Critique: a worldwide student journal of politics
Cultural Fractionalization and Policy Response
to COVID-19: A Comparative Study on the
Case of OECD Countries
Yidong Yang
China Foreign Affairs
University Beijing, China
Abstract
This study investigates the relationship between cultural fractionaliza-
tion within a state and the stringency of COVID-19 policy responses
in 38 countries. Using quantitative metrics including ethnic, religious,
and linguistic diversity to measure cultural fractionalization, the study
applies a standardized process of quantitative measurement to analyze
the data. From a constructivist perspective, the study argues that do-
mestic policies are shaped by the knowledge structures within society
and finds that countries with more cultural fragmentation tend to have
looser quarantine policies, while those with more cultural homogeneity
tend to have stricter ones.
For three years, COVID-19 had been raging across all countries,
during which every country stood on the same ground to fight the
pandemic, which made a great impact both on economic growth and
political development. There is no doubt that a pandemic is a difficult
problem for all counties because no country has had adequate
knowledge about the disease and potential solutions. Some may argue
that a pandemic is inherently different from other crises, such as floods
or earthquakes, because the cause of the crisis is clear, but during the
very first stage of the pandemic, it was not (Rodriguez et al. 2007). Yet
these uncertainties caused different governments to act quite
differently under pressure to put forward their policies in order to stop
the spread of such a novel disease and to deal with the disastrous
impacts. Global action on the crisis has been triggered, but not at the
same moment, nor in the same way, or with the same stringency.
Measures like quarantine, distancing, closure of schools and airports,
testing, and face-covering for example, are the most common policies
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a government would adopt, but even though, the balance of the
mixture and the stringency of each policy differs a lot among all
countries.
It must be said that the pandemic has been a highly valuable
opportunity for all political science research since all counties have
responded to the same crisis in different ways which would provide us
with sufficient information on how specific states have acted during
the crisis and what could have caused the states to act in this way. Thus,
this paper would like to make full advantage of the opportunity to
practice a thematic study on world politics
During the ongoing pandemic, many policy-related aspects
require further research. However, this article focuses on the policies
that countries put forward to control the spread of the disease, which
include full-scale to partial lock-downs, quarantine measures of various
intensities and models, social or physical distancing, various ‘track, test,
and trace’ measures and so on. To be clear, the speed and timing of
political response to the pandemic could also differ among countries,
but the timeline of response depends on when countries confirmed
their first cases of the virus, which makes the evaluation of the timing
highly uncertain. Because it cannot be sure whether countries act on
the discovery of the virus outside the country or their own first
confirmed cases, or both.
One may question how these different policy responses are
related to the factors within states. This research believes that in this
matter, that in this matter is the domestic culture has an impact on
policymaking, it may not be the most influential factor but still has its
effects directly or indirectly. This article take culture as an idea itself.
Further discussion on methodology will be provided below. To begin
the research, it is be assumed that domestic culture will somehow affect
the policy-making process even related to an emergency such as the
COVID-19 outbreak, based on common sense and true faith,
To simplify the problem, this research takes the stringency of
different policies as the dependent variable, which is an index
measured on twelve different indicators including testing policies, face-
Critique: a worldwide student journal of politics
covering policies, and travel controls. And to treat cultural
fractionalization within states as the independent variable, and
question whether there is a connection between these variables or not.
Cultural fractionalization is an index measuring whether the
culture within a country is formed in a homogeneous or a
heterogeneous way and how diversified it is, which is surely an inclusive
concept that can be measured from many aspects. This article defines
culture in a boarder sense that differs from political culture studies,
where scholars consider only civic culture. In this short paper, three
key indexes would be measuring diversity levels: ethnic diversity,
religious diversity, and linguistic diversity. To be clear, it is unclear how
the three factors are independently related to outcome. It is unclear
whether any cultural diversity is related to policy stringency in any way.
The main reason to adopt such a method of dividing cultural
fractionalization is only that it is at least one of the measures mostly
used by researchers to study the problem of cultural fractionalization.
At least one of the measurements must be adopted to measure cultural
fractionalization, for the continuation of the research, and indeed, the
trichotomy of culture could be a better solution. And these three
indexes are intuitive, quantitative, and easy to evaluate, as this article
would argue further below.
