May 2012

1. The Relative Economic and Technical Efficiency of Selected International Container Terminal Operators May 2012 Dr. Emanuele D’Agostino (PhD in Transport economics, Genoa, Italy) Dr. Fedele Iannone (University of Genoa, DIEM, Genoa, Italy) Prof. Claudio Ferrari (University of Genoa, DIEM, Genoa, Italy) E-mail: ferrari@unige.it

2. Structure of the presentation • The international container terminal industry • Review of DEA applications • Overview of the methods and data used to assess the ICTOs’ efficiency • The DEA models used for evaluations • The panel approaches used for the ICTOs’ DEA over time • Descriptive statistics of models’ variables and price data • Some analysis’ results • Main conclusions and future research

3. The international container terminal industry(1) Source: author’s processing

4. The international container terminal industry(2) Source: author’s processing based on data by Drewry

5. The international container terminal industry(3) Source: author’s processing based on data by Drewry Source: author’s processing based on data by Drewry

6. The international container terminal industry (4) Source: author’s processing based on data by Drewry

7. The international container terminal industry (5) Source: author’s processing based on data by Drewry

8. The international container terminal industry (6) Source: author’s processing based on data by Drewry

9. The international container terminal industry(7) Source: author’s processing based on data by Drewry

10. The international container terminal industry(8) Source: author’s processing based on data by Drewry

11. The international container terminal industry(9) “Question marks” “Stars” “Cash cows” “Dogs” Source: author’s processing based on data by Drewry

12. The international container terminal industry(10) Source: author’s processing based on data by Drewry and companies’ annual reports

13. The international container terminal industry(11) EBITDA margins of selected ICTOs during 2008-2010 Source: author’s processing based on data by Drewry and companies’ annual reports

14. Review of DEA applications (1) Cont’d

15. Review of DEA applications (2) Cont’d

16. Review of DEA applications (3) Cont’d

17. Review of DEA applications (4)

18. Overview of the methods and data used to assess the ICTOs’ efficiency (1) Two groups of models based on different inputs and outputs variables have been constructed. The (a) group is summarized in the above table. The (b) group in the next table.

19. Overview of the methods and data used to assess the ICTOs’ efficiency (2) Two groups of models based on different inputs and outputs variables have been constructed. The (b) group is summarized in the above table.

20. The DEA models used for evaluations (1) Input oriented CCR and BCC models (Charnes, Cooper and Rodes, 1978; Banker, Charnes and Cooper, 1984) Assuming there are nDMUo (o = 1,…, n) and they use m inputs xio(i = 1,…, m) to produce s outputs yro(r = 1,…, s) with the unit input costs cio (i = 1,…, m and o = 1,…, n). The production possibility setP is: Cont’d

21. The DEA models used for evaluations (2) Input oriented CCR and BCC models (Charnes, Cooper and Rodes, 1978; Banker, Charnes and Cooper, 1984) Using the set P, we solve the following LP for each DMUo:

22. The DEA models used for evaluations (3) Output oriented CCR and BCC models (Charnes, Cooper and Rodes, 1978; Banker, Charnes and Cooper, 1984) Using the set P, we solve the following LP for each DMUo:

23. The DEA models used for evaluations (4) New-Cost model (Tone, 2002) Assuming there are nDMUo (o = 1,…, n) and they use m inputs xio(i = 1,…, m) to produce s outputs yro(r = 1,…, s) with the unit input costs cio (i = 1,…, m and o = 1,…, n). The cost-based production possibility setPc is: Cont’d

24. The DEA models used for evaluations (5) New-Cost model (Tone, 2002) Using the set Pc, we solve the following LP for each DMUo:

25. The DEA models used for evaluations (6) New-Revenue model (Tone, 2002) Assuming there are nDMUo (o = 1,…, n) and they use m inputs xio(i = 1,…, m) to produce s outputs yro(r = 1,…, s) with the unit output prices pro (r = 1,…, s and o = 1,…, n). The price-based production possibility setPp is: Cont’d

26. The DEA models used for evaluations (7) New-Revenue model (Tone, 2002) Using the set Pc, we solve the following LP for each DMUo:

27. The panel approaches used for the ICTOs’ DEA over time • Contemporaneous analysis involves the estimation of T frontiers, one for each year. This approach allows for technical progress and regress. It also allows for intersecting frontiers, which would signal local progress in a region of output-input space and local regress in another region. • Window analysis involves the estimation of a sequence of overlapping pooled panels, each consisting of a few time periods of arbitrary lenght. This approach allows for tracking efficiency trends through successive overlapping windows. It also alleviate volatility in efficiency estimates. • Intertemporal analysis involves the pooling of data to estimate a single grand frontier. This approach assumes an un-varying best practice technology, which may be tenable in short panels. It generates T efficiency estimates for each DMU, alla against the same standard, and trends in efficiency estimates of individual DMU may be of interest. • Time series analysis involves the estimation of separate single frontiers comparing each single DMU with its own efficiency across time. When there is a change of efficiency, it is difficult to determine whether the cause is an external or internal factor.

28. Descriptive statistics of models’ variables and price data

29. Some analysis’ results (1) Average results from contemporaneous analysis, window analysis, intertemporal analysis, and time series analysis

30. Some analysis’ results (2)

31. Some analysis’ results (3)

32. Some analysis’ results (4)

33. Some analysis’ results (5)

34. Some analysis’ results (6) VRS New-Cost models (a) Cont’d

35. Some analysis’ results (7) VRS New-Cost models (a) Cont’d

36. Some analysis’ results (8) VRS New-Revenue models (a) Cont’d

37. Some analysis’ results (9) VRS New-Revenue models (a)

38. Some analysis’ results (10) VRS New-Cost models (b) Cont’d

39. Some analysis’ results (11) VRS New-Cost models (b) Cont’d

40. Some analysis’ results (12) VRS New-Revenue models (b) Cont’d

41. Some analysis’ results (13) VRS New-Revenue models (b)

42. Some analysis’ results (14) Correlations among the efficiency measures from input oriented models (a)

43. Some analysis’ results (15) Correlations among the efficiency measures from output oriented models (a)

44. Some analysis’ results (16) Correlations among the efficiency measures from input oriented models (b)

45. Some analysis’ results (17) Correlations among the efficiency measures from output oriented models (b)

46. Main conclusions and future research • More attention to be paid to the cost side than to the revenue one • Build flexibility in costs • Customer involvement in business strategies • Peer ICTOs and the performance targets, including most productive scale size (MPSS) targets • Profit efficiency measurement issues • Comparison of non parametric and parametric frontier estimation techniques • Decomposition of the ICTOs performances into seaparate measures of technological change and technical efficiency change • Comparison of the efficiency levels of ICTOs and shipping lines