vytautas magnus university
TRANSCRIPT
VYTAUTAS MAGNUS UNIVERSITY
FACULTY OF ECONOMICS AND MANAGEMENT
BACHELOR STUDIES
Nikita Kuznetsov
ANALYSIS OF THE IMPACT OF PANDEMICS ON THE ECONOMY
Bachelor final thesis
Economics study program, state code -6121JX034
Study field of Economics
Supervisor Lect. Vitalija Kardokaitė-Šimanauskienė
(degree, name, surname)
Defended assoc. prof. dr. R. Bendaravičienė
(Dean of the faculty)
Vilnius, 2021
2
Nikita Kuznetsov. ANALYSIS OF THE IMPACT OF PANDEMICS ON THE ECONOMY.: Bachelor
thesis in Economics and Finance / Supervisor Vitalija Kardokaitė-Šimanauskienė / Vytautas Magnus
University, Faculty of Economics and Management, Department of Economics. – Vilnius, 2021. – .55 p.
SUMMARY
In this paper the economic impact of pandemics is analyzed and the relationship between the
governmental measures, used to mitigate the effects of pandemics, and the economic performance of the
country is evaluated. The importance of this topic is also proved by the great interest of scientists and the
abundance of scientific articles. The problem addressed in this paper is to asses, what is the impact of
pandemics on countries’ economies? The object of the research is the impact of pandemics of countries‘
economies. The aim of the paper is to analyze the impact of pandemics on countries’ economies. The
methods used in the first part of the thesis is the systematization of scientific literature, assessing the
impact of the pandemic on the economy. The methods used in the second part of the paper: a
mathematical model to determine the development of the stages of a pandemic and a comparative logical
and graphical analyses of the economic indicators carried out to evaluate the impact of different policy
approaches used to fight against the effects of the pandemics on the countries‘ economies. Research
results revealed that the pandemic itself considerably contributes to a fall in the economic activity and
spending.
Keywords: economic activity, financial support, mitigation measures, pandemic, SEIR model.
3
Nikita Kuznetsov. PANDEMIJOS POVEIKIO EKONOMIKAI ANALIZĖ: Ekonomikos baigiamasis
darbas / Darbo vadovas vardas Vitalija Kardokaitė-Šimanauskienė / Vytauto Didžiojo universitetas,
Ekonomikos ir vadybos fakultetas, Ekonomikos katedra. – Vilnius, 2021. – 55 p.
SANTRAUKA
Baigiamajame bakalauro darbe yra analizuojamas pandemijos poveikis šalių ekonomikoms ir
vertinamas ryšys tarp vyriausybės taikomų pandemijos poveikio ekonomikai slopinimo priemonių ir
šalies ekonomikos būklę charakterizuojančių ekonominių rodiklių. Nagrinėjamos temos svarbą įrodo
mokslininkų susidomėjimas bei mokslinių straipsnių gausa. Šio baigiamojo darbo problema nagrinėja
koks yra pandemijos poveikis šalių ekonomikai. Darbo objektas - pandemijos poveikis šalies
ekonomikai. Baigiamojo darbo tikslas - išanalizuoti pandemijos poveikį šalies ekonomikai.
Baigiamajame darbe naudojami tyrimo metodai: mokslinės literatūros, vertinančios pandemijos poveikį
ekonomikai, analizė ir sisteminimas; matematinio modelio SEIR, pandemijos stadijų raidai nustatyti,
taikymas; lyginamoji loginė bei grafinė ekonominių rodiklių analizė, atskleidžianti kovos su pandemija
metodų poveikį šalies ekonomikai. Gauti tyrimo rezultatai atskleidžia, jog pandemija ženkliai prisideda
prie ekonominės veiklos apimčių ir išlaidų sumažėjimo.
Raktiniai žodžiai: ekonominė veikla, finansinė parama, pandemija, pandemijos poveikio
ekonomikai mažinimo priemonės, SEIR modelis.
4
CONTENTS SUMMARY ........................................................................................................................................ 2
SANTRAUKA ..................................................................................................................................... 3
INTRODUCTION ............................................................................................................................... 5
1. THEORETICAL ASPECTS OF THE IMPACT OF PANDEMICS ON THE ECONOMY .......... 7
1.1 Theoretical concept of pandemics ............................................................................................... 7
1.2 Theoretical aspects of the impact of pandemics on the economy ................................................. 8
1.3 Review of research analyzing the impact of pandemics on the economy ....................................16
2. ANALYSIS OF THE IMPACT OF PANDEMICS ON THE ECONOMY: CASES OF SWEDEN
AND DENMARK...............................................................................................................................23
2.1 Methodology of the analysis of the impact of pandemics on Sweden and Denmark economies ..23
2.2 An overview of pandemic situation in Sweden and Denmark.....................................................27
2.3 Results of the analysis of the impact of pandemics on Swedish and Danish economies and the
comparison of the results with other research ..................................................................................35
CONCLUSIONS ................................................................................................................................44
REFERENCES ...................................................................................................................................46
STATISTICAL INFORMATION SOURCES .....................................................................................50
ANNEXES .........................................................................................................................................51
5
INTRODUCTION
Relevance of the topic. Several years ago, the author of the book “The Black Swan: The Impact
of the Highly Improbable” Nassim Taleb called viruses the greatest threat to humanity in the coming
years (Avishai, 2020). In 2019, the World Health Organization released A World at Risk report (GPMB,
2019), warning humanity of the potential devastation of the pandemics. As it can be seen now, many
countries were not prepared for this type of threat. The spread of the virus was greatly facilitated by
globalization, so revered by the modern consumer society and so favored by it. Now people must face
the downsides of this process.
The economic system, due to its complexity, cannot adapt so quickly to current changes.
According to the forecasts of the European Commission, as of December 2020, the European economy
will lose about 7% of GDP, government budgets of the European countries will hit budget deficit of 8%
on average and the have already lost trust of the citizens significantly. Also, according to many
dictionaries and source search engines (Merriam-Webster, 2020), “pandemic” was declared the word of
the year, so the relevance of the pandemic studies is higher than ever.
The topic of the mitigation policy strategies is of particular relevance today as well. Quarantine
seemed to be the only way to solve the problem in many countries. However, unlike most European
countries and Scandinavian peers, Sweden has abandoned tough containment measures since the start of
the pandemic. This decision was vigorously discussed all over the world throughout 2020.
The problem addressed in this paper is to assess, what is the impact of pandemics on countries’
economies?
The object of the paper is the impact of pandemics on countries’ economies.
The aim of the paper is to analyze the impact of pandemics on countries’ economies.
To reach the aim of the paper the following objectives were set:
1. To analyze the theory and concept of pandemics and its socio-economic impact.
2. To present, compare and evaluate previous researches that analyzed the impact of pandemics on
the economy.
3. To analyze the impact of pandemics on the economy, developing the research methodology and
adapting it to perform the analysis of pandemic impact on Sweden and Denmark economies.
4. To present the results of the research and compare them with the results of previous research.
This paper is structured in 2 main parts.
In the first part pandemics in terms of an external shock leading to a crisis of the economic
system are analyzed. It examines 4 pandemics, from the Great Plague of the 14th century to the 2009
6
swine flu pandemic. The last chapter of the theoretical part provides an overview of the researches
evaluating the impact of pandemics on the economies, specifically of COVID-19. The second empirical
part examines the impact of pandemics on the economies of Sweden and Denmark, taking into account
the mitigation measures used.
Research methods. The analysis, systematization, and generalization of scientific literature are
performed. The comparative logical and graphical analyses are used to analyze data. A mathematical
model of the spread of the epidemic is applied, on the basis of which a conclusion is made about the
appropriateness of the mitigation measures taken in Sweden and Denmark. Then, the cross-sectional
comparison of the main economic indicators is used. Lastly, the main findings and results of the analysis
are provided and compared with other studies on the topic.
Information sources. In the theoretical part, the analysis and evaluation of scientific articles
were performed. In the second part a comparative analysis of statistical data and graphical analysis are
used. Coronavirus statistics and economic data were taken from John Hopkins Database, Our World in
Data website, Statistiska Centralbyran (Swedish Statistical Webpage), Danmarks Statistik (Danish
Statistical Agency), reports of the European Commission and the website of the International Monetary
Fund.
7
1. THEORETICAL ASPECTS OF THE IMPACT OF PANDEMICS ON
THE ECONOMY
In this part of the paper the impact of the pandemic on the country’s economy in theoretical
terms will be analyzed and an interpretation of the concept of a pandemic will be provided, as well as a
grouping of pandemics according to certain features.
The possibilities of analyzing the impact of a pandemic on the economy are examined by
analyzing scientific articles already performed and empirical researches on this topic already published.
1.1 Theoretical concept of pandemics
The topic of epidemics and pandemics is very complex. In 2020, it gained even more interest
and will attract more and more researches over the next few years. To begin with, it is worth giving
definitions to these objects of the study. World Health Organization (WHO) - directing and coordinating
authority of international health uses the definition, proposed by the Oxford Dictionary of Epidemiology.
The definition of an epidemic is:
The occurrence in a community or region of cases of an illness, specific
health-related behavior, or other health-related events clearly in excess of
normal expectancy. The community or region and the period in which the cases
occur are specified precisely. The number of cases indicating the presence of
an epidemic varies according to the agent, size, and type of population
exposed; previous experience or lack of exposure to the disease; and time and
place of occurrence…Generally, a disease that exhibits large inter-annual
variability can be considered as epidemic. (Dictionary of Epidemiology, 2014)
So, according to the definition, epidemic is always some sort of unusually large number of
health-related events, and it can be used to define not only virus outbreaks, but some other high-volume
events affecting health. However, a lot of attention in this paper is devoted to COVID-19 and it has a
different official status - on March 11, 2020, the World Health Organization announced that the outbreak
of coronavirus infection COVID-19 had become a pandemic (WHO, 2020). Thus, the definition of a
pandemic, presented by Porta in A Dictionary of Epidemiology is the following: “An epidemic occurring
over a very wide area (several countries or continents) and usually affecting a large number of people”
(Porta, 2014). Thus, pandemics are just epidemics of a higher scale, spreading along many countries and
inflicting a substantial part of the population.
8
Diseases have always accompanied people. Somewhere they hardly disturbed the humanity,
somewhere they forced it to fight back and prosper, and somewhere they made a huge contribution to the
destruction of the empires (Gray, 2020, para. 3). Nevertheless, humanity does not stand still, improving
medicine, social policy and living conditions - never in history has the standard of living been so high.
Consequently, the fact that humanity does not face so many infectious diseases in the quality of massive
outbreaks like in industrial era and before can be attributed to these changes in people’s wellbeing (Bloom
& Cadarette, 2019). All the more unexpected and destructive is the emergence of a new virus capable of
undermining the foundations of a modern society based on the intertwining and codependency of sectors,
countries and regions.
The world of the 21st century is a world of uninterrupted trade and the pursuit of wealth. The
stronger are the negative consequences that humanity is forced to endure after epidemics and pandemics,
from changes in trade due to supply chain disruptions to bans on voluntary movement imposed by states
to prevent the spread of the virus (Snower, 2020, p. 4).
The potential economic losses can be enormous as many sectors are affected - tourism,
healthcare, agriculture and transport (Delivorias & Scholz, 2020). All of this is underpinned by
accelerated urbanization, increased international travel and climate change, making the outbreak of any
virus a global problem.
Nevertheless, finding problems in the modern economic system, realizing its vulnerability to
external factors, people can make it better, help to adapt and make it more flexible. Overall, the economic
impact of a pandemic is sometimes difficult to quantify. The qualitative and quantitative costs incurred
by both society and individual households can vary significantly depending on the severity of the
pandemic, the long-term effects and projected costs (indirect or direct). The historical context is also very
important - in the past, demography played the main role in shaping economic activity, but now
information spreads instantly, so business is much more sensitive. Thus, pandemics and their impact on
world economies is a very serious topic requiring much effort to analyze thoroughly to make the system
more resilient to it.
1.2 Theoretical aspects of the impact of pandemics on the economy
This part of the paper explores 4 infectious diseases, which affected the economic activity of
countries. The reason why these particular epidemics were chosen is very simple - they all influenced
the development of economic and social relations in countries around the world to one degree or another.
So, the Spanish woman instilled in Asia a culture of wearing masks, which was reflected in the current
9
trends in the spread of the virus, while in European countries this culture eventually disappeared (Horii,
2014, para. 3). SARS and the 2008-2009 swine flu pandemic are recent examples of how even mildly
contagious and not very fatal diseases can lead to regional or global destabilization.
