2015-8
Determinants of University Tuition in Japan
Fumitoshi Mizutani
Noriyoshi Nakayama Tomoyasu Tanaka

Determinants of University Tuition in Japan

Fumitoshi Mizutani
Kobe University
Graduate School of Business Administration
2-1 Rokkodai, Nada-ku, Kobe 657-8501 Japantoshi@kobe-u.ac.jp

Noriyoshi Nakayama
Nagoya City University
Graduate School of Economics
1 Yamanohata, Mizuho-cho, Mizuho-ku, Nagoya 467-8501 Japan
nakayama@econ.nagoya-cu.ac.jp

Tomoyasu Tanaka
Kinki University
Faculty of Business Administration
3-4-1 Kowakae, Higashi-Osaka 577-8502 Japantanakatomo@bus.kindai.ac.jp
Correspondent Author: Fumitoshi Mizutani
toshi@kobe-u.ac.jp1Determinants of University Tuition in Japan[Abstract]: The main purpose of this study is to find the factors involved in determining private
university tuition level. Furthermore, we test whether or not university tuition of prestigious (higher
standard deviation score) private universities is lower than that of less prestigious private universities.
Japanese researchers have drawn conflicting conclusions about this issue. We obtain the following
results. First, important factors affecting tuition are (i) the size of university (i.e. the number ofattending students), (ii) the quality of the university (i.e. standard deviation scores), (iii) urban andcompetition factors (i.e. the inverse of HHI), and (iv) subsidies to private universities. Second,
empirical results show that university tuition is higher at very prestigious universities, a tendency
similar to that in the US. Third, bigger university size and greater availability of subsidies tend to
reduce the tuition level.
[Key Words]: Determinants of tuition, Universities, Quality of education, Competition[JEL Classification]: I21, I22, I23

1. Introduction
Because of recent serial reforms implemented by the Ministry of Education, Culture,
Sports, Science and Technology (hereafter MEXT), universities in Japan face a difficult situation.
Further reforms may become necessary in order to address various problems resulting from previous
reforms and other environmental factors.
The main purpose of this study is to identify the factors that currently determine private
university tuition cost. Furthermore, we test whether or not the tuition of highly competitive private
universities is lower than that of less academically competitive private universities. While the tuition
at prestigious private universities in the US is higher than at less prestigious private universities, inJapan, the opposite is the case. It is the national, or public, universities that are considered the mostprestigious. While in the US, Ivy League universities have historically been considered the mostprestigious and selective, in Japan the seven national former imperial universities1 are considered
the most desirable, and all have a long history of educating leaders in many fields. Students atnational universities in Japan pay much lower tuition than their counterparts at private universities,

1

The seven national universities are the University of Tokyo, Kyoto University, Tohoku University,
Kyushu University, Hokkaido University, Osaka University and Nagoya University. Alumni of these
universities include high-achieving scientists, engineers, government officials, and corporate leaders.

2

so that the nation’s top students almost invariably aim to matriculate at these low-cost, high-prestige
national universities. This situation has created a dilemma for prestigious private universities, which,because they are competing with national universities to attract the same low-tuition seeking,high-achieving students, are unable to impose high tuition costs. Therefore, even as the quality ofeducation at prestigious private universities increases, their tuition costs must remain low if these
institutions are to attract the best students.
Regarding the question of whether or not prestigious private universities charge lower
tuition rates than less prestigious universities, empirical studies show different results. Maruyama(1991, 1994) obtains the result that it is not true that prestigious universities have lower tuition in
Japan. He maintains that the tuition level of private universities in Japan can be explained as a“market result” rather than as a form of “scholarship.” On the other hand, Urata (1998) obtains
totally different results, finding that universities with better conditions and higher selectivity tend tohave lower tuition and fees. Thus, it is not clear whether private universities’ tuition increases areproportionate with national universities’ competitiveness.
This paper consists of six parts after the introduction. The second section summarizes
characteristics of universities in Japan by comparing the Japanese university system with systems inother industrial countries. The third section evaluates factors which previous studies have shown tohave an effect on university tuition. Based on previous studies, we build empirical models in the
fourth section. In this section, three equations are built: (i) the university tuition equation, (ii) the
number of students equation, and (iii) the standard deviation score equation. In the fifth section, data
for the estimation are explained. The section six shows empirical results. In this section, thesimultaneous approach is used. The last section summarizes the major findings.

2. Overview of Universities in Japan
2.1 International Comparison Among Selected Countries
First, we will explain the current situation of universities in Japan. Table 1 shows aninternational comparison of universities. Selected countries for comparison are the US, the UK,
France and Germany.
In Japan, there are 752 universities, a large number when compared with the number inEuropean countries. The number of universities per capita is almost the same as in the US.
Furthermore, private universities in Japan account for 79%, similar to the percentage in the US. In
contrast, most universities in European countries are public institutions.
3***********
Table 1***********The number of students per capita in Japan is similar to that in France and Germany,
while in the US and the UK, these numbers are relatively larger, presumably because the US and UK
attract many foreign students.
The second important point is that public expenditure on higher education in Japan islower than in other countries, as Table 2 shows. As for this measure, the ratio in both per GDP and
per total public expenditure in Japan is significantly low. In Japan, expenditures on higher education
are covered to a large extent by households, which contribute about 50% of the total.

