Discussion Paper No. 138
What do high-achieving graduates bring to
nonacademic track high schools?
Yuta Kuroda

December, 2023

Data Science and Service Research
Discussion Paper

Center for Data Science and Service Research

Graduate School of Economic and Management
Tohoku University27-1 Kawauchi, Aobaku

Sendai 980-8576, JAPANWhat do high-achieving graduates bring to nonacademic
track high schools?
Yuta Kuroda *December 6, 2023

Acknowledgments
This work was supported by JSPS KAKENHI Grant Numbers 19K23217 and 21K13315.
The administrative data for high schools used in this study were provided by the Ministryof Education, Culture, Sports, Science and Technology (MEXT). I am grateful to
DAIGAKUTSUSHIN Corporation for providing the data on the high school students'
acceptance performance used in this study. I would also like to thank Kawaijuku forproviding the data on the level of difficulty of the entrance examinations for theuniversities. I deeply appreciate the helpful comments and suggestions provided by
Kentaro Nakajima. I also appreciate the participants of the Rokko Forum at Kobe
University in March 2021 and the Kansai Labor Economics Workshop in May 2023. Anyerrors that remain are my own. The views expressed are the author's own and do notnecessarily reflect those of any organizations with which the author is affiliated.

*

Graduate School of Economics, Osaka Metropolitan University, 3-3-138, Sugimoto,Sumiyoshi-ku, Osaka, 558-8585 Japan (E-mail: kuroyu0725@gmail.com)iAbstract
In this study, the effects of high-achieving graduates in nonacademic track high schools
with low university enrollment rates are investigated. Japanese high schools are stratified,with each high school having nearly fixed tiers of universities to which their graduates
advance. Because it is so rare for students from nonacademic track schools to be accepted
into top universities, students who are accepted can serve as accidental role models,positively affecting the motivation, aspiration, and knowledge of academic procedures of
their lower schoolmates. I have created and used various definitions of nonacademic trackschools and high-achieving graduates by using the university acceptance data of almost
all high schools in Japan from 2001 to 2021. The results showed that the quasirandom
appearance of high-achieving graduates improved the university acceptance outcomes of
nonacademic track high schools for subsequent years. Additionally, the appearance ofhigh-achieving graduates is not related to factors such as teacher‒student ratios, regionalsocioeconomic characteristics, or changes in the school district system. Therefore, theobserved performance improvement may result from changes in student beliefs andmotivations or from the accumulation of school expertise rather than from peer effects or
systemic changes.

JEL classifications: I21, I24, D83, R23
Keywords: University choice, College choice, School stratification, Role model,
Subjective expectations

ii

1.

Introduction
Access to a selective university conveys significant financial and nonfinancial

benefits and thus is important in reducing income inequality and improving social
mobility (Chetty et al., 2020). However, because advancing to higher education iscomplex and psychologically taxing, factors other than academic preparation andfinancial constraints can become barriers (Page and Scott-Clayton, 2016). Schools withmany low-income or minority students and fewer college-bound students tend to lackinformation about application processes and available scholarships (Roderick et al., 2011;
Mulhern, 2021). Additionally, because beliefs and aspirations are socially constructed andaffected by their communities, students from high schools with historically low academic
achievement may have low self-confidence and tend to underestimate themselves (Janzen
et al., 2017; Guyon and Huillery, 2021). Moreover, in recent years, university selection
by parental socioeconomic status has begun in primary education, and students from moreadvantaged schools increasingly move to more selective next-level schools (Berkowitz
and Hoekstra, 2011; Delaney and Devereux, 2020). Therefore, students who drop out of
the academic track at an early stage may go on to less selective universities even when
they have high academic potential (Hoxby and Avery, 2013; Hoxby and Turner, 2015). 1
Input from appropriate role models can address this lack of knowledge, selfconfidence, and aspirations and its resulting disparities in higher education. People

1

Academic and nonacademic tracks are sometimes institutional and sometimes not. Dueto student choice and regional characteristics, schools that are not institutionally classified
may be substantially separated into academic and nonacademic tracks. In this paper, thedistinction between academic and nonacademic tracks is used when the graduates' career
paths differ substantially, even if they are not institutionally distinct.
1improve their aspirations, efforts, and achievements through social observation, learning
about the successes of their role models' with similar characteristics (Sequeira et al., 2016;
Janzen et al., 2017). Additionally, in educational investments, students look to theirpredecessors with similar characteristics and abilities (even if they are not directly related)to determine their career paths based on their peers and organizational norms (Mulhern,
2021; Barrios-Fernández, 2022). Therefore, nonacademic track high school students withpotentially high academic ability can choose a more suitable career path through exposure
to appropriate role models (Hoxby and Avery, 2013; Carrell and Sacerdote, 2017).
There are many studies on the effects of role models and noteworthy predecessors indeveloping countries, where student aspirations, beliefs, and information are often scarce(Conley and Udry, 2010; Bernard et al., 2019). In developed countries, the effect of suchrole models as female entrepreneurs and scientists on women’s access to higher educationand STEM programs is attracting attention (Porter and Serra, 2020). However, we find it
challenging to infer the impact of role models on nonacademic track students from theexisting evidence. Because aspirations and beliefs are relative, distinct role model effects
are expected between developed and developing countries and between academic andnonacademic tracks. Additionally, role models whose abilities are overly advanced fornonacademic track students may not be effective because people better compare
themselves to people with similar characteristics (Clots-Figueras, 2012; Sequeira et al.,2016; Barrios-Fernández, 2022). Additionally, disadvantaged students may incorrectly
understand the information or develop erroneous beliefs (Bettinger et al., 2012; Loyalka
et al., 2013). Therefore, the role model effect in the nonacademic track, which is a relative
disadvantage in developed countries, is still not understood.
Therefore, the focus of this study is on high schools with low university enrollment2

rates, and the impact of unexpected student acceptance by prestigious universities (highachieving graduates, high achievers) on such high schools is analyzed. This study is
focused on the university enrollment numbers of high school rather than on individual
student enrollment choices. The Japanese high schools analyzed here have been
traditionally separated into those catering to selective universities (academic track) andthose not catering to university admissions (nonacademic track). Since it is rare for a
nonacademic track high school student to be accepted into a prestigious university, suchan event can be considered quasirandom. Thus, high achievers can serve as
nonexperimental and incidental role models who influence lower-level students andfaculty. I found many such rare cases in the university acceptance data for almost all
Japanese high schools between 2001 and 2021 and used them in this study, in which an
event study framework is applied to analyze changes in the enrollment patterns ofgraduates in universities from high schools that had not previously sent many (or any)
students to top universities following the appearance of high achievers.
As a result, I found that in those schools where high achievers appeared, the average
selectivity of the universities to which students were accepted persistently increased. On
the other hand, the relevant student university enrollment and application rates did notchange. The critical challenge in identifying the impact of high achievers is verifying thatthe appearance of these high achievers is not related to any other factor. I present a varietyof robustness checks to support the identification of causal effects. First, I confirmed thatthe appearance of high achievers was not associated with changes in teacher‒student
ratios or changes in the school district system. The main results also remain robust to
estimation using samples that exclude small and private schools, which are prone tochanges in school structure. Furthermore, the appearance of the high achievers did not3attract potentially talented students from the surrounding high schools, confirming thatchanges in newly enrolled students do not explain the main results.
At least four mechanisms explain the findings of this study. First, the success of closerole models may affect beliefs and efforts by making students aware of a reward for theirefforts (Ersoy, 2023). Second, such high achievers may inform students that selective
university attendance is a realistic option. When high schools are stratified, their statusperceptions form stereotypes, and their students may apply to universities they perceive
as appropriate to their high school status (González-Jiménez, 2022). High achievers canovercome such stereotypes and provide a new understanding of selective university
enrollment. Third, schools can accumulate further experience and information about
selective university admissions, increasing the options of potential applicants. Fourth,teachers' expectations and beliefs about students' abilities can change, leading to teacherspresenting students with more challenging career options (Cherng, 2017). Although thesealternative mechanisms cannot be perfectly distinguished, I argue that the impact of highachievers on the knowledge, motivation, and beliefs of younger students and faculty
members is a primary driver of my results.
This study contributes to the literature by identifying the role model effects on
relatively low-performing schools in developed countries. Experiments that attempt tochange student higher education behavior through information interventions or rolemodel effects sometimes work (Hoxby and Turner, 2015; Mulhern, 2021) but sometimes
fail to exert the desired effect (Kerr et al., 2020; Rizzica, 2020). The inability of lowachieving or disadvantaged students to correctly interpret the information they areprovided is one reason why such interventions sometimes fail (Bettinger et al., 2012;
Loyalka et al., 2013). Additionally, role model interventions that use overly-different4models from the focal students or present options that exceed student expectations may
not be helpful to students in forming career choices (Clots-Figueras, 2012; Sequeira et al.,2016; Barrios-Fernández, 2022). Alternatively, the provision of detailed information
about university admissions can often act as a negative surprise that reduces student
motivation by modifying their optimism (Loyalka et al., 2013; Kerr et al., 2020). In
contrast, high achievers in this study overcome the shortcomings of role model provisionand information interventions presented in these previous studies. High achievers have
similar characteristics to regular students, and their acceptance into a prestigious
university is easier to understand than statistical information and less likely to act as anegative surprise. In this study, I present the potential for more effective interventions byidentifying the effects of high achievers.
The findings of this study have important implications for educational discussion,
including such topics as ability tracking and school choice. The empirical results oftracking policies have been mixed, with some finding positive impacts on average ((Duflo
et al., 2011; Guyon et al., 2012) and others finding further increases in educational
inequality (Pekkarinen et al., 2009; Matthewes, 2021). School choice policies have thepotential to improve educational achievement through better matching, but they can also
promote stratification and academic disparities (Hsieh and Urquiola, 2006; Lavy, 2021).
According to my findings, such sorting may have a notable and persistent negative impact.
If tracking or school choice places potential high achievers in elite schools, this could
increase disparities by reducing the knowledge and confidence of students in
disadvantaged schools. In contrast, my results suggest the effectiveness of policies suchas the Top Ten Percent Plan, which geographically distributes outstanding students (Blacket al., 2023). If the outstanding students assigned to relatively disadvantaged schools

5demonstrate a high achiever effect, it could promote social mobility and reduce social
inequality.
This study differs from the literature on peer effects in education. In recent years, anincreasing number of studies have been focused on the peer effects of extremely highperforming students (e.g., Mouganie and Wang, 2020; Modena et al., 2022). In contrast,
this study analyzes the role model effect of successful graduates rather than the peer effectof highly performing classmates. Additionally, the focus of this study is not on the effectsof contact with high achievers but rather on the indirect effects, such as awareness of thehigh achiever's accomplishments or changes in faculty member's beliefs or knowledge.
In some ways, this aligns with the literature on regional norms or entrepreneurship rooted
in the community (Sorenson, 2017). Compared to peer effects, which primarily affectpeers in the classroom, the norms or knowledge generated by high achievers may be moreconsequential for educational policy because they remain in the school.
The remainder of this paper proceeds as follows. Section 2 details the Japanese
education system. Section 3 introduces the data used in the analysis. Section 4 presents
the empirical strategy and identifying assumptions. Section 5 presents the main results
and describes a series of robustness checks, and Section 6 concludes.

