Note: The following text was published on 29 July 2021. It is based on the state of research at that time and it does not reflect the most recent developments.
Terminology and focus
All countries have reacted to the Covid-19 pandemic with policies aiming to contain the spread of the virus within their territory. Most countries have done so by severely limiting basic freedoms of their inhabitants. The word “lockdown” has been used to refer to very different types of containment policies, most of which have never been applied before or at least not for a comparable time and space.
At the end of 2019, the definition of “lockdown” on Wikipedia read: “an emergency protocol that usually prevents people or information from leaving an area”. At that time, there was no reference to epidemiological measures. By May 2021, the definition had changed to “A lockdown is a restriction policy for people or community to stay where they are, usually due to specific risks to themselves or others if they can move and interact freely.” However, “lockdown” nowadays means very different things in different places. The closure of schools and businesses is often referred to as a lockdown even when people are allowed to circulate freely. In many places, lockdown was referred to as (mass) quarantine. Yet, in the scientific literature as well as in most countries’ public communication, there is a clear distinction between quarantine and lockdown.
Quarantine refers to people who are likely to have been exposed to the virus, either because they were in close contact with an infected person or because they recently travelled from a place with a high incidence of the virus. At least that is the theory, in fact, there are numerous instances of countries enforcing quarantine on travellers from another country with a lower incidence. Even individuals travelling from a “high incidence” country are very unlikely to be infectious themselves. When a person travels from a country with a 7-day incidence of 300 new infections per 100,000 inhabitants (a high incidence many countries never reached), this means that an individual’s risk of having recently contracted the virus is 0.3% – assuming an even distribution of the virus through the entire population. Of course, the likelihood is much lower if this person has no symptoms or has even tested negatively. Persons travelling from high incidence countries are nevertheless treated the same as e.g. the spouse or close co-worker of an infected person: They are forced to stay home for a period of between 5 and 14 days, which sometimes can be evaded or shortened by testing negatively. A quarantine is mostly even stricter than a general lockdown. While people under a lockdown are normally allowed to leave their house for a set of reasons such as work or essential shopping, quarantined individuals are not allowed to leave their house under any circumstances.
If a person was tested positive or shows symptoms, separating this person from non-infected people is usually referred to as isolation but often it is erroneously called quarantine. While the term lockdown is frequently used for policies that do not fit its above-mentioned definition (e.g. for business closures), the terms quarantine and isolation usually refer to “lockdown” measures based on the individual risk of exposure rather than for entire populations. As described above, this does not mean in all cases that the individual risk is indeed higher. If, for instance, a person flies from a place with an incidence of 200/100,000 to a place with one of 250/100,000 and even tested negatively prior to departure, there is clearly no higher individual risk of being infected, but still, this person would be confined to their home under the current policies of many countries.
Another term frequently used to refer to “lockdown” policies is stay-at-home order. Because the word lockdown is so frequently misused, I will mostly stick to the term stay-at-home order in this project. A stay-at-home order, according to Wikipedia, “is an order from a government authority that restricts movements of a population as a mass quarantine strategy for suppressing or mitigating an epidemic or pandemic by ordering residents to stay home except for essential tasks or for work in essential businesses”. A curfew limits people’s movement to certain hours. Usually, a curfew bans leaving the house during the night. In this project, I will focus on stay-at-home orders and curfews. I will also include policies that impose conditions for leaving the house, e.g. the obligation to wear a mask outside independent of the distance towards others.
All these policies and countless others are often referred to as “lockdown policies” in public discourse. Another term frequently used, particularly in the scientific literature is non-pharmaceutical interventions (NPI). When Covid-19 was a new disease, there have only been NPI while now there are vaccines, i.e. pharmaceutical interventions.
Most countries have issued some sort of stay-at-home order during the Covid-19 pandemic but the details of these orders vary dramatically. Stay-at-home orders are mandatory regulations that prevent citizens either from leaving their homes or from entering public space. The most important practical differences between stay-at-home orders are which “good reasons” people could bring up to justify being outside of their home and how they had to do so. The exact term differed, for instance, UK citizens needed a “reasonable excuse” to go out. In all countries, “essential work” justified leaving the house, although there have been huge differences in which work was deemed essential and whether remote work was mandatory where possible. Urgent medical visits and health emergencies were other reasons that were accepted everywhere. Essential shopping was allowed everywhere too, but often only in very limited time frames. This included supermarkets, pharmacies, gas stations, bank services, and often different other shops that were seen as important. Walking a dog was regarded as a good reason to leave the house in most places, often limited to a very short time. A large difference between stay-at-home orders in different countries was whether physical activity was treated as a good reason to leave the house. In some places, this was not the case, in others, physical exercise was allowed limited to a certain time or only in close proximity to home. Other countries allowed going for a walk without time constraints. The impact of a stay-at-home order is probably different in both cases. If individual exercise or going for a walk is allowed at all times this might decrease the burden on physical and mental health as compared to a stay-at-home order that literally requires everyone to stay at home. It is also more difficult to enforce a complete ban on gatherings when individual movement is allowed which decreases the negative effects of complete social isolation for those who dare to breach the law and meet in private places. But what does a stay-at-home order mean if going for a walk is a good reason to leave the house that could always be brought up in controls? It still implies two things that were very unusual in European countries until March 2020: First, even when walking remains allowed, a stay-at-home order still means that sitting, lying or standing in public spaces for a long time is not allowed. Second, law enforcement officers are always able to control you and ask for your reasons for being outside.
The view that you could need “a good reason” or even “a reasonable excuse” to be outside is perhaps the most drastic cultural shift in post-war Europe. Unfortunately, neither the underlying ideology nor the consequences of this new post-individualist culture have been addressed much in research so far. My dataset on stay-at-home orders is purely descriptive and does not imply any value judgements in itself, but my motivation for this project is that I am deeply convinced that humans should not require a “good reason” to be outside. Being outside is a good reason in itself and needs no excuse. It is against human nature to be locked in buildings and to be deprived of sunlight and other environmental influences. You have a good reason no matter what they tell you.
TL;DR: “Lockdown” has become an ambiguous term that is used for many different things. This project focuses on lockdowns in a narrow sense, i.e. on stay-at-home orders and curfews. These are different from quarantine, which is an individual stay-at-home order based on likely exposure to the virus and from isolation which refers to infected individuals. Under a stay-at-home order, people need “a good reason” to leave their homes. This will be a major focus of this project.
Data and other lockdown policy trackers
There are several projects tracking government responses to the Covid-19 pandemic using public sources. One of the largest of these projects is the CoronaNet Research Project. Over 500 research assistants worldwide contribute to this comprehensive dataset comprising of 60,000 entries as of 2 May 2021. “A good reason” was inspired by the CoronaNet project: I participated as a research assistant with the task of keeping track of policy changes in Honduras and later in the German state of Schleswig-Holstein. Initially, I meant to use CoronaNet data to write a short report about stay-at-home orders in different countries. Sadly, I found out that despite the enormous efforts of hundreds of volunteers, the dataset was still very incomplete. CoronaNet researchers are consistently working on updating and completing their dataset and I hope my project can contribute as an additional source of information for them as well as for other researchers.
Perhaps the most comprehensive and most cited policy tracker is the Oxford COVID-19 Government Response Tracker (OxCGRT). It provides data on 17 indicators of government response collected by over one hundred Oxford University students and staff from different parts of the world. One of the included indicators are stay-at-home requirements which are classified among two dimensions: First, countries are put into four different categories depending on the severity of stay-at-home requirements and coded like this:
0 – no measures
1 – recommend not leaving the house
2 – require not leaving the house with exceptions for daily exercise, grocery shopping, and “essential” trips
3 – require not leaving the house with minimal exceptions (e.g. allowed to leave only once every few days, or only one person can leave at a time etc.)
Second, it is noted whether the policy is targeted or not meaning whether the policy only applies to a sub-region or jurisdiction.
While this is generally a very well-maintained dataset, it does hardly allow for a more nuanced analysis of the effects of stay-at-home orders in different countries. Most European countries have been grouped into category 2 for most of the time even though the exceptions did not in all cases include “daily exercise, grocery shopping, and “essential” trips” or the definition of what is an essential trip varied dramatically. Spain and Austria have been grouped in the same category for instance when Austrians could leave their house “for recreational purposes” at all times while in Spain physical exercise was not treated as “essential” and even going for a short walk was banned for many weeks. Assigning Spain and Austria into the same category neglects the potentially very different effects lockdown policies had in these countries, especially on the mental health of their populations.
The Oxford COVID-19 Government Response Tracker further does not distinguish between curfews and all-day stay-at-home orders. In reality, it makes a huge difference whether people are free to move during the day and are “only” forced to stay home for example from midnight to 5 AM as was the case in most of Germany in May 2021. The Oxford COVID-19 Government Response Tracker researchers calculate a Stringency Index, a composite measure of nine measures including stay-at-home requirements. While acknowledging the extraordinary effort it surely took to calculate such a measure, I would like to stress that it can lead to dangerously misleading results if decision makers depend on such a rough composite measure summarising an immensely complex topic in a single number.
The Oxford Supertracker provides an overview of Covid-19 policy trackers and surveys. On 2 May 2021, 151 policy trackers are listed, most of them specialised on distinct regions or countries and on specific types of policy intervention. There are a number of policy trackers that cover stay-at-home orders and intend to do so worldwide or at least for all European countries, but a quick scan through these revealed that none of them offers complete data covering all European countries for the entire period from the beginning of 2020 until now.
The described limitations of the CoronaNet and Oxford datasets are the main motivation for this project. The core question my data aims to address is: Could you go for a walk? This question might sound almost silly in pre-2020 terms. There is little research on this because it was seen as self-evident to most researchers at least in Europe that you could just open your door and go out to take a breeze, no matter at what time and with which motivation. As we all know, this fundamental freedom has been taken away from most people, but there is little structured evidence so far. Established datasets such as the Oxford Government Response Tracker take into account whether a country imposed a stay-at-home order or not but they do not address the question of what this effectively meant if you lived there and just wanted to go out to take a walk.
TL;DR: This research project deals with the question of whether it was banned to leave the house to go for a walk in European countries in 2020 and 2021. There are other projects tracking lockdown policies in different countries, but none of them addresses the question of whether stay-at-home orders allowed individual recreational outdoor activity as an accepted reason to leave the house or not. This project aims to provide the most comprehensive data on whether and under which conditions it was allowed to go out for a walk in different European countries.
Aspects of lockdowns
The point of departure for this project is the widespread notion of lockdowns being a useful or even necessary tool to fight the Covid-19 pandemic. Lockdowns are a new policy, at least in the context of 21st century technology and in democratic societies. The public belief of lockdowns being a successful or indispensable tool of public health policy will very likely lead to the continued application of lockdown policies not only in the Covid-19 pandemic, but likewise in future epidemics and possibly in other crises not related to a virus. It is naïve to assume that this pandemic is a “once in a lifetime” event as epidemics and pandemics occur regularly and their probability rises with growth in world population of humans and livestock, life expectancy and mobility.
I will review existing evidence concerning different effects of lockdowns, many of which I believe are underrepresented in the public discourse. There are numerous examples of media portraying lockdowns as “inevitable” or making statements such as “country X was forced back into lockdown”. This spin on lockdowns being inevitable has spread widely in the general public as well. All sorts of events are cancelled “due to Covid” when it was actually “due to Covid policies” which are, or at least should be, a matter of public debate.
At least in my home country, Germany, there is much less of a public discussion about the side effects of lockdown policies than there is about the side effects of vaccines. While the latter underly a rigorous control of public health authorities, there is no similar scientific evaluation of the costs and benefits of non-pharmaceutical interventions. You probably heard about people worrying whether they should take the Astra Zeneca vaccine or not because it was associated with an increased risk for blood clots? There have been pages of newspapers and hours of podcasts filled with discussions on whether the vaccine is safe. Exact estimations differ, but the risk of developing critical blood clots after taking the shot was estimated to be around 5 over 1 million which even seems to be smaller than the risk of suffering from such blood clots after an infection with Covid-19. On the other hand, the effectiveness of the vaccine to prevent infections has been proven in large clinical studies. Personally, I am happy about the rigorous quality control of vaccines and I can understand that even rare side effects make people feel uneasy. I also observe a very disturbing tendency of pressurising people to get vaccinated against their will. Nonetheless, the public focus is odd for me. There is a vaccine that most people can still choose to use or not, well-proven to prevent severe cases of Covid-19 that has an extremely rare side effect which even happens to be a possible symptom of coronavirus. And you have coercive lockdowns which have not proven to be nearly as effective as a vaccine. In fact, it is highly disputed whether they had any effects to prevent infections, but we will come to that. Lockdowns come with all sorts of side effects including depression, anxiety, postponed health check-ups leading to belated treatment of cancer or cardiovascular conditions etc. that will all be addressed further below. These side effects are far more prevalent than 5 in one million. So why does the public discourse focus on criticizing a voluntary intervention that has proven benefits and comparably small risks compared to a coercive one that has large risks and unproven benefits? In other words: If lockdowns were a vaccine, they would have never been approved. I shall add that I personally know people who have been pressurised to get vaccinated against their will. With increased vaccine availability, several countries have introduced regulations that discriminate against unvaccinated people to an extent that vaccination can hardly be called voluntary anymore. There seems to be a continuity with lockdown rules though in the sense that instead of explicit coercion, “lockdown” regulations are applied to the unvaccinated but not to the vaccinated. For lockdowns in a narrow sense of the word which are the topic of concern on this website, this has not been the case so far, but I will cover such regulations if they emerge in the future.
Before I continue my analysis, let me address a critical point about this project: I consider myself as both an (amateur) researcher and an activist. I am aware that combining research and activism is a difficult balancing act. As an economist, I learned to distinguish between positive statements (what is?) and normative statements (what should be?) where the first can be proven or discarded empirically but the latter cannot. This entire project is driven by the profound conviction of large-scale lockdowns being a terrible policy. This normative statement has been, from the beginning, backed by several positive statements, e.g. predictions of adverse effects on poverty, hunger, or mental health. Yet, my rejection of lockdowns is also deep-rooted in my personal aversion against authoritarianism and in my love for being outside. In the following chapters, I will carefully disentangle different aspects of lockdowns. I will avoid mixing up positive and normative statements as much as possible which will not always be possible.
In the following, I will first give an overview of the costs and benefits of lockdowns. I will start with the epidemiological evidence of what lockdowns should achieve and whether they were successful doing so. I will then address several costs associated with lockdowns. In the third chapter, I will look at existing cost-benefit calculations of lockdowns. Lastly, I will briefly address the question which factors contributed to the emergence of lockdown policies across the world.
When I started this research, I was prepared to argue why I think lockdowns are morally wrong and probably not cost-efficient despite their supposedly unquestionable epidemiological benefits. But after reviewing the literature, I no longer believe that stay-at-home orders have proven effective in slowing the spread of SARS-Cov-2. I would not go as far as saying that they had no effect at all and I am almost certain that lockdowns contributed a little to “flattening the curve” in some places. Still, their effect is not nearly as big as lockdown proponents claim and it seems to be insignificant in many, if not most countries.
Based on the data I collected, I grouped all European countries into four categories depending on the strictness of lockdown policies. As the initial response in the spring of 2020 differed in many countries from the response to the second and third waves in the winter and spring of 2021, I assigned countries into a category depending on their first wave reaction and in one for their second/third wave reaction. As an end of the first wave period, I chose 31 August 2020. At that time, case numbers were low practically everywhere in Europe and most stay-at-home orders imposed during spring 2020 had ended. The entire period from 1 September 2020 will be referred to as the “second wave” even when there have been more than two waves.
The “no lockdown” category contains all countries that did not introduce stay-at-home orders during the respective time. The “less strict lockdown” category contains countries which did impose a stay-at-home order, but allowed individual outdoor activities, e.g. going for a walk. In some places, outdoor activities were limited to the municipality of residence or some maximum distance from home. The “strict lockdown” category includes all countries with a stay-at-home order that only allowed for minor exceptions. This includes countries that allowed going out only according to a timetable, with special permission e.g. after sending a text message, or having to carry a form at all times or where outdoor activities were limited to an area smaller than the municipality, e.g. to just 1 km around the house. A fourth category of countries only enforced night curfews that usually did not allow any outdoor leisure activities and had either no stay-at-home order applied during daytime or one with relatively broad exceptions.
While a general mask mandate is also a condition set to being outside and would therefore in theory disqualify a country for the “no lockdown” category, I did not consider outside mask mandates in this particular classification. First, a mask mandate is generally not viewed as a stay-at-home order (even though it could be easily rephrased to “stay at home unless you wear a mask”). Therefore, it felt strange to include countries with an outside mask mandate in the group of countries with a strict lockdown. Second, for reasons of simplicity, I did not want to open more categories. Third, the exact rules on masks and their enforcement vary a lot. In the dataset, I included all outside mask mandates that were independent of distance towards other people. But in practice, there have been places with a general mask mandate that was only enforced when people were close to others. On the other side, there were countries in which a general rule like “masks have to be worn wherever a distance of 2m cannot be ensured” were translated into general mask mandates for busy streets that were enforced even at times when a distance could be kept. Generally, countries with general outdoor mask mandates usually also had curfews or stay-at-home orders. Among the countries listed in the “no lockdown” category in the tables below, only Lithuania (first wave), Andorra (second wave), Ukraine and Bulgaria (locally during the second wave) had outdoor mask mandates.
