Institute for Economic Studies, Keio University
Keio-IES Discussion Paper Series

Access to and Demand for Online School Education
during the COVID-19 Pandemic in Japan
Hideo Akabayashi、Shimpei Taguchi、Mirka Zvedelikova

26 february, 2022
DP2022-006
https://ies.keio.ac.jp/en/publications/18516/

Institute for Economic Studies, Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan
ies-office@adst.keio.ac.jp
26 february, 2022

Access to and Demand for Online School Education during the COVID-19 Pandemic in
Japan
Hideo Akabayashi、Shimpei Taguchi、Mirka Zvedelikova
Keio-IES DP2022-006(revised edition: DP2021-013)
26 february, 2022
JEL Classification: I24; I28; J81
Keywords: Online education; COVID-19; Family background; Parental work; Japan

Abstract
The COVID-19 pandemic has forced schools around the world to close, and Japanese
schools were no exception. While many previous studies have identified an inequality inthe access to online school education based on family background, few studies havesimultaneously examined the access to online education both at school and outsideschool, and no study has examined parents’ views about online school education, an
important demand side factor. Using a panel dataset collected in May and December
2020, we examine the determinants of at-school and outside-school online experience.
We observe that children in private schools and those from high-income households
received more online education at school, and children from high-income households
and those with a highly educated parent experienced more online education outsideschool. Further, we find that a greater increase of COVID-19 between May andDecember was associated with increased access to online education outside the school,especially for children in private schools and those with a highly educated parent, while
we do not observe this trend in at-school online education. We also find that householdincome and parent’s high educational level are also associated with higher demand for
at-school online education, while mothers being employed in regular contracts andfathers in non-regular contracts decreased this demand in the short term.

Hideo Akabayashi
Faculty of Economics, Keio University
2-15-45, Mita, Minato-ku, Tokyo
hakab@econ.keio.ac.jp
Shimpei Taguchi
Graduate School of Economics
2-15-45, Mita, Minato-ku, Tokyo
shimpei.taguchi@keio.jp

Mirka Zvedelikova
Graduate School of Economics
2-15-45, Mita, Minato-ku, Tokyo
zvedmi@gs.econ.keio.ac.jp

Acknowledgement: We thank the Cabinet Office of the Japanese Government forproviding us the data from “Survey on Lifestyle Attitudes and Behavioral Changes
during the COVID-19 Pandemic.” We also thank Soichi Ohta, and the participants at thegraduate labor seminar at Keio University for their comments. This work was
supported by KAKENHI Grant Numbers 16H06323 and21H04982 from the Japan Society for the Promotion of Science, Mitsubishi Foundation,
and Keio Gijuku Academic Development Funds.

Access to and Demand for Online School Education during the COVID-19 Pandemic in Japan †Hideo Akabayashia
Shimpei Taguchib
Mirka Zvedelikovac
February 26, 2022
Abstract
The COVID-19 pandemic resulted in school closures worldwide, including in Japan, where remoteeducation at schools is underdeveloped. Using a unique panel dataset collected in May and December
2020, we examine the determinants of access to online education at and outside schools and parents’preference towards at-school online education. We observe that children from more privileged family
backgrounds received more at-school as well as outside-school online education. We also find thathousehold income and parent’s educational level are associated with higher demand for at-school online
education, while mothers working full-time and fathers in non-regular contracts decreased this demand
temporarily.

Keywords: Online education; COVID-19; Family background; Parental work; JapanJEL Classification: I24, I28, J81

We thank the Cabinet Office of the Japanese Government for providing us the data from “Survey on Lifestyle
Attitudes and Behavioral Changes during the COVID-19 Pandemic.” We also thank Soichi Ohta, and theparticipants at the graduate labor seminar at Keio University for their comments. This work was supported by
KAKENHI Grant Number 16H06323 from the Japan Society for the Promotion of Science and Keio Gijuku
Academic Development Funds. The authors declare that they have no relevant or material financial interests that
relate to the research described in this paper.
a: Corresponding author. Faculty of Economics, Keio University, 2-15-45, Mita, Minato-ku, Tokyo 108-8345,
Japan. https://orcid.org/0000-0002-6347-2895. Email: hakab@econ.keio.ac.jp
b: Graduate School of Economics, Keio University, 2-15-45, Mita, Minato-ku, Tokyo 108-8345, Tokyo, Japan.
https://orcid.org/0000-0003-3922-9948. Email: shimpei.taguchi@keio.jp
c: Graduate School of Economics, Keio University, 2-15-45, Mita, Minato-ku, Tokyo 108-8345, Tokyo, Japan.
https://orcid.org/0000-0002-3085-7502. Email: zvedmi@gs.econ.keio.ac.jp†

11. Introduction
With the outbreak of the COVID-19 pandemic, schools worldwide were forced to close leaving out 800
million school-aged children from receiving education as of early 2021 (UNESCO 2021). As the impact
of COVID-19 persists and prolongs school closures, the educational opportunity divide between
children with and without access to some form of online education becomes apparent. Japan, where
school education has been provided with relatively high equity regardless of family background (OECD2012), is not an exception. With the first wave of COVID-19, Japanese schools nationwide were ordered
to close and children were deprived of face-to-face education. Only 5% of Japanese children had accessto interactive online education during the period of school closure. 1 Before the pandemic, Japan ranked
the lowest in computer usage for schoolwork outside school in the developed world (OECD 2020a).
Despite its importance, not much research has been conducted on Japanese children’s access to online
education, related parental views and their heterogeneity during the COVID-19 crisis based on arepresentative sample.
There are mainly two strands of research documenting children’s learning experience during
the pandemic. The first one investigates children’s online learning activity gap using time surveys,
typically done using data from developed countries. Grewening et al. (2020) used a time-use survey ofschool-aged children in Germany to investigate students’ learning time. They reported that highachievers engaged in school online learning activities more frequently than low-achievers. Andrew etal. (2020) used the UK Time Use Survey to investigate how the lockdown impacted the time use andlearning of children between ages 4 and 15. They suggest that educational gaps between children frompoorer and better-off families are likely to have been reinforced. Using data from an online learning
service company, Ikeda and Yamaguchi (2020) investigated the online study time of junior and highschool students in Japan pre-COVID-19 and during COVID-19-related school closures and observed apositive association between online education quality and children’s study time. The second approach
typically utilized for developing countries examines the overall access to remote education. Cappelle etal. (2021) showed that access to technology, along with family and social backgrounds, affected the useof remote learning modalities in India. Likewise, Hossain (2021), analyzing data from Ethiopia, India,
Peru, and Vietnam, showed that access to remote schooling was positively correlated with household
wealth and internet access.
We aim to document both children’s access to online education at school and outside schooland parental preferences towards at-school online education during the COVID-19 pandemic, and toreport how they were associated with family backgrounds. We use unique governmental survey datacollected at two points during the pandemic from the same households—in May 2020, immediately
after school closures ended, and then half a year later, in December 2020, during the third wave. No
other existing data would allow us to examine both the rapid response and the long-term changes in the
online educational experience brought about by the pandemic.
First, we analyze and document the online educational experience of children in elementary,junior high and high schools, both at school and outside school, based on the type of school (public or
private) attended, household income and parent’s educational level. Second, we use the difference-indifference and triple difference estimation methods to further highlight the potential inequality in accessto online education by family backgrounds triggered by an increase in COVID-19 cases. Third, we
examine the parental preferences towards at-school online education, how they are shaped by theexperience of online education and factors such as household income, parental education and parents’
employment status and work styles, using probit and ordered logit models. For these analyses, we utilize
the longitudinal nature of our dataset to investigate the situation at both survey time points separately
as well as over time.
1“Survey of Study and Instruction (Gakushu Shidou Torikumi Chousa)” conducted on all boards of education,
which operate public schools, by the Ministry of Education, Culture, Sports, Science and Technology (MEXT)
on April 16, 2020.
2

The contribution of this study is two-fold. First, this is the first study to examine the impact of
the COVID-19 pandemic on online educational practices in Japan as experienced by children at school
and outside school. Outside-school education is a long-standing topic of interest in Asia, however its
impact on learning inequality has lately been gaining attention worldwide. 2 To fully understand theramifications of this pandemic, in addition to at-school education, it is important to examine outsideschool learning, such as private tutoring, prep schools, or many different types of programs typicallyoffered after regular school hours. These learning opportunities have the potential to influencechildren’s academic performance and equity in education. Outside-school educational options areabundant in Japan and are not a substitute to at-school education as they are attended on top of school,
typically to get ahead of ones’ peers to gain access to prestigious institutions of higher learning. Ignoring
potential heterogeneity in access to online education outside school may lead to an underestimation of
the extent of inequality in online learning opportunity among children. Second, adopting a broader
perspective than previous studies, using data from a nationally representative survey we simultaneously
analyze both children’s actual experience and parental preferences towards online education, and how
they differed based on socioeconomic backgrounds. Parental preferences can potentially influencechildren’s access to online learning through the support of at-home learning and purchases of outsideschool educational services or appropriate Internet-connected devices. Looking at the determinants ofparental preferences can help policy makers identify the key factors needed for creating the homeenvironment necessary for children’s equitable access to online learning. These two contributions makeour study unique while providing the first evidence on online educational access gap during the COVID19 pandemic in Japan. To the best of our knowledge, no previous studies have presented an overall
picture of the determinants of children’s access to and parental demand for online education ascomprehensively as our paper.
Our main findings are that children in private schools and those from high-income households
received more online education at school, while children from high-income households and those witha highly educated parent experienced more online education outside school. Next, we find that a greater
spread of COVID-19 between May and December 2020 was associated with increased access to online
education outside school for children in private schools and children with a highly educated parent,
however, we do not observe this trend in at-school online education. Ignoring the socioeconomic
differences in the access to online education outside school would thus lead to a substantial bias in theestimates of the online learning volume gap. Further, our analysis revealed that parents of children who
had an experience of online education at school tended to have higher preference for at-school online
education. We also observe more positive views on online education in highly educated parents andhigh-income families, factors associated with better outside-school online educational access. Moreover,
we observe that mothers employed in regular contracts and fathers in non-regular contracts tended tohold negative views of at-school online education.
2. Background
One of the early major measures to prevent the spread of the COVID-19 pandemic in Japan was to closeschools about 2 weeks before the 2-week-long spring break. On Thursday, February 27, 2020, thegovernment requested all elementary, junior high, and high schools to temporarily close from thefollowing Monday, March 2, 2020, until the beginning of the new school year on April 1, 2020. Survey
by the Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT) found that
33% of all elementary and junior high schools and 35% of high schools reopened at the beginning of
the school year, with schools in the most impacted urban areas 3 staying closed (MEXT 2020a).
Park et al. (2016) in their review stress the need for more comprehensive research on supplementary education.
Entrich (2020) examined access to outside-school education in 63 societies and found a persistent socioeconomic status related access gap.
3
Chiba, Fukuoka, Hyogo, Kanagawa, Osaka, Saitama prefectures, and Tokyo metropolis.
23

