Does instruction time improve student achievement and motivation?
Evidence from Japan
Minae Nikia*aKobe University, Kobe, Japan*Email: minae.niki@people.kobe-u.ac.jp

Does instruction time improve student achievement and motivation?
Evidence from Japan
This paper examines how instruction time affects students’ cognitive ability andmotivation, using data from Japanese 4th grade students from Trends inInternational Mathematics and Science Study. I estimate a fixed-effect modelusing within-student panel data in two subjects. To remove teacher fixed effects, I
restrict the sample to classes taught by the same teacher. The results show that
instruction time positively affects both score and motivation. Further, I find apositive heterogeneity effect on score for schools with an absence problem, while
the magnitude of the effect on motivation is about twice as large in boys relative togirls.
Keywords: Instruction time, education policy, cognitive ability, motivation tolearn, educational inequity.

1. Introduction
This study examines the impact of instruction time on students’ cognitive ability andmotivation to learn, using the change in instruction time by revision of Japanese
education as a natural experiment.

1

Across the global landscape in 2020, many schools have had to close due to the
COVID-19 pandemic. The UNESCO web page 1 reported that 84.3% of the world student
population has been affected by school closures since April 1, 2020 due to the pandemic.
Concerns are rising regarding the effect of this disruption in education on children’s
future. Beyond academic achievement, children’s health condition, physical strength, andnon-cognitive abilities, such as sociability, are all at risk of harm. Therefore, it is urgent todiscuss in depth the issue of education in the context of the COVID-19 pandemic. One
especially pressing issue is how to mitigate the education gaps caused by families’
economic background. Due to the rapid expansion of online education, the global
education system may change dramatically. With many students having to attend online
lessons at home, children’s learning environment is expected to be strongly influenced bytheir economic background. In this context, we have little evidence on the impact ofschool instruction time.
One key issue to consider is the importance of classroom teaching for students.
Do in-person school lessons encourage cognitive ability and non-cognitive ability? An
accurate examination of the effects of instruction time in school is necessary.

1

https://en.unesco.org/covid19/educationresponse#schoolclosures (2021/5/5)

2

Education is greatly relevant to the development human capital (Schultz 1961;
Becker 1962). In economics, studies of return to education have mainly focused onunderstanding the effect of extending one’s education period (Acemoglu and Angrist
2000; Pischke 2007; Fischer et al. 2020). Recently, it has become important to design
education policies based on evidence; the effects of particular policies on the education
production function can be examined. Moreover, since Coleman et al.’s (1966) report,
analysis of schools’ education resources has become particularly important in shaping
educational reform. Endogeneity must be excluded to estimate the pure causal effect ofeducation resources on student performance.
School education is among the most important policy areas in most countries.
Debate continues regarding how lessons can most effectively contribute to improving
children’s ability. Education policies under which instruction time is increased or
instruction contents changed can easily gain support from many people because they do
not need additional resources at the school level. In other words, such policies are
generally expected to improve children’s ability at a low cost. Therefore, changes to
instruction at school play a key role in many countries’ education reform. In this regard, it
is important to discuss around effective instruction time and school days (Gromada and
Shewbridge 2016).

3

Among other countries, Japan has been discussing instruction time and content
for the past 30 years. Japanese children have equal access to compulsory education acrossJapan under the Basic Act on Education (Kyoiku kihon ho) and the School Education Act(Gakko kihon ho), based on the spirit of the Japanese Constitution. Furthermore, allpublic schools’ curricula in Japan must follow the curriculum guidelines (Gakushu shido
yoryo) notified by the Ministry of Education, Culture, Sports, Science and Technology(MEXT). The Japanese public education curricula, including instruction time andcontents, are determined by these guidelines. Of course, these guidelines merely set thestandards that schools should abide by, so some schools deviate from them. Further, theguidelines are revised every 10 years to keep pace with the latest developments. In 2002
the curriculum guidelines were substantially changed. Ahead of this change, from 1995 to
2002, all public schools gradually saw a shift to the five-day school week, having
previously also attended school on Saturday. Furthermore, school instruction time andcontents were substantially reduced. This education reform was controversial and many
people, especially children’s parents, were concerned about a decline in children’s
academic ability.
Generally, an increase in school instruction time is equated to increased input and,in turn, improved academic ability. Figure 1 shows Japanese 4th and 8th grade students’4

average test scores and their total standard instruction time in mathematics and sciencefrom 1995 to 2015. The lines illustrate changes in scores on the Trends in International
Mathematics and Science Study (TIMSS), which is an international academic survey,
while the bars indicate changes in the accumulated standard lesson times for students ofeach grade in the two subjects. There is no apparent simple correlation between TIMSS
score and school instruction time in Figure 1.
<Please insert Figure 1 here>
This study examines whether increasing instruction time really improves students’
academic ability. It is hard to estimate the pure effect of instruction time because that it
encompasses not only the time but also the content of the lessons. Thus, I have tried toanalyze only the effect of instruction time. In addition, to estimate the causal effect of
instruction time, it is necessary to eliminate the individual fixed effects of students’ability and teachers’ quality of teaching, as well as the characteristics of each school andclass.
To estimate the causal effect, previous studies have conducted naturalexperiments using an exogenous change in schooldays due to weather or policy (Marcotte
2007; Bellei 2009; Aucejo and Romano 2016; Battistin and Meroni 2016). Some studies
have used a fixed-effect model employing panel data for different academic subjects

5

(Lavy 2015; Rivkin and Schiman 2015; Cattaneo, Oggenfuss, and Wolter. 2017; Motegi
and Oikawa 2019). These studies have shown that the effect of instruction time on
students’ academic ability is heterogeneous due to numerous factors Gromada and
Shewbridge 2016). Furthermore, while instruction time’s effect on children’s cognitive
ability has been examined, its effect on children’s non-cognitive ability, which has animportant role in human capital development (Heckman, Stixrud, and Urzua 2006), is still
unknown.
To find the pure effect of instruction time, I estimated a between-subjects
fixed-effect model using panel data comprising the scores of Japanese 4th grade students
on the TIMSS in 1995, 2003, and 2007. My estimation removed both student and teacher
fixed effects that related closely to students’ outcome. Moreover, in addition to the testscore, I use students’ motivation to learn as a second outcome variable. I confirmed theheterogeneity of the effects by analyzing the interaction terms.
The results show that increasing the instruction time leads to a significant rise in
students’ test scores and their motivation to learn. The value of the coefficient of testscores indicates roughly the same effect size as previous studies have reported.
Additionally, I estimated the heterogeneity of the effects of instruction time by adding
variable interactions. The results suggest that schools facing a high student absence rate

