A Study Based on Qualitative Comparative Analysis:
Multi-Cause Analysis and Mechanism Exploration of
“Underachieving Students Against COVID-19” in Europe
Caikun Cheng
*
School of International Relations and Public Affairs, Shanghai International Studies University,
Shanghai, 1550 Wenxiang Road, China
Keywords: COVID-19 Epidemic, Europe, Underachieving Students Against COVID-19, Qualitative Comparative
Analysis, Combinations Of Causes.
Abstract: The COVID-19 epidemic has been spreading around the world for three years. Europe, which has the highest
concentration of countries with a high human civilization index, has shown extremely poor performance
against the pandemic. I paid attention to this phenomenon and conducted qualitative comparative analysis on
the cases of 58 European countries, to explore the reasons and mechanisms for the European region to become
the concentration area of "underachieving students against COVID-19". The analysis results showed that there
were four parallel combinations of causes that made European countries "underachieving students against
COVID-19": (1) "Loose lockdown" * "Democratic regime" * "High population density"; (2) "Loose
lockdown" * "Democratic regime" * "High tertiary GDP share"; (3) "Loose lockdown" * "Non-democratic
regime" * "Low vaccination rate" * "Low tertiary GDP share"; (4) "Democratic regime" * "High vaccination
rate" * "High population density" * "High tertiary GDP share". Combinations of four causes comprehensively,
Europe as "underachieving students against COVID-19" has concentrated the main mechanism for: Europe's
high democracy index under the voice of the people for freedom and the demands of the development of the
third industry and traffic control measures, had to give up severely coupled with a high population density
helped the spread of the virus, finally led to COVID-19 outbreak of a pandemic. In addition, the results show
that high vaccination rates did not become a major factor in the success of the European epidemic, while the
adoption of stringent containment measures had a very crucial impact on the success of the European
epidemic.
1 INTRODUCTION
According to the World Health Organization, as of 19
September 2022, COVID-19 has infected
250,786,965 and killed 2,086,940 people in Europe,
making it the world region with the highest
cumulative total number of cases and the second
highest total number of deaths. Even now (19
September 2022), Europe is still one of the most
severely affected regions in the world, with 1,205,467
infections and 2,897 deaths per day. (World Health
Organization) It is truly a concentration area of
"underachieving students against COVID-19".
At the same time, however, European countries
have begun to relax their containment measures and
*
Corresponding author Email
adopt a strategy of "living with the virus" despite the
severity of the COVID-19 pandemic at home. In
Europe, for example, the government response index
has fallen below 25 in Britain, France, Canada, Italy
and so on, the lowest level since April 2020,
(University of Oxford) according to Oxford
University. Another example is the EU Council's
adoption on 30 June 2020 of a recommendation on
the possible phasing out of temporary restrictions on
non-essential travel into the EU and the
implementation of an "Open EU" plan to allow non-
essential travel for vaccinated persons as well as
those recovering from COVID-19 from 1 March
2022. (European Union)
So what are the mechanisms that make Europe
such a concentration area of "underachieving
766
Cheng, C.
A Study Based on Qualitative Comparative Analysis: Multi-Cause Analysis and Mechanism Exploration of “Underachieving Students Against COVID-19” in Europe.
DOI: 10.5220/0012043900003620
In Proceedings of the 4th International Conference on Economic Management and Model Engineering (ICEMME 2022), pages 766-777
ISBN: 978-989-758-636-1
Copyright
c
2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)
students against COVID-19"? And what is the
process of mechanism? To answer the above
two questions, this paper will select 58
European countries as study cases and analyze
them through computer technology.
2 LITERATURE REVIEW
The study of the COVID-19 pandemic has already
yielded rich results. I conducted a literature review
and found that scholars have mainly investigated the
study of the COVID-19 pandemic from three
perspectives:
The first perspective is the study of the pathogen
of COVID-19, which explores the origin of the virus,
its ability to infect cells, its ability to mutate, and its
safety. This part of the research has received
sufficient attention since the early stages of the
epidemic and has produced numerous notable
achievements. For example, Marco Ciotti and
Massimo Ciccozzi and others have conducted an
excellent literature review on the spread and latency
of the COVID-19 virus in the early stages of the
epidemic. (Ciotti, 2020) Alessandro Vespignani and
Tian Huaiyu and others have modeled the spread of
COVID-19. (Vespignani, 2020) The second
perspective is the prevention and control measures of
the COVID-19 pandemic. This perspective
investigates how to effectively block and control the
virus, emphasizing the pre-positioning of virus
prevention and control methods and the evaluation of
prevention and control measures. For example, Arefi
Maryam Feiz and Poursadeqiyan Mohsen argue that
strict virus detection and social distancing will
effectively prevent the COVID-19 pandemic. (Arefi,
2020) Roy M. Anderson, Hans Heesterbeek and
others. evaluated epidemic prevention measures in
China, Japan, and South Korea, and argued that
closing schools and closing public places are the main
pillars of epidemic containment. (Anderson, 2020)
The third perspective is the impact of the COVID-19
pandemic. This perspective focuses on the changes
that occur at all levels, from society to individuals,
under the pandemic, and also examines the impact of
the COVID-19 pandemic outside of human society.
