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The Knowledge Economy and Digitalization: Assessing the Impact on Economic Growth of Russian Regions

  • SPATIAL FEATURES OF SECTORAL DEVELOPMENT
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Abstract—

In the knowledge economy, information and communication technologies (ICT) have accelerated the digitalization of organizations, households, and management, which leads to the economic growth of countries and regions. Russia lags behind developed countries in investing in knowledge economy sectors and in the share of these sectors in GDP. Although ICT has been assigned a decisive role in the digitalization of regions, the digital divide and fragmentation of the knowledge economy at the mesolevel are holding back growth. The objective of the study is to assess how the economic growth of regions is impacted by the spending of developing the knowledge economy, including the ICT sector, and indices of digitalization of households and organizations. An econometric model of endogenous growth has been proposed and tested on the data from Rosstat and the HSE University. Indices of digitalization of households and organizations for 2017 were compiled for 80 regions. In the index of digitalization of households, the first five positions were occupied by Moscow oblast, the Republic of Tatarstan, Tyumen oblast, Moscow, and St. Petersburg; in the index of digitalization of organizations, Moscow, St. Petersburg, Leningrad oblast, Stavropol krai, and Tambov oblast; at the same time, many regions of Asian Russia and the North Caucasus lagged significantly behind. The hypothesis that ICT spending leads to an increase in GRP per capita growth rate by 1 percentage point and is complementary to the spillover of spending on higher education has been confirmed. Digitalization indices have positive but statistically insignificant regression coefficients. Spending on other sectors of the knowledge economy—science, higher education, and healthcare, which form human capital—were unable to significantly affect economic growth of regions in 2017, perhaps due to underfunding of their development (versus developed countries). The article concludes that in order to ensure the economic growth of Russian regions in the context of digital transformation, systemic actions are needed in managing all sectors of the knowledge economy and knowledge spillovers, a breakthrough in domestic science and ICT. The results obtained in the study can be useful in managing the knowledge economy in the context of digital transformation of Russian regions.

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Notes

  1. Expert assessment by A.G. Aganbegyan (2021, p. 43).

  2. Information Society Development Strategy–2030: Basic Information. https://d-russia.ru/strategiyarazvitiya-informatsionnogo-obshhestva-2030-osnovnye-svedeniya.html.

  3. For example, Digital Economy Report 2019: Value Creation and Capture: Implications for Developing Countries / UNCTAD. https://unctad.org/en/PublicationsLibrary/der2019_en.pdf. Accessed March 5, 2020.

  4. In global practice, the World Bank’s Knowledge for Development (K4D) approach makes it possible to assess the readiness of countries for transition to a knowledge-based development model using the knowledge economy index, which includes indices characterizing the institutional regime, innovation, education, and ICT (Chen and Dahlman, 2005). A.G. Aganbegyan (2017) also includes healthcare and biotechnology as part of the knowledge economy.

  5. This question is beyond the scope of our study, but it is interesting to consider it further.

  6. Regions of Russia: Socioeconomic Indicators. 2019. Moscow: Rosstat, 2019.

  7. Digital economy indices: 2020: Statistical Digest, Moscow: HSE University, 2020; Information society: Main Characteristics of Federal Subjects: Statistical Digest, Rosstat; HSE University, Moscow: HSE University, 2018.

  8. In the article, the borders of Russia are considered in accordance with the Constitution of the Russian Federation adopted by popular vote on December 12, 1993, with amendments approved during the All-Russian vote on July 1, 2020.

  9. Initially, the basic endogenous growth model as a function of science spending was developed jointly with M.A. Kaneva, who supplemented it with healthcare spending and studied in greater detail the impact of KE on the economic growth of this sector.

  10. Calculated by factor analysis of four components: share of specialists with higher education; share of university graduates; share of employed people aged 15–30, and the control variable is the share of people employed in agriculture in the total number of people employed in the regional economy.

  11. They take into account the spatial structure of the country and are calculated by weighting the analyzed factor by the matrix of inverse distances between Russian regions.

  12. Information Society: Main Characteristics of Federal Subjects: Statistical Digest, Rosstat; HSE University, Moscow: HSE University, 2018. The sample size of 80 regions for 16 indices was sufficient, since Fisher’s test for the significance of zero regression coefficients was able to judge the reliability of calculations for 2017 (see Table 5). The author does not pretend to approximate the results for the forecast, because for this, longer series of panel data are needed, etc.

  13. Different dependent variables were used (GRPpc growth rate and GRP in (Litvintseva and Karelin, 2020)) and methods for constructing indices, which once again indicates the ambiguity and importance of different empirical estimates.

  14. See: Abdulli, R., Poll: Almost 40% of Russians do not work in their specialty; most often they are sales managers. https://daily.afisha.ru/news/49270-opros-pochti-40-rossiyan-ne-rabotayut-po-specialnosti-chasche-vsego-eto-menedzheri-po-prodazham/.

  15. The cross-sectional model does not claim to have high predictive properties, which was not a direct goal of the study. However, as noted in the literature on econometric analysis, an equally important methodological principle for assessing growth dynamics, in particular, according to (Durlauf et al., 2009), favored the discovery of stable positive correlations. In our model, they are found between GRPpc growth rate and ICT spending.

  16. Significant digital transformation, especially in the context of sanctions and insufficiently rapid import substitution of a number of significant software products, can be discussed, according to experts, no earlier than the next 3 to 5 years (see (Balashova, A. and Yasakova, E., Experts have assessed the threat of sanctions to Russia’s digital transformation. https://www.comnews.ru/content/220022/2022-04-27/2022-w17/eksperty-ocenili-ugrozu-sankciy-dlya-cifrovoy-transformacii-rossii).

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Funding

The study was carried out under research plan of the Institute of Economics and Industrial Engineering, Siberian Branch, Russian Academy of Sciences, project no. 121040100283-2 “Regional and Municipal Strategic Planning and Administration in the Context of Upgrading State Regional Policy and Development of the Digital Economy.”

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  1. Institute of Economics and Industrial Engineering, Siberian Branch, Russian Academy of Sciences, 630090, Novosibirsk, Russia

    G. A. Untura

  2. Novosibirsk National Research State University, 630090, Novosibirsk, Russia

    G. A. Untura

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Untura, G.A. The Knowledge Economy and Digitalization: Assessing the Impact on Economic Growth of Russian Regions. Reg. Res. Russ. 13, 397–406 (2023). https://doi.org/10.1134/S2079970523700909

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