ISSN (Print) 0473-453X

Discussion Paper No. 1207

ISSN (Online) 2435-0982

SCHOOL ICT RESOURCES, TEACHERS,AND ONLINE EDUCATION:EVIDENCE FROM SCHOOL CLOSURESIN JAPAN
DURING THE COVID-19 PANDEMIC

Hideo Akabayashi
Shimpei Taguchi
Mirka Zvedelikova

Revised April 2024
March 2023

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

School ICT resources, teachers, and online education: Evidence from school
closures in Japan during the COVID-19 pandemic
Hideo Akabayashia
Shimpei Taguchib
Mirka Zvedelikovac

April 15, 2024

Abstract
During the COVID-19 pandemic, schools switched to online education. Using Japan’snationwide administrative data, we examine the impact of schools’ ICT equipment andteachers’ IT skills on the provision of online classes, communication with students’families, and teachers’ working hours in early 2020. To isolate supply-side effects, we
exploit differences in ICT resources between public elementary and junior high schools
at a municipality level, 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.
However, IT skills were associated with teachers’ working hours.

Keywords: COVID-19; remote education; teachers’ skills; school resources
JEL classification: I20, J22, H75aCorresponding 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.jpbGraduate 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.jpcInstitute of Social and Economic Research, Osaka University, 6-1 Mihogaoka, Ibaraki, Osaka

567-0047, Japan. https://orcid.org/0000-0002-3085-7502. Email: zvedelikova@iser.osakau.ac.jp

11. Introduction
In early 2020, the COVID-19 pandemic forced many schools worldwide to close,
resulting in widespread children’s learning loss (UNESCO, 2021; Cortés-Albornoz et al.,2023). During these closures, many countries turned to online tools to secure a degree ofcontinuity in children’s education. Related studies have predominantly focused on the
demand side, investigating how students’ family backgrounds influenced access toremote/online education and the associated educational access gap, generally finding anegative impact (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 andteachers’ IT skills. Only a few studies have examined how school ICT resources affected
online learning provision 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 separating the demand and supply sides of ICT
resources and access to online education. It is likely that schools in wealthier areas where
demand for high-quality education might be higher are better equipped and staffed bymore highly qualified teachers, making the effects of the supply side difficult to identify.
An equally important concern is the overwork of teachers, as even schools with top-level
ICT equipment and skilled staff cannot effectively provide online education if teachers
are overwhelmed.
Japan has a very high availability of high-speed Internet connection (OECD 2020a), yet
the use of ICT in school education is largely lacking (OECD 2020b, 2020c). During theschool closures in the first wave of the pandemic, only a small portion of schools provided
live online classes and other forms of digital education. This paper aims to examine how
ICT resources at school affected the online education provision and teachers’ working
hours during the early stages of the pandemic using data on Japanese public elementaryand junior high schools, the compulsory stage of schooling. Specifically, the aspects of2

online education we investigate are the length of the school closures, the provision of live
online classes and live online communication with students’ families. These threeoutcomes each describe a different aspect of school closures, yet collectively
comprehensively capture the closure experience and the degree of its digitalization. While
the first two outcomes are the natural focus of interest, we contribute to the literature by
simultaneously investigating the channel schools used to communicate with students’families. Since we analyze online education provided to young children, we also consider
it important to document how schools communicated relevant information and monitored
students’ learning and daily lives during this turbulent period.
Japan is also a country where teachers commonly work unusually long hours (OECD2019), a problem that could be alleviated by better working conditions. In addition tofacilitating students’ learning, school ICT resources are expected to improve teachers’
work efficiency. Most teachers did not switch to remote work during the closures, making
school resources highly relevant. We, therefore, also investigate the impact of teachers’
IT skills on their overtime during the school closures and the remainder of the affectedschool term.
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 schools’
response to the COVID-19 pandemic, a survey about school ICT resources, and a survey
about teachers’ overtime work. The combined dataset includes information for bothelementary and junior high schools at a municipality level. Public schools in Japan are
operated by municipality-level local boards of education (BoE), which also allocate major
resources across schools, including ICT, following strict legal procedures. Utilizing a
BoE-level fixed effects model, we exploit the variation in schools’ ICT resources withineach BoE district to isolate the causal effect of the supply side. 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. We run a series of additional analyses toconfirm the robustness of our results against several threats to causal interpretation. In
addition to this empirical analysis, we conducted an online survey of public elementaryand junior high school teachers to further our understanding of the results.

3

Our results show that better ICT equipment was more relevant than teachers’ IT skills tothe provision of online education, but neither had any effect on communication withstudents’ families using live online tools. However, weak IT skills resulted in a higher
percentage of teachers working extra hours, especially extreme overtime, in the months
following schools' reopening. These results suggest that the impact of various ICT
resources differs between students and teachers. A bottleneck to implementing online
education in Japan was created by a shortage of ICT equipment, distributed at the BoElevel, but was not contributed to by teachers’ IT skills. However, the persistent overworkof 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 policymakers should be aware of
the importance of matching appropriate policy tools to their targets. Improving the
supply-side issues should be a priority in promoting access to online education andteacher welfare in Japan.
By presenting this research, we contribute to the growing literature covering pandemicrelated school closures by simultaneously investigating the effect of school ICT resources
on both the provision of online education and the choice of communication channel withstudents’ families while also examining teachers’ working conditions during and after
school closures using Japanese data. Our results shed light on the effects of ICT resources
on both students and teachers, allowing causal interpretation. To our knowledge, ourpaper is the first to comprehensively examine these topics.
2. Previous Literature
Whether ICT technology can improve teachers’ teaching styles and, thereby, children’s
learning outcomes has been a central issue in educational policy in recent years. Many
studies have empirically 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 increased teachers’
computer use had no or negative effect on children’s learning. Goolsbee and Guryan(2006) analyzed the effect of Internet access investment subsidies in US public schools,
concluding that students' test scores were unaffected while there was a significant increase4

in Internet access at treated schools. Machin et al. (2007) examined the effect of a change
in policy rules allocating ICT funding to public schools, suggesting an improvement inEnglish and science but not in math outcomes. Using a randomized trial, Barrow et al.
(2009) found that assigning computer-aided instructions improved pre-algebra andalgebra test scores. Bass (2021) examined the effect of eligibility for ICT vouchers for
public schools in California, finding voucher use had a significant effect on student
achievement. Most recently, Lomos et al. (2023), using 2018 data from secondary schools
in Luxembourg, a country with ample school ICT resources yet a relatively low ICT use
in classroom practice, reported that teachers’ technological knowledge was an important
predictor of ICT classroom use. These studies examined the effects of ICT facilities onlearning at school; however, during the COVID-19 school closures, the effective use ofschool ICT was crucially important to providing instruction and supporting learning forstudents at home.
Early in the COVID-19 pandemic, Dincher and Wagner (2021) surveyed Germanelementary and secondary school teachers, finding that at-school ICT infrastructure did
not predict the 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 initiallevel of ICT resources in Germany was vastly different from that in Japan, allowing
Dincher and Wagner (2021) to focus specifically on teachers’ attitudes. Furthermore,their paper does not attempt to separate the role of supply-side factors at school from thedemand-side factors such as student and family characteristics. For evidence from acountry with a lower initial level of ICT resources, Akah et al. (2022) examined theavailability and use of a wide array of ICT resources during the COVID-19 pandemic atpublic universities in Nigeria. They concluded that academic staff with good IT skills
used ICT in teaching to a higher degree than their lower-skilled counterparts. Collectively,
Dincher and Wagner (2021), Akah et al. (2022), and Lomos et al. (2023) show thatteachers’ IT skills likely play an essential role in the impact of school ICT equipment on
both at and out-of-school learning.
In a typical school environment, principals and teachers hold discretion over how thebenefits of improved productivity provided by additional ICT resources are distributed.
For example, teachers may react to additional ICT resources by reducing the time and5effort 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, as pointed out inreview articles by Bulman and Fairlie (2016) and Escueta et al. (2017).
Our paper contributes to two strains of literature: the provision of online school
educational practices during COVID-19 school closures and the impact of ICT onteachers’ workstyles. While these topics may seem unrelated, ultimately, it is teachers
who facilitate school education. Comprehensively examining the supply side of school
education presents a more accurate picture of the issues schools faced during the
pandemic and can thus better inform policy makers.
3. Data and Setting
At the end of February 2020, shortly before spring break, Japanese schools were ordered
to close to prevent the risk of community transmission of COVID-19. Schools reopenedat the beginning of the new school year on April 1, 2020, and closed again after a partial
state of emergency declaration on April 7, 2020, and nationwide one on April 16, 2020.
This state of emergency was 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, making it the period of our interest.