2.

Institutional context

2.1. Education system in JapanThe Japanese education system includes six years in elementary school, three years
in junior high school, three years in high school, and four years in university. Only
elementary and junior high schools are compulsory, but approximately 99% of Japanese
students advance to high school. The main career paths after high school include job

6placement, vocational school, two-year junior college, and four-year university. The
university enrollment rate was approximately 55% in 2020 and is on the rise.
High schools, whether public or private, have entrance exams. If students do not pass
the admissions test, they mostly apply for additional openings at lower-ranked highschools. Almost all students are finally assigned to one of the high schools, while very
few do not go on to high school or try again next year. Because of the high stakes of theadmissions process, some students enter high schools that they do not really desire toattend.
University entrance examinations are primarily high-stakes academic tests (generalexamination), and admissions are further based on high school grades and interviews. 2
Typically, national universities test many subjects, while private universities often test
only the specialized areas in each department. Unlike high school, if students fail theentrance exam of their preferred university, they sometimes attend a preparatory schoolfor a year and retake the exam the following year. Such students who are neither highschool nor university students are referred to by the slang term “Ronin” in Japan, andsome spend years as Ronin before being accepted into highly selective universities.

2.2. Academic achievement disparity and stratification in high schools
Japanese high schools are broadly classified into two categories: schools that aredesigned to prepare students for higher education (general courses) and schools thatprimarily provide vocational training (e.g., industrial, commercial, and agricultural2

The examination system differs at the department level as well as at the university level,

but for the sake of simplicity, I explain it here only at the university level.
7

courses). 3 After 2000, the percentage of students in general courses was approximately
72-73%, while the percentage in vocational courses was 18-22%. These courses are notinstitutionally classified into academic and nonacademic tracks. Graduates from any highschool or course can apply to all types of universities. However, if the subjects requiredby the target university were not included in the high school curriculum, applicants muststudy independently or at a preparatory school.
Although there are no institutional differences, significant disparities exist among theacademic performances of high schools. Even in high schools with regular courses, there
is a clear distinction between substantial academic track high schools and nonacademic
track high schools. Traditional academic track schools send many students to prestigious
universities every year. Most students in academic track schools study intensively foruniversity admission. The school also has expertise in university admissions, and special
lectures are sometimes given to prepare students for the entrance exams of prestigious
universities. On the other hand, nonacademic track schools have few or no students
aiming to go to university, and many students find employment or go on to vocational
schools. Nonacademic track schools offer fewer opportunities to obtain information about
university admissions and only provide a little preparation for entrance exams.
Thus, in Japan, attending a selective high school is crucial for advancing to a selective
university. However, such selective high schools are not evenly distributed throughout
the country, with many clustered in urban areas. Additionally, to enroll in such selective
high schools, students must invest in education early on, such as private cram schools andtutoring. Therefore, children from nonurban areas and those with parents of low
3

In addition to general and vocational courses, there are specialized courses such asmusic, art, sports, and foreign languages.
8socioeconomic status face greater difficulty enrolling in selective high schools.
Consequently, children with relatively disadvantaged parents sometimes attend
nonacademic track high schools and consequently enroll in nonselective universities
despite their potentially high academic ability.

2.3. Selectivity and classification of Japanese universities
In 2020, Japan had 86 national universities, 94 public universities, and 615 private
universities. National universities tend to be more prestigious and selective than private
universities. Public universities are similar to national universities but less academicallyselective, and they include specialized institutions such as nursing and arts. The
dispersion of selectivity among private universities is vast, and while there are someoutstanding universities, there are also many less selective universities, some of whicheven have open admissions.
There are several ways to measure the selectivity and authority of Japanese
universities, and one typical indicator is the hensachi score (Araki et al., 2016; Goodman
and Oka, 2018). The leading preparatory schools report hensachi scores, which arestandardized scores from previous mock exams taken by the students accepted to each
university. Hence, the hensachi score is not a standardized version of an entrance exam
score but rather an overall indicator of the approximate difficulty and selectivity ofadmission to a given university or department. The Hensachi score is standardized with amean of 50 and a standard deviation of 10, and its distribution is roughly normal. Thus, auniversity with a 50 hensachi score is moderately selective, while a university with a 70
hensachi score is very selective and prestigious. In the Japanese labor market, graduationfrom a university with a high hensachi score is highly valued and increases the probability

9

of finding a higher-income job. Therefore, for students choosing a university, the hensachiscore is essential.
In addition to hensachi scores, other criteria for university prestige and selectivity
include groups of universities such as the Ivy League in the United States and Oxbridgein the United Kingdom. University groups are sometimes classified according to their
historical background, while other times, the leading preparatory schools createclassifications for reputable universities. For instance, the former imperial universities areamong the most highly selective and prestigious Japanese national universities.
Additionally, university groups such as MARCH are prestigious private university groups
defined by the leading preparatory schools and commonly used in Japan. 4 In this study,
such university groups are used to define high achievers.

3.

Data
There are four types of data used in this study: the university hensachi scores, thenumber of students accepted to universities by high school, the characteristics of highschools, and the neighborhood characteristics around the high schools. This study is
aimed at identifying the impact of high achievers in nonacademic track high schools on
the school’s university acceptance performance of the high school in the following year
and beyond. However, no institutional criteria exist for either nonacademic track highschools or high achievers. Therefore, the first step is to define nonacademic track highschools and high achievers based on hensachi scores and the acceptance performance by

4

MARCH is an acronym for the five leading private universities in the Kanto region:

Meiji University, Aoyama Gakuin University, Rikkyo University, Chuo University, andHosei University.
10

high school. Second, a measure of each high school's university acceptance performanceeach year is developed based on how many students from each high school were acceptedby universities of any selectivity level.

3.1. Hensachi score
To measure a university's selectivity, I use the hensachi scores published by
Kawaijuku. Kawaijuku is one of the most well-known preparatory schools in Japan, andthese data are the same as those referenced by high school students wishing to enter
universities. These data include almost all universities that require academic achievement
tests for admissions. 5 Hensachi scores are presented in increments of 2.5 and range from35 to 72.5. For universities with fewer applicants than openings that have establishedopen admissions, the hensachi score cannot be calculated (and is described as “borderfree” in the data), so the hensachi score for such a university is considered 30. 6
While hensachi scores exist for different departments and entrance exam types, thenumber of students accepted to universities by high school, which is discussed below,
does not distinguish between departments or entrance exam types. Therefore, the averagehensachi scores of the first exams (the most primary exam) of all departments is
considered the university's total hensachi score. Furthermore, the average of these scoresfrom 2012 to 2021 is a measure of the university's selectivity. In other words, hensachi

5

The universities where hensachi scores cannot be used include nursing, arts, and sports,

where the admission exam consists of an interview or tests practical skills. Appendix
Table A1 shows the representativeness and capture rate of the available datasets.
6

Excluding such universities from the analysis did not cause any change in the main

results.
11

scores are time-invariant variables, forming one unique value for each university. Thisapproach is plausible because the difficulty of admission, prestige, and reputation of each
university barely changed during the analysis period, with an autocorrelation coefficientof approximately 0.98 for the hensachi scores.
Figure 1 shows a histogram of hensachi scores by national, public, and private
universities. National universities are, on average, highly selective, and admission to mostsuch universities requires a hensachi score of 50, which represents an average or above
academic ability. Public universities also tend to have higher average hensachi scores but
lower scores than national universities. In contrast, while some private universities havehensachi scores above 60, the overwhelming majority have hensachi scores below 50.
The figure also shows that national universities are, on average, more selective andprestigious in Japan, with some prestigious private universities following closely behind. 7

7

The exception is medical schools, whether national or private, which are incredibly

selective. One limitation of this study is that it cannot distinguish between medical
schools and others. However, the cost of admission of such schools is very high, as private
medical schools require tuition fees that are several to dozens of times higher than thoseof other faculties. Additionally, the number of medical schools is small, and the students
who apply differ significantly from the general student population. Therefore, this study
assumes that successful applicants to medical school are not affected by a role model
effect and seriously impact the identification strategy.
12

Figure 1. Histogram of hensachi scores by university type
Note: Black, dark gray, and light gray represent national, public, and private universities,
respectively.

3.2. Classification of university groups
In this study, three groups of universities are defined based on selectivity, prestige,
and reputation as published by preparatory schools. Figure 2 shows each group of
universities, their numbers, and their mean hensachi scores. Group A consists of the 11
most selective universities, corresponding to the top 10% academically, and the graduatesof these universities are highly valued in the labor market. Group B includes 27
universities, consisting of the 11 universities in Group A and the 16 next most selective
universities. Group B includes prestigious national universities and groups of well-known
private universities located in Japan's two largest cities, the Kanto and Kansai
metropolitan areas. Group C includes 217 institutions and comprises Group B plus allnational and public universities. Group C universities are not highly prestigious, but they
are moderately selective, requiring at least average academic ability for admission.

13

Figure 2. Classification of university groups and average hensachi scoresNote: The full name and hensachi score of each university and its rank in the majorinternational university rankings are listed in Appendix Table A2.

3.3. University acceptance data by high school
The number of students accepted from each high school to each university every year
is obtained from the “Extra issue of Sunday Mainichi, High School Achievement”
published by the Mainichi Shimbun Publishing Inc. This dataset is based on an annual
survey performed on four-year universities by the private company DAIGAKUTSUSHIN
Corporation. These data report the total number of students accepted rather than thenumber of enrollments, so if one student is accepted to more than one university, thatstudent is counted more than once. The number of high schools and universities surveyed
differs slightly each year, but data are included for approximately 5,000 high schools and
500 universities. The data are highly accurate and representative, covering approximately
99% of high schools and 95% of national and public universities in Japan. Approximately
55% of private universities are included in the survey, as new and defunct private

14universities and private universities with very few students are not included. 8 In thisstudy, 21 years of data are available, covering 2001 to 2021.
Combining the number of students accepted by each university with the hensachiscore, the average hensachi score of the universities accepting each high school graduate
is calculated and used as the university acceptance performance of each high school. For
instance, if two students from a given high school are accepted to universities withhensachi scores of 50 and 60, then the placement rate of the high school would be 55. The
minimum and maximum hensachi scores of accepting universities are also used todetermine whether the high achievers improved the average performance and whether theupper or lower groups increased their performances.
There are several advantages to using the average selectivity of accepted universities.
In countries with high university enrollment rates, the value of a university degree in thelabor market is declining, and enrollment in more selective universities is valued.
Therefore, changes in student behavior that university enrollment rates cannot capture
can be identified by considering the selectivity of the universities they accepted.
Additionally, because nonacademic track high schools tend to avoid taking comparable
academic tests between schools, they often fall short of empirical analysis due to a lack8

Appendix A1 compares the number of universities published by the Ministry of

Education, Culture, Sports, Science and Technology (MEXT) with the number ofuniversities included in the High School Achievement. Appendix A3 also compares thenumber of accepted students published by universities with those reported in the “High
School Achievement” for national and private universities around the 25th, 50th, 75th,
and 99th percentiles of hensachi scores. The table shows that the higher the hensachi scoreis, the higher the capture rate, but even in universities with lower hensachi scores, morethan 90% of the accepted students are captured.
15

of available data. This study provides new evidence on the nonacademic track by using
university acceptance performance across all high schools in the country.
Although these data have certain advantages by capturing the outcomes of nearly all
high schools across Japan over time, the potential for investigating the mechanisms
underlying the effects found is limited. First, since the number of successful applicants
per department or faculty is not available, it is impossible to analyze whether students
choose the same department or faculty as high achievers. Additionally, since there is no
information on the types of entrance exams and student applications, we cannot identify
whether the changes in placement rates are due to changes in ability or changes in career
choices. Therefore, other administrative high school data provide a complementary
exploration of the underlying mechanisms..

