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An Overall Measure of Technical Inefficiency at the Firm and at the Industrial Level: The 'Lost Return on the Dollar' Revisited

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  • Juan Aparicio

    (University Miguel Hernandez of Elche)

  • Jesus T. Pastor

    (University Miguel Hernandez of Elche)

  • Subhash Ray

    (University of Connecticut)

Abstract

As a measure of overall technical inefficiency the Directional Distance Function (DDF) introduced by Chambers, Chung, and Färe ties the potential output expansion and input contraction together through a single parameter. By duality, the DDF is related to a measure of profit inefficiency, which is calculated as the normalized deviation between optimal and actual profit at market prices. As we show, in the most usual case, the associated normalization represents the sum of the actual revenue and the actual cost of the assessed firm. Consequently, the corresponding dual formulation of the DDF has no obvious economic interpretation. In contrast, in this paper we allow outputs to expand and inputs to contract by different proportions. This results in a modified DDF that retains most of the properties of the original DDF. The corresponding dual problem has much simpler interpretation as the lost return on outlay that can be decomposed into a technical and an allocative inefficiency component. JEL Classification: C61, D20 Key words: Data Envelopment Analysis, Directional Distance Function, Profit Inefficiency

Suggested Citation

  • Juan Aparicio & Jesus T. Pastor & Subhash Ray, 2012. "An Overall Measure of Technical Inefficiency at the Firm and at the Industrial Level: The 'Lost Return on the Dollar' Revisited," Working papers 2012-02, University of Connecticut, Department of Economics.
    • Handle: RePEc:uct:uconnp:2012-02
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    References listed on IDEAS

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    1. M C A S Portela & E Thanassoulis, 2007. "Developing a decomposable measure of profit efficiency using DEA," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(4), pages 481-490, April.
    2. William Cooper & Jesús Pastor & Fernando Borras & Juan Aparicio & Diego Pastor, 2011. "BAM: a bounded adjusted measure of efficiency for use with bounded additive models," Journal of Productivity Analysis, Springer, vol. 35(2), pages 85-94, April.
    3. Kuosmanen, Timo & Kortelainen, Mika & Sipiläinen, Timo & Cherchye, Laurens, 2010. "Firm and industry level profit efficiency analysis using absolute and uniform shadow prices," European Journal of Operational Research, Elsevier, vol. 202(2), pages 584-594, April.
    4. Pastor, J. T. & Ruiz, J. L. & Sirvent, I., 1999. "An enhanced DEA Russell graph efficiency measure," European Journal of Operational Research, Elsevier, vol. 115(3), pages 596-607, June.
    5. Silva Portela, Maria Conceicao A. & Thanassoulis, Emmanuel, 2005. "Profitability of a sample of Portuguese bank branches and its decomposition into technical and allocative components," European Journal of Operational Research, Elsevier, vol. 162(3), pages 850-866, May.
    6. Cooper, W.W. & Pastor, Jesus T. & Aparicio, Juan & Borras, Fernando, 2011. "Decomposing profit inefficiency in DEA through the weighted additive model," European Journal of Operational Research, Elsevier, vol. 212(2), pages 411-416, July.
    7. Leleu, Hervé & Briec, Walter, 2009. "A DEA estimation of a lower bound for firms' allocative efficiency without information on price data," International Journal of Production Economics, Elsevier, vol. 121(1), pages 203-211, September.
    8. 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.
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    Cited by:

    1. Nurhan Davutyan & Canan Yildirim, 2013. "Competitiveness in Turkish Banking: 2002-2011," Working Papers 774, Economic Research Forum, revised Sep 2013.

    More about this item

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D20 - Microeconomics - - Production and Organizations - - - General

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