Advertisement

Advanced Semi-parametric and Parametric Methods to Assess Efficiency in the Postal Sector

  • M. Meschi
  • M. R. Pierleoni
  • S. GoriEmail author
Chapter
First Online:
Part of the Topics in Regulatory Economics and Policy book series (TREP, volume 50)

Abstract

This paper uses Two-stage Data Envelopment Analysis (“TS DEA”) and Stochastic Frontier models (“SF models”) to compare the efficiency performance of national postal operators. It applies TS DEA and SF methods to the same postal operator dataset, and compares their efficiency rankings and the way they account for the effect of exogenous variables. Section 2 contains a literature review. Section 3 applies two-stage DEA with bootstrapped Tobit regression and SF models to the database used in Pierleoni and Gori (2013). Section 4 concludes. The critical aspect of this paper is limited data availability (77 observations, seven operators for 11 years). This calls for caution in interpreting the results; there is a need for a combination of qualitative and quantitative analysis to fully grasp differences in performance between postal operators.

Keywords

Exogenous Variable Efficiency Score Postal Operator Time Path Stochastic Frontier Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.

References

  1. Aigner, D. J., Lovell, C. A. K., & Schmidt, P. (1977). Formulation and estimation of Stochastic frontier production function models. Journal of Econometrics, 6, 21–37.CrossRefGoogle Scholar
  2. Amornkitvikai, Y., & Harvie, C. (2010). Identifying and measuring technical inefficiency factors: evidence from unbalanced panel data for Thai listed manufacturing enterprises. Economics Working Papers.Google Scholar
  3. Banker, R. D., & Morey, R. C. (1986a). Efficiency analysis for exogenously fixed Inputs and outputs. Operations Research, 34(4), 513–521.CrossRefGoogle Scholar
  4. Banker, R. D., & Natarajan, R. (2004). Statistical tests based on DEA efficiency scores, chapter 11. In W. W. Cooper, L. Seiford, & J. Zhu (Eds.), Handbook on data envelopment analysis (pp. 299–321). Norwell: Academic.Google Scholar
  5. Barnum, D. T., Gleason, J. M., & Hemily, B. (2008). Estimating DEA confidence intervals for Canadian Urban Paratransit Agencies using panel data analysis. A Great Cities Institute Working Paper.Google Scholar
  6. Battese, G. E., & Coelli, T. J. (1995). A model for technical inefficiency effects in a Stochastic frontier production function for panel data. Empirical Economics, 20, 325–32.CrossRefGoogle Scholar
  7. Battese, G., & Corra, G. (1977). Estimation of a production frontier model with application to the pastoral zone of Easter Australia. Australian Journal of Agricultural Economics, 21(3), 167–179.CrossRefGoogle Scholar
  8. 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
  9. Coelli, T., Rao, D., O’Donnell, C., & Battese, G. (2005). An introduction to efficiency and productivity analysis (2nd ed.). New York: Springer.Google Scholar
  10. Comandini, V., Pierleoni, M. R., Consiglio, A., Gori, S., & Piccinin, E. (2010). The Altmark ruling and approaches to measuring efficiency of postal operators. In M. A. Crew & P. R. Kleindorfer (Eds.), Heightening competition in the postal and delivery sector. Northampton: Edward Elgar Publishing.Google Scholar
  11. Cuesta, R. A. (2000). A production model with firm-specific temporal variation in technical inefficiency: With application of Spanish dairy farms. Journal of Productivity Analysis, 13, 139–158.CrossRefGoogle Scholar
  12. Fare, R., Grosskopf, S., & Lovell, C. A. K. (1994). Production frontiers. Cambridge: Cambridge University Press.Google Scholar
  13. Farrell, M. J. (1957). Measurement of productive efficiency. Journal of the Royal Statistical Society, 20(Series A), 253–281.CrossRefGoogle Scholar
  14. Gori, S. (2013). A comprehensive approach on the application of cost efficiency methods to network industries: special focus on the postal sector. Berkeley: Institute of Governmental Studies, University of California. http://escholarship.org/uc/item/4d60z5dvGoogle Scholar
  15. Gori, S. (2014). A cross country study of the cost efficiency of the postal sector. Ph.D. thesis. Bristol Business School, discussed on February and submitted June 2014.Google Scholar
