The Effect of Private Junior High School Attendance on Educational Expectations
and Academic Self-Concept in Japan*
ISS Discussion Paper Series
F-188
September 2017
Izumi Mori
Institute of Social Science
The University of Tokyo
Email: i-mori@iss.u-tokyo.ac.jp

Key words: Private school, educational expectation, academic self-concept,
propensity score matching, Japan*The earlier version of this paper was presented at the summer meeting of the ResearchCommittee on Social Stratification and Mobility (RC28) of the International Sociological
Association held in Columbia University, New York, August 7-10, 2017. The author
acknowledges support from the Japan Society for the Promotion of Science (JSPS)
KAKENHI Grant Number 26780482. The revised Japanese version of this paper is to appear
at the Volume 101 of the Journal Educational Sociology [Kyouiku Shakaigaku Kenkyuu]
edited by the Japan Society of Educational Sociology.

1

Abstract
This study examined the effect of private junior high school attendance on
students’ educational expectations and academic self-concept in Japan using
propensity score matching as the methodology. The study used the 8th gradestudent data from 2001 TIMSS (Trends in International Mathematics and Science
Study) in Japan. The results suggest that advancement to a private junior highschool makes it easier for students’ educational expectations to increase whencompared to students with similar characteristics attending a public junior highschool. Specifically, it is easier for students of lower SES to experience thisincrease in educational expectations. Furthermore, advancement to a private juniorhigh school makes it easier for students’ academic self-efficacy to decrease
compared to students with similar characteristics attending a public junior highschool. Among the students in private junior high schools, those withcomparatively high academic achievements are more likely to experience a
decrease in their academic self-efficacy. These results together suggest that private
junior high schools in Japan have both positive and negative academic impacts,
and that these effects may differ by student characteristics, especially favoring
students of lower socioeconomic status.

21. Introduction
Since the latter half of the 1980s, an increasing number of students in Japan
have been entering private junior high schools. According to the School Basic
Survey of the Ministry of Education, the percentage of students enrolled at private
junior high schools was approximately 3% in the early 1980s, but increased to over
5% in the mid-1990s and to more than 7% by 2000.
Indicated as background for this trend is an increase in the number ofhouseholds choosing private schools consequent to a change in public schools toa five-day schedule (while private schools continue to hold classes on Saturdays)(Takeuchi et al. 2006). Further reasons include parental anxiety about the change
to “yutori education” (offering a reduced number of curriculum hours andcontent) in public schools resulting from the revision of the Curriculum
Guidelines by the Ministry of Education, Culture, Sports, Science andTechnology in 2002 (Nishimura 2006).
In the academic fields of educational sociology and economics, the topic
of selecting private schools is frequently argued in relation to the socioeconomic
background of households. Parental education and occupational status as well ashousehold income are repeatedly identified as characteristics determining which
students will choose private schools (Kataoka 2009; Kobari 2004; Tachibanaki
and Matsuura 2009). Furthermore, regarding the work of mothers, full-time
homemakers are indicated as being more suited for providing the emotional
support and dedication of time required from parents of students who will sit forthe private junior high school entrance exams (Hirao 2004; Tachibanaki andMatsuura 2009). The tendency to select a private school is especially strong inurban areas, for both junior high and elementary school examinations (Kataoka2009). In terms of academic abilities, on average, the higher a student’s grades,
the more likely that student will desire to attend a private school (Hida 1993).
While the background of students proceeding to private junior highschools has thus become clearer, Japanese research is limited on the effects
stemming from what occurs after a student has entered a private junior highschool. One exception is Nishimaru (2008), who reported that educational
attainment more readily increases for students who attend national or private
3junior high schools. This was attributed to the continued curriculum throughjunior high school to senior high school at the same institution, eliminating theneed to sit for a separate high school entrance exam. Tsumura, Nishimaru, andOda (2011) found that among recent cohorts born from 1971–1980, the effects on
the years of educational attainment were significant for persons who attended aprivate junior high school. However, these reports focused on the effects on
educational attainment; therefore, the impacts on students during their enrollment
in private junior high schools remain unclear. Consequently, this paper examines
the impacts on students' educational expectations (i.e., what level of schoolingthey expect to achieve) and academic self-concept as effects observed during
enrollment at a private junior high school.
The merits of attending a private junior high school are promoted inmagazines and at juku (private preparatory schools where students receive special
training for entrance exams). Furthermore, private junior high schools have an
established positive public image. Parents urge their children to attend private
schools based on their expectations regarding the positive aspects including
unique educational policies, high public esteem, and the fact that attendance
makes it easier for a student to advance to university (Hida 1993). However, there
are still unanswered questions regarding educational expectations and academic
self-concept. Compared to equivalent-level students in public schools, dostudents at private junior high schools truly experience the effects they hope for?
2. Research on the Effects of Private Schools
While Japanese research has not focused on the effects of private schools,
since the 1980s, many studies have been published overseas, especially in the
United States. The contemporary debate continues on various topics including
methods of analysis and interpretation. Using the High School and Beyond(HS&B) survey, Coleman and his colleagues claimed that enrollment at private
high schools (especially in Roman Catholic high schools) had certain effects interms of scholastic abilities and advancement to university (Coleman et al. 1982).
From the following year and thereafter, using longitudinal data from HS&B,
studies criticizing Coleman's analysis methods and findings were published4