6

see a positive effect on the test score with increasing instruction time, although the size ofthe effect on motivation is about twice as strong in boys as in girls. In addition, there is apositive effect on test scores and motivation for the combination of instruction time andstudents’ study-time at home.
The contributions of this paper are threefold. First, I show the effect of
instruction time on students’ motivation to learn, which previous studies have notexamined. Second, by considering instruction time for the whole year, the estimate of
the real change generated by instruction time is likely more accurate than in previous
studies focused on weekly instruction time. Third, I performed detailed estimations
using a fixed-effect model to remove student and teacher fixed effects, using Japanese
student data, which have a high average score.
The rest of this paper is organized as follows: Section 2 explains the Japanese
education system and reforms. Section 3 summarizes the existing literature on the effectof instruction time and content of lessons. Section 4 presents the data and describes
identification strategies. The results are reported and discussed in Section 5. Finally,
Section 6 presents conclusions.

7

2. The Japanese education system and school instruction time
The TIMSS provides all the data for participating countries, but this study focuses
especially on Japan to investigate the effect of instruction time in a society where schools
have many resources and students achieve high test scores. The TIMSS data show thatJapanese students in compulsory schools have high cognitive ability on average.
Furthermore, Japanese public education provides equal standards of education
nationwide, so there are only small gaps in student ability. I examine the effect of
instruction time under such an environment.
Despite their high achievement, Japanese students have lower motivation to learn
on average than their peers in other countries. It is thus important for Japanese education
policy to raise students’ motivation as well as their scores. The effect of the learning
environment on students’ motivation is an important area in which this study can provide
valuable knowledge.
The Japanese compulsory education system comprises six years of elementary
school education and three years of junior high school education, and is regulated by alaw called the School Education Act (Gakko kihon ho). The school year begins in Apriland ends in March the following year. Generally, children attend elementary school from
6 to 12 years of age, and junior high school from 12 to 15 years of age.

8Japan’s National School Curriculum (Gakusyu shido yoryo) ensures that children
can receive a consistent standard of education nationwide. Besides, the School Education
Law (Gakko kyoiku ho shiko kisoku) sets the standards for instruction time and contents
for all subjects, and each school makes its own curriculum in accordance with thesestandards. The National School Curriculum is revised about every 10 years to keep pacewith the latest developments. The revision in 2002 greatly changed Japanese education
policy, marking a shift from merely inputting knowledge toward fostering thinking andself-creation. Japanese public schools introduced the five-day school week in April 2002.
As Saturdays ceased to be school days, the annual number of lessons for each grade
dropped by about 70. 2 The 2002 revision also greatly reduced standard instruction time.
As many discussions ensued regarding a possible decline in children’s academic ability,
the standard instruction time was raised in the following revision in 2011, but the five-day
school week was retained and, from 2020, English was added as a new subject atelementary school.

2This represents a 3,150-minute (52.5-hour) reduction because each lesson in the Japaneseelementary school system lasts 45 minutes.

9For all that the standard instruction time provides by school guidelines and that all
schools are required to follow, the actual number of school days and the curriculum isdecided by each school based on community and school conditions. Therefore, there
might be a difference in instruction time between the standard number and the true
number.
<Please insert Figure 2 and Figure 3 here>
Figure 2 shows differences between schools and changes across years in the
instruction time of 4th grade students in public elementary schools. Standard indicatesschools whose instruction time matched the curriculum guidelines, while low and highrespectively indicate schools in which instruction time was lower and higher than thestandard instruction time. I recognize that many schools had longer instruction time than
the national standard. In addition, the difference between the school with the highest
instruction time (1121 lessons) and the school with the lowest instruction time (944lessons) is over 100 lessons. Figure 3 shows the distribution of total school days each year
for all Japanese public elementary schools, and how this distribution changed between
2002 and 2008. Over 90% schools had 196–205 school days between 2002 and 2008;
some schools had more than 206 days while a few schools had less than 195 days, so thedifference could be over 10 days between schools.

10Thus, during the period of time this study has focused on, many Japanese publicschools had an instruction time over the standard time, and the amount of instruction timeand school days varied by school and area. Consequently, I cannot treat the change to
instruction time in the revision of the National School Curriculum as an exogenous
variable in the analysis. Furthermore, the curriculum is determined by each school, so
instruction time is influenced by characteristics of the school, teachers, students, andschool area. To estimate the causal effects of instruction time, it is necessary to remove
these endogenous factors as much as possible. In Japan, elementary schools have large
class sizes (up to 40 students), and the teacher has significant responsibility and workload.
Furthermore, an elementary school teacher needs to teach almost all the subjects, whereasa junior high school teacher focuses on a specific subject. The teacher has a strong
influence on student performance, so I need to remove teacher fixed effects to estimate
the impact of instruction time accurately.

3. Related literature
To identify how school education resources can be most effectively allocated, manystudies have focused on how education reform affects instruction (Gromada and
Shewbridge 2016). Their findings suggest that an increase in instruction time generally

11improves children’s cognitive ability. However, there is a lack of data on whether
instruction time and children’s academic performance are positively correlated. Since
the 2000s, many studies have tried to estimate detailed causal analyses using microdata
since. These studies have used natural experiments to estimate the causal effects of
instruction, using measures of instruction time or number of school days. The mostcommon method is a difference-in-difference estimation following an area’s education
reform (or other policy change) that serves as a natural experiment.
Some studies have shown that instruction time positively affects students’cognitive ability. Several of these studies used the event of German education reform,
which led to increased instruction time but a reduction in the high school year
(Dahmanna 2017; Huebener, Kuger, and Marcus 2017; Huebener and Marcus 2017).
Elsewhere, Bellei (2009) evaluated the impact of increasing instruction time as a resultof the education reform in Chilean high schools, while Lavy (2020) used Israeli highschool data.
One study reported a 0.05 standard deviation (SD) improvement in English
test scores for students who engaged in a program delivering increased instruction
time in low-scoring elementary schools and junior high schools in Florida (Figlio,
Holden, and Ozek 2018). Thompson (2020) found that a one hour increase in

