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

School ICT resources, teachers, and online education:
Evidence from school closures in Japan during the COVID-19 pandemic
Hideo Akabayashi, Shimpei Taguchi, Mirka Zvedelikova
15 March, 2023
DP2023-008
https://ies.keio.ac.jp/en/publications/22469/

Institute for Economic Studies, Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan
ies-office@adst.keio.ac.jp
15 March, 2023

School ICT resources, teachers, and online education: Evidence from school closures in Japan
during the COVID-19 pandemic
Hideo Akabayashi, Shimpei Taguchi, Mirka Zvedelikova
Keio-IES DP2023-008
15 March, 2023
JEL Classification: I20, J22, H75
Keywords: COVID-19, remote education, overtime work, teachers’ skills, school resources
Abstract
As schools worldwide were forced to close due to the COVID-19 pandemic in early 2020,
they struggled to switch to online education. Using Japan’s nationwide administrative data,
we examine the impact of schools’ ICT equipment and teachers’ IT skills on the provision of
online classes, communication with students’ families, and teachers’ working hours during
and shortly after the closures. To isolate supply-side effects, we exploit differences in ICT
resources between public elementary and junior high schools using a municipality-level fixed
effects model, the level at which ICT resources are decided. We find that basic ICT
equipment was critical to implementing online classes, but IT skills were not. Furthermore,
we observe no effect of ICT resources on schools’ communication with families. However, IT
skills were associated with teachers’ working hours. In particular, weak IT skills resulted in ahigher percentage of teachers working overtime.
Hideo Akabayashi
Faculty of Economics, Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan
hakab@econ.keio.ac.jp
Shimpei Taguchi
Graduate School of Economics, Keio University (Graduate student)
2-15-45 Mita, Minato-ku, Tokyo, 108-8345, Japan
shimpei.taguchi@keio.jp
Mirka Zvedelikova
Institute of Social and Economic Research, Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japanzvedelikova@iser.osaka-u.ac.jp
Acknowledgement : We thank the Japanese Ministry of Education, Culture, Sports, Scienceand Technology (MEXT) for providing us the data from “Survey Results on ICT in School
Education” and “Survey on Reform of Working Conditions in Schools by the Local Boards ofEducation.” We also thank the participants at various seminars and academic conferences
for their comments.

School ICT resources, teachers, and online education:
Evidence from school closures in Japan during the COVID-19 pandemic
Hideo Akabayashia
Shimpei Taguchib
Mirka Zvedelikovac
March 15th, 2023
Abstract
As schools worldwide were forced to close due to the COVID-19 pandemic in early 2020, theystruggled to switch to online education. Using Japan’s nationwide administrative data, we examine
the impact of schools’ ICT equipment and teachers’ IT skills on the provision of online classes,
communication with students’ families, and teachers’ working hours during and shortly after the
closures. To isolate supply-side effects, we exploit differences in ICT resources between public
elementary and junior high schools using a municipality-level fixed effects model, the level at which
ICT resources are decided. We find that basic ICT equipment was critical to implementing online
classes, but IT skills were not. Furthermore, we observe no effect of ICT resources on schools’
communication with families. However, IT skills were associated with teachers’ working hours. In
particular, weak IT skills resulted in a higher percentage of teachers working overtime.
Keywords: COVID-19; remote education; overtime work; teachers’ skills; school resources
JEL classification: I20, J22, H75------------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,
Japan. https://orcid.org/0000-0003-3922-9948. Email: shimpei.taguchi@keio.jp
c: Institute of Social and Economic Research, Osaka University, 6-1 Mihogaoka, Ibaraki, Osaka
567-0047, Japan; Graduate School of Economics, Keio University, 2-15-45 Mita, Minato-ku, Tokyo,108-8345, Japan. https://orcid.org/0000-0002-3085-7502. Email: zvedelikova@iser.osaka-u.ac.jp
11. Introduction
In the early days of 2020, the COVID-19 pandemic forced many schools around the world
to close, resulting in a widespread children’s learning loss (UNESCO 2021). During these closures
many countries turned to online education. Related studies have predominantly focused on the
demand side, investigating how students’ family backgrounds influenced access to remote/online
education and the associated educational access gap (Andrew et al., 2020; Bacher-Hicks et al., 2021;
Grewening et al., 2021; Ikeda and Yamaguchi, 2021; Akabayashi et al., 2023). However, many
countries also faced supply side issues in providing high-quality online education given schools’
information and communication technology (ICT) resources, namely ICT equipment and teachers’
IT skills. Only a small number of studies have examined how school ICT resources affected theprovision of online learning during the pandemic (Dincher and Wagner, 2021; Akah et al., 2022).
The first reason for this gap in the literature is the lack of data. Very few countries havesystematic data on schools’ ICT resources or educational practices during the pandemic. The second
reason is the difficulty of separating the demand and supply sides of both ICT resources and accessto online education. It is likely that schools in wealthier areas where demand for high-quality
education might be higher are better equipped and staffed by more highly qualified teachers, making
the effects of the supply side difficult to identify. An equally important concern is the overwork ofteachers, as even schools with top-level ICT equipment and skilled staff cannot effectively provide
online education if teachers are overwhelmed.
The aim of this paper is to examine how ICT resources at school affected the online
education provision and teachers’ working hours during the early stages of the pandemic using dataon Japanese public elementary and junior high schools, the compulsory stage of schooling in Japan.
Public schools in Japan are operated by local boards of educations (BoE), who also allocate major
resources across schools, including ICT, following strict legal procedures. During the pandemicinduced school closures in Japan, most teachers did not switch to remote work, making ICT
equipment available at school highly relevant. Notably, among developed countries, Japan has a very
high availability of high-speed Internet connection (OECD 2020a), yet the use of ICT in education inschools is largely lacking (OECD 2020b, 2020c). During the school closures in the first wave of thepandemic, only a small portion of schools provided live online classes and other forms of digital
education. Japan is also a country where teachers commonly work unusually long hours (OECD2019), a problem that could be alleviated by better working conditions. Therefore, improving the
supply side issues should be a priority in promoting access to online education and teacher welfare in
Japan.
Whether ICT technology can improve teachers’ teaching styles and thereby children’s
learning outcomes has been a central issue in education policy in recent years. Many studies haveempirically investigated how policies providing computers to students or incentivizing ICT
equipment purchases have affected students’ learning, yielding mixed results. Angrist and Lavy
(2002) conducted the first study formally examining the effects of funding for educational computers
in Israeli schools, suggesting that an increase in teachers’ computer use has no effect or a negative
effect on children’s learning. Goolsbee and Guryan (2006) analyzed the effect of Internet accessinvestment subsidies in US public schools, concluding that, while there was a significant increase inInternet access at treated schools, students’ test scores were unaffected. Machin et al. (2007)
examined the effect of a change in policy rules allocating ICT funding to public schools, suggestingthat there was an improvement in English and science but not in math outcomes. Using arandomized trial, Barrow et al. (2009) found that assigning computer-aided instructions improves
pre-algebra and algebra test scores. Bass (2021) examined the effect of eligibility for ICT vouchers
for public schools in California, finding a significant effect of voucher use on student achievement.
Most recently, Lomos et al. (2023) using 2018 data from secondary schools in Luxembourg, acountry with ample school ICT resources yet a relatively low ICT use in classroom practice, reported
that teachers’ technological knowledge is an important predictor of ICT classroom use. These studies
examined the effects of ICT facilities on learning at school; however, during the COVID-19 school
closures, the effective use of ICT at school was crucially important to providing instruction andsupporting learning for students at home.
Early in the COVID-19 pandemic, Dincher and Wagner (2021) surveyed German2elementary and secondary school teachers, finding that at-school ICT infrastructure did not predictthe use of online teaching tools during school closures, while teachers’ technical affinity did. The
setting of this study is close to our paper; however, the initial level of ICT resources in Germany was
vastly different from Japan, allowing Dincher and Wagner (2021) to focus on teachers’ attitudes
specifically. Furthermore, their paper does not attempt to separate the role of supply-side factors at
school from the demand-side factors such as the characteristics of students and families.
Nevertheless, Dincher and Wagner (2021) and Lomos et al. (2023) show that teachers’ IT skills
likely play an essential role in the impact of school ICT equipment on both at and out of school
learning.
Moreover, school ICT equipment is expected not only to facilitate students’ learning but
also to improve teachers’ work efficiency. In a typical school environment, principals and teachers
hold discretion over how the benefits of improved productivity provided by additional ICT resources
are distributed. For example, teachers may react to additional ICT resources by reducing time andeffort spent preparing classes.1 It is important to consider the distributional effects of a productivity
increase when interpreting why previous studies on the effects of in-school ICT use produced mixed
results. However, only a limited number of studies have directly assessed the impact of ICT
resources on teachers’ efforts at school (Bulman and Fairlie, 2016; Escueta et al., 2017).
In this study, we use a dataset combining several sets of government administrative datacollected in 2020 from the entire Japanese public school system: a survey about school ICT
resources; a survey about public schools’ response to the COVID-19 pandemic; and a survey aboutteachers’ overtime work. The combined dataset includes information about schools’ ICT equipment,teachers’ IT skills, length of school closures, types of remote education and communication channels
with students’ families during school closures, and teachers’ overtime hours during the closures andin the following months at a municipality level for both elementary and junior high schools.
Utilizing a municipality-level fixed effects model, we exploit the variation in schools’ ICT resources
within each municipality to isolate the supply side effect. Due to the centralized nature of resource
allocation, the variation in within-BoE’s ICT resources is likely related to BoE’s budget
implementation process. In addition to this empirical analysis, we conducted an online survey of
public elementary and high school teachers to further the understanding of our results.
By presenting this research, we contribute to the growing literature covering pandemicrelated school closures by simultaneously investigating the effect of school ICT resources on boththe provision of online education and the choice of communication channel with students’ families
while also examining teachers’ working conditions during and after school closures using Japanesedata. Our results shed light on the distributional effects of ICT resources on both students andteachers, allowing causal interpretation. To our knowledge our paper is the first to comprehensively
examine these topics.
Our results show that, during school closures, better ICT equipment was more relevant
than teachers’ IT skills to the provision of online education, but neither had any effect oncommunication with students’ families using live online tools. However, weak teacher IT skills
resulted in a higher percentage of teachers working extra hours, especially extreme amounts ofovertime in the months following the reopening of schools. These results suggest that the impact ofvarious ICT resources differs between students and teachers. A bottleneck to implementing online
education in Japan was created by a shortage of ICT equipment, the distribution of which isdetermined at the municipal level but was not contributed to by teachers’ IT skills. However, thepersistent overwork of teachers in Japan may be due to a lack of IT skills at the individual level.
Therefore, the effect of ICT resources is multi-dimensional and policy makers should be aware of1

Reducing teachers’ work hours should not necessarily be viewed negatively. In a society like Japan
where teachers’ hours of work are the longest among developed countries (OECD 2019), the stagnant useof ICT at public schools resulting in unappealing working environment may have created a difficulty inrecruiting high-quality teachers. Additionally, Hojo (2021) showed that class size, in Japan typicallylarge, is associated with teachers’ overwork and their level of stress.

3the importance of matching appropriate policy tools to their targets.
2. Data and setting
At the end of February 2020, shortly before the spring break, Japanese schools wereordered to close to prevent the risk of community transmission of the COVID-19 virus, first
confirmed in Japan in January 2020. Many schools reopened at the beginning of the new school year,
starting on April 1st, 2020, and closed again after a state of emergency was declared for Japan’s mosturban prefectures on April 7, 2020. The state of emergency was extended to all prefectures on April
16th, 2020, and lifted in waves from mid-May to late May 2020, prompting schools to reopen. This
was the only period of pandemic-related mandated school closures in Japan. During this period, theJapanese Ministry of Education, Culture, Sports, Science and Technology (MEXT) implemented aseries of surveys asking BoEs nationwide to report the details of school closures for the publicschools in their jurisdiction, typically corresponding to a municipality. These details included thenumber of schools closed, specific steps taken to provide remote education to students, and steps
taken to communicate with students’ families. For the purposes of this paper, we use data collected
through the Survey on Learning and Instruction during the COVID-19 Pandemic2 as of June 23rd,
2020. According to this survey, conducted after schools fully reopened, the average length of school
closures reported by BoEs was 24.6 school days for public elementary schools and 24.5 school days
for public junior high schools. The percentage of BoEs reporting that live online classes were held in
their district during the closures was 8.1% for elementary schools and 8.9% for junior high schools,
and the percentage reporting the use of live online tools to communicate with students’ parents was
9.4% and 10.3% for elementary and junior high schools, respectively. However, the survey asks only
a binary question whether online education was implemented in a BoE at all, it does not inquire as toits extent. We come back to this point later in Section 4.3.
Regarding schools’ ICT equipment, public school resources are customarily determined by
the school founding body, the local BoE. BoEs are highly sensitive to an equal provision of
resources and teacher assignments to each public school of the same level, elementary or junior high.
However, the weights placed on school levels might differ across municipalities. MEXT annually
collects information on public schools’ ICT resources, specifically ICT equipment and teachers’ IT
skills, through the Survey on ICT in School Education3, of which we use the 2019 school year
iteration, collected as of March 1st, 2020. While the survey results provide information about eachpublic school separately, we aggregate the data on a BoE level to correspond with the data regarding
the pandemic response. Across BoEs nationwide, 67.4% of elementary schools and 68.2% of juniorhigh schools were equipped with high-speed (over 100 Mbps) Internet, and 83.6% of elementary and83.5% of junior high schools were equipped with Wi-Fi. The average number of presentation
devices capable of projecting digital materials (projector, digital whiteboard, digital TV) per regularclassroom was 0.96 for elementary and 0.75 for junior high school. In order of elementary schools
and junior high schools, the percentage of schools equipped with commercial digital instructional
materials for teachers such as premade lesson presentation slides, was 57.4% and 64.3%. Digitaltextbooks for students were available in 9.5% and 10.0% of schools, and 76.5% and 77.1% usedmanagement software. Finally, 65.7% and 66.2% of schools had a set information policy regarding
internal information.
The same survey also inquired about teachers’ IT skills in four categories with four
subcategories each, rating all teachers on a scale of “lacking,” “mostly lacking,” “mostly proficient,”
and “proficient” for each of the 16 items. The first category, A, is labeled as teachers’ ability to use
ICT for class preparation, grading, and administrative tasks. Taking the average across A-1 to A-4
subcategories, the BoE average of teachers considered as proficient in skill A was 43.4% for2

Shingata Korona Uirusu Kansensho no Eikyo wo Fumaeta Gakushu Shido nado ni kan suru Jokyo
Chosa (MEXT)
3
Gakko ni okeru Kyoiku no Johoka ni kan suru Chosa (MEXT).

