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The efficiency analysis of container port production using DEA panel data approaches

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Abstract

Applications of Data Envelopment Analysis (DEA) to container port production have been largely restricted to standard DEA models using cross-sectional data. The efficiency results derived may be biased; for instance, as the result of random effects or a recent investment in future production. In overcoming this problem, panel data on container port production may be more suitable for medium- to long-term efficiency analysis. This paper evaluates available DEA panel data approaches by applying them to a sample of 25 leading container ports. Empirical results validate the necessity of utilizing panel data and reveal that considerable waste exists in container port production. It also provides a basis for assessing the competitiveness of container ports, for benchmarking best practice and identifying specific sources or causes of inefficiency.

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References

  • Al-Eraqi AS, Mustafa A, Khader AT, Barros CP (2008) Efficiency of Middle Eastern and East African seaports: application of DEA using window analysis. Eur J Scientific Res 23(4): 597–612

    Google Scholar 

  • Ali AI, Seiford LM (1993) The mathematical programming approach to efficiency analysis. In: Fried H, Lovell CAK, Schmidt S (eds) The measurement of productive efficiency: techniques and applications. Oxford University Press, Oxford, pp 160–194

    Google Scholar 

  • Anderson TW, Darling DA (1952) Asymptotic theory of certain “goodness-of-fit” criteria based on stochastic processes. Ann Math Stat 23: 193–212

    Article  Google Scholar 

  • Ashar A (1997) Counting the moves. Port Dev Int 13: 25–29

    Google Scholar 

  • Banker RD, Charnes A, Cooper WW (1984) Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manage Sci 30(2): 1078–1092

    Article  Google Scholar 

  • Barros CP (2006) A benchmark analysis of italian seaports using data envelopment analysis. Maritime Econ Logist 8(4): 347–365

    Article  Google Scholar 

  • Barros CP, Athanassoiu M (2004) Efficiency in European Seaports with DEA: evidence from Greece and Portugal. Maritime Econ Logist 6(2): 122–140

    Article  Google Scholar 

  • Bendall H, Stent A (1987) On measuring cargo handling productivity. Maritime Policy Manage 14(4): 337–343

    Article  Google Scholar 

  • Caves DW, Christensen LR, Diewert WE (1982) The economic theory of index numbers and measurement of input, output and productivity. Econometrica 50: 1393–1414

    Article  Google Scholar 

  • Charnes A, Clark CT, Cooper WW, Golany B (1985) A developmental study of Data Envelopment Analysis in measuring the efficiency of maintenance units in the U.S. air forces. Ann Oper Res 2: 95–112

    Article  Google Scholar 

  • Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2: 429–444

    Article  Google Scholar 

  • Cooper WW, Seiford LM, Tone K (2000) Data envelopment analysis: a comprehensive text with models, applications, references and DEA-Solver software. Kluwer Academic Publishers, Boston

    Google Scholar 

  • Cullinane KPB, Ji P, Wang TF (2005a) The relationship between privatization and DEA estimates of efficiency in the container port industry. J Econ Bus 57(5): 433–462

    Article  Google Scholar 

  • Cullinane KPB, Song DW, Wang TF (2005b) The application of mathematical programming approaches to estimating container port production. J Prod Anal 24(1): 73–92

    Article  Google Scholar 

  • Cullinane KPB, Wang TF, Song DW, Ji P (2005c) A comparative analysis of DEA and SFA approaches to estimating the technical efficiency of container ports. Transp Res A Policy Pract 40(4): 354–374

    Article  Google Scholar 

  • Cullinane KPB, Song DW, Ji P, Wang TF (2004) An application of DEA windows analysis to container port production efficiency. Rev Netw Econ 3(2): 186–208

    Google Scholar 

  • Cullinane KPB, Wang TF (2006) The efficiency of European container terminals: a cross-sectional data envelopment analysis. Int J Logist Res Appl 9(1): 19–31

    Article  Google Scholar 

  • De Borger B, Kerstens K, Costa A (2002) Public transit performance: what does one learn from frontier studies. Transp Rev 22(1): 1–38

    Article  Google Scholar 

  • De Monie G (1987) Measuring and evaluating port performance and productivity. United Nations Conference on Trade and Development (UNCTAD) monographs on port management No. 6 (UNCTAD: Geneva)

  • Fare R, Grosskopf S, Norris M, Zhang Z (1994) Productivity growth, technical progress and efficiency change in industrialized countries. Am Econ Rev 84: 66–83

    Google Scholar 

  • Forsund FR, Sarafoglou N (2002) On the origins of data envelopment analysis. J Prod Anal 17: 23–40

    Article  Google Scholar 

  • Glass GV, Peckham PD, Sanders JR (1972) Consequences of failure to meet assumptions underlying fixed effects analyses of variance and covariance. Rev Educ Res 42: 237–288

    Google Scholar 

  • Gonzalez MM, Trujillo L (2009) Efficiency measurement in the port industry: a survey of the empirical evidence. J Transp Econ Policy 43(2): 157–192

    Google Scholar 

  • Harwell MR, Rubinstein EN, Hayes WS, Olds CC (1992) Summarizing Monte Carlo results in methodological research: the one- and two-factor fixed effects ANOVA cases. J Educ Behav Stat 17: 315–339

