The technological system of Chinese manufacturing industry: A sectorial approach

China Economic Review 20 (2009) 767–776

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China Economic Review

The technological system of Chinese manufacturing industry: A sectorial approach Jiancheng GUAN a,⁎, Zifeng CHEN b a b

School of Management, Fudan University, 670 Guoshun Road, 200433 Shanghai, PR China School of Management, Beijing University of Aeronautics and Astronautics, Beijing 100083, PR China

a r t i c l e

i n f o

Article history: Received 2 February 2008 Received in revised form 27 April 2009 Accepted 3 May 2009 JEL classification: O14 O30 O33 P27 Keywords: Inter-sector innovation diffusion China I-O analysis Network

a b s t r a c t The purpose of this paper is to analyze the technological system of Chinese manufacturing. The input–output method and network analysis are applied to investigate the structure and performance at the system level and the role of each sector at the individual level in 1997 and 2002. Firstly, the R&D-flow matrices for per unit output are constructed to examine the technology intensity and constitution of each sector. And then they are combined with the economic size and R&D investment structure in order to have a more comprehensive understanding of the system at the gross level, and reveal the main technology diffusion providers, acquirers and techno-economic flows between sectors. Results of both years show that the R&D performance and technology providing for diffusion are more concentrated than technology acquiring, and this difference becomes even greater in 2002. There are fewer sectors which act as main technology diffusion sources while more sectors participate as acquirers of technology diffusion. The techno-economic flows have high dependence on a few traditional sectors and the contribution of high-tech sectors such as ICT is quite limited. © 2009 Elsevier Inc. All rights reserved.

1. Introduction The significance of technological competitiveness, either at the country level, regional level or the firm level, has been a prominent issue in the theory of economic development and business literature. Innovation is widely regarded as the central process driving technological competitiveness improvement (Cooke, Heidenreich, & Braczyk, 2004; Guan & Ma, 2003). Technological innovation and its effective diffusion are central and crucial to the growth of economic output, productivity and employment (Porter, 1990; Nelson, 1993). Moreover, the technological innovation system is considered to be a complex system involving various institutions in the creation, diffusion, and utilization of innovations (Rosenberg,1972; Porter, 1990; Mowery & Rosenberg,1989; Liu & White, 2001). With the increasingly sophistication of innovation process research, numerous studies and extensive research in innovation management have linked innovation to competitiveness and economic performance at the national level (Porter, 1990; Nelson, 1993), the regional level (Sternberg & Arndt, 2001), and the firm level (Brockhoff & Guan, 1996; Guan & Ma, 2003). The performance of technological innovation depends on the ability of innovation diffusion as well as innovation creation. In this paper, we adopt the structured notion of technological system proposed by Leoncini and Montresor (2000), that there are not only innovative relationships but also techno-economic ones. The technological system is constituted by four building blocks: a hard core of techno-scientific knowledge; a constellation of technical systems devoted production; the market environment; and the institutional interface. The flows of knowledge and technology along with the economic interactions, such as the transactions of intermediate product and capital goods, constitute the techno-economic relationships of the technological system. The techno-economic relationships are important relations in the economic system and they are the focus of the present study.

⁎ Corresponding author. Tel.: +86 21 25011165; fax: +86 21 65642412. E-mail addresses: [email protected], [email protected], [email protected] (J. Guan). 1043-951X/$ – see front matter © 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.chieco.2009.05.001

