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A spatial econometric panel data examination of endogenous versus exogenous interaction in Chinese province-level patenting

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  • James LeSage
  • Yuxue Sheng

Abstract

We examine the provincial-level relationship between domestic Chinese intellectual property (IP) and knowledge stocks using a space–time panel model and data set covering monthly patent activity over the period 2002–2010. The goal of the modeling exercise is to explore the elasticity response of IP to knowledge stocks classified by type of creator (universities and research institutes, enterprises, and individuals). A focus is on spatial and time dependence in the relationship between knowledge stocks and IP, which implies spatial spillovers and diffusion over time. Many past studies of regional knowledge production have focused on patent applications as a proxy for regional output from the knowledge production process. However, this ignores the distinction between patent applications and patents granted, with the latter reflecting a decision and ability to convert knowledge produced into IP. This study differs in its focus on the regional relation between IP and knowledge stocks and the space–time dynamics of these. Using patents granted as a proxy for IP, and past patent applications as a proxy for regional knowledge stocks, allows us to explore the implied quality of knowledge production by various types of creators. Because Chinese patent applications have grown by 22 %, questions have been raised about the quantity versus quality of these applications. Our findings shed light on this issue. Copyright Springer-Verlag Berlin Heidelberg 2014

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  • James LeSage & Yuxue Sheng, 2014. "A spatial econometric panel data examination of endogenous versus exogenous interaction in Chinese province-level patenting," Journal of Geographical Systems, Springer, vol. 16(3), pages 233-262, July.
    • Handle: RePEc:kap:jgeosy:v:16:y:2014:i:3:p:233-262
    DOI: 10.1007/s10109-014-0198-7
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    Cited by:

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    4. Wang, Yongpei & Yan, Qing & Yang, Jieru & Komonpipat, Supak & Zhang, Qian, 2024. "Can inter-provincial transmission reduce regional carbon emissions? Evidence from China," Energy Policy, Elsevier, vol. 184(C).
    5. Zhaoliang Li & Minghao Jin & Jianwei Cheng, 2021. "Economic growth of green agriculture and its influencing factors in china: Based on emergy theory and spatial econometric model," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(10), pages 15494-15512, October.
    6. Rodrigo Perez-Silva & Mark D. Partridge & William E. Foster, 2019. "Are foreign-born researchers more innovative? Self-selection and the production of knowledge among PhD recipients in the USA," Journal of Geographical Systems, Springer, vol. 21(4), pages 557-594, December.
    7. Wang, Jian & Wu, Qun & Yan, Siqi & Guo, Guancheng & Peng, Shangui, 2020. "China’s local governments breaking the land use planning quota: A strategic interaction perspective," Land Use Policy, Elsevier, vol. 92(C).
    8. Yanchao Feng & Xiaohong Wang & Wenchao Du & Jun Liu, 2018. "Effects of Air Pollution Control on Urban Development Quality in Chinese Cities Based on Spatial Durbin Model," IJERPH, MDPI, vol. 15(12), pages 1-21, December.
    9. James P. LeSage & R. Kelley Pace, 2018. "Spatial econometric Monte Carlo studies: raising the bar," Empirical Economics, Springer, vol. 55(1), pages 17-34, August.
    10. Liang, Cong & Hui, Eddie C.M. & Yip, Tsz Leung, 2018. "Time on market (TOM): The impact of new residential stamp duty," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 1117-1130.
    11. Anthony J. Glass & Karligash Kenjegalieva, 2024. "Returns to scale, spillovers and persistence: A network perspective of U.S. bank size," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 2049-2076, April.
    12. Ye Yang & Osman Doğan & Süleyman Taşpınar, 2023. "Observed-data DIC for spatial panel data models," Empirical Economics, Springer, vol. 64(3), pages 1281-1314, March.
    13. Lina Meng & Bo Huang, 2018. "Shaping the Relationship Between Economic Development and Carbon Dioxide Emissions at the Local Level: Evidence from Spatial Econometric Models," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 71(1), pages 127-156, September.

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    More about this item

    Keywords

    Static and dynamic space–time panel data models; Log-marginal likelihoods; Local versus global spatial spillovers; C11; C23; O47; O52;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
    • O52 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Europe

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