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Productivity in China's high technology industry: Regional heterogeneity and R&D

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  • Zhang, Rui
  • Sun, Kai
  • Delgado, Michael
  • Kumbhakar, Subal

Abstract

This paper analyzes the impact of Research and Development (R&D) on the productivity of China's high technology industry. In order to capture important differences in the effect of R&D on output that arise from geographic and socioeconomic differences across three major regions in China, we use a novel semiparametric approach that allows us to model heterogeneities across provinces and time. Using a unique provincial level panel dataset spanning the period 2000-2007, we find that the impact of R&D on output varies substantially in terms of magnitude and significance across different regions. Results show that the eastern region benefits the most from R&D investments, however it benefits the least from technical progress, while the western region benefits the least from R&D investments, but enjoys the highest benefits from technical progress. The central region benefits from R&D investments more than the western region and benefits from technical progress more than the eastern region. Our results suggest that R&D investments would significantly increase output in both the eastern and central regions, however technical progress in the central region may further compound the effects of R&D on output within the region.

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  • Zhang, Rui & Sun, Kai & Delgado, Michael & Kumbhakar, Subal, 2011. "Productivity in China's high technology industry: Regional heterogeneity and R&D," MPRA Paper 32507, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:32507
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    Cited by:

    1. Delgado, Michael S. & McCloud, Nadine & Kumbhakar, Subal C., 2014. "A generalized empirical model of corruption, foreign direct investment, and growth," Journal of Macroeconomics, Elsevier, vol. 42(C), pages 298-316.
    2. Peng, Xin-Yi & Lin, Ching-Yi & Mai, Te-Ke, 2024. "Unraveling the dynamics of R&D and firm productivity: Empirical insights from 27 Chinese industries with regional and ownership differences," Finance Research Letters, Elsevier, vol. 59(C).
    3. 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).
    4. Xie, Hualin & Wang, Wei & Yang, Zihui & Choi, Yongrok, 2016. "Measuring the sustainable performance of industrial land utilization in major industrial zones of China," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 207-219.
    5. Chen, Wei & Shen, Yue & Wang, Yanan & Wu, Qun, 2018. "How do industrial land price variations affect industrial diffusion? Evidence from a spatial analysis of China," Land Use Policy, Elsevier, vol. 71(C), pages 384-394.
    6. Wang, Ya & Pan, Jiao-feng & Pei, Rui-min & Yi, Bo-Wen & Yang, Guo-liang, 2020. "Assessing the technological innovation efficiency of China's high-tech industries with a two-stage network DEA approach," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    7. Marian Rizov & Xufei Zhang, 2014. "Regional disparities and productivity in China: Evidence from manufacturing micro data," Papers in Regional Science, Wiley Blackwell, vol. 93(2), pages 321-339, June.
    8. Subal C. Kumbhakar & Kai Sun & Rui Zhang, 2016. "Semiparametric Smooth Coefficient Estimation of a Production System," Pacific Economic Review, Wiley Blackwell, vol. 21(4), pages 464-482, October.
    9. Gong, Binlei, 2018. "Agricultural reforms and production in China: Changes in provincial production function and productivity in 1978–2015," Journal of Development Economics, Elsevier, vol. 132(C), pages 18-31.
    10. Geng, Xin & Sun, Kai, 2019. "Gradient estimation of the local-constant semiparametric smooth coefficient model," Economics Letters, Elsevier, vol. 185(C).
    11. Jinfa Li & Ruijie Qin & Hongbing Jiang, 2022. "Measurement of Innovation Efficiency in China’s Electronics and Communication Equipment Manufacturing Industry-Based on Dynamic Network SBM Model," Sustainability, MDPI, vol. 14(3), pages 1-18, January.
    12. Hasan, Mostafa Monzur & Cheung, Adrian (Wai-Kong), 2018. "Organization capital and firm life cycle," Journal of Corporate Finance, Elsevier, vol. 48(C), pages 556-578.
    13. Alemayehu, Fikru K. & Kumbhakar, Subal C. & Landazuri Tveteraas, Sigbjørn, 2022. "Estimation of staff use efficiency: Evidence from the hospitality industry," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    14. Zhang, Gongyi & Zhao, Shukuan & Xi, Yujuan & Liu, Na & Xu, Xiaobo, 2018. "Relating science and technology resources integration and polarization effect to innovation ability in emerging economies: An empirical study of Chinese enterprises," Technological Forecasting and Social Change, Elsevier, vol. 135(C), pages 188-198.
    15. Anaya, Karim L. & Pollitt, Michael G., 2017. "Using stochastic frontier analysis to measure the impact of weather on the efficiency of electricity distribution businesses in developing economies," European Journal of Operational Research, Elsevier, vol. 263(3), pages 1078-1094.
    16. Shan, Siqing & Jia, Yingwei & Zheng, Xianrong & Xu, Xiaobo, 2018. "Assessing relationship and contribution of China's technological entrepreneurship to socio-economic development," Technological Forecasting and Social Change, Elsevier, vol. 135(C), pages 83-90.

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    More about this item

    Keywords

    China; Research and Development; Productivity; Semiparametric smooth coefficient model;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • L00 - Industrial Organization - - General - - - General

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