This paper presents the influence of innovation systems on regional innovation pe rformance in China for the period 1998-2008. It places special emphasis on the effects of institutional factors, namely marketization level. The findings indicate that the innovation system contributes greatly to increasing the level of innovation. Among the factors of innovation systems, the openness of the regio n and government expenditure on education plays key roles. Market-oriented institutional arrangements also increase innovation performance.
China’s transformation from a centrally planned system to a market-oriented economy has been a great success: Since the beginning of the “Reform and opening up policy” in 1978, China’s gross domestic product (GDP) has increased by approximately 9.8% per year. With the economic boom, China’s level of innovation has also been increasing rapidly, and consequently, innovation performance has already become a crucial factor for national competitiveness.
Currently, researches are paying more attention to the innovation system in innovation research. According to a report by the Organization for Economic Cooperation and Development (OECD), innovation is the result of a complex interaction between various actors and institutions. Technical change does not occur in a perfectly linear sequence, but through feedback loops within this system. The innovation system includes a network of enterprises, universities, research institutes and governments, where the flows of technology, information and knowledge among people are key to the innovative process [
Research on innovation systems was initially carried out at the national level. Patel and Pavitt [
In emerging countries like China, regional diversity is greater than in industrialized ones; thus, research at the national level may reflect real situations inaccurately. Moreover, many articles ignore institutional transformation factors such as marketization, which is seen as one of the most important reasons for technical progress in China [
Although Furman, Porter, and Stern [
This paper is organized as follows: Section 2 presents the theoretical basis and econometric model; Sections 3 and 4 demonstrate the dataset and research results and Section 5 is the conclusion.
This paper is based on the FP & S model [
According to the knowledge production function, knowledge production is a function of new input into research and development (R & D) and the stock of knowledge. Technological progress and the accumulation of knowledge are both the consequence of economic development and forces that promote economic development at the same time. The knowledge production function can be formulated as follows:
A ˙ t = δ L A , t λ A t ϕ (1)
where A ˙ t stands for the output of the new knowledge and innovation in year t, L A , t λ for human capital which is invested in R & D and A t ϕ for knowledge accumulation in year t.
Because the growth of knowledge stock depends on R & D professionals, the influence of the state should not be ignored. Good governance is a “good” process of decision-making and the process by which decisions are implemented (or not implemented) [
Porter develops the theory of national competitive strategy, namely the diamond model, which evaluates the competitiveness of industries and companies in a national cluster. In a domestic cluster, there are four important factors: 1) factor conditions including product factors such as human capital, real capital and knowledge resources, 2) competition promoting the innovation and productivity of enterprises; however, stress does not only come from local competitors, but also from international rivals, which depends on the level of openness, 3) demand conditions influencing innovation behavior, when sophisticated domestic clients pressure firms to be more efficient and create more advanced products and 4) related and supporting industries providing the fundamental infrastructure, which strengthens the knowledge spill-over effect through communication among geographically nearby industries and reduces transaction costs. Two additional factors should not be discounted: one is chance, which affects competition but is beyond the control of a firm. The other is government, which can influence each of the four determinants above (either positively or negatively). Traditional innovation policy focuses mainly on correcting market failure and maintaining competition orders through competition policy, while the innovation system theory emphasizes the interaction synthesis effect between different actors. Innovation-oriented competition in domestic clusters determines the innovation performance of firms and industries [
Similar to Porter, Nelson attaches great value to the impact of institutions and systems [
A ˙ j , t = δ j , t ( X j , t I N F , Y j , t C L U S , Z j , t L I N K ) L j , t A λ A j , t ϕ (2)
where A ˙ j , t denotes the innovation production of region j in year t, L j , t A λ the input of capital and human resources and A j , t ϕ the knowledge stock. The vector X j , t I N F is the entire innovation infrastructure, the R & D activities of the government and the openness of the region; vector Y j , t C L U S is the cluster-specific circumstance for innovation in the region, particularly universities and research institutes and Z j , t L I N K is the linkage between the innovation infrastructure and the cluster.