12weekly instruction time was associated with test-score improvements of 0.018 SD inmathematics and 0.006 SD in reading in 3rd through 8th grade students in Oregon.
Furthermore, increased instruction time through supplementary lessons has beenfound to improve junior high school students’ mathematics scores in the UnitedStates (Taylor 2014) and in south Italy (Meroni and Abbiati 2016). Conversely,
Meyer and Klaveren (2013) found no effect of instruction time on mathematics andlanguage achievement for Dutch elementary school students using instrumental
variables (IV) estimation. Lengthening the school day has been found to positively
impact on student test scores following a change in climatic conditions, such as
heavy snow (Marcotte 2007; Aucejo and Romano 2016).
Besides natural experiments, some studies have adopted a within-student
between-subject identification approach using a fixed-effect model (Lavy 2015; Rivkin
and Schiman 2015; Cattaneo, Oggenfuss, and Wolter (2017)). They estimated the effectof instruction time with an international comparison using Programme for International
Student Assessment (PISA) data, and found that instruction time has a significant
positive effect on student test scores. In addition, Cattaneo, Oggenfuss, and Wolter(2017) found heterogeneity on instruction time; specifically, students with typically
better academic results benefitted more. Lopez-Agudo and Marcenaro-Gutierrez (2019)13

analyzed PISA 2015 data for Spain and showed that the effect of instruction time differs
between areas.
In Japan, Motegi and Oikawa (2019) found that the change in instruction time
following education reform had heterogeneous effects on 8th grade students’ TIMSS
scores (1999 and 2003) due to differences in instruction quality. Likewise, the reductionof instruction time as a result of the education reform in Japan caused a decrease of 0.5
years for Japanese women’s education period (Kikuchi 2014) and increased the gap inacademic achievement between children from different socioeconomic backgrounds(Kawaguchi 2016). Meanwhile, Bessho et al. (2019) showed that supplementary lessons
totaling 45 hours resulted in a 0.13 SD improvement on Japanese language art scores of
3rd and 4th grade students in Japan.
Other studies have focused on instruction contents (Machin and McNally 2008;
Van Klaveren 2011; Schwerdt and Wuppermann 2011). Machin and McNally (2008)
analyzed the effect of the Literacy Hour policy in the United Kingdom using a
difference-in-difference approach; they found this policy to be particularly effective forchildren with weak ability to read and write, and also more cost-effective than othereducation policies. Van Klaveren (2011) concluded that the proportion of lesson time
devoted by teachers to lecturing the class is unrelated to students’ cognitive14

performance in the Netherlands. However, Schwerdt and Wuppermann (2011) indicatethat traditional lecture-style teaching is associated with higher student achievement. In
the United States, McMullen and Rouse (2012) estimated the effect of year-roundschooling and confirmed that this did not affect average student performance.
The above studies used student test scores (indicating cognitive ability) as thestudent outcome measure. Improving cognitive ability is the main purpose of education
because it is closely related to future productivity. However, following Heckman’s
studies (Heckman and Rubinstein 2001; Heckman, Stixrud and Urzua 2006; Cunha andHeckman 2007), non-cognitive ability, motivation, and behavior in school, arerecognized as being of equal importance to cognitive ability. As for the analysis of
education resources and non-cognitive factors, I consider the effect of class size (Deeand West 2011; Ito and Nakamuro and Yamaguchi 2020) and the effect of teacher andteaching (Blazar and Kraft 2017). Concerning the effect of instruction time, Lavy
(2020) found that increasing the weekly instruction time has a positive effect onacademic achievement with no behavioral cost, while Meroni and Abbiati (2016) used
the students’ attitudes and behaviors toward studying as the outcome index. However,
little research has investigated the effect of instruction time on students’ motivation.

15

4. Data
4.1. TIMSSThis study uses Japanese 4th grade data from TIMSS 1995, 2003, and 2007. TIMSS is
an international comparative assessment aimed at improving teaching and learning inmathematics and science for students around the world. It is carried out every four years
by the International Association for the Evaluation of Educational Achievement (IEA)
to test the mathematics and science achievements of 4th and 8th grade students. Besides
students’ educational achievement data, it collects information on the teaching andlearning of mathematics and science from students, teachers, and school principals. The
number of participating countries grew from 46 in 1995 to 58 in 2019. TIMSS 1995,
1999, 2003, 2007, 2011, 2015, and 2019 data for each country are available from the
IEA’s webpage. 3
In Japan, about 140–150 schools and 4,000–5,000 students have participatedeach year at both 4th and 8th grade levels. The survey data provide accurate estimates ofnational student populations with a two-stage sample design, but students registered at a3

https://timssandpirls.bc.edu.

16

special needs school or attending associated classes are excluded at the sampling stage.
Japanese investigations were carried out in February or March of each survey year. The
Japanese academic year starts in April, so I consider that the results of estimations using
TIMSS data show the effects of educational resources in that year. I can also control the
influence of students’ cognitive or non-cognitive abilities because TIMSS test scores ofeach student can be matched with the answers given by each student, their teacher andtheir school principal for each questionnaire about school resources and students’attitudes toward study and motivation.
There are two reasons why I use data from TIMSS 1995, 2003, and 2007. First,
Japanese school instruction time was substantially changed between 1995 and 2007
because of revision to the National School Curriculum in 2002. Motegi and Oikawa(2019) captured the effect of this revision as an exogenous change and estimated theeffect of instruction time using Japanese 8th grade data from TIMSS 1999 and 2003.
This study differs from them in that change of instruction time considering
non-exogenous change. However, that great change of instruction time during
1995-2007 makes enough changes to catch the effect in the estimation.

17

Second, this study tries to control teacher fixed effects. Assuming that theteacher strongly affects students’ performance through their lessons, I control for that
effect by limiting the sample to classes taught by the same teacher in mathematics andscience. After 2007, many Japanese elementary schools have two or more teachers inclass, especially for mathematics. I thus decided to use TIMSS 1995, 2003, and 2007
data to enable estimations without restricting many samples that have two or moreteachers in class. 4

4.2. Empirical modelTo estimate the causal effects of instruction time, I need to remove the effects ofunobserved student and school factors that correlate with instruction time and student
outcomes. I considered using IV estimation to remove endogeneity for instruction time,
but were unable to find an appropriate instrumental variable in the TIMSS data.
Furthermore, these data are cross-sectional, not panel data. Therefore, I created panel datausing the difference in instruction time between subjects, following the approach of Lavy

4

We cannot identify whether a school is public, national, or private. There are three national or
private schools in 2007.

