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.

In Stochastic Frontier Analysis the presence of outliers in the data, which can often be safely ignored in other forms of linear modelling, has potentially serious consequences in that it may lead to implausibly large variation in efficiency predictions when based on the conditional mean. This motivates the development of alternative stochastic frontier specifications which are appropriate when the two-sided error has heavy tails. Several existing proposals to this effect have proceeded by specifying thick tailed distributions for both error components in order to arrive at a closed form loglikelihood. In contrast, we use simulation-based methods to pair the canonical inefficiency distributions (in this example half-normal) with a logistically distributed noise term. We apply this model to estimate cost frontiers for highways authorities in England, and compare results obtained from the conventional normal-half normal stochastic frontier model. We show that the conditional mean yields less extreme inefficiency predictions for large residuals relative to the use of the normal distribution for noise.

This is a repository copy of The case for the use of multiple imputation missing data methods in stochastic frontier analysis with illustration using English local highway data.

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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. road transport is dominant, but rail still has a non-trivial mode share in both the passenger and freight market. In LICs, experience varies. In Sub-Sharan Africa, the lack of railway infrastructure means that, for passenger flows outside of the long distance segment (which is small in any case), the road mode is dominant. As such, the rail transport that exists is biased towards freight. In South Asia, however, LICs can have substantive railway transport focused on passenger flows (for example in Bangladesh and Myanmar).More broadly, road and rail services are different because roads are open to private users whilst rails have much stronger access restrictions. Studies on the regulation, concession design, and institutional skills requirements for planning, designing, and monitoring transport services have been reviewed. Given the access restrictions in rail, these issues are more applicable to the rail sector. The situation for roads is different, where the state usually provides the infrastructure either directly or indirectly via letting tenders, but private users are also free to use the infrastructure. The issues with respect roads are more focused on regulations on driver hours, road safety, and preventing the over loading of vehicles and tackling corruption. Such reforms aim to deliver efficiency gains and service quality improvements at lower costs for users.Common to both road and rail are the long and costly clearance times at border crossings. Arvis [4] shows that the cost in the bottom three quintile LICs is three times higher, and its paperwork twice as high as that for the top two quintiles LICs. The literature also indicates that transit time and the reliability of transit impact freight choices and that cross-border delays significantly increase freight tariffs. Existing evidence on alternative technologies that can help improve cross border crossing is also considered. In many LICs, it is clear that the opportunities presented by strategic roads and rails are not being fully realised for a variety of reasons.Specific experiences in LICs...