Discussion Paper No. 142
The role of prosociality and social capital in changes insubjective well-being during the COVID-19 pandemic
Yuta Kuroda, Takaki Sato and Yasumasa Matsuda

August, 2024

Data Science and Service Research
Discussion Paper

Center for Data Science and Service Research

Graduate School of Economic and Management
Tohoku University27-1 Kawauchi, Aobaku

Sendai 980-8576, JAPANThe role of prosociality and social capital in changes insubjective well-being during the COVID-19 pandemic ∗Yuta Kuroda1, Takaki Sato2, Yasumasa Matsuda3

1

Graduate School of Economics, Osaka Metropolitan University, Osaka, Japan
2

3

Faculty of Economics, Musashi University, Tokyo, Japan

Graduate School of Economics and Management, Tohoku University, Miyagi, Japan

Abstract
This study examines the role of local social capital, individual personality, and theirinteraction on changes in subjective well-being (SWB) during the COVID-19 pandemic.
Our estimations use tracking panel data based on a unique survey of approximately
25,000 people in Japan from 2019 to 2022. The results show that before the pandemic,
individuals with high prosociality had higher SWB, whereas individuals with low andmoderate levels of prosociality had no significant difference in SWB. Additionally, therelationship between individual prosociality and local social capital did not affect SWB.
However, after the pandemic, the SWB of non-prosocial individuals changed
heterogeneously depending on the level of local social capital. Non-prosocial individuals
in areas with high social capital showed little change in SWB, whereas non-prosocial
individuals in areas with low social capital showed significantly decreased SWB. These
results may be caused by the possibility of free-riding on the reduced risk of infection due∗ Corresponding author: Yuta Kuroda (kuroyu0725@gmail.com).

Ito the preventive actions of others in areas with high social capital.

JEL classifications: H00, I3, D64, I18, Z18
Keywords: Subjective well-being, Prosociality, Social capital, COVID-19, Online survey

Acknowledgments
This work was supported by JSPS KAKENHI grant number 21H03400. We appreciate
the cooperation of Macromill, Inc., in providing us with valuable data. We would also
like to thank the participants at the Symposium of Yotta Informatics at Tohoku University
in March 2023. Any errors that remain are our own. The views expressed are those of theauthors and do not necessarily reflect those of any organizations with which the authors
are affiliated.

Declaration of competing interests
The authors declare that they have no known competing financial interests or personal
relationships that could have appeared to influence the work reported in this paper.

Data and code availability statement
The raw survey data cannot be shared because they contain personal information. Allother datasets are publicly available. Processed datasets and R-code to reproduce ourresults can be provided upon request.

II

1.

Introduction

Like civic and environmental issues, pandemics are collective action problems in which
individual and social interests conflict (Fang et al., 2022). To control pandemics,
individual activities should be limited. However, the benefits of individual preventive
action spill over into society such that individually optimal behavior does not coincidewith socially optimal behavior (Farboodi et al., 2021). When considering these public
goods dilemmas, social norms, altruism, and prosociality play essential roles (Fehr &Schmidt, 1999). Communities and individuals with high levels of compliance andprosociality can solve collective action problems because they willingly strive to improvetheir social welfare. However, the problem becomes more complex when many
individuals are willing to free ride on this voluntary cooperation. Free riders enjoy greater
benefits at lower costs, whereas collaborators receive only lower benefits at higher costs.
Therefore, social welfare in the case of collective action problems must consider the
relationship between community norms and individual prosociality. However, comparedwith many theoretical studies on this issue, existing studies provide little empiricalevidence.
This study focuses on the recent COVID-19 pandemic and uses subjective well-being(SWB) to analyze social dilemmas and collective action problems. More specifically, we
focus on the relationship between individual prosociality and community social capital.
Previous studies have treated social capital as a comprehensive measure of norms andbeliefs and have found that cities with higher social capital have higher levels of
prevention (Barrios et al., 2021; Bartscher et al., 2021; Durante et al., 2021; Kuroda et al.,2022). Previous studies have revealed that individuals who are more prosocial are more
likely to comply with policies and voluntary prevention (Campos-Mercade et al., 2021;

1

Müller & Rau, 2021). Furthermore, while disasters and pandemics can severely affect
SWB (Brodeur et al., 2021), cities with high social capital and prosocial individuals areless likely to experience a decline in SWB (Laurence & Kim, 2021).
However, in cases of collective action dilemmas, the SWB of prosocial individuals isheavily affected by the norms of the community. Free riders can spoil individual efforts
if cooperative norms are not solid, and SWB may decline. Alternatively, SWB may
decrease due to frustration that others are not taking appropriate action (Hensel et al.,2022). Selfish individuals living in prosocial neighborhoods can improve their SWB by
free-riding on the preventive behaviors of others.
This study addresses the above issues using SWB survey data for approximately
25,000 people from 2019 to 2022. By using data from the same monitors surveyed
continuously before the start of the COVID-19 pandemic, we analyze changes in SWB
before and after the pandemic. Because the survey includes a measure of personality thatis a proxy for prosociality, we perform an estimation using the interaction term between
that proxy and community social capital as measured by national election turnout, thenumber of volunteer firefighters, and so on. The results show that the relationship between
individual prosociality and community social capital did not affect SWB before thepandemic. Furthermore, during the pandemic, non-prosocial individuals living in areas
with low social capital had lower SWB, while those living in areas with high social capital
had increased SWB. Additionally, the analysis of outdoor consumption behavior
suggested that areas with low social capital may have decreased the frequency of outdoor
activities during the pandemic. Therefore, in areas where the norm of cooperation is
scarce, SWB is reduced by the failure of preventive actions, whereas in other areas, SWB
is increased by free-riding on the preventive actions of others.

