Prepared for the IIPS Symposium on

Globalization andJapan’s Science and Technology Strategy
19 – 20 November 2007
Tokyo

Session 1
Monday, 19 November 2007

Comparative Study overInnovative Region vs. Innovative Nation
China and Other Countries

Chen, Xiangdong
Beijing University of Aeronautics & Astronautics

Institute for International Policy Studies (IIPS) Conference 2007
Nov. 19-20, Tokyo, Japan

Comparative Study over Innovative Region vs. Innovative Nation
China and Other Countries

Professor Chen, Xiangdong
The School of Economics & Management
Beijing University of Aeronautics & Astronautics
Beijing, 100083
P. R. China
Introduction:
It is no doubt that the most determined source for generating sustainableeconomic progress lies in technology innovation and continual knowledge
accumulation. Based on this crucial resource, national and regional technology
capability as well as economic development can be continually running on acompetitive base, therefore, strategic policy for effective technology development
becomes key issue for both country level national innovation system (NIS) and forregional level innovation system (RIS). However, these two systems are different, andstrategic policy consideration for technology capabilities is usually a more relevant
topic on a national level, rather than in regional boundaries, in spite of social, cultural,and regional varieties (Archibugi, 2005). On the other hand, ownership based
technology capability / innovation resources has been discussed actively along with
National Innovation System in China as well as in other countries in recent years,
together with international background of rapid pace of globalization in which
technology innovation occurs almost without country boundaries. The key question
here is how do we combine regional innovation movement in an international sense,with ownership based national innovation system as a key policy issue, in a rather
national sense. This research paper, based on author’s recent research work, focuses
on relationships between regional innovation and national innovation, in order toprovide implication for strategic policy purposes. The paper argues that regionalinnovation movement is fundamental and RIS is basic platform in composition ofnational innovation system, while ownership based technology innovation policy isplaying a vitally important role only in complementary cases where market failure
may exist.

1

II Regional innovation vs. National Innovation system: related Concepts.
It should be noted that regional innovation system and NIS are in common in
many important characters, such as knowledge generation and knowledge diffusion
process. According to Archibugi and Coco (2005), technology side of nationalinnovation capability can be summarized as following three parts, which can alsocharacterize important nature in regional innovation movement:(a) Embodied/Disembodied, since it is recognized that technological capabilities areembodied in capital goods, equipment, infrastructures, and in disembodied forms suchas human skills and scientific and technical expertise, and both types of capability
contribute vitally to the technological base of a country(b) Codified/Tacit, As codified component of knowledge is usually represented bymanuals, blueprints, patents, and scientific publications are as important as the tacit
components associated with learning by doing and by using (Lundvall and Johnson,1994).
(c) Generation/Diffusion. As it has been long recognized that both the production ofknowledge
and its diffusion and imitation provide a valuable technological resource for thecountry and the region concerned.
The three parts of movement can actually cover development of technology based
resources both in regional and national level in three stages, namely, dynamic part as
new knowledge production and diffusion (described in C), technology transfer partas a knowledge formation process (described in B), and technology accumulation partas knowledge integration (described in A).
However, there are policy sides of innovation systems, and the concepts of both
NIS and RIS in strategic policy terms focuses on different phenomena, which may
further imply important policy differences.
Major components and related activities related to national level system usually
include development factors on both market competitive resources and resources
which may encounter market failure. Thus an appropriate framework of National
Innovation System or national technology capability system can be shown as follows(Furma, Porter, Stern, FSM model, 2002):

