Doshisha University Life Risk Research Center
Discussion Paper Series No. 2021-01

Social Costs of COVID-19 and the Nature of
Behavioral Change
Tadashi Yagi
Faculty of Economics
Doshisha University

Discussion Paper SeriesSocial Costs of COVID-19 and the Nature of Behavioral Change

Abstract

The coronavirus disease 2019 (COVID-19) pandemic has not only caused widespread medical damage in termsof death, severe illness, and sequelae, but also economic damage, including bankruptcy, business closures, andunemployment resulting from measures such as the state of emergency declared in Japan aimed at preventing the

spread of infection. In addition, there is a fear of becoming infected with the disease. The abovementioned economic

losses have resulted in further consequences, including increased domestic violence due to the need to stay at home,increased prevalence of depression, education stagnation due to the closure of schools, and loneliness due to cessationof human interaction.

In this paper, we estimate the social costs of the COVID-19 pandemic and clarifying thenature of people’s behavioral changes. This research is important for evaluating the policies that have been

implemented so far to combat infectious diseases. In addition, we clarified what factors determine the evaluation ofpolicies such as the basic income policy, and what factors determine the abovementioned societal changes in

consciousness that serve as the basis for behavioral changes based on support or disapproval of the Swedish strategy.

1. Introduction
The coronavirus disease 2019 (COVID-19) pandemic has not only caused widespread medical damage in termsof death, severe illness, and sequelae, but also economic damage, including bankruptcy, business closures, andunemployment resulting from measures such as the state of emergency declared in Japan aimed at preventing the1

spread of infection. In addition, there is a fear of becoming infected with the disease. The abovementioned economic

losses have resulted in further consequences, including increased domestic violence due to the need to stay at home,increased prevalence of depression, education stagnation due to the closure of schools, and loneliness due to cessationof human interaction.

Accurately understanding the social costs of the COVID-19 pandemic and clarifying the nature of people’s

behavioral changes are important tasks for evidence-based evaluation of the policies that have been implemented so

far to combat infectious diseases. These tasks are essential to considering appropriate responses to the threat of

infectious diseases that are expected to continue in the future.

Many researchers have investigated the effects of the COVID-19 pandemic on society. Barceló and Sheen (2020)

empirically identified barriers to wearing masks, and found that individuals who are younger, more educated, lesssensitive to infection, and more introverted are more likely to refuse to wear masks. Ebuenyi (2020) empiricallyshowed that the risk of COVID-19 infection depends on social factors such as poverty as well as health and hygiene

status, suggesting the need for reform of the social system in order to combat infectious diseases. Hall et al. (2020)

empirically demonstrated the magnitude of the trade-off between preventing the spread of COVID-19 infection andthe economy, showing that a mortality rate of 0.44% results in an economic loss of 28%. Tisdell (2020) empiricallydemonstrated the effectiveness of public policies in preventing the spread of COVID-19, with particular criticalexamination of the proposition that lockdowns are effective in preventing the spread of COVID-19. His paper poses

questions regarding the extent to which individual freedom of choice should be restricted, and the conditions underwhich policies to prevent the spread of infection can be balanced while minimizing economic costs. Algara et. al.
2

(2021) empirically analyzed how scientific knowledge influences behavioral change amid the COVID-19 pandemic,

providing a comparative behavioral analysis of the various stresses and social problems caused by COVID-19

policies.

Considering the abovementioned studies, the present study aimed to empirically clarify the state of behavioral

changes in the general population for preventing the spread of COVID-19 in Japan and to analyze societal changes

in consciousness that serve as the basis for behavioral changes and their impact on well-being. In addition, weclarified what factors determine the evaluation of policies such as the basic income policy, and what factors determine

the abovementioned societal changes in consciousness that serve as the basis for behavioral changes based on support

or disapproval of the Swedish strategy.

The structure of this paper is as follows. Section 2 provides an overview of the data, and Section 3 clarifies thestate of behavioral changes toward measures that have been put in place in order to prevent the spread of infection.

Section 4 empirically analyzes the impact of behavioral changes and changes in consciousness using estimates ofcompensation income. Section 5 analyzes the determinants of attitudes toward the basic income policy, and Section

6 examines the relationship between support for or disapproval of the Swedish strategy and attitudes toward infection

prevention.

2. Overview of data
The data analyzed in this study were derived from a questionnaire survey entitled “Impact of the Spread of a New

Type of Virus Infection on Society and the Economy” conducted September 8–9, 2020. The survey was part of the3

“Research Project on COVID-19 Infections” at Doshisha University. Of the 13,502 questionnaires distributed, 2,274

were returned completed (16.8% response rate). After excluding responses those takes quite short time in answering,

finally 2,183 were included in the analysis.

The demographic composition of the respondents by age group and the percentage by gender was found to be nearly

equal to the actual demographic composition. That is, men were 49.3%, women were 50.7%, those aged less than 40

years were 29.4%, those aged between 40 and 60 years were 36.5%, and those aged over 60 years were 34.2%.

When preparing the survey questions, we referred to part of a report by the National Institute for Research

Advancement (NIRA) Research Institute (2020).

The data in Table 1 show distributions according to city size, final education, occupation, and personal income

before taxes. Large cities were the areas of residence for nearly 40% of the respondents, and a university degree orhigher was held by about 50% of the respondents. Retail, transportation and communication, and service industries

accounted for 3.8%, 3.8%, and 21.7% of all occupation categories, respectively, and these industries continue to be

severely affected by the COVID-19 pandemic. In addition, an annual personal income of less than 6 million yen

before tax was reported by 85.1% of the respondents.

Table 1 Distribution of respondents according to city size, education, occupation and annual personal income before
tax.
％

Final education

％

Large
city(populationof one million orlarger)38.4

Junior high school graduates

1.88

Agriculture,and fisheries

MiddleCity(population

15.8

High school graduate

27.71

Construction

City size％

Occupation

4

forestry

Individual
annualincomebeforetaxation (10,000 yen)

％

0.6

0～200

45.4

4.1

201～600

39.7

of less than one
million)
Other cities

38.9

Colleges, vocational

19.10

Manufacturing

11.1

601～1000

11.5

Towns or villages

6.8

Graduated from university

45.76

Wholesale business

2.6

1000～

3.4

Total

100.0

Graduate School

5.22

Retail

3.8

Total

100

Total

99.68

Finance and Insurance

2.5

Real Estate

1.9

Transportation
andTelecommunications

3.8

Electric gas

0.7Service industry

21.7

Others

8.5

Unemployed

38.7

Total

100

3. Behavioral changes toward measures implemented to prevent the spread of infection
3.1 Determinants of wearing masks

In Japan, it is not uncommon to see people wearing masks of their own volition in daily life for various reasons

such as to prevent the spread of airborne diseases including the common cold, and to protect themselves from getting

sick. However, since COVID-19 began to spread, nearly everyone now wears a mask in almost all public places.

Wearing a mask can be inconvenient, bringing about issues such as feeling suffocated and not being able to see eachother’s mouths, which makes smooth communication difficult to achieve. Despite these problems, many people stillwear masks. So, why have people been wearing masks since COVID-19 began to spread?

There are five factors that address the question posed above, and each have their own corresponding questions

that are answered according to a 5-point scale (where 1 = strongly agree and 5 = strongly disagree). The five factors

and corresponding questions are as follows.
51) Intention to take preventive measures: After learning that the COVID-19 epidemic had become a pandemic(early February), did you try to take preventive actions (infection avoidance actions)?

2) Attitude toward the effectiveness of infection prevention measures: Do you think that preventive actions(infection avoidance actions) are effective in avoiding infection from COVID-19?

3) Subjective norm: To what extent do you think your family and friends expect you to avoid contracting COVID-

19?

4) Knowledge of infection prevention measures: Do you know how to avoid contracting COVID-19 (infection

avoidance behavior)?

5) Risk-averse attitude: When you go out, do you usually bring an umbrella if there is a chance of rain according

to the weather forecast?

The mean value of question 2) was the highest at 4.17. The second highest one is question 1).

The question on the reasons for wearing a mask was asked as follows, using a 5-point scale (where 1 = don't

think so at all and 5 = strongly agree).

To what extent do you support the following reasons for wearing a mask?

1. I think it is necessary to prevent the spread of infectious diseases.

2. I am simply following the rules given by my company, etc.
6

3. I feel that I will be blamed if I do not wear a mask.

4. Because others around me are wearing masks, it would be bad for them if I don’t wear one too.

5. I feel like I will be left out if I don’t wear a mask.

The first is answer option is a medical reason, but many people cite “peer pressure” as the reason for wearing amask. According to Konoue and Sato (2020, p. 5), peer pressure means yielding to the command to follow the“atmosphere” of the majority or mainstream group. They point out that a system unique to Japan called seken is at

the root of this “pressure to conform”. The direct translation of seken is “society.” However, it has special additional

meanings such as the mutual monitoring system. The authors also point out that there are four rules that constituteseken: the rule of return, the rule of status, the rule of human egalitarianism, and the rule of witchcraft (Konoue andSato, 2020, Part 1). In the present survey, we assumed the questions in the present survey followed the rule of returnand the rule of human egalitarianism. The rule of return can be seen in answer option 4, where we see that people

around us are wearing masks, so we consider not wearing a mask as bad in return. The rule of human egalitarianism

is observed in answer options 2, 3, and 5, which express how we spend time with one another and that we have thesame friends, and are represented by exchanges such as “thank you for the other day” and “please keep in touch”.

In terms of the status of the responses, the medical reason (I think it is necessary to prevent infectious diseases

and to prevent their spread) has the highest mean value as a reason for wearing a mask (see descriptive statistics Table2). The reason, “I feel that I will be blamed if I do not wear a mask,” has the next highest mean value followed by“Because others around me are wearing masks, it would be bad for them if I don’t wear one too”. Thus, the medical

reason was found to provide a greater motivation to wear a mask compared with peer pressure.
7

Next, we attempted to analyze the determinants of wearing masks. The estimated equation for the determinantsof wearing masks is shown in equation (1).

