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Efficiency in Public Sector: A Neural Network Approach

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  • Francisco J. Delgado

Abstract

Here artificial neural networks (ANNs) are employed for efficiency purposes. First, the main features of ANNs are presented. Then, common techniques of the efficiency literature are reviewed: parametric (deterministic and stochastic) and non-parametric (Data Envelopment Analysis [DEA] and Free Disposal Hull [FDH]). ANNs are proposed for frontier approximation. Their advantages and drawbacks in the efficiency context are examined. Finally, these various methodologies are applied to refuse collection services using a sample of Spanish (Catalonian) municipalities. The results are compared with Pearson´s correlation and Spearman rank-correlation coefficients

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  • Francisco J. Delgado, 2004. "Efficiency in Public Sector: A Neural Network Approach," Computing in Economics and Finance 2004 81, Society for Computational Economics.
    • Handle: RePEc:sce:scecf4:81
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    References listed on IDEAS

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    1. 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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    More about this item

    Keywords

    Neural Networks; Efficiency; DEA;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • H72 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Budget and Expenditures

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