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Actors in the system interact with and learn from others to improve their technology stock and adaptive ability in the face of highly competitive environments. Innovation actors can obtain technology not only through internal R&D but also by absorbing technology from other sectors. A study of ten OECD countries has found that some countries and sectors obtain their technology from acquiring elsewhere more than from their own R&D (Norihisa & Papaconstantinou, 1997). This is because R&D investments have a dual role: in addition to developing new products and processes, they are also directed at helping firms absorb and assimilate technology developed elsewhere (Cohen & Levinthal, 1989). The term technological diffusion is used here to refer to the way in which innovations spread, from their very first implementation to different consumers, countries, regions, sectors, markets and firms, and this through market or non-market channels. Without diffusion, an innovation has no economic impact (OECD/Eurostat, 2005). Broadly speaking, there are two types: disembodied diffusion and product-embodied diffusion, which are differed from whether or not necessary involving the purchase of machinery and equipment incorporating new technology developed elsewhere for the diffusion of knowledge and technology. The distinction is clear conceptually, but not very clear in practice. Product-embodied diffusion is the introduction into the production process of machinery, equipment and components which incorporate new technology. It happens within the so called techno-economic space and accompanies with the techno-economic flows (DeBresson, 1996). These industries are mainly in the R&D-intensive manufacturing sectors (Norihisa & Papaconstantinou, 1997). They sell their technology intensive intermediate and capital goods to downstream sectors (both manufacturing and nonmanufacturing). So the downstream sectors can use the obtained technology to improve their product and product processing, which has a similar effect as when R&D activities are conducted within the system. Input–output analysis is a useful tool for modeling the techno-economic flows and inter-sector R&D spillover. It displays the essential conditions of a system in an integrated manner by examining the supply chain. Although there are some limitations to this method, it has two main advantages. The first is that it reflects actual economic transactions. The purchase activities act as carriers of technology across industrial sectors through interactive learning processes. Another advantage is that this methodology simultaneously takes into account both supply and demand factors and thereby goes beyond the “technology-push” and “demand-pull” debate (DeBresson, Sirilli, Hu, & Luk, 1994). Combining this methodology with network analysis, a study has been performed for comparing the technological systems between Italy and Germany. It showed clearly the different structural nature of the two diffusion systems (Leoncini, Maggioni, & Montresor, 1996). It has also been used to study the technology diffusion in the service sectors to show that services were more technologically sophisticated than what is usually thought (Amable & Palombarini, 1998). A recent study (Chang & Shih, 2005), employing a similar methodology, compared patterns of intersectoral innovation diffusion in Taiwan and China using the data of 1999 and 1997 respectively, to find that China had a lower systemic connection at the whole level but had a less hierarchical structure, indicating that Taiwan created relatively efficient diffusion but Chinese sectors share more symmetric advantages of structural position. Taiwan moreover focused on high-tech sectors while China was still concentrated more on the traditional sectors. This study also found the key sectors in the two innovation systems as well as some isolated ones. As a technology latecomer, China has made great efforts to boost its technological competitiveness by improving its innovative capability. The target of building up an innovative country has been considered an important position in the national development strategy since 2005. Innovation and R&D activities are reinforced and independent innovation is stimulated and encouraged by the government (Guan & Ma, 2007). It is believed that China's long-term economic performance will ultimately depend upon its ability to acquire, adapt and innovate new technologies (Guo, 2008). Manufacturing sectors are generally considered as main actors of technology innovation and serve as technology diffusion sources for other sectors. They have played a key role in the rapid growth of the Chinese economy over the past two decades. The Chinese central government has been consistently emphasizing the importance of technology development in the manufacturing sector and viewing technology development as an engine for the process of catching-up with technology leaders. Thus it is necessary to analyze the technological system of Chinese manufacturing. All manufacturing sectors are considered as actors in the system and the system is studied by comparing its structure and performance in 1997 and in 2002. As a dynamic system, the innovation creation and diffusion activities changed all the time, influencing and changing the structure and performance at the system level and the role of each sector at the individual level. This study combines R&D expenditure with the input–output table in order to measure the techno-economic flows and adopts network analysis to investigate where the technology comes from and where it goes. The remaining parts of the paper are organized as follows. Section 2 defines the methodology and data used in this study, including the input–output analysis and network analysis. Section 3 deals with the empirical study, describes the technology intensity for per unit output, the main technology providers and acquirers, and the main techno-economic flows. Finally, conclusions are provided in Section 4. 2. Methodology and data 2.1. Constructing the R&D-flow matrices Technology transfer and acquisition are the most important types of relationships in technological systems (Carlsson, Jacobsson, Holmen, & Rickne, 2002), some of which take place via markets, some via non-market interactions. Although there are various channels for technology diffusion, the identification of product-embodied R&D flows is a major first step in understanding the structure of a national technological system (Drejer, 2000). Product-embodied diffusion, in which intersectoral innovation

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diffusion takes place via purchasing intermediate input and capital goods embody new technology from upstream industries. The usefulness of input–output analysis for mapping technology flows was originally suggested by Terleckyj (1974), then used in other studies (Leoncini et al., 1996; Norihisa & Papaconstantinou, 1997; Chang and Shih, 2005). Combining the input–output table and sector R&D expenditures to construct inter-sector R&D-flow matrices can identify the main techno-economic relationships, the most pervasive and the most dependent sectors, discover to which extent innovation diffusion between sectors takes place, and find out which sectors depend more on their own R&D performance, and which ones depend more on innovation acquisition from elsewhere. In this paper, two kinds of R&D matrices are constructed using the total R&D input for 1997 and 2002 respectively. One kind is used to measure the R&D diffusion for per unit output and the other one is for the total output. The use of R&D-flow matrix can be traced back to the work of Terleckyj (1974). The input–output system is then defined as: X = ðI − AÞ

−1

Y

ð1Þ

where X is the vector of gross output per sector, and A is the matrix of input coefficients, Y is the vector of current final demand. A is obtained by dividing the columns of the intermediate inputs according to the corresponding gross output levels. A = W · diagðX Þ