Countries were always classified according to the strictest rules in place during the time covered. Poland’s New Year’s one-night curfew was ignored in the classification, however. When rules differed across a country, the country was placed in the category that fits best for most of the country. In the second wave, many countries have worked with a tier system where restrictions were automatically introduced if the incidence in a certain region exceeded a certain value. In this case, the entire country is grouped according to the rules in the regions of highest incidence. Conversely, if only single provinces or municipalities introduced stricter measures, the country is grouped according to the rules that were in place in the majority of regions and an asterisk signalises more stringent local restrictions. When stay-at-home orders were only applied to parts of the population (e.g. the elderly), the entire country is put into the respective category.
The tables show that no country adopted night curfews as their primary containment measure during the first wave but it was the most common category during the second wave. Countries that had a strict all-day lockdown in the first wave (e.g. Spain or Romania), as well as countries that imposed no lockdown during the first wave (e.g. Netherlands and most of Germany), opted for a strict ban on movements during the night when addressing the second waves of the pandemic. There are countries who were in the same category in both times and countries that switched categories, most notably Serbia that had a very strict lockdown in 2020 but refrained from reimposing any renewed curfews or stay-at-home orders. The map shows a clear north-south divide when it comes to lockdown measures.
Lockdown measures during the first wave:
|No stay-at-home order||Belarus, Bulgaria*, Croatia, Denmark, Estonia*, Finland, Germany*, Iceland, Latvia, Liechtenstein, Lithuania, Netherlands, Norway, Sweden, Switzerland|
|Night curfews with either no daytime stay-at-home order or broad exceptions during daytime|
|Stay-at-home order with broad exceptions e.g. for outdoor walks||Austria, Belgium, Czech Republic, Hungary, Luxembourg, Monaco, Slovakia, Slovenia, United Kingdom*|
|Stay-at-home order with only minor exceptions (e.g. timetables, special permissions, limited duration)||Albania, Andorra, Bosnia and Herzegovina**, Cyprus, France, Greece, Ireland**, Italy, Kosovo, Malta**, Moldova, Montenegro, North Macedonia, Northern Cyprus, Poland, Portugal, Romania, Russia, San Marino, Serbia, Spain, Turkey, Ukraine**|
*more stringent restrictions locally
**strictest rules only for some groups (e.g. elderly)
Lockdown measures during the second wave:
|No stay-at-home order||Andorra, Belarus, Bulgaria, Croatia, Denmark, Estonia, Finland, Iceland, Liechtenstein, Malta, Norway, Russia*, Poland**, Serbia, Sweden, Switzerland, Ukraine|
|Night curfews with either no daytime stay-at-home order or broad exceptions during daytime||Albania, Belgium, Bosnia and Herzegovina, Czech Republic, Germany, Hungary, Kosovo, Latvia, Luxembourg, Monaco, Montenegro, Netherlands, North Macedonia, Romania*, San Marino, Slovakia, Slovenia, Spain|
|Stay-at-home order with broad exceptions e.g. for outdoor walks||Austria, Ireland***, Lithuania, United Kingdom***|
|Stay-at-home order with only minor exceptions (e.g. timetables, special permissions, limited duration)||Cyprus, France, Greece, Italy, Moldova, Northern Cyprus, Portugal, Turkey|
*more stringent restrictions locally
**night curfew only on New Year’s Eve
***Unlimited walks allowed, but only within few km around home
First, let us have a look at some descriptive statistics. The graphs show cumulative Covid-19 cases and deaths per 100,000 inhabitants for the different categories of countries as listed above. I downloaded the data on cases and deaths from the OxCGRT website. They take the data from John Hopkins University themselves, but I found it more comfortable to work with the Oxford data. Northern Cyprus was excluded from the calculations because in the OxCGRT data, no data was provided for this territory. For the calculations, I added up the cases and deaths for each category and day, respectively, and then divided them by the sum of these countries’ population and multiplied the result with 100,000. Note that countries are not regrouped on a daily basis, but remain in the strictest category that applies to them over the course of the depicted time period.
For the first wave, I decided to depict the “no lockdown” category twice, once excluding Germany. Most of Germany was not under a stay-at-home order, but 6 of 16 states did impose stay-at-home orders allowing for individual outdoor walks. Few municipalities even imposed stricter stay-at-home orders. At the same time, Germany is one of the most populous countries in Europe and thus has a big effect on the result for the entire category. Given that Germany cannot easily be grouped into a category and given its size, I found it worthwhile looking at the other countries in its category alone.
We can see no big difference between the different categories of countries. Countries without a lockdown had the lowest deaths per capita, including Germany or not. Deaths per capita were highest in the group of countries with a lockdown that allowed for relatively unlimited outdoor activities. This is driven by Belgium and the UK which were ranked first and third in deaths per capita as of 31 August 2020, excluding microstates (second was Spain). Cases per capita were highest in countries with a strict lockdown. You could think that we are facing a problem of reverse causality here. Maybe countries with more cases were adapting stricter measures. Sebhatu et al. (2020) have shown that this was not the case in the first wave. The decision whether a country locked down or not was not driven by the local incidence. I will discuss this study further below. The hypothesis of countries with more cases imposing stricter lockdowns (ignoring other factors) is also disproved by my data. The category of countries with a strict lockdown had the lowest cases per capita when lockdowns were imposed in March and April, but they were less successful in “flattening the curve” than the other countries.
Of course, such a simple graph is not enough to derive any conclusions on whether lockdowns worked or not. Too many factors are left out. Countries that locked down probably were significantly different from countries that did not lockdown. As you can see in the maps, locked down countries were mostly Southern European while countries in Northern Europe who are richer on average tended not to impose any stay-at-home orders. Probably Norway, Iceland and Finland do not have the lowest cumulative numbers as of (30 June 2021) because they did not apply strict lockdown measures, but at least in big parts due to country-specific characteristics like a low population density, a large proportion of single households, high income and a huge tertiary sector of people who can work from home. On the other hand, it was not only the rich Nordic countries and Switzerland who opted not to lockdown. During the second wave, Serbia had no curfew for instance. Neither did Bulgaria. Belarus had the most liberal restrictions in whole Europe. Given that they did not face a catastrophic overwhelming of their health systems, it seems that not locking down is not a luxury only rich countries could afford. Let us see how the curves look for the second and third waves:
I depicted the “no lockdown” category twice again, this time excluding Russia for similar reasons as excluding Germany during the first wave. Russia is Europe’s most populated country and thus has a big effect on the results. At the same time, Russia does not perfectly fit into the “no lockdown” category as there has been a mandatory stay-at-home order for elderly people in Moscow. Additionally, I should note that it is hard to research Russian policy measures without understanding Russian or any other Slavic language, so I might have missed some policies.
Again, the different curves look similar with the exception of the “yellow” category. This is dominated by the United Kingdom where the emergence of the B 1.1.7 variant contributed to a spike in infections (and tests) earlier than in many other countries. The drop in cumulated cases for the red category on 20 May is due to France correcting their statistics. Around 350,000 infections had been double counted. This example sheds light on the limited reliability of case statistics. It is entirely possible that all differences between different categories of countries are driven by different testing regimes. Countries with tighter restrictions might perform more tests and thus discover more asymptomatic infections. Perhaps looking at deaths can be more insightful:
As with cases, countries without a lockdown performed best, but when excluding Russia, there is no difference to countries with a strict lockdown. Again, you can see yellow curve rise in January due to the situation in the UK. Unlike during the first wave, we can see a stronger flattening of the curve for countries with a strict lockdown. This might indicate that during the second wave, mandatory stay-at-home orders with strict bans on outside activities did contribute to reducing Covid-19-related deaths while nighttime curfews and stay-at-home orders allowing for outdoor leisure did not. But keep in mind that strict lockdown rules might be correlated with many other measures like school closures or travel bans. We might see the effects of these other measures here, or we might see a development that is completely unrelated to any policy measures. Correlation does not equal causation. Even if we assume that in this case correlation does imply causation, this does not say that lockdowns have been beneficial when taking into account their costs which I will outline further below.
I have to admit that I am not sure to which degree statistics on Covid-19 mortality are comparable across countries. Dividing deaths by cases yields a case fatality rate that implausibly differs between over 4 percent in Bosnia and Herzegovina and Bulgaria and under 0.5 percent in Iceland and Cyprus as of 30 June 2021. All-cause mortality is an unbiased measure that is completely independent of different testing regimes. Euromomo has charts of weekly excess mortality for many European countries. Unfortunately, you cannot download the raw data from their website. Just looking at the graphs, I cannot see any clear relationship between lockdowns and excess mortality.
If lockdowns had a strong effect, you would assume that infections in locked down countries would rise more slowly over time. While this was not the case in the first wave, there might have been such an effect for the strictest countries during the second wave. It has not been strong enough to push the cumulated deaths under the level of those countries that did not lock down, however. Also, lockdowns are regularly justified by the outlook to exponential growth in infections that countries would face if they did not resort to these draconic measures. This is clearly rejected by the evidence. With or without stay-at-home orders: No country in Europe has seen sustained exponential growth up to the point where herd immunity was reached. This is the same for other regions. An early academic paper that noted the fact that infections with SARS-CoV-2 dropped everywhere and the reproduction number Rt fell to 1 or below everywhere independent of local restriction is Atkeson et al. (2020).
A detailed analysis of this pattern is provided by Philippe Lemoine in his text “The Case against lockdowns”. I highly recommend this piece to anyone who is interested in more analysis of this type. While I focus on Europe, Lemoine also features some comparisons between different US states during the second wave that prove the relative ineffectiveness of lockdowns even more than comparisons between European states. States like Florida, North Dakota, South Dakota, Georgia arguably give a better counterfactual than e.g. Sweden as restrictions in these US states have been even smaller while other US states had very strict lockdown policies. See also this (German) article for a comparison between North Dakota and South Dakota, two neighbouring US states with similar geography and demographics that differed significantly in the severeness of their Covid restrictions. While North Dakota mandated masks, South Dakota did not for instance.
To cite Lemoine: “Even if someone has been able to find a large effect of non-pharmaceutical interventions on transmission with a more sophisticated statistical analysis, the fact it doesn’t jump at you when you look at this kind of simple graphs should make you sceptical of that finding and, the larger the effect, the more sceptical you should be, because if non-pharmaceutical interventions really had a very large effect it should be easy to see it without fancy statistics. I think that, in general, one should be very suspicious of any claim based on sophisticated statistical analysis that can’t already be made plausible just by visualizing the data in a straightforward way. (To be clear, this doesn’t mean that you should be very confident the effect is real if you can, which in many cases you shouldn’t.) That’s because sophisticated statistical techniques always rest on pretty strong assumptions that were not derived from the data and you should usually be more confident in what you can see in the data without any complicated statistical analysis than in the truth of those assumptions. So visualizing the data provides a good reality check against fancy statistical analysis.”
I will address which conclusions scholars have derived on the subject through fancy statistical analysis further below. But I think we can conclude two things from my superficial and Philippe Lemoine’s more detailed analysis: First, there are clearly other factors that are far more important in determining the spread of the virus than the question whether a country imposed lockdowns or not. Second, it is clear that not locking down does not automatically lead to an exponential growth of cases until herd immunity is reached and thus to overwhelmed hospitals. Remember that this is what lockdowns were meant to achieve back in early 2020. There was no talk of “Zero Covid”, there was no sports-like global competition which country had the lowest case numbers, and there were no arbitrary thresholds like “not more than 50 per 100,000 new infections over 7 days”. The single reason large parts of the public pushed for lockdowns was the perspective of overwhelmed hospitals! Luckily, while there have been local bottlenecks, the dystopia of a complete collapse of the health system with people dying from untreated injuries because hospitals are full of Covid patients did not become reality in most countries – whether they locked down or not.
Of course, health workers have worked under incredible pressure last year, but they have done so before. Of course, locally, ICU capacities were overwhelmed, e.g. in parts of Northern Italy at the beginning of the pandemic. In India and some South American countries, health systems really seemed to be at the brink of collapse, but remember that people have always died from completely preventable causes in India. There are still millions of children suffering from deadly hunger in India. With all respect for this beautiful country: India has been a public health disaster before the pandemic already, although both the pandemic and the lockdowns certainly aggravated existing problems.
Also, when looking at the data on lockdowns, please bear in mind that I work with a rather narrow definition of lockdowns, i.e. stay-at-home orders. Nearly all countries in the “no lockdown” category still introduced strict and unprecedented rules on events and gatherings. Theoretically, it seems far more likely that bans on gatherings have an effect on infections than restrictions to the individual right of movement have. There is no way of getting infected without coming close to someone infected (or touching something the infected person touched before but this is only a minor channel of transmission for SARS-CoV-2). If you prevent two healthy people to meet, this has no effect on infections. If you prevent someone, even if infected, to go for a walk on himself, this has no effect on infections. The only possible way of stay-at-home orders to reduce infections would be by preventing infected people to meet others. But even most countries that did not implement stay-at-home orders have introduced strict rules on gatherings and nearly all countries have forced tested or symptomatic patients to isolate. This is a plausible explanation why stay-at-home orders seem to have had no significant effect. As the author “zacki” explains here (in German), “Lockdowns nearly only affect loose contacts between non-infected. Therefore, they have little effect.”
Another possible reason is that there have been several exceptions to stay-at-home orders. In most cases, it was always allowed to leave for work, often restricted to “essential” sectors which mostly included the entire primary and secondary sector. This led to bizarre scenarios: In nearly all countries, it was perfectly legal to stay in a badly ventilated room with many others as long as it was work-related. In most places, this was not only true for the most indispensable type of work. In large parts of the world, people could legally work together to produce, say, hiking equipment but if the same people would hike in the forest this would have been illegal despite a much lower risk of infection outdoors.
The third reason why it is not at all implausible that lockdowns had no effect is perhaps the most important one: In the face of the pandemic, most people changed their behaviour in a radical manner – with or without coercive measures. Human beings are not computer programmes that only respond to commands. Except for young children and seriously mentally handicapped people, we are capable of estimating risks and applying preventive measures to protect ourselves.
The scientific literature on lockdown effectiveness
So what does the science say? Do fancy statistical models come to different conclusions than straightforward visualisations? In the following, I will present some studies that analysed the effectiveness of lockdown measures. My literature review has not been systematic, but it includes the most frequently cited studies on the subject. If you know of another study that I should include here, feel welcome to reach out to me.
Perhaps the most influential study on the effects of lockdown measures was published in Nature by Imperial College’s Flaxman et al.: “Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe”. Their paper has been cited in over 1000 other publications according to Google Scholar and it has been one of the main sources for politicians and journalists who have claimed that “lockdowns work”. The time frame they analysed was from February 2020 to 4 May 2020. Flaxman et al. write “We find that across 11 [European] countries 3.1 (2.8-3.5) million deaths have been averted owing to interventions since the beginning of the epidemic.”. This is the premature conclusion that made it to the news. It is premature or even misleading due to several serious flaws in their methodology:
In the words of the authors: “The counterfactual model without interventions is illustrative only, and reflects the assumptions of our model. We do not account for changes in behaviour; in reality, even in the absence of government interventions we would expect Rt to decrease and therefore would overestimate deaths in the no-intervention model.” Flaxman et al. worked with a model that only allows two things to change the reproduction number Rt: Policy measures and past infections, i.e. herd immunity. Except for correcting for herd immunity, they assume Rt to be constant and to only change every time a policy intervention comes into force. They assume “that changes in Rt are an immediate response to interventions rather than gradual changes in behaviour”. This is crucial. They completely neglect any voluntary changes in behaviour. As anyone who has lived through last year can tell, this is a completely unrealistic assumption. They also largely neglect other factors that caused infection dynamics such as seasonality. Their reasoning is somewhat circular: They made a model that only allows policy interventions to slow infections, matched it to data of falling infections and, surprise, found that policy interventions were the cause for the fall in infections.
As the authors state themselves, it is hard to disentangle the effects of different interventions given that they were implemented in rapid succession. According to their estimations, lockdowns have caused a significant reduction in Rt of 81% (95 percent confidence interval: 75% to 87%). Other measures, e. g. school closures, were not identified to have an effect on Rt. The exception is Sweden where despite not locking down, infections fell (even if slightly slower than in many other countries). As is shown by Soltesz et al. (2020), Flaxman et al.’s model is extremely sensitive to changes in NPI definitions. Soltesz et al. also show that Flaxman et al.’s model only works by assuming an unrealistically large country-specific effect that caused the pandemic to slow down in Sweden despite not having introduced a lockdown. This point was also raised by Philippe Lemoine in a blog article, so if you want some more technical reasoning why Flaxman et al.’s paper is bad science, please read there.
