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Data Envelopment Analysis for Performance Evaluation: A Child's Guide

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  • Subhash C. Ray

    (University of Connecticut)

  • Lei Chen

    (University of Connecticut)

Abstract

In this paper we offer a simple exposition of the neoclassical production theoretic foundations of Data Envelopment Analysis. The concepts of technical efficiency (both input- and output-oriented), scale efficiency, and cost efficiency are explained and the corresponding DEA models are described in details. We offer step-by-step instruction on how to write the codes for solving the various DEA models using the Solver option in the widely accessible MS Excel software. An important feature of this paper is a detailed exposition of how to write various Visual Basic Macro programs for solving DEA problems. We also describe the non-convex Free Disposal Hull (FDH) procedure and the second-stage regression analysis that seeks to account for variation in measured efficiency scores due to external factors.

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Bibliographic Info

Paper provided by University of Connecticut, Department of Economics in its series Working papers with number 2009-38.

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Length: 52 pages
Date of creation: Nov 2009
Date of revision:
Handle: RePEc:uct:uconnp:2009-38

Note: This paper builds on the material from a keynote address delivered by the first author at the CDE-Swiss Re Workshop on Performance Measurement held by the Delhi School of Economics in Delhi in January 2009.
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Keywords: Efficiency; Linear Programming; Benchmarking; Excel Solver;

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  1. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
  2. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
  3. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
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