what is Data Envelopment Analysis (DEA) ?


Data Envelopment Analysis (DEA) is a “data-oriented” approach for evaluating the performance of a set of peer entities called Decision-Making Units (DMUs), which convert multiple inputs into multiple outputs. The definition of a DMU is generic and flexible. Recent years have seen a great variety of applications of DEA for use in evaluating the performances of many different kinds of entities engaged in many different activities in many different contexts in many different countries. These DEA  pplications have used DMUs of various forms to evaluate the performance of entities, such as hospitals, US Air Force wings, universities, cities, courts, business firms, and others, including the performance of countries, regions, etc.


As pointed out in Cooper et al. (2007), DEA has also been used to supply new insights into activities (and entities) that have previously been evaluated by other methods. For instance, studies of benchmarking practices with DEA have identified numerous sources of inefficiency in some of the most profitable firms – firms that had served as benchmarks by reference to this (profitability) criterion – but DEA has provided a vehicle for identifying better benchmarks in many applied studies. Because of these possibilities, DEA studies of the efficiency of different legal organization forms such as “stock” vs. “mutual” insurance companies have shown that previous studies have fallen short in their attempts to evaluate the potentials of these different forms of organizations. Similarly, a use of DEA has suggested reconsideration of previous studies of the efficiency with which pre-and postmerger activities have been conducted in banks that were studied by DEA.

 Since DEA was first introduced in 1978 in its present form, researchers in a number of fields have quickly recognized that it is an excellent and easily used methodology for modeling operational processes for performance evaluations. This has been accompanied by other developments. For instance, Zhu (2003a, 2009) provides a number of DEA spreadsheet models that can be used in performance evaluation and benchmarking. DEA’s empirical orientation and the absence of a need for the numerous a priori assumptions that accompany other approaches (such as standard forms of statistical regression analysis) have resulted in its use in a number of studies involving efficient frontier estimation in the governmental and nonprofit sector, in the regulated sector, and in the private sector. See, for instance, the use of Data Envelopment Analysis to guide removal of the Diet and other government agencies from Tokyo as described in Takamura and Tone (2003). In their originating article, Charnes et al. (1978) described DEA as a “mathematical programming model applied to observational data [that] provides a new way of obtaining empirical estimates of relations – such as the production functions and/or efficient production possibility surfaces – that are cornerstones of modern economics.”

DEA is a methodology directed to frontiers rather than central tendencies. Instead of trying to fit a regression plane through the center of the data as in statistical regressions, for example, one “floats” a piecewise linear surface to rest on top of the observations. Because of this perspective, Data Envelopment Analysis proves particularly adept at uncovering relationships that would remain hidden from other methodologies. For instance, consider what one wants to mean by “efficiency,” or more generally, what one wants to mean by saying that one DMU is more efficient than another DMU. This is accomplished in a straightforward manner by DEA without requiring explicitly formulated assumptions and variations that are required with various types of models such as linear and nonlinear regression models.


Edited, W. C. W. (2010). Handbook on data Envelopment analysis (2nd ed.). Boston, MA: Springer Science+Business Media.