Container port efficiency in emerging and more advanced markets

Transportation Research Part E 46 (2010) 1030–1042

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Transportation Research Part E journal homepage: www.elsevier.com/locate/tre

Container port efficiency in emerging and more advanced markets Yen-Chun Jim Wu a,*, Mark Goh b a b

Dept. of Business Management, National Sun Yat-Sen University, 70, Lienhai Rd., Kaohsiung 80424, Taiwan School of Management, University of South Australia, Adelaide, Australia

a r t i c l e

i n f o

Article history: Received 23 April 2009 Received in revised form 1 December 2009

Keywords: Container ports Efficiency Emerging markets Data envelopment analysis DEA

a b s t r a c t The literature on container port efficiency has typically centered on ports in advanced markets or comparisons within regions. This study compares the efficiency of port operations in emerging markets (BRIC and the Next-11) with the more advanced markets (G7). We use data envelopment analysis to evaluate the container ports based on the import and export cargo volumes in 2005. Our results suggest that none of the ports in the advanced markets are role models for the field. This study provides a first step towards gaining insights into port efficiency in emerging markets. Ó 2010 Elsevier Ltd. All rights reserved.

1. Introduction As the economies of the newly emerging countries grow and fuel infrastructure development, demand for commodities in these countries such as Brazil, Russia, India, and China (BRIC) has risen markedly. Wilson and Purushothaman (2003) have identified three common characteristics of the BRIC economies, namely, plentiful natural resources, relatively young populations, and large land areas, that have prompted analysts to classify them as markets that will significantly alter the global landscape. Despite the scholarly attempts to measure a country’s productivity or efficiency, a working definition remains elusive (Kao et al., 2008). O’Neill et al. (2005) have looked at the economic development potential of another set of developing countries, referred to as ‘‘The Next Eleven (N-11)”, which collectively have the potential to rival or even surpass the BRIC nations. The N-11 are South Korea, Indonesia, Vietnam, the Philippines, Pakistan and Bangladesh in Asia, Nigeria and Egypt in Africa, Mexico in North America, Iran in the Middle East, and Turkey. O’Neill et al. (2005) estimate that, by 2050, the combined gross domestic product (GDP) of the N-11 would equal that of the United States or four Japans. Cognizant of these economic development trends, many multinational corporations have made these emerging markets, such as BRIC, their primary investment choices, with an eye to reducing production and labor costs thereby gaining a decisive competitive advantage. Clearly, these investments have in turn fuelled the development of the logistics sector in those markets. Razzaque (1997) indicated that logistics is an important component of a country’s economy since it affects productivity (and hence competitiveness), distribution efficiency, interest rates, energy availability and energy costs. With the rise of China and her attendant hunger for raw materials and her propensity to export finished goods in containers, other emerging markets, especially the non-landlocked ones, will also realize the importance of an emphasis on trade and logistics, and the volumes of their import and export cargo will inevitably expand. Indeed, some scholars assert that port efficiency is an important criterion for a country in international competitiveness (Tongzon, 1989; Chin and Tongzon, 1998). Thus far, container port operations have attracted significant attention from academics and practitioners alike (Kim et al., 2003; Lee et al., 2007; Li et al., 2009; Parola and Sciomachen, * Corresponding author. Tel.: +886 7 5251005; fax: +886 7 5254698. E-mail addresses: [email protected] (Y.-C.J. Wu), [email protected], [email protected] (M. Goh). 1366-5545/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.tre.2010.01.002

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2005; Rodríguez-Álvarez et al., 2007). According to UNCTAD (2007), the global containership fleet increased by 17 million dead weight tons or 15.5% y-o-y, with the high growth rate reflecting greater international trade in manufactured goods and increasing containerization. For this reason, improving port efficiency or productivity has become a critical yet challenging task in the development of many countries (Turner et al., 2004). As ports are an important link in the logistics chain, the level of port efficiency affects to a large extent a country’s productivity and competitiveness. Countries need to focus on the factors that affect the efficiency of their ports and benchmark on the degree of efficiency, both between ports within a region and with the ports of other regions (González and Trujillo, 2008). However, few international studies have been conducted on the port industry in emerging markets either for the lack of data or interest. Hence, spawned by the recent economic growth of emerging markets such as those of India and China, exploring issues relevant to the port sector of such markets is of considerable importance, despite the dearth of literature. This research thus seeks to evaluate the efficiency of container ports in emerging and advanced markets. We first review and discuss the container port efficiency of the various countries of interest. Next, we apply the DEA technique to measure the efficiency of container ports. Lastly, the paper analyzes the results, and presents conclusions and some suggestions for the port sector.