***********
Table 2***********

Information on university tuition is not available for all countries. However, when wecompare university tuition in Japan with that in the US, the following result can be seen. Public
universities in Japan charge tuition almost equivalent to that in the US, but tuition at private
universities in Japan is much lower than in the US. In fact, there is an important reason that tuition at
Japanese private universities is kept low: student scholarships are almost nonexistent in Japan. As
Table 3 shows, scholarships account for only 0.6% of total support. Direct support to private
universities from the government is lower than in continental European countries.
From these results, it can be seen that the organizational structure of universities in Japanis relatively similar to that in the US. The feature showing the most difference between the twocountries is the tuition costs at private universities.

***********
Table 3***********

2.2 Private-National University Comparison of Tuition Level in Japan
A comparison of the current level of university tuition at private and national universities4

is shown in Table 4. In Japan, university tuition covers three main categories. Tuition is mainly for
lectures and experiments. Admission fees are paid only once at the time of enrollment in a university.
Facility maintenance fees are charged by private universities, but not by national universities, which
include these in tuition.

***********
Table 4***********

This table shows that the tuition at a private university is about 60% higher than at anational university. While there are still big differences between the two kinds of universities, thedifference was formerly much greater. Figure 1 shows trends in university tuition. However, in thisfigure, the facility maintenance fees of private universities are not included because this measure is
not available. For example, in 1975, the tuition level of private universities was 5.1 times higher thanthat of national universities. Since then, the difference has been amended by increasing tuition costs
at national universities. Since 1986, the tuition difference has been suppressed less than twice.

***********
Figure 1***********

3. Previous Studies
3.1 Previous Studies
In this section, we will overview previous studies analyzing the determinants ofuniversity tuition. There are numerous studies, such as Hartford and Marcus (1986), Koshal andKoshal (1998, 2001), Dimkpah et al. (2004) and Elliot and Soo (2013) in the US and Maruyama(1991, 1994), Yonezawa (1994), Urata (1998) and Funabashi (2008) in Japan.
Harford and Marcus (1986) investigate what kinds of factors determine university tuitionlevel. They apply the hedonic approach to tuition for U.S. private colleges by using data from1982-1983. They find that a college’s tuition tends to become higher concomitant with an increase in
student quality as expressed by SAT scores, and with an increase in teaching quality as expressed byan increased ratio of PhD holders. From these results, they conclude that the determinants of college
5tuition are the quality of both students and teachers.
In contrast to the situation in the US, prestigious universities in Japan have lower tuition
rates. Maruyama (1991, 1994) examines whether the tuition of prestigious private universities is
actually low, and he investigates the determinants of tuition in Japan. By applying regression to thedata of Japanese private universities in the year 1989, he obtains the following results. First, it is nottrue that prestigious universities have lower tuition in Japan; rather, the tuition level has a positive
relationship with the difficulty of entrance into a university. He maintains that the tuition level ofprivate universities in Japan can be explained as a “market result” rather than as a form of“scholarship.”Yonezawa (1994) also investigates the determinants of college tuition. His approach issimilar to Harford and Marcus (1986), which uses the hedonic approach but investigates Japanese
private universities and colleges for the academic year 1991-92. However, his findings are not as
simple as those of the previous Harford and Marcus (1986) study. He concludes that there are several
patterns in the economic behavior of Japan’s private universities, and that the behavioral patterns andtuition levels are substantially influenced by a university’s competence or position in the higher
education market.
Urata (1998) investigates what would be factors affecting the tuition at private
universities in Japan. He analyzes 1997 data of private universities in Japan and obtains the resultsthat universities with better conditions and higher selectivity tend to have lower tuition and fees.
These results are different from Maruyama’s (1991), and the tendency he describes is different fromthe situation at private universities in the U.S. He offers the following reasons for his results. First, inJapan, it is students’ parents who pay university tuition, so that it tends to be necessarily suppressed.
Second, prestigious universities are in general established institutions with a long history and stable
management conditions, making tuition increases unnecessary. It is also possible that there is a
widespread and self-fulfilling view in Japan that high-quality higher education should be provided atlower cost. This philosophy seems to have been put into practice at private universities as well.
Koshal and Koshal (1998) analyze the determinants of tuitions. They build and estimate amodel of supply and demand functions for university education in the U.S. The data used are 360
comprehensive universities for the year 1990-91. They estimate simultaneous equations for themodel by applying the 2SLS and obtain the result that there exists a perfectly competitive market foruniversity education. The quality of students, cost of education, average SAT score, class size, levelof highest degree offered, and tier of the institution are the main factors accounting for tuition cost.
Koshal and Koshal (2000)’s study investigates the relationship between state6