Maski =
α + β Zi + γ X i + ε i .

(1)Here, subscript i is individual i, Mask i is the variable for wearing a mask when going out, Z i is a vector of variablesconsisting of five factors that generally influence infection prevention behavior, X i is a vector of socioeconomic
attributes of individual i, β and γ are vectors of the coefficients of the explanatory variables, and ε is the error term.
When estimating equation (1), the five factors mentioned above are separately estimated as explanatory variables.

The estimation results are shown in Table 2. Model 1 is the estimation result in which variables excluding control

variables are the explanatory variables, and Model 2 is the estimation result in which the control variables are includedin the explanatory variables. When the control variables were included in the explanatory variables, “I feel that I willbe blamed if I do not wear a mask” became non-significant, and “I feel like I will be left out if I don’t wear a mask”

became significant at 10%.

Factors that influence general infection prevention behavior, such as intentions, attitudes, norms, and knowledge

about infection prevention, were all found to be significant at the 1% level. Among those with a high level ofknowledge about general infection prevention measures, the use of masks decreased, but among those with a highlevel of other factors influencing infection prevention behavior, the extent of mask use increased. This was considered

to be a reasonable result.

As shown in models 1 and 2, “Because others around me are wearing masks, it would be bad for them if I don’t

wear one too ” (the rule of return) takes a positive sign and contributes to mask wearing. On closer examination of8the details of peer pressure in models 1 and 2, “I am simply following the rules given by my company, etc.” has anegative coefficient, suggesting that those who responded with this reason tend not to wear masks in places where

the rules do not apply. This can be interpreted to mean that these respondents had doubts about the medical reason

for wearing masks but wore them because of corporate rules. In terms of the control variables, women tended to wear

masks more often compared with men, and in terms of age, those who tended to not wear masks were in their late40s, with a U-shaped distribution. High school graduates tended to wear masks less often compared with middle

school graduates, and those who were unemployed also tended not to wear masks.

We found that there was a correlation between the five reasons (items) for wearing masks, and that the coefficientschanged depending on the combination of items. Therefore, we performed principal component analysis of the fiveitems in response to the question about the reason why respondents wore masks, as shown in Table 3)[[Please update

the reasons in the table to match the wordings used in the manuscript.]]. The first component was strongly correlatedwith the instructions of companies and other organizations and the rules of public constructs, which we called thesympathetic pressure component. The second component was strongly correlated with the medical reason, so wecalled this the medical reasons component. The same estimation using these two components led to models 3 and 4

in Table 2. Model 3 is the estimation result in which variables other than the control variables are the explanatory

variables, and model 4 is the estimation result in which the control variables are the explanatory variables. Using

these models, the results were the same, indicating that the presence or absence of the control variables was notimportant. In both models 3 and 4, the medical reason component, “I think it is necessary to prevent infectious

diseases and to prevent their spread” was the most common reason for wearing masks, with a coefficient value about9twice as large as that of the synchronous pressure component. (In Table 2, the standardized coefficients are not shown,

but they are parallel to the magnitude of the regression coefficients.)

10

Table 2 Estimated results: determinants of wearing masks
Explained variable: Mask when you go out. 1=Not applicable at all,...,5=Very
applicable

Factors thatgenerally affectinfection
prevention
behavior

Reasons forwearing a mask

Model 1Model 2Model 3Model 40.178
[8.76] ***0.0727[2.89] ***0.0571[3.22] ***-0.1018[-4.88] ***0.31370.1642[8.06] ***0.069[2.75] ***0.0638[3.49] ***-0.0988[-4.72] ***0.3052

0.184[9.05] ***0.0859[3.44] ***0.0562[3.15] ***-0.0988[-4.73] ***0.1712[8.40] ***0.0825[3.30] ***0.0626[3.41] ***-0.096[-4.57] ***[13.66] ***-0.0339[13.21] ***-0.0343[-2.29] **0.0474[2.16] **0.0609[-2.31] **0.0275[1.24]
0.0592[2.79] ***[2.71] ***0.023[1.28]

0.0316[1.75] *0.1121[7.32] ***0.2731[14.44] ***0.0089[1.02]-0.0001[-0.01]0.1028[6.68] ***0.2621[13.65] ***0.0078[0.90]
0[-0.00]0.0547[1.44]
0.0745[0.98]
0.0787[0.75]

0.0535[1.40]
0.0581[0.76]
0.06[0.57]

0.1412[3.87] ***0.0751[2.20] **-0.0043[-2.16] **0.1317[3.61] ***0.0693[2.03] **-0.0039[-1.93] *-0.104-0.1113[-3.12] ***[-3.33] ***-0.9294-0.9443[-2.73] ***-0.028[-1.94] *0.0026[0.03][-2.76] ***-0.0274[-1.91] *-0.013[-0.13]Full-time and full-time employees withregular employment(Ref).
Contracts, temporary staffing, dispatch, parttime jobs, internal employment, etc.

-0.049-0.0511

Self-employed[-0.95]0.0579[-0.98]0.0423[0.81][0.59]

Intended to take infection prevention
measures
Attitude toward effectiveness of infection
prevention measures
Subjective norms
Knowledge of infection prevention
measures
I think it is necessary to
Medicalprevent the spread ofreasons
infectious diseases
I am simply following therules given by my company,etc.
I feel that I will be blamed if
I do not wear a mask
Tuning
Becauseothers around me arepressure
wearing masks, it would be
bad for them if I don’t wear
one tooI feel like I will be left out if I
don’t wear a mask
Tuning pressure component
Ingredients for medical reasons

Risk aversion
attitude
IncomeRisk aversion attitude
Income (10,000 yen)
Unmarried (Ref)
MarriedMarital Status

Divorce
Bereavement
Male (Ref)

Gender

Women
Age

Age

Age square
Junior high school graduate (ref)Education

High school graduates, vocational school
graduates (including vocational schools),
technical college graduates, junior college
graduates
Graduated from university and graduate
schoolDon't know
Management and Executives

Employment
status11

Students-0.1792[-1.17]-0.1094[-1.98] **0.0201[0.15]
0.0512[0.79]
0.015[0.21]
0.0134[0.21]

Unemployed
Others
Large city
Middle City
City size

Other cities-0.1887[-1.23]-0.1158[-2.09] **0.0143[0.11]
0.0545[0.84]
0.0219[0.31]
0.0094[0.15]

Towns or villages(ref)Constant
Adj-r-squaredNNote)* ,**and* indicate significant at 10%, 5% and 1% levels,
respectively. [ ] is a t-value.

2.0519[18.55] ***0.31611944

1.9166[9.95] ***0.33141944

3.6772[32.55] ***0.31121944

3.4736[17.98] ***0.3258
1944

Table 3 Reason for wearing a mask: principal component analysis
The first Principal
Component

The second
Principal
Component

I feel like I will be left out if I don’t wear a mask

0.822

-0.06

Because others around me are wearing masks, it would be bad forthem if I don’t wear one too

0.814

0.238

I feel that I will be blamed if I do not wear a mask.

0.793

0.2

I am simply following the rules given by my company, etc.

0.69

-0.492

I think it is necessary to prevent the spread of infectious diseases

0.128

0.891

3.2 Behavioral changes and changes in consciousness brought about by COVID-19

COVID-19 has affected various industries and people. The possible effects can be predicted using models. For

example, Kaplan et al. (2020) performed simulation analysis using a model consisting of epidemiological andeconomic sectors to clarify the trade-offs between preventing the spread of infection and economic activities.

However, we believe that empirical analysis of people’s behavioral changes to prevent the spread of infection will

enable us to more accurately predict policy effects and trade-off relationships. In this paper, we used a questionnaire

survey to identify behavioral changes to prevent the spread of infection and clarified the relationship between

behavioral changes and socioeconomic factors.

12

In the questionnaire survey, the respondents were asked about the impact of the spread of COVID-19 for eightitems as follows. They were asked the same question for the eight items, “Since January 2020, how do you think thefollowing has changed in daily life since the spread of the COVID-19?” The results are shown in Table 4. The eightitems were classified as behavioral changes or changes in consciousness. The behavioral changes included changes

in working hours; housework, childcare, and nursing care hours; sleeping hours; leisure time; pre-tax householdincome, and the total workload. The changes in consciousness included changes in overall job satisfaction and overall

life happiness. Figure 1 plots the distribution of the responses in Table 4. Most respondents answered “no change”

for all items. The items to which most of the respondents answered “decreased” were working hours, sleeping hours,

pre-tax household income, total workload, overall job satisfaction, and overall life happiness. The items to whichmost of the respondents answered “increased” were housework, childcare, and nursing care hours and leisure time.

The number of respondents who answered that sleeping hours decreased and those who answered that sleeping hours

increased appeared to be roughly equal, but slightly more respondents answered that sleeping hours would increase.

Table 4 Effects of COVID-19 on daily life

Decreased alot
Decreased alittleNo change
Increased alittleIncreased alot
Number of
resp.
Mean value

Working
hours

Housework,
childcare andnursing care hours

Sleepinghours

Leisuretime

Householdincome (before
tax)

Total
workload

Overall job
satisfaction

Overall life
happiness

119

21

39

64

139

97

92

129

265

30

172

152

361

281

224

493

876

1750

1751

1508

1636

842

955

1462

57

302

200

376

42

95

59

82

21

80

21

83

5

23

8

17

1338

2183

2183

2183

2183

1338

1338

2183

2.7

3.18

3

3.12

2.73

2.75

2.75

2.71

13

Figure 1 Distribution of the effects of COVID-19 on daily life

2000
1800
1600
1400
1200
1000
800
600
400
200
0

No change
Increased a little
Increased a lot

Increased a lot
Decreased a little

Working

Housework,

hours

childcare andSleep time

Leisure time

Household Total amount Overall job
incomenursing care(before tax)

of worksatisfaction

Happiness in
life

hours

To examine what attributes were affected by the spread of COVID-19, we conducted regression analysis on theaffected items. The estimated equation is shown in equation (2).

