Discussion Papers In Economics
And Business

The Influence of Education on Preference:
A Natural Experiment by the School Grouping Policy
Shoko Yamane
Michiko Nakabayashi

Discussion Paper 24-02

February 2024

Graduate School of Economics
Osaka University, Toyonaka, Osaka 560-0043, JAPANThe Influence of Education on Preference:
A Natural Experiment by the School Grouping Policy

Shoko Yamane*
Michiko Nakabayashi†‡

Abstract
We reveal the effect of education on academic performance and preferences byanalyzing a special selection system for high school entrance examinations in Japan, the“school grouping policy.” Owing to the random assignment of students, the difference inperformance between the schools in a group was due to their educational features. Education
affected both academic performance and hometown loyalty, and the instruction on how tochoose a college to apply to strongly influenced students’ choices. While there is significant
selection bias in most empirical studies, our findings are free from such bias and are importantwhen considering educational policies.

Keywords: Natural experiment, Education, School choice, Hometown loyalty
JEL classification: I26, I28, J24

Graduate School of Economics, Osaka University, and Papalaka Research Institute, Ltd., 3-21-5-307,
Kandasakumacho, Chiyoda, Tokyo, 101-0025, JAPAN. Email: shoko@papalaka.com, Tel: +81-3-3538-6642†
Faculty of Economics, Kindai University, 3-4-1 Kowakae, Higashi-Osaka, Osaka, 577-8502, JAPAN, E-mail:shin@eco.kindai.ac.jp, Tel: +81-6-6721-2332‡
We are deeply grateful to the teachers at the two high schools who provided the data. An earlier version of thispaper was presented at Ohtake-Sasaki seminar in Osaka university. We thank all seminar participants for theirvaluable comments. This work was supported by JSPS KAKENHI Grant Number JP20K01737.
*

I. Introduction
Education has a wide range of effects. In economics, its impact on lifetime
earnings has long been studied in depth, along with other outcomes such as health,mortality, and crime (see Lance 2011). Of course, there has been a significant body ofresearch on the influence of education on academic performance, which has in turn led tostudies on school effectiveness. Education also influences individuals’ preferences, waysof thinking, and social capital. In fact, many studies empirically showed the influences of
education on individuals’ preferences. For example, Denny (2003) identified the positiveeffect of years of education on social capital, as measured by individuals’ participation involuntary and community activities. Using a meta-analysis, Huang et al. (2009) found astrong and robust relationship between education and social capital, which can be definedas such as social trust and social participation. Using data from public elementary schools
in Japan, Ito et al. (2015) found that education increases altruism, cooperation with others,
reciprocity, and national pride.
Therefore, education has been shown to have a positive effect on many fronts;
however, how can we measure the effects of education? While there are many empiricalstudies on the effects of school education, it is not easy to capture the “pure” influence ofschool education, mainly because of endogeneities. The simplest way to examine theeffectiveness of a certain curriculum is to compare schools that have adopted thatcurriculum with those that have not. However, this method poses serious endogeneity
problems because the characteristics of the schools that have adopted a certain curriculum
are most likely to differ from those who have not adopted it. For example, Epple, Newlon,
and Romano (2002) found that the degree of competition with private schools increases
1the probability of adopting school tracking in public schools. As such, a comparison of
public schools that adopt school tracking with those that do not reflects a difference in thelevel of competition with private schools. This endogeneity makes it impossible to extract
the pure effects of school tracking. In school tracking research, Betts (2011) noted that it
is natural for schools with a mixture of races, religions, and immigrants to use tracking todifferentiate students. Again, we cannot distinguish the effects of school tracking fromthe influence of demographic characteristics.
Other endogeneity problems caused by school-by-school comparisons because
school choice is based on the preferences of each student or their parents. For example, apreference for competition affects a student’s choice of school. As Nierderle and
Vesterlund (2007) showed, there is a gender difference in the preference for competition,
which means that male students tend to choose high-ranked schools. This selection bias
harms to measure the effects of school education. In addition, parents who consider thatcertain curricula or educational methods have a positive influence on their children tend
to move to regions in which schools adopt these curricula or educational methods. These
parental characteristics also hinder identifying the influences of school education. Somestudies have attempted to decrease the effect of this endogeneity using propensity scores(Betts and Shkolnik 2000); however, it is difficult to control perfectly. Therefore,
comparing schools (or classes) is not an adequate way to determine the pure influence ofschool education.
Another way to examine the influence of education is to compare it before andafter the adoption of a certain educational method in the same school. Although a policychange is needed to achieve this, some studies used adequate settings. Examples include
Pekkarinen et al. (2009) in Finland, Meghir and Palme (2005) in Sweden, and Galindo2

