Panel Data Research Center, Keio University
PDRC Discussion Paper Series

Trends in income and well-being inequality during the COVID-19
pandemic in Japan
Kayoko Ishii, Isamu Yamamoto
4 March, 2024
DP2023-003
https://www.pdrc.keio.ac.jp/en/publications/dp/8984/

Panel Data Research Center, Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan
info@pdrc.keio.ac.jp
4 March, 2024

Trends in income and well-being inequality during the COVID-19 pandemic in Japan
Kayoko Ishii, Isamu Yamamoto
PDRC Keio DP2023-003
4 March, 2024
JEL Classification: D31; I31; I14
Keywords: inequality; well-being; COVID-19

Abstract
Although the COVID-19 pandemic could have caused distributional changes, existing studies only
investigated its immediate monetary impacts. This study examines the medium-term impacts on income
and well-being inequality using individual longitudinal data from the Japan Household Panel Survey. Gini
coefficients before and after the pandemic are calculated to analyze income inequality. Various well-being
measures such as mental health, life satisfaction, sleeping hours, and work engagement are used toanalyze well-being inequality. The findings reveal no increase in income inequality. Progressive income
growth ensured stable inequality throughout the pandemic. Conversely, well-being worsened, particularly
among the low-income group, and well-being inequality increased. Furthermore, people in the highincome group benefited from flexible work arrangements, contributing to improved well-being,suggesting that the increase in well-being inequality was associated with income inequality during the
pandemic. Thus, although income inequality did not change, overall inequality, including subjective wellbeing, increased during the pandemic.

Kayoko Ishii
Faculty of Economics, Keio University
108-8345
2-15-45, Mita, Minato-ku, Tokyo, JAPANishiikayoko@keio.jp
Isamu Yamamoto
Faculty of Business and Commerce, Keio University
108-8345
2-15-45, Mita, Minato-ku, Tokyo, JAPAN
yamamoto@fbc.keio.ac.jp

Acknowledgement: The data for this analysis, “Japan Household Panel Survey (JHPS/KHPS)” and“JHPS Special Survey for COVID-19” were provided by the Keio University Panel Data ResearchCenter, for which we gratefully acknowledge. The authors are grateful for the comments andsuggestions by Jonathan Portes, Monia Costa Dias, Robert Joyce, Yoshio Higuchi, Jun Saito, Kotaro
Tsuru. This work was supported by JSPS Grantsin-Aid for Scientific Research 22H04911 and
JRP20211706.

Trends in income and well-being inequality during the COVID-19
pandemic in Japan

Kayoko ISHII

Isamu YAMAMOTO

Keio University

Keio University

Abstract
Although the COVID-19 pandemic could have caused distributional changes, existingstudies only investigated its immediate monetary impacts. This study examines themedium-term impacts on income and well-being inequality using individual
longitudinal data from the Japan Household Panel Survey. Gini coefficients before andafter the pandemic are calculated to analyze income inequality. Various well-being
measures such as mental health, life satisfaction, sleeping hours, and work engagement
are used to analyze well-being inequality. The findings reveal no increase in income
inequality. Progressive income growth ensured stable inequality throughout the
pandemic. Conversely, well-being worsened, particularly among the low-income group,
and well-being inequality increased. Furthermore, people in the high-income group
benefited from flexible work arrangements, contributing to improved well-being,suggesting that the increase in well-being inequality was associated with income
inequality during the pandemic. Thus, although income inequality did not change,
overall inequality, including subjective well-being, increased during the pandemic.
JEL classification: D31 Personal Income, Wealth, and Their Distribution, I31 General
Welfare, Well-Being, I14 Health and Inequality

11. Introduction
Owing to the enormous economic and social damage caused by the COVID-19
pandemic, several studies have investigated its distributional impact in many countries.
Although some studies have shown an increase in income inequality even after
considering regressive policy interventions (Crossely et al., 2021; Angelov andWaldenstrom, 2023), most indicate that income inequality after redistribution did not
significantly increase (O’Donoghue et al., 2020; Almeida et al., 2021; Clark et al.,2021; Blundell et al., 2022; Stantcheva, 2022). As Clark et al. (2021) and Tanaka(2022) indicate, regressive policies using existing redistributive mechanisms and special
intervention schemes fully offset the severe negative impact for lower-income
households, suggesting that income inequality based on disposable income did notincrease significantly, at least at the beginning of the pandemic. However, subsequentchanges or the medium- and long-term impacts of the pandemic are not known from theexisting studies.
Furthermore, few studies have focused on the distributional impact of thepandemic on nonmonetary aspects, such as subjective well-being. Even if income
inequality did not change during the pandemic, well-being inequality could increase.
For instance, the negative impact of the pandemic may have been greater amongindividuals with poor well-being before the pandemic than those with better well-being.
As infectious diseases such as COVID-19 could have a greater psychological thaneconomic impact, several studies have investigated the heterogeneous impact of the
COVID-19 pandemic on well-being variables such as mental health, anxiety, general
health, happiness, and satisfaction (Banks and Xu, 2020; Nagasu et al., 2021;
Yamamura and Tsutsui, 2022; Adam-Prassl et al., 2022). For example, Banks and Xu(2020) and Nagasu et al. (2021) show that the pandemic had severe consequences forindividuals with poor well-being before the pandemic, such as young people, women,
and low-income households. This suggests a possibility that well-being inequalities
increased during the pandemic. However, few studies have measured the distributionalchanges in these variables during the pandemic. Among existing studies, Delhey et al.
(2023) find that the COVID-19 pandemic did not increase inequality in life satisfactionin Germany and the United Kingdom from 2020 to 2021. However, Sudo (2022) reports2