18

(2015), Rivkin and Schiman (2015), and Motegi and Oikawa (2019). Then, I estimatedthe following equation as the production function of education using a fixed-effectmodel:

𝑦𝑦𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 𝜆𝜆𝐻𝐻𝑠𝑠𝑠𝑠𝑠𝑠 + 𝜷𝜷𝜷𝜷𝒊𝒊𝒊𝒊𝒊𝒊𝒊𝒊 + 𝜸𝜸𝒁𝒁𝒔𝒔𝒔𝒔𝒔𝒔 + 𝜇𝜇𝑖𝑖 + 𝜃𝜃𝑠𝑠 + 𝜙𝜙𝑡𝑡 + 𝜂𝜂𝑐𝑐 + 𝜔𝜔𝑐𝑐𝑐𝑐 ＋𝜋𝜋𝑖𝑖𝑖𝑖𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖(1)

where subscripts i, s, t, and c indicate the i th student at the s th school at the t th time
(t=1:1995; t=2:2003; t=3:2007) in the c th subject (c=1: mathematics; c=2: science).
Variable 𝑦𝑦𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 is students’ outcome variable, representing either the test score or learning

motivation. 𝐻𝐻𝑠𝑠𝑠𝑠𝑠𝑠 is the total instruction time in one year, 𝑿𝑿𝒊𝒊𝒊𝒊𝒊𝒊𝒊𝒊 is a vector of thecharacteristics of the student and their family, and 𝒁𝒁𝒔𝒔𝒔𝒔𝒔𝒔 is the vector of school

characteristics. As control variables X, I use dummy variables of students’ birth monthand sex. Variable Z represents teachers’ experience and sex.
Also in equation (1), variable 𝜇𝜇𝑖𝑖 is the student fixed effects, capturing unobserved

student ability, family background, and constant non-cognitive skill. Variable 𝜃𝜃𝑠𝑠 is theschool fixed effects, capturing unobserved school characteristics such as school quality
and the environment of the school area. As we limited the sample to classes taught by thesame teacher, 𝜃𝜃𝑠𝑠 also includes teacher fixed effects, such as teacher quality. I can remove
these effects ( 𝜇𝜇𝑖𝑖 , 𝜃𝜃𝑠𝑠 ) by estimating a fixed-effect model. Furthermore, 𝜙𝜙𝑡𝑡 and, 𝜂𝜂𝑐𝑐19

represent time and subject effects, while 𝜔𝜔𝑐𝑐𝑐𝑐 shows the time effect for each subject andcaptures the influence of each subject by year. I control 𝜙𝜙𝑡𝑡 , 𝜂𝜂𝑐𝑐 , and 𝜔𝜔𝑐𝑐𝑐𝑐 , using subject

dummies and time dummies. The variable 𝜋𝜋𝑖𝑖𝑖𝑖𝑖𝑖 captures individual-by-school-by-subject

fixed effect. If this variable does not occur at random and affects student educationachievement, the estimation would be biased. For example, if a student chooses a schoolthat provides more instruction time for a specific subject in which that students wants
order to improve their test score, this may result in upward bias in the estimates. However,elementary-level students in Japan generally attend the public school in their district.
Nonetheless, it is necessary to pay attention to the results on the effect of instruction time.
Finally, 𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 is an error term.

In estimating model (1), I make two assumptions, namely that the effects of

instruction time are equal for both subjects and include spillover between the two
subjects.
This study’s main purpose is to find the effect value of coefficient (λ) for
instruction time (H). Our estimation uses the annual instruction time (H), as the unit
used in education guidelines. This guideline determined the standard annual instruction
time and lessons of 45 minutes at elementary schools. I consider this variable moresuitable than other times (for example, hours, minutes per week) because I can check20

the consistency of TIMSS data with real school operations in each year, by checking
whether instruction time changes along with the education guideline changes.
TIMSS does not enquire directly about annual school instruction time, so I
created this variable through the following steps. First, I obtained the instruction time(minutes) per week from the question, ‘How many minutes per week do you teachmathematics (science) to the students in the TIMSS class?’ Second, I obtained theannual school instruction days 5 from the question, ‘How many days per year is yourschool open for instructing students?’ Third, I calculated the number of school weeks
per year by dividing annual school instruction days by the number of school days per
week (5 or 6 days). Finally, I calculated the annual instruction time as instruction time(minutes) per week times the number of school weeks per year, and then divide this by
45 minutes (the standard school instruction time in elementary school).
Table 1 shows the standard instruction time for mathematics and science by
curriculum guidelines for each grade of elementary school in 1995, 2002, and 2007.

5

We subtracted 10 days from the data provided by the school to account for days with nostudying instruction, such as school events. The average of such days is about 10 as per MEXT
investigation.

21

Figure 4.1 depicts a histogram of annual instruction time for 4th grade students inmathematics and science by year. It shows three peaks in 2003 and 2007, following the
reduction in instruction time in the 2002 revision. Many schools’ instruction times for
4th grade students exceed those of the guidelines (Table 1 shows mathematics, 150,
science, 90), which is consistent with the results shown in Figure 2 and indicates that
instruction time is an endogenous variable. Figure 4.2 combines all the data from thethree years and shows the difference of instruction time for mathematics and science.
<Please insert Table1, Figure 4.1, and Figure 4.2 here>
I adopt three dependent variables (y) to measure the instruction time effects: thestandardized test score (score) and two indexes of learning motivation (joy and positive).
I use a TIMSS standardized raw score with a mean of 50 and a standard deviation of 10
within each country. 6 The motivation indexes joy and positive are calculated using
responses from the student questionnaire. Joy is captured by responses on a 4-point scale

6

TIMSS provides five scores called “plausible values” to enable inter-countries comparisons.
Jerrim et al. (2017) pointed out that it is not appropriate to use this value in this fixed-effectmodel, so some studies created their own test scores. However, I decided to use the original
standardized scores from TIMSS to avoid introducing other biases into the estimation.

