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Stringency in policy responses to Covid-19 pandemic
and social distancing behavior in selected countries
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WORKING PAPER
A H M Belayeth Hussain*
April 20, 2020
Abstract
This paper investigates the relationships of government’s stringencies in policy measures to respond
to the Covid-19 pandemic and “social distancing” behavior across countries. Amid coronavirus
(nCovid-19) pandemic, the trending advice for “social distancing” has become one of the most popular
yet contentious recommendations in many countries. Studies have shown that cities and states that
had early and broad isolation and preventive measures had a 30% to 50% lower outbreak and fatality
rates than other cities during the Spanish Flu nearly a century ago. This paper finds that the “social
distancing” performance of communities significantly depends on the government’s policy stringency
level. The countries with stricter government responses and measures have experienced higher
compliance with “social distancing” advice, and hence experienced slower coronavirus growth rates
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than the countries with softer stringencies. Among the coronavirus hardest-hit countries, a 1.00 unit
increase in government’s stringency to respond to the Covid-19 decreases 0.69 unit in people’s
workplace mobility. For the same countries, 1.00 unit rise in government’s stringency contributes to
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0.31 unit more “stay at home”. Among the studied 32 countries, a 1.00 unit increase in government
stringency measure reduces 0.57 unit of workplace mobility among the working people. The countries
with 1.00 unit more stringency level contribute to 0.25 unit more “stay-at-home.” This paper uses two
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sources of data: OxCGRT Stringency Index and Google’s community mobility data. The paper
concludes that governments across the countries with more stringent policies to respond to Covid-19
pandemic could give rise to “social distancing” performance, which further contributes to curve the
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death tolls.
Key words: Covid-19; Coronavirus; Social distancing; Governments; Stringency index
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____________________________
*Senior Lecturer
Centre for Research on Women and Gender (KANITA)
Universiti Sains Malaysia (USM)
belayeth@usm.my
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This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=3586319
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1. Introduction
Amid coronavirus (nCovid-19) pandemic, the trending advice for "social distancing” has become one
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of the most popular yet contentious recommendations in many countries. Studies have shown that
cities and states that had early and broad isolation and preventive measures had a 30% to 50% lower
outbreak and fatality rates than other cities during the Spanish Flu nearly a century ago. Studies
investigated the hypothesis that the Spanish Flu multiple waves were caused by people avoiding
potentially infectious contacts, a behavior termed “social distancing” interventions, which could play
a significant role in mitigating the public health impact of future influenza pandemics (Caley et al.,
2007). During this current pandemic situation around the world, some studies also have found a
positive impact of “social distancing” to flatten the curve of coronavirus outbreak. People’s restricted
mobility to workplaces and other commonly traveled places help reduce the coronavirus rise
(Yilmazkuday, 2020). This can have a better outcome when government measures across the countries
become more stringent as the outbreak gets spiked (Hale et.al., 2020). To restrict people’s mobility
during the pandemic, government’s policy choices play a decisive role that helps curve the rise of the
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death toll. Governments’ decisions on social distancing, movement restrictions, and lockdowns have
negative associations with people’s mobility to retail and recreation centers, groceries and pharmacies,
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parks, transit stations, and workplaces (Hussain, 2020). The same decisions have a positive association
with people’s stay-at-home behavior. The World Health Organization (WHO) recommends people to
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maintain at least one-meter distance between persons (WHO, 2020). However, this advice seems
purely a "physical distancing" between persons (among whom there might be a potential carrier of
coronavirus). On the other hand, the Centers for Disease Control and Prevention (CDC) explains
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"social distancing" which they also call "physical distancing" as a way of people’s mobility to keep them
distant from others (CDC, 2020). This definition includes at least two meters distance from other
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people; to avoid gathering in groups; and staying out of crowded places and mass gatherings (CDC,
2020). Some researchers argue that the term "social distancing" should be replaced with “spatial
distancing” along with “social closeness”. They recommend that public health advice should approach
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the threat of Covid-19 by promoting "spatial distance" together with "social closeness" (Abel and
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McQueen, 2020).
As governments around the world have responded to the Covid-19 pandemic, the latest information
about their policies is essential to offer further avenues. Oxford Covid-19 Government Response
Tracker (OxCGRT) has developed a new robust index that demonstrates levels of policy measures
that the governments across countries have taken to tackle the spread of pandemic (Hale et al., 2020).
