Competitiveness Factors of Russian Regions

This study evaluates the factors of the regional economy that affect its competitiveness. The study describes the theoretical foundations for assessing the competitiveness of the regional economy, including an overview of the factors of the regional economy that affect its competitiveness. Using data from Rosstat and the Central Bank for 2014-2019, using panel regression models with random effects, the impact of factors of the regional economy of 85 regions of the Russian Federation on the regional competitiveness index derived by the author is assessed. As a result, it was revealed that the lower the regional cluster in terms of competitiveness, the higher the impact on the competitiveness index of the indicator of the volume of goods shipped by the category of mining (the influence of the indicator (sign) varies depending on the cluster). The lower the competitiveness cluster of the region, the higher the impact on the competitiveness index of the export indicator. The lower the competitiveness cluster of the region, the higher the impact on the competitiveness index of the indicator of the balanced financial result of the activities of the organizations in the region. The lower the competitiveness cluster of the region, the higher the impact on the competitiveness index of the incidence rate per 1000 population. Also, for the first time for the regional analysis of the region's competitiveness, it was revealed that there is a significant positive relationship between the group of parameters of financial literacy of the region's population (in particular, the general index of financial literacy) and the level of regional competitiveness, which is the scientific novelty of this work. In summary, a multivariate econometric analysis was carried out using clustered data and panel regression models with random effects. Various methods of evaluating and comparing the results of these methods have been investigated. A large and up-to-date panel database with a large number of variables (as part of the preliminary selection of parameters) was used, consisting of data for 85 regions for 6 years from 2014 to 2019, which is about 510 observations and allows you to take into account more correlations. than on smaller subsamples. Regions were clustered to obtain more efficient parameter estimates, taking into account regional differences. For the first time, within the framework of assessments of regional competitiveness, data on the financial literacy of the population of the regions were effectively used. Key words: regional economy, regional competitiveness, regional competitiveness index, clustering, panel regression model with random effects.