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Operational Research

, Volume 13, Issue 2, pp 271–287 | Cite as

Measuring productive efficiency using Nerlovian profit efficiency indicator and metafrontier analysis

  • Richard Mulwa
  • Ali Emrouznejad
Original Paper

Abstract

The aim of this paper is to illustrate the measurement of productive efficiency using Nerlovian indicator and metafrontier with data envelopment analysis techniques. Further, we illustrate how profit efficiency of firms operating in different regions can be aggregated into one overarching frontier. Sugarcane production in three regions in Kenya has been used to illustrate these concepts. Results show that the sources of inefficiency in all regions are both technical and allocative, but allocative efficiency contributes more to the overall Nerlovian (in)efficiency indicator.

Keywords

Productive efficiency Nerlovian profit efficiency indicator Metafrontier Data envelopment analysis Sugarcane farming 

Notes

Acknowledgments

The authors thank the anonymous reviewers and Professor Constantin Zopounidis, the editor of Operational Research: An International Journal for their insightful comments and suggestions.

References

  1. Battese GE, Rao DSP, O’Donnell CJ (2004) A metafrontier production function for estimation of technical efficiencies and technology gaps for firms operating under different technologies. J Prod Anal 21:91–103CrossRefGoogle Scholar
  2. Chambers RG, Chung Y, Färe R (1998) Profits, directional distance functions, and Nerlovian efficiency. J Optim Theory Appl 98(2):351–364CrossRefGoogle Scholar
  3. Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision-making units. Eur J Oper Res 2:429–444CrossRefGoogle Scholar
  4. Cherchye L, Kuosmanen T, Post T (2000) What is the economic meaning of FDH? A reply to thrall. J Prod Anal 13:263–267CrossRefGoogle Scholar
  5. Coelli T, Rao DSP, Battese GE (1998) An introduction to efficiency and productivity analysis. Kluwer Academic Publishers, DordrechtCrossRefGoogle Scholar
  6. Emrouznejad A (2005) Measurement efficiency and productivity in SAS/OR. J Comput Oper Res 32(7):1665–1683CrossRefGoogle Scholar
  7. Emrouznejad A, De Witte K (2010) COOPER-framework: a unified process for non-parametric projects. Eur J Oper Res 207(3):1573–1586CrossRefGoogle Scholar
  8. Emrouznejad A, Parker B, Tavares G (2008) Evaluation of research in efficiency and productivity: a survey and analysis of the first 30 years of scholarly literature in DEA. Socio-Econ Plan Sci 42(3):151–157CrossRefGoogle Scholar
  9. Färe R, Grosskopf S (1997) Profit efficiency, Farrell decompositions and Mahler inequality. Econ Lett 57:283–287CrossRefGoogle Scholar
  10. Färe R, Grosskopf S (2004) New directions: efficiency and productivity. Kluwer Academic Publishers, DordrechtGoogle Scholar
  11. Färe R, Grosskopf S, Weber W (1997) The effect of risk based capital requirements on profit efficiency in banking. Discussion paper series no. 97-12, Department of Economics, Southern Illinois University at CarbondaleGoogle Scholar
  12. Fukuyama H, Weber WL (2004) Efficiency and profitability in the Japanese. In: Färe R, Grosskopf S (eds) New directions: efficiency and productivity. Kluwer Academic Publishers, Dordrecht, pp 133–146Google Scholar
  13. Hayami Y, Ruttan VW (1971) Agricultural development: an international perspective. Johns Hopkins University Press, BaltimoreGoogle Scholar
  14. Lau LJ, Yotopoulos PA (1971) Test for relative efficiency and an application to Indian agriculture. Am Econ Rev 61:94–109Google Scholar
  15. Matawie KM, Assaf A (2008) A metafrontier model to assess regional efficiency differences. J Model Manag 3(3):268–276CrossRefGoogle Scholar
  16. Mulwa R (2006) Economic and environmental performance of sugarcane production in Kenya: non-parametric frontier approaches, farming and rural systems, vol 84. Magraf-Verlag Publishers, GermanyGoogle Scholar
  17. Mulwa R, Emrouznejad A, Muhammad L (2009) Economic efficiency of smallholder maize producers in western Kenya: a DEA meta-frontier analysis. Int J Oper Res 4(3):250–267CrossRefGoogle Scholar
  18. Nerlove M (1965) Estimation and identification of Cobb-Douglas production functions. Rand McNally Company, ChicagoGoogle Scholar
  19. Oh D (2010) A metafrontier approach for measuring an environmentally sensitive productivity growth index. Energy Econ 32(1):146–157CrossRefGoogle Scholar
  20. Portela MCAS, Thanassoulis E (2007) Developing a decomposable measure of profit efficiency using DEA. J Oper Res Soc 58:481–490CrossRefGoogle Scholar
  21. Rao DSP, Battese GE, O’Donnell CJ (2003) Metafrontier functions for the study of inter-regional productivity differences. Working paper series no. 01/2003. School of Economics, University of Queensland, AustraliaGoogle Scholar
  22. Varian HR (1992) Microeconomic analysis, 3rd edn. W. W. Norton and Company, NYGoogle Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  1. 1.University of NairobiNairobiKenya
  2. 2.Operations and Information Management Group, Aston Business SchoolAston UniversityAston, BirminghamUK

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