WHICH IS BETTER INNOVATIVE INVESTMENT
An Empirical Analysis of Statistics from Chinese Industrial Undertakings
Zongyuan Huang
1,2
1
School of Economics and Management, Beijing Jiaotong University, Beijing 100004, China
2
School of Economics and Management, Guangxi Normal University, Guilin 541004, China
Keywords: Technical innovation, The relations of innovation capital and characteristics of technology and economy,
The best form of innovation capital.
Abstract: The analysis of this paper proves that, due to the differences in terms of characteristics of technology and
economy between the developing countries or less-developed regions and the developed countries, and the
industrial structure in these regions is located in the non-frontier, so the effects of various innovative
investment modes in technological innovation differ from that in the developed countries. The significant
relation, i.e. the effects of current venture investment in US is three times of the R&D investment effects,
turns out to be the fact that the R&D investment produces four times effects than the venture investment
effects in China. Therefore, as to current industry system of China, venture investment is definitely not the
best innovative financing method, while the R&D investment may be much better.
1 INTRODUCTION
Technology innovation is the major source of
technology advancement, which plays significant
guiding and supporting role in the formation of
national competitiveness in the long run. Among the
various factors affecting the efficiency of technology
innovation, innovation investment occupies a crucial
position. A popular notion holds theoretically that
among various innovation investment forms, the
stimulus from venture investment to technology
innovation is much profounder than that of other
investment forms. For example, the research results
of the scholars, Tykvova (2000), Ueda and
Hirukawa (2003), which explores in the angle of
resources supplementation, show that venture
investment can adapted better to the characteristics
and demands of technology innovation, while
traditional financing modes can not be the major
sources of corporation’s technology innovation
investment. Gebhardt’s study aiming at the angle of
curbing budget found that, as to the financing of
innovative projects, venture investment is much
more effective than traditional financing methods
and is able to promote technology innovation better.
(Gebhardt, 2000; 2006), Keuschning applied general
equilibrium in his study and found that the services
including capital and management provided by
venture investment can effectively raise the success
probability of running business, and guarantee the
smooth advancement of technology innovation
under the conditions of general equilibrium.
(Keuschning, 2004), Lv Wei proposed that venture
investment mechanism is a breakthrough of original
technology innovation, causing the lifting of
corporate ability of technology innovation, and as a
result can accelerate greatly technology innovation
(Lv Wei, 2002).
Empirical statistics from some developed
countries like USA and EU give strong support to
the above statements. For example, Kortum and
Lerner carried out empirical analysis on the
relationship between venture investment and
technology innovation according to the statistics
from the USA. The result indicates that the stimulus
of venture investment is approximately three times
of that of R&D. (Kortum, Lerner, 2000) Engel and
Keilbach conducted their study taking German
statistics as samples and they studied the effects of
venture investment on small and medium sized high-
tech businesses, and the result illustrates that the
total number of patents from the businesses with
venture investment is much more than from those
without (Engel, Keilbach, 2007).
Due to the modeling effects of the developed
countries, the above notions and experiences are opt
to become the policy models of industrial
617
Huang Z..
WHICH IS BETTER INNOVATIVE INVESTMENT - An Empirical Analysis of Statistics from Chinese Industrial Undertakings.
DOI: 10.5220/0003614806170628
In Proceedings of the 13th International Conference on Enterprise Information Systems (PMSS-2011), pages 617-628
ISBN: 978-989-8425-56-0
Copyright
c
2011 SCITEPRESS (Science and Technology Publications, Lda.)
technology innovation in the developing countries
and less-developed regions, and produce significant
effects. However, indiscriminate acceptance of these
notions may contain potential dangers: the decision
makers might ignore the real situation of that nation
and region, and over-react to these new innovative
investment modes like venture investment and
reduce their attention to tradition innovation
investment and relative administration, which results
in damages to technology innovation practices in
that nation and region. Until now, some crucial
problems haven’t gained adequate attention and
research: for the developing countries and less-
developed regions whose technology and economy
are relatively lagging behind, is venture investment
the best innovation investment mode in their
technology innovation? In the technology
innovation movement in the developing countries
and less-developed regions, if there exists a relation
that the stimulus of venture investment to patent
innovation is larger or times of the effects of R&D
?
2 THE THEORETICAL
EXPLANATION MODEL OF
THE FUNCTIONAL
PRINCIPLES OF INNOVATIVE
INVESTMENT IN
TECHNOLOGY INNOVATION
2.1 Theoretical Model
In order to answer these questions, first we need to
establish a model about how innovative investment
functions in technology innovation practice, and
clearly elaborate the functional mechanism and
movement principles of innovative investment in
technology innovation practices theoretically.
