Today, digital transformation has a significant impact on the labor market, changing the employment structure and forming new requirements for the qualifications of employees. The purpose of the study is to analyze the impact of digital literacy on the employment status of the population of the Republic of Kazakhstan in the context of rapid technological changes and the consequences of the COVID-19 pandemic. Research methods include correlation analysis to identify the relationship between the level of digital literacy and employment structure, as well as a comparative method to assess the dynamics of changes in the labor market. The empirical base of the study is based on statistical data for 2010-2022 collected from official statistical collections of Kazakhstan and reports of the International Labor Organization (ILO). The results of the study show that an increase in the level of digital literacy leads to a decrease in the proportion of employees and the self-employed, which is associated with automation, the development of digital platforms and changing forms of employment. Correlation analysis revealed a significant negative relationship between the level of digital literacy and the proportion of employees (r = -0.75), as well as a strong positive relationship with the number of pensioners (r = 0.75) and dependents (r = 0.94). The results obtained confirm the trend towards the transition to remote forms of work and the use of digital technologies in everyday life. Future research may focus on developing strategies to increase digital literacy among the population for balanced labor market development and reduce the digital divide, especially in rural areas.

The present paper analyzed the impact of the digital economy and innovations on Kazakhstan’s labor resource transformation from theoretical and empirical perspectives. By means of correlation analysis the factors that were the most significant for the result variable - the employed population in high-tech and knowledge-intensive sectors of the economy were determined (R2>0,8). However, the correlation analysis revealed the multicollinearity - close linear relationship between all factors. In this regard, the method of statistical equations of dependencies was applied for further research. During the study, a multifactorial equation of dependencies was calculated. Key socio-economic factors influencing population employment in high-tech and knowledge-intensive sectors of the economy were determined. The degree of influence of each factor on the result variable was calculated. Thus, the level of employment in high-tech and knowledge-intensive industries of Kazakhstan is most influenced by four key factors: the share of Internet users, the degree of influence of this indicator is the most significant and amounted to 38.28%; the share of computer users – 28.27%; gross domestic product per capita - 19.47%; and internal expenditure on research and development work – 11.69%. Taking into account the fact that the digital innovation era today is almost at the very beginning of its development, the digital processes occurring in the economy, in particular in the labor market, require monitoring and in-depth analysis for the timely development of management levers and control of their impact, that only emphasizes the relevance of this study.

Currently, Kazakhstan’s higher education system is undergoing drastic transformations due to the transition of the national economy to digital technology platforms. Practice shows the need for such a transition, which is related to the training of qualified specialists with innovative knowledge who can adapt to the new conditions of the labor market. This study aims to assess the relationship between the development of the higher education system and the level of innovative development in the Republic of Kazakhstan. The research uses correlation and regression analysis, modeling of linear equations, structural, functional, and comparative analysis, and the grouping method. Statistical data from the Bureau of National Statistics of the Republic of Kazakhstan and international sources for 2013-2023, covering over 80 countries in terms of education and high technology exports, were used as an empirical base. The results of the correlation analysis showed that such factors as the gross domestic product (correlation coefficient - 0.95), the number of students in higher education institutions (0.66), as well as the number of innovation costs in the industry (0.61), have the greatest impact on internal R&D costs in Kazakhstan. At the same time, despite the positive dynamics of quantitative indicators, several systemic barriers remain in Kazakhstan, hindering the transformation of academic knowledge into market innovations. Future research paths may include a deeper analysis of the role of digital technologies in the transformation of higher education, as well as the development of indicators for assessing the innovative potential of universities.

Today, poverty remains a significant problem affecting various population groups and economic stability. Understanding the key determinants of poverty is an important prerequisite for developing effective poverty reduction strategies. This study aims to identify the relationship between the poverty level and the population’s monetary income, as well as to assess the regional features of its spread in Kazakhstan. The methodology is based on an analysis of statistical data for 2001-2023 collected from official data from the Bureau of National Statistics of the Republic of Kazakhstan, the World Bank, and the United Nations. The article uses descriptive statistical methods to study the dynamics of poverty and correlation and regression analysis to identify the relationship between poverty and indicators such as average nominal income per capita, Gini coefficient, unemployment rate and household size. The results showed significant regional differences in poverty levels, with the highest poverty rates observed in the Turkestan region (9%) and the Abai region (8%). Regression analysis confirmed a significant impact of the cash income deficit on the poverty rate (R2=0.86, p<0.01). A high correlation between the poverty rate and the Gini coefficient (0.89) was revealed, indicating a significant impact of income inequality. The prospects for further research include an in-depth analysis of the impact of educational attainment on poverty, a study of the impact of digital financial technologies on household incomes, and an assessment of the effectiveness of government programs to reduce poverty.