2Our findings contribute to the literature on social cooperation, unity, and trust whendisasters and collective action problems occur. Previous empirical studies suggest thatmutual support and strong altruism can lead to informal care (Laurence & Kim, 2021),
which may alleviate psychological distress (Aldrich & Meyer, 2015; Moore & Carpiano,2020). However, evidence on the impact of crisis on community norms and social trust
presents conflicting arguments (Wu et al., 2022). In communities where norms of mutual
support and altruism are high, these norms may be reinforced when residents help eachother overcome challenges (Rayamajhee & Bohara, 2021; Yamamura et al., 2015).
Conversely, in areas where altruism and prosociality are low, selfish behavior may occur
in times of crisis, and social norms may be undermined (Yamamura et al., 2015). Previous
studies on COVID-19 suggest that social trust may have been particularly impaired
among people with lower socioeconomic status, minorities, and people at greater risk ofinfection (Borkowska & Laurence, 2021; Lo Iacono et al., 2021; Wu et al., 2022).
However, the mechanism still needs to be clarified, and unemployment, lower incomes,
and a lack of informal care have been cited as potential possibilities. We present a possible
novel channel for the impact of collective action problems on community norms byinvestigating the effects of the relationship between individual personality andcommunity norms on SWB.
This study is also related to the social science literature on COVID-19. The empirical
study of COVID-19 began in 2020 and has reached a new phase. While the first few years
after the beginning of the pandemic focused on the impact of the policy on the level of
infection and prevention, current interest has shifted to the optimal level of prevention
policy. When considering optimal prevention levels and policy interventions, we mustevaluate cost-effectiveness. However, the difficulty of monetary valuation due to many3

externalities complicates evaluation. For example, financial losses and deaths from
infectious disease are easily observed, but mental illness and resulting suicides are easily
overlooked. Therefore, to evaluate social welfare and address effective policies, weshould use comprehensive measures, such as subjective well-being and life satisfaction(Levinson, 2012). Studies examining the early stages of the pandemic revealed that
infection decreased SWB with significant heterogeneity in the effects (Belot et al., 2021;
Laurence & Kim, 2021; Sandner et al., 2023). However, given that infectious diseases areshocks that can last for several years and that behavioral fatigue is also an issue, analyzingchanges in SWB over time is essential (Brodeur et al., 2021; Sibony, 2020). This study
provides new insights into changes in social welfare over the years by using SWB datacollected continuously since before the COVID-19 pandemic.
Compared with previous studies, this study has advantages and disadvantages. We
have access to a sample of households first interviewed in December 2019, allowing us
to control for prepandemic characteristics, an empirical challenge. Additionally, whereas
most related studies have focused on the early stages of the pandemic, we use a four-year
follow-up study to capture the medium-term impact of the pandemic. Additionally, theresults of this study have external validity because Japan is not extreme in terms of time
preference, risk preference, reciprocity, altruism, or trust (Falk et al., 2018). On the other
hand, a limitation of this study is that we cannot attribute our findings to causal effects.
Prosociality, social capital, and norms are essentially endogenous to society, culture,politics, and institutions. Since these factors cannot be observed perfectly and haveinterdependent and complex relationships, our empirical analysis must be correlational.
However, because Japan is highly homogeneous in terms of culture, religion, race, andother characteristics that are difficult to quantify, the effects of confounding factors are4relatively small compared with those in Western countries (Central Intelligence Agency,2024). Additionally, due to data availability, evidence of the mechanism is only suggestive.
Therefore, the above limitations should be considered when interpreting the findings ofthis study.
The remainder of this paper proceeds as follows. Section 2 introduces the data usedin the analysis. Section 3 presents the empirical strategy. Section 4 presents the main
results and describes a series of robustness checks, and Section 5 concludes.

2.

Data
We obtained survey data, including a wealth of personal characteristics on

approximately 25,000 people throughout Japan, from the market research company
Macromill, Inc. The survey targets the monitors of a home scanner dataset called the quick
purchase report (QPR), provided by Macromill, Inc. The QPR data are intended toprovide detailed marketing information by collecting nationally representative panel dataon consumer purchasing behavior. The QPR monitors are picked at a specific rate fromeach region, reflecting the population estimates of the Statistics Bureau of the Ministryof Internal Affairs and Communications, so they are representative. If a monitor drops
out, the sample size is ensured by adding individuals with similar characteristics. Personal
characteristics include zip code, age, income, family structure, and personality andlifestyle questionnaires. Although the original purpose of collecting QPR data ismarketing, it is also used in the economics literature because of its high representativenessand rich individual characteristics (e.g., Kuroda, 2022; Sato et al., 2024).
The QPR data contain a wealth of individual characteristics, but information on wellbeing is lacking. Therefore, we conduct an additional independent survey that target QPR

5

monitors to obtain records of their SWB and preexisting medical conditions. The surveys
were conducted from early December 2019 through 2022, with four years of data
available. 1 December 2019 was before the COVID-19 pandemic and is considered
normal in our analysis. In 2020, the first case of COVID-19 was confirmed in Japan at
the beginning of the year, and the infection was widespread in December. This was before
the development of the vaccine, and the main preventive action was refraining from goingout. In December 2021, the risk of infection decreased, but the fear of the virus persisted.
The main preventive actions changed from refraining from going outside to vaccinating
and limiting the number of people in a room. In December 2022, the pandemic wasgradually ending, although there were still cases of infection. Although government-led
infection control programs have weakened, voluntary infection prevention measures haveremained.
There were approximately 25,000 respondents to the survey, excluding individuals
who completed the survey incorrectly or did not answer the questionnaire. Approximately
14,000 individuals responded to all four surveys. Table 1 shows the descriptive statistics
from the first- and third-year surveys. The primary outcome SWB was a discrete variablefrom the response to the following question: Currently, how happy do you feel? Score thedegree of your happiness between 1 (very unhappy) and 10 (very happy). SWB is
correlated with suicide rates at the regional and individual levels (Helliwell, 2006) and isplausibly strongly correlated with other observable variables (Konow & Earley, 2008).