The Black Death.
The most important pandemic in many ways is (at least for the European history) the Black
Death. According to various estimates, it carried out lives of one-fourth to one-third of the European
population (Khan, 2003, p. 273), meanwhile producing a significant effect on culture, politics and
economics. The mortality rate was sometimes so high that some cities lost up to three quarters of their
citizens.
At the same time, the level of wealth of the population before the epidemic itself (until 1347)
did not differ in any way from the well-being of the inhabitants of China, but immediately after the events
of this epidemic, there was a sharp jump in the income of the population (Sharp, 2012). Undoubtedly,
this was facilitated by the loss of population, directly affecting the land-labor ratios, respectively,
significantly raising wages. All this is a component of the Malthusian model, built in 1798 by the famous
English demographer Thomas Malthus. The implication of his growth model was that all forms of life
with an abundance of resources are characterized by exponential population growth. However, at some
point, resources begin to be scarce and growth slows down. This is called the Malthusian trap - a typical
situation in the pre-industrial era, when at some point in time population growth outstripped the growth
in food production due to limited soil fertility. However, this system stopped working in the industrial
era, because the concept of capital appeared, which helps to increase production and develop
technological progress. Thus, having wiped out a significant part of the population of Europe, the plague
helped the survivors to rise to a new level of equilibrium steady point of the Malthusian model.
An interesting observation is that all groups, regardless of income and age, were equally affected
by the virus (Cohn, 2003, p. 67), which is not the case with the current epidemic (UNDP, 2020, p.
11).Also, this outbreak became a spark that set fire to the conditions of the social order. The feudal system
suffered a blow from which it was ultimately unable to recover. Villages were depopulated, land values
fell, production costs dropped. Many fiefdoms have disintegrated into more modern contractual
relationships (Bell & Lewis, 2004).
According to the research made by Voigtländer: “… the mortality channel alone can account
for at least half of the increase in per capita incomes in early modern Europe. The largest component
came from more frequent warfare. Diseases spread by trade… made smaller contributions” (Voigtländer
10
2012, p. 30). However, no one should forget that the plague created a more "attractive" background for
constant military conflicts, increasing the income of the population, thus giving the war causation.
Thus, the Black Death played a very important role in creating the foundation for evolving social
relations and is a good example of how a terrible in its scale endogenous catastrophe can have positive
consequences in the long term. This is why Africa's deadly HIV epidemics could potentially increase per
capita income (Young, 2005, p. 423).
The Spanish Flu.
The 1918-1919 influenza pandemic, also called the Spanish flu pandemic, has one of the highest
death tolls during the healthcare crises in human history. This outbreak of influenza is accounted for at
least 40 million people dead, however some of the researches affirm, that number is very close to 100
million people worldwide (Quammen, 2012, p. 331).
There are several theories regarding the causes of the emergence of this virus, and none of them
has been proven to be the one and only (Kolata, 1999, p. 53). The virus could originate in China and get
to Europe through the military divisions that sailed to participate in the First World War. Another theory
is that the virus was first encountered in the combat positions - in trenches in France and Germany. And
finally, the third theory, which has the highest number of supporters as well as it is cited much more
often - the virus came from the Midwestern United States, along with the military (Barry, 2005, p. 117).
There is no reliable source saying that the virus first appeared in Spain. The reason why this
virus began to bear the name of this country is simple - Spain did not take part in the First World War,
so its media didn’t censor what was happening at the front like other countries did. Therefore, information
about the unknown virus killing people in the trenches of France, Germany and other countries
participating in the war was first spread in Spain. Global troop movements during the war and
demobilization afterwards had greatly contributed to the spread of the virus in a world where human
movement had generally been at much lower levels.
When it comes to a detailed analysis, it becomes very hard to determine the targeted impact on
the world economy caused by the pandemic precisely, as many countries were in the protracted war,
which contributed to the economy destruction a lot by itself. Particularly destructive was that the profile
of mortality, as it was everything different from what was expected - instead of a regular U-shape, when
2 age-specific groups primarily affected were young children and the elderly, it was more of a W-shape
(Figure 1). Its distinguishing characteristic was that men and women of the age from 15 to 44 were
primarily affected (Brainerd & Siegler 2003, p. 5). As the influenza was enormously deadly to the
primary labor force, it, in fact, severely affected the economy damaging the labor supply thus affecting
11
businesses and families economically. This effect was amplified by the fact that many young people lost
their lives in the war, thus each subsequent life of a person of the "working age" became even more
valuable. Meanwhile, in the USA, these deaths by the Spanish flu surpass the number of combat deaths
during World War I, World War II, the Korean War, and Vietnam all together (Brainerd & Siegler 2003,
p. 2). In their studies, Brainerd and Siegler demonstrated the impact of the pandemic on economic trends
in the United States in the first decade after the pandemic (1920-s) and in the following to even-out all
the possible disruptions caused by the independent factors.
Figure 1. Age-Specific Death Rates from Influenza and Pneumonia in the U.S.
Note: Compiled by an author using data from “The economic effects of the Influenza pandemic” by Brainerd &
Siegler research, 2003, Centre for Economic Policy Research. № 3791, p. 36
They used the standard neoclassical Solow model (1956) (which came to be used to estimate
economic growth in the industrial era, where the Malthusian model was no longer applicable), implying
a diminishing marginal utility of capital. According to it, regardless of the shock occurring in the
economy due to the diminishing marginal utility of capital, the growth in utility will return to the steady-
state level k0, where k is the amount of capital per worker. Even if the shock’s effect is positive and the
amount of capital per worker goes up, it will slowly return to the equilibrium point. Moreover, the larger
the shock (pandemic in this case), the larger the initial increase in output per worker as well as in the
capital per worker, and the more negative will be the subsequent growth in output per worker. (Figure
2a). In a simple AK model, according to Romer (1987), implying a constant utility from capital, due to
shocks such as pandemics, a new state will be formed, from which the utility of capital will subsequently
0.00
500.00
1000.00
1500.00
2000.00
2500.00
3000.00
< 1 1 - 4 5 - 1 4 1 5 - 2 4 2 5 - 3 4 3 5 - 4 4 4 5 - 5 4 5 5 - 6 4 6 5 - 7 4 7 5 - 8 4 8 5 +
DEA
TH R
ATE
AGE
Death rate (M), 1918 Death rate (W), 1918
Death rate (M) 1914-1916 Death rate (W) 1914-1916
12
grow. Thus, in contrast to the Solow model, the following growth rate is positive since the amount of
capital produced by every worker will continue to grow (Figure2b).
Figure 2a. Solow model Figure 2b. AK growth model.
Source: The economic effects of the Influenza pandemic (2003).
The most important conclusions of their study, where they used the regression analysis, was that
both the overall mortality rate from influenza and pneumonia in 1918 and 1919, and the mortality rate
among people of the working age are significantly and positively associated with the subsequent growth
in real income per capita from 1919-1921 to 1930 in the US states. Thus, it was found out that: “the
epidemic is positively correlated with subsequent economic growth in the United States, even after taking
into account differences in population density, urbanization, levels of income per capita, climate,
geography, the sectoral composition of output, human capital accumulation, and the legacy of slavery”
(Brainerd & Siegler, 2003, p. 27). The results show that one death per thousand led to an average annual
growth rate of real income per capita over the next ten years to at least 0.15 percent per year. Thus, it is
fair to say that in this particular “pandemic” case Romer’s AK model worked better, setting up a new
equilibrium point with the higher capital utility. However, they also found that in 1919-1920 the number
of business failures was unusually high, possibly due to deaths and the economic fallout from the
pandemic. This means that the post-flu gain is, at least in part, only a return to the trend.
Additionally, some communities started implementing non-pharmaceutical intervention (NPIs)
in order to reduce transmission by decreasing contacts between people, at least in the US (Bootsma &
Ferguson, 2007, p. 7588). The research suggests by completing the correlation analysis, that: “the most
important conclusion from this work is that the timing of public health interventions had a profound
influence on the pattern of the autumn wave of the 1918 pandemic in different cities. Cities that
k0 – capital per worker. Steady-state level k1 - capital per worker. New level
13
introduced measures early in their epidemics achieved moderate but significant reductions in overall
mortality. Larger reductions in peak mortality were achieved by extending the epidemic for longer”
(Bootsma & Ferguson, 2007, p. 7591). However, it was also found that right after the restrictions were
lifted, transmission rebounded.
An important fact is what Crosby wrote in his book: “the states with the highest excess mortality
rates – Pennsylvania, Montana, Maryland, and Colorado – had little indeed in common economically,
climatically or geographically. Unlike previous epidemics which traveled on a slow east-west axis, the
Spanish Lady struck in a sudden, random fashion.” (Crosby, 1989, p. 66). Undoubtedly, it is also
important to remember about the First World War, which also had a significant impact on economic
trends in the country, but the authors of the article tried to minimize the impact of variables that are not
shocks from the pandemic.
The SARS-CoV-1 epidemic.
Severe acute respiratory syndrome (SARS) was the very first deadly epidemic that people
encountered in the 21st century. The infection quickly spread from China in just over six months, infecting
people in several dozen countries, but China and Hong Kong were the ones most severely affected by
the outbreak. The strain of this virus showed a mortality rate much higher than its "peace-loving" brother
SARS-COV-2 - out of 8437 cases of infection by July 2003, 813 deaths were recorded, which is
equivalent to almost 10% (Lee & McKibbin 2004, p. 119). It is noteworthy that this virus, like SARS-
CoV-1, disproportionately affected the elderly population, especially those with existing chronic
conditions (mortality in this group of the population was over 50%) (Siu & Wong, 2004, p. 72).
Even though the virus was “mothballed” rather quickly, negative economic consequences could
not be avoided, luckily, they turned out to be short-lived. For example, in Hong Kong, the demand curve
suffered the most, as people were afraid of getting infected, so they preferred to stay at home. As a result,
retail sales in April 2003 fell by 15% compared to the previous year, and the use of public transport
decreased by 20% (Liu, Hammitt & Wang, 2003, p. 17).
The tourism sector suffered the most: the flow of tourists decreased significantly (from 1.5 million a
month to 700,000). albeit only for 3 months (see Figure 3). Overall, the number of travel from/to Hong
Kong by any means of transport has been reduced. Thus, the volume of travel by land transport decreased
by 50%, by sea by 72%, and by air by 77% (Siu & Wong, 2004, p. 81). Additionally, under SARS,
countries had to take measures to stimulate the economy by introducing relief packages. Taiwan, for
example, has approved a $ 1.4 billion stimulus package to cover business and medical expenses. In turn,
Hong Kong approved $ 1.5 billion for economic recovery (Liu, Hammitt & Wang, 2003, p. 22).
14
Figure 3 Number of visitors arriving in Hong Kong (August 2002 to August 2003).
Note: This graph was adopted from “Economic Impact of SARS: The Case of Hong Kong” by Siu & Wong, 2004,
Asian Economic Papers, 3(1), p. 77.
SARS has proven to be important in understanding the economic impact of the epidemic. He
clearly demonstrated that the economic impact is not limited to the loss of lives and health care, which
were low in SARS case. Most of the economic losses are caused by psychological factors; fear and
uncertainty reduced demand, and expectations for the future are redefined. The SARS epidemic also set
a precedent for government involvement in the fight against the epidemic to reduce the economic losses
it causes.
Even though the economic consequences of the epidemic were short-lived, they seriously
puzzled scientists, politicians and the world community. The author put it: “As the world becomes more
integrated, the global cost of a communicable disease like SARS is expected to rise.” (Lee & McKibbin
2004, p. 115). As it can be seen now, these predictions were not founded.
The H1N1 pandemic of 2009.
H1N1 is the last pandemic that humanity went through before the advent of COVID-19. It all
started suddenly - appearing in Mexico in May 2009, it took the virus only 5 weeks to reach all the
inhabited continents, forcing the WTO to proclaim this outbreak a pandemic. As for the official statistics
of morbidity and mortality, there are conflicting estimates. WHO has confirmed about 18,000 deaths,
while some studies suggest that 151,700–575,400 deaths in the first year of a pandemic were attributed
to the influenza virus (Dawood, Iuliano & Reed, 2012, p. 692). A notable feature of this influenza virus
0
200
400
600
800
1000
1200
1400
1600
1800
Nu
mb
er o
f vi
sito
rs (i
n t
ho
usa
nd
s)
Visitors arriving in Hong Kong
15
is its targeting - 80% of all fatal cases occurred in the age group under 65, while usually influenza acts
differently, as the percentage of the age group from 65 is accounted for 80-90% of all influenza-related
human losses.
