山形大学大学院社会文化創造研究科社会文化システムコース紀要

第２０号（２０2３）19－26

Altruism and Health Status During the COVID-19 Pandemic
Fusae Okaniwa(Social System Program)to conduct a preliminary analysis of the relationshipbetween altruism and health status.

1. Introduction
Traditional economics assumes that individuals are
selfish and rational. However, altruism has gained
increasing prominence in the field of behavioral
economics. This study focuses on the relationshipbetween individual behavior and altruism during the
COVID-19 pandemic. The hypotheses of this study are
twofold. First, individuals with higher levels of altruism
may be more willing to exert effort or make sacrifices
to take socially better actions or those deemed desirable(Lagarde and Blaauw, 2014). Thus, individuals
with higher levels of altruism are speculated to have
implemented more infection-prevention behaviors
during the COVID-19 pandemic. Second, previous
studies have indicated that altruistic behaviors, suchas volunteering, can lead to better health (Schwartz
et al., 2003; Borgonovi, 2008). However, individuals
with high levels of altruism, such as nurses, tend tosacrifice their mental health to support others (Sasaki etal., 2016; Dill et al., 2016). It is thus hypothesized that
individuals with higher levels of altruism who caredfor others during the COVID-19 pandemic may haveexperienced a negative impact on their health.
In public economics, infection-prevention measures
are considered public goods, similar to police and fireservices. Public goods are non-excludable, meaningthat it is not possible to provide services only to those
who have paid for them. Consequently, when someone
bears the cost of providing a public good, there is afree-rider problem, because those who have not paid
can still benefit from it. Therefore, if the supply ofsuch public goods is left to the market, a problem mayarise in which many individuals attempt to free-ride,
resulting in significant undersupply (market failure).
During the COVID-19 pandemic, individuals with lowlevels of altruism who did not take infection-prevention
measures may have become free riders, potentiallyreducing the utility of individuals with higher levels of
altruism who did take more preventive measures. This
study focuses on health status as an aspect of utility
and uses the results of an original questionnaire survey

2. Methods－19－This study utilizes the results of the "Survey on
Individual Behavior and Preferences during the
COVID-19 Pandemic" conducted by Okaniwa andYoshida (2020). This survey aimed to understand therelationship between individual preferences and changes
in lifestyle and behavior before and after the spread of
COVID-19. The survey covered a wide range of topics
related to people’s behavior during the pandemic,
including the use of the “Go To Travel” campaign, theuse of the 100,000 yen cash handout, measures taken toprevent infection, preferred sources of information, andindividual preferences such as altruism, risk aversion,and time preference. The survey was conducted online
through an Internet survey company and took placeover a two-day period from December 18 to 19, 2020.
The survey targeted 1,036 respondents selected basedon the population structure of the national census. The
survey design and implementation were conducted inaccordance with the regulations of the Ethics Review
Committee of the Graduate School of Economics,
Tohoku University, and were approved in December
2020. The survey was conducted as part of theAdvanced Graduate Program for Future Medicine and
Health Care at Tohoku University.
The survey was conducted during the third wave of
the pandemic in Japan, when the number of domestic
cases increased (Figure 1). On December 3, Osaka
Prefecture declared a “state of medical emergency”
owing to the surge in severely ill patients; the decision
to dispatch the Self-Defense Forces to Hokkaido,
where the medical system was under severe strain,
was made on December 8th, rendering the domestic
situation tense. On December 11, the US Food and Drug
Administration issued an emergency authorization for
the vaccine, and mass vaccinations began in earnest in
Europe and the United States. It is important to notethat these social circumstances significantly influenced
the survey results.

Altruism and Health Status During the COVID-19 Pandemic（Fusae Okaniwa）（Con�irmed cases）
8,000

7,000
6,000

5,000
4,000

3,000
2,000

1,000

0

2020/4/1

2020/5/1

2020/6/1

2020/7/1

2020/8/1

2020/9/1 2020/10/1 2020/11/1 2020/12/1

Source: Prepared by the author based on the Ministry of Health, Labour and Welfare (2023).

