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Adequacy and Equity in Nevada School Funding: A School-Level, Cost Function Analysis. Wen Wang, Ph.D., Indiana University-Purdue University Indianapolis Anna Lukemeyer , J.D., Ph.D., University of Nevada Las Vegas. Special Acknowledgements.
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Adequacy and Equity in Nevada School Funding: A School-Level, Cost Function Analysis Wen Wang, Ph.D., Indiana University-Purdue University Indianapolis Anna Lukemeyer, J.D., Ph.D., University of Nevada Las Vegas
Special Acknowledgements • Carrie Sampson, Ph.D. Candidate, University of Nevada Las Vegas • Jaewon Lim, Ph.D., University of Nevada Las Vegas • Robin Mendoza, MPA Student, University of Nevada Las Vegas • The Lincy Institute, Las Vegas, Nevada (for funding and support)
Introduction • Nevada ranks among the lowest states in the nation in terms of both student achievement and school funding • 39th (2013 NAEP 4th grade reading) • 37th (2013 NAEP 4th grade mathematics) • 43rd (2012 spending per pupil)
Introduction • A number of school funding reform bills are currently under consideration in the Nevada legislature. • This study uses cost function methodology to analyze the adequacy and the equity of the Nevada school finance system • Presentation today: preliminary findings
Current Nevada School Finance System • No state funding for capital outlay (i.e., buildings, etc.) • Only 17 school districts (coterminous with counties) • Per pupil funding (state and local) determined largely at the state level. There is little leeway for additional local discretionary spending.
Current Nevada School Finance System • Based on enrollment with adjustments made for district economic and geographic characteristics and ability to raise revenue. • Does not structurally account for the relatively large number of ELL or low-income students. • Only allocation for at-risk students is for special education. • Categorical state funding exists for specific purposes such as class-size reduction and early childhood programs.
Research Objectives • Using education cost function methodology and school level data: • Estimate the amount of funding needed to provide students in a hypothetical “average” school a reasonable opportunity to achieve specified performance outcomes • Estimate adjustments to this level of funding based on resource costs and student characteristics
Data and Methods • Primary challenges; • No integrated dataset with the variables needed for a cost function study of Nevada • N=17 school districts
Data and Methods • Current dataset; • School level performance, demographic, and spending data for almost all public schools for school years 2012-2013 and 2013-2014 (approximately 635 of the 700 public schools for each year) • District-level data for property tax base, community income and demographics, etc.
Data and Methods • Data underlying findings presented today • Omits combined schools (K-12, K-8, 6-12), state sponsored charter schools, and special purpose schools. • Cost function study also omits outliers and schools in very sparsely populated counties (Esmeralda and Eureka).
Data and Methods • Cost function model and variables • E=f(P, R, St, Ef) where: • E = Per pupil operating expenditures • P = Student performance (% attaining proficiency in math and reading, treated as endogenous) • R = Resource costs (Comparable Wage Index) • St = Student and family characteristics (enrollment, enrollment squared, FRL, ELL, IEP)
Data and Methods • Cost function model and variables (cont’d) • E=f(P, R, St, Ef) where: • Ef = Efficiency • Income, property value, aid/income ratio, education (proportion of bachelors or higher), district type – large urban, small centralized, rural, small enrollment • Efficiency index (DEA)
Conclusions and Next Steps • Current Nevada school finance system does not formally account for student characteristics and in fact directs money away from school districts with the highest needs (Clark and Washoe). • Suggests that at-risk students require additional funding to achieve
Conclusions and Next Steps • Next steps: • Refine model. • Estimate model using spatial econometric techniques • Calculate Cost Indices and make recommendations • Add to dataset