Efficiency Changes at Major Container Ports in Japan: a Window Application of Data Envelopment Analysis

RURDS Vol. 14, No. 2, July 2002

EFFICIENCY CHANGES AT MAJOR CONTAINER PORTS IN JAPAN: A WINDOW APPLICATION OF DATA ENVELOPMENT ANALYSIS

Hidekazu Itoh

Graduate School of Policy and Planning Sciences, University of Tsukuba, Tsukuba, Japan

Container ports in Japan are not experiencing rapid cargo handling growth. Recently, however, a reorganization of port use is taking place due to a variety of factors like growing container ship size, diversified Asian shipping routes, increasing import cargo and the Great Hanshin earthquake in Kobe in 1995. This paper analyzes the operational efficiency of eight major international container ports using a “window” application of data envelopment analysis for the period between 1990 and 1999. The paper shows a distinction between ports with timely development and sound demand growth and those with deferred re-planning and slow demand recovery. The Port of Tokyo has successfully redeveloped to receive large-sized container vessels and is observing increased and well- balanced incoming and outgoing traffic, and hence remains DEA efficient in our analysis. The Port of Kobe still needs to implement further measures to attract new users and previous users due to the Great Hanshin earthquake. The BCC model of the DEA takes into account small-scale operations of Yokkaichi and Shimizu Ports, and appraises their recent increasing demand for Asian routes. The appropriate design of window length is also proposed in the study.

1. Introduction

1.1 Port efficiency and DEA

Efficient development and operation of ports is indispensable for Japan because the country’s island geography necessitates a trade-oriented economy. As a component of the overall Japanese economy, the port sector comprises only 0.24% of the GDP. The share of this sector in total employment is 0.09%, while capital stock is 0.14%. Efficiency in providing port service based on such limited resources, therefore, is an extremely important issue for the eco- nomic development of Japan. The majority of cargo handled at ports, value-wise, is containerized. As inputs to the container port operation system, a port requires an extensive set of both water- and land-side infrastructure. Shippers and shipping lines as well as many other port users require various administrative and related financial and other services. The efficiency of port operations is dependent on the design and maintenance of berths, channels, navigation aids, other water- side facilities, stacking areas, cargo handling equipment, warehouses, container freight sta- tions, accessibility and other land-side facilities. Port management, particularly the type of

ownership and administration, stevedoring labor, and the existence of competition in port net- works determine the operating efficiency of port facilities. Information technology also helps in improving their operating performances. In this context, ports tend to allocate a different set of inputs for their own operation purposes and environments. Three types of cargo flows are handled at ports: local exports, local imports and trans- shipments. Import cargo handling requires larger port areas than export cargo. Ports with regional hub functions transship cargoes between large ships on trunk routes and small ones on feeder routes. Although Japan is basically a country of shippers, or cargo origination, some transshipment is observed at such major ports as Yokohama and Kobe. Transshipment vessels and containers demand well-planned operations. Moreover, ports are visited by container vessels on different shipping routes, of different size, and at different frequencies. International trunk routes such as North American routes and European routes tend to handle high-value added cargoes that seek reliable port operations, while Asian routes tend to handle low-value- added cargoes that often prefer low-priced service. We apply the Data Envelopment Analysis (DEA) approach to evaluate the efficiency of ports in this study. DEA estimates the efficiency of systems with multiple inputs and multiple outputs. The system for evaluation is called a decision-making unit (DMU), and can be a company or a nonprofit organization. The efficiency of a DMU according to the DEA analysis is defined by its ability to transform an input into the maximum amount of aimed output. A DEA generalizes this single input/single output technical efficiency measure to a multiple output/multiple input case by constructing a relative efficiency measure in terms of the fraction of a single “virtual” output and a single “virtual” input. The efficiency defined here is composed of or is achieved through two components, namely technical efficiency and scale efficiency. Technical efficiency is improved by allocating and taking use of an appropriate combination of different inputs to produce intended outputs. Scale efficiency is improved by choosing an appropriate size of production if a scale economy exists. The DEA technique measures the efficiency of DMUs with multiple inputs and multiple outputs in a non-parametric fashion without requiring any explicit a priori determination of relationships between the outputs and inputs.1 Such relationships may be called the “stan- dards” criteria, and are required for the conventional efficiency estimation using production functions, which measure the absolute efficiency. The DEA approach, on the other hand, de- cides the relative efficiency order within a set of analyzed units. As pointed out earlier, ports handle different flows and volumes of containerized cargoes based on a combination of a variety of inputs. This study covers the container operation of eight ports in Japan, namely, the Ports of Tokyo, Yokohama, Shimizu, Nagoya, Yokkaichi, Osaka, Kobe, and Kitakyushu. As will be seen in the next subsection, their operations differ considerably and the DEA approach becomes a very relevant and useful tool for efficiency measurement in consideration of unique characteristics of each port’s operation. Besides their diversified operations, the container ports are recently experiencing a number of noticeable changes, which seriously concern port performance and policy. The Great Hanshin earthquake of 1995 damaged the Port of Kobe and caused a sudden decline in port