2. Measures of port efficiency The methodologies employed in estimating container port efficiency include Stochastic Frontier Analysis (SFA) (Cullinane and Song, 2003; Cullinane et al., 2002; Estache et al., 2002; Liu, 1995), DEA (Barros and Athanassiou, 2004; Cullinane et al., 2005a; Kaisar et al., 2006; Roll and Hayuth, 1993; Valentine and Gray, 2001), multiple linear regression (Tongzon, 1995), Total Factor Productivity (TFP) (Estache et al., 2004; Cheon et al., 2010), and Free Disposal Hull (FDH) (Wang et al., 2003; Cullinane et al., 2005a). González and Trujillo (2008) use a translog distance function to gauge if 10 Spanish ports improved their technical efficiency during three waves of reforms. The study not only found clear changes in the development of port activities, it also observed a substantial improvement in the use of technology in the case of Spain. Liu (1995) used SFA to study ports in the UK, with data from 1983 to 1990 to compare the technical efficiency and evaluate the impact of privatization and nationalization on port operations efficiency. Estache et al. (2002) used three SFA models in their study (Cobb-Douglas, Translog – no technological change, and Translog – no technical inefficiency) to evaluate the technical and allocative efficiencies of 11 Mexican ports between 1996 and 1999. Cullinane et al. (2002) adopted stochastic frontier production models in their study, for three distributions (half-normal, exponential, and truncated normal) to compare the efficiency of 15 container ports and terminals in Asia between 1989 and 1999. Cullinane and Song (2003) also applied the same assumptions in their study to compare the productivity of two Korean container terminals and three UK container terminals between 1978 and 1996. Later, Tongzon (1995) used multiple linear regression to build a model of port efficiency and estimated the relative efficiency of 23 international container ports. Recently, Estache et al. (2004) used the TFP Malmquist Index to evaluate the state of operations at 11 major Mexican ports from 1996 to 1999. Most recently, González and Trujillo (2009) systematized existing studies to assess the economic efficiency and productivity of the port industry. However, due to the organizational complexity that exists across multiple port activities, the paper only highlighted the efficiency of container ports for emerging and advanced markets. On the DEA models to study the efficiency of container ports, Roll and Hayuth (1993) have used the CCR model, based on constant returns to scale, to evaluate and determine the efficiency of ports for advanced economies; and their work was treated as a theoretical exploration of applying DEA to the port sector rather than as an actual application since no data were collected or analyzed. The CCR model was later favored by Kaisar et al. (2006), Valentine and Gray (2001). Martinez-Budria et al. (1999) used the DEA-BCC model to evaluate 26 Spanish ports, collecting data from 1993 to 1997, to compare their relative efficiencies consequently dividing them into three tiers. In addition, some studies have combined two or three methods, with the research becoming more complex and explicit. For instance, Itoh (2002) used the CCR, BCC models and the DEA Window analysis to evaluate the relative performance of eight international ports in Japan between 1990 and 1998. Cullinane et al. (2004) applied the DEA Window analysis to compare the world’s top 30 leading container ports (ranked in 2001) to adduce relative efficiency over time. Further, Al-Eraqi et al. (2008) determined the relative efficiency of 22 cargo ports in the Middle East and Africa through a DEA cross-sectional data and Window analysis. An important advantage of such a window analysis is that it increases the discriminatory power of DEA by increasing the total number of decision making units (DMUs), i.e. the number of ports, providing results capable of tracking recent changes in performance and the stability of the port over time (see Charnes et al. (1994) for the details). Andersen and Petersen (1993) have proposed the A&P model, capable of differentiating the relative efficiency levels of DMUs with efficiency ratings of one. In the evaluation of container port efficiency, Lin and Tseng (2007) applied the A&P model to discriminate among the major container ports in the Asia–Pacific that were rated as efficient under the DEACCR model. Similarly, Wu and Lin (2008) ranked the efficiency of container ports in India, the G7, and the BRIC nations, differentiating their relative strengths and weaknesses through the A&P model. From the perspective of international container ports, So et al. (2007) applied the output-oriented CCR and BCC models to examine the efficiency of 19 major container ports in Northeast Asia including Korea, China, and Japan. As the facilities and scales of these container ports were similar, the selected DMUs were adequate for analysis. Tongzon (2001) chose the

