Data Environment Analysis
Abstract
Data Envelopment Analysis (DEA) began in generalization of the usual scientific-engineering efficiency valuation of a single input, single output system as the ratio of the output input (in the same physical measure, e.g., energy) to multi-input, multi-output systems (or organizations or production units) without known physical laws or the same measure for all inputs and outputs. This was accomplished by (i) reduction of the multi-inputs and outputs to single virtual inputs and outputs, (ii) replacing absolute efficiency by efficiency relative to all members of a sample of units (called DMU's) having the same inputs and outputs, (iii) evaluating a unit's relative efficiency as the maximum of the ratio of its virtual output to virtual input subject to virtual outputs being less than or equal to virtual inputs for each (all) of the DMU's. The origin, history, current status and problems of Data Envelopment Analysis (DEA) on empirical multi-input, multi-output data are surveyed in relation to efficiency valuation, production function determination and stochastic frontier estimation.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 01, 1989
- Accession Number
- ADA227432
Entities
People
- Abraham Charnes
- William W. Cooper
Organizations
- University of Texas at Austin