Panel Data Research Center, Keio University
PDRC Discussion Paper Series

Gendered Effects of Telework on Subjective Well-being during Covid-19:
The Case of Japan
Zhi-xiao Jia, Risa Hagiwara
15 March, 2023
DP2022-008
https://www.pdrc.keio.ac.jp/en/publications/dp/8517/

Panel Data Research Center, Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan
info@pdrc.keio.ac.jp
15 March, 2023

Gendered Effects of Telework on Subjective Well-being during Covid-19: The Case of Japan

Zhi-xiao Jia, Risa Hagiwara
PDRC Keio DP2022-008
15 March, 2023
JEL Classification: I31; J16; J22
Keywords: Covid-19; gender; subjective well-being; telework; time allocation

Abstract
We investigate the effect of telework on employees’ subjective well-being (SWB) in Japan during theCovid-19 period, with an emphasis on the potential gender discrepancies in the telework effect. We
use the latest 4 available waves of Japan Household Panel Survey (JHPS) and its Covid-19 module
conducted between early 2020 to early 2021 (i.e., within one year of the Covid-19 outbreak). Telework
effect is estimated via both Fixed Effect (FE) and Difference in Difference (DiD) methods. We find that,telework decreases employees’ SWB during Covid-19. However, this negative effect is significant only
for males. We also find that, telework results in an expansion of housework and childcare hours forfemales. This is in line with gender expectations that females are caretakers, which would increase
female teleworkers’ affective utility following gender identity theory, and thus partially offset thenegative affective effect from the increasing domestic burden. On the other hand, males’ working hours
are decreased when teleworking, which contradicts gender norms and would hence attenuate maleteleworkers’ affective utility. Although males’ leisure time increases when teleworking, which would
improve their SWB, the combined effect is still significantly negative. Thus, our results imply thatJapanese males might still lexicographically value the conservative gender norm that males arebreadwinners.

Zhi-xiao Jia
Faculty of Economics, Keio University
2-15-45 Mita, Minato-Ku, Tokyozxjia@keio.jp
Risa Hagiwara
Faculty of Economics, Meikai University
1 Meikai, Urayasu-City, Chiba
hagiwara@meikai.ac.jp

Acknowledgement: We are grateful to the Panel Data Research Center (PDRC), Keio University
for providing the micro data of Japan Household Panel Survey (JHPS/KHPS) and its Covid-19
module, JHPS COVID-19 Special Survey (JHPS-COVID19). This study is supported by the Japan
Society for the Promotion of Science (JSPS) Grant-in-Aid for Specially Promoted Research(22H04911) and Grant-in-Aid for Young Scientists (18K12799).

Gendered Effects of Telework on Subjective Well-being during Covid-19: The Case ofJapan*

Zhi-xiao Jia

Risa Hagiwara

Keio University

Meikai University

zxjia@keio.jp

hagiwara@meikai.ac.jp

Abstract

We investigate the effect of telework on employees’ subjective well-being (SWB) in Japan during theCovid-19 period, with an emphasis on the potential gender discrepancies in the telework effect. We use
the latest 4 available waves of Japan Household Panel Survey (JHPS) and its Covid-19 module conducted
between early 2020 to early 2021 (i.e., within one year of the Covid-19 outbreak). Telework effect isestimated via both Fixed Effect (FE) and Difference in Difference (DiD) methods. We find that, telework
decreases employees’ SWB during Covid-19. However, this negative effect is significant only for males.

* We are grateful to the Panel Data Research Center (PDRC), Keio University for providing the micro data of Japan Household

Panel Survey (JHPS/KHPS) and its Covid-19 module, JHPS COVID-19 Special Survey (JHPS-COVID19). This study is
supported by the Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Specially Promoted Research (22H04911)
and Grant-in-Aid for Young Scientists (18K12799).
i

We also find that, telework results in an expansion of housework and childcare hours for females. This
is in line with gender expectations that females are caretakers, which would increase female teleworkers’
affective utility following gender identity theory, and thus partially offset the negative affective effect fromthe increasing domestic burden. On the other hand, males’ working hours are decreased whenteleworking, which contradicts gender norms and would hence attenuate male teleworkers’ affective utility.
Although males’ leisure time increases when teleworking, which would improve their SWB, the combined
effect is still significantly negative. Thus, our results imply that Japanese males might still lexicographicallyvalue the conservative gender norm that males are breadwinners.

Keywords: Covid-19, gender, subjective well-being, telework, time allocation

JEL classifications: I31; J16; J22ii

1. Introduction

The unprecedented worldwide epidemic of the Covid-19 virus has altered numerous aspects of human
life, one of which is manifested in the massive and rapid adoption of telework as an anti-infection
measure (Herrera et al., 2022, Okubo, 2022). Telework is a work style which allows employees toaccomplish work at home due to advances in information and communications technology (ICT).
Among its many claimed advantages (for instance, it enables a more flexible work scheduling, whichmight improve workers’ perceived autonomy (Petcu et al., 2021)), its potential to enhance work-lifebalance is considered to be the most prominent one (Tremblay et al., 2006, Tremblay and Thomsin, 2012,
Sullivan, 2012, Herrera et al., 2022). For instance, workers, especially female ones, could utilize remote
work to concentrate more on domestic activities (e.g., caregiving) simultaneously while working(Giménez-Nadal et al., 2018, Kurowska, 2020). However, opposite views state that telework would blurthe boundaries between paid work and family, and such “role ambiguity” would intensify the work-family
conflict (Glavin and Schieman, 2012, Sullivan, 2012). Since work-life conflict is one crucial determinantof individuals’ subjective well-being (SWB) (Shams and Kadow, 2019, Piccitto et al., 2022), it is thus
ambiguous that whether telework finally results in a positive effect on workers’ SWB. In fact, someliteratures have found that telework reduces individuals’ SWB (e.g., Song and Gao, 2020, Xiao et al., 2020,
Schifano et al., 2021, Senik et al., 2022, Gueguen and Senik, 2022).

In addition, as Chung and Lippe (2020) summarized, there exists potential gender discrepancies in theoutcome of telework. For instance, females may conduct more housework and childcare when working
1from home while male teleworkers may not do so, which could be attributed to the pre-existing gendereddivision of labor that females are caretakers (Piotrowski et al., 2019, Sato, 2022). If telework does lead togendered adjustments of subsequent behaviors, which associates with work-life interfaces, we could
expect that the effect of telework on SWB also varies by gender. In fact, the gender differences in theeffect of telework on SWB has also been documented in previous studies (e.g., Giovanis, 2017, GiménezNadal et al., 2018, Song and Gao, 2020, Senik et al., 2022).2 If the abovementioned gender norms areingrained, individuals are likely to be hesitant to act in ways far removed from their own gender. As aresult, adhering to ((reluctant) deviation from) the gender expectations would augment (attenuate)
individuals’ affective utility following the gender identity theory of Akerlof and Kranton (2000). From
this perspective, we could expect that more commitment to housework and childcare through teleworks
would increase the SWB for female teleworkers under the gender role division attitudes, since they could
better meet family demands (Sato, 2022).

