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Measuring inefficiency in the presence of bad outputs: Does the disposability assumption matter?

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  • Benjamin Hampf

    () (Darmstadt University of Technology)

Abstract

Abstract Multiple approaches to incorporate environmental pollution in production technologies have been proposed. In this paper, we analyze the consequences of these theoretical models for applied research. Our comparison focuses on alternative disposability assumptions imposed on bad outputs and their effects on the measurement of productive efficiency. Using empirical data on electricity generation in the US, we quantify and analyze differences among the theoretical models. Moreover, Monte Carlo simulations are conducted to evaluate the ability of the models to correctly estimate inefficiency given varying specifications of the pollution-generating process.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:empeco:v:54:y:2018:i:1:d:10.1007_s00181-016-1204-3
    DOI: 10.1007/s00181-016-1204-3
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    1. Fernandez C. & Koop G. & Steel M.F.J., 2002. "Multiple-Output Production With Undesirable Outputs: An Application to Nitrogen Surplus in Agriculture," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 432-442, June.
    2. Benjamin Hampf & Jens J. Krüger, 2015. "Optimal Directions for Directional Distance Functions: An Exploration of Potential Reductions of Greenhouse Gases," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 97(3), pages 920-938.
    3. 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.
    4. Chien-Ming Chen & Magali A. Delmas, 2012. "Measuring Eco-Inefficiency: A New Frontier Approach," Operations Research, INFORMS, vol. 60(5), pages 1064-1079, October.
    5. Knox Lovell, C. A. & Pastor, Jesus T. & Turner, Judi A., 1995. "Measuring macroeconomic performance in the OECD: A comparison of European and non-European countries," European Journal of Operational Research, Elsevier, vol. 87(3), pages 507-518, December.
    6. Fare, Rolf & Grosskopf, Shawna, 2004. "Modeling undesirable factors in efficiency evaluation: Comment," European Journal of Operational Research, Elsevier, vol. 157(1), pages 242-245, August.
    7. Atakelty Hailu & Terrence S. Veeman, 2001. "Non-parametric Productivity Analysis with Undesirable Outputs: An Application to the Canadian Pulp and Paper Industry," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(3), pages 605-616.
    8. Jeanneaux, Philippe & Latruffe, Laure, 2016. "Modelling pollution-generating technologies in performance benchmarking: Recent developments, limits and future prospects in the nonparametric frameworkAuthor-Name: Dakpo, K. Hervé," European Journal of Operational Research, Elsevier, vol. 250(2), pages 347-359.
    9. Aparicio, Juan & Pastor, Jesus T. & Zofio, Jose L., 2013. "On the inconsistency of the Malmquist–Luenberger index," European Journal of Operational Research, Elsevier, vol. 229(3), pages 738-742.
    10. Timo Kuosmanen, 2005. "Weak Disposability in Nonparametric Production Analysis with Undesirable Outputs," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(4), pages 1077-1082.
    11. Dakpo, K & Jeanneaux, Philippe & Latruffee, Laure, 2015. "Empirical comparison of pollution generating technologies in nonparametric modelling: The case of greenhouse gas emissions in French sheep meat farming," 2015 Conference, August 9-14, 2015, Milan, Italy 211557, International Association of Agricultural Economists.
    12. Rolf Färe & Shawna Grosskopf, 2003. "Nonparametric Productivity Analysis with Undesirable Outputs: Comment," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(4), pages 1070-1074.
    13. Mark Andor & Frederik Hesse, 2014. "The StoNED age: the departure into a new era of efficiency analysis? A monte carlo comparison of StoNED and the “oldies” (SFA and DEA)," Journal of Productivity Analysis, Springer, vol. 41(1), pages 85-109, February.
    14. Hanley, Nick & Shogren, Jason, 2007. "Introduction," Journal of Forest Economics, Elsevier, vol. 13(2-3), pages 73-74, August.
    15. Reinhard, Stijn & Knox Lovell, C. A. & Thijssen, Geert J., 2000. "Environmental efficiency with multiple environmentally detrimental variables; estimated with SFA and DEA," European Journal of Operational Research, Elsevier, vol. 121(2), pages 287-303, March.
    16. 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.
    17. W. Liu & W. Meng & X. Li & D. Zhang, 2010. "DEA models with undesirable inputs and outputs," Annals of Operations Research, Springer, vol. 173(1), pages 177-194, January.
    18. repec:wly:japmet:v:31:y:2016:i:7:p:1407-1429 is not listed on IDEAS
    19. Fare, Rolf, et al, 1989. "Multilateral Productivity Comparisons When Some Outputs Are Undesirable: A Nonparametric Approach," The Review of Economics and Statistics, MIT Press, vol. 71(1), pages 90-98, February.
    20. Seiford, Lawrence M. & Zhu, Joe, 2002. "Modeling undesirable factors in efficiency evaluation," European Journal of Operational Research, Elsevier, vol. 142(1), pages 16-20, October.
    21. Cropper, Maureen L & Oates, Wallace E, 1992. "Environmental Economics: A Survey," Journal of Economic Literature, American Economic Association, vol. 30(2), pages 675-740, June.
    22. Emir Malikov & Subal C. Kumbhakar & Mike G. Tsionas, 2016. "A Cost System Approach to the Stochastic Directional Technology Distance Function with Undesirable Outputs: The Case of us Banks in 2001–2010," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1407-1429, November.
    23. Hampf, Benjamin, 2015. "Estimating the materials balance condition: A stochastic frontier approach," Darmstadt Discussion Papers in Economics 226, Darmstadt University of Technology, Department of Law and Economics.
    24. Chambers, Robert G. & Chung, Yangho & Fare, Rolf, 1996. "Benefit and Distance Functions," Journal of Economic Theory, Elsevier, vol. 70(2), pages 407-419, August.
    25. Mekaroonreung, Maethee & Johnson, Andrew L., 2012. "Estimating the shadow prices of SO2 and NOx for U.S. coal power plants: A convex nonparametric least squares approach," Energy Economics, Elsevier, vol. 34(3), pages 723-732.
    26. Scheel, Holger, 2001. "Undesirable outputs in efficiency valuations," European Journal of Operational Research, Elsevier, vol. 132(2), pages 400-410, July.
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    More about this item

    Keywords

    Environmental pollution; Nonparametric efficiency analysis; Technology modeling; Disposability assumptions;

    JEL classification:

    • Q53 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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