1

Reducing teachers’ work hours should not necessarily be viewed negatively. Japanese teachers’

work the longest hours among developed countries (OECD, 2019). The stagnant use of ICT atpublic schools resulting in unappealing working conditions may thus have created a difficulty inrecruiting high-quality teachers. To the best of our knowledge, no previous research exists on thedeterminants of teachers’ overtime in Japan. The research on teachers’ working hours outside
Japan typically focuses on absenteeism instead of overtime (Duflo et al., 2012; Nunoo et al.,2023). However, research shows that overtime is a source of mental distress for Japanese teachers(Bannai et al., 2015; Matsushita and Yamamura, 2022), making this topic relevant also outside of
the topic of school closures.

6During the closures, the Japanese Ministry of Education, Culture, Sports, Science andTechnology (MEXT) asked BoEs nationwide to report closure details for the publicschools in their jurisdiction, typically corresponding to a municipality. We use datacollected through the Survey on Learning and Instruction during the COVID-19
Pandemic as of June 23, 2020. Appendix A Table A1 further introduces the surveys usedin this study. The summary statistics of the closure-related variables are reported in Panel
A of Table 1. The average length of school closures was 24-25 school days, live online
classes were held in 8-9% of BoEs, and live online tools were used to communicate withstudents’ families in 9-10% of BoEs. BoEs, on average, reported shorter closures andhigher degrees of both remote practices for junior high schools. However, the survey asksa binary question about online education implementation; it does not inquire about its
extent.
Regarding schools’ ICT equipment, public school resources are customarily determinedby 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 BoEs. MEXT annually collects information on public schools’ ICT resources,
specifically ICT equipment and teachers’ IT skills, through the Survey on ICT in School
Education, of which we use the 2019 school year iteration, collected as of March 1, 2020.
While the survey results provide information about each public school separately, weaggregate the data on a BoE level to correspond with the pandemic response data.
Panel B of Table 1 contains the summary statistics of the ICT variables. The ICT survey
inquiries about teachers’ IT skills in four categories with four subcategories each, ratingteachers on a scale of ‘lacking,’ ‘mostly lacking,’ ‘mostly proficient,’ and ‘proficient’.
The main categories are teachers’ ability to use ICT for class preparation, grading, andadministrative tasks, the ability to teach classes using ICT, the ability to teach students touse ICT, and the ability to instruct students in the knowledge and attitude needed to utilize
information. The English translation of the skill-related questions is available inAppendix C. As the relationship between underlying skills and the MEXT-defined
categories is unknown, we run a principal component analysis for proficient and lacking
ranks over all 16 items and schools and then aggregate the data by school level to a BoE
7

level. We then standardize these IT skill indices to have a mean of 0 and a standard
deviation of 1. On average, junior high school teachers had better IT skills than theirelementary school counterparts. Next, within-BoE ICT equipment is described by sevenvariables defined in Appendix A Table A2. Junior high schools were generally better
equipped than elementary schools.

ELEMENTARYSCHOOLJUNIOR HIGHSCHOOL

Mean

SD

Mean

SD

Days closed (min = 0, max = 61)

24.57010.30424.46610.308

Live online class (dummy)

0.081

0.273

0.089

0.285

Live online communication withfamily (dummy)

0.094

0.292

0.103

0.304

IT skill index (Proficient)-0.016

0.975

0.016

1.025

IT skill index (Lacking)-0.200

0.757

0.200

1.160

High-speed Internet (ratio)

0.674

0.431

0.682

0.444

Wi-Fi (ratio)

0.836

0.336

0.835

0.348

Presentation device (E: min = 0, max =3) (JH: min = 0, max = 5)

0.958

0.425

0.745

0.506

Digital instructions (ratio)

0.574

0.447

0.643

0.440

Digital textbook (ratio)

0.095

0.230

0.100

0.257

Management software (ratio)

0.765

0.392

0.771

0.399

Security policy (ratio)

0.657

0.431

0.662

0.449

Panel A: Remote education practices

Panel B: ICT resources

Notes: N = 1,711 for Days closed and ICT resources, corresponding to 19,603 elementary and 9,113 junior highschools. N = 1,693 for online education outcomes.

Table 1: Summary statistics – remote education practices and ICT resources
Next, to examine how ICT resources affected teachers' workloads during school closuresand in the months after reopening, we use data collected through the Survey on Reformof Working Conditions in Schools by the Local Boards of Education for 2019 and 2020.
For each school term month, MEXT asks BoEs to report the percentage of school staff
working 0 to 45, 45 to 80, 80 to 100, and over 100 overtime hours per month. We
8summarize these categories into two variables: the ratio of teachers working over 45 hoursof overtime and over 80 hours of overtime, a threshold recognized by the Japanese
government as dangerous to health. The survey does not distinguish between types ofstaff; however, as the staff is predominantly made up of teachers, we consider the data to
be representative of teachers. As Japanese teachers are known to work long hours, we use
the 2019 data to establish the baseline rate of overtime.

Overtime by month, 2020
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
April

May

June

July August

AprilOver 45 hours

May

June

July AugustOver 80 hours

Elementary school

Junior high school

Figure 1: Overtime in 2020

Figure 1 shows the average overtime for 2020, with the full summary statistics inAppendix A Table A3. For all months and thresholds, junior high school teachers worked
longer overtime than elementary school teachers. However, the actual amount of overtime
hours, especially in 2020, was likely much higher, as the atypical situation likely
prevented schools from keeping accurate records. Furthermore, the 2019 survey round,9an initial one, suffered from non-respondence.2 To use all the available 2020 data, we use
a single imputation by simple average to fill in the missing 2019 data points.
4. Empirical Strategy
To answer the question of how ICT resources affected schools’ pandemic response, we
employ the following model as our baseline:

𝑅𝑒𝑚𝑜𝑡𝑒 𝑒𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝛼𝛽 ∗ 𝐼𝐶𝑇 𝑒𝑞𝑢𝑖𝑝𝑚𝑒𝑛𝑡𝛾 ∗ 𝐼𝑇 𝑠𝑘𝑖𝑙𝑙𝑠

𝛿

𝜇𝐷

𝜖 ,
(1)

where remote education stands for the length of school closures measured in school days
and dummy variables indicating the implementation of live online classes and live online
communication with families in BoE 𝑖 at school level 𝑗. The term 𝛿 is a BoE 𝑖 fixed
effect, 𝐷 is a school-level dummy variable and 𝜖 represents the error term. Standard
errors are two-way clustered at a BoE and prefectural level.
Next, we analyze the impact of ICT resources on teachers’ overtime hours using thebaseline model (2). In this analysis, we focus on teachers’ skills only and utilize ICT
equipment variables as controls as teachers operate under set conditions.

𝑂𝑣𝑒𝑟𝑡𝑖𝑚𝑒𝛼𝛽 ∗ 𝐼𝐶𝑇 𝑒𝑞𝑢𝑖𝑝𝑚𝑒𝑛𝑡

𝜎 ∗ 𝑂𝑣𝑒𝑟𝑡𝑖𝑚𝑒 𝑖𝑚𝑝𝑢𝑡𝑒𝑑𝛿𝜇𝐷𝛾 ∗ 𝐼𝑇 𝑠𝑘𝑖𝑙𝑙𝑠𝜌 ∗ 𝑂𝑣𝑒𝑟𝑡𝑖𝑚𝑒

𝜖 .
(2)

2

Unlike ICT survey, this survey is not legally mandated by MEXT, leading to a larger percentageof missing data. The wording of the relevant question also assumes digital record keeping ofworking hours. We find that relatively smaller BoEs are more likely to be missing; however,whether it affects our estimates based on within-BoE difference is not clear.

