Abstract—Based on the endogenous growth model, collected patent data from 31 regions including provinces, municipalities and autonomous regions in Chinese Mainland from 1985 to 2004, studied factors that can affect the regional innovative capacity with the empirical research. The results of the study showed population of employment on Hi-tech enterprises was also an obvious factor to reflect the regional innovative capacity of the Hi-tech enterprises in china. It has been proved that the inertia have huge influence to innovative activities, so it can be made out that innovative activities have great relation to local accumulation of culture. Accordingly, we inquired into the factors to affect the regional innovative capacity in china and gave policies and suggestions. At the same time, study verified our standpoint that defined capital gathering as a core.

Index Terms—Regional innovative capacity, Population

of employment on Hi-tech enterprises, human capital, endogenous

I. INTRODUCTION HE essential of innovation is the creation, spreading, updating and transformation of knowledge. Cooke (1998)

considered that innovation is the commercialization of the knowledge, and the use course of the knowledge; innovation can be defined as “succeed in utilizing new knowledge”. According to this understanding, the regional innovation is “succeed in utilizing new knowledge in the regional” [1]. Lundvall (1992) summarized regional innovation as “the interaction course of certain societies or areas, which can not be understood if its system and background are not considered”[2]. A regional innovative capacity is defined as its potential to produce a stream of commercially relevant innovations[3]. Jaffe [4], Feldman and Florida [5] considered three key issues centralized in regional innovative capacity: the stock of R&D funds, the available labor pool, and the quality of educational institutions reflected in human capital. Stern, Porter and Furman [3] consider the stock of R&D funds as the most important issue, whether based on the industry or based on the university, which can both support new technologies, designs, ideas, and innovative production methods, thereby affecting the marginal product of R&D for

Manuscript received March 15, 2009. F.A, S.B,T.C,F.D,School of Management and Economics, UESTC,

Chengdu, 610054

innovative capacity. Used a time sequential data, Jaffe [4] found out the university R&D spending had a positive and significant influence on corporate patents, especially on certain high-tech sectors. Anselin, Varga and Arcs [6] found that there is a significant spillover effect among university research, innovative activity and corporate R&D investment. Specially, universities could provide a muster of skilled labor meeting advanced, high-tech, human-capital requirements. Both basic and applied research in universities redound to embarking in enterprises, thereby, it is no accident that the location of high-tech canters are about the research university. Feldman and Florida [5], Anselin, Varga and Arcs [6], and Wilkerson (2002)[7] study the spatial interactions, spillover effects and geographic patterns of university, federal and private R&D spending. They find that university research had spillover effects on regional technological innovation and the high-tech labor market. Wilkerson (2002) [7] indicated city size is an important factor that determinates the size of high-tech labors. Stern, Porter and Furman (2000) [3] put forward the stock of ideas can influent the rate of generating new idea. In research of regional innovative capacity, Jaffe (1989)[4], Feldman and Florida (1994)[5], and Anselin, Varga and Arcs (1997)[6] studied the marginal product of industrial and government R&D expending, regarding employment size of high-tech as an exogenous factor. But Romer (1990)[8] proposed the endogenous growth model treating employment size of high-tech as an endogenous factor, and rendered the relationship between new knowledge generation, economic growth, and the existing pool of innovative capacity. Stern, Porter and Furman (2000)[3] used the endogenous growth model to investigate these relationships of economies in different countries. Riddel and Schwer (2003)[3] used this model to analyze regional growth among the states in the U.S.A.

These literatures are mainly interesting in the influence, like overflow effect and labor market which actually is innovation development are brought by R&D. It is neglected the relationship between employment increase and regional technology innovation ability. Shao Yunfei（2006）（2005） [13-14]did demonstration study on regional technology innovation ability. This article will analyze and prove involved factors which influence China technology innovation ability.

Empirical Research on Regional Innovation Capacity based on Economic Endogenous

Growth Model―Come from 31 provinces in Chinese Mainland

Frist. A.CHEN Xinyou, Second. B. SHAO Yunfei, Third, C. ZENG Yong, and Four, D. DU Yifei,

T

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II. REGIONAL INNOVATIVE CAPACITIES MODEL BASED ON THE ENDOGENOUS GROWTH

Romer (1990)[8] proposed the endogenous growth model, which was applied by Stern, Porter and Furman (2000)[3]. Combined Romer’s endogenous technical growth model (Romer. 1990) with Nelson’s literatures on national innovative capacity (Nelson, 1993) and Porter’s concept of industrial competitive advantage (Porter, 1990), Riddel and Schwer (2003)[9] proposed a start-level production for new ideas as:

φλθδ tjtAjtjtjtj AHXA ,,,,, = (1)

tjA , is the growth rate of new technologies in state j

during year t, tAjH , is the devotion of the stock of capital and

labor ideas production, tjA , is the stock of ideas in state t

during year j, and tjX , are state-level variables may influence innovative capacity, such as economic geography, expenditures on education and fiscal support, etc.

