Journal of the Knowledge Economy

, Volume 7, Issue 2, pp 613–629 | Cite as

Efficiency Measurement and Determinants of the Public Transport Industry in Tunisia

  • Ahmed Ayadi
  • Sami Hammami


This paper uses data envelopment analysis (DEA) to investigate the efficiency of the public transport sector in Tunisia over the period 2000–2010 (Stage 1 Analysis—Cost Efficiency Estimation). Cost efficiency of the regional transport companies (RTC) is measured by the use of three input and two output variables. Capital (fleet size), labour and energy are considered as inputs and the number of seats per kilometre and turnover as outputs. The major findings showed that there are 4.7–43.6 % inefficiencies in these RTCs under the cost specification “variable returns to scale”. Besides, in the second stage of our study, we attempt to shed light on the determinants of efficiency. Our results indicate that the RTCs’ efficiency has not improved by increasing financial performance (return of equity (ROE)) and the number of trips. This means that the RTCs are not making the best use of the production factors and that the subsidy granted by the State negatively and indirectly affected (The lack of the managers and employees motivation is due to the belief that the government certainly funds the annual deficits through a subsidy) these companies’ financial performance.


Public transport Cost efficiency DEA Inefficiency factors 

JEL Classification

L91 C23 D61 M41 


  1. Agarwal, S., Yadav, S. P., & Singh, S. P. (2011). DEA based estimation of the technical efficiency of state transport undertakings in India. Opsearch, 47(3), 216–230.CrossRefGoogle Scholar
  2. Ang, J., Lauterbach, B., & Schreiber, B. Z. (2002). Pay at the executive suite: how do US banks compensate their top management teams? Journal of Banking & Finance, 26(6), 1143–1163.CrossRefGoogle Scholar
  3. Banker, R. D., Charnes, A., & Cooper, A. A. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30, 1078–1092.CrossRefGoogle Scholar
  4. Barro, J. R., & Barro, R. J. (1990). Pay, performance and turnover of bank CEOs. Journal of Labor Economics, 8(4), 448–81.CrossRefGoogle Scholar
  5. Barros, C. P. (2006). A benchmark analysis of Italian seaport using data envelopment analysis. Maritime Economics and Logistics, 8, 347–365.CrossRefGoogle Scholar
  6. Boame, A. K. (2004). The technical efficiency of Canadian urban transit systems. Transport Research Part E, 40(5), 401–416.CrossRefGoogle Scholar
  7. Bogetoft, P., & Otto, L. (2011). Benchmarking with DEA, SFA, and R. LLC: Springer Science Business Media.CrossRefGoogle Scholar
  8. Brown, L. D., & Marcus, L. C. (2009). Corporate governance and firm operating performance. Review of Quantitative Finance and Accounting, 32(2), 129–144.CrossRefGoogle Scholar
  9. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429–444.CrossRefGoogle Scholar
  10. Coelli, T., (1998). A guide to DEAP version 2.1: a data envelopment analysis (computer)Google Scholar
  11. Cowie, J. (2002). Acquisition, efficiency and scale economies: an analysis of the British bus industry. Transport Reviews, 22(2), 147–157.CrossRefGoogle Scholar
  12. Farrell, M. J. (1957). The measurement of productive efficiency. Journal Royal Statistics Society Series. A General, 120(3), 253–281.CrossRefGoogle Scholar
  13. Farsi, M., Filippini, M., & Kuenzle, M. (2006). Cost efficiency in regional bus companies: an application of new stochastic frontier models. Journal Transport Economics Policy, 40(1), 95–118.Google Scholar
  14. Filippini, M., Maggi, R., & Prioni, P. (1992). Inefficiency in a regulated industry: the case of the Swiss regional bus companies. Annals of Public and Cooperative Economics, 63, 437–455.CrossRefGoogle Scholar
  15. Gedajlovic, E., & Shapiro, D. M. (2002). Ownership structure and firm profitability in Japan. The Academy of Management Journal, 45(3), 565–575.CrossRefGoogle Scholar
  16. Greer, M. (2006). Are the discount carriers actually more efficient than the legacy carriers: a data envelopment analysis. International Journal of Transport Economics, 33, 37–55.Google Scholar
  17. Hilmola, O. P. (2007). European railway freight transportation and adaptation to demand decline: efficiency and partial productivity analysis from period of 1980–2003. International Journal of Productivity and Performance Management, 56(3), 205–225.CrossRefGoogle Scholar
  18. Jong, G.C.D., Cheung, Y.H.F., (1999). Stochastic frontier models for public transport, paper presented at the eighth WCTR, AntwerGoogle Scholar
  19. Kennedy, P. (1985). A guide to econometrics (2nd ed.). Cambridge: The MIT Press.Google Scholar
  20. Lin, L. C., & Tseng, C. C. (2007). Operational performance evaluation of major container ports in the Asia-Pacific region. Maritime Policy and Management, 34(6), 535–551.CrossRefGoogle Scholar
  21. Lozano, S. (2009). Estimating productivity growth of Spanish ports using a non-radial, non oriented Malmquist index. International Journal of Shipping and Transport Logistics, 1(3), 227–248.CrossRefGoogle Scholar
  22. M’raihi, R., (2001). Efficacité-cout de l’industrie de transport collectif public en Tunisie : frontière de coût déterministe et stochastiqueGoogle Scholar
  23. Martín, J. C., & Reggiani, A. (2007). Recent methodological developments to measure spatial interaction: synthetic accessibility indices applied to high-speed train investments. Transport Reviews, 27(5), 551–571.CrossRefGoogle Scholar
  24. Odeck, J. (2008). The effect of mergers on efficiency and productivity of public transport services. Transport Research Part A, 42(4), 696–708.Google Scholar
  25. Prowse, S. D. (1992). The structure of corporate ownership in Japan. Journal of Finance, 47(3), 1121–1140.CrossRefGoogle Scholar
  26. Saxena, P., & Saxena, R. R. (2011). Measuring efficiencies in Indian public road transit: a data envelopment analysis approach. Opsearch, 47(3), 195–204.CrossRefGoogle Scholar
  27. Scheraga, C. (2004). Operational efficiency vs. financial mobility in the global airline industry: a data envelopment analysis and Tobit analysis. Transportation Research A, 38, 383–404.Google Scholar
  28. Thiry, B., & Tulkens, H. (1992). Allowing for inefficiency in parametric estimation of production functions for urban transit firms. Journal of Productivity Analysis, 3, 45–65.CrossRefGoogle Scholar
  29. Viton, P. (1997). Technical efficiency in multi-mode bus transit: a production frontier analysis. Transportation Research Part B, 3, 23–39.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.University of Sfax (Tunisia), Faculty of Economics and Management of Sfax, Laboratory UREDSfaxTunisia

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