Facoltà di scienze economiche

Application of panel data models in benchmarking analysis of the electricity distribution sector

Farsi, Mehdi ; Filippini, Massimo ; Greene, William

In: Annals of Public and Cooperative Economics, 2006, vol. 77, no. 3, p. 271–290

This paper explores the application of several panel data models in measuring productive efficiency of the electricity distribution sector. Stochastic Frontier Analysis has been used to estimate the cost-efficiency of 59 distribution utilities operating over a nine year period in Switzerland. The estimated coefficients and inefficiency scores are compared across three different panel data models.... Plus

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    Summary
    This paper explores the application of several panel data models in measuring productive efficiency of the electricity distribution sector. Stochastic Frontier Analysis has been used to estimate the cost-efficiency of 59 distribution utilities operating over a nine year period in Switzerland. The estimated coefficients and inefficiency scores are compared across three different panel data models. The results indicate that individual efficiency estimates are sensitive to the econometric specification of unobserved firm-specific heterogeneity. This paper shows that alternative panel models such as the “true” random effects model proposed by Greene (2005) could be used to explore the possible impacts of unobserved firm-specific factors on efficiency estimates. When these factors are specified as a separate stochastic term, the efficiency estimates are substantially higher suggesting that conventional models could confound efficiency differences with other unobserved variations among companies. On the other hand, refined specification of unobserved heterogeneity might lead to an underestimation of inefficiencies by mistaking potential persistent inefficiencies as external factors. Given that specification of inefficiency and heterogeneity relies on non-testable assumptions, there is no conclusive evidence in favor of one or the other specification. However, this paper argues that alternative panel data models along with conventional estimators can be used to obtain approximate lower and upper bounds for companies’ efficiency scores.