2

National Innovation Policy focus

Market competition focus

Based on this model, it can be summarized that Regional Innovation System (RIS)
is different from NIS in following three aspects:
1. National Innovation System (NIS) is a policy oriented nation-wide commitment
and an action system with more strategic planning for the whole country, which
may cover typical concerns as follows:a) Government – University – Industry collaboration and relationships focused;b) Start-Ups and high tech enterpreneourial companies focusedc) Key industrial sector development oriented;d) Key product / media component focused;e) Energy / Environmental sustainable technology focused;f) National security focused;g) Overseas company competition concerned.
Therefore, NIS is a system of action primarily from government of particularcountries, vision oriented, strategies focused, and international political andeconomical relation backed;
2. Regional Innovation System (RIS) is a primarily local market driven network,
clustering based and technology accumulation with technical traditions in local
industries, generated and developed through market competition and market
selection, combined with local innovation resources including local government
policies which can only have limited impact under market mechanism.
3. Externalities are usually effective only in bounded regions. Although access tointernational technology and knowledge flows is especially important fordeveloping countries, as Romer (1986); Lucas (1988), Grossman and Helpman(1991) argue, that externalities such as knowledge spillovers or learning by doing3are the driving force for today’s economic growth in a long run, however, suchlearning by doing activities and tacit type innovation are usually bounded inparticular region, and externalities do have geographic limits, external knowledge
and technology transfer can be effective usually within certain regions and serveas important vehicle for regional economic growth. With such kind of evidence
that knowledge spillovers are geographically localized (Jaffe et al., 1993;
Branstetter, 2001), some further implications can be drawn as important reference
on regional innovation.
(1) Regions with a larger agglomeration of firms grow faster because
regional concentration of firms facilitates knowledge spillovers.
(2) Foreign trade and FDI policy are more regional bounded, which
provide important source for technology spillovers;(3) High tech policy is usually localized as high tech sectors need
important support from local resources, such as higher education
facilities, qualified human resource, technology accumulation fromcertain traditional sectors, and market preference, as well as local
purchasing power.
It can be concluded that RIS is a platform for NIS and therefore innovation
activities in regional level is fundamental to national innovation performance on thewhole.
However, differences between NIS and RIS can vary from country to country. It
can be sharply significant in some countries (mainly latecomers or developing
countries such as in China), also can be modestly marked or even faded in some othercountries (typically in European countries).
In order to clarify differences and relationships between RIS and NIS, this paper
provide following chart to combine different type of innovation activities and provide
concept of two kinds of policy, namely, Ownership Oriented Policy and Lead Market
Oriented Policy.

4

Lead Market oriented

Innovative Nation

Innovative Region
Local firm imitation for higher value-added product。。
。 Higher value added product competition / innovation

Foreign firms’ innovation。
。。。

Ownership oriented

Foreign capital R&D

Thiad Patenting
Key Sector Innovation / local firms
Regional / sector Innovation
Policy support for Special technical areas
high tech sector / R&D consortium
University innovation / Start-Ups /Frim level R&D
Education / HR input
。
。。。

。
。。。

（RIS）

NIS

Social environment for innovation

Strategic policy Focus
Figure 1. Policy Focus on National Innovation System andRegional Innovation system

The differences in two kinds of policies can be further described throughfollowing five issues.
(1) NIS is established based on regional innovation platform, in which innovation
activities are clustering from various kinds of sources including overseas, thus
Lead Market Orientation policy is the most important in defining promisingregional market.
(2) NIS related strategic policy is primarily ownership oriented, indicating important
positioning for certain type of technology as well as particular sectors in whichmarket mechanism can not fully apply.
(3) Ownership Oriented policy / strategy is fundamental for both local firms and forthe nation as a whole that owns capabilities competing in national andinternational market; however, Lead Market Oriented policy is equally importantin that the market in innovative region is usually functioned as clustering to hosthigh tech firms as well as high tech itself.
(4) Lead Market Oriented policy is primarily regional and is fundamental for
innovative region as well as for innovative nation, which accept various kinds ofcollaborations, while ownership based national innovation movement accept
collaboration with policy limitations.
(5) Innovative nation and innovative region can be evaluated according to appropriate
indicator system, reflecting ownership innovation performance of the nation in theformer case and reflecting activeness of innovation in regions in the later case.
Finally, it should be further noted that Lead Market indicates, according to Bartlett
5

Cross-Nation
Innovation Clusters

Collaboration
Value chain based

Market Competition / Collaboration
Sectors based / differentiatedCross region /Cross boarderInnovation Clusters

Sectoral differentiatedCollaboration / Market Competition

Collaboration
Value Chain based

Country A
Regional Innovation Sys. / Sub-National Innov. Sys. / National Innov. Sys / Pan-NISs）

Country B
Regional Inno. Sys. / Sub-national Inno. Sys. / National Inno. Sys. / Pan-NISs

and Ghoshal (1990), a geographical location, where the market is created by local
demand and nature resource embedded. The Lead Market Oriented policyemphasizes places with the first adoption of new designs and techniques rather
than the place to host first invention. Thus the related region can be considered asa kind of multinational corporation that host a globalized product (Raffée andKreutzer, 1989), and of cause still retain possibility to host both (including firstinvention). Moreover, under Lead Market framework, collaboration space can befurther developed and extended. Following chart shows space and possible
positioning of different policy system.

