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Low-enrollment weighting: purpose, court history and modeling trade-offs
Summary
Nick Myers and KLRD reviewed the low-enrollment weighting (an economies-of-scale adjustment enacted in 1992) and presented models showing effects of removing it, lowering its threshold, or rolling dollars into the base. Staff warned that eliminating it without offsets would sharply reduce funding for the smallest districts.
Nick Myers briefed the task force on the low-enrollment weighting, its statutory mechanics, historical rationale and modeling results.
What the rule does: Myers explained that low-enrollment weightings are scale factors applied to districts below a statutory threshold; the weighting increases per-student funding for very small districts to reflect higher per-student fixed costs. He said the weighting "was enacted in 1992 as part of the SDF QPA" and that the Kansas Supreme Court reviewed challenges to the low-enrollment structure (USD 229 v. State) and upheld a rational-basis determination in the 1992 decision.
Model runs and dollar magnitudes: KLRD presented alternative scenarios. One illustrative number: removing the low-enrollment weighting entirely would free about $232.5 million of state foundation aid; combined with the computed local option budget effect that staff modeled, the total redistribution shown in that run was approximately $313.6 million. Other runs considered applying the low-enrollment scale only to districts below 500 students (the state median), below 1,000 students, or reverting to the original 1,900 threshold used when the weighting was enacted.
Distributional concern: staff and task force members repeatedly noted that the low-enrollment weighting is designed to address real economies-of-scale and that removing it without offset would disproportionately harm very small, often geographically large, districts. Director Rooker and others pointed to prior study materials (Lori Taylor and related peer review work) that analyze cost drivers such as density, comparable wages and other regional price differences and cautioned that any change should be data-driven.
Policy options discussed included targeting sparsity or isolation measures (for example, treating a district differently if it is very remote), adjusting thresholds, or permitting voluntary service-sharing or consolidation with legislative incentives. Several members asked KLRD to produce maps showing small districts, population density and proximity to larger districts so the task force could consider sparsity-based approaches.
Ending: Staff will provide the requested maps, the Taylor study materials and combined models (paired high/low scenarios and alternative thresholds) so the task force can examine targeted options rather than an across-the-board repeal.
