Analysing Technical and Scale Efficiency in Asian Container Ports with Data Envelopment Analysis

Transcription

1 9 Analysing Technical and Scale Efficiency in Asian Container Ports with Data Envelopment Analysis Susila Munisamy In the last decade, the shipping industry and the global seaborne trade has witnessed a rapid growth due to globalisation of the world economy, boom in international trade and borderless investments. More than 80 percent of international trade in goods is being carried by sea transport, accounting for more than 8.17 billion tonnes in 2008 (UNCTAD 2009). The fact that shipping plays such a dominant role in freight transportation and its position in the international supply chains means that ports play an important role in contributing to the region s economic development. As such its competitiveness could affect the regions viability, prospects and propensity for growth. The rapid sea-traffic growth has also increased competition among neighbouring ports. Given the current phase of globalisation and competition, port performance is of major importance for port competitiveness and indicators of plausible improvements in port management and operational planning becomes significantly important. In this context, it is important to evaluate the efficiency levels of ports in utilising the resources which will reflect their current status quo and reveal their strengths and downsides in the competitive environment. Therefore, this chapter will compare the efficiency of container ports against bestpractices in the Asian region. This study would find the best port practices in this region providing guidelines to improve port performance. The theory of efficiency analysis began with the works of Koopmans (1951), Debreu (1951) and Farrell (1957) who made the first attempts at estimating efficiencies for a set of observed production units. A popular 109

2 Statistics in Research non-parametric approach for estimating efficiency is Data Envelopment Analysis (DEA). Charnes, Cooper and Rhodes (1978) and Banker, Charnes and Cooper (1984) popularised the linear programming DEA technique with the impositions of constant and variable returns to scale assumptions, respectively, on the production frontier. DEA in container port efficiency has been popularised by Roll and Hayuth (1993). Following Roll and Hayuth s (1993) work, many empirical studies have used DEA to measure technical efficiency of ports (see Cullinane and Wang 2006; Tongzon 2001; So, Kim, Cho and Kim 2007; Valentine and Gray 2001; Song and Sin 2005; Wang, Song, and Cullinane 2002; Barros 2003). A detailed review of the application of the DEA technique in ports is provided by Gonzalez and Trujillo (2007). The DEA technique attempts to trace out a production frontier based on observed input and output levels for individual ports and a port s technical efficiency is evaluated relative to the frontier. This chapter employs the non-parametric DEA technique to evaluate the technical and scale efficiency of performance of 69 major Asian container ports. Port Efficiency Measurement Framework The Concept of Efficiency The process of production is ways of converting certain inputs into outputs. A production function expresses the relationship between inputs and outputs, Y= f(k,l). The production function illustrates the maximum amount of products and services that can be produced by utilising alternative combinations of inputs such as land, labour and capital. The maximum amount of products given the inputs can define a production frontier. A firms efficiency cannot exceed the limits set by the production frontier and in the real world, there are numerous observable firms that operate below the frontier. These firms are regarded as inefficient. Farrell (1957) defines two different concept of efficiency in production: technical and allocative efficiency. Technical efficiency is the conversion of physical inputs into outputs, which is also known as productive efficiency. Allocative efficiency is the ability to combine inputs and outputs in optimal proportions in light of prevailing prices, in order to minimise production costs. The focus of this study is on technical efficiency. Measurement of Technical Efficiency In a framework based on the works of Koopmans (1951), Debreu (1951) and Farrell (1957), a container port production can be formulated as a set 110