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DEA-CCR and DEA-additive models to study the efficiency of four Australian and 12 other international container ports, and tested if the differences in output affected the performance and efficiency of the ports. Other studies used both the CCR and BCC models to analyze the efficiency of two Greek and four Portuguese ports (Barros and Athanassiou, 2004) and to evaluate the world’s top 30 container ports to determine if container port privatization benefits efficiency (Cullinane et al., 2005b). In the context of emerging economies, Chudasama and Pandya (2008) investigated 12 major ports in India adopting the DEA-CCR and DEA-BBC models, identifying sources of inefficiency to port authorities. Rios and Maçada (2006) established a model to measure the relative efficiency of the MERCOSUR (MERCOSUR is a Spanish acronym for Mercado Común del Sur comprising Argentina, Brazil, Paraguay, and Uruguay) trading bloc, using the DEA-BCC model. They concluded that DEA is useful for both governmental regulatory bodies and port managers in analyzing operational efficiency. FDH and DEA were used by Wang et al. (2003) to analyze the efficiency of container terminals at 28 of the world’s most important international ports. The two models yielded different results, with nine out of the 57 terminals being deemed as efficient when the DEA-CCR input-oriented model was applied, as compared to 23 and 37 terminals when the DEA-BBC input-oriented and FDH models were applied, respectively. Cullinane et al. (2005a) obtained similar results in that different models yielded different assessments of efficient terminal numbers. The FDH model assumes strong input and output disposability, i.e. any given of output(s) remains feasible if any of the inputs is increased, and likewise with given inputs it is always possible to reduce output(s). Both studies noted that FDH modeling was an insufficiently sensitive tool due to the nature of its underlying logic and step function solution algorithm. Indeed, the FDH model concluded that DMU performance was efficient when it was actually not. Cullinane et al. (2006) used SFA and DEA to evaluate 28 of the world’s most important international ports and the efficiency of their container terminals. Their work highlights that the SFA model was preferable when analyzing port operations as the hypothesis of constant returns to scale in the production frontier could not be rejected. Thus, from the extant literature on SFA, FDH, and DEA techniques and their applicability, the DEA approach appears most suitable for this study as it is not only non-parametric but also it does not require an explicit a priori determination of relationships between the inputs and outputs, nor the setting of rigid importance weightings for the various factors. It also has the advantage of being an objective efficiency evaluation model.

3. Methodology Based on the literature, it is clear why research which have addressed the port efficiency of emerging, advanced, and international markets have relied primarily on the DEA-CCR and DEA-BCC models, despite the fact that information technologies in emerging markets are not as advanced as those of developed countries (Emrouznejad et al., 2008). Hence, we adopt these models as its base. Wang et al. (2003) mentioned that, in terms of model orientation, input-oriented models are closely related to operational and managerial issues, while output-oriented models are more associated with planning and strategy formulation. With the rapid expansion of globalization and international trade, many container ports are compelled to review regularly their capacity to ensure that they can provide satisfactory service to port users and maintain their competitive edge. From that perspective, this study adopted the output-oriented CCR and BCC models to evaluate the efficiency of container port operations. The CCR model as proposed in Charnes et al. (1978) assumes that the production process yields constant returns to scale. When the returns to scale vary, production combinations will be scaled accordingly. As such, inefficiencies can be attributed to operations with different returns to scale. Banker et al. (1984) then improved on the constant returns to scale model by proposing a variable returns to scale BCC model. When the CCR and BCC models assign a value of one to the efficiency of DMUs, it is impossible to rank the efficiency and differentiate the relative strengths and weaknesses of already efficient container ports anywhere, be it in India, the G7, the BRIC nations, or the N-11. To overcome this limitation, the A&P (Andersen and Petersen) model was applied to reinforce the discriminatory power of the CCR and BBC models in ranking the relative efficiency of container ports in emerging and advanced markets. 3.1. Data Our study collected data from the Containerization International Yearbook (2007), including the statistics for lift-on/liftoff containers handled as well as conventional berths. The DMUs for our DEA model were selected as the largest container port in 2005 in each country. Twenty-one countries were included in our DEA analysis (there was a lack of data for Nigeria). They were classified into two categories: advanced markets (G7: USA, France, Italy, Canada, Germany, Japan, UK), and emerging markets (Brazil, Russia, India, China, Bangladesh, Egypt, Indonesia, Iran, Korea, Mexico, Pakistan, the Philippines, Turkey, and Vietnam), as shown in Table 1. 3.2. Input and output variables Dowd and Leschine (1990) have argued that the productivity of container ports/terminals depends on the efficient use of land, labor, and capital. In earlier studies, the variables were mainly classified into three major categories as follows.

