Analysis of Cost Estimates and Staffing Schedules in General Education Schools: New Approaches to Optimization and Efficiency (Evidence from Namangan Region, 2020–2025)

Efficient management of cost estimates (smeta) and staffing schedules (shtat jadvali) is critical for the quality and sustainability of general education schools financed from the state budget. Conventional normative approaches based on fixed class-size and staffing coefficients remain effective for baseline planning, yet their rigidity, limited responsiveness to demographic shifts, and weak linkage to performance indicators motivate investigation of more flexible, data-driven optimization methods. This study evaluates the structure of school cost estimates and staffing schedules in Namangan region over 2020–2025 and compares traditional normative practice with multi-criteria optimization approaches that integrate pupil–teacher ratios, wage-fund shares, non-wage current expenditures, and digital monitoring tools. Official statistical series show that the number of general secondary schools rose from 726 (2020/21) to 767 (2024/25), while the pupil contingent increased by 52.7 thousand (10.3 %) to approximately 563.2 thousand. Teacher numbers expanded from roughly 38.4 thousand to 42.2 thousand. Wage and wage-related payments consistently accounted for 60–65 % of total school expenditures. Application of adjusted normative coefficients combined with digital monitoring reduced estimated redundant staffing positions by 4–7 % and improved alignment of cost estimates with actual enrollment dynamics. The findings demonstrate that efficiency cannot be judged solely by compliance with existing norms; resource reallocation capacity, transparency of cost estimates, and post-allocation performance must be considered together. The proposed approaches offer a technically credible pathway for improving budget utilization in general education while preserving educational quality. Further refinement of digital tools, regional calibration of norms, and pilot-scale validation are recommended.