According to the supplementary information of their paper, Flaxman et al. define lockdowns like this: “As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks.” This is an extremely vague definition and results in 10 countries (all except Sweden) having locked down: Italy, Spain, Austria, France, Belgium, Denmark, Switzerland, Germany, Norway and the UK. How should they come to any conclusions about the effectiveness of a “lockdown” when they do not even decide what they mean by lockdown? Spain turned itself into an open prison for 2 to 3 months while in Denmark people were allowed to go out whenever they wanted and meet up with up to 9 other people at a time. This can hardly be treated as the same policy.
The initial R value they modelled the counterfactual model with was 3.8 on average, somewhat higher or lower in the different countries and they worked with early estimations of the infection fatality rate (IFR) of around 1.38%. The IFR is the proportion of deaths from infection compared to the total number of infected individuals, diagnosed or not. An R0 of 3.8 is somewhat higher than many more recent estimates but was probably fair to assume in the spring of 2020. An IFR of 1.38% is probably an overestimation, however. A meta-analysis by John Ioannidis published in the Bulletin of the WHO found a median IFR of 0.27%. Another meta-analysis by Meyerowitz-Katz and Merone found an estimated IFR of 0.68%. Due to an aged population, the IFR is likely to be significantly higher in Europe than on global average. A serological study on a super-spreading event in Gangelt, Germany by Streeck et al. (2020) published in Nature calculated an IFR of 0.36%, but according to Dimpfl et al. (2020) this figure needs to be corrected to 0.46% due to deaths reported after the end of the study period. Combining the Gangelt data with data from a serological study from another hotspot (Ischgl, Austria) and national data on tests and deaths in Germany, Dimpfl et al. calculate an IFR of 0.83% for Germany. Whatever the real IFR is, it is most likely far below 1.38%.
In conclusion, given the unrealistic assumption of no voluntary behavioural changes, the disregard of seasonality and other important factors, the vague definition of different policy interventions, the incapacity to explain the case of Sweden, and the extremely high IFR estimate, the paper of Flaxman et al. should not be taken as evidence for the effectivity of lockdown. Aside from these methodological flaws, their analysis only covers 11 countries for less than 3 months. Over one and a half years into the pandemic, we can draw on more comprehensive data.
In the same issue of Nature from 8 June 2020, another very influential paper on lockdown measures was published: Berkeley’s Hsiang et al.’s “The effect of large-scale anti-contagion policies on the COVID-19 pandemic” was cited over 600 times according to Google Scholar. The authors analysed regional data from six countries (Italy, France, China, South Korea, Iran and the United States) until 6 April 2020 and estimate that all policies combined reduced the average growth rate of infections by 25%. However, they also ignore behavioural adaptation in their analysis: “If individuals alter their behaviour in response to new information unrelated to anti-contagion policies, such as seeking out online resources, this could alter the growth rate of infections and thus affect our estimates. If increasing availability of information reduces infection growth rates, it would cause us to overstate the effectiveness of anti-contagion policies.”
They further find that when policies are assumed not to have an immediate effect on infections but recalculate their model in a way that a policy only becomes effective between one and fifteen days after it is introduced, the estimated effects of policies and the significance of these effects shrink. It is not just a fair assumption, but absolutely clear that any policy does not affect (diagnosed) infections the next day given an incubation period of about 5 days. The included policies were very heterogenous but included home isolation in China, Italy, Iran, France and the US. In France, the effect of home isolation was combined with business closures on the same day and was estimated to amount to a 15% decrease in per day growth while in Italy, home isolation was associated with a 3% rise in per day growth rate (though not significant).
So, just like the Imperial College team, the Berkeley scientists estimate their model based on the unrealistic assumptions that voluntary behavioural changes and seasonality do not influence infections and additionally they ignore the incubation period of SARS-CoV-2. Even if these serious flaws would have been addressed, the time frame they analysed was rather short and behavioural changes could very well differ between the first wave in March 2020 and current or future infection dynamics so that any effects found are unlikely to be repeatable in the present or future. But remember that effects of behavioural changes are discarded from the beginning, making the paper useless for deriving policy recommendations.
Islam et al. (2020) used the Oxford Covid-19 Government Response Tracker to estimate the impact of non-pharmaceutical interventions on infections in 149 countries or regions. For their lockdown variable, they combined stay-at-home regulations with restrictions of movement within a country. Following their approach, even Sweden had a lockdown. Out of 149 countries, only Belarus, Iceland and Tanzania did not impose a lockdown. It is not clear what lead to this coding, but I assume that they included countries with recommendations to stay home, which according to the Oxford data, included Sweden and other countries that did not impose mandatory stay-at-home orders. Islam et al. found that this “lockdown” together with school closures, workplace closures, restrictions on mass gathering and the closure of public transport had a joint effect of reducing the incidence of Covid-19 by 13%. As they do not work with a completely unrealistic counterfactual scenario and only use real-world data, this estimate could be more realistic than those presented in the above-mentioned papers. Their work does not give any estimate on the impact of lockdowns in particular though. If lockdowns combined with a set of other measures like school closures, business closures, or restrictions on gatherings, reduced the incidence by 13%, this figure could be interpreted as an upper boundary of the effect of lockdowns alone.
Haug et al. (2020) analysed the impact of NPIs in 79 countries in March and April of 2020 using data from the Complexity Science Hub Covid-19 Control Strategies List (CCCSL), the above-mentioned Coronanet Project and the WHO-PHSM dataset. They disaggregated several different NPIs and found small gathering cancellation to be the most effective one. National lockdowns (including stay-at-home orders in US states) and individual movement restrictions (lockdowns and curfews) both were among the most effective interventions with an estimated reduction in Rt of about 0.1. As with all other studies, it is difficult to clearly isolate the effect of lockdowns given that many other measures were taken simultaneously. As Haug et al. write, lockdowns can be seen as the “‘nuclear option’ of NPIs: highly effective but causing substantial collateral damages to society, the economy, trade and human rights”. They also stress that “communicating on the importance of social distancing has been only marginally less effective than imposing distancing measures by law.”
Brauner et al. (2021) published a paper in Science in 2021. They collected information on non-policy interventions between 22 January and 30 May 2020. In their study, Brauner et al. reestimated their model several times under different assumptions to arrive at an estimate of the effects of single NPIs which they categorised as having either a small (<17.5%) effect on Rt, a moderate one (between 17.5% and 35%) or a large one of more than 35% reduction of Rt. According to their study, school and university closures had a large effect as did bans on gatherings to 10 people or less. When other interventions were already in place, issuing a stay-at-home order was only associated with a small effect on Rt. As Brauner et al. write they “found that issuing a stay-at-home order had a small effect when a country had already closed educational institutions and nonessential businesses and had banned gatherings. In contrast, Flaxman et al. and Hsiang et al. included the effect of several NPIs in the effectiveness of their stay-at-home order (or “lockdown”) NPIs and accordingly found a large effect for this NPI.” Just as the other studies, Brauner et al.’s model does not account for voluntary behavioural changes.
Banholzer et al. (2020) analysed the infection dynamics in 20 countries through 15 April 2020. They find lockdowns to be the least effective among seven investigated NPIs. According to their estimations, lockdowns were responsible for a 5% decrease in reported cases while venue closures, border closures, work bans on non-essential activities and event bans all had a substantially larger effect of 23% to 36%. Banholzer et al. argue that “the moderate effect of lockdown may be explainable by event bans, venue closures and gathering bans catching already a substantial part of the impact of a lockdown”. Their analysis is subject to the same limitations as most other research nonetheless: Their model implicitly attributes all changes in cases to NPIs and neglects voluntary behavioural changes. Banholzer et al. did not assume NPIs to have an immediate effect on infections though, but assumed a 7-day delay which corresponds to a typical SARS-CoV-2 incubation period.
Liu et al. (2021) analyse the Oxford data for 130 countries for the time between 1 January and 22 June. With the same limitations in mind with regard to the reliability of this data and to the assumption that only NPIs affected behaviour, their results are interesting as they conflict with those of e.g. Flaxman et al. Stay-at-home requirements were not associated with a reduced reproduction of the virus. According to Liu et al., school closures and internal travel restrictions were the NPIs most strongly associated with a lower Rt.
Chaudhry et al. (2020) looked at the 50 countries with most cases (as of 1 April 2020) until 1 May 2020. They do not find lockdowns to have an effect on cases but they do find that lockdowns are positively associated with recovery rates. They cannot offer an explanation why lockdowns should affect recovery rates if they affect neither critical cases nor overall mortality. Instead, higher Covid-19 caseloads were associated with higher obesity prevalence, median population age, and longer time to border closures from the first reported case. Increased mortality was associated with higher obesity prevalence and GDP per capita.
Bjørnskov (2021) compares weekly mortality data in the first half of the years 2017, 2018, 2019 and 2020 in 24 European countries. Using general mortality data is independent of differences between countries in reporting behaviour. Also, general mortality is a much better figure to inform policy decisions as it is comparable across years. Counting only Covid-19-related deaths bears the danger of overreacting as there is no “normal” number of such deaths since SARS-CoV-2 is a newly emerged virus. But as those who die following an infection with the virus are usually old or sick, a significant proportion of them would have died anyway. Comparing general mortality means that we no longer compare against zero, but against the usual mortality that we have been used to and that we accept as a society. Bjørnskov accounts for the potential endogeneity of lockdown policies by including lags of mortality rates and uses two different specifications of the Oxford policy stringency index. In his paper, he finds no relationship between lockdown severity and mortality.
Koh et al. (2020) used the Oxford data and found that physical distancing measures were effective in containing the spread of the virus if applied early. Mandatory stay-at-home orders were not more effective than recommendations to stay home.
As stated before, the literature review at hand is not intended to be comprehensive and many more studies could be included here. Regarding the last point, I would like to quote a literature review performed by Allen (2021):
“There are, by my count, close to twenty studies that distinguish between voluntary and mandated lockdown effects. Although they vary in terms of data, locations, methods, and authors, all of them find that mandated lockdowns have only marginal effects and that voluntary changes in behavior explain large parts of the changes in cases, transmissions, and deaths.”
Loewenthal et al. (2020) claim to have found a clear correlation between the time at which a country entered lockdown and the mortality rate, but no such relation between lockdown strictness or lockdown duration and mortality rate. The authors define “lockdown” very differently from most other sources though, indeed I would argue that their wording is somewhat inaccurate. Loewenthal et al. (2020) do not look at explicit non-pharmaceutical interventions. They did not study laws and news articles. Instead, they used data on people’s movement for OECD countries collected from using maps on iPhones. They define the time at which a country went into lockdown as the time when actual mobility dropped. “Lockdown strictness” refers to how much mobility fell. This is certainly influenced by NPIs as Loewenthal et al. found the steepest fall in mobility in Spain (88%), which banned all non-essential movement with fewer exceptions than most other European countries. For my interpretation, Loewenthal et al.’s paper shows how little coercive lockdowns helped in containing the pandemic. Mobile phone data clearly shows that there has been a substantial reduction of people’s movement in all countries, independent of whether the country introduced a stay-at-home order or not. In Sweden, mobility dropped by 29%. Loewenthal et al.’s data suggest that the largely voluntary, but more moderate decrease in mobility in countries like Sweden were enough to yield the same benefits as the drastic decrease in countries like Spain. Loewenthal et al. further mention that the drop in people’s movement does not always coincide with the timing of government orders.
Several studies have shown that the peak of infections was reached before a strict contact ban was introduced in Germany, including a stay-at-home order in 6 of 16 states. People’s changed behaviour, possibly combined with earlier restrictions such as bans on large events, were sufficient in flattening or even bringing down the curve and keeping the reproduction number below one (see for example Wieland (2020) and Berlemann and Haustein (2021). For more evidence on this pattern for other countries, please refer to Philippe Lemoine’s “The case against lockdowns” I already advertised further above. A German source showing that the peak of infections was reached before lockdown in Germany, Austria and Ireland is Zacki (2021).
If there is such an unclear correlation between the strictness of containment measures and success in containing SARS-CoV-2, what is it that drives the huge differences between countries? I do not want to dig deep into this subject, but I would like to point to De Larochelambert et al. (2020) who used the Oxford data to investigate whether stricter or any lockdown rules were associated with lower Covid-19 death rates. What sets their paper apart from many of the aforementioned ones is that they looked at a broad range of other indices from five domains (demography, public health, economy, politics, environment) that could affect Covid-19 mortality. Lockdown strictness (though measured by the rather imprecise Oxford stringency index) was not associated with Covid-19 mortality but many other factors were: Obesity and inactive lifestyle, a higher mortality rate from cardiovascular diseases and cancer, GDP, a high or declining life expectancy, a lower mortality rate from infectious diseases and some geographical variables. My interpretation of these findings is that stay-at-home orders could even increase Covid-19 mortality in the medium run: If obesity and an inactive lifestyle are strongly associated with higher mortality, but lockdowns are not, how do you expect mortality to decrease when physical inactivity is enforced by locking people in their homes?
Many countries resorted to night-time curfews instead of all-day lockdowns during the second wave in winter and spring 2020/21. Without going too much into detail, I want to point to few studies analysing the effects of such curfews. Given the ambiguous effect of full-time stay-at-home orders, it seems very unlikely that part-time stay-at-home orders, i.e. curfews, have a huge effect. This is claimed by many governments nonetheless. When Germany introduced a national curfew (implemented locally depending on the local incidence), they used the work of Sharna et al. (2021) to prove the effectiveness of curfews (see the official justification). The authors collected data on NPI implementation, infections and deaths in 114 regions of 7 European countries between 1 August 2020 and 9 January 2021. Analyzing regions and not states takes account for the large subnational and even subprovincial variation in infections as well as in NPIs. Sharna et al. did not look at daytime stay-at-home orders, but one of the measures analysed were nighttime curfews. Sharna et al. estimate that night-time curfews had a moderate effect of reducing Rt by 12% but noted that curfews, due to their broad nature, interact with several other NPIs. The Closure of all non-essential businesses reduced Rt by 35% and a ban on all gathering reduced Rt by 26%. One of the main authors later criticised politicians for misinterpreting the study by concluding that nightly curfews would reduce Rt in Germany by 13%. He highlights the large uncertainty margins and the interconnectedness with other NFIs as well as the fact that the curfews were of very different duration.
Aside from Sharna et al., the German government cited two other studies in their official justification of night curfews: Domenico et al. (2021) estimated a model using data from the second wave in France estimating that curfews have brought down Rt by 15%. The authors acknowledge that it is difficult to single out the effect of curfews as they came together with other social distancing policies. The third study, Ghasemi et al. (2021), uses Canadian data to show that curfews were successful in reducing nighttime mobility, but did not address the epidemiological effects i.e. whether these contacts have led to infections.
In my eyes, the German government could have had a closer look at another study that provides empirical evidence on the effects of curfews in the German state of Hesse: De Haas et al. (2021) study the effects of night-time curfews from 9 PM to 5 AM in Hesse. The period of analysis was 18 November 2020 until 28 February 2021. Of the 26 counties in Hesse, 15 imposed a night curfew for some time during the observation period. Unlike studies that compare countries with very different demographic, economic or cultural characteristics, De Haas et al. worked with a quite homogenous sample. Their quasi-experimental study found no effect of night curfews on infections.
So, to shortly summarise the existing evidence on the effects of lockdown measures on the pandemic itself, we can certainly repeat the standard sentence in conclusions of scientific papers: More research has to be done. Existing evidence gives different results: Some studies find stay-at-home orders to be effective (though not necessarily efficient) in “flattening the curve” (e.g. Flaxman et al., Hsiang et al., Haug et al.) while others do not find lockdowns to significantly contribute to curbing the spread of SARS-CoV-2 (e.g. Brauner et al., Banholzer et al., Liu et al., Chaudhry et al., Bjørnskov). All studies face difficulties in attributing the dynamics of the coronavirus spread to certain measures given that many measures were implemented simultaneously, given that people adapted their behaviour voluntarily and given the many other factors that determine the path different countries take in the pandemic, e.g. seasonality and demography. Evidence for other NPIs is similarly unclear: Some studies find school closures to be highly effective for example (Brauner et al., Liu et al.) while others find them to have only a very small effect if any at all (Flaxman et al., Banholzer et al.). There seems to be no evidence that stricter lockdown rules are any more effective in fighting the pandemic given that several studies find voluntary social distancing to be equally effective as enforced confinement (Loewenthal et al, Koh et al., Islam et al.). Evidence from several countries points to the fact that people voluntarily reduced their movements (and probably their contacts) before any stay-at-home orders were introduced (e.g. Wieland, Berlemann and Haustein, Lemoine). As with full-time stay-at-home orders, the effects of curfews are disputed with some studies suggesting that they had a moderate effect on reducing infections (Sharna et al., Domenico et al.) and others not finding any effect (De Haas et al.).
The much-repeated claim that stay-at-home orders were necessary to prevent an unstopped exponential growth in infections with unavoidably overloaded hospitals can easily be discarded by looking at infection dynamics in the many places that did not confine their residents. Despite popular belief, Sweden is not a huge exception in this regard, but is in the same team as other Nordic countries (Denmark, Finland, Iceland and Norway), Switzerland, Croatia (despite strict internal movement restrictions), Estonia, Serbia, Poland, Bulgaria (the four latter only during the second wave) and many other countries around the world. Evidence from the United States does not show a clear difference in infection trajectories between those states that locked their citizens in their houses and those that did not. If stay-at-home orders have had any effects on infection dynamics, they are by far not as substantial as many people believe. Lastly, there seems to be little difference in effects between countries with extremely strict lockdowns like Spain during the first wave and those with lockdowns that still allowed going for a walk (e.g. Germany during the first wave). In the next section, I will address the other side of the coin, i.e. the undesired side effects of stay-at-home orders.