1

We were unable to obtain personal characteristics such as income and family structure

in 2022, so we used 2021 indicators as a proxy. We confirmed changes in individual
characteristics from 2019 to 2021 and confirmed that they did not change significantly
over the year, so this should not seriously affect the results.
6

Therefore, SWB can be a valuable measure for analyzing social welfare during apandemic.

2019 (before the pandemic)
Individual characteristics
Subjective well-being
Prosociality
Age
Male
ChildrenHousehold Size
Chronic diseases
Household income
Regional characteristics
Social capital
Population density
Average age
Ratio of elderly people
Ratio of foreigner
Average taxable income
Number of monitors

2021 (during the pandemic)

Mean Std. Dev.

Min.

Max.

Mean Std. Dev.

Min.

Max.

6.293
3.869
46.060
0.503
0.566
2.933
0.214
6.032

1.953
0.797
14.695
0.500
0.496
1.279
0.410
3.641

1
1
15
0
0
1
0
1

10
5
75
1
1
11
1
25

6.074
3.843
46.369
0.502
0.561
2.899
0.234
6.087

2.022
0.791
14.361
0.500
0.496
1.265
0.423
3.668

1
1
15
0
0
1
0
1

10
5
75
1
1
10
1
25

-1.0430.430
46.09926.1350.014
3.291

0.595 -2.641
0.493
0.000
2.542 38.624
4.455 15.101
0.000
0.012
0.584
2.120
25,017

6.236
2.238
61.63353.7050.146
9.799-1.0460.435
46.06026.0740.014
3.297

0.592 -2.641
0.495
0.000
2.538 38.624
4.461 15.101
0.012
0.000
0.585
2.088
23,7336.236
2.238
65.98260.4850.146
9.799

Table 1. Sample summary statistics

Prosociality, age, gender, marital status, the presence of children, household size, thepresence of chronic disease, household income, education level, occupation, family
structure, and housing type were based on the QPR data. The prosociality measure is afive-level discrete variable that takes a value of five if the respondent agrees that“sociability and cooperativeness are essential” and one if the respondent disagrees. The
prosociality measure changes slightly yearly for the same monitors, with anautocorrelation coefficient of approximately 0.5. Appendix Table A1 is a matrix ofchanges in prosociality from 2019 to 2021; it shows that few individuals experience7

extreme increases or decreases in prosociality. We also use several other individual
personality questions as placebo tests to check the robustness of the main results.
Chronic disease is a dummy variable that takes a value of one if diabetes,
hyperlipidemia, or hypertension is present. Household income is a variable divided intonine groups: less than 2 million JPY, 2 to 4 million JPY, 4 to 6 million JPY, 6 to 8 million
JPY, 8 to 10 million JPY, 10 to 12 million JPY, 12 to 15 million JPY, 15 to 20 million JPY,
and more than 20 million JPY. In Table 1, annual household income is converted to themedian value for each group (converted to 25 million JPY above 20 million JPY).
Education is a five-level variable: middle school, high school, junior college, college, andmaster's degree or higher. Occupations include 12 categorical variables, such as public
employees, clerical company employees, technical company employees, self-employedworkers, and part-time workers. Family structure is a five-category variable: single,
married couple, two-generation, three-generation, and other. There are six categoricalvariables for housing type: owned detached houses, rented detached houses, owned
apartments, rented apartments, company and government housing, and others.
We use social capital measures as a proxy variable for community social norms. To
measure social capital, the literature uses measures such as voting rates (Barrios et al.,2021), association densities (Giuliano & Wacziarg, 2020; Satyanath et al., 2017), and thenumber of civil offices and religious organizations (Rupasingha et al., 2006). Following
related studies, we developed a social capital measure based on voter turnout in national
elections, participation in volunteer fire companies, the number of community centers per
capita, and assembly facilities per capita at the municipal level (Kuroda et al., 2022).
Voting is the most representative example of a privately costly activity with no direct
reward that is socially beneficial. We obtained voter turnout rates for the 2012, 2014, and82017 national elections from the Ministry of Internal Affairs and Communications (MIC)and used the average of these rates. Because participation in volunteer fire brigades
contributes to the community but participation is voluntary and the rewards are small (thenational average is approximately $300 per year), participation rates can capture the normof willingness to contribute to the community. We obtained the participation rate of thefire brigade in 2021 from the Fire and Disaster Management Agency of the MIC. The
number of community centers and meeting facilities per capita is a good measure of thestrength of community bonds and the willingness to provide public goods. The 2008
Social Education Survey obtained the number of community centers and meeting
facilities for each municipality. We use the first principal component of these four factors
as the composite measure of social capital. Additionally, population density, average age,percentage of elderly individuals, percentage of foreigners, and taxable income at themunicipal level are obtained from various government statistics and used as control
variables.
Fig. 1 shows the distribution of SWB across the four survey years. The average SWB
is approximately 6, with few reporting extraordinarily high or low SWB scores. Therefore,
the results are not biased because SWB does not increase or decrease further. Appendix
Table A2 also compares the differences in summary statistics between surviving anddropout monitors. The table shows that the dropout monitors have slightly higher SWB,
confirming that those who experienced an unfortunate event, such as losing their jobs, are
not more likely to drop out of the survey. Furthermore, students and younger people are
more likely to drop out because of schooling, changing jobs, or moving, whereas people
with stable lives, such as those with children and elderly individuals, are less likely todrop out. There is almost no significant difference in the regional characteristics. The

9

correlation coefficient between community social capital and prosociality isapproximately 0.01, indicating that community norms are unrelated to individuals’
personalities.

Fig. 1. Distribution of SWB from 2019-2022
Note: The horizontal axis represents each SWB level, and the vertical axis represents thenumber of individuals at each SWB level.

