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Spatial Modelling of the Impact of R&D Potential on the Dynamics of Scientific and Technological Development of Russian Regions

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  • Ilya V. Naumov
  • Sergey S. Krasnykh

Abstract

The study of the scientific and technological development of Russia's regions is important for several reasons. Firstly, the development of advanced production technologies is crucial for enhancing the competitiveness of Russian industry and ensuring the country's technological sovereignty. Secondly, analyzing the impact of science expenditures, the number of researchers and the number of organizations on the development of advanced technologies will help to identify the factors that either promote or hinder scientific and technological progress in different regions. This, in turn, can serve as the basis for developing proposals to update the Strategy of Scientific and Technological Development of the Russian Federation, as well as the development strategies of federal districts and constituent entities of the Russian Federation. The purpose of this study is to assess the impact of the dynamics of the regions' R&D potential on the dynamics of advanced production technologies developed within those regions using spatial modelling methods. The following hypothesis has been proposed - increasing budget expenditures on science and technology has a positive impact on the development of advanced manufacturing technologies in Russian regions. The novelty of the methodological approach lies in the use of spatial modelling methods applying several spatial weight matrices. In the course of the study, it was confirmed that the dynamics of the newly developed advanced production technologies is positively influenced by the engineering and technical personnel based in the neighboring regions who are engaged in research and development, as well as by the financial resources allocated to scientific organizations of the surrounding regions to conduct fundamental research. According to Durbin's model, the number of R&D organizations operating in the surrounding regions and the amount of funding allocated for applied research and development have a negative impact on the dynamics of developed advanced technologies. The theoretical significance of the study lies in the identification of factors affecting the creation of domestic advanced manufacturing technologies. The practical significance lies in the possibility of using these results to form strategies to promote scientific and technological development of the regions of the Russian Federation under modern conditions.

Suggested Citation

  • Ilya V. Naumov & Sergey S. Krasnykh, 2023. "Spatial Modelling of the Impact of R&D Potential on the Dynamics of Scientific and Technological Development of Russian Regions," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 22(3), pages 630-656.
  • Handle: RePEc:aiy:jnjaer:v:22:y:2023:i:3:p:630-656
    DOI: https://doi.org/10.15826/vestnik.2023.22.3.026
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    References listed on IDEAS

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    1. Lin Zou & Yi-Wen Zhu, 2021. "Universities’ Scientific and Technological Transformation in China: Its Efficiency and Influencing Factors in the Yangtze River Economic Belt," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-17, December.
    2. Lambert, Dayton M. & Brown, Jason P. & Florax, Raymond J.G.M., 2010. "A two-step estimator for a spatial lag model of counts: Theory, small sample performance and an application," Regional Science and Urban Economics, Elsevier, vol. 40(4), pages 241-252, July.
    3. Rong Wang & Fayuan Wang, 2022. "Exploring the Role of Green Finance and Energy Development towards High-Quality Economic Development: Application of Spatial Durbin Model and Intermediary Effect Model," IJERPH, MDPI, vol. 19(14), pages 1-17, July.
    4. Malecki, Edward J., 1981. "Science, technology, and regional economic development: Review and prospects," Research Policy, Elsevier, vol. 10(4), pages 312-334, October.
    5. Liangen Zeng, 2021. "China’s Eco-Efficiency: Regional Differences and Influencing Factors Based on a Spatial Panel Data Approach," Sustainability, MDPI, vol. 13(6), pages 1-19, March.
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    More about this item

    Keywords

    scientific and technological development; spatial modeling; Russian regions; spatial lag model (SAR); spatial error model (SEM); spatial lag and error model (SAC); Spatial Darbin model (SDM).;
    All these keywords.

    JEL classification:

    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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