OSIPP Discussion Paper: DP-2021-E-003

How can a college's admissions policies
help produce future business leaders?
January 25, 2021
Kenjiro Hirata
Faculty of Economics, Kobe International University*
Shinpei Sano
Graduate School of Economics, Kobe University**
Katsuya Takii
Osaka School of International Public Policy, Osaka University***【Keywords】 Admission Policy, Business Leaders, Managerial Human Capital【JEL Classification】 I23, J24, M12【Abstract】 This paper examines the long run impacts of expanding the range ofsubjects in higher education admission examinations using a historical event, thereform of Japanese entrance examinations in 1979. Our results show that degree
programs that are forced to increase the number of subjects increases theprobability of graduates being appointed onto the board of directors of publicly
traded companies despite reducing the measured average intellectual ability ofstudents in the program.

This suggests that by broadening the range of subjects,

colleges can select students who can master a wide range of knowledge andtransform them into future business leaders.
We would like to thank Charles Yuji Horioka, Takuma Kamada, Nobuyoshi Kikuchi,
Shuhei Kitamura, Masayuki Kudamatsu, Mai Seki and seminar participants at theContract Theory Summer Camp, Fukuoka University; Kansai Labor Workshop, Kyoto
University; Osaka City University; OSIPP Economics Brownbag Seminar, Workshop on the
Economics of Human Resource for their helpful comments. Financial supports from theJapanese Ministry of Education, Culture, Sports, Science and Technology (a Grant-in-Aid
for Scientific Research 18330051, 21243024, 25245040, 18H03636) and Japan Securities
Scholarship Foundation are gratefully acknowledged.
*
khirata@kobe-kiu.ac.jp**
sano@econ.kobe-u.ac.jp***
takii@osipp.osaka-u.ac.jp

How can a college’s admissions policies help
produce future business leaders?
Kenjiro Hiratay, Shinpei Sanozand Katsuya Takiix

Abstract
This paper examines the long run impacts of expanding the range ofsubjects in higher education admission examinations using a historicalevent, the reform of Japanese entrance examinations in 1979. Our results show that degree programs that are forced to increase the numberof subjects increases the probability of graduates being appointed onto
the board of directors of publicly traded companies despite reducing themeasured average intellectual ability of students in the program.

Thissuggests that by broadening the range of subjects, colleges can select students who can master a wide range of knowledge and transform them into
future business leaders.
We would like to thank Charles Yuji Horioka, Takuma Kamada, Nobuyoshi Kikuchi,
Shuhei Kitamura, Masayuki Kudamatsu, Mai Seki and seminar participants at the Contract
Theory Summer Camp, Fukuoka University; Kansai Labor Workshop, Kyoto University; Osaka City University; OSIPP Economics Brownbag Seminar, Workshop on the Economics of
Human Resource for their helpful comments. Financial supports from the Japanese Ministryof Education, Culture, Sports, Science and Technology (a Grant-in-Aid for Scienti…c Research
18330051, 21243024, 25245040, 18H03636) and Japan Securities Scholarship Foundation are
gratefully acknowledged.
y Faculty of Economics, Kobe International University, 9-1-6 Koyocho-naka, Higashinadaku, Kobe, Hyogo 658-0032, Japan. khirata@kobe-kiu.ac.jp
z Graduate School of Economics, Kobe University, 2-1 Rokkodai-cho, Nada-ku, Kobe,
Hyogo Prefecture, 657-8501, Japan. sano@econ.kobe-u.ac.jp
x Correspondence: Osaka School of International Public Policy, Osaka University, 1-31,
Machikameyama, Toyonaka, Osaka, 560-0043, Japan. takii@osipp.osaka-u.ac.jp1

1

Introduction

Evidence shows that some elite colleges produce a disproportionately large shareof managers or directors (e.g., Cappelli & Hamori, 2004; Zimmerman, 2019). If
educating these business leaders is one of the missions of the elite colleges,
their admission policy must select appropriate candidates. How can a college’s
admissions policies help produce future business leaders?
We can …nd some clues to answer this question in the corporate governance and labor economics literature, which shows that successful managers
are more likely to have a broad career (e.g., Custódio et al., 2013; Frederiksen& Kato, 2017) and students who wish to become entrepreneurs in future may
take broader courses during their Master of Business Administration (MBA)degree (e.g., Lazear, 2004, 2005, 2012)1 . This …nding provides the hypothesisthat people who have a high ability to learn broad topics from both their academic life and work experience are candidates for future managers. Therefore,
the admission policies to produce future business leaders should include somemechanism to test this ability.
This paper examines whether expanding the range of subjects in the entrance
examinations for colleges also increase the number of business leaders among thegraduates from these colleges. A di¢ culty is that each college can choose theirpreferred breadth of subjects.

Hence, the simple regression analysis may bebiased due to the endogeneity problem: i.e., prestigious colleges attract manybusiness leaders and require broader subjects in their admission policy without
any causal relationship between the two.
We use a historical event in Japan to solve this endogeneity problem. The
Japanese government introduced a nationwide standardized entrance examination, the Joint First-Stage Achievement Test (JFSAT), in 1979.

As a result,

1 How much general human capital is important for managerial tasks is one of the mostimportant empirical questions in the literature on corporate governance because market-based
views on managerial compensation explain the high chief executive o¢ cer (CEO) compensation in the United States as resulting from competition to employ scarce general managerial
talent (e.g., Gabaix & Landier, 2008; Terviö, 2008).

2the number of subjects in the entrance examination required for admission to
public universities was increased to seven. The subjects are Japanese, Mathematics, English, two natural sciences and two social sciences.

We measure

the breadth of subjects by the number of subjects in the entrance examination
and analyze how this exogenous change in the number of subjects a¤ected theshare of board members of listed companies from the degree program graduates,
where a degree program means a faculty and university pair.
We conduct two-way …xed e¤ect instrumental variable (IV) estimates usinga sample of public universities between 1975 and 1985. Using the fact that the
number of subjects exogenously increased to seven, we construct a mechanicalIV, which consists of the number of subjects in the admission examinations in
1975 when the examination year is prior to 1978 and is seven after 1979. As
control variables, we not only control for the degree program’s …xed e¤ect andyear dummy, but also the interaction of the year dummy with several degree
program characteristics.
Our analysis shows that increasing the number of subjects in a degree program’s admission examination signi…cantly increases the probability of being
appointed onto the board of directors of a listed company. This result passes
several robustness checks.

Moreover, its magnitude is substantial.

The re-sults show that increasing the number of subjects by one in a degree program
increases the likelihood of degree program graduates being appointed as directors by about 1.26 to 1.6 times for the average degree program in our sample.
Hence, we can conclude that increasing the number of subjects in an entrance
examination is an e¤ective way to screen for future business leaders.
Having obtained these main results, the next step is to identify the mechanism behind the results: i.e., why does increasing the number of subjects inan entrance examination of a degree program increase the number of futurebusiness leaders among its graduates?

In this paper, we try to separate twopotential types of students that can be selected by increasing the number ofsubjects: 1) Increasing the number of subjects screens out students who havehigher academic skills. 2) Increasing the number of subjects in the entrance3

examinations selects students who have a comparative advantage in learning awide range of topics. Changing the type of students in a degree program not
only a¤ects the type of student directly, but it also a¤ects the type of student
by changing the type of friends that students in a degree program can interact
with. Through these two e¤ects, the type of students selected in the entrance
examination surely changes the type of graduates from the degree program.
For this purpose, this paper examines the impact on the degree program’s
selectivity scores to investigate whether the increase in the number of subjects
simply selects intellectually capable students. Selectivity scores are the scores
reported by Japanese pre-University schools as an indicator of the di¢ culty inentering a degree program. As discussed below, the selectivity score represents
the average competence of students who enter a degree program with respect tothe subjects required to pass the entrance examination for the degree program.
Several previous studies …nd that the selectivity score is the important measureof the intellectual ability of the average graduate of a degree program (Abe, 2002;
Araki et al., 2016; Goodman & Oka, 2018; Ito & Nakamura, 2019). Hence, if
increasing the number of subjects screens out the students with higher academic
skills, the selectivity score is likely to increase.
The analysis shows that as the number of subjects in a degree program’s
admissions exam increases, the selectivity scores of the degree program decreaseslightly but signi…cantly. Our estimates show that with an extra entrance exam
subject, the selectivity score decreases by about 0.01% for the average degree
program in our sample. This result suggests that an increase in the number ofsubjects is unlikely to improve the average intellectual ability of degree program
graduates.
In summary, the results of this study support the hypothesis that by broadening the range of subjects for entrance examinations, colleges can select people
who can learn a wide range of knowledge and produce future business leadersfrom their graduates.
This result is consistent with the …ndings of existing research that managerial
skills are general.

