Global COE Hi-Stat Discussion Paper Series 271

Research Unit for Statisticaland Empirical Analysis in Social Sciences (Hi-Stat)

Fewer School Days, More Inequality

Hi-Stat Discussion Paper

Daiji Kawaguchi

January 2013

Hi-Stat
Institute of Economic Research
Hitotsubashi University
2-1 Naka, Kunitatchi Tokyo, 186-8601 Japan
http://gcoe.ier.hit-u.ac.jp

FEWER SCHOOL DAYS, MORE INEQUALITY1

Daiji Kawaguchi
Hitotsubashi University, RIETI, TCER, and IZA

January 11, 2013

Abstract
This paper examines how the intensity of compulsory education affects the time use andacademic achievement of children with different socioeconomic backgrounds. The
impact is identified off the school-day reduction of Japan in 2002 that resulted when all
Saturdays were set as public-school holidays. An analysis of time diaries and test scores
before and after the school-day reduction reveals that the socioeconomic gradient of 9th
graders’ study time becomes 80% steeper and the socioeconomic gradient of academic
achievements of 8th and 10th graders becomes 20-30% steeper. Intensive compulsory
education contributes to equalizing the academic performances of children withdifferent socioeconomic backgrounds.

JEL Classification Code: I24, I28
Key Words: Compulsory Education, Inequality, Socioeconomic Gradient.
Financial support was provided by the Japan Society for Promotion of Science. Accessto the survey of time use and leisure activities was granted by the Ministry of Internal
Affairs and Communications. I am grateful to Yoichi Arai, Daniel Hamermesh,
Hidehiko Ichimura, Edward Lazear, Patrick Puhani, Rebecca Ryan, Jay Stewart, Till
von Wachter, Ken Yamada, and many seminar participants at Hitotsubashi University,
University of Maryland, GRIPS, Singapore Management University, University ofTokyo, Academia Sinica, University of Hanover, and the East-West Center forcomments.

1

1

I.

Introduction
One purpose of compulsory education is to assure uniform educational

opportunities for all children regardless of their socioeconomic backgrounds. Indeed, afew studies show that expanding the years of compulsory education reduces thedependence of children’s educational attainment or risk attitudes on their parents’
educational attainment (Meghir and Palme (2005), Aakvik et al. (2010), Hryshko et al.
(2011), Brunello et al. (2012)). Then, how does the intensity of compulsory education
affect the intergenerational dependence of educational attainment? Although this is an
important question to address, since policymakers determine the national curriculum
and choose the intensity of compulsory education, only a few studies examine the effectof the intensity of compulsory education on the intergenerational dependence of
educational attainment.
This paper demonstrates that intensive compulsory education homogenizes
children's time for studying and dampens the effect of parental socioeconomic
background on their children's academic performance. This idea is related to previous
research findings indicating that the socioeconomic gap of students’ achievement
widens after summer breaks, because the out-of-school environment is moreheterogeneous by socioeconomic status than the in-school environment (Downey et al.
(2004), Alexander et al. (2007)). Another strand of related literature examines the“incarceration” effects of schooling (other than cognitive ability) on youth behavior,such as the increase in property crimes committed by youth on off-session days (Jacoband Lefgren (2003)), or the reduction of teenage pregnancy resulting from more years ofcompulsory education (Black et al. (2008)).
To examine the effects of the intensity of compulsory education on children's
2

time use and academic performance across socioeconomic classes, this study uses theschool-day reduction in Japan that took place in 2002 for identification. For the purposeof reducing the time pressure on students and attaining 2 full days off per week for
public-school teachers, the Japanese government set all Saturdays as holidays starting in
April 2002, whereas half-day instruction had been given on the first, third, (and fifth)
Saturdays of each month before the policy change. The change of time use of 9th graders
who are subject to compulsory education is examined, based on the 1996, 2001, and2006 waves of the Survey of Time Use and Leisure Activities (社会生活基本調査,
JTUS), which includes the time diaries of the second and third weeks of October in eachsurvey year, as well as background variables for all household members. In addition to
the time-use survey, the 1999 and 2003 waves of the Trends in International
Mathematics and Science Study (TIMSS), which targets 8th graders, and the 2000 and2003 waves of the OECD Programme for International Student Assessment (PISA),
which targets 10th graders, are used to assess the policy change’s effect on students'
academic performance.
The analysis of the JTUS data reveals that students' time for studying on
Saturdays declined by one third from 2001 to 2006, on average, but the decrease of
study time was more significant among children with less-educated parents, effectively
making the socioeconomic gradient of study time 80% steeper. This increased
socioeconomic gap of study time resulted in a widening gap in test scores: The
socioeconomic gradients of test scores became 20-30% steeper after the policy change.
These results imply that study time is a valuable input for the academic achievement of
disadvantaged students and that time-intensive compulsory education contributes toreducing the gap of academic achievement between socioeconomic classes.
3This paper also contributes to existing literature on the intergenerational
dependence of socioeconomic status in Japan (Kariya (2001) and Tanaka (2008), Ueda
(2009), Hojo and Oshio (2010) and Yamada (2011)). This paper demonstrates that study
time is an important channel of this dependence and that the intensity of compulsory
education determines the degree of the dependence.

II. Family Background, Students' Time Use, and Academic Performance
In this section, I lay out a simple model that conceptualizes the relationships
among students' family background, time use, and academic performance to motivate
the empirical analysis. I employ simple functional forms for the purpose of exposition,
but it should be clear that the model's basic logic is robust to alternative assumptions.
I assume that a parent maximizes the utility function by choosing her child's timespent on studying, t, given the resources for her child’s education, p. The parental
resource, p, refers to parental human capital that contributes to the production of achild’s human-capital production or a child’s innate ability inherited from the parent.
The utility function consists of her child's test score, y, and her child's study time, t. I
assume that the parent feels the pain of her child’s studying and that this is the only costof letting her child study. By assuming that the marginal cost of studying does notdepend on parental resources, I abstract away from the heterogeneity of the liquidity
constraint. The child's test score, y, is determined through the production function f(t, p),wheresatisfies

0,

0,

0,

0 . Assuming that the utility function islinear in the test score and time spent on studying, the utility function is given as:

,

.

4

(1)

Compulsory education sets the minimum amount of time that must be spent on
studying,∗. The optimal t, denoted as

and the marginal cost, so that∗,, is determined to equate the marginal benefit

1 in the case of an inner solution orin the case of a corner solution. The consequent test score is determined as
∗

,

∗

∗

.
It should be noted that the change of compulsory-education policy affects thetime allocation of only those children who are at the corner solution. Then, who is at thecorner solution? Are they children from wealthy families or poor families? The answer
depends on whether time and parental resources are substitutes or complements in f(t,p).
Let us first consider the case in which two inputs are perfect complements andthe production function is,,

The optimal study time is determined as
The test score is given as

,
∗

.
if

(2)
and

∗

if

.

