Benchmarking and firm heterogeneity: a latent class analysis for German electricity distribution companies

Abstract

In January 2009, the German Federal Network Agency introduced incentive regulation for the electricity distribution sector based on results obtained from econometric and nonparametric benchmarking analysis. One main problem for the regulator in assigning the relative efficiency scores is unobserved firm-specific factors such as network and technological differences. Comparing the efficiency of different firms usually assumes that they operate under the same production technology, thus unobserved factors might be inappropriately understood as inefficiency. To avoid this type of misspecification in regulatory practice, estimation is carried out in two stages: in the first stage observations are classified into two categories according to the size of the network operators. Then separate analyses are conducted for each subgroup. This article shows how to disentangle the heterogeneity from inefficiency in one step, using a latent class model for stochastic frontiers. As the classification is not based on a priori sample separation criteria it delivers more robust, statistically significant, and testable results. Against this background, we analyze the level of technical efficiency of different subgroups from a sample of 200 regional and local German electricity distribution companies for a balanced panel data set (2001–2005). Testing the hypothesis if larger distributors operate under a different technology than smaller ones, we assess if a single step latent class model provides new insights to the use of benchmarking approaches within the incentive regulation schemes.