Optimizing Public School Teacher Salary Schedules

Abstract

The structure of public school teacher salary schedules has a significant impact on teacher retention, recruitment, and student outcomes in the United States. While traditional step-and-lane salary structures provide incremental pay increases based on experience and educational attainment, they often lack strategic alignment with teacher effectiveness. This thesis introduces an optimization-based framework for designing teacher salary schedules aimed at maximizing cumulative teacher effectiveness within existing budget constraints. By using synthetic data based on publicly available metrics from Michigan public schools, we model various salary functional forms—linear, logarithmic, and power—and explore their implications for teacher retention and effectiveness. Our findings suggest that a concave power function, which prioritizes higher salary increases in the early career stages, aligns best with district-level goals of fostering retention and improving educational outcomes. Through comparative analysis across Michigan’s statewide data and district-specific data from Detroit and Bloomfield Hills, we demonstrate that the power model yields more consistent and realistic salary distributions, particularly in districts facing high turnover rates or early-career attrition. Additionally, this framework provides a practical approach for districts to adapt salary schedules to their unique profiles without requiring policy overhauls. This work contributes to the literature on teacher compensation by offering a scalable, data-driven method that bridges the gap between traditional pay structures and optimized salary schedules. The model serves as a foundation for further research and policy development aimed at enhancing teacher effectiveness and educational equity through more intentional salary design.