According to the scenarios that were developed by Warwick J McKibbin, this pandemic was
recognized as mild, based on epidemiological estimates of the virus mortality and its prevalence rate
(mild, moderate, severe, ultra) (McKibbin,2009). The 2009 swine flu virus has been shown to be
relatively mild, and therefore competent public health measures can help with this type of pandemic
(Verikios et al., 2011). The mortality rate was calculated at 0.026% (BBC, 2009).
However, due to the low mortality rate of the virus, little is known about its economic impact.
However, the virus has still impacted health systems around the world, especially in Latin America. For
example, in Chile, the pandemic resulted in losses of $ 16 million due to decreased productivity of
workers due to illness. If we extrapolate this study to the US economy while keeping the pandemic the
same, then in the US, a decrease in productivity would result in a loss of $ 2 billion. The tour also suffered
badly. industry in Mexico, losing nearly a million visitors in 2 years, resulting in a loss of $ 2.8 billion
(Delivorias & Scholz, 2020).
In England, for example, the cost of treating patients with this strain of influenza was estimated
at an additional £ 45.3 million (the first wave of £ 20.5 million in additional spending on patients, and
the second £ 24.8 million) over the two years from 2009 to 2011.
Table 1
Epidemics and Pandemics analyzed.
TIME PERIOD DEATH TOLL STATUS
The Black Death 1347-1351 25 M Pandemic
The Spanish Flu 1918-1919 50 M Pandemic
SARS-COV-1 2003 813 Epidemic
H1N1 Pandemic 2009
18,000
(151,700–575) Pandemic
COVID-19 (2020-12-
28) 2019-present 1.8 M Pandemic
Note: Compiled by the author based on the information about pandemics from CDC and Encyclopedia Britannica
All epidemics, with the exception of the plague of 1347, broke out unexpectedly, spread with
the lightning speed, and then dried up in just a year. Even though the mortality rate of 2 of them (SARS
16
and influenza of the 21st century) was insignificant, they still exerted a very strong pressure on the
economies of the countries in which the most massive outbreaks of infections occurred. Spheres, that
were hurt the most were the once largely dependent on the consumeristic choice and substitutional
variability – tourism and retail. However, such an event could potentially have much larger and
widespread economic consequences. The duration and unknown nature of the disease, coupled with its
high infectiousness, can create a lot of uncertainty for both politicians with investors and ordinary
citizens. This is exactly what happened in 2019.
1.3 Review of research analyzing the impact of pandemics on the economy
The coronavirus has taken hold in people's lives. No one can be surprised by the mask mode,
the need to maintain a distance and other precautions in order to not to let the infection to spread. There
are many ways to combat the spread of the virus. As the Imperial College COVID-19 Response Team in
their Report 9 highlights, states can combine preventive and isolation protection measures, which lead to
a big variety of different scenarios - it is worth at least looking at how the governments of countries
within Europe itself reacted differently, and there are many more scenarios around the world. A group
of scientists presenting their report about “Non-pharmaceutical interventions” proposed two fundamental
strategies (Ferguson, 2020, p. 3):
1) Suppression. This strategy aims at reducing the reproduction number (R), thus the virus will have
less carriers and eventually burns down. It can be reached by eliminating human-to-human contacts or
dropping it down to the very essential minimum.
2) Mitigation. This strategy aims at reducing the health impact of epidemic, not eradicating it
completely, until the “herd immunity” builds up. This is when the outbreak will fizzle down on its own
and the pathogen is no longer able to find a host easily.
The choice of measures ultimately depends on the relative feasibility of their implementation
and their likely effectiveness in different social contexts. For example, the American sociologist Charles
Murray describes the importance of the geographical aspect and population density when choosing
restrictive measures: “The relationship of population density to the spread of the coronavirus creates sets
of policy options that are radically different in high-density and low-density areas ”(Murray, 2020). In
several of his studies, he considers 3 American fractions: New-York, dozen large cities beyond New-
York as well as small cities, towns and the countryside. In the US 91% of the population live in counties
with the population density of 2000-3000 people per square mile – where the amount of COVID-19 cases
is unproportionally lower than in highly populated cities: “the sensible thing for government to do about
17
the pandemic in a small town or small city is different from the sensible thing for government to do in a
big, crowded city” the author says. Thus, the strategy to fight the pandemic may vary not only within one
country, but within one city sometimes, which may make things way more difficult for the politicians
and legislators. However, this is the case for America, where the urbanization criteria are different from
the European standards.
However, disregarding the population density, the population-wide social distancing should be
implemented, as this policy has the largest impact .Additionally with some other intervention policies –
school and university closure, home isolation of cases, quarantining, tracing – has the potential to restrain
transmission below the threshold of R=1. R is a measurement showing an average rate of a pathogen’s
contagiousness (Bates, 2020, para. 3). Thus, in order for the virus not to spread, this indicator must be
below 1, then less than 1 person will pick up the virus from each infected person, which means that the
pandemic should not fade away. However, there is another important variable – k, which means how
dispersed the pathogen is. Thus, the unpredictability of the virus is shown. For 11 months of collecting
epidemiological statistics, it became clear that the pathogen of the COVID-19 virus is overdispersed,
which means that it tends to spread in clusters (Cevik, Marcus & Buckee, 2020, p. 8). From the other
hand, diseases like flu are very deterministic and tend to show the right picture of transmission with just
R variable. Therefore, a more comprehensive system needs to be developed with the coronavirus.
Excessive variance makes it very difficult to deduce patterns and learn lessons from any events.
For example, infection / non-infection events are asymmetric in their knowledge contribution. For
example, if we consider an event that could potentially lead to the infection of many people: the actively
discussed case in Springfield, Missouri (Hendrix, Walde & Findley, 2020). Hairdressers who were
infected with the COVID-19 continued to work with masks, having contact with 139 clients. None of
their clients were infected - 67 people were tested and the rest did not report symptoms. And, since no
one was infected, it can be concluded that masks are significantly useful in preventing infection. But, on
the other hand, knowing the chaotic nature of the spread of the infection and the applicability of the
Pareto principle, in the event of infection of one of the clients, this would have become even a more
weighty proof that masks do not help to contain the infection.
While over-variance makes some conventional methods of studying cause and effect difficult,
there is a sphere that should be studied to avoid turn bad luck into disaster – socio-economic policies due
to their high dependency on numbers.
The developing COVID-19 pandemic can truly be called the most significant pandemic since
1918’s influenza. Economically speaking, it caused the contraction as severe as the Great Depression
almost a century ago (Fan, Jamison & Summers., 2020, p.129). This crisis affected many spheres of
18
people’s functioning – according to the forecasts, economic output will shrink by 5.2% in 2020
(European Commission, 2020, p. 172), World Health Organization claims it a public health crisis, as
many countries were (and are) struggling with the amount of people in need of medical help entering
medical facilities on the daily bases with addition of the shortage of personal protective equipment and
ventilators. In the developing countries people are experiencing food limitations, so they are forced to
choose - death by hunger or COVID-19. This is due to the problem, that is caused by the social security
systems in poor countries, giving people no choice but to work on the interconnected supply chain to
make a living. Failing that, supply chain system will crunch, thus affecting the rich (developed countries).
Realizing how complex political decisions and their consequences are, economists reacted quickly by
providing analyzes and forecasts of scenarios, giving politicians a qualitative and qualitative analysis to
work with.
This part of the thesis is devoted to reviewing the research and analyzing the lessons that can be
drawn from them. Most of the research has been based on a time-tested epidemiological model called
SIR, in which economic variables have been introduced to manage public health and the economic impact
of pandemics. These models formalize the compromise that all countries must come to in order to form
the optimal (if possible) socio-economic strategy to combat the pandemic.
SIR is a simple mathematical model that shows how the infection spreads across the population.
SIR is an acronym which stands for Susceptible, Infected and Recovered (or removed). All people of the
population are divided between these 3 groups. Susceptible (S) are those at risk been infected, as they
are not immune to it. Infected (I) people are those who are undergoing the process of the disease and can
be contagious. Removed (R) are people who cannot be infected due to different reasons (either being
immune to it, deceased or immune after successfully going through the status of being infected). Dynamic
in this model is measured on the basis of two parameters: β, the rate at which infected individuals
encounter susceptible individuals and successfully transmit the virus, and γ, the rate at which infected
people recover or die:
Recovery rates are ranging widely even inside one disease: Ebola’s recovery rate was from 75%
to 10% in some outbreaks. Influenza recovery rate depends on the strain of it going up to 99.998% (18
deaths per 100,000 people) (CDC, 2020).
Another important indicator is the “basic reproduction number”, which reflects how many
people will be infected by one person with the virus before he/she recovers from it. Talking about the
basic reproduction number (R0), it is important to give some examples of what it can be. Thus, measles
is very infectious R0 = 15, which means that every infected person is responsible for 15 new
contaminations, SARS in 2003 was more infectious than cold R0=3 but way less than measles and regular
19
flu ranges R0= 0.9-2.1. Needless to say, these are the in-born attributes of the diseases and it is up to the
policies implemented to drop the contamination by public regulatory rules.
Additionally, economists imply one more category – Exposed (E) to account for the individuals
who are infected but not infectious yet. Implying this parameter, the model imposes that there is an
incubation time in a new host for the virus to become infectious.
The first research, done by Atkeson (2020) (see Table 2), shows SEIR model, implying that the
transmission rate (β) can change to account containment measures implemented by a government or
change of behavior. The analysis reproduces models at different levels of transmission to simulate the
rate at which the virus spreads. Also, in this research, the models varied in the absence / presence of
containment measures for the spread of the virus. Depending on the presence and severity of the virus
containment measures applied, the transmission rate ranged from 3.0 (no containment measures) to 1.0
(strict quarantine). The bottom line of this study is that regardless of the measures taken to control and
protect the population, in the long term it is impossible to contain β <1.0, which translates into a
widespread of the virus and, consequently, in a lot of fatalities (Atkeson,2020).
However, Atkeson's study did not look at the impact of the pandemic on economic societal
relations. Accordingly, in their study, Eichenbaum, Rebelo, and Trabandt: “allow for the interaction
between economic decisions and rates of infection (Eichenbaum, Rebelo & Trabandt, 2020, p. 1)”. In
addition to the standard indicators used in the SIR model, it is considered that infection can occur at work
or during shopping. They also consider that sick people make their decisions differently - they may not
go to work or ask someone to go shopping for them. Thus, this system takes into account how people
make economic decisions in a pandemic, because for many categories of citizens, utility of consumption
is more important than the utility of being infected. Thus, people decide to work less in exchange to stay
safe and sound. In their study, they apply the models (the medical preparedness model, the treatment
model, the vaccination model), including various social scenarios, and also consider models that are
based on the measures taken to contain the spread of the virus. (shelter-in-place orders or bars and
restaurants closure). They found out, that the optimal measures of virus control reduce the peak level of
infection to 3% from 5% as well as the death toll to 0.21% from 0.26% (Eichenbaum, Rebelo & Trabandt,
2020, p. 21).
The next research conducted by Alvarez, Argente, and Lippi (2020) was based on SIR model
with the inclusion of lockdown measures to find the specific type of lockdown which minimizes the
economic losses as well as the lives carried out by the pandemic (and measures implemented). Their
quantitative analysis classifies the features of the optimal lockdown policy – shape, intensity and duration
of it. Additionally, they do not imply testing in their model. As a result, their optimal policy looks like
20
“the implementation of the severe lockdown beginning couple weeks after the outbreak, which covers
60% of the population in a month and gradually weaken it to 20% of the population after 3 months”
(Alvarez, 2020, p. 1). Most importantly, everything comes down to the lockdown implementation and its
effectiveness – ineffective lockdown makes the optimal time of lockdown shorter. Thus, economic costs
of the ineffective lockdown rapidly overweight the positives of it, as the lockdown doesn’t succeed with
its main function – decrease the transmission of the virus (Alvarez,2020).
Similarly, Jones, Philippon, & Venkateswaran (2020) include the mitigation policy. However,
they support suppressive measures of a way higher scale and, as a result, they conclude that both the
cumulative death rate may drop to 1.75% from 2.5% and the death rate to 0.15%. This comes from their
“learning by doing” assumption that says that people working at home are getting better at this in time
and from hospital congestion when the infection rates are high (Jones, Philippon, & Venkateswaran,
2020, p. 11).