α β X i + εi .
Z i =+(2)

Here, subscript i is individual i, and Z i is the eight items of behavioral changes and changes in consciousness. X iis the vector of socioeconomic attributes of individual i, consisting of income, age, gender, city size, marital status,
education, employment status, number of children, and occupation. β is the vector of coefficients of explanatory
variables, and ε is the error term. The eight items of behavioral changes and changes in consciousness are working
hours; housework, childcare, and nursing care hours; sleeping hours; leisure time; household income (before tax),

total workload, overall job satisfaction, and overall life happiness. The eight items were answered using the 5-point

method but were treated as continuous variables in the estimation.

The estimation results are shown in Table 5. The variables that were found to be statistically significant wereincome, age, marital status, education, self-employed employment status, and finance and insurance occupations.
14Higher income tended to increase with increased total workload, sleeping hours, leisure time, and overall life

happiness, suggesting a more favorable situation.

Age had an effect on housework, childcare and nursing care hours; leisure time; and overall life happiness. The

influence was not linear, but U-shaped or inverted U-shaped. The burden of housework, childcare, and nursing care

hours was concentrated among married women with children, but it peaked in the early 40s, with an inverted U-shape.

On the other hand, leisure time decreased with age, but after the early 40s it began to increase, and the degree of

impact was a U-shaped and opposite to that of housework, childcare, and nursing care hours. Overall life happinessalso decreased with age, but increased after the early 50s, with a U-shaped degree of influence.

Relative to unmarried respondents, married respondents were affected in terms of working hours (+:+(-) meansplus(minus) of coefficient),housework, childcare, and nursing care hours (+), leisure time (−), household income(before taxation) (+), total workload (+), and overall overall life happiness (+). Although there was damage in theform of increased working hours; increased housework, childcare, and nursing care hours; decreased leisure time;and increased total workload, there was an increase in pre-tax household income and overall life happiness. Married

respondents were more affected compared with unmarried people because they have family relationships in addition

to their social relationships. In terms of education, compared with those who have graduated from university andgraduate school, those who graduated high school, vocational school, technical college, and junior college wereaffected by housework, childcare, and nursing care hours (−), household income (before taxes) (−), total workload(−), and overall job satisfaction (−).

15

In terms of employment status, compared with full-time regular employees and regular staff, those who were self-

employed were affected by working hours (−), pre-tax household income (−), total workload (−), overall job

satisfaction (−), total workload (−), overall job satisfaction (−), and overall life happiness (−). These negative impacts

were significant, and as work decreased, overall job satisfaction and overall life happiness also decreased.

Respondents who were self-employed found it difficult to work remotely because they needed to be in contact withother people. Therefore, they were likely to be more susceptible to such negative changes. Managers and executives

were affected by overall job satisfaction (−). The number of children significantly increased housework, childcare,and nursing care hours. In terms of occupation, compared with the manufacturing industry, the finance and insurance

industry was affected by working hours (+), sleeping hours (+), household income (before tax) (+), and total workload(+). Although their household income increased, their total workload also increased.

Thus, those who experienced relatively large negative impacts were in their early 40s, married, either high school,

vocational school, technical college, or junior college graduates, and self-employed. In contrast, those who

experienced positive impacts had higher incomes, with happiness observed to increase among married respondents

and also with age among respondents who were older.

16

Table 5 Determinants of behavioral changes
Workinghours

IncomeIncome:10,000yen
Age

Age

Age square
Male(ref)Sex

FemaleLarge city
Middle city

City Size

Other city
Towns orvillages(ref)0.00060.02130.03180.01390.02280.01330.0241[0.87]
0.0296[0.57]-0.0034[-1.04]0[.]-0.0112[-0.21]-0.0118[-0.12]-0.0781[-0.75]-0.0614[-0.63][0.08]
0.1132[3.22]***-0.0078[-3.55]***0[.]0.1299[3.62]***
0.0534[0.81]-0.0078[-0.11]
0.009[0.14][2.62]***-0.032[-0.83]0.0021[0.86]
0[.]-0.0334[-0.85]-0.0083[-0.11]0.0016[0.02]
0.0089[0.12][3.04]***-0.1237[-2.50]**0.0085[2.76]***
0[.]0.0107[0.21]
0.0451[0.48]
0.101[1.01]
0.1068[1.15][1.48]-0.0209[-0.47]0.0005[0.18]
0[.]-0.0038[-0.08]-0.0337[-0.40]-0.0258[-0.29]-0.0272[-0.32][2.09]**
0.0076[0.15]-0.0023[-0.71]0[.]-0.0557[-1.05]0.1051[1.08]
0.0668[0.64]
0.0443[0.45][1.36]-0.0429[-0.93]0.0017[0.60]
0[.]-0.0498[-1.06]
0.0317[0.36]-0.002[-0.02]-0.0432[-0.50][2.36]**-0.119[-2.46]**0.0064[2.12]**
0[.]-0.0008[-0.02]0.0554[0.61]
0.065[0.67]-0.0053[-0.06]

0

0

0

0

0

0

0

[.]

[.]

[.]

[.]

[.]

[.]

0

0

0

0

0

0

0

0

[.]
0.1765[2.88]***
0.1159[1.07][.]
0.1129[2.72]***
0.024[0.33][.]-0.0555[-1.22]-0.0618[-0.77][.]-0.1073[-1.84]*-0.03[-0.29][.]0.0911[1.74]*
0.0015[0.02][.]
0.1192[1.96]*
0.0776[0.72][.]
0.0443[0.81]-0.0266[-0.28][.]0.1282[2.25]**
0.1166[1.16]

Bereavement0.25610.19680.1034-0.2573

0.09440.243

0.2938-0.1096[1.12][1.27][0.61][-1.18][0.48][1.07][1.45][-0.52]Junior highschoolgraduate(ref)0.2526-0.1239-0.1127-0.2470.36220.0139-0.00610.1718[1.15][-0.83][-0.69][-1.18][1.93]*[0.06][-0.03][0.84]High school
graduates,
vocational
schoolgraduates
juniorcollege
graduates-0.075-0.0747

0.00670.0698-0.0784-0.1096-0.0746-0.0708[-1.52][-2.23]**[0.18][1.49][-1.86]*[-2.23]**[-1.70]*[-1.55]

0

0

0

0

0

0

0

0

Don't know[.]
0.6148[1.36][.]-0.461[-1.50][.]-0.1454[-0.43][.]-0.2366[-0.55][.]-0.1719[-0.44][.]-0.1761[-0.39][.]-0.2059[-0.51][.]-0.301[-0.72]

Management
andExecutives-0.2002-0.0191-0.0083-0.0964-0.0454-0.188-0.23250.0191[-1.64][-0.23][-0.09][-0.83][-0.44][-1.55][-2.14]**[0.17]

0

0

0

0

0

0

0

0

[.]

[.]

[.]

[.]

[.]

[.]

[.]

[.]

-0.0587

0.0497-0.00220.0325-0.0633-0.0206

0.0032-0.0635

Divorce

Full-timeand regular(Ref).
Contracts,
temporarystaffing etc.

[-0.91][1.13][-0.04][0.53][-1.14][-0.32][0.06][-1.06]-0.15430.0568-0.05230.1155-0.309-0.22-0.1946-0.1522

Students[-1.78]*-0.0704[-0.42][0.97]-0.1038[-0.92][-0.81]-0.0655[-0.53][1.40]
0.1161[0.73][-4.17]***0.0849[0.60][-2.55]**-0.0153[-0.09][-2.53]**0.1616[1.09][-1.89]*0.1453[0.94]

N. ofchildren-0.0338

0.0581-0.0122-0.0416-0.0329-0.02110.0092-0.027[-1.14][2.90]***[-0.56][-1.48][-1.30][-0.72][0.35][-0.98]Selfemployed

N. ofChildren0.0095[.]

Graduatedfromuniversity
and graduate
schoolEmployment Status

A sense ofwell-being inlife

0MarriedEducation

Overall job
satisfaction[.]

Unmarried(ref)

MaritalStatusExplainer variable: 1 = decreased considerably, ... , 5 = increased considerably.
Housework,
Householdchildcare andSleeping
LeisureincomeTotal
nursing care
hours
time
(beforeworkload
hours
taxation)17

Agriculture, Forestry
andFisheries
and Mining-0.1765-0.0095-0.1576

Construction[-0.74][-0.06]0.10030.0084[0.99]
0

Manufacturing(ref)
Wholesalebusiness
RetailOccupationFinanceandInsurance
Real EstateTransportat
ion andtelecommu
nicationsElectricity,gas, waterand heat
supplyServiceindustry
OthersConstant

Adj-rsqu*redN-0.3015

0.4318[-0.89][-1.32][2.11]**

0.0758-0.05770.1362[0.12][1.01][-0.60][1.57]

0

0

0

0

[.]

[.]

[.]

[.]

-0.05180.10380.1214[-0.43]0.0047[0.05][1.27]-0.0294[-0.42][1.35]
0.0463[0.61]

0.27050.1081[2.21]**-0.0182[-0.13]0.33970.0592-0.1296[1.42][0.28][-0.58]0.17920.04780.0293[1.78]*[0.53][0.31]

0

0

0

[.]

[.]

[.]

[.]

