essay on covid 19 lockdown

Read these 12 moving essays about life during coronavirus

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

by Alissa Wilkinson

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.

• The Vox guide to navigating the coronavirus crisis

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?

• A syllabus for the end of the world

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.

• What day is it today?

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.

• Vox is starting a book club. Come read with us!

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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Open Access

Peer-reviewed

Research Article

The good, the bad and the ugly of lockdowns during Covid-19

Contributed equally to this work with: Talita Greyling, Stephanie Rossouw, Tamanna Adhikari

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation School of Economics, University of Johannesburg, Johannesburg, South Africa

Roles Conceptualization, Data curation, Investigation, Methodology, Project administration, Software, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation School of Economics, Auckland University of Technology, Auckland, New Zealand

Roles Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing

• Talita Greyling, 
• Stephanie Rossouw, 
• Tamanna Adhikari

• Published: January 22, 2021
• https://doi.org/10.1371/journal.pone.0245546
• Peer Review
• Reader Comments

Amidst the rapid global spread of Covid-19, many governments enforced country-wide lockdowns, with likely severe well-being consequences. In this regard, South Africa is an extreme case suffering from low levels of well-being, but at the same time enforcing very strict lockdown regulations. In this study, we analyse the causal effect of a lockdown and consequently, the determinants of happiness during the aforementioned. A difference-in-difference approach is used to make causal inferences on the lockdown effect on happiness, and an OLS estimation investigates the determinants of happiness after lockdown. The results show that the lockdown had a significant and negative impact on happiness. In analysing the determinants of happiness after lockdown, we found that stay-at-home orders have positively impacted happiness during this period. On the other hand, other lockdown regulations such as a ban on alcohol sales, a fear of becoming unemployed and a greater reliance on social media have negative effects, culminating in a net loss in happiness. Interestingly, Covid-19, proxied by new deaths per day, had an inverted U-shape relationship with happiness. Seemingly people were, at the onset of Covid-19 positive and optimistic about the low fatality rates and the high recovery rates. However, as the pandemic progressed, they became more concerned, and this relationship changed and became negative, with peoples' happiness decreasing as the number of new deaths increased.

Citation: Greyling T, Rossouw S, Adhikari T (2021) The good, the bad and the ugly of lockdowns during Covid-19. PLoS ONE 16(1): e0245546. https://doi.org/10.1371/journal.pone.0245546

Editor: Francesco Di Gennaro, National Institute for Infectious Diseases Lazzaro Spallanzani-IRCCS, ITALY

Received: July 29, 2020; Accepted: December 30, 2020; Published: January 22, 2021

Copyright: © 2021 Greyling 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: All relevant data are within the paper and its Supporting Information files.

Funding: The following authors received salaries from their institutions, whom were also the funders of the research. 1. Prof T Greyling: University of Johannesburg via the University Research Fund. 2. Dr Stephanie Rossouw: Auckland University of Technology via the Faculty of Business, Economics and Law. 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

In an attempt to curb the spread of Covid-19 and minimise the loss of life, governments around the world have imposed their version of mandatory self-isolation through implementing lockdown regulations. Unfortunately, restricting people's mobility and depriving them of what matters most might intensify the negative effect on happiness levels.

In an extreme country case, this might be amplified. In this study, we treat an extreme country as a country with very strict lockdown regulations, with likely high infection rates, amidst low levels of well-being. We define well-being as those aspects of life that society collectively agrees are important for a person's quality of life, happiness and welfare. One of the dimensions of well-being, material (income) hinges on a bleak economic outlook.

To this end, our primary aim in this study is to use the Gross National Happiness Index (GNH), a real-time measure of well-being, derived from Big Data, to investigate if lockdown regulations in itself caused a decrease in happiness. Secondly, we determine which factors matter most (factors significantly influencing happiness) to happiness under these changed circumstances. We accomplish these aims by using two econometric techniques: difference-in-difference (DiD) and ordinary least squares (OLS).

Against this backdrop, the current study makes several contributions to the literature:

• Determining whether lockdown regulations cause a decline in happiness –in an extreme country case scenario.
• Investigating specifically the determinants of happiness during a lockdown, whereas other studies have focused on mental well-being and related matters (see section 2).
• Being one of the few studies (see also Rossouw, Greyling and Adhikari; Greyling, Rossouw and Adhikari [ 1 , 2 ]) that investigates the effect of lockdown on happiness making use of real-time Big Data . Other studies such as Hamermesh [ 3 ] and Brodeur et al. [ 4 ] also use Big Data, though limited to Google Trends (see section 2).

These results give policymakers the necessary information to take action in increasing the happiness of the nation and set the scene for increased economic, social and political well-being. It also allows them to reflect on happiness outcomes due to their policy actions. An additional benefit of the current study is that policymakers do not need to wait for extended periods to see the consequences of their policies, as we are making use of real-time data, with immediate information. Usually, policymakers can only evaluate their own decision making, with significant time-lags, prolonging the implementation of corrective actions.

Our results indicate lockdown itself causes a decrease in happiness. Furthermore, in an extreme country case (a country under stringent lockdown regulations coupled with low levels of well-being) what matters most to happiness under lockdown is the factors directly linked to the regulations that were implemented. These factors can be classified as (i) social capital issues; lack of access to alcohol (and tobacco), increased social media usage, and more time to spend at home, of which all are negatively related to happiness except the stay-at-home factor, and (ii) economic issues; concerns over jobs and the threat of retrenchments, which are negatively related to happiness. The finding on the stay-at-home order is interesting as even though lockdown itself caused a decline in happiness, it seems that people adjust and over time begin to appreciate the benefits of staying at home.

Noteworthy is that Covid-19, proxied by new deaths per day, had an inverted U-shape relationship to happiness. Seemingly people were, at the onset of Covid-19, positive and optimistic as the fatality rate was relatively low and recovery rates high. However, as the pandemic progressed, they became more concerned, and this relationship changed and became negative, with peoples' happiness decreasing as the number of new Covid-19 deaths increased.

The rest of the paper is structured as follows. The next section contains a brief background on South Africa and briefly discusses literature about happiness and studies conducted on the impacts of pandemics and consequently lockdown regulations. Section 3 describes the data, the selected variables and outlines the methodology used. The results follow in section 4, while the paper concludes in section 5.

2. Background and literature review

2.1 south africa.

In this study, we focus on South Africa because it presents us with a unique case to investigate the effect of a lockdown on happiness when levels of well-being are already low. Health and income, two dimensions of well-being, was significantly affected, although in opposite directions. Health was positively affected by the lockdown since it limited the spread of Covid-19. At the stage of writing the paper (3 June 2020), the number of new Covid-19 cases were nearly 120,000 (John Hopkins University [ 5 ]). On the other hand, the economic outlook of the country, and therefore peoples' incomes, was negatively affected. This opposite effect has led to significant debates on the value of the implementation of the lockdown.

Furthermore, South Africa implemented one of the most stringent lockdown regulations (comparable to the Philippines and Jordan), which exacerbated the costs to well-being and the economy while already experiencing a severe economic downturn. Therefore, South Africa is an example of an extreme country case which unfortunately amplifies the effects of the difficult choices made by policymakers. Therefore, we take advantage of this unique country case and determine how stringent lockdown regulations impact happiness during a one in 100-year event.

In South Africa, there are five levels of differing lockdown regulations, with alert level 5 being the most stringent and alert level 1 the most relaxed. The idea behind these levels is to curb the spread of Covid-19 and give time to South Africa's health system to prepare itself. Additionally, as they move down in levels, South Africans receive increasingly more of their previous liberties back. During level 5, which was announced 23 March 2020 and implemented on 27 March 2020, South Africans were only allowed to leave their homes to purchase or produce essential goods. All South Africans were instructed to work from home, there was no travel allowed, the sale of alcohol and tobacco were banned, people were not allowed to exercise outside their homes, and the police and defence force ensured compliance to the restrictions. South Africa moved to level 4 lockdown on 01 May 2020. With this move, they received back the ability to exercise outside from 6 am—9 am, purchase more than just essential goods, including food deliveries as long as it was within curfew.

Interestingly, the sale of alcohol and tobacco was still banned. On 01 June they moved to level 3, allowing restricted sales of alcohol (Mondays to Thursdays) and the re-opening of certain businesses. However, the services industry, especially beauty and tourism, remained closed. At the time of writing this paper, South Africa was still under level 3 lockdown.

Whereas everybody understands that the Covid-19 infections curve needs to be flattened, there are grave concerns that these stringent lockdown regulations will also flatline South Africa's well-being and economy. Before the Covid-19 lockdown, South Africa's average happiness levels were 6.32 compared to an average of 7.23 and 7.16 in Australia and New Zealand, respectively (Greyling et al. [ 2 ]). Furthermore, South Africa had a 29 per cent unemployment rate, and the gross domestic product (GDP) has been estimated to shrink by 7 per cent in 2020 (Bureau of Economic Analysis [ 6 ]). According to the South African Reserve Bank [ 7 ], an additional 3 to 7 million people can potentially become unemployed as a direct consequence of the pandemic, thereby increasing unemployment rates to approximately 50 per cent. The country's sovereign credit rating was downgraded to junk status in March 2020, which impacted on political stability, the level of the national debt and debt interest payments. Add to this already grim situation, the fact that consumption of South Africans has been declining in 2020, with a significant decrease seen after lockdown, then one can very easily see how the well-being and happiness levels in South Africa can plummet.

2.2 Happiness

Why should we care whether people's happiness is adversely impacted by not only a global pandemic but also by the response from the government? The studies of Helliwell, Layard, Stiglitz et al., Veenhoven, Diener and Seligman and others [ 8 – 12 ], have shown beyond a shadow of a doubt that if policymakers want to maximise the quality of life of their citizens, they need to consider subjective measures of well-being. Piekalkiewicz [ 13 ] states that happiness may act as a determinant of economic outcomes: it increases productivity, predicts one's future income and affects labour market performance. By measuring happiness, individuals themselves reveal their preference and assigned priority to various domains, which cannot be identified by a measure such as GDP. As was pointed out by Layard [ 9 ], while economists use exactly the right framework for thinking about public policy, the accounts we use of what makes people happy are wrong. In layman's terms, we say that utility increases with the opportunities for voluntary exchange. However, Layard [ 14 ] argues that this overlooks the significance of involuntary interactions between people. Policymakers should formulate policy to maximise happiness or well-being, as is the main aim of many constitutions. This can be achieved by directing economic, social, political and environmental policy to maximise well-being while acknowledging that people's norms, aspirations, feelings and emotions are important. Thereby underscoring that understanding and measuring happiness should be an integral part of the efforts to maximise the quality of life.

On the other hand, if people's happiness is negatively affected, such as it was in the wake of the Covid-19 pandemic and the implementation of lockdown regulations, there are far-reaching consequences.

These consequences are as follows:

• Social capital: unhappier people display less altruistic behaviour in the long run (Dunn et al. [ 15 ]). They are also less active, less creative, poor problem solvers, less social, and display more anti-social behaviour (Lyubomirsky et al. [ 16 ]). If unhappier people display more anti-social behaviour, South Africa could see an increase in behaviour such as unrests, violent strikes and perhaps higher crime rates.
• Health care: unhappier people are less physically healthy and die sooner (Lyubomirsky et al. [ 16 ]). Additionally, unhappy people engage in riskier behaviour such as smoking and drinking, thereby placing unnecessary pressure on national health systems.
• Economic: unhappy workers are typically less productive, in particular in jobs that require sociability and problem solving (Bryson et al. [ 17 ]). If an economy can raise the rate of growth of productivity, by ensuring their workers are happier, then the trend growth of national output can pick up.

2.3 Literature on the determinants of happiness during a lockdown

Having established that policymakers should strive to maximise the happiness of their people, it is necessary to know what determines happiness. Previous studies have investigated, at a macro-level, what influences happiness and found that economic growth, unemployment and inflation play a significant role (Stevenson and Wolfers, Perović, Sacks et al. [ 18 – 20 ]). However, these studies were conducted during 'normal' periods and not under such conditions that are currently plaguing the world. The current study will have the opportunity to investigate this exact question, namely what determines happiness during a lockdown driven by a global health pandemic.

