R-ECONOMY

Relevance. Corruption remains a persistent issue in many countries, including Kazakhstan. By exploring the relationship between the socio-economic characteristics of specific regions and corruption, this research can provide a foundation for informed policy-making and targeted anti-corruption efforts and thus help mitigate its negative impact on regional development. Research Objective. The research aims to assess the impact of corruption on regional socio-economic development in Kazakhstan through the creation and application of a multifactor corruption index. Data and Methods. The study uses official statistical data on corruption offenses and regional socio-economic indicators, including industrial production, fixed asset investments, household expenditures, unemployment rates, and foreign trade volumes. A multifactor index methodology was employed, using Pearson correlation coefficients to calculate averaged absolute values of sub-indices for each indicator. Results. The study found strong correlations between corruption and socio-economic indicators in regions like East Kazakhstan, Abay, Akmola, and Kostanay. The economic structure of these regions plays a key role: East Kazakhstan and Akmola, with dominant mining industries, are more vulnerable to corruption due to public contracts and licensing. Kostanay’s agricultural sector, central to its economy, is prone to corruption in land allocation, subsidies, and procurement. The economic importance of these sectors amplifies the impact of corruption on development, strengthening the correlation. Conversely, regions with lower index values show weaker correlations in the analysis, likely due to economic diversity, incomplete data, or less effective governance mechanisms. Conclusions. The regional specificity of the interrelation between corruption and socio-economic development in Kazakhstan necessitates tailored approaches that consider the unique conditions of each region. These findings can be of interest to policymakers and other stakeholders. The proposed methodology allows for a more precise assessment of both hidden and visible corruption risks, highlighting critical areas for implementing effective anti-corruption measures.

Relevance. The global imperative for adopting a low-carbon economy resonates worldwide, yet comprehensive assessments specific to the Russian economy remain scant. This is especially important considering the significant differences in the level of transition to sustainable development among Russian regions. Research Objective. This study aims to introduce a robust methodology for evaluating and analyzing the international trade of low-carbon goods (LCGs) across various Russian regions and assessing its effects on fuel combustion emissions. Data and Methods. Data on LCGs trade were obtained from the Federal Customs Service of Russia. In conjunction, datasets from Rosstat and the Central Bank of Russia were incorporated for comprehensive econometric modeling. The analytical framework employed Tobit and quantile regressions. Results. The study uncovers significant disparities among Russian regions regarding the intensity of low-carbon goods exports and imports. This variation highlights the diverse competencies in LCGs production, as well as differing ecological agendas and consumption patterns across regions. Additionally, the research demonstrates that, although the widespread adoption of advanced production technologies is positively correlated with increased fuel combustion emissions, a U-shaped relationship exists where higher LCGs exports are associated with reductions in fuel combustion emissions across Russian regions to a certain degree. Conclusions. This research highlights important implications for both federal and regional industrial and environmental policies. It advocates for the development of targeted incentives that encourage the adoption of low-carbon goods (LCGs) and advanced technologies. By doing so, policymakers can effectively promote sustainable development tailored to the unique needs and conditions of various regions, thereby fostering ecological resilience and economic growth across diverse regional landscapes.

Relevance. Fostering well-being ranks high on regional social policy agendas. With the dynamic shifts in the international economic landscape, known as geo-economic fragmentation, there’s a pressing urgency for stakeholders to optimize resource allocation at the regional level, increasing interest in efficient strategies to adapt to sanctions while enhancing overall well-being. Research objective. This article aims to investigate the dimensions and determinants of the eco- and human capital efficiency in Russian regions in the context of geo-economic fragmentation and sanctions pressure. Data and methods. A proposed three-stage approach integrates factor analysis to identify subjective well-being indicators, data envelopment analysis (DEA) to evaluate socio-eco-efficiency, and panel tobit regression to examine the determinants of efficiency. Microdata from the Rossat Comprehensive Observation of Living Conditions database were utilized, covering the period from 2014 to 2022. To assess efficiency, a DEA model is employed. The output indicators from this model were the estimated measures of subjective well-being. These indicators were validated through factor analysis and included professional satisfaction, safety assessment, accessibility and quality of social and cultural infrastructure in the regions. Results. In the given period, people reported feeling increasingly satisfied with jobs and quality of life, though there was a noticeable slowdown in the growth of human capital development indicators, environmental investments, and real income by early 2023. Efficiency varied significantly among the regions. Industrially developed mining areas and republics in the North Caucasus consistently showed high socio-eco-efficiency, despite limited resources. The efficiency benefited both from digitalization and increased per capita gross regional product, but urbanization had a negative impact. Conclusions. Amid geo-economic fragmentation, regional communities and job markets face significant challenges in adaptation. With the looming risk of declining satisfaction and perceived quality of life, it is imperative for regional policies to bolster tangible well-being indicators and invest in social capital and infrastructure to address these issues effectively.

Relevance. Similar to other countries, Indonesia’s economy was significantly impacted by the COVID-19 pandemic, especially at the district and city levels. As the second-largest contributor to Indonesia’s GDP, East Java faced noticeable economic downturns. Industry, the region’s main economic sector, played a key role in these challenges, making it essential to evaluate all sectors from a regional economic perspective to navigate this turbulence effectively. Research objective. This study investigates the regional economy’s sectoral competitiveness in East Java, with a particular focus on the 17 sectors categorized by Statistic Indonesia (Badan Pusat Statistik-BPS) before and after COVID-19. Data and methods. The study relies on data from Badan Pusat Statistik (BPS)). The dataset includes GDP information for 11 regions, namely 7 districts and 4 cities, in East Java from 2018 to 2022, covering the pre- and post-pandemic periods. Methodologically, the study employed Location Quotient (LQ) analysis and Mix and Share Analysis. LQ analysis was used to assess the concentration and comparative advantage of East Java’s regions. Mix and Share and Shift-Share analyses were applied to identify the competitive industries in specific regions and their advantages. Results. The findings show positive economic growth in most regions of East Java before and after the pandemic, except for two regencies that saw a decline. The study emphasizes the need to strengthen regional resilience at the village level using the Village Fund from the national budget. Conclusions. Regional stakeholders, central government interventions, and continued development of leading sectors are essential for mitigating the effects of COVID-19. According to regional economic theory, collaboration between the government and businesses is crucial for enhancing competitive advantage and increasing the number of leading sectors.

Relevance. As interregional competition intensifies, state support for innovation in regional economies becomes increasingly important. To improve the effectiveness of such support, it is necessary to gain a better understanding of the link between innovation and economic development. Research Objective. The study aims to assess the impact of innovation activity on regional economic growth, focusing on the case of a large industrial region in Russia. Data and Methods. The analysis uses socio-economic data for Sverdlovsk region from 2000 to 2023 and applies an autoregressive distributed lag (ARDL) model to assess the relationship between innovation and economic performance. The study introduces an Innovation Activity Index, which incorporates several key components, such as the number of research personnel, internal R&D expenditures, and other relevant indicators, for a more comprehensive evaluation. Results. The study traces the region’s innovation activity, revealing a general upward trend. However, while innovation clearly has a positively influence on economic growth in the short term, its effects over medium- and long-term periods are less obvious, likely due to structural factors in the regional economy. Conclusion. The study proposes recommendations to enhance regional innovation support within the «smart specialization» framework, including backing innovation projects, developing a sustainable innovation ecosystem, and investing in human capital.