TL;DR: It is not clear whether lockdowns were effective in reducing the spread of SARS-CoV-2. Simple comparisons show no notable difference between countries that imposed lockdowns and countries that did not. While it is possible that lockdowns affected the course of the pandemic, other factors seem to be much more important in explaining differences between countries or regions. The initial goal of “flattening the curve” was reached everywhere, with tough restrictions or without. The scientific literature about the effects of lockdowns is inconclusive and suffers methodological difficulties. Different studies come to completely different results. Many studies finding lockdowns to be effective use rather broad definitions of lockdowns and thus cannot be used to justify the strictest types of such policies.
In the last section, we took a look into the effectiveness of stay-at-home orders, i.e. whether they were successful in achieving what they were intended to achieve. Now I will focus on the undesired effects, lockdowns had. As noted above, I will focus on stay-at-home orders and curfews, but I will touch on the effects of other Covid policies that are closely related.
Usually, I cite scientific papers and news articles here, but I would like to give a big shoutout to the subreddit LockdownSkepticism, one of the few places where people discuss and criticise lockdown policies in a very rational and civilised manner and an important source for my research. Concerning the costs of lockdown policies, reddit user freelancemomma has expressed it perfectly: “The costs include not only measurable outcomes such as job loss or drug overdoses, but intangibles such as shattered dreams, social starvation, and existential despair. These costs are no less real for being difficult to quantify.”
It is important to keep in mind that the evidence I refer to in the following is necessarily incomplete as lockdowns have affected every aspect of our lives. The variables that lockdowns are intended to affect are not hard to measure: Infections, free hospital beds, deaths. The costs are manifold and hard to quantify. It is equally difficult to establish causation, i.e. attribute them to lockdowns, specifically because the pandemic itself has side effects that go beyond the direct costs of infections, like the widespread fear of the virus.
There are several serious undesired side effects of Covid-19 containment policies that I would like to address here shortly even though they are not directly related to stay-at-home orders: The economic costs of stay-at-home orders are probably small compared to those of business closures and restrictions and border closure. But as these measures were often applied together with stay-at-home orders and were jointly branded as “lockdown” policies, they are closely related. For an overall assessment of the effect of non-pharmaceutical interventions as a response to the Covid-19 pandemic, it is of utter importance to take the economic effects and economics-related health effects more seriously than it has been done so far: Economically illiterate social media warriors have not ceased to exclaim that the priority should be to “save lives, not the economy” without taking into consideration that people live off the economy. Even in rich countries such as the UK, Denmark, and Germany, evidence shows that poor people die several years earlier on average as compared to rich people. A policy that makes people poorer might thus not be very suitable to save lives in the long run.
Economic stress and unemployment are risk factors for many serious health conditions. Researchers from the University of Oxford have calculated that the financial crisis has caused more than 10,000 additional suicides between 2008 and 2010 across the US, Canada and Europe. And suicides are just the tip of the iceberg. Unemployment is strongly associated with worse physical and mental health (see e.g. here and here).
In rich countries, the costs of business closures can be made up for by government transfers, but not the psychological effects of unemployment, and in this case, of the message that they are officially branded as “inessential” for the society. Complete compensation of lost income is not feasible in poorer countries and even in richer countries the precariously or unofficially employed do not benefit from compensation schemes. A brothel owner in Germany might be financially recompensated by the government, but the Eastern European prostitute in the street cannot claim any financial benefits. Farmers were allowed to continue working and Germany was quick in creating a website where German students could register as farm workers. At the same time, Eastern European seasonal labourers lost their planned income of an entire season.
The connection between wealth and health is much more extreme in poorer countries. Lockdowns have destroyed the livelihoods of millions in developing countries where little social safety nets exist. It should be taken into account that even “our” containment measures in the richer countries have detrimental effects on the world’s poor. Business closures in Europe disrupt global supply chains, at the expense of workers in Asia who are laid off. Also think of countries like Thailand that depend heavily on tourism, a sector that was crushed by travel restrictions. Dr. David Nabarro, WHO’s Special Envoy on Covid-19 made clear in October 2020 that the WHO does not advocate lockdowns as the primary means of control of this virus due to the effects on poverty and child malnutrition. Oxfam warned that lockdown-related hunger could claim more lives than the virus itself. Hunger and malnutrition have been widespread in 2020 among children who would, under normal circumstances, receive food at school. On the peak of school closures, the World Food Programme counted 369 million children missing out on school meals globally. In 2021, the FAO reports that after five years of virtually no change, the global prevalence of undernourishment has increased 1.5 percentage points in one year. Note that we are touching very sensitive ethical questions here: How many starving children are okay to save how many 80-year-olds from dying? My personal answer would be zero.
It will probably never be possible to make an exact calculation of how many people died earlier as an effect of Covid-19 containment policies and how many they saved from dying earlier because we have no counterfactual. Had the world not locked down, people would have changed their behaviour nonetheless. An economic crisis could have emerged only from the pandemic itself and individual, voluntary reactions to this threat. For instance, people would likely travel less even if there were no travel restrictions. Nonetheless, the economic turmoil would have certainly been much less severe without drastic authoritarian containment measures.
Non-pharmaceutical interventions to curb the spread of coronavirus have had tremendous implications for physical and mental health. An important health-related side-effect of lockdown measures has been an increased reluctance of people to seek medical care. This could have been caused by the fear of getting infected at the doctor’s practice or by a wish not to contribute to an expected overwhelming of the health system. But given that primary care contacts dropped at a time when health systems were clearly not overloaded (though expected to) it is likely that some people decided not to see a doctor due to the social pressure of the virtual #stayhome mob. It was after their furore turned into policy that primary care contacts in the UK for nearly all analysed conditions dropped significantly (Mansfied et al. 2020). By July 2020, contacts for nearly all analysed health conditions had not recovered to pre-lockdown levels. The conditions included in the study covered several mental conditions (e.g. depression, self harm, eating disorders) but physical conditions, too, such as heart failure and myocardial infarction. It is unlikely that the lockdown caused the number of heart attacks to fall from one day to the other. In the words of Mansfield et al. “it is more likely the reduced primary care contacts we saw represent a substantial burden of unmet need (particularly for mental health conditions) that could be reflected in subsequent increased mortality and morbidity.” As I will address further below, mental health problems were seriously exacerbated by the lockdown thus the number of people seeking care should have risen rather than fallen.
The same pattern was observed in other countries, e.g. in Germany where stationary cases in hospitals went down by 30% between March and May 2020 as compared to the normal level. Between mid-March and early April of 2020, hospital admissions for heart attacks fell by 28% in Germany and for strokes by 15%. This development is more likely to reflect social trends rather than rational thinking given that less than two percent of all beds were occupied by Covid-19 patients in the respective time period thus there has been no reason not to go to the hospital with a heart attack out of fear the hospital might be at full capacity. A comprehensive summary of the evidence of first-wave restrictions on cancer care is provided by Heneghan et al. (2021).
Many of the most striking public health problems of this age are related to an unhealthy lifestyle. As ever more people spend most of their day sitting, obesity has become a pandemic over the last decades. According to WHO estimates, more than half of Europeans were overweight in 2008 and more than a fifth was obese. As the WHO Regional Office for Europe writes on their website: ”Participation in 150 minutes of moderate-intensive aerobic physical activity each week (or equivalent) is estimated to reduce the risk of ischaemic heart disease by approximately 30%, the risk of diabetes by 27%, and the risk of breast and colon cancer by 21–25%. In addition, it has positive effects on mental health by reducing stress reactions, anxiety and depression and by possibly delaying the effects of Alzheimer’s disease and other forms of dementia. In Europe, estimates indicate that over one third of adults are insufficiently active.”
As we see, physical activity is beneficial for preventing some of the most prevalent physical and mental health issues. This is not limited to ambitious exercise. A simple walk in the park already brings about similar benefits. See for example this article for an incomprehensive list of health benefits that come with spending time outside). But without citing any more articles, I would assume that it has long been common sense that regular physical activity or exercise, fresh air and the right amount of sunlight exposure are beneficial to health.
It is hardly needed to cite any scientific studies to know that physical activity declines when people are recommended to, pressured to, or, as in most countries, forced to stay home. For numbers, you can see, for instance, Wilke et al. (2021) who performed a multinational study in 14 countries. Moderate-to vigorous physical activity declined by 41% and vigorous physical activity declined by 42.2% in April and early May of 2020 as compared with pre-lockdown levels. Wilke et al. find a correlation between the Containment and Health Index (CHI, the stringency index mentioned above calculated with the Oxford data) and reductions in physical activity. They note Spain among the outliers due to its low CHI index and high reduction in physical activity which, in my eyes, shows the inappropriateness of the CHI as Spain imposed some of the strictest restrictions on movement of all European countries.
Largely neglected in general debate, the world is facing a second pandemic of mental nature. It is unclear to what extent the mental pandemic is caused by fear of the virus itself and to what extent it is caused by political and social changes as a reaction to the pandemic. Looking at statistics on psychopharmaceutical prescriptions, it comes clear that the mental health pandemic was ongoing for years when the pandemic started, but it worsened a lot over the course of 2020: In a representative YouGov survey conducted in November 2020 in 16 countries and territories, between 44% (Germany) and 65% (United Kingdom) of respondents reported that the pandemic had detrimental effects on their mental health. More than 42% of people surveyed by the US Census Bureau in December reported symptoms of anxiety or depression in December, an increase from (not of!) 11% the previous year. UK adults reporting symptoms of depression almost doubled from 10% before March 2020 to 19% in June 2020.
The NGO Deutsche Depressionshilfe (German Depression Aid) conducted a representative survey in June and July of 2020 among 5,178 people between 18 and 69 years old and living in Germany. 1,094 participants have been diagnosed with depression at least once in their life and 197 suffered from acute depression. The fear of infection with SARS-CoV-2 was equal among the subsamples of depressed and the entire sample (43% vs. 42%), but in the sample of acutely depressed, a higher percentage (retrospectively) reported negative feelings about the first four weeks of lockdown (74% vs. 59%). Also, while the majority thought that social cohesion increased during the first four weeks of lockdown (54% agreement), the majority of depressed did not agree to that statement (44% agreement). In June and July, 68% of acutely depressed felt negative about the current situation as compared to 36% in the entire sample. In a second survey conducted in February 2021, equally representative for 18- to 69 years olds in Germany, 89% of acutely depressed reported that they are missing contacts to others and 87% reported not having sufficient exercise.
With the worsening of public mental health, treatment worsened too. In Germany, it was already extremely difficult to find a therapist before the pandemic and waiting lists were long. The pandemic further exacerbated this problem. In both surveys of Deutsche Depressionshilfe, more than every second patient suffering from acute depression reported cancelled therapy sessions or other constraints to therapy (Reich et al. 2021). In Wales, people referred to talking therapies fell by a third compared to pre-lockdown times while the prescription of antidepressants increased.
It is incredibly hard to properly estimate the mental health implications of lockdown measures. Most studies on mental health rely on typically rather small samples of volunteers. Everywhere, different non-pharmaceutical interventions were applied at the same time or in quick succession thus making it difficult to isolate the effect of stay-at-home orders. Additionally, huge mental stress cannot only arise from the fear of the virus or from lockdown measures, but from the fear of anticipated lockdown measures too. Personally, I can report that I spent many weeks in extreme distress caused by the fear of being confined and the fear of people around me pushing for confinement even though the most radical voices finally did not get as far as they wanted and leaving the house was allowed at all times in my region during the first wave of the pandemic. (I flew to Sweden during the second wave where I could live without such fears.)
To be fair, we have to acknowledge that some measures that came with the “lockdown” label had a positive effect on some people’s mental health. Many studies on the subject (e.g. Pieh et al. 2021, Ahrens et al. 2021) have found very heterogeneous mental health effects of non-pharmaceutical interventions: While some were affected negatively, others benefitted from reduced levels of stress due to working from home or not working at all.
Evidence from Germany suggests that (self-reported) stay-at-home orders are associated with higher levels of depression and anxiety among younger adults in particular (Benke et al. 2020). The German situation was particularly useful for such an analysis as some states issued a stay-at-home order and others did not. As stay-at-home orders in Germany during the first wave allowed outdoor walking at all times (except in a few villages), the effects are likely to be smaller than in countries that did not allow these exceptions. Reich and Klotzbücher (2020) make use of data from Germany’s largest telephone and online crisis helpline service Telefonseelsorge. Reich and Klotzbücher had access to anonymised data on all recorded contacts from 1 January 2019 to 10 May 2020. They find a significant increase of calls of about 20% following the national “lockdown” in Germany. They control for Covid cases on a state level in order to separate the effect of the lockdown itself from the effect of the spread of the virus. Reich and Klotzbücher considered regional differences in what “lockdown” meant, i.e. whether there was a stay-at-home order in place, whether it was allowed to have a picnic in public or whether it was allowed to visit acquaintances at home. Only for four states, they could assure that helpline calls were not redirected across state borders so that it was possible to definitely say where the calls were from. These were four of the five most populous states: Northrhine-Westfalia, Bavaria, Baden-Württemberg and Hesse. Among these states, they found that the most substantial increase in helpline calls was observed in Bavaria, the state with the strictest lockdown rules followed by Hesse, Northrhine-Westfalia and Baden-Württemberg – in the exact order of strictness of lockdown measures. After the fourth week of lockdown, helpline calls slowly started to decrease again and at the end of April, Reich and Klotzbücher found no statistically significant difference anymore. Not only the timing of the spike in helpline calls suggests that the cause are social distancing policies rather than fear of the virus or the economic crisis: There was no increase in talks about physical health or financial problems, but a significant increase in topics such as loneliness, depression, and anxiety. They also point to the fact that in the German-speaking regions of Switzerland, where social distancing measures were less strict, there was no significant increase in helpline calls (Brülhart and Lalive 2020)
Further evidence for adverse effects of lockdowns on mental health is provided by Pieh et al. (2021) who found the prevalence of depressive, anxiety, and insomnia symptoms to be significantly higher in the UK four weeks into the first lockdown relative to pre-pandemic epidemiological data. In Austria, Pieh et al. (2020) found the prevalence of depression and anxiety to be higher four weeks into the lockdown than in pre-pandemic times and found the lockdown to be especially stressful for younger adults. In the UK, the factor most highly associated with high anxiety during lockdown was loneliness. People who “often or always” felt lonely were almost five times more likely to report high anxiety than those who “never” feel lonely. Of course, stay-at-home orders and contact bans directly cause loneliness. Rossi et al. (2020) found high levels of post-traumatic stress symptoms, adjustment disorder, high perceived stress, anxiety, depression and insomnia associated with the first lockdown in Italy in a non-representative sample. In the United States, lockdowns were associated with a stark increase in drug overdoses. They jumped 18% in March of 2020, 29% in April and 42% in May (all compared with 2019) according to this Washington Post report. In Europe, drug overdoses are far less common. Drug use changed during the lockdowns, but it is not clear whether there is any causal relationship. According to Manthey et al. (2021), alcohol use has decreased in Europe while tobacco and cannabis consumption increased. There is a lot of heterogeneity though as some users reported drinking much more while others reported drinking much less. That there are no huge changes on average does not neglect the possibility of an increase in problematic consumption patterns among some groups. The OECD notes that 43% of surveyed persons in 11 OECD countries reported drinking more frequently during “lockdown” while 26% report drinking less. Drinking more was specifically common among women, parents of small children, people with a higher income, and people with symptoms of anxiety and depression.
Few studies have made cross-country comparisons of the effects of lockdowns on mental health. Brodeur et al. (2020) used Google Trends to check if people searched for mental health-related expressions more or less often after a lockdown was announced in a country or territory covering the United States and nine Western European countries. They found that around the announcement of lockdowns, searches for boredom and loneliness spiked. Searches for loneliness decrease throughout lockdown in Europe but not in the US. Searches for sadness did not increase immediately after the announcement of a lockdown but were on the rise until three weeks after a lockdown was announced. Surprisingly, searches for suicide fell in both regions. This is in line with Pirkis et al. (2021) who found that in 21 analysed countries, suicides either decreased or stayed constant between April and July 2020 as compared to pre-lockdown levels. It is important to keep in mind that suicides are seldom a spontaneous reaction to external conditions, but often the result of years of suffering, most commonly from depression. Even if lockdown measures have not caused an immediate spike in suicides, their effect on the prevalence of depression risks causing more suicides in the future.