Adding a multiplicative stochastic component then taking logs, we can derive a linear estimable form of the model, as formula (2):

jtjtjtAjtjtj AHXA νεφλθβ +++++= ,,,,, lnlnlnln (2)

The variable jν represents errors in the state-level

intercept, tj ,ε is a mean zero, constant variance error term that

varies with the state and the time period. The current research obtained mixed data and formed the following formula (3), by combining the formula (2) (Riddel & Schwer, 2003)[9] and data of provinces in China from 1995 to 2004, and then variable jν was discarded.

tjtjtAjtjtj AHXA ,,,,, lnlnlnln εφλθβ ++++= (3) Find corresponding index:

uiii

iiiiii

eNAP

PSQUGPEFGFHTPPAQ

××

×××××=7

7

66

55

44

33

221

β

ββββββ

(4)

III. EMPIRICAL ANALYSES

The Hypothesis of Empirical Analysis： As the number of high-tech enterprises employed in the Technological advances factors and regional technological innovation capability with a significant relationship.

A. Method and Data Stern, Porter and Furman (2000) [3] and Jaffe (1989) [4]

used patents as a reflection of growth in innovative capacity. Anderson, Anderstig and Harsman (1990)[10] indicated patents as a good measure of technological innovation. We also took number of patent applications granted as the amount

of increased innovation. Patents included creations and inventions, utility models and designs. The patent stock can be calculated in the formula:

Patents Stockt1

(1 ) Patents Stockit i

iρ

∞

−=

= −∑ (5)

The current paper considered the degenerative rate of knowledge is not a constant value; the knowledge degenerated urgently at beginning then become slowly. The knowledge of last year degenerated P and remained (1-P) in this year, well then, the patent stock this year can be represented by (1-P) of number of patent applications granted last year, square of (1-P) of number of patent applications granted the year before last, …, the sum of remained number of patent applications granted in all years before this one.

As application and acceptation of patents since April of 1985 in China, patent stock also was calculated since April of 1985. P represented the degenerative rate of knowledge, Kondo (1999)[11] and Alston, Craig and Pardey (1998)[12] separately investigated assumed 10% and 15% as value of P. Considered the term of patents of creations and inventions is 20 years, and the term of patents of utility models and designs is 10 years, based on the principle that after the period of validity the remained value tended to 0, we took 10%, 15% and 12.5% as P.

Riddel and Schwer (2003) separated the human capital devotion variable into several indexes: the number of nonfarm employees, number of jobs in high tech, R&D in university and university degrees issued. As the statistics index in common use in China, this study adopted number of nonfarm labors, number of employees in high-tech, government funds in the sources of funds for S&T activities, funds raised by enterprises in the sources of funds for S&T activities, and the number of graduates from colleges.

Thereunto, number of nonfarm labors account for variation in the size of the labor pool among the provinces. Subtracted the number of employees in the primary industry from the number of employed persons in whole society, is the number of nonfarm employees. It is a dimension of labors size, including employees both in the secondary and tertiary industry, just because the mass of perusing patents centralized in the secondary and the tertiary industry. We adopted this measurement to detect the relationship between the patents and the scale of whom doing patents. Employment in high technology accounted for the main bodies of pursuer science and technology, which was used to detect the relationship between the patents and the dimension of whom concentrative doing patents. Graduates from colleges accounted for the increasing main bodies of whom pursuer science and technology, which was used to detect the relationship between the patents and increasing main bodies of doing science and technology. Government funds in the sources of funds for S&T activities and funds raised by enterprises in the sources of funds for S&T activities accounted for national R&D input and corporation R&D input separately. The current study mainly investigated the relationship between input and output of patents. Thereby, the formula used in this study is

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iiii

iiii

uNAPPSQUGPEFGFHTPPAQ

+++++++=

765

4433220

lnlnlnlnlnlnln ββββ

(6)

B. Dependent variable and Independent variable

Dependent variable：Number of patents--- iPAQ

Independent variable：The log of number of jobs in high tech--- 2iHTP ； the log of government funds in the

sources of funds for S&T activities--- 3iGF ，the log of funds raised by enterprises in the sources of funds for S&T activities 4iEF ， the log of number of graduates from

colleges--- 5iUGP ，the log of patent stock--- 6iPSQ ，the log

of nonfarm employment in the province--- 7iNAP 。

0β ---Intercept， iU :--- Noise

C. Estimate, Verification and Conclusion We use many methods to estimate and verify the model. First, using original data and taking rates of degenerate as

ρ=10%，ρ=15%，ρ=12.5% respectively, calculating step by step throw regress. Conclusions indicate PSQ and HTP labors have evidently influenced.