Rueda and Vignoles (2007) in the UK. In this comparison, the differences by year that
included the influence of other policies or birth cohorts would harm the results. In addition,
the endogeneity in school choices remains. Marcenaro-Gutierrez et al. (2007) tried toreduce the endogeneity issues in attending selective schools using propensity scores;
however, their results implied that selectivity bias persisted. In addition, even if
endogeneity was reduced, a perfect pure comparison could not be conducted due tocommon shocks and cohort effects that arise from using before-and-after data.
As such, the question is: how can we determine the influence of school
education? Recently, some econometric methods have been developed to reduce theseendogeneity problems, such as instrumental variables, regression discontinuity, and theempirical Bayes methods (see Angrist, Hull, and Walters 2023). However, a setting inwhich school assignments are random would be free from endogeneity problems,
including selection bias in the first place. In this study, we found such a setting, namely
natural experimental situation to reveal the influence of school education. The naturalexperimental situation we used is a policy called the “school grouping system,” adopted
in specific areas in Japan during specific periods. As described in Section II, under thispolicy, junior high school students chose a “school group” to apply to, which generally
consisted of two high schools. After taking the entrance examination, they were randomly
assigned to one of the high schools in that school group under the policy. Because of thisrandom assignment, our data are free from endogeneity problems and the characteristicsof the incoming students in the two schools are statistically equal. Therefore, we caninterpret that the difference in performance between the two schools is due to theeducational features of each school. Usually, the difference in outcomes between two
schools comes from educational methods (including curriculum, teaching method,3

tracking, and school spirit), selection bias, and peer effects, but it is difficult to separate
these factors from each other. However, under the school grouping policy, the differencein outcomes between two schools belonging to the same school group comes only fromeducation, because there is no selection bias and no difference in the size of the peer effectunder this random assignment.
Using this natural experimental setting, we reveal the features of education foreach school cleanly. Two educational outcomes are considered in this study: academic
performance and preference. We use college choice for both indicators. We focus on thepreference of loyalty to one’s hometown. In terms of college choice, in Japan, it is usualfor high school students to decide on two things at the same time: how high-ranked are
the universities they will apply to and how far away are these universities? The difficultyof entering university can be considered to reflect academic performance, and the decision
to attend a local university is a measure of hometown loyalty. As it is relatively common
for adults to live with their parents in Japan, it is a huge decision for high school students
to live alone in distant regions. It is often observed that a student has the ability to go toa high-ranked university in an urban area, but they choose a local university which islower-ranked. For example, according to the School Basic Survey (2020), the averagerate of students who attend university in the same prefecture as their high school is 42.7%(the largest is 77.8% in Okinawa Prefecture and the smallest is 20.0% in Shiga Prefecture)out of the number of students who attend four-year universities in their prefecture. If
education increases students’ loyalty to their hometowns, the number of students applying
to local universities will increase.
Some extant studies have shown that loyalty to one’s hometown is an importantfactor in college choice in Japan. For instance, based on a survey of high school students4