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Y.-C.J. Wu, M. Goh / Transportation Research Part E 46 (2010) 1030–1042 Table 1 Operational performance of decision making units. Markets

Country

Container ports (DMUs)

Advanced markets

USA France Italy Canada Germany Japan UK

Los Angeles Le Havre Gioia Tauro Vancouver Hamburg Tokyo Felixstowe

Emerging markets

China Russia Brazil India Bangladesh Egypt Indonesia Iran Korea Mexico Nigeria Pakistan Philippines Turkey Vietnam

Shanghai Saint Petersburg Santos Jawaharlal Nehru Chittagong Damietta Tanjung Priok Shahid Rajaee Pusan Manzanillo N/A Karachi Manila Ambarli Ho Chi Minh City

3.2.1. Input variables 3.2.1.1. Labor inputs. Broadly speaking, labor inputs include stevedoring, port authority employees, container terminal labor and other labor expenditures. Tongzon (2001) used the number of port authority employees as a proxy for the number of stevedores due to the lack of information. Similarly, González and Trujillo (2008) chose the number of port authority employees as an input variable. Itoh (2002), however, suggested using the number of container terminal workers as the labor input unlike Martinez-Budria et al. (1999) who used labor expenditure. Cullinane and Song (2003) preferred the total wages and salaries paid to employees. While Barros and Athanassiou (2004) also argue for using the number of laborers, it is unclear which variable among stevedore labor, port authority employees or container terminal laborers is preferred. 3.2.1.2. Land/capital/equipment input. Ports require a significant amount of investment in land, equipment, and infrastructure. In terms of land, the literature has often used terminal area as a variable. As for equipment, the number of quayside gantries, yard gantries and straddle carriers are often selected, while the number of berths and the total length of terminals are frequently used as infrastructure variables. On the measurement of capital, Liu (1995) used the net value of fixed capital, including land, buildings, docks, berths, roads, storage, and equipment as the input variable for capital while Cullinane and Song (2003) chose the net book value of fixed equipment, buildings, land, and mobile and cargo handling equipment as the input variable. 3.2.1.3. Other input variables. Several other input variables have been proposed, for example, Tongzon (2001) proposed the number of tugs and the amount of delay time as inputs while Martinez-Budria et al. (1999) suggested depreciation charges and other expenditures with Roll and Hayuth (1993) using uniformity of service instead. 3.2.2. Output variables 3.2.2.1. Actual throughput. This paper uses the total tonnage throughput, number of containers, and the total cargo throughput to assess port operation performance. Most research use the number of containers or cargo throughput as the output variable. 3.2.2.2. Service level output. Roll and Hayuth (1993) utilized the level of service and user satisfaction as outputs while Tongzon (2001) suggested the ‘‘ship work rate” as an indicator of a ship’s operating speed. Ship work rate refers to the number of containers moved per hour per ship and is an indicator of port service quality. 3.2.2.3. Other output variables. González and Trujillo (2008) argues that the weight of the liquid bulk cargo and the number of passengers should be considered when assessing a port’s output performance. Liu (1995) suggests using ownership, size and location of the port and capital intensity as dummy variables to assess port performance. Many variables have been proposed as indicators of port efficiency. Tables 2 and 3 summarize the input and output variables respectively, as surveyed in the literature on DEA from 2003 to 2006. As shown, terminal area, total quay length, and

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Table 2 Key input variables for DEA analysis. Study

Martinez-Budria et al. (1999)

Tongzon (2001)

Valentine and Gray (2001)

Wang et al. (2003)

Itoh (2002)

d d d

Barros and Athanassiou (2004)

Cullinane et al. (2005a)

Cullinane and Wang (2006b)

d

d

Kaisar et al. (2006)

Cullinane et al. (2005b)

Cullinane et al. (2006)

d

d

d d d d

d d d d

d d d d d d d

d

d

d

d

d d d d d d d d d

d d d d

d

d d d d

d d

d d

Note: Labor-exp = labor expenditures; Depreciation = depreciation charges; Other-exp = other expenditures; Cranes = number of cranes; Berths = number of berths; Delay time = amount of delay time; Employees = number of port authority employees; Berth = total length of berth; Terminal = total length of terminal; Container = container berth length; Quay length = total quay length; Quayside = number of quayside gantries; Yard = number of yard gantries; Straddle carriers = number of straddle carriers; Termi-workers = number of container terminal workers; Workers = number of workers.