appropriation and tuition costs. They apply a simultaneous equation model by using 1990 data on 47
continental states in the US. They obtain the results that tuition depends on state appropriation,
median family income, out-of-state enrollment and regional factors.
Dimkpah et al. (2004) analyze the impacts of university quality and location attributes on
university tuition by using four-year private universities in the U.S. Observations in this study are
684 for 1999-2000, and they estimate the hedonic price equation. They conclude that the quality
attributes (i.e. highly and moderately competitive university, percentage of faculty with doctoral
degree, student/faculty ratio, age of university) of a university are the important determinants oftuition.
Funabashi (2008) investigates the factors affecting private university tuition level byusing data on Japanese private universities in 2004, and applying regressions. He obtains the resultsthat factors affecting tuition are (i) the level of difficulty of the entrance examination (+), (ii) the
history of the university (-), (iii) the number of students per teacher (+), (iv) the size of the university(+), and (v) tuition costs at neighboring universities (+)2.
Elliott and Soo (2013) investigate the relationship between tuition fees of MBA programsand the number of applications to these programs. They apply Three-Stage-Least-Squares methods
for simultaneous equations by using a panel dataset comprising universities from countries acrossthe world. They find that higher application numbers encourage universities to charge higher fees in
the future, but higher fees in turn curtail application numbers.
Although his study does not focus on the determinants of tuition, McDuff (2007) uses US
data to investigate the determinants of the number of applications to in-state public colleges anduniversities. He finds that student willingness to pay for quality is quite large.
Heath and Tuckman (1987) also maintain that tuition levels are more likely to affectapplications, while financial aid, since it is usually awarded after acceptance, is more likely to affectchoice at the point of matriculation, at least at the undergraduate level.
Noorbakhsh and Culp (2002), by using data from Pennsylvania, analyze how tuition
increases affect enrollment in higher education. They conclude that a large tuition increase coupledwith an elastic demand caused a significant loss of nonresident enrollment and tuition revenue in theState System of Higher Education.
Fethke (2005) takes a theoretical approach to exploring the strategic interactions ofsubsidies and tuition in public higher education. He explains that when the revenue of the university
2

The sign of these five factors shows the relationship of the university tuition. The plus (+) signshows that the factor works to increase tuition.
7

is important relative to students’ welfare in legislative preferences, commitment to the subsidy prior
to the setting of tuition leads to a lower subsidy and a higher tuition than will occur if legislatures
determine tuition.
Neill (2009) investigates the effect of tuition fee increases on demand for a university
education. He uses data on Canadian universities and makes both single equation estimates andsystem estimates. He concludes that system estimates that take into account endogeneity of fees
show large effects relative to single equation estimates. And he obtains the result that an increase of
one thousand Canadian dollars in university tuition fees reduces enrollment rate by 2.5 – 5%.
Finally, although not primarily about determinants of university tuition, research has
been done on outside budget purchasing behavior (e.g. Yoshida (2007)), quality assurance (e.g.
Toma and Naruo (2009)), and university administrators’ behavior (e.g. Coates and Humphreys(2002), Coates et al. (2004)).

3.2 Summary of Previous Studies
Here we will summarize the main features of previous studies, regarding such matters as
analytical methods used, data, important factors determined to affect tuition levels, and so on.
First, most studies use regression analysis, but there are some variations. The majority ofstudies use single equation for the tuition model, such as Harford and Marcus (1986), Maruyama(1991, 1994), Yonezawa (1994), Urata (1998) and Dimkppah et al. (2004). Of these, some studies,such as Harford and Marcus (1986), Yonezawa (1994) and Dimkpah et al., use hedonic specificationof university tuition. These studies suggest that university tuition can be explained as quality ofuniversity service.
Some studies, such as Koshal and Koshal (1998, 2000) and Elliot and Soo (2013), use
simultaneous equations, indicating that university tuition is considered an endogenous variable. In
fact, there is no guarantee that tuition is an exogenous variable from other quality variables of
education. Therefore, the simultaneous equation approach could be better than the single equation
approach.
Second, data for analysis in most previous studies have comprised a pooling of individual
universities. However, some studies use a cumulated data set. Koshal and Koshal (2000) estimate
tuition based on the state data set. This approach focuses on the demand side and factors affecting
regional difference. As we focus more on a university’s behavior in pricing, we use a data set ofindividual universities.
Third, as for factors affecting tuition level, there are six categories. The first category is
8university size. For example, Funabashi (2008) takes this factor. A big university might have a scaleadvantage in cost, or this might be a disadvantage.
The second category is the age of the university. Dimkpah et al. (2004) and Fubabashi(2008) take this factor. Older universities have a bigger stock of education facilities so that theymight be able to provide better services with relatively lower costs.
The third category is the university’s competitiveness factor. Prestigious universities,
having built excellent reputations, are able to attract more applicants, creating a demand that allows
them to charge higher tuition than universities perceived to be less prestigious. This has been thecase with prestigious private universities in the US. In fact, many previous studies, such as
Maruyama (1991, 1994), Urata (1998) and Funabashi (2008) in Japan and Koshal and Koshal (1998)
and Dimkpah et al. (2004) in the US, include the competitiveness factor.
The fourth category is education quality. Student-teacher ratio, class size, the percentageof doctoral degree holders among teaching staff, and so on, are typical variables used in studies by
Harford and Marcus (1986), Koshal and Koshal (1998), Dimkpah et al. (2008).
The fifth category is the competition factor with other universities. Funabashi (2008), forexample, includes this factor when he examines tuition at universities in close proximity, who set
tuition at levels allowing them to compete with each other for applicants.
The sixth category is the availability of subsidies from government. The more subsidies auniversity can receive from the government, the more it is able to suppress tuition rates.
It is also important to consider universities’ costs, information about which isunfortunately unavailable in Japan. However, on average, this cost might be correlated with
university size.

