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

5 July, 2021
DP2021-013
https://ies.keio.ac.jp/en/publications/14240/

Institute for Economic Studies, Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan
ies-office@adst.keio.ac.jp
5 July, 2021

Access to and Demand for Online School Education during the COVID-19 Pandemic in
Japan
Hideo Akabayashi、Shimpei Taguchi、Mirka Zvedelikova
Keio-IES DP2021-013
5 July, 2021
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 Number 16H06323 from the Japan Society for the
Promotion of Science 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

July 5, 2021

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 in the access to online school
education based on family background, few studies have simultaneously examined the access to online
education both at school and outside school, and no study has examined parents’ views about online
school education, an important demand side factor. Using a panel dataset collected in May andDecember 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 outside school. Further, we find that a greater increase of COVID19 between May and December was associated with increased access to online education outside theschool, 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 household income and parent’s
high educational level are also associated with higher demand for at-school online education, whilemothers being employed in regular contracts and fathers in non-regular contracts decreased this demandin the short term.

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.
a: Corresponding author. Faculty of Economics, Keio University, Tokyo, Japan. https://orcid.org/0000-00026347-2895. Email: hakab@econ.keio.ac.jp
b: Graduate School of Economics, Keio University, Tokyo, Japan. https://orcid.org/0000-0003-3922-9948. Email:shimpei.taguchi@keio.jp
c: Graduate School of Economics, Keio University, 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 schoolin the developed world (OECD 2020a). One of the explanations of low prevalence of online learning isthat Japanese parents prefer face-to-face instruction as teachers can pay more attention to each childgiven a relatively large class size. Traditional teaching style that emphasizes understanding of basic
skills and group-oriented socialization has also been highly valued in the Japanese society (OECD 2012).
However, forced introduction of online educational practices may permanently change parental
attitudes towards online learning both at and outside school while maintaining the equity of access tohigh quality education. Despite its importance, not much research has been conducted on Japanesechildren’s access to and experience of online education and related parental views during the COVID19 crisis, especially by family socioeconomic status and type of school attended (public or private)based on a representative sample. The impact of the COVID-19 contagion on online education in Japanremains understudied.
To fill this gap, we aim to document both children’s actual experience of online education at
school and outside school and parental preferences towards online education during the COVID-19
pandemic, and to report how they were associated with family backgrounds. We use governmental
survey data collected at two points during the pandemic from the same households—in May 2020,
immediately after the first wave of the pandemic and the end of the associated school closures, and thenhalf a year later, in December 2020, in the middle of the third wave. This data allowed 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 attended,household income and parent’s educational level. Second, we use the difference-in-difference and triple
difference estimation methods to further highlight the potential inequality in access to online education
by aforementioned family backgrounds triggered by increase in COVID-19 cases. Third, we examine
the parental preferences towards at-school online education, how they are shaped by the experience of
online education and factors such as household income, parental education and parents’ employment
status and change in work styles during the pandemic, using probit and ordered logit models. For bothanalyses, the longitudinal nature of our dataset allows us to investigate changes in online educational
access and parental attitudes during the pandemic.
The contribution of this study is two-fold. First, to the best of our knowledge, this is the firststudy to examine the impact of the COVID-19 pandemic on online educational practices in Japan asexperienced by children at school and also outside school. It is important to include both at-school andoutside-school learning experiences to understand the true impact of this pandemic, especially in acountry like Japan where outside school educational options are abundant and highly influencechildren’s academic performance and equity in education. Ignoring potential heterogeneity in access toonline education outside school may lead to an underestimation of the extent of inequality in online
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