We investigate the effect of telework on happiness, mental health (Kessler Psychological Distress Scale(K6)), and subjective productivity (self-evaluated job performance) in Japan during the Covid-19 period,and both overall effect as well as heterogenous effect by gender are estimated. Japan is suited to ouranalyses due to following reasons. First, it was not until the Covid-19 pandemic that the telework system
was adopted by Japanese firms. According to the aggregated data from Japanese Panel Study of
Employment Dynamics (JPSED), 14.1% and 15.5% of employees were eligible for telework in December

2 However, no consensus has been reached on which gender is happier while telecommuting.

2

2020 and December 2021, respectively, while the corresponding percentage was only 4.2% in December
2019. The fact that teleworkers have become a non-negligible share of all Japanese employees since theoutbreak of pandemic in early 2020 ensures the meaningfulness of our analyses. In addition, the rapid
expansion of telework should be attributed to the Covid-19 shock. Through utilizing such exogenous
switch to home-based work, the causality between telework and SWB could be inferred. Second, Japan isa country where the gender role attitude remains strong (Taniguchi and Kaufman, 2014, Piotrowski et al.,2019, Sato, 2022). As Kurowska (2020) illustrated, the “doing gender effect” (West and Zimmerman,
1987), that is, to behave in line with gender stereotypes, is stronger in societies with more traditional
gender norms. We therefore expect the existence of the gendered behavioral adjustments in response to
the change of workplace, which in turn, implies the potential gendered effect of telework on SWB.

One contribution of this study is to focus on the gender-specific effect of telework, and try to explainsuch gender discrepancies by gender identity theories. Hence, this paper could be expected to shed somelight on the gender normative views in Japan. Another contribution of our work is to use an intensive
household panel survey which has four available waves from early 2020 to early 2021 (i.e., within one yearof the Covid-19 outbreak). Literatures on the effect of working from home on SWB during the Covid19 period using large-scale micro surveys are sparse (e.g., Senik et al. (2022) for German, Etheridge et al.
(2020), Gueguen and Senik (2022) for Britain, Schifano et al. (2021) for several E.U. nations),3 and one

3 Some other studies relied on self-collected data (e.g., Xiao et al., 2020). These inferences, however, are likely to be biased dueto the small sample size and unrepresentative sampling.
3reason is the lack of intensive (e.g., biannual, or monthly) longitudinal survey covering 2020 to 2022
especially outside European countries and the U.S. The SWB and the prevalence of telework kept
fluctuating in response to the seriousness of pandemic (e.g., the number of infections/deaths)
throughout 2020 to 2022, and such fluctuation is also associated with the strictness of anti-Covid-19
policies (Clark and Lepinteur, 2022, Okubo, 2022). Japanese government issued the so-called State ofEmergency Declaration (SED) in April to May of 2020 national-wide, a kind of pandemic lockdown,
which accelerated the switch to telework. SED was rescinded in the remaining months of 2020, while it
was re-implemented in metropolitan areas (e.g., Tokyo, Osaka, and Aichi) in January to March of 2021.
The resulting frequent variations in SWB and telework rate may not be sufficiently captured by merely
the annual or biennial surveys, and the estimates would be attenuated if relied on such datasets. This
paper extends the analysis of the telework effect on SWB during the Covid-19 period to Japan, relying
on the latest 4 available waves of Japan Household Panel Survey (JHPS) and its Covid-19 module, JHPS
Covid-19 Special Survey (JHPS-Covid19), conducted in February 2020, May-June 2020, OctoberNovember 2020, and January 2021, i.e., since the virus outbreak. Hence, our data could be expected to
exploit the abovementioned variations to infer the effect of telework on SWB. Considering that theextensive transfer to remote work took place in April 2020 (that is, with the promulgation of SED)
although the telework had already been gradually adopted by Japanese firms to avoid infections in
February to March 2020, we also estimate the effect of such switch to telework using a Difference in
Difference (DiD) approach.

4

We found that, telework decreases SWB during the Covid-19 period. However, this negative effect issignificant only for males, especially husbands (married males). By analyzing the effect of telework on
how individuals allocate their time for paid work and domestic production, we found the doing gendereffect which could be used to interpret our findings that females are less negatively affected by telework.
The remainder of this paper is structured as follows: Section 2 introduces the related literature, Section
3 describes the data and empirical method, Section 4 presents the results, and Section 5 concludes.

2. Literature review

When it comes to the term “work from home”, existing literatures have examined both the effect oftelework or telecommuting, that is, formal paid work at home during normal business hours (Song andGao, 2020, Kazekami, 2020), and informal unpaid overtime work at home (Ojala et al., 2014, Song andGao, 2020). We do not estimate the effect of the latter in the current study, since our data does notcontain such information. Throughout this paper, we use the term “telework”, “work from home”,“home-based work”, etc. interchangeably, while all of them refer to the home-based formal paid work
during normal business hours. One mainstream of literature investigated the effect of working fromhome on perceived work-life balance and intra-household time allocation. For example, Giovanis (2017)
and Giménez-Nadal et al. (2018) documented a reduction (increase) in working hours (leisure time andhousework time) due to telework.4 In recent years, a few literatures have used large-scale micro data to

4 Meanwhile, Ojala et al. (2014) found that informal overtime work at home induces a neglection of home issues, and makes

spouse consider that the respondent works too hard.
5directly estimate the effect of working from home on employees’ SWB. Giménez-Nadal et al. (2018) used
the Well-being Module of American Time Use Survey (ATUS) conducted in 2012 and 2013. Their OLS
inference showed that, male teleworkers enjoy higher “net” happiness (i.e., the feeling of happiness minus
the average feeling of pain, sadness, fatigue, and stress) than male commuters. On the other hand,telecommuting exerts an insignificant minor negative effect on net happiness for female teleworkers.

Song and Gao (2020) pointed out that OLS takes no account of unobservable induvial heterogeneities,
which might distort the estimation. They used 2010, 2012 and 2013 Well-being Module of ATUS, andestimated by fixed effect (FE). Although ATUS is cross-sectional, each respondent reports her activity(working, doing chores, etc.) and instantaneous feeling (happy, pain, etc.) three times on the survey day.
In other words, each respondent is observed three times, which means that the longitudinal inference isfeasible. They found that telework brings more stress for both females and males. Specifically, they foundthat the effect of telework varies with parental status (whether having child or not). For instance, mothers(fathers) who telework feel less happy (more stressed), while telework does not affect the instantaneous
feelings of child-less females and males.5 Such gender inequality in SWB was also illustrated in Giovanis(2017), in which female teleworkers were found to be happier than female commuters, while nosignificant gap in happiness between teleworkers and non-teleworkers was found for males. In a recent
study, Kazekami (2020) used Japanese Panel Study of Employment Dynamics (JPSED) collected in 2017and 2018, and estimated the effect of telework on SWB in Japan. Using ordered logit regression, they

5 Song and Gao (2020) also estimated the effect of bringing work home. They found that it deteriorates SWB.

6

showed that teleworkers are happier and enjoy higher life and work satisfactions than their commuter
counterparts. However, teleworkers also suffer from more stress.

We extend the analyses to the Covid-19 period using a comprehensive panel household survey spanning2020 to 2021 with 4 waves (Section 3 shows details). Several studies have estimated the effect of teleworkon employment, income, health and living habits, etc. during the pandemic. For instance, Angelucci et al.
(2020) relied on the Understanding America Study (UAS), a panel survey, conducted from March to Julyof 2020 with 9 waves. Using FE method, they found that non-remote workers are more vulnerable tounemployment, and show higher subjective infection risk during the pandemic. Their work conveys thatteleworkers are more satisfied during the pandemic. Meanwhile, studies directly estimating the telework
effect on SWB (life satisfaction, mental stress, subjective productivity, etc.) have also been accumulated.
These studies relied on either independently-designed or large-scale micro survey and documented either
a detrimental effect of telework (Xiao et al., 2020, Schifano et al., 2021), or an insignificant effect(Etheridge et al., 2020). Some studies analyzed the effect of stringency of lockdown and found that it
deteriorates life satisfaction (Clark and Lepinteur, 2022), which implies a negative effect of telework on
SWB, considering that the strength of lockdown, or, the seriousness of the pandemic, is positively
corelated with the propensity of telework (Okubo, 2022).

