Discussion Papers In Economics
And Business

Valuing Medical Schools in Japan:
National versus Private Universities
Mototsugu Fukushige, Hideo Yunoue

Discussion Paper 06-02

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANValuing Medical Schools in Japan:
National versus Private Universities
Mototsugu Fukushige, Hideo Yunoue

Discussion Paper 06-02

January 2006

この研究は「大学院経済学研究科・経済学部記念事業」
基金より援助を受けた、記して感謝する。

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANValuing Medical Schools in Japan:
National versus Private Universities
Mototsugu Fukushige a,∗ , Hideo Yunoue aaGraduate School of Economics, Osaka University, Machikaneyama 1-7, Toyonaka,
Osaka 563-0033, Japan

Abstract
Medical school usually has the highest tuition fees among the university
departments. The reason why students pay such expensive fees is that they estimatethat their earnings will greatly increase after graduation. We construct a model about
student behavior on entering college and estimate the value-added of medical schools
using college data from Japan. Our results show that a school with a long tradition ofproviding high quality education is evaluated as rendering high value-added to students.
Those empirical results enable us to simulate the effects of the privatization of a public
university. This simulation indicates that there is no difference between public andprivate schools when the tuition fees of the public university become as high as those of
the private university.

JEL: I21, L33
Keywords: Value-added of University, Medical school tuition fee, Public and private
schools, Privatization

1. Introduction
Medical school usually has the most expensive tuition fees among the various∗

Corresponding author. Tel.: +81 6 6850 5248; fax: +81 6 6850 5248.
Email address: mfuku@econ.osaka-u.ac.jp (M. Fukushige).
1

schools in universities. For example, according to “Tuition and Student Fees, First-Year
Medical School Students 2004-2005” by the Association of American Medical Colleges,
average first-year student payments for medical school in the US, including tuition fee,amount to approximately US$35,000. In Japan, first-year students must pay 3,800,000
yen (or 37,000 US dollars, based on an exchange rate of $1=¥105). In comparison, theaverage university tuition fee in the US is about $2,100 for a public school and $20,000
for a private school. In Japan, a private school costs about 1,400,000 yen, whereas apublic school costs about 800,000 yen. This example shows that medical school isextremely expensive compared to other fields of higher education.
A student who enters medical school is prepared to pay the requisite tuition fees
because he or she expects the value-added from the school to outweigh its burden. The
value-added components, including human capital accumulation and the developmentof human networks, directly increase the student’s income. The purpose of this paper isto measure how the student expects to gain from the school. The estimatedvalue-added for each student can be used to evaluate the total value of medical
education.
The evaluation of medical schools is important for the determination of social
policies, not only when higher education is being considered, but also when decisions
are made about the national medical systems, such as determining the total number of
medical doctors, providing primary and secondary care network systems, and so on.
However, as Schneider (2002) and Chun (2002) mentioned, it is not easy to evaluate
value-added of higher education because their output is not clearly defined. In a recent
work, Cawley et al. (1999) found there was a relation between wages and test scores
but it has not only an intuitive and simple relation but also theoretical background.

2

Cawley et al. (1999) insisted that the test scores do not fit the measure of value-added.
Yunker (2004) provided a method for measuring higher education in an accounting
school. He defined the value-added as the difference between the observed records ofaccounting examination and the estimated scores. Spetz (2002) focused on thevalue-added for nursing education and showed that students chose higher education
because of the advantages conferred by working positions or personal endowments.
In the present paper, we build upon the accumulated empirical results by proposing
an economic model to measure the value-added of universities for each student and by
conducting an empirical research. We use medical school tuition fees and data on
school characteristics as independent variables to explain the difficulty level of thecollege. We consider that the difficulty level of the university reflects the student’s effort
level. Benjamin and Hersh (2002) mentioned that college rankings do not report thecollege value-added because the variety of output by universities creates ambiguities.
However, in this paper, we avoid this ambiguity regarding outputs by focusing on
medical school. Almost all students attend the medical school to become medical
doctors.
The model clarifies the relationship between the difficulty level and the value-addedof the medical schools, and formulates an estimable equation with observable data. The
empirical result allows us to evaluate not only the value-added of the medical schoolsfor each student, but also the total value-added of the school. Using the estimatedvalue-added, we made a comparison of the public and private schools. This estimatedrelationship is useful for conducting a simulation study of the process of privatization of
public schools. This simulation provides interesting information about the effects ofuniversity system reform in Japan. In 2004, all the national universities had an

3

institutional change from directly managed entities to agencies that entrusted auniversity education.
This paper is organized as follows. Section 2 presents the model, section 3
describes the data set, and section 4 describes the results of the estimation and thesimulation. Finally, section 5 concludes the paper.