Naturally, the number of studies being conducted to examine the effect of Covid-19 is growing exponentially. This increasing interest in the effect of a global pandemic as well as the policies implemented by governments on peoples' well-being, come on the back of relatively few studies conducted during prior pandemics such as SARS and the H1N1. When SARS hit in 2002 and then again when H1N1 hit in 2009, scholars were only truly starting to understand that for governments to formulate policies to increase well-being, you needed to measure well-being. Of the current studies being conducted on the effect of Covid-19 or lockdown regulations on all affected domains, not many studies are in a position to use real-time Big Data, such as we do.

In layman's terms, Big Data is a phrase used to describe a massive volume of both structured (for example stock information) and unstructured data (for example tweets) generated through information and communication technologies such as the Internet (Rossouw and Greyling [ 1 ]). At the time of writing this paper, the following studies were closest aligned with our study and focused on:

• nationwide lockdown on institutional trust, attitudes to government, health and well-being, using survey data collected at two points in time (December 2019 and April 2020) (1003 respondents) (Sibley et al. [ 21 ]). Their preliminary results showed a small increase in people's sense of community and trust. However, they also found an increase in anxiety/depression post-lockdown and hinted at longer-term challenges to mental health.
• the happiness of married and single people while in government-imposed lockdown by running simulations to formulate predictions, using Google Trends data (Hamermesh [ 3 ]). Not surprisingly, married people were more satisfied with life than single people.
• the timing of decision-making by politicians to release lockdown based on a comparison of economic benefits with the social and psychological benefits versus the cost, increase in deaths if policymakers released lockdown too early (Layard et al. [ 22 ])
• the stages of GNH using a Markov switching model in New Zealand (Rossouw et al. [ 23 ]). They found that happiness was at a lower level and the unhappy state lasted longer than was expected. Furthermore, they found that the factors important for New Zealand's happiness post-Covid-19 were related to international travel, employment and mobility.
• exploring Covid-19 related determinants of life dissatisfaction and feelings of anxiety in a cross-country study using survey data collected between 23 March and 30 April (de Pedraza et al. [ 24 ]). They found that persons with poorer general health, without employment, living without a partner, not exercising daily and those actively seeking out loneliness report higher dissatisfaction and higher anxiety. Additionally, they found that the effect of Covid-19 on dissatisfaction and anxiety levels off with a higher number of cases.

2.4 Literature on the causal effect of a lockdown

To the knowledge of the authors, there are only two papers that investigated the causal effect between lockdowns and population well-being. Brodeur et al. [ 4 ] investigated the changes in well-being (and mental health) in the United States and Europe after a lockdown was implemented, using Google Trends data. They found an increase in searches for loneliness, worry and sadness, which indicated a negative effect on mental health. Greyling et al. [ 2 ] conducted a cross-country study investigating the effect of lockdown on happiness. They found that lockdown caused a negative effect on happiness, notwithstanding the different characteristics of the countries (South Africa, New Zealand and Australia), the duration and the type of lockdown regulations. When they compared the effect size of the lockdown regulations, they found that South Africa, with the most stringent lockdown regulations incurred the greatest happiness costs.

Brodeur et al. [ 4 ] study analysed data from one Big Data source, Google Trends and collected data for a short period between only 01 January 2019 and 10 April 2020 in countries that had introduced a full lockdown by the end of this period. Greyling et al. [ 2 ] study used both Google Trends and the GNH index but did not investigate the determinants of happiness after lockdown for an extreme country case.

In summary, taking all of the above into consideration, there is not one study which determines causality between lockdown and happiness and analyses the determinants of happiness in an extreme country case using real-time , Big Data . Therefore, our study is the first of its kind.

3. Data and methodology

To estimate the causal effects of a lockdown on happiness, we use a Difference-in-Difference (DiD) approach (see section 3.3.1). The technique compares happiness (dependent variable), before and after the treatment (the lockdown) to a counterfactual time period in the year before. For the control period, we select the same time period, with the same number of days in 2019, corresponding to the number of days in 2020, thus 152 days in each year (01 January 2020 to 03 June 2020, excluding 29 February 2020). Our results should thus be interpreted as the average impact of the lockdown on happiness, comparing pre and post-lockdown in 2020 to the same time period in 2019, which we assume had normal levels of Gross National Happiness (see a discussion on the GNH in section 3.2.1). In this manner, we also account for seasonal trends in happiness.

In the analyses, we make use of daily data for South Africa. As high-frequency data available at almost real-time, is scarce, we make use of novel Big Data methodologies to harvest data. Additionally, we use the Oxford Stringency dataset that was released in May 2020, which includes data related to lockdown regulations, such as time-series data on the stay-at-home index, Covid-19 cases and Covid-19 deaths (Hale et al., Roser et al. [ 25 , 26 ]).

3.2 Selection of variables

The selection of the variables included in our estimations is based on the reviewed literature, the contents of tweets related to the lockdown and data availability.

3.2.1 Gross National Happiness Index–the dependent variable.

To measure happiness (the dependent variable), we make use of the Gross National Happiness Index (GNH), which was launched in April 2019 (Greyling, Rossouw and Afstereo [ 27 ]). This project measures the happiness (mood) of a country's citizens during different economic, social and political events.

Since February 2020, the researchers extended the project that initially analysed the sentiment of tweets, to incorporate the analysis of the emotions underpinning tweets. The team did this to determine which emotions are most prominent on specific days or events.

To construct the GNH index, the researchers use Big Data methods and extract tweets from the voluntary information-sharing social media platform Twitter. Big Data, such as Twitter, provides real-time information for policymakers to assist them when facing short-term deadlines with imperfect information. Big Data also allows governments to 'listen' and capture those variables which their citizens deem to be important for their well-being, rather than relying on pre-defined economic utility theories. Big Data offers governments the opportunity to observe people's behaviour and not just their opinions. This approach of revealed preferences unveils a reflexive picture of society because it allows the main concerns of citizens (and the priority ranking of those concerns) to emerge spontaneously, and it complements as such the information captured by gross domestic product. Lastly, Big Data does not suffer from non-response bias (Callegaro and Yang [ 28 ]).

Greyling, Rossouw and Afstereo [ 27 ] apply sentiment analysis to a live Twitter-feed and label every tweet as having either a positive, neutral or negative sentiment. This sentiment classification is then applied to a sentiment-balance algorithm to derive a happiness score. The happiness scores range between 0 and 10, with five being neutral, thus neither happy nor unhappy.

All tweets per day are extracted, and a happiness score per hour is calculated. The index is available live on the GNH website (Greyling, Rossouw and Afstereo [ 27 ]). In South Africa, the average number of tweets extracted for 2020 is 68,524 per day. South Africa has approximately 11 million Twitter users, representing almost 18 per cent of the population (Omnicore [ 29 ]). Although the number of tweets is extensive and represents significant proportions of the populations of the countries, it is not representative. However, Twitter accommodates individuals, groups of individuals, organisations and media outlets, representing a kind of disaggregated sample, thus giving access to the moods of a vast blend of Twitter users, not found in survey data.

Furthermore, purely based on the vast numbers of the tweets, it seems that the GNH index gives a remarkably robust reflection of the evaluative mood of a nation. Also, we correlate the GNH index with 'depression' and 'anxiety', derived from the 'Global behaviors and perceptions at the onset of the Covid-19 Pandemic data ' survey, for the period from 01 March 2020 (OFS [ 30 ]). We find it negative and statistically significant related, therefore, it seems that the GNH index derived from Big Data gives similar trends to survey data. (We would have appreciated the opportunity to correlate the GNH to a happiness measure–but a happiness measure, as such, was not included in the survey).

Considering the GNH index over time, we found that the index accurately reflects a nation's emotions, for example, when South Africa won the Rugby World Cup on 02 November 2019, the happiness index accurately depicted the joy experienced by South Africans ( Fig 1 ). The hourly happiness score was 7.9 at 13:00, the highest score ever measured, at the exact time that the final whistle was blown to announce the victory of the Springboks over England.

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Source: Authors' calculations using GNH dataset (Greyling et al. [ 27 ]).

https://doi.org/10.1371/journal.pone.0245546.g001

Also, when the famous American basketball player, Kobe Bryant and his daughter Gigi, tragically passed away on 27 January 2020, the happiness index once again captured the negative mood of the nation, and the happiness score decreased to 5.8, significantly below the mean (see Fig 2 ). The result of the GNH mirrors the one determined by the Hedonometer, which recorded an average happiness score of 5.89 on the day of Bryant's death. The top three words that made this day sadder than the previous seven were 'crash', 'died' and 'rip'.

https://doi.org/10.1371/journal.pone.0245546.g002

3.2.2 The selection of covariates included in estimations.

We found ourselves in uncharted territory, as there are limited studies estimating happiness functions during a lockdown (see Brodeur et al., Greyling et al., Rossouw et al. [ 2 , 4 , 23 ]). As a result, we considered these studies and the tweets to determine the factors to consider, which might influence happiness during a lockdown , as well as the most tweeted subjects. It was evident from the tweets that the main topics of discussion related to economic concerns, the prohibition of the sale of alcohol and tobacco, the stay-at-home orders and the Covid-19 pandemic itself.

To estimate our difference-in-difference model, we restricted our covariates to the lockdown variable, a year effect, the difference-in-difference estimator and controlled for new Covid-19 deaths, job searches and searches for alcohol. We were restricted in the number of covariates due to the limited observations and potentially encountering the issue of over-identification of the models. Therefore, we selected those variables which were available for both 2019 and 2020, and which were also trending subjects during the lockdown period. We were not able to add a stay-at-home variable which captures the lack of mobility, as the counterfactual time period is then not comparable to 2020.

Lockdown, our treatment variable, divides the sample into two distinct time periods: before the announcement of the lockdown, 23 March 2020 and thereafter. We make use of the date of the announcement of the lockdown rather than the date of the implementation, as this showed the severest effect on happiness (see Brodeur et al. [ 4 ]).

The Covid-19 pandemic and consequent spread of the virus is the reason for the lockdown. As such, we include the number of new daily Covid-19 deaths as well as its square. This will allow us to control for the likelihood of a U-shaped relationship between the number of Covid-19 deaths and happiness. Furthermore, there is likely a lagged effect on happiness due to Covid-19 deaths being reported in the media only the following day. Therefore, we lag these variables by one day. We derive the data from the Oxford Stringency data set (Hale et al. and Roser et al. [ 25 , 26 ]).

To measure jobs (a proxy for future job uncertainty) and the sale of alcohol and tobacco, we use the methodology as set out by Nuti et al. and Brodeur et al. [ 4 , 31 ] and use daily searches on Google Trends (see also Simionescu and Zimmermann [ 7 ]). We considered searches for both the alcohol and tobacco topic; however, the variables follow the same trends during the lockdown period and are highly correlated (r = 0.83). We are, furthermore, restricted in the number of covariates to include in the model and decided to include only 'alcohol' in the estimations. We justify this decision since the ban of alcohol affects a larger proportion of the population. It is estimated that 41 per cent of males and 17.1 per cent of females consume on average 9.3 litres of alcohol per capita annually whereas only 17.6 per cent smoke (Peltzer et al. and Reddy et al. [ 32 , 33 ]). However, as a robustness check, we also run all estimations using the searches for tobacco.

It should be noted that when we use Google Trends data, it is expressed as an index between 0 and 100 with 0 being the "least" interest and 100 being the "most" interest shown in the topic for the year. However, the series are not comparable across years as the underlying data is sourced from different search requests for each of the two years. To address this, we use a scaling procedure outlined in Brodeur et al. [ 4 ]. First, we generate "weekly" interest weights for each day by expressing the average weekly score that a particular daily score fell on, as a proportion of the average yearly score. Then, we multiply the daily scores with these weights to obtain weighted search trends. Finally, we normalise these weighted search trends to render us a score between 0 and 100, which is comparable across years.