Vectors X j , t I N F , Y j , t C L U S and Z j , t L I N K complement each other and play a role similar to indicators of innovation input and knowledge accumulation. These three factors are introduced in exponential form; thus, Equation (2) would be rewritten into a new form, A ˙ j , t = δ X j , t I N F δ 1 Y j , t C L U S δ 2 Z j , t L I N K δ 3 L j , t A λ A j , t ϕ [
ln A ˙ j , t = δ 1 ln X j , t I N F + δ 2 ln Y j , t C L U S + δ 3 ln Z j , t L I N K + λ ln L j , t A + ϕ ln A j , t + ϵ j , t (3)
Furman, Porter and Stern [
Regional decentralization has shaped China’s transition. In China’s institutions, which are viewed as a regionally decentralized authoritarian system, the central government has control over personnel affairs, while local governments are responsible for the economy [
Unlike the OECD countries, China is a latecomer and has experienced massive reforms and a process of transformation to a market-oriented economy in the last decades. Marketization refers to building an order of fair competition and an economic system where the market plays a fundamental role in resource allocation. Park, Li and David [
Thanks to the Chinese marketization index of the Chinese National Economic Research Institute [
In the following sections, we evaluate the extent to which the regional innovation system influences China’s local innovation performance, thereby analysing the innovation infrastructure, cluster milieu and linkages between them with the help of the model of Furman, Porter and Stern [
1Due to missing values we exclude the data of Tibet.
We established a panel dataset with information on the innovation activities of 30 Chinese provinces, autonomous regions and directly controlled municipalities in mainland China (hereinafter called provinces) between 1998 and 20081, which is partly comparable to that employed by Furman, Porter and Stern [
We chose the number of patents granted as an indicator of regional innovation output. Patents are a frequently used variable for innovative activity in the literature on innovation research, and the association between these two factors is widely recognized [
Generally, the process from patent application to granting lasts a period of time, so we had to consider the lag between research input and patent output. Furman and Hayes [
the intensity of innovation across regions.
According to the FP & S model, innovation input includes professional labor forces (or real capital) and knowledge stock. For labor input, we used the number of full-time equivalents of R & D personnel in a province from the China Statistical Yearbook on Science and Technology from 1999 to 2009 (PERSONAL), which contains all of the full-time R & D staff in research institutes, universities and enterprises. For knowledge stock, we employed the GDP per capita of the province. Per capita GDP captures the ability of a country, or in this case a province, to translate its knowledge stock into a realized state of economic development (and thus yields an aggregate control for a country’s, or province’s, technological sophistication) [
Innovation infrastructure refers to factors of fundamental institutions and the role of the government. We imported the percentage of international trade volume in relation to GDP (OPENNESS) and the share of education expenditure of total government spending (ED_SHARE). Another important element is the
marketization process. The experience of industrial countries has proven that the best way to modernize is to build a market-based economic system [
Besides infrastructure, regional innovation performance also depends on the milieu of concrete clusters at a meso-level. The variable that we chose as indicator of the properties of clusters and industry structure is the share of the tertiary sector of GDP. As Porter [