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 influence children’s access to online
learning through parents’ support of the child and purchases of appropriate Internet-connected devices.
Looking at the determinants of parental preferences can help policy makers identify the key factors
needed for creating the home environment necessary for children’s equitable access to online learning.
These two contributions make our study unique while providing the first evidence on online educational
access during the COVID-19 pandemic in Japan.
Our main findings are that greater spread of COVID-19 is associated with increased access toonline education outside school, especially for children in private schools and those with highly
educated parents, while we do not observe this trend in at-school online education. Therefore, ignoring
the socioeconomic differences in the access to online education outside school would lead to a
substantial bias in the estimates of the inequality in the amount of online learning done by children.
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 and high-income families, which is consistent
with factors associated with better access to outside-school online education. We also observe thatmothers employed in regular contracts and fathers in non-regular contracts tended to hold negative
views of at-school online education. These findings suggest the need for carefully designed online
educational policies to prevent a rise in inequality of educational opportunity, with a focus on building
online learning environment accessible to all children and supporting families with difficulties instaying home with children, while considering the hidden inequality in the online educational accessoutside school.
This paper is organized as follows: section 2 introduces related literature and section 3 provides
an overview of the COVID-19 pandemic in Japan and the related school closures. Section 4 introduces
our data, section 5 describes the utilized methodology, section 6 discusses the estimation results andfindings, and finally, section 7 concludes the study.
2. Related literatureChildren’s access to online education can be heavily influenced by parents’ socioeconomic status,
through paid access to both private schooling and private educational services outside school.
Correspondingly, parental demand for online education over in-person education is likely to be
motivated by parents’ overall preferences and their evaluation of the quality of online education theirchildren have been receiving. It is also likely to be affected by the family’s lifestyle and living
arrangements, such as the employment status of the mother, parents’ ability to work remotely or thesize and structure of the household. Therefore, the overall impact of the COVID-19 crisis on children’s
learning can only be comprehensively understood by encompassing all these factors and circumstances
simultaneously. To truly meet the complex needs of families which all experience a crisis, such as the
COVID-19 pandemic, differently, governments designing policies regarding remote education need toapproach this topic with a broad perspective. However, as of the time of drafting this paper in June
2021, very few studies have jointly investigated the relationship between children’s online learning
experience, both at school and outside school, parents’ stated preferences regarding online education,and socioeconomic backgrounds.
There is an increasing number of studies investigating children’s online learning activity gap
using nationwide time surveys. Grewening et al. (2020) used a time-use survey of school-aged childrenin Germany to investigate students’ learning time. They reported that high-achievers engaged in school
online learning activities more frequently than low-achievers. They also found that overall study time
reduction during the lockdown was higher for low-achievers than for high-achievers.

3

Andrew et al. (2020) used the UK Time Use Survey to investigate how the lockdown impacted
the time use and learning of children between ages 4 and 15. They suggest that educational gaps between
children from poorer and better-off families are likely to have been reinforced. They also present
evidence that the frequency of online study activities provided by the school is positively correlatedwith children’s total learning time. Using data from an online learning service company, Ikeda andYamaguchi (2020) investigated the online study time of junior and high school students in Japan preCOVID-19 and during COVID-19-related school closures and observed a positive association between
online education quality and children’s study time.
Although these studies analyzed the actual learning time of children, they did not investigate
the heterogeneity in parents’ preferences regarding online education due to their work status and work
style changes during the pandemic, which can have broad policy implications as to how and to what
extent the government should expand the online education activities and access at school.
Next, we introduce several studies that investigated how school closures affected parents’
employment during the COVID-19 pandemic lockdowns. Russell and Sun (2020) found evidence thatin the US, the unemployment rate of women with young children increased due to the closure of childcare centers. In Japan, Yamamura and Tsustsui (2021) used a unique online survey data and found that
school closures were likely to have affected parents’ workstyles, especially parents with children inelementary school. Using an online survey data of mothers with children aged between 4 and 10 inJapan, Takaku and Yokoyama (2021) ascertained that school closures led to weight gain in children andmother’s increased anxiety over child-rearing.
However, studies using Japanese data mainly relied on data from commercial online surveys orservices, typically making use of timing of school closures to evaluate the effect on children, and do
not consider access to outside-school education and parental attitudes. Our paper is unique in that, using
data from a nationwide representative survey collected by the Japanese government, we document a
gap in children’s access to online education, both at and outside school, while also examining parents’preferences regarding online schooling, how they are affected by family backgrounds and parents’ work
styles. In doing so, we contribute to three rapidly expanding areas of research of the ongoing COVID19 crisis.
3. Background
Japan announced its first confirmed case of the COVID-19 infection on January 16, 2020, in a returnee
from China, and the first case of domestic transmission on January 28, 2020. One of the early major
measures to prevent the spread of the pandemic in Japan was to close schools about 2 weeks early before
the 2-week-long spring break was set to start. On Thursday, February 27, 2020, when the cumulative
number of domestic cases stood at 195 at the number of deaths at 4, the government requested allelementary, junior high, and high schools to temporarily close from the following 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 andjunior high schools and 35% of high schools reopened at the beginning of the school year, with schools
in the most impacted urban areas2 staying closed (MEXT 2020a).
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 from2

