarktpolitik vor dem Hintergrund der Covid-19-Krise. Vienna: Institute for Advanced Studies; 2020. https://irihs.ihs.ac.at/id/eprint/5388/7/ihs-report-2020-hofer-titelbach-fink-oesterreich-arbeitsmarktpolitik-covid-19.pdf . Accessed 30 Oct 2020

Müller M. The start of the Austrian response to the COVID-19 crisis: a personal account. Wien Klin Wochenschr. 2020;132(13–14):353–5. https://doi.org/10.1007/s00508-020-01693-y .

de Wit L, Jelsma JG, van Poppel MN, Bogaerts A, Simmons D, Desoye G, et al. Physical activity, depressed mood and pregnancy worries in European obese pregnant women: results from the DALI study. BMC Pregnancy Childbirth. 2015;15:158.

Loder AKF, Schwerdtfeger AR, van Poppel MNM. Perceived greenness at home and at university are independently associated with mental health. BMC Public Health. 2020;20(1):802. https://doi.org/10.1186/s12889-020-8412-7 .

Cenik F, Keilani M, Hasenöhrl T, Huber D, Stuhlpfarrer B, Pataraia A, et al. Relevant parameters for recommendations of physical activity in patients suffering from multiple myeloma: a pilot study. Wien Klin Wochenschr. 2020;132:124–31. https://doi.org/10.1007/s00508-019-01582-z .

Clever K, Schepper F, Pletschko T, Herschbach P, Christiansen H, Martini J. Psychometric properties of the fear of progression questionnaire for parents of children with cancer (FoP-Q-SF/PR). J Psychosom Res. 2018;107:7–13. https://doi.org/10.1016/j.jpsychores.2018.01.008 .

Herrmann C, Snaith RP BU. Hospital anxiety and depression scale–deutsche version (HADS-D). Verlag Hans Huber: Ein Fragebogen zur Erfassung von Angst und Depressivität; 1995.

Sipötz J, Friedrich O, Höfer S, Benzer W, Chatsakos T, Gaul G, et al. Health related quality of life and mental distress after PCI: restoring a state of equilibrium. Health Qual Life Outcomes. 2013;11:144. https://doi.org/10.1186/1477-7525-11-144 .

Greimel E, Kato Y, Müller-Gartner M, Salchinger B, Roth R, Freidl W. Internal and external resources as determinants of health and quality of life. PLoS One. 2016;11(5):e0153232. https://doi.org/10.1371/journal.pone.0153232 .

Mautner E, Ashida C, Greimel E, Lang U, Kolman C, Alton D, et al. Are there differences in the health outcomes of mothers in Europe and East-Asia? A cross-cultural health survey. Biomed Res Int. 2014;856543. https://doi.org/10.1155/2014/856543 .

Probst T, Stippl P, Pieh C. Changes in provision of psychotherapy in the early weeks of the COVID-19 lockdown in Austria. Int J Environ Res Public Health. 2020;17(11):3815. https://doi.org/10.3390/ijerph17113815 .

Tullio V, Perrone G, Bilotta C, Lanzarone A, Argo A. Psychological support and psychotherapy via digital devices in Covid-19 emergency time: some critical issues. Med Leg J. 2020;88(2):73–6. https://doi.org/10.1177/0025817220926942 .

Van Daele T, Karekla M, Kassianos AP, Compare A, Haddouk L, Salgado J, et al. Recommendations for policy and practice of telepsychotherapy and e-mental health in Europe and beyond. J Psychother Integr. 2020;30(2):160–73. https://doi.org/10.1037/int0000218 .

Prajapati N, Łaszewska A, Franco D, Ericson R, Leroy S, Lindert J, et al. What are the COVID-19 Lockdown-induced illnesses and why should European public health systems be investigating their epidemiology, treatment, and prevention? The Association of Schools of Public Health in the European Region. 2020. https://www.aspher.org/download/531/aspher-lockdown-induced-illnesses.pdf . Accessed 21 Dec 2020.

Middleton J, Lopes H, Michelson K, Reid J. Planning for a second wave pandemic of COVID-19 and planning for winter. A statement from the Association of Schools of Public Health in the European region. Int J Public Health. 2020;65:1525–7. https://doi.org/10.1007/s00038-020-01455-7 .

Devaux M, Sassi F. Social disparities in hazardous alcohol use: self-report bias may lead to incorrect estimates. Eur J Pub Health. 2016;26(1):129–34. https://doi.org/10.1093/eurpub/ckv190 .

Mulder J, de Bruijne M. Willingness of Online Respondents to Participate in Alternative Modes of Data Collection. Survey Practice. 2019;12(1). https://doi.org/10.29115/SP-2019-0001 .

Yetter G, Capaccioli K. Differences in responses to web and paper surveys among school professionals. Behav Res Methods. 2010;42(1):266–72.

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We would like to say thank you to all survey participants and to colleagues at the Department of Health Economics for piloting the survey.

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JS, RW and CV conceived the study idea, and developed the conceptual framework and methods of the research. JS provided the resources to this study. TH and AL executed the survey and conducted the analysis supervised by JS. JS, TH and AL wrote the manuscript which was reviewed by all. All authors provided critical feedback and helped shape the research, analysis and manuscript. All authors approved the final manuscript.

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Simon, J., Helter, T.M., White, R.G. et al. Impacts of the Covid-19 lockdown and relevant vulnerabilities on capability well-being, mental health and social support: an Austrian survey study. BMC Public Health 21 , 314 (2021). https://doi.org/10.1186/s12889-021-10351-5

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What We’ve Learned About So-Called ‘Lockdowns’ and the COVID-19 Pandemic

By Lori Robertson

Posted on March 8, 2022

SciCheck Digest

Plenty of peer-reviewed studies have found government restrictions early in the pandemic, such as business closures and physical distancing measures, reduced COVID-19 cases and/or mortality, compared with what would have happened without those measures. But conservative news outlets and commentators have seized on a much-criticized, unpublished working paper that concluded “lockdowns” had only a small impact on mortality as definitive evidence the restrictions don’t work.

Multiple lines of evidence back the use of face masks to protect against the coronavirus, although some uncertainty remains as to how effective mask interventions are in preventing spread in the community.

Lab tests, for example, show that certain masks and N95 respirators can partially block exhaled respiratory droplets or aerosols, which are thought to be the primary ways the virus spreads.

Observational studies, while limited, have generally found mask-wearing to be associated with a  reduced   risk  of contracting the virus or  fewer   COVID-19   cases  in a community.

A  few   randomized controlled  trials have found that providing free masks and encouraging people to wear them results in a small to moderate reduction in transmission, although these results have  not always  been statistically significant.

Masks should not be viewed as foolproof, as no mask is thought to offer complete protection to the wearer or to others. The Centers for Disease Control and Prevention recommends that people wear the most protective mask that fits well and can be worn consistently. Loosely woven cloth masks are the least protective. Layered, tightly woven cloth masks offer more protection, while well-fitting surgical masks and KN95 respirators provide even more protection and N95 respirators are the most protective.

Link to this

In the early months of the COVID-19 pandemic in 2020, as the virus spread around the globe, many countries implemented restrictions on movement and social gatherings in an effort to flatten the curve — or reduce sharp spikes in caseloads to avoid overwhelming health care facilities. Without vaccines or evidence-based treatments, these non-pharmaceutical interventions, or NPIs, were the only public health measures available for months to combat the pandemic.

There have been a lot of studies assessing whether and to what extent so-called “lockdowns” and various NPIs have been effective, and plenty of research that has concluded these measures can limit transmission, or reduce cases and deaths. For instance, a study published in Nature in June 2020 found that “major non-pharmaceutical interventions—and lockdowns in particular—have had a large effect on reducing transmission” in 11 European countries. It estimated what would have happened if the transmission of the virus hadn’t been reduced, finding that 3.1 million deaths “have been averted owing to interventions since the beginning of the epidemic.” The estimate doesn’t account for behavior changes or the impact of overwhelmed health systems.

In May 2020, the same journal published a study that estimated the number of cases in mainland China would have been “67-fold higher” by the end of February 2020 without a combination of non-pharmaceutical interventions.

But one working paper posted online in January — and not peer-reviewed — has gotten a lot of attention in conservative circles for its conclusion that “lockdowns have had little to no effect on COVID-19 mortality.” The paper, which is an analysis of other studies, has been touted as a “Johns Hopkins University study,” but it’s not a product of the university’s Bloomberg School of Public Health, whose vice dean — among other public health experts — has criticized the paper.

“The working paper is not a peer-reviewed scientific study,” Dr. Joshua Sharfstein, vice dean of the Johns Hopkins Bloomberg School of Public Health, said in a Feb. 8 statement sent to us in an email. “To reach their conclusion that ‘lockdowns’ had a small effect on mortality, the authors redefined the term ‘lockdown’ and disregarded many peer-reviewed studies. The working paper did not include new data, and serious questions have already been raised about its methodology.”

Sharfstein said that early on “when so little was known about COVID-19, stay-at-home policies kept the virus from infecting people and saved many lives. Thankfully, these policies are no longer needed, as a result of vaccines, masks, testing, and other tools that protect against life-threatening COVID-19 infections.”

The authors of the working paper are economists: Steve H. Hanke , a senior fellow at the libertarian Cato Institute and founder and co-director of the Johns Hopkins Institute for Applied Economics, Global Health, and the Study of Business Enterprise; Jonas Herby , a special adviser at the Center for Political Studies in Copenhagen, Denmark; and Lars Jonung , a professor emeritus at Sweden’s Lund University.

Fox News published a Feb. 4 story questioning why other mainstream media outlets hadn’t written stories about the working paper, saying there had been “a full-on media blackout,” and “Fox & Friends” co-host Brian Kilmeade asked in a Facebook post , “Will some people get an apology after this?” On Feb. 21, former Republican vice presidential nominee Sarah Palin posted a video to Facebook highlighting the working paper and asking if lockdowns were about “power,” not “safety.”

But the non-peer-reviewed paper isn’t the definitive or final word on lockdowns, and the attention it has received has, in turn, sparked criticism of the paper’s analysis.

Criticisms of the Working Paper

The working paper was a literature review and meta-analysis , meaning it searched the available scientific literature and identified studies that met certain criteria, and then combined similar studies statistically to reach a conclusion. It identified 24 papers, published or posted as of early July 2021, that met its criteria for the meta-analysis — 17 of which were peer-reviewed. Among the criticisms: The paper excluded many relevant studies, broadly defined “lockdown,” and overwhelmingly based one of its headline figures on a study whose conclusions it rejected. That study also didn’t estimate the delayed effect of government restrictions on death rates a few weeks later, according to experts we consulted. Instead, it only assessed the effect of current death rates on current policies.

Excluded research. One of the criticisms is that the working paper excluded a lot of relevant research. The paper said it considered “difference-in-difference” studies, which would compare outcomes in areas or populations that were subject to a restriction with those that were not, and limited its analysis to the impact on mortality. The paper excluded studies that use modeling on mortality, that compare before and after a “lockdown” and that consider the timing of restrictions. Gideon Meyerowitz-Katz , an epidemiologist working on his Ph.D. at the University of Wollongong in Australia, said in a long Twitter thread: “Many of the most robust papers on the impact of lockdowns are, by definition, excluded.”

He called the working paper “a very weak review that doesn’t really show much, if anything.” It excluded “modelled counterfactuals,” which would compare what happened with what would have happened without the intervention. “Because this is the most common method used in infectious disease assessments, this has the practical impact of excluding most epidemiological research from the review,” Meyerowitz-Katz said.

Hanke told us: “Models are fine if they are based on empirical observations,” meaning from experience, “rather than assumptions. In those circumstances, models are able to reliably forecast the real world. But the models used during the pandemic have been inaccurate, as they, for the most part, have not been based on empirical observations but assumptions,” he said in an email. “A prime example of modelers gone astray is the Imperial College London study of March 16, 2020.”

That March 2020 report , early in the pandemic, estimated that 2.2 million lives would be lost in the U.S. in “the (unlikely) absence of any control measures or spontaneous changes in individual behaviour.” As we’ve written before , it wasn’t intended to be a practical estimate, as doing absolutely nothing was, in the author’s words, “unlikely.”

One of the authors of that report has been critical of Hanke’s working paper. Neil Ferguson, director of the MRC Centre for Global Infectious Disease Analysis, Jameel Institute, Imperial College London, said in a statement that the working paper “does not significantly advance our understanding of the relative effectiveness of the plethora of public health measures adopted by different countries to limit COVID-19 transmission.”

Ferguson said that NPIs “are intended to reduce contact rates between individuals in a population, so their primary impact, if effective, is on transmission rates. Impacts on hospitalisation and mortality are delayed, in some cases by several weeks. In addition, such measures were generally introduced (or intensified) during periods where governments saw rapidly growing hospitalisations and deaths. Hence mortality immediately following the introduction of lockdowns is generally substantially higher than before. Neither is lockdown a single event as some of the studies feeding into this meta-analysis assume; the duration of the intervention needs to be accounted for when assessing its impact.”

Ferguson said because NPIs affect transmission rates, “the appropriate outcome measures to consider are growth rates (of cases or deaths) over time, with appropriate time lags – not total cases or deaths.”

Definition of “lockdown.” The working paper also had a very broad definition of “lockdown”: “Lockdowns are defined as the imposition of at least one compulsory, non-pharmaceutical intervention (NPI),” it said. “NPIs are any government mandate that directly restrict peoples’ possibilities, such as policies that limit internal movement, close schools and businesses, and ban international travel.”

The paper did not examine the impact of voluntary behavior or recommendations, as opposed to mandates. “Our definition does not include governmental recommendations, governmental information campaigns, access to mass testing, voluntary social distancing, etc., but do include mandated interventions such as closing schools or businesses, mandated face masks etc.”

The paper then divided the 24 studies it considered into three groups: studies using a stringency index for restrictions, studies on shelter-in-place orders and those looking at specific NPIs. The last category included 11 studies on various measures, including face mask policies and limits on gatherings.

Stringency index studies. The authors examined seven studies on the impact of more severe restrictions, calculating from those studies that, compared with a policy of recommendations, “lockdowns in Europe and the United States only reduced COVID-19 mortality by 0.2% on average” — the figure that conservatives have cited . But six of the seven studies concluded that lockdown policies helped reduce mortality, and the 0.2% figure is overwhelmingly based on one study that mistakenly estimated the wrong effect, according to economists we consulted. 

The studies in this group used the Oxford COVID-19 Government Response Tracker , which looked at government responses worldwide to the pandemic and created a stringency index, measuring how strict the measures were over time. The index is from 0 to 100, with 100 being the most stringent restrictions. For instance, the OxCGRT heat map shows that many countries around the world had stringency levels above 70 in April 2020. 

The working paper calculates mortality impact estimates for each of the seven studies aiming to show the effect of the average mandated restrictions in Europe and the United States early in the pandemic compared with a policy of only recommendations. The paper then calculates a weighted average, giving more weight to studies that said their findings were more precise. Nearly all of the weight — 91.8% — goes to one study, even though the working paper rejects the conclusions of that study. 

That study — coauthored by Carolyn Chisadza , a senior lecturer in economics at the University of Pretoria, and published on March 10, 2021, in the journal Sustainability — looked at a sample of countries between March and September 2020 and concluded: “Less stringent interventions increase the number of deaths, whereas more severe responses to the pandemic can lower fatalities.”

The working paper claims the researchers’ conclusion is incorrect — but it uses the study’s estimates, saying the figures show an increase in mortality due to “lockdowns.”

Chisadza told us in an email that the study showed: “Stricter lockdowns will reduce the rate of deaths than would have occurred without lockdown or too lenient of restrictions.” But Hanke said the data from Chisadza and her colleagues only show that “stricter lockdowns will reduce mortality” relative to “the worst possible lockdown,” meaning a more lenient lockdown that, under the study, was associated with the highest rate of deaths.