4

elementary schools and 46.9% for junior high schools. 1.2% of teachers in elementary and 1.8% injunior high schools were rated as lacking. Skill B is described as teachers’ ability to teach classes
using ICT. For elementary schools, 26.9% of teachers were considered proficient in skill B and 3.6%
lacking, with these numbers for junior high schools standing at 27.3% and 5.8% respectively. Skill C
is the ability to teach students to use ICT. 29.0% of elementary school teachers were rated proficientand 3.6% lacking in skill C, while 28.8% and 5.9% of junior high school teachers were rated as such.
Finally, skill D is described as the ability to instruct students in the knowledge and attitude needed toutilize information. For elementary school teachers, 36.1% were deemed proficient in skill D and1.6% lacking. For junior high school teachers, 34.3% were rated proficient and 2.6% lacking. As therelationship between underlying ability and the A-D categories and subcategories is unknown, we
use principal component analysis over all 16 items and schools, then separate by school level before
collapsing data to a BoE level to reveal the difference in overall IT skills for proficient and lacking
ranks. For the purposes of our analysis, we standardized the IT skill indices to have a mean of 0 anda standard deviation of 1. Table 1 contains the summary statistics of the variables discussed so far.
Next, to examine how ICT resources affected teacher’s workload during school closuresand in the months after reopening, we use data collected through the Survey on Reform of Working
Conditions in Schools by the Local Boards of Education4 for years 2019 and 2020.5 For each monthfrom April to August, MEXT asks BoEs to report the percentage of school staff in their respective
districts working 0 to 45, 45 to 80, 80 to 100, and over 100 overtime hours per month. The survey
does not distinguish between types of staff; however, as staff is predominantly made up of teachers,
we consider the data to be representative of overtime hours worked by teachers specifically. As
Japanese teachers are known to work long hours, we use the 2019 data to establish the baseline rateof overtime. From here, we summarize the categories provided into two variables, one showing thepercentage of teachers working over 45 hours of overtime and another over 80 hours of overtime, athreshold recognized by the Japanese Ministry of Health, Wealth and Labor (MHLW) as dangerous
to health. Table 2 provides the averages across BoEs for elementary and junior high school teachers.
In the 2019 April-June period, around 56% of elementary school teachers worked over 45
hours of overtime and 16% worked over 80 hours on average across BoEs. In the same period,
around 63% of junior high school teachers worked over 45 hours of overtime and 24% over 80 hours.
In July and August, months partially overlapping with the summer break, these numbers decreased
significantly. In April and May 2020, months during which mandatory school closures occurred, thepercentage of teachers working overtime was similar to the summer break period for both thresholds,
as all school activities were suspended. From July 2020, after schools reopened, the percentage ofteachers working overtime increased dramatically; however, they did not reach pre-pandemic levels,
likely because school activities remained limited to prevent infection. For all observed months andthresholds, junior high school teachers worked more overtime hours than elementary school teachers.
However, the atypical situation, particularly the school closures, likely prevented schools fromkeeping accurate overtime records. The actual amount of overtime hours, especially in 2020, waslikely much higher. Therefore, our results require a careful interpretation.
The 2019 round of the survey was its initial round and, while it inquired about several
months in 2018, data for the full April-August period is available only for 2019. Additionally, not all
BoEs provided data for each month, resulting in varying sample sizes, and fewer BoEs responded in2019 than 2020. To use all the available 2020 data, we use single imputation by simple average tofill in the missing 2019 data points.
3. Empirical strategy

4

Kyoiku Iinkai ni okeru Gakko no Hatarakikata no tame no Torikumi Jokyo Chosa (MEXT).
Unlike ICT survey, this survey is not legally mandated by MEXT, leading to a larger percentage of
missing data. The wording of the relevant question also assumes digital record keeping of working hours.
We find that relatively smaller municipalities are more likely to be missing; however, whether it affects
our estimates based on BoE difference is not clear.
55

When selecting our empirical strategy, we faced three potential issues in the causal
interpretation of the effect of ICT resources on remote education and teachers’ overtime hours. First,
schools in urban areas might be better equipped than schools in remote locations, both in terms of
ICT equipment and more skilled teachers. Urban areas were also likely harder hit by the pandemic,
possibly resulting in a higher demand for remote education and longer closures.6 The fixed effects
model at a BoE level allows us to address this concern, eliminating BoE-specific factors common toboth elementary and junior high schools, such as the socioeconomic standing of the municipality or
the degree of demand for remote education due to COVID-19. We also use a school-level dummy
variable to control for level-specific effects, such as parents of older students likely having a higher
income or higher demand for online education. The second concern is whether ICT resources are
exogenous to the pandemic. Because ICT resources were measured before school closures, we
believe this concern is negligible. Given the unexpected nature of the pandemic and the rigidities ofthe public school system, it is unlikely that more proactive BoEs at the time of the survey equippedschools with ICT resources in expectation of online education, which was first widely implemented
in Japan during the pandemic. Third, as discussed in Section 2, most of the data are aggregated at the
BoE level, the decision-making level of school-level resources allocation, making BoE the unit of
our analysis. It is therefore possible that our estimates suffer from bias due to classical measurement
error. We examine these concerns in Section 4.3.
The main underlying assumption of our analysis is that the BoE level fixed effects control
for BoE characteristics that affect the provision of ICT resources common to both school levels
within a single municipality, isolating the effects of the education supply side through the differencein ICT equipment and teachers’ IT skills between elementary and junior high schools. This strategy
is valid in identifying the effect of ICT resources on outcomes only if there is a sufficient withinmunicipality variation between the two school levels, and the within-municipality variations are notsystematically correlated with unobserved factors that potentially affect variation in remoteeducation and work hours of teachers. As presented in Table 1, the national average of somevariables does not have a substantial variation; however, averages obscure the distribution of thevariation. The within-municipality distribution reveals that the fixed effects strategy is suitable. Next,
although there is no perfect way to test the second condition, we use simple linear regression to
examine whether within-municipality differences in ICT equipment and teachers’ IT skills between
the two school levels are systematically associated with a set of municipality characteristics(population size and per capita income) and the number of COVID-19 cases in April 2020 in thecorresponding prefecture. We do not confirm systematic correlation, but we further examine thevalidity of our assumptions in the robustness check.
To answer the question of how ICT resources affected schools’ pandemic response, we
employ the following model, assuming an equal effect on both elementary and junior high schools:
𝑅𝑒𝑚𝑜𝑡𝑒 𝑒𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝛼𝛽 ∗ 𝐼𝐶𝑇 𝑒𝑞𝑢𝑖𝑝𝑚𝑒𝑛𝑡𝛾 ∗ 𝐼𝑇 𝑠𝑘𝑖𝑙𝑙𝑠

𝛿

𝜇𝐷

𝜖
(1)

where remote education stands for the length of school closures measured in school days anddummy variables indicating the implementation of live online classes and live online communication
with families in BoE 𝑖 and on school level 𝑗. The term 𝛿 is a BoE 𝑖 fixed effect, 𝐷 is a school level
dummy variable and error term 𝜖 is two-way clustered at BoE and prefectural level. This model
identifies the effect of the differences in schools’ ICT equipment and teachers’ IT skills between
elementary and junior high schools within a BoE district on a common outcome.
6

A major limitation of COVID-19 pandemic-related research in Japan is the lack of detailed
epidemiological data, as the Japanese government publishes the number of infections only on aprefectural level. While many municipalities disclose their data, these are typically metropolitan areas
covering only a portion of our sample. Using a BoE level fixed effect model allows us to work aroundthis problem and thus analyze the full national sample.

6

Expanding on the model in Equation (1) to capture the heterogeneous effect of the schoollevel 𝑗, we incorporate interaction terms as shown in Equation (2):𝑅𝑒𝑚𝑜𝑡𝑒 𝑒𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝛼𝜖𝛽 ∗ 𝐼𝐶𝑇 𝑒𝑞𝑢𝑖𝑝𝑚𝑒𝑛𝑡𝛾 ∗ 𝐼𝑇 𝑠𝑘𝑖𝑙𝑙𝑠𝛾 ∗ 𝐼𝑇 𝑠𝑘𝑖𝑙𝑙𝑠 ∗ 𝐷

𝛿

𝜇𝐷
(2)

where 𝛾 represents the baseline effect of IT skills and 𝛾 represents the effect of IT skills that are
additional to the school-level 𝑗.
Next, we analyze the impact of ICT resources on teachers’ overtime hours during theschool closures in April and May 2020 and after schools reopened for June-August 2020. In thisanalysis we focus on teachers’ skills only and utilize ICT equipment variables as controls as teachers
operate under set conditions. Equivalently to the previous analysis, we specify a baseline model (3)and a model incorporating interactions (4).
𝛼𝑂𝑣𝑒𝑟𝑡𝑖𝑚𝑒𝛽 ∗ 𝐼𝐶𝑇 𝑒𝑞𝑢𝑖𝑝𝑚𝑒𝑛𝑡
𝜎 ∗ 𝑂𝑣𝑒𝑟𝑡𝑖𝑚𝑒 𝑖𝑚𝑝𝑢𝑡𝑒𝑑𝛾 ∗ 𝐼𝑇 𝑠𝑘𝑖𝑙𝑙𝑠
𝛿 𝜇𝐷

𝜌 ∗ 𝑂𝑣𝑒𝑟𝑡𝑖𝑚𝑒𝜖(3)𝑂𝑣𝑒𝑟𝑡𝑖𝑚𝑒𝛼𝛽 ∗ 𝐼𝐶𝑇 𝑒𝑞𝑢𝑖𝑝𝑚𝑒𝑛𝑡𝛾 ∗ 𝐼𝑇 𝑠𝑘𝑖𝑙𝑙𝑠𝛾 ∗ 𝐼𝑇 𝑠𝑘𝑖𝑙𝑙𝑠 ∗ 𝐷
𝜌 ∗ 𝑂𝑣𝑒𝑟𝑡𝑖𝑚𝑒𝜎 ∗ 𝑂𝑣𝑒𝑟𝑡𝑖𝑚𝑒 𝑖𝑚𝑝𝑢𝑡𝑒𝑑
𝛿 𝜇𝐷
𝜖
(4)

is the percentage of teachers working over a specific
The outcome variable 𝑂𝑣𝑒𝑟𝑡𝑖𝑚𝑒
number of overtime hours in month 𝑘 (April-August) of year 𝑡 (𝑡 2020) in BoE 𝑖 and school type𝑗. To account for the seasonality in working conditions over a school year, we also include the prepandemic overtime baseline 𝑂𝑣𝑒𝑟𝑡𝑖𝑚𝑒. As some values of 𝑂𝑣𝑒𝑟𝑡𝑖𝑚𝑒
are imputed, we
also include a dummy variable 𝑂𝑣𝑒𝑟𝑡𝑖𝑚𝑒 𝑖𝑚𝑝𝑢𝑡𝑒𝑑
to account for the fact. The definition of
the remainder of the terms in Equations (3) and (4) is identical to those used in Equations (1) and (2).
All fixed effects models were estimated using Stata 17 xtreg command.
4. Results
4.1 Remote education
In Japan, where telework during the pandemic remained limited even for jobs easily
performed remotely, teachers generally did not work from home during the school closures, making
the analysis of at-school ICT resources relevant. Better school resources should generally contributeto children’s learning, here measured by the provision of remote education, as schools cannot utilize
a mode of remote education they are unable to provide.
However, our analysis focuses on the differences in the designated variables between school
types on the BoE unit of observation and thus does not determine which factors contributed to thepandemic response in case the response or the provision of ICT resources was identical for bothelementary and junior high schools within a municipality. We therefore find it instructive to startwith a naïve specification showing overall association before presenting the results from our main
specification. We display the results from Equation (1) omitting the BoE fixed effect 𝛿 in Table 3.
Examining the results without BoE fixed effect, teachers’ IT skills show the expected signs andsignificance. Better IT skills were linked to a higher likelihood of implementing remote education incolumns (3) and (5) with weak IT skills decreasing this likelihood (columns (4), (6)) while also
prolonging school closures (column (2)). High-speed internet was also associated with both longer
school closures and higher likelihood of remote education. Moreover, schools better supplied withhigh level ICT equipment such as presentation devices in normal classrooms, commercial digital
instructional materials for teachers and digital textbooks for students were more likely to adopt live
online tools to hold classes or communicate with students’ families. However, while omitting BoE
7