The authors of OxCGRT have collected various policy responses to Covid-19 pandemic based on 11
indicators, namely, school closure, workplace closure, cancellation of public events, closure of public
transports, closure of public campaigns, imposing movement restrictions, imposing international travel
controls, introducing fiscal and monetary measures, emergency investment in health care, investment
in vaccines, testing policy, and, contact tracing, etc.
Google (2020) has introduced a report on people’s mobility as a response to social distancing advice
during the Covid-19 pandemic. This report is made possible to track people’s movement using location
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services in the countries where this service is available. Google identifies six major locations where
people are used to traveling and moving in their everyday needs. The six defined areas are: retail and
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recreation centers that include restaurants cafes, shopping centers, theme parks, museums, libraries,
and movie theatres; groceries and pharmacies that include the locations of grocery markets, food
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warehouses, farmers markets, food shops, drug stores, and pharmacies; various parks that include
national parks, public beaches, plazas, and public gardens; transit stations are the locations including
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subways, bus and train stations; workplaces where people use to travel during workdays; and,
residential places where people stay.
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This paper finds that the “social distancing” performance of communities significantly depends on the
government’s policy stringency level. To define “social distancing,” this study considers three areas of
people’s mobility during the pandemic. The “social distancing” indicators of people’s movement are
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This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=3586319
— “stay-at-home,” “mobility towards workplaces,” and “mobility towards public parks.” The study
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finds that the countries with stricter government policy responses and measures have experienced
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higher compliance with “social distancing” advice, and hence experienced a slower coronavirus growth
rate than the countries with softer stringencies. Among the coronavirus hardest-hit countries, a 1.00
unit increase in government’s stringency to respond to the Covid-19 decreases 0.69 unit in people’s
workplace mobility. For the same countries, 1.00 unit increase in government’s stringency contributes
to 0.31 unit more “stay at home.” Among the studied 32 countries, a 1.00 unit increase in government
stringency measure reduces 0.57 unit of workplace mobility among the working people. For these
countries, with 1.00 unit more stringency level contributes to 0.25 unit more “stay-at-home.” This
paper utilizes two sources of data: OxCGRT Stringency Index and Google’s community mobility data.
2. Data
This paper appropriates two sources of data: OxCGRT Stringency Index (Hale et al., 2020) and
Google’s data (2020) on community mobility (Chan et al., 2020). During the pandemic, the
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governments across countries have declared and implemented some initiatives that include social
distancing, movement restrictions, public health measures, socio-economic measures, and lockdown
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declarations. ACAPS (2020) has compiled all the measures implemented by governments worldwide.
As a relatively robust way of indexing, the OxCGRT has developed a stringency index for each
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countries depending on its policy responses to the Covid-19 pandemic. Using 11 indicators on
governments’ responses to Covid-19, the OxCGRT developed a “0 to 100 point Index” for countrylevel stringency. On the other, Google mobility data reports how people’s community mobility at
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different places has changed compared to a baseline. As explained by Google, the benchmark is a
median value of the five weeks, January 3, 2020, to February 6, 2020. Provided that data from both
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sources are available from February 16, 2020, to March 29, 2020, this paper chooses 32 countries from
all geographic regions. The selected countries are ­— Australia, Austria, Belgium, Brazil, Canada,
Denmark, Finland, France, Germany, India, Ireland, Israel, Italy, Japan, Malaysia, Netherlands, New
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Zealand, Philippines, Norway, Singapore, Portugal, Saudi Arabia, South Africa, South Korea, Spain,
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Sweden, Switzerland, Taiwan, Thailand, Turkey, United Kingdom, United States of America.
3. Results
Stringent measures and social distancing
Figure 1 demonstrates different governments’ stringencies to ensure social distancing across countries
over time (February 16, 2020, to March 29, 2020). Australia, Ireland, Japan, Singapore, South Korea,
Sweden, Taiwan, Thailand, United Kingdom, and the United States had been experiencing a softer
level of stringencies than the other studies countries mentioned in the Figure. On the other, the
countries with Stringency Index over 90 and above are Austria, Denmark, Finland, France, India,
Israel, Italy, Malaysia, Netherlands, New Zealand, Norway, Philippines, Saudi Arabia, South Africa,
Spain, and Turkey. Among the coronavirus hardest-hit countries, the United States and the United
Kingdom had the least level of stringencies from their governments. As of April 26, 2020, the Unites
States share around one-fourth of total death tolls in the world, while among the European nations,
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United Kingdom has the third highest deaths per million populations of the country. Asian countries
are among the top, where governments have stricter advice for their citizens. India, Israel, Malaysia,
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the Philippines, Saudi Arabia, and Turkey are among them. However, some Asian nations, for example,
Japan, Singapore, South Korea, and Taiwan, had softer advice for their citizens amid the Covid-19
pandemic. Singapore, which had a minimal number of confirmed cases up until late March 2020 has
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now the highest among the ASEAN countries.