(Alexander and Pallas 1983; Willms 1985). In response, Coleman's groupreanalyzed the data by utilizing the characteristics of longitudinal data, and again
found certain effects due to enrollment at private high schools. They especiallyhighlighted major impacts on the scholastic abilities of students from householdsof low socioeconomic status (SES) (Coleman and Hoffer 1987). Using the samedata, a further study found that enrollment at Catholic high schools was especiallyeffective for minority students, specifically African-American students with low
household income (Bryk et al. 1993). Results of an analysis of PISA (Programme
for International Student Assessment) by the Organisation for Economic Cooperation and Development (OECD) indicate that in multiple nations, it ispossible that enrollment in a private high school has positive effects on academic
performance (Dronkers and Robert 2008).
Meanwhile, other studies have not determined such positive effects forprivate schools, including the National Assessment of Educational Progress(NAEP) for elementary and junior high students in the United States and the Early
Childhood Longitudinal Study, Kindergarten Cohort (ECLS-K), which tracks
students from preschool through middle school. One study claimed that publicschools have a greater impact on scholastic abilities when analyzed while
controlling a plethora of school-level factors (Lubienski and Lubienski 2014).
Thus, at least in the research conducted outside Japan, there are mixed
findings regarding the effects of private schooling and the extent of these effects
when they do exist. A general statement of such effects is difficult, especiallysince the survey periods, levels of education focused on, and analysis methods
vary. Even if the effects of private schools exist, their extent may be minimal(Alexander and Pallas 1983). However, when viewed from the perspective ofheterogeneous effects, private schools (especially Catholic high schools) areconsidered as having stronger effects for students who come from householdswith non-affluent socioeconomic backgrounds, and major evidence opposing
these findings has yet to emerge (Bryk et al. 1993; Coleman and Hoffer 1987).
The present paper considers these findings in its investigation of the effects ofprivate junior high schools in Japan.

5

3. Previous Studies on Educational Expectations and Academic Self-Concept
3.1. Mechanisms related to Educational Expectations
As effects of private junior high schools, this paper focuses on educational
expectations and academic-related self-concept. Until now, many studies haveidentified a strong relationship between educational expectations and actual
educational attainment. Numerous prior studies focusing on the mechanisms thatshape such expectations exist, both abroad and in Japan (Fujita 1979; Sewell et al.
1969). Prior research presented the idea that educational expectations are formed at
an early period and impacted by family SES including parental education,
occupation, and household income. Other studies clarified that factors related tosocial stratification do not have fixed effects, but are changeable as students progress
through their school years and encounter various forms of guidance, organizational
culture, academic “tracks,” and the influence of teachers and fellow students(Andrew and Hauser 2011; Bozick et al. 2010; Nakamura 2010; Ojima 2001). In
research using panel data in the United States, 40% of the students surveyed madean unchanging decision regarding educational expectations during their school years
from the 4th grade in elementary school to the 11th grade in high school (year 2 ofsenior high school in Japan). The remaining 60% of surveyed students changed theireducational expectations during the same academic years (Bozick et al. 2010).
Prior research also determined that students from non-affluent households
more readily change their level of educational expectations. According to Bozick etal. (2010), students with higher SES are sent signals from surrounding others thatuniformly encourage them to advance to university. In this case, these students’ levelof educational expectations is easily fixed at an early period. Conversely, students
with lower SES are not subject to similar uniform messages. Furthermore, thescarcity of their possessed resources readily changes their level of educational
expectations based on the influences of their surroundings. Applying these findings
to the present study, I hypothesize that among the students attending private juniorhigh schools, those with lower SES are more likely to change their educational
expectations. As such, I hypothesize that students with lower SES—surrounded byfellow students with high expectations—are more likely to experience an increased
level of educational expectations, because of these novel stimulations. Conversely,6

it is possible that the level of educational expectations of students with higher SES
is more difficult to change, despite attending a private junior high school. The
following hypotheses summarize the abovementioned points:
Hypothesis 1: Students are more likely to have higher educational expectations if
they attend a private junior high school (compared to students with similar
characteristics who attend a public junior high school).
Hypothesis 2: Students with lower SES are especially more likely to have higher
educational expectations if they attend a private junior high school.
3.2. Mechanisms of Academic Feelings of Self-Efficacy
The subjective index of students’ academic self-concept has been employedin many studies in the field of educational psychology. In this and other fields, theacademic self-concept is noted as an important factor impacting academic
achievement and educational attainment. If students have self-confidence and astrong motivation regarding their schoolwork, such feelings are not only importantin themselves, but also related to desirable study habits and improved scholastic
abilities, all of which likely affect educational attainment.
Thus, feelings of academic self-efficacy can be defined as students showing
confidence that when placed in a certain situation, they have the abilities to achieve
their goals. This self-efficacy is not only due to inherent individual differences, but
has aspects that are changeable based on environmental effects. The consciousnessof self is formed socially by comparing oneself to surrounding others. This has
previously been explained in sociology in the context of the “relative deprivation”
caused by comparisons with a reference group (Ishida 2015; Merton 1957). This
framework has been introduced into the context of education-related research suchas the “Big Fish Little Pond” effect. These studies have found that one's selfconsciousness has aspects that are relatively determined by comparing the self to areference group (Furuta 2016; March 1987; Toyama 2008).
One environment where self-consciousness is especially vulnerable to such
effects is when the provided guidance divides students into groups based on their7