0.1315-0.079-0.0598-0.065-0.0343[1.14]-0.1802[-1.84]*[-0.76]-0.0977[-1.11][-0.50]0.0267[0.26][-0.60]-0.0572[-0.63][-0.30]0.0113[0.12]

0.1619-0.0158

0.33250.21470.10590.105[1.30]-0.1498[-1.57][1.79]*
0.0807[0.77][-0.14]0.054[0.40][3.18]***
0.2248[1.87]*[1.76]*-0.0669[-0.48][0.97]
0.131[1.04][0.92]
0.011[0.08]

0.13770.0161

0.04860.0211-0.0495

0.09770.01930.0477[1.35][0.23][0.64][0.22][-0.57][0.97][0.21][0.50]

0.164-0.0274

0.1772-0.10930.14070.30950.15210.297[0.79][-0.19][1.15][-0.55][0.79][1.50][0.82][1.54]

0.09950.01240.0454-0.11240.02250.1493-0.00540.0637[1.50]
0.1518[1.77]*
2.61[0.28]
0.0357[0.61]
2.567[0.93]
0.0423[0.66]
3.0274[0.40]
0.0864[1.18]
2.7782[2.27]**
0.1668[1.95]*
2.6002[-0.09]-0.021[-0.27]2.9196[1.04]-0.0033[-0.04]3.0373[12.17]***[17.67]***[19.04]***[-1.78]*-0.1092[-1.33]3.4547[16.90]***[15.15]***[12.18]***[15.30]***[15.23]***0.01870.0519-0.00120.018

0.04230.03330.01330.0214

1200

1200

1200

1200

1200

1200

1200

1200

Note)* ,**and* indicate significant at 10%, 5% and 1% levels, respectively. [ ] is a t-value.

4. Impact of behavioral changes on changes in consciousness
4.1 Impact of COVID-19 on behavioral changes in terms well-being

We analyzed the impact of COVID-19 on behavioral changes in terms of well-being. The estimated equation isshown in equation (3).

Happinessi =α + β Zi + γ X i + ε i ,(3)where subscript i is individual i, Happiness i is individual i’s sense of well-being, Z i is the items of behavioral changes,
X i is the vector of socioeconomic attributes of individual i, β and γ are the coefficients of the explanatory variables,18

and ε is the error term. There are nine items of behavioral changes and changes in consciousness, as follows: working
hours; housework, childcare, and nursing care hours; sleeping hours; leisure time; personal income; householdincome; total workload; overall job satisfaction; and overall life happiness. These items were answered using the 5-

point method, and for the responses to behavioral changes and changes in consciousness, “increased significantly”

and “increased somewhat” were combined into “increased,” “no change” was unchanged, and “decreased

significantly” and “decreased somewhat” were combined into “decreased” in the three response groups that wereused as dummy variables. As shown in Table 6, when estimating equation (3), the nine items were estimated as

explanatory variables one at a time, rather than at the same time. In the estimation, the income explanatory variable

was used as a class value. For example, income between 1 million and 2 million yen was converted to a monetary

value such as 1.5 million yen. This method was used when calculating the compensation variant, which is analyzed

later in this paper.

The results presented in Table 6 were analyzed, focusing on statistically significant cases. As housework, childcare,and caregiving increased, and as sleeping hours decreased, the sense of well-being decreased. The cause might beincreased burden and stress. As for leisure time and personal income, the sense of well-being decreased in the“decreased” group and increased in the “increased” group. This was also true for overall job satisfaction and overall

life happiness. The results for household income were the same, but these results were not significant in the “increased”

group. These findings were considered reasonable.

19

Table 6 Impact of behavioral changes and changes in consciousness on well-being

ReducedBehavioral
change items

IncomeNo change (ref)The description variable (happiness of the whole life): 1 = decreased considerably, and 5 = increased considerably.
Housework, childcare
Working
Sleeping
LeisurePersonal
Household
Total
and
hours
hours
time
incomeincomeworkload
nursingcare hours-0.17382
0.223622-0.28068-0.54523-0.45232-0.50734-0.168796[-1.34][0.75][-1.78]*[-3.55]***[-3.99]***[-4.68]***[-1.29]

Increased
Income(10,000yen）
Unmarried(ref)MarriedMarital Status

Divorce
Bereavement-0.23779[-0.95]-0.24484[-1.98]**-0.00704[-0.05]0.220315[1.96]**

0.649554[2.25]**Overall job
satisfaction

a sense ofwell-being
throughout
one's life

-0.744940[-5.47]***-1.01658039[-10.31]***0.370176[1.22]-0.00316[-0.02]0.7337928[2.83]***

1.27583049[6.16]***

0.000803

0.000795

0.000792

0.000745

0.000755

0.000736

0.0007708

0.0007472

0.00071026[3.38]***[3.81]***[3.79]***[3.58]***[3.63]***[3.53]***[3.25]***[3.21]***[3.54]***

1.036126[7.41]***-0.07962[-0.29]-0.73736[-1.26]

1.179877

1.157336

1.191526

1.132994

1.14728[10.29]***[10.22]***[10.54]***[10.07]***[10.21]***-0.11223[-0.49]0.413594[1.32]-0.11734[-0.52]0.39902[1.27]-0.12356[-0.55]0.42542[1.36]-0.12623[-0.56]
0.379786[1.22]-0.10734[-0.48]0.401678[1.29]

1.0367952[7.43]***-0.077150[-0.28]-0.724469[-1.24]

1.0372247[7.56]***-0.050171[-0.18]-0.846301[-1.47]1.10139252[10.14]***-0.16592357[-0.76]0.42910431[1.42]

0.438112[3.24]***-0.55116[-4.10]***0.039662[4.77]***

0.662428[6.22]***-0.38088[-3.74]***0.028182[4.69]***

0.646712[6.11]***-0.40094[-3.94]***0.029512[4.93]***

0.643336[6.09]***-0.38328[-3.78]***0.028205[4.73]***

0.633972[6.04]***-0.38382[-3.79]***0.02865[4.81]***

0.634363[6.04]***-0.39373[-3.89]***0.029035[4.88]***

0.4320969[3.20]***-0.550014[-4.09]***0.0397352[4.77]***

0.4663673[3.51]***-0.540107[-4.09]***0.0394478[4.82]***

0.66298924[6.52]***-0.34673301[-3.55]***0.02706629[4.71]***

0.302805

0.166393

0.157860.170954

0.164139

0.150979

0.2934957

0.2710636

0.13983823[2.38]**-3.03537[-2.59]***0.807338[14.86]***[1.69]*-2.96406[-2.95]***0.829522[1.60]-2.91711[-2.91]***0.815442[1.74]*-2.88378[-2.89]***0.821809[1.67]*-3.12614[-3.13]***0.817469[1.54]-2.83516[-2.84]***0.813745[2.30]**-3.038186[-2.60]***0.8085588[2.16]**-2.93755898[-2.56]**0.7554132[19.53]***[18.98]***[19.44]***[19.34]***[19.24]***[14.86]***[13.97]***[1.48]-2.8057895[-2.92]***0.74316905[17.99]***0.501537

0.517386

0.514925

0.531867

0.543037

0.539959

0.5107622

0.5758613

0.46721941[1.58][1.68]*[1.67]*[1.73]*[1.77]*[1.76]*[1.60][1.84]*[1.58]-0.01912-0.11598-0.12272-0.11594-0.09215-0.11156-0.0235568-0.03712566-0.06610321[-0.12]0.164521[0.75][-0.81]0.202051[0.95]
0.196023[0.43]
0.139798[0.90]
0.738363[1.88]*
0.359568[1.86]*
0.222675[1.06]
0.14808[0.77][-0.77]0.189353[0.90]
0.169767[0.38]
0.172893[1.11]
0.70094[1.80]*
0.339814[1.77]*
0.192997[0.93]
0.123923[0.65][-0.61]0.304542[1.44]
0.170434[0.38]
0.07049[0.45]
0.687414[1.76]*
0.345154[1.80]*
0.207517[0.99]
0.135113[0.71][-0.74]0.288369[1.36]
0.180183[0.40]
0.073439[0.47]
0.66693[1.71]*
0.357926[1.87]*
0.23585[1.13]
0.155883[0.82][-0.15]0.1646476[0.75][-0.23]0.2478887[1.15]

0.831153[2.02]**
0.300864[1.19]
0.390648[1.42]
0.173675[0.68][-0.77]0.206008[0.97]
0.218454[0.48]
0.163223[1.05]
0.712439[1.82]*
0.370197[1.92]*
0.226246[1.08]
0.157082[0.82]

0.823219[2.00]**
0.28312719[1.11]
0.36563487[1.33]
0.15872339[0.62]

0.765137[1.89]*
0.26393293[1.06]
0.34586368[1.28]
0.17179674[0.69][-0.45]0.29090398[1.43]
0.28692733[0.66]
0.13966348[0.93]
0.62289047[1.65]*
0.34096415[1.84]*
0.17958349[0.89]
0.19080812[1.04]

4.009802[6.91]***
0.285258
1200

3.293694[7.18]***
0.30371
1944

3.436364[7.47]***
0.303154
1944

3.350357[7.33]***
0.309021944

3.468989[7.58]***
0.310314
1944

3.550803[7.77]***
0.310863
1944

4.01429367[6.89]***
0.28486587
1200

4.23718686[7.40]***
0.30859085
1200

3.65649061[8.27]***
0.3568396
1944

Male(ref)Sex

FemaleAge

Age

Age square
Junior high, Highschool, etc

Education

University, Graduate
schoolDon’t know

HealthHealthy
Management andExecutives

Employment
StatusFull-time andregular (Ref).
Contracts,
temporary staffing
etc.
Self-employedStudents
Unemployed
Others
Large city
Middle city

City Size

Other city
Towns orvillages(ref)Constant
Adjusted R2
Sample sizeNote) *, **, and *** are significant at 10%, 5% and 1% levels, respectively. [ ] is a t-value.

