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A Reverse Directional Distance Function to Reconcile Between Competing Efficiency Goals: An Application to Indian Manufacturing

Author

Listed:
  • Subhash C. Ray

    (University of Connecticut)

  • Kankana Mukherjee

    (Babson College)

Abstract

In analysis of technical efficiency in production, either the maximum proportional increase in all outputs or a minimal proportional scaling down of all inputs is generally selected as the criterion. In the recent literature, the Directional Distance Function combines the two objectives by increasing output and reducing input simultaneously to the extent possible. This paper introduces a Reverse Directional Distance Function as one possible approach to resolve the conflict between incompatible goals through a minimal adjustment applicable to both of the two objectives. In our empirical analysis we apply the proposed method to Indian manufacturing where increasing the output and conserving energy use are both highly important objectives. A comparison of the feasible targets obtained through the conventional and the Reverse Directional Distance Functions yields an estimated tradeoff between output increase and CO2 reduction (due to energy conservation) along the frontier.

Suggested Citation

  • Subhash C. Ray & Kankana Mukherjee, 2017. "A Reverse Directional Distance Function to Reconcile Between Competing Efficiency Goals: An Application to Indian Manufacturing," Working papers 2017-08, University of Connecticut, Department of Economics.
  • Handle: RePEc:uct:uconnp:2017-08
    Note: Subhash Ray is the corresponding author
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    File URL: http://web2.uconn.edu/economics/working/2017-08.pdf
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    References listed on IDEAS

    as
    1. Aparicio, Juan & Pastor, Jesus T. & Ray, Subhash C., 2013. "An overall measure of technical inefficiency at the firm and at the industry level: The ‘lost profit on outlay’," European Journal of Operational Research, Elsevier, vol. 226(1), pages 154-162.
    2. Tulkens, Henry & Vanden Eeckaut, Philippe, 1995. "Non-frontier measures of efficiency, progress and regress for time series data," International Journal of Production Economics, Elsevier, vol. 39(1-2), pages 83-97, April.
    3. Li, Xiao-Bai & Reeves, Gary R., 1999. "A multiple criteria approach to data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 115(3), pages 507-517, June.
    4. Kumar Mandal, Sabuj & Madheswaran, S., 2010. "Environmental efficiency of the Indian cement industry: An interstate analysis," Energy Policy, Elsevier, vol. 38(2), pages 1108-1118, February.
    5. Mukherjee, Kankana, 2010. "Measuring energy efficiency in the context of an emerging economy: The case of indian manufacturing," European Journal of Operational Research, Elsevier, vol. 201(3), pages 933-941, March.
    6. Chambers, Robert G. & Chung, Yangho & Fare, Rolf, 1996. "Benefit and Distance Functions," Journal of Economic Theory, Elsevier, vol. 70(2), pages 407-419, August.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Data Envelopment Analysis; Minimax Solution; Output Growth; Energy Saving;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • L60 - Industrial Organization - - Industry Studies: Manufacturing - - - General

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