fixed effect allows us to benefit from the original variation across school types and BOEs, it does notconsider within-municipality unobserved local factors, making causal interpretation difficult. It is
possible that better equipped schools are located in more urban areas, which could be harder hit by
the pandemic resulting in a higher demand for remote education and longer closures. We thus for theremainder of this paper adopt the BoE fixed effects approach to eliminate common local factors,
allowing for causal interpretation. The following discussion is made in this context.
Results from Equation (1) including the BoE fixed effect 𝛿 are displayed in Table 4. As
seen in columns (1)-(2), we no longer confirm any consistent link between schools’ ICT resources
and the length of school closures. As schools were ordered to close, differences in school closure
length likely occurred toward the end. The positive and significant estimate of the elementary school
dummy suggests that other considerations were made, possibly related to students’ age.
Turning to live online classes and live online communication with students’ families, wefind no effect of teachers’ IT skills (proficiency or lack of skill) on live online classes within BOEs;
rather, physical ICT equipment seemed to have enabled schools to transition to online education.
The results in Table 4 columns (3)-(4) show that within BoEs, a higher percentage of schools
equipped with Wi-Fi led to a higher likelihood of live online classes; however, this effect issignificant only at a 10% level and not significant for live online communication with parents incolumns (5)-(6). Likewise, schools better equipped with presentation devices in regular classrooms
were at a 5% of significance more likely to provide live online classes to students within BOEs.
Regarding teaching resources, the availability of commercial digital instructional materials forteachers increased the likelihood of live online classes and, as expected, had no effect oncommunication with parents. These results should be viewed within the limitations of our study, yetthey paint a picture consistent with the way online education was typically depicted by the Japanese
media: one teacher per empty classroom using a combination of digital presentation tools and aphysical board in front of a camera.
Furthermore, utilizing Equation (2) to examine the impact of school type-specific teacher IT
skills we present the results in Table 5. We do not confirm any statistically significant effect ofschool type-specific IT skills on remote education outcomes. We also explore the complementarityof IT skills and ICT equipment by including their interactions in Equation (1) to examine the impact
on the probability of holding live online classes. We create two dummy variables each for both ICT
equipment confirmed relevant and IT skills, indicating a value above the sample mean and sample
median (a positive value for digital textbooks as the median value is 0) for each school type. The
results are displayed in Table 6. We do not confirm any significant effect of the interaction terms,
defined as the interactions of the original continuous variables, the two types of dummy variables, ortheir combinations. However, the variation in these dummy variables remaining in the fixed effects
model is small. Albeit limited, this result suggests that the null effect of IT skills on the likelihood of
online classes implementation is not caused by school-type specific factors or by a shortage of ICT
equipment, possibly preventing teachers from manifesting their IT skills.
To further our understanding of these results, we conducted a survey of teachers on a web
platform operated by a large Japanese education company. This platform7 aims to provide its 73,000
freely registered users with lesson resources and opportunities to exchange ideas. We collected
responses for one month in August 2022 receiving 461 valid answers. Limiting this sample to public
elementary and junior high school teachers, we arrived at a sample of 424. This sample consists of
83% elementary and 17% junior high school teachers, is 60% female, with approximately equal age
distribution from 20s to 50s. We believe this supplemental survey well reflects our research setting.
The survey results show that 32% of the sampled teachers conducted online education atleast once during the pandemic, with over 90% of them broadcasting lessons from school. This result
confirms that it is the school environment, not teachers’ home environment, that should be
7“Foresta Net” owned by Sprix, Ltd.
https://foresta.education/

8

considered in the analysis of remote education. Moreover, teachers with an experience of online
classes selected presentation devices (such as projectors, digital TV, or digital blackboard) as the
ICT equipment used to hold these classes, while only a small percentage responded that they used
PCs or tablets only. These findings are consistent with our results. Furthermore, 25% of the sampledteachers considered presentation devices the key to a future smooth implementation of online
education, in addition to basic ICT infrastructure. As this survey was conducted over two years after
the initial school closures, it might not accurately reflect the conditions at the time, overstating theextent of online education. However, these findings are in line with our results, and we believe theystrengthen our interpretation.
To summarize, during the early days of the COVID-19 pandemic, the differences in thepace at which schools reopened and in the provision of remote education within a BoE were likely
not related to teachers’ IT skills, defined as either common skill from Equation (1) or school levelspecific skill from Equation (2). Rather, ICT equipment essential for accessing the Internet in asocially distanced environment, a wireless network, and the tools necessary to hold an online class(digital materials for teachers and presentation devices capable of projecting digital materials)
seemed to be the factors that increased the supply of online classes. These results suggest, and a
supplemental teachers’ survey reinforces, that one obstacle to providing remote education during
school closures was a lack of basic ICT infrastructure on the part of schools. We also confirm thatusing live online tools for classes and communication with students’ families is different in nature,with the latter being less equipment dependent. However, considering the presence of measurement
error, these results are likely lower bounds of the actual effects.
4.2 Overwork
The expected impact of ICT resources on teachers’ work hours during and after school
closures is not theoretically clear. Better ICT resources might enable teachers to perform their jobs,
especially in cases where basic infrastructure is largely missing, possibly increasing work hours; yetteachers with better IT skills might be able to prepare for online classes more efficiently, possibly
reducing the time required. It should be noted that teachers with weak IT skills might be deficient inother skills as well, lowering the relevance of IT skills specifically and creating a need for a morecomprehensive approach. Further, considering the limitations of the data discussed in section 2, thefollowing results likely underrepresent the actual situation, suggesting that more accurate data would
likely produce more significant results.
The results using Equation (3) are displayed in Table 7. We divided the standardized
coefficient estimates of the impact of IT skill indices by the average ratio of teachers working overthe specific threshold of overtime hours in each month to arrive at the effect size, visualized on atimeline in Figure 1. Teacher proficiency in IT skills had only a limited impact on overtime hours interms of statistical significance controlling for the pre-pandemic amount of overtime. Proficiency,
however, decreased the percentage of teachers working overtime for nearly all months andthresholds with a notable exception of August 2020, a month typically reserved for a summer break.
Focusing on the significant effects, 1 standard deviation improvement in IT skill proficiency would
result in 14% decrease in teachers working over 80 overtime hours in May 2020 and 4.7% decreasein July 2020. Both months were transitional—from closures to in-person classes for May and tosummer break with supplementary classes for July—suggesting a beneficial role of IT skills when
adapting to a changing situation. However, the impact for April, the month with the most dramatictransition, is not statistically significant.
Conversely, the effect of the overall lack of IT skills was more pronounced after schools
reopened, especially in the over 80 hours of overtime per month category. During school closures, in
April 2020, the lack of IT skills at a 10% level of significance increased the percentage of teachers
working over 45 hours of overtime 3.7% per standard deviation, but had no impact on the percentageof teachers working over 80 overtime hours. For all months after reopening, the lack of IT skills had
a statistically significant impact on extreme overtime, increasing the percentage of teachers working
over 80 extra hours per month. The effect size of 1 standard deviation deterioration in IT skills stoodat 6.9% and 5.7% for June and July respectively and 24.1% for August 2020, confirming again how
unusual August 2020 was compared to the typical year. As the percentage of BoEs reporting9

implementation of live online classes during school closures stood at just 8–9%, working hours
during closures in most BoEs would likely not have been spent preparing or providing online
education, thus lowering the importance of IT skills as defined by MEXT. However, even in BoEs
that did not provide online classes, teachers were tasked with compensating for learning loss
incurred during closures when students returned to classrooms furnished to some degree with ICT
equipment, making IT skills pertinent. Regardless of significance, the direction of the effect of lackof skills is positive for all overtime thresholds and all months, meaning more teachers working
overtime.
Investigating the role of school level-specific IT skills using Equation (4), we again present
the results on a timeline in Figures 2 and 3 by school type. Equivalently to the preceding analysis,
the estimated impact of lack of skills is statistically significant in more instances than proficiency.
As an elementary school-specific effect, teachers being proficient in IT skills in general reduced
overtime at both thresholds, although the effects lack statistical significance. Conversely, a lack ofassociated IT skills significantly increased the percentage of teachers working over 45 hours in all
months except August and those working over 80 extra hours in April and May, likely reflecting thedifficulty of providing education to very young students during a sudden pandemic outbreak. For
junior high schools, proficiency showed a similar trend to elementary schools, decreasing work
hours but largely missing statistical significance. Compared to elementary schools, the significant
detrimental effect of lack of IT skills materialized later, as it was concentrated largely in the postclosure period, especially for the over 80 overtime hours category. This result is consistent with theabove interpretation that IT skills, especially the lack thereof, are more relevant when teachers
actually teach.
The teachers’ survey provides anecdotal evidence as to how the lack of IT skills affectedteachers’ overtime work. For example, after schools reopened, teachers were required to get
accustomed to providing online education utilizing existing ICT equipment to prepare for possible
subsequent closures. As this demand came shortly after reopening, when schools were expected to
conduct face-to-face classes comparable to the pre-pandemic period, it might have heavily alteredteachers working hours. Teachers also cited insufficient BoE provided IT training and a lack of IT
support, limited to only several days a month. Additionally, teachers were required to set up devices
newly provided to students, create their Wi-Fi profile settings or install required software, while
simultaneously following information security regulations, securing students’ privacy and teaching
students how to use ICT devices appropriately. These new duties were on top of the usual workload.
Therefore, especially in the months closely following the school closures, teachers likely spend moretime in schools to complete these tasks, and the lack of IT skills might have severely affected thetime required to do so.
Overall, our analysis, which likely underestimates the actual effects, suggests that overtime
hours are associated with a lack of teachers’ IT skills rather than proficiency, again demonstrating
the importance of removing supply side bottlenecks, with the supplemental teacher survey
supporting these findings. We also confirm a heterogeneous effect of IT skills based on school level
and timing, indicating the complex nature of the pandemic response. Nevertheless, it is important tostress that IT skills might be representative of a broader skill set, making a possible interventiondifficult to design. Additionally, our results suffer from limitations due to data restrictions and theconsequently adopted empirical strategy, and more research into the topic is needed to help alleviate
the burden teachers in Japanese schools face.
4.3 Robustness check
To address the potential concerns raised in Section 3 regarding the validity of our approach,
we run a series of checks to investigate the robustness of our results. This follow-up analysis, which
also uses the fixed effects framework, systematically confirms our results.
To start with, we investigate the first concern, that is, the validity of the BoE-level fixed
effects approach in addressing unobserved common local factors that simultaneously influence both
ICT resources and the schools’ response to the pandemic. We adopt two approaches—splitting thefull sample into subsamples of interest and adding interaction terms.
First, our BoE-level fixed effects approach assumes that the within-BoE difference in10