For example, the team of Yao Hua, Chen Junhua and
Xu Yongfeng started from the impact of the pandemic
on social mental health and argued that the COVID-
19 pandemic has caused a range of illnesses such as
fear, anxiety and depression, and people with mental
illnesses may be more affected. (Yao, 2020) From an
environmental perspective, Saeida Saadat, Deepak
Rawtani and others found that COVID-19 lockdown
significantly improved air quality in numerous global
cities and reduced water pollution in some parts of the
world. (Saadat, 2020)
In summary, we found that academic research on
COVID-19 pandemic tends to focus on immediacy or
predictability, but lacks retrospective studies. For
example, the academic community has yet to come
up with a clear answer and explanation for the cause
of the COVID-19 pandemic. Even if there are
attribution studies on the COVID-19 pandemic, they
are commonly focused on a single country and single-
cause studies (such as Tim Colbourn's study on the
possible failure of British public policies under the
epidemic situation) (Colbourn, 2020), rather than
regional integrated analysis and multi-cause
induction from the perspective of comparison. But
the lack of research in this area will be somewhat
supplemented in this paper.
Finally, I think it is necessary to explain the
logical structure of this paper. The third section after
the introduction and literature review is devoted to
the experimental methods, the selection of the
experimental samples, the selection of the
experimental variables, and the measurements used
in this study. This section will be designed in strict
accordance with the standards of computer
experiments to ensure the accuracy of the
experimental results. The fourth section mainly
expounds the experimental procedure using SPSS,
fsQCA3.0 and other computer software. In this
section, I also proceed with the drawing of a clear set
of data table, the necessary conditional tests, analysis
of causes combinations, and robustness inspection.
The objective and accurate multi-cause pathways and
mechanisms are explored through the application of
techniques related to data science. The main content
of fifth section is the empirical analysis of the
experimental results. I will first modify our
assumptions on the variables, and then perform
empirical analysis on four multi-cause paths with
specific typical cases to enhance the empirical
plausibility of the experimental results. The last
section is the conclusions.
3 EXPERIMENTAL DESIGN
In this part, I present the experimental methods,
experimental sample and variable selection of this
study. Later, I will also perform experimental
measurements and encoding.
A Study Based on Qualitative Comparative Analysis: Multi-Cause Analysis and Mechanism Exploration of “Underachieving Students
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767
3.1 Experimental Methods
I will adopt the Experimental method of qualitative
comparative analysis, namely qualitative
comparative analysis (hereinafter referred to as QCA)
method.
According to Arend Lijphart, a political scientist,
a large number of cases should be studied statistically,
while a small number of cases should be studied
comparatively. If a small number of cases suffer from
the difficulty of "too many variables but too few
cases", we should expand the number of cases to
make it into a large sample. (Lijphart, 1971)
However, limited by the conditions of the study, it is
difficult to expand the number of samples in some
specific condition. For example, the number of
European countries studied in this paper is relatively
stable. At the same time, although my research
accommodates 58 samples, due to the complexity and
great differences among European countries, when
applied to statistical methods, there are often too
many variables to deal with and the problem of
"multicollinearity" exists. Therefore, my research is
also faced with the difficulty of "too many variables
but too few cases", which makes it difficult to
conduct statistical analysis.
The QCA method can effectively solve the
problems encountered in my study. It is a method that
integrates the logic of quantitative analysis into
qualitative inference, with the use of set theory and
Boolean algebra at its core. The methodology of QCA
requires the researcher to manipulate or assign values
to variables. In fact, it can be regarded as judging
whether a case is affiliated to (clear set analysis,
where "1" and "0" are commonly used to represent
"belong to" and "don't belong to" respectively) or to
what extent (fuzzy set analysis, where the value is
usually between "0" or "1"), For example, 0.5 is a set
of intermediate values between "completely
affiliated" AND "completely unaffiliated". (Hao,
2016) At the same time, the computational system of
Boolean algebra is adopted, which is mainly
represented by logical AND (denoted by symbol "*",
which is equivalent to "conjunctive" in logic), logical
OR (denoted by symbol "+", which is equivalent to
"disjunction" in logic) and logical negation (denoted
by symbol "~"). For example, "A*B+~C=D" means
"the presence of both A and B or the absence of C can
lead to the appearance of D". Therefore, the QCA
method can help us to compare and analyze multiple
cases through the logic of Boolean algebra on the
premise that the effect of a single variable on the
result is known, and finally obtain the "configuration"
explanation or multiple causal path analysis on the
occurrence of a specific result. It has particular
advantages over quantitative analysis and
comparative analysis in general. (Gao, 2014)
However, the method of QCA is too complicated
in practice, so here I think it is worth introducing the
computer software that will be used in the following
analysis, fsQCA3.0, which was developed by Charles
Ragin's team and is commonly used in QCA software
for clean and fuzzy set analysis. (Charles Ragin) The
software greatly improves the usability and feasibility
of the QCA method, as it can efficiently simplify and
analyze complex truth tables and accurately output
the resulting combinations of causes. The usage
process is shown in Figure 1.
Figure 1: The flowchart of using fsQCA3.0.
.
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3.2 Selection of Experimental Samples
The experimental samples were mainly selected from
countries whose COVID-19 epidemic data were kept
by the World Health Organization (WHO), with 59
eligible samples. However, the sample data of the
country "Montenegro" was abnormal, therefore it was
removed, leaving 58 eligible samples. Among them,
there were 48 positive cases ("underachieving
students against COVID-19") and 10 negative cases
("non-underachieving students against COVID-19").
3.3 Selection of Experimental Variables
In this paper, we mainly select the following factors
as independent experimental variables to investigate:
the degree of democracy in the country, the severity
of epidemic containment and management measures,
the vaccination rate, the size of the population
density, and the proportion of the GDP of the
country's tertiary industries.
3.3.1 Degree of Democracy in the Country
The first experimental variable is related to the
democracy of the country's actual political system,
which is based on the research of Joseph H. Anson
and Haijie Wang's team. According to Joseph H.
