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Maintaining the Regular Ultra Passum Law in data envelopment analysis

Author

Listed:
  • Olesen, Ole B.

    (Department of Business and Economics)

  • Ruggiero, John

    (School of Business Administration)

Abstract

The variable returns to scale data envelopment analysis (DEA) model is developed with a maintained hypothesis of convexity in input-output space. This hypothesis is not consistent with standard microeconomic production theory that posits an S-shape for the production frontier, i.e. for production technologies that obey the Regular Ultra Passum Law. Consequently, measures of technical efficiency assuming convexity are biased downward. In this paper, we provide a more general DEA model that allows the S-shape.

Suggested Citation

  • Olesen, Ole B. & Ruggiero, John, 2012. "Maintaining the Regular Ultra Passum Law in data envelopment analysis," Discussion Papers on Economics 2/2012, University of Southern Denmark, Department of Economics.
    • Handle: RePEc:hhs:sdueko:2012_002
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    File URL: https://www.sdu.dk/-/media/files/om_sdu/institutter/ivoe/disc_papers/disc_2012/dpbe2_2012.pdf
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    References listed on IDEAS

    as
    1. Podinovski, V. V., 2005. "Selective convexity in DEA models," European Journal of Operational Research, Elsevier, vol. 161(2), pages 552-563, March.
    2. Finn Førsund & Lennart Hjalmarsson, 2004. "Are all Scales Optimal in DEA? Theory and Empirical Evidence," Journal of Productivity Analysis, Springer, vol. 21(1), pages 25-48, January.
    3. Ginsberg, William, 1974. "The multiplant firm with increasing returns to scale," Journal of Economic Theory, Elsevier, vol. 9(3), pages 283-292, November.
    4. Niels Christian Petersen, 1990. "Data Envelopment Analysis on a Relaxed Set of Assumptions," Management Science, INFORMS, vol. 36(3), pages 305-314, March.
    5. Cinzia Daraio & Léopold Simar, 2007. "Conditional nonparametric frontier models for convex and nonconvex technologies: a unifying approach," Journal of Productivity Analysis, Springer, vol. 28(1), pages 13-32, October.
    6. Olesen, Ole Bent & Petersen, Niels Christian, 2011. "Scale properties in data envelopment analysis," Discussion Papers on Economics 4/2011, University of Southern Denmark, Department of Economics.
    7. Peter Bogetoft, 1996. "DEA on Relaxed Convexity Assumptions," Management Science, INFORMS, vol. 42(3), pages 457-465, March.
    8. Cazals, Catherine & Florens, Jean-Pierre & Simar, Leopold, 2002. "Nonparametric frontier estimation: a robust approach," Journal of Econometrics, Elsevier, vol. 106(1), pages 1-25, January.
    9. A. Zellner & N. S. Revankar, 1969. "Generalized Production Functions," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 36(2), pages 241-250.
    10. Ruggiero, John, 1996. "On the measurement of technical efficiency in the public sector," European Journal of Operational Research, Elsevier, vol. 90(3), pages 553-565, May.
    11. Fare, R & Primont, D, 1995. "On Inverse Homotheticity," Bulletin of Economic Research, Wiley Blackwell, vol. 47(2), pages 161-166, April.
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    Cited by:

    1. Miningou, Élisé Wendlassida & Vierstraete, Valérie, 2013. "Households' living situation and the efficient provision of primary education in Burkina Faso," Economic Modelling, Elsevier, vol. 35(C), pages 910-917.
    2. Olesen, Ole B., 2012. "A homothetic reference technology in Data Envelopment Analysis," Discussion Papers on Economics 14/2012, University of Southern Denmark, Department of Economics.
    3. Olesen, Ole B., 2014. "A homothetic reference technology in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 233(3), pages 759-771.

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    More about this item

    Keywords

    Data envelopment analysis; homothetic production; S-shaped production function; non-convex production set;
    All these keywords.

    JEL classification:

    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General

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