4. Empirical Model
Based on previous studies, we will build the structure of the model. The model consistsof three equations: (i) the university tuition equation, (ii) the number of students equation, and (iii)the standard deviation score equation. These three equations are explained as follows.
ln PTUI = 0 + 1 ln QATS + 2 ln SDS +3 ln (1/HHI) + 4 ln SUB + 5 ln SAL(1)

ln QATS = 0 + 1 ln SDS + 2 ln PTUI +3 DUMLMA(2)

ln SDS = 0 + 1 ln PTUI + 2 ln QATS + 3 ln AGEUNIV +4 ln RSCSC + 5 ln EDCSTS + 6 ln EDCSTA
Where, PTUI : university tuition,
9(3)

QATS : number of attending students,
SDS : standard deviation scores,
HHI : Herfindahl-Hirschman index,
SUB : subsidies to private universities per student,
SAL : annual salary of a professor,
DUMLMA : large metropolitan area dummy,
AGEUNIV : age of university,
RSCSC : science research fund per academic staff,
EDCSTS : number of students per academic staff,
EDCSTA : number of students per campus areas.

As these equations show, the university tuition, the number of students and the standard
deviation scores are all endogenous variables, and the other variables are exogenous. We explain themeaning of these equations in Figure 2.

***********
Figure 2***********First, university tuition equation explains how the university tuition is determined. The
level of university tuition is affected by several factors, among which the most important is thenumber of attending students (ln QATS). A university with more students might enjoy scale merit. Or,
to the contrary, there might be scale demerit. University tuition can be considered a proxy variable ofthe unit cost of university education. As revenue sources are limited at private universities in Japan,
the number of students is the most important factor affecting tuition at those institutions.
Second, top private universities such as Waseda University and Keio University arecompeting with top national universities such as the University of Tokyo and Kyoto University.
Furthermore, private universities of the first tier (i.e. higher than 56 points in standard deviation
scores) are competing with national and municipal universities to acquire high school students withhigher grades. Therefore, the standard deviation score (ln SDS) could affect the level of tuition. Bothsigns are possible for this variable. If a university provides a better quality of university education to
attract excellent students, the coefficient should be plus. However, if first tier private universities
compete with national universities, the coefficient should be negative because the tuitions of national
10and municipal universities are much lower than those of private universities. In fact, the empiricalresults for the coefficient of this variable are controversial (see, for example, Maruyama (1991,
1994) and Urata (1998)).
Third, we introduce the inverse of Herfindahl-Hirschman index (ln (1/HHI)) as acompetition variable in this equation. However, in this equation, we divided the higher education
market into three tier groups in each region. In general, the more competition there is, the more
likely tuition levels are to be suppressed.
Fourth, the availability of subsidies from the government (ln SUB) also affects the levelof tuition. If the subsidies are larger, the tuition becomes lower.
Last, the annual salary of a professor (ln SAL) is also important. The coefficient of thisvariable could be positive.
Therefore, by evaluating the sign of these variables, we can see the effects of thesefactors.

5. Data and Definition of Variables
5.1 Data
As the main purpose of this study is to find the determinants of university tuition, wecollected observations from private universities in Japan. Because tuition at public universities suchas national universities and municipal universities is decided by the government and set at almost
uniform levels, we limited our sample to private universities only. Furthermore, as there is muchvariation among major subjects of study, we focused here on social science majors such as
economics, management, commerce, law, government and public policy. We selectedfaculties/schools of social science from 165 universities in 2010. As a result, the total sample size is297.

5.2 Definition of Variables
Table 5 shows the definition of all variables used in this study. First, university tuition(PTUI) in this study is defined as the sum of annual tuition, facility fees, training fees and various
miscellaneous fees for undergraduate education. As there is wide regional variation in prices,
university tuition is adjusted according to The Consumer Price Index Regional Differentials(Shohisya Bukka Chiikisa Shisu).

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***********
Table 5***********

As for the total number of attending students (QATS), we use total full time undergraduate
students of each faculty for this variable. Graduate students, research students, part-time students
and so on are not included in this variable. Data for this variable are obtained from Asahi Shinbun
Publications (2010).
As for a measure of difficulty of entrance to a university, we use the Standard Deviation
Score (SDS), known as “Hensachi” in Japan. The SDS is often used in Japan to measure howdifficult it is to enter individual universities based on data from trial entrance examinations
conducted by preparatory schools for these universities. The score, T of the SDS is measured as T =10 (x – x)/x + 50, where x: individual’s raw score, x: mean of raw score, x: standard deviation of
raw score. The expected required score of SDS to enter each university is reported by several
preparatory schools. The SDS is based on Asahi Shinbun Publications (2010).
As for competition factor, we choose the Herfindahl-Hirschman index (HHI). We divide
Japan into 8 regions3 in terms of area, and divide into 3 academic levels4 in terms of SDS,
calculating HHI based on the number of students.
Subsidies to a private university also affect the level of tuition. In this study, we definesubsidies to a private university (SUB) as the sum of general subsidies and special subsidies divided
by total number of students. The SUB is also adjusted according to The Consumer Price Index
Regional Differentials (Shohisya Bukka Chiikisa Shisu). Data on subsidies are obtained from The
Promotion and Mutual Aid Corporation for Private Schools in Japan (Kyosai Jigyodan Shigaku
Shinko Jigyo Honbu).
Salary of a professor (SAL) is the sum of annual salary and bonus and is also adjusted by
The Consumer Price Index Regional Differentials (Shohisya Bukka Chiikisa Shisu). Data on salaryare obtained from The Basic Survey of Wage Structure (Chingin Kozo Kihon Tokei Chosa).