Figure 2.

Collaboration Space under Different Innovation Policy in Two Countries A & B

Under this framework, other two special innovation systems can be further
suggested, Sub-National Innovation System between Regional Innovation System
and National Innovation System, and the other, Pan-National Innovation Systems
which are widely extended over different NISs.. Sub-NIS is a Clustering based
innovation system which emphasizes innovation networking and clustering, while the
Pan-NISs indicates collaboration in a wider scope across borders but on the country
level. Clearly, both NIS and Pan-NIS’s can be heavily strategic issue.

6III. China’s Case on Regional Technology Innovation, in Comparison with
Policy Support.
This paper now turns to regional innovation issues, particularly on un-evenly
distribution of innovation resources and innovation performances, which can provide
important implications for policy studies.
(1) Regional Innovation in China: Input and Outcome
This paper will use R&D input data and patent data as major indicators forpolicy oriented innovation performance.
Patent system is one of effective way to protect technical inventions, and patentcan be used not only as an indicator for innovation outcome, but also as a kind ofresources for further innovation in production place. Apparently, patent data have
weakness as only about 50% of patent can be adopted in real production (Guellec and
Van Pottelsberghe,1999), on the other hand, not all manufacturing companies arewilling to patent their new designs. Therefore, values of patent protection for
innovation output can vary from industry to industry. However, patent data are still
acceptable for most policy researchers regarding to innovation activities, especiallywhen considering those inventions that can bring gains through upgrading
productivity（Acs et al 2002).
High tech export data are also taken into consideration in this paper, however, it
should be noted that Foreign Direct Investment (FDI) firms play very important rolein high tech sectors in China, almost dominating high tech export from China.
Figure 1 contrasts typical economic performance in each region in China withoverall R&D input and government based science and technology financial input, in a
relative terms (R&D ratio to local gross regional production, R&D / GRP; and local
government S&T financial input to all input from local government). It is very clearthat innovation input develops partially in parallel with economic performance,
however, there are exceptions in several typical regions.
25000

6

20000

5
4

15000

3
10000

2

GDP

R&D /GSP

Xi Zang

Ning Xia

Gan suXin Jiang

Yun NanShaanxi

Nei Menggu

Guangxi

An Hui

HuNan

FujianSichuan

Shanghai0
He Nan0

Jiangsu

1
Guangdong

5000

Local Gov. Ratio

Figure 1. Comparison of Regional Economic Development and Innovation Input (2005)
Source: edited based on China Science & Technology Statistic Year Book, 2006

7

90
80
70

25.00%

20.00%

60
50
40
30

15.00%
10.00%

20
10
0

5.00%

Figure 2.

High Tech Ratio

Hai NanXi Zang

Jiangxi
Gan suNing Xia
Qing Hai

Shan Xi
Xin Jiang
Chong qing
An HuiGui ZhouNei Menggu
Ji LinShaanxi

He BeiYun Nan
Guangxi

Sichuan
HuNan
Hei Longjiang
Hubei

亿元

Shandong
He NanTianjinFujianZhejiang
Beijing
Jiangsu
Liaoning

GuangdongShanghai

0.00%

Ratio to total

Comparison of Regional Innovation Input and High Tech Output(patent share and high tech export share, 2005)

Source: edited based on China Science & Technology Statistic Year Book, 2006

Figure 2, on the other hand, indicates how effective local innovation input iscorrelated to economic performance in high tech sectors. All regions in China are
ranked by R&D input value (0.1 billion RMB, 2005), with comparison of regional
output in patent (patenting share to national total in 2005) and in high tech productexport (share of export value to national total)
It can be summarized that innovation performance is independent in certain
important regions, not in parallel with economic development, while the innovation
output in terms of patenting records and high tech export shares follows similar
pattern, as Figure 1 shown, with levels of innovation input in Chinese regions,however, the innovation output in different regions: namely patent share and high techexport share, provide the most closely correlated pattern, which may suggest that
innovation in most regions in China are driven by market forces, primarily in foreign
capital dominated high tech sectors.
(2) Regional Innovation in China: Innovation Performance through Multiple
Indicator Analysis
In order to investigate through multiple characters, this paper also adopts
Principle Component Analysis (PCA) methodology on following seven different
indicators over 31 Chinese geographical regions. Two principle components areachieved, representing primarily on market driving forces (F1) and Policy driving
forces (F2). Correlation parameters among the seven indicators and extracted
principle components are listed below for further reference.