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This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=3586319
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Figure 1: Stringencies across countries
Figure 2 brings together two different charts demonstrating the state of stringencies and resultant
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social distancing performances across the countries. In the first chart, scatters show the linear increase
of stringency levels in the countries. The same chart also shows a sharp decline in people’s workplace
presence and appearance in public places. Conversely, people’s propensity to stay at home had
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increased compared to their baseline time (median score of previous months). The chart indicates that
from mid-February until mid-March 2020, the growth rate of confirmed coronavirus cases has risen
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that follow a slow decline after mid-March. As time advanced, the consequences of relative rise in the
stringency levels of governments’ policies and pieces of advice fell into the reductions in citizen’s
community mobility towards workplaces and public parks that indicate the sharp rise in social
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distancing performances. As the chart shows, after mod-March until the end of the month, another
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indicator of social distancing, people’s stay-at-home has increased than the baseline period. From all
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five lines in the chart, the paper infers that the stricter the government rules, the more the social
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distancing performance can achieve by a country.
Figure 2: Governments' stringencies and social distancing
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This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=3586319
In Figure 2, the second chart shows two comparative regression lines on top of a scatter plot of
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stringency levels across countries. Using the data wrangling and tidying procedures in R programming,
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I made another group of data-frame that has observations from the coronavirus hardest-hit countries.
They are United States, United Kingdom, Italy, France, Germany, and Spain. In the chart, the “blue”
linear regression line indicates overall (32 countries) predicted increase in people’s “stay-at-home”
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behavior.
Figure 3: Governments' stringencies and social distancing
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as a result of stricter public health advices from the government. The “red” regression line indicates
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the predicted contingency for the top hit six countries. The chart confirms that the coronavirus
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hardest-hit countries would have better “stay-at-home” performance (as an indicator of social
distancing) than the overall result of all 32 countries. For them, a 1 unit increase in stringencies of
government measures results in a 0.31 unit increase in “stay-at-home (Table 2). This result is statistically
significant, with a p-value of 0.000 and R-squared of0.77 (Table 2). On the same social performance
indicator, among 32 countries, with 1 unit increase in stringencies of government measures results in
a 0.25 unit reduction in “stay-at-home.” This result is also statistically significant, with a p-value of
0.000 and R-squared of 0.66.
Likewise, Figure 3 brings together two charts demonstrating the level of stringencies and consequent
social distancing performances across the countries. In the first chart, the “blue” linear regression line
indicates a sharp reduction in people’s mobility towards workplaces. The findings infer that this
reduction is a result of stricter public health advice from the government. However, when it comes to
comparing the top six affected countries, it is seen that the hardest-hit countries have even sharper
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decline than the overall fall in workplace presence. The “red” regression line indicates the predicted
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dependency of people’s restricted mobility towards workplaces on the stringency levels of the
governments' measures to curve the Covid-19 pandemic in the hardest hit six countries. The lines in
this chart confirm that the coronavirus hardest-hit countries would have a sharper decline in workplace
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presence (as an indicator of social distancing) than the overall result of all 32 countries. For 32
countries, with 1 unit increase of stringencies in government measures reduces -0.57 (p-value = 0.000;
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R-squared = 0.58) unit of workplace presence during the pandemic (Table 1). For the hardest-hit six
countries, a 1 unit increase in stringencies in government measures leads a -0.69 unit decline (p-value
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= 0.000; R-squared = 0.75) in community mobility towards workplaces (Table 2).
The second chart demonstrates that the studied countries have experience of controlled community
mobility towards public parks. This result also has the same confirmation as it is showed in the first
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This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=3586319
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than all 32 countries. However, the trend is similar in both groups of countries.