2021/1/1

2021/2/1 2021/3/1

Figure 1: Number of New Positive Cases in Japan (April 2020–March 2021)

Ordinary least squares (OLS) and ordered probit
models were used to statistically verify that individuals
with high levels of altruism were more likely to (1)practice infection-prevention behaviors and (2) havepotentially worse health conditions or lifestyles during
the pandemic.

3. Data
3.1 Altruism
Altruism refers to a concern for the interest andhappiness of others over one’s own. There are two
incentives that act when people engage in altruistic
behavior (Margolis, 1982). First, purely altruistic
behavior is motivated by the innate desire to experience
joy from others' happiness. Second, altruistic behavior
is driven by selfish motives such as expecting future
rewards or anticipating an increase in one's evaluationby acting on behalf of others. It is difficult to strictly
distinguish between these motivations, so this study
focuses on "altruistic behavior" that encompasses both
motives. The analysis was based on the premise that
individual preferences did not change over time.
Altruism was measured based on ten questions
derived from Rushton et al. (1981) and Oda et al. (2013)(Table 1). Each question is answered on a 5-point scale(1 = never, 2 = once, 3 = a few times, 4 = often, and 5= very often). The numerical values of the responses
were directly used to calculate the total score for the ten
questions (hereafter, the "total altruism score"), whichranged from a minimum of 10 points to a maximumof 50 points. A higher total altruism score indicates agreater tendency to engage in altruistic behavior.

Table 1 shows the descriptive statistics for eachquestion (A1–A10) related to the total altruism scores.
Although all questions were collected to measure
altruism, slight differences in the nature of the questions
resulted in slight variations in the average response
values. For example, for question A3, “I have opened
and closed the door for a stranger when I was near
the elevator button," the average value was 3.39, andmany participants responded that they had performed
this action several times. However, for question A5, “I
have lent something to a stranger," the average value
was low, at 1.58, and more than half of the respondents
answered "never.”The correlations among the altruism-related
questionnaire items are presented in Table 2. The
correlation coefficients between each item (A1 to
A10) range from 0.15 to 0.47, indicating no strong
relationships between them. However, correlations
between the total altruism score and each item (A1to A10) reveal high values ranging from 0.55 to 0.72.
Altruistic behaviors exhibited various levels andcharacteristics, and the mean values differed between
the individual items. However, note that the total
altruism score could capture an individual's behavioral
tendencies as an indicator that integrates these various
factors. Therefore, the total score was chosen as anindicator of altruism rather than relying on specificquestionnaire items.
3.2 Infection-Prevention Measures
Table 3 shows the data on infection-prevention
measures. The questionnaire included eight items (B1-－20－

Altruism and Health Status During the COVID-19 Pandemic（Fusae Okaniwa）
Table 1. Altruism Questionnaire and Statistics
In your daily life, please choose one applicable answer from
"1. never" to "5. very often".
A1 I have given directions to a stranger.
A2 I have donated money.
I have opened and closed the door for a stranger when I
A3
was near the elevator button.
I have told a clerk the correct amount of money whenA4
the clerk gave me extra change by mistake.
A5 I have lent something to a stranger.
I have given strangers information or knowledge that
A6
only I know.
A7 I have done volunteer work.
I have helped an unacquainted person with disabilities
A8
or elderly people to cross a pedestrian crossing.
I have given up my seat to a handicapped or elderly
A9person on a train or bus.
I have helped a stranger's bicycle to get up when it was
A10
on the ground.
Total Altruism Score

Obs

Mean

S.D.

1,036

3.39

1.10

1,036

1.58

1,036
1,036
1,036
1,036
1,036

1,036
1,036
1,036

2.87
2.47

1.00
1.07

2.38

Min

1

2.03

5

1.13

1

5

1

5

1

1.07

1,036 23.06
Source: Prepared by the author based on Okaniwa and Yoshida (2020).