in rural areas in Japan, Tomie (1997) showed that the eldest child tends to choose to go
to a local college on purpose. Hirao and Shigematsu (2006) conducted an original surveyof high school students in Yamaguchi Prefecture, which is not an urban area in Japan, andfound that students who have strong loyalty to their hometowns (students who answeredthat they want to work with living in their parents’ houses or work in their home
prefecture) are not active in job hunting. For example, such students were not willing towork, did not have the job they wanted, and could not picture themselves as working.
Interestingly, however, they well discussed with their parents about job hunting or futurejobs. Kudoh (2006) conducted an original survey of high school students from threeschools in the urban and rural areas of Japan. He showed that there are large differences
between urban and rural areas in the reasons for attending university and choosing acareer path. They also observed differences in social capital. In rural areas, high school
students tend to ask for advice from adults, including their parents and teachers, when
they encounter an important event, whereas students in urban areas tend to ask theirfriends.
Therefore, the outcome that we use in this study is the number of students who
have passed university, which we compare in two dimensions: the number of students
who apply to high-ranked universities and the number of students who apply to local
universities. We compare these numbers between the schools in the same school group,
and if there are some differences in the school grouping policy between the two schools,
we can conclude that each school’s education influences the growth of the loyalty to one’s
hometown and academic performance. Details of Japanese universities and their entrance
examination systems are explained in Section II.C.
The rest of this paper is organized as follows. Section II presents our data,5

including an explanation of the details of the school grouping policy and university
entrance examination system in Japan. Section III highlights the results. Finally, Section
IV presents the discussion and conclusions.

II. Method
A. School Grouping Policy
The school grouping system aimed to decrease discrimination among highschools. For this purpose, two or three high schools form a “school group” and thestudents choose which “school group” they apply to. Students who pass the entrance
examination are randomly assigned to one of the schools in that school group. Students
are thus unable to choose a specific high school. There is no way to change the results of
the random assignment or make an objection; hence, this system was known to produce
great dissatisfaction among students. Official documents that clearly state the allocation
method prove that this was done completely randomly.
The school grouping system has been adopted in only six prefectures in Japan(Tokyo, Chiba, Aichi, Gifu, Mie, and Fukui). The period during which this policy was
adopted varied between prefectures, for example, from 1967 to 1981 in Tokyo and from1973 to 1988 in Aichi. However, all school grouping policies ended in 2004.

B. Our Data
Our data are collected from two schools in Toyohashi City, Aichi Prefecture.
Toyohashi City is located at the middle point between Tokyo and Osaka, which are thelargest and second largest cities in Japan, respectively. It takes approximately two hours
from Toyohashi to both Tokyo and Osaka by bullet train. Toyohashi is in the easternmost
6

part of Aichi Prefecture, and the nearest large city is Nagoya.
Although the Toyohashi School area of Aichi Prefecture has two school groups,
we analyzed only one (which contains two high schools). This is because only one of theschool groups gave us permission and access to their data. We visited each high school
and collected paper documents such as school bulletins as noted in detail below. There isno online data nor government statistic data. Our data contained two high schools
belonging to the same school group: traditional school A and new school B. These schools
are preparatory schools, not vocational ones. Aichi Prefecture applied the school grouping
policy to students who began high school between 1973 and 1988. This means that thestudents graduated from high school between 1975 and 1990. Hereafter, we used thegraduation years as a time indicator.
Our data were obtained from school bulletins. Both schools published annual
school bulletins for the students and their parents, which contained information on how
many students passed the entrance examination for each college, the awards in club
activity, reports of school events, short essays by teachers, and results of physicalmeasurements. However, there is limited common information between the two schools,
except for the number of college admissions.
Data were not gathered from three years because bulletins were not available forschool A in 1975 and for school B in 1977 and 1995. Therefore, we omitted these years
for both schools from the following analysis. The school group system was applied tothese two high schools from 1975 to 1990. We only used data for this period; that is, ourdata consisted of pairwise data for 14 years. The average number of students per grade
during this period was 412.7 (SD = 25.31, min = 360, max = 461) in school A and 343.00

