Assel Bekbossinova

In the context of the increasing spatial differentiation of agricultural development and the need to switch to a growth model with higher added value, the task of scientific substantiation of the territorial location of agricultural farms in the regions of Kazakhstan is being actualized. The purpose of the study is to analyze the dynamics of the development of the regions of Kazakhstan and determine the prerequisites for the formation of agrohabs based on industrial, infrastructural and social determinants. The methodological framework includes descriptive statistical analysis, normalization of indicators using the min-max method, and calculation of an integral index. The typologization of the regions was carried out using the quartile method, and a socially oriented SWOT diagnosis was additionally carried out for regions with high and medium potential levels. The information base was the official data of the Bureau of National Statistics and the National Bank for the period 2010-2024. The results showed that during the analyzed period, gross agricultural output increased from 1.82 trillion tenge to 8.31 trillion tenge (an increase of 4.56 times; average annual rate of 11.5%), while employment decreased from 2.29 million to 1.03 million people and labor productivity increased from 0.79 to 8.09 million tenge per employee. The integral index revealed a pronounced regional differentiation: the highest values were recorded in Turkestan (0.80), Almaty (0.69), Akmola (0.66) and North Kazakhstan (0.65) regions, the lowest in Mangystau (0.37) and Ulytau (0.10) regions. The results obtained confirm the need for a differentiated regional policy and the use of integrated assessment as a tool for strategic planning of agrohabs.

Type of the article: Research ArticleAbstractGiven the instability of global energy markets and recurring price shocks, it is increasingly important to assess the sustainability of economic growth in countries heavily dependent on oil and gas, such as Kazakhstan. The purpose of this study is to assess the impact of the oil and gas sector’s production, fiscal and external sector transmission channels on Kazakhstan’s economic growth during 2010–2024. The empirical base includes official data from the Bureau of National Statistics of Kazakhstan, the Ministry of Finance, and the World Bank, as well as economic indicators related to exchange-rate dynamics and inflation. The study employs a combination of cointegration analysis, vector error correction models (VECM), and short-term ordinary least squares (OLS) specifications. The results show that oil production and GDP are linked by a stable long-term relationship, with the error-correction term indicating that about 24% of deviations from equilibrium are eliminated per year. Gas production emerges as a key short-term growth factor: a one-thousand-ton increase in gas production is associated with a 5.26 billion tenge (11.8 million USD) in GDP (p = 0.002). Tax revenues from the oil and gas sector exert a moderate positive effect on economic dynamics (p = 0.065), reflecting fiscal sensitivity to external conditions. The external sector channel exhibits the strongest short-term effect: a one-unit depreciation of tenge is associated with an average increase of 2.61 × 10⁵ tenge in nominal GDP (p < 0.01). Overall, the results indicate that Kazakhstan’s economic growth is shaped by long-term dependence on production and short-term transmission of price and financial shocks.AcknowledgmentsThis paper has been funded by the Science Committee MSHE RK (Grant “Strategy for the structural and technological modernization of the basic sectors of the economy of the Republic of Kazakhstan, taking into account ESG approaches: criteria, implementation mechanisms, and forecast scenarios” BR24993089).

Today, business valuation is a key tool for making managerial and investment decisions, especially in restructuring, strategic planning, and capital raising. One of the most common approaches to valuation is the discounted cash flow method (hereinafter referred to as DDP), which allows you to bring future cash flows to their current value. The purpose of this study is to identify the potential risks of using the DDP model to assess the market value of a business, using the example of the Toyota Motor Corporation, and to propose approaches to minimise them. The work used Toyota Motor Corporation's open financial reports, including revenue, cost, EBITDA, net profit, and macroeconomic indicators of the Japanese economy. The research methodology combines theoretical literature analysis and practical modelling: a DDP model was built based on data from a public manufacturing company, using elements of risk management (sensitivity analysis, scenario analysis, and modelling of the post-forecast period). The results of the study showed that Toyota Motor Corporation's net profit increased (+103.4%), and the forecast of free cash flows increased from 426,564 to 513,563 million yen in the period 2025-2030. With a discount rate of 10.59% and a fixed growth rate of 1.06%, the high sensitivity of the final cost to changes in key parameters, especially the discount rate and macroeconomic assumptions, was revealed. The results of the study show the high sensitivity of the final assessment to changes in key parameters, particularly the discount rate and growth rates, as well as the model's vulnerability to macroeconomic volatility and production risks.

