ISS Discussion Paper Series
F-179
March 2016

Trends in Socioeconomic Achievement Gap in Japan:
Implications for Educational Inequality

Izumi Mori
The University of Tokyo

ABSTRACTJapan underwent rapid economic expansion as well as economic development after theSecond World War. However, since the 1990s, the country faced long recession and theincrease in social and economic inequality. During such time, education policy also
shifted toward favoring differentiated curriculum and choice-based system. The purposeof this paper is to reveal how the relationship between family background and students’
academic achievement has changed over time in Japan in the recent decade. Using theThird International Mathematics and Science Study (TIMSS), we applied hierarchicallinear modeling and examined the proportion of between-school variance in academic
achievement as well as the effect of family background on achievement over time. As aresult, we found evidence on increase educational inequality in Japan during this decade.
This paper suggests that changes in educational policy, such as ensuring equal
opportunity and providing adequate resources to schools may be necessary to reducesuch growing educational inequality in Japan.

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BACKGROUND
Japan underwent major economic development after the Second World War,with an average GDP growth of 10% during the 1960s and subsequent higher growth in
GDP until the 1980s. During the postwar decades, Japan also experienced a dramatic
educational expansion at all school levels. High school enrollment rate increased from
51.5% in 1955 to over 90% in the 1970s, and tertiary enrollment rate increased fromaround 50% in the 1980s to over 70% in 2000 (MEXT 2015).
More recently, since the late 1990s, Japan experienced a radical neoliberal
restructuring of its social and economic systems due to changes in its politicalleadership. Amid such transformation, the country faced a long recession and rise in
unemployment rate (MIC 2015). Moreover, a rising Gini coefficient over the period
suggested growing income inequality (MHLW 2008). In terms of people’s perceptions,
recent survey showed that young Japanese adults identified themselves as being in a
relatively low class within the whole Japanese society (Shirahase 2010).
These social and economic changes corresponded with changes in educational
policy over time. The education system in Japan was once known for its egalitariancharacteristics that provided equal opportunities for all students (Cummings 1982). In
1987, the U.S. Department of Education introduced the success of Japanese education ina book titled Japanese Education Today (Leestma & Dorfman 1987) to provide lessons
for the United States. However, neoliberal and market-oriented educational reform inJapan since the late 1990s introduced school choice, ability grouping, and accountability
in the mainstream school system, which seemed to have caused the breakdown of
postwar egalitarianism in Japanese education (Fujita 1997). For example, Figure 1
shows that the number of municipalities introducing school choice in Japan dramatically3increased between 1997 and 2012, both for elementary schools and middle schools.

Figure 1.

The Number of Municipalities that Introduced School Choice by Year

Source: MEXT (Ministry of Education, Japan), 2013

The Japanese government also changed the national curriculum standards
towards more yutori (relaxation or latitude) over the past decades, aiming to address
problem-solving skills and individuality as they reduced curriculum contents and schooldays. However, since the revision of national curriculum guidelines in 2008, there has
been a quick return to a more emphasis on academically-focused curriculum.
Summarizing the recent key arguments on educational reforms in Japan,
Takayama (2007) described the Japanese education system in the late 1990s to the
2000s as the collapse in “educational pipeline system,” and indicated a widening
socioeconomic gap in academic achievement and learning motivation based on previous
studies (Honda 2005; Kariya 2001; Kariya et al. 2002; Yamada 2004).

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Under such circumstances, a question arises as to what consequences result
from these socioeconomic and educational changes with respect to social stratificationand educational inequality. This paper addresses this question by examining whether
and how the socioeconomic gap in academic achievement in Japan has changed overtime during the past decade. To this end, we examined the trends in between-school
differences in student achievement and the relationship between socioeconomic
background and student achievement. We used data from the Trends in International
Mathematics and Science Study (TIMSS) and focused on the most recent five cohorts(1995, 1999, 2003, 2007, and 2011) of eighth-grade students in Japan.

PREVIOUS STUDIES
There has been an increasing number of Japanese studies that revealed therelationship between family background (SES) and academic achievement. For example,
Kariya and his colleagues (2002) compared the district achievement data in Westernareas of Japan between 1989 and 2001 and showed that academic achievement ofelementary and middle school students has not only declined but also widened its gap
over the years. The authors showed that family background and use of private
supplementary tutoring are among the possible reasons for such inequality inachievement. They also suggested that changes in the national curriculum standards
toward yutori since 1998 caused wealthy families to rely on private tutoring, whichconsequently increased socioeconomic gap in achievement.
Mimizuka and his colleagues (2008) collected and analyzed data from students,
parents, and school principals from three major areas in Japan (metropolitan area,
suburban area, and rural area) from 2007 to 2008. Sponsored by the Ministry of5

Education in Japan, their study analyzed whether parental education, occupation,
household income, and expenditures on out-of-school tutoring had any influences on
students’ academic achievement. According to their analysis, they found substantialsocioeconomic gap in academic achievement.
Although these studies offer important evidence on the relationship between
SES and academic achievement in recent Japanese society, their findings are somewhat
limited in that these studies relied on data that are not nationally representative,
precluding generalization for a much larger population. In addition, neither of thesestudies documented the trend in the relationship between SES and academic
achievement by including both the period before and after 2002, a year that introduced
significant deduction in curriculum time. To better understand the trend in educational
inequality over time in Japan, we should analyze nationally representative data withrepeated cross-sectional design including cohorts from the most recent decade.
To summarize, this study seeks to offer empirical evidence on the growing
educational inequality in Japan, as it seeks to understand the consequences ofsocioeconomic and educational changes in recent Japanese society.

