Discussion Papers In Economics
And Business
Impact of Temporary School Closure Due to
COVID-19 on the
Academic Achievement of Elementary SchoolStudents
Shinsuke Asakawa
Fumio Ohtake

Discussion Paper 21-14
September 2021

Graduate School of Economics
Osaka University, Toyonaka, Osaka 560-0043, JAPANImpact of Temporary School Closure Due to COVID-19 on the
Academic Achievement of Elementary School Students1
Shinsuke Asakawa23

Fumio Ohtake45

Abstract
This study examines the effects of COVID-19 related temporary school closures on the academic
performance of fifth- and sixth-grade primary school students in Japan. Difference-in-differencesand event studies were conducted using "Manabi Nara" data, a math achievement test administered
to fourth-sixth graders at each term-end in Nara City. Children who experienced temporary school
closure made the treatment group while inexperienced one-year older children were the control
group. The results showed lowered math scores in the short term, but scores significantly increased
six months after school closure. Further, the lower the students' academic achievement was, thegreater was their improvement in their math scores. We found that increased motivation and attitude
shifts toward math during this period contributed to improved scores. Finally, students withdisadvantaged living conditions around school vacations saw their math scores and motivation andattitude toward math fall, particularly in the bottom 25% of their fourth-grade academic
performance.

Keywords：COVID-19, School Closure, Learning Loss, Elementary School Students,
Math Scores, DID and Event Study, Living conditions, Learning Disparity
JEL Code: I21, I24, I28

1

We are grateful for the substantial comments and advice from Masaru Sasaki, Michihito Ando,

Nobuyoshi Kikuchi and the participants at the 2021 Labour Economics Conference. The authors are solely
responsible for any remaining errors. JSPS KAKENHI (grant number: 20H05632 and Osaka University
funded this study. We have no competing interests with other people or organizations that could influenceor bias the contents of this paper, no other positions, and no other relatives/partners.
2
Specially Appointed Researcher and Ph.D. student, Graduate School of Economics, Osaka University,
1-7, Machikaneyama, Toyonaka, Osaka 560-0043, Japan. Email: shinsuke.asakawa@gmail.com
3
Research Fellow, Japan Society for the Promotion of Science
4
Professor, Graduate School of Economics, Osaka University, 1-7, Machikaneyama, Toyonaka, Osaka
560-0043, Japan. Email: ohtake@econ.osaka-u.ac.jp
5Specially Appointed Professor, Center for Infectious Disease Education and Research (CiDER), Osaka
University

11. Introduction
In 2020, approximately 1.5 billion students across 188 countries were forced to stay home
due to temporary school closures (OECD, 2021; UNESCO, 2021). Almost a year after
the spread of COVID-19, more than half of the OECD countries have not yet fully
reopened their schools (OECD, 2021).
Existing studies examining the relationship between temporary school closures to stop
the spread of COVID-19 and students' academic performance have shown that temporary
school closures hurt students' academic performance in the short term, 6 and that theeffects of temporary school closures vary among students.7 This result is consistent withother existing studies that have found that a temporary school closure or a reduction inschool days decreased students' academic achievement.8 Furthermore, some estimatessuggest that the learning loss due to temporary school closure is about $16,000 per student

6

Many existing studies have shown that temporary school closures due to COVID-19 reduced students'
academic performance in the short term and that students with more disadvantaged home environments had
lower learning time (Aucejo, French, Araya & Zafar, 2020; Bonal & González, 2020; Grätz & Lipps, 2021;
Reimer, Smith, Andersen & Sortkær, 2021). Others show that students with lower academic performanceand home environments reported lower academic performance (Agostinelli, Doepke, Sorrenti & Zillibotti,
2020; Andrew, Cattan, Costa-Dias, Farquharson, Kraftman, Krutikova, phimister et al., 2020; Engzell, Frey
& Verhagen, 2021; Kuhlfeld, Soland, Tarasawa, Johnson, Ruzek & Liu, 2020; Van Lancker & Parolin,
2020; Maldonado & De Witte, 2020; Schult, Mahler, Fauth & Lindner, 2021).
7
The difference in the ability to adapt to online education is seen as a factor causing the heterogeneity inschool closure effects. While online education was adopted in many countries during the temporary school
closure, existing studies show significant differences in adaptability to online education depending on
students' academic ability, household income, internet environment, and region of residence (Bacher-Hicks,
Goodman & Mulhern, 2020; Bansak & Starr, 2021; Bayrakdar & Guveli, 2020; Chetty, Friedman, Hendren,
Stepner & The Opportunity Insights Team, 2020; Ikeda & Yamaguchi, 2020; van der Velde, Sense, Spijkers,
Meeter & van Rijn, 2021).
8
Studies examining the effects of school closures and reduced class days include summer vacations(Atteberry & McEachin, 2020; Cooper, Nye, Charlton, Lindsey & Greathouse, 1996; Downey, Von Hippel& Broh, 2004; Kuhfeld, 2019; Kuhfeld, et al. 2020; von Hippel, Workman & Downey, 2018); natural
disasters (Andrabi, Daniels & Das, 2020; Hansen, 2011; Goodman, 2014; Marcotte, 2007; Marcotte &Hemelt, 2008; Sacerdote, 2012; Thamtanajit, 2020); infectious diseases (Meyers & Thomasson, 2020);
teachers' strikes (Belot & Webbink, 2010; Wills, 2014); and reduced class days (Aucejo & Romano, 2016;
Kawaguchi, 2016; Motegi & Oikawa, 2019).

2

(Azevedo et al.,2020) or 3% of the lifetime income (Hanushek & Woessman, 2020).

Compensating for this learning loss due to school closure has become an important policy
issue in many countries. On the other hand, the medium-term impact of temporary school
closures remains unresolved due to the difficulty of obtaining test data with highfrequency and comparability among multiple cohorts.
This study, therefore, examines the short-term — third term of the fiscal year (FY) 2019and first term of FY2020 — and mid- and long-term — second and third term of FY2020— effects of the COVID-19 related temporary closure of elementary schools in Japan,
using the "Manabi Nara" math test data of elementary school students in Nara City, which
is one of the core cities with a population of more than 200,000.9
However, Nara City implemented two policy interventions after the temporary closureof schools — the shortening of the summer vacation and the distribution of one tablet(ICT) terminal per student. The summer vacation was shortened to compensate for the
drastic reduction in school days due to the temporary closure. Moreover, to increase theefficiency of education, Nara City distributed an ICT terminal to each elementary school
student in order of readiness from September 2020 onward. As a result, the students'
academic performance by the end of the second term of FY2020 may be higher than
before. The number of days to introduce ICT terminals also varied by about one monthacross schools, which may cause heterogeneity in academic performance when measured
from the end of the second term. Therefore, we consider the effect of the shortened

9“Manabi Nara.” has two advantages. First, we can capture short term variations as a result of the impact
of temporary school closures because the same math test is administered to all students in the same gradeat the end of each term. The second advantage is we can obtain information on math scores not only forchildren born between April 2, 2009, and April 1, 2010 in the COVID-19 experienced cohort (FY2020 P6
cohort) but also for children born between April 2, 2008, and April 1, 2009 in the COVID-19 unexperienced
cohort (FY2019 P6 cohort). See Appendix Table A-1 for the correspondence of year and grade by cohort.

3

summer vacation and the earliness of the ICT terminal’s introduction to identify the
impact of the shorter school days due to temporary school closures.
The baseline analysis in this study is a difference-in-differences (DID) analysis.10 The
first difference compares the math test scores between children who experienced
temporary school closures due to COVID-19 and children who did not experience school
closure in grades 5 and 6. The second difference is the difference before and after
temporary school closures. Then, by adding the cross-terms of the COVID-19 experiencedummy and the “third term of grade 6” dummy, we decompose and separately estimate
the entire period after the policy intervention of temporary school closure (from the third
term of grade 5 to the third term of grade 6) and after the second term of grade 6. This
procedure enables us to estimate the short- and medium-term effects of temporary school
closures, considering the shortened summer vacation and benefits of the introduction of
ICT. In addition, we conduct an event study and estimate the difference between the twogroups for each term to confirm the parallel trend before the second term of grade 5 andtest whether the effects of temporary school closure vary by term.
So far, the existing studies have not yet examined the effects of living conditions,
including the home environment and mental health, on academic performance during andafter the temporary school closure due to COVID-19. Therefore, as a part of this study,
we also use the answers to the "Living Conditions Survey" conducted in Nara City in
May (during the school closure) and June (after the school closure) 2020 to examine

10

When COVID-19 temporary school closures occurred in March 2020, the FY2020 P6 cohort was at theend of the third term of fifth grade, and the FY2019 P6 cohort was at the end of the third term of the sixth
grade. Therefore, we consider the period from the end of the third term of the fifth grade to the end of thesecond term of sixth grade as the policy intervention for reducing school days due to temporary school
closure. The difference between the pre-intervention (the first term and the second term of the fifth grade)and the post-intervention (f the third term of t fifth grade to the second term of the sixth grade) was thesecond difference.

4

whether disadvantaged living conditions affected students' academic performance after
the temporary school closure.
Based on the above discussion, we examine the following questions.

1.

Did the reduction in school days due to temporary closures lower the academic
performance of students?

2.

Did the effects of temporary school closures vary by academic quartile ordisadvantaged living conditions?

3.

Did students' motivation and attitudes toward math change after a certain period
had passed since the temporary school closure?

This study obtained the following evidence: For the first question, considering the term
immediately prior to the temporary closure (the third term of grade 5), the decrease inschool days resulted in a decline in academic performance for students with lower
academic performance, as of the third term of grade 4. However, math scores increased
significantly after six months from the school closure (the second and third terms of grade
6). Thus, the negative effects before the first term of grade 6 were canceled out for the
lower-performing students, and their math scores in the second and third terms of grade
6 improved. Regarding the second question, students with low math scores a year before
the temporary school closure were the most negatively affected by school closure in the
short term. However, they too turned around and showed the highest improvement inmath test scores in the medium to long term. Furthermore, in the lowest quartile, thestudents who faced disadvantaged living conditions during and after school closure weresignificantly more hindered in the growth of their math scores from the second semester5

of grade six. Consequently, students who were disadvantaged during and after the
temporary closure of the school experienced a greater decline in math scores in the shortterm and had lower growth in math scores even during the improvement phase after thesecond semester of grade 6. Finally, for the third question, the motivation and attitude
toward math were also enhanced when math scores rose (the second and third terms ofgrade 6). However, disadvantaged living conditions also had negative impacts, especiallyin the lowest academic ability groups.11
This study makes two main contributions. First, using high-frequency data, such as theresults of math tests conducted every semester, we examined the effect experiencing thechange in the school days due to temporary school closures or shortened summer
vacations under the COVID-19 epidemic had on the math scores of students. Moreover,
we also clarified if these changes in school day reduce the gap in academic achievement.
Existing studies have shown that the summer vacation decreases academic achievement,
especially in the lower grades12, while the effect of shortening the summer vacation isstill unknown. On the other hand, with regard to the effect of ICT terminals, online
learning under temporary school closure improves academic performance when teachers
support students, while it significantly reduces the study time of low-achieving students
when sufficient support is not available (Carlana & La Ferrara, 2021; Grewenig,
Lergetporer, Werner, Woesmann & Zierow, 2020). However, ICT implementation in faceto-face classes has not yet been proven to be effective. Therefore, our contribution is to
examine the impact of the combination of two policy interventions on student

11

Students in the lowest quartile of the third term math scores as of fourth grade did not study printouts
during the school closure, were unable to concentrate on their studies after the closure, did not get enoughsleep, and felt unmotivated and anxious during and after the closure.
12
See Downey et al. (2004); Kuhfeld (2019); Kuhfeld et al. (2020); von Hippel et al. (2018).

6

achievement: the increase in school days due to the shortened summer vacation and theintroduction of ICT terminals in face-to-face classes with enhanced teacher support.
The second contribution is to examine whether disadvantaged living conditions during
and after the temporary school closure harm students' academic performance and whether
the effects are heterogeneous across academic strata. Existing studies show that the effectof home study under temporary school closure differs depending on students' abilities andhousehold characteristics during temporary closure.13 Moreover, many studies show thatthe living conditions themselves worsen due to school closures.14 Thus, we cannot deny
the possibility that the deterioration of living conditions creates heterogeneity in theeffects of temporary school closures. Therefore, our second contribution is to examine
the causal relationship between living conditions and students' academic achievement
during and after school closures.
This paper consists of seven sections: Section 2 describes the changes in school daysin elementary schools in Nara City, including temporary school closures and shortenedsummer vacations, Section 3 describes the data, Section 4 explains the estimation method,
Section 5 presents the results of the baseline empirical analysis, and Section 6 examines
the heterogeneity of the effects of temporary school closures. Finally, Section 7 provides
a summary of the study.

13

Study time (Aucejo et al., 2020; Bonal & González, 2020; Gratz & Lipps, 2020; Reimer et al., 2021);
online learning (Bacher-Hicks et al., 2020; Bansak & Starr, 2021; Bayrakdar & Guveli, 2020; Chetty etal., 2020; Ikeda & Yamaguchi, 2020; van der Velde et al., 2021)
14
Many existing studies show that mothers are more affected by COVID-19 than fathers. For example,
COVID-19 reduces women's employment (Alon, Doepke, Olmstead-Rumsey & Tertilt, 2020; Collins,
Landivar, Ruppanner & Scarborough, 2021; Craig & Churchill, 2021; Heggeness, 2020), increases mothers'
additional parenting time (Del Boca, Oggero, Profeta & Rossi, 2020; Farré, Fawaz, Gonzalez & Graves,
2020; Yamamura & Tsutsui, 2021b; Zamarro & Prados, 2021), worsening parental mental health and wellbeing (Cheng, Mendolia, Paloyo, Savage & Tani, 2021; Huebener, Waights, Speiss, Siegel & Wagner, 2021;
Takaku & Yokoyama, 2021; Yamamura & Tsutsui, 2021a), and increased domestic violence (Pereda &Díaz-Faes, 2020; Baron, Goldstein & Wallace, 2020; Hsu & Henke, 2021).

7

2. School closure and shortening of summer vacation in Nara City
Across Japan, primary and junior high schools, high schools, and special-needs schools
have been temporarily closed since March 2, 2020, to arrest the spread of the COVID-19
epidemic.15
In response to the government's request, Nara City implemented temporary school
closures for approximately three months from the same date. As a result, students enrolled
in elementary and junior high schools, high schools, and special-needs schools in FY
2019-2020 lost 23 class days in the third term of FY 2019 and 54 class days in the firstterm of FY 2020 compared to the previous year.16 On the other hand, Nara City shortened
the summer vacation by 20 days in FY2020 from the previous year to compensate for the
fewer class days (see Figure 1). Thus, this study considers the temporary school closureand the associated shortened summer vacation as two policy interventions regarding theschool day and examines the policy effects of each intervention. The following sections
provide an overview of each policy.

(Figure１around here).

15

As of March 5, 2020, 18,923 of the 19,161 elementary schools in Japan were closed (The Japan times,2020). However, since the temporary school closure was not mandatory but requested by the Prime Minister,
the timing of the closure varied by local governments（Yamamura & Tsutsui, 2021a; 2021b）。

16

In response to a government request, Nara City has temporarily closed the city's elementary, junior high,and senior high schools from March 2 to April 5, 2020 (with a spring break from March 25 to April 5). As
a result, the third term of the 2019 school year was shortened by 23 days (from March 2 to March 24,
including weekends and holidays) compared to the previous year. In addition, after the school opening
ceremony and explanation of the school closure schedule on April 6, the school was closed again for one
month, from April 7 to May 7. During this period, due to the nationwide COVID-19 pandemic and thedeclaration of a state of emergency on April 16, on April 28 Nara City decided to extend the re-opening
deadline to May 31. As a result, the first term of the 2020 school year was shortened by only 54 days (April
8 to May 31, 2020, including weekends and holidays) compared to the previous year.

82.1. Temporary school closure (Primary school)
During the temporary school closure, students were required to study at home. 17
Elementary school students watched videos at home streamed by teachers and studied
independently using paper-based study handouts. The teachers collected and graded thefilled-in handouts to check the students' understanding because they could not learn bythemselves if they studied at home for a long time.18 Thus, given that all elementary
schools in Nara City were closed temporarily at the same time and in the same manner,
this study considers the temporary closure of elementary schools as the first policy
intervention and examines their effects.

2.2. Shortened summer vacation
As shown in Table 1, the summer vacation in Nara City was drastically shortened to 16
days (August 8 to August 24) in FY2020 (from 36 days in the previous year) tocompensate for the delay in learning caused by the temporary school closure due to
COVID-19. As a result, students' academic performance at the end of the second term
may have increased (not decreased due to the summer vacation) compared to the previous
year because the learning interruptions caused by the summer vacation were greatly
reduced in elementary schools.
(Table１around here).

17Children (grades 1-6) who were unable to stay at home due to their parents' employment or other reasons,could attend elementary school during regular class hours from Monday to Friday. During this time theteachers did not conduct classes, and the students who attended school engaged in self-study. Students withany type of fever or cold symptoms were not allowed to attend school.
18
Since May, Nara City has been lending school-based tablets and Wi-Fi routers to junior high school
students from households that do not own tablets and/or have an internet connection. Junior high andelementary students were also given tablets to use, which allowed elementary school students to study athome using study handouts during most of the temporary school closure. As a result, the distribution andcollection of assignments and teachers' study guides were also able to be completed online.

9Before FY2018, the summer vacation duration was fixed between the first weekday
after July 21 and September 1. However, in FY2019, before the COVID-19 epidemic, thesummer vacation was already shortened by seven days compared to the previous year
(FY2018). This reduction was mainly to accommodate increases in the curriculum, suchas introducing English as a compulsory subject. Thus, the effect of the summer vacation
reduction in FY2020, which was to compensate for the temporary school closure due to
COVID-19, on students may vary depending on their academic performance.
Therefore, after considering the summer vacation itself, this study estimates the impactof each policy intervention on children's academic achievement by considering (1) the
shortening of summer vacation before the COVID-19 epidemic; and (2) the shortening ofsummer vacation after the COVID-19 epidemic as different policy interventions.

3. DataThis study uses information collected over five terms (from the first term of the fifth grade
to the third term of the sixth grade) from the panel data "Manabi Nara." The survey tracks
the math test score at the end of each term over three years (from the fourth to the sixth
grade) for every child in all 43 elementary schools in Nara City (about 2,700 students per
grade). The cohort used and the test timing are shown in Figure 2.

(Figure 2 around here).