3

8 regions are Hokkaido, Tohoku, Kanto, Koshinetsu-Hokuriku, Chubu, Kansai, Chugoku-Shikoku,
and Kyushu-Okinawa.
4
Three academic levels in SDS in this study are (i) higher than 56 point, (ii) between 55 and 46
point, (iii) lower than 45 point, in SDS score.
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Large metropolitan area dummy (DUMLMA) is a dummy variable to show large
metropolitan areas in Japan. In this study, the Tokyo and Osaka metopolitan areas are considered5.
Therefore, universities located in these metropolitan areas are taking one in this dummy variable.
The age of the university (AGEUNV) is defined as the number of years having elapsed
between the university’s establishment and 2009. Data for each university’s year of establishment
are obtained from Asahi Shinbun Publications (2010).
Science research fund per academic staff (RSCSC) is obtained by dividing scientificresearch fund from the Ministry of Education, Culture, Sports, Science and Technology by the
number of academic staff at a university. This variable is also adjusted by each region’s consumer
price index. Data for both scientific research fund and number of academic staff of a university are
obtained from Asahi Shinbun Publications (2010), and consumer price index is obtained from the
Ministry of Internal Affairs and Communications.
Number of students per academic staff (EDCSTS) and number of students per campus area
(EDCSTA) are obtained by dividing the number of students by the number of academic staff and total
land area of campus individually. Data on the number of students, number of academic staff and total
land areas of campus are obtained from Asahi Shinbun Publications (2010).

6. Empirical Analysis
6.1 Relations Among Key Variables
Although the main purpose of this study is to find the determinants of university tuition
by using regression analysis, first we can look at the sample distribution of key variables. In this case,by picking up three variables such as university tuition, number of attending students, and standard
deviation scores, we can detect a relationship among them, which can be seen in Figure 3 to Figure 5.
First of all, the relationship between university tuition and the number of attending students (Figure4) seems to be negative; that is, the bigger the university, the lower the university tuition per student.
As for the relationship between the standard deviation score and the number of attending students(Figure 5), the standard deviation score seems to be positively related to the size of a university.
However, there seems to be a clear relationship between university tuition and the standard deviation
score, as Figure 3 shows. It is necessary to control other conditions in order to find the real5

In this study, Tokyo metropolitan area includes the prefectures of Ibaraki, Tochigi, Gunma, Chiba,
Tokyo, Kanagawa, and Yamanashi. Osaka metropolitan area includes the prefectures of Shiga, Kyoto,
Osaka, Hyogo and Wakayama.
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relationship among these variables. Therefore, we apply regressions.

***********
Figure 3**********************
Figure 4**********************
Figure 5***********

6.2 Regression Results
The main purpose of this study is to find the determinants of university tuition (ln PTUI).
Our study is characterized by the fact that we specify three variables (i.e. university tuition (ln PTUI),
number of attending students (ln QATS), and standard deviation scores (ln SDS)) as endogenous
variables. Therefore, these three dependent variables are estimated by the use of the simultaneous
equation model. The estimation method of these equations is the three stage least square method(3SLS). We also estimate these three dependent variables one by one in single equations in order tocompare the results to those from the simultaneous equations. The estimation method for the single
equation is the ordinary least square method (OLS). The estimation results of the regressions areshown in Table 6.

***********
Table 6***********

From these results, sign and magnitude of coefficients in most variables are similar
between these models. However, some variables are different in significance level, sign ofcoefficients (e.g. standard deviation scores (ln SDS) in the university tuition equation, large
metropolitan area dummy (DUMLMA) in the number of attending students equation).
14

One reason why there are differences in these models is that the single equation model
does not consider endogeneity conditions. Therefore, we test whether or not three variables,
university tuition (ln PTUI), number of attending students (ln QATS), and standard deviation scores (lnSDS), are really endogenous variables, by using the so called endgeneity test. We apply the Hausman
Test for these three equations. Test results show that these three variables are not considered
exogenous variables 6 .

From this result, as the simultaneous equation models are better, weinterpret the coefficients based on the simultaneous equation models.
First, in the university tuition model, the important factors affecting tuition are numbersof attending students, standard deviation scores, inverse of HHI, and subsidies to private universities.
As the number of attending students and subsidies to private universities increase, the university
tuition decreases. On the other hand, as the standard deviation scores (i.e. difficulty of entering theuniversity) and the inverse of HHI (i.e. more competition in the region) increase, the university
tuition increases. These results, except for the inverse of HHI, seem reasonable factors in the pricing
of university tuition. Among these variables, based on the degree of their coefficients, standard
deviation scores and the number of attending students most affect university tuition. The inverse of
HHI shows a positive relationship is that this perhaps reflects the urban rather than the competition
factor.
Second, in the number of attending students equation, the important factors are theuniversity tuition and standard deviation. As university tuition increases, the number of attending
students decreases. On the other hand, as the standard deviation scores (i.e. greater difficulty ofentering the university) increases, the number of attending students increases. From these results, thesize of university (i.e. number of attending students) has a positive relationship not with university
tuition but with the standard deviation scores. In fact, highly competitive private universities such as
Waseda University and Keio University are very large. Therefore, we can get these results.
Third, in the standard deviation scores equation, the important factors are both number ofattending students and age of university. These variables both have a positive relationship. These
results show that as the university size increases and the university becomes old, the standard
deviation scores (i.e. difficulty of entering the university) become higher.