22

(1=strongly disagree, 2=disagree, 3=agree, 4=strongly agree) to the statement ‘I enjoy
learning mathematics (science)’. A higher number means greater motivation to study.
Positive is the second index for student motivation to learn. I make it by principal
component analysis using responses on a 4-point scale (1=strongly disagree, 2=disagree,
3=agree, 4=strongly agree) to three questions: ‘I enjoy learning mathematics (science)’;‘I usually do well in mathematics (science)’; and ‘I learn things quickly in mathematics(science)’. I estimated and got the principal component scores from the first principal
component of covariant variable. Table 2 reports the descriptive statistics for thevariables for each TIMSS year and aggregated. It shows that the mean instruction timesof mathematics is 186 in 1995, 173 in 2003, and 175 in 2007; for science, they are 111 in
1995, 102 in 2003, and 103 in 2007. Therefore, both subjects decreased instruction timeafter 2002, when the guidelines were revised.
<Please insert Table 2 here>

5. Results
5.1. Ordinary least squares
As the first step in my analysis, I checked the relationship between variables by ordinary
least squares (OLS) using pooled data. Columns (1)–(6) of Table 3 report the coefficient23

estimates. Specifically, columns (1)–(2) shows the results for score, columns (3)–(4)show the results for positive, and columns (5)–(6) show the results for joy. Columns (7)–(8) then show the results for joy by ordered logistic estimation, which is used because joy
is the variable made from the ordered number of answers. The coefficients on instruction
time in columns (1)–(6) are all positive, but the only significant value is in column (1): thesignificance disappears when the control variables are added in column (2).
<Please insert Table 3 here>
Regarding the school control variables, school size is significantly positively
related to score and positive, while female teacher is significantly positively related to joy.
However, teaching experience is not significantly related to any of the dependent
variables. Notably, the coefficient of the girls shows a significantly negative value for all
dependent variables, indicating that 4th grade girls have a lower understanding of andmotivation to learn mathematics and science than 4th grade boys.
I ran the estimation using birth month 2,3 and birth month 4 dummies. These
coefficients show the difference from the birth month 1 dummy, which denoted students
born in January, February, and March. As all Japanese public schools start in April,
students born from January to March are the youngest among their class. With one
exception, the coefficients on the birth month 2-4 dummies are all significantly positive24

for score and birth month 2,3 dummies are significantly positive for positive, and increasein magnitude for older children. These results suggest there is a clear effect of relative age.
Conversely, all the birth month dummies show no significant correlations with joy. Study
time at home is significantly positively related to positive and joy, but does not influence
score. I cannot rule out the possibility of reverse causality: increasing study time at home
may follow from increases in motivation and joy resulting from an increase in instruction
time. Finally, the ordered logistic results in columns (7)–(8) do not greatly differ from the
OLS results for joy. The only differences are that the coefficient on instruction time isnegative (but still insignificant) and that the positive coefficient on school size becomessignificant (at the 10% level).
These OLS estimation results explain some correlations between the variables,
but it is difficult to control all variables related to instruction time, such as teacher
quality and student ability, because of that these variables do not have observable data.
The instruction time changed greatly after the 2002 guidelines revision; this canbe seen in Figure 4.1, but the size of the change differs between schools. This meansthat instruction time is an endogenous variable likely influenced by school
characteristics, so I need to remove the endogeneity from the estimation. To estimate
causal effects of instruction time, previous works clearly demonstrate the need to25

control for factors that may affect students’ test scores and learning motivation, such as
their individual ability, family background, and teacher quality.

5.2. Fixed-effect model
5.2.1 Basic estimation results
The extent of the change in instruction time in Japan was determined by the actual
situation of students, teachers, and the school district environment. Therefore, I applied a
fixed-effect model using a within-student between-subject identification approach toaddress endogeneity with respect to instruction time. This helps remove fixed effects ofstudents, schools and teachers.
Table 4 reports the results of estimating equation (1) using the fixed-effect model.
For all estimations I used robust standard errors clustered at the school level andcontrolled for year and subject fixed effects. Panel A shows the results of full-sample
estimations with score, positive, and joy as dependent variables. The coefficients on
instruction time are significantly positive for score and joy (5% level) and for positive(10% level). However, these coefficients have small magnitudes: if instruction time wereto increase by 100 lessons (4,500 minutes), students’ score is predicted to rise by only
0.85 and their motivation by only 0.18 for positivity and 0.15 for joy.

26

<Please insert Table 4 here>
To compare our findings with those of previous studies, I calculated my results forweekly instruction time adopting the methods used by previous studies and obtained astandard deviation value of 0.027 for score. This is very similar to the value of 0.02–0.03
reported by Rivkin and Schiman (2015), and quite close to the value of 0.05–006 reported
by Lavy (2015). Bessho et al. (2019) found that remedial education programs forJapanese 3rd and 4th grade students had a positive effect of 0.13 SD on test score ofJapanese language arts, but no effect on mathematics. These remedial programs comprise
45 hours of instruction time. I recalculated my effect for the same time of them, and it’s
0.029SD, that is around 1/4th of the size of the effect reported by Bessho et al. (2019)
result. However, they used the Japanese language arts test score (vs. mathematics andscience in my study) and estimated the effect for remedial lessons (vs. normal school
instruction time). These differences in their approach compared to mine at least partly
explain the differences between their results and my findings.

27

Panel A of Table 4 shows that instruction time is positively related to bothdimensions of learning motivation, i.e., joy and positive. 7 However, this study cannot
provide sufficient evidence on whether instruction time directly affects student
motivation, or what other variables affect student motivation. For example, when
students receive more school lessons, their understanding should be enhanced,
potentially leading to higher motivation. How students’ learning motivation increases
remains a matter for further discussion.
Panels B and C of Table 4 show the results of robustness tests for the above
estimations. Specifically, both tests restricted the sample to consider 𝜋𝜋𝑖𝑖𝑖𝑖𝑖𝑖 as a

non-exogenous variable. Although my estimation removed school fixed effects andstudent fixed effect, some students might move the school, which decide to increase theinstruction time for special subject after the education revision in 2002, in order to learn
more that subject at the school. This might affect the result of the estimation. Therefore,
Panel B shows the results after limiting the sample to students taking 50–250 lessons.
That number range represents standard hours ±100 in mathematics in Japan from 2003.

7

We also ran a fixed effect ordered logistic estimation for joy and found no clear difference withthe result from Table 4 (see Appendix A).

