Income inequality remains a critical issue in Kazakhstan, despite overall improvements in living standards. This study aims to explore the impact of various economic factors on income inequality across 16 regions of Kazakhstan from 2001 to 2022, addressing gaps in existing research. The study's primary goal is to identify key variables, such as the funds coefficient, household consumption income, unemployment rate, inflation, and minimum subsistence level, that influence the Gini coefficient, which measures income inequality. The research utilizes panel data models, specifically applying fixed effects and random effects models, to conduct a comprehensive analysis of these factors. The data were sourced from official records, including those of the Bureau of National Statistics of the Republic of Kazakhstan. The study includes a Hausman test to determine the appropriate model for analysis. The findings reveal that the funds coefficient, household consumption income, unemployment levels, and poverty rate have a significant positive impact on income inequality, indicating that disparities between the wealthiest and poorest groups contribute to increasing inequality. Conversely, inflation exhibits a negative but minor effect on inequality, suggesting that price stability policies might mitigate income disparity slightly. These results underscore the importance of targeted economic policies to reduce income inequality and support the poorest population groups. The study's limitations include not accounting for migration flows and social factors like access to education, which could further influence inequality levels. The results can inform policy development aimed at improving social and economic conditions in Kazakhstan. Future research should incorporate social and institutional factors to provide a more precise analysis of the dynamics of inequality. This study contributes to the understanding of how macroeconomic variables shape regional income distribution in a transitioning economy.
This study investigates how financial growth connects to regional environmental performance within the framework of policies aimed at reducing carbon emissions. It uses a comprehensive panel dataset covering the period from 2010 to 2024. Although Kazakhstan has set ambitious targets, significant differences in financing levels and institutional development across regions pose substantial obstacles to achieving the target emissions reductions. Employing regional panel data, we use a random-effects model to assess links among banking loans, governmental funding metrics, employment statistics, and pollution measurements. Principal component analysis is utilized to tackle potential collinearity and reveal fundamental patterns. This approach reflects the inherent differences between regions rather than evolutionary shifts. The obtained empirical data demonstrate a significant relationship between high levels of bank loans and reduced carbon emissions. Regions with better access to financial services are better positioned to invest in energy efficiency, green infrastructure, and green innovation. Conversely, increases in regional budgets are associated with rising emissions, as tax revenue growth primarily comes from industries most dependent on fossil fuels. Dependence on the national budget for subsidies exacerbates distortions in regional budgets’ relationship with the regions’ transition to low-carbon development. The findings confirm the importance of regional financial management in determining the path to reducing greenhouse gas emissions. Based on this, it is proposed to transform the mechanism of interbudgetary relations to grant regions greater financial autonomy and to localize credit resources at the regional level to accelerate the transition to a low-carbon economy in Kazakhstan.
This paper presents the first international empirical evaluation of the current practice of environmental reporting in the Kazakhstan oil industry, based on an assessment of the period following 2019, when the regulation was modernized. It employs a mixed-methods research design, combining content analysis, econometric modeling, and qualitative interviews, to assess the impact of environmental transparency on corporate sustainability performance. To measure the quality of disclosure in priority companies (KazMunayGas, TengizChevroil, and CNPC-Aktobe), the GRI (304-306) and TCFD criteria were incorporated with the Environmental Code of Kazakhstan (2021), resulting in the creation of a new composite measure, the Environmental Reporting Quality Index. The results show that there is a high level of heterogeneity in these scores (0.58-0.79) due to the structure of ownership, foreign participation and listing status. The regression analysis indicates that report quality is negatively correlated with the CO2. Intensity with a significant negative association which demonstrates that the more successful the reporting standards of companies are, the less they produce emissions and the less they cause environmental incidents. Qualitative evidence can reinforce this causal explanation: the more successful the reporting, the greater investments in monitoring, verifying and controlling systems. Despite the improvement in the regulatory regime, Kazakhstan is at the bottom of the transparency list when it comes to the external assurance, Scope 3 coverage and board level climate governance. The net effect is that the entire industry is moving towards the aspect of reactive compliance towards proactive transparency owing to policy changes and investor pressure. The study is part of the replicable research methodology in the emerging economies in that the enhanced disclosure has been found to be an accountability measure, as well as a catalyst to quantifiable changes in environmental performance.
Type of the article: Research Article AbstractWhether digital transformation in the public sector and in financial services jointly contributes to banking stability – or whether the two strands proceed along parallel trajectories – remains an open empirical question for post-socialist economies undergoing both reforms simultaneously. This study addresses the question in three components. First, a cross-country mediation analysis covers up to 130 economies over 2018–2024 (853 country-year observations), drawing on the World Bank GovTech Maturity Index, the IMF Financial Access Survey, and the IMF Financial Soundness Indicators, with panel OLS, country-clustered standard errors, and bootstrap mediation tests. Second, the results are decomposed via fixed-effect deviations for three post-Soviet economies from distinct EBRD regions: Ukraine, Armenia, and Kazakhstan. Pre-shock GovTech maturity is positively associated with digital banking adoption (β = +2.91, p = 0.017); sub-pillars differ in channel: core government systems for transaction intensity, public service delivery for account ownership. Bootstrap mediation tests do not support the indirect path through digital banking adoption (six specifications, lowest p = 0.132). GovTech maturity instead shows a substantial direct association with the non-performing-loan ratio – a 13-percentage-point reduction per unit increase in GTMI (p = 0.037) – plausibly operating through institutional infrastructure such as property registries, e-courts, and tax-credit information systems. The two strands are linked but not chained: GovTech is associated with digital banking adoption, yet the route to lower non-performing loans runs through institutional infrastructure. Country-level decomposition reveals heterogeneous GTMI trajectories and identifies reform priorities across public service delivery and core government systems. AcknowledgmentThis article was prepared based on the results of a study funded by the Ministry of Education and Science of Ukraine entitled “GovTech for Ukraine: A Digital, Secure, Transparent, and Equitable State in Times of War and Post-War Reconstruction” (registration number: 0126U000544).
Breast cancer is one of the most prevalent and life-threatening diseases worldwide, making accurate early diagnosis critical for improving patient outcomes. This study investigates the effectiveness of logistic regression models for breast cancer detectionusing the Wisconsin Breast Cancer (Diagnostic) Dataset. Various optimization techniques, including Ordinary Least Squares, Gradient Descent, Newton’s Method, L2 Regularization, Stochastic Gradient Descent (SGD), and GridSearch-based fine-tuning, were applied to determine their impact on model performance. Standard evaluation metrics—Accuracy, Precision, Recall, and F1-score—were used to compare methods. The results demonstrate that GridSearch-based fine-tuning consistently yields the highest overall performance, highlighting the importance of systematic hyperparameter optimization in enhancing model robustness and predictive accuracy. These findings emphasize that even classical models like logistic regression can achieve state-of-the-art results in medicaldata analysis when combined with appropriate optimization strategies.