These data have two advantages. First, students in the same grade take the city-wide
math test at the end of each term (three times a year: July, December, and March). Second,
we can obtain information on the students who experienced the COVID-19 school closure

10

(FY2020 P6 cohort) and compare it with the students one grade higher (FY2019 P6
cohort) who did not experience the COVID-19 epidemic. The high frequency of test
results across the two grades is a significant advantage of the present data.
Using the DID and event study, we estimate the impact of the COVID-19 temporary
school closure on academic achievement by comparing the children who experienced
COVID-19 school closure with those who did not (first difference) with the difference
before and after the period in which the school days were reduced due to COVID-19(second difference). Therefore, we create a COVID-19 experience dummy “COVID19”
that takes the value 1 for the FY2020 P6 cohort and 0 for the FY2019 P6 cohort torepresent the first difference.

3.1. Outcome variable: math test scoresThis study uses math test scores at the end of each term as the outcome variable toexamine changes in the academic performance of students.
We first check the average math scores per term for the FY2020 P6 cohort and the
FY2019 P6 cohort in Figures 3 and 4 to see if the school closure associated with COVID19 affected students' math test scores. Figure 3 shows that the academic performance of
the FY2020 P6 cohort significantly decreased in the first term of grade 6 compared to thatof the FY2019 P6 cohort. In contrast, it increased dramatically after the second term ofgrade 6. Figure 4 shows the mean outcome variables per term, divided into four quartiles,
calculated by the scores at the end of the third term of the fourth grade. The results
indicate that, especially in the lower quantiles (bottom 50%), both the decline in academic
achievement before and after the temporary school closure (third term of grade 5 and firstterm of grade 6), and the increase after the second term of grade 6, are more prominent.

11

(Figures 3 and 4 around here).

The following sections analyze which policy interventions caused the changes in
Figures 3 and 4 and whether the changes in academic performance differed depending on
the living conditions during the temporary school closure, using DID and event studies.

3.2. Temporary school closure related to COVID-19
Nara City implemented temporary school closure from the third term of the fifth grade(March 2) to the first term of the sixth grade (May 31). However, before the temporary
school closure, some elementary schools were already able to take the end-of-term testfor grade 5 students, usually held in March 2020, while others started school closures
without taking the test due to the government's urgent request to close schools.
Typically, the end-of-term tests are conducted after the examination content has been
completed. However, the Ministry of Education, Culture, Sports, Science, andTechnology (MEXT) had already notified the prefectures and designated cities' educationcommittees of the possibility of temporary school closures as of February 18, and thegovernment even requested temporary school closures on February 27.19 As a result,some elementary schools may have moved up the examination schedule keeping the19

The notification can be found at the following URL (https://www.mext.go.jp/content/20200218mxt_kouhou02-000004520_3.pdf). In the third term of the fifth grade in the FY2020 P6 cohort, many
elementary schools did not administer math exams due to the reduced school days as a result of thetemporary school closures and failure to complete the test content. If the test scores of students in the
FY2020 P6 cohort who took the test in the third term of fifth grade in 2020 varied from those who did not,a self-selection problem would arise. Therefore, we tested for differences in the means and variances of testscores during periods other than the third term of the fifth grade between the following groups: 1) students
who took the test in the third term of the fifth grade and those who did not, and 2) students who took thetest in the third term of the fifth grade and the whole FY2020 P6 cohort. The results show that the mean
and variances of both groups did not differ at the 10% significance level (see Appendix Table A-2).
Therefore, this study also uses the third term of the fifth grade of the FY2020 P6 cohort for analysis.

12possibility of temporary school closures in mind. Existing studies show that the test scores
decline when exogenous shocks lead to an earlier examination date (Goodman 2014;
Marcotte 2007; Marcotte and Hemelt 2008). Therefore, students' academic performance
may have also declined in the third term of grade 5, just before the temporary school
closure.
This study defines the third term of the fifth grade and later as the period after the
temporary school closure, while the previous term is defined as the period before school
closures. Specifically, to perform DID/Event study estimation, we create a dummy for
temporary school closure “P5T3_P6T3,” which takes the value one after the third term ofgrade 5, and 0 before the temporary school closure.

3.3. Shortened summer vacation
As shown in Table 1, the summer vacation in Nara City was significantly shortened to
16 days, from August 8 to August 24, 2020. However, the previous year's summer
vacation was also shortened by approximately one week compared to the summer
vacation before FY2018 in response to the curriculum expansion.
Therefore, we first create a dummy for the summer break (summer break) to estimate
the effect of the summer break itself, taking the value of 1 when the test is conducted in
the second term, which is just after the summer break, and 0 in the other terms. We also
define the number of shortened days (shortened days) as the number of days obtained bysubtracting the summer vacation periods in 2019 (36 days) and 2020 (16 days) from the
43-day summer vacation period in FY2018 and assigned to the end of the second term of
the FY2020 P6 and FY2019 P6 cohorts, and 0 otherwise. The coefficient of shorteneddays shows the effect per day of shortening the summer vacation.

13

Furthermore, we construct a dummy for the end of the third term of the sixth grade“P6T3,” which takes the value 1 for the second term of the sixth grade and 0 for the priorterms. The variable decomposes the effect of reducing school days due to COVID-19 andshortens summer vacation. Thus, we can verify whether the change in academic
achievement after the shortening of the summer vacation shortening following the
temporary closure is different from that of the previous year by checking the coefficientof the intersection term between COVID19 dummy and P6T3 dummy.

3.4. Heterogeneity of living conditions
In Nara City, most elementary school students studied at home in the same manner(video viewing and paper-based print learning) at the same time without any physicalcontact with teachers (see Section 2). However, existing studies suggest that the living
conditions of elementary school students during temporary school closures, such as theirhome environment and physical and mental conditions, differ among students. 20 For
example, Yamamura and Tsutsui (2021a) found that COVID-19-induced temporary
school closure aggravated mothers' mental health among elementary school-aged
children and a low educational background. This finding suggests that the effects of
temporary school closure on the family environment and the mental health of parents andchildren may differ depending on the characteristics of the primary guardian during theclosure, such as gender and educational background.
Therefore, we examined whether the effect of temporary school closure differs

20

See the following studies: parental employment (Alon et al., 2020; Collins et al., 2021; Craig & Churchill,
2021; Heggeness, 2020), parenting time (Del Boca et al., 2020; Farré et al., 2020; Yamamura & Tsutsui,2021b; Zamarro & Prados, 2021), parental mental health and well-being (Cheng et al., 2021; Huebener etal., 2021; Takaku & Yokoyama, 2021; Yamamura & Tsutsui, 2021a), domestic violence (Pereda & DíazFaes, 2020; Baron et al., 2020; Hsu & Henke, 2021)

14depending on living conditions during and after temporary school closure.21 Data arefrom the questionnaire results of "Survey of living conditions during the vacation (May)"and "Survey of living conditions after school re-opening (June)," which were conductedin conjunction with "Manabi Nara." Specifically, we first created living condition dummy
variables for each of the two questionnaire items. We assigned one if the respondent
answered "quite applicable/applicable" to a disadvantaged living condition or "not quite
applicable / not applicable" to an advantaged living condition and 0 otherwise. Then, weused the average of the May and June questionnaire results as a variable to indicate thedifference in living conditions.22 By adding these dummy variables to the explanatory
variables, we estimate the effects of temporary school closures, considering the effect ofheterogeneity caused by disadvantaged living conditions.23

3.5. Introduction of ICT terminals
In Japan, the lag in the introduction of ICT to education compared to OECD countries
has been a problem for a long time (OECD 2020). Therefore, MEXT had planned to
distribute one tablet (ICT) terminal per student from around FY 2018, before the COVID19 epidemic, to coincide with the introduction of the new education guidelines in FY
2020. Later, the temporary school closure reaffirmed the priority of online education, and21Video production skill and the speed at which the class is taught may differ among schools and classes.
Therefore, this study deals with the unobservable heterogeneity of school and classroom units bycontrolling for school and class fixed effects, as described below.
22
We create a variable equal to 1 if both May and June are equal to 1, 0.5 if one equals 1, and 0 if both areequal to 0. Thus, the two variables "studied using handouts from school (May)" and "sometimes havedifficulty concentrating on studies (June)" are used in the analysis without averaging them as different
variables. However, even if we assume that both variables mean "I can't concentrate on my studies" anduse both averages as variables, the main results of the analysis remained the same.
23
See Appendix Table A-3 for a detailed description of the disadvantaged living condition dummy andAppendix Figure 1 for a histogram of the variables for the disadvantaged living conditions.

15

as a result, the introduction of ICT terminals was promoted nationwide after the school
closure in Japan. For example, in Nara City, ICT terminals were sequentially introduced
to elementary schools from September 14 to October 12, as shown in Figure 5.

(Figure 5 around here).

Since ICT terminals enable individualized and proficiency-based learning, thegovernment expects that the learning effect is higher than traditional group learning.24
Therefore, at the end of the second term of the sixth-grade students and later, the ICT
terminal introduction is likely to have affected the students' academic achievement inaddition to the shortened summer vacation. Furthermore, the difference of 28 days
between the earliest and the latest elementary schools receiving the ICT terminals could
have a heterogeneous effect on academic achievement.
However, the effect of the introduction of ICT terminals and the shortened summer
vacation cannot be distinguished because both interventions were timed after the end of
the first term test. Thus, we consider the coefficient of the intersection term between the
COVID-19 dummy and the After P6T2 dummy to be the effect of ICT introduction in
addition to the shortening of the summer vacation. We focus on the difference in the earlydays of introduction and examine the heterogeneity of the effects. This is measured as the24

The program to promote ICT in education in Japan is called the "GIGA School Program" and can befound at URL:https://www.mext.go.jp/en/content/20200716-mxt_kokusai-000005414_04.pdf. The
program has three objectives (https://www.mext.go.jp/content/20200625-mxt_syoto01-000003278_1.pdf).
The first is to realize interactive teaching by allowing teachers to understand the responses of each student.
The second is to enable individualized learning based on each student's level of achievement. The third isthat by sharing each person's opinions in real-time, it is possible to obtain various opinions. Therefore, theprimary goal of the GIGA School Program was to improve classes to realize active, interactive, and deeplearning and the ICT introduction was a means to achieve this goal.

16

difference between the start date of the use of ICT terminals in each elementary school
and the start date of the slowest elementary school to receive the terminals. We thus use
the variable “ICT early days” as a control variable.
Table 2 shows the descriptive statistics for the variables explained in Sections 2.1-2.5.

(Table 2 around here).

4. Estimation method
This study uses DID as the main estimation method. In addition, we estimate the event
study type DID (Event study) to confirm the medium- and long-term effects of the policy
intervention and test the trends of the treatment and control groups before the policy
intervention (i.e., common trend).

4.1. Full sample analysis
In this section, we first test the hypothesis that temporary school closures due to
COVID-19 lowered the academic performance in math.
The FY2019 P6 cohort cannot be considered a control group without considering theimpact of the shortened summer vacation because the summer vacation was shortened in
Nara City even before 2020. Therefore, we use the following estimation model as abaseline to identify the effects of temporary school closures alone.

4.1.1. Baseline DID
𝑌!,# = 𝛼% 𝐶𝑂𝑉𝐼𝐷19! × 𝑃5𝑇3_𝑃6𝑇3𝑡 + 𝛼& 𝐶𝑂𝑉𝐼𝐷19! + 𝛼' 𝑃5𝑇3_𝑃6𝑇3𝑡 + 𝜇( + 𝜇𝑔 + 𝜇) + 𝜖!, # (1)17𝑌#,% is the math score of an individual 𝑖 in term 𝑡. 𝐶𝑂𝑉𝐼𝐷19# is a dummy variable thattakes the value of 1 for the FY2020 P6 cohort, and 𝑃5𝑇3_𝑃6𝑇3% is a dummy variable
that takes the value of 1 when the testing period is after the third term of the fifth grade.
𝜇& , 𝜇' and 𝜇( are the school (s), grade (g), and classroom (c) fixed effects, respectively.
The parameter of interest is 𝛼) , which shows if the temporary school closure had asignificant effect on the math achievement of the treatment group (𝐶𝑂𝑉𝐼𝐷19# ).
Therefore, the temporary school closure to prevent the COVID-19 epidemic and theshortening of summer vacation to compensate for the significantly reduced school days
can be considered as a set of policy interventions related to changes in the number ofschool days. Thus, we estimate the following model in addition to the baseline DID toseparately identify the effects of school closures and shortened summer vacation.

4.1.2. Baseline DID + Summer break (SB)
𝑌#,% = 𝛼) 𝐶𝑂𝑉𝐼𝐷19# × 𝑃5𝑇3_𝑃6𝑇3% + 𝛼+ 𝐶𝑂𝑉𝐼𝐷19# + 𝛼, 𝑃5𝑇3_𝑃6𝑇3%+ 𝛼- 𝑆𝑢𝑚𝑚𝑒𝑟 𝑏𝑟𝑒𝑎𝑘% + 𝛼. 𝑆ℎ𝑜𝑟𝑡𝑒𝑛𝑒𝑑 𝑑𝑎𝑦𝑠#,% + 𝜇& + 𝜇' + 𝜇( + 𝜖#, %(2 )𝑆𝑢𝑚𝑚𝑒𝑟 𝑏𝑟𝑒𝑎𝑘% is a dummy variable that takes the value one if the test period is theend of the second term immediately after the summer break, and 𝑆ℎ𝑜𝑟𝑡𝑒𝑛𝑒𝑑 𝑑𝑎𝑦𝑠#,% isthe number of shortened days in 2019 and 2020 compared to the summer break in 2018.
The new parameters of interest are 𝛼- and 𝛼. , where 𝛼- measures if the summer
vacation itself has a significant impact on math achievement, while 𝛼. indicates if
shortening the summer vacation in 2019 had a significant impact on math achievement.
We also add an interaction term of 𝐶𝑂𝑉𝐼𝐷19# and 𝐴𝑓𝑡𝑒𝑟 𝑃6𝑇2𝑡 to the estimation
equation to extract the effects of the second and third terms of grade 6 only.

18

4.1.3. Baseline DID + SB + After P6T2
𝑌#,% = 𝛼) 𝐶𝑂𝑉𝐼𝐷19# × 𝑃5𝑇3_𝑃6𝑇3% + 𝛼+ 𝐶𝑂𝑉𝐼𝐷19# + 𝛼, 𝑃5𝑇3_𝑃6𝑇3%+𝛼- 𝑆𝑢𝑚𝑚𝑒𝑟 𝑏𝑟𝑒𝑎𝑘% + 𝛼. 𝑆ℎ𝑜𝑟𝑡𝑒𝑛𝑒𝑑 𝑑𝑎𝑦𝑠#,% + 𝛼/ 𝐶𝑂𝑉𝐼𝐷19# × 𝐴𝑓𝑡𝑒𝑟 𝑃6𝑇2𝑡+ 𝛼0 𝐴𝑓𝑡𝑒𝑟 𝑃6𝑇2𝑡 + 𝜇& + 𝜇' + 𝜇( + 𝜖#, %(3 )The notable parameter here is 𝛼/ , which shows that only the treatment group changed
its math achievement after the shortened summer vacation. In other words, by adding
𝐶𝑂𝑉𝐼𝐷19# × 𝐴𝑓𝑡𝑒𝑟 𝑃6𝑇2𝑡 , the parameters 𝛼- and 𝛼. were modified to coefficients
indicating only the summer break effects other than those of the sixth grade in thetreatment group. As a result, we can decompose the impact of the summer vacation ingrade 6 after the COVID-19 epidemic and the impact of summer vacation at other times.
Next, we estimate the following baseline event study model to confirm the policy
intervention's medium- and long-term effects and clarify whether the common trend
assumption before the policy intervention holds.

4.1.4. Baseline Event study,

𝑌#,% = L 𝛽% 𝐶𝑂𝑉𝐼𝐷19# × 𝑆𝑐ℎ𝑜𝑜𝑙 𝑐𝑙𝑜𝑠𝑒% + 𝜇& + 𝜇' + 𝜇( + 𝜖#

(4)

%12+

𝑆𝑐ℎ𝑜𝑜𝑙 𝑐𝑙𝑜𝑠𝑒% is a dummy variable here that takes the value of 1 when the test period
is 𝑡.25 Checking each parameter enables us to test the following hypothesis: 𝛽2+ and𝛽2) show that the effect of temporary school closures cannot be identified because no

We define 𝑡 = 0 as the end of the third term of the fifth grade when the temporary school closure began.
For the correspondence between 𝑆𝑐ℎ𝑜𝑜𝑙 𝑐𝑙𝑜𝑠𝑒 and each grade and term, see Appendix Table A-4.

25

19

parallel trend is established between the treatment and control groups. 𝛽3 ~𝛽+ represent
the fact that temporary school closure had a significant effect on the math achievementof the treatment group in period 𝑡.
We also estimate the following model to identify the summer break effects in addition
to the temporal school closure effects:

4.1.5. Baseline DID + SB,

𝑌#,% = L 𝛽% 𝐶𝑂𝑉𝐼𝐷19# × 𝑆𝑐ℎ𝑜𝑜𝑙 𝑐𝑙𝑜𝑠𝑒% + 𝛽- 𝑆𝑢𝑚𝑚𝑒𝑟 𝑏𝑟𝑒𝑎𝑘% + 𝛽. 𝑆ℎ𝑜𝑟𝑡𝑒𝑛𝑒𝑑 𝑑𝑎𝑦𝑠#,%
%12+

(5 )

+ 𝜇& + 𝜇' + 𝜇( + 𝜖#

The value of 𝛽- helps determine that the summer vacation itself significantly affects mathscores. 𝛽. indicates that shortening the summer vacation in 2019 has an impact on mathachievement.

4.2. Subsample analysis
The effects of the temporary school closure may differ depending on students’ academic
performance before the intervention. Therefore, the full sample analysis may offset theestimated effects if the effects are heterogeneous across student achievement quartiles.
Thus, we calculate the quantiles for each treatment and control group based on the mathscores one term prior to the period used in the analysis (i.e., the third term of the fourth
grade) and divided the students into four subsamples. Then, we examine whether theeffect is heterogeneous across achievement quartiles by estimating only those equations
for which the common trend assumption is satisfied in the event study.

20

5. Results
In this section, we first present the DID and event study results using the full sample. We
then show the subsample analyses for each quartile in the third term of the fourth grade.

5.1. Full sample results
The baseline estimations using equations (1) and (4) are shown in Columns 1 and 4 of
Table 3. We also present the DID and event study results, controlling for variables related
to the summer vacation, in Columns 2 and 5 of Table 3. Column 3 of Table 3 shows theestimated effects in the second term of the sixth grade.

