Analysis of the Dynamics of Socio-Economic Development of the Siberian Federal District Based on Spatial Data

Traditional approaches to economic modeling often ignore the spatial structure of data, which leads to distorted results in the presence of spatial dependencies. The scale of differences in the level of gross regional product per capita between the regions of the Siberian Federal District (SFD) reaches 10–15 times, which exceeds similar indicators in most developed countries. The analysis showed that external shocks can radically change the structure of regional imbalances, leading to unexpected effects of convergence or divergence. The purpose of the study is to analyze key indicators of the spatial development of the Siberian Federal District based on a comprehensive analysis. Within the framework of the work, the following tasks were solved: a systematic analysis of theoretical approaches to modeling the spatial development of regional economic systems was carried out; spatial autocorrelation was analyzed based on the calculated Moran indices; the dynamics of regional differentiation in the context of external economic shocks (pandemic, sanctions) was studied. The methodological basis of the research consists of the principles of system analysis, the theory of spatial economics, and the concepts of new economic geography. The methods of spatial econometrics and statistical methods of panel data processing were used in the work. The scientific novelty of the study is to identify and theoretically substantiate the phenomenon of «crisis convergence» in regional development, when external shocks lead to the smoothing of regional differences and establish the dominance of external shocks over internal factors of spatial development in modern conditions. The practical significance of the results obtained is determined by the possibility of using the results obtained by state authorities at the federal and regional levels to form spatial development strategies, increase the effectiveness of regional policy, and predict socio-economic processes in the face of external challenges.