abilities. For example, when student abilities are evaluated based on the schools theyattend, all students affiliated with a high-ranking school are automatically includedin the student group having high academic abilities. Thus, if no students aroundoneself have low academic abilities—that is, having no one with whom to compare
oneself boosts one’s own feelings of self-efficacy—it is easier for self-efficacy todecrease (Chmielewski et al. 2013; Marsh et al. 1995). This hypothesis is assumed
to hold for the private junior high school students who are the focus of this paper. In
other words, if the academic abilities of one’s peer group—fellow students serving
as one’s reference group—is high overall, then relative deprivation occurs. Students
affiliated with such student groups are more likely to experience a decrease in theiracademic feelings of self-efficacy.
Regarding the academic self-efficacy of students in this situation, it isassumed that the higher the academic abilities, the more vulnerable the student to adecrease in self-efficacy. For example, when students with high academic abilities
choose to attend a public school characterized by wide-ranging levels of student
academic abilities, capable students may be ranked high in academic ability in theschool overall, with a corresponding increase in self-efficacy. However, if theyattend a private junior high school, which includes students with mostly homogenous
family backgrounds, they may be overwhelmed by other students with relatively
higher academic abilities and fail to reach the top, despite high academic skills. This
results in a decrease in self-efficacy. The above discussion thus leads to thefollowing two hypotheses:
Hypothesis 3: Students are more likely to have lower academic self-efficacy if theyattend a private junior high school (compared to students with similar characteristics
who attend a public junior high school).
Hypothesis 4: Students with higher academic achievements are especially more
likely to have a lower academic self-concept if they attend a private junior highschool.

8

4. Data and Method
4.1. Data
To investigate the issues described in this paper, data from the Trends inInternational Mathematics and Science Study (TIMSS) conducted by theInternational Association for the Evaluation of Educational Achievement (IEA) was
employed. Since 1995, TIMSS has been conducted once every four years. The
subjects are 4th grade elementary school students and 8th grade middle school
students (in Japan, this is second-year junior high school students) in participating
countries. The most recent survey was conducted in 2015 in more than 60 countries
and regions worldwide. The sample for each country or region for each of the survey
years and grades was approximately 150 schools and 4,500 students. Thus, thesurvey data can be employed to appropriately ascertain country/regional-level trends
and changes in students’ academic abilities and learning status. The surveyed
academic subjects were mathematics and science. The study reported in this paper
used data from the 2011 Japan survey of 8th grade students, based on student andschool questionnaire items.
The 2011 TIMSS survey was conducted in March in Japan, which is the endof the academic school year for second-year Japanese junior high school students(National Institute for Educational Policy Research 2013). Thus, at the time of thesurvey, students had already experienced approximately two years of schooling since
entering junior high school, which was considered appropriate timing forinvestigating the effects of entrance to a private school.
4.2. Variables
Table 1 provides an overview of the major variables used in the analysis.
“Private junior high schools,” the treated variable, is a category comprised of bothnational and private junior high schools. Overall, selectivity among national juniorhigh schools is just as high as for private junior high schools, accompanied with asmaller variation in students’ academic achievement. Therefore, I do not consider it
problematic to consider national junior high schools as equivalent to private juniorhigh schools. Within the stratified sampling framework of the study presented in thispaper, which targets second-year junior high school students in Japan, the “national
9and private schools” variable comprises a single stratum. The other strata comprisefour strata, with public schools divided according to the size of the municipality(metropolitan district, city, etc.) in which they are located (National Institute for
Educational Policy Research 2013).
Mathematics achievement is used as the index for academic ability (acovariate in this study) (1), because it easily expresses differences due to “willfulacquisition” (i.e., student study habits and efforts) and is considered an important
subject in private junior high school entrance exams. Conforming to this decision,
mathematics is also used as the target school subject for academic self-efficacy.
Considerations regarding entrance to private junior high schools must take
into account that in certain localities, the majority of students attend public schools
and the selection of a private school may not be an option (owing to the absence ofa private school in the local area). Thus, certain regions are excluded from theanalysis, as described hereafter. First, responses in the questionnaire for schools in
the lowest population category were excluded—a population of 3,001–15,000
persons in the municipality (city, town, village, “ward” for Tokyo’s 23 ward districts).
Similarly, schools responding that they were located in the smallest category for siteof residence (“small town or village”) were excluded. Note that there was a partial
overlap of these schools and those responding as such. Consequently, the results offour public schools (total of 128 students) were excluded from the analysis.
Furthermore, excluding variables with missing data meant that ultimately,
the number of cases used for analysis were as follows: 278 students attending
national or public schools (number of schools: 11 including 7 private and 4 national
schools), and 1,914 students attending public schools (number of schools: 111)(2).
[Insert Table 1 here]

