A conditional full frontier approach for investigating the Averch-Johnson effect

This paper applies a probabilistic approach in order to develop conditional and unconditional Data Envelopment Analysis (DEA) models for the measurement of sectors’ input oriented technical and scale efficiency levels for a sample of 23 Greek manufacturing sectors. In order to capture the Averch and Johnson effect (A-J effect), we measure sectors’ efficiency levels conditioned on the number of companies competing within the sectors. Particularly, various DEA models have been applied alongside with bootstrap techniques in order to determine the effect of competition conditions on sectors’ inefficiency levels. Additionally, this study illustrates how the recent developments in efficiency analysis and statistical inference can be applied when evaluating the effect of regulations in an industry. The results reveal that sectors with fewer numbers of companies appear to have greater scale and technical inefficiencies due to the existence of the A-J effect.