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Secondary schools efficiencyand non-controllable inputs: sensibility of the results to different DEA model specifications

Author

Listed:
  • José Manuel Cordero Ferrera

    () (Universidad de Extremadura)

  • Francisco Pedraja Chaparro

    (Universidad de Extremadura)

  • Javier Salinas Jiménez

    () (Instituto de Estudios Fiscales. Universidad de Extremadura)

Abstract

The aim of this paper is to study how much sensitive efficiency scores are when different approaches proposed in the literature are used to include exogenous factors in efficiency analysis. Specifically, we will concentrate on two crucial issues. One the one hand, although we have an extensive data set on these variables derived from school surveys, it is not possible to use all these data, so we consider the possibility of either including the most relevant variables in the efficiency analysis or using Principal Components Analysis to summarise information contained in such variables. On the other hand, two alternative methodologies are considered to include these factors in efficiency analyisis, we will compare the approach according to which such factors must be taken into account to calculate the final efficiency indices and alternative multi-stage approaches. The analysis covers 79 public high schools in the region of Extremadura (Spain) for the 2001-02 school year.

Suggested Citation

  • José Manuel Cordero Ferrera & Francisco Pedraja Chaparro & Javier Salinas Jiménez, 2005. "Secondary schools efficiencyand non-controllable inputs: sensibility of the results to different DEA model specifications," Hacienda Pública Española, IEF, vol. 173(2), pages 61-83, June.
  • Handle: RePEc:hpe:journl:y:2005:v:173:i:2:p:61-83
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    References listed on IDEAS

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    1. Greene, William H., 1980. "Maximum likelihood estimation of econometric frontier functions," Journal of Econometrics, Elsevier, vol. 13(1), pages 27-56, May.
    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. B. Hollingsworth & P. Smith, 2003. "Use of ratios in data envelopment analysis," Applied Economics Letters, Taylor & Francis Journals, vol. 10(11), pages 733-735.
    4. Cinzia Daraio & Léopold Simar, 2005. "Introducing Environmental Variables in Nonparametric Frontier Models: a Probabilistic Approach," Journal of Productivity Analysis, Springer, vol. 24(1), pages 93-121, September.
    5. H. Fried & C. Lovell & S. Schmidt & S. Yaisawarng, 2002. "Accounting for Environmental Effects and Statistical Noise in Data Envelopment Analysis," Journal of Productivity Analysis, Springer, vol. 17(1), pages 157-174, January.
    6. Hanushek, Eric, 1971. "Teacher Characteristics and Gains in Student Achievement: Estimation Using Micro Data," American Economic Review, American Economic Association, vol. 61(2), pages 280-288, May.
    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. Bradley, Steve & Taylor, Jim, 2002. "The Effect of the Quasi-market on the Efficiency-Equity Trade-Off in the Secondary School Sector," Bulletin of Economic Research, Wiley Blackwell, vol. 54(3), pages 295-314, July.
    9. Muniz, M. A., 2002. "Separating managerial inefficiency and external conditions in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 143(3), pages 625-643, December.
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    More about this item

    Keywords

    Data Envelopment; Analysis (DEA); secondary schools; non-controllable inpunts;

    JEL classification:

    • D20 - Microeconomics - - Production and Organizations - - - General
    • I22 - Health, Education, and Welfare - - Education - - - Educational Finance; Financial Aid

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