Other topics that are trending are related to the 'stay-at-home' orders. The Oxford Stringency dataset includes a time series variable on the stay-at-home orders. It differs on a day to day basis according to its stringency. It is an ordinal variable plus binary of geographic scope. It takes the value 0 if there are no stay-at-home orders and 1 if the government recommends not leaving your house. Value 2 represents people not leaving their homes with the exceptions of daily exercise, grocery shopping, and 'essential' trips. Not leaving your home with minimal exceptions (e.g. allowed to leave only once a week, or only one person can leave at a time, etc.) takes the value 3 (Hale et al. [ 25 ]).

Furthermore, we include the number of tweets per day, as it forms part of the Twitter data extracted daily for South Africa (Greyling et al. [ 27 ]), which is a proxy for connectivity. It also gauges the opportunity cost of not being able to have face to face interactions, which seems to be negatively related to happiness (Chae and Wilson et al. [ 34 , 35 ]). Interestingly the number of tweets increased markedly during the lockdown period, from an average of 60,708 to 80,000 tweets per day. Table 1 provides descriptive statistics for the variables included in the estimations.

https://doi.org/10.1371/journal.pone.0245546.t001

3.3 Methodology

3.3.1 difference-in-difference..

Where GNH it is the daily happiness for South Africa at time t . The treatment variable lockdown takes the value of 0 pre-announcement day (23 March) and one post-announcement of lockdown in both the year of the actual lockdown (2020) as well as the year before the lockdown (2019). Year is a dummy variable where 1 is the year 2020. We control for new deaths per million with a one-day lag as well as the quadratic effect of new deaths per million on GNH. Additionally, we control for the effect of job and alcohol searches. As a robustness test, we use the number of new Covid-19 cases instead of new Covid-19 deaths (see Table 4 in S1 Appendix ).

Our main coefficient of interest is the interaction between the lockdown and the year variable. If it is found to be significant, it provides evidence of a causal effect of the lockdown on the dependent variable, in the current year, notwithstanding the trend in 2019.

3.3.2 OLS regression.

Here, y t refers to the Gross National Happiness Index (GNH) for each day and X t is a vector of several relevant covariates to account for the changes in the happiness levels during the lockdown period. μ t is the error term.

Due to the various factors that affect happiness, some of our independent variables may be correlated with the error term, leading to endogeneity concerns. Depending on the direction of the correlation between the error term and the X-variable, the coefficient could be biased upwards or downwards. For instance, the coefficient on the indicator for jobs is likely biased upwards as it, in all likelihood, shows the effect of concerns about jobs as well as some other negative economic shock on happiness. In the absence of panel data or an appropriate instrument, it is difficult to ascertain causality to Eq ( 2 ). However, simply correlating the covariates and the error term we find all levels of correlation to be less than 0.3, although a very basic test, this still indicates that the likelihood that endogeneity might bias estimations is relatively small. A natural extension of the work, as better data becomes available with time, would be to address these concerns.

We cannot rule out the probability of autocorrelation and heterogeneity in our data, especially due to its time-series nature. We use robust standard errors to account for this. The choice of our controls, however, comes with a caveat. Seeing as we only have 81 daily observations using a larger battery of covariates would lead to problems arising due to overfitting of the model. This issue is considered in Green [ 36 ], who suggests a minimum of 50 observations for any regressions as well as an additional eight observations per additional term. As a robustness test, we included tobacco rather than alcohol products (see Table 5 in S1 Appendix ).

4. Results and analysis

4.1 difference-in-difference estimation.

Fig 3 tracks the dependent variable (GNH) over the time period before and after the date of the lockdown (23 March) in the year of the lockdown (2020) and the year preceding it. On the day of the announcement of the lockdown and for a few successive days, we see a sharp downwards departure from the 2019 trend, assumed to be normal.

https://doi.org/10.1371/journal.pone.0245546.g003

Table 2 provides the results for the difference-in-difference specification, which helps us to make causal inferences on the effect of the lockdown on the GNH. At the outset, we notice a negative and significant effect of the 'year' variable (p<0.001), showing that on average the GNH was lower in 2020 than in 2019.

https://doi.org/10.1371/journal.pone.0245546.t002

We control for trends in job searches (a proxy for job uncertainty) and alcohol searches (a proxy for increased interest in alcohol-related topics in the specification). Both variables show a negative association with GNH, implying if there are more searches for jobs or alcohol, reflecting a scarcity in these items, GNH decreases. The negative effect of alcohol is statistically significant at the 1% level (p<0.001). We also control for lagged new Covid-19 related deaths and lagged new Covid-19 related deaths squared, both are significant (p<0.001). Interesting is the finding of the significant inverted U-shaped relationship between new Covid-19 deaths and happiness ( Fig 4 ). In the earlier stages of the pandemic, with very few new Covid-19 deaths, it appears that people were positive and optimistic as the fatality rates were very low and the recovery rates very high. However, as time progressed, the higher fatality rates turned the relationship around so that the number of new Covid-19 cases were negatively related to happiness.

https://doi.org/10.1371/journal.pone.0245546.g004

To determine if the decrease in GNH was due to the lockdown (the treatment) specifically and not just due to the year trend, we must consider the estimated coefficient of the interaction variable 'lockdown and year'. We report a negative and statistically significant coefficient (p-value 0.064) on the interaction variable, indicating that 'lockdown' caused, on average a decrease in GNH of 0.101 points when compared to its mean values for average 2019 values, controlling for the general trend in the two years. Thus, we can conclude that the lockdown caused a decline in GNH in 2020 compared to 2019. The decline of 0.101 may seem small at first but given the low general levels of happiness in South Africa compared to other countries (Greyling et al. [ 27 ]) the reduction is substantial.

4.2 Regression analysis

To address the second research question, namely, to determine the factors that are related to happiness after the lockdown was implemented, we consider the results of Table 3 .

https://doi.org/10.1371/journal.pone.0245546.t003

Table 3 shows that job searches (p-value 0.09), searches for alcohol (p-value<0.001) and the number of tweets is negatively related to happiness. In contrast, the stay-at-home index is positively related to happiness (p-value<0.001). The squared relationship between new Covid-19 deaths and happiness is negative and statistically significant (p-value<0.001), indicating that this relationship changed over time as was highlighted in section 4.1. Suppose we consider the relatively low mortality rate (0.02 per cent of confirmed cases in the early stages) compared to countries such as the USA (3.9 per cent), the U.K. (15.4 per cent) and Spain (9.4 per cent). In that case, it could explain the initial positive relationship between the number of new Covid-19 deaths and happiness. Although as time passed and the death rate increased (currently, the mortality rate is at 1.5 per cent of confirmed cases), this relationship became negative. As a robustness test, we used the number of new Covid-19 cases and its square instead of the new Covid-19 deaths and found similar results (see Table 4 in S1 Appendix ).

As expected, job searches, a proxy for uncertainty about the future job market is negatively related to happiness (p-value<0.001). Analysing the tweets, we realised that this is a major concern, which is closely related to economic concerns. The economic performance of South Africa in the last year has been weak with high levels of unemployment (increase to 50 per cent), low growth rates (GDP is expected to contract with 7 per cent in 2020) and high debt to income ratios (government debt as a percentage of GDP– 80 per cent). In a recent survey conducted by Statistics South Africa on behavioural and health impacts of the Covid-19 pandemic (Statistics South Africa [ 37 ]), it was found that 95 per cent of the respondents were very concerned about the economy. In contrast, only 60 per cent was concerned about the Covid-19 pandemic itself. This supports our current findings in that economic factors matter more to South Africans happiness levels than Covid-19 itself.

Alcohol-related searches are also found to be negatively related to happiness (p-value<0.001). Considering the close correlation between alcohol and tobacco products, we can assume that what holds for alcohol products, also holds for tobacco products. As a robustness test, we excluded the alcohol variable and included searches for tobacco variable and found very similar results (see Table 5 in S1 Appendix ). South Africa is one of the very few countries globally that have banned alcohol and tobacco sales during the Covid-19 pandemic. It is argued that these products contribute to the negative effects of the virus. The banning of these products had severe implications on different levels of society. Individuals see this as a major infringement of their human rights, negatively affecting their happiness. Furthermore, research done by Sommer et al. [ 38 ] proved that because of the presence of hordenine in beer, it significantly contributes to mood-elevation. In South Africa, which is well-known for its high per capita beer and alcohol consumption (Statistics South Africa [ 39 ]), also related to 'socialising', the ban on these products had a severe negative effect on happiness. Even in level 3, when the ban on alcohol sales was lifted, but still restricted, we found this negative relationship.

The restricted sale of alcohol and tobacco has indirect consequences for South Africans happiness via the economic impact since these industries are two of the largest industries in South Africa. They employ people across the whole supply chain from production to retail. Due to the ban on these industries, people can potentially lose their jobs. Lastly, the government sector forgoes all taxes on these products. This is against the backdrop of the recent downgrade of South Africa's debt rating to junk status in an already very uncertain fiscal environment. If all of these factors are aggregated, we can understand that the cumulative effect of the banning and restriction of sales of these products severely decreases the happiness levels. In Table 5 in S1 Appendix , we use tobacco searches instead of alcohol to estimate the determinants of happiness, which gives us results that are qualitatively similar to Table 3 .

The number of tweets is negatively related to happiness (p-value<0.001). Previous research has shown that increases in the use of social media are negatively related to happiness (Rolland et al., Chae and Wilson et al. [ 34 , 40 , 41 ]). Noteworthy is that the number of tweets during the lockdown period increased significantly from an average of 60,708 per day before the lockdown to 80,000 per day after the lockdown indicating that more people used social media during the lockdown period.

Interesting is the result of the stay-at-home orders being positively related to happiness (p-value<0.001). On analysing the contents of the tweets, we find the following. South Africans are wary of contracting Covid-19, and therefore, they abide by the stay-at-home orders and social distancing regulations to minimise the risk. That means that the stay-at-home orders in itself increase happiness; it is only once the other lockdown regulations are added that a total decrease in happiness levels are experienced.

Additional benefits revealed from analysing the tweets show that being at home provides a more peaceful and calmer environment compared to the rushed experience outside their homes. Also, people in the suburbs seem to be more convivial, with strangers greeting one another as people went for short walks around their neighbourhoods. In general, people have more time to spend with their loved ones. People earning salaries incur major savings, as there is less opportunity to spend money. People also save on commuting to and from workplaces and other destinations. One of the unexpected benefits of the stay-at-home orders is the much lower crime rates experienced in the country. Homes are constantly occupied, limiting the risk of residential robberies (-3.8 per cent). Other types of crimes such as murder (-72 per cent), rape (-87.2 per cent) and carjacking (-80.9 per cent) are much lower as well (Adapted from the speech of Police Minister Cele 2020 [ 42 ]).

In summary, what changed when the lockdown regulations to curb the spread of Covid-19 were implemented? It caused a significant decrease in happiness, and factors related to the lockdown regulations became relevant determinants of happiness.

5. Conclusions

In this paper, we used the Gross National Happiness Index (GNH), a real-time measure of well-being, derived from Big Data, to investigate whether lockdown regulations caused a decrease in happiness. Additionally, we determined which factors matter to happiness under these changed circumstances. We accomplished these aims by using two models: difference-in-difference and ordinary least squares.

We added to the current literature by determining causality between lockdown and happiness and analysing the determinants of happiness after a lockdown in an extreme country case using real-time , Big Data . Subsequently, this was the first study of its kind.

To determine if the decrease in GNH was due to the lockdown, specifically, we considered the estimated coefficient of the interaction variable 'lockdown and year'. We found a negative and statistically significant coefficient on the interaction variable, indicating that the lockdown caused a decline in GNH in 2020 compared to 2019.

As regards to the factors that are related to happiness after the lockdown was implemented, we found searches for alcohol (tobacco), the number of tweets and uncertainty about the future job market to be negatively related to happiness. In contrast, stay-at-home orders are positively related to happiness. Interesting is the negative and statistically significant squared relationship between new Covid-19 deaths and happiness, indicating that this relationship initially was positive but became negative over time.