Literature review
In recent research, many scholars have used the comparative
method to question why different policy responses between different
countries occurred, but most of those studies lack discussions of
domestic cultures.
Existing institutional arrangements can be a key factor in
influencing the behavior of governmental responses to public health
crises. Capano, Howlett, Jarvis, Ramesh and Goyal demonstrated how
preparation and experience could impact leaders’ decisions on fighting
the pandemic (Capano et al. 2020). Countries were divided into four
categories, some of the Asian countries that fought with SARS-CoV-
1, H1N1, and MERS in their early years would deal with the recent
pandemic more steadily and confidently. For example, China and Italy,
where the virus first affected, were not prepared to deal with the novel
virus, which caused chaos at the beginning. Preparation has been
identified through their research as a key factor, but it fails to help
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explain why countries with advanced public health systems show very
different policy responses to the pandemic. This article would like to
focus more on political institutions rather than on medical experiences.
In addition to public health management, governmental and
political factors can have various impacts on controlling the pandemic.
Toshkov, Carroll and Yesilkagit, asking what factor could account for
the various response to the first wave of COVID-19 in European
countries, built their research on variables including (1) general
governance capacity, (2) crisis management preparedness, (3) health
care specific capacity and organization, (4) political institutions, (5)
government type, (6) party-political ideology, and (7) societal factors
(Toshkov and Yesilkagit 2021). They were successful in organizing
different factors into groups and the result is very comprehensive. But
in contrast to their intricate independent variables, they set their
dependent variables rather succinctly, which only measure school
closure and national lockdown. The term ‘cultural factor’ that this
article would like to argue in this paper is similar to the term ‘societal
factors’ mentioned above, but the authors measured the quality of
society only by a survey about three factors of societal value, on which
this article would tend to dig deeper into the core of the question to
understand where culture lies.
About how to sort out different measures adopted by
governments, Nihit Goyal and Michael Howlett, use CoronaNet as a
database and sort different policies into a dataset in the order of
semantic categories, and keep notes on how long each policy takes its
effect, from which they obtained their exciting findings (Goyal and
Howlett 2021). The paper concludes that there are 16 key policies in
response to COVID-19 and thus the authors questioned how it is
related to governing resources. It’s interesting that they also conducted
research on the key terms associated with the discussed policies, and
measured these terms by their occurrence and exclusivity, by doing so,
the authors aimed to measure the balance of the policy mix. Their idea
of organizing the data and sorting them into groups is enlightening for
this research, and this research will be using a similar method.
Critique: a worldwide student journal of politics
Similarly, in searching for the factors affecting different policy
responses, Moshe Maor and Michael Howlett also conducted their
research by comparing different political responses among countries
and concluded that three independent factors could affect politicians
in making their policies during the COVID-19 pandemic (Maor and
Howlett 2020. The three factors mentioned in the paper are
psychological factors, including elite panic and limited government
attention spans, institutional factors, implying the level of government
effectiveness, and strategic factors, including political considerations.
This article will question a similar research problem but by addressing
the influence of cultural factors, the differences is that their previous
article proposed the factors after asking the question, while this article
will do the opposite.
Different from foregoing comparative research on mass data,
Paula Serafinia and Noelia Novoselb did regional research on
Argentina alone, questioning how local cultural understanding
underpins COVID-19 policy response (Serafini and Novosel 2021).
Research found that freelance workers are suffering the most from the
control policies. In response, Argentina adopted a series of measures
to protection of cultural works. In the paper, the authors conclude that
Argentina’s diverse culture made the government adopt a caring but
limited cultural policy of solidarity during the COVID-19 pandemic.
The following research will be conducted not in one country alone, but
in 38 different countries, but the research will be looking into the effect
of cultural factors similarly. There is no doubt that culture had a great
influence on pandemic restrictive policies, but how it is related, was
not clarified in the previous paper.
In summary, while all these scholars tried from different
perspectives to question what could be the factors in the complex
formation process of policy responses to COVID-19, this article
attempts to have cultural and social dimensions to understand the
various policies. It should be noted that sorting out different factors
during the decision-making process was never an easy task, before or
after the pandemic, but the novel virus brought us much closer to the
secret answer of policy decision-making than did any of those political
events of the past since all countries act towards the crisis
synchronously. But the questions remains for scholars from all schools
of thought. For example, prior research on choices of policy responses
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would largely ignore the role of domestic culture and any qualities to
do with it, such as cultural diversity, identity, and national character,
which will be the main focus of the discussion in this short paper.