Y.-C.J. Wu, M. Goh / Transportation Research Part E 46 (2010) 1030–1042

Variables Manpower Capital Uniformity Labor-exp Depreciation charges Other-exp Cranes Berths Number of tugs Terminal area Delay time Employees Berth Terminal Container Quay length Quayside Yard straddle carriers Termiworkers Workers

Roll and Hayuth (1993)

Study

Variables Total tons throughput Service level User satisfaction Ship calls Total cargo moved through docks Revenue obtained from rental of port facilities Ship working rate Number of containers Number of ships Total containers handleda Total cargo handledb a b

Roll and Hayuth (1993)

MartinezBudria (1999)

Tongzon (2001)

Valentine and Gray (2001)

Wang et al. (2003)

Itoh (2002)

Barros and Athanassiou (2004)

Cullinane et al. (2005a)

Cullinane & Wang (2006)

Kaisar et al. (2006)

Cullinane et al. (2005b)

Cullinane et al. (2006)

d

d

d

d

d

d d d d d

d

d

d

Loaded and unloaded. Dry and liquid cargo, and loaded and unloaded cargo.

d d

d

d

d d d d

Y.-C.J. Wu, M. Goh / Transportation Research Part E 46 (2010) 1030–1042

Table 3 Key output variables for DEA analysis.

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Table 4 Input and output variables of selected major international container ports.

a

No. of pieces of equipmenta

Country

Terminal area (ha)

Total quay length (m)

Advanced markets USA France Italy Canada Germany Japan UK

No. of containers (TEU)

647.7 205.0 130.0 158.3 531.8 102.1 177.6

9278 6075 3155 4019 9248 4016 4026

143 90 138 60 326 95 125

7,484,624 2,118,509 3,160,981 1,767,379 8,087,545 3,593,071 2,750,000

Emerging markets China Russia Brazil India Bangladesh Egypt Indonesia Iran Korea Mexico Nigeria Pakistan Philippines Turkey Vietnam

617.0 59.9 69.1 68.8 1.5 60.0 165.6 10.7 392.2 31.8 N/A 36.9 184.5 78.3 142.2

7542 2203 2160 1280 450 1050 3192 6190 12,610 2205 N/A 1200 8102 5620 3731

343 59 33 71 25 29 133 43 252 28 N/A 26 77 68 64

18,084,000 1,119,346 2,267,921 2,666,703 783,353 1,621,066 3,281,580 1,292,962 11,843,151 873,976 N/A 850,000 2,665,015 1,185,768 1,911,016

No. of quayside gantries, yard gantries (rail-mounted and rubber typed), and straddle carriers (unit: piece).

Table 5 Correlation between inputs and output. Input

Terminal area (ha)

Total quay length (m)

Pieces of equipment (No.)

Output Number of containers

0.8508

0.6598

0.9055

Table 6 Statistics for the sample. Input

Output

Terminal area (ha)

Total quay length (m)

Pieces of equipment (number)

No. containers (TEUs)

Max 647.7

12610.0

343

18,084,000

Min 1.5

450.0

25

783,353

Average 184.34

4635.81

106.10

3781,332

SD 189.77

3161.98

90.93

4193,896

the number of quayside gantries, yard gantries, and straddle carriers are commonly used as inputs, while container volume is often used as the output. In addition, these variables are found in the Containerization International Yearbook (2007). As a result, these six variables form the input and output variables for our DEA model (see Table 4). In terms of the inputs, we adopt Cullinane and Wang’s (2006) approach of combining three variables (the number of quayside gantries, yard gantries, and straddle carriers) into a single composite variable, referred to as the ‘‘number of pieces of equipment”.

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The data used for this paper are summarized in Table 6. The analysis of related coefficients (see Table 5) shows a positive correlation between the factors, in conformity with the isotonicity assumption. As a rule, the number of DMUs should be twice the number of inputs and outputs. In this study, the DMUs, the 21 container ports, conformed to this rule.