We reached out to a third party about this disagreement. Victor Chernozhukov , a professor in the Massachusetts Institute of Technology’s Department of Economics and the Statistics and Data Science Center, along with Professor Hiroyuki Kasahara and Associate Professor Paul Schrimpf , both with the Vancouver School of Economics at the University of British Columbia — the authors of another study that was included in the working paper — looked at the Chisadza study and provided FactCheck.org with a peer review of it . They found the Chisadza study only measured the correlation between current death growth rates and current policies. It did not show the lagged effect of more stringent policies, implemented three weeks prior, on current death growth rates, which is what one would want to look at to evaluate the effectiveness of “lockdowns.”

In an email and in a phone interview, Chernozhukov told us the Chisadza study made an “honest mistake.” He said the working paper is “deeply flawed” partly because it relies heavily on a study that “estimates the wrong effect very precisely.”

In their review, Chernozhukov, Kasahara and Schrimpf write that the Chisadza et al. study “should be interpreted as saying that the countries currently experiencing high death rates (or death growth rates) are more likely to implement more stringent current policy. That is the only conclusion we can draw from [the study], because the current policy can not possibly influence the current deaths,” given the several weeks of delay between new infections and deaths.

The effect that should be examined for the meta-analysis is “the effect of the previous (e.g., 3 week lagged) policy stringency index on the current death growth rates.”

Chernozhukov, Kasahara and Schrimpf conducted a “quick reanalysis of similar data” to the Chisadza study, finding results that “suggest that more stringent policies in the past predict lower death growth rates.” Chernozhukov said much more analysis would be needed to further characterize this effect, but that it is “actually quite substantial.”

If the Chisadza study were removed from the working paper, according to one of the paper’s footnotes, the result would be a weighted average reduction in mortality of 3.5%, which Hanke said doesn’t change the “overall conclusions.” He said it “simply demonstrates the obvious fact that the conclusions contained in our meta-analysis are robust.”

But experts have pointed out other issues with the meta-analysis. Chernozhukov also said the paper “excluded a whole bunch of studies,” including synthetic control method studies, which evaluate treatment effects. He also questioned the utility of looking at a policy index that considers the U.S. as a whole, lumping all the states together. He said the meta-analysis is “not credible at all.”

Among the other six stringency index studies included in the meta-analysis, only one concluded that its findings suggested “lockdowns” had zero effect on mortality. In a review of 24 European countries’ weekly mortality rates for the first six months of 2017-2020, the study, published in CESifo Economic Studies , found “no clear association between lockdown policies and mortality development.” The author and Herby , one of the authors of the working paper, have written for the American Institute for Economic Research , which facilitated the controversial Great Barrington Declaration , an October 2020 statement advocating those at low risk of dying from COVID-19 “live their lives normally to build up immunity to the virus through natural infection,” while those at “highest risk” are protected.

The other studies found lockdown policies helped COVID-19 health outcomes. For instance, a CDC study published in the agency’s Morbidity and Mortality Weekly Report in January 2021, on the experience of 37 European countries from Jan. 23 to June 30, 2020, concluded that “countries that implemented more stringent mitigation policies earlier in their outbreak response tended to report fewer COVID-19 deaths through the end of June 2020. These countries might have saved several thousand lives relative to countries that implemented similar policies, but later.”

A working paper from Harvard University’s Center for International Development , which looked at 152 countries from the beginning of the pandemic until Dec. 31, 2020, found that “lockdowns tend to significantly reduce the spread of the virus and the number of related deaths.” But the effect fades over time, so lengthy (after four months) or second-phase “lockdowns” don’t have the same impact.

A study published in World Medical & Health Policy in November 2020 — that looked at whether 24 European countries responded quickly enough — found that the fluctuating containment measures, from country to country and over time, “prohibited a clear association with the mortality rate.” But it said “the implementation speed of these containment measures in response to the coronavirus had a strong effect on the successful mitigation of fatalities.”

Many studies found restrictions worked. Meyerowitz-Katz noted that the working paper authors disagreed with the conclusions of other studies included in the review, pointing to one included in the group of shelter-in-place orders. Meyerowitz-Katz said that study “found that significant restrictions were effective, but is included in this review as estimating a 13.1% INCREASE in fatalities.”

That study, by Yale School of Management researchers, published by The Review of Financial Studies in June 2021 , developed “a time-series database” on several types of restrictions for every U.S. county from March to December 2020. The authors concluded: “We find strong evidence consistent with the idea that employee mask policies, mask mandates for the general population, restaurant and bar closures, gym closures, and high-risk business closures reduce future fatality growth. Other business restrictions, such as second-round closures of low- to medium-risk businesses and personal care/spa services, did not generate consistent evidence of lowered fatality growth and may have been counterproductive.” The authors said the study’s “findings lie somewhere in the middle of the existing results on how NPIs influenced the spread of COVID-19.”

In terms of hard figures on fatality reductions, the study said the estimates suggest a county with a mandatory mask policy would see 15.3% fewer new deaths per 10,000 residents on average six weeks later, compared with a county without a mandatory mask policy. The impact for restaurant closures would be a decrease of 36.4%. But the estimates suggest other measures, including limits on gatherings of 100 people or more, appeared to increase deaths. The authors said one possible explanation of such effects could be that the public is substituting other activities that actually increase transmission of the virus — such as hosting weddings with 99 people in attendance, just under the 100-person limitation.

Another study in the shelter-in-place group is the study by Chernozhukov, Kasahara and Schrimpf, published in the  Journal of Econometrics in January 2021 . It looked at the policies in U.S. states and found that “nationally mandating face masks for employees early in the pandemic … could have led to as much as 19 to 47 percent less deaths nationally by the end of May, which roughly translates into 19 to 47 thousand saved lives.” It found cases would have been 6% to 63% higher without stay-at-home orders and found “considerable uncertainty” over the impact of closing schools. It also found “substantial declines in growth rates are attributable to private behavioral response, but policies played an important role as well.”

The working paper considered 13 studies that evaluated stay-in-place orders, either alone or in combination with other NPIs. The estimated effect on total fatalities for each study calculated by the authors varied quite widely, from a decrease of 40.8% to an increase of 13.1% (the study above mentioned by Meyerowitz-Katz). The authors then combined the studies into a weighted average showing a 2.9% decrease in mortality from these studies on shelter-in-place orders.

Sizable impact from some NPIs. The working paper actually found a sizable decrease in deaths related to closing nonessential businesses: a 10.6% weighted average reduction in mortality. The authors said this “is likely to be related to the closure of bars.” It also calculated a 21.2% weighted average reduction in deaths due to mask requirements, but notes “this conclusion is based on only two studies.”

As with the shelter-in-place group, the calculated effects in the specific NPIs group varied widely – from a 50% reduction in mortality due to business closures to a 36% increase due to border closures. The paper said “differences in the choice of NPIs and in the number of NPIs make it challenging to create an overview of the results.”

“The review itself does refer to other papers that reported that the lockdowns had a significant impact in preventing deaths,” Dr. Lee Riley , chair of the Division of Infectious Disease and Vaccinology at the University of California, Berkeley School of Public Health, told us when we asked for his thoughts on the working paper. “The pandemic has now been occurring long enough that it’s not surprising to begin to see many more reports that now contradict each other. As we all know, the US and Europe went through several periods when they relaxed their lockdowns, which was followed by a resurgence of the cases.”

Riley said that “many of the studies that this review included may suffer from the classic ‘chicken-or-egg’ bias. Whenever there was an increase in cases of deaths, lockdowns got instituted so it’s not surprising that some of the studies showed no impact of the lockdowns. If there was no surge of cases or deaths, most places in the US did not impose restrictions.”

Meyerowitz-Katz noted on Twitter that “the impact of ‘lockdowns’ is very hard to assess, if for no other reason than we have no good definition of ‘lockdown’ in the first place. … In most cases, it seems the authors have taken estimates for stay-at-home orders as their practical definition of ‘lockdown’ (this is pretty common) And honestly, I’d agree that the evidence for marginal benefit from stay-at-home orders once you’ve already implemented dozens of restrictions is probably quite weak.”

But, “if we consider ‘lockdown’ to be any compulsory restriction at all, the reality is that virtually all research shows a (short-term) mortality benefit from at least some restrictions.”

Additional Studies

We’ve already mentioned two studies beyond those in the working paper: the Nature June 2020 study by Imperial College London researchers that estimated interventions in 11 countries in Europe in the first few months of the pandemic reduced transmission and averted 3.1 million deaths; and the Nature May 2020 study that estimated cases in mainland China would have been 67-fold greater without several NPIs by the end of February.

There are many more that aimed to evaluate the effectiveness of various mitigation strategies, not included in the working paper’s analysis.

• A 2020 unpublished observational study — cited in the working paper as the basis for the Oxford stringency index but not included in the analysis — found that more stringent restrictions implemented more quickly led to fewer deaths. “A lower degree of government stringency and slower response times were associated with more deaths from COVID-19. These findings highlight the importance of non-pharmaceutical responses to COVID-19 as more robust testing, treatment, and vaccination measures are developed.” In considering nine NPIs, the authors said the average daily growth rates in deaths were affected by each additional stringency index point and each day that a country delayed reaching an index of 40 on the stringency scale. “These daily differences in growth rates lead to large cumulative differences in total deaths. For example, a week delay in enacting policy measures to [a stringency index of 40] would lead to 1.7 times as many deaths overall,” they wrote.
• A more up-to-date study by many of the same authors, posted July 9, 2021, by the journal Plos One, looked at data for 186 countries from Jan. 1, 2020, to March 11, 2021, a period over which 10 countries experienced three waves of the pandemic. In the first wave in those countries, 10 additional points on the stringency index — in other words more stringent restrictions — “resulted in lower average daily deaths by 21 percentage points” and by 28 percentage points in the third wave. “Moreover, interaction effects show that government policies were effective in reducing deaths in all waves in all groups of countries,” the authors said. 
• A  Dec. 15, 2020, study in Science used data from 41 countries to model which NPIs were most effective at reducing transmission. “Limiting gatherings to fewer than 10 people, closing high-exposure businesses, and closing schools and universities were each more effective than stay-at-home orders, which were of modest effect in slowing transmission,” the authors said. “When these interventions were already in place, issuing a stay-at-home order had only a small additional effect. These results indicate that, by using effective interventions, some countries could control the epidemic while avoiding stay-at-home orders.” The study, like many others, looked at the impact on the reproduction number of SARS-CoV-2, or the average number of people each person with COVID-19 infects at a given time. It notes that a reduction in this number would affect COVID-19 mortality, and that the impact of NPIs can depend on other factors, including when and for how long they are implemented, and how much the public adhered to them.
• A study in  Nature Human Behaviour on Nov. 16, 2020 , considered the impact on the reproduction number of COVID-19 by 6,068 NPIs in 79 territories, finding that a combination of less intrusive measures could be as effective as a national lockdown. “The most effective NPIs include curfews, lockdowns and closing and restricting places where people gather in smaller or large numbers for an extended period of time. This includes small gathering cancellations (closures of shops, restaurants, gatherings of 50 persons or fewer, mandatory home working and so on) and closure of educational institutions.” The authors said this doesn’t mean an early national lockdown isn’t effective in reducing transmission but that “a suitable combination (sequence and time of implementation) of a smaller package of such measures can substitute for a full lockdown in terms of effectiveness, while reducing adverse impacts on society, the economy, the humanitarian response system and the environment.” They found that “risk-communication strategies” were highly effective, meaning government education and communication efforts that would encourage voluntary behavior. “Surprisingly, communicating on the importance of social distancing has been only marginally less effective than imposing distancing measures by law.”
• Another study in Nature in June 2020 looked at 1,700 NPIs in six countries, including the United States. “We estimate that across these 6 countries, interventions prevented or delayed on the order of 61 million confirmed cases, corresponding to averting approximately 495 million total infections,” the authors concluded. “Without these policies employed, we would have lived through a very different April and May” in 2020, Solomon Hsiang, the lead researcher and director of the Global Policy Laboratory at the University of California at Berkeley, told reporters . The study didn’t estimate how many lives were saved, but Hsiang said the benefits of the lockdown are in a sense invisible because they reflect “infections that never occurred and deaths that did not happen.”
• A more recently published study in Nature Communications in October , by U.K. and European researchers, found that closures of businesses and educational institutions, as well as gathering bans, reduced transmission during the second wave of COVID-19 in Europe — but by less than in the first wave. “This difference is likely due to organisational safety measures and individual protective behaviours—such as distancing—which made various areas of public life safer and thereby reduced the effect of closing them,” the authors said. The 17 NPIs considered by the study led to median reductions in the reproduction number of 77% to 82% in the first wave and 66% in the second wave.
• A February 2021 study in Chaos: An Interdisciplinary Journal of Nonlinear Science estimated large reductions in infections (by 72%) and deaths (by 76%) in New York City in 2020, based on numerical experiments in a model. “Among all the NPIs, social distancing for the entire population and protection for the elderly in public facilities is the most effective control measure in reducing severe infections and deceased cases. School closure policy may not work as effectively as one might expect in terms of reducing the number of deceased cases,” the authors said.

Near the end of his lengthy Twitter thread on the working paper, Meyerowitz-Katz said he agrees that “a lot of people originally underestimated the impact of voluntary behaviour change on COVID-19 death rates – it’s probably not wrong to argue that lockdowns weren’t as effective as we initially thought.” He pointed to the Nature Communications study mentioned above, showing less of an impact from NPIs in a second wave of COVID-19 and positing individual safety behaviors were playing more of a role in that second wave.

“HOWEVER, this runs both ways,” Meyerowitz-Katz said. “[I]t is also quite likely that lockdowns did not have the NEGATIVE impact most people propose, because some behaviour changes were voluntary!”

He and others examined whether lockdowns were more harmful than the pandemic itself in a 2021 commentary piece in BMJ Global Health . They concluded that “government interventions, even more restrictive ones such as stay-at-home orders, are beneficial in some circumstances and unlikely to be causing harms more extreme than the pandemic itself.” Analyzing excess mortality suggested that “ lockdowns are not associated with large numbers of deaths in places that avoided large COVID-19 epidemics,” such as Australia and New Zealand, they wrote.

Editor’s note:  SciCheck’s COVID-19/Vaccination Project  is made possible by a grant from the Robert Wood Johnson Foundation. The foundation has  no control  over FactCheck.org’s editorial decisions, and the views expressed in our articles do not necessarily reflect the views of the foundation. The goal of the project is to increase exposure to accurate information about COVID-19 and vaccines, while decreasing the impact of misinformation.

Herby, Jonas et al. “ A Literature Review and Meta-Analysis of the Effects of Lockdowns on COVID-19 Mortality .” Studies in Applied Economics, Institute for Applied Economics, Global Health, and the Study of Business Enterprise, Johns Hopkins University. posted Jan 2022.

World Health Organization. “ Coronavirus disease (COVID-19): Herd immunity, lockdowns and COVID-19 .” 31 Dec 2020.

Flaxman, Seth et al. “ Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe .” Nature. 584 (2020).

Lai, Shengjie et al. “ Effect of non-pharmaceutical interventions to contain COVID-19 in China .” Nature. 585 (2020).

Sharfstein, Joshua, vice dean of the Johns Hopkins Bloomberg School of Public Health. Statement emailed to FactCheck.org. 8 Feb 2022.

Best, Paul. “ Lockdowns only reduced COVID-19 death rate by .2%, study finds: ‘Lockdowns should be rejected out of hand .'” Fox News. 1 Feb 2022.

Meyerowitz-Katz, Gideon. @GidMK. “ This paper has been doing the rounds, claiming that lockdown was useless (the source of the 0.2% effect of lockdown claim). Dozens of people have asked my opinion of it, so here we go: In my opinion, it is a very weak review that doesn’t really show much, if anything 1/n .” Twitter.com. 4 Feb 2022.

Hanke, Steve H., founder and co-director of the Johns Hopkins Institute for Applied Economics, Global Health, and the Study of Business Enterprise. Email interview with FactCheck.org. 18 Feb 2022.

