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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
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    1. 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.
    2. 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.
    3. 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.
    4. C. Lovell, 2003. "The Decomposition of Malmquist Productivity Indexes," Journal of Productivity Analysis, Springer, vol. 20(3), pages 437-458, November.
    5. 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.
    6. repec:bla:scandj:v:98:y:1996:i:2:p:303-13 is not listed on IDEAS
    7. 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.
    8. 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.
    9. Toloo, Mehdi & Mensah, Emmanuel Kwasi & Salahi, Maziar, 2022. "Robust optimization and its duality in data envelopment analysis," Omega, Elsevier, vol. 108(C).
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
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