Norwegian Electricity Distribution Research Articles

Efficiency Measurement in Norwegian Electricity Distribution: A Generalized Four-Way-Error-Component Stochastic Frontier Model

In this paper, we introduce a new model to estimate efficiency by generalizing the state-of-the-art panel stochastic frontier model, the salient feature of which is decomposition of inefficiency into a persistent and a transient component. The proposed model introduces an autoregressive process to allow for temporal dependence in transient inefficiency. Both firm heterogeneity and persistent inefficiency components are allowed to be correlated with some exogenous and endogenous covariates in the model. Our model solves the endogeneity problem and it also introduces determinants of both persistent and transient inefficiency. Since the transient component is autoregressive, the likelihood function is not available in closed form. To address this problem we use the Maximum Simulated Likelihood and (Simulated or Bayes) Generalized Method of Moments method to estimate the parameters and several other quantities of interest, including transient and persistent inefficiency. Since the model is dynamic and accommodates determinants of inefficiency, it is useful to production managers who wish to identify how much of their present inefficiency is affected by past inefficiency, as well as how and in what ways efficiency can be improved. We use Norwegian electricity distribution data to showcase an application of our model.

Performance Effects of Network Structure and Ownership: The Norwegian Electricity Distribution Sector

Transmission and distribution networks are capital intensive segments of the electricity sector and are generally considered natural monopolies. Due to their non-competitive nature, these are subject to independent regulation to prevent the abuse of monopolistic power and to induce competitive behaviour. Effective economic regulation of the electricity networks has become a key target in most developed economies after the 1980s. In Norway, incentive regulation and efficiency benchmarking were introduced in 1997. In Norway, the electricity grid is divided into three levels, namely, central, regional and distribution networks. In this paper, we study two overlooked aspects when analysing the performance of electricity networks: vertical integration and ownership structure. We use a stochastic frontier analysis approach to analyse the performance of Norwegian electricity distribution utilities for the period 2007–2014. We observe that vertical integration between distribution and regional transmission implies higher cost inefficiencies. This indicates that the efficiency gains due to separate management of the networks exceed the economies of coordination from vertical economies of scope. In addition, we find that council ownership entails higher efficiencies. This could be explained by the state having an interest in high-voltage electricity networks, rather than low-voltage ones, and the decentralised model from which the now centralised system was once developed.

Determinants of allocative and technical inefficiency in stochastic frontier models: An analysis of Norwegian electricity distribution firms

Recent econometric advances have made it possible to distinguish between persistent and transient technical inefficiency along with allocative inefficiency in stochastic frontier models for panel data. Kumbhakar et al. (2020) and Lai and Kumbhakar (2019) introduce a methodology that allows for the estimation of these inefficiency components and costs therefrom, while including determinants of both components of technical inefficiency. We extend these models to include technical change and determinants of allocative inefficiency (input misallocation). Including a set of variables that influence input misallocation, we are able to determine the effects of these variables on the cost of allocative inefficiency. We provide empirical evidence on the costs of all three types of inefficiency using data on 149 Norwegian electricity distribution firms between 2000 and 2016. We find that the cost of input misallocation is only slightly lower than that of technical inefficiency. Our results reject a commonly imposed modeling assumption that firms are fully allocatively efficient.

Disentangling Costs of Persistent and Transient Technical Inefficiency and Input Misallocation: The Case of Norwegian Electricity Distribution Firms

Numerous studies have focused on estimating technical inefficiency in electricity distribution firms. However, most of these studies did not distinguish between persistent and transient technical inefficiency. Furthermore, almost none of the studies estimated the cost of input misallocation arising from non-optimal use of inputs. One reason is that the cost function (input distance function) typically used in the literature does not allow for the separation of technical inefficiency and allocative inefficiency. In this study, we estimate both the persistent and transient components of technical inefficiency and input misallocation of Norwegian electricity distribution firms, using panel data from 2000 to 2016. Our modeling and estimation strategy is to use a system approach, consisting of the production function and the first-order conditions of cost minimization. Input misallocation for each pair of inputs is modeled via the first-order conditions of cost minimization. We also estimate the costs of each component of technical inefficiency and input misallocation by deriving the cost function for a multi-output separable production technology. Our modeling and estimation strategy handles endogeneity of inputs. Finally, we allow for inclusion of determinants of persistent and transient technical inefficiency. Our results show that the costs of input misallocation of Norwegian electricity distribution firms are non-negligible.

