Spatial modeling of human potential in the Republic of Bashkortostan - I. A. Lakman; A. V. Gorshechnikova; N. K. Shamsutdinova; V. B. Prudnikov (Статистика и экономика, 2019)
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.