The agricultural sector plays a key role in ensuring economic stability and food security, but the factors determining farm profitability remain a matter of debate. The purpose of the study is to assess the impact of subsidies, farm size and age on the annual income of agricultural enterprises in the North Kazakhstan region. Panel data from 456 farms for 2010-2023 (6354 observations) was used as a database. Methodologically, the work is based on multifactorial regression using a random effects (REM) model and stable standard errors. Using balanced panel data from 2010 to 2023, a multiple regression model with robust standard errors was applied. The results show that the size of the farm has the most significant and statistically significant positive impact on income (𝛽=1,278, p < 0.001), confirming the existence of economies of scale. The impact of subsidies (𝛽= − 0.143, p = 0.789) and farm age (𝛽 = 0.050, p = 0.305) turned out to be statistically insignificant. The results indicate the need to reorient agricultural policy to support effective farm consolidation, improve subsidy allocation mechanisms, and develop targeted programs to help young farmers. Furthermore, the application of the robust standard errors technique, which is rarely used in prior domestic studies, strengthens the methodological reliability of the results. Based on the findings, several policy recommendations are proposed: subsidies should be redirected toward enhancing productivity and innovation, supporting large and efficient farms, and improving financial and institutional mechanisms for young farmers.

This study empirically examines the effectiveness of agricultural subsidies in enhancing farm productivity in the Zhambyl region of Kazakhstan over the 2010–2025 period. Despite substantial public expenditure and the widespread use of subsidies as a core instrument of agricultural policy, their actual contribution to productivity growth remains contested. Using a quantitative research design, this study analyzes panel data from 34 farms (510 observations) obtained from the Ministry of Agriculture of the Republic of Kazakhstan. A two-way fixed effects regression model, validated through the Hausman test, is employed to estimate the relationship between subsidy amounts and farm productivity, controlling for land area and fixed assets. Diagnostic tests confirm the model’s compliance with key classical regression assumptions. The results reveal that agricultural subsidies have no statistically significant effect on farm productivity (β = 0.228, p > 0.05). This finding remains robust across sub-period analyses (2010–2015 and 2016–2025), indicating that increased subsidy volumes have not translated into measurable productivity gains. The study identifies structural inefficiencies in subsidy design, including disproportionate allocation to large farms, weak conditionality, and a focus on cost compensation rather than performance stimulation. These findings contribute to the growing body of evidence questioning the efficacy of untargeted financial support and underscore the urgent need for policy reform. The paper advocates for a transition toward “smart,” region-specific, and results-based subsidy mechanisms linked to technology adoption, sustainable practices, and robust monitoring frameworks. The study provides actionable recommendations for policymakers seeking to enhance the efficiency and sustainability of public spending in Kazakhstan’s agricultural sector.

The agricultural sector plays a critical role when it comes to economic development, food security, and environmental sustainability. This particular study looks at factors that affect total factor productivity (TFP) in agricultural state-owned enterprises (SOEs) and private farms. The research identified and evaluated land use efficiency, labor productivity, capital investment, and the adoption of sustainable practices for over 11,982 farms in Kazakhstan within a ten-year timeframe. The results exhibit productivity differences between SOEs and private farms. A larger share of private farms outcompetes SOE due to more efficient labor utilization and allocation of resources while the SOEs are more productive due to self-sufficient capital investment and infrastructure. As expected, lagged TFP (TFP from the last period) turns out to be an important predictor of TFP within the industry, for which there has been a great emphasis in the cross-section analysis. Additionally, the consequences of land use and agricultural sustainability positively contributed to TFP. These results confirm the expectations about responsible farming with a focus on resource optimization. On the other hand, crop yield was discovered to have an inverse relationship with TFP. This implies low levels of TFP growth because of efficiences associated with over-relies on inputs. This research is useful for policymakers and stakeholders as it expands the range of possibilities for multi-targeted approaches. Keywords: Total Factor Productivity (TFP), agriculture, capital investment, sustainable farming practices, State-Owned Enterprises (SOEs)

This study examines regional agricultural credit allocation and intensity in Kazakhstan and their relevance to financial inclusion. Data cover 17 harmonised units in 2014–2023. Two-way fixed-effects models use 153 observations for 2015–2023 (T = 9), after lagging output. Outcomes comprise total real credit, credit per agricultural worker, and credit per hectare. Credit remains concentrated: the 2014–2023 Spearman rank correlation is 0.93, and 13 of 17 units remain in the same quartile. The modest decline in the top-three share is not statistically distinguishable from zero. A 1% increase in lagged agricultural output is associated with 0.63% higher current credit. Subsidies are positively associated with credit, with moderate evidence of a stronger association in initially high-credit regions. The interaction has weaker bootstrap support (p = 0.061; 95% CI [−0.01, 0.30]), so the distributional evidence is suggestive rather than definitive. Separate and joint models support a positive branch-density association; ATM-density estimates are imprecise. Regional differences and the main associations persist in the normalised outcomes. These non-causal associations concern geographic financial access and cannot establish borrower-level exclusion. Aggregate credit growth alone does not establish geographically inclusive agricultural finance.