Abstract
The uneven economic and social development of various regions and municipalities in many countries is a negative consequence of the inefficient spatial organization of the economy. A system of spatial development indices is developed in the article using the example of Russian regions. A significant and positive spatial autocorrelation was revealed as a result of calculations of the global Moran index using the matrix of weights of inverse distances and the boundary matrix of weights. Local Moran indices were calculated in the process of identifying groups of territories with a low level of spatial development; a spatial scattering diagram and a geographical map were constructed. The results obtained make it possible not only to determine the spatial development factors of specific regions but also to develop recommendations for improving the spatial organization of the country’s economy as a whole.
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References
Affandi, Y., Anugrah, D. F., & Bary, P. (2019). Human capital and economic growth across regions: A case study in Indonesia. Eurasian Economic Review, 9(3), 331–347.
Anselin, L. (1995). Local indicators of spatial association - LISA. Geographical Analysis, 27, 93–115.
Balash, O. S. (2012). A statistical study of spatial clustering of Russian regions. Bulletin of Tula State University. Economic and Legal Sciences, (2–1), 56–65.
Bogachkova, L. Y., & Khurshudyan, S. G. (2018). The development of tools for spatial economic research on the effectiveness of energy efficiency policies by the example of the regions of the Russian Federation. Information Economics: development stages, management methods, models: [Collective monograph], Kharkov, pp. 143–158.
Demidova, O. A. (2014). Spatial-autoregressive model for two groups of interconnected regions (on the example of the eastern and western parts of Russia). Applied Econometrics, 34(2), 19–35.
Elhorst, J. P. (2014). Spatial econometrics: From cross-sectional data to spatial panels. New York: Springer.
Geary, R. C. (1954). The contiguity ratio and statistical mapping. Incorporated Statistician, 5, 115–145.
Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24, 3.
Krugman, P. (1991). Increasing returns and economic geography. Journal of Political Economy, 99, 483–499.
LeSage, J. P., & Pace, R. K. (2009). Introduction to spatial econometrics. Boca Raton, FL: CRC Press.
Ministry of Economic Development of Russia. (2019). Spatial development strategy of the Russian Federation until 2025. [pdf]. Accessed April 2, 2019, from, MEDR http://economy.gov.ru/minec/activity/sections/planning/sd/201817081
Moran, P. A. P. (1948). Interpretation of statistical maps. Biometrika, 35, 255–260.
Moscow School of Management Skolkovo. (2018). Index “Digital Russia”. Accessed April 24, 2019, from, MSMS https://finance.skolkovo.ru/ru/sfice/research-reports/1779-2018-10-001-ru/
Pavlov, Y. V., & Koroleva, E. N. (2014). Spatial interactions: Estimation based on global and local Moran indices. Spatial Economics, 3, 95–110.
Pfeiffer, D., Robinson, T., Stevenson, M., Stevens, K., Rogers, D., & Clements, A. (2008). Spatial analysis in epidemiology. Oxford: Oxford University Press.
Pisati, M. (2012). Exploratory spatial data analysis using Stata, [pdf]. Accessed April 30, 2019, from https://www.stata.com/meeting/germany12/abstracts/desug12_pisati.pdf
Timiryanova, V. M., Zimin, A. F., & Zhilina, E. V. (2018). Spatial component in changing the retail market of goods. Regional Economy, 14(1), 164–175.
Vakulenko, E. S. (2015). Analysis of the relationship between regional labor markets in Russia using the Ouken model. Applied Econometrics, 40(4), 28–48.
Acknowledgment
The reported study was funded by the RFBR according to the research project №. 19-010-00562.
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Kozonogova, E., Dubrovskaya, J. (2020). Assessment of the Features of the Spatial Organization of the Russian Economy Based on the Global and Local Moran Indices. In: Bilgin, M., Danis, H., Demir, E., Tony-Okeke, U. (eds) Eurasian Economic Perspectives. Eurasian Studies in Business and Economics, vol 15/1. Springer, Cham. https://doi.org/10.1007/978-3-030-48531-3_14
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DOI: https://doi.org/10.1007/978-3-030-48531-3_14
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