IDEAS home Printed from https://ideas.repec.org/a/spr/cejnor/v31y2023i4d10.1007_s10100-023-00853-5.html
   My bibliography  Save this article

Comparing Malmquist and Hicks–Moorsteen productivity changes in China’s high-tech industries: exploring convexity implications

Author

Listed:
  • Xiaoqing Chen

    (Nanjing University of Information Science and Technology)

  • Xinwang Liu

    (Southeast University
    National School of Development and Policy of Southeast University)

Abstract

The high-tech industry, as a two-stage system consisting of technology development and economic transformation, plays an important role in the transformation and upgrading of China’s economic development. Productivity analysis is the primary method for assessing the performance of economic growth. Hence, it is important to accurately measure productivity changes, which can provide valuable guidance for its further development. Accordingly, based on the provincial-level sample data of the high-tech industry, this paper measures the productivity changes of the whole system and each stage by adopting Malmquist and Hicks–Moorsteen indices both under the convex and nonconvex measures. Further, a comparative analysis of productivity changes from the national and regional high-tech industry perspective has been performed. Empirical results suggest that productivity changes differ significantly among the national, Eastern, and Northeast regions only for the variable returns to scale assumption under the nonconvex measure, specifically for the whole system. Moreover, more contradictory results between Malmquist and Hicks–Moorsteen indices occur under variable returns to scale and nonconvex technology. Furthermore, productivity growth for the whole system is primarily attributed to that of the technology development stage, except for the Central region. In addition, the differences in productivity changes across regions are reduced. Finally, some constructive suggestions have been made so that policymakers can provide theoretical references when making decisions for the high-quality development of the high-tech industry.

Suggested Citation

  • Xiaoqing Chen & Xinwang Liu, 2023. "Comparing Malmquist and Hicks–Moorsteen productivity changes in China’s high-tech industries: exploring convexity implications," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(4), pages 1209-1237, December.
  • Handle: RePEc:spr:cejnor:v:31:y:2023:i:4:d:10.1007_s10100-023-00853-5
    DOI: 10.1007/s10100-023-00853-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10100-023-00853-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10100-023-00853-5?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Qingxian An & Fanyong Meng & Beibei Xiong & Zongrun Wang & Xiaohong Chen, 2020. "Assessing the relative efficiency of Chinese high-tech industries: a dynamic network data envelopment analysis approach," Annals of Operations Research, Springer, vol. 290(1), pages 707-729, July.
    2. Kerstens, Kristiaan & Vanden Eeckaut, Philippe, 1999. "Estimating returns to scale using non-parametric deterministic technologies: A new method based on goodness-of-fit," European Journal of Operational Research, Elsevier, vol. 113(1), pages 206-214, February.
    3. Kerstens, Kristiaan & Van de Woestyne, Ignace, 2014. "Comparing Malmquist and Hicks–Moorsteen productivity indices: Exploring the impact of unbalanced vs. balanced panel data," European Journal of Operational Research, Elsevier, vol. 233(3), pages 749-758.
    4. Aparicio, Juan & López-Torres, Laura & Santín, Daniel, 2018. "Economic crisis and public education. A productivity analysis using a Hicks-Moorsteen index," Economic Modelling, Elsevier, vol. 71(C), pages 34-44.
    5. Caves, Douglas W & Christensen, Laurits R & Diewert, W Erwin, 1982. "The Economic Theory of Index Numbers and the Measurement of Input, Output, and Productivity," Econometrica, Econometric Society, vol. 50(6), pages 1393-1414, November.
    6. Walter Briec & Kristiaan Kerstens & Philippe Venden Eeckaut, 2004. "Non-convex Technologies and Cost Functions: Definitions, Duality and Nonparametric Tests of Convexity," Journal of Economics, Springer, vol. 81(2), pages 155-192, February.
    7. C. Lovell, 2003. "The Decomposition of Malmquist Productivity Indexes," Journal of Productivity Analysis, Springer, vol. 20(3), pages 437-458, November.
    8. W. Briec & K. Kerstens, 2009. "Infeasibility and Directional Distance Functions with Application to the Determinateness of the Luenberger Productivity Indicator," Journal of Optimization Theory and Applications, Springer, vol. 141(1), pages 55-73, April.
    9. repec:bla:scandj:v:98:y:1996:i:2:p:303-13 is not listed on IDEAS
    10. Samoilenko, Sergey & Osei-Bryson, Kweku-Muata, 2013. "Using Data Envelopment Analysis (DEA) for monitoring efficiency-based performance of productivity-driven organizations: Design and implementation of a decision support system," Omega, Elsevier, vol. 41(1), pages 131-142.
    11. Yu, Anyu & Shi, Yu & You, Jianxin & Zhu, Joe, 2021. "Innovation performance evaluation for high-tech companies using a dynamic network data envelopment analysis approach," European Journal of Operational Research, Elsevier, vol. 292(1), pages 199-212.
    12. Shoufu Lin & Ji Sun & Shanyong Wang, 2019. "Dynamic evaluation of the technological innovation efficiency of China’s industrial enterprises," Science and Public Policy, Oxford University Press, vol. 46(2), pages 232-243.
    13. M. J. Farrell, 1959. "The Convexity Assumption in the Theory of Competitive Markets," Journal of Political Economy, University of Chicago Press, vol. 67(4), pages 377-377.
    14. Toloo, Mehdi & Mensah, Emmanuel Kwasi & Salahi, Maziar, 2022. "Robust optimization and its duality in data envelopment analysis," Omega, Elsevier, vol. 108(C).
    15. Fare, Rolf & Shawna Grosskopf & Mary Norris & Zhongyang Zhang, 1994. "Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries," American Economic Review, American Economic Association, vol. 84(1), pages 66-83, March.
    16. Li, Lan-bing & Liu, Bing-lian & Liu, Wei-lin & Chiu, Yung-Ho, 2017. "Efficiency evaluation of the regional high-tech industry in China: A new framework based on meta-frontier dynamic DEA analysis," Socio-Economic Planning Sciences, Elsevier, vol. 60(C), pages 24-33.
    Full references (including those not matched with items on IDEAS)

