Panel Data Research Center, Keio University
PDRC Discussion Paper Series

Impact of Teleworking on Childcare Time During the COVID-19 Pandemic:
The Role of Owner-Occupied Housing
Takuya Ishino, Yoichi Mizumura, Kazuto Sumita,
Takuya Yoshida, Norifumi Yukutake
19 September, 2021
DP2021-004
https://www.pdrc.keio.ac.jp/en/publications/dp/7384/

Panel Data Research Center, Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan
info@pdrc.keio.ac.jp
19 September, 2021

Impact of Teleworking on Childcare Time During the COVID-19 Pandemic: The Role of Owner-Occupied Housing

Takuya Ishino, Yoichi Mizumura, Kazuto Sumita, Takuya Yoshida, Norifumi Yukutake
PDRC Keio DP2021-004
19 September, 2021
JEL Classification: J13; C23; R20
Keywords: telework; childcare participation; COVID-19 pandemic; housing environment

Abstract
To prevent further virus transmission during the COVID-19 pandemic, several countries imposed
stay-at-home restrictions to refrain people from going out. This exogenously encouraged many workers
and companies to adopt telework. This study discusses the relationship between teleworking andchildcare participation, considering the housing environment. Data from the Japan Household Panel
Survey and its supplementary modules on COVID-19, conducted in 2020, were utilized. After
controlling for individual and household attributes, regions, and housing characteristics, we foundthat male workers who telework in large-sized, owner-occupied housing significantly increased theirchildcare hours. For the male teleworkers living in owner-occupied detached housing, if telework time
per day increases by an hour, childcare time per day will increase by 0.16 hours. This is an increaseof approximately 17% in childcare time per day, relative to the sample average for the male subset.
This suggests that sufficient space and quality of housing may have a substantial effect on time
devoted to childcare by men.

Takuya Ishino
Faculty of Economics, Kanazawa Seiryo University
Ushi 10-1, Gosho-machi, Kanazawa-shi, Ishikawa
ishino@seiryo-u.ac.jp
Yoichi Mizumura
5-28-20 Hakusan, Bunkyo-ku, Tokyo
yoichi.mizumura@gmail.com
Kazuto Sumita
Faculty of Economics, Toyo University
5-28-20,Hakusan, Bunkyo-ku, Tokyo
sumita@toyo.jp
Takuya Yoshida
Graduate School of Economics, Toyo University
5-28-20,Hakusan, Bunkyo-ku, Tokyo
yoshironzai@gmail.com

Norifumi Yukutake
College of Economics, Nihon University
1-3-2,Misaki-cho, Kanda, Chiyoda-ku, Tokyo
yukutake.norifumi@nihon-u.ac.jp

Acknowledgement: This work was supported by Grants-in-Aid for Scientific Research, Grant-in-Aid
for Specially 17H06086 2017-2021.

Impact of Teleworking on Childcare Time During the COVID-19
Pandemic: The Role of Owner-Occupied Housing
Takuya Ishino1, Yoichi Mizumura2, Kazuto Sumita3*, Takuya Yoshida4, andNorifumi Yukutake5
1Faculty of Economics, Kanazawa Seiryo University; ishino@seiryo-u.ac.jp
2Graduate School of Economics, Toyo University; yoichi.mizumura@gmail.com
3Faculty of Economics, Toyo University; sumita@toyo.jp
4Graduate School of Economics, Toyo University; yoshironzai@gmail.com
5College of Economics, Nihon University; yukutake.norifumi@nihon-u.ac.jp

Abstract
To prevent further virus transmission during the COVID-19 pandemic, several
countries imposed stay-at-home restrictions to refrain people from going out. This
exogenously encouraged many workers and companies to adopt telework. This study
discusses the relationship between teleworking and childcare participation,
considering the housing environment. Data from the Japan Household Panel Survey
and its supplementary modules on COVID-19, conducted in 2020, were utilized. After
controlling for individual and household attributes, regions, and housing
characteristics, we found that male workers who telework in large-sized, owneroccupied housing significantly increased their childcare hours. For the maleteleworkers living in owner-occupied detached housing, if telework time per day
increases by an hour, childcare time per day will increase by 0.16 hours. This is anincrease of approximately 17% in childcare time per day, relative to the sample
average for the male subset. This suggests that sufficient space and quality of housing
may have a substantial effect on time devoted to childcare by men.
Keywords: housing environment, telework, childcare participation, COVID-19
pandemic
JEL: J13, C23, R20

1

1. Introduction
A nationwide state of emergency was declared in Japan from April 7 to May
25, 2020, due to the coronavirus disease (COVID-19) pandemic. During this period,
some industries that provide face-to-face services were asked to close temporarily,
and telework was encouraged. Working from home was particularly promoted
because people were required to refrain from leaving their homes to prevent furthertransmission of the virus. In Japan, a low telework rate has been attributed to thecorporate system and culture, but a system for telework implementation had to be
developed with the onset of the COVID-19 pandemic. Therefore, it is worthwhile toanalyze and discuss how the shift toward telecommuting had brought about changes
in Japanese behavior, during this period.
This study aimed to analyze the impact of teleworking on childcare time
during the COVID-19 pandemic.1 Many elementary and high schools in Japan closed
temporarily during the period of the emergency declaration. Since children spent
more time at home, parents had to take care of them over longer periods of time.
Therefore, in this study, we define childcare time in a broader sense to include thetime taken to care for preschoolers and help elementary to high school students with
their homework, to estimate the impact of working from home on childcare time.
We analyzed the quantitative effect of telecommuting on childcare time using
the difference-in-differences (DID) and triple difference (TD) methods. Traditionally,
telecommuting has been chosen as a method to balance work and household
responsibilities. However, in this situation, endogeneity arises between
telecommuting and childcare time, making it difficult to accurately identify
quantitative effects. In the case of the COVID-19 pandemic, alleviation of these
problems is expected because telecommuting tends to be implemented based on
criteria different from work-life balance and childcare. This study also aims to explore
the association between working from home and housing characteristics. To workfrom home, information and communications technology (ICT) tools (i.e., Internet)
and a space to work in, are needed. Thus, the relationship between housing
attributes and telecommuting may become more apparent. Therefore, we createdshort panel data from the Japan Household Panel Survey (JHPS/KHPS) and its
supplement modules on COVID-19, surveyed in May and October 2020, and analyzed
them to measure the impact of working from home on childcare time, during the
COVID-19 pandemic.

1Herein, teleworking is interchangeable with telecommuting. Since the variables of teleworking
are derived from questions about working from home, teleworking equals working from home.

2

The remaining paper is structured as follows. In section 2, related literature isbriefly reviewed. Then, we describe the data set used for the analyses in Section 3
and present the empirical methodology and results of the analyses in Section 4.
Section 6 discusses the results and section 5 summarizes the conclusions drawn fromthe study.

2. Review of Related Literature
2.1 Impact of the COVID-19 pandemic on childcare and telecommuting
In many countries, the COVID-19 pandemic has promoted telework andhelped change gender roles in the household. A study by Alon et al. (2020) discussed
the impact of the COVID-19 pandemic on gender inequality in the United States (U.S.)
They analyzed data on the distribution of women, men, and couples by occupation,and the division of labor time. The results showed that COVID-19 greatly increased
the burden on women. In particular, the COVID-19 pandemic, unlike normal
recessions, was more likely to reduce employment in sectors where women make upa large portion of the labor force.
Craig and Churchill (2020) conducted a survey on the COVID-19 pandemic andcollected 2,722 responses over a three-week period during the Australian lockdown
from May 7–30, 2020. This survey evaluated changes in employment status and placeof work before and after the COVID-19 pandemic. The results showed that the
mandatory restrictions during the pandemic increased fathers’ childcare time in dualearner households, narrowing the burden gap between couples.
Teleworking was not widespread in Japan before COVID-19 hit, but the
pandemic clearly promoted it. Okubo et al. (2021) analyzed the effects of COVID-19
on working from home, using data from a unique survey on telework conducted by
Keio University and the Nippon Institute for Research Advancement (NIRA). They
found that, despite teleworking becoming more prevalent compared with working as
normal, the efficiency of new teleworkers was reduced by approximately 20% on
average during the COVID-19 pandemic. Conversely, the efficiency of those who had
already worked from home pre-pandemic, was maintained.