that inequality in life satisfaction increased during the COVID-19 pandemic in Japan in
2020. However, little is known about the medium- and long-term distributional changes
in subjective well-being during the pandemic.
In addition, no existing studies have investigated an association between the
distributional changes in subjective well-being and those in income. There is apossibility that richer people had better access to healthcare and infection-preventive
workstyles, such as remote work, which may have enhanced their well-being. In thiscase, even if income inequality does not change, overall inequality, including subjective
well-being, is widening. This suggests the importance of investigating an association
between income and well-being inequality.
Considering these research gaps in the existing literature, this study examines
the medium-term changes in overall monetary (income) and non-monetary (subjective
well-being) inequalities and their association during the COVID-19 pandemic in Japan.
Representative longitudinal data from Japan, the collection of which started before the
COVID-19 pandemic, allows us to analyze these impacts on income and well-being
inequality considering pre-pandemic economic positions. As the well-being variables
included in the data are self-reported, the use of a longitudinal survey that started before
the pandemic is important to exclude retrospective biases. Unlike previous studies thathave investigated the immediate impacts on income distribution, we attempt tounderstand the medium- and long-term impacts on inequality by using data until 2022.
This is based on concerns that the COVID-19 pandemic has changed the existing
structure and trends of income and well-being inequality.
In the analysis, we first examine the changes in income inequality using threedescriptive approaches: observing fluctuations of the Gini coefficients, income
fluctuations, and dynamic transitions or mobility among income groups. Second, we
conduct a similar descriptive analysis for well-being inequality, observing changes in
the mean and variation of well-being variables. Third, we examine an association
between income and well-being inequality by focusing on workstyle, such as remote
work as potential factors that cause the association. After observing a descriptive
relationship, we estimate fixed-effects models for well-being variables such as mental
health and work engagement, in which we include pre-pandemic income quintile
dummies as well as their cross-terms with the after-the-outbreak dummy. We focus on3the estimates of the coefficients of the cross-terms because they indicate how the prepandemic income level is associated with the changes in well-being after the outbreak.
We also conduct mediation analysis by adding a remote work dummy as an explanatory
variable. If the coefficients of the cross-terms become insignificant after adding a
remote work dummy, we can indicate that the association between income and wellbeing inequality during the pandemic may have been caused by the changes inworkstyle during the pandemic.
This study is related to three strands of the literature. First, it contributes to theliterature on the monetary distributional impact of the COVID-19 pandemic. Owing to
the lack of timely available official data, early studies assessing the immediate impact ofthe pandemic and government interventions on inequality have used either
microsimulation (O’Donoghue et al., 2020; Palomino et al., 2020; Almeida et al., 2021),
rapidly collected survey data (Clark et al., 2021; Gambau et al., 2022), or alternative
data such as bank records (Aspachs et al., 2020) and high-frequency transaction data
(Hacıoğlu-Hoke et al., 2021). These studies reveal that although the pandemic had asevere impact on low-wage and low-income households, the policy response offset the
regressive effect in the early stages of the pandemic. Studies using official statistics orrepresentative data are limited, and most of them also focus on the immediate impact ofthe pandemic (e.g., Angelov and Waldenstrom, 2023; Aina et al., 2023). Thus, little is
known about the medium- and long-term impacts on income inequality.
Second, this study contributes to the literature on the impact of the pandemic onindividual well-being. Several studies have investigated the heterogeneous impact of the
COVID-19 pandemic on well-being in many countries (e.g., in the UK: Banks and Xu,
2020; Bu et al., 2020; Etheridge and Spantig, 2022; in the US: Adam-Prassl et al., 2022;
in Germany: Schmidtke et al., 2021; Huebener et al., 2021; in Canada: Beland et al.,2022; and in Japan: Nagasu et al., 2021; Yamamura and Tsutsui, 2022). However, mostof these studies do not examine the distributional changes in well-being during the
pandemic.
Third, this study contributes to the literature on well-being inequality. With thepioneering study of Veenhoven (1990), subsequent studies have been conducted toclarify the negative association between well-being inequality and individual well-being
within various sizes of groups (Delhey and Kohler, 2011; Okulicz-Kozaryn and Mazelis,42017; Goff et al., 2018; Dickinson and Morrison, 2022; Grimes et al., 2023). However,
little is known about the changes in well-being inequality during the COVID-19
pandemic, except Delhey et al. (2023) and Sudo (2022), who focus on the short-term
impact of the pandemic. Therefore, the challenge of this study is to identify themedium-term changes in overall income and subjective well-being inequalities and theirassociations during the COVID-19 pandemic in Japan.
The remainder of this paper is structured as follows. Section 2 explains the data
and variables used in the analysis. Section 3 examines changes in income inequality.
Section 4 examines changes in well-being inequality and their association with income
inequality. Finally, Section 5 presents the conclusions of this study.

2. Data and Variables
2.1 Data
This study uses data from the Japan Household Panel Survey (JHPS). The JHPS is a
representative longitudinal survey in Japan that follows the same individuals to trackchanges in various aspects, such as working conditions, time use, health, well-being,
and income. The JHPS began in February 2004. In the first year of the survey, therespondents were approximately 4,000 men and women as well as their spouses. The
samples were selected using two-stage stratified random sampling methods to minimize
selection bias. Since February 2004, surveys have been conducted every February. To
compensate for attrition of the sample, new samples were added in 2007, 2009, 2012,
and 2018. In April 2020, to capture the impact of the COVID-19 pandemic onhouseholds, the JHPS Special Survey for COVID-19 (JHPS-COVID19) was conducted,
which targeted the respondents of the JHPS 2020. The JHPS-COVID19 was conducted
six times in spring and autumn from 2020 to 2022. This study partially uses the JHPSCOVID19.
To correct for the bias caused by the attrition in the JHPS and the JHPSCOVID19, we utilize a sampling weight for all calculations. The sampling weights are
created by an iterative proportional fitting or raking method to match the distributions ofgender, age groups, working conditions, and the number of people living together ineach wave of the JHPS with those of the Japanese population. Population data are5

obtained from the Labour Force Survey (Ministry of Internal Affairs andCommunications) for the same period. We fully match the distributions of thesevariables to those from the Labour Force Survey using weights.

