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Flexible Measures–Classifying Inputs and Outputs

In: Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis

Author

Listed:
  • Wade D. Cook

    (York University)

  • Joe Zhu

    (Worcester Polytechnic Institute)

Abstract

In standard data envelopment analysis (DEA), it is assumed that the input versus output status of each of the chosen analysis measures is known. In some situations, however, certain measures can play either input or output roles. Consider using the number of interns on staff in a study of hospital efficiency. Such a factor clearly constitutes an output measure for a hospital, being one form of training provided by the organization, but at the same time is an important component of the hospital’s total staff complement, hence is an input. This chapter presents DEA models to accommodate such flexible measures. Both an individual DMU model and an aggregate model are suggested as methodologies for deriving the most appropriate designations for flexible measures.

Suggested Citation

  • Wade D. Cook & Joe Zhu, 2007. "Flexible Measures–Classifying Inputs and Outputs," Springer Books, in: Joe Zhu & Wade D. Cook (ed.), Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis, chapter 0, pages 261-270, Springer.
    • Handle: RePEc:spr:sprchp:978-0-387-71607-7_14
    DOI: 10.1007/978-0-387-71607-7_14
    as

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