Aside from the immediate physical and mental health deteriorations associated with lockdowns, the stress caused by fear-mongering public communication and extreme policy measures is likely to have devastating long-term effects. Of course, most people believe lockdowns were an effective means to control the pandemic. Many of these people may have experienced lowered stress levels during lockdown as they felt safer. But as I will highlight further below, the public communication and policy that has been prevalent since March 2020 in most countries has lead many people to grossly overestimate both the social risk and their personal risk to fall seriously ill from Covid-19. Mislead policy increased stress levels in the first place which have then been reduced for some by lockdown measures. It shall not be underestimated how lockdowns and other extreme policy measures can increase fear as people think the danger has to be extraordinary if such extraordinary measures are taken. So for those who believe in lockdowns, their effect on stress can go in both directions. It is likewise worth noting that, as reddit user Reasonable-World-154 puts it, “for the pro-lockdowners it is clearly a source of strength and encouragement that their sacrifices are having a positive impact on the state of the world; their misery is saving lives”. For the (not so small) minority that is forced into lockdown against their will, it is straightforward that the measures mean extreme stress. Personally, I have been feeling constantly stressed since March 2020. As Schippers (2020) writes “acute stress in a healthy human is quite harmless, but stress that last for days, weeks, months or years can be harmful. It can result in a state of chronic systemic inflammation which in turn results in the development of chronic diseases.” and “chronic stress in advanced age will accelerate aging and dysfunction of the immune system.”.
Aside from younger adults, patients of care facilities were another group that was affected by lockdown measures to a particularly extreme degree. In my database of stay-at-home orders, I excluded regulations on quarantines of care facilities to be able to make comparisons for the general population. Elderly and handicapped people living in care facilities have been subject to quarantine even in countries where no population-wide lockdown measures were imposed. Conditions in care facilities have left room for improvement before the age of lockdowns, but with Covid-19 restrictions, many care facilities have effectively turned into prisons. The enforced isolation is especially difficult to process for patients with dementia. Research from Italy and the UK, point at a serious worsening of symptoms among dementia patients. A survey among 950 experts in care homes and 1000 experts in ambulant care services in Germany found that the mood and life satisfaction of 74% of all care home residents has worsened during the pandemic. 82% of surveyed care home workers reported that a general ban on visits was imposed in their facility. Note that a big share of those who have died with Covid-19 were residents of care homes thus the lowered mood of surviving residents may be explained by these deaths to a certain extent. Yet, it is beyond doubt that the isolation that was enforced on care home residents had its toll on their wellbeing.
Pouso et al. (2021) is the only study I came across that explicitly acknowledges differences in lockdown stringency regarding individual outdoor movement. As they summarise: “Importantly for the COVID-19 crisis, research suggests that maintaining contact with nature during stressful life events, such as relationship breakdown or job loss, can “buffer” individuals against stress; with those living in greener areas reporting fewer mental and physical symptoms of poor-health during, and shortly after, the stressful event. It is also important that while ‘home’ is generally considered a restorative environment in psychological literature, during the COVID-19 crisis, school’ closures and the increase of telework might have compromised its restorative potential. Under these special circumstances, being able to maintain contact with nature from home might have had a relevant positive effect in mental health, compensating the loss of the restorative effect of the home, to some extent.” Pouso et al. distributed an online survey internationally and got 6,080 valid responses. Only including countries with at least 100 responses, they obtained a non-representative sample of 5,218 people from nine countries: Spain, United Kingdom, Germany, France, United States, Portugal, Italy, New Zealand and Mexico. They grouped countries into three levels with level 1 countries restricting access to outdoor public spaces (e.g. Spain), level 2 countries allowing for some outdoor activity (e.g. UK) and level 3 countries not restricting access to outdoor space. Which level a respondent’s region was in was self-reported by the respondent. They “found a clear negative effect of severe confinement on mental health, with people who had restricted access to outdoor public spaces (Level 1) more likely to show symptoms of mental health disorders than people who had partial (Level 2) or no restriction (Level 3) to access to outdoor spaces.” In countries with a strict lockdown, natural views as well as access to nature, e.g. in the form of a private garden, were associated with a lower risk of anxiety and depression. The Spanish subsample was analysed further and Pouso et al. found that a private garden or patio contributed more to mental health than a balcony. As with the economic effects of containment measures, we find a similar pattern of inequality here: People in urban environments without natural views and without a balcony are more likely to suffer mentally from being locked down than those who have access to land. A limitation of Pouso et al.’s study is that the sample is not random and highly educated people are particularly overrepresented as the sample was drawn using a “snowball” technique where the researchers sent the survey to their acquaintances with the demand to forward it. This is not uncommon of course, and you should generally be aware that much research, e.g. in the field of psychology, is carried out using samples of university students leaving people with lower formal education severely underrepresented.
Given the tremendous health benefits from physical activity and from experiencing nature, preventing people from going out is likely to have a devastating effect on public health. You should assume that policy makers worldwide would only take such a measure if its immediate benefits unambiguously exceed its risks. However, there has never been a plausible causal explanation or evidence for a significant infection risk when being outside keeping a distance towards others. Stay-at-home orders have been justified with their indirect effects: When you are not able to go out, you are also not allowed to meet anyone. On the other hand, most countries that did not implement stay-at-home orders have introduced strong rules on gatherings thus reaping the potential rewards of social distancing without the costs of disallowing individual exercise. Even when assuming that (private) social interactions do not reach the absolute minimum when only gatherings are banned and individuals can freely circulate, I doubt that the net effect on health of this extra reduction of social interactions would be positive. I think everyone knows moments in which meeting a friend is priceless. Especially in times of crisis, people may need a hug, a shoulder to lean on, or just someone to talk and cry. When the government regulates the maximum number of people who are allowed to meet, there are two breaking points: The first is when any restriction is set in place as it means that the government suspends the right of assembly. If a democratic government starts to suspend such a fundamental right, this has strong implications on the self-conception of the society and the relationship between the governors and the governed. But aside from political and philosophical questions, the immediate effects on the lives of most people are arguably negligible (except for some, e.g. event managers). The marginal costs of a reduction from 100 to 50, to 10, or maybe even to 5 people are fairly small, but the costs rise when numbers are becoming very low. When no more than 3 people are allowed to meet, two befriended couples could not meet anymore, for instance. I hypothesise that the marginal costs of reducing the number of people someone is allowed to meet from 1 to 0 has the highest marginal costs. Loneliness is not an enjoyable state for most people. And for suicidally depressed or for people with an addiction this one other person could save their life. Free individual movement probably ensures that those who do emergently need to see a friend (but do not want to bother to explain themselves to the police) will just do so despite it being prohibited. A stay-at-home order with a complete ban on meeting people outside the own household is certainly very harmful and almost certainly lethal for some people.
On top of the huge toll lockdowns take on people’s mental health with increased prevalence of anxiety, depression and substance abuse, interpersonal problems might be exacerbated by lockdowns: The combination of economic and social stress factors with lockdown policies have dramatically increased cases of domestic violence in almost all countries. For many, home is not a safe place and the freedom of movement to leave home at all times has been taken for granted by almost everyone until March 2020. For some, the loss of this freedom might only mean a minor annoyance, but for others, It puts them at serious risks. Being able to see a friend or even sleep at a friend’s place cannot only save the lives of some suicidally depressed but it can be the only rescue for people facing intimate partner violence.
While their freedom of movement has always been more restricted, children might suffer most from enforced isolation. While the overwhelming of hospitals never became a reality in Germany, the social response to Covid-19 has caused an overwhelming of the psychiatric system. The spokesperson of the German Professional Association of Pediatrics in May 2021 spoke of triage in psychiatric wards for children and adolescents: “There are psychiatric illnesses on a scale we have never experienced before. The child and youth psychiatries are full, there is a triage there. Those who are not suicidal and “only” have depression are no longer admitted at all.” It would be unfair to blame children’s poor mental health on stay-at-home orders, however, as most of Germany only experienced strict night curfews which do not affect children who are usually not outside after 9 PM anyway. Here, we are probably seeing the side effects of school closures, mask mandates and an unprecedented campaign of fear. It is often stated that children are adaptive and therefore the closure of playgrounds and kindergartens would not cause excessive suffering. But clearly, there are boundaries to children’s adaptiveness. Complete isolation from other children for a longer period is unnatural and may seriously inhibit children’s development. For small children, there are developmental “windows of opportunity” in which they typically learn certain behaviours (similar windows exist for children’s immune system). Making up for the lost opportunities to learn and develop can be hard or even impossible. And we cannot know what it does to children to grow up in an environment of fear where the message they learn every day is that they are dangerous and every human contact is potentially dangerous. Joffe (2021) summarises:
“Especially concerning are the effects on children during “the early years” of life, increasingly recognized as the period of greatest vulnerability to, and greatest return on investment from, preventing adverse long-term outcomes that can have lasting and profound impacts on future quality of life, education, earning potential, lifespan, and healthcare utilization. The early years of life are a critical period when a child’s brain develops from social interaction and experiences, thus providing the foundation for their entire future life potential. During the pandemic children are being exposed to increased intimate partner violence, family financial crises, disrupted education, an increasing achievement gap (i.e., low-income families who do not have access to a computer, internet, space, food, and parental support cannot participate in online learning), loneliness, physical inactivity, and lack of support services (e.g., school lunches, access to early childhood services and aids for those with disability). These adverse childhood experiences have permanent impacts that cannot be compensated for by later improvements in social situations.”
Speaking of children, another cost of lockdowns that cannot be compensated for some is the missed opportunity to conceive children. Women have only a limited time to conceive children and their fertility declines significantly in their 30s and 40s. If a woman was kept from finding a partner due to social distancing policies or from seeing her partner due to travel restrictions, it could be too late to have children after lockdowns are lifted. This is another example of costs of social distancing policies that are impossible to capture in a statistic but are nevertheless real.
A last area of costs is culture – an intangible that is even more difficult to measure than mental health or wellbeing. Culture always changes, but I was used to society valuing cultural heritage so I think the destructive impact of lockdown policies on parts of our culture should be taken into account. These costs are mostly not caused by stay-at-home measures though but are due to “milder” policy interventions such as bans on gatherings and venue closures. We became used to seeing football matches without cheering fans. Carnival was cancelled entirely. Night clubs have been closed for 15 months in many countries by the time of writing. Large concerts have not taken place and in some countries, there is no perspective of mass events being ever allowed again to the same extent as before 2020. No one says they want to ban mass events forever, but given that the current incidence in most European countries is extremely low and all risk groups have been offered vaccination; Covid-19 will not cause overloading hospitals anymore. Restrictions remain in place to protect people who either decided against being vaccinated or who face an extremely low chance of being hospitalised due to Covid-19. But culture changed. For many people, safety from pathogens has become a main priority in life. Enjoying music with strangers is not valued by the median voter anymore. Life expectancy is at an all-time high in human history, yet prolonging the life of older people has been giving priority over maintaining the life quality of all. No matter whether you liked going out on a night dancing before Covid: We need to acknowledge that a lot of cultural activities just vanished with unclear chances of resurrection. The same holds for theatres, for cinemas, basically for all places where strangers came together to enjoy cultural experiences. I am certain that the perspective of the stranger as a threat to our health instead of someone who increases our pleasure in enjoying something has tremendous consequences for social cohesion. I am still hopeful that much of pre-Covid culture could survive the crisis, but I do not think this will happen if we do not start to value it and if we keep letting extremely anxious people make the rules for society as a whole. Of course, culture is made by people. Many cultural venues have not survived the lockdowns while many others have been saved by government benefits or private donations. It will be interesting to see how musicians and artists will process this time and how they will react to the society cancelling them for over a year for being “inessential”. Another aspect of culture that was heavily affected by lockdowns is the sense of self of our societies and the ethical foundations of what used to be liberal societies. I will address these aspects in some more detail further below in the section on ethical considerations.
In summary, costs of stay-at-home orders are difficult to quantify as they coincide with costs resulting from the pandemic itself as well as costs of other policy interventions. There is clear evidence of stay-at-home orders and other interventions labelled as “lockdown” to have devastating effects on many people’s mental health with increasing rates of anxiety and depression. Furthermore, there is some evidence that substance abuse has risen among parts of society. The mental health effects of lockdown measures are not distributed equally. Several studies found younger people, including children, to be particularly vulnerable to suffer from mental health issues following lockdown policies. For strict stay-at-home orders, those without private outdoor access suffer significantly more than those with access to a garden. Another subgroup that suffered tremendously have been elderly people in care facilities and dementia patients whose symptoms worsened. Cases of domestic violence have spiked during lockdowns with reported cases probably only being the tip of the iceberg of aggravated within-household violence. Strict stay-at-home orders caused people to be less active which has potentially huge adverse effects on physical and mental health if lasting for a longer time. Another lockdown-related cost to physical health comes from skipped health check-ups and delays in treatment e.g. of cancer patients.
Other measures labelled as “lockdowns” that are milder than stay-at-home orders have contributed to all of the mentioned effects on physical and mental health too. School closures have deprived millions of children of their right to education and in poorer countries have caused hunger among children who would usually receive a meal at school. Business closures, bans on gatherings and border closures have caused an economic crisis that undoubtedly has a huge toll on people’s physical and mental health and destroyed a lot of intangible cultural heritage that was suddenly deemed “inessential”. These measures alone would surely not have had the same impact on physical and mental health as stay-at-home orders had.
TL;DR: The undesired side effects of lockdown policies are manifold but hard to quantify. Lockdowns have affected all aspects of our lives, including many unquantifiable ones. It is hard to disentangle the effects of the pandemic itself, lockdowns in a narrow sense, and other containment measures often referred to as “lockdown”. Covid-19 containment measures in their entirety have had substantial undesired effects including – but not limited to – causing or exacerbating an economic crisis and aggravating hunger in poor countries. Lockdowns in a narrow sense have caused – among other things – a rise in the prevalence of depressive and anxiety disorders, a fall in overall wellbeing for some groups and an increase in behaviour detrimental to health in some groups. The costs of lockdowns are distributed unevenly. Groups disproportionally affected include young people, children and their parents, people in care homes, and people with little or no private access to nature, e.g. with a garden.
With everything written in mind, I assume the costs of lockdown policies outweigh the benefits by large. But this is just an amateur assumption that could well be wrong. What is truly scandalous in my eyes is that most governments (if not all) have not even bothered to perform a comprehensive cost-benefit analysis before announcing this set of unprecedented far-reaching policies (see e.g. Allen 2021).
Which currency we measure costs and benefits in? GDP would not be the best choice: The vast majority of people dying from Covid-19 in richer countries are retired, so if we limit our cost-benefit analysis to economic effects, we would see elderly people as net receivers rather than contributors and their death would probably increase GDP per capita. This is not how most people look at the situation. More common are claims to “save lives”. Almost everybody assumes that saving lives is a good thing. This seems to be a common ethical ground for most people at first glance. But confront people with a trolley problem like this: A trolley is bound to roll down a track to which a toddler is tied. If you pull a lever, the trolley is redirected to another track to which two elderly persons are tied. In this scenario, many people would be willing to sacrifice two elderly people for one baby. The largest experiment made in this direction was the Moral Machine Experiment conducted with half a million volunteers from all over the world. Confronted with a situation where an automated car had to decide between killing different people, respondents showed a strong preference for saving children over saving the elderly. So, to leave the metaphor, I am not alone with my opinion that public health policies should not have the only aim to minimise the absolute number of deaths at all costs. We should rather take into account the number of life years that are saved which implicitly means that children are given greater weight than old people. For an early application of the trolley problem framing to Covid-19 containment policies, I recommend Paul Frijters’ blog article from March 2020.
When evaluating public health policies, the WHO has used the measures of “Years of potential life lost” or disability-adjusted life years (DALYs) that weighs years lived with a disability less. A related concept is quality-adjusted life years (QALYs) where life quality is taken into account. All these calculations may seem cruel to people unfamiliar with public health. They are in fact commonly used nevertheless. The WHO calculates with DALYs and QALYs are used in cost-utility analyses of public health interventions, e.g. in the United Kingdom. The rationale is that if people are given the hypothetical choice to live x years with a disability and y years without, for people to be indifferent, x is usually considerably larger than y, i.e. people would accept to die earlier but in a good overall health condition.
As a side note, and at the risk of being called heartless, I do not think that it is fair to assume that dying from Covid is always worse than not dying from Covid. Even though most of us do not like to think about it: We are all going to die. It is a noble cause to try to maximise everyone’s life span, but realistically this is not always in the best interest of the respective person. There are millions of ailing old people suffering from chronic pain or fighting a hopeless battle against cancer. There are millions of old people suffering from severe dementia who cannot take any conscious responsibility for their life (or death) anymore but whose younger self would feel nothing but agony if it knew how they would live their last years. We all knew people whose death was nothing but a relief for themselves and their loved ones. Modern medicine can keep people alive for a long time but in some cases, it is a life deprived of dignity. Given that nearly all people who died from Covid-19 were old or sick, it is fair to assume that for many, Covid-19 meant a relatively quick death that prevented them from years of suffering. Of course that does not hold for all and maybe not even for most Covid deaths but given that the median of Covid-19-related deaths is above 80 in Germany, it is likely that a significant share of them was suffering or was about to suffer from conditions that make death look more like a promise than a threat.
