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

 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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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

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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

（

）

Completely unaffiliated

（

）

Result

"underachie

ving

students

against

COVID-19"

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

Long and severe

lockdown measures were

taken

Short or loose lockdown

measures were taken

Oxford

University,

OAG,

Bloomberg

Having a

high

vaccination

rate

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

Population density ≥115/

㎡

Population density

＜

115/

㎡

World

Population

Reviews

Tertiary

industry has

a higher

proportion

of GDP

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.

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Table 2: Data table of causative conditions and results (based on clear sets).

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

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.

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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.

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