−1

ð2Þ

where W denotes the matrix of domestic intermediate deliveries. The symbol diag() is used to indicate the diagonal matrix as obtained from the corresponding vector. Then the R&D-flow matrix for per unit final demand can be constructed as: CR&D = diagðR&DÞ · diag ðX Þ

−1

ðI −AÞ

−1

ð3Þ

where R&D indicates the vector of sectorial R&D expenditure. While the traditional Leontief inverse B = (I − A)− 1 is a final demand-to-output multiplier, the indicator CR&D estimates how much R&D is directly and indirectly embodied in one unit of final demand for each sector. In the previous studies, the traditional Leontief inverse has been used directly to construct the R&D-flow matrices, because that the final demand represents the market sub-system and it is an important part of the technological system (Leoncini et al., 1996; Leoncini & Montresor, 2000; Chang & Shih, 2005). The market is absolutely important in the economy system. However, in this present study, it focuses more on the innovative sub-system and the productive sub-system. We are more interested in the structural change caused by technology innovation and technology diffusion than that caused by the market. So the output-to-output multiplier has been used to measure how much R&D is embodied in the intermediate in order to get one unit of industry output. For this reason, the modified Leontief inverse which is an output-to-output multiplier is used (Miller & Blair, 1985; Leontief, 1986). Defining A− j as the A matrix without the line and the column corresponding to sector j, and aj the jth column vector of the A matrix minus the jth line, then bj is defined as:
−1 bj = I −A − j aj

ð4Þ

BT = ½bV 1 ; bV 2 ; N ; bV n

ð5Þ

The bj′ vectors are the bj vectors with a zero at the jth line. Use of the modified Leontief multiplier can avoid the problem of double counting the embodiment of each sector and the total R&D embodiment of each sector can be defined as the simple sum of direct R&D actually conducted by the sector and R&D embodied in purchased products (Amable & Palombarini, 1998). Then the R&D-flow matrices are constructed as: unit

= diagðR&DÞ · diagðX Þ

−1

sum

= diagðR&DÞ · diagðX Þ

−1

R R

BT BT · diag ðX Þ

ð6Þ ð7Þ

indicates how much R&D is The first matrix Runit indicates R&D embodied in intermediates for per unit output. Its element runit ij embodied in intermediate goods from sector i to sector j directly and indirectly in order to produce one unit output for sector j. The sum of the ith row indicates how much R&D embodied in intermediates has been diffused from sector i to all other sectors, in order to produce one unit output for each sector. The sum of the jth column indicates that how much R&D embodied in intermediates has been diffused to sector j from all other sectors in order to make a unit output of sector j. So the technological innovation acquired from elsewhere can be shown as the sum of column of R&D-flow matrix Runit, while the technology self-performed can be represented as the R&D investment per unit output. Besides the diffusion for per unit output, we are also interested in the R&D diffusion for the whole output. So the second matrix Rsum is used to indicate the total R&D embodied in intermediates for gross output in the considered year. Its element rsum indicates ij

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the total volume of R&D diffused from sector i to sector j directly and indirectly through intermediates. The sum of the ith row and column indicate the total R&D provided and used by sector i through intermediates respectively. 2.2. Network analysis After using input–output analysis to obtain R&D-flow matrices for per unit output and gross output, we use network analysis to obtain more insight into the patterns of the diffusion as in previous studies (Leoncini et al., 1996; Chang & Shih, 2005). Network analysis is a recently developed method for social structures study, and has been widely used to examine the relationship such as scientific collaboration (Newman, 2001), and actor network of Hollywood (Albert & Barabasi, 2000). Network analysis which is derived from a graph theory uses quantitative techniques to study and describe the structure of interactions (edges) between entities (nodes). In this study, sectors interact with each other by the flow of R&D embodied in intermediates and they construct a network in which the nodes represent the sectors and the direct edges represent the R&D flow, so the network analysis is extremely suitable for examining the patterns of intersectoral technology diffusion. Networks can be represented by graphs as well as matrices. Here we use matrices constructed by the input–output method to construct our graphs. Almost all manufacturing sectors have a positive R&D investment and every two sectors have directly or indirectly intermediate input. So all elements of the R&D-flow matrices are positive and it means that it represents a network with a density equals to 1. To distinguish the important and unimportant techno-economic relationships between sectors, we choose a cut-off value k to construct our networks. If the R&D flow from sector i to sector j is greater than or equal to k, then a direct edge from i to j is formed, else there is no link from i to j. The selection of k is important for the structure of the network. However, it is arbitrary to some extent. To control this arbitrary, we choose k at several different levels in order to investigate techno-economic flows at different important levels. We use every element as a cut-off value to get a distribution of density of each year, then choose the k according to the density distribution. Network density (in a directed network) is defined as the fraction of the number of exit links e to the maximum possible number of links in a network composed by n nodes, i.e. D = e / n(n − 1). After construction of the networks, we will use certain indicators developed in the network theory to help us analyze those networks. We use the outdegree (number of outward connections) of node j to measure how many sectors have benefited from sector j, and the indegree (number of inward connections) of node j to measure how many sectors sector j has benefited from. While degree indices are used to measure the individual actor, now we use two system level indices, the inward (Hin) and outward (Hout) degrees of centralization, to study the scale of structural hierarchy of technological systems. It can be defined as (Freeman, 1978): P
Hin =