Chiba, Fukuoka, Hyogo, Kanagawa, Osaka, Saitama prefectures, and Tokyo metropolis.

4April 1 to June 23, on an 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.3
As of the time of drafting this paper in June 2021, the central government had not requested
schools to close again, even during subsequent waves of the pandemic. However, schools might haveclosed for short periods of time independently of the central government to contain local outbreaks. Fig.
1 shows the timeline of the COVID-19 spread in Japan, the state of emergency and the data collection
points, from January 16 to December 31, 2020.
4. 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. 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. We then excluded respondents who did not produce a definitive answer about thetype of education their child had been receiving (answer “do not know”) in either wave to finalize oursample at 530 respondents.
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 an average,
our sample is more educated than the general population, with 75% of respondents having attained apost-secondary education. The data on the educational attainment of respondent’s spouse were notavailable. Based on the 2010 national census, 44% of those in the 25–49-year age category, which ismost 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 withannual household income over 6 million yen, which is the highest income bracket common to bothrounds of the 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
the governmental statistics (MHLW, 2020). Other properties of the sample are described in Table 1.
Ages of both parents and children were collected in the survey but not disclosed.
The questionnaires of our survey in May and December enquire 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 “noonline education received.”
3“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In May, 34% of the 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 421 respondents. The descriptive statistics for this limited sample are presented
in Appendix Table A1.4
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. 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-personschooling in principle or completely. In December, these proportions shifted to 5% and 86%,
respectively. Generally, in both surveys the younger the child, the more the parent preferred in-person
learning. Moreover, parents with children in public schools preferred in-person learning over parents ofchildren 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.

4

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.

6

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.5 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.
5. Empirical strategy
5.1 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 backgrounds:
𝑂𝑛𝑙𝑖𝑛𝑒𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝐴𝑐𝑐𝑒𝑠𝑠,𝛽𝛽 ∗ 𝑆𝑐ℎ𝑜𝑜𝑙

𝛽 ∗ 𝐹𝑎𝑚𝑖𝑙𝑦,

𝛽 ∗ 𝐶𝑜𝑣𝑖𝑑

,

𝜀

(1)
where 𝑂𝑛𝑙𝑖𝑛𝑒𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝐴𝑐𝑐𝑒𝑠𝑠 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,
𝑂𝑛𝑙𝑖𝑛𝑒𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝐴𝑐𝑐𝑒𝑠𝑠 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, 𝑂𝑛𝑙𝑖𝑛𝑒𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝐴𝑐𝑐𝑒𝑠𝑠 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. 𝐶𝑜𝑣𝑖𝑑 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 𝜀 represents an error term, which is for all models assumed to be clustered on aprefectural level.
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:
𝑂𝑛𝑙𝑖𝑛𝑒𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝐴𝑐𝑐𝑒𝑠𝑠𝛽𝛽 ∗ 𝑆𝑐ℎ𝑜𝑜𝑙
𝛽 ∗ 𝐹𝑎𝑚𝑖𝑙𝑦∗ 𝑂𝑛𝑙𝑖𝑛𝑒𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝐴𝑐𝑐𝑒𝑠𝑠𝜀𝛽 ∗ 𝐶𝑜𝑣𝑖𝑑

𝛽(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:
𝑂𝑛𝑙𝑖𝑛𝑒𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝐴𝑐𝑐𝑒𝑠𝑠

𝛽

𝛽 ∗𝐷

∗ 𝐷𝑒𝑐𝑒𝑚𝑏𝑒𝑟𝛽 ∗𝐷𝛽 ∗ 𝐷𝑒𝑐𝑒𝑚𝑏𝑒𝑟

𝜀
(3)
in
where the definition of 𝑂𝑛𝑙𝑖𝑛𝑒𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝐴𝑐𝑐𝑒𝑠𝑠 is identical to that in Equations (1) and (2). 𝐷
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 𝐶𝑜𝑣𝑖𝑑 and 𝐶𝑜𝑣𝑖𝑑 .