Anson, dogmatic authoritarianism can get additional
support under the epidemic, while the epidemic
prevention and control of authoritarianism is more
recognized. On the contrary, the democratic regime is
mired in an epidemic. (Manson, 2020) This view is
also borne out by Wang's team's study of 143
countries from 1 January 2020 to 31 January 2021,
which concluded that the daily number of new
COVID-19 cases and deaths increases when the
ruling party's ideology is more liberal and
democratic. (Wang, 2021) In this paper, I will use The
Economist's 2021 Democracy Index assessment (The
Economist, Democracy Index 2021) as a measure of
how democratic European countries are during a
pandemic. At the same time, based on the conclusions
of previous studies, I also put forward a hypothesis -
- Hypothesis 1: European countries with higher
democracy index (democratic countries) are more
likely to be "underachieving students against
COVID-19".
3.3.2 Severity of Epidemic Containment and
Management Measures
The second experimental variable is related to the
measures taken by countries during the pandemic,
which is based on the research of Roy MAnderson,
Hans Heesterbeek and Wei Liu's team. Roy M
Anderson and Hans Heesterbeek argue that strict
epidemic prevention and management measures are
essential to stop the COVID-19 pandemic and urge
the government to close schools and strictly lock
down public places. (Anderson, 2020) Liu's team,
which has studied China's success, believes that strict
containment and efficient management of huge data
are the country's "magic weapons". (Liu, 2020) In this
paper, I will use the response index from Oxford
University, (University of Oxford) OAG global flight
frequency data, (OAG) and Bloomberg's ranking of
COVID-19 lockdown intensity (Bloomberg) to
measure the severity of epidemic prevention, control
and management measures in European countries
during the pandemic. At the same time, based on the
conclusions of previous studies, I also propose a
hypothesis here -- Hypothesis 2: European countries
that take strict prevention and control measures are
less likely to be "underachieving students against
COVID-19".
3.3.3 Vaccination Rate
The third experimental variable, which is related to
vaccination outcomes in European countries, was
based on research by Mangalakumari Jeyanathan's
team and Enahoro A.Iboi's team. Mangalakumari
Jeyanathan's team believes that once vaccines are
widely administered around the world, human
societies will develop strong resistance to the virus.
(Jeyanathan, 2020) Meanwhile, Enahoro A.Iboi's
team is adamant that only 82 percent vaccine
coverage and 80 percent vaccine effectiveness will
stop the COVID-19 pandemic. (Iboi, 2020) In this
paper, I use a combination of incomplete vaccination
rates and complete vaccination rates (incomplete
vaccination rates are used for statistical needs in the
temporal dimension, as detailed below) (World
Health Organization) from WHO statistics as
indicators of vaccination rates across countries during
the pandemic. At the same time, based on the
conclusions of previous studies, I also propose a
hypothesis -- Hypothesis 3: European countries with
higher vaccination rates are less likely to be
"underachieving students against COVID-19".
3.3.4 Size of the Population Density
The fourth experimental variable, which is related to
the population and area ratio of European countries,
is based on research by Joacim Rocklov, Henrik
Sjodin and Arunava Bhadra's team. Joacim Rocklov
and Henrik Sjodin pointed out in their study that high
A Study Based on Qualitative Comparative Analysis: Multi-Cause Analysis and Mechanism Exploration of “Underachieving Students
Against COVID-19” in Europe
769
population density will catalyze the spread of
COVID-19, because under conditions of high
population density, it is difficult for people to
maintain proper social distance, making the virus
more contagious. (Rocklöv, 2020) Arunava Bhadra's
team, in order to test the Johns Hopkins Bloomberg
School of Public Health's conclusion that the spread
of COVID-19 is not related to population density,
modeled population density in relation to COVID-19
infection and death rates in India and concluded that
population density is positively related to COVID-19
infection and death rates. (Bhadra, 2021) In this
paper, I will use the population density of European
countries based on World Population Reviews
statistics (World Population Review, 2022) as an
indicator of the size of the population density of
European countries. At the same time, based on the
conclusions of previous studies, I also put forward a
hypothesis -- Hypothesis 4: European countries with
high population density are more likely to be
"underachieving students against COVID-19".
3.3.5 Proportion of the GDP of the Country's
Tertiary Industries
The fifth experimental variable is related to the
country's industrial structure system. The setting of
this variable mainly refers to the research of Naveen
Donthu, Anders Gustafsson and Abid Haleem, Mohd
Javaid. Naveen Donthu and Anders Gustafsson
conducted a study on all walks of life under the
epidemic. Among them, a series of tertiary industries
such as business and tourism are facing great
difficulties and are even on the verge of collapse,
while countries with tertiary industry as the main
industrial structure are the most affected by the
epidemic. (Donthu, 2020) The study by Abid Haleem
and Mohd Javaid highlights the fragmentation of
socio-economic ties caused by the COVID-19
pandemic, which is triggering a range of responses,
making it necessary and imperative for countries to
focus on real economic development. (Haleem, 2020)
While the two studies do not explicitly identify a
relationship between the tertiary sector's share of
GDP and the failure to respond to the pandemic, they
do suggest that it is the sector most affected by the
pandemic. If a country takes the tertiary industry as
its pillar, it will have to make a choice between
"controlling the epidemic" and "stabilizing the
economy". According to related studies, European
countries generally have a high proportion of tertiary
industries in their economic structure, so I have
reason to believe and assume - Hypothesis 5:
European countries with a higher share of GDP from
tertiary industries are more likely to be
"underachieving students against COVID-19".
Second, in this paper I will use the rate of service
sector contribution to GDP of European countries
calculated by the World Bank (World Bank) as an
indicator of the proportion of tertiary industries in the
GDP of European countries.