1 Alternative approaches such as Total Factor Productivity (TFP) approach and index approach do not have the efficiency frontier as the evaluation base in contrast to the DEA approach, which assumes the efficiency frontier. The efficiency frontier is based on a kind of the Pareto optimum concept in the case of more than one measures. See Char- nes et al. (1994), Sengupta (1995), Nakajima (2001) for more on the TFP and index approaches and the DEA analysis.

handling demand. The size of container vessels is becoming larger2 partly due to the strategic movement of global alliances forged by shipping lines since 1995,3 and is requiring construc- tion of 15m-deep container berths. Japan’s trade balance has been long characterized by export dominance, but in 1994, import container cargo handling exceeded that of export. Finally, during the last decade a number of regional ports other than the five major ports were developed to receive container traffic on Asian routes. Some containers are even shipped out of such regional ports by feeder vessels and transshipped to main vessels for trunk routes at the Port of Pusan. It is of great interest to evaluate how the major container ports have reacted to these recent changes by re-planning their facilities and operation. Responses of the ports to such recent changes cannot be analyzed by a cross-sectional DEA application in one period of time. To analyze the efficiency changes of such ports over a time period, a “window” application of the DEA method is employed. A window is a kind of panel application, i.e., both cross-sectional and time-series. Since the design of the DEA window application is not formalized, our secondary objective is to discuss its appropriate design. The rest of the paper is organized as follows. The second half of Section 1 discusses the recent trends of port changes. Section 2 reviews the literature on the applications using the DEA method. Section 3 outlines the methodology and window application of DEA. Section 4 introduces the data sets and window application design of the DEA for this study. Section 5 discusses the results and policy implications of the analysis. It also provides the application results of different window lengths to propose an appropriate window analysis design. Section 6 concludes.

1.2 Recent trends in port changes

In 1999, container handling volume at Japan’s eight major ports was 88.6% for exports, 87.9% for import. Total (export and import) container handling at the Ports of Tokyo, Yoko- hama and Kobe ranged from 1.5 million to two million TEUs. The level of container handling at Nagoya and Osaka was approximately one million TEUs each. The levels at Kitakyushu, Shimizu and Yokkaichi were less than 500,000 TEUs. Figure 1 illustrates the trend of container handling volumes in terms of TEU (20-footer equivalent units) by export and import for the eight ports. Although Kobe handled the most during the first half of 1990s for both export and import cargo, the Great Hanshin earthquake reduced the volume to less than half of the pre-earthquake level. The level has recovered to an extent, but has not reached the pre-earthquake level. The previous demand from Kobe has shifted to nearby Osaka and, to a lesser extent, to Yokohama and Nagoya, especially in the case of import cargo. While both export and import handling volumes at Tokyo increased gradually since 1993, Tokyo caught up with Yokohama in 1998. The handling volumes at Shimizu, Yokkaichi, and Kitakyushu were smaller compared to the major five ports, but growing due to increasing Asian route demand.