i

T i Cin − Cin



ðn − 1Þðn − 2Þ

; Hout =

 P
T i i Cout − Cout ðn − 1Þðn − 2Þ

ð8Þ

where ⁎ represent the sector with the largest degree. These two indicators measure the difference in centrality between the most central sector and other sectors. The higher these indicators are, the more hierarchic the system. In this study this means that the technological system with high centralization index is less conductive to interactive innovation diffusion than a low centralization system with an evenly distributed structure (Leoncini et al., 1996; Chang & Shih, 2005). 2.3. Data Our study investigates the patterns of intersectoral techno-economic interaction with respect to manufacturing sectors in China for 1997 and 2002. We choose these 5 years because the input–output table of 2002 is the most recent one available, and a five-year period is suitable for appreciable changes of technological system. Input–output data are taken from the China input–output table for 1997 and 2002 from the website of the Chinese Input–Output Association.1 In the world scope, the average life cycle of technology innovation is 70 years in the 19th century, 50 years in the postwar era, 10 years in the 80s of the 20th century, and presently just 3 to 5 years (Zeng & Williamson, 2008). So considering the delay of R&D output, in this study we use the average R&D expenditure of 5 years previous to the examined year to construct the R&D-flow matrices. Although the choice of a temporal lag in building up R&D stock is arbitrary to some extent, the 5 years lag is a reliable choice. The data is derived from the China Statistical Yearbook on Science and Technology 1995, 1996, 1997, 1998, 2000, 2001, 2002, and 2003 (National Bureau of Statistics). Since there is a little difference in sector classification between the 2 years, we adjust them to 15 sectors according to the classification in 2002 (see Appendix A). 3. Empirical study In this paper, we study the technology flows within 15 manufacturing sectors for China in 1997 and 2002. First, the R&D-flow matrices Runit and Rsum are built for each year. Although only manufacturing sectors are studied here, product-embodied R&D can flow indirectly from one manufacturing sector to another through a non-manufacturing sector such as farming or service. For 1

www.iochina.org.cn/touruchanchubiao.htm.

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Fig. 1. Indirect diffusion from one manufacturing sector to another.

example, a manufacturing sector A may sell a machine which embodies some technological innovation to a service sector B, then B absorbs and uses some of this technology to improve their product and finally sell them to another manufacturing sector C (see Fig. 1). During this process technological innovation has been diffused from sector A to sector C indirectly. So during the construction of the modified Leontief multipliers, all sectors have been included in the calculation in order to get the exact indirect R&D flow. The part of manufacturing is then taken from the result. 3.1. Technology profile for per unit output A comparison of technology acquired by R&D-embodied flow from other sectors with technology obtained from their own R&D performance for per unit output has been done within this subsection, to check which sectors depend more on self-performance while which ones depend more on outside acquiring. The R&D volume contained in per unit (thousand) output for each sector is shown in Fig. 2. Before a detailed analysis for each sector, one notable difference between 2 years is that as a whole the technology intensity of 2002 is much lower than that of 1997. One important factor is that, although the total R&D investment of 2002 is higher than that of

Fig. 2. Technology intensity profiles.