10The outcome variable 𝑂𝑣𝑒𝑟𝑡𝑖𝑚𝑒is the ratio of teachers working over a specificnumber of overtime hours in BoE 𝑖, school level 𝑗, month 𝑘 (April–August) of year 𝑡 (𝑡2020). To account for the seasonality in working conditions over a school year, we
include the pre-pandemic overtime baseline 𝑂𝑣𝑒𝑟𝑡𝑖𝑚𝑒. As some values of𝑂𝑣𝑒𝑟𝑡𝑖𝑚𝑒aare𝑂𝑣𝑒𝑟𝑡𝑖𝑚𝑒 𝑖𝑚𝑝𝑢𝑡𝑒𝑑

imputed,wealso

includedummy

variableto account for the fact. The definition of the remainder of theterms in Equations (2) is identical to those in Equations (1). All fixed effects models were
estimated using Stata 17 xtreg command.
For this fixed effect model to identify the causal effect of ICT resources on remoteeducation, the strict exogeneity of explanatory variables conditional on the unobserved
effect 𝛿

must be satisfied (Wooldridge, 2010; p. 304), namely, 𝐸 𝜖|

𝐼𝐶𝑇 𝑒𝑞𝑢𝑖𝑝𝑚𝑒𝑛𝑡 , 𝐼𝑇 𝑠𝑘𝑖𝑙𝑙𝑠 , 𝐷 , 𝛿 ) = 0.
We faced three potential issues in the causal interpretation of the effect of ICT resourceson remote education and teachers’ overtime hours. First, schools in urban areas might bebetter equipped than schools in remote locations, both in terms of ICT equipment andmore skilled teachers. Urban areas were also likely harder hit by the pandemic, possibly
resulting in a higher demand for remote education and longer closures.3 The BoE-level
fixed effects model allows us to eliminate these BoE-specific factors common to bothschool levels. We also use a school-level dummy variable to control for level-specificeffects, such as parents of older students likely having a higher income or higher demandfor online education.
The second concern is the exogeneity of ICT resources to the pandemic. Because ICT
resources were measured before school closures, we believe this concern is negligible.
Basic BoE-level funding, including for personnel costs, is determined by a formula based3

A major limitation of COVID-19 pandemic-related research in Japan is the lack of detailed

epidemiological data, as the Japanese government published the number of infections only on aprefectural level. While many municipalities disclosed their data, these are typically metropolitan
areas covering only a portion of our sample. Using a BoE level fixed effect model allows us towork around this problem and thus analyze the full national sample.

11

on enrollment. BoEs can apply for additional funding for specific purposes, with MEXT
deciding the amount and allocation. Given the unexpected nature of the pandemic and therigidities of the public school system, it is unlikely that more proactive BoEs at the timeof the survey equipped schools with ICT resources in expectation of online education,
first widely implemented in Japan during the pandemic. The difference in BoE-schooltype level ICT resources is thus likely caused by the varying pace of budget
implementation.
The third issue is the level of data aggregation. Most of the data are aggregated at the BoElevel, making BoE the unit of our analysis and possibly causing our estimates to sufferfrom attenuation bias due to classical measurement error.
The main underlying assumption of our analysis is that the BoE-level fixed effects control
for within-BoE common characteristics affecting the provision of ICT resources to bothschool levels. The fixed effects approach thus allows us to isolate the supply side effects
through the difference in ICT resources between elementary and junior high schools. This
strategy is valid only if, first, there is a sufficient within-municipality variation, andsecond, this variation is not systematically correlated with unobserved factors potentiallyaffecting variation in outcomes. While the national averages of some variables do nothave a substantial variation, the within-municipality distribution makes the fixed effects
strategy feasible. Next, to test the second condition, we use simple linear regression to
examine whether the within-BoE differences in ICT resources are systematicallyassociated with a set of municipality characteristics (population size and per capita
income) and the number of COVID-19 cases in April 2020 in the corresponding
prefecture. We do not confirm systematic correlation. We further examine the above
concerns in the robustness check in Appendix B.
5. Results and Discussion
5.1 Remote Education
In Japan, where telework during the pandemic remained limited even for jobs easily
performed remotely, teachers generally continued working from school during the school
closures, making the analysis of at-school ICT resources relevant. Better school resources
should generally contribute to children’s learning, here measured by the provision of12

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 levels on the BoE unit of observation. Thus, it does not determine which
factors contributed to the pandemic response if the response or the ICT provision wereidentical. For completeness, the results from Equation (1) omitting the BoE fixed effect𝛿 are presented in Appendix A Table A4.

School days closed(1)Elementary school(2)

Live online class

Live online
communication

(3)

(5)

(6)
-0.00869(0.00735)(4)0.102** 0.0942**(0.0384) (0.0399)-0.0110-0.0118(0.00834) (0.00862)-0.00982(0.00710)

0.00461(0.0162)0.000248(0.00494)

0.00380(0.00454)

Teachers' IT Skill
IT skill index - Proficient-0.0188(0.0311)-0.00219(0.00336)𝑁-0.179*(0.106)-0.0427
Wi-Fi(0.0801)
0.0133Presentation device(0.0410)0.00673
Digital instructions(0.109)0.105
Digital textbook(0.182)0.0889Management software(0.0595)-0.0814
Security policy(0.102)
3,422-0.175(0.106)-0.0422(0.0795)
0.0112(0.0403)
0.00469(0.110)0.104(0.182)0.0848(0.0605)-0.0810(0.101)
3,422-0.0205-0.0202(0.0250) (0.0249)
0.0294*0.0295*(0.0161) (0.0160)
0.0209** 0.0206**(0.00954) (0.00898)
0.0312*0.0310*(0.0155) (0.0155)-0.0220-0.0220(0.0252) (0.0252)-0.0102-0.0106(0.0328) (0.0330)-0.0117-0.0117(0.0302) (0.0304)
3,386
3,386

𝑅

0.007

0.007

IT skill index - Lacking

0.00373(0.00440)

School ICT Equipment
High-speed internet

0.008

0.008

-0.00964(0.0295)
0.0277(0.0169)
0.00623(0.00951)
0.00549(0.0136)-0.0185(0.0215)-0.0170(0.0335)0.0349(0.0303)
3,386-0.0102(0.0291)
0.0276(0.0171)
0.00739(0.00908)
0.00579(0.0137)-0.0192(0.0214)-0.0162(0.0341)0.0358(0.0307)
3,386

0.005

0.005

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

Table 2: Effect of ICT resources on remote education
Results from Equation (1) are displayed in Table 2. As seen in columns (1) and (2), we
do not confirm any consistent link between schools’ ICT resources and the length of13

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 the remaining remote education outcomes, we find no effect of teachers’ IT
skills on live online classes within BoEs; rather, physical ICT equipment seemed to haveenabled schools to transition to online education. The results in Table 2 columns (3) and(4) show that within BoEs, a higher Wi-Fi provision led to a higher likelihood of live
online classes; however, this effect is significant only at a 10% level and not significant
for live online communication with parents in columns (5) and (6). Likewise, schools
better equipped with presentation devices in regular classrooms were at a 5% significancelevel more likely to provide live online classes. Regarding teaching resources, theavailability of commercial digital instructional materials for teachers increased thelikelihood of live online classes and, as expected, had no effect on communication withparents. These results are consistent with how online education was typically depicted by
the Japanese media: one teacher per empty classroom using a combination of digitalpresentation tools and a physical board in front of a camera.
Furthermore, we include interaction terms in Equation (1) to explore the impact of schoollevel specific IT skills and the complementarity of skills and equipment. First, as shownin Appendix A Table A5, we do not confirm any statistically significant effect of schoollevel-specific IT skills on remote education outcomes. Next, we create two dummy
variables each for ICT equipment confirmed relevant in Table 2 and IT skills, indicating
a value above the sample mean and sample median for each school level. As per Appendix
A Table A6, we do not confirm any significant effect of the interaction terms for either
definition. This result suggests that the null effect of IT skills on the likelihood of online
class implementation was not caused by school-level specific factors or by a shortage of
ICT equipment, possibly preventing teachers from manifesting their IT skills.