Additionally, there were the studies that include testing in the model. One of such analysis was
conducted by Piguillem & Shi (2020). They show that “if the government has no means to identify the
carriers of the virus, the observed mandatory quarantines around the world seem to be close to what it
can be considered optimal. However, if the government can increase the intensity of testing over subjects,
that is a far superior strategy “(Piguillem & Shi, 2020, p. 42).
Lastly, there is a research, analyzing the heterogeneity effects of COVID-19. Kaplan, Moll &
Violante (2020) integrate the expanded SIR model with the variables like income & wealth inequality,
sectoral occupation. In their model, economic exposure to the pandemic is strongly correlated with the
level of income. Thus, the lower the income the more exposed you were to the pandemic and the measures
accompanying it (Kaplan Moll & Violante, 2020, p. 5) (Table 2).
Table 2
Research of COVID-19 and its impact on the Economies.
DATE MODEL SUBJECT OF
RESEARCH
CONCLUSIONS
Atkeson March 2020 SEIR
Liquid virus
transmission over
time.
No mitigation policies can
deter the virus once β>1.0
Eichenbaum,
Rebelo &
Trabandt
April 14,
2020
SIR
(extended)
Relationship
between the disease
spread and the
Containment measures
reduce:
21
economy. Economic
decisions.
Infection’s peak level – 5%
3%
Death toll – 0.26%
0.21%
Alvarez, Argente
& Lippi
April 6,
2020
SIR
(extended)
Quarantining people
once suspected in the
contact. Cost of
statistical life.
Lockdown costs.
Severe lockdown 2 weeks
after the outbreak.
Effectiveness of a lockdown
is extremely important.
Jones, Philippon
&
Venkateswaran
Ap2ril
10,2020
SIR
(extended)
Working from home.
Learning by doing.
Hospital congestion.
Front-loaded mitigation
policy. Work from home
immediately.
Cumulative death rate –
2.5% 1.75%
Death toll to 0.15%
Piguillem & Shi June 8,
2020 SEIR Testing
Quarantine and enacting of
it becomes more selective.
Kaplan, Moll &
Violante
September,
2020
SIR
(extended)
Testing, Wealth &
Income Inequality,
Unequal economic
consequences of the
pandemic. Welfare costs
are large.
Note: Compiled by the author based on the research analyzed.
The spread of the coronavirus pandemic has led not only to a public health crisis, but also to an
economic one. The literature reviewed in this section makes it clear that human behavior is very
important when choosing a model to fight the virus. Ignoring the behavior of people in response to a
pandemic can result in dire consequences in both health and welfare, as happened in 1918.As authorities
choose the NPI policies to follow, it is imperative for them to consider how it affects people’s lives and
how reasonable they are, otherwise it creates a distrust effect making everyone worse-off.
This chapter has described the concept of a pandemic. Also, a historical analysis of past
pandemics was carried out, taking into account their impact on the economy. The following is a review
of scientific papers that look at pandemics in terms of their socio-economic impact. There are many
22
variables that affect the course of a pandemic. So, measures to contain infection (social distancing,
isolation etc.) have an inversely proportional effect on the mortality rate, reducing the number of infected
people, and people of low income are more exposed to negative consequences from a pandemic.
23
2. ANALYSIS OF THE IMPACT OF PANDEMICS ON THE
ECONOMY: CASES OF SWEDEN AND DENMARK
In this part of the paper the economies of Sweden and Denmark will be compared in under the
pandemic conditions. The overview of the pandemic circumstances will be given with respect to their
different approaches used. The results of the analysis of the impact of pandemics on countries’ economies
will be provided and compared with the results of previous scientific research.
2.1 Methodology of the analysis of the impact of pandemics on Sweden and
Denmark economies
The section presents a simple SEIR model for the COVID-19 pandemic. SIER models are
commonly used in order to understand the flow the virus spreads with. The reason why it is used in this
analysis is simple – the amount of days the pandemic is in action reflects the economic performance of
the country. The longer the pandemic is present, the lower the chance that the contagion policy can rely
on the strict lockdown measures, as it exhausts the economy and makes in very vulnerable. Based on the
literature review, the scale of suppression measures in place directly affects the economic activity in the
area affected. So, the main goal of this part of the analysis is to show, using the model, how differently
the pandemic has been developing in Sweden and Denmark with the help of the previous research, which
are related to the costs of lockdowns, isolations, social distancing and other NPI intervention measures.
Analysis of previous studies has identified that the direct costs of the COVID-19 pandemic related to the
amount of infected people and mortality are lower than the indirect losses conditioned by the crisis (Noy,
2020). It means that the low amount of COVID-19 cases and deaths does not necessarily correlate with
a low economic impact. Also, analysis of previous studies identified that fiscal measures, virus mitigation
measures, interconnectedness with the world supply chain are the factors that influence economic
performance under the pandemic conditions.
For the statistical information of economic indicators the official data sources are used: ECDC,
Our World in Data, Statistiska Centralbyran (Swedish Statistical Webpage), Folkhalsomyndigheten
(Swedish Public Health Agency), Danmarks Statistik (Danish Statistical Agency) in order to compare
and evaluate the COVID-19 cases of Denmark and Sweden. For the assessment of analysis of the impact
of pandemics on the economy, based on theoretical analysis, hypotheses are raised, the model of
assessment will be constructed, and the correction of variables will be made. Also, the most suitable
24
method for the investigation will be selected based on the model test. Additionally, the limitations of the
analysis will be presented.
The model that is used in this section is called SEIR. The original model was developed by
Kermack & McKendrick in 1927 and was an infectious disease dynamic model. It simulated the course
of the disease dividing the population between 3 categories and tracking changes among them over time.
SIR – S (susceptible, those who are at risk of getting the disease), I (infected, those who can transmit the
disease) and R (removed, who have been exposed by the virus and either died or recovered from it).
However, in the model of this analysis, there will be one extra category – E (exposed, those who are
infected but not infectious yet). Thus, the population (N) fraction would look like this: N = S + E + I
+R. The model implies, that the person gets infected first and has some time (σ) before he/she becomes
infectious himself, so called incubation period (Kermack & McKendrick, 1927). Other 2 important
parameters used are, the transmission rate (β) showing how “successful” the person is at transmitting the
virus to the other encountered individuals, and the recovery rate (γ), showing at which rate infected
people die or recover. With all that being said, the formulas will look like this:
1) 𝑑𝑆
𝑑𝑡= −𝛽𝑡𝑆
𝐼
𝑁
2) 𝑑𝐸
𝑑𝑡= 𝛽𝑡𝑠
𝐼
𝑁 − σE
3) 𝑑𝐼
𝑑𝑡= σ𝐸𝑡 − γ𝐼
4) 𝑑𝑅
𝑑𝑡= γI
5) β = 𝑅𝑡γ
The first equation shows that the susceptible population goes down due to the number of newly
infected. The second equation shows the number of new exposures minus the fraction developing the
symptoms. The third equation shows that the infected population goes up with people with the developed
symptoms minus the people removed (recovered or dead). The fourth equation shows the amount of
people recovered within the given timeframe. The fifth equation shows how the transmission rate is
formulated.
This model is used to create scenarios for the spread of a pandemic, taking into account various
variables. So, the model will use several variants of the variable R0, which stands for disease transmission
rate, in different situations. Depending on the location, population density, mitigation measures, personal
protective equipment, etc., disease transmission rate may vary, which directly affects the degree of
infection of the population. The value of R0 corresponds to the transmission of the disease with no
25
mitigation efforts. This is a critical parameter for evaluation the progression of the disease in the
population and the economic costs of mitigation. The problem is that this indicator is not very reliable
due to the problem of overdispersion discussed in the part 1.3. However, from the perspective of creating
a mathematical model for identifying the duration of a pandemic, this is the most appropriate variable to
reflect the concept of the spread of the virus. So, even in the reports of the World Health Organization,
the indicator is indicated at the level of 2 to 4, which creates completely different patterns of distribution.
However, the initial R0 was chosen as R0 = 2.8, (Ferguson et al, 2020), however, depending on the
adopted pandemic containment policy, this indicator may fall to below 1, which leads to the conservation
of the virus and a further drop in the level of infection by the population.
The initial conditions for all experiments are set as follows. The initial value of I is set to one in
a million for Sweden, corresponding to 10 initial cases there given a population of 10 million. As for
Denmark, I value is set to one in 500 thousand, corresponding to 10 initial cases there given a population
of 5 million. The initial value of E = 4I, corresponding to an initial 40 individuals carrying the virus but
not yet contagious for both the countries. These values roughly correspond to the initial outbreaks in
Sweden and Denmark in late February, however this data is based on a grain of improvisation, as the
number of cases is very difficult to track without the proper systems which were not available at the very
beginning.
The incubation period (Tinc) was chosen to be 5.2 days. It is a mean indicator presented by the
World Health Organization (WHO), when in reality the incubation period may differ from 2 to 14 days.
The infectious period (Tinf) is the variable showing the time period when the person is infectious
to others. This indicator also varies significantly among the sources but is chosen to be 2.3. Thus, the
data for the implementation of the mathematical model was developed.
Table 3
Parameters used in SEIR model for COVID-19 pandemic
PARAMETER DENMARK SWEDEN
Infectious period (Tinf.), in days 2.3 2.3
Incubation period (Tinc.), in days 5.2 5.2
R0 at the beginning of the outbreak 2.8 2.8
Population size 5806000 10230000
Initial suspected cases 10 10
Mitigation measures Strong/Moderate Moderate/Weak
Note: Compiled by the author using WHO data, Statistiska Centralbyran and Danmarks Statistik
26
Each figure shows 3 sets of variables – Susceptible (blue line), Infectious (orange line) which
also includes the Exposed variable component represented in the incubation period and Recovered (green
line) [see Annex 2a, b]. Figures show, that the level of Susceptible people does not change significantly.
It means, that mitigation measures work correctly, decreasing the level of contagiousness of the
population. However, the presence of such strict control measures leads to the fact that they must
eventually be abolished, leading to subsequent outbreaks of the pandemic. Thus, for the full functioning
of the society, it is necessary either to significantly reduce the number of the population in the Susceptible
category, which means that the population develops herd immunity, which means that the most
vulnerable segments of the population will have less chance of becoming infected. Or we need to develop
a vaccine that will help a society move from the Susceptible category to the Recovered (or rather
Immune) category.
The selection of Denmark and Sweden is based on the division of regulatory perceptions towards
COVID-19 by them. In the meantime, many other factors can be considered almost ceteris paribus, as
Denmark and Sweden have very similar cultures, economic structure, demographics, weather and, most
importantly, they started detecting coronavirus cases approximately at the same time. Economic data will
be used in order to compare the initial consequences of the regulatory politics of these countries. Cross-
country comparison of data from the section of economic statistics, including macro- and microeconomic
indicators of Sweden and Denmark is used keeping into the consideration the anti-contagion policies that
the countries have implemented. It is worth noting, that COVID-19 was not the one and only reason
affecting the economic performance of the countries, but it caused the greatest fall still. COVID-19 has
affected. Approaches, that these two countries have used to deal with the pandemic, have played a
significant role in the way the economy has functioned since the first day of the pandemic. Accordingly,
it also affected the behavior of the inhabitants of these countries. People started to behave more
consciously and weigh their social decisions, keeping in mind positive and negative consumption
externalities.
There are several reasons why Sweden and Denmark are considered:
• Firstly, they have very similar geographic conditions, having relatively the same temperature,
humidity, and overall climate.
• Secondly, they have a similar culture, starting with languages - the citizens of these countries
can communicate without the knowledge of English, a common historical heritage and ending
with the norms of social behavior, which include the "internal" rules of social distance, a willing
obedience and law compliance.
27
• Thirdly, these countries have very similar economic baseline: GDP PPC, income inequality,
average wages, as well as long-term interest rates.
• Finally, the circumstances under which the country “welcomed” COVID-19 – almost at the
same time – the beginning of March, when the first cases were detected in Sweden and Denmark.
Other European countries aren’t so comparable (except Norway and Finland), since some of
them experienced earlier outbreaks (Italy, Spain or France), have very different population density (Italy,
Belgium or UK) or demographics (Mediterraneans’ households are more extended).
In the following chapters, the situation with the coronavirus in these countries will be analyzed,
the main macro and microeconomic indicators will be considered, the fiscal policies of the countries will
be assessed to smooth out the consequences of the pandemic, and the mathematical model will be
analyzed.