2.2 Impact of telecommuting and workplace flexibility on childcare time
Some studies have been conducted on the effects of teleworking andworkplace flexibility on childcare time. Using matched data from the U.S. Current
Population Survey and the American Time Use Survey 2004-2005, Wight and Raley(2009) found that women who worked from home spent less time on paid work, and3

fathers who worked from home spent less time on primary childcare. Genadek and
Hill (2017) used the same data to analyze the impact of workplace flexibility on theamount of time spent with children, more comprehensively. They found that mothers
who telecommute spend more time with their children, whereas fathers do not, andfathers who work on flexible schedules spend less time with their children than those
who do not.
More recently, Pabilonia and Vernon (2020) distinguished between full-day
telecommuting and weekend or work-at-home telecommuting—an issue that had
been overlooked until their study—and analyzed the probability of being ateleworker, weekly earnings on the main job, and time use. The analysis of time use
pointed out that men spend more time on primary childcare, while women spend
more time on physical leisure activities, during work-from-home days.
Zhang et al. (2020) analyzed the telework choice based on variables related
to life stages, such as gender, marital status, and parenthood, using unique German
microdata, which were collected before the pandemic. They showed a complex
association between telework behavior and life stages.
These studies analyzed the relationship between workplace flexibility,
including teleworking and time spent with the family, such as childcare time; however,
the endogeneity of teleworking was not discussed. In analyzing the impact oftelecommuting on childcare time, an endogeneity issue is expected because having
children increases parental responsibilities and workload at home, whereas working
from home may facilitate work-life balance.

2.3 Effect of housing characteristics on telecommuting and childcare
time
Qin et al. (2021) analyzed telecommuting in relation to housing characteristics.
Their study applied a negative binomial regression model to their own survey data to
analyze the relationship between housing characteristics and ICT usage and foundthat the type of housing and the number of rooms significantly affected theprobability of telecommuting. In particular, detached and semi-detachedtownhouses and a higher number of rooms increased the probability oftelecommuting. This supports the idea that telecommuting requires a quiet andindependent environment for work.
In addition, Grinstein-Weiss et al. (2010) analyzed parental childcare time andhousing characteristics. They evaluated parental behavior that affects outcomes forchildren, by examining whether parental engagement in the home, school, andcommunity differs based on housing characteristics. The results showed that4

households in homeownership have increased organized activities and less TV/game
time, which means they are more engaged in childcare; thus, homeownership has apositive impact on child outcomes.
Accordingly, this study aimed to analyze whether increased teleworking
during the COVID-19 pandemic led to an increase childcare time, considering housing
characteristics by means of a micro-survey. Furthermore, since the COVID-19
pandemic promoted telework as “an exogenous shock” for workers (Zhang et al.,2020), the endogeneity of teleworking could be solved based on the data collectedduring the pandemic.

3. Data
3.1 JHPS/KHPS and supplementary modules on COVID-19
We analyzed data drawn from the JHPS/KHPS and its supplementary module
on COVID-19 (COVID-19 Supplement), which were conducted in February, May, andSeptember 2020, respectively. The survey covers general topics, including
employment, education, lifestyle, time allocation, health and living environment,along with more detailed subjects, such as respondents’ household composition andtheir income, expenditures, assets, and housing type. It was originally conducted as
two independent panel surveys: the Keio Household Panel Survey (KHPS) and the(former) Japanese Household Panel Survey (former JHPS). The KHPS and JHPS havebeen conducted annually since 2004 and 2009, respectively, but they were eventuallycombined in 2014. The initial sample size was 4,005 households for the KHPS and
4,022 households for the former JHPS.
The COVID-19 Supplement aimed to ask specific questions related to the
COVID-19 pandemic to understand how people's lives, attitudes, behaviors, andpsychological states were affected during the state of emergency. After asking
JHPS/KHPS respondents to participate in the COVID-19 Supplement, 3,891 of 5,470
responses were analyzed.
The regular JHPS was conducted in February 2020 (JHPS2020) and surveyed
responses at a time during which fewer new infections occurred. It collected
information about household and housing characteristics, such as annual householdincome, number of household members, housing tenure, and so on. Details of
housing and household characteristics were asked only in this survey and not in the
supplementary modules.
The first supplementary module was conducted from late May to thebeginning of July 2020. This period followed immediately after the first wave of new5

infections and the first state of emergency (April 7 to May 25, 2020). In this survey,
questions regarding the respondents’ lives one month prior (April) were used toanalyze their situations during the first state of emergency.
The second supplementary module was conducted from mid-October to theend of December 2020. This corresponds to the period after the second wave of new
infections. Herein also, respondents’ lives one month prior (September) weresurveyed.
In this study, we produced short panel data from the JHPS2020 and the firstand second supplementary modules, creating variables of household and housing
information, and variables related to COVID-19, respectively. Although we could
determine which respondents had moved during the course of the study, details ofhousehold and housing characteristics could not be observed. Therefore, households
that moved during our observation periods were excluded from the data. Based on
this data, we aim to clarify the impact of working from home and the housing
environment, on childcare participation during the COVID-19 pandemic. In this study,
due to sample size issues, the target age for childcare were individuals under 18 yearsof age.

3.2 Estimation samples and descriptive statistics
We conduct our analysis based on three samples:(i) workers with children aged 18 years and below,(ii) workers from sample (i) including those who teleworked by corporate
mandate and excluding those who teleworked at their own discretion,(iii) workers from sample (ii) with children aged from 7–18 years.
Sample (i) included respondents who were working at the time of the survey,
with children aged 18 years and below. However, it excluded those whose weekly
childcare time exceeded 70 hours; These respondents—accounting for 3% of thetotal sample—were regarded as outliers and not taken into consideration. This left
1,270 respondents after also excluding those with missing information related tovariables used in the analysis. Descriptive statistics of the variables included in sample(i) are tabulated in Table A1 in the Appendix.
Table 1 shows the means of childcare and telework time in February, April,and September 2020. Respondents were asked to indicate the number of days per
week dedicated to childcare. We then calculated childcare hours per day from the6

answers to this question.2 In February, the mean amount of childcare time per day
was 1.52 hours, increasing to 1.58 hours in April—during the first state of
emergency—and 1.55 hours in September. Although similar tendencies wereobserved between male and female samples, females spent 2.4 hours per day on
childcare in February and April, which is roughly three times higher than males’
childcare time.
Regarding telework, the ratio of teleworkers was only 5% in February and themean teleworking hours per day, 0.26 hours. In April, due to the state of emergency,
the ratio of teleworkers and telework hours per day increased to 27% and 1.95 hours,
respectively. In September, although the state of emergency was not declared during
this month, the ratio of teleworkers remained high at 19% and teleworking hours per
day were still high at 1.48 hours. From a gender perspective, males tended to perform
more telework than females. Similar tendencies were observed in another survey.
Table 1 also includes the implementation status of telework during theobservation periods. The last row of each panel indicates that approximately 3% and
6% of teleworkers from the whole sample chose teleworking on their own accord in
April and September, respectively. They could choose teleworking to enable childcare
at home and facilitate work-life balance, as stated in Section 2.2. In the regression
explaining childcare time, the coefficients related to teleworking should include thebias due to reverse causality from childcare to teleworking. Therefore, to reduce suchbias, we estimate models based on sample (ii), which excludes respondents who
voluntarily chose telework and includes only the ones who performed telework at
the behest of the company. This restricted sample retains 1,224 respondents andallows us to more accurately identify the causal effects of telework on childcare time.
In sample (iii), we further exclude respondents with children younger than 6
years, from sample (ii). Such respondents account for 21% of those with children aged
18 years and below in the whole sample (Table A1). The exclusion is made because a
portion of preschool-aged children could have continued attending daycare facilities
even under the state of emergency in April 2020, whereas elementary, middle high,and high school students were unable to attend school during that time (Yokoyama
and Takaku, 2020). Ultimately, 970 respondents remained in this restricted sample.
We conduct our analyses on samples (ii) and (iii) to identify the causality fromtelework to childcare time, in the next section.