Kaplan et al. (2012) investigate a proprietary dataset for4executives and provide evidence that general skills are important managerial
skills. Custódio et al. (2013) …nd a positive relationship between the index ofgeneral managerial skills and CEO pay. Custódio et al. (2019) show that S&P
1500 CEOs who acquired general management skills throughout their careers
drive more innovation in their …rms.

Frederiksen and Kato (2017) provide

evidence that the breadth of human capital is important for career success using
Danish registry data. This paper contributes to this literature by showing thatgeneral skills may be rooted in innate talents and education at a young age.
This paper also discusses the relation between general skills and the ability tolearn a wide range of topics. This is consistent with Lazear (2004, 2005), which
insists that entrepreneurs need balanced skills.

Compared to Lazear (2004,

2005), we focus more on the causal e¤ects of admission policies, which can beused as policy instruments.

Therefore, we can discuss appropriate admission

policies to produce future business leaders.
Several papers examine how and why entering the elite universities in‡uences
career success (e.g., Hastings et al., 2013; Zimmerman, 2019).

In particular,

Zimmerman (2019) shows that admission to an elite degree program increases
the number of leadership positions held by students by 44%. While investigationof the return to elite college quali…cations is an important and active researcharea, this paper pays more attention to the allocative role of admission policy:
how do changes in admission policy in‡uence the type of students each college
can educate?
The allocative role of admission policy can be discussed in the college choices
literature. Previous papers compare how centralized and decentralized mechanisms allocate talented students (e.g., Machado & Szerman, 2016; Hafalir etal., 2018; Tanaka et al., 2019), and examine how the timing of examination andadmission selects talented students (e.g., Avery et al., 2014). This paper casts
light on an ignored aspect of admission policy: i.e., the breadth of subjects inentrance examinations. We show that if schools wish to produce future managers, they should expand the range of subjects in the entrance examination.
We believe that our analysis nicely complements other analyses.
5The paper is organized as follows.

Section 2 brie‡y discusses the institu-

tional background and provides an overall picture of the market for Japanesehigher education.

Section 3 explains the data for our empirical studies andhow we construct our main variables: i.e., the director share, the number ofsubjects and the selectivity score. Section 4 provides the descriptive analysis of
our main variables. Section 5 introduces our empirical model and discusses theidenti…cation assumptions. Section 6 shows the results of our empirical studies.
Section 7 concludes the paper.

2

Institutional BackgroundIn this section, we brie‡y explain the Japanese education system and the institutional background behind the introduction of JFSAT. We also discuss theoverall picture of the markets for Japanese higher education.

2.1

Educational System in Japan

Since 1947, the Japanese education system has consisted of nine years of compulsory education (elementary and lower secondary education, such as juniorhigh school), three years of upper secondary education (typically high school),and higher education (ranging from two years for college and four years foruniversity).
One of the characteristics of Japanese higher education is that only a small
number of students go on to become graduate students.

According to the

Ministry of Education, only 7% of students who had a bachelor’s degree became
graduate students in 1991. Hence, most managers do not have master’s degrees.
According to the Japanese Institute for Labor (1998), only 1.9% of generalmanagers or section managers had a master’s or Ph.D. degree in 1996, while
61% and 11% of the corresponding managers have a master’s or Ph.D. degree inthe United States and Germany. As this evidence suggests, a bachelor’s degree
is quite important in Japan.

6Undergraduate programs in Japanese universities have a four-year system.
There are three types of educational establishments in Japan: i.e., national, local
public and private where we refer to both national and local public universities
collectively as public universities. In 1975, the breakdown of these universities
is 19.3%, 8.1% and 72.6%, respectively (see Ministry of Education, Culture,
Sports, Science and Technology’s Basic School Survey). Students prepare forcollege or university entrance examinations and must pass them between January and March before the …rst semester commences in April. Each university
admits students based on the score of this test until its capacity is …lled.

If

students fail all degree programs that they apply to, they must wait one year
to apply to them again.
High school students can apply to a department in a university. However,the faculty council in a university can largely in‡uence the admission, curriculumand diploma policies of departments in the faculty.

Later, we aggregate thedepartment-level data to faculty-level data and conduct panel analysis basedon faculty and university pair data. We call this faculty and university pair adegree program.

2.2

The Introduction of JFSAT

Since the current Japanese education system began in 1947, the university entrance examination system has been reformed several times. One of the mostdramatic changes was the introduction of JFSAT in 1979.
Prior to 1979, university entrance examinations were decentralized and conducted by individual universities. Decisions regarding university entrance examinations were left to the discretion of each university. By law, the decisionmaking authority rests with the university’s Faculty Council.

Therefore, thesetting of examination subjects at each university is left to the discretion of
the university faculty.

However, the Ministry of Education set guidelines forentrance examinations by issuing an “Implementation Guidelines for University
Enrollment Selection” notice. The guidelines, which were updated every year,7

include the basic policy of university entrance examinations, acceptance policy,
entrance examination method, entrance examination date, announcement of examination method, and other items. Although these guidelines were not legallybinding, universities conducted individual entrance examinations in accordancewith these guidelines.
The government planned to reform the entrance examination system in thecontext of rising demand for higher education and intensifying entrance examinations.

The demand for higher education has increased with economic

development in a period of high economic growth. The high school advancement rate exceeded 90% in 1975.

The university entrance rate was 38.4% in

1975, compared with 10% in 1950. The bene…t from graduating from college
or university was likely to have been high at this point2 .

However, it wasdi¢ cult for university establishments to expand college capacities at that time
because the College Setting Standard strictly regulated the quantity until 1991.
As the demand for higher education increased during the high-growth era, thecompetition for university admissions intensi…ed. The academic ability test for
selecting students for each university not only made it di¢ cult, but also began
to deviate from the standard high school curriculum.
The government has sought to introduce a common test that rationally selects students who are suitable for higher education.

In 1977, the National

School Establishment Law was partially revised, and the University Entrance
Examination Center was established.

In 1979, the JFSAT was introduced.

The JFSAT is a mechanism that forces students who are interested in applying
public universities to take the same examination as the …rst stage of the entrance examination. The examination contains …ve main subjects: Japanese,
English3 , mathematics, science and social studies. Each main subject counts
2 While we do not have any evidence of the perceived returns at that time, more recent

research estimated that the return to higher education was around 5 to 10% in Japan (Kikuchi,
2017; Nakamuro et al., 2017).
3 Strictly speaking, high school students can choose German or French instead of English.
However, most students choose English for their examination;
English for this paper.

8

hence we call this subject

for 200 points; thus, the total score is 1000 points. Science and social studies
include sub…elds, e.g., physics, chemistry, biology, geology, ethics, society, politics and economics, Japanese history, world history and geography. Students
must choose two sub…elds from both science and social studies.

Therefore,

JFSAT requires students to study seven subjects to enter public universities.
Before 1979, no university required more than seven subjects in their entrance
examination.
The JFSAT was conducted in January every year from 1979 until 1989.
After obtaining their JFSAT scores, applicants can decide which department of
which university they choose to apply to. All public universities conduct theirown entrance examination tests as a second-stage test and choose the successful
candidates based on both their JFSAT score and the results of the secondstage entrance examination tests. Each university and department can choosedi¤erent weights for all subjects in JFSAT and the second-stage examination
tests.
Because a large number of students take the same examination, a marksheet is used as the examination method in JFSAT. However, most universities
impose written exams as their second-stage test. Nevertheless, no university department imposes new subjects that are not included among the subjects in the
JFSAT on the students. As we wish to measure the breadth of subjects by the
number of subjects that students must study for the public university’s entrance
test, we ignore the style of examination between JFSAT and the second-stage
test, and count that the number of subjects is seven after the introduction of
JFSAT.
The Ministry of Education stipulates that the date of the second-stage examination for public universities is the same throughout Japan.

Hence, theapplicants could apply to only one public university after the introduction of
JFSAT. Before the introduction of JFSAT, they could choose to apply to two
public universities. There were two groups of national universities before 1978,
i.e., …rst- and second-tier universities. National universities in each group musthave the same examination date. Typically, local public universities also choose
9the same examination date as either group of national universities.