14

To further our understanding of these results, we conducted a survey of teachers on a web
platform operated by a large Japanese educational company. This platform 4 aims toprovide its 73,000 freely registered users with lesson resources and opportunities toexchange ideas. We collected responses for one month in August 2022, receiving 424
answers from public elementary and junior high school teachers, accounting for 83% and
17% of the responses, respectively. The respondents were approximately equallydistributed in age from 20s to 50s, and 60% were female.
The survey showed that 32% of the sampled teachers conducted online classes at least
once during the pandemic, with over 90% broadcasting lessons from school. This result
confirms that it is the school environment, not the teachers’ home environment, thatshould be examined. Moreover, teachers with experience of online classes selected
presentation devices as the main equipment used, while only a small percentage
responded that they used PCs or tablets only. Furthermore, 25% of the sampled teachersconsidered presentation devices the key equipment 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 likely overstates the extent of online
education. However, these findings are in line with our results.
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 unrelated to teachers’ IT skills. Rather, ICT equipment essential for accessing
the Internet in a socially distanced environment (Wi-Fi) and the tools necessary to holdan online class (digital materials for teachers and presentation devices to project them)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 remoteeducation during school closures was a lack of basic ICT infrastructure in schools. We
also confirm that using live online tools for classes and communication with students’families is different in nature, with the latter being less equipment-dependent.
Considering the presence of measurement error, these results are likely lower bounds ofthe actual effects; on the other hand, potential omitted variable bias would lead to our
4“Foresta Net” owned by Sprix, Ltd. https://foresta.education/15

results being overestimated. Although the issue of the potential bias remains, we confirm
the robustness of our results in the discussion in Appendix B. Our findings stand incontrast with Dincher and Wagner (2021) and Akah et al. (2022), who reported an
association between teachers’ IT skills and the increased use of online teaching and ICT
tools in elementary and secondary schools in Germany and universities in Nigeria. Whilethese studies, unlike the present one, did not provide a causal discussion, this difference
also likely stems from the difference in their settings and an overall digitalization of
education in the respective countries, highlighting the need for country-specific researchand policies.
5.2 Overwork
The expected impact of ICT resources on teachers’ work hours is not theoretically clear.
Better ICT resources might enable teachers to perform their jobs, possibly increasing
work hours, yet teachers with better IT skills might be able to prepare more efficiently,
possibly reducing the time required. It should be noted that teachers with weak IT skills
might also be deficient in other skills, lowering the relevance of IT skills specifically.
Further, considering the data limitations, the following result likely underrepresent theactual situation.
The results using Equation (2) are displayed on a timeline in Figure 2 and fully inAppendix A Table A7. To arrive at the effect size, we divided the standardized coefficientestimates by the average ratio of teachers working over the specific threshold of overtime.
Teacher IT skill proficiency had only a limited impact on overtime hours in terms ofstatistical significance. However, regardless of significance, proficiency decreased thepercentage of teachers working overtime for nearly all months and thresholds except forAugust, a summer break month used for supplementary classes. Focusing on thesignificant effects, 1 standard deviation improvement in IT skill proficiency would resultin a 14% decrease in teachers working over 80 overtime hours in May and a 4.7%
decrease in July. Both months were transitional—from closures to in-person classes for
May and to summer 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 dramatic transition, is not statistically significant.
16

Conversely, the effect of the overall lack of IT skills was more pronounced after schools
reopened, especially for extreme overtime. During school closures, in April, the lack of
IT skills at a 10% level of significance increased the percentage of teachers working over
45 hours of overtime by 3.8% per standard deviation. For all months after reopening, thelack of IT skills had a statistically significant impact on extreme overtime. The effect sizeof 1 standard deviation deterioration in IT skills stood at 6.9% and 5.7% for June and July,
respectively, and 27.8% for August. As the percentage of BoEs reporting implementationof live online classes during school closures stood at just 8–9%, working hours during
closures in most BoEs would not have been spent preparing or providing online education,
thus lowering the importance of IT skills. However, teachers in all BoEs were tasked withcompensating for learning loss after reopening, making IT skills pertinent. Regardless ofsignificance, lack of skills increased overtime for both thresholds and all months.

Figure 2: Effect of teachers’ IT skills on overtime17

To confirm whether these results are driven by IT skills, as opposed to other more generalskills, we include teacher characteristics as additional controls in Equation (2).
Specifically, we utilize information about teacher’s educational attainment, average age,and male-to-female ratio from the School Teachers Survey of the 2019 school year as aproxy for general skills. The results in Appendix A Tables A8 to A10 show that our main
results for either measure of IT skills are robust to controlling for teacher characteristics.

Figure 3: Effect of IT skills on overtime – elementary school

Next, to investigate the role of school level-specific IT skills, we add school-level and IT
skills interaction terms to Equation (2). The results are presented in Figures 3 and 4. For
elementary schools, IT skill proficiency in general reduced overtime at both thresholds,
although the effects lack statistical significance. Conversely, a lack of IT skills
significantly increased the percentage of teachers working over 45 extra hours in all
months except August and those working over 80 extra hours in April and May, likely
reflecting the difficulty of providing education to very young students during a pandemic.
18

For junior high schools, proficiency showed a similar trend, decreasing overtime but
largely missing statistical significance. Compared to elementary schools, the significant
detrimental effect of lack of IT skills materialized later, concentrating largely in the postclosure period. This result is consistent with the above interpretation that IT skills are
more relevant when teachers actually teach.

Figure 4: Effect of teachers’ IT skills on overtime – junior high school
The teachers’ survey provides anecdotal evidence as to how the lack of IT skills affectedteachers’ overtime. After schools reopened, in addition to conducting face-to-face classes,
teachers were required to get accustomed to providing online education to prepare forpossible subsequent closures. On top of that, teachers were required to set up devices
newly provided to students and teach them how to use them, while following information
security regulations. Teachers also cited insufficient BoE-provided IT training and a lackof IT support, limited to several days a month. Teachers, therefore, likely spend more19

time than usual in schools, especially after reopening, and the lack of IT skills might haveseverely affected their work hours.
Overall, our analysis suggests that overtime hours are associated with a lack of teachers’
IT skills rather than proficiency. This result again demonstrates the importance ofremoving supply-side bottlenecks, and the supplemental teacher survey supports thesefindings. We also confirm a heterogeneous effect of IT skills based on school level andtiming, indicating the complex nature of the pandemic response. These results are robust
against a variety of robustness checks discussed in Appendix B. Nevertheless, it isimportant to stress that IT skills might be representative of a broader skill set, making a
possible intervention difficult to design. Additionally, our results suffer from limitations
due to data restrictions and the consequently adopted empirical strategy, and more
research into the topic is needed to help alleviate the burden teachers in Japanese schoolsface.
6. 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
demand side, as investigating the impact of the supply side on the provision of remoteeducation is challenging due to the lack of appropriate data and the difficulty of separating
the effects of the supply and demand sides.
The percentage of boards of education reporting the implementation of online classes
during mandated school closures in their municipality at the start of the pandemic stoodat less than 10% nationwide. The paper aims to empirically isolate the effect of school
ICT resources on the provision of online education and teachers’ work hours early in the
pandemic, using nationwide administrative data and a BoE-level fixed effects model. The
unique point of our analysis compared to the currently available literature is that we
simultaneously investigate the effect of school ICT resources on both the provision of
online education and the choice of communication channel with students’ families while
also examining teachers' working conditions during and after the closures. Under theassumptions discussed in Section 4, our results allow for a causal interpretation of theeffect of ICT resources on both students and teachers.
20

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 to have enabled schools to transition to online education, suggestinga technological bottleneck 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 alack of IT skills, while the beneficial effect of IT skill proficiency is weak. Additionally,
we identify a heterogeneous effect of IT skills on teachers’ overtime by school level andtiming, 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 various 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 BoE level, not teachers’
IT skills, developed at an individual level. However, the persistent overwork of teachersin Japan may result from a lack of IT skills. Therefore, the effect of ICT resources ismulti-dimensional, which is 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
describing schools’ ICT resources and robustness checks, other unobserved school
resources and teachers’ skills may also be relevant in determining remote education
practices and teachers’ work hours. Likewise, it is possible that some measures that do
not vary much between school levels may affect both outcomes, yet they are notconsidered in our analysis, given our analytical framework. It is also clear that our results
are specific to the 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 isneeded to generalize our findings to a broader context of other societies and circumstances.
However, as ICT is a convenient tool to ensure children’s continued education in timesof crisis in general, such as during conflicts or in case of natural disasters, we believe ourresults are informative beyond the context analyzed in this study.
21

Acknowledgement
We thank the Japanese Ministry of Education, Culture, Sports, Science and Technology
for providing us the data used in this study. We also thank the participants at various
seminars and academic conferences for their comments. This work was supported by
KAKENHI Grant Number 21H04982, 16H06323 and 20H05631 from the Japan Society
for the Promotion of Science, and by Mitsubishi Zaidan and Keio Gijuku Academic
Development Funds.
Declaration of interest
None.
Data accessibility statement
The data that support the findings of this study are available from the Japanese Ministryof Education, Culture, Sports, Science and Technology. Restrictions apply to theavailability of these data, which were used under license for this study.