A new stochastic frontier model with cross-sectional effects in both noise and inefficiency terms

This paper develops a new stochastic frontier model that allows for cross-sectional (spatial) correlation in both the noise and inefficiency terms, which are likely to be of a different nature. The main econometric novelty of the proposed model is that it can be estimated in a straightforward manner by maximum likelihood and non-linear least squares. The proposed model is useful when there are omitted but spatially correlated variables, the firms benefit from best practices implemented by other firms, or common factors prevent the firms to improve their performance. An application to the Norwegian electricity distribution sector is also provided.

Lost economies of scope and potential merger gains in the Norwegian electricity industry

In 2016, the Norwegian Parliament amended the Energy Act, with changes taking effect from 2021. The amended legislation will introduce strict separation of all generation and distribution companies within the electricity industry in Norway. Economies of scope studies from Norway show evidence of large economies of scope. Further, the companies in the industry could utilize the economies of scale potential if they merged. In this paper, we perform merger analysis to investigate potential merger gains in the Norwegian electricity distribution industry. By providing a method of testing for optimal mergers, we can present the best merger combination to the Norwegian electricity industry.

Economies of scale in Norwegian electricity distribution: a quantile regression approach

ABSTRACTIn this article, we investigate scale economies in Norwegian electricity distribution companies using a quantile regression approach. To the best of our knowledge, this is the first attempt to apply this estimation technique when analysing scale economies. We estimate the cost elasticities of the two output components: network length and number of customers, to calculate returns to scale. Our results show large potential of scale economies, particularly for the smallest companies. We also find that returns to scale is increasing over time. These findings have important implications for policymakers when they are deciding the structure of the industry in the future.

Yardstick Regulation of Electricity Distribution—Disentangling Short-run and Long-run Inefficiencies

In this paper we estimate the short-run, long-run and overall efficiency of Norwegian electricity distribution companies for the period 2000-2013 controlling for both noise and company effects. Short-run inefficiency is the part of inefficiency that is allowed to adjust freely over time for each company, but long-run (persistent) inefficiency remains constant over time, although it is allowed to vary across companies. For robustness check we also consider two additional models in which either company effects are not controlled or these are treated as inefficiency. The production technology is represented by a translog input distance function in all three models. We find that technical change and returns to scale are quite robust across the models. However, the efficiency scores across the three models we consider are not correlated strongly. We conclude that the regulators and practitioners should take extra caution in using the proper model in practice, especially when the efficiency measures are used to reward/punish companies through incentives for better performance.

Regulating Heterogeneous Utilities: A New Latent Class Approach with Application to the Norwegian Electricity Distribution Networks

Since the 1990s, electricity distribution networks in many countries have been subject to incentive regulation. The sector regulators aim to identify the best performing utilities as frontier firms to determine the relative efficiency of firms. This paper develops a nested latent class (NLC) model approach where unobserved differences in firm performance are modelled using two ‘zero inefficiency stochastic frontier’ (ZISF) models nested in a ‘latent class stochastic frontier’ (LCSF) model. This captures the unobserved differences due to technology or environmental conditions. A Monte Carlo simulation suggests that the proposed model does not suffer from identification problems. We illustrate the proposed model with an application to Norwegian distribution network utilities for the period 2004-2011. We find that the efficiency scores in both LCSF and ZISF models are biased, and some firms in the ZISF model are wrongly labelled as inefficient. Conversely, inefficient firms may be wrongly labelled as being fully efficient by the ZISF model.