This in-stitutional arrangement allowed applicants to apply to two public universities
before 1978.
While university applicants lost the option to make multiple applications topublic universities after 1979, they received informational bene…ts from taking
the JFSAT. Before 1978, applicants must apply to their preferred universities
and departments without observing any results of the entrance examination. In
contrast, after 1979, because students could apply to their preferred department
and university pair after observing the JFSAT results, they can update theirprobability of success before making their decision.
While JFSAT continued until 1989, several major revisions began in 1987.
First, students must choose one sub…eld, but not two sub…elds, out of the scienceand social studies choice sets.

As the full score of science and social studies

became 100, the total score became 800 points.

As a result, the number ofsubjects that students must take at JFSAT fell to …ve. Second, the Ministry of
Education allows the university to choose the date of the second examinationfrom two candidate dates This arrangement again allows applicants to apply
to two public universities.
In sum, the introduction of JFSAT made the following four big changes inentrance examinations for Japanese higher education between 1979 and 1986.
1. JFSAT prevents students from applying to multiple public universities.
2. JFSAT requires students who apply to a public university to study broader
subjects.
3. JFSAT requires students who apply to a public university to take a standardized exam.
4. JFSAT allows students who apply to a public university to have partial
information about their probability of success before deciding which universities to apply to.

10

Figure 1: Changes in Tuition Fees between 1970 and 1989

We focus how changes in the number of subjects in‡uences the selectivity
score of a degree program and the director share of the graduates from thedegree program in this paper.

2.3Japanese Higher Education Market Between 1970 and1989

To grasp the changes in the Japanese higher education market between 1970
and 1989, we examine the time series movements of tuition fees (the price of
higher education), capacity (the supply of higher education) and the number ofapplications (the demand for higher education) in this period. Figures 1, 2 and3 show the movements.
Figure 1 shows the time series of tuition fees by university type. Tuition fees

11

Figure 2: Changes in Capacity between 1970 and 1989

began increasing in 1975. The tuition fees of private universities are larger thanthose of public universities.

However, the di¤erences in tuition fees between

private and public universities are roughly the same across time.

There is a

steady increase in tuition fees for both private and public universities after 1975.
Figure 2 shows the time series of capacity by university type.

It shows thatprivate universities absorb more students than do public universities. Althoughboth private and public universities have gradually increased their capacity inthis period, the increase in capacities is much larger in private universities thanin public universities.
Note that there are no signi…cant discrete changes in tuition fees and capacity
around 1979. Gradual increases in tuition fees and capacity in Figures 1 and 2
around 1979 suggest that the admission reforms of 1979 were not reforms withincreases or decreases in capacity or changes in tuition.

12

Figure 3: Changes in the number of applications between 1970 and 1989

Figure 3 shows the time series sequences of the number of applications between 1970 and 1989. Compared to Figures 1 and 2, Figure 3 displays fairlydi¤erent time series patterns. The number of applications to public universities(national and local public universities) suddenly dropped in 1979 and suddenly
increased in 1987. This pattern is not observed for the number of applications
to private universities.
As we discussed above, while applicants could make two applications toeither national or local public universities before 1978 and after 1987, they could
only apply to one public university (national or local public) between 1979 and1986. This restriction reduced the number of applications to public universities
during this period. Interestingly, this restriction does not seem to increase thenumber of applications to private universities, as we do not observe any jump

13

around 1979 and 1987.
Note that these sudden changes in the demand for public universities are
not re‡ected in changes in tuition fees, which indicates that tuition fees do not
adjust to equal the demand and supply in the market for higher education in
Japan. The selectivity score, which is discussed below, is likely to be adjusted
for the impact of supply and demand in this market.

3

Data

Our empirical analysis draws on degree program (university faculty) level panel
data from 1975 to 1985. Using this data, we examine the e¤ect of the numberof subjects in degree programs on the director share in the graduates from thedegree program and the selectivity score of the degree program. In this section,
we explain how we construct the director share, the number of subjects andselectivity score.

3.1

Director Share

We obtain personal data on all directors of Japanese listed companies using
the 1990–2017 issues of Directors Data (Yakuin Shikiho), published annually by
Toyo Keizai Inc.

This directory contains detailed personal information, suchas name, birthday, name of the degree programs, i.e., faculties and universities
pairs, that directors graduated from and the year when they graduated.

To

identify the year when they have enrolled in their degree programs, we subtracted four years from the year of graduation. We report descriptive statistics
of director-level data that is used for the construction of director share in the
Appendix.
We match the degree programs listed in Directors Data with those in The
University Digest (Zenkoku Daigaku Ichiran) published by the Ministry of Education.

The University Digest contains information on the departments inuniversities, such as enrollment capacity, when the school was established, cat-14

egory of major, and location. Aggregating the capacity data in The University
Digest by university faculty level, we calculate the capacity of the degree program. Using this calculated capacity, we construct the share of degree program
graduates with experience as a board member in a Japanese listed company
after 1990 relative to the capacity of the degree program for the cohort.

Wecall this the director share, which is one of the main outcome variables in our
regression analysis.
Our measure of director share has to be viewed with caution.

Becauseof some directors’missing educational background data in the Directors Data,measurement errors in the director shares may potentially be a problem.

Asshown in Table 13 in the Appendix, the response rate increases with …rm size.
In particular, …rms that have capital stock below the 10th percentile have signi…cant drops in response rate.

Hence, we drop the …rms that have capitalstock below 10%. Table 14 in the Appendix reports that this selection criterion
does not change the results very much.
In addition, we use a sample of 142 degree programs that produce morethan …ve graduates with experience as a director in Japanese listed …rms after
1990 (hereafter, we refer to these degree programs as “major degree programs")
for our benchmark estimation. In this way, we hope to exclude samples that
contain extremely noisy information. In the Appendix, Tables 15 and 16 report
the results of the same regression using the sample of all degree programs that
produce at least one graduate with directorship experience in Japanese listed…rms after 1990 as a robustness check.

3.2

The Number of Subjects

We measure the breadth of subjects by the number of subjects that students
must study to pass the entrance examination. We obtain information on the selectivity score and exam subjects from booklets: Eikan-wo-Mezashite publishedby Kawaijuku.

The booklet lists the subject patterns required for entrance

exams.

15

The entrance examination consists of an academic test including English,
mathematics, Japanese, and other subjects4 . During the sample period, there
was a change of the names of subjects due to a change in the curriculum guidelines at high school.

We looked into the details of the curriculum guidelines

and identi…ed the names of subjects with similar content and treated them as
the same subject.
There are compulsory and optional subjects for entrance examination. Whena degree program imposes an optional subject, students must choose their preferred subjects from the proposed choice sets. Because we wish to measure thebreadth of subjects by the number of subjects, we simply count the number ofcompulsory subjects. For the optional subjects, we assume that each subject
has an equal chance of being chosen, which allows us to estimate the probabilityof choosing the subjects. Then, we add this probability if the same subject is
not included in the compulsory subjects of entrance examination for the degree
program.
It is important to understand how we count the number of subjects after
the introduction of JFSAT in 1979.

After the introduction of the JFSAT,

students who apply to public universities must take seven di¤erent subjects.
While students must take the second-stage test, it does not include new subjects.
Hence, the number of subjects remains at seven. This is consistent with our wish
that we measure the breadth of subjects by their number. Public universities
have a di¤erent number of subjects only before the introduction of the JFSAT.
We match this data with the capacity data in The University Digest andaggregate it by university faculty level with capacity as a weight. This procedure generates the number of subjects required in each degree program for ouranalysis.
4 While some degree programs use practical tests such as an essay and practical skills fortheir admissions, these degree programs are few and, typically, do not signi…cantly in‡uence
the admission system. Moreover, this does not in‡uence the reported selectivity score. Hence,
we do not consider these practical tests in this paper.

16

3.3

Selectivity Score

Kawaijuku, the publisher of Eikan-wo-Mezashite, which provides the information to construct our selectivity score, is a pre-University school that administers
a series of large-scale mock examinations5 . After conducting the mock exam,
Kawaijuku provides students who took the mock exam with their deviation score(hensachi ) calculated by the following equation:nHks=

10 (xnks

ks )

+ 50

ks

n
is the deviation score on the kth subject of the nth student at thewhere Hks

sth mock exam, xnks is the exam score on the kth subject of the nth student at
the sth mock exam, andks andks are the average and the standard deviationof the exam score on the kth subject at the sth mock exam.

Based on thestudent’s deviation score, Kawaijuku also provides them with estimates of theirprobability of being accepted to a degree program of their choice.
Kawaijuku then tracks whether the student has been accepted into the degree
program of his or her choice.

