Research Grants

This study investigates Texas’s multi-indicator at-risk model to determine if a state-administered composite measure identifies low-income students more comprehensively and sustainably than traditional federal income proxies. The researchers use a three-stage design merging district-level data to map identification overlaps, benchmark agreement with Census estimates, and evaluate the administrative feasibility for other states. The study aims to provide evidence to inform more inclusive funding formulas that capture dimensions of disadvantage often missed by household income alone.

This study develops an alternative low-income student identification index designed for the Mississippi and Tennessee funding formulas. Drawing on six domains of access, including food, childcare, housing, and broadband, and using both federal and state-level data sources, the study creates a multidimensional index to replace narrow income-based indicators. Its significance lies in offering a replicable, equity-centered framework that enables states to capture the material realities of poverty across varied geographic and community contexts without requiring costly new data investments.

This study provides causal evidence on how the eleven percent statutory cap on special education weighted-funding within the Oregon State School Fund influences district revenue and student services. The researcher will use a kink regression discontinuity design to analyze a twelve-year panel of district-level Oregon administrative data, examining district-level expenditures, and aggregate student achievement scores. This study aims to inform the 2027 Oregon legislative session’s debate on funding caps and offer insights for other states managing special education costs.

This study compares Michigan’s current binary economically disadvantaged indicator with alternative measures that capture poverty depth and persistence. Researchers will use linked administrative and geographic data from three diverse Michigan counties to conduct correlation and regression analyses of income-based, program-based, and neighborhood-based metrics. This study illustrates how states can utilize existing data to move beyond binary poverty measures and design more differentiated funding weights that account for the depth and duration of student hardship.