4.3. Method of Analysis
The study reported in this paper aimed to quantifiably measure the effects
of private junior high schools. Here, effects cannot be accurately deduced bycomparing the mean values of the dependent variables of students in public schools
10with those of students in private schools. This is because, as shown in prior research,
students who are more likely to attend private junior high schools already tend tohave a high academic achievement and parental education, which may lead toostensible differences in outcomes. Therefore, we need to discover independent
effects due to attendance at public schools (e.g., effects of the school curriculum,
school environment, and so on). To do so, it is necessary to appropriately control forvarious factors that make it easier for someone to advance to a private junior highschool. In this process, only a portion of all students in public schools is suitable for
a comparison with students attending public junior high schools. Thus, the focus
must be on public school students who, although they have entered a public juniorhigh, have similar characteristics that would have made it as easy for them to entera private school as those who did.
Based on these considerations, for an in-depth investigation of causal effects,
this study employed a propensity score analysis (Guo and Fraser 2010; Rosenbaumand Rubin 1983). This method is appropriate when remarkable differences areevident (i.e., when there is bias in data distribution) between two groups, namely atreated group (in this paper, students attending private junior high schools) and acontrol group (students attending public junior high schools). In education studies,
this method is applied in research on private school selection, advancement touniversity, and in Japan, investigations on juku attendance, for example (Nakazawa
2013).
Propensity score analysis employs a framework of counterfactualassumptions to conduct an analysis when the simultaneous observation of two
phenomena is not possible in the real world. An example is when one wants tocompare effects that would have occurred when the same individual had attended
both a private junior high school and public junior high school (Morgan and Winship2007). When this comparison is not for a single individual, but for fixed groups, it
makes it possible to reproduce situations with similarity. Considering the themes ofthis paper, propensity score analysis enables a comparison based on the following
assumption and question: “For students with the same characteristics including
parental education, family background, and academic achievement, what differences
are observed when one group attends public junior high schools and another group11

private junior high schools?”
The propensity score refers to the conditional likelihood (probability) of aphenomenon occurring under certain predicted conditions. For example, in selecting
a private school, we may ask: “To what extent was it easy for this student to enter aprivate school?” In many cases, there is a high tendency for students with higher
academic achievement and parental education to enter a private school. Using thepropensity score, “matching” these students with students having similar or identicallevels of certain conditions, but who went on to attend a public school, can be
performed. The differences between the two student groups are then considered as
effects. Note that when selecting the covariates to be used, prior research should bereferred to and as many variables as possible selected that can best predict treatedvariables. This can also impact the final outcome variables (Rosenbaum and Rubin
1983).
Regarding the analysis procedures, first, a logistic regression analysis was
conducted using covariates that can impact the selection of a private junior highschool and propensity scores were estimated(3). Next, the average treatment effects(ATE) were estimated using cases for which the scores for the treated and control
groups were balanced.
Matching, stratification, and weighting are the main types of analysis
performed using propensity scores. Nearest-neighbor matching was applied in thisstudy, which entailed grouping “close” propensity scores into pairs one by one (Guoand Fraser 2010)(4). In this study, the final outcome was derived by performing afurther multiple regression analysis using the matched data and comparing theseresults to those of the multiple regression analysis, which employed data from before
the matching procedure(5).
Figures 1 and 2 show the distribution of propensity scores before and after
matching. No changes occurred in the subjects in the treated group pre- and postmatching, and the score mean was 0.25. Conversely, major selection and reduction
occurred after matching (post-matching) for subjects in the control group. As a resultof exclusion (via matching) of subjects with low propensity scores, while the meanscore was 0.11 before matching, this increased to 0.25 after matching, the same value
as for the treated group. The figures show that the distribution of post-matching12

propensity scores became remarkably well balanced.

0.2

Predicted Probability
.4
.6

.8

Propensity Score Distrubution Before Matching

Control

TreatedFigure 1. Propensity Score Distribution Before Matching (vertical axes show rateof advancement to a private junior high school)

0.2Predicted Probability
.4
.6

.8

Propensity Score Distribution After Matching

Control

TreatedFigure 2. Propensity Score Distribution After Matching (vertical axes show rateof advancement to a private junior high school)
5. Results of the Analysis
5.1. Descriptive Statistics Before and After Matching
Table 2 shows the mean values of major variables before and after matchingfor the treated and control groups. The treated group comprises students who
enrolled in private schools, and the control group those who enrolled in publicschools. For all covariates, after matching, all differences lost significance. As
public junior high school students with the same conditions as private junior highstudents were chosen, even in the control group, students with relatively high13

academic achievements and high SES backgrounds were selected.
Regarding the dependent variables (educational expectations and selfefficacy in mathematics), with matching, differences between the treated and control
groups are equivalent to the ATE. This suggests that after matching, attendance at a
private junior high school extended the number of years of educational expectations
by approximately 0.4 years compared to attendance at a public junior high.
Conversely, self-efficacy in mathematics decreased by 1.1 points.
[Insert Table 2 here]