According to the Theory of Six Forces of Essential
Factors of Production of academician Xu ShouBo,
any economy and production are executed on the
basis of six fundamental production factors, namely
labor force, financial force, physical force, natural
force, transport force and time force (Xu Shoubo,
2006). As an important technology production
activity of human society, technology innovation
cannot be isolated from the six Essential production
factors. Innovative investment is one of these
important factors-financial force and R&D
investment and venture investment are two
significant modes of innovative investment.
Therefore, the explanation model of how innovative
investment functions in technology innovation
practice actually is an innovative model proposed by
the writer on the basis of six production factors
principle
.
It can be learned from the innovative model
based on the Theory of Six Forces of Essential
Factors of Production, technology innovation system
is a complex adaptive system, whose subject is an
adjuster that take the initiative in trying to adapt well
to circumstances, possessing limited rationale and
opportunism. The innovation result is the outcome
of the mutual function of the system subject under
the certain system structure and circumstances, and
then the rules of the system are very significant.
Thus, as an important fundamental production factor,
in what way does the innovation investment
participate in technology innovation? How does it
adjust to and influence the other factors? And what
about the function mechanism of various innovation
investments like venture investment and R&D
investment?
The writer holds that there are several basic
points to be grasped. Firstly, in the innovation
activities, the action and decision system of various
subjects is a “Target-oriented Self-adjusting
process”, whose target is to realize the maximum of
its own benefits and the minimum of comprehensive
cost (including cost of transaction and management).
Secondly, the subjects of various factors have both
limited rationale and the features of opportunism, so
their action principles are continuously repeated and
evolved towards the adjustment to the external
environment and reaction to the feedback cha ains,
integrating the features of nonlinearity, complexity
and dynamic evolvement. Therefore, in terms of
decision making methods, the subjects of various
factors all abide by “convention”, and their response
principles are adjusted dynamically on that basis,
and this is a conventional study process and
accumulation process of technology experience
(Nelson and Wentt, 1982).
If satisfactory returns could be achieved when
the subjects of these factors function conventionally,
the conventions will be continued and strengthened;
Otherwise, if abnormity occurs when the factors
function conventionally and the return is lower than
a certain level, the subjects of the factors will need
to adjust the convention, namely seeking a new
convention suitable for itself among the existing
technology and conventions, or by innovation
discovering a new emerging convention which had
never been found before. Then what are the
dimensions consisting of the conventions? The
writer holds that essentially the convention is a kind
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618
of technology program when the subject faces and
settles problems, so its dimensions are
characteristics of technology and economy.
Based on the above analysis, the writer proposed
a coupling relationship model integrating the
innovation investment and technology
characteristics (with tokens to display technology
complexity), and the economy characteristics. As
illustrated by Graph 1, this model actually is evolved
from the K Model by Mr. Herbert (Kitschelt·Herbert,
1991)
. According to this model, the coupling
relationship cross-functioned by various innovation
investment and technology characteristics and the
economy characteristics could be summed up as
follows:
(1) In those industries with more mature technology
and fierce market competition such as textile
industry, light industry, machinery industry etc.,
because the market structures are closer to perfect
competition market, so the technology innovation
have its uniqueness in the following three aspects:
first, the demand of technology innovation is strong
and diversified; second, the assets specificity is low
during the process of innovation, and the results of
innovation can hardly lead to considerable
monopoly profits; third, the technology of the
industries are relatively mature, less complex, and
the uncertainty of innovation is relatively low. The
compare analysis of the returns of innovation
subjects and the comprehensive cost indicates that
R&D investment by the corporations in this industry
and private innovation investment might produce
relatively high profits and low comprehensive cost,
while venture investment and national R&D
investment may lead to the problems of low profits
and high comprehensive cost. Therefore, corporate
R&D investment and private innovation investment
are more suitable for this kind of industry, as
illustrated by Graph 1 Section 1.
(2) In those industries whose technologies are
relatively mature and whose market structures tend
to be monopolized, such as fundamental chemistry,
steel and railway transportation, there are limited
corporations to be chosen from to expand innovation
results. The innovation results turn up in the manner
of Know-How and the asset specificity and cost of
innovation are both very high, so the corporate
center laboratory is more suitable, as illustrated by
Section of Graph 1.
(3) In those industries whose technologies are highly
complex, market structures tend to be monopoly, the
innovation demands are concentrated and the asset
specificity of innovation are very high, such as
nuclear technology, aviation industry, huge aircrafts
manufacture and telecommunication, there are
considerable risk during the innovation process and
it requires the national and corporate R&D
investment, as illustrated by Section of Graph 1.