2 Capacity of the largest container ship was 4,300TEU in 1990 and 8,000TEU in 2000. 3 See Doi et al. (2000) for details.

Figure 1. Changes in handling volume

1400000

1200000 Tokyo/Export Yokohama/Export 1000000 Shimizu/Export Nagoya/Export 800000 Yokkaichi/Export Osaka/Export TEU 600000 Kobe/Export Kitakyushu/Export 400000 Tokyo/Import Yokohama/Import 200000 Shimizu/Import Nagoya/Import 0 Yokkaichi/Import Osaka/Import 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 Kobe/Import Year Kitakyushu/Import

Figure 2 presents the balance of exports and imports. Imports exceed exports at Tokyo, Osaka, and Kitakyushu, and to smaller extents at Yokohama and Kobe. Exports exceed imports at Shimizu. Yokkaichi experienced a switch from import dominance to export dominance in 1994. Figure 3 illustrates the trend in container berth size. Kobe, Tokyo and Osaka have been equipped with berths whose average length is longer than 300m since opening, 1996 and 1994, respectively. This is due to the trend of increased ship size, pointed out earlier, and associated with the increasing number of cranes per berth.

Figure 2. The balance of imports and exports

2.50

Tokyo 2.00 Yokohama Shimizu 1.50 Nagoya Yokkaichi 1.00 Osaka Import / Export Kobe 0.50 Kitakyushu

0.00

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

Year

Figure 3. Average berth length

400

Tokyo 350 Yokohama Shimizu 300 Nagoya Yokkaichi 250 Osaka Kobe Average Berth Length (m) 200 Kitakyushu

150

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

Year

2. Literature review

While a sizable literature exists for DEA-based applications, three papers have applied the DEA approach to analyze port efficiency. Roll and Hayuth (1993) advocate the use of DEA to measure port efficiency and demonstrated, based on hypothetical port data, how the relative efficiency rating of ports can be obtained. Tongzon (2001) applied the DEA approach to provide an efficiency measurement for four Australian and twelve other international container ports, including the Ports of Yokohama and Osaka, as of 1996 and based on the as- sumption of both constant and variable returns to scale. He identified that the Ports of Mel- bourne, Rotterdam, Yokohama and Osaka were the most inefficient mainly due to the considerable extent of slack in the inputs of container berths, terminal areas, and labor with other inefficient ports. Itoh et al. (2001) analyzed the performance of the aggregated Chinese system of ports and water transport through a time series application of DEA in both constant and variable returns to scale frameworks using input-output tables and related data for 1987-1997. They detected an inefficient system operation in 1997 due to over-investment. While Roll and Hayuth introduced the usefulness of the DEA approach to the efficiency analysis of ports, which tend to have diversified investment and operations, the last two works applied the DEA method to real-world data. Tongzon’s focus was on the comparison of international container ports in different countries at one point in time, and therefore may be under- stood as a cross-sectional application. Itoh et al. focused on the time trend changes of efficien- cies with one aggregated port system, and thus may be called a time-series application. The international container ports in Japan vary in size, route and frequency of ship calls, handling volumes, and investment policy. At the same time, they have faced changes in envi- ronmental factors of port management, including shippers and economies, shipping lines, and natural causes at different points of time. To analyze the efficiency changes of such ports in

such time periods, we make a window application of the DEA method. Our objectives are not only to analyze the efficiency changes of these different ports but also to discuss the appropriate design of DEA window applications, for the analysis requires an application design in terms of the time length of the window.

