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Measuring Productive Efficiency: An Application to Illinois Strip Mines

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Author Info

  • P. Byrnes

    (Department of Economics, Southern Illinois University, Carbondale, Illinois 62901)

  • R. Färe

    (Department of Economics, Southern Illinois University, Carbondale, Illinois 62901)

  • S. Grosskopf

    (Department of Economics, Southern Illinois University, Carbondale, Illinois 62901)

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    Abstract

    The purpose of this paper is to apply a generalized version of the Farrell measure of technical efficiency to a sample of Illinois strip mines. We disaggregate the original Farrell measure (which was designed to measure lost output or wasted inputs due to underutilization of inputs) into three mutually exclusive and exhaustive components: (1) a measure of purely technical efficiency, (2) a measure of input congestion (overutilization of some input(s)) and (3) a measure of scale efficiency. This approach has the advantage that it provides additional information on the sources of inefficiency of production, which should be useful to managers, in general, not only in strip mining. Simple linear programming techniques are derived and used in calculating these efficiency measures for our sample. We find that Illinois strip mines are fairly efficient (relative to each other). The major source of inefficiency was due to deviations from the optimal scale of production. We also find suggestive evidence that inefficient mines tend to have relatively high stripping ratios, high labor-output ratios, single rather than multiple coal seams, and lower earth-moving capacity.

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    File URL: http://dx.doi.org/10.1287/mnsc.30.6.671
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    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 30 (1984)
    Issue (Month): 6 (June)
    Pages: 671-681

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    Handle: RePEc:inm:ormnsc:v:30:y:1984:i:6:p:671-681

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    Keywords: technical efficiency; mining; linear programming; farrell measure; congestion; scale efficiency;

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    Cited by:
    1. J. David Cummins & Hongmin Zi, 1997. "Comparison of Frontier Efficiency Methods: An Application to the U.S. Life Insurance Industry," Center for Financial Institutions Working Papers 97-03, Wharton School Center for Financial Institutions, University of Pennsylvania.
    2. Lee, Hsuan-Shih & Zhu, Joe, 2012. "Super-efficiency infeasibility and zero data in DEA," European Journal of Operational Research, Elsevier, vol. 216(2), pages 429-433.
    3. C. Lovell & Shawna Grosskopf & Eduardo Ley & Jesús Pastor & Diego Prior & Philippe Eeckaut, 1994. "Linear programming approaches to the measurement and analysis of productive efficiency," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 2(2), pages 175-248, December.
    4. Tauer, Loren W. & Hanchar, John J., 1993. "Nonparametric Technical Efficiency with N Firms and M Inputs: A Simulation," Staff Papers 121333, Cornell University, Department of Applied Economics and Management.
    5. Sylvain Bouhnik & Boaz Golany & Ury Passy & Steven Hackman & Dimitra Vlatsa, 2001. "Lower Bound Restrictions on Intensities in Data Envelopment Analysis," Journal of Productivity Analysis, Springer, vol. 16(3), pages 241-261, November.
    6. Zhu, Joe, 2000. "Multi-factor performance measure model with an application to Fortune 500 companies," European Journal of Operational Research, Elsevier, vol. 123(1), pages 105-124, May.
    7. Tony Flegg & David O. Allen, 2006. "Does Expansion Cause Congestion? The Case of the Older British Universities, 1994 to 2004," Working Papers 0605, Department of Accounting, Economics and Finance, Bristol Business School, University of the West of England, Bristol.
    8. Ertürk, Mehmet & Türüt-AsIk, Serap, 2011. "Efficiency analysis of Turkish natural gas distribution companies by using data envelopment analysis method," Energy Policy, Elsevier, vol. 39(3), pages 1426-1438, March.
    9. Tony Flegg & David O. Allen, 2006. "Are the New British Universities Congested?," Working Papers 0610, Department of Accounting, Economics and Finance, Bristol Business School, University of the West of England, Bristol.
    10. AT Flegg & DO Allen & K Field & TW Thurlow, 2003. "Measuring the Efficiency and Productivity of British Universities: An Application of DEA and the Malmquist Approach," Working Papers 0304, Department of Accounting, Economics and Finance, Bristol Business School, University of the West of England, Bristol.
    11. Flegg, A.T. & Allen, D.O., 2009. "Congestion in the Chinese automobile and textile industries revisited," Socio-Economic Planning Sciences, Elsevier, vol. 43(3), pages 177-191, September.
    12. Guan, Jiancheng & Chen, Kaihua, 2012. "Modeling the relative efficiency of national innovation systems," Research Policy, Elsevier, vol. 41(1), pages 102-115.
    13. Tony Flegg & David O. Allen, 2006. "An Examination of Alternative Approaches to Measuring Congestion in British Universities," Working Papers 0606, Department of Accounting, Economics and Finance, Bristol Business School, University of the West of England, Bristol.
    14. Simões, Pedro & Cunha Marques, Rui, 2009. "Performance and Congestion Analysis of the Portuguese Hospital Services," MPRA Paper 16940, University Library of Munich, Germany.
    15. Fang, Hong & Wu, Junjie & Zeng, Catherine, 2009. "Comparative study on efficiency performance of listed coal mining companies in China and the US," Energy Policy, Elsevier, vol. 37(12), pages 5140-5148, December.
    16. Banker, Rajiv D. & Chang, Hsihui & Cooper, William W., 1996. "Equivalence and implementation of alternative methods for determining returns to scale in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 89(3), pages 473-481, March.
    17. Yunos, Jamaluddin Mohd & Hawdon, David, 1997. "The efficiency of the National Electricity Board in Malaysia: An intercountry comparison using DEA," Energy Economics, Elsevier, vol. 19(2), pages 255-269, May.
    18. Tony Flegg & David O Allen, 2004. "An Examination of Alternative Approaches to Measuring Congestion in British Universities," Working Papers 0407, Department of Accounting, Economics and Finance, Bristol Business School, University of the West of England, Bristol.
    19. Rubio-Misas, María, 2009. "Productividad y eficiencia de las Mutualidades de Previsión Social/Productivity and Efficiency of Social Benefit Institutions," Estudios de Economía Aplicada, Estudios de Economía Aplicada, vol. 27, pages 571 (30 Pá, Agosto.
    20. Tsolas, Ioannis E., 2011. "Performance assessment of mining operations using nonparametric production analysis: A bootstrapping approach in DEA," Resources Policy, Elsevier, vol. 36(2), pages 159-167, June.
    21. Tony Flegg & David O. Allen, 2006. "Does it matter How We Measure Congestion?," Working Papers 0614, Department of Accounting, Economics and Finance, Bristol Business School, University of the West of England, Bristol.
    22. Kao, Chiang, 2010. "Congestion measurement and elimination under the framework of data envelopment analysis," International Journal of Production Economics, Elsevier, vol. 123(2), pages 257-265, February.
    23. Subhash Ray, 2002. "William W. Cooper: A Legend in His Own Times," Journal of Productivity Analysis, Springer, vol. 17(1), pages 7-12, January.
    24. Thompson, Russell G. & Dharmapala, P. S. & Thrall, Robert M., 1995. "Linked-cone DEA profit ratios and technical efficiency with application to Illinois coal mines," International Journal of Production Economics, Elsevier, vol. 39(1-2), pages 99-115, April.

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