(4) In those industries whose technologies are highly
complex but whose market positions are still in the
infant phase, whose market structures tend to be
competitive, and at many times the dominant
industry design and stand haven’t come into shape,
such as IT, software, artificial intelligence, genetic
engineering and pharmaceuticals etc., the technology
innovation corporations are mostly newly start-ups
which demand a large sum of investment in
technology R&D and market development,
possessing high uncertainty and risk of innovation.
However, once the innovation succeeds, a vast
market prospect and great returns will be enjoyed.
Judging from the experience of developed countries,
prior to the technology innovation in this kind of
industry, national R&D investment are needed, and
in the commercialized innovation phase, the venture
capital will be very influential, as illustrated by
Section Graph 1.
(5) In those industries, which are moderate complex
and face moderate market competition, such as
electronic equipment, household appliance,
sophisticated chemistry, machinery and automobile
manufacture, there is less demand of technology
innovation, and relatively high asset specificity may
be formed during the process of innovation.
Meanwhile, the technology in this kind of industry
may change drastically, requiring considerable input,
imposing serious demand on market scale and
producing high risk of innovation, therefore, they are
more suitable for the innovative investment forms
like R&D activities in the center laboratory of large
corporations and innovation alliance, as illustrated
by Section of Graph 1.
2.2 Theoretical Explanation of
Technology Innovation Activities in
the Developing Countries and the
Less-developed Regions
Analysing the technology innovation activities in the
developing countries and less-developed regions
applying the above mentioned model, the writer
came to an important conclusion: since the industry
structure and characteristics of technology and
economy in the developing countries and less-
developed regions are different from that of the
developed countries, therefore, as to the technology
innovation in the developing countries and the less-
WHICH IS BETTER INNOVATIVE INVESTMENT - An Empirical Analysis of Statistics from Chinese Industrial
Undertakings
619
developed regions, venture investment is not the best
innovation investment method. The relation, namely
the venture investment has much larger or times of
stimulating influence on patent innovation than the
effects of R&D, stands no ground.
First, according to the model, if two industries
differ in characteristics of technology and economy,
the innovation investment forms that they fit for will
differ accordingly. For example, as to the rising
industries with vast prospects, who have
sophisticated technology and great uncertainty, and
who face fierce competitive market without mature
standard, such as IT, artificial intelligence, genetic
pharmaceuticals, venture capital is an optimum
innovation investment. However, as to the industries
with relatively mature technology and serious
competition such as textile and light industry, due to
mature technology standards , specified market, and
high level of marketization, the suitable innovation
investment mode are corporate R&D input or private
investment. The reason is that in this kind of
industry, the growth margin is limited. If the venture
investment enters this kind of industry, the rate of
return will be very low. At the same time, since the
cost of transaction and management is very high, the
community income of society doesn’t accord with
personal income, and the rate of return of national
R&D investment will be low too. As a result, for this
kind of industry, these two kinds of innovation
investment modes may not be suitable.
Then, for the developing countries and less-
developed regions, what are the essential differences
in terms of characteristics of industrial technology
and economy between them and the developed
countries? The writer maintains that the most
distinctive difference between them lies in that the
developed countries are in the leading edge of
industrial technology and economy, while the
developing countries and less-developed regions are
mostly in the following edge. Just as Mr. Lin Yifu
point out, because the developed counties occupy
the leading positions in global industrial chain, in
most of the cases enterprises have different views on
the problem that which industry will come as next
new and promising industry in the national
economy, so they form no social consensus. Among
various investment options, projects of few
enterprises succeed, while projects from most
enterprises would fail. The continual economic
development relies on the choice of market. Later
reality proves that the investment projects of a
number of successful enterprises will promote next
round of emerging of new industry, and drive the
development of entire national economy. However,
the industries of developing countries position low
in the global chain of industry, the economic
development of developing countries positions
inside the global industrial chain, go through a
process of upgrading along the track of the current
industry with varied capital and technology
intensity. The industrial upgrading during economic
developing, the enterprises invest in the technology-
mature, product-existing-market industries inside the
global industrial chain. Which industry is new and
which is promising? The enterprises inside the
economy are opting to see eye to eye with one
another, and swarm into it one by one and form
“emergence”. (Lin Yifu, 2007) This difference
between the developing countries and the less-
developed regions decides their essential differences
in technology innovation: the technology innovation
activities in the developed countries position mainly
in the industries in Section , , and of the
model; while the technology innovation activities in
the developing countries and less-developed regions
position mainly in the industries in the Section ,
, and of the model. That is to say, in the
developing countries and less-developed regions, in
terms of industrial structure, the industries with
relative mature technology and high level of market
competition dominate. This judgment could be
proved by the proportion and changes of added
value of Chinese high-tech industry in GDP since
1996. The statistics in Table 1 indicate that the
proportion of added value of high-tech industry of
China in GDP will rise from 1.81% in1996 to 4.48%
of 2007, presenting an entire rising trend. Although
it indicates a great advancement of high-tech
industry of China over more than ten years, it, at the
same time, also presents an important fact that the
scale of the high-tech industry of China is still very
small, and other traditional industries apart from
high-tech industry still dominate the industrial
structure of China.