4.2 The economic value of behavioral changes and changes in consciousness20Behavioral and consciousness changes were found to have an impact on raising and lowering the sense of well-being. In this section, we evaluate these changes in monetary terms. When evaluating the welfare of a variable inmonetary terms, we used the compensating variation (CV) and the equivalent variation (EV), where CV is thewillingness to pay (WTP) and EV expresses unwillingness to accept compensation (WTP) or willingness to accept

compensation (WTA). In general, WTP and WTA are respectively defined as follows:v( =p 0 , Q 0 , y 0 ) υ ( p1 , Q1 , y1 − WTP ) ,v(𝑝𝑝0 , 𝑄𝑄1 , 𝑦𝑦 0 ) = 𝑣𝑣(𝑝𝑝0 , 𝑄𝑄 0 , 𝑦𝑦 0 + 𝑊𝑊𝑊𝑊𝑊𝑊),

(4)
(5)

Here, v(⋅) is the indirect utility function, p is the price level, Q is a non-market good, y is income, and the superscript

numbers 0 and 1 indicate the state before and after the non-market good is consumed, respectively. We also consideredsubjective well-being (SWB) of an individual, which was assumed to be the indirect utility function, and happiness(H) and life satisfaction (LS) were used as measures of SWB. We estimated a happiness function, as follows:

Happinessi =α + β yi + γ zi + λ X i + ε i .

(6)Here, subscript i denotes individual i, Happiness i denotes individual i’s sense of well-being, y i denotes individual i’s
income, z i is a vector of evaluations such as sleep, X i is a vector of individual i’s socioeconomic attributes, and β, γ,and λ are coefficients of explanatory variables (where γ and λ are vectors). When the state before the COVID-19
outbreak is denoted by the subscript 0 and the state after the outbreak is denoted by the subscript 1, we used the well-being function equation (6), and obtained the WTP i of individual i as follows:α + β yi0 + γ zi0 + λ X i0 + ε i =+α β ( yi1 − WTP) + γ zi1 + λ X i0 + ε i .
From this, WTP i was estimated as shown by the following equation:
21

(7)

γj 1
( z ji − z 0ji ) .
j β

WTP = ( yi1 − yi0 ) + ∑(8)Here, z1 j -z0 j are the eight items of behavioral changes and changes in consciousness due to the spread of COVID19 (overall well-being throughout one’s life is excluded). Because we did not know the respondents’ incomes after

the COVID-19 outbreak, they were assumed to be the same as before the outbreak.

Based on the results in Table 6, and compared with “no change”, we considered the compensating changes perperson for each item, as shown in Table 7(a). Because the situation worsened with increased housework, childcare,and nursing care hours and total workload, the compensating variables were negative. The unit is 10,000 yen, so thedecrease represented by minus 3.54 million yen in sleeping hours seemed quite large. However, for household income,calculations were based on annual household income, so the figure was only about 10,000 yen per day. For housework,

childcare, and nursing care hours, sleeping hours, and household income, we found only negative compensatorychanges. In terms of leisure time, the amount of compensation for decrease in leisure time was much larger than thatfor increase in leisure time, indicating that the inability to go out caused considerable damage. In terms of personal

income, the compensation for increase in income was much larger than that for decrease in income. It is very

interesting that the valuations are asymmetrically done between increase in income and decrease in income. Decrease

and increase in overall job satisfaction were evaluated to be almost the same. Overall life happiness was evaluatedhigher than the other items, with a decrease of 14.31 million yen, or about 40,000 yen per day. Increase in overall life

happiness was evaluated as greater than its decrease, and the overall result was positive. However, as seen in Table

4, for many respondents, each behavioral change and change in consciousness decreased, so the compensation change

as a whole was negative. Thus, on a per capita basis, although the compensation change was positive for overall well22

being and personal income, it was negative for housework, childcare, and nursing care hours, sleeping hours, andhousehold income. We were able to confirm that these respondents experienced damage in their daily lives. The

compensation variables for working hours and total workload were statistically insignificant, so the compensation

variable was zero.

The overall compensating variables for each item in Japan are shown in Table 7(b).

We used per capita

compensating variables (in 10,000 yen) and the population from 20s to 80s (about 100 million people) allocated toeach response based on the percentage of respondents who answered the change in each item. However, there werefewer respondents for working hours, total workload, and overall job satisfaction because only respondents who were

employed answered these questions.

Overall sense of well-being throughout one’s life was minus 326 trillion yen. Individuals rated it higher when it

increased, but the number of respondents who answered that it increased was small, so it was negative overall. All

individual items were negative, with household income having the largest negative value at minus 158 trillion yen.

This was followed by overall job satisfaction, personal income, housework, childcare, and nursing care hours,

sleeping hours, and leisure time.

According to newspaper reports, in the first quarter (April to June) of fiscal 2020, GDP fell by about 40 trillion

yen. If we multiply this by 4 to derive an annualized value, we get 160 trillion yen. The annualized value of minus

326 trillion yen is the change in compensation for overall well-being, which is about double the 160 trillion yen inabsolute terms. Because the questionnaire survey asked about changes in overall sense of well-being, the answers are

expected to reflect psychological evaluations. If this is the case, then the damage caused by psychological evaluation23

is thought to be about 160 trillion yen.

If we consider that the overall sense of well-being throughout one’s life can be broken down into various items

such as working hours, then the total of each item excluding household income would be minus 323 trillion yen.

Then, because this value is almost equal to the evaluation of overall well-being, the evaluation of overall well-being

can be decomposed into the evaluation of housework, childcare, and nursing care hours, sleeping hours, leisure time,

personal income, and overall job satisfaction.

Table 7(a) Compensation change per person (10,000 yen per person, annually)
Workinghours
Decrease
Increase

Housework,
childcare andnursing carehours

Sleeping hours-354-308

Leisuretime

Personal
incomeHouseholdincome

-732
296

-599
860

-689

Total
workload

Overall job
satisfaction

a sense of wellbeing throughout
one's life

-997
982

-1,431
1,796

Overall job
satisfaction

a sense of wellbeing throughout
one's life

-144
30
-114

-408
81
-325

Note: The ref has not changed. Statistically non-significant areas are left blank.

Table 7(b) Total compensation change (unit trillion yen, annually)
Workinghours
Decrease
Increase
Total

Housework,
childcare andnursing carehours

Sleeping hours
-34

-54
-54

-34

Leisuretime

Personal
incomeHouseholdincome

-72
62
-10

-131
20
-110

-158

Total
workload-158

Note: The ref has not changed. Statistically non-significant areas are left blank.

4.3 Impact of behavioral changes and changes in consciousness on health

COVID-19 causes the following negative mental health effects: panic attacks, anxiety, and stress. This has beenshown by Qiu et al. (2020) who conducted a questionnaire survey of health effects throughout China. In thissubsection, we focus on seven items of behavioral and changes in consciousness: working hours, housework,

childcare, and nursing care hours, sleeping hours, leisure time, personal income, total workload, and overall job

satisfaction. We also examine the impact of these items on health. The estimated equation is shown as follows:

Healthi =α + β Zi + γ X i + ε i .
24(9)Here, subscript i is individual i, Health i is the health status of individual i, Z i is the seven items of behavioral
changes and changes in consciousness, X i is the vector of socioeconomic attributes of individual i, β and γ are thecoefficients of explanatory variables (γ is a vector), and ε is the error term. The seven items were treated as continuous
variables in the estimation, and were not estimated as explanatory variables at the same time, but one by one.

The estimation results are shown in Table 8. The control variables were as follows. For the seven behavioral

changes and changes in consciousness (working hours; housework, childcare, and nursing care hours; sleeping hours;

leisure time; personal income; total workload; and overall job satisfaction), higher income was associated with better

health. In terms of marital status, respondents who were married and those who were divorced were healthier thanunmarried respondents in seven behavioral changes and changes in consciousness. Women were healthier than men.

Older age was associated with decreased health because of increased risk of disease. Education and employment

status had no effect on health. City size had an effect on health. With towns or villages as a reference, seven behavioral

and consciousness changes were found to be associated with decreased health in large and mid-sized cities (excluding

mid-sized cities with personal income). One possible reason for this decrease is the prevalence of COVID-19 in urban

areas.

Among the items of behavioral changes and changes in consciousness, the coefficients for sleeping hours, personal

income, and overall job satisfaction were positive at the 1% level. In other words, an increase in these items indicatedan increase in the sense of health. This might be due to decrease in stress. The coefficients for working hours andleisure time were positive and significant at the 10% level. Increase in working hours was thought to decrease thesense of health, but in fact the opposite was true, at the 10% significant level. This might be due to the fact that the25increase in working hours might be due to the increase in remote working hours, which reduces commuting time andthus reduces the risk of infection with the COVID-19. Increase in leisure time might also decrease stress, whereas adecrease in social contact might decrease risk of infection with COVID-19.