Considering the results mentioned above, it ultimately means that if policymakers want to increase happiness levels and increase the probability to achieve the happiness levels of 2019, they must consider those factors that matter most to peoples' happiness. These factors include allowing creatures of habits some of their lost comforts by reinstating the sale of alcohol and tobacco. In saying that, we do advocate for responsible alcohol use by all South Africans and note that the significant effect of the ban on the sale of alcohol could be confounded by the restriction on social gatherings as well.

These results are important for countries in similar well-being situations, thus low levels of happiness, a diverse state of the economy and an increasing number of Covid-19 cases to evaluate what the effect of a strict lockdown is.

Additionally, policymakers should assure citizens that there is a credible plan to get the economy, which is currently in dire straits, back on track. Such an economic plan should stimulate growth, create job opportunities and increase employment rates, supply the necessary infrastructure and deal with curbing vast budget deficits and debt burdens. Hopefully, such policies will fuel the dying embers of a dying economy and increase well-being levels.

Lastly, it would be remiss of us not to note the limitations of the study. First, we were restricted in the number of covariates we could add to our difference-in-difference model due to the limited observations and therefore potentially overidentifying the models. Second, regarding the inverted U-shaped relationship between new Covid-19 deaths and happiness, we acknowledge that there might be confounding factors at play, initially seen as ‘positives' of the lockdown, but later turned into negatives. However, using alternative sets of covariates in the regression analyses, the inverted U-shape between new Covid-19 deaths and happiness remained. Therefore we trust that the revealed relationship is robust.

Supporting information

S1 appendix. robustness checks..

https://doi.org/10.1371/journal.pone.0245546.s001

https://doi.org/10.1371/journal.pone.0245546.s002

Acknowledgments

We would like to thank our colleagues Professor Emeritus John Knight from Oxford University and Dr Kelsey O'Connor from STATEC Luxembourg, for their generosity in providing feedback on the study.

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I Thought We’d Learned Nothing From the Pandemic. I Wasn’t Seeing the Full Picture

M y first home had a back door that opened to a concrete patio with a giant crack down the middle. When my sister and I played, I made sure to stay on the same side of the divide as her, just in case. The 1988 film The Land Before Time was one of the first movies I ever saw, and the image of the earth splintering into pieces planted its roots in my brain. I believed that, even in my own backyard, I could easily become the tiny Triceratops separated from her family, on the other side of the chasm, as everything crumbled into chaos.

Some 30 years later, I marvel at the eerie, unexpected ways that cartoonish nightmare came to life – not just for me and my family, but for all of us. The landscape was already covered in fissures well before COVID-19 made its way across the planet, but the pandemic applied pressure, and the cracks broke wide open, separating us from each other physically and ideologically. Under the weight of the crisis, we scattered and landed on such different patches of earth we could barely see each other’s faces, even when we squinted. We disagreed viciously with each other, about how to respond, but also about what was true.

Recently, someone asked me if we’ve learned anything from the pandemic, and my first thought was a flat no. Nothing. There was a time when I thought it would be the very thing to draw us together and catapult us – as a capital “S” Society – into a kinder future. It’s surreal to remember those early days when people rallied together, sewing masks for health care workers during critical shortages and gathering on balconies in cities from Dallas to New York City to clap and sing songs like “Yellow Submarine.” It felt like a giant lightning bolt shot across the sky, and for one breath, we all saw something that had been hidden in the dark – the inherent vulnerability in being human or maybe our inescapable connectedness .

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But it turns out, it was just a flash. The goodwill vanished as quickly as it appeared. A couple of years later, people feel lied to, abandoned, and all on their own. I’ve felt my own curiosity shrinking, my willingness to reach out waning , my ability to keep my hands open dwindling. I look out across the landscape and see selfishness and rage, burnt earth and so many dead bodies. Game over. We lost. And if we’ve already lost, why try?

Still, the question kept nagging me. I wondered, am I seeing the full picture? What happens when we focus not on the collective society but at one face, one story at a time? I’m not asking for a bow to minimize the suffering – a pretty flourish to put on top and make the whole thing “worth it.” Yuck. That’s not what we need. But I wondered about deep, quiet growth. The kind we feel in our bodies, relationships, homes, places of work, neighborhoods.

Like a walkie-talkie message sent to my allies on the ground, I posted a call on my Instagram. What do you see? What do you hear? What feels possible? Is there life out here? Sprouting up among the rubble? I heard human voices calling back – reports of life, personal and specific. I heard one story at a time – stories of grief and distrust, fury and disappointment. Also gratitude. Discovery. Determination.

Among the most prevalent were the stories of self-revelation. Almost as if machines were given the chance to live as humans, people described blossoming into fuller selves. They listened to their bodies’ cues, recognized their desires and comforts, tuned into their gut instincts, and honored the intuition they hadn’t realized belonged to them. Alex, a writer and fellow disabled parent, found the freedom to explore a fuller version of herself in the privacy the pandemic provided. “The way I dress, the way I love, and the way I carry myself have both shrunk and expanded,” she shared. “I don’t love myself very well with an audience.” Without the daily ritual of trying to pass as “normal” in public, Tamar, a queer mom in the Netherlands, realized she’s autistic. “I think the pandemic helped me to recognize the mask,” she wrote. “Not that unmasking is easy now. But at least I know it’s there.” In a time of widespread suffering that none of us could solve on our own, many tended to our internal wounds and misalignments, large and small, and found clarity.

Read More: A Tool for Staying Grounded in This Era of Constant Uncertainty

I wonder if this flourishing of self-awareness is at least partially responsible for the life alterations people pursued. The pandemic broke open our personal notions of work and pushed us to reevaluate things like time and money. Lucy, a disabled writer in the U.K., made the hard decision to leave her job as a journalist covering Westminster to write freelance about her beloved disability community. “This work feels important in a way nothing else has ever felt,” she wrote. “I don’t think I’d have realized this was what I should be doing without the pandemic.” And she wasn’t alone – many people changed jobs , moved, learned new skills and hobbies, became politically engaged.

Perhaps more than any other shifts, people described a significant reassessment of their relationships. They set boundaries, said no, had challenging conversations. They also reconnected, fell in love, and learned to trust. Jeanne, a quilter in Indiana, got to know relatives she wouldn’t have connected with if lockdowns hadn’t prompted weekly family Zooms. “We are all over the map as regards to our belief systems,” she emphasized, “but it is possible to love people you don’t see eye to eye with on every issue.” Anna, an anti-violence advocate in Maine, learned she could trust her new marriage: “Life was not a honeymoon. But we still chose to turn to each other with kindness and curiosity.” So many bonds forged and broken, strengthened and strained.

Instead of relying on default relationships or institutional structures, widespread recalibrations allowed for going off script and fortifying smaller communities. Mara from Idyllwild, Calif., described the tangible plan for care enacted in her town. “We started a mutual-aid group at the beginning of the pandemic,” she wrote, “and it grew so quickly before we knew it we were feeding 400 of the 4000 residents.” She didn’t pretend the conditions were ideal. In fact, she expressed immense frustration with our collective response to the pandemic. Even so, the local group rallied and continues to offer assistance to their community with help from donations and volunteers (many of whom were originally on the receiving end of support). “I’ve learned that people thrive when they feel their connection to others,” she wrote. Clare, a teacher from the U.K., voiced similar conviction as she described a giant scarf she’s woven out of ribbons, each representing a single person. The scarf is “a collection of stories, moments and wisdom we are sharing with each other,” she wrote. It now stretches well over 1,000 feet.

A few hours into reading the comments, I lay back on my bed, phone held against my chest. The room was quiet, but my internal world was lighting up with firefly flickers. What felt different? Surely part of it was receiving personal accounts of deep-rooted growth. And also, there was something to the mere act of asking and listening. Maybe it connected me to humans before battle cries. Maybe it was the chance to be in conversation with others who were also trying to understand – what is happening to us? Underneath it all, an undeniable thread remained; I saw people peering into the mess and narrating their findings onto the shared frequency. Every comment was like a flare into the sky. I’m here! And if the sky is full of flares, we aren’t alone.

I recognized my own pandemic discoveries – some minor, others massive. Like washing off thick eyeliner and mascara every night is more effort than it’s worth; I can transform the mundane into the magical with a bedsheet, a movie projector, and twinkle lights; my paralyzed body can mother an infant in ways I’d never seen modeled for me. I remembered disappointing, bewildering conversations within my own family of origin and our imperfect attempts to remain close while also seeing things so differently. I realized that every time I get the weekly invite to my virtual “Find the Mumsies” call, with a tiny group of moms living hundreds of miles apart, I’m being welcomed into a pocket of unexpected community. Even though we’ve never been in one room all together, I’ve felt an uncommon kind of solace in their now-familiar faces.

Hope is a slippery thing. I desperately want to hold onto it, but everywhere I look there are real, weighty reasons to despair. The pandemic marks a stretch on the timeline that tangles with a teetering democracy, a deteriorating planet , the loss of human rights that once felt unshakable . When the world is falling apart Land Before Time style, it can feel trite, sniffing out the beauty – useless, firing off flares to anyone looking for signs of life. But, while I’m under no delusions that if we just keep trudging forward we’ll find our own oasis of waterfalls and grassy meadows glistening in the sunshine beneath a heavenly chorus, I wonder if trivializing small acts of beauty, connection, and hope actually cuts us off from resources essential to our survival. The group of abandoned dinosaurs were keeping each other alive and making each other laugh well before they made it to their fantasy ending.

Read More: How Ice Cream Became My Own Personal Act of Resistance

After the monarch butterfly went on the endangered-species list, my friend and fellow writer Hannah Soyer sent me wildflower seeds to plant in my yard. A simple act of big hope – that I will actually plant them, that they will grow, that a monarch butterfly will receive nourishment from whatever blossoms are able to push their way through the dirt. There are so many ways that could fail. But maybe the outcome wasn’t exactly the point. Maybe hope is the dogged insistence – the stubborn defiance – to continue cultivating moments of beauty regardless. There is value in the planting apart from the harvest.

I can’t point out a single collective lesson from the pandemic. It’s hard to see any great “we.” Still, I see the faces in my moms’ group, making pancakes for their kids and popping on between strings of meetings while we try to figure out how to raise these small people in this chaotic world. I think of my friends on Instagram tending to the selves they discovered when no one was watching and the scarf of ribbons stretching the length of more than three football fields. I remember my family of three, holding hands on the way up the ramp to the library. These bits of growth and rings of support might not be loud or right on the surface, but that’s not the same thing as nothing. If we only cared about the bottom-line defeats or sweeping successes of the big picture, we’d never plant flowers at all.

More Must-Reads from TIME

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• How Ukraine Beat Russia in the Battle of the Black Sea
• The Reintroduction of Kamala Harris
• Long COVID Looks Different in Kids
• What a $129 Frying Pan Says About America’s Eating Habits
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• Review Article
• 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.

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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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COVID-19 Lockdown: My Experience

When the lockdown started, I was ecstatic. My final year of school had finished early, exams were cancelled, the sun was shining. I was happy, and confident I would be OK. After all, how hard could staying at home possibly be? After a while, the reality of the situation started to sink in.

The novelty of being at home wore off and I started to struggle. I suffered from regular panic attacks, frozen on the floor in my room, unable to move or speak. I had nightmares most nights, and struggled to sleep. It was as if I was stuck, trapped in my house and in my own head. I didn't know how to cope.

However, over time, I found ways to deal with the pressure. I realised that lockdown gave me more time to the things I loved, hobbies that had been previously swamped by schoolwork. I started baking, drawing and writing again, and felt free for the first time in months. I had forgotten how good it felt to be creative. I started spending more time with my family. I hadn't realised how much I had missed them.