3.4. Defining nonacademic track high schools and high achievers
In this study, the university placement rates by high school, hensachi scores, anduniversity groups are used to define nonacademic track schools and high achievers. The
main analysis defines high schools that have not sent students to any Group C university
for at least five years as nonacademic schools. Of course, the criteria used for university
group and number of years are arbitrary, thus confirming that the results are not sensitive
to changes in these criteria. Then, we define high achievers as those students from
nonacademic track schools that have been accepted to Group C or higher universities.
Stricter criteria for high achievers, i.e., restricting enrollment to group A and B
universities, would increase the possibility that high achievers are random but would
result in small treatment groups. Because of these tradeoffs, the main analysis presents
and compares estimates using admission into Groups A, B, and C to define high achievers.

16

Figure 3 illustrates how nonacademic track high schools and high achievers aredefined and treatment groups and variables are created. In this example, students acceptedto Group A universities are defined as high achievers. First, row (A) shows the number ofstudents accepted to Group C universities, where one student was accepted in 2001 andthen no students were accepted through 2010. Therefore, this high school has been
defined as a nonacademic track school since 2007. Row (B) shows the number of years
in which there were no successful applicants in Group C and is shaded to indicate when
this value exceeds five years, that is, when it is considered a nonacademic track. Because
there may have been high achievers before the data availability period, the data prior to2005 were used only to define nonacademic track schools and were not used in theestimation.

Figure 3. Conceptual diagram of the definition of nonacademic track high schools
and high achievers

Row (C) shows the number of students accepted to Group A universities, which was
one in 2011 and two in 2014. Here, students accepted into Group A universities in 201117

from nonacademic track high schools are defined as high achievers. Thus, the treatment
variable shown in row (D) takes 1 in 2011, and the relative time since treatment isrepresented by row (E). There were also successful applicants to Group A universities in
2014, but those who were accepted after the treatment are not considered. Additionally,rarely, the status of nonacademic track schools may reoccur after the appearance of highachievers. Although the second high achiever is also not considered a treatment in themain analysis, I also perform an estimation that excludes schools where the high achiever
appears twice as a robustness check. Row (F) shows the number of students who were
accepted by any universities, and row (G) shows the average hensachi score of theaccepting universities. The key outcomes of this study are shown in row (G), and weanalyze how these outcomes change with high achievers.
Only nonacademic track schools are used in the analysis. In other words, academic
track schools who constantly send students to prestigious universities are not analyzed.
Appendix Table A5 shows the treatment timing and the number of schools in the treatment
and control groups for each of the three definitions of high achievers. Approximately
1,100 high schools, or approximately 25%, are considered nonacademic track schools.
Additionally, high achievers appear without bias during the analysis period.
There are certain advantages to defining high achievers based on their university
placement rates. Although previous studies have primarily defined high ability from testscores, such ability can be recognized within the same classroom but is not easily
recognized in other classrooms or grades. In contrast, an achievement measure assessing
whether graduates have been accepted to prestigious universities can be used to establish
the reputation of a high school, which can persist over time. In Japan, when students
achieve remarkable results in academics or sports, they are sometimes congratulated with18

signboards or banners placed in noticeable locations around the school. These areintended to enhance the school's reputation and improve student motivation andconfidence. Therefore, the appearance of high achievers likely results in a widely
recognized and continuously significant impact.

3.5. Other control variables
High school data are taken from the School Basic Survey conducted by the MEXT.
The number of graduates, pupils per teacher, and pupils per class are used as control
variables. Additionally, the main analysis is complemented with an estimation of the job
placement rates and the percentage of university applicants among graduates used as
explained variables. Since the School Basic Survey is only available after 2009, theestimation controlling for these variables covers 12 years of analysis, ranging from 2009
to 2020.
In addition to high school data, schools’ regional characteristics are also used in theanalysis. Population density and the percentage of children (under 18 years old) are
obtained from the Vital Statistics by the Ministry of Health, Labor and Welfare. The
annual taxable income for each municipality is also obtained from the Ministry of Internal
Affairs and Communications. Additionally, official land prices as published by the
Ministry of Land, Infrastructure, Transport and Tourism are aggregated by municipality,
and the average land price for the municipality in which the school is located is applied.
In addition to being used as control variables, these variables are used to confirm that
there is no significant change in the regional characteristics before or after the appearanceof high achievers.

193.6. Dataset construction and descriptive statistics
A fundamental assumption of this study's identification strategy is that the appearanceof high achievers is quasirandom. This assumption is violated when changes in the school
system or student quality affect the probability of high achievers. Therefore, only thosehigh schools that have not experienced significant structural changes during the analysis
period are used. I exclude high schools that have been newly established, discontinued,
consolidated, reorganized, changed ownership, or have become coeducational.
Additionally, only schools surveyed at least ten times during the analysis period are used.
This sample selection reduces the number of schools, including academic track schools,
from 5,249 to 3,944. From this restricted sample, nonacademic track high schools are
extracted and used for the main analysis.
Table 1 shows the descriptive statistics by treatment and control groups as well as
their significant differences. Panels A-C present the results when high achievers aredefined by acceptance into groups A, B, and C, respectively. Descriptive statistics for thetreatment group show values from one year before the high achievers appeared, while theresults for the control group show median values for the analysis period. Comparing eachpanel reveals that Group A has the slightest difference between the treatment and control
groups, while Group C has the most considerable difference. This is likely due to thestricter definition of high achievers, which further randomizes their occurrence.
Conversely, relaxing the definition of high achievers increases the probability that highachievers arise from factors other than coincidence, such as the efforts exerted by eachhigh school.

20

Schools with high achievers (oneyear before treatment)
Mean Std. Dev. Min.
Max.

Panel A: Group A
Average hensachi score39.527 2.609 36.094 50.319
Number of graduates
192.917 71.995 19.000 355.000
Number of teachers
36.639 11.960 9.000 63.000
Number of classrooms
15.808 5.200 4.000 22.000
Pupils per teacher
16.753 5.872 6.333 34.500
Pupils per class
34.742 6.402 11.400 40.917
Ratio of applicants to universities
0.225 0.189 0.020 0.992
Population density (/1000)
4.655 5.196 0.026 19.618
Ratio of children
0.091 0.012 0.056 0.122
Taxable income (1,000,000 JPY)
3.280 0.737 2.344 6.124
Land price (1,000,000 JPY)
0.168 0.215 0.012 1.168
Number of universities within 30 km 38.833 45.560 0.000 141.000
52
Number of total high schools
0.308
Ratio of private high schools
Panel B: Group B
Average hensachi score39.319 3.236 31.072 53.214
Number of graduates
183.563 78.554 13.000 445.000
Number of teachers
36.363 13.632 5.000 82.000
Number of classrooms
15.759 5.150 3.000 27.000
Pupils per teacher
16.037 5.616 3.769 54.800
Pupils per class
36.297 5.183 11.400 68.833
Ratio of applicants to universities
0.199 0.147 0.012 1.000
Population density (/1000)
3.101 4.241 0.003 19.618
Ratio of children
0.093 0.011 0.052 0.148
Taxable income (1,000,000 JPY)
3.198 0.739 2.170 10.232
Land price (1,000,000 JPY)
0.138 0.268 0.004 3.043
Number of universities within 30 km 29.492 40.048 0.000 144.000
476
Number of total high schools
0.227
Ratio of private high schools
Panel C: Group C
Average hensachi score38.912 3.322 30.500 53.214
Number of graduates
169.388 76.710 13.000 445.000
Number of teachers
34.935 13.501 5.000 82.000
Number of classrooms
14.506 5.302 3.000 30.000
Pupils per teacher
15.366 5.270 3.769 54.800
Pupils per class
35.844 5.100 11.500 68.833
Ratio of applicants to universities
0.178 0.124 0.009 1.000
Population density (/1000)
2.087 3.469 0.003 19.618
Ratio of children
0.094 0.011 0.040 0.148
Taxable income (1,000,000 JPY)
3.025 0.628 2.039 11.267
Land price (1,000,000 JPY)
0.100 0.205 0.003 3.043
Number of universities within 30 km 19.604 32.700 0.000 144.000
915
Number of total high schools
0.189
Ratio of private high schools

Schools without high achievers(median for the entire period)
Mean Std. Dev. Min.
Max.

t value

38.651 2.893 31.000 51.322
152.925 74.614 10.000 457.000
32.889 13.324 4.000 79.000
13.333 5.275 3.000 30.000
14.343 4.277 1.737 43.267
34.907 5.552 5.500 66.500
0.143 0.099 0.000 0.949
1.818 3.301 0.003 20.579
0.093 0.010 0.035 0.184
2.949 0.507 2.079 11.117
0.083 0.174 0.003 3.043
16.908 29.735 0.000 144.000
1115
0.145

1.895
3.277
1.845
2.393
2.442
-0.1302.599
3.255-1.147
2.675
2.287
2.868

37.725 2.969 31.000 51.322
130.863 68.221 10.500 416.000
29.848 12.759 4.000 74.000
11.835 5.043 3.000 27.000
13.550 4.063 1.737 32.897
33.803 5.980 5.500 65.000
0.107 0.078 0.000 0.949
1.260 2.718 0.004 20.579
0.093 0.010 0.035 0.127
2.842 0.366 2.101 4.534
0.060 0.074 0.003 0.699
11.283 22.386 0.000 139.000
559
0.129

7.129
10.062
6.992
9.902
6.965
5.884
10.422
7.025
1.079
8.125
5.014
7.541

37.179 2.884 31.000 45.881
118.154 66.335 10.500 416.000
27.591 12.557 4.000 68.500
11.276 5.034 3.000 25.000
13.357 3.946 2.750 26.770
33.376 6.207 8.833 65.000
0.092 0.085 0.000 0.949
1.588 3.401 0.004 20.579
0.092 0.010 0.057 0.120
2.870 0.398 2.206 4.534
0.067 0.088 0.007 0.699
14.420 26.575 0.000 139.000
257
0.160

7.688
10.014
7.765
7.839
6.244
5.181
11.829
1.979
2.680
4.453
3.259
2.475

Table 1. Comparison of the pretreatment and control groups

Overall, the treatment groups show larger school sizes in terms of the number ofstudents and teachers and that of university applicants. However, the quality of education21

is not high because of the large number of pupils per teacher and pupils per class. The
population density, taxable income, land prices, and the number of surrounding
universities indicate that the schools in the treatment group are located in relatively
developed urban areas. Therefore, the low cost of attending university and the large
number of students and university applicants make it more likely that high achievers
appear by chance. I control for these characteristics and use an event study framework to
identify the impact of high achievers. I also ensure that these variables are mostly
consistent before and after the appearance of high achievers.