7

(SD = 34.53, min = 272, max = 376) in school B.1

C. University Admissions in Japan
We used the number of students who passed the entrance examination in eachcollege to measure their academic performance. Before explaining the details of ouracademic performance measures, we discuss the university admission system in Japan.
In Japan, the career path of high school students involves getting a job or goingto an advanced school (university, junior college, or vocational college). Generally,
university education lasts four years, while junior and vocational college takes two years.
According to the School Basic Survey (2023), 14.2% of high school students found a job,
57.7% attended university, 3.4% attended junior college, and 21.9% attended vocational
college in 2023. The percentage of those getting a job has decreased from 42.2% in 1976,
the first year of our data, to 35.2% in 1990, the end of our data.
Japanese universities can be classified as public or private. Public universities
include national, prefectural, and urban ones. In addition, national universities have beendifferentiated into ex-imperial universities and others. The seven ex-imperial universities
are the most difficult to enter. Applicants to public universities are required to take astandardized examination (National Center Test for University Admissions), as well as
specific examinations at each university. There are only two opportunities to take aspecific examination to enter public universities; therefore, students cannot apply to three

This difference comes from the home economics course in school B, which has around 50
students per grade. We excluded this course from our analysis because the entrance examination
for the home economics course had been exempted from the school grouping policy. The classes
of general courses and home economics course are completely segregated and they have totallydifferent curricula.
81

or more public universities in the same year. However, private universities provide alarger number of entrance examinations. It is not necessary to take a standardized
examination for private university admission, and students can sometimes skip all orsome subjects of the university’s specific examination if they pass the standardized exam.
The number of subjects required also differs between public and private university
entrance exams. Public universities require all five subjects (English, Japanese, math,science, and social studies), whereas private schools demand only some of these.
Therefore, strategy and cost differ significantly depending on whether the first choice isa public or a private university.
In general, public universities in Japan are more difficult to enter than private
universities. However, there are some private universities that are as difficult to enter as
public institutions. Table 1 ranks the difficulty of the entrance exams for universities. In
this ranking, national universities hold top-ranking positions; however, some private
universities, such as Waseda and Keio, are also highly ranked.
There are also two types of two-year colleges: public and private. However, astandardized exam, either public or private, is not required. The number of examination
subjects generally ranges from one to three, and there are many opportunities to take theentrance examination because different colleges hold the examination on different days.

9

Table 1: University Ranking Based on Entrance Examination Difficulty in Japan
Rank

Name

School Type

1

The University of Tokyo

National

2

Waseda University

Private3

Kyoto University

National

4

Keio University

Private5

Sophia University

Private6

Hitotsubashi University

National

7

Tokyo Institute of Technology

National

8

Hokkaido University

National

9

Doshisha University

Private10

Osaka University

National

Notes: The data are sourced from the “University entrance difficulties ranking” by
Toshin High school (2020). We created this ranking table by excluding the faculties of
medical science because the difficulty level for entrance to these generally differs fromthe difficulty level of a specific university. The original ranking includes the difficulties
for entering each faculty, but we created this table by choosing the highest-ranked
faculty at each university.

