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A Homothetic Data Generated Technology

Author

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  • Antonio Peyrache

    (School of Economics and Centre for Efficiency and Productivity Analysis (CEPA) at The University of Queensland, Australia)

Abstract

In this paper I propose a method for constructing an enlargement of a variable returns to scale (VRS) data generated production technology that will satisfy homotheticity. The method can be used both with convex and non-convex technologies and both in the single output and multiple output setting. The method is computationally fast, therefore it provides a tool that can be used on large datasets. An empirical illustration is provided based on a dataset of Italian courts of justice

Suggested Citation

  • Antonio Peyrache, 2022. "A Homothetic Data Generated Technology," CEPA Working Papers Series WP042022, School of Economics, University of Queensland, Australia.
    • Handle: RePEc:qld:uqcepa:176
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    File URL: https://economics.uq.edu.au/files/35249/WP042022.pdf
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    References listed on IDEAS

    as
    1. Kerstens, Kristiaan & Vanden Eeckaut, Philippe, 1999. "Estimating returns to scale using non-parametric deterministic technologies: A new method based on goodness-of-fit," European Journal of Operational Research, Elsevier, vol. 113(1), pages 206-214, February.
    2. R. Russell & William Schworm, 2009. "Axiomatic foundations of efficiency measurement on data-generated technologies," Journal of Productivity Analysis, Springer, vol. 31(2), pages 77-86, April.
    3. Walter Briec & Kristiaan Kerstens, 2006. "Input, output and graph technical efficiency measures on non-convex FDH models with various scaling laws: An integrated approach based upon implicit enumeration algorithms," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 14(1), pages 135-166, June.
    4. Podinovski, V. V., 2004. "On the linearisation of reference technologies for testing returns to scale in FDH models," European Journal of Operational Research, Elsevier, vol. 152(3), pages 800-802, February.
    5. Peyrache, Antonio & Zago, Angelo, 2016. "Large courts, small justice!," Omega, Elsevier, vol. 64(C), pages 42-56.
    6. Olesen, O.B. & Ruggiero, J., 2022. "The hinging hyperplanes: An alternative nonparametric representation of a production function," European Journal of Operational Research, Elsevier, vol. 296(1), pages 254-266.
    7. Olesen, Ole B. & Ruggiero, John, 2014. "Maintaining the Regular Ultra Passum Law in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 235(3), pages 798-809.
    8. Hanoch, Giora & Rothschild, Michael, 1972. "Testing the Assumptions of Production Theory: A Nonparametric Approach," Journal of Political Economy, University of Chicago Press, vol. 80(2), pages 256-275, March-Apr.
    9. Olesen, Ole B., 2014. "A homothetic reference technology in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 233(3), pages 759-771.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Input Homotheticity; Output Homotheticity; DEA; FDH; Efficiency;
    All these keywords.

    JEL classification:

    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth

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