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Anticipating the Consequences of School Reform: A New Use of DEA

Author

Listed:
  • Shawna Grosskopf

    (Department of Economics, Oregon State University, Corvallis, Oregon 97331)

  • Kathy J. Hayes

    (Dedman College, Southern Methodist University, PO Box 750235, Dallas, Texas 75275-0235)

  • Lori L. Taylor

    (Research Department, Federal Reserve Bank of Dallas, 2200 North Pearl Street, Dallas, Texas 75201)

  • William L. Weber

    (Department of Economics, Southeast Missouri State University, Cape Girardeau, Missouri 63701)

Abstract

We use DEA-type linear programming techniques to simulate a basic component of educational reform-eliminating restrictions on the allocation of school personnel. Our technique allows us to identify potential output gains (or equivalently potential cost savings) from reform. We can also identify which personnel groups are likely to gain and lose under this reform. When we apply our model to a sample of Texas school districts, we find evidence that the educational establishment has substantial economic rents to protect from school reform, and that the primary beneficiaries of reform are likely to be affluent school districts with few minority students. The technique, which relies on the relationship between the direct and indirect distance functions, can be easily generalized to measure the potential gains from removing other input restrictions such as union work rules, environmental regulations, or deed restrictions.

Suggested Citation

  • Shawna Grosskopf & Kathy J. Hayes & Lori L. Taylor & William L. Weber, 1999. "Anticipating the Consequences of School Reform: A New Use of DEA," Management Science, INFORMS, vol. 45(4), pages 608-620, April.
  • Handle: RePEc:inm:ormnsc:v:45:y:1999:i:4:p:608-620
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    File URL: http://dx.doi.org/10.1287/mnsc.45.4.608
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    References listed on IDEAS

    as
    1. Toma, Eugenia Froedge, 1983. "Institutional Structures, Regulation, and Producer Gains in the Education Industry," Journal of Law and Economics, University of Chicago Press, vol. 26(1), pages 103-116, April.
    2. A. Bessent & W. Bessent & J. Kennington & B. Reagan, 1982. "An Application of Mathematical Programming to Assess Productivity in the Houston Independent School District," Management Science, INFORMS, vol. 28(12), pages 1355-1367, December.
    3. Grosskopf, S, 1986. "The Role of the Reference Technology in Measuring Productive Efficiency," Economic Journal, Royal Economic Society, vol. 96(382), pages 499-513, June.
    4. Subhash C. Ray, 1991. "Resource-Use Efficiency in Public Schools: A Study of Connecticut Data," Management Science, INFORMS, vol. 37(12), pages 1620-1628, December.
    5. Fried, Harold O. & Lovell, C. A. Knox & Schmidt, Shelton S. (ed.), 1993. "The Measurement of Productive Efficiency: Techniques and Applications," OUP Catalogue, Oxford University Press, number 9780195072181.
    6. Rolf Färe & Shawna Grosskopf & William L. Weber, 1989. "Measuring School District Performance," Public Finance Review, , vol. 17(4), pages 409-428, October.
    7. Hanushek, Eric A, 1986. "The Economics of Schooling: Production and Efficiency in Public Schools," Journal of Economic Literature, American Economic Association, vol. 24(3), pages 1141-1177, September.
    8. Fare, R. & Grosskopf, S. & Lovell, C. A. K., 1988. "An indirect approach to the evaluation of producer performance," Journal of Public Economics, Elsevier, vol. 37(1), pages 71-89, October.
    9. Shawna Grosskopf & Kathy J. Hayes & Lori L. Taylor & William L. Weber, 1997. "Budget-Constrained Frontier Measures Of Fiscal Equality And Efficiency In Schooling," The Review of Economics and Statistics, MIT Press, vol. 79(1), pages 116-124, February.
    10. Meyer, Robert H., 1997. "Value-added indicators of school performance: A primer," Economics of Education Review, Elsevier, vol. 16(3), pages 283-301, June.
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