  16. Gori, S., Piccinin, E., Romito, S., & Scarfiglieri, G. (2006). On the use of cost functions in the assessment of the impact of liberalization on postal universal service burden. In M. A. Crew & P. R. Kleindorfer (Eds.), Progress toward liberalization of the postal and delivery sector. New York: Springer.Google Scholar
  17. Greene, W. (2005a). Fixed and random effects in stochastic frontier models. Journal of Productivity Analysis, 23, 7–32.CrossRefGoogle Scholar
  18. Greene, W. (2005b). Reconsidering heterogeneity in panel data estimators of the stochastic frontier model. Journal of Econometrics, 126, 269–303.CrossRefGoogle Scholar
  19. Greene, W. (2008). The econometric approach to efficiency analysis. In H. O. Fried, C. A. K. Lovell, & S. S. Schmidt (Eds.), The measurement of productive efficiency. New York: Oxford University Press.Google Scholar
  20. Grosskopf, S. (1996). Statistical inference and nonparametric efficiency: A selective survey. Journal of Productivity Analysis, 7, 161–176.CrossRefGoogle Scholar
  21. Kneip, A., Park, B. U., & Simar, L. (1998). A note on the convergence of nonparametric DEA estimators for production efficiency scores. Econometric Theory, 14, 783–793.CrossRefGoogle Scholar
  22. Kopp, R. J. (1981). The measurement of productive efficiency: A reconsideration. Quarterly Journal of Economics, 96, 477–503.CrossRefGoogle Scholar
  23. Kumbhakar, S. C., & Lovell, C. A. K. (2000). Stochastic frontier analysis. Cambridge, MA: Cambridge University Press.CrossRefGoogle Scholar
  24. Kumbhakar, S. C., & Tsionas, E. (2006). Estimation of Stochastic frontier production functions with input-oriented technical efficiency. Journal of Econometrics, 133, 71–96.CrossRefGoogle Scholar
  25. Kumbhakar, S. C., Lien, G., & Hardaker, J. B. (2012). Technical efficiency in competing panel data models: A study of Norwegian grain farming. Google Scholar
  26. Meeusen, W., & van den Broeck, J. (1977). Efficiency estimation from Cobb-Douglas production functions with composed error. International Economic Review, 18, 435–444.CrossRefGoogle Scholar
  27. Murillo-Zamorano, L. R. (2004). Economic efficiency and frontier techniques. Journal of Economic Surveys, 18(1), 33–77. Blackwell Publishing Ltd.CrossRefGoogle Scholar
  28. NERA Economic Consulting. (2004). Economics of postal services: Final report. A report to the European Commission, July.Google Scholar
  29. Park, B., Simar, L., & Weiner, C. (2000). The FDH estimator for productivity efficiency scores: Asymptotic properties. Econometric Theory, 16, 855–877.CrossRefGoogle Scholar
  30. Pierleoni, M. R. (2012). Liberalization, privatization and competition in the postal services: the state of the art. CRRI Research Project.Google Scholar
  31. Pierleoni, M. R., & Gori, S. (2013). Efficiency analysis of postal operators: a comparison between United States and Europe. In M. Crew & P. Kleindorfer (Eds.), Reforming the postal sector in the face of electronic competition. Cheltenham: Elgar Publishing Inc. Chapter 18.Google Scholar
  32. Pitt, M. M., & Lee, L. F. (1981). The measurement and sources of technical inefficiency in the Indonesian weaving industry. Journal of Development Economics, 9, 43–64.CrossRefGoogle Scholar
  33. Poi, B. P. (2004). From the help desk: Some bootstrapping techniques. The Stata Journal Number, 4(3), 312–328.Google Scholar
  34. Schmidt, P., & Sickles, R. (1984). Production frontiers and panel data, Journal of Business Economics and Statistics, 2(4), 367–374.Google Scholar
  35. Simar, L., & Wilson, P. W. (1998). Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models. Management Science, 44(11), 49–61.CrossRefGoogle Scholar
  36. Simar, L., & Wilson, P. W. (1999c). Estimating and bootstrapping Malmquist indices. European Journal of Operations Research, 115, 459–471.CrossRefGoogle Scholar
  37. Simar, L., & Wilson, P. W. (2000a). Statistical inference in nonparametric frontier models: The state of the art. Journal of Productivity Analysis, 13, 49–78.CrossRefGoogle Scholar
  38. Simar, L., & Wilson, P. W. (2007). Estimation and inference in two-stage, semi-parametric models of production processes. Journal of Econometrics, 136, 31–64.CrossRefGoogle Scholar
  39. UPU database. (2003, 2004, 2005, 2007). http://www.upu.int/en/resources/postal-statistics/aboutpostal-statistics.htmlGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.FTI ConsultingLondonUK
  2. 2.University of Tor VergataRomeItaly
  3. 3.University of the West of EnglandBristolUK

Personalised recommendations

Cite chapter

Buy options