Traditional production function models may be named as econometric models. First, these models cannot simultaneously analyze several inputs and outputs (Farell, 1957). Secondly, they require strict assumptions about the functional form of the technology used. Thirdly, they do not provide relative measure of productivity that could be used for decision making.
The traditional models are useful when the productivity analysis is to be used for post mortem of the past for arriving at policy conclusions for the future. But as per the business economics framework, a decision-making unit needs an in depth analysis of alternative sets of inputs and outputs for getting the relative measure of productivity that could be used in the decision support systems (Berger and Humphrey 1997).
For such problems, there is a deterministic non-parametric specification technique for estimating the production technologies and measuring the inefficiencies in production. It is known as the Data Envelopment Analysis (DEA). Data envelopment analysis is a recent addition to the arsenal of quantitative techniques available or studying organizational performance. In this approach the technical efficiency is defined as minimum inputs(s) for any particular combination of outputs.
There are several formats of data envelopment analysis models (Charnes et. al., 1994). It is a linear programming approach, in which technical efficiency is measured against an efficiency frontier. It is also known as a frontier analysis technique because the decision-making unit lies on the efficient external frontier or is enveloped within the frontier. The decision making units that lie on the frontier are the institutions adhering to best practices and they retain a value of one and those enveloped by the frontier lie somewhere between 0 and 1.
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