Ferguson, Neil, director of the MRC Centre for Global Infectious Disease Analysis, Jameel Institute, Imperial College London. Statement posted by Science Media Centre . 3 Feb 2022.

Oxford COVID-19 Government Response Tracker . Blavatnik School of Government, University of Oxford. https://covidtracker.bsg.ox.ac.uk/. website accessed 20 Feb 2022.

Chisadza, Carolyn, senior lecturer in economics at the University of Pretoria. Email interview with FactCheck.org. 15 Feb 2022.

Clance, Matthew, associate professor in the Department of Economics at the University of Pretoria. Email interview with FactCheck.org. 16 Feb 2022.

Our World in Data. Cumulative confirmed COVID-19 deaths . website accessed 22 Feb 2022.

Bjornskov, Christian. “ Did Lockdown Work? An Economist’s Cross-Country Comparison .” CESifo Economic Studies. 67.3 (2021).

Fuller, James A. et al. “ Mitigation Policies and COVID-19–Associated Mortality — 37 European Countries, January 23–June 30, 2020 .” Morbidity and Mortality Weekly Report. 70.2 (2021).

Goldstein, P. et al. “ Lockdown Fatigue: The Diminishing Effects of Quarantines on the Spread of COVID-19 .” Harvard University Center for International Development. 2021.

Stockenhuber, Reinhold. “ Did We Respond Quickly Enough? How Policy-Implementation Speed in Response to COVID-19 Affects the Number of Fatal Cases in Europe .” World Medical & Health Policy. 12.4 (2020).

Riley, Lee, chair of the Division of Infectious Disease and Vaccinology at the University of California, Berkeley School of Public Health. Email interview with FactCheck.org. 14 Feb 2022.

Spiegel, Matthew and Heather Tookes. “ Business Restrictions and COVID-19 Fatalities .” The Review of Financial Studies. 34.11 (2021).

Chernozhukov, Victor et al. “ Causal impact of masks, policies, behavior on early covid-19 pandemic in the U.S. ” Journal of Econometrics. 220. 1 (2021).

Hale, Thomas et al. “ Global Assessment of the Relationship between Government Response Measures and COVID-19 Deaths .” medrxiv.org. 6 Jul 2020.

Hale, Thomas et al. “ Government responses and COVID-19 deaths: Global evidence across multiple pandemic waves .” Plos One. 9 Jul 2021.

Brauner, Jan M. et al. “ Inferring the effectiveness of government interventions against COVID-19 .” Science. 371.6531 (2020).

Haug, Mils et al. “ Ranking the effectiveness of worldwide COVID-19 government interventions .” Nature Human Behaviour. 4 (2020).

Sharma, Mrinank et al. “ Understanding the effectiveness of government interventions against the resurgence of COVID-19 in Europe .” Nature Communications. 12 (2021).

Yang, Jiannan et al. “ The impact of non-pharmaceutical interventions on the prevention and control of COVID-19 in New York City .” Chaos: An Interdisciplinary Journal of Nonlinear Science. 31.2 (2021).

Achenbach, Joel and Laura Meckler. “ Shutdowns prevented 60 million coronavirus infections in the U.S., study finds .” Washington Post. 8 Jun 2020.

Hsiang, Solomon et al. “ The effect of large-scale anti-contagion policies on the COVID-19 pandemic .” Nature. 584 (2020).

Chernozhukov, Victor et al. “ Comments on the ‘John Hopkins’ Meta Study (Herby et al., 2022) and Chisadza et al. (2021) .” Provided to FactCheck.org. 4 Mar 2022.

Chernozhukov, Victor, professor, Massachusetts Institute of Technology Department of Economics and the Statistics and Data Science Center. Phone interview with FactCheck.org. 8 Mar 2022.

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How Covid lockdowns hit mental health of teenage boys hardest

New research findings are contrary to what had previously been thought about pandemic’s effect on children’s wellbeing

Teenage boys were hit hardest by the Covid lockdowns, with their mental health failing to recover despite the return to normality, according to the most comprehensive academic study of its kind.

Early research into how lockdown affected children indicated that girls had suffered more significant mental health problems than boys.

However, a new study carried out by academics from three UK universities, published in the journal European Child + Adolescent Psychiatry , found that over the long term, teenage boys’ mental health was more adversely affected.

The research followed a cohort of about 200 children, aged between 11 and 14 at the time, asking them and their mothers to assess their mental state. It logged data from each child before lockdown, three months after the measures started and again at 15 months into the pandemic.

Researchers then compared this data with historical records that show the usual pattern of mental wellbeing for boys and girls during adolescence.

The academics found that while both sexes had an immediate decline in their mental health, boys then did not experience the natural improvement in mental wellbeing that usually comes with maturation as they move through the teenage years.

Dr Nicky Wright, a lecturer in psychology at Manchester Metropolitan University and a co-author of the paper, said: “The key message of this is that we expect more boys to be at risk of mental health problems now than we would before [the pandemic].

“Girls, on average, are more likely to suffer with mental health problems than boys. But the girls in the study followed their usual pattern, suggesting the experience of lockdown had a more significant impact on boys than girls.

“There wasn’t a pandemic effect on girls’ depression. When you account for puberty and development, it’s consistent with previous trends,” said Wright.

This weekend marks four years since the first UK lockdown was called on 23 March 2020. Schools were closed, leaving teenagers who were used to spending at least six hours a day surrounded by peers isolated from society. Work set by teachers for home schooling took an average of between two and three hours a day for most adolescents to complete, and with many parents working, lots of teenagers were left alone for long periods of time.

For those who were moving between primary and secondary school during the pandemic years, lockdowns also disrupted integration into new social groups and the chance to form friendships.

For older teenagers, universities and colleges switched to virtual lectures and seminars, leaving new students unable to form bonds with others.

The psychologist and writer Wendy Gregory said the findings of the study echoed changes to her client list in her private practice. “Lockdown has had a horrific impact on mental health, particularly in boys and young men, and partly I’m seeing the results of this now as I’m getting a lot more seeking therapy,” she said.

“There has been a big upturn in males seeking mental health support generally across the age ranges, and for teen boys there has been a huge uptick.”

In south London, Dr Jen Wills Lamacq, a child psychologist who works in state schools, said she has seen the pandemic effect on boys’ mental health first-hand, including increased difficult behaviour. She believes the decline in young male mental health was triggered by the rupture to their lives at a crucial point in adolescent development.

“Lots of boys, to regulate their emotions, may want to be outside, doing something active and around other people, without necessarily talking. For long periods of time, they were deprived of opportunities to regulate their mental wellbeing in a way that comes naturally to them,” she said.

For parents of teenage boys and young men, the findings may come as little surprise. Single mother Rebecca*, from London, says her teenage son, who was already receiving counselling before the pandemic, had a breakdown during lockdown that resulted in him becoming violent and police being called to restrain him.

“He was doing his GCSEs and when lockdown happened at first it was a huge relief because he didn’t have to go into school, as that was a trigger for anxiety, but losing that routine was awful and he had a breakdown. He had a psychotic episode where he was hearing voices. The police came and were very hard with him, and they put him in handcuffs in front of me,” she said.

Rebecca’s son is now an adult and his health has improved from that crisis point, but she says lockdown has had a lasting impact on his mental wellbeing. “I think it was dreadful. I think there will be repercussions for years to come for all kids,” she said.

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Essay on Lockdown in English for Students and Children

This long essay on lockdown in English is suitable for students of classes 5, 6, 7, 8, 9 and 10, 11, 12, and also for competitive exam aspirants. All important information related to how to write an amazing essay about Lockdown.

• 1.1 Definition
• 1.2 Introduction
• 1.3 Online Education During Lockdown
• 1.4 Advantages of Lockdown
• 1.5 Disadvantage of Lockdown
• 1.6 Lockdown 2021
• 1.7 Conclusion

Long Essay on Lockdown in English 800 Words

Lockdown essay in English – Lockdown is a term that exploded collectively around the world in the year 2020. With the widespread attack of an invisible virus, known as the Novel Coronavirus , the entire world was devastated by the Pandemic of this virus. It occurs during a wide variety of emergencies and it disrupts normal life.

Many words became popular after the arrival of Coronavirus, the term “lockdown” being one of them. A lockdown is a period of time when people have to stay home and are only allowed to travel in an emergency. During this period everything is closed except for some essential services like hospitals, grocery stores, medical stores, etc.

Introduction

Coronavirus has been considered the most contagious virus ever in the history of mankind. Its effects have become catastrophic within a short time. To prevent the spread of this Coronavirus in the country, our government has taken some drastic steps.

One of the most important measures implemented is a lockdown, where all businesses have been closed, all people have been confined to their homes and almost all professional, personal, and economic activities have come to a standstill.

The lockdown was announced and enforced on the 25 th of March, 2020. It has been extended, in phases, to continue till mid-June. The government has issued advisories to all citizens to practice social distancing and stay at home. The purpose of the lockdown is to prevent community transmission of this deadly virus so that the chain of transmission can be broken.

Each and every person faced many difficulties during this period but for the daily wagers, it was much more difficult. Work from home, online education , and online business were some of the options during this period, and the Indian government also helped the people a lot.

Online Education During Lockdown

For the first time, schools in India have moved to online classes. It is a struggle for the teacher as well as the students. School students, children, and their parents felt the impact to close schools and educational institutions.

The lockdown situation prompted people to learn and use digital technology and as a result, increased digital literacy.

The teaching material is easily shared among the students and the doubt questions are solved on Telegram, WhatsApp, E-mail, and various social media. Students need to learn digital skills for their own sake and improve the quality of education as well as changes in syllabus, textbooks, teacher training, and examination systems, but at the very least, the quality of online education must also improve needed.

Advantages of Lockdown

Due to the lockdown, on the one hand, while people have been forced to remain imprisoned in the house, on the other hand, many big benefits are also being seen. Some important benefits of essay on lockdown:-

• The rapidly spreading Coronavirus has been controlled by applying Lockdown.
• Due to the lockdown, the movement of vehicles has been reduced very much, factories have been closed, and the air of the cities has started to clear due to the rein in such activities.
• The impact of the lockdown is also being seen on global warming. In early April, scientists showed a hole of 1,000,000 square kilometers in the ozone layer above the North Pole. According to NASA, it has started filling these holes now.
• Earth’s vibration has been reduced by 30 to 50 percent due to less traffic, machines, and noise pollution.
• Due to Coronavirus, there has been a change in the cleanliness habits of the people. People are being more vigilant. Due to the lockdown, more time is also available for cleaning the house.
• People are learning to live with limited resources and insist on being self-sufficient (or Aatmnirbhar ) in the future so that they can produce themselves.
• During this lockdown period, we have got a lot of time for self-development and self-awareness.
• Most people in Lockdown are cooking at home and eating the same. Health will also be good due to good food.

Disadvantage of Lockdown

Some important disadvantages of the essay on Lockdown:-

• Many migrant laborers got trapped in different cities and they could not return to their homes due to which they had to face many difficulties.
• Many industries like agriculture, education, and entertainment are suffering. It has negatively impacted the world economy.
• Unemployment has increased rapidly due to the lockdown. Because of this many people have lost their jobs.
• All schools and colleges were closed due to the lockdown, due to which the students were not able to study well.

Lockdown 2021

The lockdown was imposed due to Coronavirus in March 2020 last year. The same situation is being seen again. Again in April 2021, Coronavirus is spreading rapidly due to which lockdown is being imposed in all the states one by one.

In view of this spreading Coronavirus, the CBSE board canceled the class 10 examination and postponed the class 12 examination.

Lockdown is something that affects people from all backgrounds and especially the daily wagers. Some of the main problems during a lockdown are employment, poverty, and starvation.

Overall, we should keep in mind that lockdowns are only imposed for our welfare, so it is always our duty to follow the rules of lockdown.

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Open Access

Peer-reviewed

Research Article

Do economic effects of the anti-COVID-19 lockdowns in different regions interact through supply chains?

Roles Conceptualization, Methodology, Project administration, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Graduate School of Information Science, University of Hyogo, Kobe, Hyogo, Japan

Roles Methodology, Validation, Writing – original draft

Affiliation RIKEN Center for Computational Science, Kobe, Hyogo, Japan

Roles Conceptualization, Methodology, Writing – original draft, Writing – review & editing

Affiliation Graduate School of Economics, Waseda University, Tokyo, Japan

• Hiroyasu Inoue, 
• Yohsuke Murase, 
• Yasuyuki Todo

• Published: July 30, 2021
• https://doi.org/10.1371/journal.pone.0255031
• Reader Comments

To prevent the spread of COVID-19, many cities, states, and countries have ‘locked down’, restricting economic activities in non-essential sectors. Such lockdowns have substantially shrunk production in most countries. This study examines how the economic effects of lockdowns in different regions interact through supply chains, which are a network of firms for production, by simulating an agent-based model of production using supply-chain data for 1.6 million firms in Japan. We further investigate how the complex network structure affects the interactions between lockdown regions, emphasising the role of upstreamness and loops by decomposing supply-chain flows into potential and circular flow components. We find that a region’s upstreamness, intensity of loops, and supplier substitutability in supply chains with other regions largely determine the economic effect of the lockdown in the region. In particular, when a region lifts its lockdown, its economic recovery substantially varies depending on whether it lifts the lockdown alone or together with another region closely linked through supply chains. These results indicate that the economic effect produced by exogenous shocks in a region can affect other regions and therefore this study proposes the need for inter-region policy coordination to reduce economic loss due to lockdowns.

Citation: Inoue H, Murase Y, Todo Y (2021) Do economic effects of the anti-COVID-19 lockdowns in different regions interact through supply chains? PLoS ONE 16(7): e0255031. https://doi.org/10.1371/journal.pone.0255031

Editor: Ashkan Memari, Sunway University, MALAYSIA

Received: November 1, 2020; Accepted: July 8, 2021; Published: July 30, 2021

Copyright: © 2021 Inoue et al. 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: The data for supply chain network are based on a survey done by Tokyo Shoko Research (TSR), one of the leading credit research agencies in Tokyo, supplied to us through the Research Institute of Economy, Trade and Industry (RIETI). The data are not in the public domain but are commercially available from Tokyo Shoko Research, Ltd., http://www.tsr-net.co.jp/ , [email protected] . The authors had no special access privileges to the data.

Funding: H.I., 18K04615, Japan Society for the Promotion of Science, https://www.jsps.go.jp/english/index.html , The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Y.T, 18H03642, Japan Society for the Promotion of Science, https://www.jsps.go.jp/english/index.html , 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.

1 Introduction

COVID-19, a novel coronavirus (SARS-CoV-2) disease, has been spreading worldwide. To prevent its spread, many cities, regions, and countries were or have been under lockdown, suppressing economic activities. On 18 April 2020, 158 countries out of 181 implemented measures that required temporary closure or work-from-home for some sectors in some or all cities. Although some countries later lifted their lockdowns, 95 countries remained under lockdown on 30 July 2020 [ 1 ].

Closing workplaces shrinks the economic output of regions under lockdown. The negative economic effect of a lockdown in one region may diffuse through supply chains, i.e., supplier-client relationships of firms, and to other regions that are not necessarily in a lockdown. When a firm is closed due to a lockdown strategy, its client firms located elsewhere would suffer decreased production due to the lack of supply of intermediate goods and services. Suppliers of the closed firms would also see reduced production because of a shortage of demand.

Many studies have empirically confirmed the propagation of economic shocks through supply chains, particularly shocks originating from natural disasters [ 2 – 7 ]. Some examine the diffusion of the effect of lockdowns because of COVID-19 on production across regions and countries and estimate the total effect using input–output (IO) linkages at the country-sector level [ 8 – 11 ] and supply chains at the firm level [ 12 ].

