Productivity and Efficiency Analysis Software: An Exploratory Bibliographical Survey of the Options (original) (raw)
Related papers
2017
The field of productivity and efficiency analysis is growing exponentially, both in terms of new methods and approaches proposed to assess the efficiency of productive decision making units (DMUs), and in terms of innovative and traditional empirical application of existing methods. In this landscape it is interesting to trace the evolution of the field to try to understand recent trends and unexplored areas for further research. This survey provides a bibliometric investigation on the advances of the data envelopment analysis from the seminal and influential work of Abraham Charnes in 1978 to most recent empirical and theoretical contributions. A time-based mapping and distance-based clusters of the state of the art in the field of productivity and efficiency analysis are developed with the support of CitNetExplorer and VOSviewer analytical tools (Van Eck and Waltman 2014). The visualization of professor Charnes relevant extensions is presented with the identification of applicatio...
Economic Efficiency and Frontier Techniques
Journal of Economic Surveys, 2004
Most of the literature related to the measurement of economic efficiency has based its analysis either on parametric or on non-parametric frontier methods. The choice of estimation method has been an issue of debate, with some researchers preferring the parametric and others the non-parametric approach. The aim of this paper is to provide a critical and detailed review of both core frontier methods. In our opinion, no approach is strictly preferable to any other. Moreover, a careful consideration of their main advantages and disadvantages, of the data set utilized, and of the intrinsic characteristics of the framework under analysis will help us in the correct implementation of these techniques. Recent developments in frontier techniques and economic efficiency measurement such as Bayesian techniques, bootstrapping, duality theory and the analysis of sampling asymptotic properties are also considered in this paper.
FEAR: A Software Package for Frontier Efficiency Analysis with R
This paper describes a software package for computing nonparametric 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.
Efficiency Measurement: A Methodological Review and Synthesis
Data Envelopment Analysis Journal
Measuring efficiency has been a major item on the health economics agenda over the past quarter century. A thorough review of the literature shows that almost all studies met the basic requirements proposed by Cowing and Stevenson in 1983, as they relied on the solid theoretical foundations of production economics. Many methods were nevertheless developed and used, with some grounded in statistics, others in operations research, or accounting. The objective of this paper is to show how these methods often fail to include all relevant theoretical considerations. For example, authors relying on economic theory have applied empirical methods with stochastic error terms that are sometimes at odds with certain properties of their models. In fact, almost all models can be approached as specific cases of a general model. We will show that each model implies specific assumptions on the nature of the data, and that in some cases, the models are incoherent. framework covered in this methodological synthesis, we will use a simple form of the cost function. i.e., the nonregulated cost function. It is possible to considerably generalize the firm's environment by introducing quasi-fixed or fixed inputs (also called non-discretionary inputs), regulation, technological parameters, etc. The issue of input and output quality will be discussed at a later point. Regardless, knowledge of the cost function is essential and as it is unobserved, it must be inferred from available data. The goal of what follows is therefore precisely to show how one can recover the cost function from available price and quantity data.
Productivity Analysis and Variable Returns of Scale: DEA Efficiency Frontier Interpretation
Procedia Computer Science, 2015
The main objective of this paper is to analyze DMUs efficiency from the perspective of variable returns to scale. Thus, a case study is proposed, where the efficiencies of DMUs suffer variation according to the methods used in the analysis. The classic models of DEA, CCR and BCC, and a new model proposed by the authors, will have their results compared to classical foundations of the economy. The case study will examine the efficiency of administrative units selected of Undergraduate Higher Education.
Productivity Analysis and Variable Returns to Scale: DEA Efficiency Frontier Interpretation
The main objective of this paper is to analyze DMUs efficiency from the perspective of variable returns to scale. Thus, a case study is proposed, where the efficiencies of DMUs suffer variation according to the methods used in the analysis. The classic models of DEA, CCR and BCC, and a new model proposed by the authors, will have their results compared to classical foundations of the economy. The case study will examine the efficiency of administrative units selected of Undergraduate Higher Education.
International Journal of Production Economics, 2001
Parametric frontier models and non-parametric methods have monopolised the recent literature on productive efficiency measurement. Empirical applications have usually dealt with either one or the other group of techniques. This paper applies a range of both types of approaches to an industrial organisation setup. The joint use can improve the accuracy of both, although some methodological difficulties can arise. The robustness of different methods in ranking productive units allows us to make an comparative analysis of them. Empirical results concern productive and market demand structure, returns-to-scale, and productive inefficiency sources. The techniques are illustrated using data from the US electric power industry.