On April 7, 2020, a state of emergency was declared for 7 out of the 47 Japanese prefectures,
which on April 16 was extended nationwide, leading to reopened schools closing again. As of April 22,
95% of elementary and junior high schools and 97% of high schools were closed (MEXT 2020b). The
state of emergency was partially lifted on May 14 and fully lifted on May 25. By June 1, 99% ofelementary and junior high schools and 96% of high schools were open (MEXT 2020c). Based on thedata collected by MEXT from boards of education about public schools nationwide, in the period from
April 1 to June 23, on average, public elementary schools were closed for 24.6 school days; public
junior high schools, for 24.5 school days; and public high schools, for 26.7 school days. 4
As of the time of drafting this paper in February 2022, the central government had not requested
schools to close again, however, they might have closed for short periods of time independently of thecentral government to contain local outbreaks. Fig. 1 shows the timeline of the COVID-19 spread inJapan, the state of emergency and the data collection points, from January 16 to December 31, 2020.
3. Data
We use data from the first and the second round of “Survey on Lifestyle Attitudes and Behavioral
Changes during the COVID-19 Pandemic” collected by the Cabinet Office of the Japanese government.
Both rounds were implemented online targeting a national representative sample of respondents,
stratified by age and region, over the age of 15 across Japan. The first round was conducted between
May 25 and June 5, 2020, which we call “May survey,” following the end of the state of emergency on
May 25, and collected data from 10,128 respondents. The second round, which we call “December
survey,” took place between December 11 and December 17, 2020, with 10,128 respondents of which
5,212 also participated in the first round. Both rounds of this survey included questions about the
respondent’s background, work styles, family and social life, personal well-being, and the youngest
school-aged child’s education. 5 The survey also asked about plans and wishes for the future.
To observe and understand the impact of the pandemic on children’s education more precisely,
we limited our sample to respondents who participated in both rounds of the survey, have children with
their youngest child in elementary, junior high, or high school, and have provided consistent answers
about their child’s and their own level of schooling in both rounds of the survey, arriving at a sample
of 566 respondents. To examine the impact of the pandemic on a local level, we then excluded
respondents who moved between prefectures between the survey rounds and those who did not produce
a definitive answer about the type of education their child had been receiving (answer “do not know”)
in either wave to finalize our sample at 528 respondents. Question regarding the type of school the childattends, public or private, a variable of interest in our analysis, was included only in the second survey
round. Despite a relatively modest sample size, the structure of the data and the uniqueness of theinformation contained therein is extremely valuable and thus makes this data set suitable to our research
objective.
In the final sample, 46% of respondents were female, 62% of respondents had a child in
elementary school, 21% in junior high school, and 17% in high school. Of all children, 11% attended
private schools. By school level, 2% of elementary, 12% of junior high, and 39% of high school students
in the sample attended private schools, with these numbers in the general population for the academic
year 2020 standing at 1%, 8%, and 33%, respectively (School Basic Survey, MEXT). On average, oursample is more educated than the general population, with 75% of respondents having attained a postsecondary education. The data on the educational attainment of respondent’s spouse were not available.
4“Survey of School Instructions (Gakushuu Shidou ni Kansuru Joukyou Chousa).” This survey did not coverprivate schools; however, the guidelines regarding school closures and reopening applied to both public andprivate schools.
5
A limitation of the survey design is that the age distribution of our sample is likely to be biased towards
younger children. However, the empirical literature of female labor supply has typically focused on the effect ofyoung children. Therefore, we consider the focus on the youngest school-aged children acceptable for our
purpose of analyzing the effect of employment conditions on the demand for school online education.
4Based on the 2010 national census, 44% of those in the 25–49-year age category, which is most likely
to be represented in our sample, attained post-secondary education. The sample is also composed of
respondents who are relatively well-off; 58% of the respondents live in households with annual
household income over 6 million yen, which is the highest income bracket common to both rounds ofthe survey. In 2018, the average annual household income stood at 5.52 million yen for all households
and at 7.46 million yen for households with children under the age of 18 years according to thegovernmental statistics (MHLW, 2020). Other properties of the sample are described in Table 1. Agesof both parents and children were collected in the survey but not disclosed.
The questionnaires of our survey in May and December enquire about the type of online
education the youngest school-aged child experienced at school and outside school. The responding
parent chose all applicable answers from a given selection of “online classes,” “online instruction,” and“online materials” both at school and outside school (cram school, after-school activities), “other,” and“no online education received.” As these types of online education are often complementary, theseparation between them is not clear, and their respective prevalence in the sample is small, we do not
distinguish between them. Creating at-school and outside-school online education composite variables
also allows us to implement a more detailed analysis.
In May, 34% of children in our sample received some type of online education at school,defined as at least one of “online classes,” “online instructions,” and “online materials,” with thisnumber dropping to 13% in December. In May, 51% of parents responded that their child had received
no online education whatsoever and in December this percentage increased to 76%. As May data cover
the period of mandated school closures, it is possible that during the closures, two thirds of school
children received little to no education.
Figs 2 and 3 descriptively show the type of online education experienced by level and type ofschool the child attended. In both May and December, the higher the level of school attended, the more
online education the child received at school. The opposite trend can be seen for outside-school online
education, defined as at least one of “online classes,” “online instruction,” and “online materials”
outside school. Furthermore, children attending private schools had at both time points received more
at-school online education than children in public schools, and more outside-school online education in
December.
The questionnaire also asks parents about their preferred future school format as it relates to atschool online education, but not outside-school online education. The respondents selected a single
answer from the options “100% in person,” “in principle in person,” “over 50% in person,” “over 50%
online” and “do not know.” The phrasing of this question is identical in both rounds of the survey. As
our goal was to examine the determinants of parents’ preferences for online education, we excluded the
response “do not know” in either wave from our sample and for the purposes of this analysis, worked
with a limited sample of 419 respondents. The descriptive statistics for this limited sample are presented
in Appendix Table A1. 6
Figs 4 and 5 show the distribution of parental preferences towards at school education in May
and December broken down by level and type of school in the limited sample. Comparing parents’stated preferences in May and December, parents’ views shifted in the direction of in-person learning.
In May, 11% of parents wanted school to be held mainly online and 69% of parents wanted education
to return to in-person schooling in principle or completely. In December, these proportions shifted to
5% and 86%, respectively. One interpretation of these facts is that compared to May, when the national
To check the consistency of the two samples utilised as it relates to our variables of interest, we run a probit
regression of whether a respondent declared a preference about future school format. We find that respondents
with a child who had received online education at school and respondents whose child attended private schooltended to express a specific preference. Respondents from high income households as well as single mothers—
defined as mothers not living with a spouse—were also more likely to state a preference. On the contrary,
respondents living in multigenerational households (with respondent’s or respondent’s spouse’s parents or
grandparents) were more likely to answer “do not know” in December and thus, to be omitted from our sample.
The regression results for the consistency check can be found in Appendix Table A5.
65state of emergency was in effect and the nature of the COVID-19 virus was not clearly understood, in
December, without the national emergency measure, parents generally preferred having their children
receive face-to-face education at school. Generally, in both surveys the younger the child, the more theparent preferred in-person learning. Moreover, parents with children in public schools preferred inperson learning over parents of children in private schools.
To assess the impact of COVID-19 pandemic in Japan, we utilize the officially published datasummarized by the COVID-19 Japan Anti-Coronavirus Dashboard (https://www.stopcovid19.jp/) as
they can be accessed through the software Stata. The Japanese government announces the number of
newly confirmed cases on a prefectural basis. Some municipalities independently share their numbers;
however, these do not cover all our sample, and therefore, we used the prefecture-based data. Measuresagainst the pandemic, such as the state of emergency or school closures, are generally taken at aprefectural or nationwide level; and thus, we consider prefecture-based COVID-19 numbers as
appropriate for our study. We construct two measures of the COVID-19 spread: one covering the periodof 30 days prior to the beginning of each survey round, the other covering 7 days. Both were adjusted
to show the number of newly confirmed cases in the given prefecture during the given time frame
weighted by population of the prefecture.
In the first part of the analysis, examining differences in access to online education, we use the
30-day measure which corresponds with the period the questionnaire asks about in December. 7 Then,
to analyze parents’ preferences, we turn to the 7-day measure, which we deem more relevant to personalviews. Over the 30 days prior to the May survey, the number of newly confirmed COVID-19 cases
increased in 38 out of 47 prefectures of Japan and was 0 in the rest. For the weekly measure, the numberof newly confirmed cases per capita stood at 0 for 31 prefectures and increased in 16. In December, allprefectures saw an increase during both periods.
4. Empirical strategy
4.1 Access to online education
We first examine the online education experiences at the two data points, May and December, by thetype of school attended and family backgrounds. We estimate the following probit model to measure
the likelihood of a child experiencing online education at and outside school by their background:𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑠𝑠𝑡𝑡=1,2 = 𝛽𝛽0 + 𝛽𝛽1 ∗ 𝑆𝑆𝑆𝑆ℎ𝑜𝑜𝑜𝑜𝑜𝑜 + 𝛽𝛽2 ∗ 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝑦𝑦𝑡𝑡=1,2 + 𝛽𝛽3 ∗ 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑑𝑑𝑡𝑡=1,2 + 𝜀𝜀(1)where 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 is a dummy variable taking two forms, one for at-school online
education and the other for outside-school online education. As for at-school online experience,
𝑂𝑂𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 is equal to 1 in case the child had received at least one of the three types of
online education, “online classes,” “online instructions,” or “online materials,” from school, and takes
0 otherwise. For outside-school online education, 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 takes 1 if the child hadreceived at least one of the same three types of online education outside school, such as at an afterschool program or private tutoring and takes 0, otherwise. 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 measures newly confirmed COVID19 cases over the period of 30 days prior to the survey in each prefecture and is weighted by populationof the prefecture. Term 𝜀𝜀 represents an error term, which is for all models assumed to be clustered on aprefectural level.