Productivity Development Among Norwegian Electricity Distribution System Operators — The Impact of Capital Assessment and Technological Progress

Productivity Development Among Norwegian Electricity Distribution System Operators — The Impact of Capital Assessment and Technological Progress

Specification of merger gains in the Norwegian electricity distribution industry

Electricity distribution often exhibits economies of scale. In Norway, a number of smaller distribution system operators exist and thus there is potential to restructure the industry, possibly through mergers. However, the revenue cap regulatory model in Norway does not incentivize firms to merge as merging leads to a stricter revenue cap for the merged company. Thus the regulator compensates the firms in order to create such incentives. The amount of compensation is based on the potential gains of the merger estimated using a data envelopment analysis (DEA) based frontier approach introduced by Bogetoft and Wang (2005). DEA is however only one of many possible frontier estimators that can be used in estimation. Furthermore, the returns to scale assumption, the operating environment of firms and the presence of stochastic noise and outlier observations are all known to affect to the estimation of production technology. In this paper we explore how varying assumptions under two alternative frontier estimators shape the distribution of merger gains within the Norwegian distribution industry. Our results reveal that the restructuring policies of the industry may be significantly altered depending how potential gains from the mergers are estimated.

Specification of Merger Gains in the Norwegian Electricity Distribution Industry

Electricity distribution often exhibits economies of scale. In Norway, a number of smaller distribution system operators exist and thus there is potential to restructure the industry, possibly through mergers. However, the revenue cap regulatory model in Norway does not incentivize firms to merge as merging leads to a stricter revenue cap for the merged company. Thus the regulator compensates the firms in order to create such incentives. The amount of compensation is based on the potential gains of the merger estimated using a data envelopment analysis (DEA) based frontier approach introduced by Bogetoft and Wang (Journal of Productivity Analysis, 23, 145-171, 2005). DEA is however only one of many possible frontier estimators that can be used in estimation. Furthermore, the returns to scale assumption, the operating environment of firms and the presence of stochastic noise and outlier observations are all known to affect to the estimation of production technology. In this paper we explore how varying assumption under two alternate frontier estimators shape the distribution of merger gains within the Norwegian distribution industry. Our results reveal that the restructuring policies of the industry may be significantly altered depending how potential gains from the mergers are estimated.

Malmquist Productivity Analysis Based on StoNED

We construct a Malmquist productivity index based on stochastic non-parametric envelopment of data (StoNED) method, and we study how the distributional assumptions in the second StoNED stage affect productivity change and its decompositions. Our discussion show that the distributional assumptions do not affect the estimates of overall productivity change and scale efficiency change, but that estimates of efficiency change and technical change are affected. Data on Norwegian electricity distribution companies is used to illustrate our discussion.

Optimal Scale in Different Environments The Case of Norwegian Electricity Distribution Companies

We study returns to scale in Norwegian electricity distribution companies. The scale issue of this sector has become an important political question, and it was for instance discussed by the Reiten commission (OED, 2014) in a study about the future structure and organization of the Norwegian electricity network industry. We use panel data from the Norwegian Water Resources and Energy Directorate (NVE) for the period from 2004 to 2010. The Data Envelopment Analysis (DEA) method and the Stochastic Nonparametric Envelopment of Data (StoNED) approach are applied to examine the scale issue. We show that a majority of the companies are smaller than the optimal size, in line with Kumbhakar et al. (2014). The performance of Norwegian distribution companies are influenced by a number of environmental factors, and some of these factors are negatively correlated with company size. However, our results show that controlling for environmental factors when estimating returns to scale does not have a big effect on the estimated optimal sizes.

Using Supervised Environmental Composites in Production and Efficiency Analyses

Although supervised dimension reduction methods have been extensively applied in different scientific fields, they have hardly ever been used in production economics. Nonetheless, these methods can also be useful in regulation of natural monopolies, where firms' cost and performance are affected by a large number of environmental factors. As economic theory suggests, at the presence of other relevant production or cost drivers, the traditional all-inclusive assumption is not satisfied. This paper shows that purging the data allows us to address this issue when analyzing the effect of weather and geography on efficiency in the context of the Norwegian electricity distribution networks.

Scale economies, technical change and efficiency in Norwegian electricity distribution, 1998–2010

In this paper, we investigate scale economies, technical change and efficiency in Norwegian electricity distribution companies from 1998 to 2010. The organization and structure of this sector has become an important political question. We use panel data from the Norwegian Water Resources and Energy Directorate to address this issue. Specifically, we use input distance functions to examine scale economies, technical change and technical efficiency in the distribution companies. In modeling technically inefficiency, we separate company-specific effects from inefficiency. Our results show that the potential for scale economies is generally highest among small companies. To some extent, this finding is related to the structure of small companies and is likely to have future policy implications.

