Transit costs and cost efficiency: Bootstrapping non-parametric frontiers

This paper explores a selection of recently proposed bootstrapping techniques to estimate non-parametric convex (DEA) cost frontiers and efficiency scores for transit firms. Using a sample of Norwegian bus operators, the key results can be summarized as follows: (i) the bias implied by uncorrected cost efficiency measures is numerically important (close to 25%), (ii) the bootstrapped-based test rejects the constant returns to scale hypothesis, and (iii) explaining patterns of efficiency scores using a two-stage bootstrapping approach detects only one significant covariate, in contrast to earlier results highlighting, e.g., the positive impact of high-powered contract types. Finally, comparing the average inefficiency obtained for the Norwegian data set with an analogous estimate for a smaller French sample illustrates how the estimated differences in average efficiency almost disappear once sample size differences are accounted for.     

Technical efficiency measurement and explanation of French urban transit companies

The performance of a sample of French urban transit companies is evaluated using a broad selection of nonparametric reference technologies for two specifications of the production process. In particular, the variable returns to scale Data Envelopment Analysis (DEA) models with either strong or weak disposability in both inputs and outputs, and the Free Disposal Hull (FDH) are applied. An extensive comparison of the resulting radial output efficiency measures yields the following major methodological conclusions. First, the location of the efficiency distributions differs substantially depending on the methodology and especially on the output specification considered. The latter differences vanish if the impact of outliers is eliminated. Second, convexity has a stronger influence on the efficient-inefficient dichotomy than allowing for congestion by means of a weakly disposable DEA model. For policy purposes, these efficiency distributions are explained using a Tobit model. The findings corroborate results reported elsewhere: the relevance of ownership, the use of risk-sharing incentives in contracting, the harmful impact of subsidies, etc. Furthermore, the network structure seems to account for some differences in performance. Finally, a novelty in the urban transit context is the indirect monitoring effect of the French earmarked transportation tax.