Spatial effects in human development levels among different regions of a territory are important to study in the context of the core-periphery model. We use different methods to study human development index (HDI) for 85 Russian regions. The authors studied the human development index (HDI) for 85 Russian regions. Methods of spatial statistics (econometrics) are used to estimate the ‘spatial gradient’ in economic geography (Moran’s global and local I, Geary’s C, Getis-Ord global G indices). As a weighting matrix we used a contiguity matrix, taking into account the HDI levels only in neighboring regions. Analysis of the global indices of Moran’s I, Geary’s C and Getis-Ord G and Morans scatter plots showed the presence of time-inconsistent spatial autoregressive dependence of the level of HDI in regions of Russia. The ‘spatial gradient’ of the level of human development in Russia is influenced by historically existing imbalances (due to strong oil and gas export-oriented nature of the economy) and insufficient use of human capital. To our view the regional differentiation in human development among the regions is caused primarily by the ‘catching up’ style of Russian economy: human capital is concentrated in regions with already high level of development, although in terms of growth rates Moscow and St. Petersburg are not the leaders. The territorial and geopolitical policies of Russian Federation also influence HDI distribution. For example, huge public investments in the regions of Russian Far East are often ineffective.

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Purpose: The article presents the results of an empirical analysis of the economic growth of Russian cities with a population of over 1 million people (megacities). Design/Methodology/Approach: The analyzed indicator is the city product calculated according to the UN methodology for the period from 2010 to 2016. The paper analyses the process of β-and σ-convergence across Russian megacities using methods of spatial econometrics in addition to the traditional β-convergence techniques from the neoclassical theoretical framework. Findings: The dynamics of the coefficient of variation confirmed the presence of σconvergence in city product. Empirically, positive spatial autocorrelation has been confirmed. Beta-convergence for Russian megacities is found to be significant and the spatial location of megacities significantly affects β-convergence. Control factors such as fixed capital investment per capita in 2010, average retail volume per capita in 2010, average annual number of employees of enterprises and organizations in 2010 and the dummy variable introduced for "federal cities" Moscow and St. Petersburg are all found to have positive and statistically significant impact on economic growth. Practical Implications: Policymakers may take the results into account under the planning of economical strategies for megacities and regions in Russia in order to facilitate the regional economic growth and the speed of convergence. Originality/Value: The main contribution of the study is the consideration of the economical growth for the megacities and not for the regions as it often used to be the case in similar studies. The important finding is that megacities' economies do converge and the influence of control factors is pronounced.

calculated by the authors to use a simplified methodology that takes into account the indicators of average life expectancy at birth, the weighted average monthly wages and the average monthly pension, as well as the average duration of study and literacy of the population, for each of 54 municipalities and 8 urban districts of the Republic of Bashkortostan for the period of 2007 and 2013. A comprehensive study of spatial autocorrelation in the distribution of HDI in the republic was conducted in accordance with the five-step methodology proposed by the authors. At the first stage of the study, a weighted spatial matrix of inverse distances between the administrative centers of the municipalities was calculated. This matrix defined the spatial lag structure. At the second stage, which consisted in calculating the global and local indexes of spatial auto-correlation (Moran’s and Giris), the hypothesis about the presence of spatial autocorrelation in the HDI distribution was confirmed. Under the third stage, Moran’s scatterplots were used to visualize the spatial mutual influence of the HDI for specific municipalities for 2007 and 2013. The fourth stage consisted in spatial model estimation. Two specifications were considered: spatial auto-regression (SAR) and spatial error (SEM), both permitting to identify the mutual influence in the spatial distribution of the HDI in municipalities and urban districts. Coefficients of the models were estimated by using maximum likelihood approach. The final part of the study was devoted to the interpretation of the results of spatialregression modeling. R-Studio was used as a modelling tool.Results. It was shown that the distribution of the HDI in municipalities of the Republic of Bashkortostan is characterized by sustainable positive spatial auto-correlation. Moreover, we note an increase in dynamics of positive spatial correlation in the distribution of the HDI, which could be explained by the increasing role of urbanization and concentration of human resources in relatively large cities. There is even “a competitive struggle” going on in a number of municipalities for resources that contribute to raising the HDI. A number of municipalities form, however, a cluster of territories with a low level of human development. These areas are mainly located in the Northeast of the Republic. The estimation of spatial regression models allowed us overall to quantify the spatial auto-correlation dependencies in the distribution of human capital.Conclusion. The obtained results of spatial dependencies in the distribution of human capital can be used both in the development of strategies for the long-term socio-economic development of municipalities and serve as a basis for strategic planning of the development of the region.

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