Specifically, Gueguen and Senik (2022) used the UK Household Longitudinal Survey (UKHLS) and its
Covid-19 Module. They adopted a difference in difference (DiD) approach, and found that for individuals
who are able to work from home during the pandemic (i.e., “teleworkable” ones), switching to telework
7

after the outbreak of Covid-19 deteriorates their mental health. In addition, such negative effect is more
prominent during the beginning phase of Covid-19.6 Using the method similar to Gueguen and Senik(2022), Senik et al. (2022) also documented a detrimental effect of working from home on life satisfactionin Germany.7 Just as pre-pandemic studies (e.g., Giovanis, 2017, Song and Gao, 2020), post-pandemic
ones also illustrated a heterogeneous effect of telework by gender and marital/parental status. For
example, Senik et al. (2022) found that men suffer more (women suffer less) from telework, while thisnegative effect is alleviated for married males (exacerbated for females with children above school age).
On the other hand, Lyttelton et al. (2020)’s descriptive analyses showed that, females are more vulnerable
to negative affect (anxiety, loneliness, depression, and hopelessness) than males.

3. Data and methods

6 Gueguen and Senik (2022) and some other studies (Lyttelton et al., 2020, Senik et al., 2022) also discussed the intensity oftelework i.e., the difference in subjective well-being between those who always telework and those who mix telework andoffice-based work. We do not emphasize this issue in this study, since our data does not contain reliable measurement oftelework intensity (See Section 3.2 and Footnote 7 for details).
7 In addition, Ishii et al. (2021a) relied on a Japanese cross-sectional survey conducted in April-May 2020. Their OLS and

instrumental variable (IV) inferences showed that telework increases the anxiety that one cannot receive the proper
information about work affairs. Ishii et al. (2021b) relied on Japanese Household Panel Survey (JHPS) and JHPS Covid-19
Module, the same survey series as our study. Using February 2020 wave, May-June 2020 wave, and October-November 2020
wave, they found that telework improves the mental conditions of individuals who keep teleworking throughout 2020 using
first difference (FD) method. Compared with Ishii et al. (2021b), we use one additional wave of survey (February 2021 wave).
Besides, we estimate the catch-all effect of telework, and also estimate the effect of transfer from office-based work to homebased work using DID approach. Heterogenous effect by gender and marital status is also studied. Ishii et al. (2021a) and Ishii
et al. (2021b) are only available in Japanese.
83.1 Japan Household Panel Survey (JHPS) and Japan Household Panel Survey Covid-19
Special Survey (JHPS-Covid19)

We use the Japan Household Panel Survey (JHPS) and Japan Household Panel Survey Covid-19 Special
Survey (JHPS-Covid19) conducted by Panel Data Research Center (PDRC) of Keio University. JHPS isan annual Japanese national-representative household longitudinal survey focusing on individuals aged
20 and above, and it was initiated from 2004 with a sample of 4,000 households and approximately 7,000
individuals. In order to enlarge the sample size and to reduce the sample attrition problem, new samples
were supplemented in 2007, 2009, 2012, and 2018. JHPS covers topics like education and training,
employment, time allocation, intra-household relation and living arrangement, SWB, household income
and expenses, etc., and it is conducted in each February.

Since 2020, the outbreak of Covid-19, JHPS-Covid19, a supplementary panel survey of JHPS, has been
conducted twice a year. The first wave of JHPS-Covid19 was conducted in May-June of 2020, right after
the promulgation of the first SED during April-May 2020 (Section 1 introduced the background of SED).
The 1st JHPS-Covid19 surveyed those who responded to 2020’s JHPS survey, and the response rate is
71%. The 2nd JHPS-Covid19 was conducted in October-November of 2020, and it surveyed those who
responded to the 1st JHPS-Covid19 with a response rate of 83%. JHPS-Covid19 focuses on employment,
health, time allocation, and SWB in the Covid19 period and attitudes towards pandemic.

The latest waves available are first 2 waves of JHPS-Covid19 and 2021’s JHPS. As a side note, JHPS20219was conducted in February 2021, during which the government issued the second SED to certain
metropolitan areas such as Tokyo. We append JHPS and JHPS-Covid19 to construct an integrated panel(just like what Senik et al. (2022) and Gueguen and Senik (2022) did), and the structure of this panel isshown in the following table:

Table 1 Structure of the integrated panel
survey name

JHPS2004...

JHPS2019

JHPS2020

survey period

Feb. 2004...

Feb. 2019Feb. 2020

1

...

16

17

wave no.

JHPSCovid19_1
May-Jun.
2020
18

JHPSCovid19_2
Oct.-Nov.
2020
19

JHPS2021
Feb. 2021
20

We use wave 17 (JHPS2020), wave 18 (1st JHPS-Covid19), wave 19 (2nd JHPS-Covid19), and wave 20(JHPS2021), i.e., the waves since the outbreak of the pandemic. Following Giménez-Nadal (2018), Songand Gao (2020) and Kazekami (2020), we drop unemployed and self-employed individuals, as well as
retired individuals, i.e., those aged 65 or above. After neglecting outliers and missing values, we have 4,917
observations in our basic specification.

3.2 Main variables and measurement

Since wave 8 (JHPS2011), the following question regarding SWB is asked about:(abbreviated) “Please report your feeling of happiness during the past week, on a scale of 0 to 10, with
0 being extremely unhappy, and 10 being extremely happy.”

We use this weekly happiness to measure individual’s affective utility. Throughout this study, we treat thisvariable as continuous and use linear regression design. As previous studies pointed out and shown (e.g.,
10Ferrer-i-Carbonell and Frijters, 2004), using linear model leads to similar results as using nonlinear models
such as ordered logit.

Since wave 16 (JHPS2019), the Kessler Psychological Distress Scale (K6) has been introduced to thesurvey to capture mental stress and illness. Respondents report their feelings regarding nervousness,
hopelessness, fidgetiness, depression, tiredness, and worthlessness during the past 30 days on 1-5 scales,in which 1 (5) refers to always (never) having a certain feeling. Following, for instance, Gueguen and Senik(2022), we sum answers to these 6 items to construct a continuous mental health score, i.e., K6 score,running from 6 (extremely mentally unhealthy) to 30 (extremely mentally healthy). Additionally, JHPS
and JHPS-Covid19 also inquire about self-evaluated job performance, i.e., subjective productivity, in thepast month on a 0-10 scale since wave 16, in which 0 and 10 refers to worst performance and best
performance, respectively. We also estimate the effect of working from home on K6 score and subjective
productivity, and the latter is also linearly treated as weekly happiness.

In the first wave of JHPS-Covid19, both days of telework per week and hours of telework per week areobserved in: (1) the 4th week of February 2020, (2) the 4th week of March 2020, and (3) the 4th week ofApril 2020. In the second wave of JHPS-Covid19, days and hours of telework per week are observed in:(1) the 5th week of July 2020, (2) the 5th week of August 2020, and (3) the 3rd week of September 2020.
On the other hand, JHPS2021 asks about the percentage of telework in: (1) the 4th week of April 2020,(2) the 5th week of August 2020, and (3) the 4th week January of 2021 on 0-10 scales, in which 0 and 10
refers to never telecommuting and always telecommuting, respectively.
11

We use telework status in the 4th week of April 2020, 3rd week of September 2020, and 4th week of January
2021 from 1st JHPS-Covid19, 2nd JHPS-Covid19, and JHPS2021, respectively. As for JHPS2020, since it
was conducted in February 2020, hence we use telework status in the 4th week of February 2020 from 1st
JHPS-Covid19 to measure telework status of JHPS2020’s respondents. As JHPS2020, 1st JHPS-Covid19,
2nd JHPS-Covid19, and JHPS2021 was respectively conducted in February 2020, May-June 2020,
October-November 2020, and February 2021, and SWB is measured within the past 7 days (happiness)or 30 days (K6 and subjective productivity) of these periods, hence using telework statuses closest tothese periods ensures a more convincible relation between telecommuting and SWB. Telework dummy
equals 1 if both days and hours of telework are greater than 0,8 or the percentage of telework is greaterthan 0.

