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Aggregation bias and its bounds in measuring technical efficiency

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  • Rolf Fare
  • Shawna Grosskopf
  • Valentin Zelenyuk

Abstract

The discussion about the bias due to input (or output) aggregation in efficiency measurement based on data envelopment analysis, recently revisited by Tauer (2001) and Fare and Zelenyuk (2002) is continued. Attention is focused on the direction and the bounds of the aggregation bias.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:apeclt:v:11:y:2004:i:10:p:657-660
    DOI: 10.1080/1350485042000207243
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    References listed on IDEAS

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    1. Loren Tauer, 2001. "Input aggregation and computed technical efficiency," Applied Economics Letters, Taylor & Francis Journals, vol. 8(5), pages 295-297.
    2. Rolf Fare & Valentin Zelenyuk, 2002. "Input aggregation and technical efficiency," Applied Economics Letters, Taylor & Francis Journals, vol. 9(10), pages 635-636.
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    Citations

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    Cited by:

    1. Valentin Zelenyuk, 2023. "Productivity analysis: roots, foundations, trends and perspectives," Journal of Productivity Analysis, Springer, vol. 60(3), pages 229-247, December.
    2. Darold Barnum & John Gleason, 2005. "Technical efficiency bias caused by intra-input aggregation in data envelopment analysis," Applied Economics Letters, Taylor & Francis Journals, vol. 12(13), pages 785-788.
    3. 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.
    4. A. M. Aldanondo & V. L. Casasnovas, 2015. "Input aggregation bias in technical efficiency with multiple criteria analysis," Applied Economics Letters, Taylor & Francis Journals, vol. 22(6), pages 430-435, April.
    5. Natalya Zelenyuk & Valentin Zelenyuk, 2015. "Productivity Drivers of Efficiency in Banking: Importance of Model Specifications," CEPA Working Papers Series WP082015, School of Economics, University of Queensland, Australia.
    6. 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.
    7. 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.
    8. Darold T. Barnum & John M. Gleason, 2007. "Technical efficiency bias in data envelopment analysis caused by intra-output aggregation," Applied Economics Letters, Taylor & Francis Journals, vol. 14(9), pages 623-626.
    9. Robin Cross & Rolf Färe, 2008. "Farrell efficiency under value and quantity data," Journal of Productivity Analysis, Springer, vol. 29(3), pages 193-199, June.
    10. 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.
    11. Pavlo Demchuk & Valentin Zelenyuk, 2009. "Testing differences in efficiency of regions within a country: the case of Ukraine," Journal of Productivity Analysis, Springer, vol. 32(2), pages 81-102, October.
    12. Ali Homayoni & Reza Fallahnejad & Farhad Hosseinzadeh Lotfi, 2022. "Cross Malmquist Productivity Index in Data Envelopment Analysis," 4OR, Springer, vol. 20(4), pages 567-602, December.
    13. Darold Barnum & John Gleason, 2006. "Measuring efficiency in allocating inputs among outputs with DEA," Applied Economics Letters, Taylor & Francis Journals, vol. 13(6), pages 333-336.
    14. Colamartino, Chiara & Dipierro, Anna Rita & Toma, Pierluigi & Frittelli, Massimo, 2023. "What lies behind the success of Italian GIs products? Questioning tradition in consortia via aggregated conditional efficiency," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    15. 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.
    16. Emil Heesche & Mette Asmild, 2022. "Implications of Aggregation Uncertainty in DEA," IFRO Working Paper 2022/02, University of Copenhagen, Department of Food and Resource Economics.
    17. Subhash C. Ray, 2022. "Choice of Inputs and Outputs for Production Analysis," Springer Books, in: Subhash C. Ray & Robert G. Chambers & Subal C. Kumbhakar (ed.), Handbook of Production Economics, chapter 26, pages 1083-1116, Springer.
    18. 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.
    19. 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.
    20. 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.

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