There are profound ethical reasonings for the value of a human life to be infinite or immeasurable, but this line of thinking makes it difficult to perform any cost-benefit analyses and is therefore not too common among economists. As Douglas Ward Allen (2021) writes:
“In economics, the concept of “value” is based on the idea of maximum sacrifice. How much one is willing to sacrifice, at most, for something determines that individual’s economic value of the thing. Thus, when it comes to the value of an individual’s life, this value is determined by the actual individual. In practice, what is measured is how much individuals are willing to sacrifice to extend their life a little bit by reducing some type of harm (called a ‘marginal’ value), and then use this to determine a total value of life. Every day people make decisions that directly and indirectly are based on their marginal value of life. The decisions to eat poor foods, smoke, accept dangerous employment, cross a street, drive a car, exercise, or engage with others all entail risks to life and therefore imply a value of life.”
Allen performed a cost-benefit analysis for “lockdown” policies (not limited to stay-at-home orders) in Canada. His calculations are based on the following thought experiment: Suppose you could live one year under lockdown vs. X months under pre-lockdown normal conditions: How big is X, i.e. how many months would you be willing to give up? Allen calculates that if X was 10, i.e. the average Canadian would prefer to die two months earlier for passing on a year of lockdown, the costs of lockdowns in years of lost (valued) lives exceed the benefits by the factor 3.6 even when assuming the initial Imperial College model from March 2020 that grossly overstated the number of lives lost in the absence of lockdown and which never became reality anywhere in the world. Using a perhaps more realistic model with updated assumptions Allen calculates the costs of lockdown to outweigh the benefits by the factor 282 for Canada. In a similar approach, Frijters estimates the global costs of all Covid-19 containment policies (not stay-at-home orders alone) to outweigh the benefits in healthy lives lived or happy lives lived by the factor 50.
A good read on the costs and benefits of “lockdown” policies is Joffe (2021). Ari Joffe puts some numbers in context, e.g. regarding different causes of death. He calculates that on 4 September 2020, 3,500 people had died following a SARS-CoV-2 infection per day. Writing this in June 2021, I should note that this number is not up to date and that by 18 June 2021, an updated figure is 6,800 deaths per day (3.85 million Covid-19-related deaths due to Our World in Data divided by 565 days since 1 December 2019). 6,800 deaths per day is certainly a huge number without context. Joffe (2021) provides context by pointing out some other common causes of death: 1,100 persons die of Malaria each day which predominantly affects children and thus likely causes more years of life lost than Covid-19. Even Measles with 384 daily fatalities might be comparable to Covid-19 in years of life lost. Influenza causes more than 1,000 deaths a day and HIV nearly 2,000. Motor vehicle collisions claim the life of 3,700 people a day which implies more years of life lost than by Covid-19 given that most people who die in car crashes are not over 70 years old. All these figures are dwarfed by the estimated 22,000 people daily who die prematurely due to tobacco use and the 30,000 who die prematurely due to dietary risk factors.
If we want to minimise the years of life lost which I deem ethically superior over minimising the absolute number of deaths in a given period no matter the age of the deceased, we should focus more on providing bed nets and Malaria treatment, improving road safety, raising awareness about the negative impact of smoking and on promoting healthy food. The first two issues mainly affect poorer countries while the latter two are global phenomenona. I am not convinced of the authoritarian, coercive type of policies that became fashionable lately, but even softer “nudging” interventions such as a tax on sugar, bans on tobacco ads or cooking classes in secondary school could have a huge impact on public health.
Joffe estimates that lockdowns costed a minimum of 5 times more WELLBY (a form of life satisfaction-adjusted life years) under very unrealistic assumptions. For example, Joffe assumed that lockdowns would eradicate the virus and prevent any infections which is clearly not a realistic assumption given how ineffective lockdowns are. Changing the assumptions to presumably more realistic ones renders a cost-benefit ratio of 50 to 87. Also, “importantly, this cost does not include the collateral damage discussed above (from disrupted healthcare services, disrupted education, famine, social unrest, violence, and suicide) nor the major effect of loneliness and unemployment on lifespan and disease”.
Miles et al. (2020) calculate the costs and benefits of lockdown in the United Kingdom in terms of QALYs. They calculate the cost-benefit balance for under different assumptions regarding the lives saved by lockdown and the economic costs of lockdown. Their analysis does not include any adverse effects of lockdowns on physical and mental health or any other costs except GDP lost. Even under the most unrealistic assumptions of 440,000 lives (not QALYs) saved through lockdowns and an immediate economic recovery, the costs of lockdowns exceeded the benefits by 125 billion Pound. Miles et al. calculated with a value of 30,000 Pound per QALY which is “the figure used in evidence-based resource decisions within the UK health system. (…) In using this yardstick, we are treating decisions on how to
face COVID-19 in the same way as decisions in the UK are made about resources to apply to the treatment of cancer, heart disease, dementia and diabetes“. Miles et al. acknowledge that in the United States, a QALY is valued around three times more but equally point out that the benefits would still not outweigh the costs.
These cost-benefit analyses make estimates for the entire population. It is noteworthy that even if cumulated costs exceed benefits by far, some groups benefit from lockdowns. As policy making is always an act of balancing opposing interests, its outcome can only be understood if taking into consideration who are the groups which benefit and who are the groups which lose out from an intervention. Regarding any SARS-CoV-2 containment measures, those who benefit (if the measures are effective, which they mostly are not) are those aged 60 and above who account for the vast majority of Covid-19-related hospital admissions and deaths. For coercive measures such as stay-at-home orders, those benefitting are actually only a subgroup of the elderly, namely those who value their safety from a disease more than their freedom to enjoy their last years of life according to their own will. On the other hand, younger people who are extremely anxious about infections subjectively benefit from containment measures. People who prefer authoritarian policies and like to impose their will on others clearly benefit more than those who value individual freedom. Poorer people with little space, parents, children, and young adults are among the groups most negatively affected by lockdowns while people with a lot of space, private gardens, and the possibility to work from home are among those for whom their personal cost-benefit analysis could rationally be in favour of lockdown measures.
I am not aware of any cost-benefit analysis that comes to the conclusion that lockdowns have been beneficial for society as a whole (please write me if you know of one and I will include it). The common belief that they were and are beneficial is mostly grounded on a mono-perspective focus on suppressing the virus with optimistic assumptions concerning the effectiveness of policy interventions and neglecting any negative effects. This is particularly odd when you compare it to the rigorous approval process for vaccines. In early 2021, the media was full of discussions on whether it was safe to get a shot of the Astra Zeneca vaccine after some 5 in one million developed blood clots. People were seriously worried about this side effect even though the vaccine had been approved in large clinical studies. These studies were somewhat rushed and I can understand people’s worries that too many corners might have been cut in the approval process. But side effects of non-pharmaceutical “lockdown” policies are far more common while there has been no scientifically sound admission process at all. If lockdowns were a vaccine, they would certainly be rejected.
TL;DR: Governments have failed to justify lockdowns with a comprehensive cost-benefit analysis. The few cost-benefit analyses researchers have performed find the costs of “lockdowns” to exceed the benefits by far. These analyses usually look at the entirety of Covid-19 containment policies however and it is difficult to isolate the costs and benefits of lockdowns in a narrow sense. Estimating the cost-benefit ratio of lockdowns also means having to make ethical decisions on the goals of public health policy, e.g. in trade-offs between minimising the absolute number of deaths versus minimising the years of life years lost versus taking into account life quality.
Further ethical considerations
Aside from purely economical cost-benefit calculations and utilitarian trolley problems, there are strong ethical objections to stay-at-home orders. Most European countries identify as some sort of “liberal democracy”. Lockdowns meant a radical shift in culture away from individual liberty which has long been regarded as a pillar of European societies. But what is liberal about a government that has the freedom to decide if and when citizens are allowed to leave their homes? Most modern-day “liberals” seem to be happy with free speech as the only universally granted liberty (with free speech indirectly restricted through bullying, but not through laws). In my eyes, calling a system liberal in which the government can decide if and when you can leave your house as an innocent citizen is like Orwell’s 1984 Newspeak.
As someone who grew up in reunified Germany, I have taken fundamental rights such as the freedom of movement for granted. In school, we learned how such rights are the fundament of our constitution as a “free and democratic society”. When we learned about the limits of these rights, I cannot recall any examples of blanket civil rights infringements that came close to what we have been enduring for 15 months. Prison was for criminals and everyone who obliged by the law was supposed to be free. I have witnessed limitations to the right of movement, but they were always very limited in time and space. I lived in Hamburg during the G8 summit for instance. To protect the summiteers, several streets were blocked by the police for a few days. That was about the maximum of restrictions to public life Germany has known before March 2020. Stay-at-home orders have never been an accepted policy in Germany, nor in other countries that self-identified (and absurdly still do so) as free or liberal. Even when the lockdowns end one day, the feeling that the government guarantees these basic liberties will probably never return. Once again, I would like to point to a post on the subreddit Lockdownskepticism by user sunny-beans from the UK who exclaimed this and received hundreds of comments of people from different countries sharing the same feeling:
“If someone told me in 2019 that Europe would become like this I’d never ever have believed. Maybe I was naive but I just didn’t think it was possible. I thought we as society had overcome this, that I had rights that would protect me from such horrible authoritarian laws. And now for the first time in my life I feel like I can never go back to that feeling of safety. Doesn’t matter what happens, the fear of losing it all will always be with me. The feeling that at any moment I could lose the right to see my family, to work, to have dreams, to have friends, to just live a normal life, will always hunt me. I just feel like I lost this huge part of myself and that from now on I will see the world in much harsher lenses.”
I know best about Germany, but I think the following diagnosis is relevant for other countries too: Our current political system may be derived from traditional liberalism, but it has lost all respect for individual liberty. There is free speech but everyone dissenting is bullied by the mainstream. The traditional separation of powers has been completely overthrown. Usually, the Bundestag (the parliament) would make the laws and the government would execute them. The Bundestag has for most of the pandemic passed the decision power to the government. Political decisions that were more far-reaching than nearly anything the Bundestag has ever decided were agreed on in a body that is not even previsioned by the constitution: A conference of the chancellor and the 16 heads of states. In April 2021, the Bundestag passed a law that replaces the year-long rule of the heads of states by a strict regulation that certain measures, including curfews, automatically apply to municipalities that exceed a certain level of infections. Even though over a year has passed since the first infection, the scientific justification for containment measures was weak at best. As a side note, a frequently used threshold to justify restrictions was a 7-day incidence of 50 new infections per 100,000 population within 7 days because that was the limit for public health authorities to trace contacts. In spring 2020. Apparently, the government failed to enable the public health authorities to be able to trace more contacts over the course of a year. Now, at the beginning of July 2021, the 7-day incidence is below 10, but strict restrictions on public life remain, e.g. an obligation to wear masks in public transport and all shops. Since March 2020, the state governments have been taking decisions about nearly every aspect of everyday life. When you are allowed to leave your house or your city, whom you are allowed to meet, and what you are allowed to wear have become political decisions. The public debate focuses on details of some of these regulations but there is no debate at all whether the government should even have the power to decide on these issues. The media as well as the courts support the totalitarian and authoritarian lockdown regime. “Totalitarian” and “authoritarian” are strong words that are usually applied to dictatorships so you may argue that they do not fit present-day Germany. But I looked up these words in several dictionaries and I found them quite fitting. Cambridge Dictionary defines authoritarian as “demanding that people obey completely and refusing to allow them freedom to act as they wish” There are other definitions that require an authoritarian state to be dictatorial which obviously does not apply to current-day Germany. Still, the notion of an authoritarian democracy seems to fit quite well. Then take this definition of the term “totalitarian” from Wiktionary: “Of or relating to a system of government where the people have virtually no authority and the state wields absolute control of every aspect of the country, socially, financially and politically.” This is precisely what most countries have become. The state wields absolute control of every aspect of society (including the option to “shut it down”) with the single, total goal of eradicating the virus. I am completely aware that I could not express these issues so freely in most countries that used to be called authoritarian and totalitarian. I do not neglect the freedoms we still enjoy in Germany and other European countries as little as I will neglect which freedoms have been taken from us. I am neither a political scientist nor an expert in law. To my surprise, most supreme courts have not rejected lockdown rule. Apparently, a disease that takes the lives of less than one percent of infected people, virtually all of them being relatively old or sick, classifies as an emergency that allows suspending some of the most fundamental human rights.
Personally, I find this insight extremely frightening. Climate change as well as an unhealthy lifestyle have a higher toll on public health and could be used to argue for endless emergency rule. The German constitution was written in 1949 when the country lay in ruins and there was widespread hunger. The inalienable rights it used to guarantee its citizens were not meant to apply only in good times. If we allow governments to suspend the right to free movement if they feel there is an emergency, we have no right to free movement. Lockdowns have set an example to suspend human rights in democratic societies, with the will of the majority. As long as the majority thinks that locking down is a good decision, lockdowns will come back. This means that the right to free movement, the right to assembly, the right of education, professional freedom and other basic rights are not universal or inalienable anymore but are from now on to be thought with an appendix “… as long as people are not scared enough”. That is the world we are living in now and it is not the same place as the one where I grew up. In school, I learned that these basic rights are universal and I believed they were. Everyone could have known that there will be a pandemic one day yet no one spoke about suspending free movement in this case. As I will point out further below, it seems that there have been no such plans indeed.
Not only the “liberal democracy” has been redefined in the last 15 months. “Public space” used to be a second home for me. Public space was a promise to the landless to have their individual freedom to use the land around them. Now, public space has revealed itself as less of an individual claim but rather a space that underlies the full control of the public through executive power. A place that you may be granted access to at most times, but sometimes they can take it from you indefinitely or you have to provide them with “a good reason” why you want to use public space. This general shift from a liberal democracy with strong individual rights that underlie limited social control to an authoritarian democracy with perfect social control of the individual has revealed its ugly face to many in the last months. Millions of people whose work certainly seemed essential to themselves were told that it is not essential to the society. If I feel that I need to see a friend to talk to, it does not matter as soon as society, through its institutions, decides that it is generally not essential to see friends. Individual risk assessment was replaced by a government-enforced social distancing regime. Even in a pandemic, maybe especially in times of public health crises, there would be a strong ethical cause for allowing individuals at risk to decide how they want to deal with this risk. Elderly people who prefer to maximise their life expectancy could be given individual assistance (e.g. by delivering groceries to them) while others who want to spend the rest of their earthly days playing with their grandchildren should equally be allowed to do so.
A virus does not even count as a living being as it fully depends on its host. To survive, it becomes a part of us. Viruses are inseparably connected to human life. And as it is impossible to separate them, every attack on the virus is an attack on human life too. The virus does not get locked down, it iss its carriers who happen to be us. A “war on a virus” is effectively a war on humans as it is our nature to be part of the virus’ troops. Actually, most of us have not yet received the drawing call and are non-combatants. But the lockdownists’ troops see no difference and they attack us all as potential hosts. The notion of every person being a potential threat to every other person’s health has fundamentally changed our society. “Homo homini lupus est” has been the argument for the government’s monopoly of violence since Thomas Hobbes. “Homo homini virus est” has become the argument for an escalation of government power over all aspects of our lives.
TL;DR: Lockdowns have shaken the foundations of societies that used to be identified as free or liberal. Individual liberty has been curtailed as a result of a “war” on a virus that depends on us as carriers. The universal nature of civil rights has been questioned by a new authoritarian style of democracy. Individual risk assessment has been replaced by social control exercised through the totalitarian rule of every aspect of citizen’s private lives.
But then… why?
So, if lockdowns were such a terrible policy: Why did nearly all countries resort to some type of “lockdowns” with the majority imposing literal lockdowns in the sense of stay-at-home orders? Why did most places go back into lockdown again when cases spiked again?
If you are a lockdown advocate, you might be happy that I finally come to what lockdown sceptics are frequently associated with: Conspiracy theories. In the eyes of large parts of the general public, anyone who questions that lockdowns were grounded on scientific evidence and that we just keep “following the science” is labelled a conspiracy theorist. But given that governments have not even performed a comprehensive cost-benefit analysis on lockdowns, it is safe to say that lockdowns are not a result of scientific rigour. This lack of evidence leaves a lot of people desperate enough to turn to actual conspiracy theories which still seem more plausible to many than the official communication that there is no alternative to lockdowns. Just as lockdownists’ propaganda (e.g. “2 weeks to flatten the curve”), anti-lockdown conspiracy theories rely on unrealistic assumptions from which conclusions are derived, sometimes in a logical manner, but out of touch with reality. It is the same principle of “crap in, crap out” when scientists make a study on how effective lockdowns are that fundamentally relies on the assumption of no voluntary behaviour changes or when conspiracy theorists state lockdowns were pushed for by the pharma lobby in order to sell vaccines relying on assumptions such as perfect cooperation between competing companies and all major political parties. If your conclusions are based on unrealistic assumptions, they will not be of any relevance to the real world. Personally, I try to dismiss any premature conclusions and therefore disagree with most conspiracy theorists who, in my opinion, have done more harm than good for the anti-lockdown movement and in many places have even prevented that such a movement emerges. But we should be cautious not to let fighting conspiracy theories make us incapable of developing any theories. Theories are as crucial for good science as is evidence. This includes theories about conspiracies. In the quest for knowledge, we need to focus more on developing and testing theories than on promoting them. The question of why most countries locked down can only be addressed by theories for now. I hope scientists will test each of these theories and discard those that are evidently wrong.