5

1

1.12

1.90

5

1

1.06

2.70

5

1

1.20

1.76

5
5

1.18
0.94

1.98

1
1

Max

5

1

6.77

5

10

48

Table 2. Correlation Matrix of Altruism Questionnaire
A1
A2
A3
A4
A5
A6
A7
A8
A9
A10

A1
1.00
0.42
0.46
0.35
0.24
0.35
0.23
0.34
0.44
0.30

A2

1.00
0.28
0.28
0.21
0.26
0.37
0.27
0.35
0.27

A3

A4

A5

A6

A7

1.00
0.39
0.15
0.28
0.18
0.20
0.47
0.28

1.00
0.28
0.33
0.23
0.32
0.42
0.32

1.00
0.43
0.24
0.31
0.27
0.37

1.00
0.26
0.29
0.35
0.29

1.00
0.39
0.30
0.28

A8

A9

A10 Total Altruim Score Altruism9
0.66
0.56
0.60
0.48
0.59
0.47
0.64
0.52
0.55
0.44
0.62
0.50
0.57
0.43
1.00
0.63
0.52
0.42 1.00
0.72
0.63
0.41 0.45 1.00
0.64
0.53

Source: Prepared by the author based on Okaniwa and Yoshida (2020).
Note: Altruism9 examines the correlation between the total scores of the other nine items and eliminates theinfluence of self-correlation. For example, the Altruism9 score for A1 was 0.56, indicating a correlation
coefficient of 0.56 between the total scores of items A2–A10 and A1.

Table 3: Questionnaires and Statistics about Infection-Prevention-Measures Implemented

Which of the following measures have you implemented
Obs Meanto prevent COVID-19 infection? (Multiple answers)
B1 Always wear a mask when going out.
1,036 0.93
B2 Wash hands and sanitize hands.
1,036 0.87
B3 Practice good cough etiquette.
1,036 0.64
B4 Avoid crowds and crowded places.
1,036 0.66
B5 Keep a distance of at least 2 meters from people.
1,036 0.31
B6 Use indoor ventilation.
1,036 0.44
B7 Use a contact-confirming application.
1,036 0.15
B8 Avoid going outside.
1,036 0.50
Number of Infection Prevention Measures Implemented 1,036 4.50
Source: Prepared by the author based on Okaniwa and Yoshida (2020).

－21－

S.D.

0.25
0.34
0.48
0.47
0.46
0.50
0.36
0.50
1.96

Min

0
0
0
0
0
0
0
0
0

Max

1
1
1
1
1
1
1
1
8

Altruism and Health Status During the COVID-19 Pandemic（Fusae Okaniwa）
Table 4. Descriptive Statistics by Total Altruism Score
Obs
Mean
Min
S.D.
Number of Infection Prevention Measures Implemented
0-50 1,036 4.50
1.96
0Total Altruism Score
0-19
311 4.11
1.99
0
20-29
562 4.54
0
1.87
30-39
149 5.05
1.96
0
40-50
14 6.2102.08
Better Physical Health
0-50 1,036 -0.05
0.57-3
Total Altruism Score
0-19
311 -0.04
0.53
-2
20-29
562 -0.07
0.54
-3
30-39
149 0.03
0.73
-3
40-50
-2
14 -0.29
0.61
Better Mental Health-40-50 1,036 -0.18
0.81
Total Altruism Score
0-19
311 -0.13
0.69
-3
20-29
-3
562 -0.20
0.83
30-39
149 -0.16
0.89
-4
40-50
-4
14 -0.71
1.49
Alcohol Consumption
0-50
740 2.84
0.94
1
Total Altruism Score
0.87
1
0-19
219 2.80
20-29
400 2.87
0.96
1
30-39
110 2.82
0.96
1
40-50
11 3.18
1.25
1
SNS Usage
0-50
768 3.34
0.79
1
Total Altruism Score
0-19
200 3.22
0.70
1
20-29
434 3.37
0.79
1
30-39
120 3.40
0.81
1
40-50
14 3.50
1.51
1

Max

8
8
8
8
8

4
2
2
4
0
4
4
3
4
1
5
5
5
5
5

5
5
5
5
5

Source: Prepared by the author based on Okaniwa and Yoshida (2020).