D. Measurement of Outcomes
We measured both academic performance and hometown preference based on
students’ college choices. Note that we were only aware of the number of students who
10were accepted to each college and not the number of students actually attending them.
Several universities, especially for private universities, permit candidates to take theentrance examinations multiple times; hence, the data reveal the total number ofadmissions to be greater than the number of students actually attending the college.
However, the number of admissions can be used as a reliable indicator because students
typically apply only to the universities they are willing to attend. We used the number ofadmissions for universities and two-year colleges to measure the academic performanceof each school under the school grouping policy.
To measure the preference for hometown, we defined “local colleges”. We asked
three evaluators from Toyohashi to define “local colleges.” One of the evaluators is oneof the authors who lived in Toyohashi city until entering university. The other two
evaluators were recruited through a crowdsourcing site. We recruited the evaluators on
the condition that they had lived in Toyohashi City, attended junior high and high school
there, and were born between 1957 and 1972 (that is, they had experienced schoolgrouping policy during their high school years).2 We asked the evaluators to “Mark theuniversity that you feel (1) is a local university, (2) is a broad-sense local university, or(3) is not local university.” We picked up the colleges that all three marked as “local
colleges.” We asked them to evaluate only universities, so we do not know whether twoyear colleges are considered local.
The evaluated results showed that the "local college" was considered the collegesthat the students can go to every day from their parents’ house in Toyohashi city. This
includes Hamamatsu City in Shizuoka Prefecture (the westernmost city of the

We did not actively recruit two people. We searched for individuals who met these conditions
within a sufficient time period (two weeks) and, as a result, only two people were recruited.
112

neighboring prefecture to the east of Aichi) and all areas in Aichi Prefecture (including
the southernmost area of Chita City and the easternmost area of Inazawa City). While
Gifu Prefecture and Mie Prefecture are both adjacent to Aichi Prefecture, it is difficult totravel there every day from Toyohashi City because it takes more than two hours by train.
The colleges in the Gifu and Mie prefectures are thus excluded from “local colleges”;
however, it was defined “broad-sense local colleges.” This category includes colleges that
Toyohashi residents feel a sense of locality to although it is difficult to travel to them from
Toyohashi City every day. The “broad-sense local colleges” include colleges in theadjacent prefectures, that is, Mie, Gifu, Shizuoka, and Nagano.

III. Results
A. The Impact of the School Grouping Policy
We begin with representing the impact of the school grouping policy. For thisanalysis, we included data from 1991 to 1993, that is, after the implementation of theschool grouping policy. Figure 1 represents the number of those admitted to the mostdifficult universities (ex-imperial universities) and relatively easy colleges (two-yearcolleges) from the two schools. In the following analysis, the number of admissions foreach college is shown as the ratio of the number of students each year, because the total
number of students and the number of students who attended a specific college (notgetting a job) varied from year to year.

12Figure 1: The Effects of the School Grouping Policy
Notes: Year indicates the year of graduation. The vertical dashed line represents the endof the school grouping policy.

As shown in Figure 1, a large selection bias was observed after the end of theschool grouping policy. During this policy, there were no large differences in the numberof difficult university and relatively easy college admissions between schools A and B.
However, after the end of the school grouping policy, the number of difficult university
admissions for school B decreased greatly (see the left panel of Figure 1) and that ofrelatively easy colleges increased significantly (right panel of Figure 1). This means thatstudents who wanted to attend higher-ranked universities chose school A, while students
who were not very studious went to school B. This result indicates the danger of using
13

data for periods when the school grouping policy was not in place and provides evidenceof randomization during this policy.

B. Academic PerformanceFirst, we obtained the ratio of students who were admitted to difficult universities
out of all students for each school in terms of academic performance. We conducted a
nonparametric test, the Wilcoxon signed-rank test, because it was clear that the numberof admissions in each year was not normally distributed. The Wilcoxon signed-rank test
is a nonparametric version of the paired t-test. We compared the outcomes for each yearwith those free from a time trend. The results are summarized in Table 2. The percentagesof students who are admitted to all universities, including ex-imperial, national, andprivate universities, were significantly different between the two schools at the 1% level.
As the z-value is calculated as the distribution of school A minus that of school B, negative
z-values indicate that school B has more admissions than school A. Ex-imperial andnational universities are difficult to enter; therefore, the results show that school B had
higher academic performance than school A.