4.

Identification strategy

4.1. Event study framework
To investigate how high school admissions performance changes due to theappearance of high achievers, I estimate the following event study equations.
14

𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 𝛼𝛼 + � 𝛽𝛽𝑘𝑘 𝟏𝟏[𝑡𝑡 − 𝐻𝐻𝐻𝐻𝐻𝐻ℎ𝑖𝑖 = 𝑘𝑘] + 𝑋𝑋𝑖𝑖𝑖𝑖 + 𝐶𝐶𝑐𝑐𝑐𝑐 + 𝑆𝑆𝑖𝑖 + 𝑌𝑌𝑡𝑡 + 𝐸𝐸𝑟𝑟𝑟𝑟 + 𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖 (1)𝑘𝑘=−12𝑘𝑘≠−1

where 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 is the outcome of interest in year 𝑡𝑡 for high school 𝑖𝑖 inmunicipality 𝑐𝑐 covered by the board of education 𝑟𝑟 , and 𝐻𝐻𝐻𝐻𝐻𝐻ℎ𝑖𝑖 is the year highachievers appeared in high school 𝑖𝑖. 𝑋𝑋𝑖𝑖𝑖𝑖 and 𝐶𝐶𝑐𝑐𝑐𝑐 are vectors of time-varying school andregional characteristics that may affect the results, respectively. 𝑆𝑆𝑖𝑖 is a school-fixed
effect controlling for time-invariant characteristics at the school level. 𝑌𝑌𝑡𝑡 is a year-fixed

effect controlling for year-specific factors. Ert is the intersection term of the Board of
Education and year dummy. 9 Although 𝐸𝐸𝑟𝑟𝑟𝑟 flexibly controls for unobservable annual
9

The Japanese Board of Education has authority over prefectures or ordinance-

designated cities and can determine policy for the area under its jurisdiction.
22

institutional changes and trends in the area, it is only used in the main analysis because it
absorbs most of the variation arising from the lack of schools in each area. All standard
errors are clustered at the school level to address the possibility of the temporal correlationof error terms within schools.
The relative time indicators, 𝟏𝟏[𝑡𝑡 − 𝐻𝐻𝐻𝐻𝐻𝐻ℎ𝑖𝑖 = 𝑘𝑘] are equal to 1 when the observed

value is 𝑘𝑘 = −12, … , 14 years from the year in which high achievers appeared in a givenhigh school and zero for all schools with no high achievers. The omitted year, 𝑘𝑘 = −1,

corresponds to the year before the appearance of high achievers. Thus, 𝑘𝑘 = 0
corresponds to the year that high achievers appeared, and 𝑘𝑘 = 1 corresponds to after thesubsequent year. The coefficient 𝛽𝛽𝑘𝑘 captures the trend of the outcome of interest before
and after the appearance of high achievers.

Following previous studies (Bailey and Goodman-Bacon, 2015; Acton, 2022), I alsopresent the grouped DID equation estimates to succinctly summarize the results.
𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 𝛼𝛼 + 𝛽𝛽1 𝟏𝟏[−5 ≤ 𝑡𝑡 − 𝐻𝐻𝐻𝐻𝐻𝐻ℎ𝑖𝑖 ≤ 1] + 𝛽𝛽2 𝟏𝟏[𝑡𝑡 − 𝐻𝐻𝐻𝐻𝐻𝐻ℎ𝑖𝑖 = 0]
+ 𝛽𝛽3 𝟏𝟏[1 ≤ 𝑡𝑡 − 𝐻𝐻𝐻𝐻𝐻𝐻ℎ𝑖𝑖 ≤ 2] + 𝛽𝛽4 𝟏𝟏[3 ≤ 𝑡𝑡 − 𝐻𝐻𝐻𝐻𝐻𝐻ℎ𝑖𝑖 ≤ 5]
+ 𝛽𝛽5 𝟏𝟏[𝑡𝑡 − 𝐻𝐻𝐻𝐻𝐻𝐻ℎ𝑖𝑖 ≥ 6] + 𝑋𝑋𝑖𝑖𝑖𝑖 + 𝐶𝐶𝑐𝑐𝑐𝑐 + 𝑆𝑆𝑖𝑖 + 𝑌𝑌𝑡𝑡 + 𝐸𝐸𝑟𝑟𝑟𝑟 + 𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖(2)

where the coefficients 𝛽𝛽1, 𝛽𝛽2, 𝛽𝛽3, 𝛽𝛽4 , and 𝛽𝛽5 are used to capture changes in theoutcome of interest from one to five years before, from the occurrence year, from one to

two years after, from three to five years after, and from six or more years after theappearance of high achievers, respectively. The baseline is more than six years before theappearance of high achievers, and 𝛽𝛽1 captures whether there was a different trend

between the treatment and control groups before the appearance of high achievers. 𝛽𝛽2 isthe impact of the year that high achievers appeared, and it includes the outcomes of highachievers and the peer effects arising from their effect. The students one to two years after
the treatment enrolled before the appearance of high achievers, and for them, the23treatment can be considered an exogenous shock. Thus, 𝛽𝛽3 captures the impact of highachievers on the confidence or information available to the lower grades and does notinclude the impact of their school choice. Because 𝛽𝛽4 and 𝛽𝛽5 capture the outcomechanges for students enrolled after the appearance of high achievers, they also potentially
include enrollment characteristic changes due to high achievers. The other control
variables and the clustering of standard errors are the same as those in equation (1).
Equations (1) and (2) use a two-way fixed effects (TWFE) approach that compares
the outcomes for schools in which high achievers have just appeared with the outcomesfor schools in which high achievers have yet to appear, have already appeared, or will
never appear. However, recent studies (e.g., Sun and Abraham, 2021) have suggested thatwhen the treatment timing and effect are correlated, TWFE models might provide biased
estimates. Therefore, I confirm the robustness of the results using an alternative approachthat compares the treated schools with only a untreated control group of schools (Sun and
Abraham, 2021).

4.2. Identifying assumptions
The identification strategy used in this paper relies on whether schools without highachievers can be effectively compared to those with high achievers. Confirming thiscondition requires that the parallel trend assumption is first satisfied, which can be
examined by checking the pretreatment changes in equations (1) and (2). Second, there
must be no contemporaneous changes regarding high achievers that could affect theoutcome of the treatment group. For example, such changes in a school district systemand curriculum can improve student capabilities, leading to the appearance of highachievers. While it is impossible to rule out all of these changes, several observable24

factors are addressed and examined in this study.
Here, I investigate the validity of the identification strategy by examining whether theoccurrence of high achievers in high schools is associated with changes in thecharacteristics of the relevant schools or regions. Significant correlations would indicatedthat the assumption that the appearance of high achievers is quasirandom is not satisfied.
School characteristics include the number of enrollments, teachers, and classes, andregional characteristics include population density, taxable income, and land prices. I
estimated equation (1), controlling only for school and year fixed effects, using each
effect as a dependent variable.
The results are shown in Figure 4, with the left side displaying the results for school
characteristics and the right displaying the results for regional characteristics. The top,
middle, and bottom panels show the estimation results using Groups A, B, and C to definehigh achievers. Panels (a) and (b) indicate that when acceptance into Group A defines ahigh achiever, neither school nor area characteristics are significantly related to theappearance of high achievers. Panels (c) and (e) suggest that the number of enrollments
and number of teachers tends to increase in schools with high achievers. Additionally, inpanels (d) and (f), the schools with high achievers show increases in population density
and land prices from those of prior periods. Neither shows any systematic shift
immediately before or after the appearance of high achievers, and the trends are consistentthroughout the period. One interpretation is that high achievers are more likely in areas
with a large or growing population. Therefore, the estimation gas not been confoundedby some systematic change associated with the appearance of high achievers. In the main
specification, I control for these trends in the estimation by using school and regional
characteristics as explanatory variables.

25

Figure 4. Changes in various factors surrounding the appearance of high achievers
Note: This figure shows the changes in several variables over the nine years before andafter the appearance of high achievers. Each figure shows estimates of the coefficients
𝛽𝛽𝑘𝑘 in equation (1), controlling only for year and school fixed effects. All standard errors
are clustered at the school level.

26

5.

Results

5.1. Main results
Figure 5 displays the event study estimates detailing how high achievers change theselectivity of the universities accepting students from that high school. Panels (a), (c), and(e) on the left side of the figure show baseline estimates controlling only for school- andyear-fixed effects and estimates including additional control for school and regional
characteristics. Panels (b), (d), and (f) on the right side of the figure provide estimates
controlling for the cross terms of year and board of education fixed effects and estimatesthat apply the Sun and Abraham (2021) specification. The results are all very similar,
confirming that the main results are not sensitive to changes in variables or specifications.
First, the coefficients prior to the occurrence of high achievers were hardly significant,while the acceptance performance increased significantly afterwards. The only slight
upward trend appears in the estimates with controls in panel (a), but there is no significant
difference between the treatment and control groups from one and those from six years
prior. Therefore, a parallel trend, with no gradual improvement in performance due toschool efforts or structural changes, can be confirmed.
In all results, the year in which high achievers appear significantly increases theselectivity of the accepting universities, which is unsurprising since such selectivity
includes the results of the high achievers themselves. Notably, the coefficients are always
positive after the appearance of high achievers. Panels (a) and (b), which show the resultsof defining high achievers by Group A, demonstrate that the acceptance performance
improved after the appearance of high achievers, although the statistical significance islow, and this impact has continued for almost ten years. Panels (c) and (d) show that the27

appearance of high achievers increases the selectivity of accepting universities byapproximately one point per decade. Panels (e) and (f) indicate that high achievers
improve acceptance performance and that their impact gradually strengthens this effect.
However, the fact that the estimated results of achievers defined by acceptance into GroupC, may include the effects of structural changes because of the significant difference inunobserved characteristics between the treatment and control groups.
Table 2 shows the grouped DID estimates for the main results above using equation(2). Columns (1)-(3), (4)-(6), and (7)-(9) provide the results of the estimation with highachievers defined by groups A, B, and C, respectively. The coefficients from 1‐5 years
prior to treatment are either not statistically significant or not very significant, again
confirming that there is no difference in prior trends between the treatment and control
groups. The results show that 1-2 years after high achievers appear, the hensachi scoresof accepted universities increase by approximately 0.8 to 1.8 points. This effect resultsfrom students who enrolled without anticipating the appearance of high achievers. Thus,
it is suggested that high achievers exert a role model effect, which improves theconfidence and motivation of younger students or enhances their knowledge regarding
university entrance exams. It also indicates that the positive and significant effect persists
more than three years after the appearance of the high achiever and intensifies after
another six years or longer. This might be due to the development of school reputation,
which attracts more qualified enrollments, or it could be due to the gradual accumulationof expertise in university entrance examinations.

28

Figure 5. Event study estimates of the impact of high achievers
Note: Each figure shows estimates of the coefficient 𝛽𝛽𝑘𝑘 in equation (1). All standard

errors are clustered at the school level. Panels (a) and (b), (c) and (d), and (e) and (f) show
the estimation results with high achievers defined by acceptance into groups A, B, and C,
respectively.

