Akan Nurbatsin

The study aims to analyze the innovation potential of enterprises, explain the use of the binary regression methodology, and explore different indicators of the enterprises in the regions of Kazakhstan for building an effective management strategy. Methodological substantiation is based on the complex survey data analysis, provided by the World Bank Enterprises Survey (WBES) for Kazakhstan. WBES database has covered a sociological survey of enterprises, which was conducted using a random survey and representativeness among 1,296 enterprises, mainly in the production sector. Besides, the data were collected among companies regarding their experience in environmental perception (including innovation activity), in which they worked. The results have demonstrated that the age of the company, exporter status, type, sector, or activity – all these have a positive influence on the company’s tendency to innovations. However, as part of the study, it has found out that competitors in the marketplace and regions of activity of enterprises predominantly negatively affect the prospects of introducing innovation. It was also in evidence that the same factors (foreign ownership, advanced training, type, size, and sector of an enterprise) are essential determinants of product, technology, R&D, ICT, and innovation. Obtained results can be used by enterprises for building an effective management strategy of own business innovative development, as well as by local authorities to upgrade the competitive performance.

Acknowledgments
This study has been funded by the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan (Grant “Priorities and mechanisms of inclusive regional development of Kazakhstan in the context of overcoming the economic recession” No. AP09259004).

This study aims to analyze the level of differences based on effectiveness, statistical dependence, and mutual influence of economic indicators for ranking the regions of Kazakhstan. In the paper, a systematic algorithm of actions is used, ensuring the interconnectedness, the sequence of work, and the validity of the methods used. Several model specifications were formulated to identify interregional differences in economic development indicators: the operating data environment analysis model (DEA) and the random effects regression model (RE). The information database of the Bureau of National Statistics of the Republic of Kazakhstan for 2010–2020 was used. The construction of the RE model was carried out in the SPSS program. A regression model with fixed and random effects in panel data was employed to determine the impact of the selected indicators on gross regional product produced (GRP). Based on the results, the influence of the physical volume of industrial products on GRP per capita in the regions of Kazakhstan for 11 years was revealed in both models with a high significance of coefficients. The study results can be used by public administration bodies that carry out effective strategic management by smoothing interregional differences. Moreover, they can determine the prospects for studying other parameters based on efficiency, statistical dependence, and mutual influence of economic indicators for ranking regions.

Acknowledgment
This study has been funded by the Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan (grant “Priorities and mechanisms of inclusive regional development of Kazakhstan in the context of overcoming the economic recession” AP09259004).

Despite ongoing efforts to modernize the higher education sector in Kazakhstan, the integration of strategic management and the effective allocation of public investment continue to pose significant challenges. This study aims to assess the relationship between public investment in higher education, enrollment dynamics, and innovation activity measured through the share of innovative organizations. Official statistical data for 2004–2024 were obtained from the Bureau of National Statistics of Kazakhstan and the World Bank. The study employs econometric analysis methods, including the evaluation of multiple linear regression models, vector autoregressive models, and Granger causality tests. According to the ordinary least squares (OLS) regression results, the impact of public investment in higher education on the number of students was not statistically significant (β = –0.00035; p = 0.507), suggesting insufficient targeting of funding and inefficient channels of distribution of funds. However, the number of higher education institutions shows a statistically significant positive effect on enrollment (β = 8048.5; p = 0.002), indicating that institutional infrastructure and territorial accessibility play a more significant role in expanding access to higher education than the overall level of funding. In addition, the constructed regression model is characterized by a high explanatory power (R2 = 0.8665), which indicates the estimates’ reliability and the correctness of the choice of explanatory factors. The findings highlight the importance of coordinating investment policy in higher education with the goals of innovative 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).

Type of the article: Research Article

Abstract
The purpose of this study is to determine the socio-economic and demographic factors of intra-regional inequality in Kazakhstan and their implications for cluster-based innovation in healthcare. A hybrid approach has been used, consisting of a systematic review of 181 publications using the PRISMA 2020 protocol and econometric analysis of the 2001–2024 panel data for the districts of Kazakhstan, consisting of 3,842 observations. Fixed-effects, cluster-robust, and hierarchical mixed-effects models were employed using standardized variables of population size, fertility, mortality, migration, criminality, and investments. The results reveal that the strongest and most robust predictor of intra-regional inequality in Kazakhstan is investment in fixed capital (β = 0.466, p < 0.01; β = 0.399, p < 0.01). Population size has consistently negative effects on intra-regional inequality in Kazakhstan (β = –0.240 to –0.256, p < 0.05 and p < 0.01). In the multilevel model, fertility increases intra-regional inequality in Kazakhstan (β = 0.114, p < 0.01), whereas mortality and net migration decrease it (β = –0.150 and β = –0.037, p < 0.01). The model explained 37.1% of the variance in intra-regional inequality in Kazakhstan (R2 = 0.371). The results suggest that without balanced investment and territorially differentiated policies, cluster-based innovation in healthcare can even reinforce rather than alleviate regional disparities.

Acknowledgment
This paper was carried out within the framework of the following grant projects funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan: IRN AP26198345 “Reducing socio-economic inequality in the regions of Kazakhstan through investment in and improvement of the organization ofthe healthcare system”.

Type of the article: Research Article

Abstract
Export diversification and the growth of non-resource exports remain key strategic priorities for Kazakhstan, particularly for food manufacturing firms facing organizational and institutional obstacles to participation in international markets. We seek to assess managerial, institutional, organizational, and ownership-related drivers associated with direct and broader export participation, targeting food manufacturing enterprises in Kazakhstan. The empirical analysis uses the food sampling-sector domain of the 2024 World Bank Enterprise Survey for Kazakhstan. The complete national dataset contains 1,013 establishments, and the initial food-domain subsample comprised 128 establishments. After listwise deletion of observations with missing values, the final estimation sample consisted of n = 121 establishments in the direct export model and n = 121 establishments in the any-export model. The statistical analysis applies survey-weighted logistic regression and average marginal effects. The findings suggest that organizational capabilities are important for firms’ export operations. Digital presence (β = 2.030; p = 0.004), process innovation (β = 1.789; p = 0.010), and firm size (β = 1.491; p = 0.001) are positively associated with direct export participation. Broader export involvement is associated with foreign ownership and customs restrictions. Strengthening export competitiveness therefore requires improvements in organizational preparedness, innovation, digital communication, customs procedures, and logistics.

Acknowledgments
This study is funded by the Science Committee MSHE RK under grant IRN AP23489234 “Research on new instruments for the development of agrotechnological hubs in the regions of Kazakhstan to enhance the country’s competitiveness in the Eurasian region.”