We developed a proxy measure of outgoing activity to examine the mechanisms ofchanges in SWB during the pandemic. QPR monitors scan and report the barcodes of all
products purchased daily. Then, the product name and price and the location where theproduct was purchased (supermarket, convenience store, online, etc.) are given for eachproduct. Following previous studies, we consider a monitor outgoing if he or shepurchases more than one JPY in the store (Kuroda, 2023). Since the SWB survey was
conducted in December, we use the number of days of outdoor consumption activity
during November as a proxy variable for outdoor consumption activity. Notably, thismeasure does not fully capture outdoor activity and thus provides only suggestive

10

evidence.

3.

Methodology
We apply the following specification to estimate the effect of the relationship between

an individual's prosociality and local social capital on SWB:
5

5

𝑘𝑘=1
𝑘𝑘≠3

𝑘𝑘=1
𝑘𝑘≠3

𝑆𝑆𝑆𝑆𝑆𝑆𝑖𝑖𝑖𝑖𝑖𝑖 = 𝛼𝛼 + � 𝛽𝛽𝑘𝑘 𝟏𝟏�𝑃𝑃𝑃𝑃𝑖𝑖𝑖𝑖𝑖𝑖 = 𝑘𝑘� + � 𝛾𝛾𝑘𝑘 𝟏𝟏�𝑃𝑃𝑃𝑃𝑖𝑖𝑖𝑖𝑖𝑖 = 𝑘𝑘� × 𝑆𝑆𝑆𝑆𝑐𝑐𝑐𝑐+𝑆𝑆𝑆𝑆𝑐𝑐 + 𝑋𝑋𝑖𝑖 + 𝑌𝑌𝑐𝑐 + 𝑍𝑍𝑝𝑝 + 𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖(1)

where 𝑆𝑆𝑆𝑆𝑆𝑆𝑖𝑖𝑖𝑖𝑖𝑖 is the subjective well-being of individual 𝑖𝑖 living in city 𝑐𝑐 within

prefecture 𝑝𝑝. 𝑃𝑃𝑃𝑃𝑖𝑖𝑖𝑖𝑖𝑖 is a five-level prosociality index, with the baseline of the third level(moderate level of prosociality). 𝑆𝑆𝑆𝑆𝑐𝑐𝑐𝑐 is a dummy variable that takes a value of 1 if thesocial capital of the city of residence is above the median. Thus, 𝛽𝛽 captures the impact

of prosociality on SWB in low-social capital cities and 𝛾𝛾 captures this effect in highsocial capital cities. 𝑋𝑋𝑖𝑖 is a vector of individual characteristics, which includes age, age

squared, gender, having children, health risk, household size, household income,education, occupation, household composition, and housing type. 𝑌𝑌𝑐𝑐 is a vector ofregional characteristics and includes population density, average age, proportion ofelderly individuals, proportion of foreigners, and average taxable income. Prefectural
fixed effects 𝑍𝑍𝑝𝑝 also flexibly control for the effects of prefecture-level policies and the

COVID-19 infection situation.

We estimate the equation using data from each year and analyze changes in impact
during the pandemic by comparing the coefficients 𝛽𝛽 and 𝛾𝛾 for each year. The problem
with this specification is that it does not control for endogeneity caused by the unobserved

characteristics of individuals. For example, the results could be biased by specific11characteristics associated initially with individuals with high or low SWB. Therefore,
SWB in the baseline year of 2019 is included as an explanatory variable and is estimatedusing data collected after 2020. Therefore, the impact of the relationship between
individual personality and local norms on SWB is identified after controlling for theoriginal SWB.

4.

Results

4.1. The effect of prosociality on SWB before and after the pandemic
Before the main analysis, we independently present the impact of individual
prosociality and community social capital on SWB as a baseline. Table 2 shows the results
estimated without cross terms, and columns (1) through (4) present the results when datafrom 2019 to 2022, respectively. First, column (1) shows that the higher the level ofprosociality is, the higher the SWB, regardless of the pandemic. The impact of the control
variables on SWB is consistent with the findings of previous studies and intuition,
underscoring the validity of our SWB measures. Columns (2) through (4) capture theimpact of the postpandemic outbreak. The impact of prosociality on SWB is the same as
before the pandemic but with slightly greater heterogeneity due to prosociality. The
coefficient of social capital is slightly larger but not statistically significant.

12Social capital
SWB 2019
Prosociality
LowestLow
High
HighestIndividual characteristics
Age
Age-squared
Chronic diseases
Household Size
Regional characteristics
Population density

2019
(1)
0.024
(0.026)

Baseline
2020
2021
(2)
(3)
0.039
0.033
(0.025)(0.021)

