Teng-Fei Wang

The efficiency of European container ports: A cross-sectional data envelopment analysis

This paper focuses on measuring the efficiency of container terminals in Europe using the data envelopment analysis (DEA) approach. Container terminals in Europe play an important role in the region’s economic development and, as the result of their geographic concentration, face fierce competition compared with the rest of the world. Despite this, studies of the efficiency of container terminals in the region are minimal. As a basis for further research, this paper serves to supplement existing studies by deriving estimates of relative efficiency for a sample comprising 69 of Europe’s container terminals with annual throughput of over 10,000 TEUs. The scale properties of container terminal production are also considered as part of the study, as is the relationship of efficiency to geographical influence.

Port competition between Shanghai and Ningbo

The objective of this paper is to analyse the relative competitiveness of the neighbouring container ports of Shanghai and Ningbo in China and to develop a view of the likely future outcome of the competition between them. After assessing the demand for their services within what is hypothesized as a shared hinterland, current supply and future expansion plans are detailed and considered within the two ports’ wider development strategies. The relative competitiveness of the two ports is then evaluated on the basis of price and quality of service, as embodied within the concept of generalized cost as incurred by customers. A critical political dimension is a necessary element of this analysis. It is concluded that continued economic development in the hinterland, central government policies on regional development and Chinas accession to the World Trade Organization (WTO) will all contribute to growing demand for port services. However, Ningbo will continue to gain greater market share as the result of advantages in its natural endowments (particularly depth of water), price (especially in terms of recovering the cost of capacity expansions) and quality of service improvements that are predicted to emerge as the result of currently planned enhancements to inland transport infrastructure and logistical systems.

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

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.

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

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 worlds 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.

Chapter 15 Port Governance in China

This chapter begins by describing Chinas policies of economic reform since the inauguration of its open door policy in 1978. This provides the historical context for the countrys concurrent reform of its port industry. The evolution and gradual decentralisation of the port governance system is analysed within three distinct phases of development that have taken the sector from one where ownership and decision-making were highly centralised to one where policies of corporatisation and privatisation have been recently reinforced by Chinas new Port Law of 2004. The chapter concludes by asserting that it is still too early to tell whether the latest phase of reforms will prove to be successful in solving Chinas port problems - particularly the capacity issue - and points to possible implications of the reforms for overseas investment and future levels of concentration within the market.

A Hierarchical Taxonomy of Container Ports in China and the Implications for Their Development

Prompted by a phenomenal growth in trade over the last decade, China’s container ports have been increasing in both number and importance. Within the Asian market sector, container ports in the Chinese mainland now threaten to undermine the dominance of both Hong Kong and Singapore. This chapter describes the development of China’s container ports to this point in time and, by applying a classification system based on a hierarchy of ports, seeks to deduce likely scenarios for the sector’s future development.

Empirical Results and Analysis

This chapter reports the empirical results derived from applying the alternative approaches to container port efficiency estimation discussed in Chapter 3. The efficiency results yielded by various approaches are compared using the Pearson correlation coefficient and Spearman’s rank order correlation coefficient in order to obtain some insight into the degree of variation between the results yielded by the various approaches to efficiency estimation.

The Economic Theory of Container Port Production

Understanding the economic theory that underpins container port production is a prerequisite for policy making or taking action in order to improve the performance of a container port. This is not an easy task, however, because of the complexity of container port production. This complexity is not only related to container port operations and their management, but also to the macroeconomic environment and market structure under which container ports operate. This chapter sets out to describe the economic functions of a port and the challenges for contemporary container ports arising from the complicated nature of their operations and the challenging market demands of port users and other market players.

A Theory of Container Port Production and its Empirical Validation

This chapter mainly serves to test the hypotheses formulated in Chapter 2 by using the efficiency results derived in Chapter 5. By so doing, light can be shed on the relevance of fundamental economic theory in underpinning the operation of the container port industry. Despite their respective strengths and weaknesses, as concluded from the analysis presented in Chapter 5, non-parametric and parametric models for analysing efficiency either generate similar estimates of efficiency when utilising cross-sectional data, or the former yield more convincing estimates of efficiency than the latter when significant differences exist between the efficiency scores estimated by the two approaches. For these reasons, in this chapter, the results derived from the non-parametric set of models will constitute the fundamental basis for testing the hypotheses expounded in Chapter 2.

Model Specification and Data

This chapter focuses upon the definition of the output and input variables that are required for developing a precise model specification, for the task of data collection and for estimating the efficiency measures for the sample from the port sector. Specifying erroneous or ill-defined variables for collection and analysis will inevitably lead to the wrong conclusions emerging, however elaborate the models employed may be. Sections 4.2-4.4 apply the performance measurement theory suggested by Norman and Stoker (1991) as guidance and utilise the existing literature as a basis for variable justification. DEA model orientation and the specification of the alternative functions for the testing of the parametric models are then discussed in Section 4.5. The detailed procedure to be employed for data collection is reported in Section 4.6 and, finally, Section 4.7 summarises this chapter.