3.4 Experimental Measurement and
Coding
According to the principle of QCA analysis method,
we need to use the language of "set theory" to
redefine the experimental variables. In this paper,
clean sets are used. As mentioned above, in the
analysis of clear sets, researchers commonly use
dichotomous values such as "0 and 1" to indicate
whether a variable is "completely affiliated" or
"completely unaffiliated" to a set. Table 1 shows my
criteria for assigning values to experimental variables
(will become the screening standard of SPSS).
Table 1: Experimental variable coding standard.
Variable
type
define Label
Coding standard
Data source
Completely affiliated
（
1
）
Completely unaffiliated
（
0
）
Result
"underachie
ving
students
against
COVID-19"
ud
Infection rate ≥35% or
fatality rate ≥1%
Infection rate <35% and
fatality rate <1%
World Health
Organization
Causative
condition
Democratic
country
dr Democracy index ≥6.1 Democracy index ≤6.0
The
Economist
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Taking
strict
control and
managemen
t measures
sp
Long and severe
lockdown measures were
taken
Short or loose lockdown
measures were taken
Oxford
University,
OAG,
Bloomberg
Having a
high
vaccination
rate
hv
Incomplete vaccination
rate ≥35% in 2021 and
complete vaccination
rate ≥75% in 2022
Incomplete vaccination rate
< 35% in 2021 or complete
vaccination rate < 75% in
2022
World Health
Organization
Having a
high
population
density
hd
Population density ≥115/
㎡
Population density
＜
115/
㎡
World
Population
Reviews
Tertiary
industry has
a higher
proportion
of GDP
hp
The tertiary industry
accounts for ≥60% of
GDP
The tertiary industry
accounts for <60% of GDP
World Bank
It should be noted that the criteria for judging
"underachieving students against COVID-19" are not
strict or harsh, but most countries in Europe still
become "underachieving students against COVID-
19". Secondly about whether "the strict control and
management measures" of the decision, I will be by
Oxford University to index (University of Oxford) to
judge the duration of the epidemic prevention and
control measures are strictly, and based on the global
flight data observation of OAG (OAG) Europe
through the channels of flight attendants the actual
number of entry and exit, finally to bloomberg control
strength ranking (Bloomberg) as a reference. Finally,
I would like to explain the reason for the introduction
of the indicator "incomplete vaccination rate in 2021"
in the judgment of "Having a high vaccination rate".
Because I believe that the indicators for 2022 alone
ignore the time variable, which may lead to the
inaccurate presentation of the results, I added the data
for 2021 as the judgment criterion (incomplete
vaccination rate refers to the vaccination rate with
one shot or more, and the statistical objects in 2021
are mostly incomplete vaccination rate).
The statistics of the above data sources all ended
on 19 September, 2022.
4 DATA ANALYSIS
After the screening of SPSS, we can draw a data table
based on a clear set as the basis of our analysis, such
as Table 2 (is generated after screening by SPSS and
converted to CSV format). In this table, all
experimental variables are changed to the value "1"
or the value "0", indicating the relationship of
"Completely affiliated" or "Completely unaffiliated"
to a certain set.
A Study Based on Qualitative Comparative Analysis: Multi-Cause Analysis and Mechanism Exploration of “Underachieving Students
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Table 2: Data table of causative conditions and results (based on clear sets).
Case dr sp hv hd hp ud
Case dr sp hv hd hp ud
France 1 1 1 1 1 1 Lithuania 1 0 0 0 1 1
Germany 1 0 1 1 1 1 Croatia 1 0 0 0 1 1
The United Kingdom 1 0 1 1 1 1 Slovenia 1 0 0 0 0 1
Italy 1 1 1 1 1 1 Belarus 0 1 0 0 0 0
Russian Federation 0 0 0 0 0 1 Latvia 1 0 0 0 1 1
Türkiye 0 1 0 0 0 0 Azerbaijan 0 0 0 1 0 1
Spain 1 1 1 0 1 0 Estonia 1 0 0 0 1 1
Netherlands 1 0 0 1 1 1 Republic of Moldova 1 0 0 0 0 1
Poland 1 0 0 1 1 1 Cyprus 1 0 0 1 1 1
Portugal 1 0 1 1 1 1 Armenia 0 0 0 0 0 1
Ukraine 0 0 0 0 0 1 Bosnia and Herzegovina 0 0 0 0 0 1
Austria 1 0 0 0 1 1 North Macedonia 1 0 0 0 0 1
Greece 1 0 0 0 1 1 Albania 1 0 0 0 0 1
Israel 1 0 0 1 0 1 Luxembourg 1 0 0 1 1 1
Belgium 1 0 1 1 1 1 Montenegro 1 0 0 0 0 1
Czechia 1 0 0 1 0 1 Kosovo 1 0 0 1 0 1
Switzerland 1 0 0 1 1 1 Uzbekistan 0 1 0 0 0 0
Denmark 1 1 1 1 1 1 Kyrgyzstan 0 0 0 0 0 1
Romania 1 0 0 0 0 1 Iceland 1 0 1 0 1 1
Sweden 1 0 0 0 0 0 Jersey 1 0 1 1 0 1
Serbia 1 0 0 0 0 0 Andorra 1 0 0 1 1 1
Hungary 1 0 0 0 0 1 Isle of Man 1 0 1 1 1 1
Slovakia 1 0 0 1 0 1 Faroe Islands 1 0 1 0 1 1
Georgia 0 0 0 0 0 1 Guernsey 1 0 1 1 0 1
Ireland 1 1 1 0 0 0 San Marino 1 0 0 1 1 1
Kazakhstan 0 0 0 0 0 1 Gibraltar 1 0 1 1 1 1
Norway 1 1 1 0 0 0 Liechtenstein 1 0 0 1 1 1
Finland 1 1 1 0 0 0 Tajikistan 0 1 0 0 0 0
Bulgaria 1 0 0 0 1 1 Monaco 1 0 0 1 0 1
4.1 Necessary Condition Test
Before the combinations of causes analysis is carried
out, we need to conduct the necessary condition test
on the single condition variable first (through the
computer software fsQCA3.0). The necessary
condition refers to the condition that occurs in all
cases with results or occurs frequently. Generally
speaking, if the consistency of a condition is greater
than 0.9, it is regarded as a necessary condition.
