In 2006 all ECB publications will feature a motif taken from the €5 banknote.

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

for their helpful comments on a previous version. 1 In the last two decades electricity distribution sector have witnessed a wave of regulatory reforms aimed at improving efficiency through incentive regulation. Most of these regulation schemes use benchmarking namely measuring a company’s efficiency and rewarding them accordingly. The reliability of efficiency estimates is crucial for an effective implementation of those incentive mechanisms. A main problem faced by the regulators is the choice among several legitimate benchmarking models that usually produce different results. After a brief overview of the benchmarking methodologies, this paper summarizes the methods used in the regulation practice in several OECD countries, in which the benchmarking practice is relatively widespread. Repeated observation of similar companies over time namely panel data, allows a better understanding of unobserved firm-specific factors and disentangling them from efficiency estimates. Focusing on parametric cost frontier models, this paper presents two alternative approaches that could be used to improve the reliability of benchmarking methods, and based on recent empirical evidence, draws some recommendations for regulatory practice in power distribution networks. 2 1.

Abstract: Recent policy debates on pro-growth policies have emphasized the potential role of public expenditure in infrastructure. The degree to which growth actually responds in a sustainable fashion depends on the efficiency with which capital is used. The object of this paper is to gauge efficiency in container ports. Based on non-parametric methods, the paper estimates the maximum attainable output for a given input level and gauges efficiency as the distance from the observed input-output combinations to this frontier. Three attractive features of this approach are: 1) it is based on an aggregated measure of efficiency despite the existence of multiple inputs; 2) it does not assume any particular functional relationship between inputs and outputs; and 3) it does not rely on a-priori peer selection to construct the benchmark. Based on a sample of up to 86 ports, half of which are in developing countries, the paper estimates the efficiency frontiers and estimates the degree of (in) efficiency of the ports, concluding that the margin for cost reduction is significant. The most inefficient ports use inputs in excess of 20 to 40 percent of the level used in the most efficient ports. Given that infrastructure costs represent about 40 percent of total maritime transport costs, these could be reduced by approximately 12 percent by moving from the inefficient extreme of the distribution to the efficient one. Evidence supports the hypothesis that larger ports are more efficient than smaller ones. However,

This study presents an empirical analysis of the cost efficiency of a sample of Swiss multi-utilities operating in the distribution of electricity, natural gas and water. The multi-utilities that operate in different sectors are characterized by a strong unobserved heterogeneity. Therefore the measurement of their performance poses an important challenge for the regulators. The purpose of this paper is to study the potential advantages of recently developed panel data stochastic frontier models in the measurement of the level of efficiency for multi-utility companies. These models are estimated for a sample of 34 multi-output utilities operating from 1997 to 2005. The alternative models are compared regarding the cost function slopes and inefficiency estimates. For the inefficiency estimates, the correlation between different models and the effect of econometric specification have been analyzed. The results suggest that the inefficiency estimates are substantially lower when the unobserved firm-specific effects are taken into account.

Abstract. We employ a non-parametric methodology, Data Envelopment Analysis, to estimate efficiency scores for Portuguese public universities, using data mainly for 2003. The input measures are constructed from the number of teachers and from universities ’ spending while the outputs measures are based on the undergraduate success rate and on the number of doctoral dissertations. Using frontier analysis we are able to separate universities that might qualify, as “performing well ” from those were some improvement might be possible in terms of efficiency. This could imply a better allocation by the universities of the usually scarce public financial resources available to tertiary education.

Technical efficiency in primary health care: does quality matter?

In this note we propose the artificial neural networks for measuring efficiency as a complementary tool to the common techniques of the efficiency literature. In the application to the public sector we find that the neural network allows to conclude more robust results to rank decision−making units.

We employ a non-parametric methodology, Data Envelopment Analysis, to estimate efficiency scores for Portuguese public universities for 2001. Due to the tertiary education organisational features we consider universities as well as faculties and institutes as decision entities. Using frontier analysis we are able to separate universities/faculties/institutes that might qualify, as “performing well” from those were some improvement might increase its efficiency. This is a first effort of checking efficiency and productivity in Portuguese public tertiary education using non-parametric analysis.

Abstract: In the productivity modelling literature, the disturbances U (representing technical inefficiency) and V (representing noise) of the composite errorW = V −U of the stochastic fron-tier model are assumed to be independent random variables. By employing the copula approach to statistical modelling, the joint behaviour of U and V can be parameterised thereby allowing the data the opportunity to determine the adequacy of the independence assumption. In this context, three examples of the copula approach are given: the first is algebraic (the Logistic-Exponential stochastic frontier model with margins bound by the Fairlie-Gumbel-Morgenstern copula) and the second and third are empirically oriented, using data sets well-known in produc-tivity analysis. Analysed are a cross-section of cost data sampled from the US electrical power industry, and an unbalanced panel of data sampled from the US airline industry.