. This perfect complementarity represents the case when

the child’s effort is productive up to the parental resources, including the child’s
inherited ability. In this setting, a parent without sufficient resources faces zero marginal
return to her child's study time exceeding p. Consequently, the parent lets her childstudy the minimum amount of time that is required by compulsory education. Thus, areduction of hours of compulsory education reduces the study time of children frompoor families, but it does not decrease their test scores, which is.

Another polar result is obtained when student's time and parental resources are
perfect substitutes. Suppose that child study time and parental resources are perfect5

substitutes in the test-score production function, so that:

,

.

(3)

In this production technology, the higher the parental resources, the lower the marginal
return to the child's study time. This functional form represents the case in which aninheritably smart child does not learn much from additional study time because the childalready knows enough. Thus, the parent lets her child study fewer hours. In addition, theparent who endows her child with educational resources above the threshold lets her
child study the minimum hours required by compulsory education. The solution to the
problem is∗score is

∗

1

if

1

log 1

0 if

∗

and
1

and

if
∗

1

log

. The consequent test
if

1

.

Therefore, a reduction of hours of compulsory education decreases the study time ofchildren from affluent families and decreases their test scores.
As an additional example that lies between the two polar cases, let us consider
the Cobb-Douglas production function, a functional form adopted by previous studies(Behrman et al. (1982) and Glomm (1997)):

.

,

(4)

Because the marginal productivity of study time positively depends on parental
resources, parents with affluent resources let their children study more hours. In
particular, the optimal study time is expressed as:
∗

if

∗

if

or

. Thus, the reduction of hours of compulsory education

reduces the study hours only of children with less parental resources. This reduction of6study time causes a decline in test scores of children with less parental resources.
From this exercise, we learn that reducing compulsory education can affect study
time and test scores heterogeneously, depending on family background, and the form ofdependence is determined by whether study time and parental resource are complements
or substitutes in the education production function. Thus, who is affected by the
reduction of school days in terms of study time and how the effect translates into
academic achievement are essentially empirical questions. The following empiricalanalysis examines the heterogeneous responses of study time and test scores to the
reduction of school days by parental backgrounds.

III. Institutional Background
In Japan, parents are required to have their children receive nine years of general
education (School Education Act (学校基本法) Article 16). The first six years ofeducation are called primary school, and the ages of children attending such schools
generally extends from 6 to 12 years old. The second three years of education consist of
junior-high school, and its students range from 12 to 15 years old. The school year starts
in April, and primary schools accept children who turn age 6 on April 1 or before. Since
grade repetition and postponement of school entry are rare in Japan, most students
graduate from junior-high school at age 15.
Historically, classes were given from Monday to Saturday in primary andjunior-high schools, with half-day classes given on Saturday. There was criticism,however, that children were swamped with studying class materials and were being
deprived of opportunities to spend time with their families or in local community
activities. In addition, typical workers were starting to take Saturdays off by the7mid-1990s, because the 1988 revision of the Labor Standard Act set the legal work
hours at 40 per week (Kawaguchi et al. (2008) and Lee et al. (2012)). Motivated by thisgeneral trend of reduced work hours, pressure from teachers’ unions to reduce classhours had grown steadily. In response to these demands, the government started a
gradual process of setting all Saturdays as school holidays.
As the first step, the government set the second Saturday of each month to be aholiday, beginning on September 12, 1992. In the second step, the government added
the fourth Saturday of each month as a holiday, starting from April 22, 1995. During thistransition, however, the national curriculum guidelines (学習指導要領) were notrevised. The guidelines required 5,785 class units for primary school and 3,150 classunits for junior-high school. From April 2002, all Saturdays became school holidays,
and this change was accompanied by a revision of the national curriculum guidelines,
which require 5,367 class units (7.3% reduction) for primary school and 2,940 classunits (7.7% reduction) for junior-high school.
The 2002 revision gives parents more discretion regarding their children’s timeuse on the first, third, (and fifth) Saturdays. How did children of compulsory schoolingage spend this extra time? Are there any differences in the responses by parental
backgrounds? What are the consequences of the change of time use in terms ofacademic performance? The following empirical analysis addresses these questions.

IV. Analysis of Time-use Survey
A. Data and Descriptive Analysis
The Japanese Time Use Survey (JTUS) is conducted by the Bureau of Statistics
every five years, starting from 1976. This study uses the 1996, 2001, and 2006 waves of8the survey. All waves were fielded over nine-day periods, including the second and third
weekends in October, targeting randomly sampled households. The survey covers all
household members aged 10 or older, and each respondent fills out time diaries for two
consecutive days, reporting their activities in 15-minute intervals. The number ofpre-coded activities is 20 in all waves. These 20 activities are reclassified into threemajor categories of time use: study time, leisure time, and other time use.2 The sample
is nationally representative with individual survey weights, including about 175,000
persons in each wave.
In Japan, both private and public high schools admit students largely on the basisof their performance on the entrance examination and junior-high-school GPA, and,accordingly, most students take the entrance examination at the end of 9th grade.
Because which high school a student attends significantly affects the life course,
including college advancement probability, 9th graders generally spend a long time
preparing for the entrance examination, and this study time is heterogeneous among
students. Thus we restrict our analysis sample to 9th graders. To construct the analysis
sample of 9th graders, children aged 15 who were born between April and October, as
well as children aged 14 who were born between November and March, are included in
the analysis sample. 3 About 7,600, 4,900, and 4,100 children are included in theanalysis samples in 1996, 2001 and 2006, respectively.
Child's family-background variables are constructed from responses by otherStudy time includes “school work,” “commuting to/from school,” and “studying andresearching.” Leisure time includes “TV, radio, reading,” “rest and relaxation,” “hobbies
and amusements,” “sports,” “volunteer and social activities,” and “social life.” Other
time use includes “housework,” “child care,” “shopping,” “sleep,” “personal care,” “meals,”and “medical examination or treatment.” The categories “travel other than commuting”
and “other activities” are prorated to leisure and other activities.
3 The month of birth is not recorded in 1996. Thus the birth month is randomly
assigned based on a uniform distribution for the purpose of data construction.
2

9

household members. Educational attainment of the household head, identified by thehousehold member's id, is a major variable that approximates a child’s socioeconomic
background. Educational attainment is constructed as a continuous variable that takes 9
for junior-high-school graduates or less, 12 for high-school graduates, 14 forjunior-college graduates, and 16 for 4-year-college graduates or more. Dummy variables
indicating female-headed household, single parenthood, and mother's employment, as
well as household annual income ranges, are also constructed.
The 2001 and 2006 surveys record the date of the survey so that we candistinguish the third Saturday, which is a holiday only in 2006, and the second Saturday,
which is a holiday in both 2001 and 2006. The 1996 survey does not record the date of
the survey, however, and therefore, we cannot distinguish between the second and third
Saturdays. Thus, for the analysis that pools the 1996, 2001, and 2006 waves, the second
and the third Saturdays are not distinguished, and the changes of time use on Saturdays
are roughly considered to be one half the policy impact of setting a day as a holiday. In
an additional analysis that focuses on time use on Saturdays in 2001 and 2006, thechange of time use on the second Saturday is compared with the change of time use on
the third Saturday.
Figure 1 illustrates the mean of students' study time by day of the week. On
weekdays, the study time increases between 1996 and 2001. Between 2001 and 2006, it
does not change much among children with a junior-high-school-graduate parent, but it
increases by 17 minutes per day among children with a college-graduate parent.
Although study time increases from 1996 to 2001 on Saturdays, the 2006 amount islittle more than one half of the 2001 amount among children with ajunior-high-school-graduate parent: 328 minutes in 2001 and 175 minutes in 2006. The
10