2 The question about childcare time is asked in the JHPS/KHPS, annually. Precise information of

the questionnaire is available from:https://www.pdrc.keio.ac.jp/en/paneldata/datasets/jhpskhps/ (Accessed 9 September 2020)7

Table 1: The means of childcare and telework time from the sample of workers withchildren aged 18 years and below in February, April, and September 2020
Feb.

Apr.

Sep.

Apr.–Feb.

Sep.–Apr.

N.

Mean

N.

Mean

N.

Mean

t

t

Childcare hours per week

455

10.65

438

11.03

377

10.85

0.43

-0.19

Childcare hours per day

455

1.52

438

1.58

377

1.55

0.43

-0.19

Telework (=1)

455

0.05

438

0.27

377

0.19

9.43

***

-2.92

***

Number of telework days

455

0.18

438

0.95

377

0.64

8.42

***

-2.68

***

Telework hours per week

450

0.81

435

6.79

373

3.87

8.66

***

-3.42

***

Telework hours per day

455

0.26

438

1.95

377

1.16

9.49

***

-3.54

***

Company mandate (=1)
Implemented at own
discretion (=1)
(B) Male sample

418

0.07

403

0.28

355

0.19

8.19

***

-3.08

***

418

0.03

403

0.03

355

0.06

0.09

1.60

Childcare hours per week

251

5.66

248

6.57

200

5.62

1.13

-1.07

Childcare hours per day

251

0.81

248

0.94

200

0.80

1.13

-1.07

Telework (=1)

251

0.05

248

0.31

200

0.23

8.03

***

-2.14

**

Number of telework days

251

0.17

248

1.12

200

0.78

7.45

***

-2.09

**

Telework hours per week

247

1.00

245

8.32

197

4.93

7.08

***

-2.59

***

Telework hours per day

251

0.33

248

2.31

200

1.50

7.72

***

-2.45

**

Company mandate (=1)
Implemented at own
discretion (=1)
(C) Female sample

238

0.09

235

0.31

190

0.23

6.38

***

-1.93

*

238

0.03

235

0.04

190

0.06

0.81

1.15

Childcare hours per week

204

16.77

190

16.85

177

16.76

0.05

-0.06

Childcare hours per day

204

2.40

190

2.41

177

2.39

0.05

-0.06

Telework (=1)

204

0.05

190

0.22

177

0.15

5.09

***

-1.84

Number of telework days

204

0.19

190

0.72

177

0.50

4.20

***

-1.52

Telework hours per week

203

0.58

190

4.81

176

2.68

5.06

***

-2.12

**

Telework hours per day

204

0.18

190

1.48

177

0.79

5.56

***

-2.47

**

180

0.05

168

0.24

165

0.14

5.12

***

-2.32

**

180

0.04

168

0.02

165

0.05

-0.81

Variable(A) Whole sample

Telework status

Telework status*

Telework status
Company mandate (=1)
Implemented at own
discretion (=1)

1.20

Note: Apr.–Feb. and Sep.–Apr. represent the results of a t-test of equal means between two samples,using Welch’s method under the hypothesis of heterogeneity. ***, **, and * are significant at the 1%,5%, and 10% levels, respectively.

8

4. Econometric Analysis and Results
4.1 Identification strategy
In this section, we describe the econometric methods used in this study. The
models to be estimated are divided into two categories: (1) DID method for COVID19 and (2) TD method considering housing characteristics.
We employed the DID method due to the problem that the COVID-19
pandemic had a confounding impact on both childcare time and teleworking
practices. The former can be attributed to children spending more time at home dueto the closure of schools and the instructions to refrain from leaving their homes,
which increased the amount of time dedicated by parents to taking care of them. The
latter is due to the government’s strong recommendation for individuals to workfrom home to prevent them from commuting. Therefore, simply estimating the effectof working from home using data from before and after the onset of COVID-19, islikely to overestimate the effect.
To address this problem, we used the DID method, which estimates theaverage treatment effect on the treated, based on a parallel trend assumption. We
checked this assumption by comparing the average childcare time of the survey in
February 2019 and 2020, respectively, as seen in Figure 1. From this, we can see that
this assumption is proved for sample (ii) because 95% confidence intervals overlap,
and no significant difference exists between the average childcare time of bothteleworker and non-teleworker groups, between 2019 and 2020. We further
reviewed the assumption for the subsamples, by gender. In the male sample, theupper trends in both the treatment and control groups presented no significant
difference. In the female sample, we observed a downward trend among theteleworkers and an upward trend among the non-teleworkers; however, thisdifference was not significantly different from zero. Thus, we concluded that theparallel trend assumption is satisfied in both the whole sample and subsamples bygender.

9

(A) Whole sample(B) Subsamples by gender
Note: These figures are based on sample (ii) in section 3.2.
Figure 1: Changes in average daily childcare time by teleworking status, with 95%
confidence intervals

104.2 DID approach
The DID approach is a popular method to observe the difference in thetreatment group before and after treatment. Since the COVID-19 pandemic occurred
independently of the survey respondents, we assumed that this factor is a common
shock after controlling for the regional difference due to its impact. In this study,
treatment was the number of telework hours; the DID model is written as follows,
using the notations of Lee (2016):(1)
𝑦𝑖𝑡 = 𝛽0 + 𝛽𝑔 𝐺𝑖𝑡 + 𝛽𝜏 1[𝑡0 ≤ 𝑡] + 𝛽𝑔𝜏 𝐺𝑖𝑡 1[𝑡0 ≤ 𝑡] + 𝛽𝑥𝑖𝑡 + 𝜖𝑖𝑡 ,where 𝐺𝑖𝑡 is the telework hours and 1[𝑡0 ≤ 𝑡] is the post-treatment era dummy(COVID dummy indicating April and September). Here, we did not assume
unobserved individual heterogeneity, such as the fixed effects of respondents. For
the control variables, 𝑥𝑖𝑡 includes the variables of housing characteristics (size ofdwelling space, age of building, etc.), individual and household characteristics (age of
respondents, number of children and household members, household income,
employment, industry, etc.), as well as regional and survey period fixed effects,
respectively. Since variables of household and housing characteristics are constantover the observation periods due to the nature of the construction of the dataset, wecannot estimate the fixed effects models of respondents. Therefore, we selected
pooled ordinary least squares (OLS) models as our baseline models.
Two models were estimated for three samples: whole, male, and female
samples. Estimation results of Equation (1) are presented in Table 2, and the fullestimation results are shown in Table A2 in the Appendix. The first two columns are
based on the whole sample. Column 1 reports the results of workers with children
aged 18 years and below including the teleworkers by corporate mandate. We
obtained a positive but insignificant estimate of 𝛽𝑔𝜏 , which is the coefficient oftelework hours per days × the COVID indicator. Column 2 reports the results of
respondents with children aged from 7 to 18 years. Here, we obtained positive but
insignificant results as well. Then, we estimated the model using subsamples bygender and observed insignificant results, similar to that of the whole sample.