22

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26

Appendix A: Additional tables

2019-20

2018-19

SurveyConductor

Respondent

Date

Details

Survey on ICT in School Education 2018
School Year
(Heisei 30 Nendo Gakko ni okeru Kyoiku
no Johoka ni kan suru Chosa)

MEXT

School(nationwide)

March 1,
2019

This survey is conducted as of March 1 every year by the Financial
Support and Teaching Materials Division, MEXT. All public elementary,junior-high, senior-high, and special-needs schools are mandated torespond. BoEs enforce compliance.

Survey on Reform of Working Conditionsin Schools by the Local Boards ofEducation 2019 School Year
(Reiwa Gannendo Kyoiku Iinkai ni okeru
Gakko no Hatarakikata no tame no
Torikumi Jokyo Chosa)School Teachers Survey 2019 School Year
(Reiwa Gannendo Gakko Kyoin Tokei
Chosa)MEXT

BoE(nationwide)

July 1,
2019

This survey is conducted as of September 1 every year (July 1 for initial
year) by the Financial Affairs Division, MEXT. BoEs report the work
hours and conditions of teachers in the public elementary, junior-high,senior-high, and special-needs schools in their districts. Responding tothis survey is not mandatory.

MEXT

October 1,
2019This survey is conducted as of October 1 every year by the Analytical
Research Planning Division, MEXT. All public elementary, junior-high,senior-high, and special-needs schools are mandated to respond to theschool questionnaire, including the teacher questionnaire for randomly
selected schools. BoEs enforce compliance.

Survey on ICT in School Education 2019
School Year
(Reiwa Gannendo Gakko ni okeru Kyoiku
no Johoka ni kan suru Chosa)

MEXT

School(schoolquestionnaire
nationwide,
teacherquestionnaire inselected schools)
School(nationwide)

March 1,
2020

This survey is conducted as of March 1 every year by the Financial
Support and Teaching Materials Division, MEXT. All public elementary,junior-high, senior-high, and special-needs schools are mandated torespond. BoEs enforce compliance.

Survey on Learning and Instruction during
the COVID-19 Pandemic (Shingata Korona
Uirusu Kansensho no Eikyo wo Fumaeta
Gakushu Shido nado ni kan suru Jokyo
Chosa)
Survey on Reform of Working Conditionsin Schools by the Local Boards ofEducation 2020 School Year
(Reiwa 2 Nendo Kyoiku Iinkai ni okeru
Gakko no Hatarakikata no tame no
Torikumi Jokyo Chosa)

MEXT

BOE
(nationwide)

June 23,
2020

This survey was conducted by the School Curriculum Division, MEXT.
BoEs reported pandemic response for all public elementary, junior-high,senior-high, and special-needs schools in their districts. Responding tothis survey was not mandatory.

MEXT

BOE
(nationwide)

September
1, 2020

This survey is conducted as of September 1 every year by the Financial
Affairs Division, MEXT. BoEs report the work hours and conditions ofteachers in public elementary, junior-high, senior-high, and special-needs
schools in their districts. Responding to this survey is not mandatory.

2020-21

MANDATED SCHOOL CLOSURES (April–May 2020)

Table A1: Survey overview
27

Variable
High-speed
Internet
Wi-Fi
Presentation device
Digital instructions
Digital textbook
Management
softwareSecurity policy

Definition
Ratio of schools in BoE equipped with the Internet faster than
100 Mbps
Ratio of schools in BoE equipped with Wi-Fi
Average number of presentation devices (projector, digital
whiteboard, digital TV) per normal classroom in BoE
Ratio of schools in BoE equipped with commercial digital
instructional materials for teachers (e.g., lesson presentation
slides)Ratio of schools in BoE equipped with commercial digitalmaterials for students
Ratio of schools in BoE using software to store and manage
information
Ratio of schools in BoE with a set policy regarding storing andhandling information
Table A2: ICT equipment variables definition

28

MonthN

ELEMENTARYSCHOOL
ImputedMean%

JUNIOR HIGHSCHOOL
ImputedMean%> 45 HOURS

Year 2020
April
May
June
July
AugustYear 2019
April
May
June
July
AugustYear 2020
April
May
June
July
AugustYear 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

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

0.546
0.545
0.558
0.390
0.041

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

0.028
0.013
0.084
0.066
0.007

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

0.149
0.138
0.158
0.076
0.009

-

0.186
0.131
0.574
0.583
0.205

-

0.644
0.642
0.650
0.531
0.109

35.4
34.6
31.6
23.5
24.9

-

0.034
0.018
0.158
0.183
0.027

-

35.4
34.6
31.6
23.4
25.9

0.255
0.250
0.259
0.166
0.026

35.4
34.6
31.6
23.5
24.9

35.4
34.6
31.6
23.4
25.9
> 80 HOURS

Table A3: Summary statistics – ratio of staff working over 45 or 80 overtime hours a
month

29

Elementary school

School days
closedLive online class

Live online
communication

(1)

(2)

(3)

(4)

(5)

0.258

0.808***-0.0124-0.0183**-0.0100-0.0153*(0.220)(0.298)(0.00857)(0.00796)(0.00847)(0.00869)(6)

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***(0.823)Wi-Fi(0.790)0.0228**0.0241**0.0263*0.0267*(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(0.0180)(0.0179)(0.0176)(0.0172)

Presentation device
Digital instructions
Digital textbook(1.146)(1.095)

Security policy -1.360** -1.229**

0.01290.01220.0226*0.0223*(0.607)(0.576)(0.0127)(0.0126)(0.0120)(0.0123)

𝑁

3,422

3,422

3,386

3,386

3,386

3,386

𝑅

0.045

0.062

0.013

0.013

0.013

0.012

Notes: Estimation results from linear regression. Robust standard errors clustered at BoE and prefecture level inparentheses. *** p<0.01, ** p<0.05, * p<0.1

Table A4: Effect of ICT resources on remote education – linear regression

30

School days
closedLive 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 school
Junior high school
LackingElementary school
Junior high school

Notes: Coefficient estimates from Equation (1) with added school-level dummy and IT
skill indices interaction term. The coefficients are produced using Stata command"lincom" for the respective linear combination of the baseline IT skill effect and theschool-level specific effect. Robust standard errors clustered at BoE and prefecturelevel in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Table A5: Effect of teachers’ IT skills on remote education – linear fixed effects modelwith interactions

31

Live online class
Skill continuous

Skill continuous

Skill mean

Skill mean

Skill continuous

Skill median

Skill median

ICT continuous(1)(2)

ICT mean(3)(4)

ICT continuous(5)(6)

ICT mean(7)(8)

ICT median
(9)
(10)

ICT continuous
(11)
(12)

ICT median
(13)
(14)

Teachers' IT Skill
IT skill index - Proficient -0.00176

0.00162(0.00994)(0.0140)

IT skill index - Lacking

0.00349(0.0345)0.00146(0.00945)

0.00348(0.00841)

0.0301*(0.0162)-0.00948

0.01370.0143(0.0179) (0.0175)
0.00142(0.00698)-0.00350(0.00945)
0.00459 0.00491(0.00917) (0.00907)-0.00619(0.00831)
0.00246(0.00817)
0.01670.0165(0.0121) (0.0118)
0.00342(0.0110)-0.0125(0.00781)(0.00949)

0.00481(0.0231)-0.00878(0.0228)

0.00237(0.00901)-0.00354(0.0198)

0.00388(0.0323)-0.00130(0.00818)

0.0134(0.0190)-0.00369(0.0240)-0.0137(0.0185)

School ICT Equipment
Wi-Fi 0.0292*(0.0169)
Wi-Fi * IT skill index - 0.00256
Proficient (0.00750)

Wi-Fi * IT skill index Lacking
Presentation device 0.0211**(0.00994)
Presentation device * IT skill -0.00468
index - Proficient (0.0110)

Presentation device * IT skill
index - Lacking
Digital instructions 0.0311*
Digital instructions * IT skill
index - Proficient
Digital instructions * IT skill
index - Lacking
𝑁𝑅(0.0164)0.00768(0.0115)

3,386
0.009

-0.000537(0.00584)
0.0207**(0.00918)

0.00306(0.00737)
0.0309*(0.0161)

3,386
0.009

3,386
0.005

3,386
0.005

0.0303(0.0201)-0.00340(0.0179)