3.3 Happiness and telework in Japan: before and after the outbreak of Covid-19

Figure 1 shows how average weekly happiness evolves before and after the outbreak of pandemic. In
general, there exists a slight downward trend in average weekly happiness: from around 6.20 in 2011 to
8 In JHPS and its Covid-19 module, some respondents who telework more than one day per week report that their hours oftelework per week equals zero (in pooled sample of 1st and 2nd rounds of JHPS-Covid19, for instance, 49 out of 793
individuals with weekly telework days more than one report that their weekly telework hours is zero). Hence, we use both days
and hours of telework to identify teleworkers (in comparison, Ishii et al. (2021b), which also used JHPS and its Covid-19
module to infer the telework effect (See footnote 6), only relied on days of telework to define telework dummy). We alsochoose not to study the difference between those who always telework versus those who sometimes telework, since without
explicitly measuring the level of telework intensity, “pure” teleworkers cannot be distinguished from those who mix teleworkand office-work. Although JHPS2021 directly asks the percentage of telework, which makes such inference possible, only
using one wave of data means an inconvincible estimates due to a much smaller sample and the infeasibility of panel approach.
12around 6.00 in 2021. Interestingly, a relative sharp drop appears immediately after the outbreak of Covid19: from 6.08 in wave 17 to 5.74 in wave 18.9 After wave 18, it rebounds to around 6.00, which matches
the general trend.

Figure 1 Evolvement of happiness

J2021(20)

2ndJC(19)

1stJC(18)

J2020(17)J2019(16)

J2018(15)

J2017(14)J2016(13)J2015(12)J2014(11)J2013(10)J2012(9)J2011(8)weekly happiness
5 5.25 5.5 5.75 6 6.25 6.5 6.75 7

average happiness before and after Covid19

survey (wave)

Note: J and JC refers to JHPS and JHPS-Covid 19, respectively.

Figure 2 shows the evolvement of telecommuting rate since the outbreak of Covid-19. As it shows,roughly 5% of respondents did some telework in the 4th week of February 2020, around which the virus
had just begun to spread, and firms had begun to adopt telework system. Such adoption was accelerated
by the first SED which was in effect nation-wide in April to May 2020, and the telework rate rose tonearly 25% in April 2020. The percentage of telework decreased by 10% in September 2020, i.e., after
the first SED, while rebounded to nearly 20% in February 2021, during which the government issued thesecond SED in restricted areas.10 Patterns in Figure 1 and 2 illustrate that both SWB and telework rate

9 Regressing weekly happiness on wave dummies using FE method reveals a similar pattern of happiness evolvement as thatin Fig.1.
10 Regressing telework dummy on wave dummies using fixed effect method reveals a similar trend as Fig.2.

13fluctuate during the Covid-19 period in response to the seriousness of the pandemic and/or the strictnessof anti-pandemic policies, which justifies the importance of using intensive longitudinal surveys covering
this period to better capture such fluctuations in order to alleviate attenuation in estimates.

Figure 2 Evolvement of telework rate

J2021(20)

2ndJC(19)

1stJC(18)

J2020(17)0percentage of telework
.05
.1
.15
.2
.25

.3

telework since the outbreak of covid19

survey (wave)

Subpanel (a), (b), (c) and (d) of Figure 3 presents the distribution of happiness by telecommuting status
in wave 17, 18, 19 and 20, respectively. In all these periods, happiness distributions show a similar pattern:
teleworkers seem to be slightly happier than their commuter counterparts. However, such crude
comparison does not take other covariates, especially unobservable individual characteristics (i.e.,
individual fixed effect) into consideration, which might lead to biased conclusions.11

Figure 3 Distribution of happiness by telework status(A) wave 17 (JHPS2020)

11 We also used Pooled OLS to estimate the effect of telework on happiness as preliminary analyses. After controlling several

personal and occupational characteristics (these covariates will be introduced in Section 3.4), telework exerts a significant
positive effect on happiness, which is consistent with the patterns shown in Figure 3. These results are not provided as they
are problematic without considering unobservable individual characteristics.
14

.25

happiness by telework status: wave17
telework

0.05

density.1.15.2

non-telework

0

1

2

3

4

5
6
happiness

7

8

9

10

kernel = epanechnikov, bandwidth = 0.9267(B) wave 18 (1st JHPS-Covid19).25

happiness by telework status: wave18
telework

0.05

density.1.15.2

non-telework

0

1

2

3

4

5
6
happiness

7

8

9

10

kernel = epanechnikov, bandwidth = 0.6027(C) wave 19 (2nd JHPS-Covid19).25

happiness by telework status: wave19
telework

0.05

density.1.15.2

non-telework

0

1

2

3

4

5
6
happiness

7

8

9

10

kernel = epanechnikov, bandwidth = 0.7271(D) wave 20 (2nd JHPS-Covid19).25

happiness by telework status: wave20
telework

0.05

density.1.15.2

non-telework

0

1

2

3

4

5
6
happiness

7

8

9

10

kernel = epanechnikov, bandwidth = 0.6267

15

3.4 Estimation methodologies

3.4.1 Effect of working from home since the outbreak of Covid-19

We first estimate the following equation:ℎ!" = # + %&'!" + ()!" + *+! + ," + -! + .!"(1)

Subscript / and & indexes individual and survey round (& = 17, 18, 19, 20), respectively. ℎ!" and &'!"
refers to happiness and telework, respectively. Measurement and descriptive characteristics of these twohave been shown in Section 3.2 and 3.3. In addition to happiness, self-evaluated job performance as wellas K6 are also used as dependent variables as Section 3.2 discussed. )!" and +! refers to time-variant andtime-invariant covariates, respectively. We control for several personal and occupational characteristics,
including categorical age dummies (20s (ref-group), 30s, 40s, 50s, and 60-64), categorical education
dummies (junior high school (ref-group), senior high school, junior college, university, graduate school,
and others), gender (female dummy), marital status (whether having spouse), log month income, weekly
working hour, whether joining labor union, whether being regularly employed, 17 dummies for industryof workplace, 5 dummies for size (i.e., number of staffs) of workplace, as well as 7 dummies for living
area. Daily one-way commuting minutes and weekly chore hours (housework hours plus childcare hours)
are also controlled. At last, we incorporate a dummy for living with people who need care, and a dummy
for the presence of preschool aged (aged 5 or below) child. Descriptive statistics for these covariates areprovided in the Appendix Table A-1. # and .!" refers to intercept and error term, respectively. %, (, and16

* are the coefficients to be estimated, and % refers to the telework effect. ," is the wave fixed effect (wavedummies). -! refers to unobservable individual heterogeneities which is assumed to simultaneously affecthappiness and telework status. Hence, we use fixed effect (FE) method to estimate Equation (1).

3.4.2 Effect of the switch to telework: Difference in difference (DiD) estimation

Equation (1) is estimated in wave 17-20. However, in February 2020, around which the wave 17 took
place, even though the virus had begun to spread and telework had been adopted by some leading firms,
the massive policy-oriented transfer to telework has not occurred yet. Thus, in addition to the average
effect of telework since the outbreak of Covid-19, we will also investigate whether the transfer fromoffice-based work to home-based work from wave 17 to wave 18 (as Figure 2 depicts) affects happinessor not. In other words, we are interested in whether happiness rises or falls for individuals who holdteleworkable jobs after such transfer. Hence, we apply the following DiD estimation following Gueguen
and Senik (2022):ℎ!" = # + %# 01!" + %$ &'2345! + %% (01!" × &'2345! ) + %& 9!" + ()!" + *+! + ," + -! + .!" (2)In which

9!" = 01!" × &'2345! × &'!"