2.2 An overview of pandemic situation in Sweden and Denmark
The virus spread unevenly across Europe. Some countries were affected in February 2020, while
the virus leaked to the rest of the countries in March. The virus spread chaotically, so some countries
were simply "unlucky", but much also depended on other parameters, whether it be demographic
(population density), the importance and geographic congestion of transport hubs, etc. In the short term,
the situation is somewhat disordered (See Figure 4). On the horizontal axis the amount of cases per
million citizens is indicated by March 22nd. The reason why this date was chosen is simple – this is a
week after most of the European countries implemented some restrictive measures, whether it was early
soft lockdown like in Denmark and Slovenia or late moderate lockdown like in the UK, Spain or Estonia
(Plümper & Neumayer, 2020). Most European countries took preventive measures sooner or later,
depending on their own phase of an outbreak (early, moderate or late), however this is usually when
people themselves start being cautious and obeying, using personal protective measures. So, on March
15th almost all developed countries started taking steps to fight (or hide from) the virus. So, the measures
are most likely be on effect after March 22nd. The vertical axis shows deaths to 1 million citizens ending
May 31st, as for the majority of the developed countries, it was the end of the first wave of COVID-19
(not for Sweden though), therefore the end of the lockdown. The main role here is played by the initial
"affected area". Countries, that are more to the right (Italy, Spain) were the first European countries to
be hit by the virus. As it can be seen from the figure, the bigger the initial outbreak, the greater the amount
28
of deaths. According to the data, Nordic countries had very similar initial conditions – same vertical, so
the way how these countries managed this initial outbreak regulated the amount of deaths they had to
face.
The graph shows that Sweden did very poorly in comparison to its neighbors. Surely, there are
some other countries that experienced even worse death tolls e.g. UK (due to its high connectedness to
the rest of the world and bad early disease management), Italy, Spain, not to mention Belgium (very high
population density as well as sticky connectedness to the other European countries), but they all also
experienced worse initial outbreaks which were harder to manage. On the other hand, there is also a long-
term perspective, which is more important for a comprehensive and detailed forecasting. Sweden has
implemented different strategy than other Nordic countries and were forced to meet a flurry of criticism
both from the scientific community from outside the country and from within. Many Swedish scientists,
including epidemiologists, did not support the methods of dealing with the pandemic and publicly
appealed to the authorities, calling for the introduction of measures to contain the epidemic, similar to
those taken in other countries. For example, about two thousand researchers signed an open letter with a
AT
BE
BG
CZ
DKFI
FR
DE
EL
HU
IE
IT
LV
LT
LU
NO
MTPL
PT
RO
SK
SI
ESSE
NZAU
CA
UK
IS
USA
RU
R² = 0.2789
1
10
100
1000
1 10 100 1000 10000
Dea
ths
per
mill
ion
inh
abit
ants
as
of
May
31s
t
Cases per million inhabitants on March 22nd
Deaths per million inhabitants by June 30th
Figure 4 Initial COVID-19 outbreak and the death toll of it.
Note: Compiled by the author using data Our World un Data
29
similar appeal at the end of March. (See Figure 5). This Figure considers 35 countries, and the situation
with the coronavirus that has developed in them. The horizontal axis shows COVID-19 cases per 1
million inhabitants, the vertical axis shows deaths from coronavirus infection per 1 million inhabitants
too. The correlation between cases and deaths is quite strong, R2 = 0.5839, so we can quite confidently
say that the more cases of infection in the country, the greater the death rate. The last date considered in
this figure is December 17, 2020. In the long term, the situation is somewhat different - the number of
cases of infection is associated with a huge number of factors, from the chosen containment policy, the
age characteristics of the population (since the virus is much more dangerous for the elderly population),
to the humidity of the air and frequent hand washing. Amongst Nordic countries Sweden stands out with
high amount of cases as well as unusually high death rate – death rate trend is lower than the actual
Swedish data, pointing out its excessiveness. The graph also shows that other Scandinavian countries
had not only a very small number of infected, but also a small death rate. They were below the trend line,
which indicates a good performance of the health care system in addition to the high social awareness
that the northern countries are famous for.
Austria
Belgium
Croatia
Czech Repubic
DenmarkEstonia
France
Germany
Ireland
Italy
Latvia
Lithuania
Luxembourg
Norway
Malta
Poland
Slovakia
SloveniaSpain
Sweden
Australia
United KingdomUSA
R² = 0.5839
0.0
200.0
400.0
600.0
800.0
1000.0
1200.0
1400.0
1600.0
1800.0
0 10000 20000 30000 40000 50000 60000 70000 80000
Dea
ths
per
mill
ion
inh
abit
ants
Cases per million inhabitants
Outbreak case-to-death ratio
Finland
Figure 5 COVID-19 Outbreak case-to-death ratio. Last date considered is December 17, 2020. (WHO)
Note: Compiled by the author using data from Our World in Data
30
The external characteristics of these four countries are the same, so it all comes down to one
thing - state regulatory strategies in use. This part of the empirical analysis will look at the various
policies in the Nordic countries as they represent the breaking point explaining why the number of
coronavirus cases and the death toll are the way they are. It all starts with a look at Sweden's policy. In
mid-February, more and more information was received about a deadly new virus that forced China to
impose the draconian lockdown in some of its provinces. Unfortunately, it was not possible to leave the
virus as an endemic, as it happened with the first coronavirus outbreak in 2003. It spread to other
countries: outbreaks soon began in Korea, Iran and later in Italy. It was assumed that the Scandinavian
countries, having similar political models and cultural standards, would introduce similar policies.
Instead, it turned out to be far away from the reality – Sweden decided to take a loose approach - they
kept the restaurants open, as well as the primary schools, didn’t enforce any personal protective strategies,
relying on people’s self-consciousness. In other words, they imposed the mitigation strategy, the idea of
risk reduction. So, in this context it means that Swedish strategy recognizes the problem that senior
citizens may experience and the overwhelmed healthcare system, but they do not try to eliminate the
virus completely, as it is not possible. The Swedish strategy is built on the principles of liberal-democratic
personal responsibility, not on coercion and prohibitions.
Another reason why it was decided not to impose strict quarantine is the inevitability of a return
of the pandemic after the control measures easing. Accordingly, back in March, a report was written by
Ferguson et al. (Ferguson Laydon & Nedjati-Gilani, 2020) where they modified a simple transmission
model to reflect the evolution of the COVID-19 outbreak in Great Britain. The authors of the report
concluded that depending on the stringent virus suppression measures, a transmission bounce is created
which leads to another peak. Basically, under very suppressive measures implemented early enough, a
country can conserve the virus simply postponing the outbreak to the “better” days, until good preventive
and mitigation measures are figured out. However, long lockdown can simply be too much for the regular
people, especially when it is not very effective and requires giving up your life for many months (until
the vaccine is created) [see Annex 3].
Nonetheless, the general lockdown was not implemented because of the legal base, that restricts
enforcing measures since the voluntary measures are only allowed by the Constitution and the Swedish
Infectious Disease Act (Ludvigsson, Andersson & Ekbom, 2011, p. 450). In general, Sweden decided to
take a “trust-based” approach with some limitations, eventually leading to herd immunity as a “by-
product”, concentrating only on protecting the old and the frail (see Table 3).
What about Denmark, it had its strategy very similar to those used by other European countries
with many restrictive policies and limitations (see Table 3). Denmark closed its borders, unlike Sweden,
31
even though people were free to travel inside the country. Economic restrictions as well as gatherings
were to the greater degree limited, shutting down businesses and making people stay in their safe non-
transmitting bubbles.
Table 4
Nordic countries’ restrictions due to COVID-19
SWEDEN DENMARK NORWAY FINLAND
Schools &
Universities
Schools for children
aged 17 and above
are closed.
Kindergartens
and elementary
schools opened
in April.
Kindergartens and
elementary schools
opened in late
April.
Closed schools
(expect early
education)
Travel bans
and
restrictions
Advice against
international travel.
Recommendation
against non-essential
travel
Entry banned
with exceptions.
No limitations
of travelling
inside the
country
Entry allowed only
for residents and
citizens with some
exceptions. Two
weeks of
quarantine is
compulsory
Banned movement
between the
country’s regions.
Quarantining of
some municipalities.
Economic
restrictions
Non-essential
businesses were
open (bars,
restaurants, hair
salons).
Bars, cafes and
restaurants take
away only.
Malls were
closed.
Bars with no food
closed. Shops and
shopping centers
were open
Sports, museums,
libraries, swimming
pools, youth centers
and clubs are closed.
Take out only
Public
places
Gatherings of more
than 50 people were
forbidden. Visits to
elderly care banned.
Gatherings of
more than 10
people are
forbidden
No more than 5
people together.
No more than 10
people in public
gatherings
Personal
protection
Advised personal
responsibility and
physical distancing
Compulsory
masks in
premises
Masks must be
used in closed
spaces
Recommended usage
of face masks in
public transport etc.
Note: Compiled by the author using data from Coronavirus Government Response Tracker.
- Soft measures - Moderate measures - Hard measures
32
Interestingly, state figures in Denmark show that ethnic minorities are accounted for 50% of all
the infections occurred, comprising only 9% of the total population. This is partly due to the fact that
these people are blue-collar workers forced to work on the edge of the infection transmission, so being
in the higher group of risk. In general, this type of strategy can be called the suppression strategy, aiming
at reducing the reproduction number, to below 1. The hardest part about this non-pharmaceutical
intervention (NPI) is to keep it in place (at least intermittently) until the vaccine is there for people. For
COVID-19’s case it took almost a year to create the vaccine and will take God knows how much time to
produce and supply them all over the world to immunize enough people.
The consequences in the countries of the north were very different. For example, as of June 14,
Sweden had more cases every day than all its Scandinavian neighbors combined. In fact, when you add
up the cases from the 21 EU countries with the lowest number of cases in June, Sweden still had more
cases than all of them combined. So, the cases of the disease in Sweden hadn’t been decreasing for four
months, until the end of June, when the coronavirus temporarily receded, and the degree of infection
became the same as in Denmark, Finland and Norway. However, shortly thereafter, in early September,
the virus intensified in Denmark, prompting the introduction of new restrictive measures, albeit in a
milder form, allowing businesses to breathe easy. So, this time schools and businesses were kept open,
but some restrictions were still implemented. Unfortunately, Denmark failed to contain the virus and by
December the infection rate per 100 thousand people did not differ from the Swedish, despite the
difference in approaches. Although, it should be borne in mind that Denmark has the most advanced and
comprehensive testing system for coronavirus, so the results documented in this country can be
considered as accurate as possible. As for Sweden, the infection intensified here a little later, having
caught up with Denmark by mid-October and soared significantly higher than other countries in this
comparison category. In Norway and Finland, at the same time, infection rates remained very low, which
allows residents of these countries to celebrate Christmas without listening to strong advices from
officials (see Figure 6). Another justifying point for Denmark may be the population density in the
country - it is more like the density in Western and Central Europe, exceeding the indicators of Sweden,
Finland and Norway by 5, 7 and 7.5 times, respectively.
All this is despite the fact, that Sweden didn’t work hard on testing the people until the middle
of summer [see Annex 4] Tests that turned out positive in the percentage ratio are presented there. The
lower this percentage, the higher the odds that all cases are included. In any case, determining the number
of cases of infection is not very effective, since everything is based on the number of tests performed,
which some countries do not do enough. For example, Sweden conducts far fewer tests than its neighbors
33
(in percentage terms). As a result, things were underreported A more reliable indicator here is the death
rate.
As it was stated before, Sweden decided to become an outlier in this dangerous virus game,
relying on the very different approach. Basically, they have tried to develop this immune bubble for the
inhabitants collaterally to the comfortable living conditions. This strategy gradually affected the Swedish
death toll in comparison to what can be seen in other Scandinavian countries: It has 5 times more
coronavirus deaths per million people than Denmark and 11 times more deaths than Norway, during the
first wave. Then, for 3 months straight the death rate had been going down steadily and equaled the rest
of Europe at the end of July. By that time, it was expected by the official authorities
(Folkhälsomyndigheten, 2020) that there will be no outbreaks as the peak of the infection is a matter of
the past. Unfortunately, that didn’t work out well, as Sweden has started suffering the second wave, even
though the death rate is smaller. In the meantime, other Scandinavian countries managed to cope with
the second wave and kept mortality below the peak of the first wave (See Figure 7).