11

Table 2: Estimation results of difference-in-differences models of childcare time, perday
SampleVariables

Telework hours perday
COVID (Apr. and Sep.)(=1)
Telework hours
per day × COVID (=1)
Housingcharacteristics
Individual andhouseholdcharacteristics
Regional fixed effects
Period fixed effects

Whole sample

Male sample

Female sample

1.
Workers
withchildrenaged 18
years andbelow

2.
Workers
withchildrenaged 7 to18 years

3.
Workers
withchildrenaged 18
years andbelow

4.
Workers
withchildrenaged 7 to18 years

5.
Workers
withchildrenaged 18
years andbelow

6.
Workers
withchildrenaged 7 to18 years

0.0253(0.039)0.0394(0.040)0.0174(0.035)0.0269(0.031)0.0137(0.100)0.0347(0.108)0.0181(0.117)-0.0891(0.131)0.0131(0.123)-0.0542(0.135)-0.0761(0.208)-0.228(0.219)0.0097(0.034)-0.0030(0.038)-0.0104(0.033)0.0018(0.030)0.0590(0.101)0.0084(0.111)

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

N.
1,224
970
672
503
552
467
R-squared
0.329
0.322
0.254
0.278
0.292
0.263
Note: Childcare time per weekday is the dependent variable. The cluster standard errors over
respondents are given in parentheses. ***, **, and * are significant at the 1%, 5%, and 10% levels,
respectively.

4.3 Heterogeneous effects: TD approach
We found positive but insignificant results regarding the DID parameters.
However, such effects may change depending on heterogeneity among the
respondents, such as their housing characteristics. In previous studies, housing
characteristics have been described as a confounding factor that affects bothchildcare time (Grinstein-Weiss et al., 2010) and telecommuting (Qin et al., 2021). To
capture the differences that arise due to the heterogenous effects related to housing
characteristics, we used the TD approach, which is an extension of the DID methodthat allows us to estimate separate effects of housing characteristics.
12

The TD model is written as follows, using the notations of Lee (2016):
𝑦𝑖𝑡 = 𝛽0 + 𝛽𝑔 𝐺𝑖𝑡 + 𝛽ℎ 𝐻𝑖 + 𝛽𝜏 1[𝑡0 ≤ 𝑡] + 𝛽𝑔ℎ 𝐺𝑖𝑡 𝐻𝑖 + 𝛽𝑔𝜏 𝐺𝑖𝑡 1[𝑡0 ≤ 𝑡](2)
+ 𝛽ℎ𝜏 𝐻𝑖 1[𝑡0 ≤ 𝑡] + 𝛽𝑑 𝐺𝑖𝑡 𝐻𝑖 1[𝑡0 ≤ 𝑡] + 𝛽𝑥𝑖𝑡 + 𝜖𝑖𝑡 ,where 𝐺𝑖𝑡 is telework hours and 𝐻𝑖 is an indicator of housing characteristics. TD
identifies the effect of the treated (𝐺𝑖𝑡 =𝐺 ∗ ,𝐻𝑖𝑡 =1) at the post-treatment era dummy
1[𝑡0 ≤ 𝑡] = 1, (COVID dummy indicating April and September). The parameter ofmost interest is 𝛽𝑑 , the effects of which at a specific value of telework hours 𝐺 ∗ , aresummarized as:
𝛽𝑑 𝐺 ∗ = [𝐸(𝑦𝑖𝑡 | 𝐺𝑖𝑡 = 𝐺 ∗ , 𝐻𝑖 = 1, 𝑇 = 1) − 𝐸(𝑦𝑖𝑠𝑡 | 𝐺𝑖𝑡 = 𝐺 ∗ , 𝐻𝑖 = 1, 𝑇 = 0)]−[𝐸(𝑦𝑖𝑠𝑡 | 𝐺𝑖𝑡 = 0, 𝐻𝑖 = 1, 𝑇 = 1) − 𝐸(𝑦𝑖𝑠𝑡 |𝐺𝑖𝑡 = 0, 𝐻𝑖 = 1, 𝑇 = 0)]𝐸(𝑦𝑖𝑠𝑡 | 𝐺𝑖𝑡 = 𝐺 ∗ , 𝐻𝑖 = 0, 𝑇 = 1) − 𝐸(𝑦𝑖𝑠𝑡 | 𝐺𝑖𝑡 = 𝐺 ∗ , 𝐻𝑖 = 0, 𝑇 = 0)(3)−[]−{𝐸(𝑦𝑖𝑠𝑡 |𝐺𝑖𝑡 = 0, 𝐻𝑖 = 0, 𝑇 = 1) − 𝐸(𝑦𝑖𝑠𝑡 |𝐺𝑖𝑡 = 0, 𝐻𝑖 = 0, 𝑇 = 0)}
The estimate 𝛽̂𝑑 provides information about the net change in the marginal effects
of teleworking hours of workers living in housing with specific features, represented
by 𝐻𝑖 .This model is a saturated TD model (Lee, 2016). Here, we assumed that theslopes of telework hours and housing are time-varying (e.g., 𝛽𝑔𝜏 for telework hours
and 𝛽ℎ𝜏 for housing characteristics in the post-treatment era).
However, many workers started teleworking from April: 1[𝑡0 ≤ 𝑡] = 1 .
Therefore, 𝐺𝑖𝑡 and 𝐺𝑖𝑡 1[𝑡0 ≤ 𝑡] showed high correlation, especially in the female
sample. Thus, to avoid multicollinearity, we excluded 𝐺𝑖𝑡 1[𝑡0 ≤ 𝑡] and estimated thefollowing semi-saturated model:
𝑦𝑖𝑡 = 𝛽0 + 𝛽𝑔 𝐺𝑖𝑡 + 𝛽ℎ 𝐻𝑖 + 𝛽𝜏 1[𝑡0 ≤ 𝑡] + 𝛽𝑔ℎ 𝐺𝑖𝑡 𝐻𝑖 + 𝛽ℎ𝜏 𝐻𝑖 1[𝑡0 ≤ 𝑡](4)
+ 𝛽𝑑 𝐺𝑖𝑡 𝐻𝑖 1[𝑡0 ≤ 𝑡] + 𝛽𝑥𝑖𝑡 + 𝜖𝑖𝑡
Several variables for the characteristics of housing were selected to test the
hypothesis that housing characteristics, such as the size of the dwelling, housing
tenure, and longer commuting time, affect childcare time. The estimates ofcoefficients of the interaction terms, 𝛽𝑑 , are shown in Table 3, according to the threesamples.
First, we tested the hypothesis that the size of the dwelling affects childcare
time. Dwelling size was determined as the number of empty rooms, which is definedas the difference between the number of rooms and the occupants per household,excluding children below 10 years of age as they are considered half-occupants (Sekoet al., 2019). We considered that the number of rooms are important for teleworking
to contain the noise generated by other household members, especially children (Qinet al., 2021). Dwelling spaces were controlled in the regression analysis. We
examined the hypothesis by including an indicator of one or more empty room. The
results showed negative and insignificant coefficients, indicating that the hypothesis
is not supported.