Conceptual framework
This article seeks to explain the different processes of
policymaking among countries by applying a comparative method to
the study. It argues, that the COVID-19 crisis provides a golden
opportunity for any political comparative studies because it can bridge
one of the major gaps between political facts and scientific methods
which is the replication of the same motivation. In this paper, the
article holds regional culture as the independent variable while
examining the ‘laboratory’ of the world’s political system, for
comparative policy outcomes. While the article strives to take a hard
science approach in its research, it recognizes that there are many
intermediate variables that cannot be thoroughly measured in the
‘black box’ of decision-making, which inevitably leads to differences
between politics and hard science. However, the paper will be as
precise and careful as possible in its research as it can be. It is also
considered ‘scientific’ due to its adoption of a behaviorist perspective.
In examining political phenomena, this research will focus only on
those that are observable in order to avoid epistemological critics as
much as possible. I’m aware that post-modernism distrusts any
interpretation without sorting out the horizon one’s looking, from
which one may even conclude incommensurability between paradigms,
but this article would rather abandon the possible falsehood of
epistemic intermediaries but turn to entities itself, with so-called
ontological turn. However, in this paper, the research would like to
simplify the philosophical analysis only to show that a spectator can
annotate a political phenomenon.
While most statistical researchers tend to neglect the explanatory
discussion in their study, this article attempts to argue in a
constructivist way how policies are formed radically by shared ideas
among actors and how actors act based on the knowledge they have
Critique: a worldwide student journal of politics
for the society, which eventually reinforces the knowledge itself. It
adopts a perspective of holism and idealism in order to make such an
argument. Culture, as this article argues, does not present the
institution itself, implying a causal relationship between culture as a
starting point and policy as result, but rather a constitutive relation
between them. The detailed arguments will be presented below.
This article seeks to explain the result from a constructivist
perspective, in contrast to the causal relations argued by most scholars.
Culture, or shared ideas, among individuals can significantly impact
how people act towards policy decisions. One may ask what kinds of
shared ideas could affect policymaking because there are so many kinds
of shared ideas in societies. These may include norms, rules, identities,
ideology, discourse, or ideas themselves, some of them may affect the
policymaking process while others may not. Reality is not as important
because all actors interpret it differently. As constructivist theory
suggests, both the public and politician act based on of their
knowledge (or prior understanding) of the world, and the interaction
reconstructs or enhances their knowledge of others. It is easy to
imagined that the prior understanding direct both ends of
policymaking and forms the culture between them.
Methodology
This paper gathered its data on policies mainly from two
resources. CoronaNet is a project that collects all policy responses
made by governments related to COVID-19, from the beginning of
the pandemic. Over 500 researchers gathered data from 195 cases to
collect as much information as possible. The project also includes with
a table of COVID-19 Policy Intensity Scores, which will be explain and
be referred to in a later section of this paper. Another source of policy
data is OurWorldinData.org which has also done an excellent job of
collecting information on the pandemic. Researchers on the program
have created 3165 charts addressing the world’s largest problems,
including many related to the pandemic. On their site, policies are
separated by time and region.
The ethical, religious, and linguistic diversity indexes used in this
paper were collected from the work of Alesina and Ferrara et al. who
compared ethnic, linguistic, and cultural fractionalization across 215
countries (Alesina et al. 2003). They gathered data on ‘ethnic
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composition’, ‘language’, and ‘religious affiliation’ from yearbooks of
Encyclopaedia Britannica, which collect data from official government
reports and national censuses. The fractionalization of the three
variables is represented by a numerical result from the ‘one minus the
Herfindahl index’ formula, which varies from 0 to 1. A result of 0
indicates a perfectly homogeneous countries, while a result of 1 refers
to the most fractionalized countries. This formula calculates the
probability that two randomly chosen individuals from a country
belong to different ethnic groups, speak different languages, or follow
different religions. The calculation only considers groups comprising
more than one percent of the country’s population; smaller groups are
not taken into account.