3. Data Envelopment Analysis (DEA)

DEA was originally developed as an analytical method for evaluating the relative efficiency of firms and nonprofit organizations. While Farrell (1957) conceived the original evaluation concept of DEA, much of the recent literature refers to Charnes et al. (1978) as the pioneer work of establishing the methodology.4 DEA takes enterprises or self-governing organizations as the evaluation units, which are called Decision Making Units (DMUs). A DMU is a system with multiple inputs and outputs, for which price data or other convertible measures are either unavailable or unknown. When a single entity reports a time series of its performance data, the performing system of each time period represents a DMU. DEA then evaluates the efficiency of each DMU by finding a combination of weights on input and output variables, which leads to the most preferable evaluation.5 The efficiency of a DMU is determined by its ability to transform inputs into aimed outputs. The efficiency of a single input/single output machine or process is defined as Out- put/Input ≤ 1. It is always less than or equal to unity, as some energy loss would occur during the transformation process. DEA generalizes this single input/single output technical efficiency measure to a multiple output/multiple input case by constructing a relative efficiency measure in terms of the fraction of a single “virtual” output and a single “virtual” input. The efficient frontier is then determined by defining the virtual output from the virtual input so that DMUs become most efficient. DMUs on the efficient frontier receive an efficiency score equal to 1; inefficient DMUs are graded relative to the efficient ones. Efficiency scores are relative measures, and it is not relevant for the DEA to judge if DMUs on the efficient frontier are making the optimum use of inputs to produce their outputs. Charnes et al. (1978) generalized the simple technical efficiency concept of single input and single output originally modeled by Farrell (1957), and systemized the basic DEA model of relative efficiency evaluation, which is named after them and is called the CCR model. Later, Banker et al. (1984) developed another DEA model taking into consideration the scale economy. This is called the BCC model. As pointed out in the previous section, DEA applications are designed in a number of ways, including cross-sectional and time-series analysis.6 A “window” application was introduced by Charnes et al. (1985) to handle panel data sets consisting of pooled cross-section and time-series observations. This section first presents the outlines of the CCR and BCC models of DEA in association with mathematical expressions. It then introduces the method, application procedure and accompanied problems of the window application.

4 Farrell (1957) discussed a simple system with only one input and one output as the evaluation unit. 5 Each DMU does not choose variables of non-zero values, which tend to evaluate the DMU disadvantageously. 6 See Charnes et al. (1994), Cooper et al. (2000) about the details and variations of the DEA approach.

3.1 Mathematical explanation of the DEA

DEA efficiency score h j0 ()0 ≤ h j0 ≤ 1 represents the efficiency relative to target unit j0, and can be obtained by solving the following fractional program, which is a kind of non-linear programming, repeatedly for each unit.

t m Maxmize h = u y v x j0 ∑r=1 r rj0 ∑i=1 i ij0 t m Subject to u y v x ≤1, j = 1,L, n, (1) ∑r=1 r rj ∑i=1 i ij L L ur , vi > 0, r = 1, ,t, i = 1, , m where yrj = the amount of output r from unit j, xij = the amount of input i from unit j,

ur = the weight given to output r, vi = the weight given to input i, n = the total number of units, t = the total number of outputs, m = the total number of inputs. The DEA efficiency scores of all units relative to each other can be computed by solving the model equivalent to (1). When efficiency scores of other units are evaluated by using the same weight combination as that for DEA efficient unit j0, and if they are less than one, other units are regarded as DEA inefficient. DEA model (1) is a fractional linear program that may be easily converted into a linear form so that a linear programming method can be applied. This version is expressed as

t Maxmize h = u y j0 ∑r =1 r rj0 m Subject to v x = 1, ∑i=1 i ij0 (2) t m u y − v x ≤ 0, j = 1,L,n, ∑r=1 r rj ∑i=1 i ij L L ur ,vi ≥ ε, r = 1, ,t, i = 1, ,m where ε is a small positive non-Archimedean number.

Alternatively, the same solution can be obtained by solving the dual problem of Model (2). We define the following input-oriented model as the dual problem and solve it.

t m Minimize θ − ε s + + s − CCR ()∑r=1 r ∑i=1 i n Subject to y λ − s + = y , r = 1,L, t, ∑ j=1 rj j r rj0 (3) n x θ − x λ − s − = 0, i =1,L, m, ij0 CCR ∑ j=1 ij j i + − L L L λ j , sr , si ≥ 0, j =1, , n, r = 1, , t, i = 1, , m, θ unconstrained.