(Table 3 around here).

We find that the coefficient "COVID19 × P5T3_P6T3," representing the overall effect,
including the impact of temporary school closure and shortened summer vacation, is
positive and significant (5.275) in Column 1 of Table 3. Thus, we can conclude that theoverall effect of temporary school closure and shortened summer vacation increased mathscores by approximately 5.3 points.
Next, the result from equation (2), controlling for the effect of the shortened summer
vacation, shows that the coefficient of "COVID19 × P5T3_P6T3" is still positive andsignificant (4.474), and the coefficient of “Shortened days” is positive and significant(0.2333 per day) in Column 2 of Table 3. These results suggest that the reduction insummer vacation days partly caused an overall improvement in math scores. We also
confirm that the coefficient of "Summer break" is negative and significant (-3.976),
indicating that the summer break itself lowered the math test score.

21

Column 3 of Table 3, which shows the result from equation (2) (controlling for theeffect of the shortened summer vacation), shows that the coefficient of "COVID19 ×
P5T3_P6T3" is negative (-2.082) and significant at the 10% level in Table 3, Column 2.
This result indicates that the temporary school closure lowered the math score by about
2.1 points. On the other hand, the coefficient of "COVID19 × After P6T2" is positive andsignificant (11.64), indicating that the math score increased by about 11.64 points in thesecond term of the sixth grade. We also confirm the effect of shortening the summer breakand find that the coefficient of Shortened days has changed to negative and insignificant(-0.0004). These results suggest that the effect of shortened summer break is the opposite
before the COVID19 school closure (Shortened days) and after (COVID19 × After P6T2).
The coefficient of "Summer break" is still negative and significant (-2.555), but theabsolute magnitude is smaller than that in Column 2. However, Column 2 does notdistinguish whether the shortened summer vacation was before or after COVID19. As aresult, the individual effect of the summer vacation and the effect of the COVID19 school
closure "COVID19 × P5T3_P6T3" may not have been correctly identified. Therefore, we
make our conclusions based on the results in Column 3.
In summary, the temporary closure of the school caused a drop of about 2.1 points inmathematics scores, but the shortened summer vacation and the introduction of ICT
terminals increased mathematics scores by about 9.1 points in the medium- and longterm.26
Next, we confirm the results of the event study in Columns 4 and 5. Both results showthat math scores decreased just before the temporary school closure and increased in theConsidering that the summer vacation of the FY2020 P6 cohort was 27 days shorter than that of the
FY2018 cohort due to temporary school closures, the FY2020 P6 cohort increased their math scores byapproximately 9.1 points (11.64 -0.0004×27-2.555) in the second term of sixth grade.

26

22

second term of the sixth grade. However, the results for parallel trends differ between thetwo results. In Column 4, which does not consider the effect of the shortened summer
vacation, the coefficient is significant for the second term of the fifth grade (SC = ‒1).
On the other hand, in Column 5, where the summer break dummy (Summer break) andthe shortened days of summer vacation (Shortened days) are taken into account, parallel
trends can be confirmed for the terms before the third term of the fifth grade. Therefore,
as with the DID results, we use the event study based on Column 5 for the main estimation,
adding the variable for the summer vacation.
From Column 5, we find that math scores decreased in the third term of the fifth grade
and the first term of the sixth grade by about 4.79 points and 2.97 points, respectively.
However, the coefficient of "COVID19×P6T2" and "COVID19×P6T3" are positive andsignificant (14.41 and 7.22, respectively). These results are consistent with the DID
results. Therefore, we conclude that the negative effects of the temporary school closure
remained for a short period until the end of the term following the closure. However, after
the end of the next term, academic performance turned around and improved significantly,
resulting in higher academic performance levels than that before the closure.

5.2. Subsample analysis
Next, we calculate the score quantile (P4T3) using the math scores of the third term of
the fourth grade and examine if the effects of the policy intervention differ depending on
the math performance prior to the temporary school closure. Tables 4 and 5 present thebaseline DID and event study results, including the overall effects of the temporary school
closure and shortened summer vacation, in Columns 1 to 4, and the results of equation(3) are shown in Columns 5 to 8.

23

(Tables 4 and 5 around here).

First, Columns 1-4 of Table 4 show that the temporary closure of schools due to COVID
19 eventually increased math scores in all quartiles except the highest academic quartile.
However, all but the fourth quartile showed positive coefficients at the 1% significancelevel (about 7.137, 7.410, and 6.479 points, respectively).
Next, we confirm the effects of the shortened summer vacation and the second term of
the sixth grade, and the temporary school closures separately in Columns 5-8. The results
show that the coefficient of "COVID19 × P5T3_P6T3" is negative and significant for the
first and fourth quantiles. In contrast, the coefficient of "COVID19 × After P6T2" is moresignificant for the students in the lower academic quartiles in P4T3. Furthermore,comparing the magnitudes of the estimated coefficients, "COVID19 × After P6T2" (+) ismuch larger than "COVID19 × P5T3_P6T3" (-) for all quantiles. We also find that thelower the math score quantiles in P4T3, the higher the math scores increase in the second
term of the sixth grade. These results indicate that the shortened summer break and theintroduction of ICT terminals improved math scores, especially in the lower academic
quartiles. In addition, the summer break significantly lowered the math scores in thehigher quartiles, and shortened days of summer break significantly lowered the scores
only in the first quartile and increased the scores only in the fourth quartile. Thus, thestudy reveals that the summer break itself lowered the academic performance of the
middle and upper quartiles, while the shortened summer break, on the contrary, lowered
the academic performance of the lowest quartile.
Next, we proceed with the event study results. We first examine the coefficients of"COVID19 × P5T1" and "COVID19 × P5T2" to verify the parallel trend. Similar to the24full-sample analysis results, Columns 1-4 of Table 5, which do not control the variable
on summer vacation, show that the coefficient is significant in the second term of the fifth
grade (SC = ‒1), except for the fourth quartile. On the other hand, Columns 5-8 of Table
5 show that the pre-treatment differences between the treatment and control groups are
not significant for most terms. Therefore, we mainly discuss the results in Columns 5 to8, even for the subsample analysis, where parallel trends can be assumed.27
The coefficients for "COVID19 × P5T3" are negative and significant for all quartiles;
the first to third quartiles show a larger drop in math scores due to temporary school
closure than the fourth quartile. However, the coefficients for "COVID19 × P6T1" arenegative at 5% and 1% only for the bottom 25% and top 25%, respectively. These results
suggest that while the lowest and middle quartiles (second and third quartiles) of mathscores declined by the same degree in the third term of grade 5, the middle quartiles were
faster to recover from the decline in math scores by the first term of grade 6 than thelowest quartiles. We also find that "COVID19 × P6T2" and "COVID19 × P6T3" yielded
positive and significant coefficients for all quartiles, indicating that math scores improvedin the second term of the sixth grade. Finally, we found that the summer break itself
decreased the math scores of the middle and upper quartiles, and the lower the quartile,
the lower the math score caused by the shortened summer vacation in 2019.
These findings in the event study are consistent with the DID results. Therefore, we
conclude that math scores increased significantly from the end of the second term of grade
6, especially in the lower quartiles.

27

In the fourth quartile, the coefficient of "COVID19 × P5T1" is significant, but the coefficient of"COVID19 × P5T2," which is the term before the temporary school closure, is non-significant for allquartiles. As a result, a parallel trend is established here.

25

6. Effect heterogeneity due to disadvantaged living conditions
The literature shows that living conditions, such as home environment and physical andmental conditions, differed during the COVID-19 pandemic (see Section 3.4.) Therefore,
the effect of temporary school closure on math scores may vary among students withdifferent living conditions. However, differences in the timing of the introduction of ICT
terminals may also lead to heterogeneity in the effects of ICT terminals; the earlier theschool introduced ICT terminals, the more pronounced the change in mathematics scores
would be.28 Therefore, we control for the number of days earlier that ICT was introduced
when compared to the schools with the latest introduction (ICT early days).
To test the hypothesis that disadvantaged living conditions during and after the
temporary school closure lead to heterogeneity in the effects of the temporary school
closure, we add the living situation dummies — treatment variables — and the numberof days that ICT was introduced earlier — a control variable — to equations (1)–(5).

6.1. Full sample analysis
Table 6 shows the estimation results of equations (1) through (5) with variables
representing the disadvantaged living conditions and earliness of ICT adoption as
explanatory variables. Figure 6 shows the estimated coefficients. Figure 7 also
graphically compares the estimated results from the event study that controls only for thesummer vacation dummy and school/class fixed effects (green line), with the estimatedresults when the disadvantaged living environment is also controlled for (pink line).

28

The coefficients can be both positive and negative. Based on the results of Section 5, the introduction of
ICT terminals does not lower math scores, and consequently a positive coefficient is the result of theteachers' appropriate utilization of ICT, while a negative coefficient is evidence that ICT terminals were notused appropriately.

26

(Table 6 and Figures 6 and 7 here).

From the results in Table 6, several explanatory variables show different coefficients
considering disadvantaged living conditions and the earliness of ICT adoption.
Notably, in Columns 3 and 5, the negative and significant coefficients of "COVID19 ×
P5T3_P6T3" for DID and "COVID19 × P6T1" for the event study in Table 3 havechanged to positive values. However, the coefficient of "COVID19 × P5T3," which is theresult immediately before the disadvantaged living condition treatment, is still negativeand significant. These results indicate that the greatest decline in math scores in P6T1 canbe explained by the disadvantaged living conditions created due to temporary school
closures. The graphical results also show that, compared to the results in Section 5, thecoefficients of all samples are positive and significantly more prominent in the first termof sixth grade, and that the sign has changed from negative to positive in Figure 7.
Next, we observe that the magnitude of the coefficient "COVID 19 × School close = 2"
in Column 5 has increased compared to the results in Table 6 (from 14.41 to 21.11). Figure
7 also shows that the difference between the coefficients with and without considering
the disadvantaged living conditions is larger in the second term of the sixth grade than in
the first term.
The coefficients of the living condition dummies are similar for both the DID and event
study. We have five significant coefficients at the 5% level for the disadvantaged living
condition dummies. First, students who did less print learning during the school closure,"Lack print study (May)," had lower math scores of about 8.1 points. Second, students
who had difficulty concentrating on their studies after the closure, "Lack study (June),"had lower math scores about 2.7 to 3 points. Third, students who did not get enough sleep

27

during and after the closure, "Lack sleep," had lower math scores of about 2.1-2.6 points.
Fourth, students who reported feeling uninspired during and after the closure, "No
passion," received about 5.3 points lower math scores. Finally, students who did not domuch physical activity during and after the closure, "No sport," got about 2.4 to 3.1 points
lower.
The coefficients of "ICT early days" are negative for the both estimation methods.
However, the variable is negatively correlated with the math scores before the temporary
closure of the school (see Appendix Table A-5). Therefore, the coefficient of "ICT earlydays" may become negative and significant due to the positive correlation that it develops
with the disadvantaged living environment when latter variable is taken into account.
Thus, we conduct the same estimation after matching the sample based on the grade 4
math scores just before the sample period (see Appendix A) and find that the coefficientof "ICT early days" has become non-significant at the 5% level (see Appendix Table A6).29 This finding suggests that the early introduction of ICT terminals does not affect themath scores on average, considering the self-selection problem of how early to implement
ICT based on the difference in the math scores before ICT introduction.
In summary, we can conclude that the disadvantaged living environment hindered
students' growth in math scores after school closure.

6.2. Subsample analysis
Tables 7 and 8 show the results of the subsample analysis after incorporating the variables
related to the living condition and earliness of ICT introduction; Columns 1-4 and 5-8
29

See the Appendix B for greater detail. Appendix Figure 6 shows that using the full sample without
matching estimation, the estimated coefficients with and without considering the early days of ICT adoption
are similar, and the difference is not statistically significant. Therefore, we can conclude that, regardless ofthe matching estimation, considering the early adoption of ICT does not bias the results in Table 6.

28

show the results without and with the variables related to the summer break, respectively.
Figure 8 shows only the estimated coefficients. Figure 9 graphically shows the results
obtained from the subsample event study in the same manner as Figures 6 and 9.

(Tables 7 and 8, Figures 8 and 9 around here).

Especially for the first quantile, the DID results show that "COVID19 × P5T3_P6T3"
in Column 1 in Table 7 has larger coefficients than the one in Table 4. Moreover, theoverall effect, "COVID19 × After P6T2" ‒ "COVID19 × P5T3_P6T3," estimated inColumn 5 in Table 7 also become higher than the results in Table 4 because the coefficientof "COVID19 × P5T3_P6T3" changes from negative to positive. For the event study
results in the first quartile, we also find that the coefficient of "COVID19 × P6T1" thatwas negative in Table 4 (significant at the 5% level in Column 5) changes to positive andsignificant at the 5% level in both Columns 1 and 5 in Table 8.30 These results indicatethat the decline in math scores in the first quartile was mostly derived from disadvantaged
living conditions during and after the temporary school closure. Moreover, these results
are consistent with the finding in Figure 11 that the difference in coefficients with andwithout consideration of the disadvantaged living conditions is most pronounced for thelowest-performing students.
We also found that the effects of disadvantaged living conditions on math scores
differed between the lower and upper achievement groups. Disadvantaged living
conditions had a negative impact, especially in the lower academic ability groups. For
30

Similar to Table 5, the Event study results in Columns 1-4 of Table 8 reveal that the coefficients of"COVID19 × School close=-1" are significant in the second term of the fifth grade except for the fourth
quartile. In contrast, Columns 5-8 of Table 8 show that the coefficients are non-significant in all quartiles.
As a result, we discuss the results in Columns 5-8 assuming the parallel tread.

29

almost all students, the coefficients for "Lack study (June)" are negative and significant.
Only in the first quartile, "Lack sleep" and "Bad health" show negative and significant
coefficients. For students in the bottom 50% only, we confirm negative and significant
coefficients for "Lack print study (May)" and positive coefficients for "Feel unsafe" In
the first and third quantiles of students, the math scores of the students who answered"No passion" showed negative and significant coefficients. Finally, only in the second andfourth quartiles, “No sport” shows positive and significant coefficients.
The results show that "ICT early days" have a negative and significant coefficient(about -0.46 points per day and about -0.33 points per day) only in the first and third
quartiles in both DID and event study as the full sample results.31 However, Appendix B
show that considering the early days of ICT adoption makes the estimated coefficientslarger, but the difference between the coefficients with and without considering "ICTearly days" is statistically insignificant. Therefore, we conclude that the relatively early
introduction of ICT terminals did not fatally bias the results in Tables 7 and 8 because theearly ICT adoption reduced test scores only in some quintiles, but the magnitude was not
significant.
The results for the other variables are similar to those in Tables 3 and 4. We conclude
that, especially in the bottom quartile, the students who faced disadvantaged living
conditions during and after temporary school closure did not improve their math scores
sufficiently after school closure.

31

The estimate including “ICT early days” without considering the living conditions and the matching
estimate as the full-sample analysis reveal similar results (see Appendix Tables A-7 and A-8).

30

7. Mechanism
The analysis in the previous section shows that the COVID-19 related temporary school
closure lowers students' math scores in the short term but increases their math scores in
the medium-to-long term by increasing the number of school days. However, themechanism behind the improvement in math scores has still not been clarified. For
example, increasing school days to compensate for the delay in learning during the
temporary school closure period may have caused students to dislike studying or caused
their mental health or non-cognitive abilities to deteriorate due to intense studying. If suchan adverse effect occurs, it would be difficult to rejoice in the mid-to long-term
improvement of math scores. Therefore, this section aims to elucidate the mechanism of
mid -to long-term increases in math scores by examining whether students' motivation
and attitude toward learning math changed due to the temporary school closure, as well
as looking at the estimation method introduced in Section 4.

7.1. Data
In addition to the "Manabi Nara" conducted at the end of each term to measure the levelof learning achievement in math, Nara City conducts a questionnaire for students twice ayear to understand their motivation and attitude for learning math. The survey isconducted every May and December. However, in May 2020, when the school was
temporarily closed, this survey was not conducted because the living conditions survey
introduced in Section 3.4 was conducted. However, we have four surveys (two time points
and two cohorts) conducted in December for the COVID-19 experienced group (FY 2020
P6 cohort) and the COVID-19 not-experienced group (FY 2019 P6 cohort) in grades 5
and 6. Therefore, we can make causal inferences about the effect of temporary school31

closures on the students’ motivation and attitude toward learning math by estimating the
DID between cohorts (first difference) before and after the temporary school closure(second difference). For the first difference, as in Section 4, we create a COVID-19
experience dummy “COVID19” that takes 1 for the COVID-19 experienced group and 0
otherwise. For the second difference, we make a "P6 survey" dummy that takes 1 for
December in P6 after the temporary school closure and 0 for December in P5.
We use 10 outcome variables common to the four surveys, including students'
motivation and attitudes toward learning math.32 For these questions, students choose
one of the following four options: “Yes,” “Partly Yes,” “Partly No,” and “No.” Therefore,
this study creates a dummy variable that takes one if the student chooses “Yes” or “PartlyYes,” and 0 otherwise, and uses it as the outcome variable.33
Figures 10 and 11 show the difference between the means of the outcome variables
over the two years for the full sample, and for each quartile of the fourth-grade thirdsemester math scores. Panel A shows the results for the FY2020 P6 cohort and Panel B
shows the results for the FY2019 P6 cohort.

(Figures 10 and 11 here).

7.2. Estimation method
Figures 10 and 11 show that the COVID-19 experience group has slightly improved
motivation and attitude toward learning math in both the full sample and quartile
subsamples. However, these differences in means do not consider not only the effects of32

See Appendix Table 9 for details of each question and definitions of each of the outcome variables.
See histogram of the motivation and attitude of math for FY2019 P6 Cohort and FY 2020 P6 Cohort inAppendix Figure 2 and 3, respectively.