study time of children with a college-graduate parent decreases from 405 minutes in2001 to 299 minutes in 2006. In the case of children with a college-graduate parent, thereduction of study time on Saturdays between 2001 and 2006 is mostly made up for by
an increase of study time on weekdays, because the 17-minute increase per weekday
translates into a 85-minute increase per week. Study time on Sundays generally
increases between 1996 and 2001 but not much change is observed between 2001 and2006.
The sampling weights in the data, which are roughly 5 times larger for weekdays
than for weekend days, allow us to calculate the daily average of time use over 7 days.
All in all, from 2001 to 2006, the daily average of study time decreases from 435
minutes to 406 minutes among students with a junior-high-school-graduate parent,
whereas it increases slightly from 498 minutes to 505 minutes among students with acollege-graduate parent.
Table 1 tabulates the descriptive statistics of children's and their parents'characteristics. Study time increases from 1996 to 2001 but stays almost constant
between 2001 and 2006, resulting from its increase on weekdays and its decrease on
Saturday. The change of leisure time is almost the opposite of the change of study time.
Time spent on other activities is almost unchanged during the sample period. About one
half of the analysis sample consists of girls. In 2006, around 14 percent of household
heads hold a junior-high-school degree or less. About 46 percent, 10 percent, and 31
percent of household heads have high-school, junior-college, and 4-year-college degrees,
respectively. About 15 percent of households in the sample are headed by a female, andabout 20 percent of children have only one parent. About 25 percent of mothers work.
Annual-income categories are classified so that children are equally distributed across11categories. A comparison of the 1996, 2001, and 2006 columns reveals that the
distribution of children's household characteristics does not change significantly
between 2001 and 2006.

B. Regression Analysis
Adding the third Saturday to school holidays for the nine-day survey period in2002 decreases study time on Saturdays, but this decrease of study time on Saturdays ispartly offset by an increase of study time on weekdays. This change of children’s study
time, however, seems to depend on their parents' socioeconomic backgrounds, as
approximated by the household head's educational attainment. To capture theheterogeneous change of time use across children with different backgrounds, thefollowing regression model is estimated for each activity (A=study, leisure and otheractivities), using the pooled data of 1996, 2001, and 2006:.
The dependent variable(5)
is time use measured by minutes per day inactivity A (study, leisure, and other activities) by individual i in year t. The vector ofcontrol variables,, includes dummy variables for female-headed household, single

parenthood, and 3 household annual income categories (4-5.99, 6-8.99, and 9 million
yen).
The coefficient
coefficients

andcaptures the socioeconomic gradient of time use, and thecapture the changes of the socioeconomic gradient over time.

12

It should be noted that the coefficients

andcapture the average changes oftime use between 1996 and 2001 and between 1996 and 2006, respectively, evaluated at
12.
The coefficients for other socioeconomic variables are assumed to be constant
between 2001 and 2006, because allowing for a change of coefficients for other proxyvariables for socioeconomic backgrounds tends to obscure the change of thesocioeconomic gradient through the correlation between Head Educ and variables in x.
By fixing the coefficients for x over time, the change of the socioeconomic gradient isefficiently captured by the change ofand. Thus the change ofandshould not be interpreted as the change of the effect of household head's education perse; rather, it should be interpreted as the change of the effect of family socioeconomic
background in general.

C. Basic Estimation Results
Table 2 lays out the results of the estimation of equation (5) for the study time of
9th graders. Column (1), reporting the results for weekdays, indicates that a one-yearincrease of the household head’s educational attainment increases the child’s study timeby 3.7 minutes per day, though this result is not statistically significant. The coefficientfor the interaction term of head's education and year 2001 implies that thesocioeconomic gradient does not change between 1996 and 2001 in a statisticallysignificant way. The interaction term of head's education and year 2006 implies that thesocioeconomic gradient of study time becomes 65% (=(5.80+1.41)/(5.80-1.42)-1)
steeper in 2006 compared with 2001, although the interaction term is not statisticallysignificant.
13Column (2) reports results for Saturday. An additional year of household head's
education increases a child’s study time by about 7 minutes in 1996, about 6 minutes
per day in 2001, and 16 minutes per day in 2006. These estimates imply that thesocioeconomic gradient of study time becomes twice as much in 2006 as it is in 2001.
The large negative coefficient for the 2006 dummy implies a significant reduction in the
study time of children with a high-school-graduate parent on Saturdays in 2006.
On Sundays, a child with a better-educated parent studies longer than a childwith a less-educated parent, as implied by the estimates provided in Column (3). Thisgap grows both between 1996 and 2001 and between 1996 and 2006 in statisticallysignificant ways.
Including the third Saturday as an additional school holiday during the sampling
period increases the socioeconomic gradient of study time on weekdays and Saturdays
between 2001 and 2006. What then is the average daily effect? To answer this question,
we estimate equation (5) without specifying the day of the week. Weighted least-squares
estimates, applying the sampling weight (roughly 5 times larger for weekdays than forweekend days), are reported in Column (4). These estimates reveal that thesocioeconomic gradient of study time becomes steeper in 2006 than in 1996 or 2001,
and the change is statistically significant at the 5% level. Between 2001 and 2006, thesocioeconomic gradient of study time becomes 83% (=(5.34+7.00)/(5.34+1.39)-1)
steeper. This steepening of socioeconomic gradient is not the result of a longer-term
trend because the change of the socioeconomic gradient between 1996 and 2001 is notstatistically significant.
Study time includes three subcategories of time use: school work, studying andresearching and commuting. About 75% of study time consists of school work, about14

