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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.

In this paper, we develop an econometric model to estimate the impacts of Electronic Vehicle Management Systems (EVMS) on the load factor (LF) of heavy trucks using data at the operational level. This technology is supposed to improve capacity utilization by reducing coordination costs between demand and supply. The model is estimated on a subsample of the 1999 National Roadside Survey, covering heavy trucks travelling in the province of Quebec. The LF is explained as a function of truck, trip and carrier characteristics. We show that the use of EVMS results in a 16 percentage points increase of LF on backhaul trips. However, we also find that the LF of equipped trucks is reduced by about 7.6 percentage points on fronthaul movements. This last effect could be explained by a rebound effect: higher expected LF on the returns lead carriers to accept shipments with lower fronthaul LF. Overall, we find that this technology has increased the tonne-kilometers transported of equipped trucks by 6.3% and their fuel efficiency by 5%. ispartof: CES - Discussion paper series 08.13 pages:1-31 status: published

The detection of dangerous road sites is usually performed using empirical methods which focus on observed accident frequencies and/or proportions of accidents with a given feature. The most widely used detection tools have an empirical Bayes (EB) background. The EB approaches rely on the comparison of frequencies and/or proportions of accidents at a given site with the amounts that would normally occur at similar sites. Currently, analytical techniques for accident proportions describe the number of accidents with a given feature using a binomial distribution. This paper extends to the multinomial case the general EB technique that we recently suggested to analyze road accident proportions. Our proposed approach is a full-information Bayes method that allows for both deterministic and random heterogeneity as well as spatial-correlation among the sites under investigation. The technique can also be used to analyze accident frequencies. An empirical example based on accident data taken from the Québec city database, will serve to demonstrate its usefulness. Habituellement, la détection des sites d'accidents routiers dangereux est effectuée à partir de méthodes de bayes empiriques appliquées à des taux d'accidents et/ou des proportions d'accidents qui se sont produits dans des conditions données. Ces méthodes comparent les taux et proportions observés avec ceux qui se produisent normalement dans un ensemble de sites routiers considérés comme semblables. Les approches existantes exploitent des lois de distribution binomiales. Dans le présent article, nous décrivons une méthodologie générale à information complète pour analyser le niveau de danger des sites routiers, qui fait appel à des distributions multinomiales. La technique proposée, de type bayésienne, permet de traiter simultanément les problèmes d'hétérogénéité déterministe et aléatoire ainsi que celui de la corrélation spatiale attribuable à la proximité ou l'environnement similaire caractérisant les sites à l'étude. Notre cadre méthodologique englobe des approches bayésiennes de pratique courante qui étudient les proportions d'accidents impliquant une caractéristique donnée. Les propriétés et l'intérêt de la nouvelle méthode sont démontrés à l'aide d'un exemple simple basé sur des données d'accidents de la ville de Québec.

Investment expenditures in natural gas distribution pipelines account for 70% of the rate base of Société en Commandite Gaz Métropolitain (SCGM), the natural gas utility which serves most customers in the province of Québec. In allocating these costs to rate payers, the regulatory process divides costs into an access fee which reflects the fixed costs of planning and implementing the system that is to be divided equally over all users and a user or variable cost fee reflecting the capacity they use. In this paper we estimated a cost function to provide information to regulators on how these tariffs should be set. We use a unique data set of 131 observations which represent natural gas extension projects realized by SCGM in four Québec regions (Trois-Rivières, Sherbrooke, Québec and Chicoutimi) in the eighties and early nineties, to analyze the main determinants of capital costs. It is found that capital cost is not separable into a fixed and a variable component, that the elasticity with respect to maximum daily demand is not significant, and that the elasticity with respect to pipe length is slightly less than one. Maximum daily demand by each consumer class and consumer density per kilometer play no statistically significant role.

In recent years, major advances have taken place in three areas of random utility modeling: (1) semiparametric estimation, (2) computational methods for multinomial probit models, and (3) computational methods for Bayesian stimation. This paper summarizes these developments and discusses their implications for practice.