5

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.

7

For our data, the average difference is 0.49 cases per 1,000 inhabitants. 𝐷𝑒𝑐𝑒𝑚𝑏𝑒𝑟 is a dummy 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. We estimate the following model:
𝑂𝑛𝑙𝑖𝑛𝑒𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝐴𝑐𝑐𝑒𝑠𝑠
𝛽𝛽 ∗𝐷𝛽 ∗ 𝐷𝑒𝑐𝑒𝑚𝑏𝑒𝑟 𝛽 ∗ 𝐷∗ 𝐷𝑒𝑐𝑒𝑚𝑏𝑒𝑟 𝛽
∗𝐷
𝛽 ∗𝐷
∗𝐷
𝛽 ∗ 𝐷𝑒𝑐𝑒𝑚𝑏𝑒𝑟 ∗ 𝐷∗𝐷∗ 𝐷𝑒𝑐𝑒𝑚𝑏𝑒𝑟 ∗ 𝐷
𝜀

𝛽

(4)
is a dummy variable which identifies children’s family backgrounds along 3
where 𝐷
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 Covid19 cases had heterogenous impacts on children’s online learning experiences both at school and outsideschool by family background and school types.
5.2 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:
𝑂𝑛𝑙𝑖𝑛𝑒𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝐷𝑒𝑚𝑎𝑛𝑑
𝑆𝑐ℎ𝑜𝑜𝑙𝛽𝛽 ∗ 𝑂𝑛𝑙𝑖𝑛𝑒𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝐴𝑐𝑐𝑒𝑠𝑠𝑆𝑐ℎ𝑜𝑜𝑙
𝛽 ∗ 𝐹𝑎𝑚𝑖𝑙𝑦 ,𝛽 ∗ 𝐶𝑜𝑣𝑖𝑑 ,
𝜀
,

,

𝛽 ∗

(5)
Outcome variable 𝑂𝑛𝑙𝑖𝑛𝑒𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝐷𝑒𝑚𝑎𝑛𝑑 , 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. 𝑂𝑛𝑙𝑖𝑛𝑒𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝐴𝑐𝑐𝑒𝑠𝑠𝑆𝑐ℎ𝑜𝑜𝑙 is a dummy
variable equal to 1 if respondent’s child had received some form of online education at school lately6,
and 0, otherwise. 𝑆𝑐ℎ𝑜𝑜𝑙 and 𝐹𝑎𝑚𝑖𝑙𝑦 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, 𝐹𝑎𝑚𝑖𝑙𝑦 variables include parents’ work
status or changes in parents’ work styles due to the pandemic. 𝐶𝑜𝑣𝑖𝑑 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:𝛽𝛽 ∗ 𝑂𝑛𝑙𝑖𝑛𝑒𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝐴𝑐𝑐𝑒𝑠𝑠𝛽 ∗ 𝑆𝑐ℎ𝑜𝑜𝑙
𝑂𝑛𝑙𝑖𝑛𝑒𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝐷𝑒𝑚𝑎𝑛𝑑
𝐹𝑎𝑚𝑖𝑙𝑦𝛽 ∗ 𝐶𝑜𝑣𝑖𝑑𝛾 ∗ 𝑂𝑛𝑙𝑖𝑛𝑒𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝐷𝑒𝑚𝑎𝑛𝑑
𝜀

𝛽 ∗

(6)
where we expand Equation (5) by including the lag of the outcome variable
𝑂𝑛𝑙𝑖𝑛𝑒𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝐷𝑒𝑚𝑎𝑛𝑑. Equivalently to the preceding models, the error term 𝜀 is assumed to beclustered on a prefectural level.
6

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 backgrounds and thus, likely to be endogenous.