7

In the May survey, the period in question is not specified besides “during the pandemic.” We understand this as
the period since the beginning of the new school year on April 1. Therefore, we assume that parents described theonline learning experience at the type of school stated on the day of the survey. In the December survey, the samequestion was asked again, this time specifying the period of the previous 30 days.

6

We also estimate the changes in online educational experience from May to December survey
using the following value-added probit model to see, given the online education experience in May,
how the access in December was influenced by observed school and family factors: 8
𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑡𝑡2= 𝛽𝛽0 + 𝛽𝛽1 ∗ 𝑆𝑆𝑆𝑆ℎ𝑜𝑜𝑜𝑜𝑙𝑙𝑡𝑡2 + 𝛽𝛽2 ∗ 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝑦𝑦𝑡𝑡2 + 𝛽𝛽3 ∗ 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑑𝑑𝑡𝑡2 + 𝛽𝛽4∗ 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑡𝑡1 + 𝜀𝜀

(2)
Next, to examine whether a faster increase of regional COVID-19 cases was associated withchanges in online educational experiences, we combine May and December data and estimate thefollowing difference-in-difference (DID) linear probability model treating rapid increase in COVID-19
cases as an unexpected exogenous shock:𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑎𝑎𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 = 𝛽𝛽0 + 𝛽𝛽1 ∗ 𝐷𝐷𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 ∗ 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 + 𝛽𝛽2 ∗ 𝐷𝐷𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 + 𝛽𝛽3 ∗ 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 + 𝜀𝜀
(3)where the definition of 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 is identical to that in Equations (1) and (2). 𝐷𝐷𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 in
Equation (3) is a dummy variable equal to 1 in case respondent’s prefecture of residence saw an increasein new COVID-19 cases above sample average, based on the difference between 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑑𝑑𝑡𝑡1 and
𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑑𝑑𝑡𝑡2 . 9 For our data, the average difference is 0.48 cases per 1,000 inhabitants. 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 is adummy variable identifying December survey.
To further evaluate the effect of the faster increase of COVID-19 cases and the role of family
background factors, we extend the difference-in-difference estimation to a triple difference linear
probability model. 10 We estimate the following model:
𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂= 𝛽𝛽0 + 𝛽𝛽1 ∗ 𝐷𝐷𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 + 𝛽𝛽2 ∗ 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 + 𝛽𝛽3 ∗ 𝐷𝐷𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 ∗ 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 + 𝛽𝛽4∗ 𝐷𝐷𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 + 𝛽𝛽5 ∗ 𝐷𝐷𝐶𝐶𝐶𝐶𝑣𝑣𝑖𝑖𝑖𝑖 ∗ 𝐷𝐷𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 + 𝛽𝛽6 ∗ 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 ∗ 𝐷𝐷𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 + 𝛽𝛽7∗ 𝐷𝐷𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 ∗ 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 ∗ 𝐷𝐷𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 + 𝜀𝜀
(4)where 𝐷𝐷𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 is a dummy variable which identifies children’s family background along 3
dimensions: household income, responding parent’s education and the type of school attended (public
or private). A child is considered to come from a high-income family when annual household income
is above 6 million yen. Parental education is taken as high in case the responding parent has attained
post-secondary education. With this model, we expect to evaluate how an exogenous increase of
COVID-19 cases had heterogenous impacts on children’s online learning experiences both at andoutside school by family background and school types.
A potential problem of the difference-in-difference framework is that it may confound thetreatment effects with preexisting differences in time trends across treatment groups and untreatedgroups, in our case the prefectures that experienced a rapid increase in COVID-19 cases and those that
did not. Unfortunately, to our knowledge, the variables of interest in our dataset are not available forthe pre-pandemic period, preventing us from testing the parallel trends assumption directly. We,
The value-added model has been extensively used in the literature of the education production function. In thispaper, we opt for a widely used model that includes a lagged outcome variable as an independent variable since
the literature is not conclusive as to the specification leading to the least biased estimation results (Hanushek andRivkin 2012; Koedel et al. 2015). Moreover, with two waves of panel data, there is little room for additional
controls of measurement error or endogeneity bias.
9
Prefectures for which 𝐷𝐷𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 dummy variable is equal to 1: Aichi, Hokkaido, Hyogo, Kanagawa, Nara,
Okinawa, Osaka, Saitama prefectures and Tokyo metropolis.
10
For robustness check, we estimated equations (3) and (4) using a probit model and confirmed that theestimated results do not change qualitatively.
87

therefore, construct a set of variables we consider strong predictors of the access to online educationand its family-backgrounds related heterogeneity and test whether the trends between the two groups
are statistically different. Specifically, we use the prefectural level data for the per capita GDP,
educational expenditure per household, college enrollment rate, private school enrollment rate, publicschool expenditure per child, the ratio of students needing financial assistance for school materials, andthe ratio of students attending cram schools, covering up to five years prior to the pandemic. We did
not find any significant preexisting differences in trends between the two groups, although a degree ofcaution is needed. The possibility of unobserved strong determinants of online education access withdifferent trends between the two groups remains, nevertheless, the results of these auxiliary tests
strengthen the interpretation of our results, which, based on our strategy, are likely to be causal.
4.2 Parental demand for online education
To examine parental preferences towards at-school online education both in May and in December, weestimate the following ordered logit model separately for both surveys:
𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑑𝑑𝑡𝑡=1,2 = 𝛽𝛽0 + 𝛽𝛽1 ∗ 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠ℎ𝑜𝑜𝑜𝑜𝑜𝑜𝑡𝑡=1,2 + 𝛽𝛽2 ∗𝑆𝑆𝑆𝑆ℎ𝑜𝑜𝑜𝑜𝑜𝑜 + 𝛽𝛽3 ∗ 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝑦𝑦𝑡𝑡=1,2 + 𝛽𝛽4 ∗ 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑑𝑑𝑡𝑡=1,2 + 𝜀𝜀

(5)Outcome variable 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑑𝑑𝑡𝑡=1,2 shows parental preference towards atschool online education over in-person education measured on a four-point scale, with greater numbers
indicating stronger preference for online education. 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂ℎ𝑜𝑜𝑜𝑜𝑜𝑜 is a dummy
variable equal to 1 if respondent’s child had received some form of online education at school lately 11,
and 0, otherwise. 𝑆𝑆𝑆𝑆ℎ𝑜𝑜𝑜𝑜𝑜𝑜 and 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 are vectors of school characteristics and family backgrounds,
respectively. In addition to household income and parental education, to get insights into potential
constraints on the demand side of online school education, 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 variables include parents’ work
status or changes in parents’ work styles due to the pandemic. 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 is a control for the number of
newly confirmed COVID-19 cases in respondent’s prefecture of residence over 7 days prior to thebeginning of the respective survey.
Next, to analyze how parents’ preferences changed from May to December, we employ thefollowing value-added model:
𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑡𝑡2 = 𝛽𝛽0 + 𝛽𝛽1 ∗ 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑠𝑠𝑡𝑡2 + 𝛽𝛽2 ∗ 𝑆𝑆𝑆𝑆ℎ𝑜𝑜𝑜𝑜𝑙𝑙𝑡𝑡2 + 𝛽𝛽3 ∗𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝑦𝑦𝑡𝑡2 + 𝛽𝛽4 ∗ 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑑𝑑𝑡𝑡2 + 𝛾𝛾 ∗ 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑡𝑡1 + 𝜀𝜀

(6)where we expand Equation (5) by including the lag of the outcome variable
𝑂𝑂𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛. Equivalently to the preceding models, the error term 𝜀𝜀 is assumed to beclustered on a prefectural level.
5. Results and discussion
5.1 Equality of access to online education
In this section, we describe the differences in access to online education by the type of school attended
and family background factors based on Equations (1) and (2). Table 2 reports the determinants of
online education as experienced in May (Columns (1) and (4)) and in December (Columns (2) and (5))
and provides a closer look at the changes between May and December (Columns (3) and (6)).
11

The period in question is from April 1 to the survey date for the May survey, and previous 30 days for the
December survey. We do not include outside-school online education access variable in the model, as child’s
after-school activities are related to family background and thus, likely to be endogenous.