Almost a month later, I feel so much better. I understand how difficult this must be, but it's important to remember that none of us is alone. No matter how scared, or trapped, or alone you feel, things can only get better.  Take time to revisit the things you love, and remember that all of this will eventually pass. All we can do right now is stay at home, look after ourselves and our loved ones, and look forward to a better future.

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In Their Own Words, Americans Describe the Struggles and Silver Linings of the COVID-19 Pandemic

The outbreak has dramatically changed americans’ lives and relationships over the past year. we asked people to tell us about their experiences – good and bad – in living through this moment in history..

Pew Research Center has been asking survey questions over the past year about Americans’ views and reactions to the COVID-19 pandemic. In August, we gave the public a chance to tell us in their own words how the pandemic has affected them in their personal lives. We wanted to let them tell us how their lives have become more difficult or challenging, and we also asked about any unexpectedly positive events that might have happened during that time.

The vast majority of Americans (89%) mentioned at least one negative change in their own lives, while a smaller share (though still a 73% majority) mentioned at least one unexpected upside. Most have experienced these negative impacts and silver linings simultaneously: Two-thirds (67%) of Americans mentioned at least one negative and at least one positive change since the pandemic began.

For this analysis, we surveyed 9,220 U.S. adults between Aug. 31-Sept. 7, 2020. Everyone who completed the survey is a member of Pew Research Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories.  Read more about the ATP’s methodology . 

Respondents to the survey were asked to describe in their own words how their lives have been difficult or challenging since the beginning of the coronavirus outbreak, and to describe any positive aspects of the situation they have personally experienced as well. Overall, 84% of respondents provided an answer to one or both of the questions. The Center then categorized a random sample of 4,071 of their answers using a combination of in-house human coders, Amazon’s Mechanical Turk service and keyword-based pattern matching. The full methodology  and questions used in this analysis can be found here.

In many ways, the negatives clearly outweigh the positives – an unsurprising reaction to a pandemic that had killed  more than 180,000 Americans  at the time the survey was conducted. Across every major aspect of life mentioned in these responses, a larger share mentioned a negative impact than mentioned an unexpected upside. Americans also described the negative aspects of the pandemic in greater detail: On average, negative responses were longer than positive ones (27 vs. 19 words). But for all the difficulties and challenges of the pandemic, a majority of Americans were able to think of at least one silver lining. 

Both the negative and positive impacts described in these responses cover many aspects of life, none of which were mentioned by a majority of Americans. Instead, the responses reveal a pandemic that has affected Americans’ lives in a variety of ways, of which there is no “typical” experience. Indeed, not all groups seem to have experienced the pandemic equally. For instance, younger and more educated Americans were more likely to mention silver linings, while women were more likely than men to mention challenges or difficulties.

Here are some direct quotes that reveal how Americans are processing the new reality that has upended life across the country.

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

Joint statement by ilo, fao, ifad and who.

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

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

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

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

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

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

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

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

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

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The virus that shut down the world: 2020, a year like no other

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COVID-19 is everywhere, literally, and during 2020 its spread and resulting impact has led to a global crisis of unprecedented reach and proportion. In a six-part series closing out this tumultuous year, UN News looks at the impact on people in every part of the world and some of the solutions that the United Nations has proposed to deal with the fall-out of the pandemic. In this first feature, we lay out some of the key events of the past 12 months.

As 2020 comes to an end and people around the world try to make sense of how the world has changed, they are faced with one stark and brutal statistic. The number of people who have died after catching COVID-19 , is creeping towards the two million mark.

Early in the year, international travel was severely restricted, and people like these travelers in Thailand learnt of the importance of PPE, an acronym which quickly entered the global lexicon (which is short for personal protective equipment).

Soon, there were concerns about a global shortage of PPE and the UN supported various countries in the procurement of supplies, including China where the virus first emerged.

As COVID-19 took hold, countries and cities across the world entered lockdown with the closure of schools, cultural and sports venues and all non-essential businesses.

Normally bustling city centres, like the Kenyan capital Nairobi, were eerily quiet as people stayed at home.

The United Nations did stay open for business across the world, although most of the key events, like the annual meeting of the new session of the General Assembly in New York, did look very different. Only a small number of delegates were allowed into the chamber as world leaders gave their speeches virtually.

Across the world, people were adapting to new social distancing guidelines…..

…and were reminded about the importance of handwashing as a way to reduce the transmission of diseases.

Students who were not able to go to school had to adapt to a new reality and find ways to keep up with their studies.

While Africa appeared to suffer less from the virus than other continents, at least in terms of absolute infections and deaths, the UN did voice concerns that the pandemic would push millions more into poverty.

Especially important to the UN was supporting refugees and other vulnerable people on the move across the world, such as the hundreds of thousands of Rohingya people who have sought shelter across the border in Bangladesh.

Progress has been made, in record time, by scientists developing new effective vaccines against COVID-19 and by the end of 2020, the first people, mainly in developed countries, were being inoculated.

As the world enters 2021, the pandemic is still raging and, after an apparent mid-year lull in many countries, more infections and more deaths are being reported. With more vaccines being rolled out, the international community is being urged to work together to stop the spread and follow science-based guidelines.

For a more detailed picture of how the world looked in 2020, look out for our UN News end-of-year series of special reports, as the year draws to a close.

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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.

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The Coronavirus Crisis

Personal essay: coronavirus lockdown is a 'living hell'.

A Resident Of Wuhan

Editor's note: The author of this essay asked for anonymity for fear of reprisals by authorities for speaking critically of the Chinese government.

The government lockdown orders in Wuhan, China, have emptied the city's streets. Stringer for NPR hide caption

The government lockdown orders in Wuhan, China, have emptied the city's streets.

As residents of Wuhan, China, my family and I are living in hell.

The city has been locked down for more than a month. Every night before falling asleep I have been confronted by an unreal feeling and many questions:

Read This Essay In Chinese

To read this essay in Chinese, click here.

I know that coronavirus is the reason for the lockdown — but did life in Wuhan have to become a living hell?

Why were we notified about the city lockdown at 2 a.m. on the second to last morning before the Lunar New Year?

Why have I not been given any instructions from a government officer about how to cope when an entire city is on lockdown?

I'm nearly 30 years old. My family members and I have devoted ourselves to our jobs to build a better life — and we have largely succeeded. There's only a little more to do before we reach the level of middle class.

But along the way, things did not go exactly as I'd hoped. I have been working hard in school since I was small. My dream was to become a journalist, and I passed the test to enter the best school for journalism in China.

After school, I learned that government supervision of the media meant that telling the truth was not an option. So I gave up my dream and turned to another career.

I kept telling myself that my hard work would reward me in my personal life. And to protect myself, I decided to shut up, to be silent about politics — even when I saw people treated unfairly by the government. I thought that if I followed that path, I would be secure, I would be one of the fortunate ones.

Now I realize that this is an illusion. A secure life is not an option with a political system that does not give us freedom to speak out and that does not communicate with us truthfully.

At the moment when the city was first locked down, I hoped with all my heart that China's political system, known for concentrating resources to get big jobs done, could save the Wuhanese. But infected patients were treated in the hospital in Wuhan as early as the beginning of December, and for unknown reasons, the government held off informing the public and taking effective action.

So they missed the best window of prevention due to this cover-up.

That knowledge has made me fall into desperation. The order to lock down the city appeared from nowhere on Jan. 23 at 2 a.m., without any sign or explanation to residents — even though everyone knew what was up.

People rushed to shop at 24-hour convenience stores at 3 a.m. to gather necessary food and other items. We tried every method to escape from Wuhan, but the cage was already locked.

On new year's eve, Jan. 24, I watched the glorious performances from a gala aired on CCTV, Chinese television. But our celebratory meal was sparse, pieced together from the few ingredients I'd been able to buy in that last-minute shopping trip.

Then on the second day of the new year, another order arrived out of the blue, notifying us that the Wuhanese shall not drive. But this order only survived for less than six hours — perhaps because the authorities realized that, with public transportation shut down, cars would be needed to drive medical staffers to work and back home. So community officers called upon residents of Wuhan to provide rides for many of these workers — and to get permits to do this driving. Under the pressure of massive criticism, the government had to revoke this order for residents to provide rides.

Other orders were issued that reflected the chaos. Residents were asked to donate rice and oil to feed the medical staffers at Wuhan's top hospital since there was not enough food to guarantee meals for them. But we are the taxpayers. Shouldn't the government be able to provide?

From former schoolmates who now work in the medical profession, I learned that medical workers were not given medical supplies and were exposed to a risk of death. Many people wonder: Why didn't they go on strike? It is because they were informed that if they went on strike, their licenses to practice medicine would be revoked and their family members' jobs would be affected.

Before this coronavirus, I always thought it was OK to sacrifice some level of democracy and freedom for better living conditions. But now I have changed my attitude. Without democracy and freedom, the truth of the outbreak in Wuhan would never be known.

What has happened in Wuhan is as if your house caught on fire and all your neighbors knew but forbade you from jumping out of the window. Only until the fire is out of control, and the entire town ablaze, do they slowly begin taking responsibility while highlighting their own heroic efforts.

Not everyone has the same privileges and rights. Because I knew how to get outside of the Great Firewall that blocks the Internet, I was able to obtain masks.

The younger generations, born after 1995 and in the 2000s, have good impressions about the Chinese system, putting the nation before all because they have been living in an era of prosperity and have yet to experience adversity.

The things that happened during this outbreak have greatly surprised those kids. For example, a young man scolded others on Weibo in the early days of the outbreak. He accused them of spreading rumors and argued that if we don't trust the government, there is nothing we can trust. Later, he said, when a member of his family was infected with the coronavirus but was unable to get treatment in the overcrowded hospital, he cursed and called for help.

When Li Wenliang, one of the doctors who first reported a mysterious SARS-like illness, died of the disease himself, a student commented on the Internet: "It was just the virus that killed him, so we should focus on the epidemics." But then the student's dormitory was appropriated for quarantine patients — and he was shocked and dismayed.

This is the lesson these young people are learning. When someone says we can accomplish something but we must pay a price, do not rush to applaud.

One day you may become the price that is paid.

There is a saying in Chinese that has taken on new meaning in this coronavirus era: "When the stick hits my own head, I finally understand the pain — and why some others once cried out of pain."

Perhaps it is true that only China can build a hospital in 10 days, only China can mobilize so many people to devote themselves to the anti-epidemic agenda, only China can lock down a city with millions of people at lightning speed.

But people are not thinking critically. They do not understand that if we had human rights, democracy and freedom, we would have learned about what happened in Wuhan one month earlier. And the first whistleblower would not have died for nothing.

• coronavirus

• Open access
• Published: 22 August 2024

The heterogeneous effects of COVID-19 lockdowns on crime across the world

• N. Trajtenberg   ORCID: orcid.org/0000-0002-4451-3874 1 ,
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There is a vast literature evaluating the empirical association between stay-at-home policies and crime during the COVID-19 pandemic. However, these academic efforts have primarily focused on the effects within specific cities or regions rather than adopting a cross-national comparative approach. Moreover, this body of literature not only generally lacks causal estimates but also has overlooked possible heterogeneities across different levels of stringency in mobility restrictions. This paper exploits the spatial and temporal variation of government responses to the pandemic in 45 cities across five continents to identify the causal impact of strict lockdown policies on the number of offenses reported to local police. We find that cities that implemented strict lockdowns experienced larger declines in some crime types (robbery, burglary, vehicle theft) but not others (assault, theft, homicide). This decline in crime rates attributed to more stringent policy responses represents only a small proportion of the effects documented in the literature.

Introduction

The COVID-19 pandemic involved an unprecedented change in social dynamics worldwide. The rapid diffusion of the virus and its health costs led governments to deploy policies to restrict mobility, reduce the diffusion of the virus, and avoid the collapse of health systems. The implementation of these mobility restrictions, coupled with voluntary work-from-home policies implemented by organizations and voluntary social distancing measures implemented by households, resulted in an unparalleled global reduction in human mobility, marking an extraordinarily quiet period (Lecocq et al., 2020 ). One of the key questions regarding the effects of this ‘global natural experiment’ has been its impact on crime (Boman & Mowen, 2021 ).