2.2 Variables
To analyze income inequality, we focus on household income. The JHPS questionnaire
includes several questionnaire items on household income: household gross income,household disposable income, and amount of household income by each income source.
The first two questions directly ask for the total amount of household income, whereas
in the other questions, the total amount is obtained by adding each source of income.
We use one of these variables depending on the purpose of the analysis.1
To analyze well-being, we measure mental health, sleeping hours, life
satisfaction, job satisfaction, health satisfaction, and work engagement. These six
indices can capture wide range of well-being. For the mental health variable, we use theKessler Psychological Distress Scale (K6), which comprises six questions measuringmental health, such as “Did you feel excessively nervous?” Responses are rated on a
five-point Likert scale. The total score of the K6 ranges from 0 to 24 points, with higherscores indicating more severe mental problems. Sleeping hours are defined as average
weekday sleeping hours. Life, job, and health satisfaction range from 0 to 10, withhigher scores indicating higher levels of satisfaction. The work engagement variable ismeasured using the Utrecht Work Engagement Scale (UWES-3), which is a shortversion of the Utrecht Work Engagement Scale (UWES) created by Shimazu et al.
(2008). Work engagement is positive work-related mental health characterized by vigor,
dedication, and absorption, with higher scores indicating higher work engagement.
For the analysis of workstyle, we use a remote work dummy variable that takes
the value of 1 if the respondent worked from home at least once in the fourth week ofFebruary. In addition, we use an overtime work dummy that takes the value of 1 if the
respondent worked more than 60 hours per week. Furthermore, we use an abstract task
index that measures the number of atypical and complex problems that must be solvedin a job. The JHPS 2020 contains information on the types of tasks based on Autor and
Handel (2013), who use the Princeton Data Improvement Initiative survey from
Princeton University. We combine the answers to the questionnaire items regarding6

abstract tasks, extract the first principal component, and standardize the task score forthe abstract tasks.2

3. Income inequality during the COVID-19 pandemic
3.1 Trends in the Gini coefficientFirst, we examine the trends in income inequality before and during the COVID-19
pandemic based on the Gini coefficient. The Gini coefficient is the most commonlyused inequality measure. It ranges between 0 for perfect equality and 1 for perfectinequality. We calculate the Gini coefficients using JHPS data and several official
statistics to compare the results.
Figure 1 shows the Gini coefficients from 2019 to 2021, based on the JHPS.
Income is defined as the equivalized annual household gross income, before tax andsocial security contribution deductions, as well as the equivalized annual household
disposable income, after tax and social security contribution deductions. Data for bothvariables are obtained directly from respondents. Household income is equivalized by
the squared number of household members to reflect the differences in the needs ofhouseholds of different sizes.
As shown in Figure 1, the Gini coefficients are mainly stable for both gross anddisposable income before and two years after the pandemic outbreak. The Gini
coefficient based on gross income is approximately 0.315. The coefficients based on
disposable income are naturally smaller than those based on gross income, ranging from0.280 to 0.285. Figure 1 also shows the Gini coefficients based on the equivalized
monthly household gross income using data from the JHPS-COVID19. The findings
indicate a slight decrease in inequality in April 2020. Except for this, the Gini
coefficients are mainly the same as those of the JHPS main survey.
[Figure 1. Gini coefficient from 2019 to 2021 by the JHPS]

Figure 2 compares the Gini coefficients of the JHPS with those of thegovernment statistics standardized to 2019 levels. Family Income and Expenditure(Ministry of Internal Affairs and Communications) and the Comprehensive Survey of7

Living Conditions (Ministry of Health, Labour and Welfare) are used for government
statistics. These two government statistics were the only surveys that examined income
distribution during the COVID-19 pandemic in Japan. In Figure 2, income is defined as
annual household gross income and is not equivalized by the number of household
members.
As shown in Figure 2, although the observed inequality trends differ dependingon the surveys, the changes in each Gini coefficient are small, within ±2%. This
corresponds to the changes from 0.304 to 0.301 in the levels of the Gini coefficient in
the JHPS, those from 0.330 to 0.335 in Family Income and Expenditure, and those from
0.382 to 0.388 in the Comprehensive Survey of Living Conditions.
In summary, the results indicate that increases or decreases in the Gini
coefficients are negligible during the pandemic. Thus, the COVID-19 pandemic did not
impact income inequality in Japan.
[Figure 2. Relative Gini Coefficients from 2019 to 2021 by three surveys (2019=100)]

3.2 Income fluctuation during the COVID-19 pandemic
While no overall increasing trend is observed in income inequality during the pandemic,
significant changes may have occurred in income for a particular household or income
group. Thus, following Tanaka (2022), we examine income fluctuations by income
group during the pandemic.
Using aggregated data on Family Income and Expenditure, Tanaka (2022)
calculates the changes in disposable income across income quintiles from 2019 to 2021
among the working population in Japan. Rows 1 and 2 in Table 1 present the changes
calculated using the same methods as Tanaka (2022), and Row 3 presents an extension
to 2022. Current income (regular or replicable income that significantly affects
households’ consumption behavior, such as earnings, business income, asset income,and regular social security benefits) declined among the low- and middle-income groups
from 2019 to 2020, whereas disposable income increased for all income groups. This isdue to the increase in noncurrent income (irregular income, such as gifts) caused by theSpecial Cash Payment, which is government support that provided a fixed amount of
100,000 yen to all registered persons in 2020.