8First, we discuss the results for at-school online education shown in Columns (1) to (3). In
Column (1) we observe that, in the May survey, children from high-income families and those attending
private schools were highly statistically more likely to have experienced online education at school thantheir corresponding counterparts by 17% and 23%, respectively. In Column (2), the December survey,
the sign and significance on the coefficient of the private school dummy variable is unchanged, yethigh-income household coefficient, remaining positive, retains significance only at the 10% threshold,despite a lower effect size. However, in Column (3), which reports the results from a value-added model
specification (Equation (2)), while we did not confirm any statistically significant difference between
high and low-income households, the effect of the child attending private school remained significant
at a 5% level and positive in sign, albeit with a lower effect size. This result suggests that the online
education access gap between public schools and private schools further widened even after schools
reopened.
Second, in Columns (4) to (6) in Table 2, we examine the factors associated with access toonline education outside school. Results from the May survey in Column (4) indicate that children fromhigh-income households and those with a responding parent with a post-secondary education had ahigher likelihood of experiencing online education outside school during the first wave of the pandemic
by 16%. A similar trend was observed in the December survey in Column (5), where, in addition tohousehold income and parent’s educational level, the positive effect of a child attending a private school
becomes significant. Further, a value-added model in Column (6) shows significant and positivecoefficients on all three variables, high-income household, highly educated responding parent andprivate school. This evidence collectively suggests that there is a clear association between children’s
family backgrounds and their likelihood of receiving outside-school online education, and that the gap
in access to outside-school online education increased over the course of the pandemic.
Finally, utilizing the interaction terms, we investigate the heterogeneous effect of family
backgrounds on the access to online education by school level. The results are reported in Table 3. In
Columns (1) to (3), we do not confirm any difference in access to at-school online education based on
the level of school the child attended, with the baseline set to elementary school, for households in thehigh-income category, at either survey point. Regarding outside-school online education in Columns(4) to (6), in addition to the above discussion, we find that the effect of coming from a high-income
family is especially pronounced for high school students. High schoolers from high-income households
were nearly 70% more likely to have experienced outside-school online education than high school
students from low-income households in December, as seen in Column (5), and as a change from May
to December in Column (6). Comparing the effect of having a highly educated parent between schoollevels, we observe a similar trend in December, and seen as a change from May to December, highschool students with a highly educated parent were significantly 64% more likely to have access to
outside-school online education than high school students with a parent without post-secondary
education. These findings are not unexpected, as part of 3rd year high school students would be facing
university entrance exams in January and February, possibly creating additional demand for outsideschool online education.
To summarize, during the first wave of the COVID-19 pandemic in Japan, when schools wereordered to close, there was a clear divide in terms of access to online education both at school andoutside school, based on family background, especially household income and the type of school
attended. The online educational access gap persisted into December, when Japan was experiencing athird wave of the pandemic and possibly further widened, indicating increasing inequality during the
pandemic, especially in terms of online educational opportunity outside school.
5.2 Heterogeneity of impact of COVID-19 on online education
To assess whether a greater impact of COVID-19 was associated with higher likelihood of online
educational experience, we employ a difference-in-difference model treating a rapid increase in
COVID-19 cases as an unexpected exogenous shock to education in each region. The results are shown9

in Table 4. “COVID-19 rapid increase” variable corresponds to 𝐷𝐷𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 in Equation (3) and identifies
prefectures that saw above sample average increase in newly confirmed cases from May to December.
We do not find a statistically significant effect of the interaction term of “COVID-19 rapid
increase” and “December” for either at-school or outside-school online education. Therefore, we further
estimate triple difference models, as described in Equation (4), to examine the heterogenous impact of
COVID-19 on online educational access by various measures of family backgrounds.
First, we analyze the effect of a greater impact of COVID-19 on online educational experienceby household income level and report our findings in Columns (1) and (4) of Table 5. Coefficients of
the interaction term “COVID-19 rapid increase,” “December” and “High income household” in either
column are not statistically significant. These results indicate that a greater impact of COVID-19 does
not create heterogeneous effect on access to online education, both at school and outside school, byhousehold income.
Next, we investigate the heterogenous effect of COVID-19 by parent’s educational level,
presenting the results in Columns (2) and (5) of Table 5. The coefficient of the interaction term of“COVID-19 rapid increase,” “December,” and “highly educated parent” is positive and significant at a
5% level for outside-school online educational experience in Column (5). This result suggests that, witha greater impact of COVID-19, children with a highly educated parent had a 12% higher likelihood ofexperiencing online education outside school than children with a parent without post-secondary
education.
Finally, we examine the effect of a greater impact of COVID-19 by the type of school attended,public or private, and report the results in Columns (3) and (6) of Table 5. The coefficient of theinteraction term of “COVID-19 rapid increase,” “December,” and “Private school” is not statisticallysignificant for at-school online education experience in Column (3), but it is significant at a 5% leveland positive for outside-school online educational experience in Column (6). This result indicates thatas almost all schools had resumed face-to-face education in December 2020, the more pronounced
impact of COVID-19 was not associated with difference in access to at-school online education in bothpublic and private schools. However, in prefectures that saw a greater impact of COVID-19, children
attending private schools had a 17% higher likelihood of receiving online education outside school thanchildren attending public schools.
In sum, the heterogeneous impact of COVID-19 on children’s online educational experience isonly observed outside school by children’s school type and parental education, but not by householdincome. These results imply that the greater impact of COVID-19 did not create differences in online
educational experience at school, but it did outside school, where parents have discretion over what
education their children receive. Parents who do not necessarily have higher income but are highly
educated or willing to send their children to private schools, might have higher expectations for theirchildren’s educational achievement. The stronger influence of COVID-19 might have promoted theseparents who were more highly concerned about their children’s learning during the COVID-19
pandemic to access more online education outside school, which might have otherwise been attended
in-person.
5.3 Family backgrounds and demand for at-school online education
Turning our attention to the demand side, we investigate parents’ views regarding the type of education
they want their children to receive at school with a focus on the effects of family background. Estimatesfrom an ordered logit regression of parental preference for online education are reported in Table 6 (1),with cross-sectional results (Equation (5)) from the May and December surveys in Columns (1) and (2),respectively. Column (3) shows results of the December survey from a value-added model (Equation(6)), revealing changes from May to December.
In all instances, the strongest determinant of favorable views of at-school online education is
the recent experience of at-school online education. Estimating the impact of the type and level of school
the child attended on the responding parent’s preferences, we do not find any consistently significant
10effect in either May or December. While the results discussed in Section 5.1 reveal that children in
private schools had, at both time points, greater access to at-school online education, there is nodifference in parents’ views based on the type of school attended when the actual experience of atschool online education is controlled for. 12 However, seen as a change from May, parents of children
attending private schools were at a 10% level of significance more likely to prefer face-to-face classes
than parents of children in public schools. Private schools, on top of charging tuition, typically offer
wide array of extra curricular activities and campus environment not available at public schools,
increasing the attractivity of attending classes in person.
Focusing on family backgrounds, we find that highly educated responding parents were more
likely to hold stronger preference for online education than parents without post-secondary educationin the May survey, but not in the December survey. Highly educated parents might hold jobs more
conducive to remote-work than less educated parents, allowing them to better accommodate at-homeeducation of their children. While we discuss the role of the shift in work styles thoroughly in thefollowing section, in short, we find only a very limited association, suggesting that the significance ofthe responding parent’s attained education is not caused by varying work styles, provided that teleworkor other types of flexible work were not a wide-spread practice in Japan prior to the COVID-19
pandemic (Okubo 2020). Combined with the results from Section 5.2, our interpretation of thesefindings is that highly educated parents or parents who send their children to private schools in Japando not necessarily prefer online education to face-to-face education at school; however, they seek
additional online education outside school as a supplement especially when the concerns regarding the
pandemic increase.
The opposite trend can be seen for the role of household income, which was positive but only
marginally significant in the May survey. Yet in the December survey, respondents from high income
families were at a 1% level of significance more likely to hold positive views of online education. While
the survey did not inquire about the number of Internet-connected devices in the household, which are
crucial to access online education, if they were the driving factor behind different views based onhousehold income, the effect would likely already be evident in May. It is possible that children fromhigh-income families have access to better schools than less fortunate children. Schools with moreresources might direct them towards building knowledge and infrastructure needed to provide highquality online education even after schools reopen which would consequently lead to more favorable
parental views in December.
Next, we analyze the role of parents’ work status, setting the baseline to the parent being present
at home, as either stay at home parent, unemployed parent looking for work or parent engaging in payby-volume work from home. In general, we find that in the May survey, parents who might not be able
to adapt to new circumstances easily were more likely to want their children to return to the classroom,
while in the December survey, parents likely adjusted to the situation overall and their employment
status was no longer statistically significant. Specifically, mothers employed on indefinite full-time
contracts (regular employment) and fathers in non-regular employment showed higher preference forin-person education in the May survey. Besides the possible difference in job content between
employment contract types, mothers in other than regular contracts might choose these types of jobs forthe level of flexibility they provide. On the contrary, fathers who are more likely to be the breadwinners,
face lower job security and earnings in non-regular employment than regular employees, which mightmake it difficult to support a child learning from home.
5.4 Parents’ work styles and demand for at-school online education
The full results of Table 6 (1), with and without family background controls, are available in Appendix Table
A4. In the December survey, parents of high schoolers were more likely to be open to online education incomparison to the baseline of parents of elementary school students; however, the significance of the effectdisappeared with the inclusion of family background covariates in Columns (4) and (6). Parents’ views on thetype of education their child receives, therefore, do not seem to be related to child’s age.

