Ivan Digel

This study aims to study Kazakhstan’s regions and identify places with the best potential for developing smart cities based on cluster analysis. To analyze the differentiation by the level of development, 17 regions of Kazakhstan are grouped according to 2020 data from the statistical bulletin of the National Bureau of Statistics of the Republic of Kazakhstan. The formation of groups of regions with different values of indicators was carried out based on agglomerative clustering using the single linkage, complete linkage, and Ward’s clustering methods. In agglomerative clustering, the algorithm groups regions based on observations into clusters, and indicators determine each area’s innovative development level. The instrument to build clustering is the “RStudio” software package. As a result, regions with their essential characteristics were identified, and an assessment of their prospects was obtained with the most significant potential for developing and managing “smart cities” – Atyrau region, Almaty city, and Astana city. The remaining clusters include regions where favorable conditions for the development of innovations have not yet been formed, which require more resources and efforts to build “smart cities.” Therefore, they should not be the first to implement this concept. They need a more balanced, integrated approach, ideally supported by experience in implementing the idea in more promising regions. In a sense, clustering also allowed for identifying potential (or even existing) innovation clusters in regions of Kazakhstan. The study results can be used in developing government programs to form smart cities and further study the potential of smart cities.

Type of the article: Research Article

Abstract
Despite the growth of investments in higher education and innovative infrastructure in Kazakhstan, institutional and regional differences continue to shape heterogeneous outcomes in graduate employment. This study aims to assess the impact of higher education on the economic development and employment of graduates in Kazakhstan, while accounting for regional differences. The empirical research base is formed using official and publicly available statistical data from the Bureau of National Statistics and other government agencies of the Republic of Kazakhstan for the period 2000–2024. The study employs panel data econometric methods, including fixed-effects models and extended specifications. The results of the analysis show that investments in fixed assets per capita are positively and statistically significantly related to the level of gross regional product (β = 0.2855; p < 0.01), while average wages do not have a statistically significant effect (p = 0.941). The higher education index and the level of urbanization have negative and statistically significant coefficients (p < 0.05), and innovation activity shows a positive effect only in alternative model specifications. The results obtained emphasize the importance of considering the institutional and spatial characteristics of regions when forming management policies in the field of higher education, youth employment, and regional economic development.

Acknowledgments
This paper has been funded by the Science Committee MSHE RK (Grant “Modernization of the quality assurance system of higher education in Kazakhstan based on digitalization: development of approaches, mechanisms and information base” BR24992974).