The Analysis of the Structural Changes of China’s Higher
Education System during the Massification from 1998-2004
Wen WEN, Weihe XIE, Lefu LI
University of Oxford & Tsinghua University
2007 . 9
Paper presented at the British Educational Research Association Annual
Conference, Institute of Education, University of London, 5-8 September 2007
1. Background:
The huge expansion of the tertiary education starting from 1998 is one of the most
significant changes to China’s education system. From 1998 to 2006, the number of
tertiary education students has increased from 8,440,000 to 21,000,000; the gross
enrollment ratio has increased from 9.8% to 23%. If we use Martin Trow’s term of
“Mass higher education” and take 15% as the quantitative proxy, we can safely say
that it takes no more than 10 years for China’s higher education to transform from
Elite higher education to Mass higher education, while the same process takes the
Unite States 30 years, the UK 25 years, France 28 years, Japan 23 years and Korea 24
years.
Although this expansion policy produced and put forward by central government has
largely increased the level of human capital stock which offers China the comparative
advantage in the global competition, it is criticized a lot for this rapid expansion
process has made social problems like unemployment and inequity even worse. As a
country taking radical reforms in its economic system, diversified social
contradictions have emerged. Take for instance, the unemployment and inequity are
two biggest challenges faced by today’s China. Some people even hold the opinions
that contrary to the objective and general law of the development of education, the
expansion policy is initially taken as a temporary regulatory and control measure to
alleviate the government’s employment pressure and to stimulate consumption; while
ignoring the individual interest, the government paid more attention to the macro
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objective at the state level.
It has been almost 10 years for the higher education expansion policy being
implemented in China, and it is now necessary to evaluate this policy and give
suggestion on the future development of China’s higher education. Moreover, the
experience of the development of China’s education system, which hasn’t been
studied sufficiently, can make contribution to the theory of the field of international
higher education research, and the Chinese model in education development can
provide good practice and lessons for other countries.
2. Literature Review:
With regard to the massification process in China, it is still in a fairly elementary
stage of theoretical argumentation. Most of the research mainly focused on finding
out some factors that affect the massification process, as in the examples that Deng
Xiaochun 1 and Yang Yan 2 believe that the factor that affects the recruitment of
respective disciplines is the justification of industry structure, while the structure for
disciplinary recruitment for optimized industries shall also be based on industry
justification with economic macroscopic policies. These are all rather abstract and
theorized studies. While most data related to such studies at present are the references
to some experience data in western massification, as in the case that Deng Xiaochun
has cited the data for education of postgraduates accounting for 5~10%, education of
undergraduate taking 35~40%, and education of college students taking up about
40~60% as standards, thus to derive the conclusion that the proportion of the college
education in China is slightly lower, and so on. Certainly, no research has been
reported so far on the changes in relationship between local economy and the scale of
higher eduction arising from the re-allocation of higher educational resources caused
by massifictaion.
In general, studies on the issue with Chinese higher education, just like what Feng
Xiangdong3, the famous Chinese educator, has summarized, are still in the category of
“application research”, and should make a description that tells us what an ideal
structure for higher education should be, while a more fundamental question is: how
exactly the existing structure for higher education was formed, and why the structure
of higher education in our country is not what it ought to be.”
Comparatively speaking, massive realistic data related with higher education have
been used in this study, and these data have been sourced from “Educational Statistics
1
Deng Xiaochun, A Study on Development Strategy and Resource Allocation of China Higher
Education in 21st Century. LiaoNing Education Study, 1999, 5.
2 Yang Yan, The Impact of Adjustment of Industries Structure on China Higher Educational
Subjects’ Enrollments, Educational Review, 2000,1.
3 Feng Xiangdon, The Structure of China Higher Education: An Example of Game Theory,
Higher Education Study, 2005,5.
2
Yearbook of China” , and “China Educational Finance Statistical Yearbook”, as well
as some data from China Education, with the five-year data from 1994 to 1998 after
the catalogue of disciplines had been revised used as the analytic data before the
enrollment expansion and with the six-year data from 1999 to 2004 used as the data
during the process of recruitment expansion, and with SAS9.1 software applied for
demonstration analysis.
3. Enrollment of Different Subjects:
Massification is mainly represented by a significant increase in number of student
recruitment and is very important for us to study the structural characteristics of
higher education, as it has directly determined the future employments of students in a
microcosmic manner, while it also determines in a macroscopic style as to whether or
not knowledge can promote economic growth in its maximum efficiency. According
to the Chinese method for classification, we divide the disciplines into three types.
