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Efficiency, But At What Cost? Evidence from a DEA Analysis of WV School Districts

Author

Listed:
  • Eduardo Minuci

    (West Virginia University, Department of Economics)

  • Amir B. Ferreira Neto

    (West Virginia University, Department of Economics)

  • Joshua Hall

    (West Virginia University, Department of Economics)

Abstract

West Virginia schools are consistently below the national average on the NAEP. Using Data Envelopment Analysis, we estimate the technical efficiency of West Virginia school districts. We find less variation in technical efficiency in West Virginia than in similar studies conducted in other states. This appears to be because of state policy imposing homogeneity of input usage. Due to the limited variation in technical efficiency across districts, we cannot analyze how non-school inputs such as socioeconomic factors affect technical efficiency across districts. Summary statistics organized by county economic status, however, suggest that socioeconomic status plays a role. Our results highlight an important limitation of DEA analysis on schools.

Suggested Citation

  • Eduardo Minuci & Amir B. Ferreira Neto & Joshua Hall, 2017. "Efficiency, But At What Cost? Evidence from a DEA Analysis of WV School Districts," Working Papers 17-28, Department of Economics, West Virginia University.
  • Handle: RePEc:wvu:wpaper:17-28
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    File URL: http://busecon.wvu.edu/phd_economics/pdf/17-28.pdf
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    References listed on IDEAS

    as
    1. Eric Hanushek & Ludger Woessmann, 2012. "Do better schools lead to more growth? Cognitive skills, economic outcomes, and causation," Journal of Economic Growth, Springer, vol. 17(4), pages 267-321, December.
    2. Kirjavainen, Tanja & Loikkanent, Heikki A., 1998. "Efficiency differences of finnish senior secondary schools: An application of DEA and Tobit analysis," Economics of Education Review, Elsevier, vol. 17(4), pages 377-394, October.
    3. Kalyan Chakraborty & Basudeb Biswas & W. Cris Lewis, 2001. "Measurement of Technical Efficiency in Public Education: A Stochastic and Nonstochastic Production Function Approach," Southern Economic Journal, Southern Economic Association, vol. 67(4), pages 889-905, April.
    4. Cooper, Samuel T. & Cohn, Elchanan, 1997. "Estimation of a frontier production function for the South Carolina educational process," Economics of Education Review, Elsevier, vol. 16(3), pages 313-327, June.
    5. John Ruggiero, 2001. "Determining The Base Cost Of Education: An Analysis Of Ohio School Districts," Contemporary Economic Policy, Western Economic Association International, vol. 19(3), pages 268-279, July.
    6. 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.
    7. John Ruggiero & Donald F. Vitaliano, 1999. "Assessing The Efficiency Of Public Schools Using Data Envelopment Analysis And Frontier Regression," Contemporary Economic Policy, Western Economic Association International, vol. 17(3), pages 321-331, July.
    8. Primont, Diane F. & Domazlicky, Bruce, 2006. "Student achievement and efficiency in Missouri schools and the No Child Left Behind Act," Economics of Education Review, Elsevier, vol. 25(1), pages 77-90, February.
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    More about this item

    Keywords

    Data Envelopment Analysis; Efficiency; Government; Public Schools;

    JEL classification:

    • H41 - Public Economics - - Publicly Provided Goods - - - Public Goods
    • H76 - Public Economics - - State and Local Government; Intergovernmental Relations - - - Other Expenditure Categories
    • I29 - Health, Education, and Welfare - - Education - - - Other

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