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Input aggregation and computed technical efficiency

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  • Loren Tauer

Abstract

Using data simulated from a random production function it is shown that technical efficiency estimates computed by Data Envelopment Analysis are biased even if the exact aggregator function is used to aggregate inputs.

Suggested Citation

  • Loren Tauer, 2001. "Input aggregation and computed technical efficiency," Applied Economics Letters, Taylor & Francis Journals, vol. 8(5), pages 295-297.
    • Handle: RePEc:taf:apeclt:v:8:y:2001:i:5:p:295-297
    DOI: 10.1080/135048501750157422
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    References listed on IDEAS

    as
    1. Cornes,Richard, 1992. "Duality and Modern Economics," Cambridge Books, Cambridge University Press, number 9780521336017.
    2. Zvi Griliches, 1957. "Specification Bias in Estimates of Production Functions," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 39(1), pages 8-20.
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Ma, Shuzhong & Feng, Han, 2013. "Will the decline of efficiency in China's agriculture come to an end? An analysis based on opening and convergence," China Economic Review, Elsevier, vol. 27(C), pages 179-190.
    5. 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.
    6. Monchuk, Daniel C. & Chen, Zhuo & Bonaparte, Yosef, 2010. "Explaining production inefficiency in China's agriculture using data envelopment analysis and semi-parametric bootstrapping," China Economic Review, Elsevier, vol. 21(2), pages 346-354, June.
    7. 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.
    8. Byma, Justin P. & Tauer, Loren W., 2007. "Farm Inefficiency Resulting from the Missing Management Input," Working Papers 127008, Cornell University, Department of Applied Economics and Management.
    9. 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.
    10. Ma, Hengyun & Rae, Allan N. & Huang, Jikun, 2004. "Livestock Productivity In China: Data Revision And Total Factor Productivity Decomposition," China Agriculture Project Working Papers 23691, Massey University, Centre for Applied Economics and Policy Studies.
    11. 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.
    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. 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.
    15. 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.
    16. Kim-Huong Nguyen & Tim Coelli, 2009. "Quantifying the effects of modelling choices on hospital efficiency measures: A meta-regression analysis," CEPA Working Papers Series WP072009, School of Economics, University of Queensland, Australia.
    17. Christine E Whitt & Loren W Tauer & Heather Huson, 2019. "Bull efficiency using dairy genetic traits," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-14, November.
    18. 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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