Efficiency in Public Sector: A Neural Network Approach
AbstractHere 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 PearsonÂ´s correlation and Spearman rank-correlation coefficients
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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2004 with number 81.
Date of creation: 11 Aug 2004
Date of revision:
Neural Networks; Efficiency; DEA;
Find related papers by 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
This paper has been announced in the following NEP Reports:
- NEP-ALL-2004-07-26 (All new papers)
- NEP-CMP-2004-07-26 (Computational Economics)
- NEP-PBE-2004-07-26 (Public Economics)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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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