For a given cluster innovation environment, innovation output may tend to increase with the strength of the common innovation infrastructure [
Variable | Definitions | Source | |
---|---|---|---|
L_PATENT_GRA | Log of granted patents in the province i in year (t + 3) | National Bureau of Statistics of China | |
L_PATENT_POP | Log of granted patents per million persons in the province i in year (t + 3) | National Bureau of Statistics of China | |
A j , t ϕ | L_GDP_PC | Log of GDP per capita | National Bureau of Statistics of China |
L j , t A λ | L_PERSONAL | Log of amount of full-time equivalent of R & D personnel | China Statistical Yearbook on Science and Technology |
X j , t I N F | OPENNESS | Openness level: trade volume/GDP | China Statistical Yearbook |
X j , t I N F | ED_SHARE | Share of local government’s expenditures spent on education | National Bureau of Statistics of China |
X j , t I N F | INTER | Intermediary and law system | National Economic Research Institute |
X j , t I N F | IP | Strength of protection for intellectual property | National Economic Research Institute |
Y j , t C L U S | TERTIARY | Share of tertiary sector in GDP | National Bureau of Statistics of China |
Z j , t L I N K | UNI_RD | Percentage of R & D expenditures from universities | China Statistical Yearbook on Science and Technology |
Z j , t L I N K | BANK | Contributions of bank to scientific and technical activities | China Statistical Yearbook on Science and Technology |
Variable | N | Mean | Std. Dev. | Min | Max |
---|---|---|---|---|---|
L_PATENT_GRA | 240 | 5.579 | 1.313 | 1.792 | 8.936 |
L_PATENT_POP | 240 | 2.064 | 1.158 | 0.100 | 5.946 |
L_GDP_PC | 240 | 9.093 | 0.590 | 7.768 | 10.813 |
L_PERSONAL | 240 | 9.942 | 1.117 | 6.743 | 12.050 |
OPENNESS | 240 | 29.287 | 38.272 | 3.213 | 165.227 |
ED_SHARE | 240 | 15.380 | 2.281 | 9.697 | 21.140 |
INTER | 240 | 3.868 | 2.069 | 1.150 | 12.840 |
IP | 240 | 2.621 | 3.835 | 0.010 | 25.130 |
TERTIARY | 240 | 40.540 | 6.359 | 30.048 | 69.651 |
UNI_RD | 240 | 10.039 | 5.828 | 0.958 | 30.943 |
BANK | 240 | 6.920 | 4.773 | 0.000 | 27.210 |
(1) | (2) | (3) | (4) | |
---|---|---|---|---|
L_GDP_PC | 1.438*** | 1.358*** | 1.121*** | 1.254*** |
(0.186) | (0.176) | (0.193) | (0.185) | |
L_PERSONAL | 0.874*** | 0.651*** | 0.358*** | 0.393*** |
(0.169) | (0.149) | (0.124) | (0.130) | |
OPENNESS | 0.016*** | 0.012*** | 0.009** | |
(0.003) | (0.003) | (0.004) | ||
ED_SHARE | 0.090*** | 0.062** | 0.061** | |
(0.023) | (0.023) | (0.022) | ||
INTER | 0.087*** | |||
(0.020) | ||||
IP | 0.044*** | |||
(0.014) | ||||
TERTIARY | 0.071*** | 0.070*** | ||
(0.010) | (0.011) | |||
UNI_RD | 0.004 | 0.002 | ||
(0.007) | (0.007) | |||
BANK | −0.013* | −0.014* | ||
(0.007) | (0.007) | |||
_cons | −16.180*** | −15.092*** | −12.646*** | −13.825*** |
(1.662) | (1.446) | (1.113) | (1.186) | |
N | 240 | 240 | 240 | 240 |
r2 | 0.766 | 0.806 | 0.865 | 0.864 |
r2_a | 0.764 | 0.802 | 0.861 | 0.859 |
Standard errors in parentheses. *p < 0.1, **p < 0.05, ***p < 0.01.
the number of invention patents granted in province i in year t + 3. We analysed the innovation performance in different variants through columns (1) to (4): First, we introduced only the regression with the fundamental factors of innovation input, followed by the variables of innovation systems and institutions, step by step.
According to Romer’s endogenous growth theory, the volume of knowledge stock and the factor input determine innovation productivity. In column (1), we used the logarithm of GDP per capita and full-time equivalents of R & D personnel as indicators of the basic input level. According to regression results, both of the factors have positive effects on innovation performance. If GDP per capita increases by 10%, the number of patents granted rises by approximately 14.38%. If a region hires 10% more R & D staff, the number of patents granted goes up by approximately 8.74%.
