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Unpacking technology flows based on patent transactions: does trickle-down, proximity, and siphon help regional specialization?

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Abstract

The spatial concentration of knowledge production leads to increased regional inequality, but technology flows have the potential to improve the distribution of innovation. This study examines the role of technology flows in regional specialization at the technology level in China during 2005–2016 using patent data. To unpack technology flows, we distinguish three directions based on patent transactions: trickle-down, proximity and siphon. Results show that regions are more likely to specialize in technological activities, which exhibit a greater number of external linkages characterized by relatively low relatedness and a limited number of strong links. Access to external technological linkages is identified as a key pathway for less innovative regions to achieve place breakthroughs. The technology flows of trickle-down help less innovative regions specialize in more complex technologies than their local knowledge base, while siphon does not significantly impact place breakthroughs in innovative regions.

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Acknowledgements

This work was supported by the National Social Science Foundation of China (20BJL109) and National Natural Science Foundation of China (42171179). Thanks Prof. Can Cui for writing guidence and Han Bao for dicussion related to this work.

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Authors and Affiliations

  1. Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou, 510070, China

    Xiangjie Liu

  2. Institute for Global Innovation and Development, East China Normal University, Shanghai, 200062, China

    Chengliang Liu

  3. School of Geographic Sciences, East China Normal University, Shanghai, 200241, China

    Chengliang Liu & Junxian Piao

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  1. Xiangjie Liu
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Correspondence to Chengliang Liu.

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Appendix

Appendix

In order to compare the pros and cons of the three methods, we use an inverted triangle diagram and statistics to compare the urban and technological complexity in 2001, 2005 and 2016.

First, comparing all results, Fig. 

Fig. 2
figure 2

Inverted triangle diagram of the three methods of MR, FC and GENEPY

Full size image

2 visualizes the results in 2016, where the abscissa represents the city complexity ranking and the ordinate represents the technology complexity ranking. Since the most complex cities have the most diverse technologies, the more similar the results are to the inverted triangle, the better robust the model has. It can be seen from the figure that the overflow points on the right side of the MR red line are the most scattered, so that the result of MR has the poorest robustness. In addition, the result of FC is the best, and GENEPY is between the two. Therefore, from the perspective of all urban samples, FC ≈ GENEPY > MR.

Secondly, comparing the specific values of the top ten cities (Table

Table 7 Descriptive statistics
Full size table

9), the FC coefficient fluctuates greatly, and the literature involving FC generally adopts standardized results to avoid the problem of excessive coefficient gaps in different years. MR and GENEPY results are relatively stable, but the MR in 2001 was inconsistent with the actual, including Lingshui, Pu'er, Changjiang and other less developed regions whose coefficients were too large. Due to the relatively small number of patent application data in 2001, the results are not robust, but the GENEPY results are relatively stable. As a consequence, from the specific value, GENEPY > MR >> FC.

Table 8 Correlation matrix
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Table 9 The results of the top 10 cities of the three methods of MR, FC and GENEPY
Full size table

Therefore, this paper adopts GENEPY. In fact, GENEPY is a compromise algorithm proposed for the shortcomings of MR and FC algorithms, see Sciarra et al. (2020), for details.

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Liu, X., Liu, C. & Piao, J. Unpacking technology flows based on patent transactions: does trickle-down, proximity, and siphon help regional specialization?. Ann Reg Sci (2024). https://doi.org/10.1007/s00168-024-01277-y

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