B8). Nearly 90% of the respondents reported wearing
masks and washing their hands. Additionally, morethan 60% reported avoiding crowded places. However,approximately 15% reported using contact-confirming
applications. Table 4 displays the relationship between
the altruism scores and independent variables. The
number of infection-prevention measures indicates
how many of the eight items (B1–B8 in Table 3) were
implemented. As the total altruism score increased,
the number of infection-prevention measures also
increased. This finding suggests that individuals withhigher altruism tended to implement more infectionprevention measures.
3.3 Health status
Changes in health between the present (as ofDecember 2020, the time of the survey) and theprevious year were examined. Respondents were asked
to select one of five options (1 = good to 5 = poor) for
their current health status and that of the previous year.
These numeric values were inverted (5 = good to 1 =poor) to create a variable (better physical and mental
health) that subtracts the one-year-ago health status
response from the current response, thus indicating
the change in health status over the past year. Positive

values indicate an improvement in health over the past
year, whereas negative values indicate a decline inhealth. Table 4 provides the descriptive statistics for thechanges in health. The average value for all samples
was -0.05, indicating a deterioration in physical healthover the past year. Table 4 shows the changes inmental health, with an average value of -0.18 for all
samples, indicating a greater degree of deterioration inmental health than in physical health. The group withthe highest total altruism scores had the lowest meanvalues for physical and mental health.
3.4 Lifestyle
This study examined changes in lifestyle habits
between the present (December 2020) and the previous
year with a particular focus on changes in alcohol
consumption and social networking service (SNS)usage. Participants were asked to select one of five
options ("1. increased,” "2. slightly increased,” "3. nochange,” "4. slightly decreased,” or "5. decreased ") for
their current and past behaviors. these responses werethen reversed ("5. increased,” "4. slightly increased,” "3.
no change,” "2. slightly decreased,” or "1. decreased")
to assess changes in the past year. A mean of thisvariable greater than 3 indicates an increase in alcohol－22－

Altruism and Health Status During the COVID-19 Pandemic（Fusae Okaniwa）
Table 5. Descriptive Statistics

Variable
Number of Infection Prevention Measures Implemented
Better Physical Health
Better Mental Health
Increased Alcohole Consumption Dummy (=1)
Increased SNS Using Dummy (=1)
Total Altruism Score
Male Dummy (=1)
Age
Marital Status Dummy (married=1)
University Graduate Dummy (=1)
Household Annual Income (JPY 10 thousand)Regular Employment Dummy (=1)
Non-Regular Employment Dummy (=1)
Increased Work-From-Home Dummy (=1)

Obs
1,036
1,036
1,036
740
768
1,036
1,036
1,036
1,036
1,036
841
1,036
1,036
289

Source: Prepared by the author based on Okaniwa and Yoshida (2020).

Mean S.D.
4.50 1.96-0.05 0.57
-0.18 0.81
0.16 0.37
0.31 0.46
23.06 6.77
0.48 0.50
52.47 16.81
0.64 0.48
0.44 0.50
509.5 413.6
0.31 0.46
0.19 0.40
0.39 0.49

Min Max
0
8
-3
4
-4
4
0
1
0
1
10
48
0
1
20
89
0
1
0
1
100 2,438
0
1
0
1
0
1

Table 6: Effect of Altruism on the Number of Prevention Measures Implemented

Number of Infection Prevention Measures Implemented
Coef. Std. Err.
t
P>t
Total Altruism Score
0.063
0.01
6.51 ***
Male Dummy-0.5020.14 -3.66 ***
Age
0.021
0.00
4.87 ***
Marital Status Dummy
0.284
0.14
2.06 **
University Graduate Dummy-0.3280.13 -2.45 **Household Annual Income
0.000
0.00
0.48
Regular Employment Dummy-0.2990.17 -1.74 *Non-Regular Employment Dummy -0.443
0.18 -2.52 **Constant.
2.268
0.38
6.04 ***
841
Observations
0.13
Adjusted R-squared

Note: *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively.

consumption or social media usage over the past year,
whereas a mean below 3 indicates a decrease.
The average alcohol consumption score was 2.84,
indicating a decreasing trend. However, the groupwith a total altruism score of 40 or higher had an
average score of 3.18, indicating an increase in alcohol
consumption. For SNS use, the average score for theentire sample was 3.34, indicating an increasing trend.
The higher the total altruism score, the greater the usage
time. A dummy variable, with 1 representing those who
increased their alcohol consumption or SNS usage and0 representing others, was used for the estimation.

