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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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File URL: http://www.uniovi.es/economia/prof/Economia/FrancisoJoseDelgadoRivero/Delgado%20FJ%20-%20Efficiency%20ANNs%20-%20CEF2004.pdf
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Bibliographic Info

Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2004 with number 81.

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Date of creation: 11 Aug 2004
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Handle: RePEc:sce:scecf4:81

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Keywords: Neural Networks; Efficiency; DEA;

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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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