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1997, for manufacturing the average investment from 1998 to 2002 is just one third of the average from 1993 to 1997, while the gross output is increased by 39%. These together make the direct R&D embodied in a unit output to decrease sharply and then the R&D embodied in the intermediates decreased too along with the R&D-less techno-economic flows. The R&D investment in all manufacturing sectors dropped dramatically except for the food and tobacco sector (a small increase in investment) and timber and furniture (status quo). This is related to the Chinese government policy about developing the service sectors with great effort. The increase of R&D investment mainly happened in geological prospecting and water conservancy, wholesale and retail trade and catering services, finance and insurance, health care, sports, and social welfare, education, culture and arts, radio, film and television, scientific research and polytechnic services sectors. Sectors for equipment and machinery production (11: ordinary and special machinery; 12: transport equipment; 13: electric equipment; 14: ICT; 15: instruments and office machinery) are with higher technology intensity, while sectors for commodity production (1: food and tobacco; 2: textiles; 3: garments; 4: timber and furniture; 5: papermaking and printing) and energy processing (6: petroleum and coking) are with lower technology intensity. For these low-tech sectors, the shares of acquired R&D are more than 50% (except for the food and tobacco sector in 2002) and even more than 90% (for timber and furniture in 1997 and garments in 2002). Despite the large flows of technology to these consumer good and energy process sectors, it is the equipment and machinery making sectors that remain the most technologically intensive. Sector 15 (instruments and office machinery) is the most technology intensive sector in both periods. For sector 14 (ICT), its output share rose from 5% to 9%, while its R&D share dropped from 14% to 11%, which together made its lower relative technology intensity in 2002. Sector 7 (chemical industry) is the most self-dependent sector with the highest share of R&D performed (94% in 1997 and 85% in 2002). Sectors 3 (garments), 4 (timber and furniture) and 10 (metal product) have the highest share of acquired technology (surpassed 80% in both years). The innovation diffusion through the techno-economic flows is the main technology obtaining channel for these three sectors. Now we have analyzed the technology intensity profile for per unit output for each sector. In the next subsection we combine the technology intensity with the output and R&D constitutions in order to have a deep and detailed look at the gross level. 3.2. Technology profile for gross output For the whole system the share of acquired technology is 45% in 1997 and 39.41% in 2002. Accordingly, the technology multipliers are 1.83 and 1.65 respectively. The technology multipliers are obtained as the ratio of the estimated total technology embodied in output to the R&D directly performed and they measure the total technology embodied in gross output that is