15% consists of studying and researching, and the remaining 10% consists ofcommuting. Regression of each component on the same explanatory variables in Table 2
reveals that the socioeconomic gradient becomes steeper in a statistically significant
way between 2001 and 2006 only for school work. Therefore, the increase of
extra-curricular activities or distant commuting does not explain the increase of thesocioeconomic gradient of study time.
The survey coverage of second and third Saturdays allows us to further examine
whether the reduction of a school day is the cause for the steepening socioeconomic
gradient of study time on Saturdays. Both 2001 and 2006 waves of the JTUS distinguishthe second and third Saturdays as dates of the survey. Since the second Saturday was aschool holiday before 2002, we should not observe a steepening of the socioeconomic
gradient for the second Saturdays, but we should observe a steepening of thesocioeconomic gradient for the third Saturdays because third Saturdays became aholiday from 2002. The regression result for second Saturdays, reported in Column (1)in Table 3, reveals that an additional year of parental education is associated withchildren studying 12 minutes more in 2001, and this relationship virtually does notchange in 2006. In contrast, the study time on third Saturday is not correlated withparental education in 2001, but an additional year of parental education is correlatedwith children studying 17 minutes more in 2006, as reported in Column (2) in Table 3.
The result implies that the socioeconomic gradient of study time is absent on schooldays but present on school holidays, pointing to the reduction of the school day as thereason for the steepening socioeconomic gradient of study time between 2001 and 2006.
The increase of the socioeconomic gradient after 2002 is arguably the result of
an increase of parental discretion regarding their children’s time use. Parents who are15

well-off can afford to send their children to private schools, many of which continue tooffer classes on Saturdays; 6.9% of junior-high-school students attended private schools
in 2006 (School Basic Survey). Also, wealthy parents have the option to send theirchildren to cram schools, which offer classroom teaching that focuses on problem
solving in preparation for entrance examinations. Even without sending their children toa private school or a cram school on Saturdays, parents with higher socioeconomic
status can help their children study by going over study materials together or preparing
a good environment, such as an independent study room, for their children.
The additional school holidays on Saturdays in 2006 decrease the study time ofchildren with weaker socioeconomic backgrounds. How then did children use thiswindfall of time? Changes of time spent on leisure and other activities reported in Table
5 answer this question. Column (1) implies that the greater the head's years of education,
the shorter the leisure time on the daily mean. The size of this negative socioeconomic
gradient of leisure time is comparable to the size of the positive socioeconomic gradientof study time. Children with a better-educated parent studied longer by reducing theirtime for playing. This negative socioeconomic gradient becomes even stronger in 2006.
Again, this corresponds to the increase of the positive socioeconomic gradient of study
time in 2006. Overall, the differences and changes of leisure time are almost a mirror
image of the differences and changes of study time. Among the seven leisure-time
activities, the socioeconomic gradients for sports and associating with friends increase
between 1996 and 2006 in statistically significant ways. Column (2) reports theestimation results for other activities, such as sleep, personal care, and eating. The timespent on other activities does not depend on the parent’s educational background in
1996, 2001, and 2006.
16

In sum, adding Saturdays as school holidays reduced the average study time of
only those 9th graders with weaker socioeconomic backgrounds, so that thesocioeconomic gradient of study time became about 80% steeper between 2001 and2006.

D. Estimation Results with Prefecture × Year Fixed Effects
The analysis heretofore found heterogeneous responses to the reduction ofschool days among children with different levels of parental educational attainment.
One might argue, however, that these heterogeneous responses are largely caused by theheterogeneity of responses across regions. Since private schools and cram schools are
more readily available in urban areas than in rural areas, the reduction of study time on
Saturdays could presumably affect the study time of children living in rural areas
because of the lack of alternative choices for educational opportunities. Since
better-educated parents are more likely to live in urban areas, this potential
heterogeneity could be captured by parental educational backgrounds. To address thisconcern, the following model allows for prefecture × year fixed effects.

, (6)where i, j, and t are indexes for individual, prefecture, and year, respectively. The fixed
effects

are the prefecture × year fixed effects that capture the heterogeneity of time

use across 47 prefectures varying between 1996 and 2006. The fixed effects capture all
the differences of availability of educational institutions across prefectures, including
17

private and cram schools. All the coefficients are identified off the variation of family
backgrounds across children within the same prefecture in the same year in the
fixed-effects model.
Table 5 lays out the estimation results of the fixed-effects model. The estimatedcoefficients are quite similar to the estimated coefficients in Table 2, implying thatallowing for the prefecture × year fixed effects virtually does not change the results.
The analysis of the time-use survey heretofore revealed that the reduction ofschool days made children's study time more dependent on their family background, as
approximated by household heads' educational attainment. In relation to the theoretical
discussion provided in the previous section, the fact that the reduction of school days
reduced the study time of children with weaker socioeconomic backgrounds implies thatstudy time and parental resources are not substitutes, but complements. This is because,
if study time and parental resources were substitutes, children with stronger
socioeconomic backgrounds should have reduced their study time more in response to
the reduction of school days than children with weaker socioeconomic backgrounds.
Then how does this increased inequality of study time affect the distribution oftest scores across children? Does the dispersion of study time by children'ssocioeconomic backgrounds make children's academic performances more dependent
on socioeconomic background? The answers to these questions are not obvious, assuggested by the theoretical discussion, because studying longer may have zeromarginal return for disadvantaged students. The empirical analysis in the next section
addresses these questions by comparing the socioeconomic gradients of academic
performance before and after the reduction of schooldays.

18

V. Analysis of Test Scores
A. Data
To examine the effect of additional school holidays from 2002 on the
distribution of test scores across children with different socioeconomic backgrounds, the1999 and 2003 waves of the Trends in Mathematics and Science Studies (TIMSS) andthe 2000 and 2003 waves of the OECD Programme for International Student
Assessment (PISA) are exploited.
The TIMSS, implemented by the International Association for the Evaluation of
Educational Achievement, features the participation of 38 countries in 1999 and 49
countries in 2003, including Japan for both years. The TIMSS assesses students'
academic achievements in mathematics and sciences, targeting 8th graders who belong
to one or two classes of one of the 150 randomly sampled schools.4 About 4,000
students in each wave took mathematics and science examinations, each lasting 90
minutes, based on different types of booklets.5 To assure the comparability ofachievements measured based on different booklets, standardized test scores using
psychometric methods are recorded in the data sets. The standardized scores arenormalized to have 50 as the mean value and 10 as the standard deviation. The surveys
are accompanied by background questionnaires completed by students that ask about
the number of books at home and the possession of a calculator, a computer, a desk, or adictionary at home. In addition, the 2003 questionnaires ask about the father’s andmother’s educational attainments. Although the lack of parents’ educational attainment
in 1999 is unfortunate, the 2003 data indicate that the number of books or computer
TIMSS 2003 targeted 4th graders in addition to 8th graders. The 1999 and 2003
surveys for Japan were conducted between February and May of 1999 and between
April and June of 2003, respectively.
5 Eight booklets and 12 booklets were used for 1999 and 2003, respectively.
419

possession at home predicts parental educational background well. Thereby, further
empirical analysis based on TIMSS utilizes the predicted highest years of parents’
education. The analysis sample excludes observations who were born in a foreigncountry, who repeated a grade, and who have a missing value in test scores, sex, andbooks and other items possessed at home.
The PISA features participation by 32 countries in 2000 and 30 countries in 2003,
including Japan for both years. The PISA tests mathematics, sciences, and reading
comprehension and targets 15-year-olds. The 2000 sample includes about 5,300
10th-grade full-time students of 135 randomly sampled high-school classes. The 2003
sample includes about 4,700 full- and part-time students of 144 randomly sampled
high-school classes.6 Students in each wave took reading, mathematics, and scienceexaminations, lasting 120 minutes and based on different types of booklets.7
Standardized test scores using psychometric methods are normalized to have 50 as themean value and 10 as the standard deviation. The accompanying survey for students
asks about the number of books at home and the possession of own room, a computer,and access to the internet. Only the 2003 survey asks the father's and mother's
educational attainments, and educational background of parent is predicted from thepossession of books and the other mentioned items at home. The analysis sample
excludes observations that have a missing value in test scores, sex, and books and otheritems possessed at home.
Table 6 reports the descriptive statistics of the analysis sample. Because of thesample construction, standardized test scores do not have exact mean values of 50.