1211

In this section, we investigate the association between parents’ preferences for online education andchanges in their work styles while controlling for other family backgrounds and employment type. We
expand the family background variables in the model detailed in Section 5.3 to include variables
indicating change in work styles. This analysis remains purely observational, as we are unable toconfirm whether it is parents’ work styles that impact their views regarding online education or whether
parents adjusted their work styles in response to children’s educational experience.
For this analysis, we limit the sample to parents who, in the corresponding survey, werereported as working, either as regular employees, non-regular employees, or were company executives
or self-employed. The survey in both its rounds asked respondents how had theirs and their spouses’
work styles changed since the beginning of the pandemic. The questionnaire inquired specifically about
the change in total hours worked and about the use of telework and other flexible work styles such as
flextime and staggered working hours and days. Respondents were asked to mark all applicable answers.
We divide the answers by respondent’s sex to assess the effect of mother’s and father’s work styles
separately.
As seen in Appendix Table A1, in the May survey, 38% of working mothers and 41% ofworking fathers experienced a decrease in total hours worked, while 8% and 7%, respectively, saw anincrease. In December survey, 18% of both mothers and fathers worked fewer hours, and 7% of mothers
and 12% of fathers reported more working hours. Regarding teleworking, in the May survey, 17% of
mothers and 42% of fathers utilized telework and in the December survey, 12% of mothers and 30% of
fathers teleworked. Besides teleworking, the proportion of respondents reporting other flexible work
styles was 18% for mothers and 22% for fathers in the May survey and 12% for both mothers andfathers in the December survey.
For this analysis, we first look at working mothers and fathers separately regardless of theirspouse’s employment status, and then at households with both parents working, resulting in a different
sample size for each estimation. The results of ordered logit regression are presented in Table 6 (2) with
Columns (1) to (3) displaying results from the May survey and Columns (4) through (6) results fromthe December survey. Identical to the preceding analysis on parental preference, models reported inColumns (7) to (9) include a lag of the outcome variable to showcase changes in attitudes between
survey rounds. The baseline the results refer to is set to no change in work styles as the survey asked
about them.
Overall, we observe limited association between changes in work styles and parental views
regarding online education. In the May survey, examining parents’ changes in work styles separately,
decrease in mothers’ as well as fathers’ working hours was associated with a higher likelihood of
positive views of online education, with the impact of mothers’ work styles being more pronounced.
However, neither of these effects were statistically significant in the sample of both parents working.
In a sample of households with both parents working, we observe that respondents from householdswith mothers whose working hours increased, were at a 1% level of significance more likely to preferin-person education. We do not find any significant effect of fathers’ work style changes. Turning to
the December survey, in both the cross-sectional analysis in Columns (4)-(6) and the value-added modelin Columns (7)-(9), no difference in parental views is observed based on the change in work styles.
Although the association between work styles and parental preference for online education we
identify is weak, it is consistent with the results from Section 5.3 regarding parents’ employment status.
Our findings suggest that in the short term, parents in less flexible or more demanding work-related
circumstances had a more negative stance on online education, while in the long term the difference
based on employment type or changes in work styles disappeared. Our results are also in line with
Yamamura-Tsutsui (2021), who found that it was mainly working mothers who bore the brunt of school
closures as mothers tend to be the primary child caregivers in Japan. However, more research is needed
into the topic, especially to determine whether parents adjust their employment status or work style toaccommodate children’s online learning, which could have vast policy implications.
126. Conclusion
In this study, by utilizing data from two rounds of a government survey carried out in May 2020 and
December 2020 to the same households, we analyze the impact of the COVID-19 pandemic on online
education in Japan, as experienced by children in public and private elementary, junior high, and highschools at school as well as outside school, and focus on the heterogeneity brought about by familysocioeconomic status and regional differences. We also analyze parental preferences towards online
education as opposed to in-person learning at school, which is essential for understanding why Japan islagging other OECD countries in introducing online learning at school, and how these preferences areshaped by the actual experience of online education, family backgrounds and parents’ work styles. Our
paper presents not only the first evidence on online educational access at school during the COVID-19
pandemic in Japan, covering both public and private schools, but also provides broad perspectives tounderstand the status of online education in Japan by including both at-school and outside-school
learning experiences and family backgrounds, while also examining the key factor on the demand sidefor online education, parents’ wishes.
Overall, we find that during the COVID-19 pandemic children from high-income households
and children with a highly educated parent had better access to online education, especially outsideschool. One possible reason for this result is that due to the limited access to at-school online education,
parents with high socioeconomic status felt the need to seek online educational opportunities elsewhere,
outside schools, which was especially the case for high school students, who spend years preparing foruniversity entrance exams. A rapid growth in COVID-19 cases was associated with increased access toonline education outside school, particularly for children in private schools, who already enjoyed moreaccess to online education at school than children in public schools, and for children with a highly
educated parent. We do not observe a difference in access to at-school online education based onregional differences in the spread of the COVID-19 virus. Therefore, it is evident that ignoring thesocioeconomic differences in the access to online education outside school would lead to a substantial
bias in the estimates of the inequality of the amount of online education children received.
We also show that the parents of children who had an experience of online education at school
consistently tended to express more positive views about at-school online education. Further, we findthat, in general, highly educated parents and parents in high-income households were more likely towelcome online education at school, even after controlling for the actual experience, which appeared tocontribute to the search for additional online learning opportunities outside school. However, parental
work status and work styles seemed to be potential factors creating heterogeneity in the preferences for
at-school online education. Survey respondents from households with mother in regular employment
and those in families with father in non-regular employment, preferred face-to-face education at schoolin May 2020, immediately after schools reopened after mandated closures, but not in December 2020.
These results suggest that parents who initially had conflict in having children at home with their work,
adjusted to accommodate the new remote learning style.
Overall, the results indicate an inequality in the access to online education and in preferences
for online education at school across socioeconomic status and, to a lesser degree, work status of parents.
The limited access to online education at school may create a new learning gap among children due tothe differences in access to online education outside school. This, over the course of the pandemic, may
develop into a serious educational inequality as the baseline learning time has become much shorter.
Our results also suggest that parents are more open to at-school online education once their children
experience it. Parental preferences can likely be modified by school education policies such as active
provision of appropriate remote learning devices to be used at home.
The Japanese government was quick to adopt a supplementary budget in June 2020 to provide
remote learning devices to all students in public elementary and junior high schools, but the actual
execution of this policy was very slow and would have minimal, if any, impact during the period

13

covered by the data used in this study. 13 While supply side policies are important and merit further
research, remote learning devices by themselves might be of limited benefit if parents find it difficult
to have children learn from home. Clearly, carefully designed policies targeting both demand and supply
sides are essential for effectively achieving equity in high quality online education for children. Our
study suggests that, on the demand side, we need to focus on building online learning environment
accessible to all children, supporting children whose parents feel difficulties in staying home with themwhile considering the hidden inequality in the online educational access outside school.

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15

Table 1 Descriptive statistics
obs.

May 2020 Survey
mean
s.d.
min

max

obs.

December 2020 Survey
mean
s.d.
min

max

0.490
0.122
0.388
0.155
0.308
0.252

0
0
0
0
0
0

1
1
1
1
1
1

528
528
528
528
528
528

0.602
0.015
0.184
0.025
0.106
0.068

0.490
0.122
0.388
0.155
0.308
0.252

0
0
0
0
0
0

1
1
1
1
1
1

0.473
0.345
0.363
0.375
0.421
0.373
0.235
0.285
0.224
0.500

0
0
0
0
0
0
0
0
0
0

1
1
1
1
1
1
1
1
1
1

528
528
528
528
528
528
528
528
528
528

0.131
0.059
0.057
0.061
0.119
0.078
0.038
0.036
0.028
0.758

0.337
0.235
0.232
0.239
0.324
0.268
0.191
0.186
0.166
0.429

0
0
0
0
0
0
0
0
0
0

1
1
1
1
1
1
1
1
1
1

0.303
0.371
0.472
0.450
0.324

0
0
0
0
0

1
1
1
1
1

528
528
528
528
528

0.040
0.074
0.290
0.477
0.119

0.196
0.262
0.454
0.500
0.324

0
0
0
0
0

1
1
1
1
1

0.435

0

1

528

0.748

0.435

0

1

0.493

0

1

528

0.589

0.492

0

1

0.460
0.431
0.493
0.087
0.166
0.293
0.408
0.200
0.182
0.196
0.274
0.122
0.306
0.605
0.499

0
0
0
0
0
0
0
0
0
0
0
0
0
1
0

1
1
1
1
1
1
1
1
1
1
1
1
1
3
1

528
528
528
528
528
528
528
528
528
528
528
528
528
528
528

0.708
0.258
0.413
0.008
0.030
0.903
0.797
0.036
0.036
0.034
0.083
0.011
0.100
1.438
0.464