5.2. Educational Expectations
Table 3 provides the results of the multiple regression analysis before andafter matching for educational expectations (in number of years). Although notshown in the table, before Model 1, in which a multiple regression analysis was
conducted using only private junior high school as the predictor variable, the results
will be the same as the difference in mean values after matching, as shown in Table
2. Model 1 introduces all covariates as control variables. Compared to models forwhich a multiple regression analysis was conducted before matching and nocovariates were introduced after matching, the private junior high school coefficient
is somewhat small at .352. However, even though the present model applies stricter
criteria, enrollment at a private junior high school is still indicated as having
increased educational expectations.
Model 2 was designed to demonstrate the heterogeneity of private school
effects as expressed in Hypothesis 2. Here, to more easily investigate the interactionsof each variable with private schools, some covariates were changed to a moretreatable form. First, principal component analysis was performed so that thecovariates of father’s education, mother’s education, number of books at home, andhome educational resources could be summarized into an index expressing family
SES. In addition, mathematics achievement and family SES were divided into three
categories based on the number of cases.
Considering alone the interaction terms of Model 2 clarifies that when14

students with low SES enter a private junior high school, the increase in theireducational expectations is significantly higher than that of students who come froma middle-level SES. This was assumed to some extent based on the pre-matching
multiple regression analysis. However, the newly calculated results after matchingindicate a stricter causal effect, despite the relatively small coefficient. Meanwhile,
no significant effects emerged for students with high SES, despite their enrollment
in private junior high schools. It can be claimed that enrollment in a private juniorhigh by a student with high SES does not do much to increase the student’s
educational expectations.
[Insert Table 3 here]

5.4. Self-Efficacy in Mathematics
Table 4 provides the results of the multiple regression analysis before andafter matching for self-efficacy in mathematics. The private junior high schoolcoefficient for Model 1 after matching is roughly the same as the ATE shown in
Table 2. The results here indicate somewhat large effects in the negative direction
compared to the results of the multiple regression analysis before matching. Thus,
the results confirm that students are more likely to have lower self-efficacy inmathematics if they attend a private junior high school.
Model 2 was designed to demonstrate the heterogeneity of private school
effects as expressed in Hypothesis 4. The post-matching interactions show thatcompared to students with low academic achievement, middle or high-achieving
students are more likely to decrease self-efficacy in mathematics. These results are
more reliable having undergone the post-matching multiple regression analysis.
[Insert Table 4 here]

5.4. Nature of the Effects of Private Junior High Schools
The analyses thus far clarifies that enrollment at a private junior high school15

has certain effects on the educational expectations of students and on their academicrelated self-efficacy. However, what mechanisms generate such effects? Fromamong the various characteristics of private junior high schools, which cause theseeffects? To further investigate, I first refer to the framework of Coleman et al. (1982).
In private schools in the United States, academic abilities in languages andmathematics were higher than in public schools. Coleman and his colleagues
attempted to explain these differences using observable indices. They investigatedthe levels and effects of various indices related to coursework and homework,
student behavior, and attitudes. Their examination of private and public schools
revealed that the advantages related to academic skills in private schools could be
explained by using variables that could be manipulated by specific policies.
Further studies refined this framework for school effects, for example,
research on the mechanisms of tracking effects (Gamoran 1986; Karlson 2015;
Pallas et al. 1994). Here, the effects of tracking on academic achievement andeducational attainment were explained through three aspects: instructional effects,
interpersonal (peer) effects, and institutional (symbolic) effects. Possibly, theeffects clarified in this study on educational expectations and self-efficacy inmathematics can be explained using this framework.
TIMSS data includes various indices concerning school characteristics inaddition to the variables investigated in the present study(6) . Regarding
instructional effects, for the schools analyzed in this study, the average weekly
number of instructional hours for mathematics was 220 minutes for private
schools and 156 minutes for public schools. The time spent on homework per
week was 32.7 minutes for private schools and 24.7 minutes for public schools (7) .
However, incorporating these variables into the models used in Tables 3 and 4
does not explain the coefficients for private schools, or the results for educational
expectations and feelings of self-efficacy.
Next, for peer effects, we may consider school mean academic
achievement and its standard deviation as indicators of high-achieving andhomogeneous peer groups. For the schools analyzed, the mean academic
achievement in math were 639.2 for private junior schools and 575.6 for public
junior high schools, with private schools having a lower distribution of scores16within individual schools. Similarly, when the mean academic achievement andstandard deviations for each school were incorporated into Tables 3 and 4, thecoefficients for private schools demonstrated an observable decrease in selfefficacy only. Thus, the data showed that the academic self-concept of private
junior high school students is at the least impacted by comparisons with thereference group, namely fellow classmates with high academic abilities.
Otherwise, while private schools showed somewhat higher means for items
related to student learning and behavioral norms, when these were incorporated
into the models, no significant effects were observed. For institutional effects,
since examination using observable data is difficult, I limited the paper to present
only the framework.
Based on the above, the fact that incorporating mean academic
achievement partially explained the self-efficacy of students indicates that evenin a public school with similarly high achievement level, a decrease in selfefficacy in mathematics could be observed for that school, as shown in this study
for private schools. Although we did not observe such a school in the data of thisstudy, an example of a junior high school with high academic achievement inJapan is one that provides integrated junior and senior high schools within one
public school. This type of school has grown in popularity in recent years.
Nevertheless, observable factors other than those described above cannot
provide a more detailed explanation of the mechanisms involved in the effects ofprivate junior high schools. Thus, it can be assumed that certain independent
factors and mechanisms apply in private junior high schools, and possibly thesefactors have yet to be observed. For example, numerous factors remain
unobserved among the special characteristics of private junior high schools suchas guidance for entrance exams and interactions among students and between
students and teachers. I intend to examine this aspect in my future research.
6. Conclusion and Discussion
A summary of the findings obtained in this study follows. Advancement toa private junior high school makes it easier for students’ educational expectations toincrease when compared to students with similar characteristics attending a public
17