0.0223(0.0176)-0.00377(0.0285)0.0322**(0.0144)-0.00265(0.0229)

3,386
0.008

0.0258(0.0177)0.00816(0.0162)0.0162*(0.00870)

0.0133(0.0214)-0.000432(0.0206)

0.0109(0.0192)

0.0157(0.0157)-0.000321(0.00700)

0.00695(0.0194)0.000490(0.00898)

0.0165(0.0154)0.0344(0.0216)-0.00938(0.0152)

0.0319*(0.0170)0.0187(0.0167)-0.0165-0.0184-0.000459(0.00704)-0.000795 -0.000797(0.0102)(0.0101)-0.00403(0.00746)
0.00854(0.00730)
0.0234*0.0229*(0.0131)(0.0130)
0.00288(0.0114)-0.00969-0.0152-0.0235-0.0234(0.0181)(0.0164)(0.00689)(0.0235)(0.0198)(0.0184)

3,386
0.006

3,386
0.009

3,386
0.011

0.0117(0.0161)
0.0376*(0.0206)

3,386
0.009

0.00924(0.0143)-0.0110(0.0186)

0.0185(0.0113)-0.00396(0.0192)

3,386
0.005

0.0109(0.0158)
0.0243(0.0159)

3,386
0.005

3,386
0.005

0.0144(0.0191)
0.0128(0.0250)

0.0382**(0.0150)-0.00269(0.0128)0.0178*(0.00904)

0.0225(0.0198)-0.0139(0.0165)

0.00538(0.0191)
0.0415*(0.0216)-0.00746(0.0155)
0.0133(0.0176)

0.0336**(0.0150)-0.0197(0.0212)

3,386
0.006

-0.00246(0.0156)-0.0125(0.0131)0.0240(0.0202)
0.0332*(0.0176)

3,386
0.009

Notes: Coefficient estimates from Equation (1) with added ICT equipment variables and IT skill indices interaction terms. Robust standard errors clustered at BoE and prefecture level in parentheses. *** p<0.01, ** p<0.05, *p<0.1

Table A6: Effect of ICT resources on remote education – linear fixed effects model with interactions

32

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

IT skill index
Proficient
Effect size
𝑁
𝑅

PANEL BMay 2020

June 2020

July 2020

August 2020

-0.000446-0.00184

0.000215-0.00233**-0.00552-0.00294-0.00447-0.00592*

0.00272

0.000855(0.00421)-0.24%
2,440
0.035(0.00185)-5.91%
2,440
0.017(0.00451)
0.17%
2,454
0.059

(0.00105)-15.23%
2,454
0.030

(0.00512)-1.06%
2,560
0.332(0.00385)-2.43%
2,560
0.320(0.00549)-0.89%
2,536
0.534(0.00344)-4.75%
2,536
0.525(0.00448)
1.96%
2,406
0.462

(0.00147)
5.05%
2,406
0.154

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

0.00691*(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

IT skill index
Lacking
Effect size
𝑁
𝑅

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

Table A7: Effect of teachers’ IT skills on overtime – linear fixed effects model

33

PANEL A: Teacher’s characteristics controlled
April 2020
May 2020

June 2020

July 2020

August 2020

Overtime threshold

45

80

45

80

45

80

45

80

45

80

Teacher ratio

(1)
0.183

(2)
0.034

(3)
0.133

(4)
0.018

(5)
0.528

(6)
0.127

(7)
0.510

(8)
0.132

(9)
0.136

(10)
0.018

IT skill index
Proficient-0.00535(0.00617)-0.000381(0.00231)

0.00426(0.00801)

0.000421(0.00190)-0.00639(0.0128)-0.00558(0.00843)-0.00672(0.0125)-0.00932(0.00837)-0.00685(0.00997)-0.00129(0.00212)

Graduate school ratio

0.00407(0.0359)-0.00165(0.0369)

0.0279(0.0202)
0.0166(0.0158)

0.0383(0.0610)-0.0407(0.0525)

0.0251**(0.0114)0.00998(0.0160)-0.0848(0.0819)0.00909(0.0533)-0.0492(0.0489)-0.0224(0.0347)-0.0320(0.0714)-0.0149(0.0476)-0.0106(0.0374)-0.0442(0.0351)

0.0378(0.0621)0.0733(0.0638)-0.00985(0.0151)
0.00389(0.0133)-2.92%-1.12%

3.20%

2.34%

-1.21%-4.39%-1.32%-7.06%-5.04%-7.17%

1,222
0.070

1,222
0.067

1,225
0.094

1,225
0.109

1,284
0.454

1,284
0.466

1,262
0.660

1,262
0.650

1,190
0.545

1,190
0.265

2-year college ratio

IT skill: Effect size
𝑁
𝑅

PANEL B: Teacher’s characteristics not controlled
April 2020
May 2020

June 2020

July 2020

August 2020

Overtime threshold

45

80

45

80

45

80

45

80

45

80

Teacher ratio

(11)
0.183

(12)
0.034

(13)
0.133

(14)
0.018

(15)
0.528

(16)
0.127

(17)
0.510

(18)
0.132

(19)
0.136

(20)
0.018

-0.00532(0.00612)-2.91%-0.000256(0.00227)-0.75%

0.00448(0.00779)
3.37%

0.000625(0.00189)
3.47%-0.00701(0.0134)-1.33%-0.00601(0.00858)-4.73%-0.00699(0.0127)-1.37%-0.00947(0.00838)-7.17%-0.00640(0.0101)-4.71%-0.00137(0.00215)-7.61%

1,222
0.070

1,222
0.058

1,225
0.091

1,225
0.100

1,284
0.451

1,284
0.464

1,262
0.659

1,262
0.649

1,190
0.542

1,190
0.264

IT skill index
Proficient
IT skill: Effect size
𝑁
𝑅

Notes: Estimation results from linear fixed effect model at BoE level. Panel A displays the results with educational controls included. Panel B displays the results from model inEquation (2) in the sample with educational controls available. Robust standard errors clustered at BoE and prefecture level in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Table A8: Effect of teachers’ IT skills on overtime in sample with teachers’ educational controls available – IT skill proficiency

34

PANEL A: Teacher’s characteristics controlled
April 2020
May 2020

June 2020

July 2020

August 2020

Overtime threshold

45

80

45

80

45

80

45

80

45

80

Teacher ratio

(1)
0.183

(2)
0.034

(3)
0.133

(4)
0.018

(5)
0.528

(6)
0.127

(7)
0.510

(8)
0.132

(9)
0.136

(10)
0.018

IT skill index
Lacking

0.00136(0.00715)

4.07e-05(0.00246)-0.0134(0.0114)-0.00274(0.00171)-0.00131(0.00936)

0.0121(0.00779)-0.000446(0.00841)

0.0107*(0.00560)

0.0146(0.0122)0.00322(0.00300)

Graduate school ratio

0.00374(0.0355)-0.00161(0.0374)

0.0278(0.0199)
0.0166(0.0157)

0.0322(0.0614)-0.0341(0.0512)0.0237**(0.0115)
0.0113(0.0158)-0.0871(0.0816)0.00909(0.0553)-0.0438(0.0483)-0.0278(0.0360)-0.0339(0.0707)-0.0152(0.0492)-0.00760(0.0375)-0.0488(0.0327)

0.0460(0.0618)
0.0682(0.0615)-0.00783(0.0148)0.00267(0.0126)

0.74%

0.12%

-10.08%-15.22%-0.25%

9.53%-0.09%

8.11%

10.74%

17.89%

1,222
0.069

1,222
0.067

1,225
0.100

1,225
0.114

1,284
0.453

1,284
0.471

1,262
0.659

1,262
0.651

1,190
0.548

1,190
0.269

2-year college ratio

IT skill: Effect size
𝑁
𝑅

PANEL B: Teacher’s characteristics not controlled
April 2020
May 2020

June 2020

July 2020

August 2020

Overtime threshold

45

80

45

80

45

80

45

80

45

80

Teacher ratio

(11)
0.183

(12)
0.034

(13)
0.133

(14)
0.018

(15)
0.528

(16)
0.127

(17)
0.510

(18)
0.132

(19)
0.136

(20)
0.018

IT skill index
Lacking
IT skill: Effect size𝑁𝑅

0.00130-0.000174-0.0141-0.00287*-0.000164

0.0124-0.000193

0.0103*0.01450.00336(0.00698)(0.00243)(0.0109)(0.00159)(0.00984)(0.00783)(0.00858)(0.00558)(0.0123)(0.00302)