Different sections maintain different
characteristics of industrial technology and
economy, so the suitable innovation investment
mode should be different too. In terms of the
characteristics of technology and economy, high-
tech industry positions in the section of the
model, and the suitable innovation investment mode
in the rising phase is national public R&D input, and
in the following phase is venture investment.
However, the industrial system of the developing
countries and less-developed regions is still
dominated by the industries in Section , , and
, so the innovation investment mode should be
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620
only and mainly the R&D investment from all
aspects (including the nation, corporation and
enterprises). In other words, for the developing
countries like China, the optimal innovation
investment modes haven’t advanced to the phase of
venture investment, therefore in the technology
innovation, the relation that venture investment
produces much larger or times of stimulating effects
than the effects of R&D holds no ground either.
3 EMPIRICAL ANALYSIS
TAKING THE STATISTICS OF
INDUSTRIES OF CHINA AS
SAMPLE
In the following part, the paper will proceed to
empirical analysis of the technology innovation
statistics in industries of provinces, cities and
autonomous regions in China, in order to test
whether the above mentioned theoretical analysis
result accord with the reality or not. As a developing
country, technology innovation is an important
strategy for both of the central government and the
provinces, metropolis and autonomous regions. In
fact, over the past three decades, the provinces,
metropolis and autonomous regions all have devoted
to upgrading their innovative ability, and
accumulated a great many statistics and rich
experience. The test taking these statistics as sample
eventually is trustable.
3.1 Model Testing and Sample
Description
3.1.1 Variable Selection
In order to make up the defects of the Time-series
date, this paper chooses the Cross-sectional data of
China between 2006 and 2008 and the Panel data to
analyse this problem empirically. According to the
nature and features of the problem to be tested, the
following variables are chosen:
(1) Patent: This paper chooses the number of patent
achieved as an index to measure corporate
technology innovation. There has been a lasting
dispute over the selection of technology innovation
index. Previously, usually the indexes chosen
included innovative intermediary products (such as
patents), total factor productivity (TFP), and the
terminal output of innovation (such as the number of
innovation) etc. Since the obtaining patent is the
major foundation of technology innovation result,
and the statistics about patents have strong
obtainability, this paper selects patent variables as
the measurement index of technology innovation.
The number of patent can be divided into two types:
the number of patent applied and the number of
patents authorized. This paper selects both of them
as explaining variables to research the effects of
technology innovation input and output.
(2) Venture investment: Researchers usually choose
the total number of annual venture investment
project to be the index to measure venture
investment, and some may adopt the total volume of
venture investment and the number of venture
investor instead. The total number of annual venture
investment project refers to the total number of real
investment project of venture investment institution
in that year. The annual volume of venture
investment refers to the volume of real investment of
venture investment institution in that year, indicating
the real expenditure of one country in venture
investment, so it has primly direct impact on
technology innovation. This paper chooses the total
number of annual venture investment project and the
total volume of venture investment as measuring
indexes, and select provincial statistics that are
studied by the China Growth Enterprise Market
Research Report published by China Venture.
(3) R&D investment: As the index of innovation
input, R&D sheds obvious influence on innovation
output, and is the principal explaining variable of
patented output. This paper chooses respectively the
R&D input of the whole society and the R&D of
large and medium sized enterprises as the explaining
variables, and studies their influences on technology
innovation output.
3.1.2 Sample Description
The statistics of patents in this paper are adapted
from China Statistical Yearbook of 2005-2009;
R&D statistics are from China Statistics Yearbook
of Science and Technology of 2005-2009; Statistics
of venture investment are from the Research Report
of China Growth Enterprise Market published
between 2007 and 2009. Although related statistics
of venture investment in provinces Henan, Gansu,
Ningxia, Qinghai and Tibet are not included, the
statistics of 25 provinces that are chosen have
covered the major part of China, so the statistics are
representative.
This paper selects two samples: one is the Cross-
sectional data sample, including the statistics of
various variables of 25 provinces, metropolis and
autonomous regions in 2005; another is the Panel
WHICH IS BETTER INNOVATIVE INVESTMENT - An Empirical Analysis of Statistics from Chinese Industrial
Undertakings
621
data sample, including the statistics of the year 2006,
2007, and 2008.
