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Chapter 23 Data Envelopment Analysis (DEA) and Improving Container Port Efficiency

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  • Cullinane, Kevin
  • Wang, Teng-Fei

Abstract

This chapter analyses the relevance of Data Envelopment Analysis (DEA) to the estimation of productive efficiency in the container port industry. Following an exposition of the DEA methodology, the many previous applications of the technique to the port industry are reviewed and assessed. The DEA technique is illustrated through a detailed example application using sample data relating to the world's leading container ports. The different DEA models give significantly different absolute results when based on cross-sectional data. However, efficiency rankings are rather similar. The efficiency estimated by alternative approaches, therefore, exhibits the same pattern of efficiency distribution, albeit with significantly different means. An analysis of panel data reveals that container port efficiency fluctuates over time, suggesting that the results obtained from an analysis of cross-sectional data may be misleading. Overall, the results reveal that substantial waste exists in container port production. It is also found that the sample ports exhibit a mix of decreasing, increasing and constant returns to scale. The chapter concludes that the optimum efficiency levels indicated by DEA results might not be achievable in reality, because each individual port has its own specific and unique context. Consequently, more singular aspects of individual ports should be investigated to determine the reasons that explain estimated efficiency levels.

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  • Cullinane, Kevin & Wang, Teng-Fei, 2006. "Chapter 23 Data Envelopment Analysis (DEA) and Improving Container Port Efficiency," Research in Transportation Economics, Elsevier, vol. 17(1), pages 517-566, January.
    • Handle: RePEc:eee:retrec:v:17:y:2006:i:1:p:517-566
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    3. Janda, Karel & Krska, Stepan, 2014. "Benchmarking Methods in the Regulation of Electricity Distribution System Operators," MPRA Paper 59442, University Library of Munich, Germany.
    4. Shilin Ye & Xinhua Qi & Yecheng Xu, 2020. "Analyzing the relative efficiency of China’s Yangtze River port system," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 22(4), pages 640-660, December.
    5. Angeliki Flokou & Vassilis Aletras & Dimitris Niakas, 2017. "A window-DEA based efficiency evaluation of the public hospital sector in Greece during the 5-year economic crisis," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-26, May.
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    7. Gong, Xiaoxing & Wu, Xiaofan & Luo, Meifeng, 2019. "Company performance and environmental efficiency: A case study for shipping enterprises," Transport Policy, Elsevier, vol. 82(C), pages 96-106.
    8. Ha, Min-Ho & Yang, Zaili, 2017. "Comparative analysis of port performance indicators: Independency and interdependency," Transportation Research Part A: Policy and Practice, Elsevier, vol. 103(C), pages 264-278.
    9. Yang, Dong & Liao, Shiguan & Venus Lun, Y.H & Bai, Xiwen, 2023. "Towards sustainable port management: Data-driven global container ports turnover rate assessment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
    10. Chia-Nan Wang & Phi-Hung Nguyen & Thi-Ly Nguyen & Thi-Giang Nguyen & Duc-Thinh Nguyen & Thi-Hoai Tran & Hong-Cham Le & Huong-Thuy Phung, 2022. "A Two-Stage DEA Approach to Measure Operational Efficiency in Vietnam’s Port Industry," Mathematics, MDPI, vol. 10(9), pages 1-21, April.

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