0.71%-0.51%-10.60%-15.94%-0.03%

9.76%-0.04%

7.80%

10.66%

18.67%

1,222
0.069

1,222
0.058

1,225
0.099

1,225
0.105

1,284
0.450

1,284
0.469

1,262
0.659

1,262
0.650

1,190
0.545

1,190
0.268

Notes: Estimation results from linear fixed effect model at BoE level. Panel A displays the results with educational controls included. Panel B displays the results from model inEquation (2) in the sample with educational controls available. Robust standard errors clustered at BoE and prefecture level in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Table A9: Effect of teachers’ IT skills on overtime in sample with teachers’ educational controls available – lacking IT skills

35

PANEL A: Teacher’s characteristics controlled
April 2020

May 2020

June 2020

July 2020

August 2020

Overtime threshold

45

80

45

80

45

80

45

80

45

80

Teacher ratio

(1)
0.185

(2)
0.031

(3)
0.123

(4)
0.015

(5)
0.523

(6)
0.121

(7)
0.502

(8)
0.124

(9)
0.138

(10)
0.017

IT skill index
Proficient-0.000781-0.00191-0.000503-0.00251**-0.00708-0.00410-0.00695-0.00744**

0.001290.000847(0.00403)(0.00190)(0.00429)(0.00108)(0.00504)(0.00374)(0.00523)(0.00314)(0.00410)(0.00148)

Male-to-female ratio-0.112**(0.0422)-0.00347*(0.00184)-0.0599**(0.0225)-0.00135(0.000834)-0.104**(0.0466)-0.00566***(0.00201)-0.0319*(0.0185)-0.00158**(0.000637)

0.000149(0.0575)-0.00866***(0.00245)

0.0451(0.0554)-0.00566***(0.00200)0.0291(0.0678)-0.0113***(0.00176)

0.0601(0.0604)-0.00599***(0.00221)0.113**(0.0492)-0.00600**(0.00242)

0.00757(0.0144)-2.38e-05(0.000777)-0.42%-6.16%-0.41%-16.73%-1.35%-3.39%-1.38%-6.00%

0.93%

4.98%

2,404
0.048

2,404
0.030

2,418
0.088

2,418
0.052

2,524
0.351

2,524
0.335

2,500
0.554

2,500
0.533

2,372
0.470

2,372
0.151

Average teacher age

IT skill: Effect size
𝑁
𝑅

PANEL B: Teacher’s characteristics not controlled
April 2020
May 2020

June 2020

July 2020

August 2020

Overtime threshold

45

80

45

80

45

80

45

80

45

80

Teacher ratio

(11)
0.185

(12)
0.031

(13)
0.123

(14)
0.015

(15)
0.523

(16)
0.121

(17)
0.502

(18)
0.124

(19)
0.138

(20)
0.017

IT skill index
Lacking

0.00810**0.00105

0.00535*0.00167*

0.00569

0.00983***

0.00703*

0.00882***0.0101**0.00477*(0.00365)(0.00147)(0.00301)(0.000845)(0.00453)(0.00356)(0.00402)(0.00281)(0.00448)(0.00254)

Male-to-female ratio-0.115***(0.0426)-0.00374*(0.00187)-0.0609**(0.0229)-0.00135(0.000842)-0.106**(0.0466)-0.00583***(0.00199)-0.0334*(0.0188)-0.00159**(0.000625)-0.00528(0.0584)-0.00873***(0.00241)0.0400(0.0565)-0.00593***(0.00193)

0.0233(0.0691)-0.0114***(0.00174)

0.0537(0.0616)-0.00615***(0.00217)

0.111**(0.0502)-0.00634**(0.00239)

0.00681(0.0147)-0.000193(0.000730)

4.38%

3.39%

4.35%

11.13%

1.09%

8.12%

1.40%

7.11%

7.32%

28.06%

2,404
0.053

2,404
0.030

2,418
0.090

2,418
0.050

2,524
0.351

2,524
0.340

2,500
0.554

2,500
0.534

2,372
0.473

2,372
0.160

Average teacher age

IT skill: Effect size
𝑁
𝑅

Notes: Estimation results from linear fixed effect model at BoE level in the sample with teacher average teacher age and male-to-female ratio variables available. Robust standard errors clustered at BoE
and prefecture level in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Table A10: Effect of teachers’ IT skills on overtime with male-to-female ratio and average teacher age controls
36Appendix B: Robustness check
To address the potential concerns raised in Section 4 regarding the validity of ourapproach, 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 validity of the BoE-level fixed effects approach inaddressing unobserved common local factors that simultaneously influence both ICT
resources and the schools’ pandemic response. We adopt two approaches—splitting thefull sample into subsamples of interest and adding interaction terms.
First, our approach assumes that the within-BoE difference in outcomes is uncorrelatedwith unobserved BoE-specific factors once the differences in schools’ ICT resources arecontrolled for. However, this assumption might not hold universally. One possible caseis the difference between urban and rural areas. The demand for ICT resources in juniorhigh 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 high schools. 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 unions of villages), thusroughly 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 displayedin Table B1 and Table B2 closely resemble the main ones for all subsamples. The only
exception is the negative impact of presentation devices in regular classrooms on live
online communication with parents in the subsample of BoEs with private junior highschools. However, considering the sample size and the remainder of the results, we do
not regard it as a concern. The results of the overtime analysis are presented in Tables B3,
B4, B5, and B6. We find that while the effects of both proficiency and lack of IT skills
37largely retain their signs in all subsamples, the effect of lack of skills was generally more
pronounced in rural areas and correspondingly in areas without private junior highschools.
Second, in all our models, we include a school-level dummy variable in addition to a BoE
fixed effect. 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 acrossprefectures would be correlated with the outcomes, violating our assumption on thecommon school-level dummy. To filter this potential effect out, we include school-leveland prefecture dummy interaction terms in Equations (1) and (2) for remote educationand overtime analysis, respectively. Adding these interaction terms does not qualitatively
alter the results of either of these analyses, as presented in Table B7 and Table B8,
supporting our assumption about school-level dummy. Although it is not possible toprove the validity of the BoE-level fixed effects approach completely, a series ofrobustness checks above suggest that the potential concern with our approach largely does
not seem to affect our main results.
Next, we focus on the potential endogeneity of ICT resources. To assess the robustnessof 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 the2020 level would likely create a positive bias in the effect of ICT equipment on theprovision of remote education. BoEs 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 B9 for the available variables are
consistent with the main results, supporting our interpretation that the challenge toimplementing online education in Japan was insufficient physical ICT equipment.
Although neither the model specification nor the within-BoE difference is identical to the8

The 2018 school year wave of the Survey on ICT in School Education collected in March 2019contains most of the ICT variables utilized in the main analysis, except for the prevalence of WiFi and digital textbooks for students.

38

2020 ICT equipment level analysis, we do not confirm a systematic positive bias. The
effect of significant variables in the original analysis is larger for the 2019 level ofpresentation 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 (2), obtaining essentially identical results to the main ones, as
displayed in Table B10. After controlling for 2019 ICT equipment, we find that the effectsize 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 the concern about the possible endogeneity of 2020 ICT equipment isunfounded.
Additionally, for overtime analysis, we looked further into the past, setting 2019 overtime
as the outcome and 2018 overtime as the baseline in Equation (2). Schools with, undernormal circumstances, chronically overworked teachers could have better ICT resources
to help with the workload. This could possibly create a counteractive correlation in theeffects 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 ofteachers’ IT skills on overtime to be generally larger than that in normal times. The 2018
overtime data were collected retrospectively in the 2019 wave of the relevant survey;
therefore, they cover a shorter period (April–June), and a larger percentage is missing. In
this restricted analysis, as presented in Table B11, the 2019 school year IT skill index had
a minimal impact on overtime 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 the2019 estimated effect size is slightly larger than that of 2020 only for April and IT skill
proficiency. Moreover, for 9 out of the 12 coefficient estimates, both levels 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 themain analysis. Furthermore, they strengthen our interpretation of the main findings thatIT skill proficiency is advantageous in transitional months (school year beginning), anda lack of IT skills worsens teachers’ working conditions in times of crisis.
39

Finally, one of the major limitations of our analysis is the use of data aggregated at a BoElevel. As our implicit decision model is based at school and teacher levels, using BoElevel data likely leads to a typical classical measurement error bias. To gauge the extent
of this bias in our main results, we run the analysis using Equations (1) and (2) on a
subsample of BoEs containing a single junior high school. The results in Table B12 andB13 are similar to the full sample results. Focusing on the coefficients statisticallysignificant for the full sample, we find that in the restricted sample, the effect sizes are
generally larger by factors of 1.1–2. While the characteristics of small BoEs might also
be reflected in the effect size differences, we confirm the presence of measurement error
bias in the main results. This source of bias may contribute to our results being
underestimated. It is also important to note that not finding a statistically significant effectof an ICT resource might not mean that this resource is ineffective in ‘absolute’ terms, as
our results provide guidance on ‘relative’ importance in the presence 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 remote education
provision, while the former is important for reducing 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.

