IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/56778.html

Some searches may not work properly. We apologize for the inconvenience.

   My bibliography  Save this paper

Input aggregation bias in technical efficiency with multiple criteria analysis

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/56778/1/MPRA_paper_56778.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. repec:bla:scandj:v:87:y:1985:i:4:p:594-604 is not listed on IDEAS
    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.
    13. William W. Cooper & Lawrence M. Seiford & Kaoru Tone, 2007. "Data Envelopment Analysis," Springer Books, Springer, edition 0, number 978-0-387-45283-8, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zelenyuk, Valentin, 2020. "Aggregation of inputs and outputs prior to Data Envelopment Analysis under big data," European Journal of Operational Research, Elsevier, vol. 282(1), pages 172-187.
    2. Valentin Zelenyuk, 2019. "Data Envelopment Analysis and Business Analytics: The Big Data Challenges and Some Solutions," CEPA Working Papers Series WP072019, School of Economics, University of Queensland, Australia.
    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.
    4. Atkinson, Scott E. & Tsionas, Mike G., 2021. "Generalized estimation of productivity with multiple bad outputs: The importance of materials balance constraints," European Journal of Operational Research, Elsevier, vol. 292(3), pages 1165-1186.
    5. Wang, Ke & Wei, Yi-Ming & Huang, Zhimin, 2018. "Environmental efficiency and abatement efficiency measurements of China's thermal power industry: A data envelopment analysis based materials balance approach," European Journal of Operational Research, Elsevier, vol. 269(1), pages 35-50.
    6. Aldanondo, Ana M. & Casasnovas, Valero L., 2016. "A note on the impact of multiple input aggregators in technical efficiency estimation," MPRA Paper 75290, University Library of Munich, Germany.
    7. Hampf, Benjamin & Rødseth, Kenneth Løvold, 2019. "Environmental efficiency measurement with heterogeneous input quality: A nonparametric analysis of U.S. power plants," Energy Economics, Elsevier, vol. 81(C), pages 610-625.
    8. Hampf, Benjamin, 2018. "Cost and environmental efficiency of U.S. electricity generation: Accounting for heterogeneous inputs and transportation costs," Energy, Elsevier, vol. 163(C), pages 932-941.
    9. Andreas Eder, 2022. "Environmental efficiency measurement when producers control pollutants under heterogeneous conditions: a generalization of the materials balance approach," Journal of Productivity Analysis, Springer, vol. 57(2), pages 157-176, April.
    10. Andreas Eder, 2021. "Environmental efficiency measurement when producers control pollutants under heterogeneous conditions: a generalization of the materials balance approach," Working Papers 752021, University of Natural Resources and Life Sciences, Vienna, Department of Economics and Social Sciences, Institute for Sustainable Economic Development.
    11. repec:zbw:inwedp:752021 is not listed on IDEAS
    12. Ke Wang & Yi-Ming Wei & Zhimin Huang, 2017. "Environmental efficiency and abatement efficiency measurements of China¡¯s thermal power industry: A data envelopment analysis based materials balance approach," CEEP-BIT Working Papers 108, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    13. 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.
    14. Gómez-Calvet, Roberto & Conesa, David & Gómez-Calvet, Ana Rosa & Tortosa-Ausina, Emili, 2014. "Energy efficiency in the European Union: What can be learned from the joint application of directional distance functions and slacks-based measures?," Applied Energy, Elsevier, vol. 132(C), pages 137-154.
    15. Heinz Ahn & Peter Bogetoft & Ana Lopes, 2019. "Measuring potential sub-unit efficiency to counter the aggregation bias in benchmarking," Journal of Business Economics, Springer, vol. 89(1), pages 53-77, February.
    16. Ze Tian & Fang-Rong Ren & Qin-Wen Xiao & Yung-Ho Chiu & Tai-Yu Lin, 2019. "Cross-Regional Comparative Study on Carbon Emission Efficiency of China’s Yangtze River Economic Belt Based on the Meta-Frontier," IJERPH, MDPI, vol. 16(4), pages 1-19, February.
    17. Moriah Bostian & Rolf Färe & Shawna Grosskopf & Tommy Lundgren, 2022. "Prevention or cure? Optimal abatement mix," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 24(4), pages 503-531, October.
    18. Benjamin Hampf, 2018. "Measuring inefficiency in the presence of bad outputs: Does the disposability assumption matter?," Empirical Economics, Springer, vol. 54(1), pages 101-127, February.
    19. Valentin Zelenyuk, 2022. "Aggregation of Efficiency and Productivity: From Firm to Sector and Higher Levels," Springer Books, in: Subhash C. Ray & Robert G. Chambers & Subal C. Kumbhakar (ed.), Handbook of Production Economics, chapter 25, pages 1039-1079, Springer.
    20. Ke Wang & Zhifu Mi & Yi‐Ming Wei, 2019. "Will Pollution Taxes Improve Joint Ecological and Economic Efficiency of Thermal Power Industry in China?: A DEA‐Based Materials Balance Approach," Journal of Industrial Ecology, Yale University, vol. 23(2), pages 389-401, April.
    21. Kenneth Løvold Rødseth, 2017. "Environmental regulations and allocative efficiency: application to coal-to-gas substitution in the U.S. electricity sector," Journal of Productivity Analysis, Springer, vol. 47(2), pages 129-142, April.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:56778. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.