13

Second, we tested the hypothesis that the quality of housing affects childcare
time, by including several variables such as building type and age, along withownership of the dwelling. For the type of building, we used indicators of detachedhousing and condominiums, neither of which showed significant results. Concerning
the age of the building, we used an indicator of dwellings built within the past 10
years, as the quality of housing is higher for newly built dwellings in Japan. Thisvariable also had no significant result. In terms of dwelling ownership, we included
an indicator of owner-occupied housing. In this case, we observed a significantly
positive coefficient (0.176) for the whole sample, which represented an increase of
approximately 11% in childcare time relative to the sample average. For the malesample, the coefficient was 0.178, but we could not obtain similar results from thefemale sample.
Third, we tested the hypothesis that long commuting time substitutes
childcare time for teleworkers. We estimated the model by including an indicator for
respondents whose commuting time is longer than one hour one-way. However, we
could not derive significant coefficients and therefore, could not conclude that
commuting time has transformed into childcare time due to telecommuting.
From the above results, we found that owner-occupied housing is the main
housing characteristic to influence childcare time for teleworkers.
Furthermore, we evaluated whether the heterogeneity of owner-occupied
housing affects childcare time. These results are tabulated in Table 4. For owneroccupied dwellings with one or more empty rooms, we observed a positive andsignificant coefficient if we restricted the sample to workers with have children aged
7–18 years in the entire sample, as well as the male subset. If the telework time per
day increases by an hour, the childcare time per day increases by 0.119 hours for the
male subset, which represents an increase of approximately 12% in childcare time,
relative to the sample average. We also observed significantly positive effects in thecase of owner-occupied detached housing, based on the entire sample and the male
subset. Telework per day increased by 0.16 hours for the male subset. This value
represents an increase of approximately 17% in childcare time, relative to the sample
average for the male subset. However, no such effects were observed in the case of
condominiums.

14

Table 3: Estimates of interaction terms of triple difference models
SampleVariables

Whole sample
1.
Workers
withchildrenaged 18
years andbelow

× condominium(=1)× built less than
10 years ago(=1)

× owneroccupied
housing (=1)× commuting
time(more than 1
hour, one-way)(=1)

Female sample

3.
Workers
withchildrenaged 18
years andbelow

4.
Workers
withchildrenaged 7 to18 years

5.
Workers
withchildrenaged 18
years andbelow

6.
Workers
withchildrenaged 7 to18 years-0.1950.0105-0.03170.0105-0.0317(0.164)(0.096)(0.106)(0.096)(0.106)-0.0863-0.0007-0.2210.166

0.04250.0165(0.211)(0.227)(0.251)(0.150)(0.104)(0.115)0.119

0.02630.313-0.110-0.0021-0.0387(0.215)(0.226)(0.250)(0.152)(0.087)(0.089)0.04580.03550.00500.03190.09250.0235(0.076)(0.084)(0.065)(0.072)(0.116)(0.131)0.176***

0.203***

0.178***

0.146***

0.02590.0119(0.060)(0.063)(0.054)(0.049)(0.104)(0.113)-0.0573-0.0823-0.0291-0.0618-0.0412-0.149Telework hours × COVID (=1)× one or more
empty room
-0.156(=1)(0.165)× detachedhousing (=1)

Male sample

2.
Workers
withchildrenaged 7 to 18
years(0.075)(0.078)(0.070)(0.070)(0.101)(0.115)
Note: Childcare time per week is the dependent variable. Estimates of columns 5 and 6 are calculated
based on the semi-saturated model. The same covariates as in Table 2 are used. ***, **, and * aresignificant at the 1%, 5%, and 10% levels, respectively.

15

Table 4: Estimates of interaction terms of triple difference models
SampleVariables

Whole sample
1.
Workers
withchildrenaged 18
years andbelow

× owner-occupied
condominium (=1)

Female sample

3.
Workers
withchildrenaged 18
years andbelow

4.
Workers
withchildrenaged 7 to18 years

5.
Workers
withchildrenaged 18
years andbelow

6.
Workers
withchildrenaged 7 to 18
years

0.129*

0.09160.119**-0.0023-0.0393(0.073)(0.059)(0.060)(0.098)(0.108)0.191***

0.207***

0.160***

0.160***

0.04360.0149(0.054)(0.057)(0.054)(0.052)(0.103)(0.115)-0.0596-0.0648-0.0437-0.0597-0.0360-0.0714

Telework hours × COVID (=1)× owner-occupied
housing with one
0.109
or more empty
room (=1)(0.070)× owner-occupied
detached housing(=1)

Male sample

2.
Workers
withchildrenaged 7 to 18
years(0.076)(0.079)(0.071)(0.071)(0.102)(0.112)
Note: Childcare time per day is the dependent variable. Estimates of columns 5 and 6 are calculated
based on the semi-saturated model. The same covariates as in Table 2 are used. The cluster standard
errors over respondents are given in parentheses. ***, **, and * are significant at the 1%, 5%, and
10% levels, respectively.

16

4.4 Robustness checks
To check the robustness of the outcomes of analyses in Section 4.3, we conducted ananalysis using a sample limited to February and April and another using the variables
measured in hours per week. The aim of the first analysis was to focus on the
instantaneous increase in childcare hours (see Figure 1) caused by the closure ofschools and daycare facilities due to the COVID-19 pandemic between February and
April 2020. The second analysis was based on a sample of hours per week reflecting
the number of days per week that each person teleworked. We report selected
results that are related to our main findings.
Panel A of Table 5 shows the results of the TD models for childcare hours per
day based on the sample from February and April. Estimates of the TD term related
to one or more empty rooms showed positive results like in Table 4; however, theresults are insignificant. This is partly due to the reduction of sample sizes by limiting
observation periods. Estimates of the TD term related to owner-occupied housing
showed positive and significant results, though their magnitude decreased comparedwith the same estimates in Table 4.
Panel B of Table 5 shows the results based on the variables measured inaverage hours per week. We estimated Equation (2) with 𝑦𝑖𝑡 = average childcare
hours per week and 𝐺𝑖𝑡 = average telework hours per week. These variables reflect
the average number of days per week, which was not considered before because
average hours per week is obtained by multiplying average hours per day and the
product of average number of days in a week. The results based on these variables
are similar to, but comparatively larger than, those in Table 4. Therefore, we canconclude that the results in Table 5 are robust if we use the sample defined in hours
per week, incorporating the difference in the number of telework days a week.
Overall, our results are robust if we take into consideration the longer
observation periods of February, April, and September 2020 and the number ofteleworking days.

17

Table 5: Robustness checks of estimates of interaction terms of triple difference
modelsWhole sample

Male sample

2.
Workers
withchildrenaged 7 to 18
years

Female sample

3.
Workers
withchildrenaged 18
years andbelow

4.
Workers
withchildrenaged 7 to18 years

5.
Workers
withchildrenaged 18
years andbelow

6.
Workers
withchildrenaged 7 to18 years

0.07540.08880.05080.0009(0.074)476(0.076)356(0.114)373(0.132)318

0.143**

0.111*

0.112*

0.0966

0.0619(0.064)(0.066)864
685
No. of observations(B) Dependent variable: Childcare time per
week
Telework hours per week × COVID (=1)× owner-occupied
housing with one or
0.172
0.234**
more empty room(=1)
(0.111)(0.116)1,188945
No. of observations(0.065)482(0.064)360(0.114)382(0.128)325

0.160*

0.206**

0.113

0.0849(0.089)652(0.096)491(0.191)536(0.221)454

0.278***

0.258***

0.151

0.135

Sample1.
Workers
withchildrenaged 18
years andbelow

(A) Dependent variable: Childcare time perday (Feb. and Apr. sample)
Telework hours per day × COVID (=1)× owner-occupied
housing with one or
0.05480.0821more empty room(=1)
(0.081)(0.084)849
674
No. of observations
× owner-occupied
detached housing(=1)

× owner-occupied
detached housing(=1)

0.125*

0.285***

0.319***(0.091)(0.099)(0.087)(0.086)(0.186)(0.208)
No. of observations
1,209
961
660
496
549
465
Note: The same covariates as in Table 2 are used. The cluster standard errors over respondents are
given in parentheses. ***, **, and * are significant at the 1%, 5%, and 10% levels, respectively.

