Alexander D. Stead

When a new rail station is built on an existing line, it causes an increase in in-vehicle time – typically, between 2 and 4 min – for all existing trips that now stop at the new station. To the best of our knowledge, this paper is the first to investigate the impacts of these small time penalties on through-passenger demand. Cost-benefit appraisals of new stations in the UK have proven highly sensitive to the forecasts of through-passenger demand reductions. We conduct an ex-post analysis of the effect of a new station on through-passenger demand using a Difference-in-Differences approach. We fail to find a statistically significant impact of the station opening on through-passenger demand. If there are such demand impacts, our findings suggest that these are smaller in magnitude than the standard forecasting approach implies. More research is recommended to corroborate and generalise the findings to other contexts, as these have important implications for the planning and appraisal of new stations.

The gamma and Nakagami distributions have an advantage over other proposed flexible inefficiency distributions in that they can accommodate not only non-zero modes, but also cases in which many firms lie arbitrarily close to the frontier. We propose a normal-Nakagami stochastic frontier model, which provides a generalisation of the normal-half normal that is more flexible than the familiar normal-truncated normal. The normal-gamma model has already attracted much attention, but estimation and efficiency prediction have relied on approximation methods. We derive exact expressions for likelihoods and efficiency predictors, and demonstrate direct maximum likelihood estimation of both models. Across three empirical applications, we show that the models avoid a convergence issue that affects the normal-truncated normal model, and can accommodate a concentration of observations near the frontier similar to zero-inefficiency stochastic frontier models. We provide Python implementations via the package.

Latent class and finite mixture stochastic frontier models have been proposed as a means of allowing either for technological heterogeneity or more flexible distributions of noise and inefficiency. As in the wider literature on latent class and finite mixture models, we are interested in class enumeration, particularly testing against homogeneity. We apply a modified likelihood ratio test for homogeneity in a stochastic frontier setting, based on established results for non-Gaussian latent class and finite mixture models, and provide evidence from Monte Carlo experiments which suggest the applicability of results regarding a modified likelihood ratio test to the stochastic frontier setting. We demonstrate an application to testing a model with a contaminated normal noise term against a model with a normally distributed noise term, finding that the former is preferred, with significant implications for efficiency prediction.

High passenger and freight transport costs are a barrier to economic growth and social mobility, particularly in Low Income Countries (LICs). This paper considers the current state of knowledge regarding the barriers to achieving lower generalised transport costs. It considers both the road and railway modes across passenger and freight transport. These issues include a reform on the regulations for driver hours (preventing the road infrastructure from overloading), structuring rail concessions, increasing competition, and tackling corruption. Such reforms aim to deliver efficiency gains and service quality improvements at lower costs for users. This paper identifies the knowledge gap in previous research and concludes by setting out a research agenda that builds the evidence base for how the best practices from around the world can best be applied to the specific circumstances in Low Income Countries, with a particular focus on Sub-Saharan Africa and South Asia.