8

Table 1.

Principle Components on Typical Innovation Indicators (2005)
Component
F1:

F2:

Component I

Component II

Items

Local Government Techn Development Support Ratio

.583

.559

R&F / GRP

.126

.943

Invention Patent Ratio

-.383

.687

Ratio of Transaction Volume of Local Technology Market

.311

.845

High Tech Firm Ratio

.942

.137

High Tech Export Ratio

.917

.154

Patenting Ratio

.932

.220

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.
aRotation converged in 3 iterations.

A two-dimensioned chart can be achieved based on values of each region along
these two principle measures (refer to following Figure 3).
It can be clearly shown that some typical regions (in red) in China are mainly
policy driven while others (in blue and green) are market driven, with Beijing andGuangdong as two representative in two extremes.

F2: Policy Driven Innovation Performance

B

Sh

Sa

Z

J

G

F1: Market Driven Innovation Performance

Figure 3.

Principle Component Analysis, Regional Innovation Performance in China (2005)

Notice: B: Beijing; Sh: Shanghai; G: Guangdong; J: Jiangsu; Z: Zhejiang; Sa: Shaanxi province.

II.

Dispersion of Innovation Performances: China’s Case.

Un-evenly distribution of innovation activities is a common rule in technology
transferring and technology diffusion process, especially among different countries
9and nations. It is usually considered that increased concentration of innovation is
generated after industrial concentration, or clustering. Glaeser et. Al (1992) suggested
that the increased concentration of a particular industrial sector within specificgeographic region will facilitate knowledge spillovers across firms, thus to someextent create geographically bounded clustering and so called sub-national innovation
can occur through geographically bounded networking.
Krugman（1994）states that the regional performance in economy and innovation does
not always parallel with each other, rather it is less inequality in regional economy
than in the regional innovative ability. Countries which cover larger geographic range
and maintain higher level hierarchical clusters of industry production are more likely
to show uneven distribution of innovative resources. Although there is close relation
between regional economic development and innovation activities, inherent relative
independent rule exist, which inject more research value to the study about how
regional innovation activities differ with each other on the broader international level.
Empirical study on innovation based convergence test in this study include following
parts:(1) . σ Convergence:
The convergence parameter indicates degree of over time dispersion on innovation
indicators across different geographical regions. There are numbers of typicalmeasures to examine the σ Convergence, such as Coefficient of Variation ，Herfindhal-Hirshman（HHI），Theil Index (TEC)，Entropy Index (GEI) etc.. This paper
use Coefficient of Variation (CV):
 

nr 

 
rt

  p  p 
i 1



it

rt



p

2

（1）



rt

Where r indicates sample geographical district（ r =1,2,3）, namely, Eastern
China, Mid
China, and Western China, i represents sample regions within certain country（ i=1,2,… nr ） , prt stands for sample country r ’s average number of patentapplication in duration t .
The empirical test on China’s case on σ Convergence over three geographicaldistricts proves that CV in all three district as well as in national level increase overthe investigation time duration window, which indicates that along with faster growthof local technology capability, un-evenness or dispersion level become moresignificant. Comparatively speaking, Western China district, although with the least
innovation performance, is the strongest part in terms of dispersion of innovation
activities, while Mid China district behaves as the most evenly distributed location on
innovation performance.
On the other hand, CV level in different province is usually higher than level ofdispersion on district level, intra-district dispersion is not the major explanatory factor
for nation-wide dispersion, inter-district dispersion in fact has stronger impact.
(2) β convergence and Club Convergence hypothesis:
According to Barro，Sala-I-Martin (1992,2002) and Sala-I-Martin (1996) basedon convergence hypothesis of neoclassic economic growth theory（Solow,1956；
10