Not ready for this
Before digging deeper into possible reasons that could have contributed to the spread of lockdowns across the world, let me reinforce something I was not sure about when I started my research. The type of large-scale social distancing policies we have been enduring for over 15 months by the time of writing has not been an emergency plan that was lying in the drawers of public health authorities waiting to be applied during the next pandemic. Until doing this research, I thought I was just too ignorant to know about lockdown plans. Now it seems to me as if all epidemiological guidance from before 2020 was just discarded with the pandemic. You are very welcome, dear reader, to submit pre-2020 papers that suggested 2020-style policies and I will update this section.
In October 2019, the WHO published a report on the use of NPIs for fighting epidemic or pandemic influenza. Now I can almost hear how readers will object to how this is relevant here because Covid Is not influenza and only conspiracy theorists would compare them. Well, the comparison is not that far off. While SARS-CoV-2 is certainly more deadly than the most recent strains of influenza, remember that the “Spanish Flu” was an influenza pandemic. When the WHO made a report about how to fight an influenza pandemic, they were not thinking of the seasonal, “interpandemic” strains of Influenza. Just like SARS-CoV-2, influenza is an airborne infection relatively harmless for most people, but dangerous for some and both can be transmitted in the presymptomatic phase. The reason why there are mostly publications on influenza pandemics is that they have been more frequent and many experts expected another influenza pandemic rather than a coronavirus pandemic.
In this document, they conditionally recommend the use of face masks even though it is stated that evidence is very limited and the recommendation is based purely on “mechanistic plausibility”. Furthermore, it is not differed between different settings (e.g. indoors/outdoors) and it seems from the overall direction of the paper that the authors are thinking of recommended face masks rather than mandated. An unconditional recommendation is given to face masks worn by symptomatic people. This needs to be seen in combination with the WHO not recommending quarantine even for exposed individuals. They write “there is no obvious rationale for this measure” as there was not enough evidence, voluntary (!!!) isolation of sick people is recommended, however. It is clear that the WHO did not consider a lockdown for everyone a recommended solution if even a quarantine for exposed individuals was not recommended. Internal travel restrictions were recommended only in the early phase of an extraordinarily severe pandemic. “Border closures may be considered only by small island nations in severe pandemics and epidemics, but must be weighed against potentially serious economic consequences.” In 2007, the WHO published “Ethical Considerations in developing a public health response to pandemic influenza”: “Plans related to the isolation of symptomatic individuals and quarantine of their contacts should be voluntary to the greatest extent possible; mandatory measures should only be instituted as a last resort, when voluntary measures cannot reasonably be expected to succeed, and the failure to institute mandatory measures is likely to have a substantial impact on public health;”.
The German national pandemic plan from 2016 also only speaks about voluntary isolation and quarantine. There is no historical example of anything alike the lockdowns of 2020/21. During the H1N1 pandemic of 2009, China imposed a mandatory quarantine on close contacts of infected people. Li et al. (2013) analyse whether this was an efficient policy and found that while the quarantine measure did succeed in delaying the peak of the epidemic, the costs outweighed the benefits. In hindsight, it reads unreal how scientists from Chinese institutions wrote in 2013 how “such strict preventative measures might cause misunderstanding over the justification of losing one’s movement freedom for a week”. They questioned the justification of a one week quarantine of close contacts of infected people in the middle of a pandemic! That gives an impression of the pre-2020 ethical standards in public health.
I found one practical recent example of stricter enforcement of social distancing measures for a very limited time during the Ebola epidemic in West Africa. Sierra Leone imposed two three-day national stay-at-home orders and some more local ones in 2014 and 2015 (see here, here and here). These emergency measures have been referred to as “lockdowns” at the time. According to the sources I found, people could still go outdoors but had to stay in the vicinity of their homes.
In their 2007 publication “Pandemic Influenza, Ethics, Law, and the Public’s Health”, Gostin and Berkman write: “The standard of public health necessity requires, at a minimum, that the subject of the compulsory intervention must actually pose a threat to the community. In the context of infectious diseases, for example, public health authorities could not impose personal control measures (e.g., mandatory physical examination, treatment, or isolation) unless the person was actually contagious or, at least, there was reasonable suspicion of contagion.”
Thus all evidence I found backs my feeling (confirmation bias might be strong here) that indeed “we were not prepared for this”. Public health authorities did prepare for a pandemic, however, and everyone knew that a pandemic would happen at some point. Pandemics are a form of natural disaster we cannot completely avoid. Just like earthquakes, we need to have in mind that they will eventually happen. What we were not prepared for was a quick switch to authoritarian rule of every aspect of our lives in countries that had valued individual freedom until March 2020. But if lockdowns partly go against former emergency plans, why is it that these rules were applied in most countries nonetheless?
First, I would like to have a look at some practical aspects. As stay-at-home orders are meant to ban “non-essential” contacts, it would be straightforward to put a ban on contacts instead of keeping citizens from leaving their houses. Proponents of stay-at-home orders argue that contact bans are more difficult to enforce because private houses underly special protection from law enforcement in most countries. As the authoritarian logic of stay-at-home orders comes with a deep-seated mistrust towards individuals, enforcement possibilities are crucial. Another convenient feature of stay-at-home orders for policy makers is that they implicitly include other policies governments used to respond to the pandemic. There is no need to regulate gatherings or events as it is a necessary condition to leave the house before participating in an event. In addition, policy makers are less prone to objections on the relative proportionality of containment measures, i.e. debates on why certain businesses have to close while certain other businesses remain open. While policies such as the closing of certain businesses, school closures, or the cancellation of cultural events always affect particular economic interests that are usually organised, the opponents of stay-at-home orders are not organised (We should be!). This might be a reason why relatively little resistance was shown against stay-at-home orders.
Early estimations and lockdowns
As mentioned in my discussion on Flaxman et al.’s influential paper, early predictions largely overestimated the dangerousness of the new virus. Researchers from the Imperial College London have been among the most influential players pushing for strict non-pharmaceutical interventions. On 16 March, Ferguson et al. published their report “Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand” which was cited over 2000 times according to Google Scholar. As it is stated in the abstract, Ferguson et al. “present the results of epidemiological modelling which has informed policymaking in the UK and other countries in recent weeks”. Here is how they saw the trolley problem: “Two fundamental strategies are possible: (a) mitigation, which focuses on slowing but not necessarily stopping epidemic spread –reducing peak healthcare demand while protecting those most at risk of severe disease from infection, and (b) suppression, which aims to reverse epidemic growth, reducing case numbers to low levels and maintaining that situation indefinitely. Each policy has major challenges. We find that that optimal mitigation policies (combining home isolation of suspect cases, home quarantine of those living in the same household as suspect cases, and social distancing of the elderly and others at most risk of severe disease) might reduce peak healthcare demand by 2/3 and deaths by half. However, the resulting mitigated epidemic would still likely result in hundreds of thousands of deaths and health systems (most notably intensive care units) being overwhelmed many times over. For countries able to achieve it, this leaves suppression as the preferred policy option.” Ferguson et al. acknowledged “that interventions will need to be maintained until a vaccine becomes available (potentially 18 months or more) – given that we predict that transmission will quickly rebound if interventions are relaxed”.
10 days later, Walker et al. from the Imperial College COVID-19 Response Team published another report “The Global Impact of COVID-19 and Strategies for Mitigation and Suppression” stating: “We estimate that in the absence of interventions, COVID-19 would have resulted in 7.0 billion infections and 40 million deaths globally this year. Mitigation strategies focussing on shielding the elderly (60% reduction in social contacts) and slowing but not interrupting transmission (40% reduction in social contacts for wider population) could reduce this burden by half, saving 20 million lives, but we predict that even in this scenario, health systems in all countries will be quickly overwhelmed. This effect is likely to be most severe in lower income settings where capacity is lowest: our mitigated scenarios lead to peak demand for critical care beds in a typical low-income setting outstripping supply by a factor of 25, in contrast to a typical high-income setting where this factor is 7.”
With hindsight, we know that their predictions have been terribly wrong. There have been local shortages in ICU capacities. Examples that come to my mind are the first weeks of the pandemic in Lombardy and Madrid. There are examples of overwhelmed hospitals in low-income countries as well (e.g. India), but Covid-19-related deaths are dwarfed by the devastating effects of policy interventions including a dramatic increase in hunger. There are many sources for criticism of Ferguson et al. (2020)’s predictions, see e.g. Allen (2021).
Surely, Ferguson et al.’s projections contributed to nearly all countries choosing the options of suppression rather than mitigation. Nearly all countries maintained some restrictions through the summer of 2020 and keep maintaining them in the summer of 2021 despite very few infected people and no realistic chance of overwhelming hospitals in the next weeks. I have not yet found out how this modelling translated into the “2 weeks to flatten the curve” that were all over the media in March of 2020. More research should be done on the development of the “2 weeks to flatten the curve” slogan: To what degree was this campaign based on wrong predictions based on reasonable assumptions at the time and to what degree was it based on straight lies? Or did politicians and media misinterpret the models? Or did those advocating for “2 weeks to flatten the curve” initially wanted just this and changed their motivation over the 2 weeks? Probably all four options are correct for different people. Quite possibly, many of those in favour of lockdowns did not believe that they could win a majority for their cause. Regarding the spread of lockdowns across the world, the main author of Imperial College’s paper, Neil Ferguson, said in December 2020: “It’s a communist one-party state, we said. We couldn’t get away with [lockdowns] in Europe, we thought… and then Italy did it. And we realised we could.” It is noteworthy that the lockdown in Wuhan lasted for about 2 months when most European countries decided on “2 weeks to flatten the curve”.
Overestimation of the infection fatality is a typical pattern at the beginning of an epidemic as widespread testing is not yet common and only cases with severe symptoms are reported while infections with little or no symptoms are not. Thus the infection fatality rate is inflated because the denominator of total cases is underestimated. Unlike in most historic settings, the ongoing pandemic is the first one in the age of social media. Early predictions spread like a wildfire and the economics of attention prevented the updated, more nuanced and less spectacular predictions to replace the original apocalyptic ones. The number of views is what counts in the internet economy and strong emotions such as fear generate interest and thus more clicks/views. Social psychology offers several theories that can provide explanations for the self-reinforcing overshoot of Covid containment policies.
In March 2020, 80 percent of OECD countries adopted the same “lockdown” NPIs within 2 weeks. Sebhatu et al. (2020) show the main predictors of whether a country implements an NPI were population density and the number of spatially proximate countries that already adopted the policy. Variables not predicting adoption of NPIs included the number of cases or deaths, population over 65 years old, or hospital beds per capita in the country. Sebhatu et al. explain “When the efficacy of a policy is uncertain, the number of earlier adopters can serve as a form of “social validation” of its usefulness that need not be founded in actual usefulness. Furthermore, if the policy becomes imbued with a positive normative value—that is, adoption is considered virtuous—the act of adoption signals value beyond the usefulness of the policy itself and therefore drives further adoption”. As Frijters writes “The emotional interconnectedness of the whole world shone out in this crisis, as evidenced by the quick and ubiquitous contagion of mass hysteria through social media and the popular media in February-March 2020. (…) This is all evidence of contagion of emotions and beliefs, turning individuals into fearful crowds.”
Overestimation of individual risk
In the German Socio-Economic Panel, conducted between April and July 2020, participants were asked for their estimated probability of personally experiencing a life-threatening infection with SARS-Cov-2 within the next 12 months (Hertwig et al. 2020). The representative survey found a mean perceived risk of 26% and a median of 20%. Both numbers are astonishingly high compared to the actual population risk. For comparison: As of 12 July 2021, 91,233 persons died following an infection with the novel coronavirus. In sum, this is equal to 2.4% of people who tested positive and 0.1% of Germany’s population. The median German is still under 50 years of age. 0.002% of under 50-year olds have died with Covid-19 (deaths from here, population from here) Sure, some more have suffered a life-threatening infection. Unfortunately, I could find no data on the cumulative number of hospitalised Covid patients in Germany. In Austria, 1.3% of all people who tested positive have been submitted to intensive care. This equals 0.09% of the total Austrian population (total infections taken from here). In total, 0.12% of Austrians died after a Covid-19 diagnosis. Adding the surviving ICU patients gives an estimate for the population prevalence of life-threatening infections of 0.18%. That is more than 100 times less than the mean personal estimate of a representative sample of Germans. Similarly absurd overestimations of the individual perceived risk have been found in the United States where in January 2021, under 40 years-olds still reported an average 11% probability of dying from coronavirus.
As I wrote above, meta-studies estimate the actual infection fatality rate (IFR) far below 1 percent for the entire population. The IFR is the proportion of deaths from infection compared to the total number of infected individuals, diagnosed or not. This may not be confused with the case fatality rate (CFR) that only includes diagnosed cases and is therefore highly sensitive to changes in the frequency of testing. The IFR depends on the properties of the disease, but it is dependent on the quality of treatment too. It also depends on the demography of a population. A virus like SARS-CoV-2 that is mostly dangerous to elderly people is likely to have a higher IFR in countries with an aged population than in those with a young population.
On 6 July, 86% of Covid-19 deaths in Germany were aged 70 or above. 1% was under 50. There are arguments why it may be easier to suppress a pathogen instead of targeting the protection of risk groups. But I do not believe that people make their best decisions when they are insanely misinformed. The fear-driven communication that took over established media, social media and government communication has produced unprecedented irrational health anxiety. Young people never had a high risk of falling seriously ill from coronavirus. The irrational fear is not merely the result of organic contagion of fear on social networks, but has been actively incited by governments. In the United Kingdom, for example, behavioural scientists from the Scientific Advisory Group for Emergency (SAGE), a public institution advising the government, suggested on 22 March 2020: “The perceived level of personal threat needs to be increased among those who are complacent, using hard-hitting emotional messaging.“
Excess mortality refers to “the number of deaths from all causes during a crisis above and beyond what we would have expected to see under ‘normal’ conditions”. This measure is helpful to get a feeling for the relations. Our world in data provides data on the weekly (for some countries monthly) excess mortality measured in p-scores defined as the percentage mortality lies above or beyond the 2015-2019 average. Calculating the average of weekly p-scores for some of the biggest European countries yields 14% for Italy, 12% for the UK, 10% for France and 5% for Germany. Note that this does not mean 5% of the population. It means that in an average week during the reported period, 5% more people died than in the 2015-2019 average. In Sweden, the average weekly excess mortality was 5%. If this is compared to the neighbouring countries, this might seem much (Finland 2%, Denmark 1%, Norway -3%: all without stay-at-home orders), but 5% above average could well be seen as quite a normal fluctuation. It is all a question of perspective and relations. As you can see in this chart of Swedish mortality over the years, 2020 has been the deadliest year since 2000 in absolute terms. But if you consider population growth, 2012 had about the same death rate as 2020 (both 0.97%). No one said Sweden was “sacrificing their old people” in 2012. To be fair, I would like to stress once again that these relatively small excess mortality figures incorporate the effects of containment measures. It is very probable that excess mortality would have been significantly higher if we had treated this virus the same as we treated yearly influenza. But I would like to remind that there is no evidence that the most coercive and drastic measures were the most effective ones and it is likely that voluntary behavioural change accounts for the largest part of successful mitigation.
As of 2 July 2021, 99.95% of the world population have survived the deadliest pandemic in our lifetime. The world population actually continued growing during the pandemic. The threat is hardly of a new quality, but our reaction is. How can we react proportionately to a threat that is so ridiculously overestimated by the general public? If you decide on how to battle a disease that has a chance of far below 1 percent to kill an average grown-up but you think it has a chance of 20 percent, you will not end up with an optimal solution. It might reinforce the authoritarian nature of policies as many people are aware that most people’s personal risk to die from Covid-19 are negligible. In the minds of the misinformed, fear-driven median voter, these persons are acting irrational and have to be forced into line. They probably think that they are doing us good. If I tell people that I am not scared at all to catch Covid because I am young and healthy some react as if I said that I would not be scared to catch Ebola because I am young and healthy. They think they are morally right in forcing protection on me like they would keep a drunk person from steering a car or crossing a busy street. Based on their misinformed judgement, they are acting rational and ethical.