From this information, Kawaijuku estimates a

deviation score (hensachi ) of the degree program based on the average scoreof mock exams of students whose expected probability of entering the degree
program of their choice is half.
Kawaijuku classi…es all department and university pairs into 12 or 15 ranks
according to their deviation score and reports the rank of all department anduniversity pairs. We assign a median value in the range of the deviation score,
which is represented by the rank, to the department and university pair in therank. We match the deviation score with The University Digest and calculatea weighted average of the deviation score by degree program level using theweight calculated by capacity data in The University Digest.
deviation score the selectivity score of the degree program.

We call thisThis creates thedegree program-level panel data with selectivity data.
5 For example, in 1978, according to Eikan-wo-Mezashite, the total number of students who

took the mock examinations was 242,383 in 1977.

17

It is important to note that this measure is in‡uenced by the subjects in theentrance examination for a university department.

For example, if some de-partment and university pair includes only English and Japanese as the subjects
in its entrance examination, only the English and Japanese scores in the mockexam are used to estimate the expected probability of entering the university.
Nonetheless, because it is recognized in Japan that the selectivity score roughlyre‡ects overall academic ability of an university department, many labor economists use it as a rough measure of intellectual ability (e.g., Abe, 2002; Araki etal., 2016; Goodman & Oka, 2018; Ito & Nakamura, 2019).

Adjustment of Selectivity Scores Across Years
Because our selectivity score is based on the deviation score of students
who took a mock exam in a particular year, it is not comparable across years
because the number of students who take the mock exam di¤ers across years.
Therefore, we must make some adjustments to ensure that the selectivity scores
are comparable across years.
Suppose that a person with human capital A can obtain the score A for all
subjects under a mock exam. Let Ft (A) denote the ability distribution of all
applicants in year t and let

At andAt denote the mean and the standard

deviation of a random sample generated from Ft (A).

From the de…nition of

the deviation score, a person who obtain test score A will have the deviation
score “hensachi ” Ht in year t by the following equation:

Ht =

10 (A

At )

+ 50

At

Inevitably, the observed selectivity score of the degree programs, Ht , are in‡uenced by

At andAt .

To make the selectivity score comparable across years,

we must convert the observed deviation score into the original ability measureusing the following equation:
Ait =

A;t

10

Hti

50 +

A;t

(1)

This new measure Ait is the average ability of graduates from the ith degree18program with a selectivity score of Hti in year t. To make this transformation
feasible, we must estimate

A;t andA;t from the data.

Suppose that the ability distribution of the total population aged 18 years
follows the normal distribution with a mean of 10 and standard deviation of
50,(A). Assume that high school students on the higher end of the ability

distribution apply to a degree program and take the mock exam.

Then theability distribution of high school students who apply to universities, Fth (A), is
written by:
Fth (A) =

RA

At

1

d (a)
(At )

; 1

(At ) =

Nt
Lt

where Nt is the number of applicants aged 18 years and Lt is the total population
aged 18 years. Assume that a person who applied to a degree program but could
not pass the exam applies to the same or another degree program the next year
and that the probability of passing the entrance exam in year t is pt . Let Mt
denote the total number of applicants at year t. Then the measure of applicants
who have ability less than A, Ft (A) Mt must follow the following dynamics:
Ft (A) Mt = Fth (A) Nt + (1pt 1 ) Ft 1 (A) Mt 1

Because this equation must be satis…ed for all A; the following equation must
hold for all t:
Mt = Nt + (1pt 1 ) M t 1

Rearranging all equations, the dynamics of Ft (A) must follow:
RA
Nt At d (a)Nt
Ft (A) =+ 1
Ft 1 (A)
Mt 1
(At )
Mt
RA
A

Suppose that F0 (A) = 1 0

(2)

d (a)

. Because we can estimate At from 1(At ) =
(A0 )
o
n
Nt
Nt Nt
with theLt , we can estimate Ft (A) from the data sequences
Mt ; Lt
t=0

recursive equation (2) and initial condition.

Using the estimated Ft (A), we

calibrate

A;t andA;t andA;t .

Using the calibrated

A;t , we can obtain theadjusted selectivity score of A for each degree program from equation (1). Thisis the selectivity score used in our estimation.
19

Variable
Director Share

Obs
1507

Mean
0.27

Std. Dev. Min
0.42

0.00

Max
3.13

Selectivity Score

1333

61.11

3.31

52.57

69.68

# of Subjects# of Initial Subjects (# of Subjects in 1975)

1352
1476

6.43
5.63

0.85
0.75

3.00
3.00

7.00
7.00

# of Init. Subj.<6 & Init. Slct. Score<=60# of Init. Subj.<6 & Init. Slct. Score>60# of Init. Subj.>=6 & Init. Slct. Score<=60# of Init. Subj.>=6 & Init. Slct. Score>60

1507
1507
1507
1507

0.12
0.19
0.19
0.50

0.32
0.39
0.39
0.50

0.00
0.00
0.00
0.00

1.00
1.00
1.00
1.00

Table 1: Summary Statistics of Main Variables between 1975 and 1985

4

Descriptive Analysis

This section reports the descriptive analysis of our data set. First, we report
the summary statistics of the main variables.

Then we report time series

movement of our main variables, the number of subjects, selectivity score anddirector share.

Summary Statistics: Table 1 reports the main variables of our analysis. The
table shows that the shares of directors appointed in listed companies from thedegree program graduates is 0.27%. As our main focus is the impact of changes
in the number of subjects on the director share, we limit our analysis to asample of the degree programs where more than …ve graduates have experience
as a director in Japanese listed companies after 1990. Hence, this number canbe larger than the share of the standard college graduates.
It also shows that the average selectivity score is 61, which indicates thatthe sample mainly contains elite universities. The number of subjects is 6.43
and the number of initial subjects is 5.63. As we discuss above, the number ofsubjects is seven after 1979. These numbers indicate that the sample contains
the research programs with a relatively large breadth of subjects even before
1978.

20

7
6
5
4
3

1975

1980
Y ear

1985

Pub. Univ. (# of Init. Subj.<6 & Init. Slct. Score<=60)
Pub. Univ. (# of Init. Subj.<6 & Init. Slct. Score>60)
Pub. Univ. (# of Init. Subj.>=6 & Init. Slct. Score<=60)
Pub. Univ. (# of Init. Subj.>=6 & Init. Slct. Score>60)
Private Univ.

Sample of Major Univ . and Fac.

Figure 4: The Number of Subjects for Entrance Examination to Higher Education

Changes in the main variables between 1975 and 1985: Figures 4, 5
and 6 describe the changes in the number of subjects, the selectivity score anddirector shares of four groups in public universities. The four groups are divided
by the level of selectivity score (Init. Selct. Score) and the measure of numberof subjects (# of Int. Subj.) as of 1975.
Figure 4 shows that public universities with a small number of subjects in
the 1975 university entrance exam signi…cantly increase their number of subjects
after the introduction of the JFSAT. It also shows that the number of subjects isroughly stable except for degree program for which the initial selectivity score isless than 60 and the initial number of subjects is more than or equal to six. This
group of degree programs reduces the number of subjects before 1979, probably
because they wish to maintain the selectivity score of the degree program.
Figure 5 shows that the public universities’ selectivity score falls after the
introduction of the JFSAT. This reduction is likely to re‡ect the fact that the

21

64
62
60
58
56

1975

1980
Y ear

1985

Pub. Univ. (# of Init. Subj.<6 & Init. Slct. Score<=60)
Pub. Univ. (# of Init. Subj.<6 & Init. Slct. Score>60)
Pub. Univ. (# of Init. Subj.>=6 & Init. Slct. Score<=60)
Pub. Univ. (# of Init. Subj.>=6 & Init. Slct. Score>60)
Private Univ.

Sample of Major Univ . and Fac.

Figure 5: The Adjusted Selectivity Score

number of applications to public universities dropped in 1979 as shown in Figure
3. The magnitude of the reduction in 1979 is largest for the degree programs
for which the initial selectivity score is greater than 60 and the initial number ofsubjects is less than six and the smallest for the degree programs for which theinitial selectivity score is greater than or equal to 60 and the initial number ofsubjects is greater than or equal to six. Therefore, among elite degree programs,
the selectivity scores of programs that initially have smaller number of subjects
falls with the introduction of JFSAT more than for those that initially have alarge number of subjects.
Figure 6 shows that the director share declines throughout the period. Notethat in the capacity of a degree programs for each cohort, we de…ne director
share by the share of degree program graduates with experience on a Japaneselisted company board after 1990. By this de…nition of director share, the older
the cohort, the larger number of graduates are likely to have such experience.
Hence, the declining tendency of director share is not surprising.