Several studies focusing on natural disasters [ 5 , 6 ] examine how the network structure of supply chains affects the propagation of shocks. They find that scale-free property, non-substitutability of suppliers, and loops are major drivers of such propagation. However, the role of the network structure has not been fully examined in the context of the propagation of the lockdown effect. This issue should be of great interest from the perspective of network science for the following two reasons.

First, the literature on network interventions has investigated the types of individuals or groups in a network, such as those with high centrality, who should be targeted to promote (prevent) the diffusion of positive (negative) behaviours and outcomes [ 13 , 14 ]. Similarly, we are interested in how the economic effect of imposing and lifting a lockdown in one region, an example of a network intervention, diffuses to other regions. Compared to existing research, this study is novel in many respects. For example, we consider interventions in a network of firms and their economic outcomes, while previous studies focus on the health behaviours and outcomes in human networks [ 15 ], with a few exceptions that examine economic outcomes in human networks [ 16 ]. In addition, because a lockdown is usually imposed in a city, state, or country, the scale of interventions is large. Firms targeted by such interventions are exogenously determined by geography, and thus we should assess the network characteristics of exogenously grouped nodes, rather than the endogenously connected ones identified by network centrality [ 13 , 17 ] or community detection algorithms [ 18 ].

Second, at any point during the spread of COVID-19, some regions imposed a lockdown, while others remained open. Therefore, when we evaluate the lockdown strategy of a region, the interactions between the strategies of different regions need to be considered. In other words, the economic effect of a lockdown in a region depends on whether other regions connected to it through supply chains are similarly locked down. For example, Sweden did not impose a strict lockdown, unlike other European countries. However, it still expects a 4.5% reduction in gross domestic product (GDP) in 2020, a decline comparable to that in neighbouring countries that did impose a lockdown, possibly because of its close economic ties with its neighbours [ 19 ]. Motivated by the Swedish experience, this study examines the network structure between regions—an aspect that is usually ignored in the literature on network interventions—and discusses the need for policy coordination among regions depending on their network characteristics. Some studies call for inter-regional and international policy coordination in the presence of spillover effects in the context of health, environment, and macroeconomics [ 20 , 21 ], but they do not explicitly incorporate the network structure.

The present study fills the above gaps in research on network interventions and regional interactions. We conduct a simulation analysis by applying actual supply-chain data of 1.6 million firms and their experiences of the lockdowns in Japan to an agent-based model of production. Specifically, we analyse the network characteristics of a prefecture in Japan that led to greater economic recovery by lifting its lockdown when all other prefectures remained locked down. In addition, to further highlight the interactions between regions, our simulation investigates how the characteristics of the supply-chain links between two prefectures affect their economic recovery when they simultaneously lift their lockdowns. One novelty of our study is to decompose supply-chain flows into potential and loop flow components and test the role of upstreamness (potential) in supply chains and intra- and inter-prefectural loops in diffusion.

The data used in this study are taken from the Company Information Database and Company Linkage Database compiled by Tokyo Shoko Research (TSR), one of the largest credit research companies in Japan. The former database includes information about the attributes of each firm, including the location, industry, sales, and number of employees, and the latter includes the major customers and suppliers of each firm. Due to availability, we use the data on firm attributes and supply chains from 2016. The number of firms in the data is 1,668,567 and the number of supply-chain links is 5,943,073. Hence, our data identify the major supply chains of most firms in Japan, although they lack information about supply-chain links with foreign entities. Because the transaction value of each supply-chain tie is not available in the data, we estimate sales from a supplier to each of its customers and consumers using the total sales of the supplier and the 2015 Input-Output (IO) Tables for Japan [ 22 ]. In this estimation process, we drop firms without any sales information. Accordingly, the number of firms in our final analysis is 966,627 and the number of links is 3,544,343. Although the firms in the TSR data are classified into 1,460 industries according to the Japan Standard Industrial Classification [ 23 ], we simplify this into the 187 industries classified in the IO tables. S1 Appendix provides details on the data construction process.

In the supply-chain data described above, the degree, or the number of links, of firms follows a power-law distribution [ 5 ], as often found in the literature [ 24 ]. The average path length between firms, or the number of steps between them through supply chains, is 4.8, indicating a small-world network. Using the same dataset, previous studies [ 5 , 25 ] find that 46–48% of firms are included in the giant strongly connected component (GSCC), in which all firms are indirectly connected to each other through supply chains. The large size of the GSCC clearly shows that the network has a significant number of cycles unlike the common image of a layered or tree-like supply-chain structure.

Agent-based models that incorporate the interactions of agents through networks have been widely used in the social sciences [ 26 – 28 ]. Following the literature, we employ the dynamic agent-based model of Inoue and Todo [ 5 , 6 ], an extension of Hallegatte’s [ 29 ] model, which assumes that supply chains are at the firm level. In the model, each firm utilises the inputs purchased from other firms to produce an output and sells it to other firms and consumers. Firms in the same industry are assumed to produce the same output. Supply chains are predetermined, and do not change over time in the following two respects. First, each firm utilises a firm-specific set of input varieties and does not change the input set over time. Second, each firm is linked with fixed suppliers and customers and cannot be linked with any new firm over time, even after a supply-chain disruption. Accordingly, our analysis focuses on short-term changes in production. Furthermore, we assume that each firm keeps inventories of each input at a level randomly determined from the Poisson distribution. Following Inoue and Todo [ 5 ], in which parameter values are calibrated from the case of the Great East Japan earthquake, we assume that firms aim to keep inventories for 10 days of production on average (see S2 Appendix for the details).

When a restriction is imposed on a firm’s production, both its upstream and downstream of the firm are affected. On the one hand, the firm’s demand for parts and components from its suppliers immediately declines, and thus suppliers have to shrink their production. Because demand for the products of suppliers’ suppliers also declines, the negative effect of the restriction propagates upstream. On the other hand, the supply of products from the directly restricted firm to its customer firms declines. Therefore, one way for customer firms to maintain the current level of production is to use their inventories of inputs. Alternatively, customers can procure inputs from other suppliers in the same industry that were already connected before the restriction, provided these suppliers have additional production capacity. If the inventories and inputs from substitute suppliers are insufficient, customers have to shrink their production because of a shortage of inputs. Accordingly, the effect of the restriction propagates downstream through supply chains. Such downstream propagation is likely to be slower than upstream propagation because of the inventory buffer and input substitution.

3.2 Lockdowns in Japan

In Japan, lockdown strategies were implemented at the prefecture level under the state of emergency [ 30 ] first declared on 7 April, 2020 in seven prefectures with a large number of confirmed COVID-19 cases. Because populated regions tended to be affected more and earlier, these seven prefectures are industrial clusters in Japan, including Tokyo, Osaka, Fukuoka, and their neighbouring prefectures. The state of emergency was expanded to all 47 prefectures on 16 April. The state of emergency was lifted for 39 prefectures on 14 May and for an additional three on 21 May; it was lifted for the remaining five prefectures on 25 May. (The summary of the timeline of the lockdowns in different prefectures can be found in Fig A.3 of [ 31 ]).

Although the national government declared a state of emergency, the extent to which the restrictions were imposed was determined by each prefecture’s government. Therefore, the level of lockdown in each prefecture may have varied. Although all prefectures were in the state of emergency from 16 April to 14 May, prefectures with larger numbers of confirmed COVID-19 cases, such as the seven prefectures in which a state of emergency was first declared, requested more stringent restrictions than others. The national or prefectural government can only request closing workplaces, staying at home, and social distancing rather than enforcing these actions through legal enforcement or punishment. However, strong social pressure in Japan led people and businesses to voluntarily restrict their activities to a large extent. As a result, production activities including those in sectors not officially restricted shrunk substantially (Mainichi Newspaper, 27 May 2020).

3.3 Simulation procedure

3.3.1 replication of the actual effect..

In our simulation analysis, we first confirm whether our model and data can replicate the actual reduction in production caused by the lockdown in Japan during this state of emergency. Because we cannot observe the extent to which each firm reduces its production capacity by obeying government requests, the rate of reduction in production capacity for each sector assumed in our simulation analysis depends on its characteristics. As the reduction rate, particularly during the lockdowns in Japan is not available, we follow the literature that defines the reduction rate in general settings. Specifically, the rate of reduction in a sector is the product of the level of reduction determined by the degree of exposure to the virus given by [ 9 ] and the share of workers who cannot work from home given by [ 8 ]. For example, in lifeline/essential sectors such as utilities, health, and transport, the rate of reduction is assumed to be zero; in other words, the production capacity in these sectors does not change during a lockdown. In sectors in which it is assumed that exposure to the virus is low (50%) and 13.4% of workers can work from home, such as the agriculture and fishery sectors, the rate of reduction is 43.3% (= 0.5 × (1 − 0.134)). Sectors with ordinary exposure (100%) and 47.5% of workers were working from home, such as the retail and wholesale sectors, show a reduction in production capacity by 52.5% (= 1 × (1 − 0.475)). See S1 Table for the rate of reduction of each sector.

After the lockdown in a prefecture is lifted, all the firms in that prefecture immediately return to their pre-lockdown production capacity. Moreover, we assume that inventories do not decay over time: inventories stocked before the lockdown can be fully utilised after the lockdown is lifted. The results given below are an averaged of over 30 Monte Carlo runs.

3.3.2 Interactions among regions.

After checking the accuracy of our simulation model, we examine how changing the restriction level of the lockdown in a region affects production in another region. For this purpose, we experiment with different sets of sector-specific rates of reduction in production capacity by multiplying the benchmark rates of reduction defined above by a multiplier such as 0.4 or 0.8. For example, when the benchmark rate of reduction in a sector is 52.5%, as in the case of the iron and other metal product sectors, and the multiplier is 0.4, we alternatively assume a rate of reduction of 21.0%.

Moreover, we assume that the rates of reduction can vary among prefectures, because each prefecture can determine its own level of restrictions under the state of emergency (Section 3.2). In practice, the restrictions requested by the prefectural governments were tougher and people were more obedient to the requests in the seven prefectures in which the state of emergency was first declared because of the larger COVID-19 caseloads than in other prefectures. Accordingly, we run the same simulation assuming different rates of reduction for the two types of prefectures, defined as more and less restricted groups, to investigate how different rates of reduction in one group affect production in the other.

3.3.3 Lifting lockdown in only one region.

In practice, some prefectures lifted their lockdowns earlier than others (Section 3.2). Although this may have led to the recovery of value added production, or gross regional product (GRP), the extent of such a recovery should have been affected by the links between firms in the prefecture and others still under lockdown. To highlight this network effect, we simulate what would happen to the GRP of a prefecture if it lifted its lockdown while all others were still imposing lockdowns. Next, we investigate what network characteristics of each prefecture determine the recovery from lockdown, measured by the ratio of the increase in the GRP of the prefecture by lifting its lockdown to the reduction in its GRP because of the lockdown of all prefectures.

In particular, we focus on four types of network characteristics. First, when a prefecture is more isolated from others in the supply-chain network, the effect of others’ lockdowns should be smaller. We measure the level of isolation using the number of links within the prefecture relative to the total degree of firms (total number of links from and to firms) in the prefecture.

Second, an alternative and more interesting measure of isolation is the intensity of loops in supply chains. Although supply chains usually flow from suppliers of materials to those of parts and components and then to assemblers, some suppliers use final products such as machinery and computers as inputs. This results in many complex loops in supply chains [ 32 ], in which negative shocks circulate and can become aggravated [ 5 ]. Such loops in a network are found to generate instability in the system dynamics literature [ 33 ] and more recently in the context of supply chains [ 34 ]. In the case of lifting the lockdown in only one prefecture, the loops within that prefecture may magnify its recovery because of the circulation of positive effects in the loops.

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Each dot indicates a firm. Firms with a higher Helmholtz–Hodge (HH) potential are located more upward in both panels. In the left panel, the grey lines illustrate the potential flows computed from the HHD. The red and blue node colours represent higher and lower HH potentials, respectively. The right panel shows loop flows computed from HHD, while the different colours represent different cycles.

https://doi.org/10.1371/journal.pone.0255031.g001

Third, we pay attention to the upstreamness of firms in supply chains. Theoretically, upstream firms are affected by supply-chain disruptions through a lack of demand, whereas downstream firms are affected through a lack of supply. However, the effect of upstream and downstream links can differ in size. A recent sectoral analysis [ 36 ] finds that the profits of more upstream sectors in global value chains are substantially lower than those of more downstream sectors, implying that negative economic shocks propagate upstream more than downstream. To clarify the possible effect of upstreamness, we define the upstream position of each firm i in supply chains by its Helmholtz–Hodge (HH) potential, φ i computed from the HHD. In other words, the hierarchical position of a firm can be consistently defined by focusing on gradient flows, in other words, all flows less loop flows. The HH potential is higher when the firm is located in a more upstream position. In practice, it is generally higher for firms in the mining, manufacturing, and information and communication sectors, while lower for those in the wholesale, retail, finance, healthcare, and accommodation and food service sectors [ 32 ]. We average the HH potential over the firms in each prefecture to measure the upstreamness of the prefecture in supply chains. The visualization on the map can be found in Fig B.2 of [ 31 ].

Our measure of upstreamness based on the HH potential, is conceptually similar to the upstreamness measures developed and widely used in the literature on international trade [ 37 – 41 ] in that both measure the hierarchical position in supply chains. However, a clear difference between the two types of measures is that ours is based on firm-level data while others are based on sector-level IO tables. Therefore, our measure can incorporate firm-level heterogeneity within the same sector that is ignored in others. In addition, our measure is defined by gradient flows in supply chains that are constructed by eliminating loop flows from all flows. Although many loops at the firm level are found in supply chains, even within the industry [ 32 ], upstream measures based on IO tables do not incorporate such loops. For these reasons, we will rely on our upstreamness measures at the firm level, and not on existing measures at the sector level.

Finally, even when the supply of parts and components from other prefectures is shut down because of their lockdowns, the negative effect can be mitigated if suppliers can be replaced by those in the prefecture lifting its lockdown. Existing studies [ 2 , 5 ] have found that input substitutability can largely mitigate the propagation of negative economic shocks through supply chains. By assumption, suppliers of firms in prefecture a that are in other prefectures currently under lockdown can be replaced by suppliers in prefecture a that are in the same industry and already connected. To measure the degree of supplier substitutability for prefecture a , we divide the number of the latter suppliers by the number of the former.

3.3.4 Lifting lockdowns in two regions simultaneously.

S2 Appendix describes how to calculate Pot ab , Pot ba , and Loop ab using a simple example.

Finally, when suppliers of firms in prefecture a are located outside prefectures a and b and thus are locked down, they can be replaced by suppliers in the same industry in prefecture b that are already connected with firms in prefecture a . To measure the degree of this supplier substitutability, we divide the total number of the latter suppliers by the total number of the former. See S2 Appendix for the details.

4.1 Simulation of the effect of the actual lockdown

In Fig 2 , the blue lines indicate the results of the 30 Monte Carlo runs conducted to estimate the effect of the actual lockdown in Japan given the sector-specific rates of reduction in production capacity assumed in the literature [ 9 , 36 ] and shown in S1 Table . The horizontal axis indicates the number of days since the declaration of the state of emergency (7 April) and the vertical axis represents the total value added production, or GDP, of Japan on each day. See Section 3.2 for the sequence of the state of emergency across the country. Averaged over the 30 runs, the estimated loss in GDP is 35.0 trillion yen (3,280 billion U.S. dollars), or 6.60% of yearly GDP.