8

[Table 1. Changes in household disposable income by income group among the working
population from 2019 to 2022]

Current income continued to decrease from 2020 to 2021 in the low-income
group, and the amount of income decrease since 2019 was the largest in the low-income
group. Therefore, the negative impact of the pandemic was more pronounced in the
low-income group. However, as shown in Figure 2, this did not result in a large increasein overall income inequality. Based on the same statistics, the Gini coefficients among
the working population were 0.227 in 2019, 0.232 in 2020, and 0.236 in 2021,
indicating an increase of only 4% from 2019 to 2021.3
The changes in both the current and disposable income of the middle-income
group declined from 2021 to 2022, whereas those of the other income groups increased.
The Gini coefficient among the working population was 0.220 in 2022, which was thesame as that in 2019. These findings imply that income inequality increased slightly inthe first two years of the pandemic; however, this increase was temporary, and the
pandemic did not have a long-term impact on income inequality.
This tendency is confirmed by three additional aggregations. First, we conduct
the same aggregation as Tanaka (2022) using the JHPS data in Table 2. In contrast toTable 1 and Tanaka’s (2022) study, this table focuses on the total population (rather
than the working population) and uses annual gross income (not averaged monthly
disposable income).4 Significant increases were observed in noncurrent income in all
income groups from 2019 to 2020, which prevented a decline in total income, except in
the second and fifth quintiles. However, a decrease in income was observed in almost
all income groups from 2020 to 2021, except in the second quintile. Among all groups,
the high-income group experienced the largest income decline since 2019. The Gini
coefficients were 0.281 in 2019, 0.277 in 2020, and 0.276 in 2021, indicating a 2%
decrease in the relative term. Therefore, the JHPS data confirm that the COVID-19
pandemic did not affect income inequality from 2019 to 2021.
[Table 2. Changes in household gross income by income group]9Second, taking advantage of the characteristics of the longitudinal data from the
JHPS, we examine the dynamics of income fluctuations depending on the pre-pandemic
income level, which is not confirmed by cross-sectional analysis. Figure 3 shows violin
plots5 of the distribution of income changes from 2019 to 2020 and from 2019 to 2021
among the pre-pandemic income group, based on Crossley et al. (2021). The income isequivalized annual household gross income, as shown in Figure 1.
As shown in Figure 3 (a), in all income groups, the modes and medians arelocated at almost zero, indicating that most households experienced no changes inincome. However, we also find that more households experienced income growth in thelowest quintile, whereas more households experienced income declines in the highest
quintile from 2019 to 2020. By extending the observations from 2019 to 2021, as shownin Figure 3 (b), we find clearer tendencies. These findings indicate that although incomeof the households categorized into the low-income group at each point in time slightly
declined during the pandemic, as shown in Tables 1 and 2, households that were in the
low-income group before the pandemic experienced more income growth during the
pandemic. This provides evidence of stable income inequality through higher income
mobility across income groups during the pandemic.
[Figure 3. Violin plot of the percentage of income change by income groups from 2019
to 2020 and 2019 to 2021 using the JHPS]

Third, income growth and mobility are confirmed following the method
presented by Jenkins and Van Kerm (2006). Jenkins and Van Kerm (2006) decompose
the change in inequality from time t to t+1 into the progressivity of income growth andmobility in the form of re-ranking. Progressivity is expressed as the difference between
the Lorenz curve at time t and the concentration curve of income at time t+1 by income
rank at time t, which indicates how much the poor at time t grew their income share attime t+1. Re-ranking is expressed as the difference between the concentration curve of
income at time t+1 by income rank at time t and the Lorenz curve at time t+1. This is acomparison between the current income shares of those who were previously poor andwho are currently poor. Arranging this decomposition, we examine the changes in the10income shares of each quintile directly, as shown in Figure 4, instead of drawing a
Lorenz curve and a concentration curve, which show the cumulative income share.
[Figure 4. Changes in income share by income groups before and after COVID-19
using the JHPS]

The income share is calculated in three ways: income share at time t by quintile
at time t, income share at time t+1 by quintile at time t, and income share at time t+1 by
quintile at time t+1. Comparing the first and second quintiles reveals how much theincome share of households in each quintile in the first year changed in the next year.
Likewise, by comparing the first and third quintiles, we find how much the income
share of each quintile at each time point has changed. As shown in Figure 4, in both
periods, the households in the low- and middle-income groups in the first year increased
their income share in the next year. Their income shares were greater than the income
shares of those in the same position in the next year. Conversely, the households in thehigh-income group in the first year clearly experienced a decline in their income sharein the next year.6 These changes in the income share provide additional evidence ofmobility across income groups during the pandemic.

