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The Law of One Price in Data Envelopment Analysis: Restricting Weight Flexibility Across Firms

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Author Info

  • Timo Kuosmanen

    (Wageningen University)

  • Laurens Cherchye

    (Catholic University of Leuven)

  • Timo Sipiläinen

    (Agrifood Research Finland)

Abstract

The Law of One Price (LoOP) states that all firms face the same prices for their inputs and outputs in the competitive market equilibrium. This law has powerful implications for productive efficiency analysis, which have remained unexploited thus far. This paper shows how LoOP-based weight restrictions can be incorporated in Data Envelopment Analysis (DEA). Utilizing the relation between the industry level and the firm level cost efficiency measures, we propose to apply a set of input prices that is common for all firms and that maximizes cost efficiency of the industry. Our framework allows for firm-specific output weights and variable returns-to-scale, and preserves the linear programming structure of the standard DEA. We apply the proposed methodology for evaluating research efficiency of economics departments of Dutch Universities. This application shows that the methodology is computationally tractable for practical efficiency analysis, and that it helps in deepening the DEA analysis.

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File URL: http://128.118.178.162/eps/mic/papers/0312/0312006.pdf
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Bibliographic Info

Paper provided by EconWPA in its series Microeconomics with number 0312006.

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Length: 29 pages
Date of creation: 18 Dec 2003
Date of revision: 18 Dec 2003
Handle: RePEc:wpa:wuwpmi:0312006

Note: Type of Document - pdf; pages: 29
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Web page: http://128.118.178.162

Related research

Keywords: Data Envelopment Analysis; Law of One Price; industry-level efficiency; weight restrictions; research efficiency;

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  1. SIMAR, Léopold & WILSON, Paul, 1995. "Sensitivity Analysis to Efficiency Scores : How to Bootstrap in Nonparametric Frontier Models," CORE Discussion Papers 1995043, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  2. Afriat, Sidney N, 1972. "Efficiency Estimation of Production Function," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 13(3), pages 568-98, October.
  3. Fare, Rolf & Grosskopf, Shawna, 1995. "Nonparametric tests of regularity, Farrell efficiency, and goodness-of-fit," Journal of Econometrics, Elsevier, vol. 69(2), pages 415-425, October.
  4. Laurens Cherchye & & Timo Kuosmanen, 2002. "Benchmarking Sustainable Development: A Synthetic Meta-index Approach," Others 0210001, EconWPA, revised 08 Oct 2002.
  5. 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.
  6. Banker, Rajiv D & Maindiratta, Ajay, 1988. "Nonparametric Analysis of Technical and Allocative Efficiencies in Production," Econometrica, Econometric Society, vol. 56(6), pages 1315-32, November.
  7. Peter Bogetoft, 1996. "DEA on Relaxed Convexity Assumptions," Management Science, INFORMS, vol. 42(3), pages 457-465, March.
  8. Fare, Rolf & Zelenyuk, Valentin, 2003. "On aggregate Farrell efficiencies," European Journal of Operational Research, Elsevier, vol. 146(3), pages 615-620, May.
  9. Per Agrell & Jørgen Tind, 2001. "A Dual Approach to Nonconvex Frontier Models," Journal of Productivity Analysis, Springer, vol. 16(2), pages 129-147, September.
  10. Blackorby, C. & Russell, R.R., 1996. "Aggregation of Efficiency Indices," G.R.E.Q.A.M. 96a25, Universite Aix-Marseille III.
  11. Seiford, Lawrence M. & Thrall, Robert M., 1990. "Recent developments in DEA : The mathematical programming approach to frontier analysis," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 7-38.
  12. 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.
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