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Study of total factor productivity on China’s science and technology service industry: provincial efficiency comparatively using 2010-2019 data

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  • Lu Guo
  • Zhimin Dai

Abstract

This research describes the industries’ development and efficiency distribution, investigates the process of industry efficiency influence of these industries’ agglomeration effect on total factor productivity (TFP) change based China’s science and technology service industries from 2010 to 2019 data. We analyse the main factors influencing TFP change and the stage effect difference of significant factors through using linear and non-linear regression equations by construct the panel regression equations. The empirical results show:1) the overall trend of science and technology service industry agglomeration in China is declining; 2) Provinces and regions exhibits a trend of respective agglomeration within the economic belt; 3) the different location entropies of the five regions lead to obvious differences in the development stages; 4) the influences of technology progress and foreign trade on TFP efficiency changes are generally significant; 5)Science and technology service industry have improved industrial efficiency in the central and western regions and made it easier for these regions to break through bottlenecks. We conclude that industrial efficiency improvements should be based on technology upgrading and expansion in infrastructure allocation, also offering more realistic solutions to promote system construction and interregional policy coordination.

Suggested Citation

  • Lu Guo & Zhimin Dai, 2021. "Study of total factor productivity on China’s science and technology service industry: provincial efficiency comparatively using 2010-2019 data," Applied Economics, Taylor & Francis Journals, vol. 53(51), pages 5917-5928, November.
    • Handle: RePEc:taf:applec:v:53:y:2021:i:51:p:5917-5928
    DOI: 10.1080/00036846.2021.1931658
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    Cited by:

    1. Yin, Hua-Tang & Wen, Jun & Chang, Chun-Ping, 2022. "Science-technology intermediary and innovation in China: Evidence from State Administration for Market Regulation, 2000–2019," Technology in Society, Elsevier, vol. 68(C).
    2. Xinyue Lin & Lingli Qi & Haoran Pan & Basil Sharp, 2022. "COVID-19 Pandemic, Technological Progress and Food Security Based on a Dynamic CGE Model," Sustainability, MDPI, vol. 14(3), pages 1-18, February.

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