33

32

school and classroom, but also the living conditions under temporary school closures. In
addition, Panel B of Figures 10 and 11 show that several outcomes declined from grade
5 to grade 6 in the COVID-19 non-experienced group. In such a case, the effect of thetemporary school closure may be underestimated if only the COVID-19-experienced
group is used for a comparison before and after the temporary school closure.
Therefore, we use the following DID to test the effect of temporary school closure on
motivation and attitude toward learning math:

𝑌#,% = 𝛾 𝐶𝑂𝑉𝐼𝐷19# × 𝑃6 𝑠𝑢𝑟𝑣𝑒𝑦#,% + 𝑋#,% + 𝜇& + 𝜇( + 𝜖#(6)

𝑌#,% includes the 10 outcome variables of individual 𝑖 in term 𝑡, capturing the students'
motivation and attitude toward learning math. 𝐶𝑂𝑉𝐼𝐷19# × 𝑃6 𝑠𝑢𝑟𝑣𝑒𝑦#,% takes 1 if thestudent belongs to the COVID-19 experienced group and the survey timing is Decemberof grade 6. Hence, the parameter of interest̶the coefficient 𝛾̶captures the effects of
temporal school closure on students' motivation and attitude toward learning math.
𝑋#,% include other treatment variables, representing the living conditions during and after
temporal school closures and the earliness of ICT introduction. 𝜇& and 𝜇( are the school(s) and classroom (c) fixed effects, respectively.34

7.3. Results
Figures 12 and 13 show the estimated coefficients γ of equation (6) for each outcome
variable in the full sample and subsample, respectively; Panel A shows the results without

Here, only one survey is available for each cohort in each year, and thus, the year fixed effects 𝜇! and𝑃6 𝑠𝑢𝑟𝑣𝑒𝑦",$ are perfectly consistent. Therefore, we exclude 𝜇! from the estimation in equation (6).

34

33

𝑋#,% , and Panel B shows the results with 𝑋#,% .35(Figures 12 and 13 around here).

The full sample analyses (Panel A in Figure 12) show that the COVID-19 experienced
group increased the percentage of "Yes"/"Partially Yes" responses on several outcome
variables among motivations and attitudes toward learning math, such as “Like math,”“Math will be useful,” “High motivation for Reco-sheets (reflection study sheets),” and“Motivation for other Reco-sheets.” Moreover, the results of considering 𝑋#,% (Panel B
in Figure 12) show that the coefficients of all outcome variables are statisticallysignificant and higher than those in Panel A.
The subsample analysis without 𝑋#,% (Panel A in Figure 13), indicates that the COVID19 experience group also demonstrates a higher and more significant coefficient of theoutcome variables, but no significant differences are observed in the fourth quartile group.
On the other hand, Panel B in Figure 13 also shows that higher and more significant
coefficients are observed for many outcome variables, not only for the lower quartile but
also for the fourth quantile. However, the estimated coefficients are the largest for thelowest academic achievement group, suggesting that the effect of disadvantaged living
conditions may differ across academic quartiles.
Overall, the cohort who experienced temporary school closure due to COVID-19 show
higher motivation and attitude toward math in the medium term. Specifically, students
who chose “Like math" and “High motivation for the Reco Sheet (reflective learning
sheet)" increased significantly. This finding suggests that the increase in math scores inthe second semester of the sixth grade (six months after the temporary school closure) is

35

Appendix Tables A-10 and A-11 show the DID results without and with considering 𝑋",$ , respectively.

34

not the result of being forced to learn but is the result of the students' increased motivation
to learn. Moreover, the effect of disadvantageous living conditions has resulted in moresignificant coefficients for the outcome variables, especially in the first quartile, which isalso consistent with the fact that the disadvantaged living conditions hindered the increasein math scores in the medium- and long-term. Therefore, we conclude that the math scores
increase when a certain period has passed after the temporary school closure due to theshortened summer vacation, fewer school events, and because the teachers' efforts after
the second semester in FY2020 increased the students’ motivation and attitude towardlearning math.

8. Conclusion
This study examined the short- and middle-term effects of three policy interventions onelementary school students: temporary school closures aimed at curbing the COVID-19
epidemic in Japan, a shortened summer vacation, and the introduction of ICT terminals.
However, in Japan, the National Achievement Test for elementary school students is
conducted only once a year; therefore, short-term changes in academic achievement
before and after temporary school closures cannot be captured. Hence, we used twocohorts of children — those who experienced COVID-19 school closures (FY2020 P6
cohort) and those who did not (FY2019 P6 cohort) — from the "Manabi Nara" mathachievement test. This test is administered at the end of each term to the fourth throughsixth graders in Nara City.
The analysis defined the former cohort of children as the treatment group and the latteras the control group. We then examined whether the difference between the two groupschanged before and after the temporary school closure by using the DID and event study35

to analyze the data.
We present three main pieces of evidence. First, the temporary school closure decreased
math scores, and the lower the quartile, the lower were the math scores of the student justbefore the temporary school closure (the third term of the fifth grade). Moreover, in thefirst and fourth quartiles, the negative effect on math scores persisted immediately after
the temporary school closure (the first term of the sixth grade). Second, the drastic
shortening of the summer break to compensate for the decrease in school days due toschool closure and the introduction of ICT terminals significantly recovered andimproved math scores. Once again, the lower quartile of math scores showed a moresignificant increase in math scores from the second term of the sixth grade. Third,
motivation and attitude toward math also improved along with improving math scores six
months after the school closure. Fourth, students that have disadvantaged living
conditions during and after the school closure had lower math scores and motivation andattitude toward math. Further, the students from disadvantaged living conditions were
negatively affected, especially in the lowest 25 % of academic ability groups.
These results have two implications. First, the increase in school days due to theshortened summer vacation and the introduction of ICT terminals in face-to-face classes
can counteract the decline and reduce the gap in academic performance caused bytemporary school closures. In Japan, besides the increase in school days due to theshortened summer vacation, the introduction of ICT terminals under face-to-face classes
with full support from teachers is likely to have improved academic performance,
especially among low-achieving students. These results suggest the importance of faceto-face support by teachers in promoting ICT use in education and have important policy
implications when considering how to compensate for the loss of education due to36

temporary school closures.
The second policy implication is that students' disadvantaged living conditions should
be taken care of appropriately according to their academic ability because the negative
impact of disadvantaged living conditions under temporary school closure on academic
performance varies according to students’ academic achievement. For example, Carlanaand La Ferrara (2021) found that online support for students from disadvantaged families
during temporary school closures improved their academic performance. However,existing studies have not discussed the need for support for students from more privileged
backgrounds. Therefore, our findings, which strongly support the need to improvedisadvantaged living conditions regardless of academic hierarchy, can be considered
substantial evidence to discuss the negative side effects of temporary school closure dueto the COVID-19 pandemic.
One limitation of this study is that the data is from the core city (Nara City). We used
the uniform test "Manabi Nara" for elementary school students in Nara City to examine
the effect of temporary school closure on COVID-19. While this data has the advantage
of a high frequency of testing, it has the disadvantage of not grasping the nationwide
effect of temporary school closures. Therefore, a future study will be conducted to
examine the effects of temporary school closures on the whole of Japan by using othernationwide surveys.

37

Appendix
Appendix A: Matching estimation
ICT terminals started being introduced earlier in schools with lower academic
achievement (see Appendix Table A-5). In this case, the results may not be an effect ofearly ICT adoption, but simply a comparison of the growth in P6T2 between schools withlow and high math scores. Therefore, as a robustness check, we created an ICT earliest
dummy (ICT earliest), set to 1 if the earliest adoption of ICT terminals is in the top 50%
and 0 otherwise, and matched the sample students using the following equation:

𝐼𝐶𝑇 𝑒𝑎𝑟𝑙𝑖𝑒𝑠𝑡#,% = 𝛿 𝑠𝑐𝑜𝑟𝑒# + 𝜀#,%(𝐴 − 1)𝑠𝑐𝑜𝑟𝑒# represents the math scores in the third term of the fourth grade. We comparednearest matching methods with two different distances: 0.2 calipers calculated bypropensity score (NNM caliper 0.2) and Mahalanobis distance (NNM mahalanobis).
We discuss the validity of these matching methods and distances. Appendix Figure 4
shows that, for both estimation methods, the standardized mean difference after matching
was within the range of 0.1 in terms of absolute value, which is considered a guideline.
In particular, the test score of P4T3 was -0.2 in the original model before adjustment,
which exceeded the guideline, so we can conclude that the matching was meaningful.
Next, we confirmed which students were left behind by the matching, specificallywhether the students were significantly reduced in certain quartiles. Appendix Figure 5
illustrates the cumulative density distribution (CDF, left axis) and the density distribution(PDF, right axis) of the test score before (green line) and after matching (orange and bluelines, for NNM caliper 0.2 and NNM mahalanobis), respectively. This figure shows that38

the CDF and PDF are almost unchanged before and after matching.
Based on the above results, this study considers that matching does not change the
distribution of students' test scores but mitigates the effect of self-selection on theearliness of ICT terminal adoption derived from past test scores (in grade 4).

Appendix B: Effect heterogeneity of early adoption of ICT terminals
In Section 6, disadvantaged living conditions and early adoption of ICT terminals areincluded as explanatory variables at the same time, so we could not independently
estimate the impact of the early adoption of ICT terminals on test scores. In other words,
the result of Section 6 was the effect of the early adoption of ICT terminals conditionedby the effects of disadvantaged living conditions.
Hence, here we do not consider disadvantaged living conditions and estimate thefollowing equation, which adds only the relatively earliness of ICT terminal adoption to
the baseline estimation in equation (1):Baseline DID + SB + Early ICT
𝑌#,% = 𝛼) 𝐶𝑂𝑉𝐼𝐷19# × 𝑃5𝑇3_𝑃6𝑇3% + 𝛼+ 𝐶𝑂𝑉𝐼𝐷19# + 𝛼, 𝑃5𝑇3_𝑃6𝑇3%+𝛼- 𝑆𝑢𝑚𝑚𝑒𝑟 𝑏𝑟𝑒𝑎𝑘% + 𝛼. 𝑆ℎ𝑜𝑟𝑡𝑒𝑛𝑒𝑑 𝑑𝑎𝑦𝑠#,% + 𝛼/ 𝐶𝑂𝑉𝐼𝐷19# × 𝐴𝑓𝑡𝑒𝑟 𝑃6𝑇2𝑡+ 𝛼0 𝐴𝑓𝑡𝑒𝑟 𝑃6𝑇2𝑡 + 𝛼4 𝐼𝐶𝑇 𝑒𝑎𝑟𝑙𝑦 𝑑𝑎𝑦𝑠#,% + 𝜇& + 𝜇' + 𝜇( + 𝜖#, %( 𝐴 − 2)

Baseline Event study + SB + Early ICT,

𝑌#,% = L 𝛽% 𝐶𝑂𝑉𝐼𝐷19# × 𝑆𝑐ℎ𝑜𝑜𝑙 𝑐𝑙𝑜𝑠𝑒% + 𝛽- 𝑆𝑢𝑚𝑚𝑒𝑟 𝑏𝑟𝑒𝑎𝑘% + 𝛽. 𝑆ℎ𝑜𝑟𝑡𝑒𝑛𝑒𝑑 𝑑𝑎𝑦𝑠#,%%12++ 𝛽/ 𝐼𝐶𝑇 𝑒𝑎𝑟𝑙𝑦 𝑑𝑎𝑦𝑠#,% + 𝜇& + 𝜇' + 𝜇( + 𝜖#39

( 𝐴 − 3)

The difference between equations (1) and (2) and equations (A-2) and (A-3) is the
addition of 𝐼𝐶𝑇 𝑒𝑎𝑟𝑙𝑦 𝑑𝑎𝑦𝑠#,% as an explanatory variable. This variable takes the value ofthe difference between the start date of ICT use at the elementary school of individual 𝑖and the start date of the slowest elementary school if 𝑆𝐶 = 2 or 𝑆𝐶 = 3 and 0
otherwise because ICT has not been introduced except in the second term of the sixth
grade of the FY2020 P6 cohort. The parameters of interest are 𝛼4 and 𝛽/ , and they showthat early ICT introduction significantly affects math scores.

Full sample analysis
Appendix Table A-12 shows the results of the full sample estimation using equations (A2) and (A-3) in Columns 3 and 4. In Columns 1 and 2, the results of equations (3) and (5)are included for comparison. We also graphically compare the estimated results from theevent study in Figure Appendix Figure 6, where only the summer break dummy andschool/class fixed effects are controlled for (green line), with the estimated results where
the impact of early adoption of ICT is additionally controlled for (orange line).

(Appendix Tables A-12 and Figure Appendix Figure 6 around here)

The results show that although the estimated coefficient of "ICT early days" is negativein both the DID and event studies, the significant effect of the early introduction of ICT
terminals on math scores was not confirmed. Moreover, the significance and signs of thecoefficients of the other variables are similar to those obtained in Table 3. Moreover,comparing the magnitude of the coefficients, the addition of "ICT early days," which has
a negative coefficient on test scores, resulted in a considerable estimated increase in40

scores in the second term of the sixth grade. Appendix Figure 6 also shows that while thecoefficients become larger when the effect of early ICT adoption is considered, thedifference is not significant. Therefore, we conclude that, on average, the early adoptionof ICT does not have a significant impact.

Subsample analysis
Appendix Tables A-13 and A-14 and show the results of the subsample estimation using
equations (A-2) and (A-3). Columns 1 to 4 of each table show the results of the baseline
estimation for comparison. Appendix Figure 7 shows the estimation results obtained fromthe subsample event study in the same manner as in Figure 6.

(Appendix Tables A-13 and A-14; Appendix Figure 7 here)

The results show that "ICT early days" have a negative and significant effect (at the
10% level) on math scores only in the third and fourth quartiles only in the event study.
The results for the other quartiles were similar to those for the full sample estimation.
However, Appendix Figure 7 shows that considering the early days of ICT adoption
makes the estimated coefficients larger, but the difference is not significant. In summary,the early introduction of ICT terminals lowered the test scores only in the third and fourth
quartiles, although the magnitude was not significant.

41

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47

List of Figures
Figure 1：Timelines for policy interventions
Figure 2：Cohort types and timing
Figure 3：Change in Math Test Scores by cohort
Figure 4：Change in Math Test Scores by cohort and quantiles
Figure 5：ICT Terminal Distribution
Figure 6：Effects of Policy Intervention and Living Condition Full sample
Figure 7：The Effect of Treatment Variables - Baseline vs Baseline + Early ICT + Living
Condition (Full sample)
Figure 8：Estimated coefficients (Baseline + Early ICT + Living Condition)：Subsample
Figure 9：Baseline vs Baseline + Early ICT + Living Condition：Subsample
Figure 10：Mean of Positive Responses by Cohort (Full sample)
Figure 11：Positive Student Responses by Cohort (Subsample)
Figure 12：Estimated coefficients of other outcome variables (Full sample)
Figure 13：Estimated coefficients of other outcome variables (Subsample)
Appendix Figure 1: Histogram of the disadvantaged living conditions
Appendix Figure 2：Histogram of the motivation and attitude of math (FY2019 P6
Cohort)Appendix Figure 3：Histogram of the motivation and attitude of math (FY2020 P6
Cohort)Appendix Figure 4：Standardized mean differences: Original and matched samples
Appendix Figure 5：Test score distribution (CDF & PDF): Original and matched samples
Appendix Figure 6：Baseline vs Baseline + Early ICT：Full sample
Appendix Figure 7：Baseline vs Baseline + Early ICT：Subsample

48

Figure 1: Timelines for policy interventions
FY2020 P6
Cohort① School Closure

1/7

3/2

② Shortened summer breaks

5/31

FY2019 P5 T3

8/8 8/24

FY2020 P6 T2

FY2020 P6 T1

FY2018 P5 T3

3/24 4/8

FY2019 P6
CohortSpring break

7/20

8/25

3/20

FY2020 P6 T3

FY2018 P6 T2

FY2019 P6 T1

1/7

12/24 − 1/6

FY2018 P6 T3

12/24 − 1/6

3/2 3/20

Summer break

Figure 2: Cohort Types and Timing
FY 2020 P6 Cohort (COVID-19 experienced)

FY 2019 P6 Cohort (COVID-19 not-experienced)

Jul. 2019 “Manabi Nara”(P5 first term)
Dec. 2019 “Manabi Nara”(P5 second term)

Jul. 2018 “Manabi Nara”(P5 first term)
Dec. 2018 “Manabi Nara”(P5 second term)“Student Survey”(P5 second term)

Mar. 2020 “Manabi Nara”(P5 third term)
May 2020“Living Condition Survey”
Jun. 2020“Living Condition Survey”
Jul. 2020 “Manabi Nara”(P6 first term)
Dec. 2020 “Manabi Nara”(P6 second term)
Mar. 2021 “Manabi Nara”(P6 third term)“Student Survey”(P5 second term)

Mar. 2019 “Manabi Nara”(P5 third term)
School closure
Shortenedsummer break(27 days)+
ICT terminal
introduction

Jul. 2019 “Manabi Nara”(P6 first term)
Dec. 2019 “Manabi Nara”(P6 second term)“Student Survey”(P6 second term)

Mar. 2020 “Manabi Nara”(P6 third term)

49

Shortenedsummer break(7 days)“Student Survey”(P6 second term)Figure 3: Change in math test scores (by cohort)
100

Mean

75

Cohort
FY2019 P6(COVID−19 not experienced)

50

FY2020 P6(COVID−19 experienced)
25

0
P5T1

P5T2

P5T3

P6T1

P6T2

P6T3

Grade & Term

Figure 4: Change in math test scores (by cohort and quantiles)

First quantile

Second quantile

100

100

75

Cohort
FY2019 P6(COVID−19 not experienced)

50

Mean

Mean

75

Cohort
FY2019 P6(COVID−19 not experienced)

50

FY2020 P6(COVID−19 experienced)

FY2020 P6(COVID−19 experienced)

25

25

0

0
P5T1 P5T2 P5T3 P6T1 P6T2 P6T3

P5T1 P5T2 P5T3 P6T1 P6T2 P6T3

Grade & Term

Grade & Term

Third quantile

Fourth quantile

100

100

75

Cohort
FY2019 P6(COVID−19 not experienced)

50

Mean

Mean

75

Cohort
FY2019 P6(COVID−19 not experienced)

50

FY2020 P6(COVID−19 experienced)

FY2020 P6(COVID−19 experienced)

25

25

0

0
P5T1 P5T2 P5T3 P6T1 P6T2 P6T3

P5T1 P5T2 P5T3 P6T1 P6T2 P6T3

Grade & Term

Grade & Term

50

Figure 5: ICT Terminal Distribution

Number of primary schools

6

4

2

0
09/14

09/21
09/28
10/05
Starting date of using ICT terminals

51

10/12

Figure 6: The Effects of Policy Intervention and Living Condition (Full sample)

Lack food−3.585Lack sleep−2.429Lack print study (May)−8.094Lack study (June)−2.876

