DATA ENVELOPMENT ANALYSIS IN THE ASSESSMENT OF THE TECHNICAL EFFICIENCY OF BRAZILIAN DISTRIBUTORS OF ELECTRIC ENERGY: A COMPARATIVE APPROACH BETWEEN MALMQUIST’S INDEX AND THE WINDOW ANALYSIS
DOI:
https://doi.org/10.21680/2176-9036.2018v10n1ID12404Keywords:
Distributor. Electric Energy. Efficiency.Abstract
The electrical branch plays an important role in the economic scenario, and is responsible for attending the most varied economic sectors, which involves industrial, residential, commercial and rural consumers, and the public power. It is necessary a fair remuneration capable of establishing affordability to guarantee the quality of the provision of this service. Price determination depends on the efficiency of the distributors in the period anticipating the process of tariff review. The objective of this survey is to evaluate if the distributors of electric energy have presented technical efficiency in the period 2003-2013, based on the metrics of the Malmquist’s Index and the Window Analysis. With the aim of addressing this objective, a comparison between the both methods will have place using the Data Envelopment Analysis-DEA, considering as input the expenses with opex (management costs) and the network extension, and as output the Market (energy sale). Malmquist DEA and Window Analysis have been directed toward the input with the aid of the variable return scale-VRS model, named BCC. The data have been extracted from documents available in the site of the Electric Energy National Agency-ANEEL. The research contribution has place in line with the presentation of an alternative model to Malmquist Index to measure the technical efficiency of Brazil’s electric sector. The data analysis reveals that 32.79% distributors have proved effective, when Malmquist methodology is tested, and 24.59%, when one applies the Window Analysis methodology. Five out 61 companies – CPEE, CSPE, EFLJC, JAGUARARI and RGE proved to be efficient relatively both investigated models, that is, 8.20%. The conclusion is that a marked divergence between the two models occurs when one assesses their efficiency rankings in a comparative manner.
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