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chart — the hardest-hit counties have responded to stricter stringencies of public health advice more
Stringencies and social distancing in hardest hit countries
Figure 4 has four charts that demonstrate the effects of stringencies in government decisions on the
Covid-19 case growth rate and social distancing performances in selected six countries. All the four
charts present linear regression lines that indicate the dependencies of social distancing performances
and coronavirus case growth on the stringency levels. The first chart shows that a linear increase in
confirm case growth rate as stringency levels increase. However, this trend is slow and steady. Among
the hardest-hit countries, this rate grows with a higher rate than the average growth rate in all 32
countries included in this study. Italy, Spain, and France had the strictest levels of government
measures for public health advice. On the other, the United States and the United Kingdom had softer
levels of stringencies with a higher rate of fatalities than the overall average. The second chart shows
that the nationals of Italy, Spain, and France had the highest number of people who had stayed at
home during the observed days in February and March 2020. With the stringency level of around 77,
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20% more people had stayed at home in the United States, and in Germany. In contrast, with the
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stringency levels of nearly 100, approximately 38% more people had stayed at home than the previous
base month. In the third chart, as the stringency levels grew up, people’s mobilities towards workplaces
were restricted in six selected countries.
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Like the other two charts I discussed earlier, Italy, Spain, and France performed best with the highest
levels of stringencies. With approximately the same stringency level as it is with the United States and
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Germany, the United Kingdom showed much more reductions in workplace presence. However, the
United States had the least performance in decline in workplace mobility than their counterparts. In
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the fourth chart, the trend remains the same for selected countries with an exception for the United
States. While in the other five countries, with higher levels of stringencies, people’s community
mobilities to public parks were much lower, the United States had the experience of higher flows to
parks even than the predicted line.
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This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=3586319
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Figure 4: Stringencies and social distancing in the hardest hit countries
Table 1: Government stringency impacts on social distancing (32 countries)
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Model
Workplaces ~ Stringency.Index
Stay.at.Home ~ Stringency.Index
Parks ~ Stringency.Index
Estimate
-0.69155
0.30668
-0.89580
Str. Err.
0.02476
0.01037
0.04240
R-sq.
0.7530
0.7736
0.6355
p-value
0.000
0.000
0.000
Table 2: Government stringency impacts on social distancing (6 hardest hit countries)
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Model
Workplaces ~ Stringency.Index
Stay.at.Home ~ Stringency.Index
Parks ~ Stringency.Index
Estimate
-0.56727
0.24996
-0.44415
Std. Err.
0.01315
0.00482
0.02203
R-sq.
0.5752
0.6623
0.2283
p-value
0.000
0.000
0.000
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Discussions and conclusions:
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Governments’ policy measures on social distancing comprise of limiting public gatherings, school
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closure, public service closure, and changes in prison-related policies. Social and economic measures
(including governance) have decisions related to the declaration of the state of emergency, economic
measures, activation of emergency administrative structures, and limiting export-import products.
Governments’ initiatives related to public health introduce, implement and strengthen quarantine
policies, awareness campaigns, general recommendations, public health systems, infection testing
policies, psychological assistance, and medical social work. Globally, the governments also have
introduced movement restriction policies that include surveillance and monitoring, border closures,
visa restrictions, domestic travel restrictions, additional health documents upon arrivals, curfews, etc.
Finally, with some exceptions, countries have declared partial and full lockdowns in state and territorial
levels.
The governments across the countries have declared and implemented some social, economic, public
health, and governance-related decisions. The principal reason was to keep people away from the social
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contacts of others that help flatten out the rise of the covid-19 outbreak. From the data analyses, this
paper explores that the people of most of the countries have responded to the governments’ initiatives
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to slow down the rise. As a result, people stay at home, reduce their mobility to workplaces and public
parks. Social distancing actions and policies in response to the Covid-19 pandemic have substantial
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economic benefits. In essence, three to four months of moderate distancing would save 1.7 million
lives, and the mortality benefit of moderate social distancing is about $ 8 trillion or around $60,000 per
household in the United States (Greenstone et al., 2020). As the “social distancing” policies are of great
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importance among the political leaders, public health experts, and policymakers, some studies also urge
that political leaders must introduce social-distancing policies that do not bias against any population
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group in the country (Joseph et al., 2020). This fear extends further in other studies; for example, Stein
(2020), in response to another study, Stein (2020) urges for a rationally layered social distancing.