2. The Model
Consider the model about a student’s behavior toward the entrance examinations ofa university. Suppose that a student has a utility function as follows:

U = H( w ) − F(e).

(1)

The function describes his or her utility from the consumption of ordinary goods andservices H(w) minus the disutility F(e) arising from the effort to enter the medical school.
The ideal combination of goods is represented by a quantity of w and a price of q. In
addition, we incorporate all the costs incurred to pass the entrance examination of the
medical school, including the cost of a preparation school and the time and effort
involved in preparing for the examination. We refer to these as the “effort level”, denotedby e. We assume that H(w) has a positive first order differentiation and a negative
second order derivative. F(e) is assumed to have a positive first order derivative and apositive second order derivative. We assume that the probability of passing theentrance examination, p(e), depends on the student’s effort level and that it is strictlyincreasing, p' > 0, p' ' < 0 . We assume that the student will earn a fundamental income,M, whether she attends the medical school or not. When he or she goes to medical
school, an additional income, G, will be obtained, which we refer to as “value-added” inthis paper. For example, a high quality education or the development of a human4network with the graduates of the medical school will increase the student’s wages. In
other words, G represents the wage differences between the graduates of medical
school and those of high school. Then, the student’s budget constraint can be describedas follows:

qw + p(e)C = M + p(e)G.

(2)

Here, the school tuition fee is C.1 The school tuition fee and the additional income G
must be zero when the student does not enter the college. Solving (2) with w andsubstituting it into (1), we obtain the first order condition with e as follows:

H' ( w )(G − C)p' (e) − F' (e) = 0 .
q(3) can be rewritten as(3)

H' ( w )F' (e)(G − C ) =. Then, using the monotonicity of p’(e)qp' (e)and F’(e), there exists some value of e to satisfy the equation. We derive an equation of
the effort level as follows:⎡ H' ( w )⎤e = h⎢(G − C)⎥.
⎣ q
⎦

(4)

The inverse function of e = h[w, G − C]. is equal toF' (e). Taking this equation withp' (e)

the budget constraint (2), we find that:⎡ ⎧H' ( w )⎫⎤p ⎢h⎨(G − C)⎬⎥(G − C) + M = qw.
⎭⎦
⎣ ⎩ q

(5)

The left-hand side of equation (5) is a strictly decreasing function of w. If M is a positive
value, then there is one solution for w with a given (G − C) . By contrast, if the left-sideof (5) is a strictly increasing function of (G − C) , then the consumption level w is
described as a function of the value-added of the school and the school tuition fees,1

We can make the constraint with inequality. However, if the constraint does not bind, thestudent can enjoy an extra marginal unit of consumption goods, so we assume that therelationship holds with equality.
5w = W (G − C) . Eliminating w from equation (4) of effort level, we obtain the reduced
form of the effort level:

e = k (G − C) .

(6)

Then, the effort level e can be represented as a function of G and C. This means thatthe student effort level correlates positively with the value-added and negatively with theschool tuition fees. In other words, when the value-added of the medical school is given,
the student faces a trade-off between his or her effort level and the tuition fee.2
Now, let the difficulty level of the ith medical school, Li, be a function of the student’s
effort level, so we set Li=L(ei). The relation between the difficulty level and the student’s
effort is positive, because students have to study hard to enter a difficult school.
Substituting (6) into L(ei), we obtain:

Li = L[e(Gi − Ci)] = L(Gi − Ci) .

(7)

Then, we can see that the difficulty level is a function of the additional value of theschool minus the school tuition fees. The added-value will increase the difficulty level
while the school tuition fees decrease the difficulty level. We approximate (7) by a linear
function:

Li = α + β(Gi − Ci) + δXi ,(8)

where α represents the minimum difficulty level. The differences in difficulty levels
arise because of the gap between the value-added and the school tuition fees,
converted by the parameter, β . The parameter β denotes the conversion factor fromthe cash based variables to the difficulty level. Other factors, like location or

2

Research by Epple et al. (2004) supports this result. These authors constructed atheoretical and empirical model about student and school behavior, and then concluded that
there was a negative correlation between school tuition fees and student ability.
6

environment effects, are included as δXi .3 In addition, we assume that the college
difficulty level is zero when the benefits and costs of the school are equal and theenvironmental effects are zero. No student will expend any effort for zero additional
income. Hence, the minimum difficulty level, α , becomes zero.
Next, we consider the value-added term, βGi . We separate this term into two parts,
an observable and an unobservable part. First, we represent βGi as:JβGi = γ 0 + ∑ γjZij + εi.

(9)

j=1
J

The term,∑ γ Z , which is a linear combination of the observable factors of the
j

ij

j=1

value-added of the university i is observable, as is the constant term, γ 0 . The otherterm εi , which is assumed to be an error term, represents the unobservable part. In thepresent paper, we assume that the error term, εi , has a normal distribution with a zeromean and a σ 2 variance.4 Substituting (9) into (8), we obtain the equation below:
J

Li = γ 0 − βCi + ∑ γjZij + δXi + εi.

