Data Envelopment Analysis
Data Envelopment Analysis (DEA). A nonparametric method in operations research and econometrics for multivariate frontier estimation and ranking. With the intention of being consistent with microeconomic production theory and when being conscious of the existence of inefficiencies in the production processes, frontier techniques have been developed during the last 30 years. Among the different solutions, we can find a nonparametric method called Data Envelopment Analysis (DEA), which is a linear programming methodology to measure the efficiency of multiple Decision Maker Units (DMUs) when the production process presents a structure of multiple inputs and outputs. Then, some of the benefits of it are: (1) there is no need to explicitly specify a mathematical form for the production function, (2) it has proven to be useful in uncovering relationships that remain hidden for other methodologies, (3) is capable of handling multiple inputs and outputs, (4) it can be used with any input-output measurement, (5) the sources of inefficiency can be analysed and quantified for every evaluated unit. In the DEA methodology, formerly developed by Charnes, Cooper and Rhodes (1978), efficiency is defined as a weighted sum of outputs to a weighted sum of inputs, where the weights structure is calculated by means of mathematical programming and constant returns to scale are assumed. In 1984, Banker, Charnes and Cooper developed a model with variable returns to scale.
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