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A decomposition of profit inefficiency into price expectation error, preferences towards risk and technical inefficiency

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  • Jean-Philippe Boussemart

    (IESEG School of Management (LEM-CNRS) and University Lille 3)

  • David Crainich

    (CNRS-LEM (UMR 8179) and IESEG School of Management)

  • Hervé Leleu

    ()
    (CNRS-LEM (UMR 8179) and IESEG School of Management)

Abstract

The paper addresses the decomposition of firms’ profit inefficiency (i.e. the difference between the observed profit and the maximal profit that could have been earned) in a context of output price uncertainty. More precisely, we separate this inefficiency into price expectation error, expected profit loss due to risk preference and technical inefficiency. Within this decomposition, the allocative inefficiency is explicitly defined as the result of price expectation error and risk attitude instead of being a residual (as in the traditional profit inefficiency decomposition). Our theoretical model is then implemented in a Data Envelopment Analysis framework which allows the separate estimation of each term of the decomposition. Besides, we offer an operational tool to reveal producers’ risk preferences and to measure their intensity. While the DEA approach is appealing since it imposes very few assumptions on the production set, its main drawback lies in the sensitivity of the measure to outliers. We therefore adapt our model to a robust approach.

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Bibliographic Info

Paper provided by IESEG School of Management in its series Working Papers with number 2012-ECO-04.

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Length: 19 pages
Date of creation: Mar 2012
Date of revision:
Handle: RePEc:ies:wpaper:e201204

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Related research

Keywords: Profit Inefficiency; Allocative Inefficiency; Technical Inefficiency; Risk Preference; Risk Aversion; Data Envelopment Analysis; Robust DEA;

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  1. Subal C. Kumbhakar, 2002. "Risk preference and productivity measurement under output price uncertainty," Empirical Economics, Springer, vol. 27(3), pages 461-472.
  2. 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.
  3. Chambers, Robert G, 1983. "Scale and Productivity Measurement under Risk," American Economic Review, American Economic Association, vol. 73(4), pages 802-05, September.
  4. Dervaux, B. & Leleu, H. & Minvielle, E. & Valdmanis, V. & Aegerter, P. & Guidet, B., 2009. "Performance of French intensive care units: A directional distance function approach at the patient level," International Journal of Production Economics, Elsevier, vol. 120(2), pages 585-594, August.
  5. Sandmo, Agnar, 1971. "On the Theory of the Competitive Firm under Price Uncertainty," American Economic Review, American Economic Association, vol. 61(1), pages 65-73, March.
  6. Robert G. Chambers & John Quiggin, 2002. "The State-Contingent Properties of Stochastic Production Functions," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 84(2), pages 513-526.
  7. Fandel, Günter & Lorth, Michael, 2009. "On the technical (in)efficiency of a profit maximum," International Journal of Production Economics, Elsevier, vol. 121(2), pages 409-426, October.
  8. Cazals, Catherine & Florens, Jean-Pierre & Simar, Leopold, 2002. "Nonparametric frontier estimation: a robust approach," Journal of Econometrics, Elsevier, vol. 106(1), pages 1-25, January.
  9. Batra, Raveendra N & Ullah, Aman, 1974. "Competitive Firm and the Theory of Input Demand under Price Uncertainty," Journal of Political Economy, University of Chicago Press, vol. 82(3), pages 537-48, May/June.
  10. Just, Richard E. & Pope, Rulon D., 1978. "Stochastic specification of production functions and economic implications," Journal of Econometrics, Elsevier, vol. 7(1), pages 67-86, February.
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