junior high school. Specifically, it is easier for students of lower SES to experience
this increase in educational expectations. Furthermore, advancement to a private
junior high school makes it easier for students’ academic self-efficacy to decrease
compared to students with similar characteristics attending a public junior highschool. Among the students at private junior high schools, those with comparatively
high academic achievements are likely to experience a decrease in their academic
self-efficacy. Thus, as described, Hypotheses 1–4 were all supported.
These results suggest both positive and negative academic-related effects
that occur while students attend private junior high schools in Japan. In general, an
increase in educational expectations is viewed positively. While the extent of thiseffect did not even extend on average to half a year, students from lower SES levels
were found to benefit most from it. The decrease of self-efficacy in mathematics isgenerally a negative result. This aspect became prominent after comparing superior
students with relatively high SES attending public junior high schools, whocomprised the present study’s control group. In other words, regarding students withsimilar abilities and characteristics, it is easier for students attending public juniorhigh schools to gain a high level of self-efficacy, as they compare themselves tosurrounding others. Regarding a decrease in self-efficacy (defined in this paper as
feeling that mathematics is difficult or that one’s grades in that subject are not good),
different individuals experience this decrease differently. In the challenging
environment of a private school, even when experiencing a setback, the negative
aspects of decreasing self-efficacy are likely only temporary, as long as students
work diligently with their fellow students to improve and extend their own abilities.
The suggestions obtained from this study can serve as food for thought inthe continuing discussion on the legitimacy of dividing students based on theirabilities, based on the findings of tracking studies. When configuring tracks,
instruction is provided according to student abilities. As such, an increase is
expected in the effects and efficacy of teaching. However, there is consensus thatgroup and course divisions based on abilities frequently overlap with issues of classand race (Hallinan 1994; Oakes 2005; Tsuneyoshi 2008). To enter a private juniorhigh school in Japan, academic abilities enabling one to pass the entrance
examination process are required, the preparation for which (e.g., attendance at a18

juku) requires a substantial financial burden and parents’ support. Therefore, one
concern is that private schools tend to select students from relatively higher SES
backgrounds. However, if the analysis in this paper applies to the “real life” situation,
if students with high academic abilities but low SES enter a private junior highschool (for example, through economic aid provided for skilled students with special
needs), then their educational expectations may increase, as these students best enjoy
the resources and opportunities provided in a private school environment. For
academically and financially advantaged students, our findings suggest the necessityof investigating certain demerits in terms of self-concept (i.e., lower self-efficacy)
resulting from attending a private junior high school. Certainly, it is importantwhether the problem is simply that students are divided according to their abilities,
and that the gathering of students with similar SES supports and strengthens
inequality. However, future considerations should extend to considering thepossibility of heterogeneous effects, whereby the merits obtained from private
school enrollment differ for different students, to better elucidate conceptual
meanings arising from the way educational system is institutionalized.
The present paper has two limitations. First, some caution is required
because of the small number of private junior high schools (including a portion ofnational junior high schools) and number of enrolled students used as the data in thispaper. This stems from the fact that Japan has a low ratio of students who attend
national and private schools at the junior high school level (there is no problem withthe sample-extraction framework of TIMSS itself). Therefore, similar variable setsfrom the 2003 and 2007 data were integrated and used for a supplementary analysis.
For these sets, a trend similar to the results in this study was confirmed(8).
Second, another aspect of this study that requires caution is that the data
used was cross-sectional and collected from a specific point in time. This paper
employed the propensity score matching method, considered the more aggressive
approach to the causal inference. Nevertheless, for a portion of the variables, it ispossible that time-related changes were not strictly considered. In this study, theacademic score for mathematics was one index used to facilitate “ease of entrance”
to a private junior high school. While this may express the status of mathematicalabilities before junior high, possibly, these achievements changed under post19

entrance (i.e., while attending private junior high school) influences. Still, it isdifficult to imagine that the absolute academic abilities of individual students
underwent drastic deviations according to whether the student entered a private
junior high school. The author emphasize that the use of academic scores inmathematics was intended to serve as a “rough” (general) indicator of students’
academic ability.
This study used quantitative methods to investigate the effects of attending
a private junior high school, which have not yet been sufficiently investigated inJapan. To examine this theme in the future, two directions are conceivable. One
method is to use panel survey data that tracks students from elementary school tojunior high school, and while carefully considering temporal order, to quantitatively
clarify the effects of attending a private junior high school. Another method is tointerview students attending private junior high schools, their parents and guardians,
and teachers, and to observe classroom instruction, academic-path guidance, andaspects of school life to determine the formation of consciousness and desires
regarding academic pathway selection and approaches to schoolwork (e.g., doteacher expectations have an impact, and/or are there effects due to interactions withother students, etc.). This will entail an accumulation of cases based on qualitative
observations. Applying various types of approaches as such will likely lead to a moregeneral and systematic understanding of the effects of private junior high schools inJapan.