0
100
200
300
400
500
600
700
3/6/2020 4/6/2020 5/6/2020 6/6/2020 7/6/2020 8/6/2020 9/6/2020 10/6/2020 11/6/2020 12/6/2020
Covid cases per 1 million residents
Sweden Denmark Norway Finland
The amount of cases adjusted to the
population was very demonstrative in June,
when all Nordic countries started enjoying
non-COVID summer and Sweden had to
deal with the most severe outbreak in the
entire Europe.
Second wave didn’t differ from the
one happening in Denmark
Figure 6 COVID-19 cases adjusted to population. Last date considered is December 18.
Note: Compiled by the author using data from John Hopkins Database
34
This analysis shows that the approaches chosen by Denmark and Sweden have had completely
different effects on the course of coronavirus outbreaks in these countries. On the one hand, young people
could continue to live their lives in peace, undergoing only cosmetic changes, while the older generation
was forced to watch this through the windows of their apartments (more than 90% of deaths from
coronavirus in Sweden occurred in the age group of people above 70). On the other hand, the decision
was made to introduce strict quarantine, temporarily exchanging the standard life of the 21st century for
saving hundreds or thousands of lives. Such, as it may seem, unethical compromise (if you describe the
situation too emotionally), was adopted mostly for the sake of an economic shock, or rather its absence.
As in other countries, from the very beginning of the COVID-19 pandemic, the main goal of the
governments of Sweden and Denmark has been to protect the health and lives of people by reducing
contact between people, thus reducing the number of people infected at the same time. However, Danish
containment policy was not entirely different from a typical European practice of containment measures
escalation, while Sweden's strategy remained more liberal. In addition, Sweden has never entered a
period of lockdown with forced shelter-in-place orders, school closures and mandatory business closures.
The measures taken in Sweden were of a recommendatory nature, not obligatory, relying on the regional
0.0
2.0
4.0
6.0
8.0
10.0
12.0
3/17/2020 4/17/2020 5/17/2020 6/17/2020 7/17/2020 8/17/2020 9/17/2020 10/17/2020 11/17/2020 12/17/2020
Death cases per 1 million residents
Sweden Denmark Finland Norway
Figure 7. COVID-19 deaths adjusted to population. Last date considered is December 18.
Note: Compiled by the author using data from John Hopkins Database
35
part of Swedish society - social consciousness. However, the severity of the Danish control measures
cannot be overestimated - the severity of these measures was generally below European (Hale, 2020). It
should be noted that in Sweden the level of trust in government institutions is high (for example, in
March, opinion polls showed that about 70% of respondents trust the recommendations of the medical
department), and according to both surveys and cellular data, many people actually sat at home and
canceled Easter holidays. Thus, in the spring, the Swedes maintained voluntary self-isolation at a level
comparable to that in Denmark and other European countries. However, the mortality rate in Sweden
was much higher than in any other neighboring country, and this is primarily due to mistakes that were
made in the introduction of protective measures, namely the late closure of nursing homes.
However, the looser system in Sweden should have paid off - a smaller decline in economic
activity and more positive macro and microeconomic forecasts than in Denmark. The economic aspects
of this comparison are discussed in the next chapter.
2.3 Results of the analysis of the impact of pandemics on Swedish and
Danish economies and the comparison of the results with other research
In this part of the work, the economic indicators of Denmark and Sweden will be assessed using
the example of their different approaches towards the pandemic in order to determine the economic
feasibility of quarantine measures in this particular case.
As it can be seen in the Scandinavian countries, the choice of approach to solving a problem is
a common trade-off between saving economies or saving lives. It is logical that under stricter restrictions,
the virus should spread more slowly, which means that the death toll from it will be less, but the economic
consequences of such measures are much more serious reflecting in low consumer spending, high
unemployment and the closure of enterprises and businesses. However, it is worth noting, that the virus
itself causes significant damage to the economy, as people cut back on their consumption due to different
pandemic-in-place behavior, because they understand the impact of their activities on the health of others
– the self-reasoning and common sense thriving approach. If people decrease their economic activity
voluntarily, compulsory shutdown can be not that damaging. Accordingly, this indirectly creates a
mechanism due to which some segments of the population, with the introduced lockdown, will not reduce
their active participation in the economy - with a decrease in the level of infection, shutdowns can make
people feel more safe and comfortable working, spending money and going outside. Thus, the situation
is created in which the virus is a detrimental factor in reducing economic activity, and the competent
shelter-in-place order is a factor that stabilizes economic activity for those potentially most affected by
36
the outbreak - in this case of COVID-19, these are older people at risk. In fact, a trade-off occurs, in
which sectors of high social proximity are closed, mostly constraining the economic activity of low-risk
individuals (younger people), who otherwise would be the group mostly contributing to the spread of the
virus, with the flourishing economic activity of people of the higher age due to a reduced probability of
facing the virus.
Using data on the economic activity of the population during the quarantine period, external
differences in the policy response to COVID-19 are used to determine the impact of social distancing
laws on consumer spending. Effective March 11, 2020, the Danish government introduced a number of
social distancing laws, including a ban on gatherings of more than 10 people; the closure of schools,
universities and non-essential parts of the public sector; and complete or partial cessation of the activities
of a number of private businesses. Bars, cafes and restaurants were affected, being forced to offer take-
out services only and a range of other places like cinemas, nightclubs, dental practices and shopping
malls were closed down due to their specific requirement of social proximity. Unlike Denmark, Sweden
has responded to the COVID19 outbreak with a lightweight approach, largely relying on the social
responsibility approach. This very different response is probably the result of historical differences in
constitutions: unlike Denmark, the peculiarities of the Swedish constitution make it very difficult to
quickly pass laws affecting individual freedoms.
Figure 8 shows that in both Sweden and Denmark, daily aggregate spending in January and
February 2020 developed in a similar way to the same period in 2019: cyclical patterns were the same as
in the previous year, and there was some increase in spending levels in both countries right before the
dropdown. As the COVID-19 outbreak erupted and around the day of Denmark's closure, spending in
both countries has plummeted and stayed below 2019 levels throughout the first wave. By mid-summer,
Denmark had fully recovered its economic activity and was reaching the excess of last year's indicators
until the onset of the second wave, which originated in the country by mid-October (the surge in activity
can be attributed to the announcement of the implementation of the new restrictive measures and the
corresponding desire of people to catch up). In the future, against the background of new restrictions and
prohibitions, economic activity was evenly below the zero level of last year's indicators.
As for Sweden, the economic activity of the population there stayed at the level of last year in
the middle of summer very shortly, when the number of cases of infection and mortality fell to
insignificant values of the level of other Scandinavian countries (which speaks of the factor of a conscious
approach). Until mid-summer, the indicator was in the area of negative values for 3.5 months, since
quarantine was announced in the Scandinavian countries. Since there was no quarantine in Sweden, the
first wave lasted for several months, which caused a consistent reluctance of people to limit their social
37
activity. In the fall, the coronavirus returned to Sweden, bringing the indicators back to the level of the
beginning of the first wave by December. Places that are included in this economic activity indicator are
the restaurants, cafes, shopping centers, museums, libraries, theaters, etc.
There are also confounders, that should be discussed, in order to estimate the effect of social
distancing laws being disrupted by some other variables. First, there is no different economic exposure
caused by the COVID-19 pandemic: there was not difference trajectories of Danish and Swedish main
stock market indexes. It means, that firms were equally affected by the global contraction in economic
activity and trade in each country. Thus, even taking into account the absence of official restrictions, for
the entire time of the presence of coronavirus in Scandinavia, economic activity in Sweden did not greatly
exceed Danish during the first wave. In the summer, the absence of restrictions and the low level of
infection of the population created psychologically and in fact favorable soil for the growth of
consumerism among the Danish population, while in Sweden the level of 2019 has not been restored.
-50.00
-40.00
-30.00
-20.00
-10.00
0.00
10.00
20.00
30.00
2/18
/202
0
6/17
/202
0
10/1
5/20
20
Economic activity indicator (seasonally adjusted)
Sweden Denmark
Figure 8 Economic activity in Sweden and Denmark after the pandemic has started.
Note: Compiled by the author using data from Google COVID-19 Community Mobility Reports:
https://www.google.com/covid19/mobility/
First wave lasted less than a
month in Denmark
Second wave started in the
middle of October
38
And later, in the fall, when COVID-19 recovered in Europe, both countries went into the negative pool
of values, but in Sweden the indicators were on average lower.
This part of the analysis is based on 3 analytical forecasts written by the European Commission,
department of Economic and Financial Affairs in May, July and November 2020. This was done in order
to conduct a comparative analysis of the major micro and macro indicators of the analyzed countries.
With the help of three detailed analytical articles, changes can be tracked easily to which the forecasts
have undergone. To a large extent, these predictions depend on the policies followed by the states
regarding the COVID-19 pandemic.
It seems that Sweden's economy is not much different from other Nordic countries in terms of
the GDP change predictions. Considering, that loose contagion policy was implemented mainly to avoid
economic pitfalls, this is not what they were expecting to get. The idea was to avoid the economic
downturn that could have happened with the lockdown. However, regardless the lockdown the economy
is tanking. This is not what Sweden expected. It should be borne in mind that the countries that have
-9.0%
-8.0%
-7.0%
-6.0%
-5.0%
-4.0%
-3.0%
-2.0%
-1.0%
0.0%
Sweden Denmark Finland Norway EU
Forecast of gross domestic product, volume (percentage change on preceding year)
Spring 2020 Forecast Summer 2020 Forecast Autumn 2020 Forecast
Figure 9. Gross domestic product (GDP), volume
Note: Compiled by the author using data from European Economic Forecasts of 2020.
39
introduced a lockdown have a psychological "handicap", since people are inclined to quarantine, which
means that after its end they will be more willing to be economically active.
Figure 9 shows forecasts for three stages of 2020, two of them (spring and autumn) are full-
fledged semi-annual reports of the region's economic activity, while the summer report is intermediate
in volume. There is one thing that clearly follows from the figure - the Scandinavian countries have much
more positive GDP indicators. Initially, in May, it was predicted that the level of gross domestic product
will slightly differ from the average European trend - by one and a half percent. However, according to
calculations for November, a different picture emerges - the cumulative GDP loss in the northern
countries are, on average, two times lower than the median for Europe (-3.7% to -7.4%). Interestingly,
all Scandinavian countries had positive forecast trends throughout the year, while the forecast for Europe
did not change.
However, Sweden's real GDP fell sharply in the second quarter of 2020 due to the coronavirus
crisis (see Figure 10). The decline was especially drastic in the export and private consumption markets
(see Table 4). In general, all countries in Europe have suffered to varying degrees from cross-border
supply chain disruptions, in part due to the fact that creating a safe and functional system for the transfer
of goods takes time, which is luck of in the era of uninterrupted on-demand logistical chains, and partly
due to the fact that the biggest supplier of everything – China, was temporarily out of the game due to
COVID-19 which has forced it to shut down not only small businesses but also factories and plants,
implementing the strictest (some may say even draconian) measures to stop the spread. Thus, many
industrial enterprises had to close temporarily due to logistical problems. However, Sweden has been
able to cushion the unprecedented downturn in business activity both with softer public scrutiny due to
the coronavirus and with timely policy initiatives (IMF,2020). In response to the crisis, the Swedish
authorities have implemented a series of coordinated fiscal, monetary and financial support measures to
mitigate the impact, with fiscal stimulus for 2020 amounting to more than 16% of GDP – being equal to
SEK 805bn (almost 80 billion euros), including SEK 230bn in guarantees, SEK 240bn in budget
measures and SEK 335bn in liquidity measures. A new short-term layoff system was also introduced,
under which the employee was entitled to more than 90% of his salary, while the employer could request
50% of the cost of paying salaries through government subsidies. Citizens could get self-employment
before sick leave for up to 14 days. A tax break was introduced, according to which small businesses
could pay their income tax to a special fund, 100% of which could later be returned in an emergency
requiring an injection of funds. The most significant measures are financing temporary unemployment
benefits and sick leave costs, supporting firms that have suffered significant losses in turnover, and
increasing funding for regions and local authorities responsible for health and social services
40
(Government Offices of Sweden, 2020). Short-term indicators of output, sales, employment, as well as
business and consumer confidence and spending expectations suggest that the economic recovery, which
began in the summer, will continue, albeit at an uneven pace across industries. Real private consumption
is projected to decline by 5% in 2020, as spending on services, in particular, is likely to recover only part
of the lost ground. The enlarged government deficit is expected to be around 4% of GDP in 2020. The
public debt-to-GDP ratio should rise sharply from about 35% in 2019 to about 40% in 2020 before
stabilizing at this level. Of course, this is related to the large stimulus measures for the economy, forcing
governments to spend huge chunks of the budget. In terms of inflation, the country's consumer price
index will fall by less than 1% in 2020. This is due to low import prices, which in turn stem from
weakened trade relations due to interruptions in supply systems and a decrease in consumer demand,
leading to lower prices. However, inflation can also be subject to increased fluctuations associated with
the different price impact of the pandemic on different categories of goods and services, creating
uncertainty in the monetary sphere (see Table 4).
