Journal of Productivity Analysis

, Volume 37, Issue 2, pp 155–169 | Cite as

Explaining firms efficiency in the Ivorian manufacturing sector: a robust nonparametric approach

  • Nolwenn Roudaut
  • Anne Vanhems


In this paper, we provide an analysis of Côte d’Ivoire firms performances and study the impact of qualitative external environmental factors on firm efficiencies. We adapt the one-step nonparametric robust methodology of Daraio and Simar 2005 to take in account qualitative environmental factors and we also compare the differences of behavior among two sub groups of firms characterized by different levels of technology. The sensitivity of our conclusions to environmental factors is analyzed using a bootstrapped test. We also check the robustness of our results upon time on two different years of observations.


Firm efficiency Nonparametric estimation Conditional expected frontier of order m 

JEL Classification

C13 C14 D20 



We are grateful to Leopold Simar and Jan Johannes for stimulating conversations, suggestions and advices. We also thank three anonymous referees and an associate editor for helpful comments.


  1. Aitchison J, Aitken CGG (1976) Multivariate binary discrimination by the kernel method. Biometrika 63:413–420CrossRefGoogle Scholar
  2. Aragon Y, Daouia A, Thomas-Agnan C (2005) Nonparametric Frontier estimation: a conditional quantile-based approach. Econ Theory 21:358–389CrossRefGoogle Scholar
  3. Azam J-P, Morisson C (1994) The political feasibility of adjustment in Côte d’Ivoire and in Morocco. Development Centre Studies, OECD, ParisGoogle Scholar
  4. Badin L, Daraio C (2010) Explaining efficiency in nonparametric frontier models: recent developments in statistical inference. In: Van Keilegom I, Wilson PW (eds) Exploring research frontiers in contemporary statistics and econometrics. Physica-Verlag, Berlin (forthcoming)Google Scholar
  5. Badin L, Daraio C, Simar L (2010) Optimal bandwidth selection for conditional efficiency measures: a data-driven approach. Eur J Oper Res 201(2):633–640CrossRefGoogle Scholar
  6. Battese GE, Lundvall K (2000) Firm size, age and efficiency: evidence from Kenyan manufacturing firms. J Dev Stud 36(3):146–163CrossRefGoogle Scholar
  7. Cazals C, Florens J-P, Simar L (2002) Nonparametric frontier rstimation: a robust approach. J Econ 106:1–25Google Scholar
  8. Chapelle K, Plane P (2005) Productive efficiency in the Ivorian manufacturing sector: an exploratory study using a data envelopment analysis approach. Dev Econ 43(4):450–471CrossRefGoogle Scholar
  9. Cooper WW, Seiford LM, Tone K (2000) Data envelopment analysis: a comprehensive text with models, applications, references and DEA-solver software. Kluwer, BostonGoogle Scholar
  10. Daouia A, Simar L (2007) Nonparametric efficiency analysis: a multivariate conditional quantile approach. J Econ 140:375–400Google Scholar
  11. Daraio C, Simar L (2005) Introducing environmental variables in nonparametric frontier models: a probabilistic approach. J Prod Anal 24:93–121CrossRefGoogle Scholar
  12. Daraio C, Simar L (2007) Conditional nonparametric frontier models for convex and non convex technologies: a unifying approach. J Prod Anal 28:13–32CrossRefGoogle Scholar
  13. Daraio C, Simar L (2007) Advanced robust and nonparametric methods in efficiency analysis: methodology and applications. Springer, BerlinGoogle Scholar
  14. Daraio C, Simar L, Wilson PW (2010) Testing whether two-stage estimation is meaningful in non-parametric models of production. ISBA Discussion paper, No. 1031Google Scholar
  15. Debreu G (1951) The coefficient of resource utilization. Econometrica 19:273–292CrossRefGoogle Scholar
  16. Farrell MJ (1957) The measurement of productive efficiency. J R Stat Soc A 120:253–281CrossRefGoogle Scholar
