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Measuring efficiency with neural networks. An application to the public sector

Author

Listed:
  • Francisco J. Delgado

    (Department of Economics - University of Oviedo)

Abstract

In this note we propose the artificial neural networks for measuring efficiency as a complementary tool to the common techniques of the efficiency literature. In the application to the public sector we find that the neural network allows to conclude more robust results to rank decision-making units.

Suggested Citation

  • Francisco J. Delgado, 2005. "Measuring efficiency with neural networks. An application to the public sector," Economics Bulletin, AccessEcon, vol. 3(15), pages 1-10.
    • Handle: RePEc:ebl:ecbull:eb-04c40010
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    References listed on IDEAS

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    1. Luis R. Murillo‐Zamorano, 2004. "Economic Efficiency and Frontier Techniques," Journal of Economic Surveys, Wiley Blackwell, vol. 18(1), pages 33-77, February.
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    3. Daniel Santin & Francisco Delgado & Aurelia Valino, 2004. "The measurement of technical efficiency: a neural network approach," Applied Economics, Taylor & Francis Journals, vol. 36(6), pages 627-635.
    4. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    5. Fleissig, Adrian R. & Kastens, Terry & Terrell, Dek, 2000. "Evaluating the semi-nonparametric fourier, aim, and neural networks cost functions," Economics Letters, Elsevier, vol. 68(3), pages 235-244, September.
    6. 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.
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    Cited by:

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    2. Bracalente, Bruno & Polinori, Paolo, 2010. "L’efficienza tecnico-economica dei servizi pubblici locali: i casi delle farmacie comunali e dei servizi di igiene urbana [Technical And Economic Efficiency Of Local Public Services: The Cases Of The Municipals Pharmacies And Of The Integrated Was," MPRA Paper 34455, University Library of Munich, Germany.
    3. Azadeh, A. & Ghaderi, S.F. & Anvari, M. & Saberi, M., 2007. "Performance assessment of electric power generations using an adaptive neural network algorithm," Energy Policy, Elsevier, vol. 35(6), pages 3155-3166, June.
    4. Shivi Agarwal, 2016. "DEA-neural networks approach to assess the performance of public transport sector of India," OPSEARCH, Springer;Operational Research Society of India, vol. 53(2), pages 248-258, June.

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    Keywords

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    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • H4 - Public Economics - - Publicly Provided Goods

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