    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. Chen, Xiaoqing & Kerstens, Kristiaan & Tsionas, Mike, 2024. "Does productivity change at all in Swedish district courts? Empirical analysis focusing on horizontal mergers," Socio-Economic Planning Sciences, Elsevier, vol. 91(C).
    2. Chen, Xiaoqing & Liu, Xinwang & Zhu, Qingyuan, 2022. "Comparative analysis of total factor productivity in China's high-tech industries," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    3. Kerstens, Kristiaan & Van de Woestyne, Ignace, 2014. "Comparing Malmquist and Hicks–Moorsteen productivity indices: Exploring the impact of unbalanced vs. balanced panel data," European Journal of Operational Research, Elsevier, vol. 233(3), pages 749-758.
    4. Aparicio, Juan & Ortiz, Lidia & Santín, Daniel, 2021. "Comparing group performance over time through the Luenberger productivity indicator: An application to school ownership in European countries," European Journal of Operational Research, Elsevier, vol. 294(2), pages 651-672.
    5. Aparicio, Juan & Santín, Daniel, 2024. "Global and local technical changes: A new decomposition of the Malmquist productivity index using virtual units," Economic Modelling, Elsevier, vol. 134(C).
    6. Briec, Walter & Dumas, Audrey & Kerstens, Kristiaan & Stenger, Agathe, 2022. "Generalised commensurability properties of efficiency measures: Implications for productivity indicators," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1481-1492.
    7. Jin, Qianying & Kerstens, Kristiaan & Van de Woestyne, Ignace, 2020. "Metafrontier productivity indices: Questioning the common convexification strategy," European Journal of Operational Research, Elsevier, vol. 283(2), pages 737-747.
    8. Kristiaan Kerstens & Ignace Van de Woestyne, 2018. "Enumeration algorithms for FDH directional distance functions under different returns to scale assumptions," Annals of Operations Research, Springer, vol. 271(2), pages 1067-1078, December.
    9. Pontus Mattsson & Jonas Månsson & Christian Andersson & Fredrik Bonander, 2018. "A bootstrapped Malmquist index applied to Swedish district courts," European Journal of Law and Economics, Springer, vol. 46(1), pages 109-139, August.
    10. Forsund, Finn R. & Sarafoglou, Nikias, 2005. "The tale of two research communities: The diffusion of research on productive efficiency," International Journal of Production Economics, Elsevier, vol. 98(1), pages 17-40, October.
    11. Kerstens, Kristiaan & Sadeghi, Jafar & Toloo, Mehdi & Van de Woestyne, Ignace, 2022. "Procedures for ranking technical and cost efficient units: With a focus on nonconvexity," European Journal of Operational Research, Elsevier, vol. 300(1), pages 269-281.
    12. W. Erwin Diewert & Kevin J. Fox, 2014. "Decomposing Bjurek Productivity Indexes into Explanatory Factors," Discussion Papers 2014-33, School of Economics, The University of New South Wales.
    13. Chen, Xiang & Chen, Yong & Huang, Wenli & Zhang, Xuping, 2023. "A new Malmquist-type green total factor productivity measure: An application to China," Energy Economics, Elsevier, vol. 117(C).
    14. Lin, Shoufu & Lin, Ruoyun & Sun, Ji & Wang, Fei & Wu, Weixiang, 2021. "Dynamically evaluating technological innovation efficiency of high-tech industry in China: Provincial, regional and industrial perspective," Socio-Economic Planning Sciences, Elsevier, vol. 74(C).
    15. Reza Fallahnejad & Mohammad Reza Mozaffari & Peter Fernandes Wanke & Yong Tan, 2024. "Nash Bargaining Game Enhanced Global Malmquist Productivity Index for Cross-Productivity Index," Games, MDPI, vol. 15(1), pages 1-21, January.
    16. Xiaoqing Chen & Kristiaan Kerstens & Qingyuan Zhu, 2021. "Exploring Horizontal Mergers in Swedish District Courts Using Convex and Nonconvex Technologies: Usefulness of a Conservative Approach," Working Papers 2021-EQM-05, IESEG School of Management.
    17. Zabala-Iturriagagoitia, Jon Mikel & Aparicio, Juan & Ortiz, Lidia & Carayannis, Elias G. & Grigoroudis, Evangelos, 2021. "The productivity of national innovation systems in Europe: Catching up or falling behind?," Technovation, Elsevier, vol. 102(C).
    18. Yao-yao Song & Xian-tong Ren & Guo-liang Yang, 2023. "Capacity utilization change over time," Journal of Productivity Analysis, Springer, vol. 59(1), pages 61-78, February.
    19. Diogo Cunha Ferreira & Rui Cunha Marques, 2016. "Malmquist and Hicks–Moorsteen Productivity Indexes for Clusters Performance Evaluation," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(05), pages 1015-1053, September.
    20. Diana L. Becerra-Peña & María Ximena Lemos Mejía, 2021. "La productividad del sector manufacturero: caso Colombia 2005-2016," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 16(4), pages 1-27, Octubre -.

    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:spr:cejnor:v:31:y:2023:i:4:d:10.1007_s10100-023-00853-5. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.