+ − + − Here, θ , λ j , sr , si are dual variables, and sr , sr are slack variables. Unlike other variables, θ in the dual problem is unconstrained. The slack variables evaluate the degree of possi- bilities in order to become DEA efficient, and represent the values of excessive inputs. Model (3) is generally applied as the CCR model of the DEA approach. n The BCC model needs an additional condition, λ =1 , which is shown in Model ∑ j=1 j (4). This condition implies that the output level per unit input should remain unchanged to keep the DEA efficiency. While the CCR model maintains a constant returns to scale with both input and output orientations for projection paths in the piece-wise linear envelopment surface, the BCC model provides a variable returns to scale in the surface.

t m Minimize θ − ε s + + s − BCC ()∑r =1 r ∑i=1 i n Subject to y λ − s + = y , r = 1,L,t, ∑ j=1 rj j r rj0 n x θ − x λ − s − = 0, i = 1,L,m, (4) ij0 BCC ∑ j=1 ij j i n λ = 1, ∑ j=1 j + − L L L λ j , sr , si ≥ 0, j = 1, ,n, r = 1, ,t, i = 1, m, θ unconstrained.

The score θCCR* of the CCR model (3), and θBCC* of the BCC model (4) indicate the DEA efficiency of each model. θBCC* in the BCC model corresponds to technical efficiency, which is the fraction of a single “virtual” output and a single “virtual” input as explained above. The θCCR* in the CCR model represents the combined indicator of both technical efficiency and scale efficiency. Therefore, scale efficiency (SE) for each unit is defined as

θ * SE(X ,Y) = CCR . (5) θ BCC *

Scale efficiency indicates how close the production size of a DMU is to the most productive scale. Unit j0 is efficient if the slacks are equal to zero, and at that time θCCR* is equal to unity in the CCR model. Similarly, θBCC* is equal to one in the BCC model. Note that a difference between the BCC and CCR models is that the latter does not include the convexity (additional) constraint. This enlarges the feasible region for the solution, and characterizes the data as satisfying constant return to scale.

3.2 Window application The window analysis of the DEA approach treats the same organization in a different time as “another” unit, and compares the performance of one unit not only against the performance of other organizations in the same time but also against that of the same unit in other times. This is a useful approach when different organizations perform differently, and at the

same time the same organization performs differently depending on the period of time. Charnes et al. (1985) employed the window analysis to discuss the trend of DEA efficiency changes in various maintenance units of the U.S. Air Force. DEA analytical results differ considerably depending on whether or not a remarkable change at a particular point of time is included in the selected window. For this reason, they found that the estimation of efficiency in this approach gets robust by repeatedly moving the window terms. The window analysis is specified by the time length, i.e., the number of years in this study, of a window, p, and the number of windows, w. Denoting the number of organizations, i.e., ports in this study, by n, there are np units in each window. The total number of DMUs amounts to npw. It is a common practice in the window analysis that each organization is scored once in every window, or, in other words, scored as many times as the number of windows. Since an organization is regarded as a different unit in a new window, each unit is evaluated only once in a set of window applications. For this study, an organization is evaluated based on the average of scores in the windows. No straightforward methods have been developed to determine the appropriate time length of a window. It is selected through trial and error so as to, for example, cover a full cycle of seasonal or other fluctuations and to capture investment and operational changes.7 An important advantage of the window analysis, however, is that it increases the discriminatory power of DEA by increasing the total number of DMUs. Cooper et al. (2000) proposed a methodology to design the window length so as to maximize the total number of DMUs, or npw. Applying their concept of maximizing the number of differential DMUs, five or six years is the recommended length of the window for our case of port efficiency evaluation, and therefore the length that we apply.8 In order to examine the differences in the analytical results between different window length designs, we also evaluate ports based on the window length of one year and 10 years. Each window application of the former design is, as a matter of fact, a cross-sectional application of DEA.