(3)

In Equation (2), 01!" is a dummy variable equals 1 if the survey was conducted in wave 18, 19, or 20, i.e.,completely after the transfer. &'2345! is a time-invariant dummy indicating those who have ever17

teleworked in either wave 18, 19, or 20 at least once. In other words, &'2345! = 1 denotes teleworkable
respondents (for instance, if Ms. Eve performed telework in wave 18 while went back to office in wave
19 and 20, then &'2345! equals 1 for her). 9!" is the treated variable, and its coefficient %& captures theeffect of transfer to home-based work for those who are able to telework (for Ms. Eve, 9!" respectively
equals 0 (0×1×0), 1 (1×1×1), 0 (1×1×0), and 0 (1×1×0) in wave 17, 18, 19, and 20). Since we concentrateon the effect of switch to telework that took place between wave 17 and 18, we drop respondents who
have already worked from home before such switching, that is, teleworkers in wave 17, when performing
DiD inference.

It should be noted that the telework status in wave 18 is observed in April 2020. Around that period, in
addition to the firms that had been adopting telework system, the nation-wide SED exerted an extra
exogenous shock prompting teleworkable workers to work from home. However, this extra governmental
shock is absent in wave 19, during which the SED had ended, and it is weaker in wave 20, during which
the SED was implemented only within certain prefectures. Hence, we could here conclude that theexogeneity underlying the transfer to telework is (much) stronger in wage 18, which means that it isimportant to disentangle the effect in wave 18 to those in wave 19 and 20. Thus, the following equation
will be estimated:ℎ!" = # + %# 01!" + %$ &'2345! + %% (01!" × &'2345! ) + %&$' (9!" × '$'!" ) + %&$( (9!" × '$(!" ) +%&%# (9!" × '%#!" ) + ()!" + *+! + ," + -! + .!"

(4)

18

In which dummy '$'!" , '$(!" , and '%#!" indicates wave 18, 19, and 20, respectively. Hence, %&$' , %&$( ,and %&%# refers to the effect of transfer to telework in wave 18, 19, and 20, respectively.

4. Results and discussion

4.1 Catch-all effect of telework on subjective well-being

Fixed Effect analyses

We first estimate the % in Equation (1), that is, the average effect of home-based work on SWB since theoutbreak of Covid-19 in early 2020. Results are displayed in Table 2.

Table 2 Catch-all effect of telework (FE)telework(1)
WH-.15438*(.08314)6527.014062551(2)
WH-.26789***(.09117)4917.018672127(3)
WH-.27557***(.09667)4647.022962053(4)WH-.2755***(.09685)4630.024572048(5)
K6-.33715*(.19395)
4761.055892059(6)SP-.29345***(.1088)4760.045342057

Observations
R-squared
groupspersonal and
No
Yes
Yes
Yes
Yes
Yes
occupational traits
time allocation
No
No
Yes
Yes
Yes
Yes
living arrangements
No
No
No
Yes
Yes
Yes
Robust standard errors are in parentheses. *** p<.01, ** p<.05, * p<.1.
WH and SP refers to weekly happiness and subjective productivity (self-evaluated job performance), respectively. Wave
fixed effect and constant term are controlled in each specifications.

Only wave dummies are additionally controlled in Column (1), and telework exerts a marginally significant
negative effect on short-term happiness (-0.154 with 0.05≤P<0.10). In Column (2), personal andoccupational characteristics (see Section 3.4.1) are controlled, and the effect of telework becomes greaterand much more significant (-0.268 with P<0.01). Estimates remain stable after time allocation (commutetime and chore time) and living arrangement (co-residence with people in need of care and having pre19

school aged kids) are accounted for as Columns (3) and (4) illustrate. Meanwhile, telework is also
detrimental to mental health and subjective productivity, although the estimates on mental stress is only
significant at 0.05≤P<0.10 level. These results indicate that, in general, telework deteriorates individuals’
SWB since the outbreak of the pandemic.

Difference in Difference analyses

We next estimate the %& in Equation (2), that is, the effect of the transfer from office-based work to
telework due to pandemic shock on SWB for potential teleworkers, i.e., respondents who have everworked from home in either April 2020, September 2020, or January 2021 (that is, completely after themassive switch to home-based work). Table 3 presents estimation results. For employees who holdteleworkable jobs, transfer to telework leads to a significant drop of weekly happiness (-0.320 with P<0.01)
and self-evaluated work performance (-0.318 with 0.05≤P<0.10). On the other hand, such switch does
not significantly deteriorate mental condition, although a negative effect is detected (-0.250).

Table 3 Catch-all effect of switch to telework (DiD)
(1)
(2)
(3)
WH
K6
SP
-.3202***-.24987-.31815**
Treated (T)(.11353)(.2361)(.12616)-.01631-.51443***.11744wave18+ (cd)(.08754)(.16196)(.07616).09943-.14789.08206
wave18+×teleworkable (cd×twable)(.14845)(.27917)(.14038)
Observations
4517
4648
4644
R-squared.02643.05703.04432groups
2004
2015
2013
Robust standard errors are in parentheses. *** p<.01, ** p<.05, * p<.1.
All covariates are controlled.

Table 4 presents the estimates of %&$' , %&$( , and %&%# in Equation (4), that is, the respective effect of20

switch to home-based work in wave 18, wave 19, and wave 20. We detect a substantial and significant
drop in affective utility in April 2020 (-0.414 with P<0.01), immediately after the promulgation of thenation-wide SED which exogenously sped up the switch to telework. In wave 19 and 20, during which
the SED was not issued or not implemented nationwide, however, the estimates become much smaller
and insignificant. On the other hand, we find a negative effect on self-evaluated job performance in both
wave 18 and 20, and the estimates in these two waves remain almost the same, despite the fact that SED
in January 2021 was far less strict than that in April 2020. Such pattern implies that, the potential
mechanisms behind the effect of telework on affective utility and that on subjective productivity might
be different.

Table 4 Catch-all effect of switch to telework: evolvement (DiD)
(1)
(2)
(3)
WH
K6
SP
treated in wave 18-.41422***-.3185-.35184**(T18)(.13495)(.28665)(.14457)
treated in wave 19-.1325-.35438-.17063(T19)(.17233)(.34821)(.1913)treated in wave 20-.28017-.02685-.36678**(T20)(.175)(.3479)(.18624)-.02488-.55413***.12514wave18+ (cd)(.09061)(.16304)(.07651).10027-.1457.0822
wave18+×teleworkable (cd×twable)(.14859)(.27914)(.14039)
Observations
4517
4648
4644
R-squared.0272.05731.04472
group
2004
2015
2013
Robust standard errors are in parentheses. *** p<.01, ** p<.05, * p<.1.
All covariates are controlled.

Discussions

Our finding that working from home is detrimental to employees’ SWB is consistent with those in, forinstance, Song and Gao (2020), Xiao et al. (2020), Schifano et al. (2021), Senik et al. (2022) and Gueguen
21and Senik (2022). According to Chung and Lippe (2020)’s summarization that work-family balance is oneof the crucial pathways through which telework might affect SWB, this negative effect might partiallyindicate that, telework intensifies work-life conflict in Japan, for instance, the blurring of boundaries
between household and workplace (Glavin and Schieman, 2012), which could be manifested as the
expansion in both working hours and domestic burden (Chung and Lippe, 2020). We will dig into thisissue in the next section. In addition, our DiD results that the switch to telework due to the epidemic
shock deteriorates happiness and such deteriorated effect was most prominent at the starting phase of
Covid-19 while disappeared afterward is consistent with those in Senik et al. (2022) and Gueguen andSenik (2022). Though it could have been interpreted as, based on Gueguen and Senik (2022), individuals
emotionally adapted to telecommuting gradually, the transfers to telework in wave 19, around which the
SED was not issued, and wave 20, around which the SED was implemented only in restricted areas, areless exogenous than that in wave 18, around which the SED was implemented nation-wide. Hence, we
hesitate to claim here that our results support the adaptation hypothesis (Gueguen and Senik, 2022).
Nevertheless, our findings do unfold that, telework decreases Japanese employees’ SWB, especially
immediately after that policy-oriented sudden switch to home-based work.