3.1.3 Model Testing
Based on the model mentioned above and its
features of testing problem, this paper sets the model
of analysis as follows:
log( ) log( ) log( )PRDVC


(1)
In this formula, P, RD, VC stands respectively for
the number of patents applied, number of patents
authorized, R&D input and venture investment,
while
stands for the random error.
3.2 Regressive Analysis of the
Cross-sectional Data
The OLS estimation result of the statistics of the
selected Cross-sectional data samples of the 25
provinces, metropolis and autonomous regions of
China in 2005 can be referred to Table 3 and Table
4. The result indicates that: when the venture
investment volume(VC1) and the R&D investment
from large and medium-sized enterprises (RD2)
serve as explaining variables, if the venture
investment volume increases by 1%, the number of
patent applied will increase approximately by 0.17%
(Model 1), and the number of patent authorized
increase by 0.16% (Model 6). This result proves that
venture investment imposes obvious positive effects
on patent output, complying with the conclusion of
Kortum and Lerner (2000) and Tykvova (2000)
essentially.
However, the result of Table 3 and Table 4 also
indicate that when R&D investment from large
and medium-sized enterprises increases by 1%, the
number of patent applied will increase
approximately by 0.72% (Model 1), and the number
of patent authorized can increase by 0.69% (Model
6). Therefore, R&D investment is much larger than
the effects that venture investment produces on the
output of patent, almost 4.31 times of the stimulating
effects that venture investment produces on the
patent innovation. Obviously, this result is different
from the Kortum and Lerner’s (2000) conclusion
which was drawn on the samples of American
statistics. Because according to their conclusion, the
stimulating effects that American venture investment
produces on the patent innovation are three times of
the R&D investment. It can be seen that in the
technology innovation of China, the effect that R&D
input and venture investment produce in the patent
output is obviously different from that of America.
If the number of venture investment project (VC2),
and R&D input of large and medium-sized
enterprises (RD2) are taken as explaining variables,
we can see that when the number of venture
project increases by 1%, the number of patent
applied will increase by 0.30% approximately
(Model 3), and the number of patent authorized will
increase by 0.26% (Model 8).
Moreover, if the lag terms in 1-2 period of VC
and RD are inserted into the model, (see Model 4, 5,
9, 10). It can be seen that: The insertion of lag terms
can improve the explanation ability of the model, but
the statistical coefficients of the lag terms are not
obvious and the model is not convincing. It proves
that the expenditure of VC and RD largely coincide
with the patent output, which accord with basically
the conclusion of Hall, Griliches and Hausman
(1986).
3.3 Regressive Analysis of Mixed
Cross-sectional Data
3.3.1 Chow Testing of Mixed Cross-sectional
data
Before the regressive analysis of mixed cross
section, it is necessary to research whether there are
distinctive structural changes between regressive
coefficients of each year; therefore we need to carry
out Stability Tests on the Model. This paper, by the
approaches of Chow Tests, divides 59 observed
values into three sub-samples of 2008, 2007, and
2006 in a view of testing them. The result is
illustrated by Table 5.
Seen from the result of Chow Tests, there is no
distinctive differences between the three sectional
samples, namely there is no any obvious structural
changes between 2006 ,2007and 2008, therefore, it
is feasible to conduct regressive analysis on the
cross sections by mixing up the statistics of the three
years.
3.3.2 The Result of Regressive Analysis of
Mixed Cross-sectional Data
The result of OLS estimation on the Mixed Cross-
sectional data sample statistics from the year 2006 to
2008 is displayed in table 6 and table 7. The result
indicates that venture investment has obvious
positive effects on the patent output; when venture
investment volume increases by 1%, the number of
patent applied will increase by about 0.17% (Model
1), and the number of patent authorized will increase
by 0.16% (Model 6); when R&D input from large
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622
and medium-sized enterprises increases by 1%, the
number of patent applied will increase by about
0.75% (Model 1), and the number of patent
authorized will increase by 0.72% (Model 6).
Herein, the effects that venture investment
volume produces on patent output is far less than the
effects R&D investment produces on patent, which
is basically in line with the result of regression of
Cross-section.
The result with the number of venture investment
project (VC2), and the R&D investment from large
and medium-sized enterprises (RD2)as explaining
variables also indicates that when the number of
venture investment project increases by 1%, the
number of patent applied will increase by about
0.30% (Model 3), and the number of patent
authorized will increase by 0.27% (Model 8), which
accords highly with the regression result of Cross-
section.
