Performance evaluation using bootstrapping DEA techniques: Evidence from industry ratio analysis

In Data Envelopment Analysis (DEA) context financial data/ ratios have been used in order to produce a unified measure of performance metric. However, several scholars have indicated that the inclusion of financial ratios create biased efficiency estimates with implications on firms’ and industries’ performance evaluation. There have been several DEA formulations and techniques dealing with this problem including sensitivity analysis, Prior-Ratio-Analysis and DEA/ output–input ratio analysis for the assessment of the efficiency and ranking of the examined units. In addition to these computational approaches this paper in order to overcome these problems applies bootstrap techniques. Moreover it provides an application evaluating the performance of 23 Greek manufacturing sectors with the use of financial data. The results reveal that in the first stage of our sensitivity analysis the efficiencies obtained are biased. However, after applying the bootstrap techniques the sensitivity analysis reveals that the efficiency scores have been significantly improved.Performance measurement; Data Envelopment Analysis; Financial ratios; Bootstrap; Bias correction

Projected Spin Networks for Lorentz connection: Linking Spin Foams and Loop Gravity

In the search for a covariant formulation for Loop Quantum Gravity, spin foams have arised as the corresponding discrete space-time structure and, among the different models, the Barrett-Crane model seems the most promising. Here, we study its boundary states and introduce cylindrical functions on both the Lorentz connection and the time normal to the studied hypersurface. We call them projected cylindrical functions and we explain how they would naturally arise in a covariant formulation of Loop Quantum Gravity.Comment: Latex, 15 page

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.Averch-Johnson effect; Industry regulations; Manufacturing sectors; Nonparametric analysis