6

Wald statistics (W) for each equation are 6.444 for the university tuition equation, 32.454 for thenumber of students equation, and 43.314 for the standard deviation score equation. Null hypothesisthat university tuition, number of attending students and standard deviation scores are exogenous
variables is rejected at a significance of 5% (the university tuition equation) and 1% (both thenumber of students equation and the standard deviation score equation).
157. Conclusion
The main purpose of this study is to find the factors to determine private university
tuition level. Furthermore, because previous researchers have drawn conflicting conclusions, we test
whether or not university tuition of prestigious (higher standard deviation score) private universities
is lower than that of less prestigious private universities, (see, for example, Maruyama (1991, 1994)
and Urata (1998)). From our results, we obtain the following findings.
(1) Important factors affecting tuition are (i) the size of a university (i.e. number of attending
students), (ii) the quality of a university (i.e. standard deviation scores), (iii) the urban andcompetition factors (i.e. inverse of HHI), and (iv) subsidies to private universities.
(2) University tuition is not negatively related to the difficulty of entering the university (i.e. higher
standard deviation score). Some argue that tuition of very prestigious (i.e. higher standard deviation
score) private universities is not high because these universities are really competing with top
national universities to enroll excellent students. Based on our results, this perception is wrong. The
empirical result is the same as for the US: university tuition is higher in very prestigious universities.
The misconception about Japanese universities might be based on faulty estimation results. For
example, in our single equation model, the relationship between these variables is negative, although
not to a statistically significant degree.
(3) The size of a university has a negative relationship with university tuition. This result mightshow that university tuition has a scale effect. When other conditions are held, bigger universities
can reduce tuition.
(4) The availability of subsidies to private universities has a negative relationship with university
tuition. Subsidies decrease tuition cost.
(5) “Competition” among universities is also important in determining university tuition. But this is
not exactly same as the real competition factor. The urban factor has the effect of increasing pressure
on tuition. On the other hand, the competition factor has the effect of decreasing pressure on tuition.
Unfortunately, these factors are not separated in the explanation variable (i.e. the inverse of HHI).
(6) Important factors affecting the size of a university are tuition and standard deviation.
(7) Important factors affecting the quality of university are both the number of attending students
and the age of the university.

16

References
Asahi Shinbun Publications (2010) University Ranking for Year 2011 (2011 Nenban Daigaku
Rankingu), Asahi Shinbun Publications Inc., Tokyo (in Japanese).
Coates, D. and B. R. Humphreys (2002) “ The Supply of University Enrollments: University
Administrators as Utility Maximizing Bureaucrats,” Public Choice, Vol.110, No.3-4,
pp.365-392.
Coates, D., B. R. Humphreys and M. A. Vachris (2004) “More Evidence that University
Administrators are Utility Maximizing Bureaucrats,” Economics of Governance, Vol.5, No.1,
pp.77-101.
Dimkpah, Y. O., M. O. Eseonu and U. N. Akpom (2004) “The Impact of College Quality on Tuition:
A Hedonic Analysis,” Journal for Economic Educators, Vol.4, No.2, pp.1-7.
Elliott, C. nd K. T. Soo (2013) “The International Market for MBA Qualifications: The Relationship
Between Tuition Fees and Applications,” Economics of Education Review, Vol.34, pp.162-174.
Fethke, G. (2005) “Strategic Determination of Higher Education Subsidies and Tuitions,” Economicsof Education Review, Vol.24, No.5, pp.601-609.
Funabashi, S. (2008) “Empirical Analysis of Determinants of Private University Tuitions (Shiritsu
Daigaku Gakuhi no Kettei Yoin ni Kansuru Jissho Bunseki),” Economic Science (Keizai
Kagaku), Graduate School of Economics, Nagoya University, Vol.55, No.4, pp.71-89 (inJapanese).
Harford, J. D. and R. Marcus (1986) “Tuition and U.S. Private College Characteristics: The Hedonic
Approach,” Economics of Education Review, Vol.5, No.4, pp. 415-430.
Heath, J. A. and H. P. Tuckman (1987) “The Effects of Tuition Level and Financial Aid on the
Demand for Undergraduate and Advanced Terminal Degrees,” Economics of Education Review,
Vol.6, No.3, pp.227-238.
Koshal, R. K. and M. Koshal (1998) “Determinants of Tuition at Comprehensive Universities,”
Applied Economics, Vol.30, No.5, pp.579-583.
Koshal, R. K. and M. Koshal (2000) “State Appropriation and Higher Education Tuition: What is theRelationship?” Education Economics, Vol.8, No.1, pp.81-89.
Maruyama, F. (1991) “A Study of the Determinants of Private Utiversity Tuition in Japan (Shiritsu
Daigaku Jugyoryo no Kitei Yoin Bunseki),” Research in Higher Education (Daigaku Ronshu),
Hiroshima University, No.20, pp.267-280 (in Japanese).
Maruyama, F. (1994) A Japan-US Comparative Study of Determinants of Private Universities’
Tuition (Shiritsu Daigaku no Jugyoryo Kitei Yoin ni Kansuru Nichibei Hikaku Kenkyu),
17