High schools in 2003 include high school, secondary educational school (中等教育学校),
and technical college (高等専門学校).
7 Nine booklets and 13 booklets were used for 2000 and 2003, respectively.
620

Average scores by parental educational backgrounds in the TIMSS for 2003 show that
there is a 10-point (=1 standard deviation) difference in average mathematics test scores
between children with a college-educated parent and children with ajunior-high-school-educated parent. A similar test-score gap by parental education isfound for science test scores in the TIMSS 2003. The PISA 2000 and 2003 also indicate
similar gaps in average test scores by the educational backgrounds of parents in all threesubjects.
About one half of the analysis sample consists of girls for both the TIMSS and
the PISA. The distributions of the number of books at home are stable between 1999
and 2003 in the TIMSS and between 2000 and 2003 in the PISA. Since most students
had a calculator, a desk, or a dictionary at home in 1999, the possession of these items
did not change between 1999 and 2003 in the TIMSS. It is notable that according to thissurvey, possessing a computer at home increased from 52.5% in 1999 to 83.0% in 2003.
This household information is used to predict the highest educational attainment of
parents that is missing in the TIMSS 1999.
Parents' educational attainment is indicated by the mother's or father's attainment,
whichever is higher, and it is transformed into a continuous variable that takes 16 if aparent is a 4-year-college graduate, 14 if a junior-college graduate, 12 if a high-school
graduate, and 9 if a junior-high-school graduate.
This continuous variable is regressed on the possession of books and items athome using a sampling weight based on the TIMSS 2003. The estimation of a weighted
least-squares model renders the following result:21

11.66
0.56

0.39
0.12
1.45
0.12
0.55
0.18

0.69
0.12

0.96
0.12
0.84
0.08

0.25
0.36
0.71
0.41

,

0.095,

3,429.

The more books in the home, the higher is parental educational attainment. Possessing acomputer at home is also positively associated with better parental educational
background. Based on this regression model, the highest parental educational attainmentis predicted for both 1999 and 2003 in the TIMSS. The distribution of predicted values
is compressed, compared with the distribution of parents' actual educational attainmentthat is available for 2003. Moreover, reflecting the fact that students are asked to
identify their parents’ educational background, the distribution is upward biased
compared with the distribution of educational background reported in the JTUS 2001.
To maintain the comparability of variation of predicted and actual parental educationand correct the upward bias, 16 years of education is assigned as the predicted parental
education for the observations whose predicted value lies between 100 and the 75.85
percentile, because 24.15 percent of parents are 4-year college graduates in the JTUS
2001. Similarly, 14, 12, and 9 years of education are assigned based on the percentilesof the predicted value,. The adjusted predicted parental education is denoted as. For comparability across 1999 and 2003, predicted parental education is used
for both 1999 and 2003.
Predicted parental education is similarly obtained using the PISA 2003. The
continuous variable of parental education is regressed on the possession of books,
computer, internet, and child’s own room. The estimation result is:22

10.72
0.21

3.13
0.22

3.42
0.22

3.73
0.22

3.38
0.22

3.76
0.22

0.26
0.07

3.77
0.22

0.03
0.05

0.135,

0.65
0.05

,

4,697.

Using this result, the adjusted predicted parental educationisconstructed for the PISA 2000 and 2003.

B. Socioeconomic gradients of test scores before and after 2002
Given predicted parental educational attainment as a proxy variable for a child's
socioeconomic background in the TIMSS and PISA samples, the socioeconomic
gradients of test scores in both 1999 and 2003 are estimated by following model:

12
,

where

2003

2003

7

refers to the standardized test score in subject s, which refers toeither the mathematics or science score of child i in year t, andis the parental

education predicted from the number of books and possession of items at home. The
coefficientindicates the socioeconomic gradient in 1999, the coefficientindicates the change of the socioeconomic gradient between 1999 and 2003, and thecoefficientindicates the change of the average test score of children with23

high-school-graduate parents. Since the independent variables include generate
regressors, the standard errors of OLS estimators are invalid for inferences. To address
this problem, the 95% confidence intervals are bootstrapped by a 500-times repetition.
Table 7 reports the regression results based on the TIMSS sample. Column (1)reports the results for mathematics test scores. In 1999, an additional year of predicted
parental education increases the average test score by 0.99 point. Thus the averagetest-score difference between a child with a 4-year-college-graduate parent and a childwith junior-high-school-graduate parents is about 7 points. In 2003, the coefficient foran additional year of predicted parental education increases up to 1.19 points. Thiscoefficient turns into about an 8.3-point difference between a child with acollege-graduate parent and a child with junior-high-school-graduate parents. Thispredicted gap is close to the actual gap, which is 10.1 points in 2003, as reported inTable 6. In this four-year period, the socioeconomic gradient increases by about 20%.
Column (2) reports the result for science test scores. An additional year ofparental education increases the average science test score by 0.92 in 1999 and by 1.18
in 2003. The approximately 30% increase of the socioeconomic gradient for the science
test score is more than its increase for the mathematics test score.
As a mechanism behind the increase of the socioeconomic gradient of test scores,
one might argue that the change occurred through the choice of schools. In response to
the reduction of school days, parents with more resources may well send their children
to schools that do not reduce the amount of teaching, by giving supplementary classes
or teaching more effectively in a limited amount of class time. If the correlation
between unobserved school quality and parental socioeconomic background becomesstronger because of a more significant sorting of children into schools, the OLS
24estimates of socioeconomic gradients would become larger in 2003 than in 1999. To
assess this possibility, the following school × year fixed-effects model is estimated toallow for the correlation between parental socioeconomic background and unobserved
school quality:

12
2003

where

,

2003

8

refers to the unobserved determinant of test scores that is common in school

j in year t. In this estimation, the coefficientsandare identified off thevariation of predicted parental background within a school in a specific year.
The estimation results of the school × year fixed-effects model are reported inColumns (3) and (4) in Table 7. All the estimated coefficients become slightly
attenuated, suggesting that the effect of parental background on academic achievement
is exercised through the choice of schools. The increase of the socioeconomic gradientof mathematics test scores is statistically significant, even after allowing for the school
× year fixed effects. The difference between the socioeconomic gradients of 2003 and1999 is still positive and statistically significant for mathematics test scores, but it is nolonger statistically significant for science test scores.
Table 8 reports the estimation results based on the PISA. An additional year of
education by either the mother or the father is associated with an increase of 10th graders’test scores by about 0.9 point in reading, mathematics, and science. The sizes ofcoefficients are comparable to the results based on the TIMSS. The estimated slopes in2003 become 20-30% steeper compared with the slopes in 2000. The negative25

