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Value And Quantity Data In Economic And Technical Efficiency Measurement

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  • Maria Conceição Portela

    () (Faculdade de Economia e Gestão, Universidade Católica Portuguesa - Porto)

Abstract

This paper calls attention to the implications of using cost or revenue data in efficiency measurement through Data Envelopment Analysis (DEA). In many empirical settings this issue arises, since frequently input and/or output quantities or prices are not available, and only value measures are available. When value data are used in technical efficiency models it is clearly questionable what sort of efficiency measure is being computed, since it cannot be a productive efficiency measure (that takes into account only quantities of inputs and outputs), and it cannot be either a traditional cost or revenue efficiency measure (that considers prices of factors disagregated from quantities). In this paper we call the attention for this fact and address situations where only value data are available, and situations where for some inputs (outputs) there is quantity information and for others there is value information. The main contribution of this paper is a reconciliation of previous literature on analysing these issues and on providing some guidelines on what to do and not do, when these issues arise in a data set.

Suggested Citation

  • Maria Conceição Portela, 2013. "Value And Quantity Data In Economic And Technical Efficiency Measurement," Working Papers de Economia (Economics Working Papers) 03, Católica Porto Business School, Universidade Católica Portuguesa.
  • Handle: RePEc:cap:wpaper:032013
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    References listed on IDEAS

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    1. Cross, Robin M. & Färe, Rolf, 2009. "Value data and the Bennet price and quantity indicators," Economics Letters, Elsevier, vol. 102(1), pages 19-21, January.
    2. Robin Cross & Rolf Färe, 2008. "Farrell efficiency under value and quantity data," Journal of Productivity Analysis, Springer, pages 193-199.
    3. Portela, Maria Conceição A. Silva & Thanassoulis, Emmanuel, 2014. "Economic efficiency when prices are not fixed: disentangling quantity and price efficiency," Omega, Elsevier, vol. 47(C), pages 36-44.
    4. Rajiv Banker & Hsihui Chang & Ram Natarajan, 2007. "Estimating DEA technical and allocative inefficiency using aggregate cost or revenue data," Journal of Productivity Analysis, Springer, pages 115-121.
    5. Rolf Fare & Valentin Zelenyuk, 2002. "Input aggregation and technical efficiency," Applied Economics Letters, Taylor & Francis Journals, vol. 9(10), pages 635-636.
    6. 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.
    7. Rolf Färe & Shawna Grosskopf & Hyunok Lee, 1990. "A Nonparametric Approach to Expenditure-Constrained Profit Maximization," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 72(3), pages 574-581.
    8. Ray, Subhash C. & Chen, Lei & Mukherjee, Kankana, 2008. "Input price variation across locations and a generalized measure of cost efficiency," International Journal of Production Economics, Elsevier, vol. 116(2), pages 208-218, December.
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    Keywords

    Data Envelopment Analysis; cost efficiency; revenue efficiency;

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