28

On the other hand, Panel C shows the results after limiting the sample to students in classsizes exceeding 30 students. The upper limit for class size in Japanese public elementary
schools is 40 students. The difference in class size between schools is so large that it canaffect student performance. Moreover, schools with large classes might focus on a special
subject (mainly mathematics) thus, influencing the school curriculum. It is, therefore,possible that the effects of instruction time are seen in only small classes where teachers
can devote significant attention to each student. The results in Panels B and C show thatthe positive association between instruction time and positive is no longer statisticallysignificant, while the significance level of the coefficients on score and joy drops from
5% in the basic estimation to 10% in the robustness tests.

5.2.2 Heterogeneous effects
Next, I estimate the heterogeneous effects of instruction time. I interact instruction timewith female teacher, teacher experience, schools having absence problems (absence
problem), and female students (girls) along with the school instruction time. Motegi andOikawa (2019) found that the effect of instruction time on Japanese 8th grade students’
TIMSS scores was stronger in combination with teacher quality. Table 5 shows theresults of my estimations of heterogeneous effects. I found significantly positive29

associations between score and the interaction of instruction time with teacher
experience, absence problem, and girls. Conversely, positive was significantly positively
associated with the interaction of instruction time and female teacher but significantly
negatively related to the interaction of instruction time and girls. The variable joy was not
significantly related to any of the variable interactions.
<Please insert Table 5 here>
I then applied the same restricted-sample robustness checks detailed above (50–250 lessons of instruction time, more than 30 students in a class); the results of these two
tests are reported in Tables 6 and 7, respectively. In both restricted samples, score issignificantly positively associated with the interaction of instruction time with absence
problem. These findings indicate that increasing instruction time raises the score of
students at schools with problematic student absence. Furthermore, in both restricted
samples, the coefficients of positive with the interaction of instruction time with girls are
significantly negative (at the 1% level where only classes of over 30 students are includedin the estimation). These findings suggest that increasing instruction time extends thegender gap in students’ motivation to study.
<Please insert Table 6 and Table 7 here>

30

For the interaction between instruction time and teaching experience, thecoefficient for score in the baseline estimation is 0.0003, significant at the 5% level, but
the significance disappears in the restricted sample of classes with more than 30
students. Comparing with the impact found by Motegi and Oikawa (2019), their effectof cross term (instruction time(minutes) × teacher experience(years)) indicates 0.001,
and it changes to 0.045 for our measure, so the effect size in my baseline estimation is
1/150th of the effect size they found. The difference between my result and that ofMotegi and Oikawa (2019) could be explained by our estimation removing teacher
fixed effects, which form the core of teacher quality. The difference is also likely
explained by the difference in class contents between elementary and secondary
students. In addition, both positive significant signs for the coefficient of the interaction
between instruction time and female teacher on positive and girls on score were notpresent in the restricted samples.
Finally, in Table 8, I estimate the effects of the interaction between instruction
time and study time at home. All three coefficients for the dependent variables are
significantly positive. These results clearly show that the time spent studying at home iscritical to learning, especially as instruction time alone is not significantly associated withany of the dependent variables in the first row of Table 8. These effects indicate there may

31be reverse causality between instruction time and students’ understanding and motivation,
which should be considered when interpreting the relationships.
<Please insert Table 8 here>
If study time at home, which is tied to instruction time, influences students’ score
and motivation, then increasing instruction time could expand the gap between students
of different family backgrounds. Kawaguchi (2016) found that a reduction in schooldays
after the Japanese school guidelines revision in 2002 was likely to expand the gap
between families from distinct backgrounds because of differences in the time spent
studying at home. Thinking about education policy, increasing instruction time could
decrease the gap between students with different family backgrounds.

6. Conclusion
Does school instruction time contribute to improving students’ performance? This study
analyzed the effects of instruction time on students’ test score and learning motivation
using Japanese 4th grade data from TIMSS 1995, 2003, and 2007. Within the study period,
Japanese standard instruction time changed substantially as education policy was revisedin 2002. That change reduced the instruction time of mathematics more than that ofscience. I thus formed panel data using within-student information between these subjects

32

to investigate the causal effects of instruction time in a fixed-effect model that removes
student and school fixed effects. The study also removed teacher fixed effects by limiting
the sample to students taught both subjects by one teacher. This is a key contribution of
the study to advancing understanding because the teacher has a great influence on lesson
quality. I also estimated the interaction effects of instruction time with a female teacher
dummy, years of teacher experience, a school absence problem dummy, a female students
dummy, and students’ study time at home, which served to check the heterogeneity of theeffects of instruction time.
The estimation results show that instruction time is positively related to score andjoy. The effect size for score is about the same as that reported by Rivkin and Schiman(2015). It is unclear whether the positive effect on joy result comes through students’improved score or a boost to their curiosity from expanding the lesson. This is one
limitation of this study that future research should aim to address.
The interaction estimations reveal that instruction time positively affects score in
absence-problem schools. This indicates that schools with student absence problems canimprove students’ test scores by increasing instruction time. Conversely, there is no effectof instruction time on score and motivation with female teachers and teacher experience.
With regard to female students, the effect of instruction time on positive is half the size of33

the effect for male students, but there are no gender-based differences for the effects onscore and joy. Moreover, an increase in study time at home raises the effect of school
instruction time with regard to both score and motivation.
This study’s results indicate that increasing instruction time can improve students’scores and joy for learning, but do not clearly reveal the mechanism for increasing
motivation. Further research is required to reveal what factors affect motivation.
In addition, my results suggest that the effects of instruction time are notinfluenced by the teacher’s gender or experience. The apparent absence of heterogeneity
could be explained by my study controlling for teacher fixed effects. As Motegi andOikawa (2019) point out, education policy on instruction time should be discussed to
combine any additional instruction time with other school resources like teacher quality.
Further investigations into the connection of teacher quality with instruction time andcontents or instruction method would be of value in this field. Finally, I find clear
evidence that the interaction of instruction time and study time at home influences student
performance. This may suggest reverse causality, and suggests that increasing instruction
time may expand the education gap between students with different family backgrounds.
Many schools have been forced to close during the COVID-19 pandemic, creating the
major challenge for the education sector of reduced instruction time, even with some34

lessons delivered online. We should think urgently about not only increasing instruction
time but also implementing other measures, such as remedial lessons at home.

