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An estimation of technical efficiency for Florida public elementary schools

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  • Conroy, Stephen J.
  • Arguea, Nestor M.

Abstract

We use a frontier production function estimation technique to analyze whether elementary schools in Florida are operating at an efficient level and to explain any inefficiencies. A motivation for this analysis comes from recent state and federal level educational initiatives designed to improve school accountability and reduce class sizes. Results presented here indicate that while Florida elementary schools are not operating at efficient levels (with regional mean inefficiency estimates in the 4.1-5.1% range), they compare favorably to published results for other states. One factor associated with higher inefficiency is student promotion rates--something which does lie within the purview of school administrators and may have important policy implications. However, other factors associated with higher inefficiency (percent free-lunch eligible, higher crime and violence, higher suspension rates and not having a parent-teacher organization) are indicators of conditions that lie largely beyond the direct control of public schools, casting doubt on the effectiveness of recent accountability measures to improve efficiency.

Suggested Citation

  • Conroy, Stephen J. & Arguea, Nestor M., 2008. "An estimation of technical efficiency for Florida public elementary schools," Economics of Education Review, Elsevier, vol. 27(6), pages 655-663, December.
  • Handle: RePEc:eee:ecoedu:v:27:y:2008:i:6:p:655-663
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    References listed on IDEAS

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    1. Hanushek, Eric A. & Kain, John F. & Rivkin, Steven G., 2004. "Disruption versus Tiebout improvement: the costs and benefits of switching schools," Journal of Public Economics, Elsevier, vol. 88(9-10), pages 1721-1746, August.
    2. Fare,Rolf & Grosskopf,Shawna & Lovell,C. A. Knox, 2008. "Production Frontiers," Cambridge Books, Cambridge University Press, number 9780521072069.
    3. 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.
    4. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    5. Hung-jen Wang & Peter Schmidt, 2002. "One-Step and Two-Step Estimation of the Effects of Exogenous Variables on Technical Efficiency Levels," Journal of Productivity Analysis, Springer, vol. 18(2), pages 129-144, September.
    6. 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.
    7. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    8. 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.
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    Cited by:

    1. repec:eee:regeco:v:65:y:2017:i:c:p:89-103 is not listed on IDEAS
    2. Gronberg, Timothy J. & Jansen, Dennis W. & Taylor, Lori L., 2012. "The relative efficiency of charter schools: A cost frontier approach," Economics of Education Review, Elsevier, vol. 31(2), pages 302-317.
    3. Son Nghiem & Ha Trong Nguyen & Luke B. Connelly, 2016. "The Efficiency of Australian Schools: A Nationwide Analysis Using Gains in Test Scores of Students as Outputs," Economic Papers, The Economic Society of Australia, vol. 35(3), pages 256-268, September.
    4. Kristof de Witte & Laura López-Torres, 2015. "Efficiency in Education. A Review of Literature and a Way Forward," Working Papers 1501, Departament Empresa, Universitat Autònoma de Barcelona, revised Apr 2015.
    5. Haelermans, Carla & Ruggiero, John, 2013. "Estimating technical and allocative efficiency in the public sector: A nonparametric analysis of Dutch schools," European Journal of Operational Research, Elsevier, vol. 227(1), pages 174-181.
    6. Ryan, Chris, 2013. "What is behind the decline in student achievement in Australia?," Economics of Education Review, Elsevier, vol. 37(C), pages 226-239.
    7. Shawna Grosskopf & Kathy Hayes & Lori Taylor & William Weber, 2015. "Centralized or decentralized control of school resources? A network model," Journal of Productivity Analysis, Springer, vol. 43(2), pages 139-150, April.
    8. repec:taf:applec:v:49:y:2017:i:12:p:1164-1184 is not listed on IDEAS
    9. Daniel J. Henderson & Léopold Simar & Le Wang, 2017. "The three s of public schools: irrelevant inputs, insufficient resources and inefficiency," Applied Economics, Taylor & Francis Journals, vol. 49(12), pages 1164-1184, March.
    10. Brennan, Shae & Haelermans, Carla & Ruggiero, John, 2014. "Nonparametric estimation of education productivity incorporating nondiscretionary inputs with an application to Dutch schools," European Journal of Operational Research, Elsevier, vol. 234(3), pages 809-818.
    11. Nghiem, Son & Nguyen, Ha & Connelly, Luke, 2014. "The Efficiency of Australian Schools: Evidence from the NAPLAN Data 2009-2011," MPRA Paper 56231, University Library of Munich, Germany.
    12. repec:pal:jorsoc:v:68:y:2017:i:4:d:10.1057_jors.2015.92 is not listed on IDEAS

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