4. Well-being inequality after the COVID-19 pandemic
4.1 Trends in well-being inequality
To examine the change in inequality in terms of well-being, we first show the mean andcoefficient of variation (CV) of each well-being variable before (February 2019 and2020) and after (February 2021 and 2022) the pandemic. 7 As shown in Figure 5, themean values of the K6 score, in which higher scores indicate worse mental health,increased in 2021 during the COVID-19 pandemic and stayed high in 2022. As forvariance, the CVs of the K6 exhibited an increasing trend, which was observed even
before the pandemic. Therefore, inequality in mental health increased with averagemental health deterioration during the pandemic.
Similar changes in the averages and variances of well-being are found for job
satisfaction and work engagement. Figure 5 shows a decrease in the mean values for11both measures from 2021 to 2022, which indicates lower well-being, as well as anincrease in the CVs, which indicates growing inequality during the pandemic.
Regarding life-related satisfaction measures, as shown in Figure 5, both themean and CV of life and health satisfaction declined. This indicates that the averagesatisfaction with life and health decreased, whereas the inequality in those well-being
measures shrank during the pandemic. By contrast, both the mean and CV of sleeping
hours increased during the COVID-19 pandemic, indicating that people tended to sleep
longer on average; however, the differences in sleeping hours among people became
more pronounced.
[Figure 5. Means and coefficient of variation of well-being measures]

In summary, among the measures representing well-being, work engagement,
life satisfaction, and health satisfaction worsened after the outbreak, and only sleeping
hours improved on average. The variance or inequality in mental health, job satisfaction,
work engagement, and sleeping hours increased. Thus, differences between individuals
in terms of well-being measures increased during the pandemic.

4.2 Well-being inequality associated with income inequality
To examine the association between well-being and income inequality, the mean
deviations of each well-being measure from the total mean for each pre-pandemic
income quintile group in February 2020, 2021, and 2022 are shown in Figure 6.
Inequalities existed in pre-pandemic income levels in most well-being measures. Higher
income level was associated with better well-being. In addition, individuals in the highincome group before the pandemic experienced improvements in well-being, except forwork engagement, while those in the low-income group experienced a decline in wellbeing. For instance, we find a large decrease in the K6 score (better mental health) forthe high-income group and a large increase (worse mental health) for the low-income
group in 2022. Increases in overall inequality measured by the CV are not seen for lifeand health satisfaction in Figure 5; however, an increase in the inequality associatedwith income inequality is observed in Figure 6.

12

[Figure 6. Mean deviations of each well-being measure from the total mean for eachpre-pandemic income quintile group in February 2020, 2021, and 2022]

4.3 Workstyle inequality associated with income inequality
To examine the potential factors that lead to an association between income and wellbeing inequality, the ratio of remote and overtime work, as well as abstract task scores
across pre-pandemic income groups and years, are examined in Table 3.
[Table 3. Workstyle and tasks before and after the pandemic]

The ratios of remote work were low in all income groups in 2020 before thepandemic but sharply increased in 2021 and 2022. These increases were more evident in
the high-income group, particularly in 2021.8 However, the ratio of workers with morethan 60 hours of work per week decreased more in 2021 and 2022 for the high-income
group, whereas it increased for the low-income group. In addition, we find an increasein abstract task scores in 2021 for the high-income group.
Thus, the association between income and well-being inequality may have beencaused by these changes in workstyle and tasks.

4.4 Regression analysis for the association between income and well-being inequality
Table 4 shows the estimation results of the fixed-effects model, which identifies theassociation between income and well-being inequality by controlling for individual
heterogeneity. We use data from 2020 and 2022 to examine changes from the prepandemic period to 2022. We use six well-being variables as dependent variables:
mental health score (K6), sleeping hours, life satisfaction, health satisfaction, job
satisfaction, and work engagement score (UWES3).9 As independent variables, we use
dummy variables indicating the pre-pandemic income quintiles and their cross termswith the year 2022 dummy.
The coefficients of the dummy variables for the pre-pandemic income quintile
indicate the inequality in each well-being variable in relation to the income level before
the pandemic, as shown in Figure 6. The coefficients of the cross terms with the year132022 dummy indicate whether well-being inequality in relation to income level changed
during the pandemic. As shown in Table 4, we estimate the fixed-effects models byadding a dummy variable for remote work as an independent variable. 10 This is todetermine whether the changes in well-being inequality in relation to income levels after
the outbreak became smaller or insignificant by controlling for changes in workstyle. If
the coefficients of the cross terms become smaller or insignificant, the association
between well-being and income inequality after the outbreak was due to changes inworkstyle.
Column [1] and [2] in Table 4 shows the results for mental health. The
coefficients of the fourth and fifth quintile income group dummies are significantly
negative, indicating better mental health for people in the high-income group. Thus, anassociation existed between mental health and income inequality before the pandemic.
Furthermore, the cross terms of the fifth quintile with the year 2022 dummy are
significantly negative, which indicates that the mental health of people in the highincome group improved more during the pandemic than that of those in the low- andmiddle-income groups. Therefore, mental health inequality associated with income
inequality increased during the pandemic.
However, as shown in Column [2], the significant coefficient of the cross term
disappears after we control for the remote work dummy. As the coefficient of theremote work dummy is significant and negative, people in the high-income group were
better off in terms of mental health because they worked remotely more compared withthose in the low-income group during the pandemic.
Similar results are seen for life satisfaction, as shown in Columns [5] and [6].
Life satisfaction was relatively higher for people in the fourth and fifth quintile income
groups before the pandemic, and the difference increased for those in the fifth income
group during the pandemic.
Sleeping hours are shown in Columns [3] and [4]. Opposite associations with income
inequality are observed before the pandemic. In other words, the coefficients of the
fourth and fifth quintile income group dummies are significantly negative, indicatingthat richer people tended to have shorter sleeping hours in 2020. However, regarding thechanges in sleeping hours during the pandemic, we find a positive association withincome, as the coefficient of the cross term of the fifth income group with the 202214

dummy is significantly positive. In addition, we find insignificant coefficients of thecross terms after controlling for the remote work dummy, as shown in Column [4]. Thus,
richer people tended to have better sleeping hours during the pandemic.
Health satisfaction is shown in Columns [7] and [8]. The coefficient of the crossterms for the high-income group is significantly positive and becomes slightly smaller
after we control for the remote work dummy. These results indicate that part of theincrease in health satisfaction for the high-income group during the pandemic was dueto the adoption of remote work.
Regarding job satisfaction and work engagement, we find significant andpositive coefficients for the high-income group dummies, indicating inequality inrelation to income before the pandemic. However, we find no significant coefficients forthe cross terms, indicating that the inequality of these two variables did not change
during the pandemic.
[Table 4. Determinant of well-being from 2020 to 2022: fixed model estimation results]