4. Results We now analyze the operational performance and the relative efficiency of container ports in the emerging and advanced markets using quadrantal diagrams. 4.1. DEA-CCR, BCC, and A&P models Table 7 shows the results obtained using the CCR, BCC, A&P, CCR cross efficiency, and BCC cross efficiency models to determine the efficiency of ports under study. Under the CCR model, the ports of Shanghai, China (1), Chittagong, Bangladesh (1) and Santos, Brazil (1) have efficient operations, while none of the ports in the advanced markets could be described as efficient. The port of Los Angeles, USA (0.7639) was the most efficient port of the advanced markets. In short, the average efficiency value of ports in emerging markets indicates they are more competitive than ports in the advanced markets. Apart from the ports of Shanghai, China (1), Chittagong, Bangladesh (1), and Santos, Brazil (1), the BCC model also identified the ports of Damietta, Egypt and Shahid Rajaee, Iran as having highly efficient operations. Table 7 shows that the DEA-BCC model yields higher values than the DEA-CCR model, due to the assumption of constant returns to scale by the CCR model albeit the CCR and BCC models were well correlated (r = 0.9497). We used the A&P model to differentiate the efficiency of the DMUs that are evenly ranked with CCR-model efficiency values of one, such as China and Bangladesh. The results suggest that operations at Bangladesh’s maritime freight port are more efficient than those in China. Among all the DMUs, Bangladesh has the highest container operations efficiency. Arvis et al. (2007) argue that the crucial supply chain factors, among the main benchmarks, used to evaluate logistics efficiency include the rate of physical inspections, customs clearance, export lead time (median case), import lead time (best case), import lead time (median case), number of border agencies for exports, number of border agencies for imports, possibility of review procedures, and the typical charges for 40-ft export and import containers or semitrailers. Through the use of various models to examine the indicators of logistics efficiency, Table 8 shows that the CCR and CCR cross efficiency models yielded values with significance levels of less than 0.01 for import lead time, median case (days). The BCC model also yielded a significance level of less than 0.05 for the import lead time, median case (days).

Table 7 National ports efficiency comparison. Model

CCR

A&P

BCC

CCR cross efficiency

BCC cross efficiency

Country USA France Italy Canada Germany Japan UK China Russia Brazil India Bangladesh Egypt Indonesia Iran Korea Mexico Nigeria Pakistan Philippines Turkey Vietnam

0.7639 0.3425 0.5326 0.4286 0.4715 0.7566 0.4290 1.0000 0.3950 1.0000 0.9631 1.0000 0.9645 0.5265 0.8639 0.8101 0.6107 N/A 0.5913 0.5036 0.3392 0.4518

0.7639 0.3425 0.5326 0.4286 0.4715 0.7566 0.4290 1.2687 0.3950 1.2295 0.9631 6.5279 0.9645 0.5265 0.8639 0.8101 0.6107 N/A 0.5913 0.5036 0.3392 0.4518

0.9498 0.4093 0.7103 0.4848 0.5143 0.9664 0.4740 1.0000 0.4529 1.0000 0.9955 1.0000 1.0000 0.6039 1.0000 0.9986 0.6430 N/A 0.8562 0.5905 0.3918 0.4964

0.5351 0.3006 0.4485 0.3601 0.4104 0.6417 0.3690 0.8845 0.3340 0.9062 0.7584 0.8601 0.8281 0.4500 0.4370 0.7081 0.4509 N/A 0.5121 0.3956 0.2480 0.3961

0.9012 0.5629 1.4182 0.7541 0.9860 1.4430 0.6672 2.4288 0.6539 1.9377 1.7933 8.8862 1.1407 1.0967 0.5772 1.6129 1.1272 N/A 1.2284 1.0119 0.5743 0.5859

Note: N/A = data not available.

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Table 8 Logistics performance survey questions. Source: Arvis et al. (2007)

* **

Model

Rate of physical inspection (percent)

Customs clearance (days)

Lead time export, median case (days)

Lead time import. Best case (days)

Lead time import, median case (days)

Number of border agencies, exports

Number of border agencies, import

Possibility of review procedure (percent)

Typical charge for 40-ft export container or semi-trailer (US$)

Typical charge for 40-ft import container or semi-trailer (US$)

CCR BCC A&P CCR cross efficiency BCC cross efficiency

0.067 0.079 0.371 0.469

0.424 0.628 0.187 0.204

0.308 0.265 0.547 0.648

0.164 0.356 0.396 0.12

0.01** 0.042* 0.334 0.004**

0.506 0.481 0.126 0.722

0.311 0.349 0.282 0.589

0.508 0.869 0.571 0.808

0.394 0.383 0.255 0.352

0.092 0.068 0.251 0.089

0.663

0.191

0.474

0.431

0.349

0.154

0.402

0.719

0.245

0.187

Correlation is significant at the 0.05 level (2-tailed). Correlation is significant at the 0.01 level (2-tailed).