2022
(4)
0.044
(0.035)-0.374** -0.525*** -0.289* -0.684***(0.162)(0.140)(0.162)(0.166)-0.074-0.097-0.036-0.080(0.059)(0.085)(0.085)(0.114)0.418*** 0.399*** 0.407*** 0.373***(0.032)(0.028)(0.022)(0.027)0.568*** 0.617*** 0.653*** 0.618***(0.033)(0.045)(0.036)(0.042)Controlling 2019 SWB
2020
2021
2022
(5)
(6)
(7)
0.058*
0.020
0.025(0.031)(0.029)(0.040)0.718*** 0.647*** 0.645***(0.008)(0.007)(0.009)-0.277-0.234 -0.430***(0.176)(0.166)(0.158)0.003-0.031-0.070(0.077)(0.088)(0.104)0.126*** 0.159*** 0.124***(0.038)(0.025)(0.029)0.200*** 0.284*** 0.290***(0.043)(0.037)(0.045)-0.112*** -0.119*** -0.118*** -0.115*** -0.031*** -0.049*** -0.041***(0.006)(0.007)(0.005)(0.009)(0.009)(0.009)(0.010)0.001*** 0.001*** 0.001*** 0.001*** 0.0004*** 0.0005*** 0.0005***(6.2×10-5) (7.22×10-5)(5.03×10-5)(9.72×10-5) (9.71×10-5) (0.0001) (0.0001)-0.196*** -0.220*** -0.205*** -0.211*** -0.083*** -0.093** -0.103***(0.032)(0.031)(0.033)(0.034)(0.029)(0.038)(0.030)0.038** 0.064*** 0.050*** 0.082***
0.014
0.001
0.038**(0.018)(0.018)(0.017)(0.018)(0.020)(0.017)(0.018)0.076(0.047)
Average age-0.010(0.030)
Ratio of elderly people
0.012(0.018)
Ratio of foreigner
0.748(0.771)
Average taxable income 0.075**(0.031)Other individual characteristi YES
Prefecture fixed effect
YES
Observations
25,017
Adjusted R-squared
0.13820.103*-0.0200.036(0.052)(0.037)(0.046)-0.077** -0.071** -0.079**(0.032)(0.031)(0.033)0.043** 0.038* 0.042**(0.018)(0.019)(0.019)-0.9570.446
0.668(1.42)(1.22)(1.40)
0.046
0.051
0.047**(0.032)(0.032)(0.020)
YES
YES
YES
YES
YES
YES
24,122
23,73319,0840.12650.12180.13130.075(0.051)-0.046(0.033)0.021(0.018)-1.15(1.60)
0.028(0.023)
YES
YES
14,1470.5179-0.0450.023(0.032)(0.064)-0.066** -0.092**(0.030)(0.037)0.033* 0.049**(0.017)(0.023)-1.81
0.222(1.13)(1.48)
0.059** 0.063***(0.022)(0.020)
YES
YES
YES
YES
14,102
14,102
0.46500.4690

Table 2. Changes in the impact of prosociality on SWB
Note: Standard errors appear in parentheses. *, **, and *** indicate statistical significanceat the 10%, 5%, and 1% levels, respectively.

13

Columns (5) through (7) present the results of estimating the impacts of various
variables on SWB from 2020-2022, with prepandemic 2019 SWB as the control variable.
According to the results, the importance of social capital has increased slightly since thepandemic started. Especially in December 2020, when infection was most severe before
the vaccine was developed, high norms of regional cooperation significantly increased
SWB. Additionally, even after controlling for prepandemic SWB, prosociality
significantly affected SWB after the pandemic. This implies that the difference in SWB
by prosociality increased after the pandemic, meaning that the prosocial group is happierand that the non-prosocial group is unhappier.

4.2. The effect of the relationship between prosociality and social capital on SWB
Table 3 reports the estimation results considering the cross terms of prosociality andsocial capital. Columns (1) through (4) present the estimated results without controllingfor prepandemic SWB. Column (1), which shows the prepandemic results, indicates that
more prosocial individuals had higher SWB. Less prosocial individuals tend to havelower SWB, although not statistically significantly, in cities with high social capital.
Columns (2) and (3) capture changes in SWB in the first and second years of the pandemic,
respectively. Similar to the results in Table 2, they show a gradual increase in differencesin SWB due to prosociality. Notably, in 2021, the SWB of non-prosocial individuals
declined in cities with low social capital, whereas the SWB of those in cities with highsocial capital increased significantly. The results suggest that even non-prosocial
individuals in high-social capital cities may benefit from low infection risk by free-riding
on the preventive behaviors of their surroundings. Additionally, individuals with highprosociality do not experience a decrease in SWB even if the community's social capital14

is low. Column (4) shows the results for 2022, when the pandemic had moderated; thedifferences due to prosociality are greater. However, in contrast to columns (1) through(3), the SWB of individuals with moderate prosociality in cities with high social capitaldeclines. Although unobserved heterogeneity is not controlled for and should beinterpreted with caution, given the situation in 2022, behavioral fatigue due to excessive
preventive behavior may be the cause (Brodeur et al., 2021; Sibony, 2020). Additionally,
the consistently low SWB of non-prosocial individuals in cities with low social capital
after the pandemic may be due to the failure to achieve a desirable level of prevention.

2019
(1)
Prosociality
LowestLow
High
HighestProsociality × High SC
LowestBaseline
2020
2021
(2)
(3)

2022
(4)

-0.083(0.422)0.039(0.157)-0.058(0.069)0.027(0.092)0.066(0.055)0.544** 0.151(0.264) (0.276)-0.162 -0.335*(0.135) (0.170)-0.036 -0.118**(0.045) (0.054)-0.031-0.108(0.059) (0.073)
0.056 0.135**(0.035) (0.062)

Individual characteristics YES
Regional characteristics
YES
Prefecture FE
YES
Observations
25,017
Adjusted R-squared
0.1382YES
YES
YES
24,1220.1264YES
YES
YES
23,7330.1219High
HighestSocial capital
SWB in 2019

Using 2019 prosociality
2020
2021
2022
(8)
(9)
(10)

-0.206-0.482 -0.581** -0.761*** -0.186-0.319 -0.463** -0.286 -0.541** -0.561**(0.235) (0.297) (0.217) (0.241)(0.226) (0.209) (0.228)(0.226) (0.245) (0.248)-0.059-0.1140.048
0.094-0.042-0.030
0.037
0.008
0.034
0.045
(0.086) (0.098) (0.106) (0.137)(0.103) (0.073) (0.141)(0.102) (0.121) (0.149)
0.452*** 0.429*** 0.425*** 0.432*** 0.140*** 0.136*** 0.148*** 0.120*** 0.158** 0.210***(0.045) (0.036) (0.032) (0.038)(0.033) (0.026) (0.042)(0.044) (0.063) (0.045)
0.574*** 0.604*** 0.669*** 0.671*** 0.200*** 0.267*** 0.308*** 0.180*** 0.191*** 0.285***(0.053) (0.071) (0.044) (0.049)(0.050) (0.053) (0.071)(0.048) (0.068) (0.053)-0.323(0.259)-0.030(0.135)-0.068(0.053)-0.011(0.064)0.068(0.051)Low