In summary, inequality in well-being variables, such as mental health, life
satisfaction, and health satisfaction, increased in relation to income level, and one of themain drivers of this increase was the availability of remote work. As such, the COVID19 pandemic increased the association between well-being and income inequality
through the inequality of workstyles and remote work.

5. Conclusion
This study investigated the distributional changes in income and subjective well-being
over three years before and during the COVID-19 pandemic to explore whether the
pandemic changed the existing structure and trends of inequality. Moreover, we
examined the association between income and subjective well-being inequality. The
results revealed that income inequality did not increase and indicated that progressive
income growth ensured the stability of income inequality throughout the pandemic.
Well-being inequality increased during the pandemic and was associated with income
inequality. That is, the well-being of the high-income group tended to improve, whereas

15that of the low-income group tended to deteriorate. Flexible work arrangements, such as
remote work, affected this association. Remote work became more prevalent during the
pandemic, especially among workers in the high-income group, which contributed to an
improvement in their well-being and an increase in well-being inequality.
The findings of this study broadly align with those of previous studies
investigating the immediate impact of the COVID-19 pandemic on income inequality.
Previous studies, such as Crossley et al. (2021), indicate negative impacts on income
among low-income households in the early stages of the pandemic; however, overall
inequality did not increase, partly owing to short-term policy intervention (Clark et al.,2021). Moreover, our findings demonstrated that the stability of income inequality
continued for at least two years after the outbreak of the pandemic. According to
Stantcheva (2022), income inequality in the medium and long term might increase
owing to the regressive impacts of the pandemic; nonetheless, this was not observed inour study in Japan.
However, our analyses revealed an increase in inequality in non-monetary aspects,
particularly subjective well-being. Inequality in non-monetary aspects has received lessattention than monetary inequality in the literature on inequality. Similarly, Sudo (2022)
measures the impact of the pandemic on subjective well-being in Japan and finds
positive impacts for socially advantaged groups and negative impacts for sociallydisadvantaged groups, which increased well-being inequality. Moreover, other studies
have reported that the increase in well-being inequality was associated with existing
income inequality. For instance, Okulicz-Kozaryn and Mazelis (2017) demonstrate anincreasing gap in happiness between income groups in the US.
Beginning with the pioneering work of Veenhoven (1990), a growing body ofliterature has investigated the development and distribution of non-monetary aspects,
such as happiness, satisfaction, and health. Well-being is an outcome of life, andVeenhoven (2005) proposes that inequality can be measured using well-being rather
than income. Our findings showed that the COVID-19 pandemic impacted nonmonetary aspects more than monetary ones, suggesting the need to pay more attention
to distributional changes in subjective well-being, particularly when a society is hit byeconomic and non-economic shocks. Furthermore, our finding that the increase in wellbeing inequality is associated with existing income inequality suggests that even if

16

rising inequality is not observed on the surface, overall inequality, including that in nonmonetary aspects such as happiness, satisfaction, and health, could be increasing.
Therefore, more progressive distribution policies may be required. In addition, flexible
workstyles, such as remote work, should be promoted to increase people’s well-being in
low-income or low-well-being households.
Although this study is one of the first to examine the medium-term impacts of
the COVID-19 pandemic on income and well-being inequality in Japan, limitations
exist. First, the sample size in our analysis is not necessarily large. Although we use
representative longitudinal data from JHPS and apply the sampling weight to recover
the population, the sample size is at most approximately 5,000, which is smaller thanthat of the surveys used by previous studies in other countries. Second, although we
examined medium-term distributional changes with the data until 2021, the pandemic
was such a devastating shock that it may have long-term effects on income and wellbeing. Further investigation should be conducted in future research.

17

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1

In the JHPS2022, after data cleaning, the correlation coefficient of household gross and disposable
incomes is 0.96, and that of household gross income and total amount of each source of income is 0.95.
2
The following questionnaire items regarding abstract tasks are included in the JHPS: (1) the length ofthe longest document that must be read; (2) the frequency with which knowledge of mathematics, such asalgebra, geometry, trigonometry, probability/statistics, and calculus, is required; (3) the frequency ofsolving problems that require at least 30 minutes to find a good solution; and (4) the proportion of theworkday spent managing or supervising other workers.
3
The Gini coefficients are calculated in the same way as the one presented in Figure 2. The coefficientsare the same as those presented by Tanaka (2022).
4
In Table 2, the income data are derived from the total of individual household members’ incomes. Thisdiffers from the total household income derived from the other questionnaire item presented in Figure 2.
5
In Figure 4, the violin plot is a mixture of box and kernel density plots, which shows the distribution,mode (peak of the distribution), and median (white dot) in the data.
6
Similar changes are observed in income shares before the pandemic (2018–2019).
7
Since there is no golden rule for measuring well-being inequality, the other indices for well-being
inequality, such as Jenkins’ inequality index (Jenkins, 2020) and percent maximum standard deviation(Delhey and Kohler, 2011), are also calculated. The results are shown in Appendix. The trends are almost
the same among those indices.
8
The low-income group shows a high rate of remote work, with self-employed individuals and those infamily businesses contributing the largest portion to this rate. The rate of remote work is higher among
self-employed individuals and those in family businesses than among regular employees, and the
proportion of self-employed individuals and those in family businesses is the highest in the low-income
group.
9
The regression analyses of sleeping hours, job satisfaction, and work engagement restrict the sample to
only workers.
10
In addition, we estimate the fixed-effects models by adding a dummy variable for overtime work andabstract task score as the independent variable. However, these variables do not change the significanceor magnitude of the coefficient of the cross terms.