Swan,1956）which indicates faster pace of regional economic growth if the region
initiated with lower economic development level. This study applies regional patent
data in China between 1996 and 2005, instead of GRP (Gross Regional Production )per capita, to related model to test the convergence level. By applying regionalparameter X, estimation of β convergence can also prove if there is Club
Convergence, meaning that within each of the three typical geographical districts in
China, if there is a significant convergence among related regions in terms ofinnovation measurements. Following testing formula can be further applied to thispurposeln p  ln p
i ,T

i ,0

T

    ln p  X i ,0  
i ,0

（2）
i

i represents sample region in China（ i =1,2………31），T is end year (2005) oftime window, while 0 indicates base year (1996), PiT Pi 0 is patenting numbers insample region i at end year and base year respectively. Club Convergence can betested if β is less than 0, otherwise regions in a geographical district are rather
diversified in terms of innovation performance.
Since β is achieved positive in this study, but not pass through test, the βconvergence hypothesis cannot be supported. However, when a regional virtualvariables is introduced, test on β is significant, which indicates that dispersion on
innovation performance over the three geographical districts is much stronger thanregional level dispersion.
(3) Gini coefficient
Gini Coefficient can also be applied to examine un-evenly distributed patternover different
Geographical districts under according to certain innovation performances. The Gini
Coefficient can reveal inter-district and intra-district differences, as well as levels oftechnology based convergence and related Club Convergence, usually with condition
that intra-district Gini Coefficient decrease and inter-district Gini Coefficientincrease.
Following formula applies when such conditions are satisfied:G  GI  GN  R（3）KGI   wr sr Gr

（4）

r 1

Where G represents patenting number in certain region within a district, which
can be further classified into three parts: GI for inter-district Gini Coefficient, GN
for inter-district Gini Coefficient among three major geographical districts, and R
indicates cross effect between the two; K further represents total number of sample
districts, wr is share of region r to total numbers of regions in the country; and sr
is also share of r on patenting numbers to total level of the nation.
Gini Coefficient in this study reveals that nation-wide dispersion of innovation
capacity in China comes mainly from inter-district dispersion movement on
11

Disp.

China

China

Western

Dist.

China

Gini

Gini

Coef.

Coef.

Innovat..
perform
1996

0.47340.31720.21980.44780.11000.327923.24%

69.26%

7.49%

1997

0.48700.33980.2056

0.42890.1136

0.343323.32%

70.51%

6.17%

1998

0.4916

0.34190.20670.42430.11370.348923.13%

70.96%

5.91%

1999

0.51050.35790.20160.43260.11800.363623.11%

71.23%

5.66%

2000

0.5222

0.34780.20080.42440.11640.379522.29%

72.67%

5.04%

2001

0.54690.36740.20420.44840.12280.398022.45%

72.78%

4.76%

2002

0.56210.3656

0.1996

0.46440.12350.416221.98%

74.05%

3.97%

2003

0.56990.37070.22330.49560.12730.416922.33%

73.16%

4.51%

2004

0.58530.38590.2574

0.49150.13190.429022.53%

73.31%

4.16%

2005

0.61380.41670.30200.49630.14280.444823.26%

72.47%

4.27%

12

Movement

Mid

Inter-

Dist.

Dist.

Eastern

Intra-

Contribution of

Typical Districts

wideInter-Dist.Dispers

Nation-Contribution ofYear

Intra-Dist.Dispers

Dispersion Degree (Gini Coefficient) on Regional Innovation Performance (Patent)
in China between 1996-2005
Contribution of

Table 2

Cross Intra / Inter

innovation sources, with larger contribution level (more than 60% for continuously 10
years). However, there is a tendency after 2002 that inter-district dispersion graduallyless influential on overall national dispersion, which may imply that economic andtechnical support on Western China district helps local regions to upgrade technology
level through adoption of technical hardware as well as capabilities of learning by
doing.
Compared with economic performance dispersion over different district andregions in China, the Gini Coefficient in innovation terms indicates that there ishigher degree of dispersion in innovation performance than in economic performances,
however, the degree of such difference is rather modest.
After all, regional convergence study indicates that China does not hold robust
prove for convergence hypothesis that backward region develops continually fasterthan advanced region in terms of innovation performance.. Both innovation wealthy
region and less innovative region in China have a tendency increasing gaps among
regions as well among districts in innovation terms. Policy on appropriate transfer oftechnology and advanced technical knowledge is still keen in the near future. .