The blue and green lines indicate the simulation results given the sector-specific rates of reduction in production capacity assumed in the literature [ 9 , 36 ] and shown in S1 Table and the 26.7% of those rates to calibrate the actual production dynamics, respectively. Each line represents the daily GDP from one Monte Carlo run. The red segments indicate the daily GDP estimated from pre-lockdown GDP and the post-lockdown monthly Indices of All Industry Activity (IAIA) for April and May.

https://doi.org/10.1371/journal.pone.0255031.g002

Without relying on our model and simulation, we also estimate the changes in daily GDP from pre-lockdown GDP and the post-lockdown monthly Indices of All Industry Activity (IAIA) [ 42 ]. The average daily GDP in April and May estimated from the IAIA is indicated by the red lines in Fig 2 (see S3 Appendix for the detailed procedures). The total loss of GDP estimated by the IAIA, or the pink area in Fig 2 , is 7.52 trillion yen (1.44% of yearly GDP), 21.5% of the estimate from our simulations. Our simulation thus overestimates the loss of GDP from the lockdown, possibly because the assumed rates of reduction in production capacity due to the lockdown taken from the literature [ 8 , 9 ] are larger than the actual rates in Japan. Therefore, we experiment with different rates of reduction in production capacity by multiplying the benchmark rates by a weight to calibrate changes in production. We find that a weight of 26.7% results in a close fit between our estimates and those from the IAIA, and indicate the results using green lines in Fig 2 .

In either case (blue or green lines), the production loss rises during the lockdown. For example, the value added declined monotonically from days 9 to 37, when all prefectures were in a state of emergency, assuming a fixed rate of reduction in production capacity throughout the period. This is because the economic contraction in different regions interacted with each other through supply chains, and thus worsened over time. This worsening trend in GDP is consistent with GDP estimated using the IAIA.

Another notable finding from the simulation is that prefectures that were not locked down were heavily affected by those under lockdowns. The visualization on the map can be found in Fig 3 of [ 31 ]. In addition, a video presents a temporal and geographical visualisation of this. See S3 Appendix .

Moreover, because of the network effect, the earlier lifting of the lockdown in some prefectures does not result in a full recovery of production in these prefectures. Notably, when the lockdown was lifted in 39 prefectures on day 37 (14 May), the simulated GDP show a sharp recovery but drops again substantially a few days after the recovery. This drop occurred because the lockdown remained active in eight prefectures including the top two industrial clusters in Japan, greater Tokyo and greater Osaka. Although economic activities returned to normal in these 39 prefectures, their production did not recover monotonically but rather declined again because the major industrial clusters linked with them were still locked down. This finding points to the interactions of the economic effect of lockdown between regions through firm-level supply chains.

4.2 Interactions between lockdowns in different regions

Next, we experiment with simulations assuming different levels of restrictions, or different sets of multipliers for the sector-specific benchmark rates of reduction in production capacity, between the more and less restricted groups (Section 3.3). The more restricted group comprises the seven prefectures with a large number of COVID-19 cases, whereas the less restricted group includes the other 40 prefectures. The left, middle, and right panels of Fig 3 indicate the loss in GDP for different multipliers for the more restricted group when fixing the multiplier for the less restricted group at 0%, 50%, and 100%, respectively. Here, 100% corresponds to the rates of reduction shown in S1 Table and used in the previous subsection and 0% implies no restriction. In each bar, the blue and red portions indicate the loss of value added in the more and less restricted groups, respectively.

A restriction level is defined by a multiplier for the sector-specific benchmark rates of reduction in production capacity. For example, the left bar presents the result assuming a multiplier of 0% (i.e., no restriction) for the less restricted group and 20% for the more restricted group. The red and blue portions of each bar show the loss of value added in the less and more restricted groups, respectively, as a percentage of GDP.

https://doi.org/10.1371/journal.pone.0255031.g003

As shown, the total loss of GDP increases in the levels of restrictions in both groups. For example, the total production loss is 4.18% of GDP when the multiplier is 50% for both groups (the left bar in the middle panel), while it is larger, or 9.39%, when the multiplier is 100% for both (the right panel). More interestingly, the left panel shows that while the group with fewer restrictions imposes no restrictions, its value added decreases more (i.e., the red portion in Fig 3 increases) as the group with more restrictions imposes more restrictions. When the level of restrictions in the group with more restrictions is the highest (i.e., the multiplier is 100%), the loss in value added in the group with fewer restrictions without any lockdown is large: 18.6 trillion yen, or 3.51% of its pre-lockdown value added. These results clearly indicate that even when prefectures are not locked down, their economies can be damaged because of the propagation of the effect of the lockdowns in other prefectures through supply chains.

4.3 Effect of lifting the lockdown in one region

We further examine, how the recovery of a prefecture where lockdown is lifted is determined by its network characteristics, when only one prefecture lifts its lockdown and others remain locked down. We define the recovery rate of each prefecture as the ratio of the total gain of its value added or gross regional production (GRP) from lifting the lockdown to its total loss from the lockdown of all the prefectures for two weeks. The visualization of the recovery rate can be found in Fig 5 of [ 31 ]. See S6 Fig for the bar plot of the recovery rate of each prefecture.

One notable finding is that the prefectures that recover the most, including Hokkaido, Shimane, and Okinawa, which are remote from industrial hubs in terms of both geography and supply chains, suggesting the effect of network characteristics on economic recovery by lifting a lockdown. The name and location of each prefecture can be found in Fig A.2 of [ 31 ].

We further examine the correlation between the recovery rate and network measures explained in Section 3.3 (i.e. those for isolation, loops, upstreamness, and supplier substitution) and test the significance of the correlation using ordinary least squares (OLS) estimations. Fig 4 illustrates the correlation between the recovery rate and network measures. To control for the effect of the prefecture’s economic size on its recovery ( Fig 4(f) ), we include GRP in logs in all the OLS estimations and exclude the effect of GRP from the recovery rate in Fig 4 . The number of links of each prefecture could also be controlled for; however, because its correlation coefficient with GRP is 0.965 ( S3 Table ), we do not use the total links in our regressions to avoid multicollinearity. S4 Table presents the OLS results.

The vertical axis indicates the recovery rate, defined as the ratio of the increase in the GRP of a prefecture by lifting its own lockdown to its decrease because of the lockdown of all prefectures. Except for panel (f), the effect of GRP is excluded from the recovery rate. The horizontal axis indicates the share of the links within the prefecture to its all links in (a), the share of the loop flows within the prefecture to its total flows in (b), the share of the links to other prefectures to all links in (c), the standardised potential flows in (d), the share of substitutable suppliers to all suppliers outside the prefecture in (e), and GRP in logs in panel (f). The orange line in each panel specifies the fitted value from a linear regression that controls for the effect of GRP. The blue, black, and red dots show prefectures whose GRP is among the top 10, bottom 10, and others, respectively.

https://doi.org/10.1371/journal.pone.0255031.g004

In panels (a) and (b) of Fig 4 , the supply-chain links and loops within the prefecture are found to be positively correlated with the recovery rate. These results suggest that when a prefecture is more isolated in the network and has more loops within it, the positive effect of lifting a lockdown circulates in the loops, which can mitigate the propagation of the negative effects of other prefectures’ lockdowns. By contrast, the outward links to other prefectures and the HH potential of the prefecture are negatively and significantly correlated with the recovery rate (panels (c) and (d)). These findings imply that prefectures with more upstream firms in supply chains tend to recover less from lifting their own lockdowns. Panel (e) indicates that the recovery rate is higher when more suppliers in other prefectures under lockdown can be replaced by those in the prefecture lifting its lockdown.

4.4 Effect of lifting the lockdowns in two regions simultaneously

Finally, we simulate the effect on the production of prefecture a if it lifted its lockdown together with prefecture b . We compare the recovery in prefecture a ’s GRP by lifting its lockdown together with prefecture b and that by lifting its lockdown alone, and compute the relative recovery measure, as shown in S7 Fig . Using a regression framework as above, we investigate how the relative recovery measure of prefecture a is affected by the network relationships between prefectures a and b . Fig 5 illustrates the correlation between selected key variables and the relative recovery. In the regression analysis, we always control for the GRP of prefecture b , its squares, and the number of links between prefectures a and b that may affect the relative recovery ( Fig 5(e) and 5(f) ). Following this, we exclude these effects from the relative recovery in panels (a)–(d) in the figure. S6 Table presents the results of the OLS estimations.

The vertical axis indicates the relative recovery of prefecture a , defined as the ratio of the increase in the GRP of prefecture a by lifting its lockdown together with prefecture b to its increase by lifting its lockdown alone. The effect of the GRP of b and total links between the two are excluded from the relative recovery measure. The variable in the horizontal axis is given by Eqs 3 and 4 in panels (a) and (b), respectively, Eq 5 in (c), the share of substitutable suppliers in b for those in a among a ’s locked-down suppliers in (d), the number of links between prefectures a and b in (e) and the GRP of b in logs in (f). The orange line in each panel signifies the fitted value from a linear regression that controls for the effect of the GRP of b and total number of links between a and b in (a)–(d). The blue, black, and red dots show the pairs of prefectures a and b for which the GRP of b is among the top 10, bottom 10, and others, respectively.

https://doi.org/10.1371/journal.pone.0255031.g005

Panels (a) and (b) of Fig 5 show that even after controlling for the effect of economic size and number of links between the two prefectures, the ratio of potential flows from prefecture a to b and from b to a to the total flows of a is positively correlated with the relative recovery. S8 Fig shows a similarly positive correlation for the number of links between the two, rather than potential flows, and the relative recovery. These results suggest that the recovery from lifting a lockdown is greater when two prefectures closely linked through their supply chains, regardless of the direction, lift their lockdowns together. Further, we find that prefecture a recovers more when prefectures a and b are linked through more circular flows (panel (c)), confirming that the positive impacts of lifting a lockdown can circulate and be strengthened in inter-regional supply-chain loops. Panel (d) indicates that if prefecture a ’s suppliers in other prefectures are in lockdown but can be replaced by suppliers in prefecture b easily, prefecture a ’s recovery is higher when the two prefectures lift their lockdowns together. Although the correlation between the relative recovery measure and network variables seems to be largely driven by the observations for which the GRP of prefecture b is large (depicted by the blue dots in Fig 5 ), we find that the positive correlation still exists without these observations ( S9 Fig ).

5 Discussion and conclusion

Our simulation analysis reveals that the economic effects of lockdowns in different regions interact with each other through supply chains. Our results and their implications can be summarised as follows.

First, when a firm is locked down, its suppliers and customer firms are affected because of a lack of demand and supply, respectively. Therefore, a region’s production can improve more if prefectures lift their lockdowns together when they are closely linked through supply chains in either direction ( Fig 5(a) and 5(b) ). In addition to the total number of links between the two regions, the intensity of such links compared with those with others is also important.

Second, when the firms in a region are in more upstream positions in the whole network or are predominantly suppliers of simple parts, the production of the region does not recover substantially by lifting its lockdown alone ( Fig 4(d) ). Although the negative economic effect of a lockdown can propagate downstream and upstream, firms can mitigate downstream propagation easily by using inventory or by replacing suppliers who are under lockdown. The difference between the downstream and upstream effects of lockdown is aggravated as the effect propagates further through supply chains. This finding is in line with the literature [ 36 , 43 ] that also finds the upstream accumulation of negative effects on profits and assets. In practice, our result implies that a region with many small- and medium-sized suppliers of simple materials and parts should be cautious about whether it lifts its lockdown, which may not result in a large economic benefit but could still promote the spread of COVID-19.

Third, the production of a region can recover more by lifting its lockdown when it is more isolated in the network or embodies more supply-chain loops within the region ( Fig 4(a) and 4(b) ). Similarly, the production of the two regions can recover more by lifting their lockdowns together when their inter-regional links have more loops ( Fig 5(c) ). These results imply that the positive economic effect of lifting a lockdown circulates and is intensified in loops, consistent with those in [ 5 ]. Supply-chain loops exist between two regions when the final goods produced are used as inputs by suppliers, while suppliers provide parts and components to final-good producers and the loop stretches across two regions. The importance of loops in the diffusion of the economic effects in networks is not fully recognised, either in academic literature or in policymaking.

Finally, the recovery of a region from its lockdown is greater when suppliers who are still under lockdown can be replaced by those within the region or in other regions without a lockdown in place (Figs 4(e) and 5(f) ). The role of the substitutability of suppliers in mitigating the propagation effect through supply chains has been empirically found in the literature [ 2 , 5 – 7 ]. In practice, this finding suggests two management strategies for regional governments and firms. To minimise the economic loss from lockdown, a region should develop a full set of industries to allow for the possibility of the substitution of any industry. Alternatively, the firms in a region should be linked with geographically diverse suppliers so that suppliers in a region under lockdown can be replaced by those in other regions without a lockdown.

All these results point to the need for policy coordination among regions when regional governments impose or lift a lockdown. Although this study uses the inter-firm supply chains within a country and considers the economic effect of prefecture-level lockdowns, our results can be applied to examine the effect of country-level lockdowns propagating through international supply chains. For example, many suppliers of German firms are located in Eastern Europe and many suppliers of US firms are in Mexico. Our results thus suggest that the economic gains of Eastern Europe and Mexico from lifting their lockdowns are minimal if Germany and the United States, respectively, remain under lockdown. In addition, our framework can be applied to the case of other infectious diseases, and it is likely to suggest a need for the inter-regional and international coordination of lockdown strategies to prevent the spread of infection.

Since our model does not incorporate how lockdown strategies affect the spread of COVID-19, and because it is unclear how human and economic loss should be balanced to maximise social welfare, we cannot explicitly conclude in which cases a lockdown should be imposed or lifted. However, our analysis points to the importance of coordination between lockdown strategies among regions and countries that consider their economic effect in addition to their health effect.

Supporting information

S1 appendix. data..

https://doi.org/10.1371/journal.pone.0255031.s001

S2 Appendix. Methods.

https://doi.org/10.1371/journal.pone.0255031.s002

S3 Appendix. Results.

https://doi.org/10.1371/journal.pone.0255031.s003

S1 Fig. An example of the HHD and loop and potential flow measures of prefectures.

The left panel shows the supply chains of the six firms in the two prefectures. The right top and bottom panels present the potential flows and loop flows, respectively obtained from the HHD.

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S2 Fig. An example of the substitutability measure for two regions.

The bottom shows the equation. A i is the total number of suppliers outside prefectures a and b . The lowest two suppliers are applicable. A supplier in prefecture b belongs to the same industry as the upper firm of the outside suppliers, whereas the lower firm of the outside suppliers is not substitutable. Hence, A i = 2 and B i = 1.

https://doi.org/10.1371/journal.pone.0255031.s005

S3 Fig. Loss in value added as a percentage of total GDP, assuming different restriction levels for a lockdown of 14 days, between the groups with fewer and greater restrictions.

A restriction level is defined by a multiplier for the sector-specific benchmark rates of reduction in production capacity. The red and blue parts of each bar show the loss of value added in the less and more restricted groups, respectively, as a percentage of GDP.

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S4 Fig. Loss in value added as a percentage of total GDP, assuming different restriction levels for a lockdown of 30 days, between the groups with fewer and greater restrictions.

A restriction level is defined by a multiplier for the sector-specific benchmark rates of reduction in production capacity. The red and blue parts of each bar show the loss of value added in the less and more restricted groups, respectively as a percentage of GDP.

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S5 Fig. The ratio of the improvement in GDP by lifting the lockdown in each prefecture.

The improvement is defined as the ratio of the increase in the national GDP by each prefecture lifting its lockdown to the decrease in GDP by all prefectures’ lockdowns. The horizontal axis indicates the JIS codes of the prefectures. The yellow, dark green, and light green bars show the ratio of the improvement when lockdowns persist for 14, 30, and 60 days, respectively.

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S6 Fig. Recovery rate in GRP by lifting the lockdown in each prefecture.

The recovery rate is defined as the ratio of the increase in the GRP of each prefecture by lifting its lockdown to the decrease in its GRP by all prefectures’ lockdowns. The horizontal axis indicates the JIS codes of the prefectures. The yellow, dark green, and light green bars show the recovery rate when lockdowns persist for 14, 30, and 60 days, respectively.

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S7 Fig. Relative recovery from lifting the lockdown together to the recovery from lifting the lockdown alone.