21

Figure 1. Gini coefficient from 2019 to 2021 by the JHPS

Source: Authors’ calculation using the JHPS and the JHPS-COVID19

Figure 2. Relative Gini Coefficients from 2019 to 2021 by three surveys (2019=100)

Source: Authors’ calculation using the JHPS, Family Income and Expenditure, andComprehensive survey of living condition
Notes: The definition of income in Figure 2 is household gross income. Income in the
JHPS and Comprehensive survey of living condition is annual, and Family Income
and Expenditure is averaged monthly. Income is not equivalized.

22

Figure 3. Violin plot of the percentage of income change by income groups from2019 to 2020 and 2019 to 2021 using the JHPS(a) 2019 to 2020 by income group in 2019(b) 2019 to 2021 by income group in 2019

Source: Authors’ calculation using the JHPS
Notes: 1) The definition of income in Figure 3 is equivalized annual household gross income.
2) The income group is defined by the income in 2019.

Figure 4. Changes in income share by income groups
before and after COVID-19 using the JHPS(a) Change in income share, 2019 to 2020(b) Change in income share, 2020 to 2021

Source: Authors’ calculation using the JHPS
Notes: 1) The definition of income in Figure 4 is equivalized annual household gross income2) They are calculated with consequent two years balanced panel data

23

Figure 5. Means and coefficient of variation of well-being measures(1) Mental health (K6)(2) Weekday sleeping hours(3) Life satisfaction(4) Health satisfaction(5) Job satisfaction (workers only)(6) Work engagement (workers only)

Source: Authors’ calculation using the JHPS

24

Figure 6. Mean deviations of each well-being measure from the total mean foreach pre-pandemic income quintile group in February 2020, 2021, and 2022
(1) Mental health (K6)(2) Sleeping hours (weekday)(3) Life satisfaction(4) Health satisfaction(5) Job satisfaction (workers only)(6) Work engagement (workers only)

Source: Authors’ calculation using the JHPS
Note: Income quintile is calculated by the pre-pandemic income.

25

Table 1. Changes in household disposable income by income group
among the working population from 2019 to 2022

2019-2020

2020-2021

2021-2022

Ⅰ-1,402
9,710
-270
8,577
-9,683-6,853-2,589-13,930
22,362
268
4,329
18,301

Current income
Non-current income
Nonconsumption expenditures
Disposable income
Current income
Non-current income
Nonconsumption expenditures
Disposable income
Current income
Non-current income
Nonconsumption expenditures
Disposable incomeⅡ-7,103
11,794
1,076
3,615
8,849
-9,059-2,001
1,175
9,751
-835
3,469
5,448

Ⅲ
-7,78016,411-1,2299,861
13,683-14,709-918-109-2,129100
1,577-3,606(Unit: yen)ⅣⅤ
10,656 10,713
19,659 24,451
5,887
6,584
24,427 28,580-6,683 19,714-14,714 -17,467-4,196
2,633-17,200-386
20,656 15,340-2,067
-419
5,497
3,257
13,091 11,664

Source: Authors’ calculation using Family Income and Expenditure, following Tanaka (2022) Table 2
Note: 1) The definition of income in Table 1 is averaged monthly household income, and it is not
equivalized.
2) Current income is regular or replicable income that significantly affects households’
consumption behavior, such as earning, business income, asset income, and regularly receivingsocial security benefit. Non-current income is irregular income such as donation and includes
Special Cash Payment.

Table 2. Changes in household gross income by income group2019-2020 Current income
Non-current income
Totalised household income
2020-2021 Current income
Non-current income
Totalised household income

Ⅰ

Ⅱ

Ⅲ

(Unit: 10 thousands yen)
Ⅳ
Ⅴ
Total

-1.9
9.5
8.3
-9.9
-6.6
-17.2

-16.0
12.7
-3.6
16.8
-7.1
9.3

-1.1
14.8
14.2
-2.0
-10.5
-11.2

10.8
13.7
24.4
-13.1
-9.9
-22.8

-27.1
16.8
-10.6
-23.7
-6.8
-29.9

-8.7
13.5
4.9
-8.6
-8.3
-16.7

Source: Authors’ calculation using the JHPS
Note: 1) The definition of income in Table 2 is annual household gross income, and it is not equivalized.
2) Current income includes earning, business income, asset income, interest, remittances, pension,and other regularly received social security benefits. Non-current income is other than current
income and includes Special Cash Payment.

26

Table 3. Workstyle and tasks before and after the pandemic
Rate of remote work
Jan.2020 Jan.21
Ⅰ
Ⅱ
Ⅲ
Ⅳ
Ⅴ

1%
0%
0%
0%
3%

12%
9%
13%
16%
24%

Jan.22
18%
9%
9%
13%
19%

Rate of overwork
Jan.2020 Jan.21
5.8%
7.3%
8.3%
9.3%
8.9%

6.8%
6.4%
4.2%
9.9%
8.1%

Jan.22
8.1%
6.0%
6.0%
9.3%
5.7%

Abstract task score
Jan.2020 Jan.21
-29%
-14%
-12%
8%
19%

-32%
-12%
-8%
11%
26%

Jan.22
-21%
-15%
-5%
4%
22%

Source: Authors’ calculation using the JHPS
Note: 1) The definition of ratio of remote work is engaging in remote work more than once a week.
2) The definition of ratio of overwork is working more than 60 hours a week.
3) The calculations are made using working sample only.