So what could explain the overestimation of risk both on the individual and collective level? Reddit user Sgt_Nicholas_Angel from the Lockdownskepticism believes that after the lockdowns emerged from wrong predictions, they were a “perfect storm”, self-reinforcing, specifically in combination with masks: “Everyone is staying at home as much as possible, they are masking up simply to step outdoors, and they are isolated from everyone except their immediate household. What else do they have? The news is the average person’s only connection to the outside world. This might be your local governor’s updates, twitter, or the television. There you will be bombarded with the worst covid cases, numbers that are getting worse by the day, and constantly being told to stay at home and that if you go outside without a mask you will probably die. Without lockdowns, you can be reassured by coworkers, friends, and family. Without masks, you can see that people aren’t distancing or paying attention and they are still alive. With both, you have nothing to reassure you and you fall deeper into fear, but it is not human nature to be isolated and you begin to blame others for your prolonged isolation. Pretty soon, this blame gets shifted onto those anti-maskers, and the media reinforced this with false comparisons and more fear. We now have three ways lockdowns were allowed to continue: separating people from their social group, masking everyone taking a step outdoors, and demonizing anybody that dared to disagree with what was being done. This perfect storm allowed lockdowns to continue much longer than they ever should have. (…) In conclusion, lockdowns were allowed to go on this long because of the initial belief in the two-week doctrine, the enforcement of mask wearing, the isolation and shaming of dissenters, and now the condemning of vaccine hesitancy.” Indeed, my data indicates a relationship between mask mandates and lockdowns. During the second wave, outdoor mask mandates were almost only applied in countries that also implemented some sort of confinement policy, often in the form of night curfews. Mask mandates and lockdowns could be correlated because both are strict measures aiming at the same goal. Governments who are specifically motivated to fight the pandemic and specifically uncaring about civil liberties apply both measures. Yet, masks also contribute to a self-reinforcing mechanism that keeps other restrictions in place too. First, they have a strong impact as a signal of danger and contribute to maintaining a high level of health anxiety in the population that then translates into support for other draconian measures. Second, as face masks have become the main symbol of fighting the virus, they can signalise that their wearer is associated with the same (unconsciously) political movement as the #stayhome campaigners. You cannot tell who wears a mask in the street due to their affiliation to this movement and who wears one because they are forced to.
This points to crowd effects or “groupthink”. Schippers and Rus (2020) define: “Groupthink is a phenomenon that occurs when a group of well-intentioned people makes sub-optimal decisions, usually spurred by the urge to conform or the belief that dissent is impossible. Oftentimes, these groups develop an overly narrow framing of the problem at hand, leading to tunnel vision in the search for possible solutions.” This overly narrow framing, in the case of the pandemic, was minimising the number of infections and deaths. Even when individuals develop doubts, the group shows a strong escalation to commitment bias.
Joffe (2021) addresses these issues of social psychology further in his brilliant article “Rethinking the Lockdown Groupthink”. Several cognitive biases have influenced our view of the pandemic and were re-inforced through groupthink: “Identifiable lives bias included the identifiable victim effect (we ignore hidden “statistical” deaths reported at the population level), and identifiable cause effect (we prioritize efforts to save lives from a known cause even if more lives would be saved through alternative responses). Present bias made us prefer immediate benefits to even larger benefits in the future (steps that would prevent more deaths over the longer term are less attractive). The proximity and vividness of COVID-19 cases (i.e., availability and picture superiority bias), and anchoring bias (we adhere to our initial hypothesis, and disregard evidence that disproves our favorite theory) affected our reasoning. Superstitious bias, that action is better than non-action even when evidence is lacking, reduced anxiety. Escalation of commitment bias, investing more resources into a set course of action even in the face of evidence there are better options, made us stand by prior decisions.”
Cognitive biases are powerful in explaining irrational human behaviour. Much of the research on cognitive biases builds on the work of Daniel Kahneman and Amos Tversky, two pioneers of cognitive psychology and perhaps the first and most influential behavioural economists. They investigated several cognitive biases and heuristics we apply in our judgement. Their prospect theory has been an important contribution to questioning the perfectly rational, utility-maximising homo oeconomicus as a good model for actual human economic behaviour. One of the cognitive biases they studied is the availability bias which means that people’s estimate of the relative frequency of something is influenced by the mental availability of examples. People overestimate the risk of terrorist attacks for instance because examples of terrorist acts are easily available in our minds. Statistics on infections and deaths with SARS-CoV-2 are highly available and covered by the media. Possibly, the representation and thus high mental availability leads to an overestimation of the individual risk of falling seriously ill.
Kahneman and Tversky developed the prospect theory that showed, among other things, how people treat losses and gains differently and how we are more motivated to reduce losses than to increase gains. Prospect theory was introduced with a highly relevant experiment: In Tversky and Kahneman (1981) students were introduced to the scenario of an “unusual Asian disease” that is expected to kill 600 people. Two groups of students were asked to choose between two possible policy responses which were framed differently. The first group was given the following choice: “If program A is adopted, 200 people will be saved. If program B is adopted, there is 1/3 probability that 600 people will be saved, and 2/3 probability that no people will be saved. Which of the two programs would you favor?” 72% of respondents opted for the less risky alternative. The other group was given the following choice: “If program C is adopted 400 people will die. If program D is adopted there is 1/3 probability that nobody will die, and 2/3 probability that 600 people will die. Which of the two programs would you favor?” With this framing, 78% opted for the risky alternative. Note that the expected number of fatalities is equal in all four scenarios thus homo oeconomicus would be indifferent. But the majority of respondents showed more willingness to adopt a risky program when the problem was framed as one of avoiding loss. Prospect theory has been mostly applied in economics, see here for some examples of its application in marketing.
Following Schippers and Rus (2020), this cognitive bias could contribute to explain why most people support risky policies whose effects are highly uncertain in the face of the Covid-19 pandemic. The framing of this policy choice is one of avoiding losses. All major news websites have quickly included daily scores of infections and deaths on their homepages. Countries are called a failure if their number of deaths is high and a success story if the number of deaths is low. “The problem has tended to be framed narrowly as one of avoiding deaths caused by the new coronavirus, as opposed to being framed more broadly as one of public health, or even more broadly as one of societal well-being — with all that it entails, including a healthy economy, public physical and mental health, social justice, etc. This narrow problem framing, in turn, may have influenced information elaboration and analysis of the situation and, paradoxically, may have led to riskier policy decisions than a broader problem framing would have.”
Schippers (2020) is another good summary of some of the psychological biases that influence societies to decide on lockdown even though evidence suggests that the costs outweigh the benefits. She relates a range of well-known psychological effects to lockdown measures: Prospect theory shows that people are more motivated to avoid losses than to achieve gains. As lockdown measures are framed to prevent death (even though there is poor empirical evidence that they do), which is the highest cost imaginable, this motivates people to adhere to the rules. Media and politicians often framed decisions to lockdown as false dilemmas e.g. between lives and livelihoods. At the same time, people have a tendency to seek information that confirms their beliefs and ignoring information that disconfirms their beliefs (Schippers and Rus 2020). This confirmation bias has surely influenced my own research, too, so I would be happy for suggestions of sources that contradict my beliefs.
Another good summary of the crowd dynamics is given by Paul Frijters here: He explains the overwhelming support for totalitarian policies with one of the key elements of crowd thinking. “The crowd wants to feel one and does not tolerate dissent.” Individual characteristics like status became less important as the crowd was united to fight the invisible enemy. He characterises much of the crowd behaviour as “virtue signalling” as overall effects on health and wellbeing are ignored and everything is only measured by how it could prevent coronavirus infections. This crowd effect was reinforced by commonly used war analogies (e.g. Emanuel Macron’s “Nous sommes en guerre”). Regarding this, he writes:
“Yet, the analogy with a war effort is really what it looks like. There is the same unquestioning presumption that the cause is right, that the fight will be won, that naysayers and non-combatants are basically traitors, and that there are technical solutions that will quickly overcome any apparent problem or collateral damage. There is also the same disregard and disinterest on the part of individuals in the enormity of the collateral damage, either to their own kids, people in other countries, their own futures, etc. There is even the same fatalism about the inevitability of the path they are on. These are individuals somehow enjoying not being individuals.”
Authoritarianism and cognitive dissonance
Across the world, governments gained popularity at least during the first months of the pandemic (Yam et al. 2020) due to the “rally ‘round the flag” effect that implies people increase their support for their leaders in times of crisis. My impression that this effect persists in many places. In most recent elections, candidates who supported lockdown policies won the majority of votes. Interestingly, governments in countries that did not lockdown, e.g. in the Nordic countries, receive public support, too. One explanation I could imagine is that as individual citizens, we realise that we do not have much power. Of course, the people as a whole have a lot of power in a democracy, but each individual vote does not count much. Sure, we can find many examples of governments making policy choices that a clear majority of their electorate opposes (though usually not in election years). But unlike tax policy or military interventions, lockdown policies depend on the compliance of the people. If a stay-at-home order would just be ignored by 80 percent of the people, there is little a government could do. If everyone else obliges by the law on the other hand and you find yourself being the only person on the street, you will probably get into trouble with law enforcement. But then, what is more unpleasurable? Joining in the #stayhome mob and virtue signalling how you are a good citizen? Or realising that your fellow citizens just decided to lock you in even though you have not committed any crime and raise your voice against the mob? I assume that the huge support for lockdowns, even when it became clear that they were not as effective as initially thought, has a lot to do with those who initially opposed them trying to reduce cognitive dissonance. Does it make sense to be banned from going on a walk on your own? Obviously not. But if the majority says so, I better think it does instead of feeling miserable and angry.
Schippers (2020) elaborates on this topic: “Cognitive dissonance will create tension between the belief that the sacrifices people make are necessary and the belief that some of these behaviors may be causing more harm than good in terms of mental health. The unpleasant tension stemming from conflicting beliefs then leads people to decide that the lockdown must be useful, and people also try to get doubters to reconsider their position, even in the face of clear evidence of overwhelming negative side effects.” She writes that “as the virus outbreak and media coverage spread fear and anxiety, superstition, cognitive dissonance reduction and conspiracy theories are ways to find meaning and reduce anxiety. These behavioral aspects may play a role in the continuance of lockdown decisions.” Schippers cites classical psychological experiments that show that “the effects of framing on the extent to which people obey authorities, even if the orders given are against their better (moral) judgment has been under investigation for decades”: The Asch conformity experiment, the Milgram obedience experiment, and the Stanford Prison experiment.
Those who fail to cope by adjusting their views find themselves helpless in the face of forced social distancing which increases passiveness. A corruption scandal makes people angry, higher taxes make people angry and usually touch organised interests, e.g. trade associations. Angry and organised people are the type that goes to the street and overthrows governments. But enforced social distancing made people feel lonely and isolated and has caused millions to develop symptoms of depression and anxiety rather than anger. Depressed and anxious people are rather passive. Depressed persons would rather blame themselves than the government. Depressed and anxious people lie in bed all day and watch Netflix, but they rarely go out to protest. Thus the miserable psychological condition of large parts of the society further reinforces the policies that contribute to this misery. It is a form of collective depression. At the same time, there are others who comfortable settle in their home office and do not want to return to a pre-pandemic normal life that included having to deal with annoying co-workers and traffic jams or crowded public transport.
Of course, cognitive biases and groupthink do not only shape the behaviour of citizens but of politicians as well. Possibly, politicians have a biased view of normal social activity as they are among the most socially active people living in the capital cities. Just by driving through London or Berlin and seeing millions in the street, they are likely to underestimate the degree of social distancing in the general society where all the “invisible” meetups behind the closed doors of e.g. bars or gyms do not take place. Moreover, politicians are expected to be leaders which can bias them to do rather too much than too little. They have little interest in admitting they were wrong as it could result in bad press and worse election results. Therefore, virtually everywhere where infections go down, public communication attributes the fall to the hardest measures introduced. Even when it is obviously not true because infections already went down before restrictions were escalated, this narrative is hold up, e.g. in Germany. When infections fail to fall despite strict restrictions, blame is given either to the public not abiding by the measures or to a new variant even when there is no evidence it is significantly more dangerous.
The authorities we are asked to obey have often been disguised by the slogan “follow the science”. As researcher and philosopher Matthew Crawford puts it: “The phrase “follow the science” has a false ring to it. That is because science doesn’t lead anywhere. It can illuminate various courses of action, by quantifying the risks and specifying the tradeoffs. But it cannot make the necessary choices for us. By pretending otherwise, decision-makers can avoid taking responsibility for the choices they make on our behalf.”. In this context, I found it interesting to read how an MIT study criticised anti-mask groups for believing that science is a process rather than an institution.
Where it all began
To understand the diffusion of unprecedented and previously unaccepted human rights derogations in the name of public health, we will need to look back at the history of their spread. It was on 23 January 2020 when the Chinese government imposed a lockdown on Wuhan and other cities in the Hubei province. At that time, the world was stunned by the decisiveness of Chinese authorities to contain the virus but few could imagine similar measures being implemented in liberal democracies. Even though the lockdown was not in accordance with WHO recommendations, WHO’s director-general praised China’s reaction on 30 January 2020. China reported steeply falling infection numbers (which might be false) in February and other countries began to consider following its example. On 23 February 2020, Italy put 11 villages under quarantine while still allowing for circulation within these villages. From 8 March 2020, Northern Italians needed a special form to leave their house stating a good reason for being outside. This rule was extended to the entire nation the next day. As British epidemiologist Neil Ferguson said in December 2020: “It’s a communist one-party state, we said. We couldn’t get away with [lockdowns] in Europe, we thought… and then Italy did it. And we realised we could.”
I pointed out many aspects of human psychology that could explain the persistence of lockdowns, but was fear really the only impetus for the lockdown domino? Now we are finally approaching the area of conspiracy theories. There is evidence that China deliberately influenced the public debate in Western countries. Many fake twitter accounts have spread fear of the virus while praising the Chinese approach and urging Western governments to adopt it. Propaganda activity was reported in Italy in particular. Michael Senger writes: “On March 9, Italy, the first major European country to sign onto Xi Jinping’s Belt and Road Initiative, took the WHO’s advice and became the first country outside China to lockdown. Italian Prime Minister Giuseppe Conte had long advocated closer ties with China. Chinese experts arrived in Italy on March 12 and two days later advised a tighter lockdown: “There are still too many people and behaviors on the street to improve.” On March 19, they repeated that Italy’s lockdown was “not strict enough,” saying: “Here in Milan, the hardest hit area by COVID-19, there isn’t a very strict lockdown … We need every citizen to be involved in the fight of COVID-19 and follow this policy. Italy was simultaneously bombarded with Chinese disinformation. From March 11 to 23, roughly 46% of tweets with the hashtag #forzaCinaeItalia (Go China, go Italy) and 37% of those with the hashtag #grazieCina (thank you China) came from bots.”
So while there is certainly not only one reason why such an unprecedented wave of authoritarian and totalitarian policies with little evidence of their efficiency swept across the globe, we have to consider Chinese politics as one driver behind the contagion of fear and appeal to authority that eventually became self-feeding. In the emerging “New Cold War” between China and the United States, it is potentially a huge gain in soft power that nearly the entire world adopted an authoritarian style of policy that dismisses individual liberty, a core element of identification for American (and generally “Western”) culture for a collectivist and authoritarian approach to public health that originated in China.
Writing this, nearly one and a half years have passed since the Wuhan lockdown. The events of the last 18 months have shifted our view and our rhetoric. We have become used to calling it an “easing” of restrictions when we are allowed to see some more people and have almost forgotten that there was a time when our governments did not interfere with whom we would meet. Sweden is referred to with adjectives such as “completely open” despite not allowing public gatherings of more than eight people over several weeks. Before March 2020, hardly anyone would have called a society “open” that does not allow concerts or any other types of large events. Sweden has put bans on public life that would have been regarded as insanely authoritarian in 2019 if it were the only country that did this while the rest of the world remained normal. Only by the standard of mass confinement, bans on showing your face, and complete closures of everything deemed “inessential” that the majority of the world has turned to is Sweden open. We have become used to the atrocities of lockdown and our rhetoric changed accordingly. As Sgt_Nicholas_Angel_ writes in another post on Lockdown Skepticism : “The default state going into this was “remain open unless there is evidence not to be.” Now it has become “remain closed unless there is evidence to open up.”
Everyone who values freedom has to be cautious not to accept complete government control over our private lives by simply getting used to it and now celebrating the easing of lockdowns. In my eyes, the only way to prevent lockdowns from becoming a normal policy in the tool kit of governments is to reject this “new normality” and claim every liberty we had until mid-March 2020. We need to build institutions that guarantee these liberties. I thought we had these institutions in European countries. But our constitutions and courts were not strong enough to resist the lockdown groupthink. Everyone who supports the total switch away from individual liberty and responsibility to a system of perfect control of the society over the individual should be frank about their anti-liberal stance. We can “follow the science” when we want to boil an egg, but mathematical models cannot decide for us in which society we want to live in. I referenced a lot of scientific studies that point to the downsides of lockdowns but none of these studies proves that we should not lockdown. Personally, I put high value on freedom. If you put more value on safety you could derive at different conclusions looking at the same data. I hope we can return to a society that guarantees fundamental rights such as free movement or the right to education. We decide ourselves how we want to live. I wish we did so based on facts and rational thinking and with mutual respect.
TL;DR: When lockdown policies are not the result of a rational analysis of their costs and benefits, what was it that caused governments across the globe to turn their societies upside down? This question cannot yet be answered with precision. Many factors could have been at work feeding to a self-reinforcing cycle of fear and authoritarian response both in individuals and in crowds. Early predictions over-estimated the risk, both established and social media generated attention with fear as a strong emotion. Potentially, this collective panic has been pushed for by some media or politicians (namely China), but to me, it seems to have been mostly self-reinforcing from very early on. Groupthink evoked conformist behaviour to win the “war” against the virus. Even many sceptical people adapted to the “new normality” of lockdowns to avoid cognitive dissonance while others were pacified by depression and anxiety. Deciders have masked the incertitude of their political decisions by claims to “follow the science” but to make progress as a society, we need to acknowledge the political and ethical dimension of lockdowns for which science can give no definite answer.