(10)

j=1

Our assumptions about εi make the ordinary least squares method appropriate in theestimation of equation (10).
Finally, we estimate the value-added of the university, according to the estimatedresult of equation (10):J

Li = γˆ 0 − βˆ Ci + ∑ γˆjZij + δˆ Xi + ei.

(11)

j=1

β̂ , δ̂ , and γ̂j (j=0,1,…J) are the estimated parameters of β , δ , and γj , respectively.
3

Strictly speaking, δXi consists of several components. In our empirical analysis, we set

δXi = θ0 + ∑ θjYij , where Yij is jth environmental factor of the ith school, for examplejthe population of its district.
4
It is not important to assume its mean zero because the constant term γ 0 adjusts thedeviation of the mean from zero.
7

The residual of the estimation is represented as ei. Substituting these estimatedparameters into (9) and then considering an equation (8), we can derive:Jβˆ Gi = γˆ 0 + ∑ γˆjZij + ei

(12)

j=1

= Li + βˆ Ci − δˆ Xi.
Recalling that β̂ is the conversion factor from the cash base unit to the difficulty level,
we find the cash-based value-added of the college using the inverse of β̂ from (12);Gi =Li + βˆ Ci − δˆ Xi
.
βˆ

(13)

The denominator of (13) represents the modified difficulty level of the college where theschool tuition fees and the environment effects are controlled. Measuring (13) allows us
to compare the values between universities.

3. Data
In Japan, almost every student faces severe competition when attempting to enter auniversity.5 It is especially difficult to enter medical school, more so than any otherdepartment. A student who was unfortunate enough to fail his or her entry exam must
face preparing the exam the following year. It is not a rare phenomenon that a student
will spend three or four years attempting to enter the university of his or her choice.
The only way for a student to become a medical doctor is to enter a medical school.
According to the overview of the 2003 entrance exam, the highest competition rate for
medical schools was about 40.0; that is, there is one successful candidate for every 39
failed students.6 Because medical doctors get high incomes, students rush to take the56This kind of phenomenon is observed in South Korea, China, and other Asian countries.
The average competition rate for medical schools was about 12.1, and the lowest was8entrance examinations in medical schools. This high competition rate shows that
excess demand exists for medical school.7
There are 79 medical schools in Japan, including public and private universities.
Table 1 reports the summary statistics of our data. The degree of difficulty of entrance
exams to medical schools was obtained from the College Guide 2005, a guidebook forcolleges and entrance examinations in Japan. The data was based on the survey by
Sundai Preparatory School, one of the most famous preparatory schools in Japan.
Figure 1 compares the difficulty levels of medical schools. The highest difficulty level fora medical school entrance exam was for The University of Tokyo, which reached 71.
The lowest level was 55 for Kawasaki Medical School. National universities were in the
top ranks of the lists, with private universities following. Among the national universities,
the previous imperial schools were ranked relatively higher. Of the private schools, twolong-established schools, Keio University and Nippon Medical School, have higher
difficulty levels.
School tuition fees were based on Obunsha’s Keisetsu Jidai, which is a college
guide in Japan. In Japan, medical schools have six-year courses, so we derive theschool tuition fees as the sum of the admission charge and the course fees for six years.
The admission charge is needed when a student enters university, whereas the course
fee covers lecture fees and facilities expenses. There is no difference in tuition fees
among the national schools; it costs 3,406,800 yen, which is the lower than the fees ofprivate schools. Turning to the private schools, Kyorin University charges the highest
tuition fees of 51,808,400 yen. The average fee among private schools is about 29

about 4.8.
The demand function has been observed in the US, originally by Sloan (1971) andrecently by Quinn and Price (1998).
7

9

million yen.
The variables representing the medical school value-added are the pass rate of thenational examination for medical practitioners, the year of establishment, the quota of
the school, the dummy for previous imperial universities, the dummy for previous
medical colleges, and the dummy for previous medical academies. It is important for
students to pass the national exam for medical practitioners, so its pass rate indicatesthe quality of the school’s education. The highest rate is 100% for Nagasaki University
and Jichi Medical School. The lowest score is 78% for Dokkyo University’s School of
Medicine. The average pass-rate is about 91%. The establishment year represents theeffect of school tradition. A school that has been established for some time will haveaccumulated well-organized curriculums, teaching materials, and human networks.
These factors are beneficial for students. The quota of the college also affects thedifficulty level, but its impact may be ambiguous. A smaller quota can have a positive
aspect as it means students have fewer classmates and greater access to instruction
from their professors. On the other hand, smaller quotas can have a negative effect asthey mean that fewer alumni exist.
Dummy variables reflect the status of the medical schools before World War II. The
status of medical schools is explained in Table 2. There were seven previous imperial
universities, 10 previous medical colleges, and 29 previous medical academies before
World War II in Japan. While all the previous imperial universities have become national
universities, the previous medical colleges and academies have become both national
and private schools. The dummy variables take a value of one for these schools andzero for other schools.
The previous imperial universities have a long history and have maintained their