29

Time since treatment
1–5 years priorYear of appearance
1–2 years after
3–5 years after
6+ years afterControls
Number of graduates(1)

Group A(2)

(3)

-0.049(0.399)6.12***(1.06)
1.20*(0.511)1.24*(0.611)
1.67**(0.635)1.12(0.698)9.15***(1.56)
1.78*(0.847)2.27*(0.916)
2.41**(0.922)1.21(0.789)8.42***(1.66)
1.74(0.961)1.82(1.01)
2.26*(0.994)-0.319* -0.102(0.160) (0.254)
4.81*** 5.41***(0.287) (0.432)
0.991*** 1.30***(0.232) (0.337)
0.745** 0.929**(0.238) (0.355)
1.17*** 1.32**(0.273) (0.403)-0.401(0.238)
4.39***(0.385)0.778*(0.318)0.437(0.330)0.774*(0.378)-0.223 -0.088 -0.310(0.129) (0.201) (0.185)
3.66*** 3.95*** 3.21***(0.212) (0.304) (0.280)
1.09*** 1.34*** 0.858***(0.192) (0.275) (0.254)
0.895*** 0.964** 0.426(0.211) (0.298) (0.274)
1.52*** 1.63*** 0.860**(0.251) (0.350) (0.322)

YES
YES
NO
16,2800.37360.077(0.262)0.029(0.061)
0.039(0.030)0.124(0.265)-7.82(16.1)
0.279(0.383)0.179(0.645)YES
YESNO
10,4330.39910.154(0.249)0.055(0.054)0.023(0.029)-0.266(0.307)0.584(16.2)
0.509(0.411)-0.017(0.623)YES
YES
YES
10,4330.4841

0.058(0.261)-0.029(0.060)0.072*(0.031)
0.299(0.270)-12.4(17.6)
0.062(0.404)0.027(0.393)
YES
YES
NO
8,854
0.4359-0.066(0.261)-0.004(0.057)0.048(0.031)-0.150(0.297)-0.422(16.8)
0.167(0.393)0.277(0.411)
YES
YES
YES
8,854
0.52740.118(0.232)-0.012(0.060)0.056(0.031)0.305(0.251)-18.4(16.3)
0.187(0.411)
0.047(0.384)YES
YES
NO
10,2250.4262Pupils per teacher
Pupils per classroom
Population density
Ratio of children
Taxable income
Land priceSchool FE
Year FE
Year×Region FE
Observations
Adjusted R-squared

(4)

YES
YES
NO
14,3540.4261

Group B
(5)

(6)

(7)

YES
YES
NO
16,6490.4177

Group C
(8)

(9)

0.153
(0.226)0.009(0.053)0.039(0.030)-0.161(0.292)-5.13(16.5)
0.158(0.410)0.380(0.373)YES
YES
YES
10,2250.5118

Table 2. The impact of high achievers on university selectivity toward their school
Note: Robust standard errors are clustered at the school level and appear in parentheses.
*, **, and *** indicate statistical significance at the 5%, 1%, and 0.1% levels, respectively.

5.2. Alternative outcomes
The main results show that the appearance of high achievers improves university
acceptance performance over the long term. In this section, through the use of explained
variables other than university selectivity, some alternative explanations are rejected and30

the mechanism for deriving the results is examined. Table 3, Panels a-c show theestimation results from defining high achievers by acceptance into groups A, B, and C,
respectively. Column (1) restates the baseline estimates displayed Table 2 for reference.
Identifying whether high achievers affect only the top tier of students or all students
in a school would be interesting. Additionally, one potential concern is that the appearanceof high achievers may have prompted the creation of connections (e.g., recommendation
quotas) with particular universities. 10 To address these concerns, (2) median, (3)maximum, and (4) minimum values of hensachi scores, rather than the mean value, areused for estimation. The results indicate an increase in not only the maximum value but
also the minimum and median values. Thus, the appearance of high achievers increased
the average level of university selectivity, confirming that the scenario where a few
students increase the average does not explain the results. Column (5) also shows theestimated hensachi score results from using only universities other than those accepting
the high achiever, which are almost identical to the main results. Therefore, these results
are derived from students who were accepted to different universities than those of thehigh achievers, and thus the high school-university connection explanation is not
persuasive.
Additionally, previous studies have indicated that higher education programs exert
different impacts on high school student applications, acceptance, and enrollment (e.g.,
Phillips and Reber, 2022). To understand the impact of high achievers on student behavior,

10

Appendix Figure A1 shows the average number of successful applicants by relative

time since the appearance of high achievers for the same universities as high achievers
and other universities, respectively. This shows that many students have been accepted tothe same university as the high achiever after the high achiever's appearance.
31

columns (6), (7), and (8) present estimation results from using graduate job placement
rates, university application rates, and acceptance rates for all universities as the explained
variables, respectively. The results suggest that high achievers have little or no significant
effect on employment, higher education aspirations, or university acceptance rates. The
results in Panel B, column (6), imply that there may have been a slight increase in thenumber of students who went to university rather than finding a job, but this is of little
statistical significance or impact. The findings indicate that high achievers do not increase
the number of applicants or to the acceptance rates of universities but only significantly
impact the selectivity of the universities to which they are accepted.

32Hensachi score
Baseline

Panel A: Group A
1–5 years priorYear of appearance
1–2 years after
3–5 years after
6+ years after
Observations
Panel B: Group B
1–5 years priorYear of appearance
1–2 years after
3–5 years after
6+ years after
Observations
Panel C: Group C
1–5 years priorYear of appearance
1–2 years after
3–5 years after
6+ years after
Observations
School characteristics
City characteristics
School FE
Year FE
Year×Region FE

Student behavior(4)

Excludingthe same
university(5)

Job
Ratio of University
placement university acceptancerateapplicants
rate
(6)
(7)
(8)

-0.574(1.42)
18.7***(1.75)
1.75(1.81)
2.40(1.91)
1.67(1.95)
10,4331.14(0.846)
4.43*(1.95)
1.83*(0.900)1.86(1.02)
1.94(1.05)
10,4331.16(0.699)5.32***(1.53)
1.80*(0.815)1.92*(0.919)2.36*(0.933)
10,4310.040**(0.014)0.023(0.023)0.019(0.019)0.024(0.020)0.019(0.023)
11,768-0.023*(0.010)-0.029(0.022)-0.011(0.014)-0.017(0.014)-0.017(0.017)
11,768-0.004(0.006)0.005(0.022)0.010(0.011)0.007(0.012)0.012(0.016)
11,768-0.105(0.289)4.31***(0.511)
1.13**(0.377)0.804*(0.403)
1.28**(0.456)8,854-0.935*(0.437)
13.3***(0.515)
2.46***(0.589)1.71**(0.603)2.06**(0.663)8,854

0.519*(0.222)
1.66***(0.431)0.780**(0.288)0.596(0.311)
0.961**(0.362)8,854-0.182(0.245)
1.59***(0.373)0.920**(0.323)0.536(0.338)0.994*(0.388)8,839-0.006(0.006)-0.011(0.007)-0.018*(0.008)-0.019*(0.008)-0.021*(0.010)10,085-0.003(0.004)-0.004(0.006)0.0005(0.005)-0.002(0.006)-0.004(0.006)
10,085-0.009*(0.005)0.012(0.007)-0.003(0.005)-0.005(0.006)-0.002(0.007)
10,085-0.101(0.232)
3.08***(0.352)
1.10***(0.315)0.756*(0.345)
1.48***(0.403)
10,225
YES
YES
YES
YES
NO

-0.940**(0.343)
9.11***(0.448)2.27***(0.458)
1.42**(0.489)2.01***(0.553)
10,225
YES
YES
YES
YES
NO

0.460**(0.170)1.60***(0.303)0.874***(0.243)0.812**(0.272)
1.31***(0.332)
10,225
YES
YES
YES
YES
NO

-0.196(0.195)0.551*(0.268)0.847**(0.263)0.513(0.286)
1.17***(0.342)
10,160
YES
YES
YES
YES
NO

-0.002(0.004)-0.004(0.006)-0.005(0.006)-0.006(0.007)-0.003(0.008)11,360
YES
YES
YES
YES
NO

-0.002(0.003)0.001(0.004)-0.0001(0.004)-0.002(0.004)-0.005(0.005)
11,360
YES
YES
YES
YES
NO

-0.006*(0.003)0.011*(0.004)-0.001(0.004)-0.003(0.005)0.001(0.005)
11,360
YES
YES
YES
YES
NO

Med.

Max.

Min.

(1)

(2)

(3)

1.12
(0.698)9.15***(1.56)
1.78*(0.847)2.27*(0.916)
2.41**(0.922)
10,4331.67*(0.734)8.31***(1.84)
1.60*(0.813)2.45*(0.955)
2.62**(0.982)
10,433-0.102(0.254)
5.41***(0.432)
1.30***(0.337)0.929**(0.355)1.32**(0.403)8,854-0.088(0.201)
3.95***(0.304)
1.34***(0.275)0.964**(0.298)
1.63***(0.350)
10,225
YES
YES
YES
YES
NO

Table 3. Estimation results using different explained variables
Note: Robust standard errors are clustered at the school level and appear in parentheses.
*, **, and *** indicate statistical significance at the 5%, 1%, and 0.1% levels, respectively.
33

5.3. Robustness checks
Table 4 provides the results of a series of robustness checks using sample changes.
Column (2) provides the results of using all high schools, including reorganized andnewly established schools, to ensure that the results are not derived by arbitrary sample
selection. Conversely, Columns (3)-(5) report the results of a stricter sample selection.
Column (3) presents results excluding schools with large fluctuations in enrollment, thus
more strictly eliminating the possibility that the school structure had changed. Column(4) shows the results after excluding small schools with fewer than 100 students, where
outcomes can fluctuate. Column (5) presents the results excluding private schools andintegrated middle and high schools, which are more likely to involve variance in the input
factors. These results were generally consistent with the main results, confirming that theresults are not derived from the sample characteristics.
The main analysis defines nonacademic track schools as high schools that have notsent graduates to universities included in Group C for over five years. However, as seenin Appendix Figure A1, some schools had students who met the definition of highachievers more than six years before the appearance of high achievers in that school. Suchschools may be inherently more effective than schools in the control group, which could
bias the results. Therefore, we performed estimations using samples that excluded highschools with students who had previously been accepted to the same university group as
the high achievers. The results shown in Column (6) confirm the high achiever effect evenwhen excluding such schools. Column (7) also presents the results of excluding schools
in which two or more successful applicants simultaneously met the definition of highachievers. These estimates make it more likely that the sample comprises exclusively
nonacademic track schools and confirm that, even so, the results are consistent with the34main results.