(Schneider, 2012) The result of necessary condition
test are shown in Table 3.
Table 3: Result of necessary condition test.
Conditional Variable Consistency Coverage
dr 0.833333 0.869565
˜dr 0.166667 0.666667
sp 0.0625 0.272727
˜sp 0.9375 0.957447
hv 0.270833 0.764706
˜hv 0.729167 0.853659
hd 0.520833 1
˜hd 0.479167 0.69697
hp 0.541667 0.962963
˜hp 0.458333 0.709677
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As shown in Table 3, only the consistency of
"~sp" (no "strict prevention and control measures") is
greater than 0.9, which is a necessary condition for
the emergence of "ud" (became "underachieving
students against COVID-19"). While the consistency
of other conditions is less than 0.9, which is not a
necessary condition.
4.2 Analysis of Causes Combinations
In terms of result output, fsQCA3.0 will output three
kinds of solutions, which are complex solutions,
parsimonious solution and intermediate solution.
Complex solutions are not solvable by Boolean
algebra reduction procedures and often exhibit
multiple and complex combinations of causes, so
they are not commonly used in QCA methods.
Parsimonious solutions are the most simple
combinations of causes generated by a Boolean
algebra reduction procedures, containing the least
amount of information. Intermediate solution is the
most commonly used one, which can balance the
difference between the theory and the actual facts by
certain Boolean algebra reduction procedures, and is
the main multi-factorial path chosen in the QCA
approach. Therefore, in this paper, I choose the
intermediate solution as the result of the study. Table
4 shows the intermediate solution output by
fsQCA3.0.
Table 4: Analysis of causes combinations (based on intermediate solution).
Combinations of Causes Raw Coverage Unique Coverage Consistency
dr*˜sp*h
d
0.4375 0.145833 1
dr*˜sp*hp 0.479167 0.1875 1
˜dr*˜sp*˜hv*˜hp 0.166667 0.166667 1
dr*hv*hd*hp 0.1875 0.0625 1
Solution Coverage: 0.854167
Solution Consistency: 1
As shown in Table 4, the intermediate solution
gives four different combinations of causes,
indicating that there are four different multi-cause
paths for the emergence of "ud" (became
"underachieving students against COVID-19"), and
these causal paths are parallel to each other and can
be combined with Boolean "+" sign:
dr*~sp*hd+dr*~sp*hp+~dr*~sp*~hv*~hp+dr*
hv*hd*hp
4.3 Robustness Inspection
QCA is similar to quantitative analysis in that it is
necessary to check the robustness of the results. As
shown in Table 4, fsQCA3.0 outputs coverage and
consistency, except for combinations of causes.
The coverage are divided into raw coverage,
unique coverage, and solution coverage. Raw
coverage refers to how many positive cases can be
explained by a single combination of causes. For
example, the raw coverage of the first combination of
causes "dr*~sp*hd" in Table 4 is 0.4375, which means
that this combination of causes can explain 43.75
percent of the positive cases (21 cases). The unique
coverage excludes cases that accord with more than
two reasons. For example, the unique coverage of the
first combination of causes "dr*~sp*hd" is only
0.145833, which means that this combination of cases
can explain 14.5833 percent of the positive cases (7
cases). If the unique coverage is smaller than the raw
coverage, multiple causes exist. And the solution
coverage shows how many positive cases can be
explained by the combination of causes as a whole
(dr*~sp*hd+dr*~sp*hp+~dr*~sp*~hv*~hp+dr*hv*h
d*hp). The results showed that the combination of
causes could explain 85.4167 percent of the positive
cases, and the remaining cases were considered as
special cases, which were not included in the study of
the causes for "Europe becomes a concentration area
of underachieving students against COVID-19".
Consistency is divided into consistency and
solution consistency. Consistency is commonly used
to determine whether a single combination of causes
is a subset of the result and the degree of
subordination. As shown in Table 4, the consistency of
each combination of causes is 1, meaning each
combination of causes is a sufficient condition or a
subset of the conditions for the occurrence of results.
The solution consistency is to judge whether the
combinations of causes as a whole
(dr*~sp*hd+dr*~sp*hp+~dr*~sp*~hv*~hp+dr*hv*h
d*hp) belongs to the subset of the results and the
degree of affiliation. As shown in Table 4, when the
four combinations of causes as a whole, the
coincidence degree of the solution is still 1, which
shows that the combination of causes as a whole is
also a sufficient condition or a subset of the conditions
for the occurrence of results.
A Study Based on Qualitative Comparative Analysis: Multi-Cause Analysis and Mechanism Exploration of “Underachieving Students
Against COVID-19” in Europe
773
5 MULTI-CAUSE PATHS AND
MECHANISM OF
"UNDERACHIEVING
STUDENTS AGAINST COVID-
19" IN EUROPE
I will further explore the results of the above studies
and analyses in the following. However, first of all,
we need to modify hypothesis 3 of our study, because
in addition to the third cause combination
(~dr*~sp*~hv*~hp), when "~hv" (lower vaccination
rate) is combined with other cause conditions, it will
lead to the emergence of "ud" (became
"underachieving students against COVID-19"), and
the raw coverage and unique coverage are both low.
