The role of technology

On the other hand, there are also many high-tech industries developing quickly in China, supported by foreign investment under commercial agreements at country level or by the central government itself. Figure 3.4 shows that the capital-labour ratio increases quickly over time in China. However, most of it either appears in the top agglomerated 4-digit industry table, like France and the US, or in the least agglomerated industry table like the UK. Therefore, the pattern of industrial agglomeration for the high-tech industries are still mixed based on previous analysis. Rosenthal and Strange (2001) initially applied the innovation from firms of different sizes to examine the impact from knowledge spillovers. Another factor that they applied is the education level of employment.

Devereux (2004) uses the capital-labour ratio and the proportion of skilled labours to examine the correlation between technology and agglomeration. The NBS data only included the employees’ education level in 2004. It is therefore difficult to identify the change in education level and the impact that this may have had on agglomeration.

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Moreover, industrial development in China is not innovation-motivated like in the US.

Therefore, we applied two estimators to examine the correlation between the EG index and technology: the capital to labour ratio and R&D intensity. The amount of R&D expenditure has recently received attention from the NBS, which they started to collect in 2005. Year dummies and 2-digit industry dummies are also applied as the control variables. The G, H and EG index are all incorporated in measuring agglomeration.

[Figure 2.8 about here]

[Table 3.30 about here]

In table 3.29, we find positive and significant correlations between the capital-labour ratio and geographic concentration, and an even stronger correlation with the Herfindahl index. Industrial concentration has a stronger correlation with the capital-labour ratio than with geographic concentration. It is therefore a negative and significant correlation between the EG index and capital to labour, implying that high capital-intensive firms are more likely to be less agglomerated. This is due to H having a stronger correlation than G with the capital-labour ratio, which leads to negative value on the Molecular of the EG index measurement.

In the second section of table 3.30, we examine the correlation between the agglomeration measurements and R&D intensity at the 4-digit industry level. The correlation between G and R&D intensity is negative and significant with both year dummies and industry dummies controlled. We also show that the industries with high R&D intensity would prefer to be geographically dispersed. Although the correlation between the Herfindahl index and R&D intensity is positive, it may imply that high-tech industries are industrially concentrated and that the correlation value with or without dummy controls are not significant. The EG index is negative and significantly correlated with R&D in the last two rows of the table. We can conclude that R&D intensity has a negative impact on agglomeration in China, although there are also some high-tech

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industry zones similar to Silicon Valley. The emergence of high-tech industries is highly dependent upon the environment of the economy and education level of the population.

It is hard to imagine that high-tech industries will emerge on the northwest of China or Siberia where there is a lack of productive activities. For example, silk production emerged in the richest place in China; textile industry clusters tended to be in places where machinery and textile spinning were widely available in England; Silicon Valley in California is where there are many of the world’s top universities. The silk industry development is based on warm weather in southern China and the experience with sericulture and silk reeling technology over time. The machinery spinning is also based on technological innovation and “learning by doing”. The large amount of investment by the University of Stanford into circuit-integrated production and related PC products led to the emergence of the internet and information globalisation. Therefore, China is still the recipient of technology spillovers rather than an initiator – investment in R&D is still quite limited although there are high-tech clusters in China such as Z-park³¹. Although we found some agglomerated high-tech industries, such as spacecraft (SIC-3762), supported by the central government in China, the technology itself is not innovative.

However, the constant capital and R&D accumulation can potentially bolster the technology innovations in the future.

5. Conclusions

In this chapter, we track the trends of industrial agglomeration from 1998 to 2007. In general, we find the degree of industrial agglomeration at smaller region level is different from the results at province level. The geographic concentration drive the changes of industrial agglomeration although industrial concentration also experienced a tremendous

31 Z-park is the short-name of Zhongguancun Park in Beijing. It is the first high-tech market for PC, cell phone and related products in China. The employees are mostly graduates from the universities in Beijing.

The business areas of firms located here are mainly PC assembly, repairs, and software development in Chinese. However, it has gradually become a market for selling high-tech products rather than the location of high-tech innovations.

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change over time.

The increasing on number of domestic private firms together withdecreasing of SOEs has changed the determinants of industrial agglomeration. SOEs has a negative impacts on industrial agglomeration while domestic private together with foreign firms bolster the degree of agglomeration. High wage and short firm age would encourage the productivity by promote industrial agglomeration and externalities.

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Chapter 4. Patterns of industrial agglomeration: Evidence from