3 Analysing Technical and Scale Efficiency of n ports of S n = {(x i,y i )} n i = 1 consisting of x R P + inputs used to produce y R q + outputs, which makes a production possibility set of Ψ: Ψ = {(x i,y) R p+q + x can produce y} (1) Based on this production possibility definition, we estimate the Farrell output-orientated technical efficiency measure for each port operating at (x,y) using l(x,y) = sup{ l (x, ly) Ψ}. (2) where l(x,y) 1 represents the proportionate expansion in outputs without altering inputs. Thus, the scores are bounded between unity and infinity, with unity representing a perfect efficiency score of 100%. A port is said to display technical efficiency if it produces on the boundary of the production possibility set, i.e. it maximises outputs given inputs after having chosen the technology. This boundary or frontier is defined as the best practice observed. In this framework, l(x,y) = 1 indicates the container port is on the efficient frontier which serves as the yardstick to benchmark other ports. Technical efficiency score more than 1 indicates to what extend a port should be able to equiproportionally expand its output using the given inputs to be as efficient at the technically efficient ports. Technical efficiency has two elements: pure technical efficiency and scale efficiency. Scale efficiency is the ability of each port to operate as close to its most productive scale size as possible. A firm can operate at constant returns to scale (CRS) or variable returns to scale (VRS). Pure technical efficiency is the residual efficiency element that refers to resource conservation or output enhancement efficiency. DEA Estimator In practice, Ψ is unobserved, thus to estimate output-orientated technical efficiency scores for each port we use a DEA estimator suggested by Charnes et al. (1978). We replace Ψ with its DEA estimator Ψ, which CRS is the conical hull of the estimated production frontier, allowing for CRS: Ψ CRS = {(x i,y) Rp+q + y < S n i = 1 g i y i ; x > S n i = 1 g i x i such that g i > 0, i =1,...,n} (3) The output oriented Farrell technical efficiency estimator can then be computed by taking the conical hull into consideration for the linear program: 111

4 Statistics in Research λ CRS = sup {ly < Sn i = 1 g i y i ; x > S n i = 1 g i x i such that g i > 0, i =1,...,n} (4) where l > 0 (i=1,..n) are the intensity variables over which the optimisation is made. Alternatively, the VRS estimator,, employs a convex hull by VRS including the S n i = 1 g i = 1 constraint in (3), while the non-increasing returns to scale (NIRS) estimator relaxes the constraint by applying NIRS VRS Sn i = 1 g i = <1 instead of the S n i = 1 g i = 1. Measurement of Scale Efficiency For the scale efficiency calculation we employ the method suggested by Coelli et al. (1998). We can measure the scale efficiency (SE) of the port as: SE = CRS VRS (4) where SE = 1 implies scale efficiency and SE > 1 indicates scale inefficiency. The scale efficiency scores are bounded between one and infinity. However, scale inefficiency can be due to the existence of either increasing (IRS) or decreasing returns to scale (DRS). To identify the nature of returns to scale, first the CRS efficiency score is compared with VRS efficiency scores. For a given port, if the VRS score equals the CRS score, the port is said to be operating at CRS. On the other hand, if the scores are not equal, a further step is needed to determine whether the company is operating at IRS or DRS. This is done by running the non-increasing returns to scale (NIRS) DEA model. If the VRS score equals the NIRS score, then the port is said to be operating at DRS. Alternatively, if the VRS score varies from the NIRS score, then the port is said to be operating at IRS (Coelli et al. 1998). Container Port Operations Vis and de Koster (2003) define a container port as a transhipment gateway where containers are transferred from ships to barges, trucks and trains, and vice versa. After an import container is taken off from the cargo ship using quayside cranes, the container is transferred using straddle carriers or vehicles such as prime movers or trailers to the stack. After arrangements by parties at the receiving end, the container at the stack can be transported by the vehicles to other transportation modes like barges, deep sea ships, trains or are merely collected by trucks. An export container encounters a reverse of this process. Such is the typical operation of a container port 112