10positions as headquarters of research and education since their foundation. Medical
schools in Japan are strongly connected with hospital personnel. A school with a longer
history has greater power over personnel issues than does a school with a shorter
history. The students who graduate from the former type of schools obtain a relatively
broad human network.
A previous medical college has an advantage for graduates because the medicalcolleges act as centers of medicine in their local areas. They are strongly connected not
only to schools, but also to hospitals in the district. Therefore, a student who graduatesfrom a previous medical college will find it easy to obtain a position.
The local population affects the difficulty of a school’s exams because a student
from an area with a high population faces stronger competition from rivals than do
students in other areas. The data source on local population is the National Census
from 2000. Tokyo has the maximum population and Tottori has the minimum.

4. Empirical results

4.1. Results of the estimation
The estimated results of equation (9) are shown in Table 3. First, we use all the
candidates of the explanatory variables in the estimation. We call this model the “fullmodel”. Subsequently, we drop some variables that are statistically insignificant. We callthis the “selected model”. Despite of the cross-section data, our models are well fitted
since the adjusted R2 exceeds 0.800 in both of models. In the remaining sections, we
discuss the implications arising from the selected model.
The coefficient of school tuition fees was -0.132 with 1% statistical significance. This11

results support our assumption that a student’s effort level is negatively correlated withschool tuition fees. The student makes a great effort to enter a school with low tuition
fees. There are positive signs for dummy variables. Every coefficient of the dummy isstatistically significant at 1%. The dummy for previous imperial schools is estimated to
be 4.969 with a 1% level of significance. A rich tradition, a high standard of education,
and a strong human network on a nationwide scale add value to students in previous
imperial universities. The dummy for previous medical colleges has a positive sign. The
difficulty level is raised 2.75 points by the medical college’s status. The graduates will
receive benefits from the medical college. The benefits are not only a high quality
teaching system but also the human networks developed. It is observed that thedifficulty level and the dummy for previous medical academies have a positive
correlation. It is increased about 1.36 points. Its historical performance and educationlevel are assessed but their influences are less than the previous imperial schools or theprevious medical colleges.
The coefficient of the pass rate of the national examination for medical practitioners
is estimated to be positive and significant. Because the pass rate of the national examreflects the quality of the curriculum, a student whose school achieves a higher passrate can accumulate greater human capital during his or her school days. The student iswilling to make a greater effort and pay more to enter the school.
We estimate the coefficient of population as an environment factor. It is estimated to
be positive with a 5% significant level. The results show that a school located in a larger
population area has a higher difficulty level. This supports our assumption thatcongestion causes serious competition and raises the difficulty level.

124.2. Comparing value-added
Using the estimation results above, the value-added of a medical school can bemeasured by calculating equation (13). This formula is used to obtain the value-addedin order to remove the effect of school tuition fees and the environmental influence fromthe difficulty level, and to adjust with the coefficient of school tuition fees. Figure 2reports the results of value-added per student. The bars of national schools are coloredgray while those of private school are colored black. The range of value-added is fromabout 430 million yen to 530 million yen. These values exceed the measurements of the
lifetime wages of a hospital doctor by Arai (1998), who reported that the average wage
was about 240 million yen. However, our results represent the average lifetime incomeof private practitioners, who earn much more than do hospital doctors.
The previous imperial schools have a high rank. The University of Tokyo, which has
the number one ranking has been the center of research and education in Japan. A
student improves his or her abilities by obtaining a high quality education in the school.
The previous medical colleges and academies form the group with the second
highest ranking. As these schools were established before World War II, their long
history is a major source of value-added for graduates. In contrast, a college establishedafter World War II is given a lower scores than another public university because
graduates from these new medical schools face a shortage of job opportunities becauseof their lack of human networks.
In addition, a student working in a hospital has an advantage because when he or
she is faced with an obstinate disease patient, the student is allowed to transfer thepatient to the related university hospital. The university hospitals of previous imperial
schools and previous medical colleges have relatively greater facilities than other13