  17. Florens J-P, Simar L (2005) Parametric approximations of nonparametric frontiers. J Econ 124(1):91–116Google Scholar
  18. Fried HO, Schmidt SS, Yaisawarng S (1999) Incorporating the operating environment into a nonparametric measure of technical efficiency. J Prod Anal 12:249–267CrossRefGoogle Scholar
  19. Fried HO, Lovell CAK, Schmidt SS, Yaisawarng S (2002) Accounting for environmental effects and statistical noise in data envelopment analysis. J Prod Anal 17:157–174CrossRefGoogle Scholar
  20. Hall P, Racine JS, Li Q (2004) Cross-validation and the estimation of conditional probability densities. J Am Stat Assoc 99:1015–1026CrossRefGoogle Scholar
  21. Hall P, Li Q, Racine JS (2007) Nonparametric estimation of regression functions in the presence of irrelevant regressors. Rev Econ Stat 89(4):784–789CrossRefGoogle Scholar
  22. Kneip A, Simar L, Wilson PW (2008) Asymptotics and consistent bootstraps for DEA estimators in non parametric frontier models. Econ Theory 24:1663–1697CrossRefGoogle Scholar
  23. Kneip A, Simar L, Wilson PW (2009) A computationally efficient, consistent bootstrap for inference with non parametric DEA estimtors. Discussion paper 0903, Institut de Statistiques, UCL, Louvain la Neuve, BelgiumGoogle Scholar
  24. Koopmans TC (1957) An analysis of production as an efficient combination of activities. In: Koopmans TC (eds) Activity analysis of production and allocation, Cowles Commision for research in economics, Monograph No. 13. Wiley, NewYorkGoogle Scholar
  25. Kumbhakar SC, Lovell CAK (2000) Stochastic frontier analysis. Cambridge University Press, UKGoogle Scholar
  26. Li Q, Racine J (2004) Nonparametric estimation of regression functions with both categorical and continuous data. J Econ 119:99–130Google Scholar
  27. Li Q, Racine J (2007) Nonparametric econometrics: theory and practice. Princeton University Press, PrincetonGoogle Scholar
  28. Li Q, Racine J (2008) Nonparametric estimation of conditional CDF and quantile functions with mixed categorical and continuous data. J Bus Econ Stat 26(4):423–434CrossRefGoogle Scholar
  29. Roudaut N (2006) Influences of the business environment on manufacturing firms technical efficiencies : the Côte d’Ivoire case. J Prod Anal 25:93–109CrossRefGoogle Scholar
  30. Schubert T, Simar L (2011) Innovations and export activities in the German mechanical engineering sector: an application of testing restrictions in production analysis. J Prod Anal (in press)Google Scholar
  31. Shephard RW (1970) Theory of cost and production function. Princeton University Press, PrincetonGoogle Scholar
  32. Simar L (2003) Detecting outliers in frontier models: a simple approach. J Prod Anal 20:391–424CrossRefGoogle Scholar
  33. Simar L, Wilson PW (1998) Sensitivity analysis of efficiency scores: how to bootstrap in nonparametric frontier models. Manage Sci 44:49–61CrossRefGoogle Scholar
  34. Simar L, Wilson PW (2000) A general method for bootstrapping in nonparametric models. J Appl Stat 11:67–80Google Scholar
  35. Simar L, Wilson PW (2007) Estimation and inference in two-stage, semi-parametric models of production process. J Econ 25:31–64Google Scholar
  36. Simar L, Wilson PW (2011) Inference by the m out of n bootstrap in nonparametric frontier models. J Prod Anal (in press)Google Scholar
  37. Tybout J (2000) Manufacturing firms in developing countries, how well do they do, and why?. J Econ Lit 38:11–44CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.University of South Brittany, IREABrittanyFrance
  2. 2.Toulouse Business School and Toulouse School of EconomicsUniversite de ToulouseToulouseFrance

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