18

5. Discussion
The DID results above imply that an increase in telework hours will notnecessarily increase the time spent by teleworkers with their children. This result is
consistent previous studies, such as by Wight and Raley (2009). Genadek and Hill
(2017) found that mothers who work from home increase the time spent with theirchildren aged 13 years and below. However, our results indicate that male
respondents living in larger, owner-occupied dwellings, increase the time spent with
their children.
Significantly positive effects are mainly observed in the results of the malesample. It is well known that children are mainly cared for by females in Japan. Before
COVID-19 started to spread—represented by the February sample—average daily
childcare time was 0.204 and 1.122 hours for male and female workers, respectively.
Therefore, an evident gap in childcare time existed between the two sexes before the
COVID-19 pandemic. These findings correspond to a report issued by the Cabinet
Office (2020) that stated that during the COVID-19 pandemic, the number ofhouseholds that cited an increase in the husbands’ role at home accounted for morethan 25% of total household survey coverage. However, the gap of childcare time
between male and female workers cannot be easily filled by reducing commute time
for males who work from home, as we showed in Section 4.3. Instead, housing
characteristics seemed to play a more significant role in increasing men’s childcare
time.
Regarding housing characteristics, respondents living in larger, owneroccupied dwellings tend to increase childcare time when working from home. Qin etal. (2021) showed that detached housing and dwellings with more rooms is positively
related to telecommuting. Additionally, based on Japanese data, Okubo (2021)
recently showed that workers living in owner-occupied housing tend to telework. In
Japan, it is well known that a large gap exists in the quality between owner-occupied
and rental housing. In terms of space, rental housing is significantly smaller thanowner-occupied housing. In 2013, the average space per person was 122.32 m2 forowner-occupied housing and 44.39 m2 for private rental housing (Statistics Bureau ofJapan, 2013). Homeowners can more easily secure space for both working and livingwith their family, in their respective homes.
Recently, from their unique survey, Okubo and NIRA (2021) showed thathaving own rooms in their housing increased teleworkers’ productivity. If teleworkers
living in larger housing worked in their own rooms, their productivity increased,
thereby securing additional time to spend with family and practice childcare. These
differences might be reflected in the results. Confirming that larger, owner-occupied
housing increases the childcare time of male teleworkers.
19

6. Conclusion
In this study, we measured the impact of teleworking on childcare time during
the COVID-19 pandemic. Many workers were forced to work from home to prevent
further transmission of the virus. Such an exogenous shock allowed us to measure
the average treatment effect of teleworking on childcare time by considering housing
characteristics. This study found that childcare time per day increased for male
respondents who teleworked in larger, owner-occupied housing.
Given this, it is recommended that future studies clarify the mechanism of
causality by which teleworking increases childcare hours for the respondents living inlarger, owner-occupied housing.

Acknowledgments
We thank Michio Naoi, Tsubasa Itoh, Toshihiro Okubo and the participants ofthe 2021 Virtual Joint Real Estate Conference by AsRES, GCREC, and AREUEA for theirvaluable comments. We further thank the Keio University Panel Data ResearchCenter for providing us with the JHPS/KHPS data used in this study.

Conflicts of Interest
None

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21

Appendix
Table A1: Descriptive statistics of samples of workers with child(ren) aged 18 years
and below(b)Telework(c) Non-telework

Mean
10.84
1.55
0.17
0.58
3.78
1.11

(a) Whole
S.D.
13.48
1.93
0.37
1.43
10.62
2.78

Mean
11.27
1.61
1.00
3.45
22.10
6.59

S.D.
13.60
1.94
0.00
1.50
15.97
3.14

Mean
10.75
1.54
0.00
0.00
0.03
0.00

S.D.
13.47
1.92
0.00
0.00
0.93
0.00

33.64
20.21
30.70

***
***
***

Did not want to telework

0.74

0.44

0.15

0.36

0.87

0.33

-26.65***

Requested but not granted
to telework

0.04

0.19

0.02

0.14

0.04

0.20

-1.87

*

Conducted by company
mandateImplemented at own
discretion

0.18

0.38

0.70

0.46

0.07

0.25

19.12

***

0.04

0.19

0.13

0.34

0.02

0.14

4.80

***

COVID (Apr and Sep) (=1)
Age of respondents
Female (=1)
Married (=1)
College degree (=1)
No. of household members
No. of children

0.64
41.99
0.45
0.94
0.45
3.98
1.92

0.48
6.20
0.50
0.23
0.50
1.02
0.73

0.89
42.35
0.36
0.98
0.71
3.95
1.90

0.31
6.08
0.48
0.14
0.46
0.97
0.68

0.59
41.91
0.47
0.94
0.40
3.99
1.92

0.49
6.22
0.50
0.24
0.49
1.03
0.73

11.57
0.95
-2.81
3.68
8.91
-0.46
-0.48

***

Age of children (years)
0–6 (=1)

0.21

0.41

0.24

0.43

0.21

0.40

1.00

7–12 (=1)
13–15 (=1)
16–18 (=1)

0.35

0.48

0.41

0.49

0.33

0.47

2.10

**

0.18
0.26

0.39
0.44

0.17
0.18

0.37
0.39

0.18
0.28

0.39
0.45

-0.55
-3.13

***

Monthly household income(10,000 yen)

44.9

33.5

51.4

44.9

43.6

30.5

2.44

**

Missing cases of monthly
household income (=1)

0.05

0.22

0.05

0.21

0.05

0.22

-0.22

Real saving (2010,
10,000 yen)

574.9

881.7

852.7

1278.4

518.5

765.6

3.69

Missing cases ofsaving (=1)

0.03

0.17

0.03

0.18

0.03

0.17

0.18

0.08
0.68

0.27
0.47

0.10
0.81

0.30
0.39

0.08
0.66

0.27
0.47

1.17
5.12

VariablesChildcare hours per week
Childcare hours per days
Telework (=1)
Number of telework days
Telework hours per week
Telework hours per day(b)-(c)
t
0.51
0.51

Telework status

Employment status
Self-employment (=1)
Regular employment (=1)

22

***
***
***

***

***

Non-regular employment
(=1)

0.23

0.42

Unknown type of
employment (=1)

0.00

0.03

Employment status of
spouseNo spouse (=1)
Housework (=1)
Self-employment (=1)
Regular employment (=1)
Non-regular employment
(=1)

0.06
0.14
0.06
0.48

0.23
0.34
0.24
0.50

0.02
0.20
0.07
0.36

0.25

0.44

Unknown type of
employment (=1)

0.01

0.09

0.00

0.06

0.25

0.43

0.25

0.13
0.02

0.34
0.15

0.05

Industry
Agriculture, fish, woods,
andmining (=1)
Construction andproduction (=1)
Wholesale and retail (=1)
Restaurant and hotel (=1)
Finance, insurance and realestate (=1) (=1)
Transportation (=1)
Information andcommunication (=1)
Electricity, gas, and water(=1)
Healthcare (=1)
Education (=1)
Other services (=1)
Public servants (=1)
Others (=1)
Size of the firm
1–29 workers (=1)
30–99 workers (=1)
100–499 workers (=1)
500+, public sector (=1)
Housing characteristics
Owner-occupied housing(=1)
No. of rooms (=1)
No. of rooms, 4 and above(=1)
No. of empty rooms (=1)
One or more empty rooms(=1)
Two or more empty rooms
(=1)