Moreover, the result of inserting the lag terms of
1-2 period into VC and RD (see Model 4, 5, 9, 10)
also indicates that: although the insertion of the lag
terms can improve the explaining ability of the
model, the statistical coefficients of the lag terms are
not obvious, which proves that the influences
produced by venture investment and R&D
expenditure coincident on the output of technology
innovation, and there is not obvious hysteresis
quality, which is completely in line with the
regression result of the Cross-section analysis.
4 CONCLUSIONS
In the developed countries, the effects made by
venture investment on the technology innovation
output are generally superior to R&D investment. In
the USA, it exists that the effects made by venture
investment on the output of technology innovation is
three times of that made by R&D investment.
Therefore, in terms of raising the efficiency of
technology innovation, venture investment is surely
a better innovation investment mode. However, this
paper proves theoretically that: because the industry
structure of the developing countries and less-
developed regions, as a whole, is not in the leading
edge of the world in technology and economy, and
their industries characteristics of technology and
economy are different from the developer countries,
so as a result, in the developing countries, the
influences produced by the innovation investment in
various forms are different from that of the
developed countries, so that significant relation, i.e.
in the technology innovation the effect caused by
venture investment should be times of that caused by
R&D investment, holds no ground. The empirical
analysis with statistical samples of Chinese
industrial enterprises advocates this important
conclusion: in China, though venture investment
produces obvious positive effects on the patent
output, for example, when the venture investment
volume increases by 1%, the number of patent
applied will increase by 0.17%, and the number of
patent authorized will increase by 0.16%. However,
the relation, namely the effects produced by venture
investment on the technology innovation should be
three times of that produced by R&D investment, is
not true in the technology innovation of Chinese
industrial enterprises. Instead, it occurs that the
R&D investment produces effects on technology
innovation, which is several times of that produced
by venture investment. The fact fully elaborates that
currently in the industrial technology innovation of
China; venture investment is not the best innovation
financing method. On the contrary, R&D investment
may be more suitable.
This conclusion could offer plenty helpful
enlightenment to the developing countries and the
less-developed regions in terms of the policies about
technology innovation and decision-making. First,
the policies about technology innovation should be
rooted in the actual situation of current industrial
system of that country or region. The experiences
and conclusions of the developed countries should
not be copied blindly. For example, in the traditional
industries, if the venture investment is unilaterally
stressed and the functions of R&D investment are
ignored, the policies about technology innovation
may take a wrong road and produce harmful
influences on technology innovation. Second, since
the R&D investment has not a single form, instead it
has various forms and levels, at the present stage the
effects of technology innovation made by R&D
investment in different forms at different levels
should be stressed, be studied profoundly about its
principles and be innovated continuously. For
example, the systematization, modularization and
integration innovation mode has come into the
automobile industry; it requires us to conduct
researches on the new modes of these innovation
institutions, as well as the developing trends and the
suitable R&D investment. Demanding more
attention, the research of this paper also proves that
the industry system of China is still relatively
backward, lagging greatly behind the developed
countries, and being kept away from the phase
where the new innovation investments like venture
investment are utilized efficiently. In fact, the
WHICH IS BETTER INNOVATIVE INVESTMENT - An Empirical Analysis of Statistics from Chinese Industrial
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623
practices of the developed countries have proved
that venture investment can produce tremendous
prompting effects on the development of new
industries. Therefore, how to realize the adjustment
and upgrading of industrial structure system of
China and get it used to the new trend of industry
development globally as soon as possible, are still
significant problems requiring further studies.
-------------------------------------------------------------
The answer to this question is very crucial. If the answer is
negative, there should be a necessity of reflection on certain
policies and actions in terms of technology innovation. In fact,
this question has alerted some researchers. For example, Kortum
and Lerner pointed out that what they used in their researches was
the statistics of the USA, and the empirical analysis they
conducted was about the situation of the USA, so they didn’t
answer the question that whether venture investments in other
countries could promote technology innovation or not.
Referring to Huang Zongyuan. Systematic Analysis Principle
of Industry Development in Less-developed Regions [M].Beijing:
Economic Science Press2008,P 201-202.
This model is evolved on the basis of H Model proposed by the
writer previously, and the H Model is rooted in the K Model of
Mr. Kitschelt, referring to Huang Zongyuan. Systematic Analysis
Principle of Industry Development in Less-developed Regions
[M].Beijing: Economic Science Press2008, P215.
REFERENCES
Cheng Siwei. Collections of Theses on Venture
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Construction Press, 1997
Dirk Engel and Max Keilbach. Firm-level implications of
early stage venture capital investment-An empirical
investigation [J]. Journal of Empirical Finance, 2007,
(14):150-167.
Gebhardt, G., 2000, Innovations and Venture Capital [J].
Working Paper, University of Munich.