86. Results and discussion
6.1 Equality of access to online education
In this section, we describe the differences in access to online education by the type of school the childattended, and family background factors based on Equations (1) and (2). Table 2 reports thedeterminants 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)).
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 10% 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 the level of school. 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 basedon the level of school the child attended, with the baseline set to elementary school, for households inthe high-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 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 backgrounds, especially household income, and the type of school
9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.
6.2 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 shownin Table 4. “COVID-19 rapid increase” variable corresponds to 𝐷
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 promoted these parents who
were more highly concerned about their children’s learning during the COVID-19 pandemic to seek out
more online education outside school.
6.3 Family backgrounds and demand for at-school online education

10

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 backgrounds. 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 survey and from the December survey inColumns (1) and (2), respectively. Column (3) show 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 responding parent’s preferences, we do not find any consistently significant effectin either May or December. While the results discussed in Section 6.1 reveal that children in private
schools had, at both time points, greater access to at-school online education, there is no difference inparents’ views based on the type of school attended when the actual experience of at-school online
education is controlled for.7
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 6.2, 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 not statisticallysignificant 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 thesurvey 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 in aposition to adapt to new circumstances easily were more likely to want their children to return to theclassroom, while in the December survey, parents likely adjusted to the situation overall and theiremployment 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 for in-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
7

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.

11

of jobs for the level of flexibility they provide. On the contrary, fathers who are more likely to be thebreadwinners, face lower job security and earnings in non-regular employment than regular employees,
which might make it difficult to support child learning from home.
6.4 Parents’ work styles and demand for at-school online education
In this section, we investigate the association between parents’ preferences for online education andchange 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 6.3 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 for the May survey and since May for the
December survey. The questionnaire inquired specifically about the change in total hours worked andabout 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 8% 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 more than they did before the pandemic and in the
December survey, 12% of mothers and 30% of fathers teleworked more than they did in May. Besides
teleworking, work styles became more flexible for 18% of mothers and 22% of fathers in the May
survey and 12% of both mothers and fathers 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’ working hours is associated with a higher likelihood of positive views of online
education. However, this effect was no longer statistically significant in the sample of both parents
working. In a sample of households with both parents working, we observe that respondents fromhouseholds with mothers whose working hours increased, were at a 1% level of significance more likely
to prefer in-person education. We do not find any significant effect of fathers’ work style changes.
Using the December survey, in the cross-sectional analysis, no difference in parental views isobserved based on the change in work styles. From the results of a value-added model, in Column (8),controlling for previous preference, we observe that respondents from households where fathers
teleworked more in December than they did in May were at a 10% level of significance more open to
online education than respondents from households where fathers saw no change in work styles.
However, we observe this effect in the sample of working fathers only, not in the sample of both parents
working in Column (9).

12Although the association between work styles and parental preference for online education we
identify is weak, it is consistent with the results from Section 6.3 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 virtually disappeared. Our results are also in linewith Yamamura-Tsutsui (2021), who found that it was mainly working mothers who bore the brunt ofschool closures in Japan. However, more research is needed into the topic, especially to determine
whether parents adjust their employment status or work style to accommodate the children’s online
learning, which could have vast policy implications.
7. 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 in understanding why Japan islagging behind other OECD countries in introducing online learning at school, and how these
preferences are shaped 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 to understand the status of online education in Japan by including both at-school andoutside-school learning experiences and family backgrounds, while also examining the key factor on
the demand side for 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 spent 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 infection. 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 the 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 to13the 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
than usual. Parents’ understanding and acceptance of the new remote learning style may be the key tolevelling up the access to online education at school. Our results suggest that parents are more open to
at-school online education once their children experience it.
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 has been very slow and would have minimal, if any, impact during the period
covered by the data used in this study.8 While supply side policies are important and merit further
research, remote learning devices 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 them whileconsidering the hidden inequality in the online educational access outside school.
Declarations
Funding: This work was supported by KAKENHI Grant Number JP16H06323 from the Japan Society
for Promotion of Science and Keio Gijuku Academic Development Funds.
Conflicts of interest/Competing interests: The authors declare that they have no relevant or material
financial interests that relate to the research described in this paper.
Availability of data and material: The individual data of “Survey on Lifestyle Attitudes andBehavioral Changes during the COVID-19 Pandemic (Shingata Koronauirusu Kansenshou no
Eikyouka ni Okeru Seikatsu Ishiki-Koudou no Henka ni Kansuru Chousa)” collected by the Cabinet
Office of the Japanese government can be obtained by applying through the website,
https://www5.cao.go.jp/keizai2/manzoku/index.html. The other data and materials can be provided
upon request to the corresponding author.
Code availability: The Stata code used in this research can be provided upon request to thecorresponding author.
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the COVID-19 pandemic: evidence using panel data from Japan.” Review of Economics of theHousehold, Vol.19, pp. 41-60. https://doi.org/10.1007/s11150-020-09536-5