14

Table 2: Differences in Academic Performance between Schools
Mean ofCategory

Mean ofschool A school B(1) Percentage of students accepted touniversities(2) Percentage of students accepted to eximperial universities(3) Percentage of students accepted to national
universities(4) Percentage of students accepted toprefectural or city universities(5) Percentage of students accepted to private
universities(6) Percentage of students accepted to two
years college(7) Percentage of students that took a gap year
out of the total number of students

z-valuep-value

1.168

1.286

-2.731

0.006

0.080

0.092

-1.977

0.048

0.337

0.356

-2.354

0.019

0.053

0.052

-0.596

0.551

0.771

0.869

-2.794

0.005

0.187

0.219

-1.726

0.084

0.708

0.648

2.062

0.039

0.508

0.431

2.271

0.023

(8) Percentage of students that took a gap year
out of the number of students who wereadmitted with no gap year
Notes: The percentages from (1) to (6) are based on the total number of students eachyear. The means represent the averages for all years. The z- and p-values represent theresults of the Wilcoxon signed-rank tests. The null hypothesis of this test was that the
distribution of the two schools was the same.

15

Our second academic performance indicator was the number of students in a gap
year. In Japan, it is common to take a gap year and attend the college entrance examination
the following year. According to the School Basic Survey (2023), 18% of students entered
university after one or more gap years. The more difficult it is for a student to apply to auniversity, the more likely they are to take a gap year. We obtained the number of students
who were accepted to a university after taking one or more gap year. We then obtained
the ratio of these students out of all admissions and used it to proxy academic performance.
For this analysis, we used only data obtained after 1978. School B was established in
1973, meaning that the first graduation was in 1975; therefore, there were no students
who experienced gap years at that time. As the comparison during this period isinadequate, we used data from 1978 onward, when sufficient graduates had accumulated
for school B.
The results are presented in the bottom two rows of Table 2. The number ofstudents in the gap year at school A was significantly higher than that at school B. Thisshows the differences in the strategies of choosing universities between the two schools.
The decision to take a gap year depends largely on high school education. In Japan, it iscommon for the graduates of some schools to consider applying to a difficult university
even if students have to take a gap year, while other schools do not recommend taking a
gap year.

C. Hometown Loyalty
Table 3 presents the results of the comparison regarding hometown loyalty.
Similar to academic performance, all outcomes are shown as percentages of the total
number of students. Negative z-values indicate that the percentage of school B is larger
16

than that of school A.

Table 3: Differences in Hometown Loyalty between Schools
Mean ofCategory

Mean ofschool A school B(1) Percentage of students accepted to local
universities(2) Percentage of students accepted to local
national universities(3) Percentage of students accepted to local
private universities(4) Percentage of students accepted to broadsense local universities(5) Percentage of students accepted to broadsense local national universities(6) Percentage of students accepted to broadsense local private universities

z-valuep-value

0.168

0.197

-2.542

0.011

0.168

0.197

-2.542

0.011

0.096

0.119

-2.794

0.005

0.435

0.491

-2.354

0.019

0.168

0.197

-2.542

0.011

0.272

0.308

-1.7260.084

Notes: The percentages from (1) to (6) are based on the total number of students eachyear. The means represent the averages for all years. The z- and p-values represent theresults of the Wilcoxon signed-rank tests. The null hypothesis of this test was that the
distribution of the two schools was the same.

Using the three-person evaluation, five and 31 universities were selected as local
and broad-sense local universities, respectively. Similarly, there are two local national
universities, two broad-sense local national universities, two local private universities,
17and 22 broad-sense local private universities. There is only one ex-imperial (the mostdifficult) university for Toyohashi: Nagoya University, which is included in local national
universities.
In Table 3, there are significant differences in the number of students who wereadmitted to local universities: the students of school B tended to be admitted local
universities more than the students of school A, regardless of whether the university wasdifficult or easy to enter.