Acknowledgments
I would like to thank Ryuichi Tanaka, Kazufumi Yugami, Shinpei Sano, and seminar
participants at Kobe University, Japanese Economic Association Autumn Meeting 2019.

Funding details
This work was supported by JSPS KAKENHI under Grant Number JP18K12796.

Disclosure statement
No potential conflict of interest was reported by the author.

35

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Figure 1. TIMSS score and total instruction time of Japanese 4th and 8th grade students.
600

1400

590

TIMSS score

1000

570
560

800

550

600

540

400

530

200

520
510

Total instruction
time (lessons)

1200

580

1995

2003

2007

2011

2015

0

year
G4Math Time

G4Sci Time

G8Math Time

G8Sci Time

G4MathG4Sci

G8Math

G8Sci

Notes: Total instruction time makes the aggregate number of each grade mathematics (or science)standard instruction time. G4, 4th grade; G8, 8th grade.

43

Figure 2. Annual instruction time for 4th grade students in Japanese public elementary schools,
2002–2010.

The ratio of schools for standard Instruction timelowstandard

high

Year
2002

2006

2008

2010
0%

20%

40%

60%

80%

100%

Notes: Standard represents the schools whose instruction time matches the curriculum guidelines,
while low(high) represents the schools whose instruction time is lower(higher) than is stipulated in
the curriculum guidelines.I made this graph from the data of “investigation for curriculum inelementary and junior high school (kyouikukatei no hensei jisshi jyoukyou chousa)“ by MEXT.
https://www.mext.go.jp/a_menu/shotou/new-cs/1263169.htm

44

Figure 3. Comparative distributions of annual school days in Japanese public elementary schools in2002 and 2008
The ratio of schools for school days in a year

<195

196-205

>206

Year
2002

2008

0%

20%

40%

60%

80%

100%

Notes: It is the ratio of elementary schools in japan which have their school days in a year in 2002
and 2008. I made this graph from the data of “investigation for curriculum in elementary and juniorhigh school (kyouikukatei no hensei jisshi jyoukyou chousa)“by MEXT.
https://www.mext.go.jp/a_menu/shotou/new-cs/1263169.htm

45

Figure 4.1. Total instruction time in mathematics and science for Japanese 4th grade students by
year (TIMSS data).
2003

500
0

0

100

500

1000

1500

2000

2007

0

Number of students

1000

1500

2000

1995

0

100

300

200

Math

Science

46

200

300

Figure 4.2. Total instruction time in mathematics and science for Japanese 4th grade students in all

1500
1000
500
0

Number of students

2000

2500

years (TIMSS data).

50

100

250

200
150
Instruction time/year
Math

47

Sci

300

Table1 Japanese standard instruction time in 1995,2003,2007
1995

2003/2007

Grade

Math

ScienceMath

Science

1st

136

0

114

0

2nd

175

0

155

0

3rd

175

105

150

70

4th

175

105

150

90

5th

175

105

150

95

6th

175

105

150

95

48

Table2 Summary statistics
All
Variable
Istructuin timescore
Positive
Joy

Math
ScienceMath
ScienceMath
ScienceMath
Science

Schoolschool Sizeclass Sizeteacher experience (year)
female teacher
absence problem
Student
girls
birth month1 (January-March)birth month2 (April-June)birth month3 (July-September)birth month4 (October-December)Study time at home (hours/day)Number of students

Mean

Std. Dev.

1995

2003

2007

Min

Max

Mean

Std. Dev.

Mean

Std. Dev.

Mean

Std. Dev.

177.6
105.5
51.03
51.00
4.66
5.01
2.87
3.29

22.6
15.9
9.87
9.82
1.10
1.05
0.89
0.79

37
37
7.06
-1.86
1.71
1.72
1
1

293
160
68.93
73.63
6.85
6.88
4
4

185.66110.82
53.03
52.84
4.59
4.97
2.82
3.24

20.64
12.31
9.24
9.14
0.94
0.92
0.74
0.71

172.69102.20
49.97
50.06
4.63
4.93
2.84
3.25

24.34
16.83
10.04
10.00
1.15
1.10
0.96
0.84

174.50103.36
50.06
50.08
4.78
5.12
2.95
3.41

19.94
16.82
10.01
10.03
1.20
1.10
0.95
0.79

560.98
32.65
17.69
0.60
0.21

262.59
5.50
9.34
0.49
0.40

40
5
1
0
0

1363
41
39
1
1

547.10
32.44
15.89
0.59
0.27

283.40
5.48
7.41
0.49
0.45

525.00
32.84
18.40
0.60
0.16

230.65
5.72
9.48
0.49
0.37

617.32
32.66
18.84
0.62
0.18

264.44
5.25
10.68
0.48
0.39

0.50
0.24
0.24
0.27
0.25
1.06

0.50
0.43
0.43
0.45
0.43
0.89
11607

0
0
0
0
0
0

1
1
1
1
1
4.5

0.50
0.24
0.24
0.27
0.25
1.18

0.50
0.43
0.43
0.45
0.43
0.97

0.49
0.23
0.25
0.27
0.24
0.95

0.50
0.42
0.44
0.45
0.43
0.78

0.50
0.24
0.24
0.27
0.25
1.06

0.50
0.43
0.43
0.44
0.43
0.88

3907

49

4118

3582

Table3 Results of OLS and Ordered Logistic estimation
OLS
Score

ordered logistic

Positive
(2)

(3)

Joy

VARIABLES

(1)

(4)

instruction time

0.0110*0.00860.00010.0002(0.007)(0.007)(0.001)(0.001)

instruction time

-0.0031-0.00110.00030.0004× science dummy(0.0092)(0.0091)(0.0013)science dummy

0.912

0.492

(1.321)(1.312)

Joy

(5)

(6)

(7)

(8)

0.00010.0002-0.00018-0.0001(0.001)(0.001)(0.001)(0.001)0.00000.0000-0.0002-0.0002(0.0013)(0.0010)(0.0010)(0.0021)(0.0021)0.349*

0.354*

0.424***

0.429***

0.884***

0.906***(0.185)(0.189)(0.152)(0.154)(0.308)(0.314)0.0010**0.00012**0.00010.00015*(0.0004)(0.0001)(0.0000)(0.0001)

0.16

0.03110.0375*0.0906*(0.24)(0.028)(0.0218)(0.0466)