15

Table 1 Descriptive statistics
obs.

May 2020 Survey
mean
s.d.
min

School type
Elementary school - public
530
0.602
Elementary school - private
530
0.015
Junior high school - public
530
0.185
Junior high school - private
530
0.025
High school - public
530
0.106
High school - private
530
0.068
Online learning experience in past month (multiple answer)
At school
530
0.336
Online classes 530
0.140
Online instruction 530
0.155
Online materials 530
0.168
Outside school
530
0.228
Online classes 530
0.166
Online instruction 530
0.058
Online materials 530
0.089
Other online education
530
0.053
No online education
530
0.506
Preferred school format
Over 50% online
530
0.102
Over 50% in person
530
0.166
In principle in person
530
0.332
100% in person
530
0.281
Do not know
530
0.119
Respondent's highest attained education
Highly educated (post-secondary)
530
0.747
Household annual income
High income (over 6 million yen)
530
0.583
Household typeWorking mother
530
0.698
Regular employee 530
0.247
Non-regular employee 530
0.415
Executive 530
0.008
Self-employed 530
0.028
Working father
530
0.906
Regular employee 530
0.791
Non-regular employee 530
0.042
Executive 530
0.034
Self-employed 530
0.040
Single mother
530
0.081
Single father
530
0.015
Multigenerational household
530
0.104
Number of children under 18
530
1.457
Female respondent
530
0.462
Area
Rural area
530
0.051
COVID-19 spread (week prior; per
530
0.002
1000 inhabitants)
COVID-19 spread (month prior; per
530
0.026
1000 inhabitants)
COVID-19 rapid increase
530
0.483

max

obs.

December 2020 Survey
mean
s.d.
min

max

0.490
0.122
0.389
0.155
0.308
0.252

0
0
0
0
0
0

1
1
1
1
1
1

530
530
530
530
530
530

0.602
0.015
0.185
0.025
0.106
0.068

0.490
0.122
0.389
0.155
0.308
0.252

0
0
0
0
0
0

1
1
1
1
1
1

0.473
0.347
0.362
0.374
0.420
0.372
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

530
530
530
530
530
530
530
530
530
530

0.130
0.058
0.057
0.060
0.119
0.077
0.038
0.036
0.028
0.758

0.337
0.235
0.231
0.238
0.324
0.267
0.191
0.186
0.166
0.428

0
0
0
0
0
0
0
0
0
0

1
1
1
1
1
1
1
1
1
1

0.303
0.372
0.471
0.450
0.324

0
0
0
0
0

1
1
1
1
1

530
530
530
530
530

0.040
0.074
0.289
0.479
0.119

0.195
0.261
0.454
0.500
0.324

0
0
0
0
0

1
1
1
1
1

0.435

0

1

530

0.747

0.435

0

1

0.494

0

1

530

0.589

0.493

0

1

0.460
0.432
0.493
0.087
0.166
0.293
0.407
0.200
0.181
0.195
0.273
0.122
0.305
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

530
530
530
530
530
530
530
530
530
530
530
530
530
530
530

0.709
0.258
0.413
0.008
0.030
0.904
0.798
0.036
0.036
0.034
0.083
0.011
0.100
1.440
0.462

0.454
0.438
0.493
0.087
0.171
0.295
0.402
0.186
0.186
0.181
0.276
0.106
0.300
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