40

URBAN
School days closed

Elementary school
Teachers' IT Skill
IT skill index - Proficient

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

Live online
communication

School days closed

Live online class

Live online
communication
(11)

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

0.0505(0.0315)

0.0272(0.0358)

0.00289(0.00828)

0.00580(0.00959)

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(0.0894)(0.0900)(0.0156)(0.0151)(0.0167)(0.0166)(0.0496)(0.0481)(0.0107)(0.0103)(0.0108)(0.0104)-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)-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)(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 level. Robust standard errors clustered at BoE and prefecture level in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Table B1: Effect of ICT resources on remote education – urban and rural subsamples

41

PRIVATE JUNIOR HIGH SCHOOL
School days closed

Elementary school
Teachers' IT Skill
IT skill index - Proficient

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

Live online
communication

School days closed

Live online class

Live online
communication

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

0.06430.04310.002250.001180.01020.00826

0.108**

0.101**-0.0129-0.0137-0.0132-0.0119(0.0472)(0.0480)(0.0110)(0.0109)(0.00958)(0.00924)(0.0416)(0.0424) (0.00998) (0.0102)(0.00820)(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 level. Robust standard errors clustered at BoE and prefecture level in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Table B2: Effect of ICT resources on remote education – subsamples with and without private junior high school
42

URBANPANEL 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
𝑁
𝑅

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
𝑁
𝑅

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

Table B3: Effect of teachers’ IT skills on overtime – urban subsample

43

RURALPANEL 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
𝑁
𝑅

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
𝑁
𝑅

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

Table B4: Effect of teachers’ IT skills on overtime – rural subsample

44

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
𝑁
𝑅

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
𝑁
𝑅

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

Table B5: Effect of teachers’ IT skills on overtime – subsample with private junior high school45

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
𝑁
𝑅

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
𝑁
𝑅

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

Table B6: Effect of teachers’ IT skills on overtime – subsample without private junior high schools

46

SCHOOL LEVEL AND PREFECTURE DUMMIES INTERACTION TERMS
School days closed
(1)

(2)

Live online class

Live online
communication

(3)

(5)

(4)

(6)

Elementary school 0.232***

0.232***-0.0404*** -0.0404***-0.0357*** -0.0358***(0.0154)(0.0154)(0.00278)(0.00317)(0.00277)

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

0.00374(0.0171)(0.00492)(0.00473)

IT skill index - Lacking
School ICT Equipment
High-speed internet-0.197*Wi-Fi(0.00314)-0.0117-0.00233

0.00405(0.0282)(0.00359)(0.00490)-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)

0.04240.04000.0293*0.0288*0.003840.00465(0.103)(0.104)(0.0147)(0.0147)(0.0135)(0.0137)

Digital instructions
Digital textbook
Management software
Security policy

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)

𝑁

3,422

3,422

3,386

3,386

3,386

3,386

𝑅

0.038

0.038

0.039

0.039

0.035

0.035

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

Table B7: Effect of ICT resources on remote education – school level and prefecturedummies interaction terms

47

SCHOOL LEVEL AND PREFECTURE DUMMIES INTERACTION TERMSPANEL 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
𝑁
𝑅

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
𝑁
𝑅

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

Table B8: Effect of teachers’ IT skills on overtime – school level and prefecture dummies interaction terms

48

2019 ICT EQUIPMENT
School days closed

Live online class

Live online
communication(1)
Elementary school 0.0920**

(2)

(3)

(4)

(5)

(6)

0.0842*-0.0133-0.0143-0.0112-0.00988(0.0424)(0.0437)(0.00922) (0.00951)(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-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)

Presentation device
Digital instructions
Management software
Security policy-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)

𝑁

3,418

3,418

3,384

3,384

3,384

3,384

𝑅

0.006

0.006

0.006

0.006

0.004

0.004

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

Table B9: Effect of ICT resources on remote education – 2019 ICT equipment49

2019 ICT EQUIPMENTPANEL 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
𝑁
𝑅
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%

0.001160.00360(0.00438)
0.69%

0.00813**(0.00360)
6.74%

0.00376(0.000940)
7.57%(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
𝑁
𝑅

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

Table B10: Effect of teachers’ IT skills on overtime – 2019 ICT equipment502019 OVERTIMEPANEL 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%

𝑁

1,742

1,742

1,776

1,776

1,914

1,914

𝑅

0.584

0.624

0.601

0.623

0.558

0.562

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%

𝑁

1,742

1,742

1,776

1,776

1,914

1,914

𝑅

0.584

0.622

0.602

0.622

0.558

0.562

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

Table B11: Effect of teachers’ IT skills on overtime – 2019 overtime hours

51

ONE JUNIOR HIGH SCHOOL
School days closed(1)

Live online class

Live online
communication
(5)
(6)

(2)

(3)

(4)

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)

Teachers' IT Skill
IT skill index - Proficient -0.00259

0.001260.000166(0.0269)(0.00454)(0.00585)

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

𝑁

1,100

1,100

1,088

1,088

1,088

1,088

𝑅

0.016

0.017

0.021

0.021

0.017

0.021

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

Table B12: Effect of ICT resources on remote education – subsample of BoEs with one
junior high school

52

ONE 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

0.027

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
𝑁
𝑅

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
𝑁
𝑅

Notes: Estimation results from linear fixed effect model at BoE level. Subsample of BoEs with one junior high school. Robust standard errors clustered at BoE andprefecture level in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Table B13: Effect of teachers’ IT skills on overtime – subsample of BoEs with one junior high school53

Appendix C: Teachers’ IT skills questionnaire
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 and the public.
A–3 Make use of word processing, spreadsheet, and presentation software, etc. to preparematerials such as prints or presentations used in class or documents and materials
needed for class management 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
to appropriately 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 and others, 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 and other tasks corresponding to each student’s level of learning for the
purposes of establishing knowledge and mastering technical skills.

54

B–4 Have students make effective use of computer software and other tools whensummarizing 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 to learning (character input, file management, and others).
C–2 Guide students to be able to use computers and the Internet to collect and selectrelevant and reliable information.
C–3 Guide students to be able to use word processing, spreadsheet, and presentation
software, etc. to organize research and ideas and to summarize them in text, tables,graphs, figures, and others in an easy-to-understand manner.
C–4 Guide students to use computers, software, etc. to be able to exchange, share, anddiscuss 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 andmanners 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 theInternet and other tools.
D–3 Guide students to gain the basic knowledge of information security and to
appropriately set and manage passwords to be able to use computers and the Internet
safely.
D–4 Guide students to acknowledge the usefulness of computers and the Internet andencourage students’ interest in utilizing them for learning and understanding theirmechanism.
551) 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. to prepare materials such as prints or presentations used in class or documents
and materials 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 outsideschool 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 and others 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 inteaching activities.
(Example B–1: Is able to present materials and others using computers and presentation
devices.)56

Mostly lacking: Does not know how to teach using ICT without receiving in-school orother training.
(Example B–1: Will be able to present materials and others using computers andpresentation 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 and presentation 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 operatecomputers and other 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 computersand other tools necessary to learning (character input, file management, andothers).)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
outside-school training) starting from the basics of operation.
(Example C–1: Does not know how to teach; step-by-step training (including outsideschool training) starting from the basics of operation is therefore necessary.)
Skill D: Ability to instruct students in knowledge and attitude needed to utilize
information
57

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 beable to take responsibility for their own actions participating in informationdriven society, to think of and 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
outside-school training) starting from the basics of operation.
(Example D–1: Does not know how to teach; step-by-step training (including outsideschool training) starting from the basics of operation is therefore necessary.)58