Assessing performance in Greek bauxite mining by means of frontier estimation methodologies

This paper employs for the first time data envelopment analysis (DEA) and stochastic frontier analysis (SFA) as two performance measurement competing approaches to assess efficiency in the Greek mining industry. These two frontier estimation methodologies overcome the limitations of the partial productivity measures by explicitly considering two inputs and one output in the measurement of efficiency for the period 1970–1996. The paper is also innovative in utilizing a bootstrapping approach in DEA to aggregated industry (time series) data as an alternative to the more common DEA point estimates. In particular, the bootstrapping approach used relies on the homogeneity assumption that the distribution of the efficiency scores is independently distributed over the sample; the results from DEA and SFA are more comparable under this assumption as it corresponds to the independence assumption regarding the distribution of the inefficiency term in SFA. The two different approaches to performance evaluation, as used here, do not provide confirmation of each other's findings since they are based on different principles and treat the data in different ways. Although the joint use made here of DEA and SFA provides results that are consistent with points of view that have regarded these two approaches as mutually exclusive alternatives, this paper demonstrates that from a policy perspective DEA and SFA can be utilized in tandem on a common data set to assess the efficiency and investigate the return to scale patterns at the sectoral level.