Mirroring research on crime and catastrophic events such as earthquakes (García Hombrados, 2020 ), studies at the intersection of COVID-19 and crime have centered on cities as the unit of analysis. These studies have employed a wide range of research designs, including interrupted time series, differences-in-differences, regression discontinuity, structural break analysis, and event studies (Koppel et al., 2023 ), documenting inconsistent effects across different offenses. For example, research in the US, Canada, the UK, Australia, Mexico, India, New Zealand, Sweden, and China consistently documented a significant decrease in the reported incidents of property crimes, such as robbery, theft, or burglary (Abrams, 2021 ; Andresen & Hodgkinson, 2020 ; Ashby, 2020 ; Balmori de la Miyar et al., 2020 ; Chen et al., 2023 ; Cheung & Gunby, 2021 ; Felson et al., 2020 ; Gerrell et al., 2020 ; Halford et al., 2020 ; Koppel et al., 2023 ; Langton et al., 2021 ; Payne et al., 2021 ; Poblete-Cazenave, 2020 ; Vilalta et al., 2023 ). On the other hand, other property crimes, such as vehicle theft, showed more varied outcomes. Reductions were reported for Australia, the US, China and UK (Andresen & Hodgkinson, 2020 ; Chen et al., 2023 ; Halford et al., 2020 ; Koppel et al., 2023 ; Mohler et al., 2020 ), while other studies documented no effects or even an increase for the US and Canada (Ashby, 2020 ; Hodgkinson & Andresen, 2020 ; Meyer et al., 2022 ). The relationship between the pandemic restrictions and violent crimes is less clear. While some studies focusing on cities in the US, UK, Australia, Sweden, India, Mexico, and Peru show significant reductions in reported assaults or homicides (Abrams, 2021 ; Calderon-Anyosa & Kaufman, 2021 ; Gerrell et al., 2020 ; Halford et al., 2020 ; Payne et al., 2021 ; Poblete-Cazenave, 2020 ; Vilalta et al., 2023 ), other studies in Australia, the US, and Mexico show no significant effects (Balmori de la Miyar et al., 2020 ; Campidelli et al., 2020a ; Koppel et al., 2023 ; Lopez & Rosenfeld, 2021 ; Meyer et al., 2022 ; Payne et al., 2020 ).

Since most of this evidence is based on single-city studies or, at best, studies that compare different cities within one country (e.g., Abrams, 2021 ; Ashby, 2020 ; Meyer et al., 2022 ) or a few countries (Cecatto et al., 2021 ), inconsistency across studies is not surprising given the different characteristics of cities, type of stay-at-home government restrictions, voluntary measures implemented by organizations and households, and period of analysis. The only study with broad international coverage is Nivette et al. ( 2021a ), which included 23 cities across the Americas, Europe, the Middle East, and Asia and documented an average reduction of 37% in reported offenses following stay-at-home government restrictions. Although property crimes (i.e., theft, vehicle theft, burglary, and robbery) exhibited a significant reduction, violent crimes showed a mixed picture with a reduction of assaults, while homicides showed no effects. In addition, Nivette et al. ( 2021a ) documented heterogeneity in crime reduction across locations, with the largest crime drops in cities that applied stricter lockdowns. These results are consistent with a recent systematic review, which indicated that most crimes exhibited a significant reduction following COVID-19 restrictions, except for homicides (Hoeboer et al., 2024 ).

Despite the accumulated empirical evidence on the relationship between the COVID-19 pandemic and crime across different types, one of the limitations of this literature is the focus on the generic role of the pandemic and associated measures. Considering the widespread impact of the pandemic, most studies compare current trends with a counterfactual scenario representing the expected crime rate based on pre-pandemic periods (Abrams, 2021 ; Hodkinson & Andresen, 2020 ; Nivette et al., 2021a ). Utilizing cities’ pre-pandemic trends as control groups implies the comparison of cities affected by the pandemic with those not impacted by the pandemic. This entails assessing the effects of a general ‘treatment,’ encompassing various and distinct government restrictions and stay-at-home policies associated with the pandemic and considering individual and corporate responses, which may be partially independent of government policies. Exceptionally, some studies have exploited heterogeneities across neighborhoods to identify a potential causal effect on crime at a local level. For example, when initial restrictions were relaxed in Bihar (India), crime rates increased. Still, the rise was less pronounced in areas with more stringent restrictions compared to those with less severe measures, and notably, this uptick did not extend to violent crimes (Poblete-Cazenave, 2020 ). Instead, evidence from London (UK) shows that easing national lockdown measures diminished the effect of stringent lockdowns across all property and violent crimes (Neanidis & Rana, 2023 ). Evidence from Oslo (Norway) suggests that when general COVID-19 restrictions were accompanied by the closure of bars and pubs, there were additional significant reductions in theft, violent crimes, vandalism, and fraud (Gerrell et al., 2022 ). However, the scarcity of integrated international data sets (Boman & Mowen, 2021 ; Nivette, 2021 ) means we lack causal estimates of the impact of specific types of government policies on crime at a global level.

Consequently, in this paper, we investigate how different policies implemented by local governments during the COVID-19 pandemic affected crime across cities. The goal is to identify the causal impact of strict lockdown policies on the number of offenses reported to local police. More specifically, our main research question is: What is the cross-national impact on crime of strict lockdown policies that require citizens not to leave their homes? To answer this question, we first analyze the spatial and temporal variation of government responses to the pandemic in 45 cities throughout the year 2020. We define strict lockdown conditions as instances where governments require citizens not to leave the house with no or minimal exceptions. Next, we apply a generalized synthetic control approach, which involves building a weighted combination of control groups of all the cities in those periods without strict lockdown. Thus, only cities that implemented ‘strict lockdowns’ are considered ‘treated,’ some of them intermittently, some of them permanently during the period of study. This empirical strategy allows us to isolate the effect of strict lockdowns on crime by accounting for the effects of other stay-at-home policies in cities with less stringent restrictions. We conclude by discussing the policy implications of our results for implementing crime prevention policies that may compromise individuals’ freedom of movement.

Data and methods

The outcome variable.

The outcome variable is the number of crime incidents reported to police each month in each of the 45 cities in our sample between January 2018 and December 2020 for six crime types: assault, theft, burglary, robbery, vehicle theft, and homicide. Footnote 1 The cities were selected to maximize geographical coverage. The integration, aggregation, and comparison of crimes across these units were based on the International Classification of Crime for Statistical Purposes (Bisogno et al., 2015 ). In some cities, the six crime categories were not available or did not fit our classification. For example, vehicle theft was not considered a separate category from theft in some cities (e.g., Zurich), and burglary is not clearly distinguished from another type of property crime in other cases (e.g., Montevideo). In other cities, crime counts were available but not for the relevant period of study (e.g., Tel Aviv). Additionally, some cities lacked enough monthly cases for some crimes, typically homicide (e.g., Ljubljana). As a result, some cities could not be included in some of the analyses (see Table A2 of the Supplementary Materials). Finally, due to the surge in violence following the George Floyd incident on May 25, 2020, our main results are based on data before May 2020 for the 15 US cities in our study. However, we report the results using all US data in section E of the Supplementary Materials.

The date of the time series refers to the date when the offense presumably occurred, as recorded by the police. In cases where this information was not available, the reporting date was used (e.g., Mexico City). Since not all reported crimes were investigated, the number may under-represent the volume of crime reported to the police. Additionally, for most cities, the time series starts on 1 January 2018 or 2019 and ends on 31 December 2020. Time series information and available crime categories for each city are presented in Table A2.

To compare the changes in crime trends following the onset of the COVID-19 pandemic and the implementation of stay-at-home restrictions, we calculated monthly indexes for each city and crime type. The indexes were constructed such that the 2019 average equals 100 for each time series. Figure  1 shows the indexes for all cities in a single graph, with the average trend for each crime type highlighted in orange. We used monthly time series to minimize suppression since, in some cities, the frequency of crimes per day was almost zero. This occurred most often for homicide.

Monthly crime indexes. The indexes are constructed such that their 2019 average equals 100. The average trend for each crime type is highlighted in orange

Event study design

For each crime, we have an outcome matrix with typical element \({y}_{it}\) with 45 columns corresponding to cities (N = 45) and 36 rows corresponding to the months from January 2018 to December 2020 (T = 36). We first estimated the average effect of the COVID-19 pandemic on crime incidents using a two-way fixed effects (static) regression given by:

where \({y}_{it}\) is the monthly index of crime incidents for a given type of crime in city i on month t . The (first) treatment variable \({D}_{it}^{1}\) is a dummy variable that takes the value of 1 during the period starting in March 2020 and 0 in the period preceding it. In this case, the treatment variable captures all stay-at-home restrictions implemented by governments, voluntary work-from-home policies implemented by employers, voluntary social distancing implemented by individuals, and any other change in behavior that can be attributed to the COVID-19 pandemic. In this specification, the coefficient \(\delta\) represents the average percentage change in crime incidents (relative to the 2019 average) after the onset of the COVID-19 pandemic in March 2020.

For certain crimes, patterns have been found to increase periodically. As a result, we used weather covariates and fixed effects to control for seasonal and long-term trends. The vector \({w}_{it}\) includes weather covariates (monthly average rain and temperature for each city), \({\alpha }_{i}\) denotes city fixed effects to control for factors that vary across cities but not across time, \({\theta }_{t}\) denotes month fixed effects to account for monthly seasonality, and \({\gamma }_{t}\) denotes year fixed effects to account for time trends in the data.

Next, we estimated the average effect of the COVID-19 pandemic on crime incidents for each pandemic month using a (dynamic) regression given by:

where \({D}_{it}^{j}\) are treatment dummy variables for each of the pandemic months (March to December) of 2020. In this specification, the coefficient \({\delta }_{j}\) represents the average percentage change in crime incidents for month j (relative to the 2019 average).

Matrix completion design

Our main goal is to evaluate how the implementation of strict lockdowns impacted crime trends in 2020. Let \({D}_{it}^{2}\) be the (second) treatment variable equal to 1 if city i was in strict lockdown during month t . The treatment variable has the same dimension as the outcome matrix and takes the value of 1 when strict lockdown restrictions (not just recommendations to stay-at-home) were in place in city i , while 0 represents the period before or following the implementation of strict lockdown restrictions in that city. Since strict lockdowns were implemented intermittently, the treatment adoption exhibits an on–off pattern as the date cities enter/exit a strict lockdown varies across cities.

The date lockdown restrictions were implemented across cities is not always clear. As a result, we relied on a sub-index from the Oxford COVID-19 Government Response Tracker (OxCGRT). The sub-index C6 monitors “orders to shelter-in-place and otherwise confine to the home,” takes ordinal values {0, 1, 2, 3}, and is reported daily. The index takes the value 0 if no measures are in place, 1 when “recommend not leaving house,” 2 when “require not leaving house with exceptions,” and 3 when “require not leaving house with minimal exceptions” (Hale et al., 2020 ). The sub-index C6 is plotted for each of the 45 cities in the sample in Figure B1 of the Supplementary Materials. To construct our (second) treatment variable, a city is considered under strict lockdown (i.e., the second treatment variable is 1) when the sub-index C6 takes the values 2 or 3 (“require not leaving house”). When the sub-index C6 takes the value 0 or 1, the city is considered untreated (i.e., the second treatment variable is 0). The second treatment variable is plotted in Figure B2 of the Supplementary Materials. Footnote 2

Under the potential outcomes framework (Rubin, 1974 ), for each city i and month t , there are a pair of potential outcomes, \({y}_{it}^{1}\) and \({y}_{it}^{0}\) , that correspond to the potential outcomes under treatment (strict lockdown) and control condition (stay-at-home recommendation but no strict lockdown), respectively. To assess the effect of strict lockdowns for each crime type, we need to estimate a counterfactual matrix \({y}_{it}^{0}\) in which elements represent the monthly index of crime incidents for cities that are not in strict lockdown (untreated cities). When a city is in strict lockdown ( \({D}_{it}^{2}=1\) ), the outcome variable is removed from the matrix of outcomes, and the observation is treated as missing. The objective is to “complete” the matrix of outcomes under the assumption that no lockdown has taken place (Liu et al., 2024 ). The causal quantity of interest is the average treatment effect on the treated (ATT).