Controlling 2019 SWB
2020
2021
2022
(5)
(6)
(7)

YES
YES
YES
19,0840.1314-0.1300.253
0.095-0.034 0.556*
0.443(0.298) (0.324) (0.321)(0.302) (0.327) (0.327)
0.062
0.015
-0.208-0.025-0.098-0.152(0.125) (0.125) (0.179)(0.133) (0.106) (0.178)-0.0120.047-0.045-0.085-0.044 -0.131**(0.054) (0.042) (0.061)(0.058) (0.066) (0.053)
0.033
0.040
-0.051-0.0230.037-0.134*(0.075) (0.054) (0.080)(0.068) (0.076) (0.068)
0.034-0.0200.065
0.086** 0.037 0.120**(0.041) (0.036) (0.063)(0.042) (0.044) (0.046)
0.699*** 0.634*** 0.643*** 0.705*** 0.638*** 0.642***(0.008) (0.007) (0.009)(0.008) (0.006) (0.008)
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
20,532 17,635 14,602
19,760 17,214 15,042
0.5031 0.4518 0.4661
0.5061 0.4498 0.4606

Table 3. Changes in the impact of the relationship between prosociality and social
capital on SWB

15

Note: Standard errors appear in parentheses. *, **, and *** indicate statistical significanceat the 10%, 5%, and 1% levels, respectively.

As in Table 2, columns (5) through (7) show the estimated results controlling for
prepandemic SWB. The overall pattern is consistent with columns (2) through (4) andshow that as the pandemic continues, the SWB of less prosocial individuals in cities withlow social capital decreases. In contrast, the SWB of less prosocial individuals in highsocial capital cities is relatively high, although the statistical significance is low.
Additionally, when controlling for prepandemic SWB, we find that effects such as
behavioral fatigue are weakened.
In previous estimates, we estimated the impact of a given year's prosociality on SWB
for that year. As Appendix Table A1 shows, prosociality does not vary substantially within
individuals, but it increases or decreases to some extent. Therefore, there may be bias due
to prosocial changes during the pandemic. Columns (8) through (10) therefore restrict the
sample to monitors who have continued since 2019 and estimate them using prepandemic
measures of prosociality. These results are consistent with the main results and with thoseof previous studies showing that SWB is less likely to decline in cities with high social
capital because of better access to informal care (Laurence & Kim, 2021).

4.3. Robustness checks (placebo test)
In this section, we check the robustness of the main results using a measure different
from prosociality. We have several concerns with our measure of prosociality. Because
prosocial characteristics correlate with various other characteristics, our measure may
become a proxy variable for other characteristics. It may also capture the characteristics

16

of individuals who always respond positively or negatively to personality questions.
Therefore, we use other measures of personality instead of prosociality in the estimation.
Appendix Tables A3-5 present the estimation results when family bonding, active,
and optimistic are used as personality measures, respectively. The personality that values
family bonds was created based on a 5-level response to the item, “It is very important tokeep the family together”. The active personality was created based on the response to theitem, “I think I am someone who acts before thinking”. The active personality was createdbased on the response to the item, “I am someone who does not mind so much if I cannot
do what I expect”. The results were consistent with the intuition that the more familyoriented, active, and optimistic a person was, the higher his or her SWB. This result
confirms the validity of our questionnaire's personality measures. It was also verified thatthe impact of these characteristics is unrelated to community social capital. Therefore,
factors other than prosociality do not explain the main results.

4.4. Consideration of mechanism
To examine the mechanism by which the relationship between individual prosociality
and community social capital affects SWB, we conduct an analysis using the monitors'
outgoing behavior. This estimation uses a measure of outdoor consumption activity
instead of SWB as the explained variable. The results are shown in Table 4, with the exact
basic specifications shown in Table 2. Column (2) shows that in 2020, when the risk ofinfection was highest before the development of the vaccine, the difference in outdoor
consumption activity by social capital was more significant. After 2021, the primary
infection control strategy changed from social distancing to vaccination and limiting thenumber of people in the room. Therefore, it is plausible that there is no difference in17

outdoor consumption activity depending on social capital.

Baseline
2020
2021
(2)
(3)

2022
(4)

Controlling 2019 purchases
2020
2021
2022
(5)
(6)
(7)

1.019 -6.902*** 3.996(2.708)(2.118)(3.366)-2.0131.734-0.949(1.439)(1.853)(1.833)0.126-0.525-0.039(0.599)(0.813)(0.531)1.486**
0.761
1.347(0.733)(0.830)(0.810)3.524(2.387)-0.642(1.739)0.961(0.603)
1.787*(0.996)-2.216*(1.132)0.273(0.914)-0.320(0.385)-0.166(0.414)0.389(3.050)3.005*(1.660)1.309(0.841)0.883(1.190)-1.004(0.827)8.062**(3.558)-1.812(2.692)0.875(1.022)
0.662(1.121)-1.221(0.896)-4.554(4.350)0.365(2.143)0.387(1.076)-0.397(1.321)-1.148(1.070)-1.888(3.997)-0.265(2.266)-0.628(1.132)-0.972(1.591)-0.487(0.993)

YES
YES
YES
25,0130.1496

YES
YES
YES
24,1130.1310YES
YES
YES
23,7270.1322YES
YES
YES
19,0770.1068

1.342
1.268
3.506
(2.081)(2.405)(3.306)-0.2331.047-0.236(1.362)(1.891)(2.182)0.738
1.430**
0.374(0.481)(0.559)(0.645)0.436
0.360-0.972(0.593)(0.739)(1.352)-0.935* -1.492*** -1.343**(0.497)(0.513)(0.607)0.887*** 0.837*** 0.791***(0.004)(0.006)(0.008)
YES
YES
YES
YES
YES
YES
YES
YES
YES
20,523
17,631
14,5960.77900.69710.6424

2019(1)
Prosociality
LowestLow
High
HighestProsociality × High SC
LowestLow
High
HighestSocial capital
Outdoor purchases in 2019
Individual characteristics
Regional characteristics
Prefecture FE
Observations
Adjusted R-squared-0.506(1.816)-1.617(1.712)-0.109(0.373)0.828(0.603)-1.623(1.708)-0.976(1.567)0.729**(0.342)1.041(0.682)

Table 4. Effects of the relationship between prosociality and social capital on outdoor
consumption activity
Note: Standard errors appear in parentheses. *, **, and *** indicate statistical significanceat the 10%, 5%, and 1% levels, respectively.