Type of the article: Research articleAbstractThe purpose of the study was is to assess the impact of key factors (including employment, income, poverty level, prices and social support) on the dynamics of social pressure in Kazakhstan. The analysis covered the period from 2014 to 2024 and utilized official data. The methodology combines entropy-based ranking of indicators, calculation of the Socio-Economic Pressure Index (SEPI), Social Stability Index (SSI), and Social Inertia Index (SII), as well as phase-portrait visualization. The entropy ranking revealed that the share of population below the food basket cost (di = 0.71; Wi = 0.2381) and housing assistance (di = 0.516; Wi = 0.1728) had the highest variability and the strongest influence on SEPI. Moreover, SEPI rose from 0.0967 in 2014 to its peak in 2023 – over 20 times higher – before falling to 0.53 in 2024. SSI dropped from 4.94 in 2017 to 0.44 in 2023, with minimal adaptive capacity, and partial recovery to 1.87 in 2024. There was recorded instability of positive changes and a high likelihood of renewed pressure due to the lowest value of SII (–3.24) in 2024. Thus, long-term stability and a reduction in the social sphere’s susceptibility to external and internal shocks require integrated policy measures that combine targeted support, income regulation, and adaptive governance.AcknowledgmentsThis research has been funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant “Development of mechanisms for reducing social inequality and improving the welfare of the population of Kazakhstan” AP19174744).

The gender pay gap remains a salient manifestation of social inequality and poses a significant barrier to attaining gender equality in the labour market. This study examines the structural, social, and institutional factors that influence the gender pay gap in Kazakhstan from 2013 to 2023. Based on a structural-dynamic macroeconomic approach, the research integrates descriptive analysis, correlation and regression modelling, and PESTEL analysis. The results show that the gender pay gap in Kazakhstan decreased from 32.2% in 2013 to 25.7% in 2023, with the lowest value recorded at 21.7% in 2021. A strong negative correlation was identified between the share of women with higher education and the gender pay gap (r = -0.71), confirming education as a primary factor in reducing inequality. The regression analysis also confirmed the significant impact of employment segregation: a high concentration of women in low-paid sectors, particularly trade (TradeRatio coefficient +2.27, p ≈ 0.064), substantially contributes to maintaining the pay gap. Meanwhile, the unemployment rate among women showed a positive correlation with the gender gap (r = +0.52), though it was not statistically significant in the regression model. The PESTEL analysis highlights additional barriers, including insufficient institutionalisation of equal pay control mechanisms, entrenched social norms regarding female professions, limited access to high-tech and green sectors, and weak transparency in wage systems. Future research directions include micro-level analysis of individual factors and regional disparities, as well as the effects of digitalisation and flexible employment on gender pay equality.

Gender segregation in agriculture remains a significant socio-economic problem that limits women's potential and hinders sustainable development in rural areas. Despite the critical role of women in agricultural production, their access to land, financial resources, technology, and training remains limited, leading to lower productivity and increased poverty among rural populations. This study examines gender segregation in the agricultural sector of Kazakhstan for the period 2013-2023. The use of the dissimilarity index (DDD coefficient) allows for a quantitative assessment of gender disparities in employment. A literature review shows that gender inequality in agriculture is caused by structural barriers, including uneven land ownership, limited access to agricultural subsidies, and social norms that hinder women's participation in decision-making processes. Empirical analysis reveals that the national DDD coefficient increased from 6.0% in 2016 to 9.1% in 2023, indicating a growing gender imbalance. The highest levels of segregation were recorded in regions characterized by resource-dependent and mechanized agriculture, such as Kyzylorda, Atyrau, and Aktobe. In contrast, regions with well-developed agricultural cooperatives and small-scale farming exhibited lower DDD levels. The SWOT analysis revealed both strengths and barriers faced by women in agriculture in Kazakhstan. In conclusion, recommendations are proposed to reduce the gender gap, including developing educational programs, increasing women's access to financial resources, and strengthening support for women's entrepreneurship in rural areas.

Today, digitalization and investment are becoming key drivers of agricultural transformation, but their impact on the agro-industrial sector remains controversial. The study aims to determine how investments in fixed assets, digital literacy, and Internet penetration affect the volume of gross agricultural output in Kazakhstan. The initial data includes panel data from the Bureau of National Statistics of the Republic of Kazakhstan for 2010-2023. The study uses multidimensional linear regression to assess the contribution of each factor (investments in fixed assets, the level of digital literacy, Internet penetration, and the use of mobile phones and computers) to the formation of gross agricultural output. The findings indicate that investments in fixed agricultural assets exhibit a positive but statistically insignificant relationship with agrarian production (β = 4.4474, p = 0.2221). Similarly, mobile phone use and Internet penetration in rural areas do not show a significant correlation with the growth of the agricultural sector. In addition, negative coefficients for digital literacy and Internet activity in rural areas have been identified, which may indicate barriers to introducing digital technologies into the agro-industrial complex. The novelty of this research lies in its empirical evaluation of digitalization’s role in agricultural development within a transitional economy, emphasizing the need for targeted policy interventions. The results suggest that investment efficiency, digital skills training, and improved technological adoption should be prioritized to maximize agricultural productivity. Further research is needed to explore long-term investment effects and sector-specific impacts to refine policy recommendations.