Gebhardt, G., 2006, A Soft Budget Constraint Explanation
for the Venture Capital Cycle [J]. Working Paper,
University of Munich.
Hall, B. H, Z. Griliches and J. A. Hausman. Patents and
R&D: Is There a Lag [J], International Economic
Review, 1986, No 27, pp.265-283
Huang Zongyuan. Systematic Analysis Principle of
Industry Development in Less-developed Regions [M].
Beijing: Economic Science Press, 2008.
Keuschnigg, Christian, 2004, Venture Capital Backed
Growth [J], Journal of Economic Growth, 9(2), pp239-
261.
Kortum and Lerner.Assessing the Contribution of Venture
Capital to Innovation [J]. Rand Journal of Economics,
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Kitschelt·Herbert,Industrial Governance Structure
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or Cross-National Comparative Analysis?,
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493
Lv Wei. On Technological Innovation Principle of
Venture Investment Mechanism [J]. Economic
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Lin Yifu. Wave Phenomenon and the Reconstruction of
Macro-economic Theories for Developing
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Industry [M]. Beijing: Economic Science Press, 2006.
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ICEIS 2011 - 13th International Conference on Enterprise Information Systems
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APPENDIX
Figure 1: Coupling relationship model of the innovation investment and characteristics of technology and economy.
Table 1: The Proportion of Two Industries in GDP of China Since 1996.
Year
High-tech
industry
Added
Value
(billion)
Gross
national
GDP
(billion)
High-tech
Industry
proportion
(%)
Other
industries
proportion
(%)
Year
High-tech
industry
Added
value
(billion)
Gross
national
GDP
(billion)
High-tech
industry
proportion
(%)
Other
industries
proportion
(%)
1996 1271.95 70142.49 1.81 98.19 2002 3768.58 119095.69 3.16 96.84
1997 1539.96 78060.83 1.97 98.03 2003 5034.02 135173.98 3.72 96.28
1998 1785.33 83024.28 2.15 97.85 2004 6341.30 159586.75 3.97 96.03
1999 2107.12 88479.15 2.38 97.62 2005 8127.79 184088.60 4.42 95.58
2000 2758.75 98000.45 2.82 97.18 2006 10055.51 213131.70 4.72 95.28
2001 3094.81 108068.22 2.86 97.14 2007 11620.66 259258.90 4.48 95.52
sources: Adapted from China Statistical Yearbook and China High-tech Industry Statistical Yearbook between the year 1997 and 2008,
published by China State Statistics Bureau.
Technology features
·Low complexity
·More mature
·Low uncertainty
section
Electronic equipment household
appliances
Chemical industry petrochemical
industry
Machine manufacture automobile
industry suitable innovation
investment mode:
Innovation alliance
Section
IT industry, software industry,
artificial intelligence, genetic
engineering and pharmaceutical
industry etc.
features: complex technology
fierce competition
suitable innovation investment
mode: Venture investment,
innovation club, national R&D
Section II: Fundamental chemical
industry, steel and railway
transportation etc.
features: Less complex technology,
highly monopoly,
suitable innovation investment mode:
R&D in corporate center laboratory
Section III
Nuclear technology, aviation
industry, huge aircrafts
technology and telecommu-
nication, etc.
features: Complex technology,
irreplaceable
suitable innovation investment:
National R&D and corporate
R&D
Technology features
·High uncertainty
·High immature
·High complexity
Economy feature
High monopoly
Economy feature
High competition
Section I: Traditional industries
as textile, light industry, and
machinery industry etc.
features: Simple technology,
lower irreplaceable
suitable innovation investment
mode: Adjust to innovation
investment, private investment,
and corporate R&D
WHICH IS BETTER INNOVATIVE INVESTMENT - An Empirical Analysis of Statistics from Chinese Industrial
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625
Table 2: Definition and Elaboration of Variables.
Name of variable Elaboration of Variable Index Unit
P1
Variables explained the number of patents applied
a
P2
Variables explainedthe number of patents authorized
a
VC1
Variables explainingvolume of venture investment
Million USD
VC2
Variables explainingthe number of venture investment project
a
RD1
Variables explainingR&D expenditure of the whole society
Billion RMB
RD2 Variables explaining: R&D expenditure of large and medium-sized enterprises Billion RMB
Table 3: The Regressive Result of Cross-sectional data – Taking P1 as the Induced Variable.