MidWestern

Dist

Dist

Disp.

China

China

China

Gini

Gini

Coef.

Coef.

Econom.
Perform
1996

0.41400.32180.19200.44700.10900.237926.34%

57.47%

16.20%

1997

0.41030.31820.1944

0.39840.10380.255125.29%

62.18%

12.54%

1998

0.41290.3151

0.19890.39130.10310.260424.96%

63.08%

11.95%

1999

0.41520.31210.20340.38330.10240.265924.67%

64.06%

11.27%

2000

0.42080.31390.20890.38050.10290.272824.45%

64.83%

10.71%

2001

0.41940.31370.18330.36730.10120.280124.13%

66.78%

9.10%

2002

0.42110.31460.1766

0.36610.10110.283224.00%

67.25%

8.75%

2003

0.42480.31990.17160.36050.10170.287623.95%

67.71%

8.34%

2004

0.42570.32410.1746

0.36270.10290.286024.16%

67.19%

8.65%

2005

0.43170.33170.18390.36560.10530.288524.38%

66.81%

8.80%

III.

Dispersion of Innovation Performance: Comparative Study among the US,
Japan, and China

The empirical part of this study aims to reflect dispersion degree among regions
in typical countries, through analysis of indices of innovation activities. By selecting
internationally adaptable indicators in China, the US, and Japan in the recent years,
the study is using effective means to compare the distribution pattern of innovation
activities in regional level.
This paper selects 51 states in the US, 47 provincial regions in Japan, and 31
provincial and cities in China, 129 sample regions in total over the three countries,
investigating seven typical innovation performance indicators, in contrast to common
economic performance measures(1) Comparison of Regional Innovation Activities among the Three Countries:
Regional Dispersion Level Analysis.
This paper applies a Regional Dispersion Coefficient Index (RDCI = standard
deviation / average value of specific innovation indicator) to measure degree ofconcentration of regional innovation activities, and further compare the RDCI withtypical economic performance indicators among corresponding region and countries.
As dispersion coefficient is adjusted by the average value of innovation indicator of
the sample regions in specific country, it can, to great extent, explain regional uneven
distribution pattern in specific country. It is commonly recognized that the larger thedispersion coefficient, the more significant degree of uneven characteristic ofinnovation activities in the region.
Typical innovation performance indicators are selected (refer to Table 4) through
China’s statistical yearbook as well as websites from the US and Japan, in contrast to
typical economic indicator. As is widely accepted, Gross Regional Production (GRP)
13ovement

Eastern

Dist.

wide

Cross Intra / InterInter-

Contribution of

Intra-

Inter-Dispers

Typical Districts

Contributioof

Nation-ContributioofYear

Intra-Dis.Dis

Table 3. Reference: Dispersion Degree (Gini Coefficient) on
Regional Economic Performance (GDP) in China between 1996-2005

is an aggregate measure of the value of goods and services produced in corresponding
region, indicating measure local production capacity, and thus a level of wealth, while
GRP per capita provides a measure of qualitative local market, as higher GSP per
capita will provide higher local demand for highly elastic and higher value added
market.
Table 4:Fist-class index

Typical innovation indicators

Second-class index

Description

Total R&D expenditure

Absolute value, representing volume ofinnovation input

R&D personnel
Innovation Input

Government granted fund /Relative value, representing innovation

R&D expendituredensity in terms R&D input
R&D / GRP
Inventive patent granted

Innovation Output
Innovative Environment

Number of local universities

Indicators for regional innovation output
Regional innovation environment in terms ofpotentiality of innovation resource

The value of RDCI for each of innovation indicators among China, the US, andJapan is presented in Figure 4. It can be shown that the US is holding the highest
RDCI score on most innovative indicators, except on patent record on which Japanpositions the highest dispersion. RDCI level for indicator in all three countries
exceeds regional dispersion coefficient on GDP per capita, which proves Krugman’sstatement (1994) in particular. In China’s case, dispersion in economic performance issignificantly higher than that in other two countries, while dispersions in innovation
are in the lowest place in most indicators among the three, which addresses thebiggest difference from other two developed countries.
4
3.5
3
2.5

