Abstract
In this article, we analyze the σ - and β -convergence, using the data of the socio-economic development of Russian areas, and discover the role of spatial autocorrelation in regional economic development. We are considering 80 areas of the Russian Federation for the period of 2010–2017. Moran coefficients were used to estimate spatial autocorrelation. We compare the Moran scatterplots for GDP per capita and GDP growth rates per capita in 2017 and in 2014. We study the impact on raising investment in leading capital and the costs of technological innovation. We evaluate a wide range of specifications of spatial econometric models for all kinds of weight matrices. We combine standard geographical proximity with specialization proximity to assess whether they are substitutes or additions to converging economic growth rates. The weight matrix of the neighborhood and specialization similarities are used. The weight matrix of specialization similarities of the regional economies is based on data on the structure of tax payments in 82 industries. The specialization structure of the region’s economy is related to its location. Clusters obtained by matrices of specialization proximity are well separable from each other in space. The connectivity within clusters and the boundaries between them become more apparent over time. It is shown that according to the results of estimation of conditional β -convergence models, the models of 2010–2014 and 2014–2017 differ significantly. There is a statistically significant β -convergence for the period 2010–2014. There is also the presence of spatial autocorrelation. Based on the results of valuation models constructed from data after 2014, it can be concluded that the coefficient estimates for the explanatory variables are not significantly different from zero, and accordingly there is no tendency towards regional convergence in terms of economic development. The results obtained in the work are stable for the proposed models and spatial weight matrices. Territorial proximity is a more important factor than the similarity of specialization for explanation the economic growth rates of Russian regions.
Highlights
The economic development of Russian regions is very disproportional due to distances, climatic zones, proximity to borders, historical differences in development, etc
Models of spatial econometrics are attractive for the analysis of social and economic processes occurring in large territories
The aim of the study is to test conditional β-convergence models that take into account the spatial interdependence of regional economic growth rates and assess the values of direct and indirect effects of investments and innovations
Summary
The economic development of Russian regions is very disproportional due to distances, climatic zones, proximity to borders, historical differences in development, etc. While in some regions there is a marked increase in production and investment inflows, other regions face serious economic and social problems. Models of spatial econometrics are attractive for the analysis of social and economic processes occurring in large territories. Information 2020, 11, 289 developed technological regions means that the regional activities of enterprises benefit from proximity to other firms generating innovations. Territorial interaction can be expressed through institutional, technological and social ties that affect the convergence of economic development levels in regions. The goal of our work is to assess the impact of technological innovation on the economic growth of regions using spatial econometrics models for various options of weight matrices
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