Gender equality is necessary for further economic development of a country and societal welfare in conditions of the modern demands and shifts in the labor market of Kazakhstan. The goal of this research is to reveal the significance of gender issues in the sustainable enhancement of the labor market in Kazakhstan and to suggest the possibilities of applying gender equality in management practices. Regression and correlation analysis were conducted to analyze the relationship of indicators of gender equality with the economic data. A strong positive correlation (r = 0.909, p = 0.000265) was found between the ratio of women’s wages to men’s wages and the proportion of women in economic activity groups, indicating that women’s participation in the labor market is associated with an increase in their wages. The results of the study show that there is specific progress in the labor market of Kazakhstan in relation to gender inequality, but structural barriers remain. To achieve sustainable development, comprehensive measures are needed to ensure wage equality, increase the participation of women in high-paying industries, and create a gender balance in leadership positions. Thus, it is clear that the enhancement of gender equality increases labor productivity, expands personnel stock, and enhances the resilience of the economy. Future research in managing the sustainable development of the labor market in Kazakhstan through gender equality can be aimed at studying the long-term impact of gender initiatives on economic growth and social stability and assessing the effectiveness of specific policies and programs.

The purpose of the article is to examine the main socio-economic trends in Central Asian countries and to identify prospects and opportunities for achieving sustainable growth goals. The study hypothesizes that the region has the potential for sustainable growth and strengthening its geopolitical position based on the synergy of strengths, strengthening weaknesses, and creating new opportunities. This study uses the methods of structural and logical analysis, descriptive statistics, factor analysis, predictive expert assessments, SWOT analysis of the economies of Uzbekistan and Kazakhstan, and comparative analysis of investment attractiveness. A comparative analysis of Central Asian countries was conducted at the level of individual countries, the region as a whole, and in comparison, with global averages. Key indicators used included gross domestic product (GDP), GDP per capita, GDP growth rate, unemployment rate, average salary, demographic indicators, expenditures on education, the enrollment rate in higher education, unit costs of research and development, and the human capital index. The results show that in order to strengthen their positions, the Central Asian countries must: ensure a balanced approach to economic and social development in accordance with the Sustainable Development Goals; focus government and business efforts on job creation; ensure the diversification of the national economy for a gradual transition from traditional industries to industries with higher added value. Further research requires expanding the analysis of the development of the industrial and service sectors to understand how transit potential affects the development of the economic structure; it is necessary to use econometric modeling and analysis methods.

Today, urban areas are considered significant sources of CO₂ emissions, making the problem of climate change particularly urgent. This study aims to analyze urbanization’s impact on greenhouse gas emissions, identify key economic, social, and environmental factors, and propose recommendations for sustainable urban development. The study is based on econometric analysis of panel data collected from 107 countries from 2004 to 2023 from the World Development Indicators (WDI) database, using linear regression models to examine the relationship between urbanization levels and CO₂ emissions. Results show a negative correlation (-0.361) between urbanization and emissions, indicating the potential for reducing emissions through compact urban development, with energy consumption as the main factor contributing to increased emissions (R² = 0.8541). Renewable energy use has a significant effect on reducing emissions (-0.585). In Kazakhstan, high dependence on coal-fired power leads to an increase in emissions. However, an increase in the share of renewable energy sources can significantly improve the environmental situation (-0.830). Thus, the results of the study confirm that urbanization, provided by compact urban planning and the introduction of renewable energy, can contribute to reducing CO₂ emissions per capita. It is advisable to study specific future strategies for reducing emissions in Kazakhstan, including developing smart cities and low-carbon technology. The work has practical value, offering recommendations on integrating sustainable energy use, efficient infrastructure, and environmental management into Kazakhstan’s urbanization process.