Even in the fourth cause combination (dr*hv*hd*hp),
"hv" (higher vaccination rate) became one of the
cause conditions that led to the emergence of "ud"
(became "underachieving students against COVID-
19"). Therefore, hypothesis 3 is modified here: higher
vaccination rates do not make European countries
less likely to be "underachieving students against
COVID-19". At the same time, in the necessary
condition test, we found that "~sp" (no "strict
prevention and control measures") was necessary for
the emergence of "ud". Therefore, the failure to take
strict control and management measures has become
the core reason for the emergence of "ud" ("became
"underachieving students against COVID-19""),
which has been verified by four reasons and
conditions (" ~sp "has become the Causative
condition for three of the four reasons combinations).
And according to the experimental results, we can
obtain the multi-cause mechanism model of
"underachieving students against COVID-19" as
shown in Figure 2. In the following, I will analyze
this model empirically using specific typical cases.
Figure 2: The multi-cause mechanism model of "underachieving students against COVID-19".
5.1 First Multi-Cause Path of
"Underachieving Students Against
COVID-19" in Europe
The first Multi-cause path of "underachieving
students against COVID-19" in Europe is to have all
three causative condition: "Loose lockdown" *
"Democratic regime" * "High population density".
The underlying mechanisms may be that strong
restrictions on freedom during the pandemic
triggered an upsurge of opposition that forced
governments in countries with high democratic
indices to abandon strict lockdown measures, while
high population density helped spread COVID-19,
leading to the countries as a "underachieving students
against COVID-19".
I will take the UK as a typical case to illustrate
this path. Up to April 2021, the UK government
response index remained at a high level (UK
government response index: 87.96) (University of
Oxford), and the demonstrations from April 2020 to
April 2021 put the UK government under great
pressure. In April 2021, the Prime Minister's Office
issued a press release announcing the easing of
COVID-19 containment measures from 12 April
2021, (Government.UK) and the government's
response index dropped off a cliff in the following
months. (University of Oxford) At that time, the
epidemic in Europe was still at its most severe, just as
France was constantly increasing epidemic
prevention and control measures (the French
government response index increased from 70.37 in
March to 75 in April) (University of Oxford). And the
UK's extremely dense population, which makes it
easier for people to gather, helped to spread COVID-
19, leading to a small peak in the UK in June 2021
(daily new diagnoses increased from 17,335 on 12
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April to 323,272 on 12 June) (World Health
Organization). Even at the height of the outbreak in
the UK in December 2021 (1,292,034 new cases per
day, 27 December 2021) (World Health
Organization), the UK government response index
remained at its lowest since the introduction of the
containment measures (UK government response
index: 44.06, 26 December 2021) (University of
Oxford). Finally, the UK is one of Europe's
"underachieving students against COVID-19".
5.2 Second Multi-Cause Path of
"Underachieving Students Against
COVID-19" in Europe
The second Multi-cause path of "underachieving
students against COVID-19" in Europe is to have all
three causative condition: "Loose lockdown",
"Democratic regime" and "High tertiary GDP share".
The underlying mechanism may be that the shutdown
of the tertiary industry caused by the pandemic
overwhelmed countries with a high proportion of
GDP from the tertiary industry. Protests by a large
number of workers in the tertiary sector forced the
governments of a countries with high democracy
index to abandon strict lockdown measures. But the
reopening of the tertiary sector accelerated the
population flow, provided excellent conditions for the
spread of the epidemic, and finally made the country
an "underachieving students against COVID-19".
I will take Greece as a typical case to illustrate this
path. Greece's tertiary industries account for 67.49%
of its GDP. (World Bank) It can be said that the
tertiary industry, especially tourism, is the economic
pillar and foundation of Greece, which also makes a
large number of tertiary industry workers take to the
streets to fight against the lockdown of the
government. Knowing that too severe a lockdown
would devastate the entire Greek industrial chain, the
government announced in March 2021 that Athens
would be open to holidaymakers from May 14, while
border controls would be eased in April and
designated airports would be allowed to receive
traffic from abroad. (Keep Talking Greece, 2021) We
can also find that the Greek government response
index also decreased from 87.96 on 16 April 2021 to
41.67 on 4 July 2021. (University of Oxford) As a
result, the epidemic situation in Greece has become
more severe since May 2021. By 27 December 2021,
the number of confirmed cases of COVID-19 had
risen from 2,710 on 4 May to 1,240,862, (World
Health Organization) and since then, the number of
newly confirmed cases has been extremely high, and
finally Greece has become one of the
"underachieving students against COVID-19" in
Europe.
5.3 Third Multi-Cause Path of
"Underachieving Students Against
COVID-19" in Europe
The third Multi-cause path of "underachieving
students against COVID-19" in Europe is to have all
four causative condition: "Loose lockdown", "Non-
democratic regime", "Low vaccination rate" and
"Low tertiary GDP share". The underlying
mechanism may be that non-democratic countries in
Europe have generally not made great industrial
transitions and have low per capita GDP levels.
Countries have too little social support to bear the
cost of higher vaccination rates or the economic
stagnation that severe lockdowns bring. As a result,
countries relaxed lockdown measures, allowing
COVID-19 to spread among people with low
vaccination rates, leading to become "underachieving
students against COVID-19".