outcomes is uncorrelated with unobserved municipality-specific factors once the differences inschools’ ICT resources are controlled for. However, this assumption might not hold universally. One
possible case is the difference between urban and rural areas. The demand for ICT resources injunior high schools might be higher in urban areas as opposed to rural areas possibly due tocompetition with private junior high schools that are typically located in urban areas. Private schools
are generally better equipped than public schools, possibly skewing the local demand for ICT
resources or remote education at public schools in favor of one school level. To address this concern,
we divide the sample based on an urban or rural location and the presence of private junior highschools. First, an urban location indicator is assigned to BoEs in municipalities designated as cities
and city districts, and rural location is assigned to the remaining BoEs (towns, villages, and unionsof villages), thus roughly splitting the sample in half. Second, although the BoEs that house at least
one private junior high school, representing less than 20% of the sample, are typically urban, we
consider this analysis separately. For remote education outcomes, the results displayed in Tables A1
and A2 in Appendix closely resemble the main ones for all subsamples. The only exception is thenegative impact of presentation devices in regular classrooms on live online communication withparents in the subsample of BoEs with private junior high schools. However, considering the sample
size and the remainder of the results, we do not regard it as a concern. Regarding overtime analysis,
the results are presented in Tables A3-A6 in Appendix. We find that while the effects of bothproficiency and lack of IT skills largely retain their signs in all subsamples, the effect of lack ofskills was generally more pronounced in rural areas and correspondingly in areas without private
junior high schools.
Second, in all our models, in addition to a BoE 𝑖 fixed effect 𝛿 , we also include a schoollevel dummy variable 𝐷 . However, as the state of emergency was declared and ended on a perprefecture basis, the local response might not be identical across prefectures. If prefectures placed
varying weights on school levels in terms of prioritizing online education, the omitted heterogeneity
in between-school-level differences across prefectures would be correlated with the outcomes,
violating our assumption on the common school-level dummy. To filter this potential effect, we
include school-level and prefecture dummy interaction terms in Equations (1) and (3) for remoteeducation and overtime analysis, respectively. The addition of these interaction terms does not
qualitatively alter the results of either of these analyses, as presented in Tables A7 and A8 in
Appendix, supporting our assumption about 𝐷 . Although it is not possible to completely prove thevalidity of the BoE-level fixed effects approach, a series of robustness checks provided above
suggest that the potential concern with our approach largely does not seem to affect our main results.
Next, we focus on the second concern raised in Section 3—the potential endogeneity of
ICT resources. To assess the robustness of the link of ICT equipment to remote education outcomes,
we replaced the 2020 ICT equipment variables in Equation (1) with their 2019 levels.8 Possible
endogeneity of the 2020 level would likely create a positive bias in the effect of ICT equipment on
the use of remote education as a BoE with a higher unobserved forecasting ability would have in
expectation of the educational disruption equipped schools better by the survey date of March 1,
2020. The results presented in Table A9 in Appendix for the available variables are consistent withthe main results, supporting our interpretation that the challenge to implementing online education in
Japan was insufficient physical ICT equipment. Although neither the model specification nor thewithin-BoE difference is identical to the 2020 ICT equipment level analysis, we do not confirm asystematic positive bias. The effect of significant variables in the original analysis is larger for the2019 level of presentation devices, retaining statistical significance, and is smaller for digital
instructional materials for teachers, which is now not significant.
Regarding the analysis of overtime hours, we use the 2019 level of ICT equipment as
controls in Equation (3), obtaining practically identical results to the main ones, as displayed in
The 2018 school year wave of the Survey on ICT in School Education collected in March 2019 contains
most of the ICT variables utilized in the main analysis, except for the prevalence of Wi-Fi and digitaltextbooks for students.
8

11

Table A10 in Appendix. After controlling for 2019 ICT equipment, we find that the effect size of IT
skills is on average 0.35% points smaller than that for 2020 ICT equipment controls, having
unchanged direction and one marginal result losing significance. These results suggest that theconcern about the possible endogeneity of 2020 ICT equipment is unfounded.
Additionally, for overtime analysis, we looked further into the past, setting 2019 overtime
as the outcome and 2018 overtime as the baseline in Equation (3). Schools with under normal
circumstances chronically overworked teachers could have better ICT resources to help with theworkload, possibly creating a counteractive correlation in the effects of interest. If work hours during
the pandemic were subject to a large unexpected exogenous demand shock with the school ICT
resources fixed, we expect the effect of teachers’ IT skills on overtime to be generally larger thanthat in normal times. The 2018 overtime data were collected retrospectively in the 2019 wave of theSurvey on Reform of Working Conditions in Schools by the Local Boards of Education; therefore,
they cover a shorter period (April–June) and a larger percentage is missing. In this restricted analysis,
as presented in Table A11 in Appendix, the 2019 school year IT skill index had a minimal impact onovertime hours, controlling for the 2019 school year level of ICT equipment. IT skill proficiency
lowered the percentage of teachers working extreme overtime in April 2019, and the lack of skills
had no effect. These results indicate that the 2019 estimated effect size is slightly larger than that of2020 only for April and IT skill proficiency. Moreover, for 9 out of the 12 coefficient estimates, bothlevels of IT skills changed the ratio of teachers working overtime by less than 1%. These results lend
support to our assumption of the exogeneity of the 2020 level of ICT resources in the main analysis.
Furthermore, they strengthen our interpretation of the main findings that IT skill proficiency isadvantageous in transitional months (school year beginning), and a lack of IT skills worsens teachers’
working conditions in times of crisis.
Finally, as we raised as a third concern in Section 3, one of the major limitations of ouranalysis is the use of data aggregated at a municipality level. As our implicit decision model is based
at school and teacher levels, using municipality-level data likely leads to a typical classicalmeasurement error bias. To gauge the extent of this bias in our main results, we run the analysis
using Equations (1) and (3) on a subsample of municipalities containing a single junior high school.
The results presented in Tables A12 and A13 in Appendix are similar to those of the main ones.
Focusing on estimation coefficients of the variables statistically significant for the full sample, wefind that in the restricted sample, the effect sizes are generally larger by factors of 1.1–2. While thecharacteristics of these small BoEs might also be reflected in the effect size differences, we confirm
the presence of measurement error bias in the main results, which is as expected. Although the extent
of the influence of this bias seems relatively minor, our estimation results should be interpreted as
the lower bounds of the true effects in terms of the size of the effect. It should be noted that notfinding a statistically significant effect of an ICT resource might not mean that this resource isineffective in “absolute” terms, as our results provide guidance on “relative” importance in thepresence of measurement error bias. Our results suggest that among the broad range of ICT
resources, teachers’ IT skills are relatively less important than ICT equipment in enabling remoteeducation provision, while the former is important for reducing working overtime.
In summary, we address the potential concerns related to our analysis and the validity of
our results by conducting a series of robustness checks utilizing several approaches. We confirm that
our main results are robust.
5. Conclusion
The COVID-19 pandemic forced schools in many countries, including Japan, to close,turning to online education. Many studies analyzing school closures have focused on the demandside, as investigating the impact of the supply side on the provision of remote education ischallenging due to the lack of appropriate data and the difficulty of separating the effects of the
supply and demand sides.
The percentage of Japanese boards of education reporting the implementation of online
classes during school closures in their municipality stood at less than 10% nationwide. The aim of
this paper is to, using a municipality-level fixed effects model, empirically isolate the effect of the
supply side, specifically schools’ ICT resources determined at the BoE level, on the provision of
12online education and teachers’ work hours using a dataset combining several sets of governmental
data collected in 2020 from the entire public school system. The unique point of our analysis
compared to the currently available literature is that we simultaneously investigate the effect ofschool ICT resources on both the provision of online education and the choice of communication
channel with students’ families while also examining the working conditions of teachers, who
generally continued coming to school during and after the closures, using nationwide administrative
data from Japanese public elementary and junior high schools on a municipality level. Under theassumptions discussed in section 3, our results allow for causal interpretation of the effect of ICT
resources on both students and teachers.
We find no significant effect of teachers’ IT skills, whether proficient or lacking, on theprovision of live online classes. Rather, physical ICT equipment, such as Wi-Fi access, presentation
devices in regular classrooms, and commercial digital instructional materials for teachers, seemed tohave enabled schools to transition to online education, suggesting that there was a technologicalbottleneck to implementing online education in Japan. We also confirm that live online
communication with students’ families is less equipment dependent than online classes. Moreover,
we find that teachers’ extra work hours are associated with a lack of IT skills, while the beneficial
effect of IT skill proficiency is weak. Additionally, we identify a heterogeneous effect of IT skills onteachers’ overtime by school level and timing, likely reflecting the complex nature of the pandemic
response. A supplemental survey of public elementary and junior high school teachers lends support
to our results.
These results suggest that the impact of different ICT resources differs between students
and teachers. The obstacle to implementing online education in Japan on the supply side was schools’
inadequate basic ICT equipment, determined at a municipal level, not teachers’ IT skills, developed
at an individual level. However, the persistent overwork of schoolteachers in Japan may partially bea consequence of a lack of IT skills. Therefore, the effect of ICT resources is multi-dimensional, an
important point to consider when drafting a relevant policy.
However, our results have several limitations due to data availability and structure limiting
our empirical strategy options. Although we used a wide array of variables assessing schools’ ICT
equipment and teachers’ IT skills collected in a governmental survey, it is possible that other school
resources and teachers’ skills that are correlated but not directly measured may also be relevant indetermining remote education practices and teachers’ work hours. Likewise, it is possible that somemeasures that do not vary much between school levels within a municipality and would thus not be
considered in our analysis may affect both outcomes. It is also clear that our results are specific tothe context of the public school system during the early stages of the COVID-19 pandemic in Japan,
which lagged in ICT use for education. More research is needed to generalize our findings to abroader context of other societies and circumstances.
Acknowledgement
We thank the Japanese Ministry of Education, Culture, Sports, Science and Technology(MEXT) for providing us the data from “Survey Results on ICT in School Education” and “Survey
on Reform of Working Conditions in Schools by the Local Boards of Education.” We also thank theparticipants at various seminars and academic conferences for their comments. This work was
supported by KAKENHI Grant Number 21H04982 and 16H06323 from the Japan Society for the
Promotion of Science, Mitsubishi Zaidan, and Keio Gijuku Academic Development Funds.
Declarations of interest
None.

13

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Table 1: Summary statistics–remote education practices and ICT resources

Variable

ELEMENTARY SCHOOL

Mean
School days closed (N = 1711) June 2020
Total days closed 24.570

JUNIOR HIGH SCHOOL

SD

Min

Max

Mean

SD

Min

Max

10.304061

24.46610.308

0

61

0.089

0.285

0

1

0.103

0.304

0

1

Remote education practices during school closures (N = 1693) June 2020
0.081
0.273
0
1
Live online class
Live online communication with
0.094
0.292
0
1
familyIT skills (N=1711) March 2020
IT skill index (Proficient)
IT skill index (Lacking)-0.016-0.200

0.975
0.757

-2.217-2.049

4.388
7.153

0.016
0.200

1.025
1.160

-2.217-10.226

4.388
9.526

ICT resources (N=1711) March 2020
High-speed internet
Wi-Fi
Presentation device
Digital instructions
Digital textbook
Management software
Security policy

0.674
0.836
0.958
0.574
0.095
0.765
0.657

0.431
0.336
0.425
0.447
0.230
0.392
0.431

0.0
0.0
0.0
0.0
0.0
0.0
0.0

1.0
1.0
3.0
1.0
1.0
1.0
1.0

0.682
0.835
0.745
0.643
0.100
0.771
0.662

0.444
0.348
0.506
0.440
0.257
0.399
0.449

0.0
0.0
0.0
0.0
0.0
0.0
0.0

1.0
1.0
5.0
1.0
1.0
1.0
1.0

16

Table 2: Summary statistics–ratio of staff working over 45 or 80 overtime hours a month

ELEMENTARY SCHOOL
Month

N

Mean

SD

min

max

JUNIOR HIGH SCHOOL
N

Mean

SD

min

max

> 45 HOURS

Year
2020
April
May
June
July
August
Year
2019
April
May
June
July
August

1,220
1,227
1,280
1,268
1,203

0.182
0.115
0.471
0.421
0.072

0.197
0.174
0.214
0.224
0.138

0.0
0.0
0.0
0.0
0.0

1.0
1.0
1.0
1.0
1.0

1,220
1,227
1,280
1,268
1,203

0.186
0.131
0.574
0.583
0.205

0.219
0.206
0.229
0.228
0.202

0.0
0.0
0.0
0.0
0.0

1.0
1.0
1.0
1.0
1.0

1,220
1,227
1,280
1,268
1,203

0.546
0.545
0.558
0.390
0.041

0.180
0.181
0.188
0.205
0.093

0.0
0.0
0.0
0.0
0.0

1.0
1.0
1.0
1.0
1.0

1,220
1,227
1,280
1,268
1,203

0.644
0.642
0.650
0.531
0.109

0.165
0.165
0.172
0.203
0.154

0.0
0.0
0.0
0.0
0.0

1.0
1.0
1.0
1.0
1.0

> 80 HOURS

Year
2020
April
May
June
July
August
Year
2019
April
May
June
July
August

1,220
1,227
1,280
1,268
1,203

0.028
0.013
0.084
0.066
0.007

0.097
0.077
0.131
0.129
0.059

0.0
0.0
0.0
0.0
0.0

1.0
1.0
1.0
1.0
1.0

1,220
1,227
1,280
1,268
1,203

0.034
0.018
0.158
0.183
0.027

0.107
0.083
0.168
0.180
0.072

0.0
0.0
0.0
0.0
0.0

1.0
1.0
1.0
1.0
1.0

1,220
1,227
1,280
1,268
1,203

0.149
0.138
0.158
0.076
0.009

0.165
0.164
0.189
0.149
0.051

0.0
0.0
0.0
0.0
0.0

1.0
1.0
1.0
1.0
1.0

1,220
1,227
1,280
1,268
1,203

0.255
0.250
0.259
0.166
0.026

0.193
0.192
0.211
0.182
0.103

0.0
0.0
0.0
0.0
0.0

1.0
1.0
1.0
1.0
1.0

17

Table 3: Effect of ICT resources on remote education–linear regression

Dependent Variable
Explanatory Variables
Elementary school

School days
closedLive online class

Live online
communication

(1)

(2)

(3)

(4)

(5)

(6)

0.258

0.808***-0.0124-0.0183**-0.0100-0.0153*(0.220)(0.298)(0.00857)(0.00796)(0.00847)(0.00869)