Table 9 Sensitivity analysis. DMU

USA France Italy Canada Germany Japan UK China Russia Brazil India Bangladesh Egypt Indonesia Iran Korea Mexico Nigeria Pakistan Philippines Turkey Vietnam

CCR (2005)

0.7639 0.3425 0.5326 0.4286 0.4715 0.7566 0.4290 1.0000 0.3950 1.0000 0.9631 1.0000 0.9645 0.5265 0.8639 0.8101 0.6107 N/A 0.5913 0.5036 0.3392 0.4518

Efficiency value after input deleted Terminal area

Total quay length

Number of pieces of equipment

0.7639 0.3425 0.4318 0.4286 0.4505 0.6284 0.3895 1.0000 0.3250 1.0000 0.8689 0.7260 0.9645 0.4615 0.4375 0.7454 0.4544 N/A 0.5306 0.5036 0.2537 0.4518

0.7616 0.3425 0.4817 0.4286 0.4120 0.7566 0.3904 0.8329 0.3881 1.0000 0.7816 1.0000 0.8188 0.4645 0.8639 0.7986 0.6107 N/A 0.5805 0.5036 0.3392 0.4345

0.3511 0.1748 0.4869 0.2153 0.3985 0.4646 0.3280 1.0000 0.2619 0.5301 0.9631 1.0000 0.7046 0.4788 0.2314 0.4759 0.2162 N/A 0.3595 0.1728 0.1153 0.2521

Note: N/A = data not available.

4.2. Sensitivity analysis Table 9 shows that the production equipment variable heavily influences port efficiency in most countries (the USA, France, Canada, Germany, Japan, and the UK among the advanced countries and Russia, Brazil, Iran, Korea, Mexico, Pakistan, the Philippines, Turkey, and Vietnam among the emerging countries) and is the key to their productivity. For Italy, if terminal area were excluded from the equation on determining port efficiency, the overall efficiency value for the country’s ports decreases from 0.5326 to 0.4318, clearly indicating that terminal area is critical to the efficiency of Italian ports. Bangladesh and Indonesia are similar. Meanwhile, for China, if quay length were omitted in estimating port efficiency, the overall efficiency value for the country’s ports reduces from 1.0000 to 0.8329, suggesting that quay length is crucial to the efficiency of Chinese ports. Similarly, for India. 4.3. Efficiency classification The absolute efficiency values obtained through self-assessment using the CCR model and the relative efficiency values gained through relative-assessment using CCR cross efficiency techniques had a positive correlation of 0.9362. The self-

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Fig. 1. Efficiency classification of ports.

assessment values are divided into three self-assessment groups along the y-axis, labeled as ‘‘less favorable”, ‘‘favorable”, and ‘‘more favorable”. Similarly, the relative-assessment values have been divided into three relative-assessment groups along the x-axis, correspondingly labeled as ‘‘less favorable”, ‘‘favorable”, and ‘‘more favorable”. The overall operational efficiency results derived from the CCR and CCR cross efficiency models are shown in Fig. 1, with 21 countries divided into three groups. Nigeria was not included due to a lack of data. 4.3.1. Group 1 (CCR efficiency self-assessment score = 1, CCR cross-efficiency relative-assessment score > 0.8) Group 1 comprises the ports considered to be benchmarks in the sector. Apart from the ports of Bangladesh, China, and Brazil, the ports of the remaining eighteen countries sampled were less efficient. The analysis of the ports of Bangladesh, China, and Brazil in Fig. 1 reveal that they most frequently served as operational benchmarks for the other ports in 2005. Our results further indicate that these ports should maintain their benchmark status. Also, the locations with the most efficient port operations were all from the emerging countries; none of the ports in advanced countries made the cut. 4.3.2. Group 2 (CCR efficiency self-assessment score < 1, CCR cross-efficiency relative-assessment score between 0.6 and 0.8) Group 2 ports rank highly on certain indicators of port efficiency but still exhibited flaws in a few areas. The ports in this group, located in India, South Korea, Egypt, and Japan should constantly work on specific areas of operational efficiency to stay competitive. 4.3.3. Group 3 (CCR efficiency self-assessment score < 1, CCR cross-efficiency relative-assessment score < 0.6) Group 3, (the USA, Iran, Pakistan, Mexico, Italy, Indonesia, Germany, Vietnam, the Philippines, Canada, the UK, Russia, France, and Turkey), comprised mainly ports which had no obvious competitive advantage over their counterparts in terms of port performance efficiency. Policy makers in these countries should work actively and quickly to identify and remove the factors contributing to port inefficiency. It is counter intuitive to see that G7 countries (other than Japan) ranked lowly on ocean freight terminal operations. This is perhaps due to the low priority given. 5. Conclusion This paper examines the efficiency of maritime port operations in emerging markets. Past studies have focused primarily on the operational efficiency of container ports in developed countries, on the top 30 container ports in the world, or comparing regional ports. The work on port operations in emerging markets is scant. Using the DEA-CCR, DEA-BCC, and A&P models to measure the efficiency of ports in developing countries, this study reports that the ports of Shanghai in China, Chittagong in Bangladesh, and Santos in Brazil had efficiency levels in 2005 that surpassed those in the developed G7 nations. This study used efficiency values obtained through ‘‘self-assessment” and ‘‘peer assessment” techniques to broadly analyze port operations around the world. It divided the ports studied into three separate categories. Bangladesh, China and Bra-