hospitals, a student who graduates these schools have more opportunities to curedisease than other student. This strongly connects to lifetime income for medical doctor.
Focusing now on the comparison between private and public schools, some private
schools, like Keio University or Nippon Medical School, can offer a value-added that isvery close to that of the previous imperial schools. These schools are the previous
medical colleges with a long history. In addition, although Jichi Medical School wasestablished relatively recently, it obtains a high ranking because its national examination
pass rate is high. Although some schools have relatively high rankings, most of theprivate schools obtain relatively low rankings. The reason for this result is theirexpensive school tuition fees.
The aggregated value-added of medical schools is reported in Figure 3. The
aggregated value-added is calculated as the product of the value-added per student
and the quota of school. This value could be one benchmark that represents thecontribution of the school in a year. The highest value is about 60 billion yen for Tokyo
Medical College and the second highest is about 55 billion yen for Showa University.
Both of these are private schools. Although these schools do not have a relatively
higher student value-added, their quotas are higher than those of other schools. On theother hand, Kyoto University and the University of Tokyo follow the former private
schools in terms of the aggregated value-added. From the point of view of the value of
medical schools, the difference between public and private schools is smaller than the
differences between value-added for students.

4.3. Simulation: Competition between national and private schools
In 2004, all the national universities changed their institutional system. Before the14reform, they were directly founded and operated by the country. Subsequently, they
became agencies entrusted with Japan’s university education. Under this system, thegovernment has power to guide their tuition fees using subsidy policy. However, in thenear future, it is expected that the universities will behave as private universities do,including setting the level of their tuition fees independently. Under the current
institutional reforms, the national universities will not become private universities in thenear future. However, as an extreme case, we should simulate the case where all the
public schools are privatized. This simulation is important because it will shed light on
the need for public schools in medical education. The value-added figures that were
calculated in the previous section have important roles in this simulation. National
schools will need to raise their tuition fees when the privatization occurs. Given ourstudent behavior model in section 2, such a reform will cause the difficulty level of publicschools to fall because of increasing tuition fees. The difficulty level of post-privatization
schools is re-calculated in the following simulation. We assume that the public schools
are required to increase their tuition fees to the average level of private school tuition
fees when privatization occurs. The average private school tuition fee is 29,049,900 yen.
In addition, we assume that all schools keep their value-added level before and after
privatization.
The difficulty levels of post-privatization medical schools are reported in Figure 4.
Following privatization, the highest difficulty level is achieved by Keio University, which
is a private school. Because the student effort level decreases as the national school
tuition fees are increased, the difficulty level of the national school decreases. Then, theprivate schools, especially Keio University, Nippon Medical School, and Jichi University
are ranked in the upper levels. Further, the other private schools catch up with the15national schools. Therefore, it seems that privatization will mean the difficulty levels ofprivate and public universities will be equalized. For example, the rank-sum statistics for
Figures 1 to 4 are 0.055, 0.027, -0.023, and 0.016, respectively. The absolute values ofthese statistics are close to zero, which means that the distribution of public and private
schools is less biased. This shows that there are no advantages for the public schools
after the privatization. In other words, the public schools will face more competitive
student markets. At that time, their traditions are not so important, but their educational
standard becomes more important.

5. Conclusion
This paper constructs a model that explains students’ efforts to pass an entrance
examination and then derives the value-added of medical schools using the estimatedresults of the model. This model shows a negative correlation between the difficultylevel and the school tuition fees because students do not study hard to enter a schoolwith high tuition fees. This prediction is supported by our empirical result that a student
faces a trade-off between paying high tuition fees or a high effort level. The estimatedvalue-added of medical school is consistent with Arai’s (1998) estimation because, incontrast to Arai, our results include the income of private practitioner.
Comparing the estimated value-added of each school, we initially find that a student
who belongs to a national school obtains a higher value-added than does a private
school student. The long experience of the medical schools that were established
before World War II give a higher value-added to their students.
A simulation for evaluating the privatization of national schools shows that a private
school will achieve the highest post-reform value and that the difficulty level of private

16

schools will be much closer to those of public schools. These results imply that theschool tradition is still meaningful, but the quality of education is also important.
Finally, directions for future research should be noted. It would be possible to use
our approach to evaluate colleges in other countries, such as South Korea and East
Asian countries, where college entrance is based on an examination system similar to
the Japanese system. Because of strong competition among students, the difficultylevel of the entrance exam may represent the value of schools. Another possibility is theapplication of our approach to countries that do not have difficult entrance examinations,but do require hard study for graduation. In such a case, we could evaluate the difficultylevel for graduation when applying our method of analysis.

References
Arai, K. (1998). The economics of education: an analysis of college-going behavior.
Tokyo, Springer.
Benjamin, R., & Hersh, R.H. (2002). Measuring the difference college makes: theRAND/CAE value-added assessment initiative. Peer Review 4(2/3), 7-11.
Cawley, J., Heckman, J., & Vytlacil, E. (1999). On policies to reward the value added byeducators. The Review of Economics and Statistics 81(4), 720-727.
Chun, M. (2002). Looking Where the Light is Better: A review of the literature on
assessing higher education quality. Peer Review 4(2/3), 16-25.
Epple, D., Figlio, D., & Romano, R. (2004). Competition between private and publicschools: testing stratification and pricing predictions. Journal of Public
Economics 88(7/8), 1215-1245.