Policy intervention / Living condition

Feel stressed−1.645

No passion−5.249Bad health−0.258Sign of effects

No sport

a

2.647

a
a

Not fun0.089

Feel unsafe

2.401

ICT early days

−0.093

Shortened days

0.042

Summer Break−2.688COVID−19 × P5T3−P6T3

0.804

COVID−19 × After P6T2

12.449

−10

−5

0
5
Estimated coefficitents

52

10

15

Positive effects
No effects
Negative effects

Figure 7: The Effect of Treatment Variables- Baseline vs Baseline + Early ICT + Living
Condition (Full sample)

FY2020 P6 Cohort − FY2019 P6 Cohort

20

10Treatment variables
Summer break
Summer break+ Disadvantaged living condition+ Early ICT introdction

0

−10
P5T1

P5T2

P5T3
P6T1
Grade & Term

P6T2

53

P6T3

54

Policy intervention / Living condition

Policy intervention / Living condition−10

Summer BreakCOVID−19 × After P6T2

COVID−19 × P5T3−P6T3
9.993

0
10
Estimated coefficitents

0.705

0.091

−3.835

Shortened days

Summer Break−0.093

ICT early days

2.712

1.121

Feel unsafe

1.033

3.128

Not fun−10−2.473−1.732−3.556

2.473

1.252

−2.783

Third quantile

0
10
Estimated coefficitents

No sport

Bad healthNo passion

Feel stressed

Lack study (June)

Lack print study (May)

Lack sleep

Lack food

COVID−19 × After P6T2

5.206

2.376

−0.111−0.849

Shortened days

COVID−19 × P5T3−P6T3−0.18

ICT early days

Feel unsafe

0.918

Not fun

1.395

4.169

0.976

−4.552−7.569−3.297−6.818−5.312First quantile

No sport

Bad healthNo passion

Feel stressed

Lack study (June)

Lack print study (May)

Lack sleep

Lack food20

20

19.254a Positive effects
a No effects
a Negative effects

Sign of effects

a Positive effects
a No effects
a Negative effects

Sign of effects

−10

0.98

COVID−19 × After P6T2

COVID−19 × P5T3−P6T3

Summer Break

Shortened days

7.505

0
10
Estimated coefficitents−3.027−3.2260.119−0.088Feel unsafe

2.134−0.741−0.681−0.982

2.403

0.302
−2.79

−4.6780.697

ICT early days

9.246

0
10
Estimated coefficitents−1.648−2.8080.043−0.033−1.865

5.427

3.167

0.443

−2.936

Fourth quantile−10−0.171−2.767−6.038−1.449Second quantile

Not funNo sport

Bad healthNo passion

Feel stressed

Lack study (June)

Lack print study (May)

Lack sleep

Lack food

COVID−19 × After P6T2

COVID−19 × P5T3−P6T3

Summer Break

Shortened days

ICT early days

Feel unsafe

Not funNo sport

Bad healthNo passion

Feel stressed

Lack study (June)

Lack print study (May)

Lack sleep

Lack food

14.791Figure 8: Estimated coefficients (Baseline + Early ICT + Living Condition, Subsample)

Policy intervention / Living condition
Policy intervention / Living condition

20

20

a Positive effects
a No effects
a Negative effects

Sign of effects

a Positive effects
a No effects
a Negative effects

Sign of effects

FY2020 P6 Cohort − FY2019 P6 Cohort

FY2020 P6 Cohort − FY2019 P6 Cohort

55

−10

0

10

20

30

−10

0

10

20

30

P5T2

P5T3

P5T1

P5T2

P6T1

P5T3

Grade & Term

P6T1

Grade & Term

Third quantile

P5T1

First quantile

P6T2

P6T2

P6T3

P6T3

Summer break
Summer break+ Disadvantaged living condition+ Early ICT introdction

Treatment variables

Summer break
Summer break+ Disadvantaged living condition+ Early ICT introdction

Treatment variables

−10

0

10

20

30

−10

0

10

20

30

P5T2

P5T3

P5T1

P5T2

P6T1

P5T3

Grade & Term

P6T1

Grade & Term

Fourth quantile

P5T1

Second quantile

P6T2

P6T2

P6T3

P6T3

Summer break
Summer break+ Disadvantaged living condition+ Early ICT introdction

Treatment variables

Summer break
Summer break+ Disadvantaged living condition+ Early ICT introdction

Treatment variables

Figure 9: Baseline vs Baseline + Early ICT + Living Condition (Subsample)

FY2020 P6 Cohort − FY2019 P6 Cohort
FY2020 P6 Cohort − FY2019 P6 Cohort

Figure 10: Mean of Positive Responses by Cohort (Full sample)
Panel A: FY2019 P6 Cohort
Share of positive answered students (FY2019 P6 Cohort)
Like math
Math is important
Understand math well

Outcomes

Math will be useful
Survey Timing

Concentrate in math class

P6 Dec.

Ask questions in mass class

P5 Dec.

Complete math homework
Concern about test results
High motivation for Reco
Motivation for other Reco
0.00

0.25

0.50
Mean

0.75

1.00

Panel B: FY2019 P6 Cohort
Share of positive answered students (FY2020 P6 Cohort)
Like math
Math is important
Understand math well

Outcomes

Math will be useful
Survey Timing

Concentrate in math class

P6 Dec.

Ask questions in mass class

P5 Dec.

Complete math homework
Concern about test results
High motivation for Reco
Motivation for other Reco
0.00

0.25

0.50
Mean

56

0.75

1.00

Figure 11: Positive Student Responses by Cohort (Subsample)
Panel A: FY2019 P6 CohortFirst quantile

Second quantile

Like mathLike math

Math is important

Math is important

Understand math well

Understand math well
Math will be useful
Survey Timing
P6 Dec.

Ask questions in mass class

P5 Dec.

Outcomes

Outcomes

Math will be usefulConcentrate in math class

Complete math homework

Concern about test results

Concern about test results

High motivation for Reco

High motivation for Reco

Motivation for other Reco

Motivation for other Reco
0.25

0.50
Mean

0.75

1.00

P5 Dec.

0.00

Third quantile

0.25

0.50
Mean

0.75

1.00

Fourth quantile

Like mathLike math

Math is important

Math is important

Understand math well

Understand math well
Math will be useful
Survey Timing

Concentrate in math class

P6 Dec.

Ask questions in mass class

P5 Dec.

Outcomes

Math will be useful
Outcomes

P6 Dec.

Ask questions in mass class

Complete math homework

0.00

Survey Timing

Concentrate in math class

Complete math homework

Concern about test results

Concern about test results

High motivation for Reco

High motivation for Reco

Motivation for other Reco

Motivation for other Reco
0.25

0.50
Mean

0.75

P6 Dec.

Ask questions in mass class

Complete math homework

0.00

Survey Timing

Concentrate in math class

1.00

P5 Dec.

0.00

0.25

0.50
Mean

0.75

1.00

Panel B: FY2020 P6 CohortFirst quantile

Second quantile

Like mathLike math

Math is important

Math is important

Understand math well

Understand math well
Math will be useful
Survey Timing
P6 Dec.

Ask questions in mass class

P5 Dec.

Outcomes

Outcomes

Math will be usefulConcentrate in math class

Complete math homework

Concern about test results

Concern about test results

High motivation for Reco

High motivation for Reco

Motivation for other Reco

Motivation for other Reco
0.25

0.50
Mean

0.75

1.00

P5 Dec.

0.00

Third quantile

0.25

0.50
Mean

0.75

1.00

Fourth quantile

Like mathLike math

Math is important

Math is important

Understand math well

Understand math well
Math will be useful
Survey Timing

Concentrate in math class

P6 Dec.

Ask questions in mass class

P5 Dec.

Outcomes

Math will be useful
Outcomes

P6 Dec.

Ask questions in mass class

Complete math homework

0.00

Survey Timing

Concentrate in math class

Complete math homework

Concern about test results

Concern about test results

High motivation for Reco

High motivation for Reco

Motivation for other Reco

Motivation for other Reco
0.25

0.50
Mean

0.75

1.00

P6 Dec.

Ask questions in mass class

Complete math homework

0.00

Survey Timing

Concentrate in math class

P5 Dec.

0.00

57

0.25

0.50
Mean

0.75

1.00

Figure 12: Estimated coefficients of other outcome variables (Full sample)
Panel A: Without other treatment variables

Like math

0.099

Math is important

0.022

Outcome variables

Understand math well

−0.002Math will be useful

0.03

Concentrate in math classSign of effects

0.01

Ask questions in mass class

0.042

Complete math homeworkaPositive effectaNo effect

0.018

Concern about test results

0.034High motivation for Reco

0.109

Motivation for other Reco

0.081

−0.1

0.0
0.1
Estimated coefficitents

0.2

0.3

Panel B: With other treatment variables

Like math

0.195

Outcome variables

Math is important

0.031

Understand math well

0.056

Math will be useful

0.049

Concentrate in math class

0.034

Sign of effects

Ask questions in mass class

a

0.127

Complete math homework

0.051

Concern about test results

0.091High motivation for Reco

0.21

Motivation for other Reco

0.179

−0.1

0.0
0.1
Estimated coefficitents

58

0.2

0.3

Positive effectFigure 13: Estimated coefficients of other outcome variables (Subsample)
Panel A: Without other treatment variables
First quantile

Second quantile
Like math

0.105

Math is important

0.036

Math is important

Understand math well

0.044

Understand math well

Math will be useful

0.046

Concentrate in math class
Ask questions in mass class
Complete math homework
Concern about test results

Sign of effects

0.015
0.065

a

Positive effectaNo effect−0.006

Outcome variables

Outcome variables

Like math

0.049

Math will be usefulConcentrate in math class

0.064

Concern about test results

0.06

Motivation for other Reco

0.101

Motivation for other Reco
0.4

Math is important

Understand math well

0.023

Understand math well

0.047

Complete math homework

0.003

Concern about test results

Sign of effectsaPositive effectaNo effect

Outcome variables

Outcome variables

0.026

0.013

No effect

0.4

Fourth quantile

Math is important

Ask questions in mass class

Positive effect

a

0.094

Like math

0.125

0.002

a

0.06

0.0
0.2
Estimated coefficitents

Third quantile

Concentrate in math classSign of effects

0.024

Complete math homework

High motivation for Reco

Math will be useful−0.007

0.081

0.117

Like math−0.037

Ask questions in mass classHigh motivation for Reco

0.0
0.2
Estimated coefficitents

0.114
0.01

0.037

−0.011

Math will be useful

0.04

Concentrate in math class

0.002

Ask questions in mass class−0.013Complete math homework

0.01

Concern about test results

0.004High motivation for Reco

0.124

High motivation for Reco

Motivation for other Reco

0.112

Motivation for other Reco

0.0
0.2
Estimated coefficitents

0.057
0.025

0.4

Sign of effectsa

No effect

0.099
0.055

0.0
0.2
Estimated coefficitents

0.4

Panel B: With other treatment variables
First quantile

Second quantile

Like math
0.044

Outcome variables

Understand math well
Math will be useful

0.152
0.064

Concentrate in math class
Ask questions in mass class
Complete math homework

Sign of effects

0.087
0.166

a

Positive effectaNo effect

0.066

Concern about test results

Outcome variables

Math is important

Like math

0.214

0.206

High motivation for Reco

Understand math well

−0.003

Math will be useful

0.008

Concentrate in math class

0.015

Ask questions in mass class

0.087

Concern about test results

0.11

0.4

Math is important

0.093
0.084

Concentrate in math class

Complete math homework
Concern about test results
High motivation for Reco
Motivation for other Reco

Understand math well
Sign of effects

0.026

a

Ask questions in mass class

No effect

0.4

Fourth quantile

0.039

Math will be useful

Positive effect

a

0.178

Like math

0.176

Understand math well

a

0.133

0.0
0.2
Estimated coefficitents

0.144

a

0.006

Positive effectNo effect

Outcome variables

Outcome variables

Math is important

0.122

Complete math homework

Third quantile
Like mathSign of effects

Motivation for other Reco

0.274

0.0
0.2
Estimated coefficitents

0.023

High motivation for Reco

0.299

Motivation for other Reco

0.183

Math is important

0.05

Concentrate in math class

0.023

Ask questions in mass class

0.057

Complete math homework

0.042

High motivation for Reco

0.203

Motivation for other Reco

0.168

0.0
0.2
Estimated coefficitents

0.4−0.011

Math will be usefulConcern about test results

0.095

0.076
0.017

Sign of effects

0.018
0.187
0.128

0.0
0.2
Estimated coefficitents

59

0.4

a

Positive effectaNo effect

60

Number of students (Count)

Number of students (Count)

0.00

0.25

0.50

0.75

1.00

0.00

0.25

0.50

0.5

1.0

0.5

1.0

Answer (May and June)0.0

No passion

Answer (May and June)

0.0

Number of students (Count)

Number of students (Count)

0.75

0.00

0.25

0.50

0.75

1.00

0.00

0.25

0.50

0.75

0.5

1.0

0.5

1.0

Answer (May and June)

0.0

Bad health

Answer (May and June)

0.0

Number of students (Count)
Number of students (Count)

1.00

Lack sleep

0.00

0.25

0.50

0.75

1.00

0.00

0.25

0.50

0.75

1.00

0.5

1.0

0.5

1.0

Answer (May and June)

0.0

No sport

Answer (May)0.0

Lack print study (May)

Number of students (Count)

1.00

Lack food
Number of students (Count)
0.00

0.25

0.50

0.75

1.00

0.00

0.25

0.50

0.75

1.00

0.5

1.0

0.5

1.0

Answer (May and June)

0.0

Not funAnswer (June)

0.0

Lack study (June)

Appendix Figure 1: Histogram of the disadvantaged living conditions

Number of students (Count)
Number of students (Count)

0.00

0.25

0.50

0.75

1.00

0.00

0.25

0.50

0.75

1.00

0.5

1.0

0.5

1.0

Answer (May and June)

0.0

Feel unsafe

Answer (May and June)

0.0

Feel stressed61

Share of students

Share of students

P6 Dec.

P5 Dec.

Survey Timing

AnswerYes / Partly Yes

P6 Dec.

P5 Dec.

Survey Timing

Ask questions in mass class

Partly No / No

0

25

50

75

100

Yes / Partly Yes

Answer

Partly No / No

0

25

50

Share of students

Share of students

P6 Dec.

P5 Dec.

Survey Timing

AnswerYes / Partly Yes

P6 Dec.

P5 Dec.

Survey Timing

Complete math homework

Partly No / No

0

25

50

75

100

AnswerYes / Partly Yes

Math is important

Partly No / No

0

25

50

75

100

Share of students
Share of students

75

Like mathAnswerYes / Partly Yes

P6 Dec.

P5 Dec.

Survey Timing

Concern about test results

Partly No / No

0

25

50

75

100

AnswerYes / Partly Yes

P6 Dec.

P5 Dec.

Survey Timing

Understand math wellPartly No / No

0

25

50

75

100

Share of students

100

Share of students

P6 Dec.

P5 Dec.

Survey Timing

AnswerYes / Partly Yes

P6 Dec.

P5 Dec.

Survey TimingHigh motivation for Reco

Partly No / No

0

25

50

75

100

AnswerYes / Partly Yes

Math will be usefulPartly No / No

0

25

50

75

100

Yes / Partly Yes

AnswerP6 Dec.

P5 Dec.

Survey Timing

Motivation for other Reco

Partly No / No

0

25

50

75

100

Yes / Partly Yes

AnswerP6 Dec.

P5 Dec.

Survey Timing

Concentrate in math class

Partly No / No

0

25

50

75

100

Appendix Figure 2: Histogram of the motivation and attitude of math (FY2019 P6 Cohort)

Share of students
Share of students

62

Share of students

Share of students

P6 Dec.

P5 Dec.

Survey Timing

AnswerYes / Partly Yes

P6 Dec.

P5 Dec.

Survey Timing

Ask questions in mass class

Partly No / No

0

25

50

75

100

Yes / Partly Yes

Answer

Partly No / No

0

25

50

Share of students

Share of students

P6 Dec.

P5 Dec.

Survey Timing

AnswerYes / Partly Yes

P6 Dec.

P5 Dec.

Survey Timing

Complete math homework

Partly No / No

0

25

50

75

100

AnswerYes / Partly Yes

Math is important

Partly No / No

0

25

50

75

100

Share of students
Share of students

75

Like mathAnswerYes / Partly Yes

P6 Dec.

P5 Dec.

Survey Timing

Concern about test results

Partly No / No

0

25

50

75

100

AnswerYes / Partly Yes

P6 Dec.

P5 Dec.

Survey Timing

Understand math wellPartly No / No

0

25

50

75

100

Share of students

100

Share of students

P6 Dec.

P5 Dec.

Survey Timing

AnswerYes / Partly Yes

P6 Dec.

P5 Dec.

Survey TimingHigh motivation for Reco

Partly No / No

0

25

50

75

100

AnswerYes / Partly Yes

Math will be usefulPartly No / No

0

25

50

75

100

Yes / Partly Yes

AnswerP6 Dec.

P5 Dec.

Survey Timing

Motivation for other Reco

Partly No / No

0

25

50

75

100

Yes / Partly Yes

AnswerP6 Dec.

P5 Dec.