Columns (5) through (7) also present estimates controlling for 2019 outdoor
consumption activity. Although less statistically significant, the results show decreased
18

outdoor activity in 2020 in cities with lower social capital. This estimation requires
caution in interpretation because it has solid assumptions and many limitations and is nothighly statistically significant. However, the results provide suggestive evidence of thechannels through which the relationship between social capital and prosociality affects
SWB during the pandemic.

5.

Conclusion
Using original survey data from Japan, this study examines the impact of individual

personality and community norms on changes in SWB during the pandemic. The key
findings of this study are as follows. First, the results are consistent with previous studies
that showed that the more prosocial a person was, the higher his or her level of SWB.
Second, the relationship between individual prosociality and community social capital
had no significant effect on SWB before the pandemic. In contrast, after the pandemic,
people with low prosociality were found to be significantly affected by community social
capital. A suggestive evidence-based interpretation suggests that non-prosocial
individuals may increase their SWB by free-riding in areas with high social capital.
Previous studies have pointed to the possibility of effectively solving collective action
problems by relying on community social capital and norms (e.g., Bartscher et al., 2021;
Durante et al., 2021). However, the most prosocial people continue to pay the costs.
Therefore, the longer the problem persists, the unhappier a prosocial individual becomes,
which may reduce his or her prosociality and normative behavior can be reduced. The
reliance on norms may work for short-term shocks such as natural disasters. However, forproblems such as infectious diseases and climate change, free-riding behavior canundermine people's prosociality, altruism, and social capital (Wu et al., 2022). Therefore,19

policymakers must develop policies that emphasize mutual trust and social capital rather
than simply overcoming post-emergency challenges and regulating disadvantageous
activities. The accumulation of social capital and mutual trust can be a source of economic
growth, and this accumulation may be substantial in the long term (Algan & Cahuc, 2013;
Knack & Keefer, 1997).

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Appendix
Table A1. Changes in prosociality from 2019 to 2021
Prosociality (2021)

Prosociality(2019)

LowestLow

MiddleHigh

HighestTotal

Lowest

59

28

64

36

18

205

Low

22

90

202

148

18

480

Middle

48

201

2,170

1,244

120

3,783

High

36

138

1,554

6,439

1,062

9,229

Highest

21

32

203

1,359

1,556

3,171

Total

186

489

4,193

9,226

2,774

16,868

Note: Monitors answering both the 2019 and 2021 surveys are included in the sample.

iTable A2. Comparison of surviving and dropout monitors

Individual characteristics
Subjective well-being
Prosociality
Age
Male
ChildrenHousehold Size
Chronic diseases
Household income
Regional characteristics
Social capital
Population density
Average age
Ratio of elderly people
Ratio of foreigner
Average taxable income
Number of monitors

Survivingmonitors
Mean

Dropout monitors

2020
Mean
t value

2021
Meant value

6.270
3.868
47.008
0.496
0.584
2.929
0.213
6.109

6.329
3.850
41.386
0.532
0.476
3.005
0.200
5.810

6.366
3.884
44.767
0.506
0.535
2.895
0.210
5.780

-1.0410.430
46.10626.1550.014
3.290
17,101-1.043-0.2580.433
0.354
46.099 -0.191
26.126 -0.420
0.014-0.584
3.297
0.726
5,220

ii

1.903
-1.284-20.6404.646-13.7373.518-2.033-4.4712.714
1.029-7.4491.188-5.522-1.456-0.384-4.636-1.051-0.9240.428-0.28246.066 -0.905
26.032 -1.556
0.014
0.950
3.299
0.811
3,720

Table A3. Placebo test using other personality types (family unity)
2019(1)
Personality
LowestLow
High
Highest
Personality × High SC
LowestLow
High
HighestSocial capital
SWB in 2019
Individual characteristics
Regional characteristics
Prefecture FE
Observations
Adjusted R-squared

Baseline
2020
2021
(2)
(3)

2022
(4)

Controlling 2019 SWB
2020
2021
2022
(5)
(6)
(7)

-0.458*** -0.543*** -0.513*** -0.532**(0.124)(0.137)(0.150)(0.209)-0.081-0.043-0.107-0.020(0.116)(0.089)(0.077)(0.085)0.446*** 0.516*** 0.458*** 0.439***(0.050)(0.041)(0.040)(0.037)0.818*** 0.974*** 0.947*** 0.936***(0.042)(0.042)(0.053)(0.047)-0.235-0.258*-0.107(0.154)(0.133)(0.181)
0.019-0.067-0.032(0.101)(0.060)(0.088)0.193*** 0.169*** 0.169***(0.033)(0.036)(0.038)0.380*** 0.419*** 0.388***(0.042)(0.044)(0.042)0.110(0.173)-0.016(0.169)0.041(0.062)0.059(0.057)-0.022(0.054)-0.227(0.198)0.126(0.108)0.012(0.048)-0.048(0.053)0.042(0.040)-0.063(0.255)0.064(0.137)0.025(0.052)-0.034(0.070)0.028(0.042)-0.174(0.299)-0.056(0.157)-0.001(0.057)-0.108(0.067)0.076(0.059)

