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Research on the development efficiency of regional high-end talent in China: A complex network approach

Author

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  • Zhen Zhang
  • Minggang Wang
  • Lixin Tian
  • Wenbin Zhang

Abstract

In this paper, based on the panel data of 31 provinces and cities in China from 1991 to 2016, the regional development efficiency matrix of high-end talent is obtained by DEA method, and the matrix is converted into a continuous change of complex networks through the construction of sliding window. Using a series of continuous changes in the complex network topology statistics, the characteristics of regional high-end talent development efficiency system are analyzed. And the results show that the average development efficiency of high-end talent in the western region is at a low level. After 2005, the national regional high-end talent development efficiency network has both short-range relevance and long-range relevance in the evolution process. The central region plays an important intermediary role in the national regional high-end talent development system. And the western region has high clustering characteristics. With the implementation of the high-end talent policies with regional characteristics by different provinces and cities, the relevance of high-end talent development efficiency in various provinces and cities presents a weakening trend, and the geographical characteristics of high-end talent are more and more obvious.

Suggested Citation

  • Zhen Zhang & Minggang Wang & Lixin Tian & Wenbin Zhang, 2017. "Research on the development efficiency of regional high-end talent in China: A complex network approach," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-18, December.
    • Handle: RePEc:plo:pone00:0188816
    DOI: 10.1371/journal.pone.0188816
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    References listed on IDEAS

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    1. Yasser Abdih & Frederick Joutz, 2006. "Relating the Knowledge Production Function to Total Factor Productivity: An Endogenous Growth Puzzle," IMF Staff Papers, Palgrave Macmillan, vol. 53(2), pages 1-3.
    2. James B. Ang & Jakob B. Madsen, 2011. "Can Second-Generation Endogenous Growth Models Explain the Productivity Trends and Knowledge Production in the Asian Miracle Economies?," The Review of Economics and Statistics, MIT Press, vol. 93(4), pages 1360-1373, November.
    3. Doug Fuller, 2010. "China's Emerging Technological Edge: Assessing the Role of High‐End Talent – By Denis Fred Simon and Cong Cao," Review of Policy Research, Policy Studies Organization, vol. 27(3), pages 333-334, May.
    4. Minggang Wang & Hua Xu, 2012. "A Novel Four-Dimensional Energy-Saving and Emission-Reduction System and Its Linear Feedback Control," Journal of Applied Mathematics, Hindawi, vol. 2012, pages 1-14, December.
    5. Wang, Minggang & Chen, Ying & Tian, Lixin & Jiang, Shumin & Tian, Zihao & Du, Ruijin, 2016. "Fluctuation behavior analysis of international crude oil and gasoline price based on complex network perspective," Applied Energy, Elsevier, vol. 175(C), pages 109-127.
    6. Wang, Minggang & Tian, Lixin, 2016. "From time series to complex networks: The phase space coarse graining," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 456-468.
    7. Wang, Minggang & Tian, Lixin & Xu, Hua & Li, Weiyu & Du, Ruijin & Dong, Gaogao & Wang, Jie & Gu, Jiani, 2017. "Systemic risk and spatiotemporal dynamics of the consumer market of China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 188-204.
    8. Wang, Minggang & Tian, Lixin & Du, Ruijin, 2016. "Research on the interaction patterns among the global crude oil import dependency countries: A complex network approach," Applied Energy, Elsevier, vol. 180(C), pages 779-791.
    9. Huan Chen & Lixin Tian & Minggang Wang & Zaili Zhen, 2017. "Analysis of the Dynamic Evolutionary Behavior of American Heating Oil Spot and Futures Price Fluctuation Networks," Sustainability, MDPI, vol. 9(4), pages 1-29, April.
    10. Du, Ruijin & Wang, Ya & Dong, Gaogao & Tian, Lixin & Liu, Yixiao & Wang, Minggang & Fang, Guochang, 2017. "A complex network perspective on interrelations and evolution features of international oil trade, 2002–2013," Applied Energy, Elsevier, vol. 196(C), pages 142-151.
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