17

Schneider, C.G. (2002). Can value added assessment raise the level of student
accomplishment? Peer Review 4(2/3), 4-6.
Sloan, F.A. (1971). The demand for higher education: the case of medical school
applicants. The Journal of Human Resources 6(4), 466-489.
Spetz, J. (2002). The value of education in a licensed profession: the choice of
associate or baccalaureate degrees in nursing. Economics of Education
Review 21, 73-85.
Quinn, R., & Price, J. (1998). The demand for medical education: an augmented humancapital approach. Economics of Education Review 17(3), 337-347.
Yunker, J.A. (2004). The dubious utility of the value-added concept in higher education:
the case of accounting. Economics of Education Review, in press.

18

Table 1
Definition and summary statistics of variables
Variables (unit)
Obs. Mean S. D. Max Min
55
Difficulty level of entrance exam by Sundai Prep. Scho79 63.84 3.41 71
School tuition fees (million yen)
79 12.93 13.27 51.80 3.41
Pass rate of national medical doctor examination (%) 79 0.93 0.04 1.00 0.78
Year of foundation (A.D.)
79 1961 11.50 1981 1949
Quota of school (person)
79 94.11 9.60 120
60
Population (Million person)
79 5.069 4.203 0.613 12.06
Note: Obs. means observations and S.D. means standard deviation.

19

Table 2
Types of medical colleges
The previous
imperial universities

The previous
medical colleges

The previous
medical academies

The schools establishedafter WWII

Hokkaido Univ.

Chiba Univ.

Sapporo Medi. Univ.

Asahikawa Medi. Coll.

Tohoku Univ.

Niigata Univ.

Hirosaki Univ.

Akita Univ.

The Univ. of Tokyo

Kanazawa Univ.

Fukushima Medi. Coll.

Yamagata Univ.

Nagoya Univ.

Kyoto Prefectural Univ. of MedicineGunma Univ.

Univ. of Tsukuba

Kyoto Univ.

Okayama Univ.

Tokyo Medi. and Dent. Univ. Toyama Medi. and Pharm. Univ.

Osaka Univ.

Nagasaki Univ.

Yokohama City Univ.

Fukui Univ.

Kyushu Univ.

Kumamoto Univ.

Shinshu Univ.

Yamanashi Univ.

Keio Univ.

Gifu Univ.

Hamamatsu Univ.

The Jikei Univ.

Nagoya City Univ.

Shiga Univ.

Nippon Medi. School

Mie Univ.

Shimane Univ.

Osaka City Univ.

Kagawa Univ.

Kobe Univ.

Ehime Univ.

Nara Medi. Univ.

Kochi Univ.

Wakayama Medi. Univ.

Saga Univ.

Tottori Univ.

Oita Univ.

Hiroshima Univ.

Miyazaki Univ.

Yamaguchi Univ.

Univ. of The Ryukyus

The Univ. of Tokushima

Jichi Medi. School

Kagoshima Univ.

Dokkyo Univ.

Iwate Medi. Univ.

Saitama Medi. School

Juntendo Univ.

Kitasato Univ.

Showa Univ.

Kyorin Univ.

Tokyo Medi. Coll.

Teikyo Univ.

Tokyo Women's Medi. Coll. Tokai Univ.
Toho Univ.

St. Marianna Univ.

Nihon Univ.

Kanazawa Medi. Univ.

Osaka Medi. Coll.

Aichi Medi. Univ.

Kansai Medi. Univ.

Fujita Health Univ.

Kurume Univ.

Kinki Univ.
Hyogo Coll. of Medicine
Kawasaki Medi. School
Univ. of O.E.H.J.
Fukuoka Univ.

Note: Private schools are written in bold.

20

Table 3
Estimation results for difficulty level of medical school with OLS
Estimation method
full model
selected model***
School tuition-0.131-0.132***(-6.226)(-6.589)***
Dummy for the imperial univ.
5.791
4.969***(4.032)(6.930)
Dummy for the medical college
3.533***
2.746***(2.665)(4.706)
Dummy for the medical academy 2.063*
1.360***(1.901)(3.220)***
Passrate of national exam for
0.234
0.236***
medical practitioners(5.162)(5.410)Year of foundation
0.033(0.683)
Quota of university
0.004(0.164)
Population
0.133**
0.135**(2.222)(2.234)
Constant-23.80941.582***(-0.251)(10.186)
Observations
79
79
Adjusted R-squared
0.800
0.804
Note: t-ratios are in parentheses.
* Significant at 10%.
** Significant at 5%.
*** Significant at 1%.

