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Assessing the Use of Econometric Analysis in Estimating the Costs of Meeting State Education Accountability Standards: Lessons from Texas

Jennifer Imazeki and Andrew Reschovsky
Peabody Journal of Education
Vol. 80, No. 3, Rendering School Resources More Effective: Unconventional Responses to Long-Standing Issues (2005), pp. 96-125
Published by: Taylor & Francis, Ltd.
Stable URL: http://www.jstor.org/stable/3497044
Page Count: 30
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Assessing the Use of Econometric Analysis in Estimating the Costs of Meeting State Education Accountability Standards: Lessons from Texas
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Abstract

In 2004, over 300 school districts in Texas challenged the constitutionality of the Texas system of school finance. In West Orange-Cove et al. v. Neeley et al., the plaintiffs argued that because most school districts were at or near a state-imposed property tax rate ceiling and because the share of state education funding was declining, most school districts had inadequate funds to satisfy the student performance standards mandated by the Texas Educational Accountability system. To address the empirical question of whether school districts have insufficient resources to meet the state's accountability standards, two cost function analyses were conducted. One study, entered into evidence by the state of Texas, reached the conclusion that "in aggregate, the level of education funding in Texas is more than sufficient to meet performance goals consistent with the state's accountability system." The other study, entered into evidence by the plaintiff school districts, concluded that, in aggregate, Texas school districts would need at least $2 billion in additional revenue to satisfy the requirements of the accountability system. In this article we describe the methodological similarities and differences between the two cost function studies and provide an assessment of why the two studies arrive at such different results. Based on the outcome of the case in district court-a victory for the plaintiffs-the article draws some lessons about the use of statistical-based models in a judicial setting.

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