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The Financing Efficiency of China’s Industrial Listed Enterprises Based on the Dynamic–Network SBM Model

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  • Xianhua Tan

    (School of Economics and Management, Shangrao Normal University, Shangrao 334001, China)

  • Danting Zheng

    (School of Economics and Management, Shangrao Normal University, Shangrao 334001, China)

  • Yuanyuan Zhu

    (School of Economics and Management, Shangrao Normal University, Shangrao 334001, China)

  • Sanggyun Na

    (School of Business Administration, Wonkwang University, Iksan 54538, Republic of Korea)

Abstract

Industry is an important force in China’s economic development; however, with the transformation and upgrading of the industrial structure, a large number of resources have flowed to the tertiary industry, and the funding problem has become one of the main disadvantages restricting China’s industrial enterprises’ sustainable development. This paper aims to point out the problems and improvement directions of financing efficiency of China’s industrial listed enterprises. Based on the two-stage dynamic network SBM (DNSBM) model, this paper evaluates the financing efficiency of 450 of China’s industrial listed enterprises from 2011 to 2017. The results show that: (1) the overall financing efficiency of China’s industrial listed enterprises is low, the funds are not used effectively, and there is great room for improvement; (2) the overall financing efficiency of state-owned enterprises (SOEs) is lower than that of non-state-owned enterprises (NSOEs), the average fund raising efficiency of SOEs is greater than the fund using efficiency, but the opposite is true for NSOEs; (3) the overall financing efficiency of the main-board-listed enterprises is the lowest, and that of the growth enterprise market (GEM) is the highest, the most obvious gap is in the second stage of fund using, but this gap is gradually narrowing; and (4) the overall financing efficiency of China’s industrial enterprises has obvious regional characteristics, the fund raising efficiency value in each region is not much different, while the fund using is significantly different. To improve financing efficiency, enterprises must improve their financing channels, choose the best financing method, maintain a reasonable debt-financing ratio, improve management level and profitability, increase enterprise value, enhance the debt-paying ability, and attract more capital at a low cost. In addition, the government should also provide corresponding financing support policies for different types of enterprises.

Suggested Citation

  • Xianhua Tan & Danting Zheng & Yuanyuan Zhu & Sanggyun Na, 2023. "The Financing Efficiency of China’s Industrial Listed Enterprises Based on the Dynamic–Network SBM Model," Sustainability, MDPI, vol. 15(6), pages 1-19, March.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:4723-:d:1090015
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    References listed on IDEAS

    as
    1. Fare, Rolf & Grosskopf, Shawna, 1996. "Productivity and intermediate products: A frontier approach," Economics Letters, Elsevier, vol. 50(1), pages 65-70, January.
    2. Williamson, Oliver E, 1988. " Corporate Finance and Corporate Governance," Journal of Finance, American Finance Association, vol. 43(3), pages 567-591, July.
    3. Sueyoshi, Toshiyuki & Sekitani, Kazuyuki, 2005. "Returns to scale in dynamic DEA," European Journal of Operational Research, Elsevier, vol. 161(2), pages 536-544, March.
    4. Xun Liu & Xiaoliang Yu & Simon Gao, 2019. "A quantitative study of financing efficiency of low‐carbon companies: A three‐stage data envelopment analysis," Business Strategy and the Environment, Wiley Blackwell, vol. 28(5), pages 858-871, July.
    5. Stulz, ReneM., 1990. "Managerial discretion and optimal financing policies," Journal of Financial Economics, Elsevier, vol. 26(1), pages 3-27, July.
    6. Tone, Kaoru & Tsutsui, Miki, 2009. "Network DEA: A slacks-based measure approach," European Journal of Operational Research, Elsevier, vol. 197(1), pages 243-252, August.
    7. Xianhua Tan & Sanggyun Na & Lei Guo & Jing Chen & Zhihua Ruan, 2019. "External Financing Efficiency of Rural Revitalization Listed Companies in China—Based on Two-Stage DEA and Grey Relational Analysis," Sustainability, MDPI, vol. 11(16), pages 1-21, August.
    8. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    9. Kao, Chiang & Hwang, Shiuh-Nan, 2008. "Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan," European Journal of Operational Research, Elsevier, vol. 185(1), pages 418-429, February.
    10. Kaoru Tone & Miki Tsutsui, 2014. "Slacks-Based Network DEA," International Series in Operations Research & Management Science, in: Wade D. Cook & Joe Zhu (ed.), Data Envelopment Analysis, edition 127, chapter 0, pages 231-259, Springer.
    11. Karthik Balakrishnan & John E. Core & Rodrigo S. Verdi, 2014. "The Relation Between Reporting Quality and Financing and Investment: Evidence from Changes in Financing Capacity," Journal of Accounting Research, Wiley Blackwell, vol. 52(1), pages 1-36, March.
    12. He, Ying & Chen, Cindy & Hu, Yue, 2019. "Managerial overconfidence, internal financing, and investment efficiency: Evidence from China," Research in International Business and Finance, Elsevier, vol. 47(C), pages 501-510.
    13. Jayaraman, Sudarshan, 2012. "The effect of enforcement on timely loss recognition: Evidence from insider trading laws," Journal of Accounting and Economics, Elsevier, vol. 53(1), pages 77-97.
    14. Gheeraert, Laurent & Weill, Laurent, 2015. "Does Islamic banking development favor macroeconomic efficiency? Evidence on the Islamic finance-growth nexus," Economic Modelling, Elsevier, vol. 47(C), pages 32-39.
    15. Forrester, Sydney P. & Reames, Tony G., 2020. "Understanding the residential energy efficiency financing coverage gap and market potential," Applied Energy, Elsevier, vol. 260(C).
    16. Ma, Hoi-Lam & Wang, Z.X. & Chan, Felix T.S., 2020. "How important are supply chain collaborative factors in supply chain finance? A view of financial service providers in China," International Journal of Production Economics, Elsevier, vol. 219(C), pages 341-346.
    17. Jin, Yi & Gao, Xiaoyan & Wang, Min, 2021. "The financing efficiency of listed energy conservation and environmental protection firms: Evidence and implications for green finance in China," Energy Policy, Elsevier, vol. 153(C).
    18. Demirguc-Kunt, Ash & Levine, Ross, 1996. "Stock Markets, Corporate Finance, and Economic Growth: An Overview," The World Bank Economic Review, World Bank, vol. 10(2), pages 223-239, May.
    19. Tone, Kaoru & Tsutsui, Miki, 2010. "Dynamic DEA: A slacks-based measure approach," Omega, Elsevier, vol. 38(3-4), pages 145-156, June.
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