coefficient for 2003 implies that the test scores of students with high-school-graduate
parents decrease by 4 points for reading and 5 points for mathematics and science in
2003. Girls score better in reading and score worse in mathematics than boys.
Allowing for school × year fixed effects attenuates the coefficient for parental
education, as reported in Columns (4) to (6), implying that students with better-educatedparents are selected in better schools. This attenuation is quite understandable because
the PISA targets 10th graders who study at high schools, whereas the TIMSS targets 8th
graders who study at junior-high schools. Only 6.9% of junior-high-school students
attend private schools in 2006 (Basic School Survey), and the majority ofjunior-high-school students attends local public schools that are not academicallysegregated. In contrast, public and private high schools select their students based onacademic performance measured by an entrance examination at the end of 9th grade andjunior-high school GPA. Therefore, high-school students in Japan are sorted into
schools based on their academic performance. This school sorting explains why asignificant part of the socioeconomic gradient of test scores is explained by school ×
year fixed effects.
Overall, students' academic performances depend on parental socioeconomic
backgrounds, as found in a previous study including Japan (Hojo and Oshio (2010)).
Furthermore, the slope becomes steeper after all Saturdays become school holidays in
2002. Combined with the evidence found in the previous section, the widening gap ofchildren’s study time by their socioeconomic background results in a widening
inequality of test scores among students with different socioeconomic backgrounds.

26VI. Local Average Treatment Effect of Study Time on Students’ Achievement
The findings that the reduction of school days decreases the study time ofchildren with weaker socioeconomic backgrounds and reduces their relative test scores
implies that the time spent in a classroom has a positive effect on academic achievement
among students with weaker socioeconomic backgrounds. Although previous studies
point to the importance of the quality of school education as a determinant of academic
achievement (Hanushek (2003) for example), there is limited evidence regarding theeffect of study time on academic achievement.8 This is presumably because of a serious
endogeneity of time spent on studying in the educational production function. The
reduction of school days creates an exogenous variation of study time and allows us toestimate the local average treatment effect of study time on academic achievement
among students with weaker socioeconomic backgrounds.
Since neither the JTUS nor test-score data contain both study time and academic
achievement in a sample, the idea of the two samples instrumental variable (TSIV)
estimator by Angrist and Krueger (1992) needs to be applied.
An equation that expresses the causal impact of study time on a test score isexpressed as:,

where y is test score,is study time, and9

is a vector of explanatory variables

that includes parental educational attainment, an indicator for girl, and an indicator for

8

A notable exception is Stinebrickner and Stinebrickner (2008), who estimate thecausal impact of study time on the academic achievement of liberal arts college students
using the possession of a video game by a randomly assigned roommate.
27

school-day reduction. Even with a data set that contains all relevant variables, the causal
effectcannot be consistently estimated because of a possible correlation between

and(i.e., students with higher ability study longer and score well). To deal withthis endogeneity, the study time is instrumented with an exogenous policy change that
affects students' study time, expressed by the following first-stage equation:

,

where

10

is the interaction of an indicator for school-day reduction and parental

educational attainment. The key maintained assumption here is thatwith u and v. Using the estimates
study time,is uncorrelated

and the TIMSS or PISA sample, the predicted

is obtained. Note that the TIMSS or PISA sample is used

to obtain the predicted value instead of the JTUS sample for the purpose of gaining
efficiency in the estimation, as articulated by Inoue and Solon (2010). The estimatedequation is:.

11This equation is estimated by OLS for the TIMSS sample and by IV for the
PISA sample, using the number of books at home as the IV. The 95% confidence
interval of the estimator is calculated by bootstrap with 500 repetitions, because thepresence of a generated regressor makes usual standard errors inconsistent.9
The sample of 9th graders is used to estimate equation (10), because the study
9

Inoue and Solon (2010) note that Murphy and Topel's (1985) method is the correct
method for obtaining standard errors, unless relying on the bootstrap.
28

time of 8th or 10th graders does not have sufficient variation so that the interaction termof parental education and after-policy change dummy is statistically significant. Eighthand 10th graders are used to estimate equation (11), however, because the test scores areavailable only among them. The change of the socioeconomic gradient of test scores
after the school-day reduction is expected to be larger for 9th graders than for 8th and10th graders, and thus the estimatedis interpreted as the lower bound of the causal

effect of study time on test scores.
Table 9 Column (1) reports the estimation result of equation (10) based on the
JTUS, which is the first-stage equation. The estimated coefficient indicates that thesocioeconomic gradient of study time becomes steeper by 6.76 minutes per day after
2002. This estimated coefficient is statistically significant with a p-value of 0.057.
Columns (2) and (3) report the estimation results of equation (11) for mathematics andscience scores based on 8th graders in the TIMSS. These estimation results indicate that
if a student studies 1 hour more every day, the mathematics score increases by 9.6
points. Similarly, one additional hour of study increases the science score by 8.4 points.
Considering the fact that the gap of mathematics test scores between students withjunior-high-school-graduate parents and 4-year-college-graduate parents is 10.1 points,
an additional one hour of study per day fills a significant amount of the gap between
disadvantaged and advantaged students.
Columns (4) to (6) report the OLS estimates of study time on test scores basedon the PISA. The coefficient in Column (4) implies that if a student studies 1 hour more
every day, the reading score increases by 2.4 points. Similar results are obtained for themathematics and science scores, as reported in Columns (5) and (6). One additionalhour of study per day increases the mathematics test score by 2.4 points and the science29

score by 1.8 points, while the 95% confidence interval marginally crosses 0.
The effects of study time on test scores are identified off the reduction of study
time of students with less-educated parents caused by the school-day reduction. Thus
the estimates are interpreted as the local average treatment effects among students with
less-educated parents. Therefore, the results imply that extending study time is
productive even among students with weak socioeconomic backgrounds. Some may
argue that letting socially disadvantaged students study longer is a waste of their timeand public funds, but this argument is not supported by empirical findings.

VII. Conclusion
This paper examined the heterogeneous responses of students' time use andacademic achievements to the increase of school holidays, using the all-Saturdays-off
policy implemented from 2002 in the Japanese compulsory education system.
The theoretical discussion articulated that the reduction of compulsory education
can affect study time and test scores heterogeneously, depending on family background.
Who is affected by the policy change and how they are affected depend on the way
study time and parental resources are combined in the production of students'
achievement.
The analysis of the time-use survey of 9th graders revealed that setting a
Saturday as an additional school holiday increases the socioeconomic gradient of study
time by about 80%. This increase of the socioeconomic gradient of study time translates
into an increase in the socioeconomic gradient of academic achievement of 8th and 10th
graders. The analysis of the TIMSS and the PISA revealed that the socioeconomic
gradient of test scores became 20-30% steeper in 2003 compared with 1999 or 2000 in
30

reading, mathematics, and science.
The results reported in this paper directly indicate that time-intensive
compulsory education suppresses the heterogeneity of children’s time use that depends
heavily on parental socioeconomic background. The results also imply that study time isa valuable input for academic achievement, even among socioeconomicallydisadvantaged children. Therefore, time-intensive compulsory education contributes tohomogenizing the academic achievement of children across the range of socioeconomic
backgrounds. Consequently, reducing the intensity of compulsory education is likely tomake the rich get richer and the poor get poorer through giving more discretion toparents on their children's time use. After all, schools are a leveling institution rather
than a labeling institution.