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zil in Group 1 had the ports with the highest ‘‘self-assessment” and ‘‘peer assessment” efficiency values, and these ports serve as benchmarks for the sector. This study’s results could be used by port operators as a reference when developing their facilities and improving operational performance. Bangladesh and China, though both belong to Group 1 as models of efficiency, have different container throughputs. Bangladesh’s container volume was 783,353 TEU while China’s was 18.08 million TEU in 2005. Despite such stark differences between the two countries in terms of inputs and outputs, both were highly efficient. This suggests that regardless of the volume of inputs and outputs, any port that plans its facilities based on the actual cargo demand can achieve outstanding operational efficiency. Also, no advanced country had a port ranked in Group 1, and only the port of Tokyo, Japan made it to Group 2. The ports of the other G7 countries studied were in the least efficient Group 3. This suggests that while the ports of the emerging countries may not have the same level of equipment than those in advanced countries, they nevertheless are more competitive than their more advanced counterparts on operational efficiency. This contradicts the result of Bookbinder and Tan (2003) who compared the logistics systems in Europe and Asia, and reported that, other than the two advanced countries (the UK and France) that were classified in the second tier, the other advanced European countries were all ranked in the top tier, while most of the countries in the lowest tier were emerging countries. One explanation for this contradiction could be that, over time, developing countries have caught up on the efficiency of their marine transport services. Ports (except those in China, Brazil and Bangladesh) need to upgrade their facilities and capacity urgently or risk suffering from the severe bottlenecks as trade volume expands. Policy makers should also consider improving the port catchment area as Dowd and Leschine (1990) have argued that the productivity of container ports and distribution stations depends on their land, equipment, and how efficiently workers are deployed. For example, India’s biggest infrastructural deficiency is its lack of electricity, due to the high levels of economic growth leading to an inordinate increase in electricity demand which have far outpaced the speed at which new power plants can be built. Also, its ports suffer from a lack of connectivity to major highways and industrial clusters, and that its inland transportation network is inefficient and slow because of the soaring costs of fuel (Chandrasekaran and Kumar, 2004). If such ports can rapidly improve their links to external transportation networks, have better integrated service provision, and improve in-port railroad transportation services, they will be able to achieve better operational efficiency (Kaisar et al., 2006). Likewise, based on the port of Chittagong in Bangladesh and through the use of in-depth interviews and Delphi techniques, Islam et al. (2006) highlighted that the lesser-developed economies can develop faster if only their inland freight transport systems support multimodal transport. In short, more efficient customs clearance, a shift in freight transport, technology & systems, and modern logistics concepts and practices must be implemented and constantly improved upon. Aside from the factors of port services, local inland conditions, availability, convenience, logistics costs, and regional centers and connectivity, research have identified other factors that influence the choice of shipping companies and owners with regard to ports. These include the size and level of activity of the free trade zones, the efficiency of the inland transportation network, inland transportation costs, the rate of physical inspection, customs clearance, and export and import lead times (Arvis et al., 2007; Yeo et al., 2008). All these factors that can strengthen the efficiency and hence port productivity are strategic options for port operators to carefully consider when pondering on improvements to their facilities. 5.1. Limitations and future research As in any research, our study has its limitations. According to Liu (2008), the nature of the DEA-CCR and DEA-BBC models overlook environmental factors, managerial inefficiency, and statistical noise. Further study is needed to see if the proposed revised models may enhance the practicality of DEA. We recognize that numerous other non-tangible factors affect container port efficiency. This study has adopted a focus on land and equipment as variables primarily because of a lack of data on worker efficiency. If better data were available, container port efficiency could be more thoroughly explored and detailed. Acknowledgements The authors are thankful to National Science Council of Taiwan for providing grant support (NSC 97-2511-S-110-006-MY2). Our deepest appreciation is extended to Mr. Chia-Wen Lin for his assistance in data collection and analysis. The authors also wish to thank the referees for their constructive comments. The valuable suggestions of Professor Wayne K. Talley to improve this paper are also gratefully acknowledged. Any errors or omissions remain the sole responsibility of the authors. References Al-Eraqi, A.S., Mustaffa, A., Khader, A.T., Barros, C.P., 2008. 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