Assessment and Modelling of Spatial Interactions in the Development of Research Personnel in Russian Regions
DOI:
https://doi.org/10.17059/ekon.reg.2023-3-13Keywords:
пространственная неоднородность, кадровый научно-исследовательский потенциал, пространственный автокорреляционный анализ, матрица пространственных взаимовлияний Анселина, тест Грэнджера, регрессионное моделированиеAbstract
The current spatial heterogeneity of the localisation of research personnel and mutual spatial influences between the main centres of its concentration and neighbouring regions in Central Russia, according to the hypothesis, lead to its further growth in these centres. The present paper assessed the localisation of research personnel using spatial autocorrelation analysis. The spatial interactions between regions were analysed by the method of Anselin, considering various systems for measuring distances. The Granger test was applied to confirm the presence of the established interactions. Additionally, the study built regression models of interregional spatial interactions, assessed the concentration of factors for the development of research personnel in Russian regions and examined relevant efficiency indicators. As a result, the following mutual spatial influences in Russia were determined: between Moscow city and Saint Petersburg, Tver, Bryansk and Vladimir oblasts; between Moscow and Ivanovo, Vladimir, Oryol oblasts and the Chuvash Republic; between Nizhny Novgorod and Tula oblasts; between Saint Petersburg and Tambov, Bryansk, Vladimir, Smolensk and Yaroslavl oblasts. Spatial interactions between the regions of the Ural, Volga and Siberian districts were not identified. This result, along with the increasing dynamics of the concentration of research and development human resources in the central regions, contributes to the deepening of spatial heterogeneity of research personnel in Russia. About 65% of all research personnel in Russia are located in 22 regions, and only 4 regions (cities of Moscow and Saint Petersburg, Moscow and Nizhny Novgorod oblasts) have spatial interactions with the neighbouring regions. 60.5% of research and development human resources are concentrated there. The findings can be used to develop mechanisms for reducing the spatial heterogeneity of the development of research personnel in Russia.
References
Abar, H. (2022). An analysis of causal relationship between economic growth and financial development for Turkey: A MODWT — Granger causality test. Economics and Business Review, 8(22), 59-81. DOI: 10.18559/ebr.2022.3.4
Abdullah, R. & Nasirin, W. (2022). Types of linkages between Islamic bank financing, interest rate and economic growth factors: evidance from Aceh province with granger causality test. Al-Bay’: Journal of Sharia Economic and Business, 1(2), 44-54. DOI: 10.24952/bay.v1i2.6001.
Abdullin, A. R. (2013). Personnel potential of science: introduction in the perspective and the research problem definition. Naukovedenie, 1, 01NVN113. (In Russ.)
Anselin, L. (1988). Spatial Econometrics: Methods and Models. Dordrecht: Kluwer Academic Publishers, 304.
Averina, L. M. & Sirotin, D. V. (2020). Assessment of Spatial Effects from Innovation Activities in the Industrialized Russian Regions. Ekonomika regiona [Economy of region], 16(1), 268-282. DOI: 10.17059/2020-1-20 (In Russ.)
Ayyubova, N. S. (2022). Econometric analysis and modeling of the dynamics of the balance of payments’ development in Azerbaijan. Statistika i ekonomika [Statistics and Economics], 19(2), 14-22. DOI: 10.21686/2500-3925-2022-2-14-22. (In Russ.)
Balash, O. S. (2012). Econometric Modeling of Spatial Interaction. Izvestiya Saratovskogo universiteta. Ser. Ekonomika. Upravlenie. Pravo [Izvestiya of Saratov university. economics. management. law], 12(3), 30-35. (In Russ.)
Dubovik, M. V. & Dmitriev, S. G. (2022). Correlation Analysis of Gross Regional Product and Industries of Regional Economy. Vestnik REU im. G. V. Plekhanova [Vestnik of the Plekhanov Russian University of Economics], 19(3), 109-118. DOI: 10.21686/2413-2829-2022-3-109-118. (In Russ.)
Dubrovin, S. S. (2009). Research of cause-effect relation at operations on the stock market. Izvestiya Tulskogo gosudarstvennogo universiteta. Estestvennye nauki [Izvestiya Tula State University. Natural Sciences], 2, 167-173. (In Russ.)
Evstigneeva, L. M. & Kiseleva, V. V. (2016). Creating innovation and international trade: cause or consequence? Innovatsii [Innovations], 12(218), 49-55. (In Russ.)
Fischer, M. M. & Griffith, D. A. (2008). Modeling spatial autocorrelation in spatial interaction data: An application to patent citation data in the European Union. Journal of Regional Science, 48(5), 969-989. DOI: 10.1111/j.1467-9787.2008.00572.x.
Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-Spectral Methods. Econometrica, 37(3), 424-438.
Grigoryev, R. А. (2019). Granger causality among world stock markets: multiple solutions. Terra Economicus, 17(3), 146-168. DOI: 10.23683/2073-6606-2019-17-3-146-168. (In Russ.)
Kivi, L. & Paas, T. (2021). Spatial interactions of employment in European labour markets. Eastern Journal of European Studies, 12, 196-211. DOI: 10.47743/ejes-2021-SI09.