Table 8 Estimated results: effects of behavioral change and change in consciousness on HealthWorking hours

IncomeIncome(10,000yen）

Explainer variable (current health condition): 1= not healthy, ... 5 = healthy
Housework,
childcare andSleeping
Personal
HouseholdLeisure time
nursing carehours
incomeincomehours

Total workload

0.0516

0.05480.04990.05340.05330.05190.0488[3.41]***[4.26]***[3.92]***[4.15]***[4.14]***[3.42]***[3.26]***

Unmarried(ref)MarriedMarital Status

Divorce
Bereavement

0.19920.23310.25840.25630.2422

0.208

0.197

[2.68]***[3.83]***[4.36]***[4.26]***[4.04]***[2.79]***[2.69]***

0.26890.2376

0.26140.24840.2456

0.27610.2845[1.81]*[1.95]*[2.17]**[2.04]**[2.02]**[1.85]*[1.94]*

0.34180.16280.18390.17880.16110.35620.2817[1.09][0.97][1.11][1.07][0.96][1.14][0.91]

Male(ref)Sex

Age

FemaleAge

0.14140.138

0.15340.146

0.14710.14240.1547[1.95]*[2.41]**[2.73]***[2.57]**[2.59]***[1.96]*[2.16]**-0.0308-0.0229-0.0254-0.026-0.0251-0.032-0.0283[-2.21]**[-2.18]**[-2.48]**[-2.50]**[-2.42]**[-2.29]**[-2.06]**

0.03770.03490.03020.039

0.03820.04120.0244[0.55][0.66][0.58][0.74][0.72][0.60][0.36]

Don’t know

-0.0958-0.5622-0.5745-0.5766-0.6317-0.0446-0.0263[-0.15][-1.05][-1.08][-1.07][-1.18][-0.07][-0.04]

Management and Executives-0.0611-0.1006-0.1043-0.0967-0.0968-0.0735-0.0239[-0.37][-0.61][-0.65][-0.59][-0.59][-0.44][-0.15]Contracts, temporary staffing
etc.

-0.0595-0.0622-0.0584-0.0599-0.0476-0.0633-0.0634[-0.67][-0.75][-0.71][-0.72][-0.58][-0.71][-0.72]

Self-employed-0.0743-0.1007-0.0806-0.1014-0.0588-0.0821-0.0355[-0.63][-0.70][-0.31]

Junior high, High school, etc

Education

University, Graduate school

Full-time and regular (Ref).

Employment
Status[-0.88][-0.71][-0.89][-0.51]Students

0.12930.0283

0.09690.0953[0.54][0.12][0.41][0.40]

Unemployed-0.0764-0.0763-0.0653-0.0911[-1.05]

Others
Large city
Middle city
City Size

Other city[-0.88][-0.89][-0.76]0.44250.42950.44490.41750.41970.43970.4014[2.03]**[2.04]**[2.14]**[1.98]**[1.99]**[2.01]**[1.86]*-0.2269-0.1728-0.1773-0.1723-0.1673-0.2279-0.234[-1.68]*[-1.67]*[-1.74]*[-1.67]*[-1.62][-1.68]*[-1.75]*-0.3363-0.2227-0.2254-0.2278-0.2188-0.3422-0.34[-2.30]**[-1.98]**[-2.03]**[-2.03]**[-1.95]*[-2.34]**[-2.36]**-0.0935-0.0601-0.0653-0.0644-0.0567-0.0975-0.0843[-0.69][-0.59][-0.64][-0.63][-0.55][-0.72][-0.63]0.075

0.05920.312

0.06390.10290.019

0.2586[1.85]*[1.31][6.90]***[1.86]*[2.58]***[0.47][5.70]***

Towns or villages(ref)Items of behavioral change and consciousness
changeConstant
Ajusted R2

3.09352.96872.245

2.96772.886

3.24732.5816[14.89]***[15.02]***[11.88]***[17.46]***[16.78]***[15.67]***[12.05]***0.0346

0.05710.034126

0.03550.03710.03150.0572Sample size

1200

1944

1944

1944

1944

1200

1200

Note) *, **, and *** are significant at 10%, 5% and 1% levels, respectively. [ ] is a t-value.

Because the mortality rate of COVID-19 is higher among older adults, this population is likely to be more

susceptible to health effects. Therefore, we examined those aged 65 years or older and the crossover effects of theseven items of behavioral and consciousness changes. Table 9 shows only the results for age, those aged 65 years orolder, and crossover effects. The results for the other explanatory variables were almost the same as in Table 8, sothey are omitted in Table 9. The coefficient of age was negative and significant, even when the crossover effect asconsidered, and the crossover effect between those aged 65 years or older and each behavior change item was positiveand significant at the 1% level. Take, for example, the case of sleep duration, as sleeping hours increased, sense ofwell-being increased in the group aged 65 years or older. A similar interpretation was possible for leisure time,

personal income, and overall job satisfaction. However, when the total workload, housework, childcare, nursing care

hours, and working hours increased, the sense of well-being in those aged 65 years or older increased, even though

in the case where stress was expected, and the sense of well-being decreased. This finding might be due to the factthat people aged 65 years or older refrained from social contact due to the COVID-19 outbreak, thereby reducing therisk of infection, which might in turn have increased the sense of well-being among people in this age group.

Kimura et al. (2020) used data from the Japan Gerontological Evaluation Study (JAGES) to show that contact

with others and social activities can cause dementia and functional impairment among older adults amid the COVID-

19 pandemic. To prevent these problems, the necessity of practicing social distancing is advocated. Similarly, in thepresent study, contact with others possibly reduced sense of well-being, suggesting that social distancing is aneffective way to maintain well-being while leading a normal life.
27

Table 9 Crossover effects of seven items among respondents aged 65 years or older
Explainer variable (current health condition): 1= not healthy, ... 5 = healthy

Working
hours

Age

Age

-0.0468[-2.99]***Behavioral change items

Behavioral
change items

Housework,
childcare andnursing carehours

-0.0463[-3.62]***

0.0663[1.63]

Sleepinghours-0.0464[-3.70]***0.056[1.24]

0.3011[6.65]***

Leisuretime-0.0438[-3.48]***0.0519[1.50]

Personal
incomeTotal workload-0.0481[-3.81]***-0.0475[-3.02]***0.0881[2.20]**Overall job
satisfaction-0.0444[-2.85]***0.0111[0.27]

0.2473[5.43]***

Under 65* Behavioral Change
Items (Ref)Over 65 years old * Behavioral
change items

0.1024[2.21]**

Constant

0.0862[3.20]***

3.2183[14.97]***0.0797[2.90]***

3.1423[15.36]***0.065[2.51]**

2.4264

3.1286[12.21]***[17.24]***0.093[3.17]***

0.0981[2.13]**

3.0872[16.88]***0.0962[2.19]**

3.3674[15.70]***2.7175[12.20]***Adjusted R2

0.03720.03920.06070.0381

0.0416

0.03430.0602

Sample size

1200

1944

1944

1944

1944

1200

1200

Note) *, **, and *** are significant at 10%, 5% and 1% levels, respectively. [ ] is a t-value.

5. COVID-19 and basic income
As a result of imposed economic restraints during the COVID-19 pandemic, economic activities have been greatly

reduced, and economic deprivation has worsened, especially among workers in the restaurant, travel, andtransportation industries, as well as among those who are economically vulnerable, such as non-regular workers. In

the midst of this economic crisis, the Japanese government provided a special flat-rate benefit to all citizens who

wished to receive it in FY2020. This special flat-rate benefit was a basic income type of social security policy in thesense that there was no eligibility criteria and all citizens could receive it. Such a fixed amount of payment for all

citizens was previously provided in 2009 as an emergency economic measure against the Lehman shock, ranging

from 12,000 yen to 20,000 yen per person (for those aged over 65 years and those under 18 years), but the highamount of 100,000 yen per person in FY2020 was extremely unusual. This special flat-rate benefit was important inpredicting the feasibility of a basic income and its effects, and was thought to have raised social interest in a basic

income.
28

In this context, this survey included a question about basic income. The question was, “The system in which thegovernment provides a certain amount of income to all citizens is known as basic income. What do you think about

this system?” The survey asked respondents to answer according to a 5-point scale (where 1 = they are against and 5= they are in favor). The results of the survey are shown in Figure 2. For reference, the results of a survey conducted

in 2009 in the Tokyo metropolitan area and the Kinki region (sample size: 2,389) are also shown (see Itaba and Shiozu(2011)).

According to the results of the present survey, 39.6% of the respondents were in favor of a basic income, whereas

18.3% were against it. However, the percentage of “undecided” respondents was high at 42.2%. These results mightreflect the fact that when the question was asked, it was difficult to determine whether the introduction of a basic

income would result in the abolition of existing social security systems, such as welfare, or whether it would resultin the use of financial resources. Compared with the 2009 survey, in which the same questions were asked, thepercentage in favor of a basic income was slightly higher. It is difficult to confirm whether the reason for the lower

percentage in the present study was due to the different regions surveyed or the effects of COVID-19.

We compared the results of an overseas survey with those of the present study in Japan as follows. The results of

the overseas survey are shown in Table 10, which was conducted by the European Social Survey (ESS) in 2016. That

survey asked participants if they were in favor of or against a basic income, with no “undecided” option. In addition,

the question clearly stated that the current social security system will be abolished if a basic income is introduced.

The differences between the questionnaires in these countries were analyzed in detail by Vlandas (2021) and Lee(2018). In general, both researchers noted that in countries with poor current unemployment insurance, a larger
29

percentage of participants were in favor of the introduction of a basic income.

Our analysis of the comparison of the results of the present survey with those of the other countries in the 2016

survey is as follows. Unlike the 2016 survey, the present survey included an “undecided” option, which raises thequestion of how to handle this option. If all the respondents who answered “undecided” were in favor of theintroduction of a basic income, then 81.8% would be in favor; in the opposite scenario, 39.6% would be opposed. In

the former case, the percentage would exceed Lithuania’s 80.61%. In the latter case, it falls between Germany andSweden. If we assume that half of the respondents who answered “undecided” are in favor, the percentage of thosein favor would be 60.7%, which is the second highest after Slovenia. Vlandas (2021) and Lee (2018), mentioned

above, point out the relationship between conditions of current unemployment insurance benefits and other factors

in favor of the introduction of a basic income. International comparisons of unemployment insurance are difficult;however, as an example, we can consider the replacement rate (the ratio of take-home pay when unemployed to take-home pay when employed). The replacement rate in Japan is close to that in Belgium (see Iwai (2013)). Thus, theratio in favor of the introduction of a basic income is relatively high in Japan.