Regional
Disppersion 2
Coefficient 1.5

The US
Japan
China

1
0.5
0

R&D
personnel

R&D

university R&D/GSP G/R&D

patent

GSP per
capitaInnovation Indicators

Table 4. Comparison of Dispersion Level on Typical Innovation Performance in Contrast to
Dispersion of Economic Performance among the US, Japan, and China.
Source: edited based on data from China Science & Technology Statistic Year Book, 2006, China bureau of
statistics, http://www.jpo.go.jp, http://www.census.gov

14

Based on theoretical findings from Glaeser et. al (1992) and Krugman (1994),
facts reflected in this study that geographical dispersion of innovation performance isa more common case in developed nations, it could be further concluded that local
policy support for geographically bounded innovation should be selective and should
be well harmonized in terms of sector difference with other regions, and to someextent highly relate to local market and resource backed endowment factors, rather
than one size policy for whole nation. On the other hand, regional economic
performance in terms of typical income indicators such as GDP per capita, should be
more evenly distributed, which may be benefit from more effective innovation output
and cross region transfer of both tangibles and intangibles in a more economic way.
(2) Comparison of Regional Innovation Activities among the Three Countries:
Market Driven vs. Policy Driven
This study also applies Principle Component Analysis (PCA) method to thisthree-country analysis in order to find difference in regional innovation activities
among total 129 regions in three countries.
Through KMO and Bartlett Test (KMO value: 0.628 and significant probability ofBartlett is less than 1％), six innovation indicators are proved eligible for FCA
process. All indicators are standardized for the analysis. Table 5 provides correlation
between original indicators and the two principle components extracted, with 80% ofaccumulated variance contribution over total variation, which indicates that the two
principle components containing enough information from original performancemeasures.
Table 5.

Correlation between principle component and original indicators
Principle Componentst

TypicalInnovation Indicators
Number of Universities
Number of Patent
R&D personnel
Total R&D expenditure
Government / R&D
R&D/GRPThe 1 principle component
Composite Innovation
Indicator(F1)

The 2nd principle component
Innovation Intensity
(F2)

0.886
0.817
0.789
0.721
-0.035-0.062-0.199-0.076-0.047
0.083
0.991
0.986

Based on these correlations, first principle component (F1) can be considered as
geographical composite indicators of innovation in absolute value, including
innovation environment (university numbers), innovation input (R&D expenditure,
R&D personnel), and typical innovation output (numbers of invention patent granted),
while the second principle component (F2) mainly refers to proportional measures onpolicy oriented nature and innovation intensity indicators, such as R&D/GRP、
government granted fund to total R&D.
A two-dimensional chart (Figure 5) can also be produced based on these two
principle components to reveal distribution of 129 regions in three countries regarding
innovation performance.
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Figure 5: Regional dispersion coefficients for three countries (2003)
Source: edited based on data from China Science & Technology Statistic Year Book, 2006, China bureau of
statistics, http://www.jpo.go.jp, http://www.census.gov

