Data Envelopment Analysis is a decision making tool based on linear programming for measuring the relative efficiencies of a set of comparable units. DEA is intitaly developed by Charnes, Cooper, Rhodes (1978).
Named after its developer Chames, Cooper and Rhodes, this is the first and fundamental DEA model, built on the notion of efficiency as defined in the classical engineering ratio. The CCR ratio model calculates an overall efficiency for the unit in which both its pure technical efficiency and scale efficiency are aggregated into a single value. The obtained efficiency is never absolute as it is always measured relative to the field. The Chames et al (1978) article marked the birth of DEA, and despite the numerous modified models that have appeared, the CCR model is still the most widely known and used of DEA models.
A factor which can assume only a predefined set of discrete values. Categorical variables are frequently used to indicate the presence or absence of an attribute. An analysis on fast-food restaurants could use a categorical variable assigned to I to indicate the presence of a drive-in window, with the value O denoting its absence. The use of categorical variables requires modifications in DEA models as indicated in Banker and Morey (1986). See also: ordinal variable.
– Banker R D and Morey R (1986) ‘The use of categorical variables in data envelopment analysis’, Mgmt. Sci., 32, pp 1613-1627.
Generally speaking, the results are biased if they favour a particular unit and/or unfairly assess the efficiency of some inefficient units. Although the actual DEA technique can be considered fair, bias can be introduced into the analysis through the omission of a critical factor or a lack of homogeneity in the field.
VII North American Productivity Workshop
June 6-9 – 2012
Rice University, Houston, Texas
for further information please see http://economics.rice.edu/~napw2012/
The 8th Asia-Pacific Productivity Conference, July 25-27, 2012, KMITL, Bangkok, Thailand
for further detail splease see: http://www.APPC2012.org