0.08

-7.75

***

0.24
0.33
0.24
0.50

-3.68
2.45
0.73
-3.91

***
**

0.24

0.43

2.55

**

0.01

0.08

1.26

0.00

0.07

0.44

0.25

0.43

-0.02

0.09

0.29

0.14
0.03

0.34
0.17

-2.19

**

0.22

0.10

0.30

0.04

0.20

2.86

***

0.03

0.18

0.01

0.10

0.04

0.19

-3.32

***

0.06

0.24

0.21

0.41

0.03

0.17

6.45

***

0.01

0.10

0.01

0.12

0.01

0.09

0.64

0.17
0.06
0.12
0.08
0.00

0.38
0.24
0.33
0.27
0.05

0.01
0.10
0.13
0.08

0.10
0.30
0.34
0.27

0.20
0.05
0.12
0.08
0.00

0.40
0.23
0.32
0.27
0.05

-13.83
2.00
0.46
0.09

***
**

0.26
0.16
0.21
0.38

0.44
0.36
0.40
0.49

0.20
0.11
0.18
0.51

0.40
0.31
0.39
0.50

0.27
0.17
0.21
0.35

0.45
0.37
0.41
0.48

-2.48
-2.49
-0.96
4.39

**
**

0.86

0.34

0.86

0.35

0.87

0.34

-0.40

4.76

1.46

4.57

1.37

4.79

1.47

-2.10

0.82

0.39

0.78

0.41

0.83

0.38

-1.51

0.87

1.48

0.76

1.38

0.89

1.50

-1.28

0.56

0.50

0.58

0.49

0.56

0.50

0.66

0.30

0.46

0.27

0.44

0.31

0.46

-1.31

23

0.28

0.27

0.44

0.00

0.03

0.14
0.40
0.26
0.48

0.06
0.13
0.06
0.51

0.33

0.47

0.02

0.14

***

***

**

Housing space (m2)
Housing space missing (=1)
Age of housing (years)
Less than 5 (=1)
Less than 10 (=1)
Detached housing (=1)
Condominium (=1)

96.03
0.10
17.92
0.15
0.34
0.77
0.20

69.56
0.30
12.77
0.35
0.47
0.42
0.40

85.26
0.09
16.74
0.17
0.33
0.72
0.26

41.48
0.29
11.33
0.37
0.47
0.45
0.44

98.21
0.10
18.16
0.14
0.34
0.78
0.19

73.79
0.31
13.03
0.35
0.48
0.41
0.39

-3.56
-0.71
-1.62
0.98
-0.34
-1.99
2.34

***

Owner-occupied housingwith 4 or more rooms (=1)
Owner-occupied housingwith 1 or more empty room
(=1)

0.76

0.43

0.71

0.46

0.77

0.42

-1.90

*

0.51

0.50

0.52

0.50

0.51

0.50

0.29

Owner-occupied detachedhousing (=1)

0.76

0.43

0.69

0.46

0.77

0.42

-2.25

**

Owner-occupied
condominium (=1)
Commuting time(exceeds 1 hour) (=1)

0.11

0.32

0.17

0.37

0.10

0.30

2.34

**

0.21

0.41

0.44

0.50

0.17

0.37

7.54

***

0.19

0.39

0.39

0.49

0.15

0.36

6.81

***

0.32

0.47

0.30

0.46

0.32

0.47

-0.47

Feb. (=1)

0.36

0.48

0.11

0.31

0.41

0.49

-11.57***

Apr. (=1)

0.34

0.48

0.56

0.50

0.30

0.46

7.05

***

Sep. (=1)

0.30

0.46

0.33

0.47

0.29

0.45

1.19

No. of new infectionsin prefecture625.471167.60

1205.25

1430.23507.971069.97

6.76***

City size
Major cities (=1)
Other cities (=1)

0.34
0.58

0.47
0.49

0.44
0.54

0.50
0.50

0.32
0.59

0.46
0.49

3.48
-1.33

***

Regions
Hokkaido (=1)
Tohoku (=1)
Kanto (=1)
Chubu (=1)
Kansai (=1)
Chugoku (=1)
Shikoku (=1)
Kyushu (=1)

0.03
0.05
0.34
0.21
0.19
0.04
0.04
0.10

0.16
0.22
0.47
0.41
0.39
0.21
0.19
0.29

0.01
0.02
0.52
0.16
0.21
0.01
0.01
0.06

0.10
0.15
0.50
0.37
0.41
0.10
0.12
0.23

0.03
0.06
0.30
0.23
0.19
0.05
0.04
0.10

0.17
0.23
0.46
0.42
0.39
0.22
0.21
0.31

-2.48
-2.66
5.90
-2.18
0.63
-4.50
-2.97
-2.62

**
***
***
**

No. of observations

1,270

Owner-occupied housingwith commuting timeexceeding one hour (=1)
Owner-occupied housing
built less than 10 years ago
(=1)

**
**

Survey month

214

1,056

Note: (b)–(c) represents the results for a t-test of equal means between two samples, using Welch’s
method under the hypothesis of heterogeneity. ***, **, and * are significant at the 1%, 5%, and

10% levels, respectively.

24

***
***
***

Table A2: Full estimation results of DID models of childcare time per day
SampleWhole sample

Male sample

Female sample

1.
Workers
withchildrenaged 18
years andbelow

2.
Workers
withchildrenaged 7 to18 years

3.
Workers
withchildrenaged 18
years andbelow

4.
Workers
withchildrenaged 7 to 18
years

5.
Workers
withchildrenaged 18
years andbelow

6.
Workers
withchildrenaged 7 to18 years

0.0253(0.0394)

0.0394(0.0395)

0.0174(0.0349)

0.0269(0.0313)0.0137(0.100)0.0347(0.108)0.0181-0.0891

0.0131-0.0542-0.0761-0.228(0.117)(0.131)(0.123)(0.135)(0.208)(0.219)0.0097-0.0030-0.0104

0.00180.05900.0084(0.0376)(0.0375)(0.0330)(0.0302)(0.101)(0.111)2.673***(0.962)
1.897***(0.584)4.878***(0.568)0.157(0.789)0.752(0.547)3.224***(0.835)2.845***(0.881)

5.399***(0.947)30–34 (=1)

referencereferencereferencereferencereferencereference

35–39 (=1)

0.0248(0.244)-0.647***(0.235)-1.224***(0.240)-1.388***(0.281)-1.704***(0.385)-1.478***(0.519)-0.770(0.493)1.365***(0.191)0.241(0.542)0.125(0.141)0.506(0.389)-0.0592(0.348)-0.583(0.361)-0.879**(0.360)-0.879**(0.423)-1.115(0.734)-0.551(0.551)
1.239***(0.232)0.0083(0.559)0.210(0.168)0.0516(0.283)-0.348(0.255)-0.846***(0.225)-0.679**(0.297)-0.937***(0.298)-0.674(0.594)-0.879(0.546)0.271(0.473)-0.00206(0.453)-0.511(0.424)-0.536(0.432)-0.419(0.463)-1.055(0.655)-0.950(0.662)-0.239(0.469)-0.985**(0.447)-1.744***(0.510)-2.575***(0.571)-3.022***(1.031)-4.141***(1.371)

0.945(0.663)0.300(0.575)-0.283(0.632)-1.030(0.652)-1.161(1.027)-2.884*(1.510)0.0914(0.541)0.0070(0.138)0.0541(0.523)-0.122(0.152)0.0058(0.750)-0.0550(0.291)-0.266(0.754)0.366(0.351)0.0794(0.274)0.0915(0.280)-0.103(0.280)-0.0718(0.222)

0.0942(0.526)0.495(0.604)

Variables

Telework hours per day
COVID (Apr. and Sep.)(=1)
Telework hours per day
× COVID (=1)
Individual characteristics
Age of respondents(years)
20–24 (=1)
25–29 (=1)

40–44 (=1)
45–49 (=1)
50–54 (=1)
55–59 (=1)
60–64 (=1)
65–69 (=1)
Female (=1)
Married (=1)
College degree (=1)
Type of employment
Self-employment (=1)

