Standard data envelopment analysis (DEA) does not provide adequate detail to identify the specific sources of inefficiency embedded in interacting divisions of an organization. On the other hand, network DEA gives access to this underlying diagnostic information that would otherwise remain...

Many managers, economists, and policy-makers have long believed that the production of a private enterprise is more efficient than that of a public one. This paper investigated whether it was good policy to privatize Taiwan's telecommunications industry by comparing the changes in efficiency in...

This paper describes a software package for computing non-parametric efficiency estimates, making inference, and testing hypotheses in frontier models. Commands are provided for bootstrapping as well as computation of some new, robust estimators of efficiency, etc.

The Italian gas distribution industry presents a high degree of fragmentation. However, the tendency between 1970 and 1998 was a concentration process. Available evidence supports the thesis that local distributors have undertaken a process of scale enlargement. This raises the question of...

Data Envelopment Analysis (DEA) is a nonparametric approach to evaluating the relative efficiency of decision making units (DMUs) that use multiple inputs to produce multiple outputs. An assumption underlying DEA is that all the data assume the form of specific numerical values. In some...

The failure of data envelopment analysis to provide adequate discrimination between a small number of alternatives is illustrated using a facility location problem. A straightforward modification which finds a desirability (or relative efficiency) score for each input variable provides greater...

Provisions for controlling factor weights constitute a significant extension of the data envelopment analysis (DEA) methodology, as an effective tool for measuring efficiency. This paper suggests a conceptual framework for the treatment of factor weights in DEA. First, the paper proposes general...