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Input aggregation bias in technical efficiency with multiple criteria analysis

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  • Casasnovas, Valero L.
  • Aldanondo, Ana M.

Abstract

We extend the Tauer (2001) and Färe et al. (2004) analyses of aggregation bias in technical efficiency measurement to multiple criteria decision analysis. We show input aggregation conditions consistent with multiple criteria evaluation of overall efficiency in conjunction with variation in aggregation bias.

Suggested Citation

  • Casasnovas, Valero L. & Aldanondo, Ana M., 2014. "Input aggregation bias in technical efficiency with multiple criteria analysis," MPRA Paper 56778, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:56778
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    References listed on IDEAS

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    2. repec:bla:scandj:v:85:y:1983:i:2:p:181-90 is not listed on IDEAS
    3. Toshiyuki Sueyoshi, 1999. "DEA Duality on Returns to Scale (RTS) in Production and Cost Analyses: An Occurrence of Multiple Solutions and Differences Between Production-Based and Cost-Based RTS Estimates," Management Science, INFORMS, vol. 45(11), pages 1593-1608, November.
    4. Benjamin Hampf, 2014. "Separating environmental efficiency into production and abatement efficiency: a nonparametric model with application to US power plants," Journal of Productivity Analysis, Springer, vol. 41(3), pages 457-473, June.
    5. Loren Tauer, 2001. "Input aggregation and computed technical efficiency," Applied Economics Letters, Taylor & Francis Journals, vol. 8(5), pages 295-297.
    6. Tone, Kaoru & Tsutsui, Miki, 2010. "An epsilon-based measure of efficiency in DEA - A third pole of technical efficiency," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1554-1563, December.
    7. Rolf Fare & Valentin Zelenyuk, 2002. "Input aggregation and technical efficiency," Applied Economics Letters, Taylor & Francis Journals, vol. 9(10), pages 635-636.
    8. Tim Coelli & Ludwig Lauwers & Guido Huylenbroeck, 2007. "Environmental efficiency measurement and the materials balance condition," Journal of Productivity Analysis, Springer, vol. 28(1), pages 3-12, October.
    9. Hampf, Benjamin, 2014. "Separating Environmental Efficiency into Production and Abatement Efficiency - A Nonparametric Model with Application to U.S. Power Plants," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 69997, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    10. Rolf Fare & Shawna Grosskopf & Valentin Zelenyuk, 2004. "Aggregation bias and its bounds in measuring technical efficiency," Applied Economics Letters, Taylor & Francis Journals, vol. 11(10), pages 657-660.
    11. Kaoru Tone & Miki Tsutsui, 2010. "An epsilon-based measure of efficiency in DEA revisited -A third pole of technical efficiency," GRIPS Discussion Papers 09-21, National Graduate Institute for Policy Studies.
    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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    Cited by:

    1. Aldanondo, Ana M. & Casasnovas, Valero L., 2015. "More is better than one: the impact of different numbers of input aggregators in technical efficiency estimation," MPRA Paper 64120, University Library of Munich, Germany.
    2. Aldanondo-Ochoa, Ana M. & Casasnovas-Oliva, Valero L. & Almansa-Sáez, M. Carmen, 2017. "Cross-constrained Measuring the Cost-environment Efficiency in Material Balance Based Frontier Models," Ecological Economics, Elsevier, vol. 142(C), pages 46-55.
    3. Aldanondo, Ana M. & Casasnovas, Valero L. & Almansa, M. Carmen, 2016. "Cost-constrained measures of environmental efficiency: a material balance approach," MPRA Paper 72490, University Library of Munich, Germany.

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    More about this item

    Keywords

    Data Envelopment Analysis; Input aggregation; multiple objectives;
    All these keywords.

    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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