-15.0%
-10.0%
-5.0%
0.0%
5.0%
10.0%
15.0%
Q1 Q2 Q3
Profiles (qoq) of quarterly GDP, volume (percentage change from previous quarter)
Sweden Denmark Finland Norway EU
Figure 10. Quarter to quarter GDP estimation.
Note: Compiled by the author using data from European Economic Forecasts of 2020.
41
When it comes to Denmark, it had faced a sharp and deep economic downturn due to the
pandemic, with real GDP declining by 6.8% y/y in the second quarter of 2020.Short-term indicators show
that the recovery is in full swing, but real GDP is still projected to decline by about 4% in 2020.
Nevertheless, Figure 10 shows the presence of positive trends in GDP, associated mainly with the
recovery in both domestic and foreign demand. However, due to the start of the second wave of
coronavirus in Denmark and the introduction of a new package of restrictive measures, there are doubts
not only about the growth of the GDP indicator in the 3rd quarter of 2020, but also about maintaining the
positive trend.
Consumer spending plummeted in the spring when the government imposed tight lockdown
measures to contain the pandemic but has since normalized as the measures were phased out. The
recovery in private consumption was supported by stable disposable income thanks to the government
emergency.
In spring 2020, when the pandemic hit Europe, Denmark quickly implemented a wage support
scheme to avoid mass layoffs and stave off rising unemployment. The turda market showed resilience,
quickly resuming its activity after the end of the deadly lockdown. So, the number of people who are on
the state support program quickly fell from 250 thousand in April to 30 thousand in August. Thus, the
unemployment rate, thanks in part to this program, will rise from 5% in 2019 to 6.1% in 2020. (Danmarks
Statistik, 2020). When it comes to fiscal and monetary initiatives, there is a stimulating fiscal package to
support the health care system and the economy turned out to be 3 times less than in Sweden (as a
percentage of GDP) and 6 times less in absolute terms. In the section on compensation and subsidies to
private business, a fixed costs compensation program was introduced. Depending on the degree of
decrease in turnover, compensation can range from 25 to 100% (for companies with mandatory fixed
costs coverage). It is this part of the stimulating package that is the most financially advanced, accounting
for 65% of the total. Compensation for self-employed will be up to 90%, when a decrease in turnover is
more than 30%
As in Sweden, the global recession and disruption of cross-border value chains have had the
most severe impact on Denmark's foreign trade. In the second quarter, exports and imports declined by
17% and 14% YoY, respectively. The export of goods related to tourism, shipping and construction
decreased. Exports of agricultural and pharmaceutical products have not declined due to their low
sensitivity to business cycles. Exports are expected to decline more than imports, -10.5 and - 8.7,
respectively. The consumer price index in 2020 will be at 0.3%. It is also expected that the increase in
oil prices and economic recovery in 2021 will increase inflation to 1%. Gross government debt to GDP
42
ratio will increase from 33% to 45%, which is associated with both a significant drop in GDP and the
necessary increase in government debt.
Table 4
Main economic indicators of Denmark and Sweden
Year to year change
DENMARK SWEDEN
2019 2020 2019 2020
GDP (%) 2.8 -3.9 1.3 -3.4
Private consumption
(%) 1.4 -2.9 1.3 -4.6
Public consumption (%) 1.2 1.6 0.1 0.3
Unemployment rate
(% of total labor force) 5 6.1 6.8 8.8
General government
gross debt (%) 33.3 45 35.1 39.9
General government
balance (%) 3.8 -4.2 0.5 -3.9
Inflation, (%) 0.7 0.3 1.7 0.6
Export, (%) 5.0 -10.5 3.3 -7.4
Import, (%) 2.4 -8.7 1.1 -8.2
Fiscal stimulus 15 billion euros (4.8% of GDP) 80 billion euros (16% of GDP)
Note: Compiled by the author using data from European Commission reports, Statistiska Centralbyran and
Danmarks Statistik
The Scandinavian countries are renowned for their low levels of public debt. Sweden and
Denmark, thanks to their consistent and timely financial and fiscal measures, did not allow the economy
to lose much in the first two quarters of 2020. However, the incentive check put forward by the Swedish
government turned out to be much larger than what was adopted in Denmark. This is due to different
methods of fighting unemployment - while in Denmark people were transferred to a special temporary
reduction program, in Sweden people were left on special unemployment benefits. Also, Denmark has
adopted a gradual economic stimulus program.
43
This part examined the economies of two countries - Sweden and Denmark. First of all, a
mathematical model was considered to determine the scenario for the development of a pandemic in the
region. It turned out that the number of people who are not immune to the disease will be very high
regardless of the methods used by countries (both in Denmark and in Sweden, the level of "Recovered"
was negligible), even though Sweden was determined to get herd immunity as a ‘by-product” of its loose
pandemic policy. It is worth noting that the case here focuses on the current epidemiological criteria of
the coronavirus and belief in the monitoring system. It may be that many people who have had
coronavirus are not counted, but it remains as a limitation of the method.
Then, the coronavirus situation is examined using the example of these same countries. Since
the methods of containment practiced by Sweden and Denmark were different, the results were very
different. Sweden, very different from the rest of Europe, did not use lockdown. Nevertheless, the
inhabitants of Sweden significantly reduced their economic activity, mobility, although the measures
were only advisory in nature. However, the death rate in Sweden was one of the highest in all of Europe.
This is partly due to the late closure of nursing homes, as people in this age group are most susceptible
to coronavirus.
Also, the economic activity of the population differed by age group. For example, in Denmark,
due to the pandemic, the economic activity of young people has significantly decreased, while the
economic activity of the elderly has increased due to the sense of security caused by the lockdown
(Anderson, 2020).
44
CONCLUSIONS
Revealing the concept of pandemics, it can be said that pandemics are the outbreaks of
communicable disease taking place over a very wide area, usually affecting a lot of people. Based on the
information provided in the theoretical part, it can be stated that pandemics always affect socio-economic
activity of people, usually negatively in the short-term, leading to both healthcare and economic crises
and can have positive contribution in the long-term. The effects of the pandemic are particularly acute in
the tourism as it is tied to travel, which is strongly restricted by states to contain the spread of viruses,
which is especially important in an age of comprehensive globalization.
The economic impact of a pandemic is sometimes difficult to quantify. The qualitative and
quantitative costs incurred by both society and individual households can vary significantly depending
on the severity of the pandemic, the long-term effects and projected costs. The historical context is also
very important - in the past, demography played the main role in shaping economic activity, but now
information spreads instantly, so business is much more sensitive. Thus, pandemics and their impact on
economies is very current topic to help the economic systems to become more resilient to pandemics.
An analysis of scientific research assessing the impact of a pandemic on national economies
allows to state that the impact of pandemics on the socio-economic environment of the country is
especially emphasized. The research paid a lot of attention to the suppression measures to weaken
pandemics effects on the economy, such as lockdown, shelter-in-place order, social distancing, tracking,
contact tracing and other non-pharmaceutical intervention measures. Additionally, economic indicators
are considered too, including but not limited to countries’ level of wealth, economic decisions, cost of
statistical life, inequality of income, learning by doing principle etc. The research emphasizes the need
to take comprehensive and coordinated measures to combat the pandemic, not only at the level of country,
but also at the cross-country level.
In order to analyze the impact of pandemics on the economy, the mathematical model for
determining the development of a pandemic (SEIR) is used and is applied to two analyzed countries –
Sweden and Denmark. Then, the overview of pandemic situation is presented. Lastly, the comparative
and graphic analysis of economic indicators of these countries is performed, taking into account the
peculiarities of the methods chosen to reveal the impact of pandemics on the economies.
The results of the analysis of the impact of pandemics on Swedish and Danish economies
revealed that these two countries as well as other Nordic countries were hit less by the pandemic, than
the rest of Europe, whether it is the cumulative fall of GDP in the first six months, unemployment level,
economic activity or the service sector. This is due to high degree of authorities trust in Scandinavian
45
countries and a voluntary adherence to recommended and/or legal rules. However, despite differing
approaches to the pandemic, Sweden's economic performance is not positively related to Denmark's,
while being accompanied by a much higher mortality rate.
Analysis of the economic macro and micro indicators revealed that Sweden and Denmark were
successful to provide timely and considerable financial support to households and businesses.
Additionally, the analysis of the impact of pandemics on economies showed, that the pandemic
itself considerably contributes to a fall in spending and consumption. However, it differs along the
different age groups – in Denmark social distancing laws severely limit the spending choices of people
with low health risks: this group would have spent significantly more during a pandemic if the availability
of goods and services with high social proximity were not limited, despite the higher prevalence of the
virus.
There are several limitations to the research conducted. The pandemic is still in progress; thus,
the tendencies and trends of economic fluctuations may differ in the future, resulting in substantial change
in people’s spending approaches. The picture of the world is now changing very quickly, so the
adjustment of macroeconomic indicators can be caused by reasons that are currently not yet determined.
As for the mathematical model, the biological characteristics of the virus that were used were up to date
but may differ and vary in the future because information about the pandemic is not yet explicit.
Future studies could further explore the impact of the mitigation strategies, including any
differential effects of specific measures. The distributional effects of social distancing in Sweden and
other Nordic countries can also be explored further. The evidence so far suggests very different effects.
Recent research in this area show that past epidemics have increased inequality, posed threats to the
availability of sensitive information. Besides, many scholars concentrate on the downfalls of economic
activity and try to evaluate the extents resulted from government-imposed restrictions on activity against
the voluntary choice of people to stay safe. Scientists may agree on the fact, that social distancing laws
cause small losses of economic activity only in sparsely populated societies and economies
(Scandinavia).
46
REFERENCES
1. Alvarez, F., Argente, D. & Lippi, F. (2020). A Simple Planning Problem for COVID-19 Lockdown.
University of Chicago, Becker Friedman Institute for Economics, Working Paper № 2020-34. doi:
http://dx.doi.org/10.2139/ssrn.3569911
2. Andersen, A. L., Hansen, E. T. & Johannesen, N. (2020). Pandemic, Shutdown and Consumer Spending:
Lessons from Scandinavian Policy Responses to COVID-19. University of Copenhagen and CEBI.
arXiv:2005.04630v1
3. Atkeson, A. (2020). What Will Be the Economic Impact of COVID-19 in the US? Rough Estimates of
Disease Scenarios. National Bureau of Economic Research, Working Paper No. 26867. doi:
https://doi.org/10.3386/w26867
4. Avishai, B. (2020, April 21). The Pandemic Isn’t a Black Swan but a Portent of a More Fragile Global
System. The New Yorker. Retrieved from https://www.newyorker.com/news/daily-comment/the-
pandemic-isnt-a-black-swan-but-a-portent-of-a-more-fragile-global-system
5. Barry., J. M. (2004). The Great Influenza: The Story of the Deadliest Pandemic in History (1st ed.). New-
York: Viking Press.