Footnotes(1) TIMSS data has five plausible values for each index of academic achievement.
When academic achievement is used as a dependent variable, all five plausible
values should be used in the calculation. However, when academic achievement isused as an independent variable, it is common practice to use the average of these
five plausible values (Buchmann and Park 2009). This paper also adopts this method.
(2) Although calculations were performed by supplementing missing values using
multiple imputation (MI), as no major differences occurred in terms of the main
results (outcomes), this study simply excluded the missing values.
20(3) The “p-score” command in Stata (Becker and Ichino 2002) was employed tocreate the propensity scores. When confirming the distribution of propensity scores,as the common support domain, we confirmed specific overlaps between the treatedand control groups. In this study, we considered that individuals in the control group(n=35) who scored less than the minimum values of the treated group as not includedin the common support domain. Matching was performed after excluding these
individuals.
(4) In the paired nearest-neighbor matching, differences in propensity scores were
within a 0.25 standard deviation (Guo and Fraser 2010).
(5) The method whereby covariates are introduced to the post-matching data forfurther regression analysis was considered effective as a “doubly robust estimator”(Hoshino 2009).
(6) Referenced here are responses from the teacher questionnaires, student
questionnaires, and school questionnaires.
(7) However, mean scores for the number of items taught in the separate domains of
numbers and algebra, figures, and probability, were around 50 for both private andpublic schools. Thus far, as can be concluded from the schools surveyed here, despite
few differences in terms of basic instructional parameters, private schools spend
more time engaged in teaching these items in detail than do public schools.
(8) However, since there is less data from 2003 and 2007 for the variable concerning
parental involvement in education than in the 2011 survey, matching accuracy was
somewhat weaker for this variable.

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25

Table 1. Overview of Variables
Variables
Outcome Variables

Explanation

Junior high school=9 years; high school=12 years; specialty school, vocational school,
Educational expectation junior college=14 years; specialty university/academy (meteorological university,(in years)
national defense academy, etc.)=15 years; university=16 years; graduate school=18
years; "Do not know" responses were treated as missing values.
Educational expectation
Desire to attend university or graduate school=1, other=0(university dummy)
Four items (survey questions) were used: "I usually do well in mathematics,""Mathematics is more difficult for me than for many of my classmates," "Mathematics
Self-efficacy inis not one of my strength," "I learn things quickly in mathematics." For each of themathematics
four items, responses were indicated as follows (note that items 2 and 3 were reverse
coded): "Strongly agree" = 4 to "Completely disagree" = 1. The four items are thensummed in total (for scores of 4–16).
Treatment Variable
Private junior highStudents attending national or private junior high schools=1, Public junior highschoolschools=0
Covariates
Mathematics
Average of five plausible variables on mathematics achievement, with a scale having
achievement
an international mean of 500 and an international standard deviation of 100.
Final educational level of father converted to number of years of education (codings
Father's education
are same as educational expectation (in years) above).
Final educational level of mother converted to number of years of education (codings
Mother's education
are same as educational expectation (in years) above).
Number of books at
home

Number of books owned by family: almost none (0–10 books)=1, enough for one book
shelf (11–25 books)=2, enough for one bookcase (26–100 books)=3, enough for two
bookcases (101–200 books)=4, enough for three or more bookcases (more than 200
books)=5.

Home educational
resources

Total number of items owned among 11 items: Computer, study desk, personal books(other than textbooks), own room, Internet connection, calculator, dictionary,mathematical puzzle sold at bookstores, astronomical telescope, globe, illustrated plantreference book (for total scores of 0 to 11)

Female dummy

Female=1, Male=0

Foreign-born dummy

Student born outside of Japan=1, Born in Japan=0

Age

Continuous variable considering a student's birth year and birth monthFour items (survey questions) were used: "My parents ask me what I am learning at
school," "I talk about my schoolwork with my parents," "My parents make sure that I
Academic involvement set aside time for my homework," "My parents check if I do my homework." For eachof parentsof the four items, responses were indicated as follows: "Strongly agree" = 4 to"Completely disagree" = 1. The four items were then summed in total (for scores of 4
–16).
Use of computer athome

Computer used at home=1, No computer used at home=026

Table 2. Descriptive Statistics Values for Dependent Variables and Covariates Before and After Matching
Before Matching

Outcome Variables
Educational expectation (in years)
Educational expectation (university dummy)
Self-efficacy in mathematics
Covariates
Mathematics achievement
Mathematics achievement (standardized)
Father's education
Mother's education
Number of books at home
Home educational resources
FemaleForeign born
Age
Academic involvement of parents
Use of computer at home

Treated Group/
Private School(N=278)Control Group/
Public School(N=1914)

Mean

Mean

S.D.