530
530

0.051
0.153

0.220
0.091

0
0.002

1
0.320

0.030

0

0.095

530

0.511

0.347

0.023

1.240

0.500

0

1

530

0.483

0.500

0

1

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)
2

Pseudo R
Observations

Outside schoolMay

December(1)0.228***(0.061)
0.065*(0.038)0.238***(0.054)0.166***(0.044)0.021(0.040)0.446(0.475)(2)
0.109***(0.038)-0.008(0.028)0.095**(0.041)0.038*(0.020)0.027(0.030)0.050(0.042)

December(with lag)(3)0.079**(0.038)-0.019(0.027)0.060(0.039)0.016(0.018)0.022(0.027)0.041(0.041)

0.122
530

0.081
530

0.119
530

May

December(4)
0.004(0.081)
0.052(0.043)-0.114**(0.050)0.156***(0.037)0.058*(0.031)1.178***(0.306)(5)
0.102*(0.055)0.013(0.030)-0.105**(0.047)0.101***(0.031)
0.097***(0.034)0.053**(0.024)

December(with lag)(6)0.095**(0.039)0.005(0.030)-0.079*(0.042)0.068**(0.028)0.081**(0.032)0.040(0.026)

0.057
530

0.078
530

0.182
530

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, 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 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
2

Pseudo R
Observations

Outside schoolMay

December(2)0.123*(0.071)0.027(0.085)
0.061*(0.034)-0.092(0.068)-0.035(0.070)0.033(0.041)-0.123(0.080)0.112(0.091)

December(with lag)(3)0.132*(0.071)0.024(0.084)0.042(0.029)-0.103(0.068)-0.034(0.065)0.0432(0.039)-0.144*(0.081)
0.071(0.091)(4)-0.055(0.096)-0.141(0.137)0.184***(0.039)-0.134(0.083)-0.017(0.107)-0.018(0.046)
0.261***(0.099)0.045(0.137)(5)-0.041(0.062)-1.472***(0.130)0.096**(0.038)-0.026(0.070)0.704***(0.072)0.056(0.047)0.089(0.089)0.702***(0.082)

December(with lag)(6)-0.009(0.063)-1.377***(0.116)
0.063*(0.037)-0.022(0.068)0.690***(0.077)0.058(0.046)0.036(0.095)0.642***(0.078)

0.102
530

0.141
530

0.071
530

0.090
530

0.192
530

May

December(1)-0.007(0.100)0.068(0.108)0.154***(0.050)0.018(0.086)
0.039(0.108)-0.046(0.055)0.080(0.135)0.205*(0.108)0.128
530

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). Junior high school, High school (withElementary 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 fromMay survey. Full results are available in the Appendix Table A3. Robust standard errors clustered at prefectural level are shown inparentheses. 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
2

Pseudo R
Observations

At school(1)
0.076**(0.035)-0.193***(0.027)-0.025(0.033)0.299***(0.028)Outside school
(2)
0.042
(0.031)-0.120***(0.021)
0.023(0.033)0.208***(0.020)

0.065
1,060

0.026
1,060

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) or in the pastmonth (December). COVID-19 rapid increase is a dummy variable equal to 1 inprefectures where the difference in newly confirmed cases per capita over 1 monthprior 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

At school
(1)
(2)
(3)
0.007
-0.041
0.055(0.068)(0.073)(0.039)-0.125*** -0.217*** -0.188***(0.037)(0.047)(0.030)
0.007
0.017
0.004
(0.047)(0.070)(0.041)

Heterogeneity in parental income
High-income household 0.151***(0.043)
COVID-19 rapid increase * High-income household 0.083(0.092)
December * High-income household -0.128***(0.043)
COVID-19 rapid increase * December -0.029* 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

0.112**(0.050)0.078(0.060)-0.035(0.044)-0.017(0.087)-0.046(0.048)0.156**(0.077)0.032(0.046)-0.057(0.074)

December * Private school
COVID-19 rapid increase * December* Private school
2

0.084**(0.039)-0.017(0.046)-0.045(0.039)0.120**(0.058)0.321***(0.101)
0.180(0.111)-0.054(0.091)-0.262(0.162)COVID-19 rapid increase * Private school