Table 1 Profiles of each sector. Producer

Share

Performer

Share

Provider

Share

Acquirer

Share

Technology

Share

1997 7 1 2 11 8 9 3 13 12 10 14 5 6 4 15

15% 14% 9% 9% 9% 8% 6% 5% 5% 5% 5% 4% 3% 2% 1%

7 12 14 11 9 8 13 2 15 1 6 5 3 10 4

22% 18% 14% 13% 12% 5% 4% 3% 3% 2% 1% 1% 1% 1% 0%

7 9 12 11 14 2 13 15 8 6 5 10 1 3 4

27% 24% 15% 10% 9% 3% 3% 3% 2% 2% 1% 1% 0% 0% 0%

11 13 8 10 1 12 3 9 2 14 5 6 4 15 7

27% 11% 8% 8% 7% 7% 6% 5% 5% 4% 4% 2% 2% 2% 2%

11 12 7 14 9 13 8 10 1 2 3 5 15 6 4

19% 13% 12% 9% 9% 7% 7% 4% 4% 4% 3% 2% 2% 2% 1%

2002 7 9 1 11 14 12 2 13 5 3 6 10 8 4 15

15% 11% 10% 9% 9% 7% 6% 5% 5% 5% 4% 4% 4% 3% 1%

7 11 9 14 12 1 15 13 8 2 6 5 10 3 4

24% 21% 12% 11% 7% 6% 4% 4% 3% 3% 2% 1% 1% 0% 0%

7 9 11 14 15 13 12 6 8 2 5 1 10 4 3

32% 21% 17% 7% 4% 4% 4% 3% 2% 2% 1% 1% 1% 0% 0%

11 13 12 14 10 9 1 7 3 2 5 8 6 15 4

12% 10% 10% 8% 8% 7% 7% 7% 6% 6% 5% 5% 3% 3% 3%

11 7 9 14 12 13 1 8 2 15 10 5 3 6 4

18% 17% 10% 10% 8% 7% 7% 4% 4% 4% 4% 3% 3% 2% 1%

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obtained from ¥1 expenditure in R&D. It implies that due to the fact that industries use the technologically sophisticated products of other industries as inputs, the total technology embodied in the gross output is 83 and 65% higher than the value of the R&D expenditures of the manufacturing sector in 1997 and 2002 respectively. A previous study of 10 OECD countries shows that most large countries have technology multipliers in the 1.7 to 1.9 region (Papaconstantinou, Sakurai, & Wyckoff, 1998). There is no obvious difference between China and these OECD developed countries. This is maybe due to that, although as a developing country, China has an integrated manufacturing system including almost all subclasses, which is not common in the developing countries and this is an important advantage for China to become a real powerful manufacturing country from just a large manufacturing country. The decrease of the multiplier is either because industries source less technologically advanced intermediate inputs than in the past, or because they are subcontracting less to other industries. Now we combine industry structure, R&D performance together with techno-economic flows to inspect the role of each sector. Table 1 lists the profiles of each sector from five aspects: (1) the share of each sector's gross output in the manufactory as a producer; (2) the share of each sector's R&D investment in the manufactory as a R&D performer; (3) the share of each sector's contribution to the R&D diffusion pool through intermediate providing, calculated by the sum of each row of Rsum divided by the sum of Rsum; (4) the share of each sector's acquisition from the R&D diffusion pool through intermediate using, calculated by the sum of each column of Rsum divided by the sum of Rsum; (5) the share of technology contained by each sector in the manufactory, calculated by its technology volume (self-performed R&D plus acquired R&D) divided by the total technology volume of the manufactory. In general, R&D performance and technology providing are more concentrated than production and technology use, as the top five sectors account for 75%–78% R&D performance and 82%–84% technology providing, while they account for 55% product and 49%–62% technology use in total. It means that R&D performed and diffused to the technology pool mainly by several sectors, and then they are more widely used by other manufactory sectors. Sector 7 (chemical industry) has the highest share of product, R&D performance and providing in both periods, while sector 11 (ordinary and special machinery) has the highest share of technology use and technology content. Despite the highest technology intensity, sector 15 (instruments and office machinery) is neither a prominent provider nor an outstanding user for technology diffusion for its small economic size (account for just 1% in total output). In 1997, sector 7 (chemical industry) has the highest contribution to the technology diffusion while the lowest utilization from it. And it is always the most self-dependent sector as presented in previous subsection. This is consistent with some previous findings. In the study of 15 OECD countries for the mid-1990s, the chemical industry has been the most or one of the most important sources for innovation flows in seven countries (Montresor & Vittucci Marzerri, 2008). Then the resource-based smelting and pressing of metals (sector 9) is the second important source of technology for other sectors, and it is also a self-performance dependent sector. This is idiosyncratic when compared with the developed OECD countries which have been studied in Montresor and Vittucci Marzerri's (2008) work, in most OECD countries this sector are listed in the middle hierarchy by its ordinary pervasive and dependent extent. As China is on its way to industrialization, the smelting and pressing of metals sector has been considered as one of the most important strategic industries since the establishment of the new China. By continuous technology investment, both the gross output and product quality have been enhanced rapidly. The gross output of steel has surpassed 100 million tons in 1997 and 200 million in 2003 and the increase is continuing due to the stimulation of rapid economy increase. In addition, its technology has been amplified more than the other sectors. It accounts for 24% technology providing just with 12% R&D performance. That is to say the effects of R&D invested to this sector have been nearly tripled after technology diffusion process through intermediate input. So it would be meaningful for the whole manufactory if the R&D investment to sector 9 be increased. Sector 11 (ordinary and special

Table 2 Freeman's degree centrality. 1997

2002

Sector

Out

In

Out

In

Out

In

Out

In

Out

In

Out

In

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Mean Std dev Minimum Maximum Centralization Density