4.2 Heterogenous effect of telework on subjective well-being

Fixed Effect analyses

Panel (A) of Table 5 presents the estimates of % in Equation (1) by gender. Columns (1)-(2), Columns

22

(3)-(4), and Columns (5)-(6) displays estimates for happiness, K6, and subjective productivity, respectively.
We find that, males suffer more from home-based work. That is, the negative effect of telework is more
substantial for males. On the other hand, telework does not significantly decrease females’ SWB. Panel(B) shows estimates for husbands and wives (married males and females). Similarly, husbands are foundto be more unhappy and mentally stressed, as well as have poorer perceived job performance whentelecommuting. Meanwhile, wives’ SWB is not significantly decreased during at-home work. Panel (C)presents estimates for unmarried males and females.12 SWB of both unmarried males and females are
not significantly affected by telework.13

Table 5 Heterogonous effect of telework (FE)(A) males (m) and females (f)
(2)
WH: f
-.18329(.16579)
1965.04117
912

(3)
K6: m
-.5226**(.23273)2728.05737
1138

(4)
K6: f
-.16981(.34321)2033.08715
921

(5)
SP: m
-.34022**(.1344)2722.05652
1135

(6)
SP: f
-.19175(.18403)2038.05911
922

(2)
WH: w
-.19586(.19457)1269.05101
597

(3)
K6: h
-.66116**(.25767)2049.06688
827

(4)
K6: w
-.45458(.44581)1320.11588
604

(5)
SP: h
-.39611***(.15291)2045.06196
826

(6)
SP: w
-.13644(.24212)1327.07575606(C) unmarried males (um) and unmarried females (uf)
(1)
(2)
(3)
(4)
(5)
WH: um
WH: uf
K6: um
K6: uf
SP: um-.08267-.15576.20541.35242-.07102
telework(.30382)(.30464)(.54753)(.52121)(.29448)
Observations
667
696
679
713
677
R-squared.05939.05262.07213.10061.09273groups
313
319
315
321
313
Robust standard errors are in parentheses. *** p<.01, ** p<.05, * p<.1. All covariates are controlled.

(6)
SP: uf-.27349(.28497)711.10619320

telework
Observations
R-squared
groups(1)
WH: m-.34089***(.11956)2665.034281136(B) husbands (h) and wives (w)telework
Observations
R-squared
groups(1)
WH: h-.39792***(.12743)
1998.04215827

12 It should be noted that unmarried individuals include those who are not yet married, and divorced or widowed ones.
13 Nevertheless, telework is found to insignificantly improve unmarried employees’ mental condition.

23

Difference in Difference analyses

Panel (A) of Table 6 displays the estimates for %& in Equation (2) for all males and females, which reveals
a significant detrimental effect of switch to telework on happiness, mental condition, and subjective
productivity of male potential teleworkers. However, no significant effect is detected for females. Panel(B) shows results for husbands and wives. Teleworkable husbands who switch to home-based work arefound to be less happy, as well as have worse mental condition and perceived work performance. On theother hand, teleworkable wives’ SWB does not significantly fall due to such transfer. Panel (C) provides
estimates for unmarried males and females. Interestingly, we find that transfer to telework makes mental
condition of unmarried females who hold teleworkable jobs less stressed.

Table 6 Heterogonous effect of switch to telework (DiD)(A) males (m) and females (f)
(1)
(2)
(3)
(4)
WH: m
WH: f
K6: m
K6: f
-.446***-.10415-.48251*.04529Treated (T)(.13542)(.19487)(.26302)(.44261)-.03164-.00904-.24906-.95193***
wave18+ (cd)(.11385)(.14227)(.20601)(.26865)
wave18+×telework.26943-.27498-.13829-.24097
able (cd×twable)(.18472)(.24213)(.31592)(.53833)
Observations
2600
1917
2662
1986
R-squared.04025.04349.05736.09272groups
1112
892
1114
901
(B) husbands (h) and wives (w)
(1)
(2)
(3)
(4)
WH: h
WH: w
K6: h
K6: w
-.47954***-.16604-.56161**-.62514
Treated (T)(.14605)(.23043)(.2842)(.5454).01413-.18902-.09284-1.2655***
wave18+ (cd)(.13519)(.18016)(.24156)(.33435).21032-.23217-.35904.85636
wave18+×teleworkable (cd×twable)(.21287)(.3132)(.34903)(.63479)
Observations
1943
1244
1993
1295
R-squared.04917.05327.06842.12128groups
808
585
808
592
(C) unmarried males (um) and unmarried females (uf)
(1)
(2)
(3)
(4)
WH: um
WH: uf
K6: um
K6: uf(5)
SP: m-.38501**(.15634).07755(.09714).14697(.17033)
2655
.0578
1111

(6)
SP: f
-.19722(.21324).16551(.12547).01315(.25117)
1989.05703
902

(5)
SP: h
-.39092**(.17941).08212(.10931).09301(.17993)
1988.06449
807

(6)
SP: w
-.23427(.2785)-.14757(.1553).21881(.32211)1301.07492594(5)
SP: um(6)
SP: uf24

-.32416.00261-.010391.2253*-.22648(.34029)(.35468)(.68322)(.71081)(.30977)-.128.37078-.69169-.35399.09014wave18+ (cd)(.20565)(.23528)(.42124)(.44882)(.22322)
wave18+×telework.48244-.30519.46005-2.18366**.20258able (cd×twable)(.39121)(.36073)(.7535)(.97693)(.42227)
Observations
657
673
669
691
667
R-squared.06634.07013.0744.12405.09518groups
307
311
309
313
307
Robust standard errors are in parentheses. *** p<.01, ** p<.05, * p<.1. All covariates are controlled.
Treated (T)-.11125(.3479).6786***(.20931)-.37889(.41234)
688.10968312

Estimates of %&$' , %&$( , and %&%# in Equation (4) by gender and marital status are provided in Table 7.
For teleworkable males who actually switched to telework, their weekly happiness, mental condition andsubjective productivity significantly declined in wave 18. While their mental status recovered in wave 19
and 20, happiness and subjective productivity dropped again from wave 19 to 20 after rebounded fromwave 18 to 19. SWB of husbands exhibits similar patterns. For females and wives, no significant trends
are detected in general. For unmarried ones, single female teleworkers are found to be less mentally
stressed in wave 18, while no significant trends exist for single males.