Survey Timing

Concentrate in math class

Partly No / No

0

25

50

75

100

Appendix Figure 3: Histogram of the motivation and attitude of math (FY2020 P6 Cohort)

Share of students
Share of students

Appendix Figure 4: Standardized mean differences: Original and matched samples
NNM (caliper 0.2)

NNM (mahalanobis)

distance
Test score (P4T1)

Test score (P4T1)

SampleAdjusted

SampleAdjusted

Test score (P4T2)

Unadjusted

Unadjusted

Test score (P4T2)

Test score (P4T3)
Test score (P4T3)−0.2 −0.1

0.0

0.1

0.2

−0.2

Standardized Mean Differences

−0.1

0.0

0.1

Standardized Mean Differences

Appendix Figure 5: Test score distribution (CDF & PDF): Original and matched samples

1.00

0.75

0.02
0.50

0.01
0.25

0.00

0.00
0

25

50

75

100

Cumulative composition of cumulative number of students

63

Histogram: Density

Cumulative composition of test score

0.03

Data
Original
NNM (caliper 0.2)
NNM (mahalanobis)

Appendix Figure 6: Baseline vs Baseline + Early ICT：Full sample

FY2020 P6 Cohort − FY2019 P6 Cohort

20

10Treatment variables
Summer break
Summer break+ Early ICT introdction

0

−10
P5T1

P5T2

P5T3
P6T1
Grade & Term

64

P6T2

P6T3

65

FY2020 P6 Cohort − FY2019 P6 Cohort

FY2020 P6 Cohort − FY2019 P6 Cohort

−10

0

10

20

30

−10

0

10

20

30

P5T2

P5T3

P5T1

P5T2

P6T1

P5T3

Grade & Term

P6T1

Grade & Term

Third quantile

P5T1

First quantile

P6T2

P6T2

P6T3

P6T3

Summer break
Summer break+ Early ICT introdction

Treatment variables

Summer break
Summer break+ Early ICT introdction

Treatment variables

−10

0

10

20

30

−10

0

10

20

30

P5T2

P5T3

P5T1

P5T2

P6T1

P5T3

Grade & Term

P6T1

Grade & Term

Fourth quantile

P5T1

Second quantile

P6T2

P6T2

Appendix Figure 7: Baseline vs Baseline + Early ICT (Subsample)

FY2020 P6 Cohort − FY2019 P6 Cohort
FY2020 P6 Cohort − FY2019 P6 Cohort

P6T3

P6T3

Summer break
Summer break+ Early ICT introdction

Treatment variables

Summer break
Summer break+ Early ICT introdction

Treatment variables

List of Tables
1
2
3
4
5

Change in summer break days . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
Summary statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
Results of DID and event study estimation: Full sample . . . . . . . . . . . . . . . . 68
Results of DID: Baseline and Baseline + Summer break (treatment eﬀect by quantiles) 69
Results of Event study: Baseline and Baseline + Summer break (treatment eﬀect by
quantiles) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
6
Results of DID and event study estimation: Treatment eﬀect of disadvantaged living
condition (Full sample) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
7
Results of DID: Treatment eﬀects of disadvantaged living condition by pre-treat score
quantile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
8
Results of Event study: Treatment eﬀects of disadvantaged living condition by pretreat score quantile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
A-1 Corresponding list between cohort, year, and grade/term . . . . . . . . . . . . . . . 74
A-2 Balance test between students tested in FY2020 P5T3 and the others: T-test and F-test 74
A-3 Definitions for disadvantaged living condition dummy . . . . . . . . . . . . . . . . . 75
A-4 Corresponding list between "School close" and grade/term . . . . . . . . . . . . . . 75
A-5 Correlation Test: Early days of ICT introduction and school characteristics . . . . . . 75
A-6 Comparison of original and matching results (DID & Event study): Treatment eﬀectof living condition and early ICT introduction (Full sample) . . . . . . . . . . . . . . 76
A-7 Matching results of DID: Treatment eﬀect of ICT introduction and living condition
by pre-treat score quantile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
A-8 Matching results of Event study: Treatment eﬀect of ICT introduction and living
condition by pre-treat score quantile . . . . . . . . . . . . . . . . . . . . . . . . . . 78
A-9 Definition of outcome variables for motivation and attitude toward math . . . . . . . 79
A-10 Results of DID: Motivation and attitude toward math (Full sample) . . . . . . . . . . . . . 80
A-11 Results of DID with covariates: Motivation and attitude toward math (Full sample) . . . . . 81
A-12 Results of DID and event study estimation: Treatment eﬀect of ICT introduction (Fullsample) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
A-13 Results of DID: Treatment eﬀect of ICT introduction (By pre-treat score quantile) . . 83
A-14 Results of Event study: Treatment eﬀect of ICT introduction (By pre-treat score quantile) 84

66

Table 1: Change in summer break days
Fiscal Year
2017
2018
2019
2020

PeriodDays

Decreased days from 2018

Jul. 21−Aug.31
Jul. 21−Sep.2
Jul. 20−Aug. 25
Aug. 8−24

41
43
36
16

2
0
7
27

Grade
FY2020-P6 Cohort FY2019-P6 Cohort
P4
P5
P6

P4
P5
P6
-

Table 2: Summary statistics

Statistic
Test score
COVID19
P5T3_P6T3
After P6T2
Summer break
Shortened days
ICT early days
Lack foodLack sleep
Lack print study (May)
Lack study (June)
Feel stressed
No passion
Bad healthNo sport
Not funFeel unsafe

N

Mean

St. Dev.

Min

Pctl(25)

Pctl(75)

Max

25,25525,25525,25525,25525,25525,25525,255
24,11824,11824,11824,11824,11824,11824,11824,11824,11824,118

67.67
0.44
0.62
0.32
0.38
3.55
0.98
0.01
0.08
0.03
0.08
0.08
0.05
0.06
0.07
0.05
0.03

22.24
0.50
0.49
0.47
0.48
7.52
3.66
0.09
0.24
0.17
0.28
0.23
0.19
0.20
0.22
0.17
0.14

0
0
0
0
0
0
0
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

53
0
0
0
0
0
0
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

85
1
1
1
1
7
0
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

100
1
1
1
1
27
28
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00

67

Table 3: Results of DID and event study estimation: Full sample
Dependent Variable:
Model:
COVID19 × P5T3_P6T3

Test score
Baseline DID(1)(1) + SB(2)(2) + After P6T2
(3)

5.275∗∗∗(1.063)4.474∗∗∗(1.043)-1.382∗(0.8335)-2.175∗∗(0.8526)-2.082∗(1.090)11.64∗∗∗(0.7283)-1.370(0.8528)-1.322∗∗(0.5755)-2.555∗∗∗(0.4650)-0.0004(0.0244)

COVID19 × After P6T2
COVID19
After P6T2
-3.976∗∗∗(0.4208)
0.2333∗∗∗(0.0235)

Summer break
Shortened days
COVID19 × P5T1 (SC = -2)
COVID19 × P5T2 (SC = -1)
COVID19 × P5T3 (SC = 0)
COVID19 × P6T1 (SC = 1)
COVID19 × P6T2 (SC = 2)
COVID19 × P6T3 (SC = 3)

Baseline Event(4)(4) + SB
(5)0.5094(0.7945)-3.238∗∗∗(0.9010)-5.465∗∗∗(1.662)-1.301∗(0.7764)6.393∗∗∗(0.7292)
8.608∗∗∗(0.6756)-1.307∗∗∗(0.4112)-0.3001∗∗∗(0.0879)
0.0797(0.8091)-0.2613(1.033)-5.858∗∗∗(1.652)-2.692∗∗∗(0.8232)
14.41∗∗∗(2.036)7.220∗∗∗(0.7362)

School FE
Grade FEClassroom FE

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Observations

25,117

25,117

25,117

25,117

25,117

Note: "SC" is the abbreviation for School close and indicates the diﬀerence in semesters from P5T3, the first to beaﬀected by the temporary school closure. "Event" represents the results of event study estimation. "SB" implies theestimation adding variables related to summer vacation. "After P6T2" is the estimation including interaction term of"COVID19 × P6T2." "COVID19" is a dummy variable taking one if the student belongs to the FY2020 P6 cohort.
"School close" means the diﬀerence of term from P5 T3 term. Standard errors in parentheses are clustered at theclassroom level. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01

68

Table 4: Results of DID: Baseline and Baseline + Summer break (treatment eﬀect by quantiles)
Dependent Variable:

Test score

Model:

Baseline DID

Baseline DID + SB + After P6T2

Score QT (P4T3)

1st QT(1)

2nd QT(2)

3rd QT(3)

4th QT(4)

1st QT(5)

2nd QT(6)

3rd QT(7)

4th QT(8)

COVID19 × P5T3_P6T3

7.137∗∗∗(1.759)7.410∗∗∗(1.547)6.479∗∗∗(1.157)1.369(0.8997)-1.840(1.308)-1.435(1.071)-0.6142(0.8661)

0.4238(0.7079)-3.594∗(1.830)18.06∗∗∗(1.292)-1.264(1.317)-2.457∗∗(1.010)-0.8205(0.7137)-0.1830∗∗∗(0.0428)-1.586(1.571)14.38∗∗∗(1.187)-1.484(1.096)-3.549∗∗∗(0.8751)-2.711∗∗∗(0.6282)
0.0213(0.0365)0.2937(1.164)9.628∗∗∗(1.028)-0.7572(0.8709)-1.059(0.7778)-3.576∗∗∗(0.6124)0.0394(0.0328)-3.389∗∗∗(1.036)
7.093∗∗∗(0.8459)
0.1665(0.7122)0.0504(0.5366)-3.158∗∗∗(0.4851)
0.0770∗∗∗(0.0263)

School FE
Grade FEClassroom FE

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Observations

5,253

5,975

5,905

6,624

5,253

5,975

5,905

6,624

COVID19 × After P6T2
COVID19
After P6T2
Summer break
Shortened days

Note: "SB" means the estimation adding variables related to summer vacation. "After P6T2" is the estimation including interaction
term of "COVID19 × P6T2." "COVID19" means a dummy variable taking one if the student belongs to the FY2020 P6 cohort.
"School close" means the diﬀerence of term from P5 T3 term. Standard errors in parentheses are clustered at the classroom level.
∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01

69

Table 5: Results of Event study: Baseline and Baseline + Summer break (treatment eﬀect by quantiles)
Dependent Variable:

Test score

Model:

Baseline Event

Baseline Event + SB

Score QT (P4T3)

1st QT(1)

2nd QT(2)

3rd QT(3)

4th QT(4)

1st QT(5)

2nd QT(6)

3rd QT(7)

4th QT(8)

COVID19 × P5T1 (SC = -2)-0.4796(1.314)-4.234∗∗∗(1.425)-7.152∗∗∗(2.230)-2.102(1.526)
7.718∗∗∗(1.472)
12.91∗∗∗(1.410)0.5384(1.015)-3.400∗∗∗(1.178)-7.337∗∗∗(2.745)0.7495(1.305)9.091∗∗∗(1.260)10.97∗∗∗(1.247)1.724∗∗(0.8426)-2.596∗∗(1.059)-5.427∗(3.068)2.290∗∗(0.9553)
7.927∗∗∗(0.9055)
10.13∗∗∗(1.026)2.852∗∗∗(0.7193)-0.9564(0.7845)-4.203∗∗(1.656)-2.216∗∗(0.9053)
3.992∗∗∗(0.6615)
4.819∗∗∗(0.6299)-0.5599(1.329)-0.3226(1.710)-7.214∗∗∗(2.227)-3.738∗∗(1.557)20.67∗∗∗(3.977)
11.28∗∗∗(1.455)-0.2804(0.7127)-0.5301∗∗∗(0.1587)-0.0148(1.029)0.4150(1.418)-7.835∗∗∗(2.751)-1.019(1.374)19.44∗∗∗(3.254)9.207∗∗∗(1.313)-1.660∗∗∗(0.5863)-0.3871∗∗∗(0.1379)

1.123(0.8448)0.5355(1.227)-5.997∗(3.050)0.7778(1.034)15.59∗∗∗(2.816)8.625∗∗∗(1.116)-1.831∗∗∗(0.6330)-0.2719∗∗(0.1271)

2.461∗∗∗(0.7052)
1.059(0.9333)-4.562∗∗∗(1.656)-3.188∗∗∗(0.9296)8.914∗∗∗(2.279)3.849∗∗∗(0.6789)-1.186∗∗(0.4879)-0.1743∗(0.0977)

School FE
Grade FEClassroom FE

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Observations

5,253

5,975

5,905

6,624

5,253

5,975

5,905

6,624

COVID19 × P5T2 (SC = -1)
COVID19 × P5T3 (SC = 0)
COVID19 × P6T1 (SC = 1)
COVID19 × P6T2 (SC = 2)
COVID19 × P6T3 (SC = 3)
Summer break
Shortened days

Note: "SC" is the abbreviation for School close and indicates the diﬀerence in semesters from P5T3, the first to be aﬀected by the temporary
school closure. "Event" represents the results of event study estimation. "SB" implies the estimation adding variables related to summer
vacation. "After P6T2" is the estimation including interaction term of "COVID19 × P6T2." "COVID19" is a dummy variable taking one if
the student belongs to the FY2020 P6 cohort. "School close" means the diﬀerence of term from P5 T3 term. Standard errors in parentheses
are clustered at the classroom level. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01

70

Table 6: Results of DID and event study estimation: Treatment eﬀect of disadvantaged living condition (Full sample)
Dependent Variable:

Test score
Baseline DID + DLC&ICT(1)(1) + SB(2)(2) + After P6T2
(3)

7.349∗∗∗(1.196)

7.131∗∗∗(1.216)-0.7213(0.8195)-1.489∗(0.8507)0.1729∗∗∗(0.0391)-3.769(2.918)-2.069∗∗(0.9988)-7.974∗∗∗(1.725)-2.684∗∗∗(0.9559)-1.451(0.9655)-5.327∗∗∗(1.335)-0.0756(1.315)3.082∗∗∗(1.145)0.1617(1.422)2.563(1.557)-3.971∗∗∗(0.4678)0.2260∗∗∗(0.0503)
0.0328(0.0852)-3.765(2.924)-2.117∗∗(0.9977)-7.967∗∗∗(1.724)-2.704∗∗∗(0.9591)-1.479(0.9646)-5.341∗∗∗(1.333)-0.1105(1.313)3.007∗∗∗(1.144)0.1460(1.419)2.529(1.556)0.8043(1.206)12.45∗∗∗(0.7357)-0.8532(0.8493)-1.648∗∗∗(0.5714)-2.688∗∗∗(0.4939)0.0416(0.0492)-0.0926(0.0764)-3.585(2.902)-2.429∗∗(0.9856)-8.094∗∗∗(1.726)-2.876∗∗∗(0.9546)-1.645∗(0.9612)-5.249∗∗∗(1.326)-0.2577(1.307)2.647∗∗(1.144)0.0888(1.406)2.401(1.554)

School FE
Grade FEClassroom FE

Yes
Yes
Yes

Yes
Yes
Yes

Observations

24,11824,118Model:
COVID19 × P5T3_P6T3
COVID19 × After P6T2
COVID19
After P6T2
Summer break
Shortened days
ICT early days
Lack foodLack sleep
Lack print study (May)
Lack study (June)
Feel stressed
No passion
Bad healthNo sport
Not funFeel unsafe

Baseline Event + DLC&ICT(4)(4) + SB
(5)-0.1854∗∗(0.0835)-3.579(2.913)-2.649∗∗∗(0.9796)-8.145∗∗∗(1.728)-2.974∗∗∗(0.9600)-1.799∗(0.9536)-5.250∗∗∗(1.325)-0.4283(1.310)2.353∗∗(1.154)-0.0250(1.402)2.214(1.555)1.214(0.7733)-2.618∗∗∗(0.8869)-4.666∗∗∗(1.771)2.990∗∗∗(0.8620)13.01∗∗∗(1.437)13.01∗∗∗(0.7386)-1.305∗∗∗(0.4107)-0.3025∗∗∗(0.0879)-0.1885∗∗(0.0837)-3.587(2.916)-2.648∗∗∗(0.9797)-8.132∗∗∗(1.728)-2.972∗∗∗(0.9603)-1.796∗(0.9535)-5.260∗∗∗(1.325)-0.4337(1.310)2.359∗∗(1.153)-0.0191(1.402)2.226(1.555)0.7840(0.7831)0.3732(1.014)-5.058∗∗∗(1.761)1.586∗(0.9117)
21.11∗∗∗(2.371)
11.60∗∗∗(0.8051)

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

24,11824,11824,118COVID19 × P5T1 (SC = -2)
COVID19 × P5T2 (SC = -1)
COVID19 × P5T3 (SC = 0)
COVID19 × P6T1 (SC = 1)
COVID19 × P6T2 (SC = 2)
COVID19 × P6T3 (SC = 3)

Note: "SC" is the abbreviation for School close and indicates the diﬀerence in semesters from P5T3, the first to be aﬀected by the temporary
school closure. "DLC" means the disadvantaged living conditions during school closure due to COVID19. "ICT" is equivalent to "ICTearly days". Standard errors in parentheses are clustered at the classroom level. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.0171

Table 7: Results of DID: Treatment eﬀects of disadvantaged living condition by pre-treat score quantile
Dependent Variable:

Test score

Model:

Baseline DID + DLC&ICT(1)−(4) + SB + After P6T2

Score QT (P4T3):

1st QT(1)

2nd QT(2)

3rd QT(3)

4th QT(4)

1st QT(5)

2nd QT(6)

3rd QT(7)

4th QT(8)

COVID19 × P5T3_P6T3

12.31∗∗∗(2.053)

6.139∗∗∗(1.855)6.161∗∗∗(1.263)0.8430(0.9671)-2.089∗(1.244)-1.433(1.100)-0.5415(0.8706)

0.1209(0.7383)

0.0924(0.0687)
3.436(5.082)-4.690∗(2.461)-6.795∗∗∗(2.338)-3.054∗∗(1.471)
1.574(1.958)-7.517∗∗∗(2.537)-4.332∗∗(2.183)1.539(2.140)1.245(2.488)5.411∗(2.982)0.2479∗∗∗(0.0558)
5.200(4.603)-1.012(1.677)-5.700∗∗∗(2.167)0.0763(1.295)-2.738∗(1.574)-3.016(1.863)0.8027(1.966)3.756∗∗(1.447)-1.929(2.167)9.259∗∗∗(2.137)0.1253∗∗∗(0.0427)
2.703(2.778)1.454(1.450)-2.691(1.953)-1.521(0.9873)-2.233∗(1.327)-3.565∗∗(1.578)3.173∗∗(1.410)1.272(1.356)1.274(1.701)
3.122(1.943)0.1679∗∗∗(0.0459)-5.025(3.892)
0.6741(1.200)2.601(1.692)-2.686∗∗∗(0.9747)-0.8937(0.9514)-0.7208(1.544)-0.5646(0.9369)2.512∗∗∗(0.9415)0.6515(1.176)
1.206(1.610)2.376(2.026)19.25∗∗∗(1.385)-1.752(1.260)-3.131∗∗∗(1.043)-0.8487(0.7974)-0.1113(0.0931)-0.1802(0.1393)
4.169(4.994)-5.312∗∗(2.453)-6.818∗∗∗(2.344)-3.297∗∗(1.455)1.395(1.939)-7.569∗∗∗(2.528)-4.552∗∗(2.178)0.9756(2.140)0.9184(2.500)5.206∗(2.989)-1.648(1.814)14.79∗∗∗(1.203)-1.566(1.151)-3.583∗∗∗(0.8703)-2.808∗∗∗(0.6691)
0.0434(0.0771)-0.0333(0.1304)
5.427(4.572)-1.449(1.656)-6.038∗∗∗(2.184)-0.1707(1.279)-2.767∗(1.570)-2.936(1.869)0.4432(1.942)
3.167∗∗(1.455)-1.865(2.148)9.246∗∗∗(2.142)0.7045(1.239)9.993∗∗∗(0.9768)-0.8585(0.8684)-1.093(0.7722)-3.835∗∗∗(0.6159)
0.0905(0.0574)-0.0930(0.0902)
2.473(2.784)
1.252(1.426)-2.783(1.973)-1.732∗(0.9782)-2.473∗(1.323)-3.556∗∗(1.565)
3.128∗∗(1.402)1.033(1.360)1.121(1.692)2.712(1.945)-3.027∗∗∗(1.085)7.505∗∗∗(0.8627)-0.3085(0.7517)-0.1706(0.5438)-3.226∗∗∗(0.5237)
0.1191∗∗(0.0514)-0.0885(0.0841)-4.678(3.914)0.3023(1.216)2.403(1.655)-2.790∗∗∗(0.9695)-0.9824(0.9546)-0.6813(1.522)-0.7408(0.9247)
2.134∗∗(0.9255)
0.6970(1.153)0.9805(1.594)