Krasova, E. V. (2019). Trends and problems in development of the research infrastructure personnel potential in Far Eastern Federal District. Territoriya novykh vozmozhnostey. Vestnik Vladivostokskogo gosudarstvennogo universiteta ekonomiki i servisa [Territory of new opportunities. Bulletin of the Vladivostok State University of Economics and Service], 11(4), 180-192. (In Russ.)
Lukyanova, R. R. (2010). Assessment of human resources for regional innovation activity. Ekonomika regiona [Economy of region], 1, 61-65. (In Russ.)
Magaji, S., Abubakar, M. M. & Temitope, Y. А. (2022). Impact of International Trade On Economic Growth: The Granger Causality Test Approach. International Journal of Accounting and Management Sciences, 1(2), 113-130. DOI: 10.56830/NUCB7716.
Mazilov, E. A. (2021). Problems of developing personnel potential of Russian science: regional aspect. Problemy razvitiya territorii [Problems of territory’s development], 25(5), 7-20. DOI: 10.15838/ptd.2021.5.115.1. (In Russ.)
Moran, P. (1948). The interpretation of statistical maps. Journal of the Royal Statistical Society, 10, 243-251. DOI: 10.1111/j.2517-6161.1948.tb00012.x.
Myasnikov, A. (2018). Maximum likelihood and generalized least squares estimation of spatial lag models with endogenous spatial coefficients: a Monte Carlo simulation. MPRA, Paper No. 86696. Retrieved from: https://mpra.ub.uni-muenchen.de/86696/ (Date of access: 10.02.2023).
Naumov, I. V. (2021). Research and modeling of spatial localization and movement of bank capital. Ekonomika. Nalogi. Pravo [Economics, taxes & law], 14(6), 41-51. DOI: 10.26794/1999-849X-2021-14-6-41-51. (In Russ.)
Pavlov, Yu. V. & Koroleva, E. N. (2014). Spatial interactions: evaluation with the help of global and local Moran’s index. Prostranstvennaya ekonomika [Spatial Economics], 3, 95-110. (In Russ.)
Petrov, M. B., Serkov, L. A. & Kozhov, K. B. (2021). Modelling the Heterogeneity of the Mutual Influence between Russian Regions in the Manufacturing Industry. Ekonomika regiona [Economy of region], 17(3), 944-955. DOI: 10.17059/ekon.reg.2021-3-16. (In Russ.)
Ren, X., Li, J. & Shi, Y. (2022). Can digital economic attention spillover to financial markets? Evidence from the time-varying Granger test. Journal of Digital Economy, 1, 102-116. DOI: 10.1016/j.jdec.2022.11.002.
Sargsyan, L. N. (2019). Analysis of the bilateral causality relationship between economic growth, export and the inflow of foreign direct investment in Armenia. In: Nauka i innovatsii — sovremennye kontseptsii: sbornik nauchnykh statey Mezhdunarodnogo nauchnogo foruma [Science and innovation — modern concepts: a collection of scientific articles on the results of the International Scientific Forum] (pp. 28-33). Moscow: Infiniti Publishing House. (In Russ.)
Serkov, L. A., Petrov, M. B. & Kozhov, K. B. (2021). Modeling the Interaction of the Regions of Russia and the Republic of Belarus in the Sphere of the Processing Industry. Journal of Applied Economic Research, 20(2), 217-240. DOI: 10.15826/vestnik.2021.20.2.010. (In Russ.)
Skripnyuk, D. F. & Kikkas, K. N. (2016). Analysis of cause-and-effect relationships of the real and financial sectors of the world economy. Ekonomika i sotsium: sovremennye modeli razvitiya [Economics and society: contemporary models of development], 6(4), 103-114. (In Russ.)
Timiryanova, V. M. (2020). Assessing the Spatial Dependence of the Shipped Products Volume in Dynamics. Statistika i ekonomika [Statistics and Economics], 17(5), 49-58. DOI: 10.21686/2500-3925-2020-5-49-58. (In Russ.)
Varshavsky, L. E. (2006). Problems of development of the human resources potential of science. Nauka. Innovatsii. Obrazovanie [Science. Innovation. Education], 1, 90-103. (In Russ.)
Willekens, F. (1983). Log-Linear Modeling of Spatial Interaction. Papers in Regional Science, 52(1), 187-205. DOI: 10.1111/j.1435-5597.1983.tb01658.x.
Zaytseva, E. V., Zapariy, V. V., Klyuyev, A. K., Kulpin, S. V. & Shkurin, D. V. (2016). Organizatsionno-kadrovyy potentsial universiteta: metodologiya i metodika izmereniya [Organizational and personnel potential of the university: methodology and measurement technique]. Ekaterinburg: Ural University Publishing House, 215. (In Russ.)
Zyryanov, V. V., Mosicheva, I. A., Prudnikova, M. V. (2018). Personnel potential of modern Russian science. In: Issledovatel XXI veka: formirovanie kompetentsiy v sisteme vysshego obrazovaniya [Researcher of the XXI century: the formation of competencies in the system of higher education] (pp. 143-174). Moscow: Geoinfo. (In Russ.)
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