Pseudo R
Observations

Outside school
(4)
(5)
(6)
-0.0190.055
0.076**(0.039)(0.046)(0.034)-0.102*** -0.087** -0.122***(0.032)(0.033)(0.024)0.037-0.0670.004(0.052)(0.056)(0.035)

0.092
1,060

0.071
1,060

0.126
1,060

0.114
(0.120)-0.315**(0.127)0.019(0.055)0.171**(0.074)
0.056
1,060

0.037
1,060

0.038
1,060

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 outsideschool (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 variable equal 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 are dummy variables. Robust standard errors clustered at prefecturallevel 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

May

December

December(with lag)(1)0.989***(0.220)0.642(0.404)0.377*(0.213)-0.274(0.355)0.325(0.211)0.561***(0.140)(2)
1.363***(0.215)-0.273(0.311)0.142(0.257)0.246(0.256)0.601***(0.226)0.296(0.239)(3)
1.273***(0.211)-0.560(0.344)0.014(0.244)0.268(0.247)0.503**(0.242)0.184(0.245)-0.511*(0.291)-0.219(0.227)1.297(0.817)-0.672(0.651)-0.330(0.256)-0.089(0.215)0.779(1.392)-0.728(0.907)-0.280(0.225)-0.058(0.200)0.430(1.551)-0.899(0.994)-0.502(0.449)-1.528*(0.834)-0.433(0.513)-0.413(0.902)-0.945(0.707)-1.729(1.064)-0.126(0.946)-0.475(0.869)-1.007(0.739)-1.604(1.090)-0.376(0.996)-0.457(0.849)Working mother
Regular employee
Non-regular employee
Executive
Self-employed
Working father
Regular employee
Non-regular employee
Executive
Self-employedLag (May survey)
2

Pseudo R
Observations

✓
0.058
421

0.071
421

0.103
421

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),
High-income household (household annual income over 6 million yen), Highly educated parent (responding
parent attained post-secondary education), Regular employee, Non-regular employee, Executive, and Selfemployed for mother 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 motherhousehold and Single father household dummy variables, Number of children and COVID-19 spread (weekprior) 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. Levelsof 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)

(2)

December
(3)

(4)

0.323
(0.294)-0.062(0.288)0.332(0.252)-0.972***(0.353)0.0343(0.471)0.017(0.340)-0.065(0.267)0.312(0.423)(5)

December (with lag)
(6)

(7)

0.00817(0.521)-0.052(0.460)0.122(0.395)0.384(0.546)0.192(0.476)0.025(0.302)-0.121(0.251)
0.228(0.384)(8)(9)

Working styles - mother
More teleworking
More flexible work
Fewer working hours
More working hours

0.302(0.284)-0.208(0.261)0.420**(0.193)-0.464(0.359)0.133(0.510)-0.050(0.411)0.028(0.357)0.338(0.479)Working styles - father
More teleworking

0.169(0.188)0.172(0.223)0.341(0.212)0.343(0.425)More flexible work
Fewer working hours
More working hours-0.236(0.205)0.250(0.351)0.100(0.278)0.661(0.476)0.241(0.176)0.362(0.308)-0.108(0.399)0.243(0.298)0.195(0.335)0.494(0.344)-0.588(0.437)0.398(0.374)0.293*(0.173)0.198(0.353)-0.223(0.406)0.318(0.275)0.262(0.354)0.298(0.403)-0.668(0.408)0.468(0.348)Working spouse

✓

✓

✓

✓

✓

Single parent household

✓

✓

✓

✓

✓

✓

✓

✓

✓

0.113
305

0.105
377

0.124
265

Lag (May survey)
2

Pseudo R
Observations

0.067
296

0.055
378

0.079
261

0.083
305

0.074
377

0.100
265

✓

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 aredummy variables equal to 1 in case respondent chose corresponding options in questions about their own and spouse’s changes
in work styles since the beginning of the COVID-19 pandemic in May survey and since May in December survey. 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

Fig. 1 Timeline of COVID-19 incidence and state of emergency in Japan23

Fig. 2 Online educational experience by school level24

Fig. 3 Online educational experience by school type25

Fig. 4 Preferred school format by school level26

Fig. 5 Preferred school format by school type27