Second, we standardize all indicators in order to overcome influence, which is brought by difference of indicators dimension, and conclusions indicate that it has more prominence to the equation. It will not be detailed because of space.

Third, in this article I will use Panel Data’s fixed utility model to estimate and verify.

-6 0

-4 0

-2 0

0

2 0

4 0

6 0

-1 0 0

0

1 0 0

2 0 0

3 0 0

4 0 0

2 5 5 0 7 5 1 0 0 1 2 5 1 5 0 1 7 5 2 0 0 2 2 5 2 5 0

R e s i d u a l A c tu a l F i tte d

Fig1. Residual Fig1 is the residual distributing throw regress software

from original data. Blue line delegates the residual distributing. We can see that Heteroskedasticity of random interferential data presents descending trend, which means Heteroskedasticity is already eliminated.

Data in this article involve time sequence and cross section. It accords with panel data hypothesis. We would better enactment a fixed effect model to process panel data in order to descending the error to its lowest from accumulating every year’s data. At the same time, it will have better see the influence, which verified and estimated can’t be observed just only by one method.

To process original data: throw interceptor dummy variable to observe fixed effect.

Some area, PAQ is high to more than 170 but some is just 0.01. So PAQ is not on a same level because we adopt comparative data from some region. With economic growth, provinces and cities focus on patents’ weightiness to economic growth. So, they all give more strength on patent happen to coincide to better promote economic growth. We can hypothesis that regress equation’s intercept terms will change when provinces, cities and times change. In condition of an unknown quantity has same slope coefficient; regress equation can be assumed as:

itititit

ititiii

iiiiiit

NAPPSQUGPEFGFHTPDDD

DDDDDPAQ

77665544

3322998877

66554433220

ββββββααα

αααααα

+++++++++

+++++=

D2，D3，D4，…… D9 are the suppositional variance about year. So if the observation belongs to the second year, and we can get iD2 =1, otherwise equal to 0; and if the

observation belongs to the third year, also we can get iD3 =1

， or else it equal to 0. In this way, if the observation belongs to the ninth year, we can get iD9 =1, or else it equal to 0. We just need use 8 suppositional variances because only have 9 years’ data. In other words, 0α delegates the first year’s

intercept, will 2α ， 3α ， … … 9α and interceptor

coefficient can explain the second year，… the ninth year’s intercept’ different compare to the first year’s. Data regress as Table 1.

PSQ coefficient is 1.183696792. It means every percent boost. PAQ will add 1.184 percent. It is a big numerical value in this regress equation. HTP coefficient estimates 0.1216824345. It means HTP has positive influence to PAQ. Because use suppositional variances, we lose degree of freedom a lot and parameters are too much. Numerical value of 2R will increase with number of parameters’ increase. We have 16 parameters in this equation, so goodness of fit about

2R is high to 95%. It just can be treated as our consultation; regress estimate standard error SE=12.19870, it means the error between PAQ’ estimate value and real value is 12.19870 which is big comparatively. Regress equation is remarkable to the goodness of fit to independent variable coefficient. (corresponding a big numerical value of t, but corresponding p equal to 0) Its F value high to 358 means these modified regresses have a remarkable influence to collectivity. DW estimate D value 2.087203. It means there is not exist first order autocorrelation。

TABLE 1 REGRESS RESULT

Dependent Variable: PAQ

Method: Panel Least Squares

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Date: 03/07/06 Time: 20:22

Sample: 1 30

Cross-sections included: 9

Total panel (balanced) observations: 270

Variable Coefficient Std. Error t-Statistic Prob.

C 8.579006 2.813894 3.048803 0.0025

D2 -1.030899 3.211262 -0.321026 0.7485

D3 -9.264085 3.309386 -2.799337 0.0055

D4 -6.182752 3.278776 -1.885689 0.0605

D5 -6.875878 3.315135 -2.074087 0.0391

D6 -2.936243 3.423310 -0.857721 0.3919

D7 -7.276872 3.348491 -2.173180 0.0307

D8 -12.88151 3.354197 -3.840415 0.0002

D9 -15.09681 3.348498 -4.508532 0.0000

HTP 0.121682 0.019376 6.279931 0.0000

GF -0.027829 0.005342 -5.209225 0.0000

EF 0.105468 0.017393 6.063911 0.0000

UGP -0.387354 0.043297 -8.946345 0.0000

PSQ 1.183697 0.050010 23.66934 0.0000

NAP 0.077412 0.012163 6.364614 0.0000

R-squared 0.951581 Mean dependent var 45.33046

Adjusted R-squared 0.948922 S.D. dependent var 53.97568

S.E. of regression 12.19870 Akaike info criterion 7.894489

Sum squared resid 37946.12 Schwarz criterion 8.094401

Log likelihood -1050.756 F-statistic 357.9636

Durbin-Watson stat 2.087203 Prob(F-statistic) 0.000000 We can get the regress equation as:

PAQ = 8.579006436 - 1.030899405*D2 - 9.264084589*D3 - 6.182751659*D4 - 6.875878462*D5 – 2.936243354*D6 - 7.276872498*D7 - 12.88150894*D8 - 15.0968094*D9 + 0.1216824345*HTP - 0.02782877609*GF+ 0.1054675017*EF - 0.3873542075*UGP + 1.183696792*PSQ + 0.07741235627*NAP

Meanwhile, we notice that D2 and D6’s regress coefficient is not so prominence (p value remarkable difference from 0) in these 8 estimate values. These two values mean each province or city’s slope coefficient of total patent authorize regress equation close to the fiducially year’s. It also means patents authorizer’s input is almost equal in each province and city in these two years and the fiducially year. While, number of patents authorize has difference in each province and city in the other years. And the scale of difference is decided by the strength of reform and development and conditions of economic and education’s development.

So, we can get: in all the factors of PAQ, PSQ has the most degree of influence. HTP has greater influence than EF. Although NAP does not take great influence, it also involves.

IV. CONCLUSIONS We can get major conclusions as listed throw

demonstration analyze to China’s data. 1) Result indicates that HTP is also a prominence factor in

regional technology innovation. And this is accord with our study hypothesis. We should take more care about manpower capital in the filed of high-tech, just because HTP is a prominence factor. Also it is necessary to enlarge the employment scale in high-tech for the same reason.

2) Patent activity is also a major factor in the factors influence regional innovation, and it has a high elasticity coefficient. This means new patents mostly rely on PSQ in our country’s technology innovation. This article proves that innovation has great relation with culture accumulation in the angle of quantization because PSQ has inertial influence effect. It explains why should change people’s innovation thought from the angle of quantization It indicates that imitational innovation takes a high

proportion in our country and self-innovation is not enough from these major conclusions and patents further analyze. So we should advance our self-innovation ability.

V. REFERENCE [1] P Cooke, M Heirdenreich, Regional Innovation System: the role of

goverance in a globalized world, UCL Press Limited, 1998 [2] Bjorn Asheim, Michael Dunford, “Regional futures”. Regional

Studies,1997, 31(5), p445-455 [3] S Stern, M.E. Porter, J.L. Furman, “The Determinants of National

Innovative Capacity”. National Bureau of Economic Research Working Paper 7876: Cambridge, MA. 2000

[4] A.B Jaffe, “Real Effects of Academic Research”. American Economic Review 79, p957-70, 1989

[5] M.P. Feldman, R. Florida, “The Geographic Sources of Innovation: Technological Infrastructure and Product Innovation in the United States”. Annals of the Association of American Geographers 84(2), p210-29, 1994.

[6] L Anselin, A. Varga, Z. Acrs,” Local Geographic Spillovers Between University Research and High Technology Innovations”, Journal of Urban Economics 42, p422-48, 1997

[7] C Wilkerson. “How High-Tech is the 10th District?” Federal Reserve Bank of Kansas City, Economic Review 87(2), p27- 53,2002

[8] P. Romer. “Endogenous Technological Change”, Journal of Political Economy 98, p71-102, 1990.

[9] M Riddel, P.K. Schwer, “Regional Innovative Capacity with Endogenous Employment: Empirical Evidence from the U.S.” The Review of Regional Studies 33(1), p73-84, 2003

[10] A.E. Anderson, , C. Anderstig, B. Harsman, “Knowledge and Communications Infrastructure and Regional Economic Growth”. Regional Science and Urban Economics 20, p359-76,1990

[11] M Kondo,., “R&D Dynamics of Creating Patents in the Japanese Industry”. Research Policy 28, p587~600, 1999

[12] Alston,J.M., B. Craig and P.G. Pardey, “Dynamics in the Creation and Depreciation of Knowledge and the Return to Research”. IFPRI, EPTD Discussion Paper NO.35, Washington, 1998

[13] Y F Shao, X W Tang. ““Linear Empirical Analyses of the Capacity of Regional Technological Innovation”. Management Review. 2006(4): 14－22 .

[14] Y F Shao, X W Tang. ““Principal-Components Empirical study on the Capacity of Region Technical Innovation of China.” Journal of Engineering Management. 2005(3):71-77.

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