(college degree or higher=1), annual household income,and employment status, including dummy variables forregular and non-regular employment. To control for the
impact of changes in work style on health status andlifestyle, a dummy variable for increased work fromhome as the explanatory variable was included. The
small sample size for the increased work-from-homedummy is due to the exclusion of workers who did notwork at home because of COVID-19. Table 5 presents
the descriptive statistics of the entire dataset used forthe estimation.

3.5 Other Control Variables
Other control variables were sex (male=1), age,marital status (married=1), university graduation

The impact of altruism on the number of preventive
behaviors is shown in Table 6. As the total altruism
score increased, there was a statistically significant－23－

4. Results

Altruism and Health Status During the COVID-19 Pandemic（Fusae Okaniwa）
Table 7: Effects of Altruism on Workers’ Health Status

Total Altruism Score
Male Dummy
Age
Marital Status Dummy
University Graduate DummyHousehold Annual Income
Regular Employment Dummy
Non-Regular Employment Dummy
Increased Work-From-Home Dummy
Constant.
Observations
Adjusted R-squared

Better Physical HealthCoef. Std. Err.
t
P>t
-0.0080.01 -1.67 *
0.004
0.08
0.06
0.002
0.00
0.62
-0.0520.08 -0.68
0.044
0.07
0.62
0.000
0.00 -1.23
0.112
0.10
1.10
-0.0620.11 -0.56
0.017
0.07
0.24
0.132
0.22
0.61
248
0.04

Better Mental HealthCoef. Std. Err.
t
P>t
-0.0150.01 -1.83 *-0.2400.13 -1.85 *
0.001
0.00
0.31
-0.0750.13 -0.58
0.105
0.12
0.89
0.000
0.00
0.06
0.195
0.17
1.15
-0.0340.18 -0.18
0.011
0.12
0.09
0.169
0.36
0.47
248
0.04

Note: *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively.

Table 8: Effects of Altruism on Workers’ Lifestyle

Total Altruism Score
Male Dummy
Age
Marital Status Dummy
University Graduate DummyHousehold Annual Income
Regular Employment Dummy
Non-Regular Employment Dummy
Increased Work-From-Home Dummy
Observations
Pseud R-squared

Increased Alcohol
Consumption Dummy
Coef. Std. Err.
z
P>t
0.039
0.02
2.55 **
0.268
0.26
1.05
0.010
0.01
1.14
0.288
0.24
1.19
-0.2510.21 -1.17-0.0010.00 -2.18 **
0.379
0.32
1.18
-0.1860.37 -0.51
0.390
0.21
1.82 *
214
0.10

Increased SNS using Dummy

Coef. Std. Err.
z
P>t
0.044
0.01
3.01 ***-0.5020.22 -2.23 **-0.0190.01 -2.32 **
0.171
0.24
0.72
0.215
0.21
1.01
0.000
0.00 -0.42
0.432
0.34
1.27
0.486
0.37
1.31
0.181
0.21
0.86
214
0.12

Note: *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively.

increase in the number of preventive measures taken
against infection.
Table 7 presents the estimated results of the impactof altruism on health status. Regression analysis was
conducted for changes in physical and mental health,and the sign of the partial regression coefficient for
the total altruism score was significantly negative forboth. This suggests that individuals with higher levelsof altruism experienced worsening physical and mental
health compared with the year before the outbreak. The
magnitude of the partial regression coefficient indicatesa high level of mental health deterioration.
Table 8 presents the estimated results of the impactof altruism on lifestyle changes during the one-year
period before and after the outbreak. First, regarding
changes in alcohol consumption, as the total altruism

score increased, alcohol consumption also significantly
increased. Second, with respect to SNS usage, as
the total altruism score increased, SNS usage time
significantly increased.