5 Analysing Technical and Scale Efficiency with throughput greater than 100,000 TEUs a year. On the other hand, at a container port with through put less than 100,000 TEUs throughput, mobile cranes may be used instead of quayside cranes, reach stackers instead of straddle carriers. There may be no use of gantry cranes for transport of containers within the stack, while more common uses of utility trailers and prime movers. To model the port operations, we capture the main resources used by the ports (inputs) for acquiring the main goods and services produced (outputs). Under the traditional microeconomic framework, capital and labour are necessarily the input for production. A common issue in the empirical studies of container port efficiency performance is finding a proxy to reflect labour or the number of workers. According to Notteboom, Choeck and Broeck (2000), expert analysis shows that there is a stable and positive relationship between the number of yard gantries with the number of dock workers. A study by Wang et al. (2005) showed that the average number of workers per crane is six. Hence, we take the total yard equipments, i.e. sum of straddle carriers, yard gantries, reach-stackers, front-end handlers, and forklifts, as an input factor, which proxies the labour that is required. We enlist another four inputs encompassing berth length, terminal area, total reefer points, and total quayside cranes (and/ or mobile cranes) to reflect the capital inputs in the industry. The single output used is the total throughput of the container port. Empirical Results Data We obtain secondary data for 71 major Asian container ports from Containerisation International Yearbook 2007 covering 17 countries i.e. Bangladesh, Brunei, Cambodia, China, India, Indonesia, Japan, Hong Kong, Malaysia, Pakistan, Philippines, Singapore, South Korea, Sri Lanka, Taiwan, Thailand, and Vietnam. The descriptive statistics for variables used to calculate the DEA efficiency estimates are as shown in Table 9.1. Technical Efficiency The DEA efficiency scores in this study are estimated using the computer program, Efficiency Measurement System, EMS Ver. 1.3, developed by Professor Holger Scheel, University Dortmund (Scheel 2000). In this study two ports which were considered to be outliers were removed from the dataset i.e. the port Ningbo and Zhangjiang in China and the DEA analysis 113

6 Statistics in Research Table 9.1: Major Asian Container Ports 2007: Descriptive Statistics for Inputs and Outputs Berth Length (m) Terminal Area (m 2 ) Inputs Total Reefer Points Total Quayside Cranes Total Yard Equipments Output Total Throughput (TEU) Min ,700 Max 12,610 6,169,837 7, ,935,500 Mean 2,525,90 892,461 1,130, ,239, St.Dev. 2, ,194, , ,690, was conducted using the remaining 69 ports. The DEA models run in this study is under the assumption of output maximisation (also known as output orientation). Table 9.2 presents the results of the application of the output-oriented DEA analysis giving the CRS and VRS DEA scores, scale efficiency and the nature of returns to scale. The CRS DEA scores provide the overall technical efficiency (average = 2.93). The overall technical efficiency is broken down into pure technical efficiency given by the VRS DEA score (average = 2.07) and scale efficiency (average = 1.42). The results reveal that technical inefficiency is the source of overall technical inefficiency in Asian ports rather than scale inefficiency. This technical inefficiency could be due to inefficient management practices. Effective management practices enable flexibility to rationalise factor inputs in order to maximise outputs. Ports should modernise their administration and management practices by introducing more suitable systems, working practices, or equipment and tools. They should adopt the best practices of other ports. The technical efficiency scores presented in Table 9.2 indicate the potential for ports to increase outputs while maintaining existing inputs. The efficiency scores for the ports indicate the presence of and extent of inefficiency of output production. For example, on average, the ports are 48.5% efficient (1/2.065), based on the pure technical efficiency score. The average efficiency suggests that given the inputs, the ports outputs can be expanded by 51.6 percent. Twenty One of the 69 container ports (30%) are fully efficient with a score of 1. The remaining 48 ports (70%) are inefficient and have efficiency scores ranging from to These ports either need to reduce their inputs or expand their outputs to become efficient. This indicates that the container ports in Asia have room to improve on their technical efficiencies. 114