This paper selected 38 countries that are members of the
Organization for Economic Co-operation and Development (OECD)
for analysis. These countries are generally highly developed, and vary
in size, population, culture and faith. The selection of countries from
different continents allows for a broad range of development levels
and policy systems to be analyzed. Membership in the OECD signifies
a country's interest in participating in globalization and could also be
seen as an indication of their economic development and
modernization. The process of selection can be considered
representative and neutral as the set of countries wasn’t chosen by any
scholar or organization but rather arose itself throughout history.
In choosing the countries for this study, the availability and
credibility of social survey data was also taken into consideration.
Developed countries tend to have more mature and reliable social
survey systems, often with independent agencies responsible for their
administration, such as the Bureau of Economic Analysis in the US,
INSEE in France, or the Office for National Statistics in the UK. As
an epidemic, COVID-19 spreads through the circulation of materials
and people. Therefore, relatively developed countries, which have a
higher level of globalization and were more likely to come into direct
contact with the virus at the beginning of the outbreak, were selected
for this study. On the other hand, less globalized countries may not
Critique: a worldwide student journal of politics
have had sufficient exposure to the virus at the beginning of the
epidemic, as was the case for many less developed countries in Asia,
Africa, and Latin America at the time this paper was written.
Globalization was therefore an important factor in the selection of
countries for this study.
Secondly, the fact that most OECD countries are developed gives
their governments greater confidence in their ability to make rational
and well-considered decisions. The economic success of these
countries also reflects the strength of their government and the
cooperation of the public. In comparison to less developed
governments, developed and complete governments are better able to
respond to their domestic culture, making them more valuable subjects
of study. Without a mature bureaucratic system and policy-making
process, political decisions made by the government may be unstable
and inaccurate, and therefore lack value in research. As mentioned at
the beginning of this research, it is essential to ensure that domestic
cultures in the countries have at least some influences on the policy-
making process, which can be achieved through a mature bureaucratic
system. It is not to say that undeveloped systems are incapable of this,
but rather that it would be difficult to ensure. In the development of
political systems, those that act stable and accurately and interact with
their cultural groups tend to succeed, while those that do not either
reform or face failure.
Another reason for the paper's choice of OECD countries is to
exclude China from consideration due to its unique position in the
COVID-19 timeline as the place where the virus was first discovered.
Other countries faced the virus more or less simultaneously, so
excluding China could potentially reduce uncertainty resulting from
the diachronic sequence. From today's perspective, China has indeed
implemented a different pandemic policy compared to other countries,
so it may be advisable to exclude China from the list of options.
Taking these considerations into account, it can be argued that
OECD countries are a good choice for this research as they represent
a wide range of world cultures, from Asia to America, and from
Christian to Islamic. This diverse selection will likely increase the
sample size and credibility of the research. While the OECD countries
may not be an exhaustive choice, they provide a balance between
comprehensiveness and rationality.
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The question then arises about policy responses. There are several
ways to classify these responses. Goyal and Howlett (2021) divided
them into 16 groups: curfew and lockdown, border restriction,
quarantine and tracing, government services, information
management, (non)essential business, testing and treatment, public
gathering, education, physical distancing, funding and stimulus,
advisory and warning, protective equipment, public event, health
screening, health resources. It can be seen that M. Howlett et al. have
adopted a broad concept in the evaluating policy responses, including
those aimed at stimulate economic growth and social propaganda. G.
Capano et al. classified different policies into 18 groups: tax payment
deferral, tax regulation relaxation, business loan, leave and
underemployment, travel advisory and restriction, social distancing,
monetary policy, health facilities, medical supplies, social security,
immunization and treatment, patient care, information and advice,
support for the vulnerable, school and university closure, COVID-19
epidemiology, financing relief, health-care spending. G. Capano et al.
have chosen an even border concept in defining the policy responses
for COVID-19, including epidemiology research and financing. In the
‘CoronaNet’ research project, researchers classified policies into 6
different categories, which are business restrictions, health resources,
health monitoring, school restrictions, mask policies, and social
distancing. The classification is rather rough but easy to manage for
such a sizeable project like ‘CoronaNet’.