YES
YES
YES
25,0170.1537

YES
YES
YES
24,1220.1467YES
YES
YES
23,7330.1409YES
YES
YES
19,0840.1469-0.1590.020-0.084(0.185)(0.220)(0.216)0.153
0.005-0.096(0.122)(0.114)(0.158)-0.0090.065
0.014(0.041)(0.042)(0.054)-0.050-0.040-0.091(0.042)(0.057)(0.063)0.045-0.0020.048(0.033)(0.032)(0.059)0.690*** 0.625*** 0.637***(0.009)(0.007)(0.009)
YES
YES
YES
YES
YES
YES
YES
YES
YES
20,532
17,635
14,6020.50600.4552
0.4674

Note: Standard errors appear in parentheses. *, **, and *** indicate statistical significanceat the 10%, 5%, and 1% levels, respectively. The personality variable is based on
responses to the item “It is essential to keep the family together” and is a five-level
discrete variable that takes the value of 5 for agreement and 1 for disagreement.

iii

Table A4. Placebo test using other personality types (activist)
2019(1)
Personality
LowestLow
High
Highest
Personality × High SC
LowestLow
High
HighestSocial capital
SWB in 2019
Individual characteristics
Regional characteristics
Prefecture FE
Observations
Adjusted R-squared

Baseline
2020
2021
(2)
(3)

2022
(4)

Controlling 2019 SWB
2020
2021
2022
(5)
(6)
(7)

-0.200*** -0.216*** -0.107*-0.101(0.058)(0.068)(0.057)(0.075)0.144*** 0.064* 0.099** 0.105**(0.045)(0.034)(0.038)(0.046)0.210*** 0.294*** 0.228*** 0.251***(0.041)(0.039)(0.070)(0.080)0.325*** 0.316*** 0.433*** 0.402***(0.090)(0.101)(0.112)(0.088)-0.102*(0.053)0.006(0.032)0.134***(0.046)0.138(0.095)-0.006(0.070)0.021(0.034)0.059(0.055)0.197(0.124)0.006(0.070)0.025(0.036)0.075(0.065)0.206*(0.113)0.109(0.075)-0.039(0.051)
0.062(0.046)0.090(0.135)0.008(0.034)0.046(0.086)0.045(0.048)-0.085(0.080)0.037(0.140)0.034(0.038)0.026(0.083)0.014(0.055)0.041(0.097)0.124(0.178)0.014(0.033)-0.030(0.092)0.015(0.058)0.058(0.091)-0.013(0.167)0.035(0.037)

YES
YES
YES
25,0170.1296YES
YES
YES
24,1220.1180YES
YES
YES
23,7330.1124

YES
YES
YES
19,0840.12180.024
0.018-0.085(0.074)(0.095)(0.084)0.043-0.014-0.018(0.040)(0.043)(0.036)-0.0710.018
0.009(0.080)(0.074)(0.078)-0.042-0.048-0.127(0.123)(0.199)(0.190)
0.032
0.016
0.045
(0.035)(0.025)(0.038)0.703*** 0.640*** 0.649***(0.008)(0.007)(0.008)
YES
YES
YES
YES
YES
YES
YES
YES
YES
20,532
17,635
14,6020.50220.44961 0.4636

Note: Standard errors appear in parentheses. *, **, and *** indicate statistical significanceat the 10%, 5%, and 1% levels, respectively. The personality variable is based on
responses to the item “I think I am someone who acts before thinking” and is a five-level
discrete variable that takes a value of 5 for agreement and 1 for disagreement.

iv

Table A5. Placebo test using other personality types (easygoing)
2019(1)
Personality
LowestLow
High
Highest
Personality × High SC
LowestLow
High
HighestSocial capital
SWB in 2019
Individual characteristics
Regional characteristics
Prefecture FE
Observations
Adjusted R-squared

Baseline
2020
2021
(2)
(3)

2022
(4)

Controlling 2019 SWB
2020
2021
2022
(5)
(6)
(7)

-0.542*** -0.711*** -0.472*** -0.474*** -0.286*** -0.204*** -0.100(0.056)(0.054)(0.080)(0.087)(0.051)(0.074)(0.064)-0.150*** -0.068 -0.139*** -0.152***-0.036 -0.095*** -0.048(0.052)(0.042)(0.048)(0.053)(0.040)(0.033)(0.046)0.350*** 0.413*** 0.422*** 0.414*** 0.143*** 0.167*** 0.167***(0.050)(0.037)(0.042)(0.058)(0.036)(0.040)(0.050)0.357*** 0.684*** 0.695*** 0.630*** 0.468*** 0.323*** 0.232**(0.099)(0.117)(0.092)(0.079)(0.070)(0.098)(0.088)-0.061(0.084)0.045(0.076)0.047(0.068)0.168(0.125)-0.004(0.043)0.143(0.103)-0.039(0.058)-0.004(0.052)0.128(0.174)0.026(0.034)-0.009(0.105)0.021(0.065)0.072(0.064)0.041(0.156)0.013(0.033)-0.015(0.097)0.084(0.058)0.076(0.068)0.067(0.123)0.011(0.048)

YES
YES
YES
25,0170.1431YES
YES
YES
24,1220.1347YES
YES
YES
23,7330.1292YES
YES
YES
19,0840.13700.028-0.025-0.125(0.087)(0.096)(0.078)-0.013-0.034-0.014(0.048)(0.058)(0.054)0.00080.051
0.020(0.048)(0.052)(0.066)-0.0850.134
0.175(0.099)(0.149)(0.122)
0.034
0.011
0.032
(0.030)(0.032)(0.049)0.694*** 0.630*** 0.641***(0.008)(0.007)(0.009)
YES
YES
YES
YES
YES
YES
YES
YES
YES
20,532
17,635
14,6020.50520.4542
0.4663

Note: Standard errors appear in parentheses. *, **, and *** indicate statistical significanceat the 10%, 5%, and 1% levels, respectively. The personality variable is based on
responses to the item, “I am someone who does not mind so much if I cannot do what I
expect” and is a five-level discrete variable that takes a value of 5 for agreement and 1
for disagreement.

v