Figure 2 Preference for a basic income

30

2009 survey

10.8

2020 survey

13.6
0%

18.8

36.1

26.0
20%

22.8

42.2
40%

11.8

60%

They are in favorrather agree.

I'd rather disagree

They are against

11.5

6.5

80%

100%

I can't say either way

Source: The results of the present survey and Itaba and Shiozu (2011).

Table 10 Preference for the introduction of a basic income in European countries (%)
In favor of
Againstintroduction introduction
Lithuania
80.61
19.39
Hungary
69.51
39.49
Slovenia
65.03
34.97
Belgium
58.62
41.38
Italy
58.58
41.42
Poland58.51
41.49
Portugal
58.39
41.61
Ireland56.61
43.39
Finland55.74
44.26
Czech Republic
52.2
47.8
UK
50.83
49.17
Netherlands
49.8
50.2
Spain
49.56
50.44
France48.83
51.17
Estonia
46.72
53.28
Iceland
46.02
53.98
Austria
45.96
54.04
Germany
45.75
54.25
Sweden37.55
62.45
Switzerland
34.74
65.26
Norwegian
33.67
66.33
Source: Based on Figure 1 in Lee (2018).

We examined the impact of seven items of behavioral changes and changes in consciousness (working hours,

housework, childcare, and nursing care hours, sleeping hours, leisure time, personal income, total workload, andoverall job satisfaction) on attitudes toward basic income policies. The estimated equation is given by:

BI i =
α + β Zi + γ X i + ε i .
31

(10)

Here, BI i is support for basic income (the higher the value, the greater the support), subscript i is individual i, Z i
is the seven items of behavioral and consciousness changes, X i is the vector of socioeconomic attributes of individual
i, β and γ (vectors) are the coefficients of explanatory variables, and ε is the error term. The seven items were obtainedusing the 5-point Likert method, but were treated as continuous variables in the estimation and were not estimated as

explanatory variables at the same time, but one at a time.

The estimation results are as shown in Table 9. When the items of behavioral changes and changes in consciousness

were not used as explanatory variables, those who were low-income earners, male sex, and younger were in favor ofa basic income. The low-income earners expected that basic income would improve their economic status. The

tendency for men to be more in favor of a basic income than women was also found by Itaba and Shiozu (2011). We

expected that women would be more likely to be in favor of a basic income because they often take on more family

caregiving responsibilities, and/or because they are in a precarious position in the labor market due to marriage and/or

childbirth. The risk of unemployment in the labor market is likely to be lower among men than among women.

However, those who are dissatisfied with the requirements for receiving unemployment insurance and the amount ofbenefits in the event of unemployment, are more likely to be in favor of a basic income. Because the latter situation

prevails in the present survey, men were considered more likely to be in favor of a basic income.

One reason why those respondents who were young were more likely to be in favor of a basic income is that, as in

the case of men, young people face a more difficult labor market and therefore view basic income as a way to avoid

risk. We found that marital status, education, employment status, and city size were not statistically significant. There

were also no conflicting opinions on the introduction of a basic income among these attributes.
32

When the items of behavioral changes and changes in consciousness were added to the explanatory variables andestimated, the coefficients of the male dummy become smaller and non-significant in terms of working hours, total

workload, and overall job satisfaction. Because these items are larger among men, the coefficient of the male dummy

seemed to be overestimated in the estimation equation when these items were not included as explanatory variables.

The coefficients for working hours, total workload, and overall job satisfaction were negative and significant. In otherwords, smaller values for these items indicated that the respondents were favor of a basic income. This can be

attributed to the fact that the spread of COVID-19 has reduced working hours and workloads, which has increased

the risk of unemployment and other problems, which has in turn led to support for a basic income.

We found that the coefficient for leisure time was positive and significant at 10%. Increase in personal income wasobserved to lead to a lack of support for a basic income. Increase in personal income was not shown to indicate

support for a basic income because an increase in income reduces the need to rely on a basic income. Sleeping hours

did not have a significant effect. Thus, it can be argued that the spread of COVID-19 leads to increased support forbasic income through increased risk in the labor market.

33

Table 11 Estimated results: impact on basic income adoption
Explainer variable (support state of basic income): 1 = opposite, ... , 5 = yes
No behavioral
change items
IncomeIncome(10,000yen）

Working
hours

Housework,
childcare andnursing carehours

Sleepinghours

Leisuretime

Personal
incomeTotal
workload

Overall job
satisfaction-0.0503-0.0349-0.0504-0.0503-0.0515-0.0486-0.0342-0.0341[-4.00]***[-2.41]**[-4.01]***[-3.99]***[-4.09]***[-3.87]***[-2.35]**[-2.35]**-0.0962-0.0446-0.0894-0.0963-0.0871-0.0905-0.0564-0.0583[-1.64][-0.63][-1.50][-1.64][-1.48][-1.55][-0.79][-0.82]-0.0542-0.073-0.0503-0.0543-0.0512-0.0546-0.0829-0.0946[-0.67]

Unmarried(ref)MarriedMaritalStatusDivorce
Bereavement
Male(ref)

Sex

Age

[-0.46][-0.51][-0.42][-0.46][-0.43][-0.46][-0.58]-0.134-0.3437-0.1303-0.1341-0.1259-0.1222-0.3583-0.333[-0.82][-1.15][-0.79][-0.82][-0.77][-0.75][-1.20][-1.12]0.1402

0.09620.13570.14020.141

0.14060.10050.1048[2.52]**[1.39][2.43]**[2.52]**[2.54]**[2.54]**[1.44][1.51]

FemaleAge-0.0167-0.0273-0.0177-0.0167-0.0178-0.0163-0.0268-0.0265[-1.64][-2.06]**[-1.72]*[-1.64][-1.75]*[-1.61][-2.00]**[-2.00]**-0.01220.0101-0.0109-0.0122-0.0109-0.0130.00970.0104[-0.24][0.15][-0.21][-0.23][-0.21][-0.25][0.15][0.16]-0.48910.3056-0.5-0.4892-0.4824-0.4266

0.19710.1932[-0.93][0.51][-0.95][-0.93][-0.92][-0.81][0.33][0.32]-0.1041-0.1708-0.1043-0.1041-0.1001-0.1094-0.1553-0.1723[-0.65][-1.07][-0.65][-0.65][-0.63][-0.69][-0.97][-1.08]-0.0523-0.0116-0.0516-0.0523-0.0514-0.0696-0.0044-0.0039[-0.65][-0.14][-0.64][-0.65][-0.64][-0.86][-0.05][-0.05]0.131

0.15010.133

0.13090.12650.08170.15470.142[1.17][1.34][1.19][1.17][1.13][0.73][1.37][1.26]-0.3349

0.1886

Junior high, Highschool, etc

Education

University, Graduate
schoolDon’t know
Management andExecutives
Full-time and regular(Ref).
Contracts, temporary
staffing etc.

Employment Status

Self-employedStudents-0.3655-0.3708-0.365-0.3861[-1.56][-1.59][-1.56][-1.65]*[-1.44]

Unemployed-0.0814-0.0789-0.0814-0.0755-0.0558[-0.93][-0.96][-0.89][-0.66]

Others

0.09930.1601

0.09580.0992

0.09460.10310.1679[0.48][0.77][0.46][0.48][0.46][0.50][0.80][0.90]

Large city

0.04820.16890.05040.04820.04450.04670.17820.1713[0.48][1.31][0.50][0.48][0.44][0.46][1.37][1.32]

Middle city

0.07730.17230.078

0.07740.0713

0.074

0.189

0.1806[0.70][1.23][0.71][0.70][0.65][0.68][1.35][1.29]

Other city

0.04730.15150.04840.04730.04110.04570.16340.1493[0.47][1.17][0.48][0.47][0.41][0.46][1.25][1.15]-0.1512-0.0299-0.00160.0605-0.134-0.0961-0.161[-3.90]***[-0.68][-0.04][1.80]*[-3.44]***[-2.46]**[-3.67]***[-0.96]City Size

Towns or villages(ref)Behavioral change items
Constant

3.64333.91583.74193.64793.4653

3.99313.75593.9403[28.06]***[20.05]***[19.15]***[19.89]***[21.23]***[24.25]***[19.33]***[19.42]***Adjusted R2

0.01530.02350.01510.01480.01650.02080.01590.022

Sample size

1944

1200

1944

1944

1944

1944

1200

1200

Note)* ,**and*** indicate significant at 10%, 5% and 1% levels, respectively. [ ] is a t-value.

34

6. Support for or disapproval of the Swedish strategy and awareness of infection prevention

6.1 Definition of the Swedish strategy

According to Kamerlin and Kasson (2020), the Swedish strategy is a strategy to prevent the spread of infection by

ensuring social distancing without lockdown. It does not force cafes and restaurants to refrain from operating, butthey are required to ensure social distancing on their premises. The Swedish measures are not necessarily aimed atachieving herd immunity, but rather at isolating older adults while maintaining social distancing to avoid collapse of

the health care system. The Swedish strategy is based on the assumption that it will take time to achieve herd

immunity, and that long-term sustainable countermeasures against infectious diseases can be taken without stopping

economic activities.

The Swedish strategy is a way of thinking that prioritizes living a normal life as much as possible without

requesting excessive self-restraint and taking only appropriate countermeasures against infection. Thus, it can beconcluded that it has the potential to increase the sense of well-being by reducing stress and improving quality of life.

In the present study, we confirmed whether or not this hypothesis was actually true, and we clarified the factors thatcause differences in attitudes toward preventing the spread of COVID-19 between those who support the Swedish

strategy and those who do not. This clarification helps us to understand how individuals perceive COVID-19

differently and is expected to provide important information for considering measures to increase the effectivenessof infection control.

