Data envelopment analysis (original) (raw)

Data Envelopment Analysis in Stata

2009

In this presentation, we present a procedure and an illustrative application of a user-written Data Envelopment Analysis (DEA) program in Stata. DEA is a linear programming method for assessing the efficiency and productivity of units and a popular managerial tool for measuring performance of organizations. It has been used widely for assessing the efficiency of public and private sectors, such as banks, airlines, hospitals, universities, defense firms, and manufacturers. The DEA program in Stata will allow DEA users to easily access the Stata system and to conduct not only the standard optimization procedure but also more extended managerial analysis. The Mata programming, an extension of the DEA program code developed in the Stata programming language, will be discussed for the cases where the data capacity matters. We will also discuss the returns to scale options in DEA. Unfortunately, to date no DEA options are available in Stata, but an SFA model is available. The user-written...

Data envelopment analysis (DEA) – Thirty years on

European Journal of Operational Research, 2009

This paper provides a sketch of some of the major research thrusts in data envelopment analysis (DEA) over the three decades since the appearance of the seminal work of Charnes et al. (1978) [Charnes, A., Cooper, W.W., Rhodes, E.L., 1978. Measuring the efficiency of decision making units. European Journal of Operational Research 2, 429-444]. The focus herein is primarily on methodological developments, and in no manner does the paper address the many excellent applications that have appeared during that period. Specifically, attention is primarily paid to (1) the various models for measuring efficiency, (2) approaches to incorporating restrictions on multipliers, (3) considerations regarding the status of variables, and (4) modeling of data variation.

ENHANCING ESTIMATION OF EFFICIENCY OF ECONOMIC ENTITIES: DATA ENVELOPMENT ANALYSIS

BSEU, 2021

The objective of this paper is to describe the Data Envelopment Analysis technique (DEA) used to measure the relative efficiency of decision-making units (DMUs), description of the DEA models and processes, as well as analysis of the technique concerned. The paper shows that DEA plays an important role in measuring relative efficiency and works well even with a small sample of organizations. In accordance with the technique, efficiency and productivity are measured by computing the output to input ratio. The DEA method is used throughout the world by various researchers for evaluating efficiency of different organizations, such as banks, universities and hospitals in different countries.

Editorial: Special issue on data envelopment analysis

Central European Journal of Operations Research

Data Envelopment Analysis (DEA) originally developed by Charnes et al. (1978) is an optimization method of mathematical programming to generalize the Farrell (1957) single-input/single-output technical efficiency measure to the multiple-input/multipleoutput case by constructing a relative efficiency score as the ratio of a single virtual output to a single virtual input. Thus, DEA become a new tool in operational research for measuring technical efficiency. Since 1978 over 10,000 articles, books and dissertation have been published (Emrouznejad and Yang 2018) and DEA has rapidly extended to many other fields with applications to evaluate and compare educational departments (schools, colleges and universities), health care (hospitals, clinics), agricultural production, banking, armed forces, sports, market research, transportation (highway maintenance), courts, benchmarking, index number construction and many other applications. The International DEA Society organized several international DEA conferences in order to spread the use of DEA in both theoretical and application views. Past DEA conferences were held in many countries including Russia,

Advances in data envelopment analysis

Annals of Operations Research, 2014

Since its introduction in 1978, Data Envelopment Analysis (DEA) has become one of the preeminent non-parametric methods for measuring efficiency and productivity of decision making units. Charnes, Cooper, and Rhodes (1978) provided the original DEA constant returns to scale (CRS) model, later extended to variable returns to scale (VRS) by Banker Charnes, and Cooper (1984). These 'standard' models are known by the acronyms CCR and BCC, respectively, and are now employed routinely in areas that range from assessment of public sectors, such as hospitals and health care systems, schools, and universities, to private sectors such as banks and financial institutions (Emrouznejad, et al, 2008, 2011). The main objective of this volume is to publish original studies that are beyond the two standard CCR and BCC models with both theoretical and practical applications using advanced models in Data Envelopment Analysis.

Introduction to Data Envelopment Analysis and Its Uses: With DEA-Solver Software and References

2005

Section 3.9 in Chapter 3 introduced the topic of non-discretionary variables and appUed it to examples drawn from a study of Texas schools. In that study it was necessary to allow for variations in "minority," "economically disadvantaged" and "low English proficiency" students who had to be dealt with in different schools. These input variables were "non-discretionary." That is, they could not be varied at the discretion of individual school managers but nevertheless needed to be taken into account in arriving at relative efficiency evaluations. The approach taken in Chapter 3, as adapted from Banker and Morey ^ (1986), took form in the following model: mi n 0-si'^s-+Y,st]

Introduction to Data Envelopment Analysis and its Applications

Handbook of Research on Strategic Performance Management and Measurement Using Data Envelopment Analysis

This chapter provides the theoretical foundation and background on Data Envelopment Analysis (DEA) method and some variants of basic DEA models and applications to various sectors. Some illustrative examples, helpful resources on DEA, including DEA software package, are also presented in this chapter. DEA is useful for measuring relative efficiency for variety of institutions and has its own merits and limitations. This chapter concludes that DEA results should be interpreted with much caution to avoid giving wrong signals and providing inappropriate recommendations.