7 Analysing Technical and Scale Efficiency Table 9.2: Efficiency Results of Major Asian Container Ports No. Container Ports Country DEA CRS Model Overall Technical Efficiency DEA VRS Model Pure Technical efficiency Scale efficiency Returns to Scale 1 Xiamen China CRS 2 Yantian China CRS 3 Guangzhou China CRS 4 Lianyungang China CRS 5 Tianjin China CRS 6 Chittagong Bangladesh CRS 7 Sihanoukville Cambodia CRS 8 Mumbai India CRS 9 Davao Philippines CRS 10 General Santos Philippines CRS 11 Zamboanga Philippines CRS 12 PSA International Singapore CRS 13 Shanghai China DRS 14 Tuticorin India IRS 15 Niigata Japan IRS 16 Bintulu Malaysia IRS 17 Shimonoseki Japan IRS 18 Mitajiri Japan IRS 19 New Mangalore India IRS 20 Subic Bay Philippines IRS 21 Visakhapatnam India IRS 22 Qingdao China DRS 23 Yantai China IRS 24 Belawan Indonesia IRS 25 Cebu Philippines IRS 26 Kaoshiung Taiwan DRS 27 Hong Kong China DRS 28 Keelung Taiwan DRS 29 Colombo Sri Lanka DRS 30 Tg. Pelepas Malaysia DRS 31 Incheon Korea DRS 32 Port Klang Malaysia DRS 33 Busan Korea DRS 115

8 Statistics in Research Table 9.2: Efficiency Results of Major Asian Container Ports (continue) No. Container Ports Country DEA CRS Model Overall Technical Efficiency DEA VRS Model Pure Technical efficiency Scale efficiency Returns to Scale 34 Jawaharlal Nehru India DRS 35 Tg. Priok Indonesia DRS 36 Qui Nhon Vietnam IRS 37 Pasir Gudang Malaysia IRS 38 Tokyo Japan DRS 39 Shekou China DRS 40 Chennai India IRS 41 Dalian China DRS 42 Shantou China IRS 43 Nagoya Japan DRS 44 Mizushima Japan IRS 45 Karachi Pakistan DRS 46 Fuzhou China IRS 47 Port M. Quasim Pakistan IRS 48 Laem Chabang Thailand DRS 49 Gwangyang Korea IRS 50 Mundra India IRS 51 Hakata Japan IRS 52 Kitakyushu Japan IRS 53 Jurong Singapore IRS 54 Yokohama Japan DRS 55 Manila Philippines DRS 56 Penang Malaysia IRS 57 Shimizu Japan IRS 58 Osaka Japan DRS 59 Kawasaki Japan IRS 60 Bangkok Thailand DRS 61 Kobe Japan DRS 62 Taichung Taiwan IRS 63 Iloilo Philippines IRS 64 Kochi India IRS 65 Pipavav India IRS 66 Yokkaichi Japan IRS 67 Kuantan Malaysia IRS 68 Da Nang Vietnam DRS 69 Muara Brunei IRS Geometric Average Standard Deviation