METHODOLOGY
Data and Sample
In this study, we seek to examine the trends in between-school differences instudent achievement and the relationship between socioeconomic background andstudent achievement. To this end, we used data from the Trends in International
Mathematics and Science Study (TIMSS) and focused on the most recent five cohorts(1995, 1999, 2003, 2007, and 2011) of eighth grade students in Japan. TIMSS is6conducted every four years and measures student performance in mathematics andscienceamong

fourthandeighth

grade

students

acrosssocieties 1 .

Asnationally-representative student achievement data is hardly available for public use inJapan, TIMSS is an important source of achievement data that provides a wealth of
information on student and school characteristics. We focused on eighth graders because
we regard the educational stage as crucial for observing educational inequality, locatedtoward the end of compulsory education period. In addition, the assessment was notconducted for fourth graders in 1999. We focused on mathematics because someprevious studies suggest that socioeconomic differences in academic achievement arestronger for math than for science (e.g., Sudo 2013). The number of samples wasapproximately 150 for schools and 4500 for students for each year.

Main Variable of Interest
Math achievement. TIMSS measures mathematics achievement by fiveplausible values with a scale having an international mean of 500 and an international
standard deviation of 100. The five plausible values were simultaneously used togenerate correct means and standard errors.
Socioeconomic background. TIMSS provides information that can be used as aproxy for family socioeconomic status (SES), including (1) father’s education, (2)mother’s education, (3) number of books in the home, and (4) home educational
resources. These four items were integrated using principal component analysis to

TIMSS is intended to measure whether students’ academic learning ability is in linewith the school curriculum, while PISA (Programme for International Student
Assessment, targeting 15-year-old students) is intended to measure students’ applicationof their academic knowledge and skills to real life.

1

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construct a proxy for a family’s socioeconomic status (SES). By aggregating thestudent-level SES measure at each school, school-mean SES measure was also created.
Note that since data on parental education in Japan in 1999 are missing, for themultivariate trend analysis, we only used the number of books and the index of homeeducational resources to construct a proxy for SES 2 . The number of books iscategorized as follows: (1) 1 = 0-10, (2) 2 = 11-25, (3) 3 = 26-100, (4) 4 = 101-200, and(5) 5 = more than 200 books. As for an index of home educational resources, a total of 8
items (computer, desk, internet connection, calculator, dictionary, telescope, terrestrial
globe, and picture book) were summed up 3 . Missing values were deleted for themultivariate analysis, although supplementary analysis showed that the results wereconsistent when multiple imputation method was applied.

Analytic Strategies
To examine the trends in the relationship between SES and student
achievement, we used a series of Hierarchical Linear Models (HLM). HLM was chosenover ordinary least squares (OLS) regression to address the nested nature of TIMSS data
(Raudenbush & Bryk 2002). We first estimated the fully unconditional model for Japan,
which contained only the dependent variable (math achievement) with no covariates.
From this model, we obtained the proportion of between-school level variance out of
total variance. The basic model was specified for each cohort year as follows:

We also conducted analysis with SES including fathers’ and mothers’ education for
2003-2011, years which these parental education variables are available. The results
were largely consistent with the results presented in this paper.
3
The variable Internet was not available for 1999.
2

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Level 1 model:(Math achievement)ij = b0j + b1j (SES)ij + rij ,where j refers to the school and i refers to the students sampled from school j; b0j is theaverage math achievement in school j; b1j is the effect of family SES on mathachievement in school j; and r1j ~ N (0, σ2) is the variability of students within school j.
The SES variables was centered on the grand mean.

Level 2 model:
b0j = γ00 + μ0j ,where γ00 is the grand mean of math achievement and μ0j ~ N (0, τ00) is the error termwith τ representing the variance between schools.

RESULTS
Descriptive Results
We first examined the trends in mathematics achievement gap between 1995
and 2011. We used all five plausible values for this descriptive analysis. Between 1999
and 2003, there was a statistically significant drop in the average math achievement.
Figure 2 shows a visual representation for this result. When we shed light on the top
10% and the bottom 10% of the distributions, its disparity seemed to have widened tosome degree over the years.

9Figure 2. Descriptive Statistics for Mathematics Achievement

Variance among Schools
Table 1 shows the between-school variance in math achievement from 1995 to2011. The results showed an increase in the proportion of between-school variance overthe past decade. In 1999, approximately 8% of the variance in math achievement was
attributable to the school level. In 2007, the corresponding proportion wasapproximately 19%. Figure 3 is the visual presentation of the result same as in Table 1.