Table 7 Heterogonous effect of switch to telework: evolvement (DiD)(A) males (m) and females (f)
(1)
(2)
(3)
(4)
(5)
WH: m
WH: f
K6: m
K6: f
SP: m
treated in wave 18-.50943***-.25397-.70806**.11318-.42272**(T18)(.16447)(.22684)(.35179)(.47837)(.17691)
treated in wave 19-.30158.26526-.43931-.13372-.27874(T19)(.214)(.28756)(.40899)(.65474)(.22877)
treated in wave 20-.45053**.00501-.13288.00429-.4053*(T20)(.20524)(.32398)(.37935)(.74342)(.21894)-.03243-.02387-.32256-.94618***.08025
wave18+ (cd)(.11835)(.14582)(.21058)(.2664)(.09778).27227-.28366-.12541-.23881.1487
wave18+×teleworkable (cd×twable)(.18475)(.24311)(.31637)(.53889)(.16995)
Observations
2600
1917
2662
1986
2655
R-squared.0407.04558.05838.09283.05805
group
1112
892
1114
901
1111
(B) husbands (h) and wives (w)
(1)
(2)
(3)
(4)
(5)
WH: h
WH: w
K6: h
K6: w
SP: h
treated in wave 18-.53089***-.3401-1.0487***-.61491-.40425**(T18)(.18222)(.25676)(.38468)(.56244)(.20593)
treated in wave 19-.41082*.23792-.24799-.4925-.405(T19)(.24056)(.36854)(.46131)(.81373)(.2562)treated in wave 20-.45441**-.05623-.03428-.79661-.35888(6)
SP: f-.20345(.24652).20804(.36841)-.52792(.36069).20812*(.12485).01355(.25213)
1989.06022
902
(6)
SP: w
-.12533(.30852).18667(.46217)-1.01761**
25

(T20)(.21651)(.38707)(.41973)(1.02639)(.25136).00687-.2042-.22284-1.24447***.0748
wave18+ (cd)(.1414)(.1846)(.24736)(.32878)(.11027).21256-.23678-.33395.85842.09404wave18+×teleworkable (cd×twable)(.21318)(.31467)(.34922)(.63599)(.1795)Observations
1943
1244
1993
1295
1988
R-squared.04935.05596.07214.12141.06452
group
808
585
808
592
807
(C) unmarried males (um) and unmarried females (uf)
(1)
(2)
(3)
(4)
(5)
WH: um
WH: uf
K6: um
K6: uf
SP: umtreated in wave 18-.47741-.10174.3417
1.39676*-.37583(T18)(.38085)(.41508)(.84647)(.79572)(.34259)
treated in wave 19.04753.44364-.9103.73028.40435(T19)(.46505)(.48664)(.96526)(.92932)(.43971)
treated in wave 20-.24474.03512-.20891.03126-.3741(T20)(.57394)(.57753)(.95623)(.89729)(.4687)-.13601.37015-.67155-.32728.11319wave18+ (cd)(.21224)(.2408)(.43286)(.45714)(.22343)
wave18+×telework.48944-.32432.44544-2.15926**.21072
able (cd×twable)(.39039)(.36253)(.75507)(.98046)(.42256)
Observations
657
673
669
691
667
R-squared.06903.07197.07812.12478.10088
group
307
311
309
313
307
Robust standard errors are in parentheses. *** p<.01, ** p<.05, * p<.1. All covariates are controlled.

(.45848)-.05157(.15206).22812(.325)
1301.08486594(6)
SP: uf-.33824(.4205).14748(.64396).38457(.54641).60265***(.21733)-.40304(.41197)
688.11426312

Discussions

We have found gender discrepancies. Specifically, males, especially married males, are more subjectively
vulnerable to change in workplace, while females are less affected. Following work-family balance
hypothesis, as concluded in Chung and Lippe (2020), flexible working would produce gendereddifferences in division of labor, which would lead to gendered discrepancies in well-being. In order toexplore such possibilities, we in the next step estimate the effect of telework on time allocation, that is,
working hours and chore hours (housework hours plus childcare hours).

We first perform fixed effect analyses. The top panel of Table 8 displays estimates for working hours. As
Column (1) shows, teleworkers work roughly 3.4 hours less per week compared to their commuter
counterparts in general. Such patterns do not vary that much with gender. By further dividing sample via
26

marital status, we find that working from home decreases weekly working hours of husbands (-3.1 hours)
and unmarried females (-6.0 hours), while does not significantly affect that of wives and unmarried males.

Table 8 Time allocation: FE(A) weekly working hours
(1)
(2)
(3)
(4)
(5)
(6)
all
m
f
h
w
um
-3.3571***-2.98031***-3.59595***-3.07308**-2.04723-2.0866
telework(.86725)(1.13969)(1.33874)(1.28104)(1.70987)(2.68236)
Observations
4782
2735
2047
2055
1332
680
R-squared.08125.10235.07291.1116.08754.13423groups
2063
1139
924
828
606
315
(B) weekly chore hours (housework hours + childcare hours)
(1)
(2)
(3)
(4)
(5)
(6)
all
m
f
h
w
um
1.5773***
1.02307*2.64775***
1.30725**
3.63012**-.33604
telework(.50441)(.52849)(1.00976)(.62469)(1.50874)(.76433)
Observations
4782
2735
2047
2055
1332
680
R-squared.04624.06644.06071.09047.09031.04808groups
2063
1139
924
828
606
315
Robust standard errors are in parentheses. *** p<.01, ** p<.05, * p<.1. All covariates are controlled.

(7)
uf-5.99295***(2.24062)
715.09262322(7)
uf
1.29489(1.20475)
715.09499322

The bottom panel of Table 8 shows estimates for chore hours. Home-based work increases weekly chore
time by 95 (≈1.6×60) minutes for all respondents. Both husbands and wives would contribute more tohome production if they work from home, while wives contribute much more compared to husbands(3.6 hours for wives versus 1.3 hours for husbands). On the other hand, home-based work does not exert
significant effect on chore time of unmarried ones. Nevertheless, we observe an imprecisely estimatedincrease (decrease) in chore hours for unmarried females (males). To sum up, female teleworkers tend toconcentrate more on household demands compared to male teleworkers.

We next examine whether transfer to home-based work affects potential teleworks’ time allocation. For
simplicity, only estimates on its separate effect throughout wave 18 to 20 are presented. The top andbottom panel of Table 9 displays results for working hours and chore hours, respectively. In general,27

switch to telework decreased weekly working hours for all potential teleworkers by 3.79 in wave 18. For
teleworkable males and husbands, their weekly working hours contracted by around 4.2 in wave 18, andthe corresponding figure for all teleworkable females is 3.1. On the other hand, no significant decline isdetected for wives and unmarried individuals. Meanwhile, teleworkable respondents contributed
additional 2.1 hours per week to household production in wave 18, and teleworkable females contributemuch more (4.0 hours), especially for wives (6.1 hours). On the other hand, additional weekly houseworkand childcare hours of males, regardless of marital status, and unmarred females is ignorable. Lastly, we
find that the estimates in wave 19 and 20 are insignificant, and the absolute values of estimates attenuatefrom wave 18 to 20 in most cases.

Table 9 Time allocation: DiD(A) weekly working hours
(1)
(2)
(3)
all
m
f
treated in-3.79485***-4.20352**-3.09439*
wave 18 (T18)(1.30267)(1.83444)(1.78659)
treated in-1.55643-.46762-4.27669
wave 19 (T19)(1.61323)(2.00132)(2.81218)
treated in-.48139-.55618-.20639
wave 20 (T20)(1.34206)(1.70482)(2.33371)
wave18+ (cd)-.40518-1.03808.46618(.63241)(.86143)(.93882)
teleworkable*-1.4668-1.12407-1.45062
wave18+(1.13679)(1.45883)(1.92604)(cd×twable)
Observations
4666
2668
1998
R-squared.08368.10349.07688
group
2019
1115
904
(B) weekly chore hours (housework hours + childcare hours)
(1)
(2)
(3)
all
m
f
treated in
2.05912***.575143.98433***
wave 18 (T18)(.71089)(.83556)(1.26132)
treated in.2324.31198-.1594wave 19 (T19)(.74953)(.83714)(1.55111)
treated in-.07828.00277.01729wave 20 (T20)(.76614)(.78376)(1.8126)
wave18+ (cd)-.00738-.06226.25804(.43128)(.40913)(.94282)
teleworkable*
1.23934**.907982.43408*
wave18+(.62424)(.69297)(1.25489)(4)h-4.24752**(2.12368)-1.39216(2.38494).14871(2.00247)-1.93812**(.97478)-1.29082(1.7359)(5)w-2.3587(2.33537)-3.23248(3.40606)-2.20848(2.3724)1.01069(1.12041)-.00433(2.48749)(6)um-3.9501(3.73641)
3.1697(2.98964)-.78259(3.11833)
1.95206(1.79824)-.10873(2.41637)(7)
uf-3.83558(2.87396)-8.21053(5.6231)1.36564(5.71456)-.81717(1.62403)-3.45845(3.21201)

1998.11209809

1306.09046594

670.14036309

692.09955314(4)h.86139(1.03094)-.1689(.97237)-.16454(.93003)-.15115(.51691)
1.41514*(.81442)(5)w
6.11281***(1.7864)-.73746(2.06821).18771(2.45827)-.38587(1.08395)
2.41174(1.64487)(6)um-.15054(.95542)1.21302(1.2229)-.56479(.80217).22344(.57062)-.79297(1.18967)(7)
uf.62729(1.52962)
2.73526(2.18965)-.7142(2.99068)
1.89176(1.80938)
2.272(1.90531)
28

(cd×twable)
Observations
4666
2668
1998
1998
1306
670
R-squared.04824.06631.07104.09471.10883.05255
group
2019
1115
904
809
594
309
Robust standard errors are in parentheses. *** p<.01, ** p<.05, * p<.1. All covariates are controlled.