22

Compared1.8
Unit: Percent
.4
.6
.2
0

1975

1980
Y ear

1985

Pub. Univ. (# of Init. Subj.<6 & Init. Slct. Score<=60)
Pub. Univ. (# of Init. Subj.<6 & Init. Slct. Score>60)
Pub. Univ. (# of Init. Subj.>=6 & Init. Slct. Score<=60)
Pub. Univ. (# of Init. Subj.>=6 & Init. Slct. Score>60)
Private Univ.

Sample of Major Univ . and Fac.

Figure 6: Director Sharewith Figures 4 and 5, we do not observe a clear jump around 1979 in Figure 6.
This suggests that we need appropriate covariates to identify the e¤ects of thechanges in the number of subjects on the director share.
Finally, Figures 4, 5 and 6 also report the time series movements of private
universities.

All …gures show that private universities do not show notablechange around 1979.

This evidence suggests that the exclusion of private

universities from our study is less likely to in‡uence our results.

5

Empirical Model

We would like to examine the e¤ects of changes in the number of subjects in theentrance examination. For this purpose, we use the fact that public universities
were forced to increase the number of subjects in the entrance examination in
1979 due to the introduction of the JFSAT.
To estimate the impacts of changing the number of subjects on several out-

23

comes, we conduct the following IV regression analysis:

Y j (i; t) =

j
0+

j

(i) +

j

(t) +

j
B B (i; t) +jj
W (t) W (i) + u (i; t) ;(3)

where Y j (i; t) is the jth outcome variables of the ith degree program, at theentrance examination year t, B (i; t) is the number of subjects that students
must study to pass the entrance examination of the ith degree program at theentrance examination year t, W (i) is the vector of predetermined variables of
the ith degree program. The parameterith degree program-speci…c …xed e¤ect,j

j
0 is the constant term,j(i) is the(t) is the t year-speci…c e¤ect,

j
W (t)

is the t year-speci…c vector of parameters for W (i), and uj (i; t) is the error
term of the ith degree program for the jth regression. The parameter

j
B

is

our parameter of interest that captures the impacts of changing the number ofsubjects in the jth regression.
As outcomes, we choose the share of agents who have been a director after
1990 of the graduates who entered the ith institute in year t, Y 1 (i; t), and theselectivity score of the ith degree program in year t, Y 2 (i; t).
The standard identi…cation assumption for the two-way …xed e¤ect model
regression (equation 3) is E B (i; t) uj (i; t) = 0, for all j, i and t. But note thatall degree programs in public universities are forced to impose seven di¤erent
subjects for the entrance examination after 1979.

Because, without loss ofjgenerality, we can assume E u (i; t) = 0 for all j, i and t, the identi…cation
assumption becomes:
E B (i; t) uj (i; t) = 0; 8j; i; t

1978

(4)

Each degree program can decide the number of subjects for entrance examinations before 1978.

In fact, Figure 4 shows that the degree programs forwhich the initial selectivity score is less than 60 and the initial number of subjects is more than or equal to six, reduced their number of subjects before 1978,
probably because they wished to maintain the selectivity score of the degree program. This may cause an endogeneity problem. In this case, the identi…cation24

assumption (equation 4) may be violated.
To deal with this possible bias, we construct a mechanical instrument Z (i; t)where:
Z (i; t)= B (i; 1975) if t

Z (i; t)=7 if t1978

1979

The standard identi…cation assumptions are:
E Z (i; t) uj (i; t)=

E [Z (i; t) B (i; t)] 6=

0; 8j; i; t;(5)0; 8i; t:(6)where s means sth year and j means jth degree programs.
As Figure 4 shows, equation (6) is likely to be satis…ed by de…nition. Hence,
it is less likely that this instrument is weak.

The remaining concern is the

exclusion restriction, which is represented by equation (5). Because all degree
programs in public universities are forced to impose seven di¤erent subjects
for their entrance examination after 1979, using the same argument as before,
equation (5) implies that:E B (i; 1975) uj (i; t) = 0; 8j; i; t

1978

(7)

Note that the equation (7) allows the possibility that time-varying unobserved characteristics of a degree program can a¤ect B (i; t) and uj (i; t) simultaneously. Because Figure 4 shows that B (i; 1975) partially in‡uences B (i; t)before 1978, equation (7) is likely a weaker assumption than equation (4). However, B (i; 1975) are still endogenous variables.

Hence, equation (7) assumes

that unobserved characteristics of a degree program that determine B (i; 1975)
do not determine uj (i; t) before 1978.
To validate the robustness of our results, we also conduct the following reduced form estimation:
Y j (i; t) =

j
0+

j

(i) +

j

(t) +

j
B Z (i; t) +25

jj
W (t) W (i) + v (i; t) :(8)

Whilej
B is di¤erent from our parameter of interest,j
B , Figure 4 shows that it

is likely to be similar. Hence, the results of this estimation provide important
information about the validity of our research design.

The identi…cation as-

sumption of equation (8) is similar to equation (7). Using the same argument
as above, the following orthogonality condition is assumed to be satis…ed:
E B (i; 1975) v j (i; t) = 0; 8j; i; t

1978

(9)

To guarantee equations (4), (7) and/or (9), the regression equations (3)and (8) control not only the ith degree program-speci…c …xed e¤ect and year
dummy, but also the interaction between predetermined ith degree characteristics and year dummy in,j
W (t) W (i).

As the elements of W (i), we choose1) national university dummy, and the …rst-tier national university dummy, 2)…elds dummy which consists of Humanities and Social Sciences, Science, Engineering, Agriculture, Health, Mercantile Marine, Home Economics, Education &Teacher Training, Arts and Others, 3) region dummy: Hokkaido, North Tohoku,
South Tohoku, North Kanto, South Kanto, Hokuriku, Koshin, Tokai, Kansai,
Chugoku, Shikoku and Kyusyu, and 4) selectivity score of a degree program in19756 and a dummy that indicates the ith degree program contained English,
Mathematics and Japanese as the subjects of its entrance examination in 1975.
Table 2 reports the summary statistics for these variables.
We control for the national universities and …rst-tier national universities
dummies because we know that applications to national universities were halved
after 1979. As the initial number of subjects is likely to be larger for national
universities and …rst-tier universities, if we do not control for these variables,
Z (i; t) may capture the di¤erential impacts of changes in the number of applications to each group. We also control for the interaction between location (…elddummies) and year dummies to capture the long-run changes in the number ofapplications due to urbanization and changes in industry structures. Finally,
6 If there are no data for 1975, we choose the year that such data …rst appeared in ourdataset as our initial value.

This strategy is applied to the construction of both the initial

number of subjects and the initial selectivity score.

26

Variable

Obs

Mean

Std. Dev.Min

Max

National Univ.

1507

0.91

0.29

0.00

1.00

1st Tier Univ.

1507

0.55

0.50

0.00

1.00

2nd Tier Univ.

1507

0.35

0.48

0.00

1.00

Initial Selectivity Score

1476

61.48

2.47

57.12

67.11

Initial English, Mathematics and Japanese 1498
Dummy 0.98

0.15

0.00

1.00

Located Area Dummy
Hokkaido

1507

0.07

0.26

0.00

1.00

Kita-Tohoku

1507

0.01

0.12

0.00

1.00

Minami-Tohoku

1507

0.06

0.23

0.00

1.00

Kita-Kanto

1507

0.04

0.19

0.00

1.00

Minami-Kanto

1507

0.20

0.40

0.00

1.00

Hokuriku

1507

0.05

0.22

0.00

1.00

Koshin

1507

0.03

0.17

0.00

1.00

Tokai

1507

0.09

0.28

0.00

1.00

Kansai

1507

0.23

0.42

0.00

1.00

Chugoku

1507

0.06

0.23

0.00

1.00

Shikoku

1507

0.03

0.17

0.00

1.00

Kyusyu

1507

0.13

0.34

0.00

1.00

Humanities

1507

0.06

0.24

0.00

1.00

Social Science

1507

0.30

0.46

0.00

1.00

Science

1507

0.05

0.22

0.00

1.00

Engineering

1507

0.35

0.48

0.00

1.00

Agriculture

1507

0.17

0.38

0.00

1.00

Health

1507

0.01

0.09

0.00

1.00

Mercantile Marine

1507

0.01

0.12

0.00

1.00

Home Economics

1507

0.00

0.00

0.00

0.00

Education & Teacher Training

1507

0.01

0.09

0.00

1.00

Arts

1507

0.00

0.00

0.00

0.00

Others

1507

0.04

0.19

0.00

1.00

Field of Faculty Dummy

Table 2: Summary Statistics of Main Covariates27we control for initial English, Mathematics and Japanese dummies to avoid
the possibility that our measure of number of subjects captures the impact oflanguage subjects.
Controlling for the interaction of year dummy with these observable degree
program characteristics, we assume that equations (4), (7) and/or (9) are satis…ed. To check the validity of equations (7) and/or (9), we estimate the impactsof B (i; 1975) on director share and selectivity score after excluding the e¤ect ofcovariates by each year. More precisely, we conduct the following regression on
the residuals, !
^ (i; t), using a date before 1978:!
^ j (i; t) =

j
0+

j

(i) +

j

(t) +

j
j
B (t) B (i; 1975) + " (i; t) ;

t

1978:

(10)

where the residual !
^ j (i; t) is de…ned by!
^ j (i; t)and ^ jW (t) is the estimate of^ jW (t) W (i) ;Y j (i; t)j
W (t) in the following regression using the wholesample:Y j (i; t) =where $j (i; t)hj(t) + jW (t)

j
0+

j

(i) +

j

(t) +

j
j
W (t) W (i) + $ (i; t) ;jjNote that !
^ (i; t) = j0 +B Z (i; t) + v (i; t).
i^ jW (t) W (i) + jB B (i; 1975) + v j (i; t) before 1978.

h

j

(i) +

Hence,ijjj^ jW (t) W (i)+W (t)B (t) B (i; 1975)+" (i; t) must re‡ect the movement ofv j (i; t). Assuming that jW (t) ^ jW (t), if equation (9) is satis…ed, B (t) 0

for all t

1978. While equation (7) is not equivalent to equation (9), Figure 4

shows that most degree programs did not change B (i; t) before 1978. Hence,
we expect that if equation (7) is satis…ed, it is also likely that
t

B (t)

0 for all1978.
Figures 7 and 8 plot the coe¢ cients

B (t) where t = 1976, 1977 and 1978.

These …gures show that we cannot reject our null hypothesis

B (t) = 0 for

all t before 1978 at the 5% level, which provides evidence that supports ouridenti…cation assumptions: i.e., equations (7) and (9).

28

Figure 7: The impacts of B (i; 1975) on director share after excluding the e¤ectof covariates by each year:

B (t).

Figure 8: The impacts of B (i; 1975) on selectivity score after excluding thee¤ect of covariates by each year:

B (t).

29

(1)Director Share
Number of Subjects

FE

(2)
(3)
National+Local Public
FE
FEIV

0.0758**(0.0338)

Mechanical Instrument

0.0761*(0.0400)
0.0731*(0.0385)

Fisrt Stage F-statControl
Observations
R-squared# of id

Y
1,320
0.501
139

(4)
FE
0.0753*(0.0382)First Stage
0.960***(0.0277)
1202

Y
1,320
0.501
139

Y
1,320
139

(5)
National
FE

FEIV
0.0829*(0.0466)

0.0796*(0.0448)
Y
1,201
0.503
126

(6)

Y
1,201
0.503
126

First Stage
0.961***(0.0328)
859.1
Y
1,201
126

Table 3: Impacts of the Number of Subjects on the Director Shares in the Listed
Companies: * 10% signi…cance, ** 5% signi…cance and *** 1% signi…cance. The
number inside the bracket is the robust standard error.
In the next section, we report our main results and report further robustness
results.

6

Results

Director Share: Table 3 shows the e¤ects of changes in the number of subjects
on the director share. The …rst three columns report the results by of the sample
of public universities (National+Local Public) and the last three column shows
the results for the sample of national universities. Columns (1) and (4) report
the results of our two-way …xed e¤ect model (3), columns (2) and (5) report the
reduced form regression (8), and columns (3) and (6) report the two-way …xede¤ects IV model.
All coe¢ cients of the number of subjects are positive and statistically signi…cant. All results show that an increase in the number of subjects increases
the director share in degree program graduates.
The magnitude is not small. An increase by one more subject in the entrance
examination increases the probability of producing directors from the degree
program graduates by about 0.08%. Table 1 shows that on average 0.27% of30

graduates in our sample become directors. It means that an extra subject inthe entrance examination is likely to increase the share of directors from thedegree program graduates by a factor of about 0:27+0:08
0:27

1:3 for the average

degree program in our sample. We will discuss the robustness of this number
later.

Selectivity Score: Having seen the positive impact of increasing the broadnessof examination subjects on the director share, we must ask the following question: why does increasing the number of subjects in an entrance examination
for a degree program increase the future business leaders among its graduates?
To provide evidence to infer the mechanism, we try to separate two potential
types of students that can be selected by increasing the number of subjects: 1)
Increasing the number of subjects screens out the students with higher academic
skills. 2) Increasing the number of subjects on the entrance examinations selectstudents who have a comparative advantage in learning a wide range of topics.
For this purpose, this paper examines the impact on selectivity scores of degree
programs to investigate whether the increase in the number of subjects simply
selects intellectually capable students.
As we discussed before, many Japanese labor economists use the selectivity
score as a proxy for the average academic skill of the graduates from a degree
program (e.g., Abe, 2002; Araki et al., 2016; Goodman & Oka, 2018; Ito & Nakamura, 2019). Hence, if increasing the number of subjects screens out students
who have higher academic skills, the selectivity score is likely to increase.
Table 4 shows the e¤ects of the number of subjects on the selectivity score.
The structure of the table is the same as Table 3.

The results show that

all coe¢ cients of the number of subjects are signi…cantly negative and that
adding one more subject to the entrance examinations reduces the selectivity
score of the degree program by about 0.5 and 0.7 in the samples of public
and national universities, respectively, which suggests that an increase in thenumber of subjects is unlikely to improve the average intellectual ability ofdegree program graduates.
31

(1)Selectivity Score

FE

Number of Subjects-0.474**(0.185)

Mechanical Instrument(2)(3)
National+Local PublicFE
FEIV-0.504**(0.213)-0.484**(0.206)

Fisrt Stage F-statControl
Observations
R-squared# of id

Y
1,320
0.560
139

(4)
FE
-0.597***(0.157)First Stage
0.960***(0.0277)
1201

Y
1,320
0.559
139

Y
1,320
139

(5)
National
FE

FEIV-0.686***(0.169)-0.659***(0.164)

Y
1,201
0.574
126

(6)

Y
1,201
0.576
126

First Stage
0.961***(0.0328)
859.1
Y
1,201
126

Table 4: Impacts of the Number of Subjects on the Selectivity Measure: * 10%
signi…cance, ** 5% signi…cance and *** 1% signi…cance. The number in thebracket is the robust standard error.
In summary, the study results support the hypothesis that by broadening
the range of subjects for entrance examinations, colleges can select people who
can learn a wide range of knowledge and produce future business leaders fromtheir graduates.

6.1

Robustness

In this section, we conduct several robustness checks. First, we conduct simple regressions using the sample of degree programs that did not change their
number of subjects between 1975 and 1978. Second, we conduct the same regression for the 1978 cohort. Third, we investigate a possibility that any otherunobserved characteristics might be correlated with our instrument variables.

Estimation with Selected Sample: Once we drop the degree programs thatchanged their number of subjects between 1975 and 1978, the number of subjects
and our mechanical instrument coincide. Hence, if this selection procedure does
not create any bias, the simple regression equation (3) with this selected sample

32

Director Share

Director ShareNational+Local Public National
FE
FE

Selectivity Score
National+Local Public National
FE
FE

Number of Subjects(Mechanical Instrument)

0.0682*(0.0403)0.0740(0.0484)-0.437**(0.220)-0.633***(0.175)Control
Observations
R-squared# of id

Y
1,131
0.518
118

Y
1,012
0.519
105

Y
1,131
0.594
118

Y
1,012
0.624
105

Table 5: Impacts of the Number of Subjects on the Director Share and Selectivity Score for the Sample of Degree Programs for Which There Were No Changes
in the Number of Subjects Between 1975 and 1978: * 10% signi…cance, ** 5%signi…cance and *** 1% signi…cance. The number in the bracket is the robust
standard error.
must identify the parameter

B under the assumption of equation (9).

BecauseFigure 4 suggests that a fairly small number of degree programs changed their
number of subjects between 1975 and 1978, we expect that the selection bias issmall and that we will obtain similar results.
Table 5 shows the results of the two-way …xed e¤ect model for the sample
of degree programs for which there were no changes in their number of subjects
between 1975 and 1978. The …rst two columns show the e¤ects on the numberof subjects on the director share. Compared with table 3, the coe¢ cients areslightly smaller and the coe¢ cients for the sample of national university is not
signi…cant at the 10% level. This is understandable because the selected sample
inevitably reduces sample size. The last two columns show the e¤ects of thenumber of subjects on the selectivity score. Similar to the director share, themagnitudes of coe¢ cients are slightly smaller than the results in the table 4.
While there are minor di¤erences between the results in the selected sample andour main results, we cannot …nd any results that force us to change our main
conclusion.