Massification is mainly represented by a significant increase in number of student
recruitment and is very important for us to study the structural characteristics of
higher education, as it has directly determined the future employments of students in a
microcosmic manner, while it also determines in a macroscopic style as to whether or
not knowledge can promote economic growth in its maximum efficiency. According
to the Chinese method for classification, we divide the disciplines into three types.
ENROLLMENT OF DISCIPLINES
1600000
INCREASE OF ENROLLMENT
1400000
PHILOSOPHY
ECONOMICS
LAW
EDUCATION
LITERATURE
HISTORY
SCIENCE
EGINEER
AGRICULTURE
MEDICINE
1200000
1000000
800000
600000
400000
200000
20
04
20
02
20
00
19
98
19
96
19
94
0
The figure has revealed that there are no crosses between these lines, indicating that
after massification, the variation in proportion and sequence of original respective
disciplinary types has not been obvious actually. What's on the top is engineer, from a
number of 350,000 people in 1994 increased to nearly 1.5 million people, a rise by
nearly 5 times. Of course no matter it was in 1994 or in 2004, the number of student
recruitment for engineering discipline had been the largest. What's in the second place
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is the economic management, from 140,000 people climbed up to one million people,
an increase by nearly 7 times. Comparatively speaking, the numbers of student
enrollments for other disciplines would be relatively smaller. An increase by only
once, or less than 3 times had been seen for the disciplines such as philosophy and
agronomy, etc. If we assume that the numbers of original students for respective
disciplines had determined the numbers of their newly increased students, then we can
refer to linear regression, taking the numbers of students in school for ten disciplines
in 1998 as the Independent Variables, named as SS. The following result will be
acquired through operation：
DSS = 2.32175*SS + 151299 + ε
0.37072
6.26
0.0002
R-Square 0.8306
187781
0.81
0.4437
D.W 1.622
Std. Error
t-Statistic
Prob.
F Value 39.22
Pr>F 0.0002
The integrated condition through the model is good, and respective inspections have
reached comparatively higher standards. R-Square is 0.8306, namely the ss indicator
as stock has interpreted more than 83 information in dss as increment, while P value
of ss itself is less than 0.001, in other words, its influence has been highly significant.
It has indicated through the model that the numbers of students in school in terms of
disciplinary types before enrollment expansion were closely related with their
numbers of student recruitment during enrollment expansion, and when the number of
students in school for certain disciplinary type is more that the number of students in
school for another disciplinary type by 100 people, its number of students in school
increased during recruitment expansion will be more than that for another discipline
by 232 people.
This has clearly shown that the increase in number of student recruitment for different
disciplinary types has been significantly influenced by the numbers of original
students, and from this point, it has indicated that path dependence has obviously
existed with massification.
If we re-classify the ten disciplines into Science Students and Liberal Arts as well as
Applicable and Basic Disciplines, then the minute changes in respective disciplines
can reflect certain trend when they are added up, while this trend is fairy good. We
have found that the proportion in Science Students and Liberal Arts has further
approached the 61：39 in 1998 grown to 51：45 in 2004. In recent years, we have
found that the disciplines such as economy and law have become more and more
important, and hence the strength on development has been enhanced. Facts have
proved that this has accelerated our economic growth. At the same time we have
found that the proportion of Basic Disciplines has also gone up along with
massification, which has been closely related with the facts that the Chinese
government has emphasized the functions of science and technology and has paid
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more attention to the input in basic disciplines. If we compare the changes in numbers
of student recruitment for respective disciplines in respective regions, another rather
important conclusion of this paper can be reached - convergence. This trend does not
merely exist in disciplines, and it is also present inside the hierarchical structure of
higher education.