    Article  Google Scholar 

  • Itoh H (2003) Efficiency changes at major container ports in Japan: a window application of data envelopment analysis. Rev Urban Reg Dev Stud 14(2): 133–152

    Article  Google Scholar 

  • Kumbhakar SC, Lovell C (2000) Stochastic frontier analysis. Cambridge University Press, Cambridge

    Google Scholar 

  • Lix LM, Keselman JC, Keselman HJ (1996) Consequences of assumption violations revisited: A quantitative review of alternatives to the one-way analysis of variance F test. Rev Educ Res 66: 579–619

    Google Scholar 

  • Malmquist S (1953) Index numbers and indifference curves. Trabajos Estatistica 4(1): 209–242

    Article  Google Scholar 

  • Marlow PB, Paixão Casaca AC (2003) Measuring lean ports performance. Int J Transp Manage 1(4): 189–202

    Article  Google Scholar 

  • Martinez-Budria E, Diaz-Armas R, Navarro-Ibanez M, Ravelo-Mesa T (1999) A study of the efficiency of spanish port authorities using data envelopment analysis. Int J Transp Econ XXVI(2): 237–253

    Google Scholar 

  • Min H, Park B (2005) Evaluating the inter-temporal efficiency trends of international container terminals using data envelopment analysis. Int J Integr Supply Manage 1(3): 258–277

    Article  Google Scholar 

  • Rios LR, Macada ACG (2006) Analysing the relative efficiency of container terminals of mercosur using DEA. Maritime Econ Logist 8(4): 331–346

    Article  Google Scholar 

  • Roll Y, Hayuth Y (1993) Port performance comparison applying data envelopment analysis (DEA). Maritime Policy Manage 20(2): 153–161

    Article  Google Scholar 

  • Seiford LM, Thrall R (1990) Recent development in DEA: the mathematical programming approach to frontier analysis. J Econom 46(1/2): 7–38

    Article  Google Scholar 

  • Tabernacle JB (1995) A study of the changes in performance of quayside container cranes. Maritime Policy Manage 22(2): 115–124

    Article  Google Scholar 

  • Talley WK (1998) Optimum throughput and performance evaluation of marine terminals. Maritime Policy Manage 15(4): 327–331

    Article  Google Scholar 

  • Tobin J (1958) Estimation of relationships for limited dependent variables. Econometrica 26(1): 24–36

    Article  Google Scholar 

  • Tongzon J (2001) Efficiency measurement of selected Australian and other international ports using data envelopment analysis. Transp Res A Policy Pract 35(2): 113–128

    Google Scholar 

  • Tongzon JL (2009) Port choice and freight forwarders. Transp Res E 45(1): 185–195

    Article  Google Scholar 

  • Tongzon J, Heng W (2005) Port Privatization, efficiency and competitiveness: some empirical eveidence from container ports (terminals). Transp Res A 39(5): 405–424

    Google Scholar 

  • Tovar B, Jara-Díaz S, Trujillo L (2007) Econometric estimation of scale and scope economies within the Port Sector: a review. Maritime Policy Manage 34(3): 203–223

    Article  Google Scholar 

  • Tulkens H, van den Eeckaut P (1995) Non-parametric efficiency, progress and regress measures for panel data: methodological aspects. Eur J Oper Res 80(3): 474–499

    Article  Google Scholar 

  • Turner H, Windle R, Dresner M (2003) North American containerport productivity: 1984–1997. Transp Res E 40(4): 339–356

    Article  Google Scholar 

  • Valentine VF, Gray R (2001) The measurement of port efficiency using data envelopment analysis. In: Proceedings of the 9th World Conference on Transportation Research, 22–27 July, Seoul, South Korea

  • van den Broeck J, Koop G, Osiewalski J, Steel MFJ (1994) Stochastic frontier models: a Bayesian perspective. J Econom 61: 273–303

    Article  Google Scholar 

  • Wang TF, Cullinane KPB, Song DW (2005) Container port production and economic efficiency. Palgrave-Macmillan, Basingstoke

    Book  Google Scholar 

  • Wang TF, Cullinane KPB (2004) Industrial concentration in container ports. In: Proceedings of the international association of maritime economists conference, Izmir, 30 June–2 July

  • Wang TF, Song DW, Cullinane KPB (2002) The applicability of data envelopment analysis to efficiency measurement of container ports. In: Proceedings of the international association of maritime economists conference, Panama, 13–15 November

  • Wang TF, Cullinane KPB (2006) The efficiency of European container terminals and implications for supply chain management. Maritime Econ Logist 8(1): 82–99

    Article  Google Scholar 

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Authors and Affiliations

  1. Transport Research Institute, Edinburgh Napier University, Merchiston Campus, Edinburgh, EH10 5DT, Scotland, UK

    Kevin Cullinane

  2. Transport Division, UNESCAP, Bangkok, Thailand

    Tengfei Wang

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  1. Kevin Cullinane
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  2. Tengfei Wang
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Correspondence to Kevin Cullinane.

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Cullinane, K., Wang, T. The efficiency analysis of container port production using DEA panel data approaches. OR Spectrum 32, 717–738 (2010). https://doi.org/10.1007/s00291-010-0202-7

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