The relative recovery measure is defined as the ratio of the increase in the GRP of prefecture a when it lifts its lockdown together with prefecture b to its increase when prefecture a lifts its lockdown alone. The horizontal axis shows the JIS code of prefecture a . The colour of each dot indicates whether the GRP of prefecture b is among the top 10 (blue), the bottom 10 (black), or others (red).

https://doi.org/10.1371/journal.pone.0255031.s010

S8 Fig. Correlation between the relative recovery and selected network measures.

The vertical axis indicates the relative recovery of prefecture a , defined as the ratio of the increase in the GRP of prefecture a by lifting its lockdown together with prefecture b to its increase by lifting its lockdown alone. The effect of the GRP of b and total links between the two are excluded from the relative recovery measure. The variable in the horizontal axis is given by Eqs 1 and 2 in panels (a) and (b), respectively. The orange line in each panel signifies the fitted value from a linear regression that controls for the effect of the GRP of b and total number of links between a and b . The blue, black, and red dots indicate the pairs of prefectures a and b for which the GRP of b is among the top 10, bottom 10, and others, respectively.

https://doi.org/10.1371/journal.pone.0255031.s011

S9 Fig. Correlation between the relative recovery and selected network measures.

See the caption of Fig 5 and S8 Fig . for the definitions of the variables used here. The green line in each panel signifies the fitted value from a linear regression that controls for the effect of the GRP of b and total number of links between a and b in (a)–(g). The black and red dots indicate the pairs of prefectures a and b for which the GRP of b is among the bottom 10 and between 11 and 37, respectively.

https://doi.org/10.1371/journal.pone.0255031.s012

S10 Fig. Correlation between the recovery rate and selected network measures.

See the caption of Fig 4 for the definitions of the variables used here. The orange line in each panel specifies the fitted value from a linear regression that controls for the effect of GRP in (b)–(f). The blue, black, and red dots indicate the prefectures whose GRP is among the top 10, the bottom 10, or others, respectively.

https://doi.org/10.1371/journal.pone.0255031.s013

S11 Fig. Correlation between the relative recovery and selected network measures.

See the caption of Fig 5 for the definitions of the variables used here. The red line in each panel signifies the fitted value from a linear regression that controls for the effect of the GRP of b and total number of links between a and b in (a)–(g). The blue, black, and red dots indicate the pairs of prefectures a and b for which the GRP of b is among the top 10, the bottom 10, or others, respectively.

https://doi.org/10.1371/journal.pone.0255031.s014

S1 Table. Sector-specific rates of reduction in production capacity.

Sectors are classified by the JSIC [ 23 ] at the two-digit level, except for industries 560, 561, and 569 for which we use three-digit codes to reflect the actual circumstances. The sector names are abbreviated. S1 Table lists the sector descriptions and abbreviations.

https://doi.org/10.1371/journal.pone.0255031.s015

S2 Table. Sector classifications and abbreviations.

https://doi.org/10.1371/journal.pone.0255031.s016

S3 Table. Correlation matrix of the variables used in Section 4.3.

The definitions of the variables are as follows. RecRatio: the recovery rate defined as the ratio of the increase in the GRP of each prefecture by lifting its lockdown to the decrease in its GRP by all prefectures’ lockdowns. GRP: gross regional product (log). Links: the degree (log). InLink: the share of links within the prefecture to all its links. InLoop: the share of loop flows within the prefecture to all its flows. OutLink: the share of outward inter-prefectural links to all the links of the prefecture. Potential: the average HH potential of the firms in the prefecture. Sub: the share of substitutable suppliers to all suppliers of the prefecture located outside the prefecture.

https://doi.org/10.1371/journal.pone.0255031.s017

S4 Table. Regression results for Section 4.3.

The dependent variable is the recovery rate. See the caption of Table S3 Table for the definitions of the independent variables. Standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

https://doi.org/10.1371/journal.pone.0255031.s018

S5 Table. Correlation matrix of the variables used in Section 4.4.

The definitions of the variables are as follows. Recov a : the relative recovery of prefecture a defined as the ratio of the increase in the GRP of prefecture a by lifting its lockdown together with prefecture b to its increase by lifting its lockdown alone. Link ab : the share of links from a to b to all links from a . Link ba : the share of links from b to a to all links from a . Pot ab : the share of potential flows from b to a to the total links of a . Pot ba : the share of potential flows from a to b to the total links of a . Sub ab : the share of suppliers substitutable by those in b to a ’s suppliers outside a and b . Sub ba : the share of suppliers substitutable by those in a to b ’s suppliers outside a and b . Loop ab : the share of loop flows between a and b to the total flows between the two. Bi ab : the number of inter-prefecture links between a and b in logs. GRP j : GRP of b in logs.

https://doi.org/10.1371/journal.pone.0255031.s019

S6 Table. Regression results for Section 4.4.

The dependent variable is the relative recovery measure. See the caption of Table S5 Table for the definitions of the independent variables. Standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

https://doi.org/10.1371/journal.pone.0255031.s020

Acknowledgments

This study used the computational resources of the supercomputer Fugaku (the evaluation environment in the trial phase) provided by the RIKEN Center for Computational Science. OACIS [ 44 ] and CARAVAN [ 45 ] were used for the simulations in this study. This study was conducted as part of a project entitled ‘Research on relationships between economic and social networks and globalization’ undertaken at the Research Institute of Economy, Trade, and Industry (RIETI). We thank Yoshi Fujiwara for advise on the Helmholtz–Hodge decomposition (HHD) computation.

• 1. Thomas Hale, Sam Webster, Anna Petherick, Toby Phillips, and Beatriz Kira. Oxford COVID-19 government response tracker. https://www.bsg.ox.ac.uk/research/research-projects/coronavirus-government-response-tracker , 2020.
• View Article
• Google Scholar
• 4. Vasco M Carvalho, Makoto Nirei, Yukiko U Saito, and Alireza Tahbaz-Salehi. Supply chain disruptions: Evidence from the Great East Japan earthquake. Technical Report No. 17-5, Columbia Business School Research Paper, 2016.
• 7. Yuzuka Kashiwagi, Yasuyuki Todo, and Petr Matous. International propagation of economic shocks through global supply chains. Technical Report E1810, WINPEC Working Paper, 2018.
• 8. Barthélémy Bonadio, Zhen Huo, Andrei A Levchenko, and Nitya Pandalai-Nayar. Global supply chains in the pandemic. NBER Working Paper Series No. 27224, National Bureau of Economic Research, 2020.
• 10. Fergal McCann and Samantha Myers. Covid-19 and the transmission of shocks through domestic supply chains. Financial Stability Notes No. 2020-3, Central Bank of Ireland, 2020.
• 11. Warwick J McKibbin and Roshen Fernando. The global macroeconomic impacts of COVID-19: Seven scenarios. Technical Report 19/2020, CAMA Working Paper, 2020.
• PubMed/NCBI
• 17. Garry Robins and Philippa Pattison. Interdependencies and social processes: Dependence graphs and generalized dependence structures. In Peter J Carrington, John Scott, and Stanley Wasserman, editors, Models and methods in social network analysis , volume 28, book section 10, pages 192–214. Cambridge University Press, Cambridge, 2005.
• 19. Peter S. Goodman. Sweden has become the world’s cautionary tale, July 2020.
• 22. Ministry of Internal Affairs and Communications, the Cabinet Office, the Financial Services Agency, the Ministry of Finance, the Ministry of Education, Culture, Sports, Science and Technology, the Ministry of Health, Labour and Welfare, the Ministry of Agriculture, Forestry and Fisheries, the Ministry of Economy, Trade and Industry, the Ministry of Land, Infrastructure, Transport and Tourism, and the Ministry of Environment, Japan. 2015 Input-Output tables for Japan. https://www.soumu.go.jp/english/dgpp_ss/data/io/index.htm , 2015.
• 23. Ministry of International Affairs and Communications. Japan Standard Industrial Classification (Revision 13) , 2013 (accessed June 1, 2020).
• 24. Barabási Albert-László. Network science . Cambridge University Press, 2016.
• 26. Axtell Robert, Blair Bruce, Bowles Samuel, DeVany Art, DeBevoise Malcolm, Durlauf Steven, et al. Generative Social Science: Studies in Agent-based Computational Modeling . Princeton University Press, Princeton, 2006.
• 30. The Prime Minister of Japan and His Cabinet. Government responses on the coronavirus disease 2019. http://japan.kantei.go.jp/ongoingtopics/_00013.html , 2020.
• 31. Hiroyasu Inoue, Yohsuke Murase, and Todo Yasuyuki. Do economic effects of the “anti-covid-19” lockdowns in different regions interact through supply chains? SSRN 3692937, Social Science Research Network, 2021.
• 36. Nicole Branger, René Marian Flacke, and Steffen Windmueller. Industry returns in global value chains: The role of upstreamness and downstreamness. SSRN 3476690, Social Science Research Network, 2019.
• 40. Thibault Fally. Production staging: measurement and facts, 2012.
• 41. Thibault Fally and Russell Hillberry. A coasian model of international production chains, 2017.
• 42. Ministry of Economy, Trade, and Industry. Indices of all industry production. https://www.meti.go.jp/english/statistics/tyo/zenkatu/index.html , 2020.
• 43. Jiangtao Fu, Petr Matous, and Yasuyuki Todo. Trade credit in global supply chains. RIETI Discussion Paper Series 18-E-049, Research Institute of Economy, Trade and Industry, 2018.
• 44. Yohsuke Murase, Takeshi Uchitane, and Nobuyasu Ito. An open-source job management framework for parameter-space exploration: Oacis. In Journal of Physics: Conference Series , volume 921, page 012001, 2017.
• 45. Yohsuke Murase, Hiroyasu Matsushima, Itsuki Noda, and Tomio Kamada. Caravan: a framework for comprehensive simulations on massive parallel machines. In International Workshop on Massively Multiagent Systems , pages 130–143. Springer, 2018.

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Effects of COVID-19 lockdown phases in India: an atmospheric perspective

Pramod soni.

Department of Civil Engineering, MNNIT Allahabad, Prayagraj, India

Associated Data

The data that support the findings of this study are available freely in public domain.

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus. It was first identified in December 2019 in Wuhan, Hubei, China, and has resulted in an ongoing pandemic. As of 5 July 2020, more than 11.1 million cases have been reported across 188 countries and territories, resulting in more than 528,000 deaths. More than 6.03 million people have recovered. The entire world population currently faces enormous challenges (i.e., social, environmental, health, and economic) due to the impact of COVID-19. In this regard, the affected countries are now trying to slow down the virus’s transmission through social-distancing, lockdowns, increasing the number of tests and treatment facilities. There have been four lockdowns (25 March 2020–31 May 2020), and two unlock periods (1 June–31 July 2020) in India. Aerosol Optical Depth (AOD) has been analyzed using MODIS satellite data during various phases of lockdowns over India. With the implementation of lockdown steps, AOD values dropped significantly over various regions. A significant reduction in AOD over the North-Central regions (up to −50%) compared to the regions in the South or Northeast India. The AOD over these regions was significantly affected by the lock/unlock phases. It was also observed that there was a considerable buildup of AOD during the pre-lockdown period in the year 2020 as compared to the past two years.

Introduction

The first case of novel coronavirus (COVID-19) was reported in the Wuhan district of China in December 2019 (Gautam and Hens 2020 ). The virus transmitted rapidly and affected several people within a month (WHO 2020 ). The first person reported in India was from the State of Kerala in late January 2020 (Gautam 2020b ), and according to his travel history, he had returned from China. Since then, there has been a significant rise in the number of COVID-19 patients in India’s various states. As of 5 July 2020, a total of 19,289 deaths have been reported with 6,74,313 infected persons over entire India (https://www.ndtv.com//:5 July 2020). Maharashtra, Tamil Nadu, and Delhi have nearly 50% of all India cases, whereas northeast states have the least number of cases. Considering the seriousness of the disease, initially, a 21-day nationwide lockdown (25 March 2020 to 14 April 2020: LD1.0) was announced by the prime minister of India, “Shri. Narendra Modi” to control the transmission of COVID-19 and due to which many industries, academic institutes, markets, as well as public gatherings were shut down. After the first lockdown (LD 1.0), there have been three more lowdown phases in succession (LD2.0: 15 April to 3 May 2020, LD3.0: 4 May to 17 May 2020, LD4.0: 18 May to 31 May 2020). After that, to restart the Indian economy, two unlock phases (UL) have also been announced (UL1.0: 1 June 2020 to 30 June 2020, and UL1.0: 1 July 2020 to 31 July 2020).

The direct outcomes of the various lockdown phases were that the mortality rate of COVID-19 and its cases were significantly controlled. However, there have been various indirect effects of these phases as such lockdowns on the mass level have not been implemented in the world for a long time. Apart from medical research, various scientists around the world have also focused on finding the environmental effects of COVID-19 lockdowns (Kanniah et al. 2020 ; Menut et al. 2020 ; Suresh et al. 2020 ; Mitra et al. 2020 ; Liu et al. 2020a , b ; Nakada and Urban 2020 ; Baldasano 2020 ). Ghosh and Ghosh ( 2020 ) reviewed 15 empirical research articles all around the world and inferred that all the studies had reported a trend of decrease in the level of concentrations of PM10, PM2.5, CO, NO, NO2, NH3, NOx, SO2 during the lockdown period. Srivastava ( 2020 ) also reviewed various studies focusing on the impact of weather on the spread and severity of COVID-19. They also found that air quality has immensely improved due to lockdown.

Indian scientists have also explored environmental and atmospheric changes incurred to COVID-19 lockdowns. Gautam ( 2020a ) analyzed NO 2 data, which were collected from the satellite (Sentinel – 5P), and found a significant reduction in its levels for the Asian and European countries due to COVID-19 lockdowns. Gautam ( 2020b ) used secondary results from the National Aeronautics and Space Administration (NASA) and found a significant reduction (50%) in the air quality of the Indian region. Lokhandwala and Gautam ( 2020 ) also found an improvement of air quality and environment during pre- and post-lockdown of this pandemic situation. Gupta et al. ( 2020 ) analyzed various harmful pollutants present in the environment and observed that over India temperature has been reduced to near about 15 degree Celsius, there is also a reduction in humidity up to 40%, particulate matter (PM2.5) reaches near about normal, i.e., 40 g/m 3 , and carbon monoxide levels have also been reduced to 10 ppm. Mahato et al. ( 2020 ) also found 40–50% improvement in air quality over Delhi. Jain and Sharma ( 2020 ) found around 30–80% reduction in pollutant concentrations in all the megacities in India. Kumari and Toshniwal ( 2020 ) found a substantial reduction in the concentration of PM10, PM2.5, NO2, and SO2 in two major cities (Delhi and Mumbai) of India post-lockdown phase. Bera et al. ( 2020 ) did a similar analysis with PM2.5 for another major city of India (Kolkata). They found a positive correlation between air pollution in Kolkata and the lethality related to COVID-19. Using Aerosol Optical Depth (AOD) from MODIS, Ranjan et al. ( 2020 ) found that the AOD level over the Indian Territory is greatly reduced ( 45%) during the lockdown periods as compared to the long-term mean AOD level (2000–2019). Aman et al. ( 2020 ) analyzed the impact of lockdown on water and air quality using remote sensing data and found a significant reduction in the average suspended particulate matter over Ahmedabad, India.

In India, there have been four lockdown phases, and one unlock phase has passed, and at the time of writing this article (for the first time), the second unlock phase is in progress. All the previous studies have focused on analyzing air quality during pre- and post-lockdown situations, mostly for individual cities. Moreover, these studies have not tried to identify major regions that contribute most to the anthropogenic air pollution in India. Since there has not been a major shutdown of various Industries/activities throughout the country at such a mass level, these lockdown phases can be taken as an opportunity to identify the hot spots (in terms of anthropogenic air pollution) in India. The present study was carried out with the following objectives:

• To analyze the impacts of various lockdown phases in India.
• To identify anthropogenic pollution hot spots of India.