15.950 (.873).921 (.270)
9.263 (2.611)

14.728.569
9.032(1.723)(.495)(2.821)636.212 (66.542).792 (.808)
15.162 (1.998)
14.665 (1.754)
3.378 (1.251)
8.791 (1.313).622 (.486).025 (.157)
14.460 (.288)
9.770 (3.206).921 (.270)

575.004 (79.807).048(.970)
13.778 (2.202)
13.381 (1.813)
3.039 (1.227)
7.994 (1.556).511(.500).007(.085)
14.478(.287)
9.228 (3.236).869(.337)

S.D.

After Matching

Difference with
Treated Group

Control Group/
Public School(N=278)Mean

S.D.

1.221 ***
.351 ***.231

15.568.809
10.320(1.341)(.394)(2.746)61.208 ***.744 ***
1.384 ***
1.284 ***
.339 ***.798 ***.111 ***.018 **-.018.541 **.051 *

633.843 (68.690).763(.834)
15.194 (1.737)
14.737 (1.674)
3.374 (1.103)
8.842 (1.259).604(.490).007(.085)
14.444(.304)
9.432 (3.101).935(.247)

Difference with
Treated Group.381 ***.112 ***-1.058 ***
2.369
.029
-.032
-.072
.004
-.050
.018
.018
.016
.338
-.014

Note: Regarding academic achievement in mathematics, standardized variables were used when calculating propensity scores.

27

Table 3. Multiple Regression Analysis for Educational Expectations (in number of years) Before and After Matching
Before Matching
Model 1Model 2
Coef. S.E.
Coef.
S.E
.454 (.078) ***.506 (.123) ***Private junior high school
Math achievement (middle)―Reference
Math achievement (low)-.832 (.087) ***-.816 (.088) ***Math achievement (high).573 (.071) ***.577 (.072) ***Family SES (middle)―Reference
Family SES (low)-.721 (.083) ***-.766 (.086) ***Family SES (high).375 (.059) ***.445 (.064) ***Female-.032 (.056)-.021 (.056)
Foreign born-.528 (.347)-.487 (.349)
Age.126 (.103).109 (.104)
Academic involvement of parents.043 (.009) ***.044 (.009) ***Use of computer at home.240 (.119) *.225 (.119)
Private j.h.s. * mid SES ―Reference
Private j.h.s. * low SES
1.098 (.256) ***Private j.h.s. * high SES-.315 (.143) *Constant
12.625 (1.479) *** 12.849 (1.489) ***
N
2192
2192
R2
.325
.333

28

After Matching
Model 1Model 2
Coef.
S.E
Coef.
S.E
.352 (.091) ***.157 (.135)-.498 (.133) ***.238 (.122)-.501 (.134) ***.229 (.124)

-.169
.150
.079
.524
.099
-.002
-.301

-.379
.067
.088
.503
.078
.000
-.286

(.101)(.094)(.092)(.176) **(.150)(.015)(.143) *(.170) *(.172)(.091)(.176) **(.150)(.015)(.138) *.420 (.200) *.160 (.193)
14.486 (2.177) *** 14.865 (2.176) ***
556
556
.139
.145

Table 4. Multiple Regression Analysis for Self-Efficacy in Mathematics Before and After Matching
Before Matching
Model 1Model 2
Coef. S.E.
Coef.
S.E
-.889 (.268) ***-.017 (.301)

Private junior high school
Math achievement (low)―Reference
Math achievement (middle)
1.487 (.122) *** 1.459 (.126) ***Math achievement (high)
3.442 (.153) *** 3.584 (.160) ***Family SES (low)― Reference
Family SES (middle).357 (.128) **.343 (.127) **Family SES (high).512 (.145) ***.496 (.145) ***Female-.885 (.109) ***-.883 (.106) ***Foreign born
.121 (.459).217 (.458)
Age-.288 (.175)-.258 (.177)
Academic involvement of parents.001 (.015).002 (.015)
Use of computer at home.133 (.118).119 (.117)
Private j.h.s. * low achievement — Reference
Private j.h.s. * middle achievement-.327 (.531)
Private j.h.s. * high achievement-1.329 (.347) ***Constant
11.753 (2.544) *** 11.295 (2.569) ***
N
2192
2192
R2
.305
.309

29

After Matching
Model 1Model 2
Coef.
S.E
Coef.
S.E
-1.081 (.278) ***-.075 (.325)
1.458 (.251) ***2.776 (.297) ***2.167 (.370) ***3.554 (.329) ***

.573
.154
-1.077
.186
-.925
.009
.208

.549
.140
-1.085
.328
-.828
.014
.163

(.249) *(.264)(.239) ***(.734)(.366) *(.034)(.364)(.248) *(.263)(.220) ***(.737)(.373) *(.033)(.361)-1.458 (.457) **-1.582 (.560) **22.422 (5.426) *** 20.555 (5.551)
556
556
.267
.284