Teachers' IT Skill
IT skill index – Proficient-0.3890.0107*0.0155***(0.453)(0.00565)(0.00566)

IT skill index - Lacking

1.444***-0.0136***-0.0104**(0.393)(0.00364)(0.00492)

School ICT Equipment
High-speed internet 3.051*** 2.856***
Wi-Fi
Presentation device
Digital instructions
Digital textbook

0.0228**0.0241**0.0263*0.0267*(0.823)(0.790)(0.0112)(0.0113)(0.0138)(0.0143)-0.580-0.2890.02350.02230.01520.0157(1.127)(1.072)(0.0168)(0.0172)(0.0197)(0.0202)-0.528-0.3200.0307*0.0310*0.02060.0231(0.983)(0.967)(0.0167)(0.0170)(0.0188)(0.0191)

0.349

0.430

0.02150.02030.0304*0.0290*(0.844)(0.837)(0.0149)(0.0148)(0.0152)(0.0155)-1.791-1.8440.0431*

0.0437*0.0441*

0.0447*(1.204)(1.190)(0.0242)(0.0241)(0.0256)(0.0254)

Management software 3.317*** 3.249***

0.002170.001290.000285-0.00191(1.095)(0.0180)(0.0179)(0.0176)(0.0172)

Security policy -1.360** -1.229**

0.01290.01220.0226*0.0223*(0.576)(0.0127)(0.0126)(0.0120)(0.0123)

Constant 21.57*** 20.92***

0.00236

0.007130.01720.0197(2.367)(2.349)(0.0243)(0.0246)(0.0204)(0.0210)BoE FE

No

No

No

No

No

No

Observations

3,422

3,422

3,386

3,386

3,386

3,386

R-squared

0.045

0.062

0.013

0.013

0.013

0.012

(1.146)(0.607)

Notes: Estimation results from linear regression. Robust standard errors clustered at municipality and prefecturelevel in parentheses. *** p<0.01, ** p<0.05, * p<0.1

18

Table 4: Effect of ICT resources on remote education–linear fixed effects model

Live online class

Live online
communication

(3)

(4)

(5)

0.102** 0.0942**-0.0110-0.0118-0.00982-0.00869(0.0384)(0.00834) (0.00862)(0.00710)(0.00735)

0.004610.000248

0.00380(0.0162)(0.00494)(0.00454)

Dependent Variable School days closed
Explanatory Variables

Elementary school

(1)

(2)

(0.0399)(6)

Teachers' IT Skill
IT skill index - Proficient
IT skill index - Lacking-0.0188-0.00219

0.00373(0.0311)(0.00336)(0.00440)

School ICT Equipment
High-speed internet
Wi-Fi
Presentation device
Digital instructions
Digital textbook
Management software
Security policy
Constant-0.179*-0.175-0.0205-0.0202-0.00964-0.0102(0.106)(0.106)(0.0250)(0.0249)(0.0295)(0.0291)-0.0427-0.04220.0294*0.0295*0.02770.0276(0.0801)(0.0795)(0.0161)(0.0160)(0.0169)(0.0171)

0.01330.01120.0209**0.0206**0.006230.00739(0.0410)(0.0403)(0.00954) (0.00898)(0.00951)(0.00908)

0.00673

0.00469

0.0312*0.0310*0.00549

0.00579(0.109)(0.110)(0.0155)(0.0155)(0.0136)(0.0137)

0.105

0.104

-0.0220-0.0220-0.0185-0.0192(0.182)(0.182)(0.0252)(0.0252)(0.0215)(0.0214)0.08890.0848-0.0102-0.0106-0.0170-0.0162(0.0595)(0.0605)(0.0328)(0.0330)(0.0335)(0.0341)-0.0814-0.0810-0.0117-0.01170.03490.0358(0.102)(0.101)(0.0302)(0.0304)(0.0303)(0.0307)24.58*** 24.59***

0.06080.0616

0.0698*0.0674*(0.143)(0.142)(0.0369)(0.0378)(0.0353)(0.0364)

BoE FE
Observations

Yes

Yes

Yes

Yes

Yes

Yes

3,422

3,422

3,386

3,386

3,386

3,386

R-squared

0.007

0.007

0.008

0.008

0.005

0.005

Notes: Estimation results from linear fixed effect model at BoE (municipality) level. Robust standard errors
clustered at municipality and prefecture level in parentheses. *** p<0.01, ** p<0.05, * p<0.1

19

Table 5: Effect of teachers’ IT skills on remote education–linear fixed effects model withinteractions

Dependent Variable School days
closedTeachers' IT skills

Live online
class

Live online
communication-0.0191-0.00590-0.00506(0.0301)(0.00866)(0.00467)

0.02460.00536

0.0112(0.0326)(0.00548)(0.00684)

0.04500.005390.0105(0.0375)(0.00739)(0.00701)-0.0367-0.00438

0.00177(0.0351)(0.00372)(0.00449)

Proficient
Elementary γ0Junior high γ0 + γ1
LackingElementary γ0Junior high γ0 + γ1

Notes: Coefficient estimates from Equation (3) with j = 0 for elementary school and j =1 for junior secondary school. The estimated coefficient on junior high school, γ0 + γ1
is produced using Stata command "lincom" for the respective linear combination ofcoefficients. Robust standard errors clustered at municipality and prefecture level inparentheses. *** p<0.01, ** p<0.05, * p<0.1

20

Table 6: Effect of ICT resources on remote education–linear fixed effects model with interactions

Dependent Variable

Live online class
Skill continuous
ICT continuous(1)(2)

Explanatory Variables

Skill continuous
ICT mean(3)(4)

Skill mean
ICT continuous(5)(6)

Skill mean
ICT mean(7)(8)

Skill continuous
ICT median
(9)
(10)

Skill median
ICT continuous
(11)
(12)

Skill median
ICT median
(13)
(14)

Teachers' IT Skill
IT skill index - Proficient -0.00176

0.001620.003490.004810.00237

0.00388

0.0134(0.0140)(0.00994)(0.0345)(0.0231)(0.00901)(0.0323)(0.0190)

IT skill index - Lacking

0.00146

0.00348-0.00878-0.00354-0.00130-0.00369-0.0137(0.00945)(0.00841)(0.0228)(0.0198)(0.00818)(0.0240)(0.0185)

School ICT Equipment
Wi-Fi
Wi-Fi * IT skill index Proficient

0.0292*0.0301*0.01370.01430.03030.02580.01330.01090.01570.01650.03440.0319*0.02250.0187(0.0169)(0.0162)(0.0179)(0.0175)(0.0201)(0.0177)(0.0214)(0.0192)(0.0157)(0.0154)(0.0216)(0.0170)(0.0198)(0.0167)0.00142-0.00340-0.000432-0.000321-0.00938-0.0139-0.000537(0.00698)(0.0179)(0.0206)(0.00700)(0.0152)(0.0165)

0.00256(0.00750)

Wi-Fi * IT skill index - Lacking(0.00584)

0.00695-0.000459-0.00269(0.0194)(0.00704)(0.0128)-0.00246(0.0156)

0.0207**0.00459

0.00491

0.02230.0162*0.009240.000490-0.000795 -0.000797

0.01440.0178*-0.00746-0.0125(0.00994)(0.00918)(0.00917) (0.00907)(0.0176)(0.00870)(0.0143)(0.00898)(0.0102)(0.0191)(0.00904)(0.0155)(0.0131)-0.00619-0.00377-0.0110-0.00403

0.01280.0133(0.00831)(0.0285)(0.0186)(0.00746)(0.0250)(0.0176)(0.0110)Presentation device * IT skill
index - Lacking
Digital instructions
Digital instructions * IT skill
index - Proficient(0.0101)

0.00306

0.00246

0.01170.01090.008540.005380.0240(0.00737)(0.00817)(0.0161)(0.0158)(0.00730)(0.0191)(0.0202)0.0311*0.0309*0.01670.01650.0322**0.0376*0.01850.02430.0234*0.0229*0.0382**0.0415*0.0336**

0.0332*(0.0164)(0.0161)(0.0121)(0.0118)(0.0144)(0.0206)(0.0113)(0.0159)(0.0131)(0.0130)(0.0150)(0.0216)(0.0150)(0.0176)0.00768

0.00342-0.00265-0.00396

0.00288-0.0197(0.0115)(0.0110)(0.0229)(0.0192)(0.0114)(0.0212)

Digital instructions * IT skill
index - Lacking

R-squared

0.00816(0.0162)

Presentation device 0.0211**
Presentation device * IT skill -0.00468
index - Proficient

Observations-0.00350(0.00945)

3,386-0.00948-0.0125-0.0165-0.0184-0.00969-0.0152-0.0235-0.0234(0.00781)(0.00949)(0.0181)(0.0164)(0.00689)(0.0235)(0.0198)(0.0184)

3,386

3,386

3,386

3,386

3,386

3,386

3,386

3,386

3,386

3,386

3,386

0.009
0.009
0.005
0.005
0.008
0.009
0.005
0.005
0.005
0.006
0.009
0.011
Notes: Estimation results based on fixed effect model at BoE (municipality) level. Robust standard errors clustered at municipality and prefecture level in parentheses. *** p<0.01, ** p<0.05, * p<0.1

3,386

3,386

0.006

0.009

21

Table 7: Linear fixed effects model–teachers’ overtime hours

PANEL A

April 2020

Overtime threshold

45
(1)

80
(2)

45
(3)

80
(4)

45
(5)

80
(6)

45
(7)

80
(8)

45
(9)

80
(10)

Teacher ratio

0.184

0.031

0.123

0.015

0.522

0.121

0.502

0.125

0.138

0.017

-0.000446(0.00421)-0.24%
2,440
0.035-0.00184(0.00185)-5.91%
2,440
0.017

0.000215(0.00451)
0.17%
2,454
0.059

-0.00233**(0.00105)-15.23%
2,454
0.030

-0.00552(0.00512)-1.06%
2,560
0.332-0.00294(0.00385)-2.43%
2,560
0.320-0.00447(0.00549)-0.89%
2,536
0.534-0.00592*(0.00344)-4.75%
2,536
0.525

0.00272(0.00448)
1.96%
2,406
0.462

0.000855(0.00147)
5.05%
2,406
0.154

IT skill index
Proficient
Effect size
Observations
R-squared

PANEL B

April 2020

May 2020

May 2020

June 2020

June 2020

July 2020

July 2020

August 2020

August 2020

Overtime threshold

45
(11)

80
(12)

45
(13)

80
(14)

45
(15)

80
(16)

45
(17)

80
(18)

45
(19)

80
(20)

Teacher ratio

0.184

0.031

0.123

0.015

0.522

0.121

0.502

0.125

0.138

0.017

IT skill index
Lacking
Effect size
Observations
R-squared

0.00691*(0.00370)
3.75%
2,440
0.038

0.000571(0.00138)
1.83%
2,440
0.016

0.00364(0.00338)
2.96%
2,454
0.061

0.00122(0.000928)
7.97%
2,454
0.027

0.00337(0.00443)
0.65%
2,560
0.332

0.00834**(0.00355)
6.90%
2,560
0.324

0.00401(0.00456)
0.80%
2,536
0.534

0.00714**(0.00281)
5.73%
2,536
0.526

0.00880*(0.00472)
6.36%
2,406
0.464

0.00470*(0.00260)
27.75%
2,406
0.162

Notes: Estimation results from linear fixed effect model at BoE (municipality) level. The outcome variable is the ratio of teachers working over a given overtime hours
threshold in each month. Teacher ratio shows the mean of the outcome variable. IT skill index standardized to mean 0. Other control variables are "High-speed internet," "WiFi," "Projector," "Digital instructions," "Digital textbook," "Management software," "Security policy" and the lag of the outcome variable with a dummy variable indicating
imputed value. Robust standard errors clustered at municipality prefecture and prefecture level in parentheses. *** p<0.01, ** p<0.05, * p<0.1

22

Figure 1: Effect of teachers’ IT skills on overtime–elementary school

23

Figure 2: Effect of teachers’ IT skills on overtime–elementary school

24

Figure 3: Effect of teachers’ IT skills on overtime–junior high school25Appendix A: Additional Tables
Table A1: Effect of ICT resources on remote education–urban and rural subsamples

URBAN
Dependent Variable School days closed
Explanatory Variables
Elementary school
Teachers' IT Skill
IT skill index - Proficient

(1)

(2)

0.0505(0.0315)

0.0272(0.0358)

Wi-Fi
Presentation device

(3)

(4)

0.002890.00580(0.00828) (0.00959)

Live online
communication

School days closed

Live online class

Live online
communication
(11)

(5)

(6)

(7)

(8)

(9)

(10)

0.00371(0.00762)

0.00812(0.00904)

0.128**(0.0583)

0.123**(0.0582)-0.0220**(0.0101)-0.0233**(0.0101)(12)-0.0197** -0.0192**(0.00842) (0.00832)

0.0320-0.00913-0.01100.000910

0.00229

0.00674(0.0518)(0.0125)(0.0121)(0.0248)(0.00471)(0.00480)