Effective management of pension assets is crucial for ensuring the long-term financial stability of national pension systems. In Kazakhstan, the Unified Accumulative Pension Fund (UAPF) plays a key role in accumulating and investing pension savings, yet its asset management strategies face challenges due to inflation, market volatility, and limited investment options. As of 2024, UAPF's total assets exceeded 18.6 trillion tenge, growing by 16% year-over-year. However, with inflation surpassing pension returns (9.8% vs. 8.3% in 2023), the real value of savings is declining. This study aims to develop a methodological approach for optimizing pension asset management using multidimensional classification techniques. The research employs a quantitative approach, utilizing data from the Bureau of National Statistics of Kazakhstan for 2013–2023. Statistical and econometric methods, including correlation analysis, cluster analysis, and variance analysis, were applied to classify assets based on risk and return characteristics. The study identifies three pension portfolio categories: (1) moderately aggressive, with returns up to 8.3% and higher risk; (2) moderately conservative, offering 5.5% returns with balanced risk; and (3) conservative, providing stable but low returns, failing to offset inflation. Findings highlight that Kazakhstan's pension system remains highly dependent on government securities (46% of total assets), limiting growth potential. The research suggests diversification strategies, including alternative assets, ESG investments, and digital financial tools. The study contributes to the literature on pension fund management in emerging markets and offers practical recommendations for enhancing investment efficiency and long-term financial security for pensioners. Keywords: pension asset management, investment strategy, financial stability, risk diversification, cluster analysis, pension fund, Kazakhstan, digital financial tools, economic modeling, ESG investments.

Understanding the impact of economic factors on bank lending is crucial in Kazakhstan’s modern economy, characterized by volatile inflation and fluctuations in real wages. This paper aims to investigate the link between bank lending and economic factors such as inflation, real wages, and consumer expenditure in a regional context. Data from the Bureau of National Statistics and the National Bank, covering the period from 2012 to 2022, were used to uncover how economic factors influence bank lending. For the analysis, various economic indicators were integrated through normalization and averaging. Analysis reveals significant regional disparities in real wages and consumer expenditures, which impact the demand for bank credit. The results of the correlation matrix showed that both real wages (P-value < 0.001) and inflation (P-value < 0.001) significantly impact bank lending, with an R² value of 0.998, indicating that the model explains 99.8% of the variation in bank lending. The regression analysis highlights that regions with higher real wages, such as Astana, Almaty, and Atyrau, provide the most favorable conditions for banking sector growth, demonstrated by a strong relationship between wages and bank lending. In contrast, regions with lower wage levels, such as Turkestan and Zhambyl, show a significantly weaker connection (around 0.65), reflecting their lower attractiveness for banking investment and emphasizing the need for policies to address social inequality. The Durbin-Watson test confirmed no autocorrelation in residuals (DW = 1.89), although heteroscedasticity was detected, suggesting the need for further model adjustments. The study emphasizes the importance of developing economic policies that can balance regional development and improve financial stability. AcknowledgmentsThis research has been funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant “Development of mechanisms for reducing social inequality and improving the welfare of the population of Kazakhstan” AP19174744).

This study examines the gender wage gap in Kazakhstan, highlighting persistent disparities despite some progress. In high-paying sectors, men earn up to 37% more than women. The objective is to identify key factors contributing to this gap from 2013 to 2023. Using official data from the Bureau of National Statistics of Kazakhstan, the research utilizes secondary data analysis, industry analysis, and multiple regression analysis. Factors analyzed include the ratio of female to male heads in higher education, the proportion of female research specialists, and the Gender Inequality Index. Results show that the proportion of female research specialists significantly reduces the gender wage gap, with a coefficient of -2.7286 and a p-value of 0.013. In contrast, the ratio of female heads and the Gender Inequality Index show no significant impact, with coefficients of 0.6690 (p-value: 0.160) and -0.2752 (p-value: 0.262), respectively. The wage gap, 27% in 2023, is most pronounced in high-paying sectors, influenced by differences in working hours and domestic responsibilities affecting women disproportionately. The study concludes that achieving sustainable gender equality in Kazakhstan requires targeted policies addressing economic and cultural barriers. Enhancing female participation in high-paying sectors and supporting their career advancement is crucial to reducing the gender wage gap and fostering a more equitable labor market.