21

0

10

20

30

40

50

60

The Univ. of Tokyo
Tokyo Medi. and Dental Univ.
Kyoto Univ.
Osaka Univ.
Tohoku Univ.
Nagoya Univ.
Hokkaido Univ.
Chiba Univ.
Yokohama City Univ.
Kobe Univ.
Kyushu Univ.
Keio Univ.
Kyoto Prefectural Univ. of Medicine
Okayama Univ.
Hiroshima Univ.
Sapporo Medi. Univ.
Univ. of Tsukuba
Kanazawa Univ.
Hamamatsu Univ.
Nagoya City Univ.
Mie Univ.
Osaka city Univ.
Nara Medi. Univ.
Nagasaki Univ.
Kumamoto Univ.
Nippon Medi. School
Gunma Univ.
Niigata Univ.
The Univ. of Tokushima
Oita Univ.
Jichi Medi. School
Osaka Medi. Coll.
Kansai Medi. Univ.
Hirosaki Univ.
Akita Univ.
Fukushima Medi. Coll.
Fukui Univ.
Gifu Univ.
Shiga Univ.
Wakayama Medi. Univ.
Yamaguchi Univ.
Ehime Univ.
Kagoshima Univ.
Showa Univ.
The Jikei Univ.
Asahikawa Medi. Coll.
Yamagata Univ.
Toyama Medi. and Pharmaceutical
Shinshu Univ.
Tottori Univ.
Shimane Univ.
Kagawa Univ.
Kochi Univ.
Univ. of O.E.H., Japan
Saga Univ.
Miyazaki Univ.
Tokyo Medi. Coll.
Yamanashi Univ.
Univ. of The Ryukyus
Juntendo Univ.
Kinki Univ.
Kurume Univ.
Nihon Univ.
Hyogo Coll. of Medicine
Kitasato Univ.
Kyorin Univ.
Tokai Univ.
Tokyo Women's Medi. Coll.
Toho Univ.
Iwate Medi. Univ.
Fujita Health Univ.
Fukuoka Univ.
Teikyo Univ.
St. Marianna Univ.
Kanazawa Medi. Univ.
Aichi Medi. Univ.
Dokkyo Univ.
Saitama Medi. School
Kawasaki Medi. School

Fig. 1 Difficulty level of medical school
Note: Public schools are colored gray and private schools are colored black.

22

70

80

0

100

200

300

400

Kyoto Univ.
The Univ. of Tokyo
Osaka Univ.
Tohoku Univ.
Keio Univ.
Tokyo Medi. and Dental Univ.
Nagoya Univ.
Nippon Medi. School
Kyushu Univ.
Jichi Medi. School
Kobe Univ.
Hokkaido Univ.
Chiba Univ.
Yokohama City Univ.
Okayama Univ.
Kyoto Prefectural Univ. of Medicine
Hiroshima Univ.
Kansai Medi. Univ.
Osaka Medi. Coll.
Nara Medi. Univ.
Kanazawa Univ.
Nagasaki Univ.
Mie Univ.
Kumamoto Univ.
Univ. of Tsukuba
Showa Univ.
Hamamatsu Univ.
Sapporo Medi. Univ.
Nagoya City Univ.
Univ. of O.E.H., JapanThe Univ. of Tokushima
The Jikei Univ.
Oita Univ.
Osaka city Univ.
Kyorin Univ.
Gunma Univ.
Kurume Univ.
Niigata Univ.
Fukuoka Univ.
Kinki Univ.
Hyogo Coll. of Medicine
Fukushima Medi. Coll.
Tokyo Medi. Coll.
Wakayama Medi. Univ.
Fukui Univ.
Akita Univ.
Shiga Univ.
Hirosaki Univ.
Ehime Univ.
Yamaguchi Univ.
Juntendo Univ.
Kagoshima Univ.
Gifu Univ.
Tottori Univ.
Shimane Univ.
Kochi Univ.
Saga Univ.
Kagawa Univ.
Toyama Medi. and Pharmaceutical Univ.
Miyazaki Univ.
Yamagata Univ.
Shinshu Univ.
Nihon Univ.
Kanazawa Medi. Univ.
Asahikawa Medi. Coll.
Iwate Medi. Univ.
Tokai Univ.
Toho Univ.
Yamanashi Univ.
Univ. of The Ryukyus
Teikyo Univ.
Tokyo Women's Medi. Coll.
Kitasato Univ.
Fujita Health Univ.
St. Marianna Univ.
Aichi Medi. Univ.
Saitama Medi. School
Dokkyo Univ.
Kawasaki Medi. School

Fig. 2 Added-Value of medical schools per student
Note: Public schools are colored gray and private schools are colored black.