0.455
0.438
0.493
0.087
0.172
0.296
0.402
0.186
0.186
0.182
0.277
0.106
0.301
0.597
0.499

0
0
0
0
0
0
0
0
0
0
0
0
0
1
0

1
1
1
1
1
1
1
1
1
1
1
1
1
3
1

0.220
0.002

0
0

1
0.007

528
528

0.051
0.153

0.220
0.091

0
0.002

1
0.320

0.030

0

0.095

528

0.510

0.346

0.023

1.240

0.500

0

1

528

0.483

0.500

0

1

School type
Elementary school - public
528
0.602
Elementary school - private
528
0.015
Junior high school - public
528
0.184
Junior high school - private
528
0.025
High school - public
528
0.106
High school - private
528
0.068
Online learning experience in past month (multiple answer)
At school
528
0.335
Online classes 528
0.138
Online instruction 528
0.155
Online materials 528
0.169
Outside school
528
0.229
Online classes 528
0.167
Online instruction 528
0.059
Online materials 528
0.089
Other online education
528
0.053
No online education
528
0.506
Preferred school format
Over 50% online
528
0.102
Over 50% in person
528
0.165
In principle in person
528
0.333
100% in person
528
0.280
Do not know
528
0.119
Respondent's highest attained education
Highly educated (post-secondary)
528
0.748
Household annual income
High income (over 6 million yen)
528
0.583
Household typeWorking mother
528
0.697
Regular employee 528
0.246
Non-regular employee 528
0.415
Executive 528
0.008
Self-employed 528
0.028
Working father
528
0.905
Regular employee 528
0.790
Non-regular employee 528
0.042
Executive 528
0.034
Self-employed 528
0.040
Single mother
528
0.081
Single father
528
0.015
Multigenerational household
528
0.104
Number of children under 18
528
1.455
Female respondent
528
0.464
Area
Rural area
528
0.051
COVID-19 spread (week prior; per 1000
528
0.002
inhabitants)
COVID-19 spread (month prior; per 1000
528
0.026
inhabitants)
COVID-19 rapid increase
528
0.483

Note: This table shows the descriptive statistics for the sample used in the analyses in Tables 2-4. The descriptive statistics for the analyses
in Tables 5 and 6 is in Appendix Table A1.

16

Table 2 Determinants of access to online education at school and outside school
At school

Dependent variable
Access to Online Education:
Private school
Junior high school
High school
High-income household
Highly educated parent
COVID-19 spread (1 month, per 1,000 inhabitants)
Pseudo R2
Observations

Outside schoolMay

December(2)
0.109***(0.038)-0.007(0.028)0.095**(0.041)0.038*(0.021)
0.027(0.030)0.051(0.042)

December(with lag)(3)0.078**(0.038)-0.018(0.027)0.060(0.039)0.015(0.018)0.021(0.027)0.043(0.040)(4)
0.004(0.081)
0.052(0.043)-0.115**(0.050)0.156***(0.037)0.058*(0.031)
1.162***(0.308)(5)0.102*(0.055)0.013(0.030)-0.106**(0.047)0.101***(0.031)
0.097***(0.034)0.053**(0.024)

December(with lag)(6)0.095**(0.040)0.005(0.031)-0.079*(0.042)0.068**(0.028)0.081**(0.032)0.040(0.026)

0.081
528

0.119
528

0.057
528

0.077
528

0.182
528

May

December(1)0.230***(0.061)
0.057(0.040)0.237***(0.054)0.171***(0.044)0.027(0.038)0.459(0.477)0.125
528

Notes: Average marginal effect estimates from probit model regression. The dependent variable is a dummy variable indicating whether
the youngest school-aged child had experienced any form of online education at school (Columns (1) to (3)) or outside school (Columns(4) to (6)) since the beginning of the school year in April (May) or in the past month (December). Private school, Junior high school, Highschool (with Elementary school as a baseline), High-income household (household annual income over 6 million yen), Highly educatedparent (responding parent attained post-secondary education) are dummy variables. COVID-19 spread variable shows newly confirmed
COVID-19 cases over 1 month prior to the survey starting date per 1,000 inhabitants in the prefecture of residence. Other controls are
Rural area, Respondent's gender. Columns (3) and (6) include lagged dependent variable from May survey. Full results are available in
the Appendix Table A2. Robust standard errors clustered at prefectural level are shown in parentheses. Levels of significance: *** p<0.01,** p<0.05, * p<0.1.

17

Table 3 Heterogeneity in determinants of access to online education at school and outside school
At school

Dependent variable
Access to Online Education:
Junior high school
High school
High-income household
Junior high school * High-income household
High school * High-income household
Highly educated parent
Junior high school * Highly educated parent
High school * Highly educated parent
Pseudo R2
Observations

Outside schoolMay

December(2)0.130*(0.070)0.027(0.085)
0.061*(0.034)-0.098(0.067)-0.036(0.070)0.034(0.041)-0.127(0.089)0.113(0.091)

December(with lag)(3)0.144**(0.070)0.023(0.084)0.041(0.029)-0.112*(0.068)-0.035(0.065)0.043(0.039)-0.151*(0.080)0.071(0.091)(4)-0.051(0.096)-0.142(0.137)0.186***(0.039)-0.138*(0.082)-0.018(0.107)-0.018(0.047)0.258***(0.099)0.044(0.137)(5)-0.038(0.063)-1.476***(0.130)0.096**(0.038)-0.028(0.070)0.706***(0.072)0.056(0.047)0.087(0.089)0.703***(0.082)

December(with lag)(6)-0.007(0.064)-1.381***(0.116)
0.063*(0.037)-0.024(0.068)0.692***(0.077)0.059(0.046)
0.035(0.095)0.644***(0.078)

0.103
528

0.143
528

0.071
528

0.090
528

0.192
528

May

December(1)-0.040(0.099)0.070(0.108)0.155***(0.050)0.036(0.085)0.037(0.108)-0.044(0.055)0.098(0.135)0.202*(0.108)0.130
528Notes: Average marginal effect estimates from probit model regression. The dependent variable is a dummy variable indicating whether the
youngest school-aged child had experienced any form of online education at school (Columns (1) to (3)) or outside school (Columns (4) to(6)) since the beginning of the school year in April (May) or in the past month (December). Junior high school, High school (with Elementary
school as a baseline), High-income household (household annual income over 6 million yen), Highly educated parent (responding parent
attained post-secondary education) are dummy variables. Other controls are Private school, Rural area, Respondent's gender, COVID-19
spread in the prefecture of residence over past month. Columns (3) and (6) include lagged dependent variable from May survey. Full results
are available in the Appendix Table A3. Robust standard errors clustered at prefectural level are shown in parentheses. Levels of significance:*** p<0.01, ** p<0.05, * p<0.1.

18

Table 4 Response to COVID-19 increase in access to online education at school and outside school
Dependent variable
Access to Online Education:
COVID-19 rapid increase
December
COVID-19 rapid increase * December
Constant
R2
Observations

At school(1)
0.072**(0.035)-0.194***(0.027)-0.022(0.033)0.300***(0.029)Outside school
(2)
0.042
(0.031)-0.121***(0.021)
0.023(0.033)0.209***(0.020)

0.064
1,056

0.026
1,056

Notes: Coefficient estimates from linear probability model regression. The dependent variable is a dummy
variable indicating whether the youngest school-aged child had experienced any form of online education at
school (Column (1)) or outside school (Column (2)) since the beginning of the school year in April (May) orin the past month (December). COVID-19 rapid increase is a dummy variable equal to 1 in prefectures where
the difference in newly confirmed cases per capita over 1 month prior to the survey between December and
May is higher than the sample average. December is a dummy variable identifying December survey. Robust
standard errors clustered at prefectural level are shown in parentheses. Levels of significance: *** p<0.01, **p<0.05, * p<0.1.

19

Table 5 Heterogeneous response to COVID-19 increase in access to online education
Dependent variable
Access to Online Education:
COVID-19 rapid increase
December
COVID-19 rapid increase * December
Heterogeneity in parental income

At school
(1)
(2)
(3)
-0.001-0.0520.051(0.066)(0.073)(0.038)-0.125*** -0.217*** -0.189***(0.037)(0.047)(0.030)
0.016
0.030
0.008
(0.045)(0.066)(0.040)High-income household 0.154***(0.043)
COVID-19 rapid increase * High-income household
0.089(0.091)
December * High-income household -0.130***(0.042)COVID-19 rapid increase * December -0.037* High-income household (0.074)
Heterogeneity in parental education
Highly educated parent
COVID-19 rapid increase * Highly educated parent
December * Highly educated parent
COVID-19 rapid increase * December* Highly educated parent
Heterogeneity in private-public difference
Private school

0.114**(0.050)0.075(0.060)-0.036(0.044)-0.015(0.087)-0.044(0.048)0.166**(0.079)0.031(0.046)-0.069(0.072)0.086**(0.039)-0.022(0.046)-0.045(0.039)0.123**(0.058)0.320***(0.101)
0.184(0.111)-0.053(0.091)-0.267(0.161)COVID-19 rapid increase * Private school
December * Private school
COVID-19 rapid increase * December* Private school
R2
Observations

Outside school
(4)
(5)
(6)
-0.0180.058
0.076**(0.039)(0.046)(0.034)-0.102*** -0.087** -0.123***(0.032)(0.033)(0.024)0.036-0.0690.004(0.052)(0.056)(0.035)

0.093
1,056

0.070
1,056

0.126
1,056

0.114
(0.120)-0.315**(0.127)0.020(0.055)0.171**(0.074)
0.056
1,056

0.037
1,056

0.038
1,056

Notes: Coefficient estimates from linear probability model regression. The dependent variable is a dummy variable indicating whether the
youngest school-aged child had experienced any form of online education at school (Columns (1) to (3)) or outside school (Columns (4) to(6)) since the beginning of the school year in April (May) or in the past month (December). COVID-19 rapid increase is a dummy variableequal to 1 in prefectures where the difference in newly confirmed cases per capita over 1 month prior to survey between December and
May is higher than the sample average. December is a dummy variable identifying December survey. High-income household (household
annual income over 6 million yen), Highly educated parent (responding parent attained post-secondary education), Private school aredummy variables. Robust standard errors clustered at prefectural level are shown in parentheses. Levels of significance: *** p<0.01, **p<0.05, * p<0.1.

