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Inefficiency

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Abstract

We introduce an ordinal model of efficiency measurement. Our primitive is a notion of efficiency that is comparative, but not cardinal or absolute. In this framework, we postulate axioms that we believe an ordinal efficiency measure should satisfy. Primary among these are choice consistency and planning consistency, which guide the measurement of efficiency in a firm with access to multiple technologies. Other axioms include symmetry, which states that the names of commodities do not matter, scale-invariance, which says that units of measurement of commodities does not matter, and strong monotonicity, which states that efficiency should decrease if the inputs and outputs remain static when the technology becomes unambiguously more efficient. These axioms characterize a unique ordinal efficiency measure which is represented by the coefficient of resource utilization. By replacing symmetry (the weakest of our axioms) with a very mild continuity condition, we obtain a family of path-based measures.

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  • Chambers, Christopher P. & Miller, Alan D., "undated". "Inefficiency," Working Papers WP2011/14, University of Haifa, Department of Economics, revised 30 Nov 2011.
  • Handle: RePEc:haf:huedwp:wp201114
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    1. Dinko Dimitrov & Thierry Marchant & Debasis Mishra, 2012. "Separability and aggregation of equivalence relations," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 51(1), pages 191-212, September.
    2. R. Russell & William Schworm, 2009. "Axiomatic foundations of efficiency measurement on data-generated technologies," Journal of Productivity Analysis, Springer, vol. 31(2), pages 77-86, April.
    3. Christopher Chambers & Alan Miller, 2011. "Rules for aggregating information," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 36(1), pages 75-82, January.
    4. Miller, Alan D., 2008. "Group identification," Games and Economic Behavior, Elsevier, vol. 63(1), pages 188-202, May.
    5. Bruno Leclerc & Bernard Monjardet, 2010. "Aggregation and residuation," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00504982, HAL.
    6. R. Russell & William Schworm, 2011. "Properties of inefficiency indexes on 〈input, output〉 space," Journal of Productivity Analysis, Springer, vol. 36(2), pages 143-156, October.
    7. Aly, Hassan Y, et al, 1990. "Technical, Scale, and Allocative Efficiencies in U.S. Banking: An Empirical Investigation," The Review of Economics and Statistics, MIT Press, vol. 72(2), pages 211-218, May.
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    Cited by:

    1. Ilya Segal & Susan Athey, 2007. "Designing Efficient Mechanisms for Dynamic Bilateral Trading Games," American Economic Review, American Economic Association, vol. 97(2), pages 131-136, May.
    2. Robert G. Chambers & Atakelty Hailu & John Quiggin, 2011. "Event‐specific data envelopment models and efficiency analysis," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 55(1), pages 90-106, January.
    3. repec:kap:ijhcfe:v:17:y:2017:i:2:d:10.1007_s10754-016-9207-3 is not listed on IDEAS
    4. Andrzej Skrzypacz & Juuso Toikka, 2015. "Mechanisms for Repeated Trade," American Economic Journal: Microeconomics, American Economic Association, vol. 7(4), pages 252-293, November.
    5. David Adamson & Thilak Mallawaarachchi & John Quiggin, 2007. "Water use and salinity in the Murray-Darling Basin: A state-contingent model ," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 51(3), pages 263-281, September.
    6. Salim, Ruhul A. & Islam, Nazrul, 2010. "Exploring the impact of R&D and climate change on agricultural productivity growth: the case of Western Australia," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 54(4), December.
    7. Hughes, Neal & Lawson, Kenton & Davidson, Alistair & Jackson, Tom & Sheng, Yu, 2011. "Productivity pathways: climate-adjusted production frontiers for the Australian broadacre cropping industry," 2011 Conference (55th), February 8-11, 2011, Melbourne, Australia 100563, Australian Agricultural and Resource Economics Society.
    8. Huettel, Silke & Narayana, Rashmi & Odening, Martin, 2011. "Measuring dynamic efficiency under uncertainty," Structural Change in Agriculture/Strukturwandel im Agrarsektor (SiAg) Working Papers 129062, Humboldt University Berlin, Department of Agricultural Economics.
    9. Mette Asmild & Tomas Baležentis & Jens Leth Hougaard, 2016. "Multi-directional productivity change: MEA-Malmquist," Journal of Productivity Analysis, Springer, vol. 46(2), pages 109-119, December.
    10. Robert Chambers & John Quiggin, 2007. "Information value and efficiency measurement for risk-averse firms," Journal of Productivity Analysis, Springer, vol. 27(3), pages 197-208, June.

    More about this item

    Keywords

    Efficiency Measurement; Coefficient of Resource Utilization; Ordinal; Choice Consistency; Planning Consistency; Path-based;

    JEL classification:

    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

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