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33

Table 1 Descriptive Statistics of the Japan Time-use Survey Sample, 9th Graders

1996

2001

2006

Study (Minutes per Day)

434

463

458

Weekdays

517

540

556

Saturday

297

366

242

Sunday

148

190

192

Leisure (Minutes per Day)

254

228

220

Weekdays

196

174

156

Saturday

371

310

374

Sunday

435

403

388

Other activities (Minutes per Day)

752

750

761

Weekdays

728

726

728

Saturday

771

763

825

Sunday

856

847

860

Girl (%)

49

50

50

Head Education=9 (%)

24

16

14

Head Education=12 (%)

47

45

46

Head Education=14 (%)

5

8

10

Head Education=16 (%)

24

31

31

Female Headed (%)

10

11

15

Single Parenthood (%)

14

13

20

Mother's Employment (%)

28

29

25

Annual Income -39 (%)

18

20

25

Annual Income 40-59 (%)

23

21

22

Annual Income 60-89 (%)

33

32

32

Annual Income 90- (%)

25

25

18

7,645

4,852

4,140

Observations

Note: Household income is measured in 100,000 yen (approximately, $US1,000). Samplingweights are used. Study includes commute, study, and research. The commute time is included
in study time because it is a fixed time cost for studying. The average commute time was 47, 38,
and 34 minutes in 1996, 2001, and 2006, respectively. Leisure includes shopping, moving,
watching TV and listening to the radio, hobbies, sports, social activities, and associations.
Tertiary and other includes sleeping, personal care, eating, working, housekeeping, nursing,child rearing, rest, medical care, and other activities.

34

Table 2 Changes of Child's Study Time by Head's Educational Attainment Before andAfter All Saturdays Became School Holidays in 2002, 9th Graders, Minutes Per Day

Head Education(Head Education -12) ×2001(Head Education -12) ×2006
2001
2006
Girl
Female Headed
Single Parent
Mother Work
Annual Income 40-59
Annual Income 60-89
Annual Income 90Constant

(1)

(2)

(3)

(4)

Mon-FriSat

Sun

Daily Mean

5.80

6.63

4.84

5.34

(2.18)(2.38)(2.38)(1.90)

-1.42

-0.44

5.63

1.39

(3.59)(3.58)(3.61)(3.08)

1.41

9.85

8.31

7.00

(4.06)(5.04)(3.82)(3.53)

21.44

67.28

34.78

24.24

(7.85)(9.04)(8.42)(7.33)

35.74-60.09

37.87

16.05

(8.67)(12.87)(8.81)(8.31)

28.31

16.42

30.74

25.76

(7.09)(9.06)(7.14)(6.23)-1.59-29.65

-6.15

7.13

(20.88)(36.73)(22.84)(18.51)-8.89-13.24

7.96

-8.31

(18.48)(34.15)(21.17)(16.40)

-8.94

-9.05

-11.50-9.70(7.97)(11.56)(7.72)(6.90)

-1.32

1.17

-5.03

6.32

(10.74)(12.02)(10.17)(9.75)

20.36

22.31

20.27

29.78

(10.04)(12.92)(10.08)(9.35)

32.02

58.58

38.26

34.00

(11.47)(13.79)(11.74)(10.43)421.01

191.0359.74

338.93(27.79)(29.77)(29.04)(24.04)

2

0.03

0.09

0.05

0.02

N

6,226

5,231

5,180

16,637R

Note: Sample includes age 15 if born between April and September, or age 14 if born between
October and March. All estimations are weighted by sampling weights. Standard errors are inparentheses. Household income is measured in 100,000 yen (approximately, $US1,000).

35

Table 3 Changes of Child’s Study Time on Saturday among 9th Graders, 2001 and 2006,

3rd Saturday Becomes Holiday from 2002.
(1)

(2)

(3)

(4)

2nd Saturday

3rd Saturday

2nd Saturday

3rd Saturday

12.39

0.24

11.17

0.44

(5.20)(3.01)(4.59)(2.95)

1.38

16.65

-1.28

17.45

(7.70)(6.65)(6.65)(5.28)-38.60-185.07--(22.75)(13.65)Prefecture × Year Fixed Effects

No

No

Yes

Yes

R2

0.06

0.21

0.72

0.78

N

1,119

1,725

1,119

1,725

Head Education(Head Education -12) × 2006
2006

Note: Sample includes age 15 if born between April and September, or age 14 if born between
October and March. All estimations are weighted by sampling weights. Standard errors are inparentheses. All specifications include a constant and dummy variables for girl, dummy
variables for female-headed household and single parenthood, and 3 household annual income
categories (4-5.99, 6-8.99, 9- million yen), but coefficients are not reported.

36

Table 4 Changes of Child's Time Use by Head's Educational Attainment Before and After

All Saturdays Became School Holidays in 2002, 9th Graders, Minutes Per Day

Head Education(H Education -12) × 2001
(H Education -12) × 2006
2001
2006
R

2

N

(1)

(2)

LeisureOther activities

-4.26

-1.09

(1.60)(1.35)

-3.11

1.72

(2.44)(2.07)

-7.30

0.30

(2.56)(2.70)-20.91-3.33(5.99)(5.04)-24.73

8.67(6.69)(5.69)

0.03

0.00

16,637

16,637

Note: The same note applies as in Table 3.

37

Table 5 Changes of Child's Time Use, Minutes Per Day, Daily Mean, Prefecture × Year

Fixed Effects Included, 9th Graders

Activity
Head Education(Head Education -12) ×2001(Head Education -12) ×2006
R

2

N

(1)

(2)

(3)

Study

LeisureOther activities

5.78

-4.46

-1.32

(1.84)(1.59)(1.38)

1.60

-2.75

1.15

(2.99)(2.40)(2.13)

7.13

-7.31

0.18

(3.39)(2.61)(2.58)

0.80

0.62

0.96

16,637

16,637

16,637

Note: The same note applies as in Table 3.