33

(1)
Director Share
Number of Subjects

FE

(2)
(3)
National+Local PublicFE
FEIV

0.109**(0.0474)

Mechanical Instrument

0.121**(0.0559)
0.117**(0.0540)

Fisrt Stage F-statControl
Observations
R-squared# of id

Y
1,184
0.528
139

(4)
FE
0.105*(0.0547)First Stage
0.966***(0.0248)
1514

Y
1,184
0.528
139

Y
1,184
139

(5)
National
FE

FEIV
0.125*(0.0661)

0.121*(0.0639)
Y
1,078
0.534
126

(6)

Y
1,078
0.535
126

First Stage
0.967***(0.0294)
1082
Y
1,078
126

Table 6: Impacts of the Number of Subjects on Director Shares in the Listed
Companies: Without the 1978 Cohort: * 10% signi…cance, ** 5% signi…canceand *** 1% signi…cance. The number in the bracket is the robust standard
error.

(1)
Selectivity Score

FE

Number of Subjects-0.468**(0.190)Mechanical Instrument(2)(3)
National+Local PublicFE
FEIV-0.505**(0.216)-0.488**(0.211)

Fisrt Stage F-statControl
Observations
R-squared# of id

Y
1,184
0.551
139

(4)
FE
-0.595***(0.160)First Stage
0.966***(0.0248)
1514

Y
1,184
0.551
139

Y
1,184
139

(5)
NationalFE

FEIV-0.693***(0.172)-0.670***(0.168)

Y
1,078
0.570
126

(6)

Y
1,078
0.573
126

First Stage
0.967***(0.0294)
1082
Y
1,078
126

Table 7: Impact of the Number of Subjects on the Selectivity Measure Excluding
1978: * 10% signi…cance, ** 5% signi…cance and *** 1% signi…cance. The
number in the bracket is the robust standard error.

34

Estimation Without the 1978 Cohort: Because the introduction of JFSAT
was announced before 1978, it might in‡uence the application decisions of students in the 1978 cohort. For example, if students failed to pass the entrance
examination in 1978, they need to wait one year to take the entrance examination. Because they know that they can only apply to one public university inthat following year, they may apply to less-competitive universities.

In fact,

Figure 3 shows some indication that applications to local public universities did
increase in 1978. This result might in‡uence our estimates.
Tables 6 and 7 report the same estimation results without the 1978 cohort.
Both tables show that overall results are the same as before, while the magnitudeof the impact on the director share is slightly larger. This indicates that changes
in the application decisions of students in the 1978 cohort do not in‡uence ourresults very much.

Unobserved Characteristics: While Figures 7 and 8 are consistent with ouridenti…cation assumptions (equations (7) and (9)), these …gures do not guarantee
our identi…cation assumptions. We conduct further robustness checks.
For this purpose, we add the interaction of the number of subjects in 1975
and the year dummy in our control variables. The results are reported in Tables
8 and 9.
Table 8 shows that the coe¢ cients of the number of subjects in the twoway …xed e¤ect model and two-way …xed IV model show a positive sign, but
are not signi…cant.

The results of the reduced form models are even worse.

The coe¢ cients of mechanical instruments are negative, but these are also notsigni…cant. Compared with the results in Tables 3 and 6, the magnitude of thecoe¢ cients is unstable. These results suggest that adding the interaction of thenumber of subjects in 1975 and year dummy in our control variables are likely
to cause multicollinearity with both our treatment variable and IV. Therefore,
it is likely that the instruments are weak.
The results in table 9 are better.

All coe¢ cients have the expected sign
35

(1)

Director Share
Number of Subjects(2)(3)
National+Local Public
FE
FE
FEIV
0.0661(0.0529)

Mechanical Instrument

0.194(0.130)-0.0591(0.0659)

Fisrt Stage F-statControl
Observations
R-squared# of id

Y
1,320
0.506
139

Y
1,320
0.506
139

(4)
FE
0.0426(0.0513)First Stage
0.945***(0.0367)
664.1
Y
1,320
139

(5)
National
FE

FEIV
0.189
(0.135)-0.0188(0.0718)

Y
1,201
0.508
126

(6)

Y
1,201
0.508
126

First Stage
0.945***(0.0435)
472.5
Y
1,201
126

Table 8: Impacts of the Number of Subjects on Director Shares in Listed Companies When We Add the [Initial Number of Subjects]x[Year Dummy] Under
Our Control: * 10% signi…cance, ** 5% signi…cance and *** 1% signi…cance.
The number in the bracket is the robust standard error.

(1)

Selectivity Score
Number of Subjects(2)(3)
National+Local Public
FE
FE
FEIV
-0.347(0.324)

Mechanical Instrument-1.380***(0.510)-0.510*(0.269)Fisrt Stage F-statControl
Observations
R-squared# of id

Y
1,320
0.561
139

Y
1,320
0.559
139

(4)
FE
-0.271(0.322)First Stage
0.945***(0.0367)
664.1
Y
1,320
139

(5)
National
FE

FEIV-1.525***(0.496)-0.696***(0.242)

Y
1,201
0.579
126

(6)

Y
1,201
0.577
126

First Stage
0.945***(0.0435)
472.5
Y
1,201
126

Table 9: Impact of the Number of Subjects on Selectivity Score in Listed Companies When We Add the [Initial Number of Subjects]x[Year Dummy] Under
Our Control: * 10% signi…cance, ** 5% signi…cance and *** 1% signi…cance.
The number in the bracket is the robust standard error.

36

(1)

(2)
(3)
National+Local Public
FE
FE
FEIV

Director Share
Number of Subjects

0.129**(0.0511)

Mechanical Instrument

0.164**(0.0722)0.165**(0.0730)

Fisrt Stage F-statControl
Observations
R-squared# of id

Table 10:

Y
1,320
0.506
139

Y
1,320
0.506
139

(4)
FE
0.112**(0.0548)First Stage
1.006***(0.0309)
664.1
Y
1,320
139

(5)
NationalFE

FEIV
0.155*(0.0826)

0.156*(0.0835)

Y
1,201
0.508
126

(6)

Y
1,201
0.508
126

First Stage
1.007***(0.0380)
472.5
Y
1,201
126

Impact of the Number of Subjects on Director Shares in the

Listed Companies When We Include the [More Than Six Initial Subjects
Dummy]x[Year Dummy] Under Our Control: * 10% signi…cance, ** 5% signi…cance and *** 1% signi…cance. The number in the bracket is the robust
standard error.
and the coe¢ cients of number of subjects in the two-way …xed IV model andthose of mechanical instruments in the reduced form models are signi…cant.
However, compared with the results in Tables 4 and 7, the magnitude of thecoe¢ cients are again unstable. Hence, the results might be in‡uenced by themulticollinearity and weak IV problems.
To avoid the possibility of multicollinearity, we instead add the interactionof the more than six initial subjects and year dummies in our control variables
and conduct the same estimation. The results are reported in Tables 10 and
11. The overall results are similar to our main results. All coe¢ cients havethe expected sign and are statistically signi…cant and stable. The magnitudeof the coe¢ cients is slightly larger than that in Tables 3 and 4. All results
show that an increase in the number of subjects of a degree program increases
the probability of being appointed to the board of directors of publicly traded

37

(1)

Selectivity Score
Number of Subjects(2)(3)
National+Local Public
FE
FE
FEIV
-0.526**(0.215)

Mechanical Instrument-0.631**(0.253)-0.635**(0.261)

Fisrt Stage F-statControl
Observations
R-squared# of id

Y
1,320
0.561
139

Y
1,320
0.559
139

(4)

(5)
NationalFE

FE-0.546**(0.234)First Stage
1.006***(0.0309)
664.1
Y
1,320
139

FEIV
-0.734***(0.271)-0.740***(0.282)

Y
1,201
0.579
126

(6)

Y
1,201
0.577
126

First Stage
1.007***(0.0380)
472.5
Y
1,201
126

Table 11: Impact of the Number of Subjects on Selectivity Score When We
Include the [More Than Six Initial Subjects Dummy]x[Year Dummy] Under
Our Control: * 10% signi…cance, ** 5% signi…cance and *** 1% signi…cance.
The number in the bracket is the robust standard error.
companies, although it lowers the selectivity score.