Because the Covid-19 pandemic disproportionately affects different groups of people, including age
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and health conditions of the victims. Therefore, a layered social distancing idea would be a decisive
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determinant and predictor of successful pandemic preparedness, and to support social distancing for
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vulnerable groups and to protect other individuals from the susceptible group (Stein, 2020). Therefore,
the effectiveness and societal impact of social distancing will largely depend on the credibility of public
health authorities, political leaders, and institutions (Joseph et al., 2020).
From the analyses, this paper finds that the coronavirus hardest-hit countries have better “social
distancing” performance, although they did not have as many stringencies as other nations did.
However, some trends might be interesting for further studies — Nordic countries and some East
Asian countries had fewer “social distancing” than the West and South European countries. This trend
might have been in practice due to the hardest hit coronavirus disease situations in the West and South
European nations. However, some Asian countries had experienced the highest level of stringencies
even they did not face severe spreads during the studied periods. These may be the case of the nature
and types of governments in the respective countries. With a variety of interventions and conditions,
it is confirmed that the less strict the social distancing, the more time it will take for life to return to
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normal, and the more lives will be at risk (Cano et al., 2020). Therefore, whatever the government
types, in this study on the stringencies and social distancing across countries, it is concluded that
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governments across the countries with more stringencies to respond to the Covid-19 pandemic could
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give rise to “social distancing” performance which further contributes to curve the death tolls.
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References:
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Abel, Thomas., McQueen, David. 2020. The COVID-19 pandemic calls for spatial distancing and
social closeness: not for social distancing!. 2020. International Journal of Public Health.
https://doi.org/10.1007/s00038-020-01366-73456789
Assessment Capacities Projects (ACAPS). (2020). Data on governments’ measures. Accessed on March
30, 2020. https://data.humdata.org/organization/acaps.
Centers for Disease Control and Prevention (CDC). (2020). https://www.cdc.gov/coronavirus/2019ncov/prevent-getting-sick/social-distancing.html
Chan, H. F., A. Skali, and B. Torgler. (2020). A Global Dataset of Human Mobility
for March 2020. Available at https://osf.io/gyafe/.
Greenstone, Michael and Nigam, Vishan, Does Social Distancing Matter? (March 30, 2020). University
of Chicago, Becker Friedman Institute for Economics Working Paper No. 2020-26. Available at
SSRN: https://ssrn.com/abstract=3561244 or http://dx.doi.org/10.2139/ssrn.3561244
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Hale, Thomas, Anna Petherick, Toby Phillips, Samuel Webster. “Variation in Government Responses
to COVID-19” Version 4.0. Blavatnik School of Government Working Paper. April 7, 2020. Available:
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www.bsg.ox.ac.uk/covidtracker
Hussain, A H M Belayeth, Do Governments’ Decisions on Social Distancing Flatten Out People’s
Mobility
During
COVID-19
Pandemic?
(April
12,
2020).
Available
at
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SSRN: https://ssrn.com/abstract=3574979 or http://dx.doi.org/10.2139/ssrn.3574979
Joseph A Lewnard, Nathan C Lo. (2020). Scientific and ethical basis for social-distancing interventions
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against COVID-19. Infectious Disease. The Lancet. https://doi.org/10.1016/S1473-3099(20)30190-
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Oliva Bendtsen Cano, Sabrina Cano Morales, and Claus Bendtsen. (2020). “COVID-19 Modelling: the
Effects of Social Distancing”. medRxiv doi: https://doi.org/10.1101/2020.03.29.20046870
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This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=3586319
Peter Caley, David J Philp and Kevin McCracken. (2007). Quantifying social distancing arising from
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pandemic influenza. Journal of the Royal Society Interface. https://doi.org/10.1098/rsif.2007.1197
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Richard Stein. (2020). COVID-19 and rationally layered social distancing. The International Journal of
clinical practice. https://doi.org/10.1111/ijcp.13501
World Health Organization (WHO). (2020). https://www.who.int/emergencies/diseases/novelcoronavirus-2019/advice-for-public (Accessed on April 1, 2020).
Yilmazkuday, H. (2020). Stay-at-Home Works to Fight Against COVID-19: International Evidence
from
Google
Mobility
Data
(April
9,
2020).
Available
at
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SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3571708
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