Panel A: Group A
1–5 years priorYear of appearance
1–2 years after
3–5 years after
6+ years after
Observations
Panel B: Group B
1–5 years priorYear of appearance
1–2 years after
3–5 years after
6+ years after
Observations
Panel C: Group C
1–5 years priorYear of appearance
1–2 years after
3–5 years after
6+ years after
Observations
School characteristics
City characteristics
School FE
Year FE
Year×Region FE

ExcludingOnly one
ExcludingNo history
upper
high
small
of highsecondary &achiever at aschoolsachievers
privatetimeschools
(4)
(5)
(6)
(7)

Baseline

All schools

Stableenrollment
schools

(1)

(2)

(3)

1.12
(0.698)9.15***(1.56)
1.78*(0.847)2.27*(0.916)
2.41**(0.922)
10,4330.118(0.745)
8.66***(1.40)
1.38(0.840)1.87*(0.852)2.30*(0.923)
12,5381.61(1.02)
11.0***(2.13)
2.70*(1.14)
2.99*(1.27)
3.31**(1.26)
5,942

1.12(0.697)
9.15***(1.56)
1.80*(0.842)2.27*(0.913)
2.42**(0.918)
10,1971.12(0.699)9.15***(1.56)
1.80*(0.843)2.28*(0.917)
2.43**(0.922)
10,2541.21(0.754)9.85***(1.76)
1.93*(0.953)
2.81**(1.01)
2.84**(0.998)
10,3811.21(0.842)
9.39***(1.87)
1.58(1.01)
2.20*(1.09)
2.17*(1.07)
10,355-0.102(0.254)
5.41***(0.432)
1.30***(0.337)0.929**(0.355)1.32**(0.403)8,854-0.140(0.234)
5.54***(0.394)
1.40***(0.317)
1.10***(0.334)
1.52***(0.377)
10,700-0.326(0.300)4.90***(0.511)
1.12**(0.405)0.926*(0.424)1.22*(0.495)5,104-0.160(0.249)5.36***(0.431)
1.25***(0.335)0.867*(0.355)
1.23**(0.404)8,644-0.123(0.256)
5.45***(0.438)
1.21***(0.337)0.883*(0.358)
1.22**(0.407)
8,679

0.299
(0.310)6.51***(0.537)
1.75***(0.395)1.31**(0.417)
1.89***(0.461)
7,623-0.312(0.319)4.57***(0.528)
1.29**(0.433)0.647(0.443)1.14*(0.486)
7,059-0.088(0.201)
3.95***(0.304)
1.34***(0.275)0.964**(0.298)
1.63***(0.350)
10,225
YES
YES
YES
YES
NO

-0.151(0.185)
3.96***(0.280)1.31***(0.255)1.01***(0.278)
1.65***(0.327)
12,032
YES
YES
YES
YES
NO

-0.321(0.252)
3.25***(0.379)1.10**(0.347)0.759*(0.373)
1.32**(0.430)
5,986
YES
YES
YES
YES
NO

-0.148(0.201)
3.84***(0.306)
1.30***(0.277)0.890**(0.301)
1.53***(0.354)
9,984
YES
YES
YES
YES
NO

-0.102(0.202)
3.95***(0.307)
1.28***(0.276)
0.923**(0.300)1.56***(0.352)
10,023
YES
YES
YES
YES
NO

0.666*(0.279)5.24***(0.416)
2.14***(0.378)
1.66***(0.400)2.23***(0.449)
6,404
YES
YES
YES
YES
NO

-0.040(0.251)
3.52***(0.385)
1.44***(0.352)0.677(0.368)
1.43***(0.428)
6,588
YES
YES
YES
YES
NO

Table 4. Robustness checks using altered sample selections
35

Note: Robust standard errors are clustered at the school level and appear in parentheses.
*, **, and *** indicate statistical significance at the 5%, 1%, and 0.1% levels, respectively.

Appendix Table A6 also presents the estimation results using alternative definitionsof nonacademic track schools and high achievers. Columns (1)-(3) present estimation
results where high achievers are defined using certain hensachi score thresholds rather
than the groups of universities shown in Figure 2. Columns (4)-(6) show the estimation
results using samples in which the threshold for defining nonacademic track schools is
extended from five to ten years. Columns (7) and (8) report estimates that define
nonacademic track schools by Group B rather than Group C. All estimated results are
consistent with the main results, confirming that the main results are not sensitive tochanges in the definition of nonacademic track schools or high achievers.

5.4. Heterogeneity of impact
Because peer and role model effects can be heterogeneous (e.g., Clots-Figueras, 2012;
Luppino, 2015), a subsample analysis is used here to examine the heterogeneous effects
of high achievers by school and regional characteristics. Table 5 presents the estimation
results from using the two divided samples according to the number of students, thetaxable income, the percentage of university graduates in the neighborhood, and whether
the school offers a regular course. Columns (2) and (3) indicate that smaller schools are
more affected by high achievers, which is interesting given that high achievers are more
likely to appear in larger schools. This could be because smaller schools are more likely
to recognize high achievers, and student confidence and application information are
scarce in such schools. Columns (4)-(7) show that the impact of high achievers is more36

remarkable in areas with lower taxable income and education, which is consistent withthe explanation that the more disadvantaged an area is, the greater the room for
improvement. Columns (8) and (9) show that the appearance of high achievers positively
impacts both general and vocational high schools but has a relatively weak impact on
vocational high schools. This indicates that the effect of high achievers is weak forstudents who do not wish to attend university, reinforcing that high achievers are the causeof the main results.

37

Year of appearance
1–2 years after
3–5 years after
6+ years after
Number of treatments
Number of controls
Observations
Panel B: Group B
1–5 years priorYear of appearance
1–2 years after
3–5 years after
6+ years after
Number of treatments
Number of controls
Observations
Panel C: Group C
1–5 years priorYear of appearance
1–2 years after
3–5 years after
6+ years after
Number of treatments
Number of controls
Observations
School characteristics
City characteristics
School FE
Year FE
Year×Region FE

With or without ageneral course

Taxable income

Large

Small

High

Low

High

Low

With

Without

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

1.12
(0.698)9.15***(1.56)
1.78*(0.847)2.27*(0.916)
2.41**(0.922)
52
1115
10,4330.847(0.961)
7.46***(1.86)
2.02(1.10)
1.83(1.14)
2.44*(1.14)
38
540
5,899

1.55*
(0.643)12.6***(2.10)
0.852(0.893)3.43*(1.38)
1.50(1.37)
14
575
4,534

0.814
(0.564)
6.24***(1.58)
1.88**(0.643)1.66(0.869)1.63(0.897)
34
546
5,482

1.53
(1.34)
12.9***(2.34)
1.51(1.78)
3.21(1.74)
3.84*(1.72)
18
569
4,951

1.51
(0.805)
7.94***(1.84)
2.29*(1.01)
2.25(1.15)
2.45*(1.16)
33
547
5,513

-0.051(1.29)
10.9***(2.39)
0.404(1.52)
1.82(1.27)
1.77(1.33)
19
568
4,920

0.667
(0.618)5.02***(1.04)
1.34(0.790)1.95*(0.955)1.70(0.933)
30
519
4,046

1.25
(1.07)
13.8***(2.27)
1.81(1.36)
1.94(1.44)
2.24(1.50)
22
596
6,387

-0.102(0.254)
5.41***(0.432)
1.30***(0.337)0.929**(0.355)1.32**(0.403)
476
559
8,854

-0.430(0.271)4.08***(0.413)0.651(0.354)0.543(0.384)0.758(0.441)
322
190
5,009

0.421
(0.510)8.55***(0.960)2.63***(0.736)1.54*(0.743)
2.28**(0.832)
154
369
3,845

-0.062(0.248)
4.28***(0.411)1.02**(0.351)0.594(0.387)0.738(0.459)
310
204
4,721

-0.374(0.600)7.53***(0.975)1.73*(0.759)1.37(0.762)2.09**(0.802)
166
355
4,133

-0.218(0.264)4.33***(0.438)1.06**(0.364)0.750(0.389)0.960*(0.457)
304
210
4,775

0.034
(0.530)7.36***(0.891)1.66*(0.684)1.08(0.702)1.79*(0.740)
172
349
4,079

0.478
(0.331)
4.76***(0.555)
1.42**(0.450)1.32**(0.487)
1.92**(0.583)
250
242
3,325

-0.473(0.352)
5.88***(0.611)
1.26**(0.470)0.690(0.491)0.954(0.545)
226
317
5,529

-0.088(0.201)
3.95***(0.304)
1.34***(0.275)0.964**(0.298)
1.63***(0.350)915
257
10,225
YES
YES
YES
YES
NO

-0.453*(0.226)
2.76***(0.328)0.710*(0.314)0.423(0.349)0.938*(0.415)
511
72
5,754
YES
YES
YES
YES
NO

0.321
(0.359)5.51***(0.545)2.09***(0.481)
1.56**(0.513)
2.39***(0.585)
404
185
4,471
YES
YES
YES
YES
NO

0.010
(0.230)3.27***(0.368)
1.09**(0.344)0.629(0.378)0.965*(0.445)
486
96
5,326
YES
YES
YES
YES
NO

-0.219(0.340)4.60***(0.491)
1.56***(0.438)
1.22**(0.471)
2.24***(0.545)
429
161
4,899
YES
YES
YES
YES
NO

-0.034(0.226)
3.43***(0.364)1.08**(0.340)0.775*(0.366)1.06*(0.422)
490
93
5,450
YES
YES
YES
YES
NO

-0.145(0.341)
4.51***(0.497)
1.60***(0.436)1.09*(0.474)
2.19***(0.559)
425
164
4,775
YES
YES
YES
YES
NO

0.516
(0.329)3.94***(0.469)1.66***(0.431)
1.48**(0.470)2.28***(0.574)
478
110
4,102
YES
YES
YES
YES
NO

-0.455(0.253)4.01***(0.400)1.18**(0.358)0.660(0.388)
1.25**(0.447)
437
147
6,123
YES
YES
YES
YES
NO

Baseline

Panel A: Group A
1–5 years prior

Ratio of university or
higher graduates

Number of students

Table 5. Subsample analysis for heterogeneous effects
Note: Robust standard errors are clustered at the school level and appear in parentheses.
*, **, and *** indicate statistical significance at the 5%, 1%, and 0.1% levels, respectively.
38

5.5. Spillover effect
The main results show that schools with high achievers have been continually
improving their admission performance for over a decade. This is a result of highachievers improving the confidence and application information of the students, thus
improving their performance and career choices. However, the reputation of high achiever
can attract outstanding students from surrounding schools. In this scenario, theappearance of high achievers can lower the placement rates of the surrounding highschools.
To address this concern, we estimate the average outcome of high schools within 10
kilometers of a given high school as the explained variable and report the results in Table
6. Columns (1)-(3) present the estimation results using the surrounding high schools'
mean, maximum, and minimum hensachi scores as explained variables. The results
showed that the appearance of high achievers had no significant impact on the university
acceptance performance of the surrounding schools. Column (4) provides the estimation
results using the number of students enrolled in the surrounding schools as the explained
variable. Although there appears to be a slight increase in the number of students
following the appearance of high achievers only when Group A is used in the definition,
it is not statistically significant. Columns (5), (6), and (7) present estimation results fromusing the rate of job placement, university application, and university acceptance in thesurrounding schools as explained variables. These results suggest that the occurrence ofhigh achievers do not attract the brightest students from the surrounding schools. Rather,high school students seem slightly more likely to choose higher education over
employment in those areas where high achievers have appeared.