23

500

(million yen)600

0

10,00020,00030,00040,00050,000（million yen）
60,000 70,000

Tokyo Medi. Coll.
Showa Univ.
Kyoto Univ.
The Univ. of Tokyo
Nihon Univ.
Tohoku Univ.
Keio Univ.
Nippon Medi. School
Kyushu Univ.
Jichi Medi. School
Kyoto Prefectural Univ. of Medicine
Hiroshima Univ.
Kansai Medi. Univ.
Osaka Medi. Coll.
Mie Univ.
Kumamoto Univ.
Sapporo Medi. Univ.
Univ. of O.E.H., JapanThe Jikei Univ.
Kurume Univ.
Nagoya Univ.
Fukuoka Univ.
Hyogo Coll. of Medicine
Kobe Univ.
Hokkaido Univ.
Chiba Univ.
Okayama Univ.
Miyazaki Univ.
Yamagata Univ.
Nara Medi. Univ.
Kanazawa Univ.
Nagasaki Univ.
Univ. of Tsukuba
Kanazawa Medi. Univ.
Hamamatsu Univ.
Tokai Univ.
Osaka Univ.
Toho Univ.
Yamanashi Univ.
Teikyo Univ.
The Univ. of Tokushima
Niigata Univ.
Tokyo Women's Medi. Coll.
Kitasato Univ.
Fujita Health Univ.
Kinki Univ.
Fukui Univ.
Akita Univ.
St. Marianna Univ.
Aichi Medi. Univ.
Saitama Medi. School
Saga Univ.
Shinshu Univ.
Dokkyo Univ.
Univ. of The Ryukyus
Kyorin Univ.
Shiga Univ.
Ehime Univ.
Juntendo Univ.
Kawasaki Medi. School
Kochi Univ.
Kagawa Univ.
Toyama Medi. and Pharmaceutical Univ.
Asahikawa Medi. Coll.
Oita Univ.
Gunma Univ.
Yamaguchi Univ.
Kagoshima Univ.
Shimane Univ.
Nagoya City Univ.
Osaka city Univ.
Fukushima Medi. Coll.
Tokyo Medi. and Dental Univ.
Hirosaki Univ.
Gifu Univ.
Iwate Medi. Univ.
Tottori Univ.
Yokohama City Univ.
Wakayama Medi. Univ.

Fig. 3 Total Added-value of medical school
Note: Public schools are colored gray and private schools are colored black.

24

0

10

20

30

40

50

60

70

Keio Univ.
The Univ. of Tokyo
Tokyo Medi. and Dental Univ.
Kyoto Univ.
Osaka Univ.
Nippon Medi. School
Tohoku Univ.
Nagoya Univ.
Jichi Medi. School
Osaka Medi. Coll.
Kansai Medi. Univ.
Yokohama City Univ.
Hokkaido Univ.
Chiba Univ.
Kobe Univ.
Kyushu Univ.
Showa Univ.
The Jikei Univ.
Kyoto Prefectural Univ. of Medicine
Okayama Univ.
Hiroshima Univ.
Univ. of O.E.H., JapanTokyo Medi. Coll.
Sapporo Medi. Univ.
Nagoya City Univ.
Nara Medi. Univ.
Osaka city Univ.
Univ. of Tsukuba
Kanazawa Univ.
Hamamatsu Univ.
Mie Univ.
Nagasaki Univ.
Kumamoto Univ.
Juntendo Univ.
Kinki Univ.
Kurume Univ.
Gunma Univ.
Niigata Univ.
The Univ. of Tokushima
Oita Univ.
Fukushima Medi. Coll.
Nihon Univ.
Hyogo Coll. of Medicine
Wakayama Medi. Univ.
Hirosaki Univ.
Akita Univ.
Fukui Univ.
Gifu Univ.
Shiga Univ.
Yamaguchi Univ.
Ehime Univ.
Kagoshima Univ.
Kitasato Univ.
Kyorin Univ.
Tokai Univ.
Tokyo Women's Medi. Coll.
Toho Univ.
Asahikawa Medi. Coll.
Yamagata Univ.
Toyama Medi. and Pharmaceutical
Shinshu Univ.
Tottori Univ.
Shimane Univ.
Kagawa Univ.
Kochi Univ.
Saga Univ.
Miyazaki Univ.
Iwate Medi. Univ.
Fujita Health Univ.
Fukuoka Univ.
Yamanashi Univ.
Univ. of The Ryukyus
Teikyo Univ.
St. Marianna Univ.
Kanazawa Medi. Univ.
Aichi Medi. Univ.
Dokkyo Univ.
Saitama Medi. School
Kawasaki Medi. School

Fig. 4 Comparison of difficulty level of medical school after privatization: a simulation
Note: Public schools are colored gray and private schools are colored black.

25

80