27

Table 4. Determinant of well-being from 2020 to 2022: fixed model estimation results
Mental health(K6)

Sleeping hours

Life satisfaction

[1]

[3]

[5]

[2]

[4]

Pre-pandemic income quintile (ref: I & II)III-0.129-0.128-0.0255 -0.0257-0.187IV-0.444** -0.445**
-0.18

V

-0.187-0.18-0.0519-0.0519[6]

Health satisfaction
[7]

[8]

Job satisfaction[9]

Work engagement(UWES3)

[10]

[11]

[12]

0.348*** 0.348***0.270*** 0.269***

0.152

0.152

0.0526

0.0526-0.081-0.0916-0.116-0.116-0.0667-0.0667-0.081-0.0916-0.103** -0.102**0.580*** 0.581***0.311*** 0.312***0.438*** 0.439***0.038

0.0379-0.0485-0.0779-0.0881-0.109-0.0624-0.0624-0.0485-0.648***-0.642*** -0.114** -0.115**-0.0779-0.088-0.1090.929*** 0.926***0.652*** 0.649***

0.863*** 0.859***

0.179*** 0.179***-0.181-0.0482-0.0481-0.0784-0.0886-0.0886-0.108-0.108-0.062-0.0621

Year 2022 dummy 0.740*** 0.761***-0.0289-0.0401-0.0935** -0.107** -0.176***-0.186***-0.0323-0.0327-0.181-0.0784-0.0015-0.0289-0.0559-0.0549-0.0443-0.05-0.0504-0.0805-0.0815-0.0424-0.043

Intersection of pre-pandemic income quintle and Year 2022 dummy (Y2022)
III * Y2022-0.0436 -0.0413
0.0457 0.0481-0.0779 -0.0792-0.111

IV * Y2022
V * Y2022

Observations
Number of id-0.044-0.0784-0.0793-0.108-0.102-0.0696-0.0698-0.185-0.185-0.0485-0.0485-0.0737-0.0737-0.0838-0.0838-0.121-0.121-0.0637-0.0638-0.261-0.2420.0765*0.0744-0.0432-0.0547

0.116

0.107

0.00934 0.00397-0.0025-0.0023-0.178-0.179-0.0453-0.0453-0.071-0.0711-0.0807-0.0809-0.113-0.113-0.0596-0.0596-0.326*-0.2940.0812*0.07430.120*

0.101

0.185** 0.170**

0.01930.00314-0.023-0.0224-0.18-0.181-0.0455-0.0456-0.0718-0.0722-0.0816-0.114-0.114-0.0598-0.0599

Dummy of remote work

Constant-0.111-0.0821-0.326*0.0907**0.201**

0.156*

0.216**-0.0081-0.193-0.042-0.0785-0.0893-0.102-0.05484.432*** 4.433***

6.431*** 6.430***

5.846*** 5.845***5.587*** 5.586***5.293*** 5.292***

3.038*** 3.038***-0.111-0.111-0.0336-0.0336-0.0481-0.0481-0.0543-0.0543-0.0754-0.0753-0.0432-0.0432

8,395

8,395

5,815

5,815

8,424

8,424

8,428

8,428

5,815

5,815

5,807

5,807

4,846

4,846

3,518

3,518

4,844

4,844

4,847

4,847

3,519

3,519

3,510

3,510

Source: Authors’ calculation using the JHPS
Note: 1) ***, **, and * indicate statistical significance at the 1 %, 5 %, and 10 % levels, respectively.
2) The sample is restricted to workers only for columns [3], [4], [9], [10], [11], and [12].

28Appendix Table. Other well-being inequality indices
2019

2020

2021

2022

0.615

0.612

0.623

0.622

Percent maximum standard deviation
Standard deviation
Coefficient of variation
Job satisfaction
Jenkins' inequlaity Index (upward-looking status)

0.496
4.587
4.833

0.502
4.622
4.975

0.512
4.970
5.004

0.519
5.052
5.130

0.597

0.598

0.597

0.596

Percent maximum standard deviation
Standard deviation
Coefficient of variation
Work engagement
Jenkins' inequlaity Index (upward-looking status)

0.480
2.392
1.047

0.475
2.359
1.005

0.472
2.346
0.993

0.475
2.364
1.024

0.552

0.552

0.554

0.552

0.448

0.448

0.454

0.452

Standard deviation
Coefficient of variation
Health satisfaction
Jenkins' inequlaity Index (upward-looking status)

1.343
0.599

1.344
0.596

1.361
0.616

1.356
0.631

0.596

0.596

0.597

0.594

Percent maximum standard deviation
Standard deviation
Coefficient of variation
Life satisfaction
Jenkins' inequlaity Index (upward-looking status)

0.477
2.361
0.970

0.480
2.364
0.958

0.467
2.311
0.931

0.457
2.259
0.890

0.586

0.587

0.587

0.585

0.427
2.072
0.693

0.434
2.105
0.715

0.427
2.078
0.703

0.424
2.064
0.695

K6
Jenkins' inequlaity Index (upward-looking status)

Percent maximum standard deviation

Percent maximum standard deviation
Standard deviation
Coefficient of variation
Source: Authors’ calculation using the JHPS

29