39Hensachi score

Panel A: Group A
1–5 years priorYear of appearance
1–2 years after
3–5 years after
6+ years after
Observations
Panel B: Group B
1–5 years priorYear of appearance
1–2 years after
3–5 years after
6+ years after
Observations
Panel C: Group C
1–5 years priorYear of appearance
1–2 years after
3–5 years after
6+ years after
Observations
School characteristics
City characteristics
School FE
Year FE
Year×Region FE

Student behavior(3)

Number of
students
enrolled(4)

Job
placement
rate(5)

Ratio ofuniversity
applicants(6)

University
acceptance
rate
(7)

-0.275(0.609)-0.434(0.859)-0.751(0.881)
0.255(0.775)0.564(0.974)
10,696-0.013(0.239)0.075(0.436)-0.061(0.278)0.167(0.326)0.035(0.327)
10,69621.4(87.7)
112.7(114.7)172.7(123.5)219.4*(111.7)161.7(122.8)
10,768-0.0007(0.005)0.007(0.009)0.010(0.009)0.012(0.009)0.016(0.011)
10,7560.004(0.004)-0.006(0.006)-0.012(0.008)-0.014(0.007)-0.012(0.009)10,7560.042*(0.018)0.053(0.030)0.040(0.025)0.070*(0.030)0.113**(0.035)
10,756-0.063(0.078)0.064(0.166)-0.125(0.139)-0.125(0.149)-0.229(0.180)
9,220

0.145
(0.138)0.238(0.175)0.109(0.177)0.348(0.191)0.354(0.238)
9,220

0.079
(0.072)0.179(0.101)
0.172(0.090)0.136(0.096)0.145(0.115)9,220

2.15(25.6)
4.02(33.9)
41.0(33.6)
18.6(33.9)
9.86(39.6)
9,271-0.004*(0.002)-0.005(0.003)-0.005(0.003)-0.008*(0.004)-0.010*(0.004)
9,259

0.003
(0.003)0.002(0.004)0.002(0.004)0.002(0.004)-0.0002(0.005)
9,259

0.007
(0.009)0.020(0.012)0.019(0.012)0.032*(0.014)0.040*(0.017)
9,259-0.061(0.104)0.070(0.182)-0.126(0.162)-0.166(0.190)-0.319(0.249)10,343
YES
YES
YES
YES
NO

0.047
(0.139)0.027(0.186)0.149(0.176)0.291(0.203)0.432(0.246)
10,343
YES
YES
YES
YES
NO

0.007
(0.063)0.020(0.083)0.033(0.080)0.002(0.092)-0.002(0.115)
10,343
YES
YES
YES
YES
NO

-1.36
(17.1)-6.48(22.5)
10.3(23.3)-17.9(26.7)-16.0(33.6)
10,398
YES
YES
YES
YES
NO

-0.004(0.002)-0.005(0.003)-0.004(0.003)-0.004(0.004)-0.004(0.004)
10,387
YES
YES
YES
YES
NO

0.004
(0.003)0.006(0.003)0.006(0.004)0.006(0.004)0.004(0.005)
10,387
YES
YES
YES
YES
NO

-0.003(0.008)0.005(0.011)0.004(0.011)0.014(0.013)0.013(0.016)
10,387
YES
YES
YES
YES
NO

Avg.

Max.

Min.

(1)

(2)

0.177
(0.261)0.760(0.632)-0.012(0.872)0.182(0.313)-0.466(0.604)
10,696

Table 6. Spillover effect on neighboring high schools (within 10 km)
Note: Robust standard errors are clustered at the school level and appear in parentheses.
*, **, and *** indicate statistical significance at the 5%, 1%, and 0.1% levels, respectively.
40

6.

Conclusion
In this study, an event study framework is used to confirm that prestigious university

acceptances (high achievers) appearing in nonacademic track high schools improve theuniversity acceptance performance of their schools over the long term. The appearanceof high achievers is unrelated to changes in school input factors, such as the teacher per
student ratio, suggesting that high achievers may change school-specific factors such as
student and faculty motivation, self confidence, and knowledge of the university
admissions process. Although it is impossible to separately identify the potential
mechanisms of the effect, it is worth pointing out that high achievers may lead to asustained improvement in acceptance performance. These results underscore theimportance of familiar role models for underrepresented students.
The findings of this study present several important policy implications. First,
providing role models for students may be a cost-effective policy because it does not
require additional expensive resources. The impact of graduate university selectivity on
income and occupation is complex, and the empirical evidence in Japan is scarce, making
it difficult to conduct a cost‒benefit analysis. Nevertheless, the result of high achievers
increasing university selectivity by approximately 0.1-0.2 standard deviations over a
decade is substantial. Policies that distribute the brightest students, such as the Top N%
Plan, can be expected to exert a long-term improvement effect through high achievers.
Conversely, policies such as school choice and tracking may increase social disparities
over the long term through reduced confidence or knowledge resulting from fewer highachiever.
The inability to identify potential underlying mechanisms is a limitation of this study.
We cannot determine whether high achievers have improved the academic performance41

or application destination. defining this pathway through student achievement test scores
and application information is a critical future task. Other survey data should also be used
to examine whether high achievers change school-specific knowledge of entrance
examinations or other factors.
Furthermore, considering the heterogeneity of high achiever effects is also a fruitful
future task. Due to the data availability issues in this study, little to nothing is known
about the characteristics of high achievers. Recent empirical studies suggest theimportance of more familiar and homogeneous role models (Kofoed and McGovney,
2019; Porter and Serra, 2020). Therefore, an analysis using high achiever race, gender,
and academic major data would help address issues such as the disparity in STEM
enrollment rates between genders.
Finally, understanding the factors that lead to the appearance of high achievers is alsoessential. Although this study assumes that the appearance of high achievers is
quasirandom, it could be that they are more or less likely to appear in a particularinstitution or environment. For example, school choice and financial aid policies may
increase sorting, making it more difficult for high achievers to appear. Conversely, aschool district system based on residency can produce a big-fish-little-pond effect as well
as a high achiever effect, resulting in increased academic achievement for all. Assessing
the academic dispersion within and between schools (within regions) is important toexamining these effects to develop a more efficient educational policy.

Declaration of competing interests
The author declares no known competing financial interests or personal relationships that
can have appeared to influence the work reported in this paper.

42

Data availability statement
The datasets provided by DAIGAKUTSUSHIN Corporation and Kawaijuku arecommercial, and the raw data cannot be shared without the providers' permission. The
raw datasets can be purchased by applying to the respective companies. Although the datafrom the School Basic Survey are also not permitted to be shared, they can be applied to
the MEXT. All other datasets are publicly available. The dataset used can be provided
upon request for reproducing the results.

43

References
Acton, R. K. (2022). Is a name change a game change? The impact of college-touniversity conversions. Economics of Education Review, 88, 102240.
Araki, S., Kawaguchi, D., & Onozuka, Y. (2016). University prestige, performance
evaluation, and promotion: Estimating the employer learning model using personnel
datasets. Labour Economics, 41, 135–148.
Bailey, M. J., & Goodman-Bacon, A. (2015). The War on Poverty’s Experiment in Public
Medicine: Community Health Centers and the Mortality of Older Americans.
American Economic Review, 105(3), 1067–1104.
Barrios-Fernández, A. (2022). Neighbors’ Effects on University Enrollment. American
Economic Journal: Applied Economics, 14(3), 30–60.
Berkowitz, D., & Hoekstra, M. (2011). Does high school quality matter? Evidence fromadmissions data. Economics of Education Review, 30(2), 280–288.
Bernard, T., Dercon, S., Orkin, K., & Taffesse, A. S. (2019). Parental Aspirations forChildren’s Education: Is There a “Girl Effect”? Experimental Evidence from Rural
Ethiopia. AEA Papers and Proceedings, 109, 127–132.
Bettinger, E. P., Long, B. T., Oreopoulos, P., & Sanbonmatsu, L. (2012). The Role ofApplication Assistance and Information in College Decisions: Results from the H&R
Block Fafsa Experiment. Quarterly Journal of Economics, 127(3), 1205–1242.
Black, S. E., Denning, J. T., & Rothstein, J. (2023). Winners and Losers? The Effect ofGaining and Losing Access to Selective Colleges on Education and Labor Market
Outcomes. American Economic Journal: Applied Economics, 15(1), 26–67.
Carrell, S., & Sacerdote, B. (2017). Why Do College-Going Interventions Work?
American Economic Journal: Applied Economics, 9(3), 124–151.
Chetty, R., Friedman, J. N., Saez, E., Turner, N., & Yagan, D. (2020). Income Segregation
and Intergenerational Mobility Across Colleges in the United States. Quarterly
Journal of Economics, 135(3), 1567–1633.
Clots-Figueras, I. (2012). Are Female Leaders Good for Education? Evidence from India.
American Economic Journal: Applied Economics, 4(1), 212–244.
Conley, T. G., & Udry, C. R. (2010). Learning about a New Technology: Pineapple in
Ghana. American Economic Review, 100(1), 35–69.
Delaney, J. M., & Devereux, P. J. (2020). Choosing differently? College application
behavior and the persistence of educational advantage. Economics of Education
Review, 77, 101998.
Duflo, E., Dupas, P., & Kremer, M. (2011). Peer effects, teacher incentives, and theimpact of tracking: Evidence from a randomized evaluation in Kenya. American

44Economic Review, 101(5), 1739–1774.
Ersoy, F. (2023). Effects of perceived productivity on study effort: Evidence from a field
experiment. Journal of Economic Behavior and Organization, 207, 376–391.
González-Jiménez, V. (2022). Social status and motivated beliefs. Journal of Public
Economics, 211, 104662.
Goodman, R., & Oka, C. (2018). The invention, gaming, and persistence of the hensachi(‘standardised rank score’) in Japanese education. Oxford Review of Education, 44(5),581–598.
Guyon, N., & Huillery, E. (2021). Biased Aspirations and Social Inequality at School:
Evidence from French Teenagers. Economic Journal, 131(634), 745–796.
Guyon, N., Maurin, E., & McNally, S. (2012). The Effect of Tracking Students by Ability
into Different Schools. Journal of Human Resources, 47(3), 684–721.
Hoxby, C., & Avery, C. (2013). The Missing “One-Offs”: The Hidden Supply of HighAchieving, Low-Income Students. Brookings Papers on Economic Activity, 2013(1),
1–65.
Hoxby, C. M., & Turner, S. (2015). What high achieving low income students know aboutcollege. American Economic Review, 105(5), 514–517.
Hsieh, C. T., & Urquiola, M. (2006). The effects of generalized school choice on
achievement and stratification: Evidence from Chile’s voucher program. Journal of
Public Economics, 90(8–9), 1477–1503.
Janzen, S. A., Magnan, N. P., Sharma, S., & Thompson, W. M. (2017). Aspirations failureand formation in rural Nepal. Journal of Economic Behavior and Organization, 139,
1–25.
Kerr, S. P., Pekkarinen, T., Sarvimäki, M., & Uusitalo, R. (2020). Post-secondary
education and information on labor market prospects: A randomized field experiment.
Labour Economics, 66, 101888.
Kofoed, M. S., & McGovney, E. (2019). The effect of same-gender or same-race rolemodels on occupation choice: Evidence from randomly assigned mentors at west point.
Journal of Human Resources, 54(2), 430–467.
Lavy, V. (2021). The Long-Term Consequences of Free School Choice. Journal of theEuropean Economic Association, 19(3), 1734–1781.
Loyalka, P., Liu, C., Song, Y., Yi, H., Huang, X., Wei, J., Zhang, L., Shi, Y., Chu, J., &Rozelle, S. (2013). Can information and counseling help students from poor rural areas
go to high school? Evidence from China. Journal of Comparative Economics, 41(4),1012–1025.
Luppino, M. (2015). Peer turnover and student achievement: Implications for classroom

