The diffusion and embeddedness of innovative activities in China

Abstract

China’s unprecedented growth largely results from industrial development having critically sustained the country’s economic transition after 1978. As common to the developmental context, catching-up capabilities have been both absorbed from external sources and generated by indigenous activities. These also represent exogenous and endogenous seeds of innovative activities respectively. The relative emphasis on the two has evolved over progressive industrialization–transition stages in China, leading the country to grow a global manufacturing hub. The volume and quality of innovative activities has however resulted unevenly distributed at a local level. Literature considers embeddedness, in particular, as one of the key features in the development of the local innovative environment. This paper investigates if the mixes of seeds may have delayed the innovative activities to gain embeddedness along their diffusion in the Chinese prefectural cities. In a great deal of stylization and methodological design, innovative activities are here approximated by the applications to the European Patent Office from China collected in the OECD REGPAT database as originally rearranged by the applicant’s and inventor’s prefectural locations. These locations are taken to build three indicators to be combined in a clustering procedure set to measure separate levels of embeddedness. The results suggest a growing diffusion and embeddedness of the innovative activities in the Chinese prefectural cities since the early-2000s, despite they remain highly concentrated in some regions, that is, mainly those having historically hosted the Special Economic Zones where more exogenous seeds appear to have actually delayed the innovative activities to gain embeddedness.

Appendices

Appendix A: Patent filings at the EPO and the SIPO compared

The EPO and SIPO patent statistics are here compared to give insights into the reliability of using the EPO data to approximate the innovative activities in China. The source of the SIPO statistics is the China Data on Line, Yearbooks Database providing information at the provincial level since 1985 onwards. The comparison is then necessarily performed on a shorter period (1985–2009) and broader units (provinces) than those considered in the analysis above. The overall correlation between the year–province counts from the two sources is 0.61, that is, strong. Tables 7 and 8 go more in depth reporting the counts of patent applications at the SIPO and the EPO per applicant, their relative size and correlation, based on the temporal and regional variability respectively. With a few exceptions, and despite a much lower number of patent applications at the EPO, correlations between the number of documents filed at the EPO and the SIPO tend to be very strong also over time and regions separately. This result suggests that referring to the EPO patent applications is statistically, not only conceptually, robust to approximate the diffusion and the features of the innovative activities in China.

Table 7 Correlation between the number of patent applications to the EPO and the SIPO by Chinese province: applicants, variability over years, 1985–2009

Table 8 Correlation between the number of patent applications to the EPO and the SIPO by year: applicants, variability over Chinese provinces, 1985–2009

Appendix B: Robustness

This appendix presents an alternative clustering procedure to that presented in Sect. 4 to check that excluding ex ante those prefectural cities where tot _is  = 0 does not lead to biased results. The procedural amendment here concerns just the treatment of the null values, now much more numerous, so that the number of observations is the same (200) in each period. Null values necessarily have a negative impact on the country’s average of the within-stage indicators, especially in the first and second stages (Fig. 7). Despite this “lowering” effect of the amendment, the results obtained here are expected to be very alike those presented in Sect. 4, except they now comprise a wide group of prefectural cities whose centroids’ values are much closer to zero. The size of this additional group is however expected to decrease over time.

Fig. 7

Source: authors’ arrangement from the OECD REGPAT database, January 2014

Country’s average of the within-stage indicator values (including the null values).

For the remainder, the clustering procedure follows the same steps as in the main analysis. The first (hierarchical) step suggests a clearly identifiable eight-cluster solution for the first stage and a five-cluster solution for the third (the clustering coefficients jump from 9.4 to 20.6 and from 5.7 to 8.4 respectively). Differently, the proposed solution is not unique for the second stage, but a six-cluster solution is preferred to a seven- one for its simpler interpretability. Then, the centroids obtained by the first step are taken as the initial seeds in the second (K-means non-hierarchical) step generating the results shown in Tables 9, 10 and 11.

Table 9 Clusters’ description including the null values: centroids and number of cases, first reform stage (1981–1992)

Table 10 Clusters’ description including the null values: centroids and number of cases, second reform stage (1993–2001)

Table 11 Clusters’ description including the null values: centroids and number of cases, third reform stage (2002–2009)

As expected, a new group of prefectural cities has emerged, notably in the period 1981–1992 (Table 9, VII). Centroids’ values in this group are very low so that it tends to largely overlap with the prefectural cities of “no innovative activity” in Sect. 4. Furthermore, three results here confirm those returned by the main analysis. First, the centroids’ values increase stage-by-stage, which is coherent with the distributional pattern previously shown. Second, the number of groups decreases over time, so that a broad reinforcement of the innovative activities is again verified. Finally, also the number of prefectural cities where innovative activities are poorly embedded locally decreases over time.

An alternative clustering procedure including the null values therefore supports the substance of the evidence returned by the main analysis in Sect. 4. The quality of this evidence is nevertheless weakened here by the observational noise due to many null values, which prevents to clearly discriminate between the prefectural cities with “no innovative activity” and those where the INV indicator prevails.