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Learning from Input-Output Mixes in DEA: A Proportional Measure for Slack-Based Efficient Projections

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  • Laurens Cherchye
  • Tom Van Puyenbroeck

Abstract

Several Data Envelopment Analysis (DEA) models use a radial distance measure that is based on the Debreu-Farrell notion of (in)efficiency. While this measure has an attractive interpretation, its use may be problematic if slacks or zeros are present in the data. The additive DEA model can perfectly deal with these problems, but the meaning of its associated scores is less intuitive than the one attached to the radial measures. We introduce an alternative efficiency measure, based on the results of the additive model, that can be decomposed in a Debreu-Farrell component and a factor that captures differences in input-output mixes with respect to those of the best practice reference observation. On an aggregate level, this second component can be considered as an indicator of the dispersion between radial efficiency measurement and results based on the Pareto-Koopmans efficiency notion. On the individual level, the measure allows us to regard relative inefficiency as resulting from (i) a divergence of implicit cost price vectors, and (ii) a cost level that is too high, even after adjustment for the implicit cost prices. Copyright © 1999 John Wiley & Sons, Ltd.
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  • Laurens Cherchye & Tom Van Puyenbroeck, 1998. "Learning from Input-Output Mixes in DEA: A Proportional Measure for Slack-Based Efficient Projections," Working Papers of Department of Economics, Leuven ces9813, KU Leuven, Faculty of Economics and Business (FEB), Department of Economics, Leuven.
    • Handle: RePEc:ete:ceswps:ces9813
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    References listed on IDEAS

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    1. Charnes, A. & Cooper, W. W. & Golany, B. & Seiford, L. & Stutz, J., 1985. "Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 91-107.
    2. 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.
    3. Robert Russell, R., 1985. "Measures of technical efficiency," Journal of Economic Theory, Elsevier, vol. 35(1), pages 109-126, February.
    4. Fare, Rolf & Knox Lovell, C. A., 1978. "Measuring the technical efficiency of production," Journal of Economic Theory, Elsevier, vol. 19(1), pages 150-162, October.
    5. Charnes, A. & Cooper, W. W. & Rhodes, E., 1979. "Measuring the efficiency of decision-making units," European Journal of Operational Research, Elsevier, vol. 3(4), pages 339-338, July.
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    1. Cherchye, Laurens & Van Puyenbroeck, Tom, 2007. "Profit efficiency analysis under limited information with an application to German farm types," Omega, Elsevier, vol. 35(3), pages 335-349, June.
    2. M C A Silva Portela & E Thanassoulis & G Simpson, 2004. "Negative data in DEA: a directional distance approach applied to bank branches," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(10), pages 1111-1121, October.
    3. Bacha Kebede Debela, 2022. "Inter-Organizational Bench-Learning to Respond to Climate Change and Reduce Trade-Offs in Urban Drinking Water Supply: The Case of Grade 2B Municipalities in Oromia National Regional State, Ethiopia," SAGE Open, , vol. 12(3), pages 21582440221, September.

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