This article would follow the classification peovided by T. Hale,
N. Angrist et al., which is as follows: school closure, workplace closure,
cancel public event, restriction on gathering, closed public transport,
public information campaigns, stay-at-home, restriction on internal
movement, international travel control, testing policy, contract tracing,
face covering, vaccination policy. These policies are specific and easy
to measure and can be assigned to a index to measure stringency of
each.
The point in time chosen for sampling in this research was mid-
March 2020, when all countries were just beginning to take action
Critique: a worldwide student journal of politics
against the threats of COVID-19. The reason for not choosing an
earlier or later time is that any earlier time would not have been
sufficient for countries to realize the severity and novelty of the virus
and to take serious action against it, and any later time would allow
countries to imitate the methods of others and make their actions
increasingly homogeneous. There is evidence showing how countries
imitate each other in response to the threat of COVID-19. However,
mid-March may be the golden period when each country acted toward
the crisis independently, without any paradigms to follow. The policy
stringency index calculated is presented in Table A2.
Data analysis
Table A1 shows the work summarized by Alesina et al. (2003),
which this paper will use in the research. This paper will use the
concept of ‘Cultural Fractionalization’ to measure this concept. To
obtain the basic value of how each country is diversified, this article
will simply add up the fractionalization index.
However, by simply adding them up, this article concludes as a
priori that the three contribute only and equally to the diversity of
culture, which this research cannot prove whether it is true or not.
‘Cultural Fractionalization’ is an ‘untouchable’ index without tools to
approach it. However, it can be argued that the concept of ‘Culture
Fractionalization’ is simply a result of mathematical calculation rather
than an index representing status of society, which would avoid the
criticism from empirical research. The result of the calculation of
‘Culture Fractionalization’ is presented in Table A2. This research will
continue further research based on such calculations.
The figure is presented at the end of the document, showing the
relationship between the policy stringency index and four different
fractionalizations linear fitting results. The graph of the result is
presented in Figure A1, Figure A2, Figure A3, and Figure A4. All four
different linear fitting results have a negative slope from the calculation,
but the slope of the linear fitting result in each figure is slightly
different. The attribute of the linear fitting result are presented in the
up-right table of each figure, including slope and R-square, which are
both important for further discussion. The data will be discussed in
the next section.
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Discussion
As the analysis above shows, all four relations indicate a negative
correlation between policy stringency and fractionalization. While this
correlation may not be immediately apparent, the linear fitting result
reveals the underlying tendency that may not be easily discernible.
From the analysis, it can be seen that as cultural fractionalization
increases, countries tend to choose a looser policy response, and as
cultural fractionalization decreases, countries tend to choose the
opposite.
Some may argue that the tendency should not be taken seriously
if the slope of the linear fitting result is not steep enough to ensure
that the two variables are truly connected. However, the first point to
refute this argument would be that this research used a large group of
data. This article chose a large group of countries in order to reduce
error, and if all four figures indicate the same trend, it can further be
argued that this should not be a coincidence due to the repetition.
The second point is that the selection of different counties may
be little related to the final result. Different countries may have very
different fractionalization indices across linguistics, religion, and
ethnicity. A country may be highly diversified ethnically, but
homogeneous in language, or vice versa, but the fitting result remains
unchanged. If a country is culturally fractionalized, it may not be
fractionalized in ethnicity or language, or religion, which makes it
unrelated to the country selected for this research. Every country
differs in its contributions to the four fractionalization indices, but the
output is the same - all four show a negative correlation, which means
the final result cannot be changed.
Policy stringency may be most closely connected with linguistic
diversity within countries because, in all four graphics, the slope of
linear fitting result of linguistic diversity and policy stringency is the
steepest. This could be explained by the fact that all policies require
language to propagate, and a more fractionalized country makes this
process more difficult. Different linguistic groups may require
additional translation for certain policies to be effective, which would
Critique: a worldwide student journal of politics
delay the propagation process. Religious fractionalization is the least
relevant to policy stringency because the linear result is nearly flat. This
can be explained by the fact that policy nowadays has little connection
with religion. Religion may play a more indirect role in policy decisions
in response to the COVID-19 crisis by affecting other factors in society,
such as the strength of conservative forces.