4. Data sets and applications

4.1 Data description The eight major container ports in Japan covered in this analysis are the Ports of Tokyo, Yokohama, Shimizu, Nagoya, Yokkaichi, Osaka, Kobe, and Kitakyushu. The time periods covered in the DEA window application are between 1990 and 1999. The sources of input and output data for these ports are: International Transportation Handbook for port infrastructure, Port Statistics Yearbook for container handling volumes, Maritime Transportation Survey, Kyushu Maritime Transportation Survey and Chubu Transport Bureau Maritime Transporta- tion Data for port labor. Based on the port operations discussed in Section 1, the output of the DEA analysis is import and export containers handled per year at each port in terms of TEUs. The inputs of port operations may be roughly categorized as port infrastructure, superstructure and labor. Container terminal area (m2) and the number of container berths are available to represent the

7 See, for example, Boussofiane et al. (1991). 8 See Appendix for the procedure of maximizing the number of DMUs.

port infrastructure data, while the number of gantry cranes is the typical indicator of the superstructure data. These three inputs are included in the basic applications.9 Another important input is stevedoring and administrative labor for container operations. Unfortunately, however, labor statistics are available for overall port operations of each port but not exclusively for container operations. For this reason, the basic applications are run without the labor variable. Labor still is the key input in the port production, and cannot be totally neglected. Therefore, we have used a proxy for container operation labor based on the total port labor, and have run an additional application with the estimated container labor as the fourth input variable. Container labor is estimated by dividing total labor into container labor and conventional cargo operation labor based on the value (not weight) share of container cargo and conventional cargo handled at each port. Figure 4 reports the estimated container operation labor.

4.2 Window application design Both the CCR and BCC models are evaluated in the window analysis with the window length of five years. The case of the six-year window length is also evaluated and compared with the five-year case. No significant differences are found in the results.

Figure 4. Estimated container terminal labor

5000 4500 Tokyo 4000 Yokohama 3500 Sh im i zu 3000 Nagoya 2500 Yokkaichi 2000 Osaka 1500 Kobe 1000 Kitakyushu 500

The number of container terminal's labor 0

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

Year

9 The port facility data are collected as of November of each year. The facilities at Kobe were seriously damaged in the earthquake, but the inventory of the restored facilities as of November 1995 is reported. It should be noted that a considerable portion of the reported inventory was not usable between January and November of that year.

Table 1 reports the results of the CCR model application, and can be used to explain the design of the window analysis. Since the window length is five years, six window applications cover the entire study periods of 10 years. The first application covers the period 1990-1994, which is called Term 1. Since each organization or port at a different year is regarded as a different DMU, each year between 1990 and 1994 for the Port of Tokyo obtains a different efficiency score in the Term 1 application. This is true for each of the eight ports; the Term 1 application includes 8 ports * 5 years = 40 DMUs. Similarly, Term 2 covers the next five years, from 1991 to 1995, and finally Term 6 from 1995 to 1999. The BCC model is applied in the same window set-up. The value under the column Ave. for Row of each window is the average DEA efficiency score of five different years for each port. The value under Ave. for All DMUs is the average DEA efficiency score of 30 DMUs, i.e., five different years and six different windows, for each port. It indicates the overall performance of the concerned port for the 10 years. Further- more, the value under Variance for all DMUs is the variance of the DEA efficiency scores of the 30 DMUs, and indicates the degree of change in efficiency at the respective port.