Study area and data used

According to the World Air Quality Report (WAQR ( 2019 )), 21 of the world’s 30 cities with the worst air pollution are in India, with six in the top ten. Considering the above fact, the present study has been carried out over India. Further, five different regions (covering Delhi, Maharashtra, Uttar Pradesh, Tamil Nadu, and northeast states) were analyzed, as shown in Fig. ​ Fig.1. 1 . As of 5 July 2020, Maharashtra, Delhi, and Tamil Nadu have nearly 50% of all India COVID-19 cases.

Study area: The regions have been named based on the state lying in it

The analysis has been carried out using the AOD at 550 nm over land regions, obtained from MODIS/Terra level-3 (MOD08_D3) satellite that is publicly available at daily temporal resolution and 1-degree spatial resolution. The MODIS is the most reliable public source of AOD around the globe. Mangla et al. ( 2020 ) compared AOD data for the 2010–2017 (8 years) from multiple satellites (MISR, MODIS, and OMI) and ground-based AOD (AERONET) over Indo-Gangetic Plains (Gandhi College, Jaipur, and Kanpur) region. They found that MODIS, as compared to other sensors, has a high correlation with AERONET.

Methodology

The AOD of the year 2020 over entire India and various regions marked in Fig. ​ Fig.1 1 was analyzed and compared with previous years (2018, 2019) AOD data. For comparison, anomalies of AOD of the year 2020 have been calculated by subtracting it from the AODs of the years 2018 and 2019 at each grid point.

The analysis is carried out for different time intervals, as shown in Table ​ Table1. 1 . The first period, pre-Lockdown, was the period before any lockdown was imposed in India from 1 January 2020 to 24 March 2020. The second period will be called as the lockdown 1.0 (LD1.0) that existed from 25 March 2020 to 14 April 2020. Subsequently, there were three more lockdowns (LD2.0, LD3.0, and LD4.0) between 15 April 2020 and 31 May 2020. After that, the first unlock period (UL1.0) started from 1 June 2020 to 30 June 2020. At present, the second unlock phase (UL2.0) is in progress.

Various lockdown/unlock phases in India due to COVID-19

Entire India

Since, for the year 2020, data were available only up to 30 June 2020, the spatial pattern of AOD (averaged over the last three years) is shown for this period in Fig. ​ Fig.2. 2 . It can be seen that compared to the southern parts of India, there is a substantial buildup of aerosols over the north and the eastern regions. The average AOD for the period of January to June reaches up to 0.9.

Spatial pattern of AOD over India from January 2020 to June 2020

Figure ​ Figure3 3 shows the anomaly for the PL period. There is a considerable increase in AOD during this period in the year 2020 compared to 2018 and 2019 over entire India. Since there was no imposition of any restriction, due to the rapid growth of Industrial activities, a considerable increase in AOD during this period was observed. On average, there was about 6.24% and 11.87% increase in AOD compared to the corresponding periods of the years 2018 and 2019, respectively.

Spatial pattern of AOD anomaly for the pre-lockdown period

The AOD anomalies from the years 2018 and 2019 after the PL phase are shown in Fig. ​ Fig.4. 4 . During the first phase (LD1.0) only, there is a considerable reduction of AOD over India. This reduction is more prominent over Indo-Gangetic Plains (IGP) as compared to other places.

Spatial pattern of AOD anomaly for different lockdown/unlock phases

An anomaly from the year 2018 shows that in the year 2020, AOD is always lower after the PL period, which shows a significant impact of lockdown phases over entire India. Compared to the year 2018, there is an average reduction of about 18.56% over entire India during the lockdown phases (LD1.0 to LD 4.0), whereas, for the same period, it is only 5.76% from the year 2019. From the anomalies of the year 2019, we can see that, as the lockdown phases end, a relative increase in AOD over the central part of India can be observed as various activities slowly start to take place. However, this change is most prominent for the central part of India.

All India averaged 10-day running mean timeservers of AOD are shown in Fig. ​ Fig.5 5 for all three years. During the PL period, AOD in the year 2020 is much higher than in the past two years. Moreover, just at the beginning of lockdown phases 1.0 and 2.0, there is a considerable decrease in AOD of the year 2020. After the unlock process begins (UL1.0), the AOD of the year 2020 begins to match with the AOD previous year (2018).

All India averaged 10-day running mean timeservers of AOD

Regional level analysis

To analyze the effects of lockdown over different regions (as shown in Fig. ​ Fig.1), 1 ), average AOD over these regions was obtained. The anomalies of AOD of the year 2020 were calculated by subtracting it from the AOD of the years 2018 and 2019 over the same region.

The 10-day running means anomalous time series from the year 2018 is shown in Fig. ​ Fig.6 6 for all five regions. From the figure, we can see that over Delhi, Maharashtra and UP, a clear distinction can be seen between the PL period and lockdown phases. Over Delhi and UP region, it is even more evident that before the lockdown began, there was a significant rise in the AOD, which declined after the lockdown phases. However, over the Tamil Nadu and northeast states, this behavior was not observed clearly. Although there is a decrease in AOD for the year 2020, still the effect of lockdown phases is not observed.

The 10-day running means anomalous time series from the year 2018

Similar plots for anomaly from the year 2019 are shown in Fig. ​ Fig.7. 7 . Over Delhi and UP regions, just before the lockdown period, there was a rise in AOD, which drops in lockdown phases. The percentage change in the AOD over various periods is shown in Tables ​ Tables2 2 and ​ and3, 3 , respectively. As compared to the year 2018, Delhi had 23.53% more AOD during the PL period. During all the lockdown periods, it reduced to a minimum of −47.97% during the LD3.0 phase. A similar pattern was observed for Uttar Pradesh also. The highest drop in AOD was observed for the Uttar Pradesh region during the LD1.0 phase (−49.67% from 2018 and −33.37% from 2019). However, as shown in Table ​ Table3, 3 , in contrast to the year 2018, during the PL period, AOD in 2020 increased for all the regions compared to the year 2019. However, the significant reduction during the lockdown phase is visible for Delhi, Maharashtra, and northeast.

The 10-day running means anomalous time series from the year 2019

% AOD anomaly from year 2018

% AOD anomaly from year 2019

Summary and conclusions

The AOD data obtained from the MODIS satellite were analyzed over India for various lockdown phases over different regions during the COVID-19 pandemic. Apart from the analyses over India as a whole, a total of five major regions (Delhi, Maharashtra, Uttar Pradesh, Tamil Nadu, and northeast states) were also chosen. The analysis was carried out for six different periods of 2018, 2019, and 2020. The first period was the pre-lockdown period (PL), which was up to 24 March 2020. Four different lockdown periods were then selected (LD 1.0 to LD 4.0), and one UL1.0 period was also selected.

There is a considerable increase in AOD for the PL period in 2020 over India compared to the years 2018 and 2019. On average, there was about 6.24% and 11.87% increase in AOD during this period compared to corresponding periods of the years 2018 and 2019, respectively. During the lockdown phases (LD1.0 to LD 4.0), compared to the year 2018, there is an average reduction of about 18.56% over entire India, whereas, for the same period, there is a reduction of only 5.76% from the year 2019.

Over Delhi, Maharashtra, and Uttar Pradesh, there was a significant rise in the AOD, which declined after the lockdown phases begin. However, over the Tamil Nadu and northeast states, such behavior was not observed clearly. As compared to the year 2018, Delhi had 23.53% more AOD during the PL period. The lowest anomaly −47.97% was observed during the LD3.0 phase for Delhi. Similar patterns were observed for Uttar Pradesh also. Overall, the most significant drop in AOD was observed for the Uttar Pradesh region during the LD1.0 phase (−49.67% from 2018 and −33.37% from 2019). In contrast to the year 2018, during the PL period, AOD in 2020 increased for all the regions compared to the year 2019.

The major conclusions from the study can be enumerated as

• There was a considerable buildup of AOD during the pre-lockdown period in the year 2020
• As the lockdown phases began, there was a sudden drop in AOD values, especially over the Indo-Gangetic Plains. The drop was as high as -47.97% for Delhi during the LD3.0 phase.
• As the unlock phase begins, the drop in AOD was flattened for Delhi and Uttar Pradesh regions.
• The industrialized regions in the north are significantly affected by the lock/unlock phases as compared to regions in the south or northeast.

Data availability

Publisher's Note

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• Aman MA, Salman MS, Yunus AP. Covid-19 and its impact on environment: Improved pollution levels during the lockdown period-a case from Ahmedabad, India. Remote Sensing Applications: Society and Environment. 2020; 20 :100382. doi: 10.1016/j.rsase.2020.100382. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Baldasano JM. Covid-19 lockdown effects on air quality by NO2 in the cities of Barcelona and Madrid (Spain) Science of the Total Environment. 2020; 741 :140353. doi: 10.1016/j.scitotenv.2020.140353. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bera B, Bhattacharjee S, Shit PK, Sengupta N, Saha S. Significant impacts of covid-19 lockdown on urban air pollution in Kolkata (India) and amelioration of environmental health. Environment, Development and Sustainability. 2020; 2020 :1–28. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Gautam S. COVID-19: Air pollution remains low as people stay at home. Air Quality, Atmosphere & Health. 2020; 13 (7):853–857. doi: 10.1007/s11869-020-00842-6. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gautam S. The influence of COVID-19 on air quality in India: A boon or inutile. Bulletin of Environmental Contamination and Toxicology. 2020; 104 (6):724–726. doi: 10.1007/s00128-020-02877-y. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gautam S, Hens L. SARS-CoV-2 pandemic in India: what might we expect? Environment, Development and Sustainability. 2020; 22 (5):3867–3869. doi: 10.1007/s10668-020-00739-5. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ghosh S, Ghosh S. Air quality during covid-19 lockdown: Blessing in disguise. Indian Journal of Biochemistry & Biophysics. 2020; 57 :420–430. [ Google Scholar ]
• Gupta N, Tomar A, Kumar V. The effect of covid-19 lockdown on the air environment in India. Global Journal of Environmental Science and Management. 2020; 6 :31–40. [ Google Scholar ]
• Jain S, Sharma T, et al. Social and travel lockdown impact considering coronavirus disease (covid-19) on air quality in megacities of India: Present benefits, future challenges and way forward. Aerosol and Air Quality Research. 2020; 20 :1222–1236. doi: 10.4209/aaqr.2020.04.0171. [ CrossRef ] [ Google Scholar ]
• Kanniah KD, Zaman NAFK, Kaskaoutis DG, Latif MT. Covid-19’s impact on the atmospheric environment in the Southeast Asia region. Science of the Total Environment. 2020; 736 :139658. doi: 10.1016/j.scitotenv.2020.139658. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kumari, P., & Toshniwal, D. (2020). Impact of lockdown measures during COVID-19 on air quality– A case study of India. International Journal of Environmental Health Research . 10.1080/09603123.2020.1778646. [ PubMed ]
• Liu, Y., Lin, X., & Qin, S. (2020a). The short-term seasonal analyses between atmospheric environment and covid-19 in epidemic areas of cities in Australia, South Korea, and Italy. arXiv preprint arXiv:2005.12264 .
• Liu, Z., Deng, Z., Ciais, P., Lei, R., Feng, S., Davis, S. J., Wang, Y., Yue, X., Lei, Y., Zhou, H., et al. (2020b). Decreases in global co _ 2 emissions due to covid-19 pandemic. arXiv preprint arXiv:2004.13614 .
• Lokhandwala S, Gautam P. Indirect impact of covid-19 on environment: A brief study in Indian context. Environmental Research. 2020; 188 :109807. doi: 10.1016/j.envres.2020.109807. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mahato S, Pal S, Ghosh KG. Effect of lockdown amid covid-19 pandemic on air quality of the megacity Delhi, India. Science of the Total Environment. 2020; 730 :139086. doi: 10.1016/j.scitotenv.2020.139086. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mangla R, Indu J, Chakra S. Inter-comparison of multi-satellites and aeronet AOD over Indian region. Atmospheric Research. 2020; 2020 :104950. doi: 10.1016/j.atmosres.2020.104950. [ CrossRef ] [ Google Scholar ]
• Menut L, Bessagnet B, Siour G, Mailler S, Pennel R, Cholakian A. Impact of lockdown measures to combat covid-19 on air quality over Western Europe. Science of the Total Environment. 2020; 741 :140426. doi: 10.1016/j.scitotenv.2020.140426. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mitra A, Ray Chadhuri T, Mitra A, Pramanick P, Zaman S. Impact of covid-19 related shutdown on atmospheric carbon dioxide level in the city of Kolkata. Parana Journal of Science and Education. 2020; 6 (3):84–92. [ Google Scholar ]
• Nakada LYK, Urban RC. Covid-19 pandemic: Impacts on the air quality during the partial lockdown in São Paulo state, Brazil. Science of the Total Environment. 2020; 2020 :139087. doi: 10.1016/j.scitotenv.2020.139087. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ranjan AK, Patra A, Gorai A. Effect of lockdown due to sars covid-19 on aerosol optical depth (AOD) over urban and mining regions in India. Science of the Total Environment. 2020; 745 :141024. doi: 10.1016/j.scitotenv.2020.141024. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Srivastava A. Covid-19 and air pollution and meteorology-an intricate relationship: A review. Chemosphere. 2020; 263 :128297. doi: 10.1016/j.chemosphere.2020.128297. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Suresh, A., Chauhan, D., Othmani, A., Bhadauria, N., S, A., Jose, J., & Mejjad, N. (2020). Diagnostic comparison of changes in air quality over China before and during the COVID-19 Pandemic. 10.21203/rs.3.rs-30482/v1.
• WAQR (2019). World air quality report (2019).
• World Health Organization, & World Health Organization (2020). Coronavirus disease (covid-2019) situation reports.

Essay on Lockdown

‘Lockdown’ refers to the suspension of the usual privileges of citizens, regarding their movement and socializing. It is imposed by a competent authority to prevent any untoward incident. In India, a lockdown was imposed for many months by the government to contain the spread of novel coronavirus disease. Find here some well-described essays to know in detail about the lockdown.

Short and Long Essay on Lockdown in English

Following short and long essays on Lockdown in different word limits are given here that is useful for students of classes 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, and class 12 in English in 100, 150, 200, 250, 300, 500 words. Also find short Lockdown essay 10 lines.

Lockdown Essay 10 Lines (100 – 150 Words)

1) Lockdown refers to the prevention of citizens from moving and socializing as usual.

2) Lockdown is imposed by the government in case of any emergency.

3) India has undergone a lockdown to control the widespread of the Corona virus.

4) The government of India imposed the first lockdown on 25 March 2020, Wednesday.

5) India observed continuous 150 days complete shutdown in the lockdown.

6) Except for the emergency services, everything was closed and restricted.

7) The lockdown was continued for a few months in four parts.

8) The lockdown helped in controlling the widespread coronavirus and massive deaths.

9) Lives and works of many people were affected by the lockdown.

10) Lockdown was not fruitful for the economy as India suffered negative GDP (Gross Domestic Product).

Essay 1 (250 Words)

Introduction

Lockdown is an emergency protocol implemented by the government of India with an objective to contain the spread of a novel coronavirus epidemic. The government implemented a 21 days countrywide lockdown at the beginning which was continued for many months in 4 parts in the entire nation and further the state governments implemented it as per their states need. India was under lockdown for more than 150 days continuously.

Lockdown – The Only Remedy against Novel Coronavirus

Novel coronavirus disease is highly contagious and it spreads fast from person to person. No other disease before has been known to spread such a fast rate as the novel coronavirus. There is no option other than to treat the affected symptomatically; however, the final recovery depends largely on an individual’s stamina and immunity. From the perspective of this scenario, the lockdown seems to be the only practical and effective solution to prevent the spread of the disease.

Although we cannot imagine such scenario for more time our government courage to take such a bold step. It was implemented to keep safe from this deadly virus. Lockdown badly effected our economy and today it is in its fracture mode.