School FE
Grade FEClassroom FE

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Observations

4,935

5,766

5,715

6,504

4,935

5,766

5,715

6,504

COVID19 × After P6T2
COVID19
After P6T2
Summer break
Shortened days
ICT early days
Lack foodLack sleep
Lack print study (May)
Lack study (June)
Feel stressed
No passion
Bad healthNo sport
Not funFeel unsafe

Note: "DLC" means the home environment during school closure due to COVID19. "ICT"(= "ICT early days") means thediﬀerence between the start date of the use of ICT terminals in each elementary school and the one in the slowest elementary
school. "Score QT (P4T3)" is calculated by the test score in P4 T3 term (= School close = −3). ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.0172

Table 8: Results of Event study: Treatment eﬀects of disadvantaged living condition by pre-treat score quantile
Dependent Variable:

Test score

Model:

Event + DLC&ICT(1)−(4) + SB

Score QT (P4T3):
Model:

1st QT(1)

2nd QT(2)

3rd QT(3)

4th QT(4)

1st QT(5)

2nd QT(6)

3rd QT(7)

4th QT(8)

COVID19 × P5T1 (SC = -2)-1.039(1.205)-4.190∗∗∗(1.385)-8.322∗∗∗(2.123)
5.122∗∗∗(1.719)18.69∗∗∗(3.068)19.99∗∗∗(1.748)0.6101(1.032)-3.483∗∗∗(1.233)-7.967∗∗∗(2.799)1.049(1.610)11.27∗∗∗(2.471)
11.55∗∗∗(1.583)

1.958∗∗(0.8939)-2.667∗∗(1.047)-5.359∗(2.956)2.952∗∗∗(1.030)11.21∗∗∗(1.522)
11.03∗∗∗(1.109)2.369∗∗∗(0.7576)-1.142(0.8189)-5.225∗∗∗(1.713)-2.060∗∗(0.9915)
5.981∗∗∗(1.279)5.013∗∗∗(0.7561)-0.3262∗∗(0.1580)
3.989(5.014)-5.774∗∗(2.436)-6.922∗∗∗(2.352)-3.448∗∗(1.449)1.070(1.945)-7.701∗∗∗(2.537)-4.852∗∗(2.198)0.5344(2.157)0.7252(2.510)5.061∗(3.015)-0.1181(0.1431)5.512(4.568)-1.691(1.652)-6.147∗∗∗(2.180)-0.2939(1.278)-2.863∗(1.574)-2.971(1.867)0.2087(1.954)2.867∗∗(1.450)-1.929(2.153)9.015∗∗∗(2.165)-0.1879∗∗(0.0906)
2.338(2.809)0.9916(1.421)-2.827(1.988)-1.823∗(0.9768)-2.681∗∗(1.314)-3.484∗∗(1.557)2.992∗∗(1.399)0.7509(1.362)0.8818(1.689)2.394(1.949)-0.1473(0.0926)-4.593(3.904)
0.2366(1.231)2.361(1.649)-2.800∗∗∗(0.9643)-0.9999(0.9501)-0.6774(1.520)-0.7838(0.9310)2.054∗∗(0.9288)0.6978(1.151)0.9308(1.602)-1.116(1.206)-0.1947(1.663)-8.380∗∗∗(2.126)3.449∗∗(1.732)32.04∗∗∗(4.843)18.32∗∗∗(1.777)-0.2587(0.7152)-0.5447∗∗∗(0.1591)-0.3300∗∗(0.1582)
3.975(5.021)-5.791∗∗(2.434)-6.897∗∗∗(2.351)-3.442∗∗(1.448)1.073(1.946)-7.712∗∗∗(2.534)-4.860∗∗(2.199)0.5216(2.158)0.7202(2.510)5.105∗(3.009)0.0529(1.050)0.3531(1.470)-8.469∗∗∗(2.806)-0.7367(1.668)21.71∗∗∗(3.714)9.764∗∗∗(1.633)-1.665∗∗∗(0.5858)-0.3900∗∗∗(0.1385)-0.1209(0.1436)
5.509(4.577)-1.693(1.654)-6.139∗∗∗(2.184)-0.2695(1.280)-2.888∗(1.576)-2.967(1.869)0.1985(1.955)2.872∗∗(1.449)-1.914(2.156)9.004∗∗∗(2.164)1.350(0.8999)
0.4814(1.204)-5.930∗∗(2.935)1.414(1.088)18.96∗∗∗(3.089)9.497∗∗∗(1.181)-1.834∗∗∗(0.6335)-0.2747∗∗(0.1274)-0.1904∗∗(0.0910)2.347(2.807)1.000(1.421)-2.824(1.987)-1.821∗(0.9762)-2.675∗∗(1.315)-3.511∗∗(1.556)2.993∗∗(1.400)0.7690(1.360)0.8971(1.692)2.416(1.951)1.979∗∗∗(0.7412)0.9007(0.9587)-5.584∗∗∗(1.715)-3.049∗∗∗(1.013)11.02∗∗∗(2.493)4.027∗∗∗(0.8052)-1.185∗∗(0.4885)-0.1785∗(0.0980)-0.1491(0.0928)-4.624(3.909)0.2460(1.229)2.376(1.648)-2.805∗∗∗(0.9653)-0.9944(0.9495)-0.6863(1.521)-0.7881(0.9290)2.063∗∗(0.9279)0.7005(1.151)0.9343(1.599)School FE
Grade FEClassroom FE

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Observations

4,935

5,766

5,715

6,504

4,935

5,766

5,715

6,504

COVID19 × P5T2 (SC = -1)
COVID19 × P5T3 (SC = 0)
COVID19 × P6T1 (SC = 1)
COVID19 × P6T2 (SC = 2)
COVID19 × P6T3 (SC = 3)
Summer break
Shortened days
ICT early days
Lack foodLack sleep
Lack print study (May)
Lack study (June)
Feel stressed
No passion
Bad healthNo sport
Not funFeel unsafe

Note: "SC" is the abbreviation for School close and indicates the diﬀerence in semesters from P5T3, the first to be aﬀected by the
temporary school closure. "Event" represents the results of event study estimation. "DLC" means the home environment during
school closure due to COVID19. "Score QT (P4T3)" is calculated by the test score in P4 T3 term (= School close = −3). "ICT"(="ICT early days") means the diﬀerence between the start date of the use of ICT terminals in each elementary school and the one in
the slowest elementary school. "School close" means the diﬀerence of term from P5 T3 term. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.0173

Table A-1: Corresponding list between cohort, year, and grade/term
Fiscal Year

Term

2018

2019

2020

Grade and Term
FY2020 P6 Cohort

FY2019 P6 Cohort

T1 (Apr.− Jul.)

-

P5T1

T2 (Sep.− Dec.)

-

P5T2

T3 (Jan.− Mar.)

-

P5T3

T1 (Apr.− Jul.)

P5T1

P6T1

T2 (Sep.− Dec.)

P5T2

P6T2

T3 (Jan.− Mar.)

P5T3

P6T3

T1 (Apr.− Jul.)

P6T1

-

T2 (Sep.− Dec.)

P6T2

-

T3 (Jan.− Mar.)

P6T3

-

Note: P5 and P6 mean that the cohort’s grades are fifth and sixth in primary school.

Table A-2: Balance test between students tested in FY2020 P5T3 and the others: T-test and F-test
Data:
Outcome variables:
Test score

Students tested in FY2020 P5T3 vs other students
T-testF-testStudents tested in FY2020 P5T3 vs all students
T-testF-testCoeﬃcientP-valueCoeﬃcientP-valueCoeﬃcientP-valueCoeﬃcientP-value−0.1504

0.7779−0.1165

0.82291.05130.12491.03930.2234

Note: ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.0174

Table A-3: Definitions for disadvantaged living condition dummy
Living Condition

Definition

Lack food= 1 if the student answered, "not applicable" or "not really applicable" to the question"I eat breakfast and lunch every day," or 0 otherwise

Lack sleep= 1 if the student answered, "applicable" or "mostly applicable" to the question"I sometimes have diﬃculty sleeping," or 0 otherwise

Lack print study (May)= 1 if the student answered, "not applicable" or "not really applicable" to the question"I studied using handouts from school during the temporary primary school closure," or 0 otherwise

Lack study (June)= 1 If the student answered, "applicable" or "mostly applicable" to the question"I sometimes have diﬃculty concentrating on studies," or 0 otherwise

Feel stressed= 1 if the student answered, "applicable" or "mostly applicable" to the question"I get upset, frustrated, or angry," or 0 otherwise

No passion= 1 if the student answered, "applicable" or "mostly applicable" to the question"I have no motivation to do anything," or 0 otherwise

Bad health= 1 if the student answered, "applicable" or "mostly applicable" to the question"I sometimes have physical problems such as a headache or stomachache," or 0 otherwise

No sport= 1 if the student answered, "not applicable" or "not really applicable" to the question"I exercise a lot," or 0 otherwise

Not fun= 1 if the student answered, "not applicable" or "not really applicable" to the question"I enjoy every day," or 0 otherwise

Feel unsafe= 1 if the student answered, "applicable" or "mostly applicable" to the question"I have felt anxious about something," or 0 otherwise

Table A-4: Corresponding list between "School close" and grade/term
School close (SC)Grade/term

−3
−2
−1

P4T3
P5T1
P5T2
(decrease in school days due to COVID-19)

0
1
2
3

P5T3
P6T1
P6T2
P6T3

Table A-5: Correlation Test: Early days of ICT introduction and school characteristics
Correlation test b/w

Between P5T1 and P6T1

Between P5T1 and P5T3

ICT early days vs # of students
ICT early days vs Test score

Estimated coef.
0.1015−0.2902∗∗∗

Estimated coef.
0.0996∗−0.1111∗∗

P-value
0.103
0.000

P-value
0.06670.0406

Note: "# of students" and "Test score" are grouped by each term and each school. "ICT early days"
means the number of days that the ICT were introduced earlier, counting from the school where they
were last introduced. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.0175

Table A-6: Comparison of original and matching results (DID & Event study): Treatment eﬀect of living condition and early ICT
introduction (Full sample)
Dependent Variable:

Test score

Matching method:
Model:
COVID19 × P5T3_P6T3
COVID19 × After P6T2
COVID19
After P6T2
Summer break
Shortened days
ICT early days
Lack foodLack sleep
Lack print study (May)
Lack study (June)

Feel stressed
No passion
Bad healthNo sport
Not funFeel unsafe

Original
DID(1)0.8043(1.206)12.45∗∗∗(0.7357)-0.8532(0.8493)-1.648∗∗∗(0.5714)-2.688∗∗∗(0.4939)0.0416(0.0492)-0.0926(0.0764)-3.585(2.902)-2.429∗∗(0.9856)-8.094∗∗∗(1.726)-2.876∗∗∗(0.9546)(1.726)-1.645∗(0.9612)-5.249∗∗∗(1.326)-0.2577(1.307)2.647∗∗(1.144)0.0888(1.406)2.401(1.554)COVID19 × P5T1 (SC = -2)
COVID19 × P5T2 (SC = -1)
COVID19 × P5T3 (SC = 0)
COVID19 × P6T1 (SC = 1)
COVID19 × P6T2 (SC = 2)
COVID19 × P6T3 (SC = 3)

Event
(2)

-1.305∗∗∗(0.4107)-0.3025∗∗∗(0.0879)-0.1885∗∗(0.0837)-3.587(2.916)-2.648∗∗∗(0.9797)-8.132∗∗∗(1.728)-2.972∗∗∗(0.9603)(1.728)-1.796∗(0.9535)-5.260∗∗∗(1.325)-0.4337(1.310)2.359∗∗(1.153)-0.0191(1.402)2.226(1.555)0.7840(0.7831)0.3732(1.014)-5.058∗∗∗(1.761)1.586∗(0.9117)
21.11∗∗∗(2.371)
11.60∗∗∗(0.8051)

NNM (caliper 0.2)

NNM (mahalanobis)

DID
(3)

DID
(5)

1.160
(1.428)13.30∗∗∗(0.8582)-1.532(0.9781)-2.393∗∗∗(0.6432)-2.406∗∗∗(0.5297)
0.0110(0.0558)-0.0506(0.0789)-2.486(3.308)-2.958∗∗(1.193)-9.076∗∗∗(2.071)-2.700∗∗(1.073)(2.071)-0.9597(1.164)-5.489∗∗∗(1.760)-1.402(1.536)1.274(1.518)1.358(1.861)
2.198(1.949)Event(4)-0.9715∗∗(0.4660)-0.3514∗∗∗(0.1008)-0.1550∗(0.0814)-2.456(3.320)-3.266∗∗∗(1.184)-9.110∗∗∗(2.076)-2.843∗∗∗(1.077)(2.076)-1.176(1.155)-5.514∗∗∗(1.758)-1.645(1.548)0.8625(1.533)1.204(1.852)
1.967(1.954)0.2114(0.9202)-0.3698(1.126)-6.870∗∗∗(1.957)2.106∗(1.106)22.21∗∗∗(2.584)12.04∗∗∗(0.9706)

0.5294(1.389)13.20∗∗∗(0.8276)-1.561∗(0.9315)-2.329∗∗∗(0.6526)-2.492∗∗∗(0.5299)
1.34 × 10−5
(0.0568)-0.0305(0.0815)-0.3640(3.069)-2.800∗∗(1.189)-8.092∗∗∗(1.800)-2.555∗∗(1.055)(1.800)-0.5216(1.121)-4.816∗∗∗(1.545)-0.6174(1.469)1.150(1.425)0.2750(1.779)2.659(1.817)Event(6)-1.162∗∗(0.4543)-0.3744∗∗∗(0.0995)-0.1194(0.0846)-0.3978(3.080)-3.121∗∗∗(1.182)-8.165∗∗∗(1.804)-2.694∗∗(1.059)(1.804)-0.7091(1.111)-4.865∗∗∗(1.546)-0.9131(1.476)0.7410(1.443)0.1313(1.772)2.422(1.825)-0.1294(0.8800)
0.0393(1.074)-7.836∗∗∗(1.541)1.396(1.072)
21.61∗∗∗(2.433)11.30∗∗∗(0.8964)

School FE
Grade FEClassroom FE

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Observations

24,11824,11815,871

15,871

16,286

16,286

Note: Standard errors in parentheses are clustered at the classroom level. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.0176

Table A-7: Matching results of DID: Treatment eﬀect of ICT introduction and living condition by pre-treat score quantile
Dependent Variable:

Test score

Matching method:

NNM PSM caliper 0.2

NNM mahalanobis

Score QT (P4T3):
Model:

1st QT(1)

2nd QT(2)

3rd QT(3)

4th QT(4)

1st QT(5)

2nd QT(6)

3rd QT(7)

4th QT(8)

COVID19 × P5T3_P6T3

2.200(2.203)19.20∗∗∗(1.614)-1.637(1.331)-3.135∗∗∗(1.199)-0.4828(0.9342)-0.1926∗(0.1120)-0.0718(0.1603)
4.174(5.942)-7.671∗∗(3.019)-8.291∗∗∗(2.899)-2.484(1.583)2.650(2.055)-6.368∗∗(2.850)-4.714∗∗(2.379)0.5737(2.387)
1.179(3.094)5.621(3.471)-1.388(1.984)15.24∗∗∗(1.274)-1.945(1.271)-3.572∗∗∗(0.9665)-3.007∗∗∗(0.7414)0.0967(0.0864)-0.1202(0.1337)
3.802(4.971)-0.9455(1.665)-3.477(2.324)-0.8534(1.470)-3.204∗(1.801)-1.811(2.035)1.297(2.011)
2.935(1.794)-3.491(2.226)7.906∗∗∗(2.339)-0.3091(1.454)
11.39∗∗∗(1.129)-1.622(1.008)-2.734∗∗∗(0.8689)-3.337∗∗∗(0.7364)0.0561(0.0772)-0.0371(0.1051)
3.548(3.276)2.621∗∗(1.302)-2.190(2.090)-2.769∗∗∗(1.055)-2.358(1.659)-3.723∗∗(1.829)1.898(1.594)0.6031(1.453)
2.198(1.971)3.809∗(2.076)-2.709∗∗(1.348)8.068∗∗∗(1.027)-0.0972(0.9651)-0.5446(0.6969)-2.800∗∗∗(0.6491)
0.1331∗(0.0695)-0.1391(0.1008)-9.541(5.859)0.0413(1.463)0.9273(2.890)-3.784∗∗∗(1.267)-0.4592(1.466)-1.461(1.754)-1.563(1.205)1.316(1.404)2.322(1.473)1.967(2.210)1.309(2.077)19.64∗∗∗(1.622)-2.266∗(1.254)-3.697∗∗∗(1.241)-0.4315(0.9571)-0.1531(0.1118)-0.1388(0.1583)
7.861(5.634)-6.133∗∗(2.936)-7.148∗∗∗(2.736)-2.106(1.564)2.020(1.891)-5.351∗(2.801)-4.169∗(2.339)-0.0242(2.417)-1.022(3.008)6.754∗∗(3.196)-2.334(1.957)14.84∗∗∗(1.275)-1.763(1.246)-3.462∗∗∗(1.009)-2.583∗∗∗(0.7290)0.0083(0.0902)0.0029(0.1412)8.450∗(4.750)-0.7080(1.691)-5.269∗∗(2.357)-0.0950(1.272)-3.405∗(1.807)-1.786(1.792)
1.132(1.949)2.944∗(1.581)-1.588(2.159)9.576∗∗∗(2.329)-0.9376(1.457)
11.14∗∗∗(1.110)-0.6037(0.9734)-2.222∗∗(0.9283)-3.654∗∗∗(0.7327)
0.0735(0.0674)-0.0640(0.0923)-0.6566(3.765)1.776(1.361)-1.215(2.046)-1.994∗(1.187)-2.112(1.566)-3.313(2.181)2.681(1.628)1.588(1.396)1.809(2.086)1.960(2.212)-2.774∗∗(1.321)8.258∗∗∗(1.049)-0.3810(0.9372)-0.6975(0.6957)-3.054∗∗∗(0.6076)
0.1232∗(0.0629)-0.1101(0.0966)-7.961(4.828)-0.2958(1.420)2.107(2.336)-3.158∗∗∗(1.211)-0.0029(1.389)0.1593(1.558)-1.472(1.085)1.086(1.232)-0.2994(1.509)1.043(1.965)School FE
Grade FEClassroom FE