20

Table 6 Determinants of demand for online education at school(1) Heterogeneity over parents’ work status
Dependent variable
Preference for Online Education at School
Online education at school
Private school
Junior high school
High school
High-income household
Highly educated parent
Working mother

Regular employee
Non-regular employee
Executive
Self-employed

Working father

Regular employee
Non-regular employee
Executive
Self-employedMay

December

December(with lag)(1)0.954***(0.212)0.657(0.403)0.343(0.217)-0.280(0.359)0.365*(0.212)0.590***(0.147)(2)
1.357***(0.216)-0.281(0.311)
0.149(0.259)0.250(0.259)0.594***(0.226)0.292(0.242)(3)
1.266***(0.212)-0.571*(0.346)0.032(0.243)0.278(0.251)
0.486**(0.240)0.172(0.250)-0.544*(0.289)-0.203(0.229)1.276(0.813)-0.683(0.656)-0.318(0.261)-0.080(0.214)0.781(1.397)-0.730(0.906)-0.260(0.230)-0.052(0.200)0.428(1.559)-0.901(0.994)-0.516(0.455)-1.517*(0.838)-0.445(0.516)-0.430(0.909)-0.931(0.706)-1.728(1.060)-0.116(0.941)-0.465(0.870)-0.984(0.737)-1.603(1.084)-0.357(0.987)-0.441(0.849)✓Lag (May survey)
2

Pseudo R
Observations

0.058
419

0.070
419

0.102
419

Notes: Coefficient estimates from ordered logit model regression. The dependent variable is the preference foronline education at school ranging from 1 (100% in person) to 4 (over 50% online). Respondents who answered“Do not know” are dropped. The descriptive statistics of this sample is shown in Appendix Table A1. Online
education at school, Private school, Junior high school, High school (with Elementary school as a baseline), Highincome household (household annual income over 6 million yen), Highly educated parent (responding parent
attained post-secondary education), Regular employee, Non-regular employee, Executive, and Self-employed formother and father are dummy variables. Baseline for working parents is set to the parent being present at home.
Other controls are Rural area, Respondent's gender, Multigenerational household, Single mother household andSingle father household dummy variables, Number of children and COVID-19 spread (week prior) variables.
Column (3) includes lagged dependent variable from May survey. Full results are available in the Appendix Table
A4 (1). Robust standard errors clustered at prefectural level are shown in parentheses. Levels of significance: ***p<0.01, ** p<0.05, * p<0.1.

21

(2) Heterogeneity over changes in parents’ work styles
Dependent variable
Preference for Online Education at School

May
(1)

Working styles - mother

More teleworking
More flexible work
Fewer working hours
More working hours

Working styles - father

(2)

0.317
(0.279)-0.199(0.259)0.411**(0.194)-0.457(0.356)More teleworking

0.152(0.189)0.164(0.229)0.372*(0.216)0.350(0.426)More flexible work
Fewer working hours
More working hours
Working spouse
Single parent household
Lag (May survey)

✓
✓

✓
✓

Pseudo R2
Observations

0.067
294

0.055
376

December
(3)

(4)

0.342
(0.287)-0.049(0.286)0.311(0.251)-0.966***(0.352)0.027(0.469)0.008(0.343)-0.067(0.266)0.415(0.417)-0.260(0.203)0.247(0.363)0.142(0.284)0.671(0.479)

0.079
259

(5)

0.228
(0.179)0.396(0.319)-0.083(0.396)0.238(0.297)
✓
✓

✓
✓

0.083
303

0.073
375

December (with lag)
(6)

(7)

-0.004(0.521)-0.057(0.463)0.111(0.393)0.497(0.540)0.182(0.474)0.016(0.304)-0.126(0.250)0.305(0.384)0.179(0.335)0.542(0.358)-0.544(0.434)0.386(0.370)

0.100
263

(8)

(9)
0.122
(0.509)-0.052(0.413)0.013(0.354)0.412(0.480)0.285(0.176)0.215(0.362)-0.214(0.402)0.311(0.274)0.248(0.355)0.330(0.418)-0.645(0.404)0.451(0.344)

✓
✓
✓

✓
✓
✓

✓

0.113
303

0.104
375

0.124
263

Notes: Coefficient estimates from ordered logit model regression. The dependent variable is the preference for online education at school
ranging from 1 (100% in person) to 4 (over 50% online). Respondents who answered “Do not know” are dropped. Sample is limited toparents who were reported as working (regular employees, non-regular employees, company executives, self-employed) in corresponding
surveys. More teleworking, More flexible work, Fewer working hours, More working hours are dummy variables equal to 1 in caserespondent chose corresponding options in questions about their own and spouse’s changes in work styles. Other controls are Online
learning at school, School type and level, Highly educated parent, High-income household, Employment type, Multigenerational
household, Rural area and Respondent’s gender dummy variables, Number of children and COVID-19 spread (week prior) variables.
Columns (7) to (9) include lagged dependent variable from May survey. Full results are available in the Appendix Table A4 (2). Robust
standard errors clustered at prefectural level are shown in parentheses. Levels of significance: *** p<0.01, ** p<0.05, * p<0.1.

22

Figure 1 COVID-19 timeline in Japan23

Figure 2 Online education experience by the level of school

24

Figure 3 Online education experience by the type of school

25

Figure 4 Parental preferences towards at school education by the level of school

26

Figure 5 Parental preferences towards at school education by the type of school27

Appendix
Table A1 Descriptive statistics of sample for Table 628

Table A2 Determinants of access to online education at school and outside school (full results ofTable 2)

Notes: Average marginal effect estimates from probit model regression. The dependent variable is a dummy variable indicating whether the youngest schoolaged child had experienced any form of online education at school (Columns (1) to (3)) or outside school (columns (4) to (6)) since the beginning of the school
year in April (May) or in the past month (December). Private school, Junior high school, High school (with Elementary school as a baseline), High-income
household (household annual income over 6 million yen), Highly educated parent (responding parent attained post-secondary education) are dummy variables.
COVID-19 spread variable shows newly confirmed COVID-19 cases over 1 month prior to the survey starting date per 1,000 inhabitants in the prefecture ofresidence. Other controls are Rural area, Respondent's gender. Columns (3) and (6) include lagged dependent variable from May survey. Robust standard errors
clustered at prefectural level are shown in parentheses. Levels of significance: *** p<0.01, ** p<0.05, * p<0.1.

29

Table A3 Heterogeneity in determinants of access to online education at school and outside school(full results of Table 3)

Notes: Average marginal effect estimates from probit model regression. The dependent variable is a dummy variable indicating whether the youngest schoolaged child had experienced any form of online education at school (Columns (1) to (3)) or outside school (columns (2) to (4)) since the beginning of the school
year in April (May) or in the past month (December). Junior high school, High school (with Elementary school as a baseline), High-income household(household annual income over 6 million yen), Highly educated parent (responding parent attained post-secondary education) are dummy variables. Other
controls are Private school, Rural area, Respondent's gender, COVID-19 spread in the prefecture of residence over past month. Columns (3) and (6) include
lagged dependent variable from May survey. Robust standard errors clustered at prefectural level are shown in parentheses. Levels of significance: *** p<0.01,** p<0.05, * p<0.1.

30

Table A4 Determinants of demand for online education at school (full results of Table 6)(1) Heterogeneity over parents’ work status

Notes: Coefficient estimates from ordered logit model regression. The dependent variable is the preference for online education at school
ranging from 1 (100% in person) to 4 (over 50% online). Respondents who answered "Do not know" are dropped. The descriptive statisticsof this sample is shown in Appendix Table A1. Online education at school, Private school, Junior high school, High school (with Elementary
school as a baseline), High-income household (household annual income over 6 million yen), Highly educated parent (responding parent
attained post-secondary education), Multigenerational household, Single mother, Single father, Rural area, Respondent female, Regular
employee, Non-regular employee, Executive, and Self-employed for mother and father are dummy variables. Baseline for working parents is
set to the parent being present at home. COVID-19 spread variable shows newly confirmed COVID-19 cases over 1 week prior to the survey
starting date per 1,000 inhabitants in the prefecture of residence. Columns (5) and (6) include lagged dependent variable from May survey.
Robust standard errors clustered at prefectural level are shown in parentheses. Levels of significance: *** p<0.01, ** p<0.05, * p<0.1.