China has totally 31 provinces and autonomous regions, which can be sub-divided
into developed regions, developing regions, and underdeveloped regions. As China
covers a territory of a large area, the discrepancies between the developed regions and
the underdeveloped regions are very large, equivalent to the degree of differences
between many developed countries and developing countries in the world. We will
further compare the average data of the abovementioned three regions and refer to the
following table：
Liberal
Arts
0.3899
0.3648
0.3839
1998 Applicable Basic
Developed
0.2294 0.7706
Developing
0.2541 0.7459
Undeveloped
0.2948 0.7052
Liberal
2004 Science Arts
Developed
0.5138
0.4861
Developing
0.5274
0.4726
Undeveloped 0.5134
0.4866
2004 Applicable Basic
Developed
0.2358 0.7642
Developing
0.248 0.7525
Undeveloped
0.271 0.7296
1998 Science
Developed
0.6101
Developing
0.6352
Undeveloped 0.6161
In a comparison with the proportion in 1998, the variation in the numbers of students
in school between Science and Liberal Arts and the Applicable and the Basic after
massification had been diminished to a great extent. While the proportion of Science
and Liberal Arts was originally rather balanced before recruitment expansion, and this
balance had still existed after enrollment expansion, only from an original 40% and
60% for the discipline of liberal arts and the discipline of science, respectively
changed into the present half and half in the three types of regions, indicating their
changing trends have been very consistent. The change in Applicable and Basic has
been rather weak, however starting from the diversities of the same year in different
regions, their structural convergence can be found in respective regions after
massification.
The abovementioned trends have corresponded to the foregoing pass dependence, and
have revealed a structural convergence. It is still not a positive fact to develop higher
education with similar models in the regions with huge economic discrepancies. We
can still get similar analytic methods and conclusions in the following analyses.
4. Hierarchical Structure of Higher Education
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What's mainly explored from the hierarchical structure of higher education is not a
proportional relationship of students in school at different levels of educational
backgrounds. Only undergraduate and junior college students are discussed in this
study, which excludes the post-graduates. Historically speaking, college and
undergraduate have shown an increase in the leapfrog fashion. At the early stage of
development in 1994, higher cost was required for undergraduates, and the number of
undergraduates was limited, while undergraduate was recognized at the same time.
People would also believe that undergraduates were much better than college graduate
students. Up to now, the scores for enrollment of dominantly majority of
undergraduate courses have been higher than those for junior colleges.
5000000
4000000
3000000
2000000
1000000
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
0
Undergraduates
Junior College Students
Total
On this account the development of undergraduate had been faster than that of the
junior college from 1994 to 1999 while after recruitment expansion the employment
pressure for undergraduates was increased and the situation of employment for some
college graduate students had turned for the better and the salaries became higher
hence junior college had developed more quickly than undergraduate speaking from a
wholeness. If similar way as described in the previous section is adopted we can
analyze the situations in the three regions. Under the condition when obvious
convergence had existed in 2004 the proportion of the undergraduates had
significantly dropped while that of the junior college had evidently risen. At the same
time our study has also revealed that the process of massification in connection with
the internal parts of respective disciplines have actually optimized the hierarchical
structures inside these disciplines. Theoretically speaking in relation to basic
disciplines the proportion of undergraduate should be larger while that of the junior
college should be smaller. Under such a rationality-related assumption we have found
that with relation to science literature and history as basic disciplines the proportions
of their undergraduates have experienced distinct stepup while with respect to
engineer agriculture medicine and other major applicable disciplines the proportion of
undergraduates has significantly decreased.
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Undergraduates Junior
1998
College
1998
Developed
70.20%
29.80%
Developing
68.50%
31.50%
Undeveloped
59.60%
40.40%
Undergraduates Junior
2004
College
2004
57.80%
42.20%
57.20%
42.80%
56.80%
43.20%
5. Distribution of Higher Education
The last part of our study is Distribution of Higher Education, i.e. the distribution of
higher educational resources. Such resources have many ways to be attributed, such as
the number of schools, the number of students, fund input, and scientific research
achievements, etc. Here for the unifying sake, we will describe distributions of such
educational resources in different places by making use of the information about
number of students in school from 1998 to 2004. Distribution of resources is very
important for a fair and balanced growth of the entire society, as well as for an
efficient service of higher educational resources to local economy. We will start with
an overall review.
In order to clarify the relationship in economic and educational resources among the
31 provinces in China,we have created new indicators GDP-Ratio and POP_Ratioi,
the former being the proportion of the local number of students in school to that of the
whole country, divided by us with the proportion of the local GDP to that of the whole
country, further subtracted with 1, as the represented gap is the smallest when
proportion is 1, while the latter being the proportion of the local number of students in
school to that of the entire country, to be divided with the proportion of local
population to that of the whole country, to be further subtracted with 1. When the two
are 0, it will be our most ideal result.
