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The effect of corruption on airport productive efficiency is analyzed using an unbalanced panel data of major European airports from 2003 to 2009. We first compute the residual (or net) variable factor productivity using the multilateral index number method and then apply robust cluster random effects model in order to evaluate the importance of corruption. We find strong evidence that corruption has negative impacts on airport operating efficiency; and the effects depend on the ownership form of the airport. The results suggest that airports under mixed public–private ownership with private majority achieve lower levels of efficiency when located in more corrupt countries. They even operate less efficiently than fully and/or majority government owned airports in high corruption environment. We control for economic regulation, competition level and other airports’ characteristics. Our empirical results survive several robustness checks including different control variables, three alternative corruption measures: International Country Risk Guide (ICRG) corruption index, Corruption Perception Index (CPI) and Control of Corruption Index (CCI). The empirical findings have important policy implications for management and ownership structuring of airports operating in countries that suffer from higher levels of corruption. [ABSTRACT FROM AUTHOR]

Abstract: A range of new spatial datasets classifying the European environment has been constructed over the last few years. These datasets share the common objective of dividing European environmental gradients into convenient units, within which objects and variables of interest have relatively homogeneous characteristics. The stratifications and typologies can be used as a basis for up-scaling, for stratified random sampling of ecological resources, for the representative selection of sites for studies across the continent and for the provision of frameworks for modeling exercises and reporting at the European scale. This paper provides an overview of five recent European stratifications and typologies, constructed for contrasting objectives, and differing in spatial and thematic detail. These datasets are: the Environmental Stratification (EnS), the European Landscape Classification (LANMAP), the Spatial Regional Reference Framework (SRRF), the Agri-Environmental Zonation (SEAMzones), and the Foresight Analysis for Rural Areas Of Europe (FARO-EU) Rural Typology. For each classification the objective, background, and construction of the dataset are described, followed by a discussion of its robustness. Finally, applications of each dataset are summarized. The five stratifications and typologies presented here give an overview of different research objectives for constructing such classifications. In addition they illustrate the most up to date methods for classifying the European environment, including their limitations and challenges. As such, they provide a sound basis for describing the factors affecting the robustness of such datasets. The latter is especially relevant, since there is likely to be further interest in European environmental assessment. In addition, advances in data availability and analysis techniques, will probably lead to the construction of other typologies in the future. [Copyright &y& Elsevier]