0.0232-0.0019-0.0019-0.00401(0.0148)(0.0015)(0.0012)(0.0027)

girls-0.314*-0.248***-0.151***-0.405***(0.1850)(0.0178)(0.0148)(0.0322)birth month 2

2.372***

0.0456**0.000988-0.014(0.24)(0.0224)(0.0171)(0.0378)

1.898***

0.0390*0.0055-0.0023(0.2330)(0.0224)(0.0184)(0.0401)

0.837***

0.0186-0.00355-0.0112school size
female teacher
teacher experiencebirth month 3
birth month 4
study time at home

(0.2350)(0.0238)(0.0184)(0.0390)0.08070.115***

0.0830***0.192***(0.1040)(0.0101)(0.0076)(0.0182)

Observations

23,21322,74022,594

22,315

22,79922,500R-squared

0.023

0.034

0.031

0.053

0.066

0.082

Pseudo-R

2

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

50

22,79922,500

0.031

0.039

Table4 Results of fixed effect estimation
Fixed effect
VARIABLES

Score

Positive

Joy

(1)

(2)

(3)

Panel AAll
0.0085**0.0018*0.0015**(0.0039)(0.0009)(0.0008)

Observations

23,213

22,594

22,799R-squared

0.001

0.069

0.144

Panel Bdrop lessons under 50& over 250

instruction time

0.0082*0.00160.0016*(0.0044)(0.0011)(0.0009)

Observations

23,05522,43722,642R-squared

0.001

0.070

0.145

instruction time

Panel C

over 30 students in a class
0.0095*0.00090.0015*(0.005)(0.0011)(0.0009)

Observations

16,33815,90916,042R-squared

0.001

0.071

0.148

instruction time

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

51

Table5 Results of Fixed effect estimation for heterogeneous effect
Fixed effect
Score

Positive

Joy

Score

Positive

Joy

Score

Positive

Joy

Score

Positive

Joy

VARIABLES

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

instruction time

0.0076*0.001150.001150.00410.00150.00120.00610.00150.0014*0.00670.0022**0.0014*(0.0042)(0.001)(0.0008)(0.004)(0.0011)(0.0008)(0.0042)(0.001)(0.0008)(0.004)(0.001)(0.0008)0.00130.0008*0.0005(0.0023)(0.0004)(0.0004)
0.0003**0.00000.0000(0.0001)(0.0000)(0.0000)
0.0057*0.00060.0003(0.0031)(0.0005)(0.0005)
0.00381*-0.0009***

0.0002(0.0023)(0.0003)(0.0003)

instruction time× female teacher

× teacher experience× absence problem

× girls

Observations

23,213

22,594

22,79923,213

22,594

22,79923,213

22,594

22,79923,213

22,594

22,799R-squared

0.001

0.070

0.144

0.001

0.070

0.144

0.001

0.070

0.144

0.001

0.070

0.144

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

52

Table6 Results of Fixed effect estimation for heterogeneous effect (drop lessons under 50& over 250)
Fixed effect (drop lessons under 50& over 250)
Score

Positive

Joy

Score

Positive

Joy

Score

Positive

Joy

Score

Positive

Joy

VARIABLES

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

instruction time

0.00680.00100.00120.00350.00120.00110.00650.00150.0016*0.00630.0021*0.0015*(0.0047)(0.0012)(0.0009)(0.0049)(0.0012)(0.0009)(0.0045)(0.0011)(0.0009)(0.0046)(0.0011)(0.0009)

0.00180.0008*0.0005(0.0024)(0.0005)(0.0004)
0.0002*0.000020.00002(0.0001)(0.0000)(0.0000)
0.0067**0.00040.0002(0.0034)(0.0005)(0.0005)
0.0039-0.0008**

0.0003(0.0024)(0.0003)(0.0003)

instruction time× female teacher

× teacher experience× absence problem

× girls

Observations

23,05522,43722,642

23,05522,43722,642

23,05522,43722,642

23,05522,43722,642R-squared

0.001

0.071

0.146

0.001

0.070

0.146

0.001

0.070

0.145

0.001

0.071

0.146

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

53

Table7 Results of Fixed effect estimation for heterogeneous effect (over 30 students in a class)
Fixed effect (over 30 students in a class)
VARIABLES

instruction time

Score

Positive

Joy

Score

Positive

Joy

Score

Positive

Joy

Score

Positive

Joy

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

0.0096*0.00070.00140.00520.00010.00100.00640.00070.00140.0082*0.00150.0016*(0.0053)(0.0012)(0.0009)(0.0054)(0.0012)(0.0009)(0.0054)(0.0012)(0.0009)(0.0049)(0.0011)(0.0009)-0.0001

0.00030.0002(0.0028)(0.0006)(0.0005)
0.00020.00005*

0.0000(0.0001)(0.0000)(0.0000)
0.0063*0.00060.0003(0.0036)(0.0006)(0.0005)
0.0028-0.0013***-0.0002(0.0024)(0.0004)(0.0003)

instruction time× female teacher

× teacher experience× absence problem

× girls

Observations

16,33815,90916,042

16,33815,90916,042

16,33815,90916,042

16,33815,90916,042R-squared

0.001

0.071

0.148

0.001

0.071

0.149

0.002

0.071

0.148

0.001

0.072

0.148

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

54

Table8 Results of Fixed effect estimation for heterogeneous effect of study time at home
Fixed effect
Score

Positive

Joy

VARIABLES

(1)

(2)

(3)

instruction time

0.00510.00080.0007(0.0039)(0.0010)(0.0008)

0.0032**0.0010***0.0009***(0.0013)(0.0002)(0.0002)

Observations

22,89822,461

22,650R-squared

0.002

0.072

0.147

instruction time× study time at home

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

55

Appendix A Results of Fixed effect Ordered Logit estimation
Joy
VARIABLES

(1)

(2)

(3)

(4)

(5)

(6)

instruction time

0.0044***0.0046***0.00230.004***

0.0048***0.0019(0.0014)(0.0016)(0.0016)(0.0015)(0.0015)(0.0015)

instruction time
0.0003× female teacher(0.0009)
0.0001**× teacher experience(0.0000)
0.0009× absence problem(0.0010)-0.0009

× girls(0.0008)
0.0025***× study time at home

(0.0005)
Observations

17,75417,75417,75417,75417,754

17,624

Pseudo-R

0.225

0.225

0.226

0.225

0.225

0.228

2

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.156