Indicators
GDP_Ratio 98
GDP_Ratio 04
POP_Ratio 98
POP_Ratio 04
Implication
STUDENTS%/GDP%-1
STUDENTS%
/POPULATION%
-1
Means
Standard
Deviation
0.1542
0.5373
-0.4679 1.5220
0.0787
0.3919
-0.5953 1.3640
0.31469
1.5052
-0.6556 6.3491
0.1493
0.8781
-0.7045 3.1729
Min
Max
It is not difficult to find by observing the following figure that as to the
abovementioned two indicators, no matter they are the means or the standard
deviations, both had obviously diminished after 2004, and through massification these
two indicators have become closer to our ideal result zero, indicating that
massification has optimized the allocation of resources in an overall manner. However,
we are not so optimistic when information about details is considered. Some evident
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deficiencies may reveal themselves if analysis is made by us about the characteristics
of industry structure in three aspects and the number of students in school. In recent
years the Chinese government is undergoing a decentralization process and has
requested the local governments to develop education according to their own local
actual situations, and allow the local university/college graduate students to make
bigger contributions to local economic growth. However, judging from the previous
analysis, we can apparently find that the local governments in the underdeveloped
regions are also learning from the governments in the developed regions, thus having
resulted in the same proportions. For this reason, from analysis of this part, we have
actually not reached any positive conclusion with regard to massification.
We have taken the 31 provinces/municipalities as samples, and have taken the
proportions of their production values from tertiary industries as the main attributes
for the indicators of economic structures, to find the Person Correlation Coefficient of
their proportions of undergraduate and junior college to those of the respective
provinces/municipalities, and have compared the deviations of these coefficients of
correlations between 1998 and 2004.
Pearson Correlation
Ratio-UC
Coefficient
1998
1998
GDP_percapital
0.42046 ;
1998
P Value: 0.0185
Pearson
Correlation
Coefficient 2004
GDP_percapital
2004
Ratio-UC
2004
0.13167 ;
P Value: 0.4801
Ratio-Tertiary
1998
0.60154;
P Value: 0.0003
Ratio-Tertiary
2004
0.47676 ;
P Value: 0.0067
Ratio-Secondary
1998
-0.6218;
P Value: 0.0002
Ratio-Secondary
2004
-0.17656 ;
P Value: 0.3435
Ratio-Primary
1998
0.50736;
P Value: 0.0036
Ratio-Primary
2004
-0.11349 ;
P Value: 0.5433
Judging the general tendency, we can easily find that the coefficient in 2004 had
obviously turned smaller than that in 1998, while its zero possibility could have
increased to certain extent, indicating that the relationship between the economic
structure and the higher educational structure is being weakened.
First of all, judging from per capita GDP, the proportion of undergraduates in the
regions with a high per capita GDP should be slightly bigger. This pattern has turned
weaker in 2004.Then judging from the production value of the tertiary industry,
generally speaking, this production value indicates the situation of economic growth
of a particular region, as the regions with high production values are more developed
regions, and vise versa. On this account, there should be more higher educational
resources in the regions typical of high production values. However, through
massification, the coefficient of correlation has slightly dropped, from 0.6 to 0.47.
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Speaking from the jobholders in the secondary industry, the larger their number is, the
proportion between undergraduate and junior college should be somewhat lower,
namely there should be more junior college graduate students engaged in industrial
production. It is indicated when the coefficient of the current period is -1.
Nevertheless after massification, such negatively correlated relationship has turned
weaker, indicating that excessive undergraduates are being developed in many
underdeveloped regions.
6. Conclusion & Further Discussion
6.1 Mechanism and tension
Mechanism here refers to the general law of the development of higher education；
tension here refers to the factors which might influence the general law, like economic
factor, demographic factor and so on. China's higher education massification exhibits
the general law of the development, which has some commonalities with the process
in other countries. It also exhibits the uniqueness caused by the unique economic and
social environment. However, since the general law of the development of Chinese
higher education hasn't been fully realized by policy makers and researchers, for most
of the time,China' s higher education is influenced more by and is adjusted to meet the
needs of outside factors. How to balance the tension between the general law and
outside factors and which should be taken as the priority is a big issue facing with the
future development of China's higher education, as well as other countries.
6.2 Quality and Structure
Although this research doesn't taken quality as its main research question, it provides
a new perspective and data analysis basis for further analysis on quality. Quality
standards vary between different subjects and different types of HEIs. Our findings of
the changes to the hierarchy of higher education system, and the changes to different
subjects implies the variances in the quality standards.ch question, it provides a new
perspective and data analysis basis for further analysis on quality. Quality standards
vary between different subjects and different types of HEIs. Our findings of the
changes to the hierarchy of higher education system,and the changes to different
subjects implies the variances in the quality standards.
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