6. Bates, V. (2020, April 20). What Is R0? Gauging Contagious Infections. Healthline. Retrieved from:
https://www.healthline.com/health/r-nought-reproduction-number
7. BBC. (2009). Swine flu less lethal than feared. Retrieved from:
http://news.bbc.co.uk/2/hi/health/8406723.stm
8. Bloom, D. E. & Cadarette, D. (2019). Infectious Disease Threats in the Twenty-First Century:
Strengthening the Global Response. Frontiers in immunology, 10, 549. doi:
https://doi.org/10.3389/fimmu.2019.00549
9. Bootsma, M. C. J. & Ferguson, N. M. (2007). The effect of public health measures on the 1918 influenza
pandemic in U.S. cities. National Academy of Sciences, 104 (18) 7588-7593. doi:
10.1073/pnas.0611071104
10. Brainerd, E. & Siegler, V. M. (2003). The economic effects of the Influenza pandemic. Centre for
Economic Policy Research. Working paper № 3791, 1-41
11. Centers for Disease Control and Prevention. (CDC), (2020). Influenza. National Center for Health
Statistics. CDC Report. Retrieved from: https://www.cdc.gov/nchs/fastats/flu.htm
12. Cevik, M., Marcus, J. & Buckee, C. et al. (2020). SARS-CoV-2 Transmission Dynamics Should Inform
Policy. Clin Infect Dis, 1-16. doi: 10.1093/cid/ciaa1442
47
13. Cohn, S. K. (2003). The Black Death Transformed: Disease and Culture in Early Renaissance Europe.
New York: Oxford University Press, 11-69. doi: https://doi.org/10.1080/03612759.2003.10527932
14. Crosby, A. W. (1989). America's forgotten pandemic (2nd ed). Cambridge University Press
15. Dawood, F. S., Iuliano, A. D. & Reed, C. et al. (2012). Estimated global mortality associated with the
first 12 months of 2009 pandemic influenza A H1N1 virus circulation: a modelling study. The Lancet
Infectious Diseases, 12(9), 687–695. doi:10.1016/s1473-3099(12)70121-4
16. Delivorias, A. & Scholz, N. (2020). Economic impact of epidemics and pandemics. European
Parliamentary Research Service. PE 646.195 Retrieved from:
https://www.europarl.europa.eu/RegData/etudes/BRIE/2020/646195/EPRS_BRI(2020)646195_EN.pdf
17. Eichenbaum, M. S., Rebelo, S. & Trabandt, M. (2020). The Macroeconomics of Epidemics. National
Bureau of Economic Research, Working Paper No. 26882. doi: https://doi.org/10.3386/w26882
18. Endo, A., Leclerc, Q. J. & Knight, G. M. (2020). Implication of backward contact tracing in the presence
of overdispersed transmission in COVID-19 outbreak. Wellcome Open Res, 5 (239). doi:
https://doi.org/10.12688/wellcomeopenres.16344.1
19. Fan, Y. V., Jamison, T. D. & Summers, H. L. (2020). Pandemic risk: how large are the expected losses?
Bull World Health Organization 2018; 96: 129–134 doi: http://dx.doi.org/10.2471/BLT.17.199588
20. Ferguson, N., Laydon, D. & Nedjati-Gilani, G. et al. (2020). Report 9: Impact of non-pharmaceutical
interventions (NPIs) to reduce COVID19 mortality and healthcare demand. Imperial College London
doi: 10.25561/77482
21. Government Offices of Sweden. (2020). Restart package for the Swedish economy – more than SEK 100
billion in the budget for 2021. Retrieved from: https://www.government.se/press-
releases/2020/08/restart-package-for-the-swedish-economy--more-than-sek-100-billion-in-the-budget-
for-2021/
22. Gray, J. (2020, June 17). How pandemics extinguished the Roman empire. NewStatesman. Retrieved
from: https://www.newstatesman.com/culture/books/2020/06/how-pandemics-extinguished-roman-
empire
23. Hendrix, M. J., Walde, C. & Findley, K. (2020, July 17). Absence of Apparent Transmission of SARS-
CoV-2 from Two Stylists After Exposure at a Hair Salon with a Universal Face Covering Policy —
Springfield, Missouri, May 2020. CDC Report: 69(28);930-932. doi:
http://dx.doi.org/10.15585/mmwr.mm6928e2
24. Horii, M. (2014). Why Do the Japanese Wear Masks? Shumei University, EJCJS, 14 (2). Retrieved from:
https://www.japanesestudies.org.uk/ejcjs/vol14/iss2/horii.html
48
25. Jones, C. J., Philippon, T. & Venkateswaran. V. (2020). Optimal Mitigation Policies in a Pandemic:
Social Distancing and Working from Home. National Bureau of Economic Research, Working Paper
No. 26984. doi: https://doi.org/10.3386/w26984
26. Kaplan, G., Moll, B. and Violante. G. L. (2020). The Great lockdown and the big stimulus: tracing the
pandemic possibility frontier for the US. National Bureau of Economic Research. Working Paper 27794.
doi: 10.3386/w27794
27. Kermack, W. O. and McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics.
Mathematical, Physical and Engineering Sciences, 115 (772). doi:
https://doi.org/10.1098/rspa.1927.0118
28. Khan, I. A. (2004). Plague: the dreadful visitation occupying the human mind for centuries. Trans R Soc
Trop Med Hyg., 98(5), 270–277. doi:10.1016/s0035-9203(03)00059-2
29. Kolata, G. (1999). The Story of the Great Influenza Pandemic of 1918 and the Search for the Virus That
Caused It (1st ed.). Farrar, Straus and Giroux.
30. Lee, J.-W. & McKibbin, W. J. (2004). Globalization and Disease: The Case of SARS. Asian Economic
Papers, 3(1), 113–131. doi:10.1162/1535351041747932
31. Liu, J-T., Hammitt, J. K., Wang, J-D et. al. (2003). Valuation of the risk of SARS in Taiwan. National
Bureau of Economic Research. Working Paper № 10011. doi: 10.3386/w10011
32. Ludvigsson, J. F., Andersson, E., Ekbom, A., et al. (2011). External review and validation of the Swedish
national inpatient register. BMC Public Health.;11(1):450. doi: 10.1186/1471-2458-11-450
33. McKibbin, W. J. (2009). The Swine Flu Outbreak and its Global Economic Impact. Brookings. Retrieved
from: https://www.brookings.edu/on-the-record/the-swine-flu-outbreak-and-its-global-economic-
impact/
34. Merriam-Webster. (2020). Word of the Year: Pandemic. Retrieved from: https://www.merriam-
webster.com/words-at-play/word-of-the-year/coronavirus
35. Murray, C. (2020, May 11). Dealing with the pandemic is not entirely rocket science. Blog post. AEIdeas.
Retrieved from: https://www.aei.org/society-and-culture/dealing-with-the-pandemic-is-not-entirely-
rocket-science/
36. Piguillem, F. & Shi, L. (2020). The Optimal COVID-19 Quarantine and Testing Policies. Einaudi
Institute for Economics and Finance, Working Papers № 2004. Retrieved from:
http://www.eief.it/eief/images/WP_20.04.pdf
37. Porta, M. (2014). A dictionary of epidemiology (6th ed.). New York: Oxford University Press. eISBN:
9780199390069
49
38. Quammen, D. (2012). Spillover: Animal Infections and the Next Human Pandemic (1st ed.). WW Norton
& Co.
39. Sharp, P., H. Strulik, & J. Weisdorf (2012). The Determinants of Income in a Malthusian Equilibrium.
Journal of Development Economics 97(1), 112–117. doi: https://doi.org/10.1016/j.jdeveco.2010.12.004
40. Siu, A., & Wong, Y. C. R. (2004). Economic Impact of SARS: The Case of Hong Kong. Asian Economic
Papers, 3(1), 62–83. doi:10.1162/1535351041747996
41. Snower, D. (2020). The Socio-Economics of Pandemics Policy. IZA Institute of Labor Economics. IZA
Policy Paper № 162. Retrieved from: http://ftp.iza.org/pp162.pdf
42. Voigtländer, N. & Voth, H-J. (2013). The Three Horsemen of Riches: Plague, War, and Urbanization in
Early Modern Europe. The Review of Economic Studies, 80 (2), 774–811. doi:
https://doi.org/10.1093/restud/rds034
43. Young, A. (2005). The Gift of the Dying: The Tragedy of AIDS and the Welfare of Future African
Generations. Quarterly Journal of Economics 120(2), 423–466. doi: 10.1093/qje/120.2.424
44. Plümper, T. & Neumayer, E. (2020): Lockdown policies and the dynamics of the first wave of the Sars-
CoV-2 pandemic in Europe, Journal of European Public Policy, doi: 10.1080/13501763.2020.1847170
50
STATISTICAL INFORMATION SOURCES
1. European Commission. (2020). European Economic Forecast. Autumn 2020. Institutional Paper 136,
1-224. doi: 10.2765/878338
2. European Commission. (2020). European Economic Forecast. Spring 2020. Institutional Paper 125, 1-
216. doi:10.2765/788367
3. European Commission. (2020). European Economic Forecast. Summer 2020. Institutional Paper 132,
1-52. doi: 10.2765/828014
4. Folkhälsomyndigheten (2020, April 30). Estimates of the peak-day and the number of infected
individuals during the covid-19 outbreak in the Stockholm region. Public Health Agency of Sweden.
Article № 20059, 1-28. Retrieved from:
https://www.folkhalsomyndigheten.se/contentassets/e1c3b83fa24f4d019e4842053ffd8300/estimates-
peak-day-infected-during-covid-19-outbreak-stockholm-feb-apr-2020.pdf
5. International Monetary Fund, (IMF). (2020). Policy Response to COVID-19. Policy Tracker. Retrieved
from: https://www.imf.org/en/Topics/imf-and-covid19/Policy-Responses-to-COVID-19
6. UNDP, Human Developments Perspective. (2020). COVID-19 and Human Development. Retrieved
from: http://hdr.undp.org/sites/default/files/covid-19_and_human_development_0.pdf
7. World Health Organization, (WHO). (2020). Timeline - COVID-19. Retrieved from:
https://www.who.int/news/item/27-04-2020-who-timeline---covid-19
8. World Health Organization (2019) Pandemic Preparedness Financing. Status update. Retrieved from:
https://apps.who.int/gpmb/assets/thematic_papers/tr-4.pdf
9. University of Oxford. (2020). Coronavirus Government Response Tracker. Retrieved from:
https://www.bsg.ox.ac.uk/research/research-projects/coronavirus-government-response-tracker
10. Hale, T., Webster, S., Petherick, A., Phillips, T., & Kira., B. (2020). Data use policy: Creative
Commons Attribution CC BY standard. Oxford COVID-19 Government Response Tracker, Blavatnik
School of Government. Retrieved from: https://www.bsg.ox.ac.uk/research/research-
projects/coronavirus-government-response-tracker
ANNEXES
Annex 1
The glossary of terms
• COVID-19 – is an infectious disease caused by a virus called coronavirus.
• Epidemic - The occurrence in a community or region of cases of an illness, specific health-related
behavior, or other health-related events clearly in excess of normal expectancy. The community or
region and the period in which the cases occur are specified precisely. The number of cases indicating
the presence of an epidemic varies according to the agent, size, and type of population exposed;
previous experience or lack of exposure to the disease; and time and place of occurrence…Generally,
a disease that exhibits large inter-annual variability can be considered as epidemic. (Dictionary of
Epidemiology)
• Fiscal stimulus - A stimulus package is a package of economic measures put together by a government
to stimulate a floundering economy. The objective of a stimulus package is to reinvigorate the
economy and prevent or reverse a recession by boosting employment and spending. (Investopedia)
• Liquidity measures – measures implying cash dotation to companies so they could pay their debt
obligations.
• Lockdown - a temporary condition imposed by governmental authorities (as during the outbreak of
an epidemic disease) in which people are required to stay in their homes and refrain from or limit
activities outside the home involving public contact (such as dining out or attending large gatherings
(Merriam-Webster)
• Non-pharmaceutical intervention - are actions, apart from getting vaccinated and taking medicine, that
people and communities can take to help slow the spread of illnesses like pandemic COVID-19 e.g.
lockdown, testing, personal protection equipment, social distancing etc. (CDC)
• Pandemic - an epidemic occurring worldwide, or over a very wide area, crossing international
boundaries and usually affecting a large number of people (World Health Organization)
• SIR model - An SIR model is an epidemiological model that computes the theoretical number of
people infected with a contagious illness in a closed population over time. The name of this class of
models derives from the fact that they involve coupled equations relating the number of susceptible
people S(t), number of people infected I(t), and number of people who have recovered R(t). Economic
layers like economic choices, government intervention, level of wealth can be added to the model to
use it in Economic science (Wolfram Math World).
Annex 2
Annex 2a. The SEIR model of Sweden where Susceptible, Recovered and Infectious
Note: Compiled by the author through WHO data and Statistiska Centralbyran
Annex 2b. The SEIR model of Denmark where Susceptible, Recovered and Infectious
Note: Compiled by the author through WHO data and Danmarks Statistik
Annex 3
Annex 3. Suppression strategies for Great Britain.
Note: was adopted from the report, written by the Imperial College COVID-19 Response Team. Retrieved from:
https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-
COVID19-NPI-modelling-16-03-2020.pdf
Cri
tica
l ca
re b
eds
occ
up
ied
per
10
0,0
00
of
po
pu
lati
on
Annex 4
Annex 4. The share of positive COVID-19 tests.
Note: Compiled by the author through the Our World in Data website using data from John Hopkins Database