R-ECONOMY

Relevance. The well-being of citizens is a key focus of national policies worldwide. Kazakhstan, however, is characterized by significant regional disparities in social well-being, which necessitates targeted programs and investments to improve conditions in less developed areas. Research Objective. This study aims to develop a national adaptive social well-being index to evaluate and spatially map the regions of Kazakhstan. Data and Methods. The methodology included identifying indicators through a literature review and regional data, conducting an expert survey to weight them, and creating a social well-being index. A spatial analysis was then used to calculate the index for each region. Results. The social well-being index shows significant disparities across regions. Astana, Almaty, and Atyrau demonstrate strong social welfare, driven by economic growth and advanced social infrastructure. In contrast, Karaganda, Pavlodar, and Shymkent show lower social welfare, highlighting the need for targeted interventions and investments. Conclusion. The findings offer valuable insights for policymakers to design strategies for sustainable socioeconomic growth in Kazakhstan. The proposed index can help national and regional authorities monitor social well-being.

Relevance. Uneven spatial development is a common challenge for all countries, driven by both subjective and objective factors. The issue lies not in regional disparities themselves but in their growing intensity. Amid crises and economic turbulence, it is crucial to have tools to assess the risks of increasing spatial unevenness and widening socio-economic disparities between regions. Research Objective. This study aims to develop and test a tool for estimating the risks of uneven spatial development in Russia’s macroregions and the growing differentiation of regions by socio-economic level, using the Urals-Siberian macroregion as a case study. Data and Methods. A two-stage approach is proposed to evaluate spatial development in terms of uniformity and regional differentiation by socio-economic level and growth rate. In the first stage, the probability of a socio-economic decline is estimated based on key indicators (risk factors) and their dynamic indices. In the second stage, the probability of an increasing variation coefficient within a macroregion, reflecting rising disparities in socio-economic development, is analyzed. A multifactor risk model is used for analysis. The study relies on data from the Federal State Statistics Service (Rosstat) covering the period from 2000 to 2022. Results. Applying this approach to the Urals-Siberian macroregion revealed persistent spatial unevenness throughout the study period, primarily due to the specialization of regions, which stabilizes their relative positions. However, during crises, spatial disparities tend to widen as regions demonstrate varying adaptive capacities and resilience - some not only recover but also improve their positions. Conclusion. The proposed tool assesses risks linked to uneven development and growing regional disparities, offering insights for sustainable macroregional strategies. The findings emphasize the need to consider regions’ specificities and adaptive capacities in spatial development policies.

Relevance. The process of new industrialization extends beyond traditional industrial sectors to include areas such as the creative industries. Since 2021, there has been a growing interest in creative activities, supported by legislative measures and strategic frameworks aimed at promoting spatial and technological development. This trend suggests a novel hypothesis: creative industries in single-industry towns can serve as catalysts for new industrialization by leveraging information and telecommunications activities. This study focuses on single-industry towns-a complex and underexplored subject-and proposes adopting a regional ‘center-periphery’ model as a framework for analysis. Research objective. The study aims to establish conceptual and methodological bases for the spatial socio-economic development of creative industries in Russia by focusing on the case of single-industry towns of Sverdlovsk and Kemerovo regions. Data and methods. This study introduces a methodological framework for evaluating the developmental potential of creative new industrialization, driven by advancements in information and telecommunications. While exploring the concepts of creative neo-industrialization and re-industrialization, the key innovation lies in classifying information and telecommunications as integral components of the creative industries, reflecting broader technological trends. This approach enables the development and testing of spatial models for creative industry growth, considering factors such as institutional constraints, concentration levels, and the potential for creative startups in single-industry towns. Results. The study highlights the concentration of creative industries around emerging local growth points, shaped by technological trends, increased industry concentration, and a declining share of startups in single-industry towns and regional administrative centers. The regions under study show different spatial models of creative new industrialization. In Sverdlovsk region, a center-semi-periphery-periphery model emerged by 2015, with Yekaterinburg at the center. In Kemerovo region, Kemerovo became the center by 2022, following growth transfers from Belovo and Leninsk-Kuznetsk in 2019 and 2021. Conclusions. This study contributes to applied research by integrating the evolutionary theory of growth with the center-periphery model and institutional economics.