0 1 0 0 0 0 13 0 7 0 7 6 1 6 1 2.8 3.868 0 13 78.06% 20%

4 2 2 1 1 1 0 5 4 4 6 4 5 2 1 2.8 1.796 0 6 24.49%

0 4 0 0 1 3 14 3 13 1 12 11 5 12 4 5.533 5.11 0 14 64.80% 40%

5 5 6 2 6 5 2 9 8 5 10 5 7 5 3 5.533 2.217 2 10 34.18%

0 9 0 0 4 8 14 8 14 3 14 13 12 13 13 8.333 5.362 0 14 43.37% 60%

11 7 8 6 8 7 7 11 8 9 11 9 11 7 5 8.333 1.886 5 11 20.41%

0 1 0 0 0 0 14 0 7 0 12 0 2 4 1 2.733 4.464 0 14 86.22% 20%

2 2 3 1 2 2 4 3 2 3 4 3 4 4 2 2.733 0.929 1 4 9.69%

2 1 0 0 0 3 14 2 14 0 14 7 5 13 8 5.533 5.5 0 14 64.80% 40%

6 4 6 3 4 5 8 4 8 5 7 6 6 7 4 5.533 1.5 3 8 18.88%

3 1 0 0 6 9 14 8 14 3 14 13 13 14 13 8.333 5.473 0 14 43.37% 60%

10 8 9 5 7 7 10 9 8 9 10 8 10 10 5 8.333 1.66 5 10 12.76%

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machinery) has done well both as a technology provider and user. Sector 12 (transport equipment) has high share of providing while low share of acquisition and it is mainly dependent on self-performance. While the other two sectors, 10 (metal product) and 13 (electric equipment) have low share of providing while high share of acquisition and they are mainly dependent on acquisition. In 2002, in general the technology acquisition has been distributed more evenly among sectors. Sector 11 (ordinary and special machinery) is still on the top of acquirers, but it is no longer dominated. Most sectors acquire technology around the average level. It means that all manufactory sectors benefit more equally from technology diffusion than before. Sector 7 (chemical industry) and sector 9 (smelting and pressing of metals) are still the most important sources for technology diffusion. At the same time they acquired from other sectors more than before and reached the average level. Sector 12 (transport equipment) has transformed from providing more (15%) and using less (7%) to proving less (4%) and using more (10%). Another sector that should be noticed is ICT (14). As a high-tech industry, its R&D investment share is much higher than average, but its technology providing share is just at average level. ICT is a new and strategic industry in China. Its high-tech product is expected to benefit other economic sectors widely. But its diffusion effects to others are limited, at least to manufactory sectors. One possible reason is that other manufactory sectors benefited from ICT mainly through capital investment rather than intermediate input. Firms pursue their informationization to improve their production efficiency and quality by purchasing ICT equipments as fixed capital. Due to data unavailability, we cannot catch the capital flows between sectors, but the aggregate data do show that there are 15% gross output of ICT converted to fixed capital in 1997 and 20% in 2002. This proportion is higher than most sectors. Now we have described the profiles of each sector and identified the main technology providers and acquirers. In the next subsection, we will have a detailed look at the main techno-economic flows. 3.3. The main R&D flows In order to construct the R&D-flow networks using R&D-flow matrices for gross output, proper cut-off values must be selected. To control the arbitrary, several different cutoffs are chosen to investigate the top 20%, 40% and 60% of the most important flows, respectively. Table 2 presents the degree centrality for these networks. There are significant higher variances of outdegree than that of indegree for all density levels in both periods, which mean that the variability for sectors providing ability is higher than the acquiring ability. This confirms that the providing ability is much more concentrated on several source sectors compared with acquiring ability in both years, so it means that many sectors obtain technology innovation actively from the techno-economic flows while a few sectors make great contribution to the technological system as a source of technology innovation diffusion. When the top 20% most important techno-economic flows are considered, the technology providing are more concentrated while the technology acquiring are more evenly distributed in 2002 than in 1997. The most traditional sectors, i.e. sectors 1 (food and tobacco), 3 (garments), 4 (timber and furniture), 5 (papermaking and printing), and the resource-based sectors 6 (petroleum and coking), 8 (nonmetal mineral product), 10 (metal product) are ‘totally’ dependent, as the value of outward centrality are nil in both years. In 1997, there are 5 sectors which benefit at least 6 other sectors and account for 93% of the techno-economic flows considered at this level. In 2002, there are just 3 sectors which benefit at least 6 sectors and about two thirds flows come from sectors 7 (chemical industry) and 11 (ordinary and special machinery). Chemical industry has the most widely influence in both periods. As the most science-based industry and the most pervasive intermediate goods provider, the chemical industry has been generally recognized as a pervasive source of technologies for the entire economic system, and its knowledge diffusion network has expanded encompassing a broad range of industries (Bowonder, 2001; Hu & Tseng, 2007). Although the total technology providing

Table 3 The main techno-economic flows. 1997 Main providers 7: 9: 12: 11: Main acquirers 11: 13:

Flow to 1(12%) 10(19%) 11(60%) 8(14%) obtain from 7(16%) 7(26%)

2(10%) 11(33%)

8(10%) 13(16%)

11(16%)

9(12%)

12(17%)

13(11%)

9(29%) 9(34%)

12(33%) 11(10%)

14(10%) 14(14%)

2(10%) 11(24%) 9(13%)

11(10%) 12(13%) 12(19%)

13(10%) 13(15%) 13(10%)

9(41%) 9(30%) 9(26%)

11(17%) 11(31%)

13(11%)

2002 Main providers 7: 9: 11: Main acquirers 11: 13: 12:

Flow to 1(10%) 10(18%) 7(11%) Obtain from 7(27%) 7(32%) 7(25%)

14(10%)

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from sector 9 (smelting and mineral product) is more than that from sector 11 (ordinary and special machinery), the latter has a wider influence and benefits 12 other sectors in 2002. The centralization index analysis agrees with the above-mentioned characteristics of the technological system. As the degree of centralization index implies that to which extent the technology system can be regarded as a hierarchical network (i.e. high centralization degree) or an evenly distributed one (i.e. low centralization degree) (Chang & Shih, 2005). Both years always show much more evenness in inward linkages than outward linkage when techno-economic flows at different levels are considered. When the top 20% flows are considered, on the inward aspect, the system is more even in 2002 than in 1997, while on the outward aspect, the system is more hierarchical in 2002 than in 1997. Next we will have a more detailed look at the techno-economic flows between main providers and main acquirers. Table 3 lists the main providers and acquirers (share no less than 10% of total) and where the technology of these providers goes and where the technology of these acquirers comes from. Technology provided by sector 7 (chemical industry) has been evenly diffused to various sectors, and the largest user of this sector just accounts 16% of its diffusion in 1997 and 10% in 2002. The second important technology source sector 9 (smelting and pressing of metals) has mainly benefit its close linked customers, i.e. sector 10 (metal product), 11 (ordinary and special machinery) and 13 (electric equipment) in both periods and sector 12 (transport equipment) in 2002 in addition. In 2002, sector 12 has transformed from a main provider to a main acquirer. While sector 11 (ordinary and special machinery) has always been both an important technology provider and acquirer. Sector 11 and sector 13 are always the largest acquirers of technology diffusion. Their technology acquired from diffusion mainly comes from the main providers for the whole manufactory. Sector 14 (ICT) acts as an important source for sectors 11 and 13 in 1997 but this importance no longer exists in 2002. In 2002, the source for the main acquirers has been dominated by sectors 7, 9 and 11. For both years, the technology flows from sectors 7 and 9 to sectors 11 and 13, from sector 11 to sector 13 are important flows from both the providers' and acquirers' view.

