IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0188816.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0188816
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0188816&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0188816?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Minggang & Xu, Hua & Tian, Lixin & Eugene Stanley, H., 2018. "Degree distributions and motif profiles of limited penetrable horizontal visibility graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 620-634.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Minggang & Zhao, Longfeng & Du, Ruijin & Wang, Chao & Chen, Lin & Tian, Lixin & Eugene Stanley, H., 2018. "A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms," Applied Energy, Elsevier, vol. 220(C), pages 480-495.
    2. Wang, Minggang & Xu, Hua & Tian, Lixin & Eugene Stanley, H., 2018. "Degree distributions and motif profiles of limited penetrable horizontal visibility graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 620-634.
    3. Xu, Hua & Wang, Minggang & Jiang, Shumin & Yang, Weiguo, 2020. "Carbon price forecasting with complex network and extreme learning machine," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    4. Guangyong Zhang & Lixin Tian & Wenbin Zhang & Xu Yan & Bingyue Wan & Zaili Zhen, 2020. "A Study on the Similarities and Differences of the Conventional Gasoline Spot Price Fluctuation Network between Different Harbors," Sustainability, MDPI, vol. 12(2), pages 1-25, January.
    5. 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.
    6. Wang, Minggang & Tian, Lixin & Zhou, Peng, 2018. "A novel approach for oil price forecasting based on data fluctuation network," Energy Economics, Elsevier, vol. 71(C), pages 201-212.
    7. An, Sufang & Gao, Xiangyun & Jiang, Meihui & Sun, Xiaoqi, 2018. "Multivariate financial time series in the light of complex network analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 1241-1255.
    8. 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.
    9. Xu, Paiheng & Zhang, Rong & Deng, Yong, 2017. "A novel weight determination method for time series data aggregation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 42-55.
    10. Wang, Chao & Zhang, Xinyi & Wang, Minggang & Lim, Ming K. & Ghadimi, Pezhman, 2019. "Predictive analytics of the copper spot price by utilizing complex network and artificial neural network techniques," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
    11. Herzer Dierk, 2022. "Semi-endogenous Versus Schumpeterian Growth Models: A Critical Review of the Literature and New Evidence," Review of Economics, De Gruyter, vol. 73(1), pages 1-55, April.
    12. Kitamura, Toshihiko & Managi, Shunsuke, 2017. "Driving force and resistance: Network feature in oil trade," Applied Energy, Elsevier, vol. 208(C), pages 361-375.
    13. Chen, Weidong & Xiong, Shi & Chen, Quanyu, 2022. "Characterizing the dynamic evolutionary behavior of multivariate price movement fluctuation in the carbon-fuel energy markets system from complex network perspective," Energy, Elsevier, vol. 239(PA).
    14. Neves, Pedro Cunha & Sequeira, Tiago Neves, 2018. "Spillovers in the production of knowledge: A meta-regression analysis," Research Policy, Elsevier, vol. 47(4), pages 750-767.
    15. Wang, Di & Zhang, Zhiyuan & Yang, Xiaodi & Zhang, Yanfang & Li, Yuman & Zhao, Yueying, 2021. "Multi-scenario simulation on the impact of China's electricity bidding policy based on complex networks model," Energy Policy, Elsevier, vol. 158(C).
    16. Herzer, Dierk, 2020. "How does mortality affect innovative activity in the long run?," World Development, Elsevier, vol. 125(C).
    17. Lixin Tian & Huan Chen & Zaili Zhen, 2018. "Research on the forward-looking behavior judgment of heating oil price evolution based on complex networks," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-18, September.
    18. Wang, Minggang & Hua, Chenyu & Zhu, Mengrui & Xie, Shangshan & Xu, Hua & Vilela, André L.M. & Tian, Lixin, 2022. "Interrelation measurement based on the multi-layer limited penetrable horizontal visibility graph," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    19. Steven Bond‐Smith, 2019. "The Decades‐Long Dispute Over Scale Effects In The Theory Of Economic Growth," Journal of Economic Surveys, Wiley Blackwell, vol. 33(5), pages 1359-1388, December.
    20. Zhou, Yang & Xie, Chi & Wang, Gang-Jin & Zhu, You & Uddin, Gazi Salah, 2023. "Analysing and forecasting co-movement between innovative and traditional financial assets based on complex network and machine learning," Research in International Business and Finance, Elsevier, vol. 64(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0188816. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.