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Setting handicaps to industrial sectors in DEA illustrated by Ethiopian industry

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  • Kidanemariam Berhe Hailu

    (National Graduate Institute for Policy Studies)

  • Kaoru Tone

    (National Graduate Institute for Policy Studies)

Abstract

In the ordinary macro-economic input-output tables, the industrial sector consists of several dozen industries and each industry in a certain sector is an aggregate of many companies in the sector. The sectoral statistics are the sum of statistics of companies in the respective sector. Usually, all sectors have the same set of inputs for producing outputs. For example, they have labour, capital and intermediate as input and amount of production as output. We can apply traditional DEA models for evaluation of efficiency regarding all sectors by means of these common input and output factors. However, there remain some insecure feelings in comparing all sectors as a scratch race. Some sectors are in fields with matured technologies, while others are in emerging fields. Some are labour intensive, while others are capital intensive. These situations lead us to compare sectors under a handicap race. In this paper, we propose a new DEA model based on the non-convex frontiers that all associated sectors may exhibit and handicaps are derived from. We apply this model to Ethiopian industry.

Suggested Citation

  • Kidanemariam Berhe Hailu & Kaoru Tone, 2014. "Setting handicaps to industrial sectors in DEA illustrated by Ethiopian industry," GRIPS Discussion Papers 14-07, National Graduate Institute for Policy Studies.
    • Handle: RePEc:ngi:dpaper:14-07
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    References listed on IDEAS

    as
    1. K. Tone & M. Tsutsui, 2015. "How to Deal with Non-Convex Frontiers in Data Envelopment Analysis," Journal of Optimization Theory and Applications, Springer, vol. 166(3), pages 1002-1028, September.
    2. Tetsushi Sonobe & John E. Akoten & Keijiro Otsuka, 2009. "An Exploration into the Successful Development of the Leather‐Shoe Industry in Ethiopia," Review of Development Economics, Wiley Blackwell, vol. 13(4), pages 719-736, November.
    3. Kaoru Tone & Miki Tsutsui, 2013. "How to deal with S-shaped curve in DEA," GRIPS Discussion Papers 13-10, National Graduate Institute for Policy Studies.
    4. Kaoru Tone & Biresh Sahoo, 2006. "Re-examining scale elasticity in DEA," Annals of Operations Research, Springer, vol. 145(1), pages 69-87, July.
    5. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    6. Rajiv D. Banker & Richard C. Morey, 1986. "Efficiency Analysis for Exogenously Fixed Inputs and Outputs," Operations Research, INFORMS, vol. 34(4), pages 513-521, August.
    7. Necmi Avkiran & Kaoru Tone & Miki Tsutsui, 2008. "Bridging radial and non-radial measures of efficiency in DEA," Annals of Operations Research, Springer, vol. 164(1), pages 127-138, November.
    8. Sebastián Lozano & Gabriel Villa, 2010. "Gradual technical and scale efficiency improvement in DEA," Annals of Operations Research, Springer, vol. 173(1), pages 123-136, January.
    9. O. Olesen & N. Petersen, 2009. "Target and technical efficiency in DEA: controlling for environmental characteristics," Journal of Productivity Analysis, Springer, vol. 32(1), pages 27-40, August.
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    Cited by:

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    2. Sanjeet Singh & Prabhat Ranjan, 2018. "Efficiency analysis of non-homogeneous parallel sub-unit systems for the performance measurement of higher education," Annals of Operations Research, Springer, vol. 269(1), pages 641-666, October.

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