IT skill index - Lacking
School ICT Equipment
High-speed internet

Live online class

RURAL-0.04930.00565

0.00883-0.0164-0.00340

0.00322(0.0576)(0.00685)(0.00836)(0.0396)(0.00381)(0.00460)-0.363-0.3840.003110.00620-0.00603-0.00147-0.174-0.170-0.0229-0.0220-0.00993-0.0103(0.319)(0.322)(0.0419)(0.0418)(0.0428)(0.0433)(0.126)(0.126)(0.0277)(0.0276)(0.0325)(0.0318)-0.245*-0.266*0.03020.03190.0613*0.0644*0.01940.02280.03090.03150.01790.0168(0.145)(0.143)(0.0359)(0.0379)(0.0312)(0.0329)(0.0868)(0.0859)(0.0192)(0.0191)(0.0220)(0.0223)

0.0992

0.09250.01820.0191-0.0118-0.0105-0.0212-0.0235

0.0216**0.0216**0.01280.0148(0.0104)(0.0894)(0.0900)(0.0156)(0.0151)(0.0167)(0.0166)(0.0496)(0.0481)(0.0107)(0.0103)(0.0108)

Digital instructions-0.247-0.2600.0666*** 0.0678***0.01740.0195

0.130

0.130

0.01610.0158-0.000583 -0.000795(0.229)(0.243)(0.0243)(0.0242)(0.0362)(0.0359)(0.116)(0.116)(0.0164)(0.0164)(0.0106)

Digital textbook-0.0423-0.0402-0.0585-0.0579

0.00480

0.00524

0.123

0.122

-0.0162-0.0166-0.0196-0.0207(0.285)(0.291)(0.0613)(0.0612)(0.0722)(0.0722)(0.202)(0.201)(0.0273)(0.0271)(0.0219)(0.0217)

0.334

0.329

0.00698

0.008100.009170.01040.07100.0671-0.0152-0.0160-0.0212-0.0203(0.230)(0.227)(0.100)(0.0991)(0.108)(0.107)(0.0631)(0.0646)(0.0406)(0.0411)(0.0384)(0.0391)

Management software
Security policy
Observations
R-squared(0.0112)0.07830.09280.03180.02830.05380.0494-0.112-0.112-0.0150-0.01470.03420.0358(0.210)(0.210)(0.0337)(0.0327)(0.0386)(0.0383)(0.109)(0.107)(0.0326)(0.0330)(0.0331)(0.0340)

1,628

1,628

1,614

1,614

1,614

1,614

1,794

1,794

1,772

1,772

1,772

1,772

0.014

0.014

0.012

0.011

0.005

0.005

0.008

0.009

0.014

0.014

0.013

0.012

Notes: Estimation results from fixed effect model at BoE (municipality) level. Robust standard errors clustered at municipality and prefecture level in parentheses. *** p<0.01, ** p<0.05, * p<0.1

26

Table A2: Effect of ICT resources on remote education–subsamples with and without private junior high school

PRIVATE JUNIOR HIGH SCHOOL
Dependent Variable School days closed
Explanatory Variables
Elementary school
Teachers' IT Skill
IT skill index - Proficient

Wi-Fi
Presentation device
Digital instructions
Digital textbook
Management software
Security policy
Observations
R-squared

Live online
communication

School days closed

(1)

(2)

(3)

(4)

(5)

(6)

(7)

0.0643(0.0472)

0.0431(0.0480)

0.00225(0.0110)0.00118(0.0109)0.0102(0.00958)

0.00826(0.00924)

0.108**(0.0416)(8)

Live online class
(9)

(10)

0.101** -0.0129 -0.0137(0.0424) (0.00998) (0.0102)

Live online
communication
(11)

(12)

-0.0132(0.00820)-0.0119(0.00846)

0.0986-0.0122-0.00560-0.000107

0.000721

0.00411(0.0603)(0.0160)(0.0213)(0.0176)(0.00538)(0.00457)

IT skill index - Lacking
School ICT Equipment
High-speed internet

Live online classNO PRIVATE JUNIOR HIGH SCHOOL-0.0331-0.00314-0.00421-0.0206-0.00198

0.00468(0.0442)(0.00528)(0.0115)(0.0331)(0.00351)(0.00462)-0.927-0.9020.01030.00198

0.05750.0517-0.164-0.160-0.0219-0.0215-0.0122-0.0130(0.975)(0.987)(0.0254)(0.0256)(0.0766)(0.0760)(0.114)(0.115)(0.0257) (0.0255)(0.0304)(0.0298)-0.170-0.1710.01910.01950.04850.0488-0.0327-0.03220.0310*0.0311*0.02650.0262(0.201)(0.202)(0.0235)(0.0246)(0.0299)(0.0302)(0.0843)(0.0838)(0.0173) (0.0172)(0.0188)(0.0189)-0.194-0.190-0.00711-0.00853-0.0344**-0.0354**0.03550.03240.0250** 0.0248**

0.01210.0135(0.178)(0.182)(0.0182)(0.0190)(0.0161)(0.0163)(0.0471)(0.0458)(0.0105) (0.00987)(0.0103)(0.00971)-0.191-0.1850.0981*

0.0959*0.04820.0466

0.03390.03200.02350.02330.00100

0.00130(0.206)(0.209)(0.0524)(0.0524)(0.0846)(0.0844)(0.107)(0.108)(0.0154) (0.0154)(0.0131)(0.0132)-0.380-0.439-0.00144

0.005480.01190.0149

0.128

0.128

-0.0253-0.0254-0.0214-0.0221(0.527)(0.541)(0.0536)(0.0525)(0.0777)(0.0814)(0.190)(0.189)(0.0264) (0.0263)(0.0219)(0.0217)

0.179

0.163

-0.0362-0.0372-0.112-0.1130.08790.0834-0.0104-0.0108-0.0130-0.0120(0.225)(0.201)(0.0256)(0.0245)(0.0953)(0.0952)(0.0622)(0.0631)(0.0343) (0.0346)(0.0339)(0.0345)

0.878

0.950

0.03780.03820.05930.0631-0.0944-0.0954-0.0123-0.01220.03440.0355(0.854)(0.862)(0.0490)(0.0524)(0.0793)(0.0923)(0.103)(0.103)(0.0309) (0.0311)(0.0310)(0.0313)

588

588

588

588

588

588

2,834

2,834

2,798

2,798

2,798

2,798

0.028

0.026

0.026

0.024

0.012

0.012

0.008

0.008

0.009

0.009

0.007

0.007

Notes: Estimation results from fixed effect model at BoE (municipality) level. Robust standard errors clustered at municipality and prefecture level in parentheses. *** p<0.01, ** p<0.05, * p<0.1

27

Table A3: Teachers’ overtime hours–urban subsample
URBAN
PANEL A

April 2020

May 2020

June 2020

July 2020

August 2020

Overtime threshold

45
(1)

80
(2)

45
(3)

80
(4)

45
(5)

80
(6)

45
(7)

80
(8)

45
(9)

80
(10)

Teacher ratio

0.181

0.038

0.122

0.020

0.542

0.135

0.536

0.145

0.154

0.024

-0.00201(0.00575)-1.11%
1,314
0.066-0.000847(0.00159)-2.25%
1,314
0.043-0.00209(0.00548)-1.72%
1,318
0.076-0.00301**(0.00136)-14.74%
1,318
0.044

-0.00750(0.0117)-1.38%
1,376
0.419-0.00639(0.00685)-4.73%
1,376
0.424-0.00920(0.0109)-1.72%
1,352
0.672-0.000485(0.00767)-0.33%
1,352
0.652

0.000823(0.00783)
0.54%
1,270
0.571

0.00203(0.00253)
8.37%
1,270
0.251

IT skill index
Proficient
Effect size
Observations
R-squared
PANEL B

April 2020

May 2020

June 2020

July 2020

August 2020

Overtime threshold

45
(11)

80
(12)

45
(13)

80
(14)

45
(15)

80
(16)

45
(17)

80
(18)

45
(19)

80
(20)

Teacher ratio

0.181

0.038

0.122

0.020

0.542

0.135

0.536

0.145

0.154

0.024

0.00320(0.00409)
1.77%
1,314
0.067

0.000432(0.00212)
1.15%
1,314
0.043

-0.00504(0.00565)-4.14%
1,318
0.078

1.52e-05(0.00114)
0.07%
1,318
0.037

0.00476(0.00706)
0.88%
1,376
0.419

0.00684(0.00550)
5.06%
1,376
0.424

0.00598(0.00787)
1.12%
1,352
0.672

0.00756(0.00641)
5.20%
1,352
0.653

0.00720(0.0105)
4.68%
1,270
0.572

0.00343(0.00460)
14.15%
1,270
0.253

IT skill index
Lacking
Effect size
Observations
R-squared

Notes: Estimation results from linear fixed effect model at BoE (municipality) level. The outcome variable is the ratio of teachers working over a given overtime hours
threshold in each month. Teacher ratio shows the mean of the outcome variable. IT skill index standardized to mean 0. Other control variables are "High-speed internet,""Wi-Fi," "Projector," "Digital instructions," "Digital textbook," "Management software," "Security policy" and the lag of the outcome variable with a dummy variableindicating imputed value. Robust standard errors clustered at municipality prefecture and prefecture level in parentheses. *** p<0.01, ** p<0.05, * p<0.1

28

Table A4: Teachers’ overtime hours–rural subsample
RURAL
PANEL A

April 2020

May 2020

June 2020

July 2020

August 2020

Overtime threshold

45
(1)

80
(2)

45
(3)

80
(4)

45
(5)

80
(6)

45
(7)

80
(8)

45
(9)

80
(10)

Teacher ratio

0.197

0.029

0.133

0.012

0.503

0.107

0.466

0.102

0.131

0.014

3.01e-05(0.00545)
0.02%
1,126
0.032

-0.00203(0.00232)-6.92%
1,126
0.030

0.00146(0.00598)
1.09%
1,136
0.068

-0.00194(0.00121)-15.67%
1,136
0.035

-0.00441(0.00515)-0.88%
1,184
0.287-0.00162(0.00376)-1.52%
1,184
0.247-0.00326(0.00614)-0.70%
1,184
0.437-0.00693*(0.00345)-6.77%
1,184
0.399

0.00272(0.00528)
2.08%
1,136
0.383

0.000507(0.00149)
3.53%
1,136
0.123

IT skill index
Proficient
Effect size
Observations
R-squared
PANEL B

April 2020

May 2020

June 2020

July 2020

August 2020

Overtime threshold

45
(11)

80
(12)

45
(13)

80
(14)

45
(15)

80
(16)

45
(17)

80
(18)

45
(19)

80
(20)

Teacher ratio

0.197

0.029

0.133

0.012

0.503

0.107

0.466

0.102

0.131

0.014

0.00734*(0.00423)
3.73%
1,126
0.037

0.000544(0.00167)
1.85%
1,126
0.029

0.00665*(0.00357)
4.99%
1,136
0.072

0.00164(0.00107)13.25%
1,136
0.035

0.00335(0.00551)
0.67%
1,184
0.286

0.00850**(0.00381)
7.97%
1,184
0.253

0.00397(0.00563)
0.85%
1,184
0.437

0.00637*(0.00375)
6.22%
1,184
0.399

0.00877(0.00550)
6.70%
1,136
0.386

0.00486*(0.00268)
33.83%
1,136
0.134

IT skill index
Lacking
Effect size
Observations
R-squared

Notes: Estimation results from linear fixed effect model at BoE (municipality) level. The outcome variable is the ratio of teachers working over a given overtime hours
threshold in each month. Teacher ratio shows the mean of the outcome variable. IT skill index standardized to mean 0. Other control variables are "High-speed internet,""Wi-Fi," "Projector," "Digital instructions," "Digital textbook," "Management software," "Security policy" and the lag of the outcome variable with a dummy variableindicating imputed value. Robust standard errors clustered at municipality prefecture and prefecture level in parentheses. *** p<0.01, ** p<0.05, * p<0.1

29

Table A5: Teachers’ overtime hours–subsample with private junior high school
PRIVATE JUNIOR HIGH SCHOOLPANEL A

April 2020

May 2020

June 2020

July 2020

August 2020

Overtime threshold

45
(1)

80
(2)

45
(3)

80
(4)

45
(5)

80
(6)

45
(7)

80
(8)

45
(9)

80
(10)

Teacher ratio

0.149

0.045

0.097

0.028

0.514

0.136

0.522

0.155

0.138

0.034

-0.00733(0.00784)-4.91%
482
0.056-0.00207(0.00419)-4.58%
482
0.168-0.00265(0.00713)-2.72%
484
0.050-0.00444(0.00311)-16.14%484
0.043-0.0239(0.0160)-4.65%
502
0.439-0.00461(0.0116)-3.38%
502
0.445-0.0181(0.0174)-3.47%
492
0.694-0.00119(0.0114)-0.77%
492
0.724-0.00764(0.0111)-5.54%
468
0.605-0.000110(0.00256)-0.32%
468
0.300

IT skill index
Proficient
Effect size
Observations
R-squared
PANEL B

April 2020

May 2020

June 2020

July 2020

August 2020

Overtime threshold

45
(11)

80
(12)

45
(13)

80
(14)

45
(15)

80
(16)