It can be seen from Figure 5 that the regions enjoy the highest scores are mostly
market driven, and, regions in the US are mostly market driven type, while regions in
China are primarily policy driven, and Japanese regions are somehow in between,
however also can be clearly classified into two groups of market driven and policy
driven.
Conclusion:
The paper concludes on following findings:
1. Innovation performance defined dispersion can be driven by policy as well as
market forces, and higher dispersion might be a effective form of innovation resource
development in a country if at the same time, economic dispersion over regions canbe thus diminished. Therefore, innovation policy at regional and national level should
clearly define ownership oriented and innovative region / country oriented functions,leaving appropriate space for active market force operating in local regions, no mattercollaborative or competitive. This innovative region based policy framework withownership innovation policy at national level as a complimentary instrument, canprovide better and more effective platform for sustainable development of technology
capability in the country concerned.
2. Since there is weak influence on innovation activities cross regions and crossdistrict, regional bounded innovation is rather separate in China, and this may imply
that innovation policy in regional level and nation level need to encourage crossregion and cross district technology and knowledge transfer, especially cross district
innovation networking or cross region innovation clusters.
3. Intra-district and inter-district dispersion on innovation performance (especially16through patent data investigation) in China tends to be high over time and thus
nation-wide dispersion becomes wider, therefore, innovation convergence hypothesis
in China’s case does not hold.
4. Overall increased dispersion level in China, both on innovation and economic
performance is primarily contributed by increasingly larger gaps among the three
districts. Based on the fact that both intra-district and inter-district dispersion increaseover the time duration between 1996 and 2005, not converging to certain level overdifferent geographical regions, it may indicates that such kind of un-evenly distributed
pattern in China is an inefficiently and rather fluctuate, which is different from othertwo typical developed countries, the US and Japan, and regional as well as nationalinnovation policy is therefore highly demanding for effectively developing innovation
resources in connection with economic development.
References
Acs Z., Anselin L., Varga.，2002, “Patents and Innovation Counts as Measures of Regional
Production of New Knowledge”，Research Policy, Vol.31, pp.1069-1085.
Balconi, M., Breschi, S., Lissoni, F., 2004, “Networks of inventors and the location of academic
research: an exploration of Italian patent data”, Research Policy, Vol.33, pp.127–145.
Bartlett, C.A., Ghoshal, S., 1990. Managing innovation in the transnational corporation. In:
Bartlett, Ch., Doz, Y., Hedlund, G. (Eds.), Managing the Global Firm. Routledge, London, pp.
215–255.
Cowell, F.A., 1995, “Measuring Inequality”, Prentice Hall and Harvester Whearsheaf, London
Dowrick,S.,Rogers,M., 2002, “Classical and Technological Convergence: Beyond the Solow-Swan
Growth Model”, Oxford Economic Papers,54, pp.369-385
Galor, 1996, “Convergence? Inferences from the theoretical models”, Economic Journal, Vol.106(July), pp.1056 -1069.
Griliches, Zvi.,1990, “Patent Statistics as Economic Indicators: A Survey”, Journal of Economic
Literature, Vol. 28 Issue 4, pp.1661-1707.
Guellec and Van Pottelsberghe., 1999, “Applications, grants and the value of patent”, Economics
Letters, Vol. 69, Issue 1, pp. 109-114.
Furman, J.L., Porter, M.E., Stern, S., 2002. The determinants of national innovative capacity.
Research Policy 31, 899–933.
Keeble D, Wilkinson F., 1999, “Collective learning and knowledge development in the evolution ofregional clusters of high technology SMEs in Europe”, Regional Studies, Vol.33(4), pp. 295-303
Krugman,Paul., 1991, “Increasing Returns and Economic Geography”, Journal of Political
Economy, Vol.99(33), pp.483-99.
Kuznets, S., 1955, “Economic Growth and Income Inequality”, American Economic Review,
Vol.45(1),18.
Lundvall, B.A., Johnson, B., Andersen, E.S., Dalum, B., 2002. National systems of
production ,innovation and competence building. Research Policy 31, 213–231.
Mansfield, Edwin., 1986, “Patents and Innovation: an Empirical Study”, Management Science,Vol. 32 Issue 2, pp173-181.
Mitchell, G.R., 1999. Global technology policies for economic growth. Technological Forecasting
and Social Change 60, 205–214.
17

Romer, P.M., 1986, “Increasing Returns and Long-run Growth”, Journal of Political Economy,
Vol.94(5), pp.1002-1037.
Solow R.M., 1956, “A Contribution to the Theory of Economic Growth”, Quarterly Journal of
Economics, Vol.70, pp.65-94.
Willianmson, J.G.,1965, “Regional Inequality and the Process of National Development: A
Description of Patterns”, Economic Development and Culture Change, Vol. V3, No.4, Part2,
pp3-45.
YIFEI SUN., 2000, “Spatial Distribution of Patents in China”, Regional Studies, Vol.34.5,
pp.441-454.
Varsakelis, N.C., 2001. The impact of patent protection, economy openness and national culture
on R&D investment: a cross-country empirical investigation. Research Policy 30, 1059–1068.
Varsakelis, N. C. 2006, Education, political institutions and innovative activity: A cross-country
empirical investigation，Research Policy

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