4. Conclusions This paper examined characteristics of the technological system in China within manufacturing sectors in 1997 and 2002. The modified Leontief inverse was employed on the R&D expenditure to measure the product-embodied R&D flow between sectors at per unit output level and gross output level respectively, and indices derived from the input–output approach and network analysis were jointly utilized to describe performance of each sector and the system structure of the 2 years. These methodologies have been successfully applied to reveal the structural nature and performance of the technological system in the 2 years. However, there are some limitations in this study. The product-embodied R&D flow has been approximated by intermediate input without involving the capital investment which is included in the final demand, and the product-embodied technology innovation has been approximated by R&D expenditure, both due to the lack of data accessibility. Despite these limitations, these methodologies are adequate for this study. The Chinese economy increased rapidly over the last decade. As the generally recognized R&D-intensive sectors, the intersectoral innovation diffusion pattern of Chinese manufacturing industry shows on one hand a similarity to some extent and notable differences on the other hand in 1997 and 2002. First, in 2002 it shows much lower technology intensity for per unit output and decreased diffusion efficiency, as indicated by the technology multipliers. The decrease of the multiplier is either because industries source less technologically advanced intermediate input, or because they are subcontracting less to other sectors. The decreased diffusion efficiency comes together with the reduced R&D investment to make much lower technology intensity in 2002. Secondly, at the system level, the R&D performance and technology providing for diffusion are more concentrated than technology acquiring from diffusion in both periods. In 2002, this difference becomes even greater, for technology providing are more concentrated, while technology acquiring are more decentralized. It indicates that the system is more hierarchical from the suppliers' point of view and more evenly from the users' point of view. That is to say, at the system level there are fewer sectors which act as a main technology diffusion source while more sectors participate as acquirers of technology diffusion. Thirdly, the chemical industry is always the most important technology provider for diffusion and its influence is widely spread to almost all other sectors. The smelting and pressing of metals sector's, as the second prominent provider, technology has been largely multiplied during diffusion through intermediate goods and mainly diffused to the machinery and equipment production sectors. The ordinary and special machinery sector and electric equipment sector are the largest acquirers in both periods, and the former is an important provider too. Another notable sector is the ICT, as a high-tech and strategic industry in Chinese, its R&D investment share is much higher than average, but it is not an outstanding technology provider. Its technology R&D diffusion to other manufactory sectors through intermediated goods is quite limited. One possible reason is that other manufactory sectors benefit from ICT mainly through capital investment rather than intermediate input. In conclusion, the R&D-embodied flow within Chinese manufacturing sectors is still highly dependent on several traditional sectors. In recent years, the Chinese government has made great efforts to develop the service sectors and reduce the R&D investment in manufacturing sectors, resulting in lower technology intensity in 2002. Though the service sectors become increasingly important worldwide and in China, the manufacturing industry is still important for the economy in China, and many service sectors obtain their technology from manufacturing sectors through product purchase. So R&D investment for the manufacturing industry is still very important and it is better to develop a polycentric technological system with higher diffusion efficiency.

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Acknowledgments This research is funded by the National Social Science Foundation of China (Project No. 08BJY031), National Natural Science Foundation of China (Project No. 70773006), and by the Shanghai Leading Academic Discipline Project (Project No. B210). Authors are grateful for the valuable comments and suggestions of anonymous reviewers and the editors, which significantly improved the paper. We also thank Prof. Ronald Rousseau of KHBO, Industrial Sciences and Technology, Belgium and Daniel Fang of the University of Washington, Seattle for their constructive suggestions and careful English corrections to the paper. Appendix A No.

Sector

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Food, beverages and tobacco Textiles Garments, leather and fur Timber, bamboo and furniture Papermaking, printing, culture, educational and sports goods Petroleum processing and coking Chemical industry Nonmetal mineral product Smelting and pressing of metals Metal product Ordinary machinery and equipment for special purpose Transport equipment Electric equipment and machinery Electronic and telecommunications equipment Instruments, meters, cultural and office machinery

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