The article would like to conclude its deduction in the following
two parts. The first dimension this article to consider is interpersonal
trust. Previous research identified a social factor influencing the
decision-making process during the pandemic, which includes trust in
government and interpersonal trust (Toshkov and Yesilkagit 2021).
These studies conclude that this trust can help government be more
confident in making tougher restrictive policies, which could also help
explain the negative correlation in the findings. People from different
ethnic groups may not tend to trust each other as much as those from
the same ethnic group, religion, or language. This dimension would
take interpersonal trust as the key factor in explaining the negative
correlation in the findings. The distrust may emerge from a person’s
deepest fear of the unknown, but if one is aware that the other person
shares the same cultural background, it would reduce the fear of the
unknown. Between different cultural groups, this research believes that
distrust could occur and be sensed by people, which would hinder
confidence in adopting policies. High-trust societies would have
enforced and endorsed tougher restrictive measures. Interpersonal
trust is a matter of ‘idea down’, it is common knowledge people within
a country would have shared. Once such common knowledge of trust
is formed, it would not change easily.
The other dimension to consider is how shared ideas are
determined by political ideologies, which is another factor that
thoroughly constituted by shared ideas among individuals. This
ideology would affect both immigration policy and policy response to
the COVID-19 crisis - looser immigration policy allows in residents
from other cultural groups and increase their cultural fractionalization.
In conclusion, it is not the fractionalization itself conduce to the loose
policy during the pandemic, but both of them are attributed to the
ideology of the government. A government that shares a looser culture
in adopting measures of policy measures would choose a looser policy
on both immigration policy and restrictive measures, resulting in the
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tendency the research has found. Ideology is another constituted idea
among actors, and it would further instruct how an actor will act.
There are a multitude of factors that could potentially impact the
relationship between cultural fractionalization and policy stringency.
However, in the current analysis, I have only focused on two key
dimensions: interpersonal trust and political ideology. These were
chosen because they were considered to be the most relevant and had
a logical connection to the main research question. Other factors, such
as economic conditions, demographic characteristics, or historical
context, were not included in the analysis because they did not fit well
within the logical framework of the study.
It is possible that these other factors could have an impact on the
relationship between cultural fractionalization and policy stringency,
but they were not considered in this analysis because they were not
deemed to be as relevant or important. Additionally, there may be other
factors that have not been identified or analysed in this study, and it is
possible that these could also have an impact on the relationship
between cultural fractionalization and policy stringency. However,
given the complexity of the research question and the limited scope of
this analysis, it was necessary to focus on a smaller set of factors and
exclude others.
In conclusion, the current analysis has provided some insights
into the relationship between cultural fractionalization and policy
stringency, but it is important to recognize that there may be other
factors at play that have not been considered in this study. Further
research is needed to fully understand the mechanisms that drive this
relationship and to identify any additional factors that may be relevant.
So, it is crucial to continue to explore and analyse this relationship in
order to gain a more complete understanding of the factors that
influence policy making in culturally diverse societies.
However, the discussions made above are only hypotheses, this
research has only revealed a possibility of explaining the result based
on basic logic, which still awaits further systemic research.
Critique: a worldwide student journal of politics
Conclusion
This study uncovers the inner connection between cultural
fractionalization and policy response to the COVID-19 virus. Most
previous studies neglect the impact culture has on policy decisions
during the pandemic. The research compared previous data calculated
on pandemic policy stringency and work done by Alesina et al. (2003)
on the fractionalization of different countries. This article has chosen
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in this research to inspect different policy responses among
38 countries that entered the OECD, which would hopefully represent
the rest of the world and provide trustworthy data on their cultural
links. To choose an early stage of the crisis, this article aimed to state
their autonomous response rather than stimulate each other’s action.
By comparing 38 countries across four variables and their policy
stringency, this article found an inconspicuous negative correlation
between cultural fractionalization and policy stringency, wherein
cultural diversity rises, stringency declines.
Though the tendency is inconspicuous, this article tried to argue
that the phenomenon itself is not a coincidence because it is unrelated
to what and how many countries this article chooses, but an implicit
relation the research has revealed. After such a phenomenon is
confirmed, this article tried to explain the such matter by two possible
logics with a constructivist flavor. The first is interpersonal trust, which
may be lacking in a fractionalized society, and will eventually reduce
confidence in restrictive policy announcements. The second is ideology,
a compact style of policy-making that would affect both immigration
and restriction measures in the pandemic crisis.