Table 1. Proportion of Variance between Schools in Mathematics Achievement,
1995-2011
Between-school variance
Within-school varianceTotal variance
ICC
Student N
School N

1995
738.825313.126051.94
12.21
5141
151

1999
502.465732.556235.02
8.06
4745
140

10

2003
868.375360.406228.77
13.94
4856
146

2007
1408.55
5873.257281.80
19.34
4312
146

2011
1075.00
6080.457155.45
15.02
4414
138

Figure 3. Proportion of Variance between Schools in Mathematics Achievement,
1995-2011

Trends in the Relationship between SES and Student Achievement
Table 2 shows the trends in the relationship between socioeconomic
background and math performance in Japan. We excluded year 1995 as there is no
measures for SES for this year. The results showed that the magnitude of the impact of
SES has increased over the years in Japan. For school-mean SES, a unit increase wasassociated with 31 score point increase in students’ math achievement in 1999. In 2007,
it has increased to 39 points, suggesting that between-school difference has widened
over the period of 1999 and 2007. After 2007, the coefficient for school-mean SES has
slightly dropped in 2011, which suggests a possible influence of recent counter-yutorireform movements. For student SES within each schools, a unit increase in student SES
was associated with 17 score point increase in math achievement in 1999. Thereafter, it
was associated with 24 score point increase in math achievement in 2003, 27 score point
increase in 2007, and 28 score point increase in 2011. These trends suggest that the11

impact of within-school SES on student achievement has grown over the recent decadein Japan, suggesting a growing educational inequality among students and theirfamilies.

Table 2. Trends in the Relationship between Socioeconomic Background and Math
Performance, 1999-2011

Fixed EffectIntercept
School-mean SES
Urbanicity
School environment
School resources
Yearly lesson hourStudent SES
FemalePrivate school
Private school*School-mean SES
Random Effect
Uj
Rj

1999
Coef.
576.97 ***31.29 ***
1.91
-1.55
-0.19
1.25
17.01 ***
-3.39
50.59 ***
19.38

2003
Coef.
573.28 ***38.80 ***
0.84
5.92 ***
3.67 *
1.80
24.60 ***
-2.67
-39.41 *111.55 ***

2007
Coef.
572.40 ***39.60 ***
1.73
9.47 ***-2.30
0.28
27.29 ***
-1.54
15.62
52.95

2011
Coef.
573.64 ***37.51 ***
7.69
4.04
0.43
6.63 *
28.53 ***
-5.26 +
-13.9354.87 *

11.98
72.74

12.44
68.53

14.06
69.27

16.82
71.44

Table 3 examined the similar model by adding year as dummy variables andinteraction terms with SES (both at student and school levels). It showed that the effects
of school-SES as well as student-SES were indeed larger compared to the base year of
1999.

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Table 3. Trends in the Relationship between Socioeconomic Background and Math
Performance (Year added as dummy) , 1999-2011

School-mean SES
Private SchoolStudent SES
Year 2003
Year 2007
Year 201103*School-mean SES
07*School-mean SES
11*School-mean SES
03*Student SES
07*Student SES
11*Student SES
Intercept
School-level varianceStudent-level variance

Model 148.47 **
21.08 **
-11.17 **-8.16 **
-11.14 **Model 240.39 **
57.38 **
21.08 **
-11.40 **-9.31 **
-12.90 **580.37 **392.045335.11577.43 **177.695336.04

Model 326.92 **
56.52 **
17.38 **
-11.27 **-9.06 **
-12.65 **12.40 *
23.56 **
14.26 *
3.33 *
4.49 **
7.52 **
577.57 **176.795324.42** <.001, * <.05
Female and urbanicity are also controlled within each model.

To facilitate a more meaningful interpretation, Figure 4 graphically depicted thetrend in the relationship between socioeconomic background and student achievementin Japan over the years. It shows that the absolute level of mathematics achievement
was the highest in 1999 and subsequently declined, and that the slope for the effect of
SES on achievement became steeper from 1999 to 2011.

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Figure 4. Trends in the Relationship between Socioeconomic Background andMath Performance

DISCUSSION
Despite educational expansion in the postwar Japan and the role of education to
support social mobility, concern on growing educational inequality is prevalent incontemporary Japanese society. To offer empirical evidence on the growing educational
inequality in Japan, this study assessed the trends in the relationship between
socioeconomic background and student achievement in Japan, using TIMSS data foreighth-grade students. The data showed increasing variance in student achievement
among schools, as well as the increasing influence on socioeconomic background on
student achievement. The evidence suggests that the achievement gap between students
from advantageous and disadvantageous home backgrounds has indeed grown overtime.
While this study does not examine the causal link between specific social and14educational reforms and the widening socioeconomic gap in student achievement, we
speculate that various social and educational transformations in Japan since the late
1990s, including the neoliberal restructuring of social and economic systems,
market-oriented educational reform in Japan, and reduced instructional hours andcurricular contents, may be responsible for growing educational inequality that we
found in our analysis. Based on the above empirical result, this paper suggests thatchanges in educational policy, such as ensuring equal opportunity and providing
adequate resources to schools, may be necessary to reduce such growing educational
inequality in Japan.

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