IV. Discussion and Conclusions
In this study, we used a natural experimental setting for the “school grouping
system” policy in Japan to reveal the pure influence of education. Under this policy, juniorhigh school students were randomly assigned to one of two schools instead of choosing
their own high school. Therefore, the features of students in each school were considered
statistically equal under this policy. Due to this unique random setting, we could capture
the pure influence of high school education on not only academic performance but also
on forming preferences about hometown loyalty. This is the major contribution of thisstudy, as only a few studies have hitherto clearly identified the effects of education.
We also found that the difference of the college choice strategy could make thedifference in academic performance between schools. This finding might be consistent
with Abdulkadiroglu et al. (2022), who showed the education effects of high-ranked highschools. They found that high-ranked schools have positive effects on academic
performance (SAT math scores) as well as they tend to offer advanced courses that enable
students obtain qualifications in Career and Technical Education or pass an industryrecognized technical assessment. Preparing these courses and recommending them to18

students is part of the strategy of each high school. In other words, school features areimportant for students making career choices.
Additionally, the large differences in academic performance between the twoschools after the end of the school grouping policy show the strength of selection bias inempirically examining the influence of education. This is one of the main contributionsof this study. Abdulkadiroglu et al. (2022) also noted a large selection bias in OLS
estimation, indicating that the education effects on academic performance were generally
overestimated. Our result also indicates the strength of traditional and school images.
Because school A had been considered “the best local high school” by the local people in
Toyohashi, this sentiment remained, so when the school grouping policy ended and thestudents were able to choose a high school, the students who aspired to higher education
chose school A. Therefore, despite 15 years of the random assignment policy, the sensethat “school A is the best local high school” did not disappear. While academic
performance during the school grouping policy was higher in school B than in school A,
most local people did not consider school B the best local school. This public image forms
a strong selection bias among students, and this is a common situation worldwide, notonly in Toyohashi.
Our findings, exhibiting the education effects on hometown loyalty preference,
provide new insights, as limited studies have demonstrated this influence. Moreover, we
revealed these effects through a natural experiment, which is free from selection bias.
Regarding hometown loyalty, Yamamura (2017) found that the effect of this preferenceis long-lasting and influences the rest of one’s personal life; for example, when the highschool baseball team of one’s hometown wins, it makes people who live far from theirhometown happy. Therefore, it is important to note that high school education can affect19

hometown loyalty, which persists over the long term of one's life. This finding also
provides an important suggestion and warning to educational policymakers becausechanging the content of education may also change students’ preferences in the long run.
One of the limitations of this study is that we cannot determine the true reason
for the differences in outcomes between schools. Betts (2011) presented the factors that
affect students’ test scores in the context of tracking, such as innate ability, other personal
traits, family characteristics, teacher qualifications, teacher effort, class size, peer effects,
curricula, pedagogical approaches, school characteristics, and educational activities ormaterials after school. All but the curricula, teacher qualifications, teacher effort,
pedagogical approach, and school characteristics were statistically equal in our naturalexperiment. However, we could not specify which of these factors caused the differences.
Although the same limitation exists in the influence of education on loyalty to
one’s hometown, the influence of alumni is a factor that can be interpreted as part of theschool culture. Our results showed that education in school B (the newer school)
increased students’ loyalty to their hometown more than for school A (traditional school).
This result indicates that students of school B preferred local colleges, whereas thestudents of school A exhibited a nationwide spirit or preferred large cities such as Tokyo.
One possible reason is that the traditional school A had more alumni than school B, andthe students of school A had opportunities to witness the remarkable activities of theirgraduates. This leads to a longing for Tokyo or other big cities and affects students’university choices. Preferences and school culture are closely connected, because
education affects the formation of preferences, thus leading to the formation of school
culture.
Our study has other limitations as well. For instance, the extent to which these20results can be generalized is questionable. To confirm the results, we can conduct thesame analysis in other regions that adopted the school grouping policy, for example, theMie or Gifu prefectures, in future studies.

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