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Observations

3,709

4,095

3,900

4,167

3,609

4,291

4,002

4,384

COVID19 × After P6T2
COVID19
After P6T2
Summer break
Shortened days
ICT early days
Lack foodLack sleep
Lack print study (May)
Lack study (June)
Feel stressed
No passion
Bad healthNo sport
Not funFeel unsafe

Note: "Score QT (P4T3)" is calculated by the test score in the P4 T3 term (= School close = −3). Standard errors inparentheses are clustered at the classroom level. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01

77

Table A-8: Matching results of Event study: Treatment eﬀect of ICT introduction and living condition by pre-treat score quantile
Dependent Variable:

Test score

Matching method:

NNM PSM caliper 0.2

NNM mahalanobis

Score QT (P4T3):

1st QT(1)

2nd QT(2)

3rd QT(3)

4th QT(4)

1st QT(5)

2nd QT(6)

3rd QT(7)

4th QT(8)

COVID19 × P5T1 (SC = -2)-1.182(1.319)-1.804(1.731)-7.863∗∗∗(2.437)3.965∗∗(1.934)27.54∗∗∗(5.318)18.73∗∗∗(1.931)-0.4413(0.8468)-0.4272∗∗(0.1792)-0.2128(0.1820)
4.166(6.010)-8.214∗∗∗(2.997)-8.400∗∗∗(2.919)-2.636∗(1.573)2.340(2.057)-6.558∗∗(2.879)-5.124∗∗(2.412)0.0387(2.433)1.041(3.115)5.469(3.493)-0.0379(1.216)-0.2088(1.611)-9.085∗∗∗(3.103)-0.5179(1.843)22.56∗∗∗(4.429)10.32∗∗∗(1.784)-1.594∗∗(0.6964)-0.3418∗∗(0.1618)-0.2290(0.1475)
3.880(4.973)-1.241(1.657)-3.555(2.317)-0.9574(1.465)-3.370∗(1.804)-1.838(2.031)1.008(2.031)
2.537(1.787)-3.602(2.223)
7.587∗∗∗(2.365)0.4662(1.043)0.9342(1.344)-8.651∗∗∗(2.703)0.5039(1.251)22.49∗∗∗(3.887)8.189∗∗∗(1.327)-1.275∗(0.7526)-0.4811∗∗∗(0.1500)-0.1452(0.1105)
3.432(3.288)2.315∗(1.295)-2.216(2.109)-2.927∗∗∗(1.048)-2.658(1.652)-3.627∗∗(1.809)1.749(1.595)0.2807(1.459)1.912(1.965)3.469∗(2.089)2.544∗∗∗(0.9760)
0.7875(1.158)-6.653∗∗∗(1.920)-1.838(1.173)12.82∗∗∗(3.108)5.159∗∗∗(0.9804)-0.5517(0.5926)-0.1808(0.1236)-0.2198∗∗(0.1100)-9.342(5.833)-0.0980(1.474)0.9159(2.894)-3.847∗∗∗(1.262)-0.5279(1.467)-1.483(1.756)-1.680(1.206)1.083(1.410)2.292(1.462)
1.865(2.214)-1.956(1.271)-1.464(1.756)-10.73∗∗∗(1.795)2.832(1.811)28.57∗∗∗(5.243)17.37∗∗∗(1.843)-0.2998(0.8835)-0.4853∗∗∗(0.1798)-0.2707(0.1773)
7.553(5.680)-6.713∗∗(2.913)-7.258∗∗∗(2.744)-2.336(1.563)1.715(1.896)-5.505∗(2.819)-4.598∗(2.374)-0.5804(2.454)-1.231(3.037)6.536∗∗(3.242)-0.3935(1.201)0.4594(1.518)-9.811∗∗∗(2.900)-1.504(1.833)20.62∗∗∗(4.351)9.141∗∗∗(1.789)-1.447∗∗(0.6708)-0.4191∗∗∗(0.1572)-0.0632(0.1586)8.558∗(4.744)-1.010(1.678)-5.404∗∗(2.348)-0.2188(1.267)-3.524∗(1.813)-1.821(1.793)0.8280(1.965)2.581(1.575)-1.625(2.166)9.313∗∗∗(2.360)1.477(1.022)
1.534(1.365)-9.154∗∗∗(2.641)
0.9519(1.259)21.26∗∗∗(3.691)8.836∗∗∗(1.350)-1.627∗∗(0.7523)-0.3957∗∗(0.1538)-0.1688∗(0.0902)-0.7962(3.793)1.366(1.368)-1.315(2.061)-2.133∗(1.177)-2.381(1.557)-3.255(2.154)2.467(1.631)
1.191(1.405)1.477(2.082)1.537(2.220)1.658∗(0.9042)0.9708(1.132)-6.067∗∗∗(1.996)-2.440∗∗(1.151)
12.48∗∗∗(3.084)4.742∗∗∗(0.9155)-1.192∗∗(0.5542)-0.1925(0.1192)-0.1714(0.1077)-7.933(4.816)-0.3833(1.428)2.062(2.334)-3.186∗∗∗(1.208)-0.0473(1.385)0.1279(1.561)-1.581(1.091)
0.9539(1.230)-0.3044(1.502)0.9707(1.978)School FE
Grade FEClassroom FE

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Observations

3,709

4,095

3,900

4,167

3,609

4,291

4,002

4,384

COVID19 × P5T2 (SC = -1)
COVID19 × P5T3 (SC = 0)
COVID19 × P6T1 (SC = 1)
COVID19 × P6T2 (SC = 2)
COVID19 × P6T3 (SC = 3)
Summer break
Shortened days
ICT early days
Lack foodLack sleep
Lack print study (May)
Lack study (June)
Feel stressed
No passion
Bad healthNo sport
Not funFeel unsafe

Note: "Score QT (P4T3)" is calculated by the test score in the P4 T3 term (= School close = −3). Standard errors in parentheses are
clustered at the classroom level. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01

78

Table A-9: Definition of outcome variables for motivation and attitude toward math
Outcome variables

Definition

Like math(Like math)= 1 if the student answered, "applicable" or "mostly applicable" to the question"Do you like to study math?," or 0 otherwise

Math important(Math is important)= 1 if the student answered, "applicable" or "mostly applicable" to the question"Do you think it is important to study math?," or 0 otherwise

Understand math well
(Understand math)= 1 if the student answered, "applicable" or "mostly applicable" to the question"Do you understand the content of the math class well?," or 0 otherwise

Math will be useful(Math useful)= 1 if the student answered, "applicable" or "mostly applicable" to the question"Do you think that what you learned in math class will be useful in the futurewhen you start working?," or 0 otherwise

Concentrate in math class(Math concentration)= 1 if the student answered, "applicable" or "mostly applicable" to the question"Do you listen carefully to the teacher in math class?," or 0 otherwise

Ask questions in math class(Math question)= 1 if the student answered, "applicable" or "mostly applicable" to the question"Do you ask questions to your teacher in math class if you don’t understandsomething?," or 0 otherwise

Complete math homework(Math homework)= 1 if the student answered, "applicable" or "mostly applicable" to the question"Do you complete your math homework regularly?," or 0 otherwise

Concern about test results(Math results)= 1 if the student answered, "applicable" or "mostly applicable" to the question"Do you care about the results of the test?," or 0 otherwise

High motivation for Reco
(Reco motivation)= 1 if the student answered, "applicable" or "mostly applicable" to the question"Do you have a high motivation to study using the Reco sheets (reflection
study sheets)?," or 0 otherwise

Motivation for other Reco
(Other Reco)= 1 if the student answered, "applicable" or "mostly applicable" to the question"You receive 3 Reco-sheets each time. Do you want to try to work on the othernumbered Reco-sheets besides your own?," or 0 otherwise

Note: Words in parentheses are abbreviations for the outcome variables used in Tables 9 and Table 10.

79

Table A-10: Results of DID: Motivation and attitude toward math (Full sample)

Estimation Method:

DID without covariates

Dependent Variables:
Model:

Math like(1)

Math important(2)

Math understand(3)

Math useful(4)

Math concentration(5)

COVID19 × P6 survey

0.0995∗∗∗(0.0272)

0.0223∗(0.0128)-0.0020(0.0156)

0.0296∗∗(0.0133)

0.0103(0.0148)

School FE
Classroom FE

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Observations

8,299

8,270

8,194

8,296

8,312

Math result(8)

Reco motivation(9)

Other reco
(10)

Dependent Variables:
Model:

Math question Math homework(6)(7)
0.0422(0.0349)

0.0180(0.0136)0.0343∗(0.0201)

0.1089∗∗∗(0.0349)0.0809∗∗(0.0356)

School FE
Classroom FE

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Observations

8,234

8,226

8,316

8,294

8,253

COVID19 × P6 survey

Note: For simplicity, the coeﬃcients for COVID19 and P6 survey are not given. We use the abbreviations of outcome variables
given in Appendix Table A-12. Standard errors in parentheses are clustered at the classroom level. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01

80

Table A-11: Results of DID with covariates: Motivation and attitude toward math (Full sample)

Estimation Method:

DID with covariates

Dependent Variables:
Model:

Math like(1)

Math important(2)

Math understand(3)

Math useful(4)

Math concentration(5)

COVID19 × P6 survey

0.1947∗∗∗(0.0309)

0.0315∗∗(0.0131)0.0555∗∗∗(0.0188)0.0487∗∗∗(0.0161)0.0340∗∗(0.0139)

School FE
Classroom FE

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Observations

7,958

7,935

7,865

7,954

7,970

Math result(8)

Reco motivation(9)

Other reco
(10)

Dependent Variables:
Model:

Math question Math homework(6)(7)
0.1273∗∗∗(0.0410)0.0512∗∗∗(0.0129)

0.0912∗∗∗(0.0220)0.2098∗∗∗(0.0386)

0.1791∗∗∗(0.0414)

School FE
Classroom FE

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Observations

7,898

7,890

7,973

7,945

7,919

COVID19 × P6 survey

Note: For simplicity, the coeﬃcients for COVID19 and P6 survey are not given. We use the abbreviations of outcome variables
given in Appendix Table A-12. Standard errors in parentheses are clustered at the classroom level. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01

81

Table A-12: Results of DID and event study estimation: Treatment eﬀect of ICT introduction (Full sample)
Dependent Variable:

Test score
Without ICT

Model:

DID
(1)

Event
(2)

ICT early days
COVID19 × P5T3_P6T3
COVID19 × After P6T2
COVID19
Summer break
Shortened days
After P6T2

-2.042∗(1.080)11.53∗∗∗(0.7357)-1.303(0.8497)-2.559∗∗∗(0.4644)0.0023(0.0247)-1.318∗∗(0.5752)

COVID19 × P5T1 (SC = -2)-1.308∗∗∗(0.4112)-0.2994∗∗∗(0.0879)

With ICT
DID
(3)

Event
(4)

-0.0429(0.0749)-1.972∗(1.112)
11.60∗∗∗(0.7285)-1.374(0.8618)-2.635∗∗∗(0.4965)
0.0228(0.0483)-1.364∗∗(0.5753)-0.1227(0.0822)0.1436(0.8036)-0.1794(1.029)-5.481∗∗∗(1.639)-2.623∗∗∗(0.8207)14.45∗∗∗(2.036)
7.214∗∗∗(0.7366)

COVID19 × P5T2 (SC = -1)
COVID19 × P5T3 (SC = 0)
COVID19 × P6T1 (SC = 1)
COVID19 × P6T2 (SC = 2)
COVID19 × P6T3 (SC = 3)

-1.308∗∗∗(0.4112)-0.2999∗∗∗(0.0878)

0.1461(0.8036)-0.1746(1.029)-5.482∗∗∗(1.650)-2.629∗∗∗(0.8204)
15.92∗∗∗(2.351)
7.208∗∗∗(0.7365)

School FE
Grade FEClassroom FE

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Observations

25,24625,24625,24625,246

Note: "ICT early days" means the number of days the ICT was introduced earlier,counting from the school where they were last introduced. "With (Without) ICT" mean
the estimation used (not used) the "ICT early days." "Event" represents the results ofevent study estimation. "School close" means the diﬀerence of term from P5 T3 term.
Standard errors in parentheses are clustered at the classroom level. ∗ p<0.1; ∗∗ p<0.05;∗∗∗ p<0.01

82

Table A-13: Results of DID: Treatment eﬀect of ICT introduction (By pre-treat score quantile)
Dependent Variable:

Test score

Model:
Score QT (P4T3):
Model:DID (Without ICT)
1st QT(1)

2nd QT(2)

3rd QT(3)

DID (With ICT)
4th QT(4)

1st QT(5)

2nd QT(6)

3rd QT(7)

4th QT(8)

0.0212(0.1330)-1.631(1.591)14.28∗∗∗(1.191)-1.421(1.117)-2.667∗∗∗(0.6689)0.0152(0.0782)-3.547∗∗∗(0.8764)-0.0661(0.0854)0.4523(1.192)9.659∗∗∗(1.035)-0.8514(0.8617)-3.731∗∗∗(0.6356)0.0708(0.0555)-1.065(0.7861)-0.0813(0.0762)-3.247∗∗∗(1.043)
7.146∗∗∗(0.8542)-0.0216(0.7266)-3.291∗∗∗(0.5182)
0.1195∗∗(0.0472)-0.0960(0.5360)

ICT early days
-3.594∗(1.830)18.06∗∗∗(1.292)-1.264(1.317)-0.8205(0.7137)-0.1830∗∗∗(0.0428)-2.457∗∗(1.010)-1.596(1.567)14.31∗∗∗(1.194)-1.458(1.097)-2.705∗∗∗(0.6296)0.0255(0.0373)-3.570∗∗∗(0.8743)

0.3437(1.159)9.568∗∗∗(1.035)-0.7410(0.8662)-3.611∗∗∗(0.6116)0.0392(0.0326)-0.9970(0.7810)-3.376∗∗∗(1.018)7.032∗∗∗(0.8667)
0.1167(0.7193)-3.153∗∗∗(0.4849)
0.0820∗∗∗(0.0276)-0.0127(0.5387)-0.0564(0.1308)-3.493∗(1.880)18.15∗∗∗(1.264)-1.369(1.327)-0.9258(0.7717)-0.1538∗(0.0863)-2.525∗∗(1.023)

School FE
Grade FEClassroom FE

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Observations

5,253

5,987

5,951

6,689

5,253

5,987

5,951

6,689

COVID19 × P5T3_P6T3
COVID19 × After P6T2
COVID19
Summer break
Shortened days
After P6T2

Note: "Score QT (P4T3)" is calculated by the test score in the P4 T3 term (= School close = −3). ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01

83

Table A-14: Results of Event study: Treatment eﬀect of ICT introduction (By pre-treat score quantile)
Dependent Variable:

Test score

Model:
Score QT (P4T3):
Model:

Event (Without ICT)
1st QT(1)

2nd QT(2)

3rd QT(3)

Event (With ICT)
4th QT(4)

1st QT(5)

2nd QT(6)

3rd QT(7)

4th QT(8)-0.0407(0.1469)-0.0133(1.027)0.4883(1.421)-7.677∗∗∗(2.718)-1.017(1.372)19.97∗∗∗(3.481)9.129∗∗∗(1.311)-1.659∗∗∗(0.5864)-0.3871∗∗∗(0.1379)-0.1453∗(0.0864)
1.182(0.8443)0.5096(1.221)-5.373∗(3.072)0.7501(1.031)17.32∗∗∗(3.045)8.641∗∗∗(1.107)-1.832∗∗∗(0.6329)-0.2729∗∗(0.1271)-0.1423∗(0.0825)
2.360∗∗∗(0.7160)
1.076(0.9399)-4.970∗∗∗(1.584)-3.157∗∗∗(0.9204)
10.57∗∗∗(2.444)3.709∗∗∗(0.7034)-1.186∗∗(0.4880)-0.1754∗(0.0977)

ICT early days
-0.5599(1.329)-0.3226(1.710)-7.214∗∗∗(2.227)-3.738∗∗(1.557)20.67∗∗∗(3.977)
11.28∗∗∗(1.455)-0.2804(0.7127)-0.5301∗∗∗(0.1587)-0.0122(1.027)0.4897(1.420)-7.668∗∗∗(2.716)-1.013(1.372)19.47∗∗∗(3.252)9.132∗∗∗(1.311)-1.659∗∗∗(0.5863)-0.3870∗∗∗(0.1379)

1.180(0.8434)
0.5062(1.221)-5.376∗(3.059)0.7562(1.032)15.60∗∗∗(2.816)8.647∗∗∗(1.107)-1.832∗∗∗(0.6329)-0.2725∗∗(0.1271)

2.371∗∗∗(0.7154)1.085(0.9388)-4.955∗∗∗(1.573)-3.138∗∗∗(0.9193)8.939∗∗∗(2.279)3.728∗∗∗(0.7049)-1.186∗∗(0.4879)-0.1749∗(0.0977)-0.1862(0.1455)-0.5687(1.330)-0.3252(1.710)-7.243∗∗∗(2.245)-3.755∗∗(1.551)23.08∗∗∗(4.409)11.26∗∗∗(1.454)-0.2807(0.7126)-0.5310∗∗∗(0.1586)

School FE
Grade FEClassroom FE

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Observations

5,253

5,987

5,951

6,689

5,253

5,987

5,951

6,689

COVID19 × P5T1 (SC = -2)
COVID19 × P5T2 (SC = -1)
COVID19 × P5T3 (SC = 0)
COVID19 × P6T1 (SC = 1)
COVID19 × P6T2 (SC = 2)
COVID19 × P6T3 (SC = 3)
Summer break
Shortened days

Note: "Score QT (P4T3)" is calculated by the test score in the P4 T3 term (= School close = −3). "Event" represents the results ofevent study estimation. "School close" means the diﬀerence of term from P5 T3 term. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.0184