5. Results and implications

5.1 CCR model analysis As discussed in Section 3, the CCR model measures the combined technical and scale efficiency. As seen in Table 1 and in Figure 5, the DEA efficiency scores (Ave. for all DMUs) are high for Tokyo (.932) and Nagoya (.931). The average score of each year (Ave for Col- umn) was consistently high for Tokyo except for 1993, but declined in the second half of the 1990s for Nagoya. The 1993 drop of the Tokyo score is due presumably to the bubble col- lapse-oriented trade cargo decline and to the terminal relocation of Maersk Line (Denmark) to Yokohama. However, the opposite terminal relocation of Hanjin Shipping Line (Korea) from Yokohama to Tokyo in 1995 probably saved Tokyo from the continued low score. Tokyo’s consistent high scoring is basically attributable to the increased ship size and growing import demand. Note that an increase in either export cargo or import cargo is sufficient to be evaluated as efficient by the DEA technique. The efficiency score of Nagoya declines the during second half of the 1990s due to declining demand. The reason for this may be that Nagoya had not constructed deep-water berths, which are necessary for large-size vessels. The DEA efficiency scores are low for Yokohama (.840), Osaka (.687) and Kobe (.747). Yokohama efficiency scores improve only temporarily in 1995 (.930) due to the earthquake- oriented demand shift from Kobe. Factors affecting Yokohama include a relatively high concentration of Asian routes, on which slowed due to the late 1990s Asian monetary crisis and smaller berth size (second smallest in the eight ports, as seen in Figure 3). Kobe’s slow demand recovery after the 1995 earthquake is detected by the score levels of around .6, and is striking with its exports, while the slack variables suggest an over-investment in its container terminal. Kobe, therefore, started reducing terminal leasing fees in 2001. Osaka finds its peak score (.872) in 1995 due to the earthquake-oriented demand shift from Kobe, but suffers from poor handling growth perhaps because of small berth size. Over-investment in cranes is sug- gested by the slack variables as a factor causing inefficiency in the second half of the 1990s.