In this paper, we used the matrix completion (MC) counterfactual estimator of Athey et al. ( 2021 ) and Liu et al. ( 2024 ). The MC estimator is given by:

where \({y}_{it}^{0}\) is the monthly index of crime incidents for a given type of crime for untreated cities, \({\alpha }_{i}\) and \({\gamma }_{t}\) denote unit (city) and time fixed effects, \(L\) is a matrix to be estimated with typical element \({L}_{it}\) (see Liu et al., 2024 ), and \({w}_{it}\) are weather covariates. Athey et al. ( 2021 ) provide an algorithm that uses regularization to estimate the model and impute the missing values in the counterfactual matrix. This estimator generates a counterfactual outcome \({y}_{it}^{0}\) for each treated observation and, as a result, we can estimate the individual treatment effect \({\delta }_{it}\) as the difference between the estimated \({y}_{it}^{0}\) and the observed \({y}_{it}\) , \(\widehat{{\delta }_{it}}={y}_{it}-{y}_{it}^{0}\) . Next, we can compute ATTs of interest as averages of the \(\widehat{{\delta }_{it}}\) for a subset of the observations under treatment. For example, we can compute the overall ATT by calculating the average for all treated observations. Alternatively, we can compute the ATT for each of the pandemic months separately or the ATT for each month relative to the beginning of a lockdown, etc. Standard errors and confidence intervals can be obtained using 1000 block bootstraps at the city level and jackknife (leave-one-out) methods, as in Liu et al. ( 2024 ).

If the treatment (strict lockdown) impacts crime, the observed frequency of monthly crime rates should be lower in the treated periods than in the counterfactual estimates. Therefore, rather than comparing the pandemic’s impact on crime in each city to what would have occurred without it (utilizing pre-pandemic crime trends as a baseline), we are examining the effect of strict lockdown measures on crime rates in cities like Mexico or London. To achieve this, we use cities such as Malmö or Stockholm, where no strict lockdown was imposed during the pandemic, as control groups. Thus, our ATT analysis yields an estimation of the reduction in crime attributable to the enforcement of stringent lockdown measures while controlling for the effect of the pandemic in cities that did not enforce such measures yet still experienced decreased mobility due to stay-at-home government policies and behavioral adjustments by organizations and individuals.

The impact of the COVID-19 pandemic on crime

We begin by evaluating the overall impact of the COVID-19 pandemic on crime. Table 1 reports the average percentage change in crime incidents for each crime type after the onset of the COVID-19 pandemic in March 2020 from Eq. ( 1 ). Our results show that, on average, across all the cities, there was a substantial drop in crime relative to the 2019 average. This effect is found to be particularly large and statistically significant for theft (31.5% drop in monthly counts), robbery (28.2% drop), burglary (20.4% drop), assault (20.1% drop), and vehicle theft (18.8% drop). In contrast, the effect on homicide was smaller (10.3% drop) and not statistically significant.

Figure  2 plots the average percentage change in crime incidents for each month after March 2020 obtained from Eq. ( 2 ). Our results show there was a large drop in crime during the first few months of the pandemic (mainly March, April, and May), followed by a moderate bounce back during the northern hemisphere summer of 2020. The drop in crime settled around 25% for burglary, robbery, and vehicle theft, and close to 30% for theft. The drop was smaller for assault, settling around a 10% drop. Finally, we observe a small and persistent drop in homicide that is not statistically significant. Overall, these estimates are consistent with those documented in the literature, although somewhat smaller in comparison to the reductions in crime reported in Nivette et al. ( 2021a ).

Average effect of the COVID-19 pandemic on crime by month

The impact of strict lockdown on crime

To evaluate the impact of strict lockdowns on crime, we need to identify the periods in which strict lockdown restrictions, not just stay-at-home recommendations, were in place in each city. In addition, we need to control for changes in behavior that can be attributed to the pandemic (e.g., voluntary work-from-home policies) but not to a strict lockdown mandated by governments. Figure B2 of the Supplementary Materials shows the temporal and spatial variation of strict lockdown for all cities and periods in our sample. Before March 2020, all cities were considered untreated. After March 2020, cities that imposed strict lockdowns were considered treated, some intermittently and some permanently. For example, Mendoza and Lima were treated during all the study periods because they imposed strict lockdowns from March until the end of 2020. In contrast, Stockholm and Malmö were control cases for the whole period because no strict lockdown was imposed. London was considered under treatment during the first three months and the last two months of 2020 but was considered a control case during spring and all summer of the northern hemisphere. Table C1 of the Supplementary Materials shows that being under strict lockdown (i.e., when the second treatment variable is equal to 1) was associated with a substantial reduction in mobility, in addition to the reduction observed in cities without strict lockdown (i.e., the control cities). For example, cities under strict lockdown experienced a drop in retail and recreation mobility that is 89.8% larger than what was observed in the control cities. Similarly, the drop was 58.8% larger for transit mobility and 64.7% larger for workplace mobility.

Table 2 presents the estimated ATT of strict lockdown using the MC counterfactual estimator. Our results show that, on average, across all the cities and all of 2020 months, the impact of strict lockdowns was negative for all crimes relative to the cases without strict lockdowns. This effect was found to be particularly large and statistically significant in the case of robbery (19.1% drop in monthly counts), burglary (14.9% drop), and vehicle theft (11.9% drop). However, the impact of strict lockdowns on assault (3.5% drop) and theft (5.4% drop) was small and not statistically significant. Finally, the effect of strict lockdowns on homicide was large (12.9% drop) but not statistically significant. Nevertheless, the analysis of homicide included fewer cities due to missing observations and a lack of sufficient volume of monthly rates (e.g., only 25 cities were included in the analysis of homicides). Footnote 3 Footnote 4

Next, we estimate the dynamic ATT of strict lockdown on crime for each month, relative to cities that did not experience a strict lockdown in that month (i.e., the difference between the estimated effect of the COVID-19 pandemic on crime for the treated and untreated each month). Footnote 5 Visual inspection of Fig.  3 reveals that the ATTs of strict lockdown on crime were observed only after May 2020. In addition, there was substantial heterogeneity across the different waves of the pandemic. For example, the monthly ATT of strict lockdown on assault exhibited a U-shape with a significant reduction in the first months of the pandemic (an additional 25% drop relative to cities with less stringent stay-at-home policies). However, the effect gradually ceased to be significant, with no effect after August. Robbery exhibited a similar trend with a more substantial and persistent initial reduction (a 30% drop) that gradually ceased to be statistically significant after the June/August period, except for a large drop in October. Burglary, in contrast, exhibited a W-shaped pattern with two drops in the period, a significant reduction in the first wave of the pandemic (a 30% drop) and a second large reduction, though smaller, during the second wave (a 20% drop). The effect of strict lockdown on theft and vehicle theft across time was less clear, and although some specific months exhibit statistically significant reductions, most months showed non-significant differences. Likewise, there was no apparent effect of strict lockdown on homicide across time due to the lack of sufficient volumes of cases.

Average treatment effect on the treated (ATT) of strict lockdown on crime by month. Cities under strict lockdown are considered treated

A follow-up question is: what is the impact of strict lockdown on crime as consecutive months of lockdown accumulate? Fig.  4 shows the ATT of strict lockdown on crime relative to the start of the strict lockdown. This was obtained by computing the ATT for treated observations that correspond to any first month of strict lockdown, then we compute the ATT for treated observations that correspond to any second consecutive month of strict lockdown, and so on. Our results show mostly non-significant reductions in crime in the first two months of strict lockdown relative to cities with less stringent mobility restrictions. However, as months of lockdown accumulated, there was an increasingly significant reduction in crime rates for burglary, robbery, and assault. In contrast, there was little effect on theft and vehicle theft, and no significant effect on homicide.

Average treatment effect on the treated (ATT) of strict lockdown on crime relative to the start of the strict lockdown

Nevertheless, these results should be interpreted with caution because the analysis was based on a reduced sample. While almost all cities in the sample (except for Stockholm and Malmo) experienced at least one month of strict lockdown, the number of cities under strict lockdown for two or more consecutive months was substantially smaller. For example, the impact of strict lockdowns on robbery that involves four consecutive months was estimated using only ten cities. Thus, as we consider the effect of longer lockdowns, we have fewer cities, and rejections of the null hypothesis of no effect are more likely to be driven by the idiosyncratic effects of the cities remaining in the sample. Moreover, we only considered the cumulative effect of consecutive periods of treatment. If the treatment (strict lockdown) was interrupted by periods of no lockdown, the new period of lockdown was considered as a new first month of treatment. For example, London had two strict lockdown periods: one lasting three months in the northern hemisphere spring and the second one of 2 months in winter.

Our findings show that cities under strict lockdown experienced substantial declines in robberies, burglaries, and vehicle thefts, compared to cities under less stringent stay-at-home orders. However, when comparing cities with strict and non-strict lockdowns, we found no significant effects on assaults, thefts, and homicides. Non-economic violent crimes, such as assaults and homicides, are often situational and linked to spontaneous conflicts in public settings associated with leisure activities (Wilcox & Cullen, 2018 ). The initial stages of the COVID-19 pandemic involved the closure of the night-time economy and the cessation of public situational contexts (e.g., pubs, bars, and other outlets) where such violent frictions are more likely to occur (Ejrnæs & Scherg, 2022 ; Gerrell et al., 2022 ). Thus, when strict lockdowns were implemented, the opportunities for these types of violent crimes were already significantly reduced. It is also possible that some shift took place from violent frictions in public settings to more private settings. There is evidence of an increase in reports of domestic incidents (Piquero et al., 2021 ), particularly by current partners and not by former partners (Ivandić et al., 2020 ). Moreover, a portion of homicides occurs within the context of organized crime activities, which was less impacted by the stringency of health measures (Hoeber et al., 2024 ).

The results regarding theft reports are puzzling. In fact, this economically motivated street crime exhibits one of the most consistent findings across the COVID-19 literature (Hoeboer et al., 2024 ). One possibility is that theft showed stability during strict lockdown because the reduction of criminal opportunities due to changes in routine activities had already taken place in the first weeks of the pandemic, where people had significantly decreased their interactions in the public sphere (Felson et al., 2020 ). Additionally, this new context, with fewer potential victims due to reduced interpersonal contacts in the streets coupled with an increase of capable guardianship at homes, might have led robbers and burglars to switch to thefts. These findings are consistent with previous evidence on “functional displacement” to other crimes, particularly with strongly motivated offenders, when there is an expectation of reducing risks, and usually toward less serious crimes (Rossmo & Summers, 2021 ; see also Johnson et al., 2014 ). Theft is a very generic category, and more fine-graded data would allow us to understand how this displacement might be associated with some specific categories of theft like shoplifting, bicycle theft, or theft of items left outside houses in porches or garages. Yet, more research is needed to understand why strict lockdowns might have had different effects on property crimes with economic motivations and which contextual and specific mechanisms explain these differences.

It is hard to know if changes in crime rates during strict lockdowns are attributable to mechanisms distinct from alterations in criminal opportunities. Although the literature mentions opportunities and strains as potential explanatory mechanisms (Campedelli et al., 2020a ; Stickle & Felson, 2020 ), research has focused mainly on the role of opportunities, with few exceptions showing how changes in interpersonal violence and violent property crimes during the pandemic can be partially explained by geographical differences associated with poverty, unemployment, and inequality (Andresen & Hodkinson, 2020 ; Campedelli et al., 2020b ). Strains are more likely to have long-term effects on crime (Eisner & Nivette, 2020 ), as government measures are relaxed, and routine changes become less relevant (Payne et al., 2021 ). For example, research conducted in the US suggests that the surge in violent crimes during reopening phases following lockdowns may be attributed to a combination of heightened opportunities and the accumulation of strains (Ridell et al., 2021 ). Nevertheless, empirical support for the role of strains in the COVID-19 literature is weak, and its relevance as an explanatory mechanism has been challenged, notably when it comes to the prediction of crimes such as domestic violence (Aebi et al., 2021 ; Hodgkinson et al., 2023 ). Our analysis does not reveal significant differences between more violent situational crimes (like assaults) and economically motivated crimes (such as theft), even after several consecutive months of lockdown. However, our findings should be taken with caution, given the limitations of our analysis (e.g., the exclusion of US cities after May 2020 and a limited number of cases with extended strict lockdowns).