38

Table 6 Descriptive Statistics of the TIMSS and the PISATIMSS, 8th

PISA, 10th

Graders

Graders

1999

2003

2000

2003

50.2

50.7

49.6

50.1

Parent 4-year-college Graduate

-

53.7

Parent junior-college Graduate

-

50.1

Parent high-school Graduate

-

47.6

Parent junior-high-school Graduate

-

43.6

50.2

50.7

49.6

50.1

Parent 4-year-college Graduate

-

53.2

Parent junior-college-Graduate

-

50.5

Parent high-school-Graduate

-

47.9

Parent junior-high-school Graduate

-

45.8

Standardized Reading Score

-

-

49.9

50.1

Parent 4-year-college Graduate--

Parent junior-college-Graduate--

Parent high-school-Graduate--

Parent junior-high-school Graduate

-

-

49.6

51.9

51.0

51.8

0-10

13.8

12.2

1-10

11.2

9.9

11-25

19.3

21.2

11-50

25.2

11.8

26-10031.2

32.1

51-10019.9

32.6

101-200

18.1

16.8

101-250

22.4

18.5

200-

17.5

17.6

251-500

12.3

17.4

501-

8.9

9.7

Standardized Math Score

Standardized Science Score

Girl (%)# of Books at Home (%)

Possession of Item at Home (%)
Calculator

99.3

99.2

Computer

63.9

53.7

Computer

52.5

83.0

Internet

36.3

60.5

Desk

97.5

96.4

Child’s room

83.2

88.9

Dictionary

99.4

99.5

4-year College

-

44.4

-

2.2

Junior College

-

17.6

-

9.3

High School

-

35.8

-

30.0

Head's Years of Education (%)

39

Junior-high School
N

-

2.2

-

58.4

4,542

3,429

4,505

4,641

Note: Mathematics and science scores in the 2000 PISA are available for 2,924 and 2,914
students, respectively. Computer possession in the 2000 PISA includes those who answered thatthey had two or more computers at home.

40

Table 7 Socioeconomic Gradient of TIMSS Scores in 1999 and 2003, 8th Graders,Standardized Mathematics and Science Scores, Mean = 50, Standard Deviation = 10

Subject

(1)

(2)

(3)

(4)

Mathematics

Science

Mathematics

ScienceParent Education

0.99

0.92

0.88

0.84

(0.77,1.11)(0.75,1.05)(0.66,1.00)(0.67,0.98)

0.20

0.26

0.13

0.18

(0.03,0.42)(0.08,0.46)(-0.04,0.36)(0.00,0.37)

0.08

-0.01

-

-

(-0.52,0.48)(-0.62,0.40)

-0.39

-1.11

-0.48

-1.18

(-0.79,-0.02)(-1.48,-0.69)(-0.87,-0.10)(-1.57,-0.76)

38.13

39.38

-

-

(36.51,41.09)(37.66,41.84)

School × year fixed effects

No

No

Yes

Yes

R2

0.06

0.06

0.97

0.97

N

9,182

9,182

9,182

9,182

(Parent Education - 12)
× Year 2003
Year 2003
Girl
Constant

Note: Bootstrapped 95% confidence intervals with 500 repetitions are reported in parentheses.
Parent education is higher educational attainment by either mother or father. Parental education
is predicted by the number of books at home, as well as possession of a calculator, a computer, adesk, and a dictionary at home.

41

Table 8 Socioeconomic Gradient of PISA Scores in 2000 and 2003, 10th Graders,Standardized Reading, Mathematics and Science Scores, Mean = 50, Standard Deviation =10

Parent Education

(1)

(2)

(3)

(4)

(5)

(6)

Reading

Math

Science

Reading

Math

Science

0.90

0.90

0.94

0.25

0.26

0.28

(0.71,1.08)(0.68,1.12)(0.70,1.12)(0.10,0.37)(0.11,0.42)(0.07,0.40)

0.26

0.25

0.20

0.23

0.05

0.11

(0.02,0.47)(-0.02,0.49)(-0.02,0.47)(0.05,0.40)(-0.12,0.23)(-0.04,0.34)

-0.38

-0.33

-0.31

-

-

-

(-0.69,0.25)(-0.67,0.38)(-0.67,0.43)

3.22

-0.47

0.39

2.12

-1.46

-0.82

(2.83,3.62)(-0.92,-0.01)(-0.07,0.84)(1.72,2.47)(-1.89,-1.05)(-1.26,-0.47)

37.33

39.30

38.24

-

-

-

(35.11,39.58)(36.44,41.91)(36.08,41.10)

No

No

No

Yes

Yes

Yes

R2

0.09

0.07

0.07

0.47

0.54

0.48

N

9,372

7,621

7,611

9,372

7,621

7,611

Parent Education-12
× Year 2003
Year 2003
Girl
Constant
School × year
fixed effects

Note: Bootstrapped 95% confidence intervals with 500 repetitions are reported in parentheses.
Parent education is higher educational attainment by either mother or father. Parental education
is predicted by the number of books at home, possession of computer, internet, and child’s own
room.

42

Table 9 Effects of Study Time on TIMSS and PISA Scores, Two Sample TSLS Estimation

SampleDependent Variable

(1)

(2)

(3)

(4)

(5)

(6)

JTUS

TIMSS

TIMSS

PISA

PISA

PISA

Study TimeMath Score

Science Score

Reading

Math Score

Science Score

Score
Study Time

-

0.16

0.14

0.04

0.04

0.03

(0.04,0.70)(0.03,0.60)(0.00,0.07)(-0.00,0.07)(-0.00, 0.07)

7.38

2.17

2.21

0.55

0.56

0.68

(1.89)(-2.38,3.81)(-2.23,3.68)(0.13,1.01)(0.07,1.10)(0.12,1.11)

22.65

-

-

-

-

-

12.55

-4.98

-4.62

-0.87

-0.80

-0.69

(8.34)(-13.64,-1.78)(-12.56,-1.59)(-1.36,-0.04)(-1.37,0.16)(-1.30,0.18)(Parent Education - 12)

1.61

-

-

-

-

-

× 2001(3.07)(Parent Education - 12)

6.76

-

-

-

-

-

× 2006(3.56)

Girl

26.63

-4.54

-4.83

2.18

-1.47

-0.41

(6.27)(-18.45,-1.13)(-12.56,-1.59)(1.26,3.12)(-2.63,-0.36)(-1.62,0.60)(in minutes per day)Parent Education
Year 2001(7.33)
After 2002

R2

0.02

0.06

0.06

0.09

0.07

0.07

N

16,637

9,182

9,182

9,372

7,621

7,611

Note: The 9th graders of the JTUS 1996, 2001, and 2006 are used to estimate Column (1). The 8th graders of the
TIMSS 1999 and 2003 are used to estimate Columns (2) and (3). The 10th graders of the PISA 2000 and 2003 areused to estimate Columns (4) - (6). Standard Errors are reported in parentheses in Column (1). Bootstrapped 95%
confidence intervals with 500 repetitions are reported in parentheses for Columns (2) and (3). Sampling weights areused for all regression models.

43

Figure 1 Students' Mean Study Time and Socioeconomic Background, 1996, 2001, 2006,

9th

Graders

530

545

Minutes per Day
200
400

522

584

405
299

260
175

Head Junior HS

Head College

Head Junior HS

Head College

1996

2001

1996

2001

2006

2006

Average
238

248

181
154

146

411

435

463

498

505

406

0

131

Minutes per Day
250
500

Sundays0

Minutes per Day
150
300

333

328

0

489

Saturdays
567

0

Minutes per Day
200 400 600

Weekdays

Head Junior HS

Head College

Head Junior HS

Head College

1996

2001

1996

2001

2006

2006

Note: All means are calculated using sampling weights. Study includes commute, study, andresearch. The commute time is included in study time because it is a fixed time cost for studying.
The average commute time is 37, 38, and 34 minutes in 1996, 2001, and 2006, respectively.

44