Model 1 Model 2 Model 3
M
odel 4 Model 5
Constant 6.030521***
(0.0000)
5.083878***
(0.0000)
6.262084***
(0.0000)
5.493210***
(0.0000)
5.642896***
0.0000
log(RD1) 0.899826***
(0.0000)
log(RD2) 0.716225***
(0.0000)
0.684397***
(0.0000)
0.716391***
(0.0000)
0.712027***
0.0000
log(VC1) 0.174657**
(0.0036)
0.069933
(0.2155)
0.171766*
(0.0074)
0.188194**
0.0049
log(VC2) 0.302276***
(0.0000)
log(RD2(-1)) 0.094017
(0.2231)
0.090564
0.2406
log(VC1(-1)) 0.058048
(0.2712)
0.049550
0.3939
log(RD2(-2) ) -0.113884
0.1396
log(VC1(-2) ) 0.090606
0.0953
R-squared 0.881717 0.898801 0.923015 0.904554 0.922629
Remarks: The numerals in()correspond to the statistics of t ***, **, and * stand for respectively the statistical markedness at the
level of 1%, 5%, and 10%.
Table 4: The Regressive Result of Cross-sectional data –Taking P2 as the Induced Variable.
Model 6 Model 7 Model 8 Model 9 Model 10
Constant 5.550355**
*
0.0000
4.749733**
*
0.0000
5.759913**
*
0.0000
5.263685**
*
0.0000
5.248052**
*
0.0000
log(RD1) 0.829870**
*
0.0000
log(RD2) 0.687432**
*
0.0000
0.667913**
*
0.0000
0.688779**
*
0.0000
0.680393**
*
0.0000
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Table 4: The Regressive Result of Cross-sectional data –Taking P2 as the Induced Variable. (cont.)
log (VC1) 0.164902**
0.0021
0.077226
0.1923
0.069328**
0.0074
0.188337**
0.0016
log (VC2) 0.264078***
0.0001
log (RD2(-1)) 0.166091
0.3487
0.058413
0.3760
log (VC1(-1)) 0.004273
0.9321
0.008879
0.8579
log (RD2(-2) ) -0.103049
0.1227
log (VC1(-2) ) 0.127898
0.0105
R-squared 0.897638 0.876635 0.924551 0.901270 0.936567
Remarks: The numerals in()correspond to the statistics of t***, **, and * stand for respectively the statistical markedness at the
level of 1%, 5%, and 10%.
Table 5: Chow Test Result.
Chow Breakpoint Test: 16 41
F-statistic 0.490538 Probability 0.812323
Log likelihood ratio 3.374632 Probability 0.760568
Table 6: Result of Regressive Analysis of Panel data-Taking P1 as Induced Variable.
Model 1 Model 2 Model 3 Model 4 Model 5
Constant 5.950044***
0.0000
5.079356***
0.0000
6.213828***
0.0000
5.574505***
0.0000
5.882852***
0.0000
log(RD1) 0.923259***
0.0000
log(RD2) 0.746766***
0.0000
0.698969***
0.0000
0.727938***
0.0000
0.720048***
0.0000
log(VC1) 0.174491***
0.0000
0.052321
0.1460
0.146557***
0.0003
0.152016***
0.0001
log(VC2) 0.300614***
0.0000
log(RD2(-1)) 0.074988
0.1195
0.044867
0.4292
log(VC1(-1)) 0.068612
0.0817
0.061908
0.1117
log(RD2(-2) ) -0.126244
0.0260
log(VC1(-2) ) 0.095440
0.0128
R-squared 0.856826 0.900252 0.898379 0.878299 0.893650
Remarks: the numerals in()correspond to the statistics of t***, **, and * stand for respectively the statistical markedness at the
level of 1%, 5%, and 10%.
WHICH IS BETTER INNOVATIVE INVESTMENT - An Empirical Analysis of Statistics from Chinese Industrial
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Table 7: Result of Regressive Analysis of Panel data-Taking P2 as Induced Variable.
Model 6 Model 7 Model 8 Model 9 Model 10
Constant 5.425997***
0.0000
4.726168***
0.0000
5.661210***
0.0000
5.091773***
0.0000
5.353154***
0.0000
log(RD1) 0.845256***
0.0000
log(RD2) 0.719684***
0.0000
0.681994***
0.0000
0.708854***
0.0000
0.695647***
0.0000
log(VC1) 0.160898***
0.0000
0.061134
0.1256
0.145119***
0.0002
0.147812***
0.0001
log(VC2) 0.267834***
0.0000
log(RD2(-1)) 0.079728
0.1596
0.041718
0.4300
log(VC1(-1)) 0.032460
0.3898
0.019042
0.5952
log(RD2(-2) ) -0.117535
0.0261
log(VC1(-2) ) 0.121967***
0.0009
R-squared 0.862644 0.864674 0.895622 0.874437 0.898560
Remarks: The numerals in()correspond to the statistics of t***, **, and * stand for respectively the statistical markedness at the
level of 1%, 5%, and 10%.
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