5. Conclusion－24－

Based on a survey conducted by Okaniwa andYoshida (2020), this study examined the relationshipbetween changes in individuals' lifestyles andbehaviors before and after the spread of the COVID-19
pandemic and their preferences. Specifically, thefocus was to statistically clarify whether individuals
with higher altruism levels implemented (1) morepreventive measures, (2) experienced deteriorating
health conditions, and (3) experienced changes in their
daily habits. The estimation method was either OLS orAltruism and Health Status During the COVID-19 Pandemic（Fusae Okaniwa）

ordered probit models.
The analysis revealed that differences in behavior
during the pandemic were influenced by individual
preferences. On the one hand, individuals with higher
levels of altruism tended to implement more infectionprevention measures. By contrast, higher levels of
altruism were associated with a decline in healthconditions, particularly mental health, before and after
the pandemic.
According to the theory of public economics,
infection-prevention measures can be considered apublic good and free riders may arise because of their
non-excludable nature. During the current COVID-19
pandemic, it is possible that individuals with low levelsof altruism who did not take preventive measuresagainst infection may have become free riders, thereby
reducing the utility of individuals with high levels of
altruism who did take preventive measures. However,this study could not establish a direct causal relationshipwith decreased utility. However, statistically significant
evidence suggests the possibility of these effects
through significant adverse health effects. Government
intervention is necessary to address the existence of
free-riders. Bonuses or other forms of relief may benecessary to reduce the burden on individuals exhibiting
high levels of altruistic behavior.

Acknowledgements
This study summarizes a presentation by Fusae
Okaniwa titled "Individual Behavior and Preferences
during the COVID-19 Pandemic" at the Symposium
on Impactful Research in Society (organized by theCenter for Research on Aging and Society, Tohoku
University) in March 2021, based on Okaniwa (2021).
I also received valuable comments from Professors
Hiroshi Yoshida, Midori Wakabayashi, Michio Yuda,
and Fengming Chen of the Faculty of Economics at
Tohoku University. I would like to express my gratitude
to them for their contributions.

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Altruism and Health Status During the COVID-19 Pandemic（Fusae Okaniwa）

Altruism and Health Status During the COVID-19 Pandemic岡庭英重

本稿の目的は，新型コロナウィルス感染拡大下における，個人の利他性と健康状態との関係を検証することにある。これまで伝統的な経済学は利己的な個人を前提としてきたが，行動経済学などの分野において，利他的な個人の存在が注目されるようになってきた。いくつかの研究では，利他性の高い個人は，社会的に求められる行動や望ましいとされる行動をとるために多少の努力や犠牲を払うことを許容する傾向があり，他人に寄り添うがあまり自身の健康状態を悪化させる可能性があることが指摘されている。本稿
では，今般の新型コロナウィルス感染拡大前後において，利他性が高い個人ほど，⑴感染予防行動を多く実施したか，⑵健康状態が悪化したか，⑶生活習慣が変化したか，について統計的に検証することに焦点を当てた。本稿では，２０２０年１２月に実施した「コロナ禍における個人の行動と選好に関するアンケート調査」（岡庭・吉田，２０２０）の調査結果を利用し，最小二乗法及び順序プロビットモデルにより，これらの関係性に関する予備的な分析を試みた。
分析の結果，⑴他人のために行動する利他性が高い個人ほど，感染予防対策を多く実施する傾向にある
ことが示された。また，感染拡大前後の比較において，⑵利他性の高い個人ほど自身の健康状態を悪化させており，特に精神的健康への悪影響がより大きいことが明らかとなった。加えて，⑶利他性の高い個人
は飲酒量やソーシャルネットワーキングサービス利用時間が増加しており，このような生活習慣の変化が健康状態の悪化に影響している可能性も示唆された。感染予防対策は公共財の一種であり，排除性を持たないことから，その費用を負担せずに便益のみを受け取るフリーライダーが生じうる。今般のコロナ禍において，感染予防対策を講じないフリーライダーの存在によって，感染予防対策を多く講じた利他性の高い人々の効用が低下した可能性がある。これらの直接的な因果関係やメカニズムの解明については，本稿の限界であり今後の研究課題である。

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