Hiroshima University, (in Japanese).
McDuff, D. (2007) “Quality, Tuition, and Applications to In-State Public Colleges,” Economics of
Education Review,” Vol.26, No.4, pp.433-449.
Ministry of Education, Culture, Sports, Science and Technology (2009) Current Situation of
Financial Supports to Students (Gakusei ni Taisuru Keizaiteki Shien no Genjo), Higher
Education Bureau, Ministry of Education, Culture, Sports, Science and Technology, September,
2009.
Neill, C. (2009) “Tuition Fees and the Demand for University Places,” Economics of Education
Review, Vol.28, No.5, pp.561-570.
Noorbakhsh, A. and D. Culp (2002) “The Demand for Higher Education: Pennsylvania’sNonresident Tuition Experience,” Economics of Education Review, Vol.21, No.3, pp.277-286.
OECE (2014) Education at a Glance 2014: OECD Indicators, OECD, Paris, France.
Toma, S-G. and S. Naruo “Quality Asurance in the Japanese Universities,” Amfiteatru Economic,
Vo.11, No.26, pp. 574-595.
Urata, H. (1998) “The Determinants of Tuition and Fees of Japanese Private Universities (Shiritsu
Daigaku Gakunoukin no Kitei Yoin Bunseki),” The Journal of Educational Sociology (Kyoiku
Shakaigaku Kenkyu), Vol.63, pp.119-136 (in Japanese).
Yonezawa, A. (1994) “Cross-Sectional Analysis on Tuition at the Private Universities in Japan:
Positive Research on the Economic Behavior of Japan’s Private Universities, Part 1 (Shiritsu
Daigaku Jugyoryo no Oudanteki Bunseki: Shiritsu Daigaku no Keiei Kodo ni Kansuru
Jisshoteki Kenkyu (1)), Kyoikugabu Kiyo, the University of Tokyo, Vol. 34, pp. 149-162 (inJapanese).
Yoshida, H. (2007) “Empirical Analysis of Acqusition Behavior on Managerial Subisidies andOutside Manetary Badget of National Universities (Kokuritsudaigaku no Uneihi Kofukin to
Gaibushikin Kakutoku Kodo ni Kansuru Jissho Bunseki),” The Journal of Finance andManagement in Colleges and Universities (Daigaku Zaimu Keiei Kenkyu), Center for National
University Finance and Management (Kokuritsudaigaku Zaimu Keiei Senta), No.4, pp.131-150(in Japanese).

18

Yen

National
University

1,200,000

PrivateUniversity

1,000,000
800,000
600,000
400,000
200,000
0
1970

1980

1990

2000

2010

2020

Year

Figure 1 Tuition of National and Private Universities in Japan

Salary ofProfessors
Competition
among
Universities

Subsidies from
Government
University
TuitionStandard
Deviation
ScoresNumbers ofStudents

Location Factor:
Large Metropolitan
Areas

Age ofUniversity

Education
Environment

Research
Level: Exogenous variable: Endogenous variable

Figure 2 Structure of the Model19yen1,500,000
1,400,000
University tuition

1,300,000
1,200,000
1,100,000
1,000,000
900,000
800,000
700,000
600,000
35

40

45
50
55
60
Standard deviation score

65

70

Figure 3 Relationship between University Tuition and Standard Deviation Score

yen
1,500,000
1,400,000
University tuition

1,300,000
1,200,000
1,100,000
1,000,000
900,000
800,000
700,000
600,000
0

1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000
Number of attending students

Figure 4 Relationship between University Tuition and Number of Attending Students

20

Standard deviation score

70
65
60
55
50
45
40
35
0

2,000
4,000
6,000
Numbers of attending students

8,000

Figure 5 Relationship between Standard Deviation Score and Number of Attending Students

21

Table 1 International Comparison of Universities

Country
Total
PublicPrivateCollege Enrollment Rate
Number of
Total
Students
Undergraduate(thousand)
Graduate
Number of
Total
Students per
Undergraduate
1,000 Population Graduate
Foreign Students (thousand)

Japan
752
21%
79%
49%
2,780
2,520
260
22
20
2
91

Number of
Universities

US
2,579
25%
75%
64%
11,000
8,480
2,520
37
29
9
565

UK
169
99%
1%
57%
2,360
1,800
560
39
30
9
249

France
94
86%
14%
41%
1,400
880
520
23
14
9
210

Germany
376
82%
18%
37%
1,990
24
190

(Source): Ministry of Education, Culture, Sports, Science and Technology (2009, p.41)

Table 2 International Comparison of Public Expenditure on Higher Education and Annual Tuition

Japan

US

UK

FranceGermany

0.8
1.8

1.3
3.5

1.3
2.7

1.3
2.3

1.4
3.1

OECD
Average
1.4
3.2

34.5

34.8

30.2

80.8

84.7

69.2

50.9

47.8

60.7

10.4

15.3

30.8

14.6

17.4

9.1

8.8

5,019

5,402

-

-

-

Private8,039
17,163(Source): OECD (2014, p.245, p.257, p.271)(Note):(1) Unit: % for item (a), US dollar for item (b).
(2) Statistics are from 2011.

