Documentos de trabajo
Consequences of omitting relevant inputs on the quality of the data envelopment analysis under different input correlation structures
Fecha
2008-02-03Autor
Ramírez Hassan, Andrés
Institución
Resumen
This paper establishes the consequences of a wrong specification on the quality
of the data envelopment analysis. Specifically, the case of omitting a relevant variable in
the input oriented problem is analyzed when there are different correlation structures
between the inputs. It is established that the correlation matrix gives relevant information
about the homogeneity of the decision making units and the intensity of inputs used in the
production process. The methodology is based on a series of Monte Carlo simulations and
the quality of the data envelopment analysis is measured as the difference between the true
efficiency and the efficiency calculated. It is found that omitting relevant inputs causes
inconsistency, and this problem is worse when there is a negative correlation structure.