45

assignment policy. Economics of Education Review, 46, 98–111.
Matthewes, S. H. (2021). Better Together? Heterogeneous Effects of Tracking on Student
Achievement. Economic Journal, 131(635), 1269–1307.
Modena, F., Rettore, E., & Tanzi, G. M. (2022). Asymmetries in the gender effect of highperforming peers: Evidence from tertiary education. Labour Economics, 78, 102225.
Mouganie, P., & Wang, Y. (2020). High-Performing Peers and Female STEM Choices in
School. Journal of Labor Economics, 38(3), 805–841.
Mulhern, C. (2021). Changing College Choices with Personalized Admissions
Information at Scale: Evidence on Naviance. Journal of Labor Economics, 39(1), 219–
262.
Page, L. C., & Scott-Clayton, J. (2016). Improving college access in the United States:
Barriers and policy responses. Economics of Education Review, 51, 4–22.
Pekkarinen, T., Uusitalo, R., & Kerr, S. (2009). School tracking and intergenerational
income mobility: Evidence from the Finnish comprehensive school reform. Journal of
Public Economics, 93(7–8), 965–973.
Phillips, M., & Reber, S. (2022). Does Virtual Advising Increase College Enrollment?
Evidence from a Random-Assignment College Access Field Experiment. American
Economic Journal: Economic Policy, 14(3), 198–234.
Porter, C., & Serra, D. (2020). Gender Differences in the Choice of Major: The
Importance of Female Role Models. American Economic Journal: Applied Economics,
12(3), 226–254.
Rizzica, L. (2020). Raising Aspirations and Higher Education: Evidence from the United
Kingdom’s Widening Participation Policy. Journal of Labor Economics, 38(1), 183–
214.
Roderick, M., Coca, V., & Nagaoka, J. (2011). Potholes on the Road to College.
Sociology of Education, 84(3), 178–211.
Sebastian Cherng, H. Y. (2017). If they think I can: Teacher bias and youth of color
expectations and achievement. Social Science Research, 66, 170–186.
Sequeira, S., Spinnewijn, J., & Xu, G. (2016). Rewarding schooling success andperceived returns to education: Evidence from India. Journal of Economic Behavior
and Organization, 131, 373–392.
Sorensony, O. (2017). Regional ecologies of entrepreneurship. Journal of Economic
Geography, 17(5), 959–974.
Sun, L., & Abraham, S. (2021). Estimating dynamic treatment effects in event studies
with heterogeneous treatment effects. Journal of Econometrics, 225(2), 175–199.

46

Appendix
Figure A1. Average number of successful applicants by relative time period since
the appearance of high achievers

Note: Panels (a), (b), and (c) show the results of defining high achievers by universities
in Groups A, B, and C, respectively. In each panel, the vertical axis represents the average

47

number of successful applicants, and the horizontal axis represents the relative time since
the appearance of high achievers. In each high school, the number of students accepted
to the same university as the high achiever is accepted to is represented by dark gray bars,
and the number of students accepted to different universities than the high achiever isrepresented by light gray bars.

48

Table A1. Representativeness and availability of university data
Availability of hensachi score

Number of universities

Total
National
PublicPrivateMEXT Survey

University acceptance data

2010
778
86
95
597

2010
2020
484 (62.21%) 507 (63.77%)
82 (95.35%) 82 (95.35%)
79 (83.16%) 92 (97.87%)
317 (53.10%) 333 (54.15%)

2020
795
86
94
615

Unique universities Universities within university
available hensachi
acceptance datascores
619
96
82
441

568 (91.76%)
92 (95.83%)
58 (70.73%)
432 (97.96%)

Note: The number of universities of each type is based on the School Basic Survey of the
MEXT. University acceptance data are based on a survey by DAIGAKUTSUSHIN
Corporation, and hensachi scores are based on reports from Kawaijuku.

49

Table A2. Hensachi scores and international rankings of the universities in theprestigious group
University name

AbbreviationType

The University of Tokyo
Hitotsubashi University
Keio University
Kyoto University
Waseda University
Tokyo Institute of Technology
Osaka University
Nagoya University
Tohoku University
Hokkaido University
Kyushu University
International Christian University
Sophia University
Rikkyo University
Meiji University
University of Tsukuba
Aoyama Gakuin University
Doshisha University
Kobe University
Chuo University
Yokohama National University
Hosei University
Gakushuin University
Kwansei Gakuin University
Ritsumeikan University
Kansai University
Hiroshima University

Tokyo
Hitotsubashi
Keio
Kyoto
WasedaTokyo Tech
Osaka
NagoyaTohokuHokkaido
KyushuICU
SophiaRikkyo
Meiji
Tsukuba
AGU
Doshisha
Kobe
Chuo
YNU
Hosei
Gakushuin
KGU
Ritsumeikan
Kansai
Hiroshima

National
National
PrivateNational
PrivateNational
National
National
National
National
National
PrivatePrivatePrivatePrivateNational
PrivatePrivateNational
PrivateNational
PrivatePrivatePrivatePrivatePrivateNational

Group
A
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

B
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

Hensachi Hensachi QS WUR THE JUR THE WUR
score
ranking
2022
2023
2023
68.9
67.1
66.6
64.9
64.8
64.4
61.3
59.5
58.6
58.3
57.5
65.8
62.9
60.2
60.1
59.8
59.5
59.4
59.0
58.1
57.9
57.5
56.9
55.9
55.5
54.8
54.1

2
5
6
11
13
15
24
32
38
40
48
8
21
27
28
30
33
35
36
42
43
47
51
58
61
67
75

23
531-540
201
33
203
56
75
118
82
145
137
801-1000
801-1000
1001-1200
1001-1200
285
1201+
1001-1200
386
801-1000
751-800
343

2
16
12
5
14
4
3
8
1
7
6
10
22
40
45
9
52
43
13
54
28
59
49
44
31
66
11

39
801-1000
68
1001-1200
301-350
201-250
301-350
201-250501-600
501-600
1500+
1001-1200
1500+
501-600
1500+
1500+
801-1000
1500+
1201-1500
1201-1500
1500+
1201-1500
1500+
801-1000

Note: International university rankings are based on QS World University Rankings 2022,
Times Higher Education World University Rankings 2023, and Times Higher Education
Japan University Rankings 2023.

50

Table A3. Number of secondary schools per government statistics and our dataset
School Basic Survey by MEXT
Only schools withcollege-bound students

All schools
High school
National
PublicPrivateUpper secondary school
National
PublicPrivate
Total

2010
4,950
17
3,630
1,303
24
2
13
9
5,225

2020
4,582
17
3,264
1,301
51
4
31
16
4,820

2010
4,844
17
3,531
1,296
23
2
12
9
4,867

2020
4,535
17
3,220
1,298
51
4
31
16
4,586

University
acceptance data
2010
4,803
17
3,496
1,290
23
2
12
9
4,826

2020
4,449
17
3,138
1,294
51
4
31
16
4,500

Note: The number of schools of each type is based on the School Basic Survey of the
MEXT.

51

Table A4. Example of the number of successful applicants in both universityreleased data and my data
Number of successful applicantsHensachiscore
National
University of Tokyo
Tohoku University
Okayama University
Niigata University
PrivateKeio University
Kanagawa University
Meikai University
Seitoku University

Published by
University

University acceptance data

2010

2020

2010

2020

68.88
58.55
53.51
50.79

3,109
2,602
1,909
2,428

3,098
2,489
2,399
2,467

3,081 (99.11%)
2,552 (98.08%)
1,907 (99.90%)
2,203 (90.73%)

3,040 (98.13%)
2,435 (97.83%)
2,336 (97.37%)
2,297 (93.11%)

66.63
45.90
38.05
34.93

10,253
8,947
753
1,964

8,590
6,167
1,085
896

1,0176 (99.25%) 8,531 (99.31%)
8,877 (99.22%) 6,133 (99.45%)
751 (99.73%) 1,073 (98.89%)
1,956 (99.59%) 883 (98.55%)

Note: The number of successful applicants published by each university is obtained fromeach university's website.

52

Table A5. Timing of treatments (appearance of high achievers)
Definition
Total number Number of
Number of treatments in each yearof highof treated never-treatedachievers 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21
schools
schools
Group A 6 4 4 2 3 4 1 3 3 5 3 2 2 3 6 1
52
1,115Group B 39 40 35 37 37 26 23 35 30 30 35 35 16 22 20 16476
559
Group C 62 61 65 75 81 58 55 68 69 54 58 59 30 38 44 38
915
257

53

Table A6. Robustness checks using alternate definitions for schools and students

Time since treatment
1–5 years prior

University groups based onhensachi scores
Above Above Above
(1)
(2)
(3)

-0.030(0.542)
Year of appearance
7.47***(1.07)
1–2 years after
0.379(0.734)
3–5 years after
0.588(0.741)6+ years after
1.08(0.834)
School characteristics
YES
City characteristics
YES
School FE
YESYear FE
YES
Year×Region FE
NO
Number of treated schools
90
Number of control schools909
Observations
8,879
Adjusted R-squared
0.3739-0.283(0.251)5.93***(0.461)
1.35***(0.346)
0.992**(0.378)
1.49***(0.451)
YES
YES
YES
YES
NO
344
521
7,497
0.4045-0.186(0.194)4.79***(0.313)1.30***(0.290)1.16***(0.329)1.67***(0.393)
YES
YES
YES
YES
NO
752
214
8,526
0.4112

Extension of years
nonacademic track definition
GroupA Group B Group C
(4)
(5)
(6)

Group Bnonacademic track
GroupA Group B
(7)
(8)

1.60
0.639
0.515*(0.939) (0.343) (0.239)
13.0*** 7.36*** 5.49***(2.48) (0.659) (0.417)
2.07
1.88*** 2.09***(1.23) (0.439) (0.348)
2.75
1.29** 1.67***(1.56) (0.486) (0.391)
3.89** 1.73** 2.27***(1.45) (0.598) (0.481)
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
NO
NO
NO
13
152
257
725
477
341
6,177
5,112
4,819
0.3948 0.4326 0.4397

0.226(0.438)
5.48***(0.846)0.838(0.588)1.18*(0.590)1.06(0.605)
YES
YES
YES
YES
NO
144
1303
13,3590.42140.091(0.171)
4.20***(0.278)1.08***(0.232)0.910***(0.255)
1.17***(0.297)
YES
YES
YES
YES
NO
958
586
13,744
0.4634

Note: Robust standard errors are clustered at the school level and appear in parentheses.
*, **, and *** indicate statistical significance at the 5%, 1%, and 0.1% levels, respectively.

54