Use of Data Envelopment Analysis for Incentive Regulation of Electric Distribution Firms

Abstract

Regulators worldwide increasingly use data envelopment analysis (DEA) for the incentive regulation of electric distribution firms. Although the production/cost frontiers estimated by DEA models provide valuable information for electricity rate setting, the benefit of DEA benchmarking in regulatory practice would be limited due to specification errors of DEA models. In this paper, we summarize and discuss existing issues of using DEA models for efficiency benchmarking from four aspects: 1. Specification of inputs and outputs, 2. Selection of costs for benchmarking, 3. Imposition of structure on benchmarking models, and 4. Treatment of contextual variables. We also give suggestions for improving the use of DEA models.