The Robustness of the Magnitude of Parameters: Let us discuss themagnitude of our estimates. After conducting several robustness checks, an increase of one more subject in the entrance examination increases the probabilityof producing directors from the degree program graduates by between 0.07 and0.16 for public universities from Tables 3, 5, 6 and 10. Table 1 shows that on
average 0:27% of graduates become directors. Hence, one more subject in theentrance examination is likely to increase the share of directors from the degree
program graduates by a factor of between 0:27+0:07
0:27

1:26 and 0:27+0:16
0:27

1:6.

This suggests a fairly large impact of an increase in the number of subjects on
the director share.
Similarly, an increase by one more subject in the entrance examination lowers
the selectivity score by between 0.44 and 0.63 for public universities from Tables

38

4, 5, 7 and 11. Table 1 shows that the average selectivity score is 61.11. Hence,an increase of one more subjects in entrance examination increases the selectivity
0:44
score by a factor of 61:11
61:11

61:11 0:63
61:11

0:99.

This result suggests thatan increase in the number of subjects has a very small impact on the selectivity
score.
In sum, the results of our robustness tests also support the main hypothesisthat by broadening the range of subjects for entrance examinations, colleges
can select people who can learn a wide range of knowledge and produce futurebusiness leaders from their graduates.

7

Conclusion

This paper examines whether expanding the range of subjects in the entrance
examinations to attend particular colleges also increase the number of businessleaders from the graduates of these colleges. To solve this endogeneity problem,
we use a historical event in Japan: i.e., the government introduced a nationwide
standardized entrance examination test, the JFSAT, in 1979.

Using the factthat the number of subjects has exogenously increased to seven, we construct amechanical IV, which consists of the number of subjects of the degree programin 1975, a year prior to 1978 when the reform was announced, and seven, which
it is after the reform was introduced.

We then conduct two-way …xed e¤ect

IV estimates using a sample consisting of public universities between 1975 and1985. Our results show that an increase in the number of subjects of a degree
program also increases the probability of appointment to the board of directorsof a publicly traded company, although it lowers the selectivity score.

This

indicates that by broadening the range of subjects for entrance examinations,colleges can select people who can learn a wide range of knowledge and produce
future business leaders from their graduates.
We can discuss the remaining questions in future research. First, while we…nd evidence that increases in the number of subjects in the entrance examinations that students must study also increase the number of business leaders
39

from the graduates of the colleges, we are not sure that this is the result ofeducation before applying to colleges or the students’innate ability. While itis beyond the scope of this paper, it would be important to separate the impactof education from innate ability as future research.
Next, it would be interesting to know if increases in the number of subjects
in the entrance examinations that students must study also increase the numberof other types of leaders from the college graduates, such as bureaucrats andpoliticians.

Some colleges may prefer to attract other types of leaders suchas bureaucrats and politicians rather than business leaders. If expanding therange of subjects also helps to produce future bureaucrats and politicians, we
can suggest that any colleges that wish to raise social leaders in general should
test ability to learn a broad range of subjects. If not, they must clarify their
mission regarding which types of leaders they wish to produce. This would also
be a subject for important and interesting future research.

8

Appendix

8.1

Descriptive Statistics of Director-Level Data

We report the summary statistics of the director-level data used for the construction of the director share. We start with a sample of 13,517 directors ofJapanese listed companies post-1990 who were born after 1955.

Of the ini-tial sample, 10,310 (76%) directors graduated from major degree programs and9,206 (68%) were directors of major companies.

Finally, because we restrict

our analysis to between 1975 and 1986, the number of observation came down
to 4,866 (36%).
Table 12 describes the …nal sample of 4,842 directors who both graduatedfrom major degree programs and have been a board member of at least one
major company.

The average age at which their …rst experience as a board

member began is 51 years. Almost one quarter of the directors graduated fromnational universities.

40

Table 12: Summary Statistics of Director Data

Table 13: Response Rate for Educational Background41

Director Share

(1)

(2)

(3)

(4)
National
FE
FEIV
FE
FEIV
0.0884*** 0.0978*** 0.0910*** 0.104***(0.0258) (0.0285) (0.0292) (0.0332)
0.0859*** 0.0925*** 0.0773** 0.0859**(0.0267) (0.0291) (0.0301) (0.0337)
0.0620** 0.0718** 0.0572* 0.0711**(0.0279) (0.0303) (0.0319) (0.0355)
0.0605** 0.0624** 0.0557*
0.0607*(0.0276) (0.0311) (0.0310) (0.0360)
0.0751** 0.0753** 0.0759** 0.0813**(0.0313) (0.0356) (0.0351) (0.0410)
0.0578*0.05380.0556
0.0557(0.0335) (0.0379) (0.0380) (0.0444)
0.0603*0.05690.06030.0623(0.0323) (0.0384) (0.0365) (0.0449)
0.0727** 0.0721*
0.0715*0.0772*(0.0337) (0.0397) (0.0381) (0.0463)
0.0758** 0.0761*
0.0753*0.0829*(0.0338) (0.0400) (0.0382) (0.0466)-0.00344 0.00267 -0.00518 0.00207(0.00320) (0.00260) (0.00373) (0.00298)
PublicAbove 90th Precentile
Above 80th Precentile
Above 70th Precentile
Above 60th Precentile
Above 50th Precentile
Above 40th Precentile
Above 30th Precentile
Above 20th Precentile
Above 10th Precentile
Below 10th Precentile

Table 14: Impact of the Number of Subjects on Director Shares in the Listed
Companies by Firm Size: * 10% signi…cance, ** 5% signi…cance and *** 1%
signi…cance. The number in the bracket is the robust standard error.
Table 13 summarizes the percentage of the available data on the directors’
educational background for deciles according to …rm size (total assets).

The

table shows that the response rate is positively related to the size of the …rm.

8.2

Appendix Estimation

Estimation by Firm Sizes: Table 14 shows the impact of the number of subjects in the entrance examination on the director by …rm size (total assets). We
report the results above the 10th percentile as our main result. The table showsthat the results are stable except for the estimation below the 10th percentile.

42

(1)
Director Share
Number of Subjects

FE

(2)
(3)
National+Local Public
FE
FEIV

0.0351*(0.0202)

Mechanical Instrument

0.0334*(0.0199)
Y
3,262
0.328
344

FE

0.0399*(0.0239)

Fisrt Stage F-statControl
Observations
R-squared# of id(4)0.0513**(0.0251)First Stage
0.836***(0.0347)
581.3

Y
3,262
0.328
344

(5)
National
FE

Y
2,756
0.353
288

344

FEIV
0.0672**(0.0312)0.0528**(0.0243)

Y
3,262

(6)

Y
2,756
0.353
288

First Stage
0.785***(0.0444)
312.3
Y
2,756
288

Table 15: Impact of the Number of Subjects on the Director Share in the Listed
Companies When We Use All Degree Programs That Produce a Director After
1990: * 10% signi…cance, ** 5% signi…cance and *** 1% signi…cance. The
number in the bracket is the robust standard error.

(1)
Selectivity Score

FE

Number of Subjects-0.261**(0.102)Mechanical Instrument(2)(3)
National+Local PublicFE
FEIV-0.234*(0.121)-0.196*(0.103)Fisrt Stage F-statControl
Observations
R-squared# of id

Y
3,262
0.420
344

(4)
FE
-0.356***(0.105)First Stage
0.836***(0.0347)
581.3

Y
3,262
0.417
344

(5)
National
FE

Y
3,262
344

FEIV
-0.356***(0.127)-0.280***(0.103)

Y
2,756
0.434
288

(6)

Y
2,756
0.429
288

First Stage
0.785***(0.0444)
312.3
Y
2,756
288

Table 16: Impact of the Number of Subjects on Selectivity Score in the Listed
Companies When We Use All Degree Programs That Produce a Director After
1990: * 10% signi…cance, ** 5% signi…cance and *** 1% signi…cance. The
number in the bracket is the robust standard error.

43

Estimation with All Degree Programs: We use a sample of 142 degree
programs that have produced more than …ve graduates who have been a directorof one or more Japanese listed …rms after 1990 as our benchmark estimation. In
this Appendix, we reestimate the same regression using the sample of all degree
programs that produce at least one graduate who has been a director of one ormore Japanese listed …rms after 1990.
Tables 15 and 16 report the results of the reestimation. Both tables showthat overall results are the same as before, while the magnitudes are slightly
smaller in both tables. These results indicate that our sample selection criterion
seems not to in‡uence the …nal result very much.

9

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