-

200 to
1,402
-

-

-

Country
PublicExpenditure
on Higher
Education(a)Proportion of
Expenditures
on Higher
Education(a)AnnualTuition Fee(b)Per GDPPer Total
PublicExpenditure
PublicSourcesHouseholdExpenditure
Other Private
SourcesPublic22

Table 3 International Comparison of Public Support for Households and Other Private Universities

Country

Japan

US

UK

FranceGermany

Direct Public Expenditure for
Institutions
Scholarships/
Other Grants to
Financial
Households
Aid toStudent Loans
Students
Others
Total
Public Support for Education
to Private Universities as a
Percentage of GDP(Source): OECD (2014, p.276)(Note):(1) Unit: %(2) Statistics are from 2011.

70.4

70.8

26.2

92.0

78.1

OECD
Average
78.5

0.6

27.9

7.5

8.0

16.3

11.6

29.0
29.6
0.23

1.3
29.2
0.39

31.7
34.7
73.8
0.99

8.0
0.10

5.6
21.9
0.31

9.2
5.1
21.5
0.31

Table 4 University Tuition Comparison between National and Private Universities in FY 2013

Kind of University

National University

Private University

Private/National
Ratio
1.61
0.94
1.61

Tuition535,800
860,072
Admission Fees
282,000
264,390
Facility Maintenance Fees
188,063
Total
817,000
1,312,526(Note):(1) These figures are obtained from several sources from the Ministry of Education, Culture, Sports,
Science and Technology.
(2) These are for undergraduate education only.
(3) Facility maintenance fees in national universities are included in tuition.
(4) Numbers for private universities are the mean of 574 private universities but numbers fornational universities are the same values for all national universities.

23

Table 5 Statistics of Used Variables

Variable
PTUI(university tuition)
QATS(number of students)
SDS(standard deviation scores)
HHI(Herfindahl-Hirschman index)
SUB (subsidies to private
universities per student)
SAL(annual salary of a professor)
DUMLMA(large metropolitan area dummy)
AGEUNV(age of university)
RSCSC (science research fund
per academic staff)
EDCSTS (number of students per
academic staff)
EDCSTA (number of students per
campus areas)

Unit

Mean

Standard
Deviation

Minimum

Maximumyen956,112102,795

717,910

1,374,751

person

1,859

1,269

220

7,182

-

50.4886.795

40.00068.000

-

0.075

0.110

0.022

1.000

yen/person

120,528

57,7950373,041thousandyen

11,744

1,094

8,819

13,028

-

0.660

0.475

0.000

1.000

year

51.89622.8027.000

89.000yen/person

304,367

372,78702,255,708-

45.61317.17910.00097.270person/m2

0.026

0.015

0.002

0.097

24

Table 6 Estimation Results

Case
Model
Estimation Method
Dependent Variable
ln PTUI(tuition)ln QATS(# of attending students)
ln SDS(standard deviation scores)ln (1/HHI)(1 / Herfindahl-Hirschmanindex)
ln SUB (subsidies to private
universities per student)
ln SAL(annual salary of a professor)
DUMLMA(large metro. area dummy)ln AGEUNV(age of university)
ln RSCSC(research fund / acad. staff)
ln EDCSTS(# of students / academic staff)
ln EDCSTA(# of students / campus area)
Constant

Case 1
Single equation
OLS

ln PTU(tuition)ln QATS(student)--1.418***(0.315)-0.055***(0.011)-0.084(0.057)0.041***(0.010)-0.002***(0.0004)
0.104(0.111)-ln SDS(standard
deviation
scores)0.001(0.055)0.066***(0.011)2.782***(0.253)

-

-

-

-

-

-

Case 2
Simultaneous equation
3SLS
ln SDSln QATSln PTU(standard(tuition) (student) deviation
scores)-2.471***-0.011(0.705)(0.170)0.158***-0.125***(0.015)(0.017)0.206***
3.896***(0.311)(0.070)0.028***(0.006)

-

-

-

-

-

-

-

0.053
(0.049)

-

-

-

-

-

-

-

-0.001*(0.001)
0.060(0.039)***0.427(0.067)

-

***

-

-

-

-

-

-

-

***

13.420(0.908)
0.197
-

***

15.645(4.833)0.486-

0.092(0.013)0.001(0.001)
0.003(0.010)0.008(0.007)3.079***(0.778)0.504-

Adjusted R2
Log of likelihood(Note):(1) Numbers in parentheses are standard errors.
(2) Statistical significance in 1% (***), 5% (**) and 10% (*).
(3) OLS estimations are White modification.

-***

13.238(0.405)26.035**(10.317)279.429

0.043**(0.018)-0.001(0.001)-0.007(0.009)0.002(0.006)2.790(2.351)[2015.3.9 1193]

25