As explained above, the regional innovative capacity depends not only on innovation input, but also on the institutional milieu. Column (2) includes all variables of the innovation infrastructure. Both of the innovation infrastructure factors, OPENNESS (0.016) and ED_SHARE (0.090), have significantly positive effects. Although China’s transition strategy since the late 1970s has been called the “reform and opening-up policy”, “reform” and “opening-up” were in fact two separate parts, meaning that they were not simultaneously implemented. Opening-up was an engine for China’s reform. Each further process of opening up brought the transition a step forward [
Indicators of cluster circumstances were then added into the regressions. Furman, Porter and Stern [
The last factor that we observed is the influence of marketization reform. Columns (3) and (4) introduce the two marketization indicators respectively: intermediary organizations (INTER) and protection for intellectual property (IP). It is remarkable that both of these have significantly positive effects. Every additional unit of INTER and IP increases the amount of patents granted by around 0.87% and 0.44%, respectively. This proves that institutions influence innovation output in China and that a market-oriented system increases regional innovation performance.
In
(1) | (2) | (3) | (4) | |
---|---|---|---|---|
L_GDP_PC | 1.406*** | 1.339*** | 1.105*** | 1.241*** |
(0.185) | (0.178) | (0.196) | (0.188) | |
L_PERSONAL | 0.836*** | 0.634*** | 0.333** | 0.370*** |
(0.164) | (0.147) | (0.126) | (0.132) | |
OPENNESS | 0.014*** | 0.011*** | 0.008* | |
(0.003) | (0.003) | (0.004) | ||
ED_SHARE | 0.085*** | 0.057** | 0.055** | |
(0.025) | (0.026) | (0.024) | ||
INTER | 0.088*** | |||
(0.019) | ||||
IP | 0.043*** | |||
(0.014) | ||||
TERTIARY | 0.072*** | 0.072*** | ||
(0.010) | (0.011) | |||
UNI_RD | 0.004 | 0.002 | ||
(0.006) | (0.006) | |||
BANK | −0.013 | −0.014* | ||
(0.008) | (0.007) | |||
_cons | −19.034*** | −18.145*** | −15.702*** | −16.932*** |
(1.628) | (1.472) | (1.244) | (1.240) | |
N | 240 | 240 | 240 | 240 |
r2 | 0.763 | 0.797 | 0.863 | 0.860 |
r2_a | 0.761 | 0.794 | 0.858 | 0.856 |
Standard errors in parentheses. *p < 0.1, **p < 0.05, ***p < 0.01.
classic determinants of innovation are also important motors for innovation performance. The other results are also robust to the modification: OPENNESS, ED_SHARE, INTER, IP, TERTIARY and BANK.
In this paper, we estimate the effects of innovation systems on regional innovation performance in China. For this purpose, we established a dataset across 30 provincial-level regions between 1998 and 2008. The results indicate that the traditional innovation factors including innovation input and knowledge accumulation, and innovation system contribute to increasing the level of innovation. Institutional arrangements also affect innovation output.
The different factors of the innovation system have different effects: innovation infrastructure, including the level of openness of a region and government expenditure on education, is necessary for innovation. Another result is that the extent of bank credit does not influence innovation output. The frequent engagement of banks in R & D financing cannot promote the level of innovation.
Finally, our results suggest that institutional factors play an important role in increasing the level of innovation. A market-oriented economic structure, especially healthy intermediary organizations and protection of intellectual property, helps a region to achieve a better outcome in innovation activities.
In political terms, the findings suggest that an ideas-driven growth model is appropriate for innovative activities in China. The provinces should not only concentrate on training sophisticated engineers and researchers but also attempt to participate in international competition and focus on the education and training of new generations. It is necessary to continue to promote China’s economic reform and transformation to a market economy. For local governments, it is beneficial to build a framework of economic fairness for the intermediary market to provide infrastructure services to innovation performers.
I would like to thank Ulrich Blum, Shi Shiwei, Rainer Frietsch, Henning Kroll, Guo Bin, Thomas Schøtt and Philipp Boeing for helpful discussions. I thank Humboldt Forum in Beijing, China; China Innovation Circles and Academy―Learning, Innovation and Competence Systems (CICALICS) academy in Hangzhou, China; and Fraunhofer Institute for Systems and Innovation Research (ISI) for valuable comments.
Kou, K. (2018) Effects of the Chinese Innovation System on Regional Innovation Performance. Technology and Investment, 9, 36-51. https://doi.org/10.4236/ti.2018.91003