Some questions may be asked to inspire further research on this
problem. The first problem is why some country, like the UK, are
limited in language and ethnicity but diversified in religion, yet they
have adopted a rather looser restrictive policy response. Those
countries may be diversified in one or two domains, but narrowed in
the rest, and whether they hold a looser or stricter policy, they will all
be a problem for the study, which may still await further investigation
into what happens in those countries. Another question is that this
short research lacks a discussion on exceptions. To explain exceptions,
the research needs to take a closer look at their domestic situation,
which may be a deviation since the topic focus on general tendency
only. Exceptions such as Canada, Iceland, and Czech could be due to
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the disease in their counties wasn’t severe in mid-March, but
researchers still need to carefully investigate such matters. The last
question would be the causal relationship between immigration policy
and restrictive policy during the pandemic. This paper conjectures that
both of them are influenced by a bigger concept, ‘political ideology’;
however, the restrictive policy could be directly influenced by
immigration policy, which is the core reason for cultural relations
within countries.
To study the policy response during the pandemic, scholars would
be benefits not only from one temporary crisis, but would also reveal
basic knowledge in policymaking. Though some of the hypothesis in
this paper still awaits further verification, the tendency the paper found
could be well enlightening.
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Appendix A
Table A1. Ethnic, linguistic, and religious fractionalization.
Country
Ethnic
Linguistic
Religious
Australia
0.0929
0.3349
0.8211
Austria
0.1068
0.1522
0.4146
Belgium
0.5554
0.5409
0.2127
Canada
0.7124
0.5772
0.6958
Czechia
0.3222
0.3233
0.6591
Denmark
0.0819
0.1049
0.2333
Finland
0.1315
0.1412
0.2531
France
0.1032
0.1221
0.4029
Germany
0.1682
0.1642
0.6571
Greece
0.1576
0.03
0.153
Hungary
0.1522
0.0297
0.5244
Iceland
0.0798
0.082
0.1913
Ireland
0.1206
0.0312
0.155
Italy
0.1145
0.1147
0.3027
Japan
0.0119
0.0178
0.5406
Luxembourg
0.5302
0.0021
0.6644
Mexico
0.5418
0.644
0.0911
Netherlands
0.1054
0.1511
0.1796
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New Zealand
0.3969
0.5143
0.722
Norway
0.0586
0.1657
0.811
Poland
0.1183
0.0673
0.2048
Portugal
0.0468
0.0468
0.1712
Slovakia
0.2539
0.0198
0.1438
South Korea
0.002
0.2551
0.5655
Spain
0.4165
0.4132
0.4514
Sweden
0.06
0.1968
0.2342
Switzerland
0.5314
0.5441
0.6083
Turkey
0.32
0.2216
0.0049
UK
0.1211
0.0532
0.6944
United States
0.4901
0.2514
0.8241
Source: Alesina et al. 2003
Critique: a worldwide student journal of politics
Table A2. The calculation of ‘Culture Fractionalization’ and policy
stringency among 30 countries (March 2020)
Country
Cultural
Fractionalization
Policy
Stringency
Australia
1.2489
19.44
Austria
0.6736
48.15
Belgium
1.309
50.93
Canada
1.9854
24.07
Czechia
1.3046
68.52
Denmark
0.4201
65.74
Finland
0.5258
37.04
France
0.6282
49.54
Germany
0.9895
32.87
Greece
0.3406
54.63
Hungary
0.7063
50
Iceland
0.3531
25
Ireland
0.3068
48.15
Italy
0.5319
82.41
Japan
0.5703
40.74
Luxembourg
1.1927
53.7
Mexico
1.2769
2.78
Netherlands
0.4361
53.7
New Zealand
1.6332
19.44
Norway
1.0353
51.85
Poland
0.3904
57.41
Portugal
0.3904
53.7
Slovakia
0.2648
63.89
South Korea
0.4175
55.56
Spain
0.8226
67.13
Sweden
1.2811
30.56
Switzerland
0.491
33.33
Turkey
1.6838
23.15
United Kingdom
0.5465
12.96
United States
0.8687
41.2
Source: OurWorldinData.org