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An Analysis on the Influence of R&D Fiscal and Tax Subsidies on Regional Innovation Efficiency: Empirical Evidence from China

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  • Weijiang Liu

    (Centre for Quantitative Economics, Jilin University, Changchun 130012, China)

  • Yue Bai

    (School of Business, Jilin University, Changchun 130012, China)

Abstract

To promote the National Mid-andLong-Term Scientific and Technical Development Program, utilizing the technical innovation data from 30 provinces of China from2002–2016, this paper evaluates the inter-provincial differences of China’s regional innovation efficiency from four aspects of technical efficiency, efficiency index change, returns to scale, and projection analysis by using the DEA-Malmquist index method and constructs of the DEA-Tobit random response model to explore the impact of government funding on regional innovation efficiency. The research results show that: (1) The local development of regional innovation efficiency in China is unbalanced, and the level of pure technical efficiency restricts the improvement of innovation efficiency. (2) In the prophase of the scientific and technical development plan, technological progress has led to the growth of total factor productivity, resulting in the formation of scale effect; in the later stage, the scale return shows an overall increasing trend, and the continuous expansion of technological scale and opportunities has improved the regional innovation efficiency. (3) The R&D fiscal and tax subsidies have policy sustainability, and the direct government funding can significantly improve innovation efficiency, while the enterprises investment is opposite, and the pretax additional deduction has a negative but not significant impact. The government should give priority to direct subsidy and supplemented by tax preference, making reasonable policy allocations to expand the policy effect.

Suggested Citation

  • Weijiang Liu & Yue Bai, 2021. "An Analysis on the Influence of R&D Fiscal and Tax Subsidies on Regional Innovation Efficiency: Empirical Evidence from China," Sustainability, MDPI, vol. 13(22), pages 1-24, November.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:22:p:12707-:d:680959
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    Cited by:

    1. Wendong Zhu & Dahai Li & Limin Han, 2022. "Spatial–Temporal Evolution and Sustainable Type Division of Fishery Science and Technology Innovation Efficiency in China," Sustainability, MDPI, vol. 14(12), pages 1-19, June.
    2. Hyensup Shim & Kiyoon Shin, 2021. "Empirical Analysis of Evidence-Based Policymaking in R&D Programmes," Sustainability, MDPI, vol. 14(1), pages 1-15, December.
    3. Yamin Du & Wonchul Seo, 2022. "A Comparative Study on the Efficiency of R&D Activities of Universities in China by Region Using DEA–Malmquist," Sustainability, MDPI, vol. 14(16), pages 1-13, August.
    4. Jiaqi Chen & Sang‐Ho Lee, 2023. "Cournot–Bertrand comparisons under R&D competition: Output versus R&D subsidies," International Journal of Economic Theory, The International Society for Economic Theory, vol. 19(1), pages 77-100, March.

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