Determinants of investment under incentive regulation: The case of the Norwegian electricity distribution networks

Investment in electricity networks, as regulated natural monopolies, is among the highest regulatory and energy policy priorities. The electricity sector regulators adopt different incentive mechanisms to ensure that the firms undertake sufficient investment to maintain and modernise the grid. Thus, an effective regulatory treatment of investment requires understanding the response of companies to the regulatory incentives. This study analyses the determinants of investment in electricity distribution networks using a panel dataset of 129 Norwegian companies observed from 2004 to 2010. A Bayesian Model Averaging approach is used to provide a robust statistical inference by taking into account the uncertainties around model selection and estimation. The results show that three factors drive nearly all network investments: investment rate in previous period, socio-economic costs of energy not supplied and finally useful life of assets. The results indicate that Norwegian companies have, to some degree, responded to the investment incentives provided by the regulatory framework. However, some of the incentives do not appear to be effective in driving the investments.

Cost Efficiency Analysis Based on the DEA and StoNED Models: Case of Norwegian Electricity Distribution Companies

Our paper applies data envelopment analysis (DEA) and stochastic non-parametric envelopment of data (StoNED) to measure cost efficiency of electricity distribution companies. The data cover 123 Norwegian electricity distribution companies during 2004-2010, and the performance of these companies is compared across the two models with and without environmental variables, i.e., variables that account for local conditions that affect the companies’ costs. The results indicate that the cost efficiency estimates with the StoNED approach are much higher than with the DEA method when we do not consider environmental variables. It shows that the choice of estimation methods is important with respect to the estimated impact of environmental variables on the performance. In addition, the inclusion of the environmental variables has considerable effect on the classification of companies with respect to local returns to scale.

Productivity change and innovation in Norwegian electricity distribution companies

Regulators of electricity distribution networks have typically applied Data Envelopment Analysis (DEA) to cross-section data for benchmarking purposes. However, the use of panel data to analyse the impact of regulatory policies on productivity change over time is less frequent. The main purpose of this paper is to construct a Malmquist productivity index to examine the recent productivity change experienced by Norwegian distribution companies between 2004 and 2007. The Malmquist index is decomposed in order to explore the sources of productivity change, and to identify the innovator companies that pushed the frontier forward each year. The input and output variables considered are those used by the Norwegian regulator. In order to reflect appropriately the exogenous conditions where the companies operate, the efficiency model used in this paper incorporates geography variables as outputs of the DEA model. Unlike the model used by the regulator, we included virtual weight restrictions in the DEA formulation to correct the biases in the DEA results that may be associated to a judicious choice of weights by some of the companies.

Efficiency effects of observed and unobserved heterogeneity: Evidence from Norwegian electricity distribution networks

Since the 1990s, efficiency and benchmarking analysis has increasingly been used in network utilities research and regulation. A recurrent concern is the effect of observable environmental factors that are beyond the influence of firms and unobserved factors that are not identifiable on measured cost and quality performance of firms. This paper analyses the effect of observed geographic and weather factors and unobserved heterogeneity on a set of 128 Norwegian electricity distribution utilities for the 2001–2004 period. We utilise data on 78 geographic and weather variables to identify real economic inefficiency while controlling for observed and unobserved heterogeneity. We use the Factor Analysis technique to reduce the number of environmental factors into few composite variables and to avoid the problem of multicollinearity. In order to identify firm-specific inefficiency, we then estimate a pooled version of the established stochastic frontier model of Aigner et al. (1977) and the recent true random effects model of Greene (2004; 2005a,b) without and with environmental variables. The results indicate that the observed environmental factors have a rather limited influence on the utilities' average efficiency and the efficiency rankings. Moreover, the difference between the average efficiency scores and the efficiency rankings among the pooled and the true random effects models imply that the type of SFA model used is highly influencing the efficiency estimates.