Our results show that the additional reduction in crime rates due to strict lockdowns was small and stronger mobility restrictions did not translate into substantially larger drops in crime. In other words, the relationship between mobility and crime does not appear to be linear as further reductions in mobility had marginal effects on crime. This result suggests that crime reductions during the pandemic were not only driven by local sanitary restrictions implemented by governments but also by people’s preventive behavior and organizations’ policies (e.g., flexible work-from-home conditions) (Barrero et al., 2021 ). Thus, when a strict lockdown was imposed, both people and organizations had already reacted, altering routine activities and crime opportunities (Stickle & Felson, 2020 ). In simpler terms, strict lockdowns did not substantially change the number of potential victims on the streets or the occupancy levels in households despite reducing mobility. These had already decreased significantly beyond the initial mobility decline prompted by the initial guidelines, as well as the precautionary measures taken by organizations and individuals. Thus, stricter lockdowns had only a marginal effect, as the new scenario did not significantly increase the difficulties or costs of finding criminal targets (Nagin, 2013 ).

Our findings carry policy implications. This study suggests that most of the crime reduction took place without the need for a costly and extensive ‘massive social incapacitation’ of citizens by the government (strict lockdown), forcing them to have a ‘house arrest experience’ (Baker, 2020 ). While the estimates in Table  2 show a negative average effect of strict lockdowns for all crimes (relative to cases without strict lockdowns), they indicate a diminishing or null effect when compared to the findings in Table  1 . This implies that achieving crime reduction can rely more on citizens’ autoregulation and less on sacrificing citizens’ freedom of movement. During the COVID-19 pandemic, policymakers explored alternatives that balance public health concerns and preserve individual liberties. Similarly, effective crime reductions can be attained through measures that are less restrictive of citizens’ freedom of circulation.

This study is not without limitations. First, many cities in the sample have a very low frequency of homicides. Although our study finds no significant effects on homicides, the low frequency of these incidents presents challenges in terms of statistical inference when determining how these crimes were affected by strict lockdowns. This is a common limitation in natural disaster studies, which focus on aggregate measures of violent crimes rather than homicides (Doucet & Lee, 2015 ). Second, our study is limited by the use of police records. This not only presents the challenge of comparing and harmonizing crime categories across different legal frameworks in various countries (Aebi, 2010 ) but also involves issues related to the reporting, recording, and publishing of data, which vary significantly across crime categories (Ashby et al., 2022 ; Buil-Gil et al., 2021 ; Xie & Baumer, 2019 ). Particularly problematic is that selection biases not only influence how victims report crimes and how police officers record them, but these processes are also heterogeneous across units of analysis (Estienne & Morabito, 2016 ; Torrente et al., 2017 ). Additionally, the pandemic might have further exacerbated bias in crime measurement. For example, underreporting might have occurred due to victims and police fearing contagion. At the same time, under-recording could have resulted from reduced police department capacity to register, respond to calls, and patrol (Wallace et al., 2021 ). Nevertheless, some studies have shown that part of the crime drop is not an artifact of underreporting by providing robustness checks by contrasting trends of different types of crimes before and after the pandemic (Abrams, 2021 ), or by triangulating police crime records with victimization surveys (Perez-Vincent et al., 2021 ). Finally, our sample has a limited geographic variance which affects the external validity of our findings. Although the sample almost doubled the number of cities in relation to Nivette et al. ( 2021a ) and included relevant cities from South America and the Caribbean, there is still an overrepresentation of North America and Europe. One challenge is to incorporate more cases from underrepresented regions and have a more representative sample in terms of low- and middle-income non-western societies (Boman & Mowen, 2021 ; Eisner, 2023 ) with more variability of crime indicators, correlates of crime, but also in terms of validity of their police crime statistics (Mendlein, 2021 ; Rogers & Pridemore, 2017 ).

Conclusions

During the COVID-19 pandemic, governments implemented a variety of stay-at-home policies to reduce mobility and prevent the spread of the virus. Cities under strict lockdowns across North America, South America, Europe, Asia, and Oceania experienced larger declines in property crimes, such as robbery, burglary, and vehicle theft, when compared to cities under less stringent stay-at-home policies. However, more stringent stay-at-home policies did not seem to have a more significant effect than less stringent policies on assault, theft, or homicide. The reduction in crime rates attributed to these more stringent policies represents only a small proportion of the overall effect of the pandemic on crime. Relevant lessons can be extracted regarding the necessity of implementing stringent measures on citizens' rights and freedom of movement to reduce crime.

Availability of data and materials

Data and codes to conduct analysis in this study are available on the website of one of the authors: www.carlosddiaz.com .

See Table A1 of the Supplementary Materials. See also the supplementary materials of the previous study (Nivette et al., 2021b ).

A city was considered treated if at least one day of the month the city was under strict lockdown. While length of strict lockdown varies significantly across the sample, most lockdowns lasted longer than 2 weeks (see Figure B2).

Placebo test results are reported in Table D1 of the Supplementary Materials. Treatment is introduced four months before the actual treatment (strict lockdown) creating an in-time placebo period. We then estimate the ATT for the placebo period using the MC estimator. We find no evidence for a lockdown effect on crime for any of the crime types considered (all p-values > 0.05).

The results using all US data are reported in section E of the Supplementary Materials. Our results for assault, burglary, robbery, theft, and vehicle theft remain mostly unchanged (see Figures E1–E3). In contrast, we now observe a large and statistically significant reduction in homicide. However, this effect was not due to a drop in homicide in the treated but due to a large increase in homicide in the control cities, which included all the US cities. For a discussion of a potential Minneapolis effect due to the Floyd case see Ratcliffe & Taylor ( 2023 ).

Figure B3 of the Supplementary Materials plots the estimated effect of the COVID-19 pandemic on crime for the two groups of cities considered (treated and untreated) for each month. The results show that cities under strict lockdown (the treated) had systematically larger reductions in crime across all types of crime compared to cities with less stringent stay-at-home policies (the untreated).

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Trajtenberg, N., Fossati, S., Diaz, C. et al. The heterogeneous effects of COVID-19 lockdowns on crime across the world. Crime Sci 13 , 22 (2024). https://doi.org/10.1186/s40163-024-00220-y

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Being Black in Germany has never been easy. Elections in eastern states could make it harder still

Germany far right racism.

ERFURT, Germany (AP) — It was a balmy summer night in 2020, shortly after the lifting of Germany’s first COVID-19 lockdown, and Omar Diallo and two friends from his home country of Guinea wanted to celebrate Eid al-Adha, the Muslim festival of sacrifice.

“We were enjoying life, playing music, walking through the city at night — we just wanted to be together again and have a good time,” Diallo, 22, told The Associated Press in Erfurt, in the eastern state of Thuringia.

He was not prepared for how the day would end. Suddenly Diallo and his friends were confronted by three black-clad white men.

“They were shouting: ‘What do you want here, f-——- foreigners, get out’!” Diallo remembered.

“First there were three, then five, seven — they were surrounding us from all sides. We couldn’t run away, and then they started chasing us,” he said.

At some point Diallo managed to call the police, and when the officers finally arrived, the attackers ran away. One of his friends was beaten up so badly that he had to be hospitalized.

“I simply tried to survive,” Diallo said. “I hadn’t done anything wrong. It all happened only because of my skin color.”

Being Black in Germany has always meant exposure to racism, from everyday humiliations to deadly attacks. In eastern Germany, the risk can be even greater.

After World War II, West Germany became a democratic, diverse society but in East Germany, which was run by a communist dictatorship until the end of 1989, residents barely had any contact with people of different ethnicities and were not allowed to travel freely abroad.

Experts say that specifically in Thuringia, radical far-right forces have created an environment that’s hostile toward minorities, including Black people.

Now, with the rise of the far-right Alternative for Germany, or AfD, Black Germans and African migrants like Diallo are growing increasingly concerned.

Thuringia, which has a population of 2.1 million,holds state elections on Sept. 1, and the fiercely anti-immigration AfD is leading the polls, on 30%.

In 2023, the NGO Ezra, which helps victims of far-right, racist and antisemitic violence, documented 85 racist attacks in Thuringia, down only slightly from 88 attacks in 2022, which Ezra described as “an all-time high of right-wing and racist violence” in the state.

“In recent years, an extreme right-wing movement has formed in Thuringia, which has contributed to a noticeable ideological radicalization of its followers. Politically, the Alternative for Germany party is the main beneficiary,” Ezra and a consortium of organizations tracking racism wrote in their annual report.

AfD’s Thuringia branch is particularly radical and was put under official surveillance by the domestic intelligence service four years ago as a “proven right-wing extremist” group.

“Authoritarian and populist forces, which are becoming very strong here now, harbor a great danger in Thuringia,” says Doreen Denstaedt, Thuringia’s minister for migration, justice and consumer protection.

Denstaedt, the daughter of a Black father from Tanzania and a white German mother, was born and grew up in Thuringia.

The 46-year-old member of the Green party said that growing up in Communist East Germany, she was “always the only Black child.” As a teenager, she was never allowed to go home on her own because of the risk of racist attacks, and she sometimes suffered racist slurs in her school.

“I actually experienced myself that people called me a foreigner, which really confused me at first, because I was born in Saalfeld” in Thuringia, Denstaedt said.

She fears that in the current political climate, racist narratives will become acceptable in the middle of society.

“My biggest concern is that people do not question (these prejudices), especially if they are not affected themselves,” she said.

It’s not exactly clear how many Black people live in Germany nowadays, as different ethnicities are not documented in official statistics, but estimates put the number of people of African descent at 1.27 million. More than 70% were born in Germany, according to Mediendienst Integration, which tracks migration issues in the country.

Germany’s history of racial discrimination begins long before the Nazis began excluding, deporting and ultimately murdering Black people in the 1930s and 1940s.

The German Empire held numerous colonies in Africa from 1884 until the end of World War I. These included territories in present-day Tanzania, Burundi, Rwanda, Namibia, Cameroon, Togo and Ghana.

The German government has only recently started dealing with the injustices committed during that period. In 2021, President Frank-Walter Steinmeier called on Germans to face the country’s cruel colonial past , and in 2023, he apologized for colonial-era killings in Tanzania over a century ago.

Daniel Egbe, a 58-year-old chemist from Cameroon who moved to Thuringia in 1994 to study, says he’s shocked how little Germans know about their colonial history. He says this ignorance may also factor into the unequal treatment of Black people.

“I’ve been teaching classes in school,” Egbe told the AP. “I tell them a bit about myself and especially the fact that Cameroon was a German colony. Many students don’t know anything about Africa or about the German past and it must be put on the map.”

Egbe, who took German citizenship in 2003, founded AMAH, an organization that helps university students and migrants from Africa when they experience discrimination in the city of Jena, in eastern Thuringia.

He’s worried about the rise of the AfD but has no intention of leaving.

“We won’t leave, we will do our part to change this society,” he said. “People are mostly afraid of what and who they don’t know. We have to change things through education.”

As for Diallo, the Guinean who was attacked in Erfurt four years ago, he also vowed to help improve the situation for Black people in Germany.

Even though the attack traumatized him, it also empowered him to fight for justice, he said. A year ago, he enrolled in university in Munich to study law, but he still visits Erfurt frequently, where he supports Youth without Borders, a network of young migrants.

“I don’t exactly know yet how I’m going to change Germany, but I know I will,” he said.

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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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