Dynamics of Competitiveness of the Northern Regions in 2008–2020: Development of an Assessment Methodology

The conceptual ambiguity of the definition of regional competitiveness hinders the development of socio-economic policy measures to increase a region’s attractiveness to people and capital. The methodology for assessing competitiveness presented in this study evaluates the attractiveness of a region for business, considering the well-being of the population, environmental conditions, economic efficiency and development of innovation and information systems. Technically, the proposed methodology relies on the linear scaling method and various approaches to accounting for interregional price discrimination. Analysis of data obtained from the northern regions of Russia confirmed the conceptual link between competitiveness and productivity and production factors. The key regional competitiveness indicators are personal income and efficiency of production factors — capital intensity and wage intensity. The study revealed low competitiveness of the majority of the northern regions due to the insufficient development of the information system, low attractiveness for business and population, poor economic efficiency and little innovation. In order to maintain high competitiveness of the most successful regions — Yamalo-Nenets Autonomous Okrug, Khanty-Mansi Autonomous Okrug and Sakhalin Oblast — authorities should pay more attention to environment and innovation. The lagging regions need to implement measures to attract investment, increase spending on transport infrastructure, and reduce poverty and unemployment. Additionally, the least competitive regions — Nenets Autonomous Okrug, Kamchatka Krai, Arkhangelsk Oblast and the Republic of Sakha (Yakutia) — are recommended to focus on improving the regions’ information infrastructure and attractiveness for business. The article demonstrated that more reliable results can be obtained by using the index of budget expenditures instead of the cost of a fixed basket of goods and services adjusted for a specific region, since it better reflects the high cost of life in the northern regions. The findings can be used to develop socio-economic policy of the northern regions of Russia.