First, we considered the ratio of support for and disapproval of the Swedish strategy, as shown in Figure 3. The

ratio of those who did not support the Swedish strategy was 25.9%, including “do not support at all” and “do not
35

support much,” whereas the ratio of those who did support the Swedish strategy was 20.1%, including “support tosome extent” and “support strongly.”

Figure 3 Distribution of support for and disapproval of the Swedish strategy
45

39.7

40
35
30
25
20
15

11.7

16.1

14.2

10

4

5
0

do not support at all

do not support much I can't say either way.

support to some

support strongly

extentIn the following subsections, we analyze the characteristics of attributes, differences in economic environment,

differences in satisfaction and happiness, differences in information access routes, differences in attitudes toward

infection control, and differences in reasons for behavioral changes by support or disapproval of the Swedish strategy.

We also clarify the factors that determine people’s behavioral changes.

6.1 Characteristics of personal attributes by support or disapproval of the Swedish strategy

We considered the level of risk aversion according to support for and disapproval of the Swedish strategy (Figure4). We found that there were relatively more people who supported the Swedish strategy in the relatively low risk

aversion group (1 to 5), and relatively more people who did not support the Swedish strategy in the relatively high36

risk aversion group (6 to 9). This result suggests that people who are able to accept some degree of risk tend to support

the Swedish strategy.

Next, we compared health status between those who supported and disapproved of the Swedish strategy (Figure5). The figure shows that many people who support the Swedish strategy are not necessarily, suggesting that the

hypothesis that people support the Swedish strategy because they are confident in their health is not valid.

Figure 4 Risk aversion

Figure 5 Health condition
40.0

30.0

30.0

20.0

20.0

10.0
0.0

10.0
1

2

3

4

5

Don't support

6

7

Support8

9

0.0

I'm not
healthy.

I'm rather not
healthy.

Ordinary

Don't support

Rather healthy

HealthySupport6.2 Differences in changes in the economic environment by support for and disapproval of the Swedish strategy

Figure 6 shows the change in working hours according to support for and disapproval of the Swedish strategy.

The percentage of respondents whose working hours had decreased significantly was higher in the group that

supported the Swedish strategy, but the difference between the percentage of respondents whose working hours hadedecreased and of those whose working hours had increased was not large in the group that did not support the Swedish

strategy. Figures 7, 8, and 9 show that differences in personal income, workload, and job satisfaction were notsignificantly different between supporters and non-supporters, indicating that differences in the economic

environment determined neither support nor non-support for the Swedish strategy. However, the percentage of

respondents who answered that there was no change was lower in the support group, suggesting that the percentage
37

of those affected by the economic environment was slightly higher among supporters of the Swedish strategy.

38

Figure 6 Working hours

Figure 7 Personal income

80.0

80.0

60.0

60.0

40.0

40.0

20.0

20.0

0.0

Decreased a Decreased a No change
lotlittleDon't support

0.0

Increased a Increased alittlelot

lot

Support

80.0

60.0

60.0

40.0

40.0

20.0

20.0

lot

littleDon't support

littlelot

SupportFigure 9 Job satisfaction

80.0

Decreased a Decreased a No change

littleDon't support

Figure 8 Amount of work

0.0

Decreased a Decreased a No change Increased a Increased a

Increased a Increased alittle0.0

Decreased a Decreased a No change
lot

lot

littleDon't support

SupportIncreased a

Increased alittlelot

Support6.3 Differences in satisfaction and happiness by support for and disapproval of the Swedish Strategy

Figure 10 shows the differences in change in life satisfaction by support for and disapproval of the Swedish strategy.

This figure shows that there is no significant difference in change in life satisfaction between the groups that supportand do not support the Swedish strategy, and that the percentage of respondents who indicated that their satisfaction

had increased was slightly higher in the support group.

Figure 11 shows the difference in sense of well-being by support for and disapproval of the Swedish strategy. This

figure shows that the percentage of people with relatively low happiness (1–5) was smaller in the group that supported

the Swedish strategy. The percentage of people with the highest sense of well-being was also higher in the group that

supported the Swedish strategy. These results indicate that the group that supported the Swedish strategy had, on
39

average, a higher sense of well-being. This finding suggest that, among supporters of the Swedish strategy, fear of

COVID-19 infection is lower and the risk of the COVID-19 pandemic is judged relatively calmly.

40

Figure 10 Life satisfaction

Figure 11 Happiness
25.0

80.0

20.0

60.0

15.0

40.0

10.0

20.0
0.0

5.0
Decreased a Decreased a No change
lotlittleDon't support

Increased a Increased alittle

0.0

lot

1

2

3

4

5

6

Don't support

Support7

8

9 10 11

Support

6.4 Information media type by support for or disapproval of the Swedish strategy

Figure 12 shows information media viewing time by support for or disapproval of the Swedish strategy. In judging

whether to support the Swedish strategy or not, we found that information about COVID-19 infection and about

prevention of the spread of infection had a great influence. The results showed that there was no statisticallysignificant difference in the amount of time spent watching TV and radio and the amount of time spent on Internet

news sites. However, the amount of time spent reading newspapers and magazines, and on social networking sites(SNS) was significantly longer in the group that supported the Swedish strategy, indicating that this group obtained

information from more diverse news sources.

The abovementioned results suggest that support for the Swedish strategy tends to become stronger when people

are able to make decisions from a broader perspective by obtaining information from a variety of news sources andfrom different perspectives. In particular, TV and other media were judged to be biased toward infection prevention

rather than as balanced reporting between strengthening immunity and infection prevention actions. Also, it can beinterpreted that obtaining information on a comprehensive infection prevention system including the immune system

41

through SNS and other media will result in a tendency to support the Swedish strategy.

42

Figure 12 Media viewing time by support for and disapproval of the Swedish strategy
2.5

2.3 2.27

2.22 2.2

2
1.5

1.22

1.45

1.11

1.45

1
0.5
0

TV, Radio

Newspapers and Internet News Sites
magazines
Don't support

SNS (Twitter,
Facebook, etc.)

SupportNote: Newspapers, magazines, and social networking sites (SNS) are significant at the 1% level.

6.5 Judgment in support of stay-at-home and lockdown by support for or disapproval of the Swedish strategy

The negative position of Swedish strategy supporters in terms of the effectiveness of stay-at-home orders andlockdown is illustrated in Figures 13 and 14. The group that supported the Swedish strategy was less supportive of

stay-at-home orders than the group that did not support it. Figure 14 shows that the percentage of respondents who

did not support lockdown at all was higher in the group that supported the Swedish strategy than in the group that

did not support the Swedish strategy. These results can be interpreted as a balanced judgment informed by a varietyof news sources, allowing for relatively accurate assessment of the costs of stay-at-home orders and lockdown, andthe decision to not support excessive infection control.

Figure 13 Stay-at-home orders

Figure 14 Lockdown

43

50.0

50.0

40.0

40.0

30.0

30.0

20.0

20.0

10.0

10.0

0.0

That's verytrue.

Somey true

I can't say

It doesn't

It doesn't

either way

apply very

apply at all

0.0

That's verytrue.

Somey true

I can't say

It doesn't

It doesn't

either way

apply very

apply at all

well

well

Don't support

Don't support

SupportSupport

6.6 Reasons for wearing masks by support for or disapproval of the Swedish strategy

Figure 15 shows the reasons for wearing a mask by support or disapproval of the Swedish strategy. The percentageof respondents who wore masks for medical reasons was lower in the group that supported the Swedish strategy at

the 1% level of significance. The proportion of respondents who abided by organizational peer pressure and the ruleof human egalitarianism was significantly higher in the group that supported the Swedish strategy at the 1% level ofsignificance. The ratio of those who succumbed to social pressure was significantly higher in the group that supported

the Swedish strategy at the 5% of level significance. However, among those who followed the rule of return, thedifference between the two groups was not significant.

Our interpretation of these results is that, in the group that supported the Swedish strategy, the decision-making

process regarding the disadvantages of wearing masks was influenced by various sources. Also, and the proportionof those who wore masks for medical reasons was smaller. In practice, the current situation does not allow for people

to refuse to wear masks, which indicates that even those who do not wear masks for medical reasons continue to wear

them for reasons such as abiding by organizational peer pressure, the rule of human egalitarianism, and social pressure.

44

Figure 15 Reasons for wearing masks by support for and disapproval of the Sweden strategy
4
3
2
1
0

Medical reasons

Organizational tuning

Social pressures

Rules of returnpressure

The Rules of
Egalitarianism

Don't support

SupportNote: Differences in the mean for medical reasons, organizational peer pressure, and the rule of human egalitarianism
are statistically significant at the 1% level. That for social pressure is statistically significant at the 5% level.

7. Conclusion

Examination of what policies should be pursued in the event of a pandemic is an extremely important issue in theworld of the future. The disruptions and economic stagnation brought about by the COVID-19 pandemic have had aserious impact on people through various routes. The serious impact has not only been realized through the channelof fear of infection, but also through the channel of mental distress associated with isolation and economic deprivation,

leading to an increase in suicides as well as deaths from infectious diseases. To achieve an optimal balance between

the prevention of the spread of infection and economic losses, it is necessary to analyze people’s behavioral change

patterns, accurately understand the changes in social welfare and social costs associated with policies, and conduct

evidence-based policy evaluation. In addition, in a pandemic, important policy choices need to be made, such aswhether to adopt the Swedish strategy or the lockdown strategy. In this study, we proceeded with our analysis withthe aim of accumulating evidence for these important policy decisions.

The results of our analysis show that awareness of policy choices differs greatly depending on individual attributes,
45

suggesting the importance of formulating detailed policies. In the future, it will be important to evaluate policies inthe event of a pandemic from a variety of perspectives, accumulate more accurate evidence, and accumulateknowledge that will enable more precise policy decision-making.

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