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Abstract
Given the large volume of resources employed in policies financed by the Structural Funds and the importance of the Objectives pursued, this paper seeks to perform an analysis of the efficiency of the application of these resources to increase productivity and employment by those regions classified as Objective 1 over the period 2000–2006. In the first place we are going to identify which are the most efficient regions by calculating both the level of efficiency according to the results obtained from the resources used (pure technical efficiency (PTE)) and the degree of efficiency according to their optimum production capacity (scale efficiency (SE)) and we have determine the “Reference Set” for inefficient regions. Finally, we will analyse the extent to which certain factors have repercussions on the efficiency such as country, geographical location and contribution of agriculture of GDP. The result indicates that the PTE of the regions are higher level although it does operate on an optimum scale. Furthermore, the country, geographical location and contribution of agriculture of GDP have significantly influences of PTE and SE.
Notes
A detailed literature based on DEA is available at www.banxia.com/frontier/bbliography.html. Analysis of the efficiency of a set of entities with DEA involves having software capable of solving the problem addressed. Free software includes that written by Coelli Citation(1996): DEAP and EMS and Scheel Citation(2000): EMS. These can be downloaded from www.uq.edu.au/economics/cepa/soltware.htm and www.wiso.unidortmund.de/lsfg/or/scheel/ems. Other commercially available programs are professional Frontier Analyst (www.banxia.com), DEA Solver Pro (www.saitech-inc.com), DEA Frontier (Zhu, Citation2002) and Warwic-DEA.
The main developments of the estimation methods of productive efficiency were put forward by Farrell Citation(1957) in the Discussion section of his paper. Forsund Citation(1999) addresses the original ideas therein and establishes connections with the determinist parametric approach, the stochastic approach and DEA.
This flexibility in the choice of weights is considered both a weakness and a strength of DEA (Boussofiane et al., Citation1991).
The reason is clear. The linear program of DEA-CCR (Expression Equation(2)) is defined by a number of constraints equal to (n + 1). However, the dual DEA-CCR linear program, the enveloping form, is subject to (s + m) constraints. Since, in general, the number of entities worked with is much higher than the total number of inputs and outputs, this is why the dual DEA-CCR model is the preferred problem to be solved (Coelli, Citation1998).
These correspond to the projection coordinates.