Success of Lockdown

Although we felt safe when the government took such a major but some experts remark lockdown as an unplanned action and it has directly affected the entire nation. Apart from India many other countries also adopted lockdown but they are strong enough to cope up with the economic damage caused due to lockdown.

It would not be wrong to say that the lockdown reduced the flow of this virus, but at the same time cases started to increase rapidly after it was unlocked in India. India became the second-highest infected country. So, in this term, we cannot say that lockdown was truly successful.

Even today schools, colleges, parks, public spaces, cinemas in India are closed. The lockdown can be still seen but the cases are decreasing comparatively. The vaccine has been developed and soon people will get rid of this deadly virus till than keep wearing your mask, wash your hand frequently, use sanitizer and follow social distancing.

Essay 2 (400 Words)

‘Lockdown’ as the name implies is a complete lockdown imposed on the usual movement of the general population of a place. A lockdown can be localized or applied over a wide area, depending on the purpose.

Lockdown in India

• First lockdown : It was 25th March 2020 when it was implemented for the first time, till the 14th of April. When the entire nation was completely shut except for some necessary grocery shops and health facilities.
• Second Lockdown : The second lockdown was announced from 15th April to 4th May with the same set of rules and regulations.
• Third Lockdown : It was implemented from the 4th of May to the 17th of May but in this phase of lockdown some special trains were run to help the daily wages workers. Some people stuck abroad were also bought back. This operation was named ‘Operation Samudrsetu’.
• Fourth Lockdown : It was implemented up to 31st of May and further different states extended as per the condition of their state. Districts were divided into three zones as per the COVID cases in the area. Red zone for most infected areas, Orange for few cases in the area whereas Green for areas with no infection.

Impacts of Lockdown

• On Novel Coronavirus Disease

This is the most significant and most desirable impact of the lockdown. The novel coronavirus is highly contagious, spreading fast from person to person. Lockdown makes social distancing effective; prohibiting human to human contact at the highest level possible. This social distancing helps a lot in containing the spread of the disease.

But at the same time, we cannot imagine continuing lockdown for a long time, because it has directly affected us in many ways.

• On The Economy

A countrywide lockdown isn’t good for the economy and is a setback for the economic growth and development of the nation. With transport suspended, railways and road transport agencies suffer losses to the tunes of crores. Small businesses and daily wage laborers are the most affected. Our GDP is going in negative decimals which is -9.6% this year and it is really a matter of fear because it will directly cause inflation.

• On Pollution Level

This is a significant positive impact of the lockdown. With all types of transport being suspended and also the people being forbidden from roaming unnecessarily, the air quality index improves drastically. The change was felt within a day or two of the lockdown.

• On Emergency Services

Lockdown was good for the emergency services and the personnel in a way that didn’t put additional stress on them. With no traffic and rush like usual days, their job becomes extremely easy and convenient.

Lockdown was very necessary for containing the coronavirus epidemic and preventing it from spreading to the community level. Despite its negative impacts; lockdown was very important. Even today although we have developed the vaccine there are many public places still closed. It is quite good in many ways.

Essay 3 (500 – 600 Words)

Lockdown is an emergency protocol imposed by the government that prohibits people from leaving their homes and venturing into public areas. In the wake of the global coronavirus pandemic spread, several governments across the globe have imposed lockdown in their respective jurisdiction, to prevent the disease from spreading further. The government of India also imposed a countrywide lockdown from midnight of 25th March and followed up to 4 months in every state and further different states followed as per the COVID cases in their states.

Why Is The Lockdown Necessary?

Ever since the coronavirus disease was first reported in China in November 2019 it affected millions of people globally. The disease is highly contagious and spreads at an unprecedented rate as never witnessed before.

The motive of a lockdown is to implement social distancing, preventing people from socializing and unnecessary gathering, so that to prevent the spread of disease from one person to another.

Effects of Lockdown

Lockdown wasn’t easy and was quite harsh experience for daily wage laborers, small businesses, and marginalized sections. These people were devoid of their livelihood and with less saving, find the lockdown financially crippling. That been said; lockdown is still necessary to save lives.

People with permanent employment, usually have the opportunity of working from home and are least affected by the lockdown. Suspension of all modes of transport for the common public caused inconvenience during this period.

Local administration relaxed the lockdown for a couple of hours every day to let people buy the necessary groceries and do other works. However, despite the relaxation people were not allowed to gather in large numbers, roam unnecessarily, and Necessary government offices and emergency services like municipalities, hospitals, police, etc. worked as usual.

Solidarity in Lockdown

Though the lockdown in India is harsh on marginalized sections of the society; people from different walks of life and several organizations have come forward for help. As soon as the lockdown was imposed, many prominent film producers, actors, and business houses have paid thousands of crore of rupees as donation to the Prime Minister Relief Fund. This money was used to be spending on food and providing monetary help to the poor during the lockdown.

Government officers distributed food packages, making sure that no person is left without food in the lockdown phase.

People of India have also displayed a great amount of respect for their emergency services personnel and medical professionals by clapping and celebrating within the premises of their own houses.

Apart from this lockdown today India is second in the list of most affected countries in the world. Lockdown saved us from community spread in India. The vaccine has been developed and soon it will be on the market. Still, some public places, schools, theatres are still closed and it is necessary until and unless all of us get the vaccine.

Lockdown is necessary to prevent the spread of coronavirus disease. It is imperative that we should be strict with the guidelines of lockdown for our own health and safety. The lockdown has been resumed but still, there are some public places under lockdown. Follow the guidelines and cooperate to stay safe and also keep others safe in this epidemic.

FAQs: Frequently Asked Questions on Lockdown

Ans . Lockdown is the policy that restricts the movement of people and states them to stay in one place.

Ans . The national emergency lockdown in India was implemented for the first time on the 25th of March, 2020.

Ans . The movement of people was restricted in the Covid-19 lockdown to curb the spread of Covid-19 infection.

Ans . Rajasthan was the first state in India to implement lockdown during Covid-19 phase 1.

Ans . Red zones are the areas that are highly infected.

Ans . The first lockdown was implemented by China in Wuhan on 23rd January 2020.

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Essay on Impact of lockdown on Students and People

Nobody ever imagined that life could turn like this. Despite being most countries democratic, people are forced to live inside their homes. The basic freedom given to us by our constitutions is taken back from us. Nobody is free to move. If anyone is found breathing in the open air he is beaten by the police and imposed with heavy penalties. What has forced all the governments to take this dictator style decision? Why are people all over the world simultaneously forced to live a completely altered life during lockdown 2020? This Essay on the Impact of lockdown on Students and People will answer all the above questions.

Essay on Impact of lockdown | Coronavirus Impact on Students and other people

With the outbreak of covid 19, the world was locked down. The fast-paced life came to a standstill. Covid 19, a disease caused by Corona Virus, started in China initially and spread all over the globe. All were helpless because the medical fraternity could not invent its antidote. So, the safest and the only option seemed was world lockdown. All the national and international borders were sealed. Some countries announced a 3-6 months stay at home order while others declared complete lockdown in phases.

People, businesses, and governments around the world have changed the way they spend, move, communicate and travel because of COVID-19. Let’s see how life has changed during the lockdown period. Did it alter our life for the better?

Lockdown 2020 in India

Indian Prime Minister Mr Modi announced a country lockdown on 21st march 2020 for 21 days. Later it got extended for more and more days. As Indians are notorious for not following the rules, everyone expected it to last for 3-4 days. But the story was different this time. Police drove away from the people who ventured on roads by giving physical punishments and charging fines. Covid 19 triggered lockdown brought a significant change in the life of all.

Impact of Lockdown on Students in India

This disease has affected all segments of the population. And students are no exception. In India, a lockdown was announced just at the time when CBSE exams were going on. Students of the 10th and 12th classes got stuck in the middle. National level entrance exams had to be postponed. Generally, the months of March and April are very crucial for students preparing for these papers. The pandemic diverted students’ focus from their studies. It has created an atmosphere of anxiety and depression among some students and parents.

Seeing from another angle, Children were the happiest creature in the world after the announcement of lockdown. But due to the setting up of virtual classrooms, their happiness did not last long. Now regular classes were going on with no escape from home assignments. However, they learned a new way of education.

Although, schools and coaching institutes have started online classes. The devices required for attending virtual classrooms are not accessible to all in India. It might create a burden on students’ psychology.

Effect of Lockdown on Senior citizens

The government officials appealed that the elderly people stay inside the home during the period of lockdown. According to doctors adults were more vulnerable to coronavirus. Morning walks and evening strolls were their only way to bring some movement in their stiff bodies. This curtailment left them immobile. But they got the company of all the family members who were otherwise too busy to talk to them. Board games and mythological serial telecasts on national television came to their rescue.

Impact of lockdown on Women

A lockdown increases the burden of household work for all families.  While all the domestic helpers were stranded at home, there was no one to share the increased household chores. In Indian families, nobody is empathetic towards the mental and physical health of women due to the increased workload.

Impact of lockdown on Men

Men are the most deeply affected victim of this pandemic. Most of the men leave their homes in the morning to complete the task of bread earning for the family. They spend their whole day outside the house. Lockdown has put them inside the four walls of the house which they are not accustomed to. The absence of professional life is making them sick. Some are lucky to do their work from home with the help of computers.

With the extensions in lockdown, they are adapting to enjoy this altered version of life. Playing online ludo and tambola is a common scene in every house. Some gentlemen are trying their hands on cooking to share a story on Instagram. Watching movies and web series, growing a beard is more a compulsion than a hobby. Sharing basic household work to cheer their better halves makes their bonding even stronger.

Conclusion: Impact of lockdown and coronavirus on people

Today, humans are in cages to save themselves from highly contagious disease covid 19. We were so blindfolded in the race of development that we neglected our spouse, our family, our culture, our environment. We were urgently in a need of some change. But nobody knew that the change would appear like this in the disguise of the Corona Virus.

This period of crisis and global volatility is a once-in-a-lifetime opportunity and we should utilise it thoughtfully and productively.

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Guest Essay

It’s Not You: Dating Apps Are Getting Worse

By Magdalene J. Taylor

Ms. Taylor is a writer covering sex and culture.

“The golden age of dating apps is over,” a friend told me at a bar on Super Bowl Sunday. As we waited for our drinks, she and another friend swiped through Bumble and Hinge, hunting for new faces and likes. Across the bar were two young men: phones out, apps open, clearly doing the exact same thing. Never did the duos meet.

What’s lamentable here isn’t only that dating apps have become the de facto medium through which single people meet. Since 2019, three in 10 U.S. adults have reported using them, with that figure rising to roughly six in 10 for Americans under 50 who have never been married. Not only are people not meeting partners in bars or any of the once normal in-person venues — they’re barely meeting them on the apps, either.

Maybe most of us just aren’t as hot as we used to be. Maybe it’s time our inflated egos got knocked down a notch. Maybe the market of people still willing to put themselves out there in an attempt to date has gotten smaller. Or maybe the apps have functionally, intentionally gotten worse, as have our romantic prospects. The more they fail to help us form relationships, the more we’re forced to keep swiping — and paying.

The internet, where so many of us spend so much of our time, has not been spared from the decline in quality that seems to plague so much of consumer life. This phenomenon was described by the writer Cory Doctorow in a November 2022 blog post and is sometimes called “platform decay”: Tech platforms like Amazon, Reddit and X have declined in quality as they’ve expanded. These sites initially hooked consumers by being almost too good to be true, attempting to become essential one-stop shops within their respective spaces while often charging nothing, thanks to low interest rates and free-flowing venture capital funding . Now that we’re all locked in and that capital has dried up, those initial hooks have been walked back — and there’s nowhere else to go.

This is precisely what is happening with dating apps now, too, with much more urgent consequences. What’s worsening isn’t just the technological experience of online dating but also our ability to form meaningful, lasting connections offline.

The collapse of dating apps’ usability can be blamed on the paid subscription model and the near-monopoly these apps have over the dating world. While dozens of sites exist, most 20-something daters use the big three: Tinder, Hinge and Bumble. (Older people often gravitate toward Match.com or eHarmony.) All three sites offer a “premium” version users must pay for — according to a study conducted by Morgan Stanley , around a quarter of people on dating apps use these services, averaging out at under $20 a month. The purpose, many believe, is to keep them as paid users for as long as possible. Even if we hate it, even if it’s a cycle of diminishing returns, there is no real alternative.

In the early heyday of Tinder, the only limits on whom you could potentially match with were location, gender and age preferences. You might not have gotten a like back from someone you perceived to be out of your league, but at least you had the chance to swipe right. Today, however, many apps have pooled the people you’d most like to match with into a separate category (such as Hinge’s “Standouts” section), often only accessible to those who pay for premium features. And even if you do decide to sign up for them, many people find the idea of someone paying to match with them to be off-putting anyway.

“If I don’t pay, I don’t date,” a friend in his 30s told me. He spends around $50 a month on premium dating app subscriptions and digital “roses” to grab the attention of potential matches. He’s gone on 65 dates over the last year, he said. None have stuck, so he keeps paying. “Back in the day, I never would have imagined paying for OKCupid,” he said.

Yet shares (Bumble’s stock price has fallen from about $75 to about $11 since its I.P.O.) and user growth have fallen , so the apps have more aggressively rolled out new premium models. In September 2023, Tinder released a $500 per month plan. But the economics of dating apps may not add up .

On Valentine’s Day this year, Match Group — which owns Tinder, Hinge, Match.com, OKCupid and many other dating apps — was sued in a proposed class action lawsuit asserting that the company gamifies its platforms “to transform users into gamblers locked in a search for psychological rewards that Match makes elusive on purpose.” This is in contrast to one of the group’s ad slogans that promotes Hinge as “designed to be deleted.”

People are reporting similar complaints across the apps — even when they aren’t taking the companies to court. Pew Research shows that over the last several years, the percentage of dating app users across demographics who feel dissatisfied with the apps has risen . Just under half of all users report feeling somewhat to very negative about online dating, with the highest rates coming from women and those who don’t pay for premium features. Notably, there is a gender divide: Women feel overwhelmed by messages, while men are underwhelmed by the lack thereof.

With seemingly increasing frequency, people are going to sites like TikTok , Reddit and X to complain about what they perceive to be a dwindling group of eligible people to meet on apps. Commonly, complaints are targeted toward these monthly premium fees, in contrast to the original free experience. Dating has always cost money, but there’s something uniquely galling about the way apps now function. Not only does it feel like the apps are the only way to meet someone, just getting in the door can also comes with a surcharge.

Perhaps dating apps once seemed too good to be true because they were. We never should have been exposed to what the apps originally provided: the sense that the dating pool is some unlimited, ever-increasing-in-quality well of people. Even if the apps are not systematically getting worse but rather you’ve just spent the last few years as a five thinking you should be paired with eights, the apps have nonetheless fundamentally skewed the dating world and our perception of it. We’ve distorted our understanding of how we’d organically pair up — and forgotten how to actually meet people in the process.

Our romantic lives are not products. They should not be subjected to monthly subscription fees, whether we’re the ones paying or we’re the ones people are paying for. Algorithmic torture may be happening everywhere, but the consequences of feeling like we are technologically restricted from finding the right partner are much heavier than, say, being duped into buying the wrong direct-to-consumer mattress. Dating apps treat people like commodities, and encourage us to treat others the same. We are not online shopping. We are looking for people we may potentially spend our lives with.

There is, however, some push toward a return to the real that could save us from this pattern. New in-person dating meet-up opportunities and the return of speed dating events suggests app fatigue is spreading. Maybe we’ll start meeting at bars again — rather than simply swiping through the apps while holding a drink.

Have you ditched dating apps for a new way to meet people, or are you still swiping left?

Opinion wants to hear your story.

Magdalene J. Taylor (@ magdajtaylor ) is a writer covering sex and culture. She writes the newsletter “ Many Such Cases .”

The Times is committed to publishing a diversity of letters to the editor. We’d like to hear what you think about this or any of our articles. Here are some tips . And here’s our email: [email protected] .

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