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Factors affecting evolution of the interprovincial technology patent trade networks in China based on exponential random graph models

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  • He, Xi-jun
  • Dong, Yan-bo
  • Wu, Yu-ying
  • Jiang, Guo-rui
  • Zheng, Yao

Abstract

Five China’s interprovincial patent trade networks over the period 2012–2016 are established on the basis of patent transfer information collection, transfer entity identification, and regional mapping. Based on the analysis of patent trade trends and the characteristics of the network structure, endogenous structural effects and exogenous factors affecting the evolution of the trade networks are proposed, and exponential random graph models (ERGMs) constructed to select the most parsimonious model. Based on the variables in the most parsimonious model, temporal ERGM is used to determine the factors of trade networks evolution among provinces. The results provide six key factors affecting network evolution over the period 2012–2016, i.e., reciprocity, eastern output effect, intensity of technological R&D, proximity to economic center, and technology openness. Moreover, analysis reveals that the concentration of technology in provinces is the key factor inhibiting evolution, while differences among provinces on economic levels, technology trade experience, the technology receiving of western provinces, and the geographical proximity of provinces exhibit a weak effect on the evolution process. Finally, suggestions to promote interprovincial patent trade are proposed.

Suggested Citation

  • He, Xi-jun & Dong, Yan-bo & Wu, Yu-ying & Jiang, Guo-rui & Zheng, Yao, 2019. "Factors affecting evolution of the interprovincial technology patent trade networks in China based on exponential random graph models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 443-457.
    • Handle: RePEc:eee:phsmap:v:514:y:2019:i:c:p:443-457
    DOI: 10.1016/j.physa.2018.09.062
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    as
    1. Garry Robins & Philippa Pattison & Stanley Wasserman, 1999. "Logit models and logistic regressions for social networks: III. Valued relations," Psychometrika, Springer;The Psychometric Society, vol. 64(3), pages 371-394, September.
    2. Hunter, David R. & Goodreau, Steven M. & Handcock, Mark S., 2013. "ergm.userterms: A Template Package for Extending statnet," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 52(i02).
    3. Gilsing, Victor & Nooteboom, Bart & Vanhaverbeke, Wim & Duysters, Geert & van den Oord, Ad, 2008. "Network embeddedness and the exploration of novel technologies: Technological distance, betweenness centrality and density," Research Policy, Elsevier, vol. 37(10), pages 1717-1731, December.
    4. Orman, Günce Keziban & Labatut, Vincent & Naskali, Ahmet Teoman, 2017. "Exploring the evolution of node neighborhoods in Dynamic Networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 375-391.
    5. Hunter, David R. & Handcock, Mark S. & Butts, Carter T. & Goodreau, Steven M. & Morris, Martina, 2008. "ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 24(i03).
    6. Hunter, David R. & Goodreau, Steven M. & Handcock, Mark S., 2008. "Goodness of Fit of Social Network Models," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 248-258, March.
    7. Zhang, Shuhong & Wang, Lin & Liu, Zhixin & Wang, Xiaofan, 2016. "Evolution of international trade and investment networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 752-763.
    8. Gupeng Zhang & Hongbo Duan & Jianghua Zhou, 2016. "Investigating determinants of inter-regional technology transfer in China: a network analysis with provincial patent data," Review of Managerial Science, Springer, vol. 10(2), pages 345-364, March.
    9. Desmarais, B.A. & Cranmer, S.J., 2012. "Statistical mechanics of networks: Estimation and uncertainty," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1865-1876.
    10. Výrost, Tomáš, 2012. "Country effects in CEE3 stock market networks: a preliminary study," MPRA Paper 43481, University Library of Munich, Germany.
    11. Pauls, Scott D. & Cranmer, Skyler J., 2017. "Affinity communities in United Nations voting: Implications for democracy, cooperation, and conflict," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 484(C), pages 428-439.
    12. He, Xijun & Dong, Yanbo & Wu, Yuying & Wei, Guodan & Xing, Lizhi & Yan, Jia, 2017. "Structure analysis and core community detection of embodied resources networks among regional industries," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 137-150.
    13. Scott W. Cunningham & Claudia Werker, 2012. "Proximity and collaboration in European nanotechnology," Papers in Regional Science, Wiley Blackwell, vol. 91(4), pages 723-742, November.
    14. Xia Gao & Jiancheng Guan & Ronald Rousseau, 2011. "Mapping collaborative knowledge production in China using patent co-inventorships," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(2), pages 343-362, August.
    15. Uwe Cantner & Andreas Meder & Anne ter Wal, 2008. "Innovator networks and regional knowledge base," Jena Economics Research Papers 2008-042, Friedrich-Schiller-University Jena.
    16. Zhang, Beibei & Zhou, Yadong & Xu, Xiaoyan & Wang, Dai & Guan, Xiaohong, 2016. "Dynamic structure evolution of time-dependent network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 347-358.
    17. Choe, Hochull & Lee, Duk Hee & Seo, Il Won & Kim, Hee Dae, 2013. "Patent citation network analysis for the domain of organic photovoltaic cells: Country, institution, and technology field," Renewable and Sustainable Energy Reviews, Elsevier, vol. 26(C), pages 492-505.
    18. Henry, Teague & Gesell, Sabina B. & Ip, Edward H., 2016. "Analyzing heterogeneity in the effects of physical activity in children on social network structure and peer selection dynamics," Network Science, Cambridge University Press, vol. 4(3), pages 336-363, September.
    19. Stanley Wasserman & Philippa Pattison, 1996. "Logit models and logistic regressions for social networks: I. An introduction to Markov graphs andp," Psychometrika, Springer;The Psychometric Society, vol. 61(3), pages 401-425, September.
    20. Xing, Lizhi & Dong, Xianlei & Guan, Jun, 2017. "Global industrial impact coefficient based on random walk process and inter-country input–output table," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 576-591.
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    Cited by:

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    2. Lianyue Feng & Helian Xu & Gang Wu & Wenting Zhang, 2021. "Service trade network structure and its determinants in the Belt and Road based on the temporal exponential random graph model," Pacific Economic Review, Wiley Blackwell, vol. 26(5), pages 617-650, December.
    3. Losacker, Sebastian, 2022. "‘License to green’: Regional patent licensing networks and green technology diffusion in China," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
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    5. Yonghong Ma & Xiaomeng Yang & Sen Qu & Lingkai Kong, 2022. "Research on the formation mechanism of big data technology cooperation networks: empirical evidence from China," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(3), pages 1273-1294, March.
    6. Cornelius Fritz & Michael Lebacher & Göran Kauermann, 2020. "Tempus volat, hora fugit: A survey of tie‐oriented dynamic network models in discrete and continuous time," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(3), pages 275-299, August.
    7. Peng, Wei & Xiong, Langyu, 2022. "Managing financing costs and fostering green transition: The role of green financial policy in China," Economic Analysis and Policy, Elsevier, vol. 76(C), pages 820-836.

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