I will take Russia as a typical case to illustrate this
path. The non-democratic countries of Europe are
usually the political and economic outcasts of
Europe. Although Russia has a strong national
mobilization capacity, its GDP per capita lags far
behind that of the developed economies of Western
Europe, and its economic market is too fragile to
withstand severe shocks. At the same time, due to the
underdeveloped economy, the government cannot
afford the high cost brought by the high vaccination
rate. For example, according to the statistics of WHO,
only 52.38 percent of the total population of Russia
has been completely vaccinated. (World Health
Organization) At the beginning of the outbreak,
Russia also took very severe epidemic containment
measures (on 30 March 2020, the government
response index of Russia was 87.04) (University of
Oxford), but after two months of lockdown, the
Russian government found itself unable to bear the
impact of the severe control measures, so in May
2020, Government officials said in a meeting with
President Vladimir Putin that COVID-19 actions and
restrictions imposed on certain industries should be
phased out and then eased until 1 September 2020.
(The Moscow Times, 2020) After that, Russia's
government response index had fallen to 37.04. And
to keep the response index between 40 and 60 for the
rest of the year, a low level. (University of Oxford)
The combination of lax epidemic control measures
and low vaccination rates has led to an increase in the
number of infections. And because of low level of
medical conditions, resulting in a high number and
A Study Based on Qualitative Comparative Analysis: Multi-Cause Analysis and Mechanism Exploration of “Underachieving Students
Against COVID-19” in Europe
775
proportion of cases and deaths in Russia (as of 19
September 2022, there were 385,837 deaths in
Russia, with a case-fatality rate of 1.90%) (World
Health Organization). In the end, Russia became one
of Europe's "underachieving students against
COVID-19".
5.4 Fourth Multi-Cause Path of
"Underachieving Students Against
COVID-19" in Europe
The fourth Multi-cause path of "underachieving
students against COVID-19" in Europe is to have all
four causative condition: "Democratic regime",
"High vaccination rate", "High population density"
and "High tertiary GDP share". The underlying
mechanism may be that Democratic countries with
strict quarantine policies increased vaccination rates
and then eased measures to some extent, but after the
relaxation of measures, high population density
aggravated the epidemic situation in the countries.
When democracies tried to return to their own
draconian containment measures, they faced pressure
from popular resistance and a depressed tertiary
industry, which resulted in the final lockdown
measures being too lax to contain the spread of the
disease in a highly concentrated and mobile
population, making them become "underachieving
students against COVID-19".
I will take France as a typical case to illustrate this
path. In fact, France took severe measures at the
beginning of the epidemic (the French government
response index in March and April 2020 was 87.96)
(University of Oxford) and only began to relax in
May 2020. In October 2020, France was hit by a
backlash (new cases per day went from an average of
less than 4,000 to 334,455 on 26 October), after
(World Health Organization) which it intensified its
epidemic control measures again (the French
government response index on 6 November 2022 was
78.70) (University of Oxford). And for a long time
after that, the French government's response
remained between 60 and 70. In November 2021,
Omicron swept across Europe. The initial intensity of
anti-epidemic measures was difficult to resist
effectively, and it was difficult to adopt severe
epidemic prevention and control measures as in the
early stages of the epidemic due to the pressure of
public resistance and the depression of tertiary
industries. Finally, the epidemic in France went out
of control (on 17 November 2022, there were
2,427,005 new confirmed cases daily in France)
(World Health Organization), making France to
become one of the "underachieving students against
COVID-19" in Europe.
Above, we have summarized the core
mechanisms that make Europe such a dense district
of "underachieving students against COVID-19", and
analyzed the multi-cause paths. Combined with the
coverage of multi-cause path (as shown in Table 4),
we can sum up the main mechanism characteristics
and main mechanisms of Europe becoming the
concentration area of "underachieving students
against COVID-19": Europe's high democracy index
under the voice of the people for freedom and the
demands of the development of the third industry and
traffic control measures, had to give up severely
coupled with a high population density helped the
spread of the virus, finally led to COVID-19 outbreak
of a pandemic.
6 CONCLUSION
This paper aims to explore the reasons why Europe
has become the concentration area of "underachieving
students against COVID-19", and through qualitative
comparative analysis of 58 European countries,
summarizes four Multi-cause paths for European
countries to become "underachieving students against
COVID-19": (1) "Loose lockdown" * "Democratic
regime" * "High population density"; (2) "Loose
lockdown" * "Democratic regime" * "High tertiary
GDP share"; (3) "Loose lockdown" * "Non-
democratic regime" * "Low vaccination rate" * "Low
tertiary GDP share"; (4) "Democratic regime" * "High
vaccination rate" * "High population density" * "High
tertiary GDP share".
We can draw conclusions from these four Multi-
cause paths，the most important condition that makes
the "underachieving students against COVID-19" is
"Loose lockdown". And "High vaccination rates" did
not become a major factor in the success of the
European epidemic. The "Democratic regime", "High
tertiary GDP share" and "High population density"
become important factors of "underachieving
students against COVID-19". All of these are worthy
of our and all of European countries’ reflections.
Finally, it can be predicted that the COVID-19
epidemic has also deeply affected the world. The
future course of the epidemic remains highly
uncertain. On the premise of keeping a clear
understanding of the epidemic situation, all countries
should gather the voices of the people to jointly fight
the epidemic. Countries should also adopt and adhere
to the policy of dynamic zeroing. At the same time,
people should avoid gathering, and public health
security should be paid attention to prevent the
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occurrence of public health emergencies. What is
most imperative is to steadily develop the domestic
economy and promote international cooperation on
epidemic prevention and control, so that all countries
in the world can get out of the shadow of the epidemic
and jointly promote the development of the world in
a better direction.