Kazakhstan’s economy remains dependent on the extractive sector, which poses risks of instability due to fluctuations in global commodity prices. In the context of globalization, the integration of the manufacturing industry into global value chains (hereinafter referred to as GVCs) is becoming an urgent task that will increase the competitiveness of the national economy. The purpose of this study is to quantify the potential of the manufacturing sector and assess the relationship between indicators of GVCs and main indicators of economic growth. Statistical data from the Bureau of National Statistics of Kazakhstan and international rating organizations like the OECD, Asian Development Bank, and Islamic Development Bank were used for this study. Regression modeling, reliability analysis using Cronbach’s alpha, and analysis of variance were used to analyze quantitative data. The results showed that the volume of non-primary exports had a statistically significant impact on GDP (p<0.05) and labor productivity (p< 0.01), but the share of manufacturing in the economy remained low and the process of export diversification and integration into global supply chains was slow. This study highlights the need for active government policies in the development of the manufacturing sector, attracting investment in non-resource industries, and deepening participation in GVCs. The findings can be used to formulate industrial policy strategies to reduce dependence on raw materials and create sustainable conditions for economic growth. Promising areas for further research include the analysis of factors affecting investments in non-primary products, the study of structural reforms in the manufacturing industry, and the assessment of government support’s impact on the development of the non-primary sector.

The problem of spatial inequality in the regions of Kazakhstan has been relevant for many years and in recent years this problem has worsened due to socio-economic changes in the areas, the consequences of the COVID-19 pandemic, the negative impact of inflationary processes, increased internal migration and other factors. The purpose of this study is to analyze spatial inequality between 16 regions of Kazakhstan covering the period from 2001 to 2017. The following scientific methods were used in the study: historical method, and statistical method. During the research, a new class of spatial econometric models was developed, which are modifications of the Durbin spatial model. These models are characterized by variable coefficients with spatial lags of the dependent and independent variables. The models were evaluated based on information about Kazakhstan’s regions, using the regional gross domestic product per capita as a dependent variable. The findings of the study show the advantages of the SDM model with fixed effects compared to alternative models, which is confirmed by the results of the assessment using the criteria of the Akaike Information Criterion (AIC) and Bayes (BIC). According to the SDM model, a 1% increase in gross regional product per capita in the base year leads to an increase in the growth rate of gross regional product per capita, all other things being equal. It is also worth noting that an increase in the unemployment rate by 1% contributes to an acceleration in the growth rate of the gross regional product per capita by 0.451, all other things being equal. An increase in government spending per unit in the region contributes to a decrease in the growth rate of the gross regional product per capita in the neighboring region, all other things being equal. The spatial lag coefficient indicates that changes in the indicators of the domestic regional product per capita in one region have an impact on changes in the domestic regional product per capita in the neighboring region. The results of the study indicate the need to use spatial weights when evaluating regional regression models.

Today, tourism and the hospitality industry are playing an increasingly important role in the economy of Kazakhstan, creating multiplier effects and contributing to the development of related industries. The aim of the study is to quantify the impact of final demand for the hotel and restaurant sectors on the economy based on the intersectoral balance model. Annual statistical reports from the Bureau of National Statistics of Kazakhstan were used as initial data, aggregated into 15 broad industries. Methods were used to calculate coefficients of direct and total costs, output multipliers, income multipliers and value-added, as well as indices of direct and inverse intersectoral relations based on the Leontief model. Results showed that the hotel-restaurant sector had the greatest multiplier effect, with a multiplier of 2.929 for output and 0.691 for income. The value added multiplier was 1.686. The values obtained reflect a high degree of interconnection between the sector and other industries, and its ability to produce a significant cumulative economic impact with increased final demand. The findings of the study highlight the importance of the hotel and restaurant sector as one of the key drivers of economic growth, especially in terms of creating added value and stimulating employment. Practical significance lies in strengthening internal cooperation, which can further enhance the effect of increasing final demand for hospitality services. Future research could include the development of dynamic and hybrid approaches to account for changes in structure, price effects, and technology in supply chains.