45
(17)

80
(18)

45
(19)

80
(20)

Teacher ratio

0.149

0.045

0.097

0.028

0.514

0.136

0.522

0.155

0.138

0.034

0.00239(0.00622)
1.60%
482
0.052

-0.00124(0.00260)-2.74%
482
0.167-0.00757(0.00833)-7.78%
484
0.053-0.00180(0.00171)-6.54%
484
0.031

0.0123(0.0116)
2.39%
502
0.433

0.0195**(0.00907)
14.31%
502
0.464

0.00305(0.0138)
0.58%
492
0.691

0.0249***(0.00824)
16.07%
492
0.736

0.0218(0.0134)
15.81%
468
0.612

0.00822(0.00508)
24.05%
468
0.319

IT skill index
Lacking
Effect size
Observations
R-squared

Notes: Estimation results from linear fixed effect model at BoE (municipality) level. The outcome variable is the ratio of teachers working over a given overtime hours
threshold in each month. Teacher ratio shows the mean of the outcome variable. IT skill index standardized to mean 0. Other control variables are "High-speed internet,""Wi-Fi," "Projector," "Digital instructions," "Digital textbook," "Management software," "Security policy" and the lag of the outcome variable with a dummy variableindicating imputed value. Robust standard errors clustered at municipality prefecture and prefecture level in parentheses. *** p<0.01, ** p<0.05, * p<0.130

Table A6: Teachers’ overtime hours–subsample without private junior high schools
NO PRIVATE JUNIOR HIGH SCHOOLPANEL A

April 2020

May 2020

June 2020

July 2020

August 2020

Overtime threshold

45
(1)

80
(2)

45
(3)

80
(4)

45
(5)

80
(6)

45
(7)

80
(8)

45
(9)

80
(10)

Teacher ratio

0.198

0.031

0.134

0.014

0.526

0.118

0.499

0.118

0.144

0.016

0.000257(0.00443)
0.13%
1,958
0.038

-0.00177(0.00187)-5.73%
1,958
0.019

0.000444(0.00473)
0.33%
1,970
0.065

-0.00217**(0.00106)-15.51%
1,970
0.033

-0.00402(0.00493)-0.76%
2,058
0.331-0.00304(0.00387)-2.57%
2,058
0.316-0.00292(0.00551)-0.59%
2,044
0.517-0.00599(0.00363)-5.09%
2,044
0.494

0.00348(0.00465)
2.41%
1,938
0.442

0.000833(0.00158)
5.22%
1,938
0.145

IT skill index
Proficient
Effect size
Observations
R-squared
PANEL B

April 2020

May 2020

June 2020

July 2020

August 2020

Overtime threshold

45
(11)

80
(12)

45
(13)

80
(14)

45
(15)

80
(16)

45
(17)

80
(18)

45
(19)

80
(20)

Teacher ratio

0.198

0.031

0.134

0.014

0.526

0.118

0.499

0.118

0.144

0.016

0.00744*(0.00403)
3.77%
1,958
0.042

0.000865(0.00148)
2.81%
1,958
0.019

0.00497(0.00337)
3.72%
1,970
0.067

0.00159(0.000973)
11.36%
1,970
0.032

0.00330(0.00463)
0.63%
2,058
0.331

0.00786**(0.00348)
6.67%
2,058
0.320

0.00471(0.00472)
0.95%
2,044
0.517

0.00547*(0.00302)
4.66%
2,044
0.494

0.00764(0.00462)
5.33%
1,938
0.444

0.00438*(0.00260)
0.0474***
1,938
0.153

IT skill index
Lacking
Effect size
Observations
R-squared

Notes: Estimation results from linear fixed effect model at BoE (municipality) level. The outcome variable is the ratio of teachers working over a given overtime hours
threshold in each month. Teacher ratio shows the mean of the outcome variable. IT skill index standardized to mean 0. Other control variables are "High-speed internet,""Wi-Fi," "Projector," "Digital instructions," "Digital textbook," "Management software," "Security policy" and the lag of the outcome variable with a dummy variableindicating imputed value. Robust standard errors clustered at municipality prefecture and prefecture level in parentheses. *** p<0.01, ** p<0.05, * p<0.1

31

Table A7: Effect of ICT resources on remote education–school level and prefecture dummies
interaction terms

Dependent Variable

School days closed

Explanatory Variables(1)

Elementary school 0.232***(2)

Live online
communication

(3)

(5)

(4)

(6)

-0.0404*** -0.0404***(0.00278) (0.00277)-0.0357*** -0.0358***(0.00317) (0.00314)

Teachers' IT Skill
IT skill index - Proficient -0.000862-5.33e-05

0.00374(0.0171)(0.00492)(0.00473)(0.0154)

IT skill index - Lacking
School ICT Equipment
High-speed internet
Wi-Fi

0.232***(0.0154)

Live online class-0.0117-0.00233

0.00405(0.0282)(0.00359)(0.00490)-0.197*-0.195*-0.0262-0.0259-0.0116-0.0121(0.105)(0.104)(0.0241)(0.0242)(0.0293)(0.0290)-0.0425-0.04190.0338*0.0339*0.0369**0.0364**(0.0944)(0.0942)(0.0174)(0.0174)(0.0178)(0.0180)

Presentation device 0.00736

0.00583

0.0167*0.0164*0.006410.00747(0.0437)(0.0440)(0.00982)(0.00943)(0.00911)(0.00884)

Digital instructions
Digital textbook
Management software
Security policy
Observations
R-squared

0.04240.04000.0293*0.0288*0.003840.00465(0.103)(0.104)(0.0147)(0.0147)(0.0135)(0.0137)0.08110.0813-0.0219-0.0219-0.0184-0.0191(0.183)(0.183)(0.0243)(0.0243)(0.0205)(0.0204)

0.04200.0404-0.00853-0.00884-0.0135-0.0129(0.0808)(0.0818)(0.0317)(0.0320)(0.0319)(0.0324)-0.0676-0.0683-0.0136-0.0137

0.03190.0329(0.0965)(0.0961)(0.0307)(0.0310)(0.0311)(0.0316)

3,422

3,422

3,386

3,386

3,386

3,386

0.038

0.038

0.039

0.039

0.035

0.035

Notes: Estimation results from fixed effect model at BoE (municipality) level. School level dummy and prefecturedummies interaction terms included (results omitted). Robust standard errors clustered at municipality andprefecture level in parentheses. *** p<0.01, ** p<0.05, * p<0.1

32

Table A8: Teachers’ overtime hours –school level and prefecture dummies interaction terms
PANEL A

April 2020

May 2020

June 2020

July 2020

August 2020

Overtime threshold

45
(1)

80
(2)

45
(3)

80
(4)

45
(5)

80
(6)

45
(7)

80
(8)

45
(9)

80
(10)

Teacher ratio

0.184

0.031

0.123

0.015

0.522

0.121

0.502

0.125

0.138

0.017

-0.000752(0.00434)-0.41%
2,440
0.145-0.00179(0.00189)-5.75%
2,440
0.076-8.74e-05(0.00454)-0.07%
2,454
0.295-0.00218**(0.000974)-14.25%
2,454
0.122

-0.00422(0.00503)-0.81%
2,560
0.418-0.00188(0.00344)-1.56%
2,560
0.419-0.00492(0.00560)-0.98%
2,536
0.592-0.00670*(0.00340)-5.38%
2,536
0.593

0.00206(0.00415)
1.49%
2,406
0.583

0.000858(0.00144)
5.07%
2,406
0.305

IT skill index
Proficient
Effect size
Observations
R-squared
PANEL B

April 2020

May 2020

June 2020

July 2020

August 2020

Overtime threshold

45
(11)

80
(12)

45
(13)

80
(14)

45
(15)

80
(16)

45
(17)

80
(18)

45
(19)

80
(20)

Teacher ratio

0.184

0.031

0.123

0.015

0.522

0.121

0.502

0.125

0.138

0.017

0.00820**(0.00342)
4.46%
2,440
0.150

0.000658(0.00114)
2.11%
2,440
0.076

0.00622**(0.00283)
5.06%
2,454
0.298

0.00135*(0.000787)
8.82%
2,454
0.120

0.00326(0.00403)
0.62%
2,560
0.418

0.00725**(0.00309)
6.00%
2,560
0.421

0.00493(0.00389)
0.98%
2,536
0.592

0.00606**(0.00287)
4.86%
2,536
0.593

0.00236(0.00410)
1.70%
2,406
0.583

0.00237(0.00239)
13.99%
2,406
0.307

IT skill index
Lacking
Effect size
Observations
R-squared

Notes: Estimation results from linear fixed effect model at BoE (municipality) level. School level dummy and prefecture dummies interaction terms included (results
omitted). The outcome variable is the ratio of teachers working over a given overtime hours threshold in each month. Teacher ratio shows the mean of the outcome
variable. IT skill index standardized to mean 0. Other control variables are "High-speed internet," "Wi-Fi," "Projector," "Digital instructions," "Digital textbook,""Management software," "Security policy" and the lag of the outcome variable with a dummy variable indicating imputed value. Robust standard errors clustered atmunicipality prefecture and prefecture level in parentheses. *** p<0.01, ** p<0.05, * p<0.1

33

Table A9: Effect of ICT resources on remote education–2019 ICT equipment

Dependent Variable School days closed
Explanatory Variables(1)

Elementary school 0.0920**(0.0424)(2)
0.0842*(0.0437)

Live online class

Live online
communication

(3)

(5)

(4)

-0.0133-0.0143(0.00922) (0.00951)-0.0112-0.00988(0.00744) (0.00771)

Teachers' IT Skill (2020)
IT skill index - Proficient 0.00311

0.000717

0.00398(0.0165)(0.00500)(0.00460)

IT skill index - Lacking(6)-0.0200-0.00247

0.00404(0.0313)(0.00333)(0.00442)

School ICT Equipment (2019)
High-speed internet -0.139-0.137-0.00436-0.00411

0.01630.0164(0.0980)(0.0995)(0.0251)(0.0251)(0.0287)(0.0287)

0.04660.04460.0249**0.0246**0.01040.0110(0.0562)(0.0553)(0.0119)(0.0117)(0.0100)(0.00981)-0.0131-0.0160

0.01400.0137-0.000786 -0.000166(0.0865)(0.0878)(0.0142)(0.0143)(0.0108)(0.0108)0.06430.06440.01180.0118-0.0176-0.0179(0.0810)(0.0788)(0.0331)(0.0329)(0.0314)(0.0314)0.02000.0151-0.0140-0.01450.02380.0255(0.0583)(0.0583)(0.0250)(0.0252)(0.0253)(0.0262)

3,418

3,418

3,384

3,384

3,384

3,384

0.006

0.006

0.006

0.006

0.004

0.004

Presentation device
Digital instructions
Management software
Security policy
Observations
R-squared

Notes: Estimation results based on fixed effect model at BoE (municipality) level. Robust standard errors
clustered at municipality and prefecture level in parentheses. *** p<0.01, ** p<0.05, * p<0.1

34

Table A10: Teachers’ overtime hours –2019 ICT equipment controls
PANEL A

April 2020

May 2020

June 2020

July 2020

August 2020

Overtime threshold

45
(1)

80
(2)

45
(3)

80
(4)

45
(5)

80
(6)

45
(7)

80
(8)

45
(9)

80
(10)

Teacher ratio

0.184

0.031

0.123

0.015

0.522

0.121

0.502

0.125

0.138

0.017

-0.000399(0.00408)-0.22%-0.00170(0.00182)-5.46%

0.000455(0.00437)
0.37%-0.00222**(0.00107)-14.50%-0.00557(0.00518)-1.07%-0.00220(0.00381)-1.82%-0.00420(0.00548)-0.84%-0.00546(0.00362)-4.38%

0.00231(0.00451)
1.67%

0.000603(0.00150)
3.56%

2,438
0.030

2,438
0.013

2,452
0.054

2,452
0.022

2,558
0.332

2,558
0.317

2,534
0.534

2,534
0.525

2,404
0.458

2,404
0.148

IT skill index
Proficient
Effect size
Observations
R-squared
PANEL B

April 2020

May 2020

June 2020

July 2020

August 2020

Overtime threshold

45
(11)

80
(12)

45
(13)

80
(14)

45
(15)

80
(16)

45
(17)

80
(18)

45
(19)

80
(20)

Teacher ratio

0.184

0.031

0.123

0.015

0.522

0.121

0.502

0.125

0.138

0.017

0.00725*(0.00370)
3.94%

0.000511(0.00133)
1.64%

0.00361(0.00337)
2.93%

0.001160.00813**(0.00360)
6.74%

0.00376(0.000940)
7.57%

0.00360(0.00438)
0.69%(0.00463)
0.75%

0.00743**(0.00293)
5.97%

0.00874*(0.00479)
6.32%

0.00495*(0.00260)
29.23%

2,438
0.034

2,438
0.012

2,452
0.056

2,452
0.020

2,558
0.332

2,558
0.321

2,534
0.534

2,534
0.526

2,404
0.461

2,404
0.158

IT skill index
Lacking
Effect size
Observations
R-squared

Notes: Estimation results from linear fixed effect model at BoE (municipality) level. The outcome variable is the ratio of teachers working over a given overtime hours
threshold in each month. Teacher ratio shows the mean of the outcome variable. IT skill index standardized to mean 0. Other control variables are 2019 levels of "Highspeed internet, "Projector," "Digital instructions," "Management software," "Security policy" and the lag of the outcome variable with a dummy variable indicating
imputed value. Robust standard errors clustered at municipality prefecture and prefecture level in parentheses. *** p<0.01, ** p<0.05, * p<0.1