25Regular employment(=1)
Non-regular
employment (=1)
Unknown type of
employment (=1)

referencereferencereferencereferencereferencereference

0.201

0.170

-0.02160.0183

0.313

0.212

(0.249)(0.267)(0.311)(0.304)(0.338)(0.372)2.343***

2.180***(0.370)Type of employment of spouse
Housework (=1)-0.329*(0.188)Self-employment (=1)-0.0284(0.287)(0.650)-0.563***(0.213)-0.194(0.291)-0.447**(0.190)-0.298(0.293)-0.442**(0.216)-0.310(0.287)0.364(0.748)0.220(0.550)-0.472(0.642)-0.352(0.552)

Regular employment(=1)

referencereferencereferencereferencereferencereference

Non-regular
employment(=1)-0.125-0.104-0.243-0.152

0.614

0.869

(0.179)(0.208)(0.173)(0.190)(0.504)(0.754)

0.461

0.281

-1.087***-0.772*2.054*

0.998(0.673)(0.499)(0.392)(0.418)(1.196)(0.882)-0.845**-1.057***-1.300***-1.407***-1.472***-2.018***(0.414)(0.370)(0.300)(0.293)(0.558)(0.603)referencereferencereferencereferencereferencereference-0.00930.00952-0.209-0.0005

0.0209-0.0846(0.209)(0.222)(0.196)(0.173)(0.454)(0.509)-0.834**-0.464-1.046***-0.762***-0.836-0.526(0.421)(0.423)(0.300)(0.276)(0.592)(0.613)-0.246-0.376

0.219

0.415

-0.982-1.319**(0.319)(0.354)(0.286)(0.335)(0.647)(0.668)-0.0583

0.0788-0.266-0.258

0.645

0.541

(0.285)(0.312)(0.258)(0.284)(1.373)(1.426)-0.173-0.103-0.321-0.2260.392-0.111(0.227)(0.255)(0.228)(0.239)(0.597)(0.703)

0.292

0.871

-0.149-0.806**

0.933

1.383

(0.464)-0.100(0.237)0.143(0.320)-0.176(0.939)-0.0736(0.260)0.324(0.354)-0.158(0.384)-0.346(0.255)0.493(0.513)0.0134(0.341)-0.380(0.231)-0.177(0.229)0.340(1.083)-0.222(0.439)0.0875(0.517)-0.360(1.282)-0.322(0.490)0.0399(0.594)-0.682

Unknown type of
employment (=1)
Industry
Agriculture, fish,
woods, and mining (=1)
Construction
production (=1)

andWholesale andretail (=1)
Restaurant andHotel (=1)
Finance, insurance andreal estate (=1)
Transportation(=1)
Information andcommunication (=1)
Electricity, gas, andwater (=1)
Healthcare (=1)
Education (=1)
Other services (=1)

26

(0.256)
0.514*(0.282)
1.192**(0.544)(0.281)0.878**(0.375)
1.956***(0.590)(0.208)0.395(0.264)(0.253)0.651*(0.332)(0.496)0.183(0.678)0.660(0.854)(0.532)0.108(0.936)
2.119**(0.909)1–29 workers (=1)

referencereferencereferencereferencereferencereference

30–99 workers (=1)

0.274
(0.247)0.0240(0.202)0.0308(0.189)0.254(0.267)-0.0053(0.215)-0.111(0.214)0.0763(0.207)-0.0059(0.182)0.107(0.183)0.187(0.180)0.279(0.182)0.317(0.216)0.666(0.424)0.0204(0.355)-0.0908(0.353)0.671(0.428)-0.125(0.394)-0.257(0.401)

No. of household
members

0.140

0.154

0.04880.0241

0.351

0.331

No. of children(0.119)-0.199(0.169)(0.127)-0.0652(0.183)(0.0798)-0.139(0.107)(0.0818)-0.0242(0.112)(0.287)-0.453(0.398)(0.304)-0.162(0.432)Monthly householdincome (10,000 yen, ln)-0.162-0.239-0.113-0.133-0.401-0.434(0.139)(0.167)(0.134)(0.133)(0.256)(0.298)-0.268-0.5220.05940.0424-1.339-1.521(0.648)(0.726)(0.594)(0.575)(1.119)(1.271)0.0020-0.0137-0.0128-0.0218

0.05110.0059(0.0291)(0.0327)(0.0248)(0.0265)(0.0673)(0.0743)-0.514-0.530

0.444

0.331

-0.657-0.734(0.511)(0.555)(0.561)(0.565)(0.870)(0.906)

0.0414(0.142)0.430(0.703)0.0204(0.162)0.214(0.789)0.0943(0.130)0.383(0.735)0.120(0.115)0.399(0.651)-0.0556(0.302)0.143(1.421)-0.0118(0.353)0.272(1.659)Owner-occupied
housing (=1)

0.02370.140-0.103-0.0568

0.101

0.258

Age of housing(0.226)-0.0124**(0.00563)(0.267)-0.0141**(0.00585)(0.220)-0.0156***(0.00480)(0.186)-0.0132***(0.00460)(0.450)-0.0191(0.0122)(0.556)-0.0253*(0.0149)

Number of new
infections in prefecture-3.27e-05-4.47e-05

1.05e-05-4.54e-06-2.86e-05

3.12e-05(5.91e-05)(6.68e-05)(7.02e-05)(6.64e-05)(9.24e-05)(0.00011)

0.0514(0.0947)

0.0762(0.100)0.119(0.107)0.0798(0.105)0.0763(0.167)0.115(0.179)Public servants (=1)
Other industry (=1)
Firm size of respondents

100–499 workers (=1)
500+, public sector (=1)
Household characteristics

Missing cases ofmonthly householdincome (=1)
Household saving(10,000 yen, 2010
prices, ln)
Missing cases ofsaving (=1)
Housing characteristics
Space (m2, ln)
Missing of space (=1)

Survey period fixed effects
Apr. (=1)
City size27

Major cities (=1)
Other cities (=1)
Regional fixed effects
Hokkaido (=1)
Tohoku (=1)
Kanto (=1)
Chubu (=1)
Kansai (=1)
Chugoku (=1)
Shikoku (=1)
Kyushu (=1)
Constant

Adjusted R-squared
No. of observations

0.871***(0.251)0.689***(0.235)0.617**(0.284)
0.538**(0.262)0.145(0.219)0.165(0.213)-0.145(0.238)-0.155(0.231)1.455***(0.471)0.979**(0.410)1.016*(0.533)0.873*(0.462)0.122(0.426)0.144(0.341)
reference-0.109(0.200)0.242(0.211)0.207(0.427)-0.308(0.332)
0.187(0.272)0.875(0.857)0.564(0.531)0.219(0.410)reference-0.255(0.222)0.242(0.227)-0.0258(0.479)-0.439(0.334)0.0316(0.297)0.843(0.985)-0.371(0.235)-0.350(0.296)
reference-0.213(0.194)0.195(0.216)-0.433(0.360)-0.111(0.340)0.216(0.265)1.675*(0.895)-0.247(0.228)-0.422*(0.240)reference-0.173(0.193)0.227(0.204)-0.406(0.316)-0.144(0.278)0.199(0.252)
1.183(0.997)2.003*(1.067)0.506(0.592)
reference-0.0233(0.397)0.115(0.422)0.312(0.814)-0.414(0.538)0.0108(0.517)3.278**(1.644)1.739(1.197)0.610(0.678)reference-0.315(0.451)-0.0647(0.482)0.0426(0.862)-0.522(0.589)-0.0980(0.539)2.286(1.961)

0.329
1,224

0.322
970

0.254
672

0.278
503

0.292
552

0.263
467

Note: Childcare time per weekday is the dependent variable. The cluster standard errors over
respondents are given in parentheses. ***, **, and * are significant at the 1%, 5%, and 10% levels,
respectively.

28