REFERENCES
Arefi, M. F., & Poursadeqiyan, M. (2020). A review of
studies on the COVID-19 epidemic crisis disease with a
preventive approach. Work, 66(4), 717-729.
Anderson, R. M., Heesterbeek, H., Klinkenberg, D., &
Hollingsworth, T. D. (2020). How will country-based
mitigation measures influence the course of the COVID-
19 epidemic?. The lancet, 395(10228), 931-934.
Bloomberg, The Best and Worst Places to Be as World
Enters Next Covid Phase,
https://www.bloomberg.com/graphics/covid-resilience-
ranking/?leadSource=uverify%20wall
Bhadra, A., Mukherjee, A., & Sarkar, K. (2021). Impact of
population density on Covid-19 infected and mortality
rate in India. Modeling earth systems and environment,
7(1), 623-629.
Colbourn, T. (2020). Unlocking UK COVID-19 policy. The
Lancet Public Health, 5(7), e362-e363.
Charles Ragin, fs/QCA3.0,
https://www.socsci.uci.edu/~cragin/fsQCA/software.sh
tml
Ciotti, M., Ciccozzi, M., Terrinoni, A., Jiang, W. C., Wang,
C. B., & Bernardini, S. (2020). The COVID-19
pandemic. Critical reviews in clinical laboratory
sciences, 57(6), 365-388.
Donthu, N., & Gustafsson, A. (2020). Effects of COVID-19
on business and research. Journal of business research,
117, 284-289.
European Union, Re-open EU, https://reopen.europa.eu/en
Gao Q.Q. (2014). From single-cause explanation to multi-
cause analysis: The turn in the study of comparative
methods. Study of political science, (3), 3-17.
Government.UK, Further easing of Covid restrictions
confirmed for 12 April,
https://www.gov.uk/government/news/further-easing-
of-covid-restrictions-confirmed-for-12-april
Haleem, A., Javaid, M., & Vaishya, R. (2020). Effects of
COVID-19 pandemic in daily life. Current medicine
research and practice, 10(2), 78.
Hao S.N. (2016). Qualitative comparative analysis and its
application in political science research. Foreign
theoretical Trends, (5), 58-67.
Iboi, E. A., Ngonghala, C. N., & Gumel, A. B. (2020). Will
an imperfect vaccine curtail the COVID-19 pandemic in
the US?. Infectious Disease Modelling, 5, 510-524.
Jeyanathan, M., Afkhami, S., Smaill, F., Miller, M. S.,
Lichty, B. D., & Xing, Z. (2020). Immunological
considerations for COVID-19 vaccine strategies. Nature
Reviews Immunology, 20(10), 615-632.
Keep Talking Greece, 2021, Greece to ease restrictions on
flights from UK in April, says Tourism Minister,
https://www.keeptalkinggreece.com/2021/03/23/greece
-uk-flights-april/
Liu, W., Yue, X. G., & Tchounwou, P. B. (2020). Response
to the COVID-19 epidemic: the Chinese experience and
implications for other countries. International Journal of
Environmental Research and Public Health, 17(7),
2304.
Lijphart, A. (1971). Comparative politics and the
comparative method. American political science review,
65(3), 682-693.
Manson, J. H. (2020). Right-wing authoritarianism, left-
wing authoritarianism, and pandemic-mitigation
authoritarianism. Personality and individual differences,
167, 110251.
OAG, Analyser Analytics Platform, https://www.oag.com/
Rocklöv, J., & Sjödin, H. (2020). High population densities
catalyse the spread of COVID-19. Journal of travel
medicine, 27(3), taaa038.
Saadat, S., Rawtani, D., & Hussain, C. M. (2020).
Environmental perspective of COVID-19. Science of
the Total environment, 728, 138870.
Schneider, C. Q., & Wagemann, C. (2012). Set-theoretic
methods for the social sciences: A guide to qualitative
comparative analysis. Cambridge University Press.
The Economist, Democracy Index 2021: the China
challenge,
https://www.eiu.com/n/campaigns/democracy-index-
2021/
The Moscow Times, 2020, Russia Moves to Gradually
Relax Coronavirus Lockdown: Officials,
https://www.themoscowtimes.com/2020/05/06/putins-
approval-rating-drops-to-historic-low-poll-a70199
University of Oxford, Oxford Covid-19 Government
Response Tracker,
https://www.bsg.ox.ac.uk/research/research-
projects/covid-19-government-response-tracker
Vespignani, A., Tian, H., Dye, C., Lloyd-Smith, J. O., Eggo,
R. M., Shrestha, M., ... & Leung, G. M. (2020).
Modelling covid-19. Nature Reviews Physics, 2(6),
279-281.
Yao, H., Chen, J. H., & Xu, Y. F. (2020). Patients with mental
health disorders in the COVID-19 epidemic.
Wang, H. J., An, K., & Zheng, M. (2021). Who has done a
better job in fighting the COVID-19 epidemic? Left or
Right?. Emerging Markets Finance and Trade, 57(8),
2415-2425.
World Health Organization, WHO Coronavirus (COVID-
19) Dashboard Table View,
https://covid19.who.int/table
World Population Review, 2022 World Population by
Country, https://worldpopulationreview.com/
World Bank, Services and value added (current US$),
https://data.worldbank.org.cn/indicator/NV.SRV.TOTL.
CD
World Health Organization, WHO Coronavirus (COVID-
19) Dashboard, https://covid19.who.int/
World Bank, GDP per capita (current US$),
https://data.worldbank.org/indicator/ny.gdp.pcap.cd
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