692.09903314

We have observed that males, especially husbands, tend to contract their working hours, while only do
marginally more chores once working from home. Females, especially wives, on the other hand, tend to
expand the domestic sphere, while only slightly reduce their working hours when telecommuting.
Although these patterns straightforwardly implies that male teleworkers enjoy more leisure time, while
their female counterparts might face more severe multi-tasking, which means that female teleworkers
might be less emotionally satisfied, the opposite results are found. We attribute it to the potential effectof the gender norms that males are breadwinners while females are caretakers (Piotrowski et al., 2019,
Sato, 2022). The fact that males work less when performing telework might violate the gender
expectations towards men, and such break of gender identity would attenuate their subjective well-being
affective utility (Akerlof and Kranton, 2000). Meanwhile, we also detected that males’ reduced working
hours are not used to increase chore hours, which is in line with gender expectation. In other words, the“doing gender” effect (West and Zimmerman, 1987) does exist.14 Although male teleworkers have moreleisure time, which should have increased their perceived well-being, the overall effect of telework is stillnegative for males. Such pattern implies that, Japanese males may still value their expected gender roles.
As for females, the increase in domestic production enables them to better cope with the family demands,
which fits the gender roles and would consequently make them more satisfied (Akerlof and Kranton,

14 Stronger statements argue that, males would use telework to expand their working spheres (e.g., Lott and Chung, 2016).

Our estimation, however, does not reveal such pattern.
29

2000). Meanwhile, we do confess that the increasing domestic burden puts pressure on women. That the
combined effect of these two opposite forces results in insignificant negative outcomes in most cases
implies that, Japanese females might not prioritise conservative gender expectations compared to males,
since otherwise we should have detected some positive outcomes (in other words, the effect from gender
identity should have overwhelmed that from work-life interfaces if women lexicographically value gendernorms).15

At last, although the estimates for unmarried ones are almost insignificant, we also detect the similar
patterns as above. Besides the relatively smaller sample size for unmarried respondents, which might leadto imprecise estimates, the insignificant estimates for unmarried sample might also reflect the fact thatthe gender role, which associates with the intra-household division of labor, might be weaker in absenceof marriage. This argument could be partially supported by the phenomenon that Japanese females stilltend to abandon full-time jobs or even quit the labor market after marriage in order to concentrate moreon domestic affairs, although it became less common since 1990s (Hagiwara, 2012). Hence, the genderedeffects could be more obviously detected in married respondents.

5. Conclusion and further discussions

We have investigated the effect of telework on SWB (specifically, happiness, mental stress, and subjective

15 This argument could be partially supported by the increasing female labor force participation rate in Japan. As summarizedin Labor Force Survey (LFS), the employment rate of Japanese females aged 15-64 increased from around 50% in early 1970s
to around 70% in 2020 (data source: LFS, Ministry of Internal Affairs and Communications).
30

productivity) in Japan during the Covid-19 pandemic with an emphasis on the potential gender
heterogeneities in telework effect. We found that, telework decreases SWB since the Covid-19 outbreak,while this negative effect is significant only for males, especially married males. Males’ working hours are
decreased when teleworking, which breaks the gender expectations and would attenuate their affective
utility following gender identity theory. Although male teleworkers’ leisure time increases, which would
improve their SWB, the combined effect is still significantly negative. This implies that, Japanese males
might still lexicographically value the conservative gender norms. We also detected that, female
teleworkers tend to expand their domestic spheres. This is in line with gender expectations, which would
enhance female teleworkers’ SWB, and hence partially offset the negative affective effect from theincreasing domestic burden. However, the non-positive combined effect implies that females may notvalue traditional gender norms as much as males do.

It should be noted that, our finding that telework deteriorates employees’ SWB does not convey thattelework itself is detrimental, since we focus on the Covid-19 period, during which home-based work is
abruptly adopted by firms and/or government to inhibit the virus spread, while the corresponding
supporting measures are not yet sufficiently arranged at all. Conditional on the fact that most of
teleworkers are working from home for the first time because of Covid-19, we could believe that mostof teleworkers are not emotionally well-prepared to manage their work-life interfaces under thisunprecedented circumstance. From this perspective, one might expect that telework exerts some positiveeffects on SWB before the Covid-19 shock (e.g., Kazekami (2020)’s finding that teleworkers are more31

satisfied compared to commuters in Japan during 2017 to 2018), or after workers get used to this newwork schedule (e.g., Gueguen and Senik (2022)’s adaptation hypothesis). Nevertheless, our results do
illustrate that the positive effects of telework on workers’ SWB should not be naively expected, andmandatory telework for collective objectives may not enhance workers’ affective utility. Considering theheterogeneities in telework effect on SWB by gender/marital status (as well as other demographicaland/or occupational characteristics), the discretionary telework according to employees’ needs isimportant.

32

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38

Appendix
Table A1 full descriptive statistics
Variabletelework
weekly happiness
K6
subjective productivityage (ref.: 20<=age<30)
30<=age<40
40<=age<50
50<=age<60
60<=age<65
education (ref.: junior high school)senior high school
junior college
university
graduate school
othersfemalemarriedlog monthly income
weekly working hours
joined union
regular staff
industry (ref.: agriculture)
fishery and forestry
miningconstruction
manufacturingretailcatering and accommodation
finance and insurance
real estatetransportation
information service
communications
energyhealth and welfare
educationother service
public affairs
othersfirm size (ref.: <5 staffs)
5-29 staffs
30-99 staffs
100-499 staffs>=500 staffs
public sector
living area (ref.: yushuo)
tohokukanto
chubu
kansaichugoku
shikoku
yushu and okinawa
daily one-way commute minutes
weekly chore hours
living with people who need careMean
14.09%
5.86
24.89
6.06

Std. Dev.
2.29
4.96
1.80

21.50%
29.32%
28.11%
11.48%
34.60%
16.82%
33.79%
4.03%
9.12%
49.65%
69.85%
12.36
34.15
43.94%
63.38%

0.64
17.08

0.13%
0.00%
5.76%
16.74%
15.54%
4.05%
4.12%
1.12%
5.89%
2.71%
1.95%
0.79%
17.21%
6.82%
10.62%
5.96%
0.14%
17.09%
17.97%
21.24%
33.04%
6.51%
6.03%
36.53%
17.70%
17.04%
5.50%
2.64%
10.46%
27.82
13.92
4.77%

22.08
17.11
39

presence of preschool aged child11.13%
wave (ref.: Feb. 2020)
May-June 2020
21.82%
Oct.-Nov. 2020
17.56%
Feb. 2021
27.77%The sample is: employees aged 20-64 in wave 17-20. Standard deviations
for dummies are not shown.

40