The presence of outliers in the data has implications for stochastic frontier analysis, and indeed any performance analysis methodology, because they may lead to imprecise parameter estimates and, crucially, lead to an exaggerated spread of efficiency predictions. In this paper we replace the normal distribution for the noise term in the standard stochastic frontier model with a Student’s t distribution, which generalises the normal distribution by adding a shape parameter governing the degree of kurtosis. This has the advantages of introducing flexibility in the heaviness of the tails, which can be determined by the data, as well as containing the normal distribution as a limiting case, and we outline how to test against the standard model. Monte Carlo simulation results for the maximum simulated likelihood estimator confirm that the model recovers appropriate frontier and distributional parameter estimates under various values of the true shape parameter. The simulation results also indicate the influence of a phenomenon we term ‘wrong kurtosis’ in the case of small samples, which is analogous to the issue of ‘wrong skewness’ previously identified in the literature. We apply a Student’s t-half normal cost frontier to data for highways authorities in England, and this formulation is found to be preferred by statistical testing to the comparator normal-half normal cost frontier model. The model yields a significantly narrower range of efficiency predictions, which are non-monotonic at the tails of the residual distribution.

The value of freight travel time savings (VFTTS) is a monetary value that is considered an important input into cost–benefit analysis and traffic forecasting. The VFTTS is defined as the marginal rate of substitution between travel time and cost and may therefore differ across firms, time and countries. The paper aims to explain variations in the VFTTS by using the meta-analysis method. The analysis covers 106 monetary valuations extracted from 56 studies conducted from 1988 to 2018 in countries across the globe. The meta-analysis method determines the factors that have an impact on these VFTTS variations. The paper briefly introduces the VFTTS concept and describes the adopted meta-analysis methodology, wherein different meta-models are used in VFTTS estimations. The results highlight the necessity of including multiple explanatory variables to ensure adequate explanation of the VFTTS variations. The findings also show that GDP per capita, transport mode and type of survey respondent are statistically significant variables. The paper sheds some light on the variations, thereby advancing the understanding of each factor’s effects on the VFTTS. Furthermore, meta-model outcomes are used to generate new values of travel time savings for different transport modes in freight transport, for several countries. These implied VFTTS can be used as benchmarks to assess existing evidence or provide new evidence to countries where no such values exist.