35

Table A11: Teachers’ overtime hours –2019 overtime hours
PANEL A

April 2019

May 2019

June 2019

Overtime threshold

45
(1)

80
(2)

45
(3)

80
(4)

45
(5)

80
(6)

Teacher ratio

0.595

0.202

0.593

0.194

0.601

0.205

IT skill index
Proficient-0.00222-0.0134*-0.00276-0.0113-0.000920-0.00170(0.00806)(0.00746)(0.00723)(0.00766)(0.00839)(0.00662)

Effect size

-0.37%-6.63%-0.47%-5.82%-0.15%-0.83%

1,742

1,742

1,776

1,776

1,914

1,914

0.584

0.624

0.601

0.623

0.558

0.562

Observations
R-squared
PANEL B

April 2019

May 2019

June 2019

Overtime threshold

45
(11)

80
(12)

45
(13)

80
(14)

45
(15)

80
(16)

Teacher ratio

0.595

0.202

0.593

0.194

0.601

0.205

IT skill index
Lacking-0.00107-0.000907

0.00571

0.00313-0.00151-0.00169(0.00701)(0.00639)(0.00644)(0.00499)(0.00735)(0.00605)

Effect size

-0.18%-0.45%

0.96%

1.61%

-0.25%-0.83%

1,742

1,742

1,776

1,776

1,914

1,914

0.584

0.622

0.602

0.622

0.558

0.562

Observations
R-squared

Notes: Estimation results from linear fixed effect model at BoE (municipality) level. The outcome variable is
the ratio of teachers working over a given overtime hours threshold in each month. Teacher ratio shows themean of the outcome variable. IT skill index standardized to mean 0. Other control variables are 2019 levels of"High-speed internet, "Projector," "Digital instructions," "Management software," "Security policy" and the lagof the outcome variable with a dummy variable indicating imputed value. Robust standard errors clustered atmunicipality prefecture and prefecture level in parentheses. *** p<0.01, ** p<0.05, * p<0.1

36

Table A12: Effect of ICT resources on remote education–subsample of municipalities with one
junior high school

Live online class

Live online
communication

(2)

(3)

(4)

(5)

(6)

Elementary school 0.208***0.200***-0.0146-0.0141-0.0199*-0.0171(0.0645)(0.0580)(0.0126)(0.0130)(0.0102)(0.0108)

Dependent Variable

School days closed

Explanatory Variables(1)

Teachers' IT Skill
IT skill index - Proficient -0.00259

0.00126(0.0269)

IT skill index - Lacking
School ICT Equipment
High-speed internet

0.000166(0.00454)(0.00585)-0.02390.001960.00878*(0.0473)(0.00405)(0.00520)-0.219-0.212-0.00549-0.00601

0.007430.00487(0.158)(0.157)(0.0283)(0.0279)(0.0342)(0.0338)

Wi-Fi 0.00875

0.01520.02230.02150.004100.00169(0.120)(0.117)(0.0212)(0.0221)(0.0202)(0.0215)

Presentation device -0.0169-0.02170.0341**0.0347**0.0268**0.0282**(0.0758)(0.0740)(0.0149)(0.0146)(0.0132)(0.0125)

0.113

0.111

0.0361*** 0.0361***0.00860

0.00945(0.135)(0.135)(0.0114)(0.0105)(0.0110)

Digital instructions
Digital textbook
Management software
Security policy
Observations
R-squared(0.0120)

0.152

0.153

-0.0179-0.0181-0.0188-0.0191(0.232)(0.231)(0.0319)(0.0313)(0.0246)(0.0243)

0.148

0.142

-0.0214-0.0208-0.0231-0.0207(0.0982)(0.0973)(0.0452)(0.0454)(0.0476)(0.0477)-0.116-0.120-0.0142-0.0137

0.03690.0381(0.126)(0.121)(0.0365)(0.0367)(0.0359)(0.0370)

1,100

1,100

1,088

1,088

1,088

1,088

0.016

0.017

0.021

0.021

0.017

0.021

Notes: Estimation results based on fixed effect model at BoE (municipality) level. Subsample of municipalities
with one junior high school. Robust standard errors clustered at municipality and prefecture level in parentheses.
*** p<0.01, ** p<0.05, * p<0.1

37

Table A13: Teachers’ overtime hours – subsample of municipalities with one junior high school
PANEL A

April 2020

May 2020

June 2020

July 2020

August 2020

Overtime threshold

45
(1)

80
(2)

45
(3)

80
(4)

45
(5)

80
(6)

45
(7)

80
(8)

45
(9)

80
(10)

Teacher ratio

0.198

0.027

0.128

0.013

0.485

0.098

0.442

0.090

0.120

0.015

0.00678(0.00754)
3.42%-0.000274(0.00320)-1.03%

0.00611(0.00828)
4.76%-0.000704(0.00138)-5.58%-0.000151(0.00631)-0.03%-0.000380(0.00444)-0.39%

0.00298(0.00658)
0.67%-0.00617*(0.00363)-6.86%

0.00458(0.00710)
3.81%

0.000883(0.00203)
5.98%

678
0.060

678
0.040

684
0.096

684
0.045

710
0.299

710
0.241

716
0.405

716
0.369

694
0.335

694
0.128

IT skill index
Proficient
Effect size
Observations
R-squared
PANEL B

April 2020

May 2020

June 2020

July 2020

August 2020

Overtime threshold

45
(11)

80
(12)

45
(13)

80
(14)

45
(15)

80
(16)

45
(17)

80
(18)

45
(19)

80
(20)

Teacher ratio

0.198

0.027

0.128

0.013

0.485

0.098

0.442

0.090

0.120

0.015

0.00807(0.00527)
4.07%-0.00110(0.00222)-4.15%

0.00560(0.00368)
4.36%0.00176(0.00136)
13.96%

0.00306(0.00677)
0.63%

0.0114**(0.00438)
11.64%

0.00407(0.00696)
0.92%

0.0106**(0.00431)
11.78%

0.00799(0.00661)
6.64%

0.00501(0.00315)
33.92%

678
0.062

678
0.041

684
0.096

684
0.047

710
0.300

710
0.254

716
0.405

716
0.375

694
0.337

694
0.139

IT skill index
Lacking
Effect size
Observations
R-squared

Notes: Estimation results from linear fixed effect model at BoE (municipality) level. Subsample of municipalities with one junior high school. The outcome variable is theratio of teachers working over a given overtime hours threshold in each month. Teacher ratio shows the mean of the outcome variable. IT skill index standardized to mean
0. Other control variables are "High-speed internet," "Wi-Fi," "Projector," "Digital instructions," "Digital textbook," "Management software," "Security policy" and thelag of the outcome variable with a dummy variable indicating imputed value. Robust standard errors clustered at municipality prefecture and prefecture level inparentheses. *** p<0.01, ** p<0.05, * p<0.1

38

Appendix B: Teachers’ IT skills questionnaire
1) Status of teachers’ ICT utilization and guidance (teachers in charge of classes in 2019)
Skill A: Ability to use ICT for class preparation, grading, and administrative tasks
A–1 Make planned use of computers, the Internet, etc. to increase education efficacy.
A–2 Make use of the Internet and other tools to collect materials used in class or materials needed
for administrative tasks, and to share information necessary to coordinate with guardians andthe public.
A–3 Make use of word processing, spreadsheet, and presentation software, etc. to prepare materials
such as prints or presentations used in class or documents and materials needed for classmanagement and administrative tasks.
A–4 Make use of computers and other tools to record, organize, and evaluate students’ work, reports,
worksheets, and others in order to comprehend learning status.
Skill B: Ability to teach class using ICT
B–1 Use computers and presentation devices to effectively present materials and others toappropriately summarize learning contents, to clearly explain tasks and raise students’ interest
and curiosity.
B–2 Have students use computers, presentation devices, etc. to effectively present opinions andothers, in order to share and compare opinions, ideas, and works.
B–3 Use learning software and other tools to have students perform repetitive learning tasks andother tasks corresponding to each student’s level of learning for the purposes of establishingknowledge and mastering technical skills.
B–4 Have students make effective use of computer software and other tools when summarizing ideas
from group discussions or collaboratively preparing reports, materials, works, and others.
Skill C: Ability to teach students to use ICT
C–1 Guide students to gain the basic skills needed to operate computers and other tools necessary tolearning (character input, file management, and others).
C–2 Guide students to be able to use computers and the Internet to collect and select relevant andreliable information.
C–3 Guide students to be able to use word processing, spreadsheet, and presentation software, etc. toorganize research and ideas and to summarize them in text, tables, graphs, figures, and others inan easy-to-understand manner.
C–4 Guide students to use computers, software, etc. to be able to exchange, share, and discuss ideas
with each other.
Skill D: Ability to instruct students in knowledge and attitude needed to utilize information
D–1 Guide students to be able to take responsibility for their own actions participating in
information-driven society, to think of and respect others, and to follow rules and manners
when collecting and sharing information.
D–2 Guide students to be able to appropriately avoid antisocial and illegal behavior, online crimes,
and other risks and to be mindful of their health when using the Internet and other tools.
D–3 Guide students to gain the basic knowledge of information security and to appropriately set andmanage passwords to be able to use computers and the Internet safely.
D–4 Guide students to acknowledge the usefulness of computers and the Internet and encourage
students’ interest in utilizing them for learning and understanding their mechanism.

2) Reference for answering 1)
Skill A: Ability to use ICT for class preparation, grading, and administrative tasks
Proficient: Generally skilled in items in question.
(Example A–3: Is able to use word processing, spreadsheet, and presentation software, etc. toprepare materials such as prints or presentations used in class or documents and materials

39

needed for class management and division of school duties.)
Mostly proficient: Knows how to use ICT equipment in question.
(Example A–3: Knows how to operate word processing, spreadsheet, and presentation software.)Mostly lacking: Unable to operate without receiving in-school or other training.
(Example A–3: Will know how to operate word processing, spreadsheet, and presentation software
after receiving in-school or other training.)
Lacking: Unable to operate without receiving step-by-step training (including outside-school
training) starting from the basics of operation.
(Example A–3: Largely does not know how to operate word processing, spreadsheet, andpresentation software.)Skill B: Ability to teach class using ICTProficient: Is able to use ICT in teaching activities.
(Example B–1: Is able to use computers and presentation devices to effectively present materials andothers to appropriately summarize learning contents, to clearly explain tasks, and raise
students’ interest and curiosity.
Mostly proficient: Using practical examples and guidebooks, is able to use ICT in teaching activities.
(Example B–1: Is able to present materials and others using computers and presentation devices.)
Mostly lacking: Does not know how to teach using ICT without receiving in-school or other training.
(Example B–1: Will be able to present materials and others using computers and presentation
devices after receiving in-school or other training.)
Lacking: Does not know how to teach using ICT without receiving step-by-step training (including
outside-school training) starting from the basics of operation.
(Example B–1: Largely does not know how to present materials and others using computers andpresentation devices.)
Skill C: Ability to teach students to use ICTProficient: Generally able to teach items in question.
(Example C–1: Is able to guide students to gain the basic skills needed to operate computers andother tools necessary to learning (character input, file management, and others).)
Mostly proficient: Using practical examples and guidebooks, is able to teach.
(Example C–1: Has and is able to explain the basic skills needed to operate computers and othertools necessary to learning (character input, file management, and others).)
Mostly lacking: Does not know how to teach without receiving in-school or other training.
(Example C–1: Does not know how to teach; in-school training is therefore necessary.)
Lacking: Does not know how to teach without receiving step-by-step training (including outsideschool training) starting from the basics of operation.
(Example C–1: Does not know how to teach; step-by-step training (including outside-school
training) starting from the basics of operation is therefore necessary.)
Skill D: Ability to instruct students in knowledge and attitude needed to utilize information
Proficient: Generally able to teach items in question.
(Example D–1: Is able to guide students to be able to take responsibility for their own actions
participating in information-driven society, to think of and respect others, and to follow
rules and manners when collecting and sharing information.)
Mostly proficient: Using practical examples and guidebooks, is able to teach.
(Example D–1: Using practical examples and guidebooks, is able to guide students to be able to takeresponsibility for their own actions participating in information-driven society, to think ofand respect others, and to follow rules and manners when collecting and sharing
information.)
Mostly lacking: Does not know how to teach without receiving in-school or other training.
(Example D–1: Does not know how to teach; in-school training is therefore necessary.)
Lacking: Does not know how to teach without receiving step-by-step training (including outsideschool training) starting from the basics of operation.
40

(Example D–1: Does not know how to teach; step-by-step training (including outside-school
training) starting from the basics of operation is therefore necessary.)41