9 Analysing Technical and Scale Efficiency The results of the comparison among nations show that the most efficient ports in Asia are located in China, Bangladesh, Cambodia, India, Philippines, and Singapore. The Chinese container ports (excluding Hong Kong), appear to be amongst the top performers with five out of 12 ports in this study ranked within the top 10 i.e. Guangzhou, Lianyungang, Tianjin, Xiamen and Yantian. In 2007, the total container throughput of the 12 ports in China is the highest, with a total of 79.9 million TEUs followed by Singapore s PSA International yielding 27.9 million TEUs. China follows the global trend in concentrating liner services at ports, hence developing their ports into hubs, to cater for the rapid development of its hinterland economy. China has emerged as the world s manufacturing powerhouse and consumer market (Cullinane et al. 2004). In addition, the ports Yantian and Guangzhou received much expertise and technology transfer from Hong Kong due to entrepreneurial interests. The least technically inefficient port is Muara Port of Brunei with a score of While the inefficiency on inputs and outputs in efficient container ports are all zero, there are too much inputs or too little output in inefficient container ports. To practically improve the efficiency of container ports, increasing changeable outputs may be more appropriate than decreasing the given inputs. In the case of Muara Port, it should increase its container throughput by 90 percent (an increase of 886,230 TEU) holding the current level of inputs, to become fully efficient. The process of production is ways of converting certain inputs into outputs and technical efficiency is the conversion of physical inputs into outputs. Technical efficiency and outputs can increase by the courses of action that can improve the ability and flexibility to rationalise factor inputs in order to maximise outputs. This is at odds with a preconception that port efficiency and inputs is most likely driven by external demand for port services. While port throughputs (output) may be strongly influenced by external demands, enhancement of port efficiency certainly requires better management practices and operational planning in terms of input use. Scale Efficiency and Returns to Scale The average scale efficiency of the Asian container ports was Thirteen (19%) ports are scale efficient while the rest 56 (81%) are scale inefficient suggesting that they are not operating at optimal scale. Of the 56 scale inefficient ports, nine ports i.e. Shanghai, Tuticorin, Niigata, Bintulu, Shimonoseki, Mitajiri, New Mangalore, Subic Bay and Visakhapatnam are technically efficient. The remaining 47 ports are both scale and technically 117

10 Statistics in Research inefficient. On the other hand, the results reveal that the port of Jurong is scale efficient but technically inefficient. The study further investigates the status of returns to scale of the container ports. The right most column of Table 9.2 indicates the status of returns to scale of each port calculated based on the DEA scores. Here, 33 ports (47.8 percent) show an increasing returns to scale, 24 ports (34.8 percent) exhibits decreasing returns to scale, while the rest of the 12 ports exhibit constant returns to scale. It is evident that the majority of the container ports with throughput of one million TEUs and higher in a year tend to operate at DRS. This is due to the fact that port capital investments are often made in large amounts irregularly with the expectation of a long working life. As such, at initiation, ports often design their capacity in advance well in excess to its current business requirement, even if port traffic only builds up gradually over time (see Cullinane and Wang 2006). On the other hand, smaller sized container ports with output below one million EUs a year, exhibit either CRS or IRS. This outcome corresponds to the findings in Wang and Cullinane (2006) in investigating efficiency of 104 container terminals in European ports. For all the ports that experience increasing return to scale in their operations, increases in inputs will result in more than a proportional increase in outputs. Hence, the ports that operate with IRS could achieve significant efficiency gains by increasing its scale of operations. The scale could be altered via internal growth or consolidation in the sector. For the ports that are operating at decreasing returns to scale, further increase in inputs would only results in a smaller proportional increase of outputs. The ports that experience DRS should eliminate their scale inefficiency by decreasing their scale of operations by giving up some of the terminal assets and operational functions to other specialised entities via concessions and leaseholds. Conclusion Given the current phase of globalisation and competition, port performance is of major importance for port competitiveness. This study analysed the technical efficiency and scale efficiency of Asian container ports by means of DEA. Our analysis shows that the main source of overall technical inefficiency in Asian container ports is due to pure technical inefficiencies rather than scale inefficiencies. This suggests that port managers must improve their management practices and operational planning at local and national levels to favour efficient ways. This could be via an optimal combination of factors of production (capital, labour and land), adequate 118

11 Analysing Technical and Scale Efficiency investment in, and adaption of, new port technologies and further training and education in the adaptation and use of new technologies. The next step would then be to improve their scale efficiencies. About 48% of the ports are exhibiting increasing returns to scale. These ports should increase its scale of operations via internal growth or consolidation in the sector. About 35% of the container ports exhibit decreasing returns to scale. These ports can decrease their scale of operations by giving up some of the terminal assets and operational functions to other specialised private entities via concessions and leaseholds. This will also help to promote intra-port competition between multiple service providers within a port which can lead to higher efficiency gains. The analysis also revealed that Chinese container ports are very competitive in comparison to others. 119

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