31

(2) Heterogeneity over changes in parents’ work styles

Notes: Coefficient estimates from ordered logit model regression. The dependent variable is the preference for online education at school
ranging from 1 (100% in person) to 4 (over 50% online). Respondents who answered "Do not know" are dropped. Sample is limited to parents
who were reported as working (regular employees, non-regular employees, company executives, self-employed) in corresponding surveys.
More teleworking, More flexible work, Fewer working hours, More working hours are dummy variables equal to 1 in case respondent chosecorresponding options in questions about their own and spouse’s changes in work styles. Online education at school, Private school, Juniorhigh school, High school (with Elementary school as a baseline), High-income household (household annual income over 6 million yen),Highly educated parent (responding parent attained post-secondary education), Multigenerational household, Single parent, Rural area,
Respondent female, Working spouse, Regular employee, Non-regular employee, Executive for mother and father (with Self-employed as abaseline) are dummy variables. COVID-19 spread variable shows newly confirmed COVID-19 cases over 1 week prior to the survey starting
date per 1,000 inhabitants in the prefecture of residence. Columns (7) to (9) include lagged dependent variable from May survey. Robust
standard errors clustered at prefectural level are shown in parentheses. Levels of significance: *** p<0.01, ** p<0.05, * p<0.1.

32

Table A5 Determinants of “No preference about online education at school”

Notes: Average marginal effect estimates from probit model regression. The dependent variable is a dummy variable equal to 1 in case therespondent answered "Do not know" to question about preference for future online education at school. Online education at school, Private
school, Junior high school, High school (with Elementary school as a baseline), High-income household (household annual income over 6 millionyen), Highly educated parent (responding parent attained post-secondary education), Multigenerational household, Single mother, Single father,
Rural area, Respondent female, Regular employee, Non-regular employee, Executive, and Self-employed for mother and father are dummy
variables. Baseline for working parents is set to the parent being present at home. COVID-19 spread variable shows newly confirmed COVID19 cases over 1 week prior to the survey starting date per 1,000 inhabitants in the prefecture of residence. Columns (5) and (6) include lagged
dependent variable from May survey. Robust standard errors clustered at prefectural level are shown in parentheses. Levels of significance: ***p<0.01, ** p<0.05, * p<0.1.

33

1st Survey on Lifestyle Attitudes and Behavioural Changes during the COVID-19 Pandemic
25 May – 5 June 2020 (‘May survey’)(Note: Questions not used in the analysis in this paper omitted.)

Q1-1. Residence① Prefecture of residence② Municipality of residenceQ2. Select everyone you live with. Reply based on their relationship to you.
1.
2.
3.
4.
5.
6.
7.
8.
9.

Do not live with anyone (live by myself)
SpouseSons or daughters (under 18 years of age)
Sons or daughters (18 years of age or over)
Parents (including parents of spouse)
Grandparents (including grandparents of spouse)
Grandchildren
Siblings (including siblings of spouse)
Other (specify)

Q4. Select an answer best describing your employment or student status.
1. Regular employee (full time employment on indefinite contract)
2. Non-regular employee (such as employee other than regular employee, part-time worker,
temporary worker, agency worker, employee on definite contract)
3. Executive
4. Self-employed (including helper)
5. Pay-by-volume work from home
6. Student (high school, specialized training college, 2-year college, university, graduate schooletc.)
7. Not in employment or education (seeking employment)
8. Not in employment or education (not seeking employment)
Working person Q9. During the pandemic, how did your work hours change?
1.
2.
3.
4.
5.
6.
7.
8.

Roughly no change (5% decrease - 5% increase)
Somewhat decreased (6% - 20% decrease)
Decreased (21% - 50% decrease)Significantly decreased (over 51% decrease)
Somewhat increased (6% - 20% increase)
Increased (21% - 50% increase)Significantly increased (over 51% increase)
Do not know

Working person Q13. Select all work styles you have experienced during the pandemic.
1.
2.
3.
4.
5.

Telework (almost 100%)
Mainly telework (over 50%) combined with periodically going to work
Mainly going to work (over 50%) combined with periodic telework
In principle going to work, sporadically teleworking
Limited number of working days (such as 3, 4-day workweek)
34

6.
7.
8.
9.

Staggered working hours or flextime
Reduced working hours due to special leave
Other (specify)
Have not implemented any of the above

Parent Q18. Did the work style of your spouse change? Select all applicable answers.
1.
2.
3.
4.
5.
6.

Telework or increased use of telework
Flexible working hours (such as staggered working hours, flextime)Work hours decrease
Work hours increase
Other change (specify)
No change

Parent Q25. Answer regarding your youngest child in elementary school or higher. Select all types of
education your child has experienced during the pandemic.
1.
2.
3.
4.
5.

Has received online classes by an instructor at school
Has received online educational instruction (such as by mail) by an instructor at school
Has received online educational materials for home use at school
Has received online classes by an instructor outside school at prep school or other lessons
Has received online educational instruction (such as by mail) by an instructor outside schoolat prep school or other lessons
6. Has received online educational materials for home use outside school at prep school or otherlessons
7. Has received other type of online education
8. Has received no online education
9. Do not know
10. Do not have child in elementary school or higher
Parent Q26. Answer regarding your youngest child in elementary school or higher. Going forward, towhat extent do you want your child to utilize online education at school?
1.
2.
3.
4.
5.
6.

Mainly online education (over 50%) combined with in-person education
Mainly in-person education (over 50%) combined with online education
In principle in-person education, sporadically online education
Completely in-person education
Do not know
Do not have child in elementary school or higher

Respondent with a spouse Q53. Describe the employment or student status of your spouse
1. Regular employee (full time employment on indefinite contract)
2. Non-regular employee (such as employee other than regular employee, part-time worker,
temporary worker, agency worker, employee on definite contract)
3. Executive
4. Self-employed (including helper)
5. Pay-by-volume work from home
6. Student (high school, specialized training college, 2-year college, university, graduate schooletc.)
7. Not in employment or education (seeking employment)
8. Not in employment or education (not seeking employment)
Students only Q54. Answer your student status
35

1.
2.
3.
4.
5.
6.
7.

High school student ( year student)
College of technology ( year student)
Specialized training college
2-year college ( year student)
University student ( year student)
Graduate school student ( year student)
Other (specify)

All respondents except students Q56. What is your highest level of education attained?
1.
2.
3.
4.
5.
6.

Junior high school graduate
High school graduate
Specialized training college graduate
2-year college, college of technology graduate
University graduate
Graduate school graduate

Respondents with son or daughter under 18 years of age (respondents who selected option 3 in Q2)
Q59. How old are your children (children under 18 years of age)? Also select the type of school andschool year they attend.
(Note: Response options redacted to reflect data availability.)Child (number)
1.
2.
3.
4.
5.
6.

Pre-kindergarten
Nursery school, kindergarten, and such
Elementary school
Junior high school
High school, college of technology
Other

Q62. What is your approximate annual household income (including taxes and social insurance
premiums)?
(Note: Response options redacted to reflect data availability.)
1. Under 2 million yen
2. 2 million – 6 million yen
3. 0ver 6 million

36

2nd Survey on Lifestyle Attitudes and Behavioural Changes during the COVID-19 Pandemic
11 December – 17 December 2020 (‘December survey’)(Note: Questions not used in the analysis in this paper omitted.)
Response options identical to May survey omitted.
Q1-1. ResidenceQ2. Select everyone you live with. Reply based on their relationship to you.
Q4. Select an answer best describing your employment or student status
Working person Q9. Compared to last December (before the pandemic), how did your work hours
change? Answer using a number taking the number of hours worked last December as 100. For
example, if your work hours decreased 20%, answer 80, if they increased 30%, answer 130. The
maximum answer is 200.
① May work hours (during the state of emergency)② Current work hours
Working person Q13. Select all work styles at the following time points: last December (before thepandemic), this May (during the State of emergency), currently.
1. Telework (almost 100%)
2. Mainly telework (over 50%) combined with periodically going to work
3. Mainly going to work (over 50%) combined with periodic telework
4. In principle going to work, sporadically teleworking
5. Limited number of working days (such as 3, 4-day workweek)
6. Staggered working hours or flextime
7. Reduced working hours due to special leave
8. Suspension of business (including being furloughed or such)
9. Other (specify)
10. Have not implemented any of the above
Parent Q24. Compared to last December (before the pandemic), did the work style of your spousechange? Select all applicable answers.
Parent Q32. Answer regarding your youngest child in elementary school or higher. Select all types of
education your child has experienced over the past month.
Parent Q33. Answer regarding your youngest child in elementary school or higher. Going forward, towhat extent do you want your child to utilize online education at school?
Respondents with a spouse Q65. Describe the employment status of your spouse.
Students only Q66. Answer your student status.
All respondents except students Q68. What is your highest level of education attained?
Respondents with sons or daughters under 18 years of age (respondents who selected option 3 in Q2)
Q74. How old are your children (children under 18 years of age)? Also select the type of school theyattend.
(Note: Response options redacted to reflect data availability.)Child (number)37

1. Pre-kindergarten
2. Nursery school, kindergarten, and such
3. Public elementary school
4. Private elementary school
5. Public junior high school
6. Private junior high school
7. Public high school (college of technology)
8. Private high school (college of technology)
9. Specialized training college
10. Working
11. Other
Q77. What is your approximate annual household income (including taxes and social insurance
premiums)?
(Note: Response options redacted to reflect data availability.)
1.
2.
3.
4.
5.
6.

Under 2 million yen
2 million – 4 million yen
4 million – 6 million yen
6 million – 8 million yen
8 million – 10 million yen
Over 10 million

38