Data envelopment analysis (DEA) is a widely used mathematical programming approach for comparing the input and output of a set of comparable decision-making units (DMUs) by evaluating their relative efficiency. The traditional DEA methods require accurate measurement of both the inputs and outputs. However, the real evaluation of the DMUs is often characterized by imprecision and uncertainty in...

The fundamental weakness of a Data Envelopment Analysis (DEA) is its weak discrimination in cases when small number of Decision Making Units (DMU) have been compared. Therefore in such cases the basic DEA model is used in combination with other methods or additional constraints are added to the model. In this paper, the Cross-efficiency method has been combined with the Potential Method (PM).

In this paper we present a method for measuring the efficiency for any economical branch. We construct the econometric approach of production frontiers, using the DEA technique (Data envelopment analysis), based on the mathematical programming approach. We effectuate an analysis of some certain data concerning the efficiency or the inefficiency of the branches, using the Efficiency Measurement ...

In this paper, we analyse economic development and growth through traditional measures (gross domestic product and human development index) and Data Envelopment Analysis (DEA) in Colombian departments over the period 1993e2007. We use a DEA model to measure and rank economic development and growth from different approaches such as poverty, equality and security. The results show considerable va...

Lack of information about technology and prices often hampers the empirical assessment of the validity of the profit maximization hypothesis. We show that the non-parametric Data Envelopment Analysis (DEA) methodology comprises natural tools for dealing with such incomplete information. In particular, we focus on the economic meaning of the DEA model that builds on assumptions of monotone and c...

We estimate a semi-parametric model of health production process using a two-stage approach for OECD countries. By regressing data envelopment analysis output efficiency scores on non-discretionary variables, both using Tobit analysis and a single and double bootstrap procedure, we show that inefficiency is strongly related to GDP per head, the education level, and health behaviour such as obes...

As a non-parametric method of relative efficient measurement of a group of decision-making units (〖DMU〗_s), Data Envelopment Analysis (DEA) is one of the most important tools in efficiency computation. One of the main concerns dealt with in DEA is dealing with return to scale in two-stage processes in which, produced outputs of the first stage inputs are used as inputs for the second stage. The...

Data Envelopment Analysis (DEA) is the methodology for evaluating the relative productive efficiency of decision making units (DMUs) that produce multiple-outputs using multiple-inputs. DEA was proposed for the first time in 1957 by Farrell; nonetheless, the wide usage of this method begun with its generalization and the linear programming formulation which is due to Charnes et al. (1978) (for ...

One of the main problems in efficiency analysis is to determinate the environmental variables that have an impact on the production process. This paper shows that applying bootstrap to data envelopment analysis (DEA) before performing classification and regression trees (CART) increase the quality of the results. In particular, employing data on the Italian Health System, the paper highlights t...

The paper uses Data Envelopment Analysis (DEA) to assess the efficiency of the Depertments of the University of Trieste, considering both research and teaching issues. General guidelines for selecting input and output flows are proposed, different DEA models are considered and discussed, and the quantitative outcomes of the evaluation are analyzed.