Table 1. CCR Results / DEA efficiency scores in window analysis 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 Tokyo Cross Section (WL1) 0.991 1.000 1.000 0.972 1.000 1.000 1.000 1.000 1.000 1.000 Window Analysis WL5* 0.882 1.000 0.999 0.772 0.830 0.997 0.983 0.992 0.976 1.000 Window Analysis WL6** 0.852 0.980 0.969 0.736 0.788 0.997 0.983 0.992 0.976 1.000 Simple Panel (WL10) 0.821 0.925 0.901 0.681 0.736 0.983 0.959 0.975 0.938 1.000 Time Series 0.901 1.000 0.989 0.733 0.787 1.000 0.959 0.982 0.957 1.000 Yokohama Cross Section (WL1) 0.916 0.978 1.000 0.954 1.000 0.931 0.867 0.959 0.837 0.804 Window Analysis WL5* 0.862 0.857 0.853 0.814 0.875 0.930 0.814 0.798 0.700 0.739 Window Analysis WL6** 0.712 0.774 0.801 0.774 0.846 0.930 0.814 0.798 0.700 0.739 Simple Panel (WL10) 0.711 0.773 0.801 0.770 0.842 0.924 0.808 0.794 0.697 0.739 Time Series 0.937 0.994 1.000 0.958 0.963 1.000 0.884 0.874 0.757 0.815 Shimizu Cross Section (WL1) 0.607 0.696 0.846 0.829 0.787 0.716 0.807 1.000 1.000 1.000 Window Analysis WL5* 0.541 0.650 0.763 0.781 0.682 0.716 0.768 0.906 0.909 0.957 Window Analysis WL6** 0.499 0.624 0.743 0.765 0.662 0.716 0.768 0.906 0.909 0.957 Simple Panel (WL10) 0.499 0.624 0.743 0.765 0.662 0.716 0.768 0.906 0.909 0.957 Time Series 0.750 0.894 0.971 1.000 0.903 1.000 0.936 1.000 0.949 1.000 Nagoya Cross Section (WL1) 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 Window Analysis WL5* 0.919 1.000 0.939 0.989 0.873 1.000 0.984 0.824 0.823 0.832 Window Analysis WL6** 0.919 1.000 0.929 0.981 0.841 1.000 0.984 0.824 0.823 0.832 Simple Panel (WL10) 0.919 1.000 0.920 0.966 0.841 1.000 0.983 0.797 0.794 0.818 Time Series 0.919 1.000 0.920 0.966 0.841 1.000 0.993 0.853 0.798 0.883 Yokkaichi Cross Section (WL1) 0.144 0.138 0.159 0.193 0.275 0.282 0.246 0.451 0.429 0.421 Window Analysis WL5* 0.124 0.127 0.141 0.145 0.231 0.278 0.205 0.323 0.308 0.382 Window Analysis WL6** 0.104 0.115 0.131 0.137 0.231 0.273 0.205 0.323 0.308 0.382 Simple Panel (WL10) 0.101 0.112 0.120 0.127 0.217 0.257 0.205 0.323 0.308 0.348 Time Series 0.644 0.718 0.706 0.737 0.884 1.000 0.678 1.000 0.954 1.000 Osaka Cross Section (WL1) 0.706 0.524 0.562 0.777 0.827 0.876 0.788 0.781 0.768 0.801 Window Analysis WL5* 0.608 0.485 0.491 0.586 0.649 0.872 0.771 0.772 0.706 0.801 Window Analysis WL6** 0.527 0.446 0.461 0.554 0.612 0.872 0.771 0.772 0.706 0.801 Simple Panel (WL10) 0.522 0.441 0.456 0.544 0.579 0.854 0.756 0.759 0.696 0.801 Time Series 0.807 0.642 0.675 0.724 0.736 1.000 0.886 0.889 0.816 0.939 Kobe Cross Section (WL1) 1.000 0.994 1.000 1.000 1.000 0.468 0.742 0.716 0.631 0.675 Window Analysis WL5* 1.000 0.965 0.945 0.969 0.912 0.447 0.631 0.593 0.533 0.606 Window Analysis WL6** 1.000 0.935 0.924 0.950 0.896 0.444 0.631 0.593 0.533 0.606 Simple Panel (WL10) 1.000 0.907 0.892 0.906 0.848 0.423 0.608 0.576 0.513 0.583 Time Series 1.000 1.000 0.995 1.000 0.983 0.531 0.789 0.738 0.621 0.692 Kitakyushu Cross Section (WL1) 0.319 0.361 0.363 0.463 0.580 0.481 0.506 0.458 0.450 0.458 Window Analysis WL5* 0.299 0.327 0.323 0.366 0.463 0.478 0.467 0.441 0.413 0.458 Window Analysis WL6** 0.250 0.296 0.304 0.350 0.442 0.478 0.467 0.441 0.413 0.458 Simple Panel (WL10) 0.242 0.285 0.296 0.341 0.430 0.466 0.451 0.436 0.408 0.458 Time Series 0.522 0.618 0.662 0.765 0.927 1.000 1.000 0.959 0.900 1.000 Notes: * indicate the value of Ave. for Column by five-window length, or these valeus are Ave. for Column in Table 1; ** indicates the value of Ave. for Column by six-window length for comparison with Window Analysis WL5.

Figure 5. CCR model results / DEA efficiency score in window analysis

1.200

1.000 Tokyo Yokohama 0.800 Sh im izu Nagoya 0.600 Yokkaichi 0.400 Osaka

DEA Efficiency Score Kobe 0.200 Kitakyushu

0.000

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

Year

Other than the five major ports, Shimizu scores high (.760) compared to Yokkaichi and Kitakyushu, and improved steadily from .541 in 1990 to .957 in 1999 due to steady demand growth. It even outscored Nagoya after 1996. Yokkaichi and Kitakyushu score low mainly due to their poor scale efficiency. Kitakyushu improved from .299 in 1990 slowly to .458 in 1999, as did Yokkaichi, from .124 to .382.

5.2 BCC model analysis The BCC model measures pure technical efficiency. (Figure 6). No remarkable differences in the trend of the DEA scores are found with the ports except for Yokkaichi between the CCR and BCC model applications. Although Yokkaichi reports the ten-year average score (Ave. for all DMUs) of .224 in the CCR analysis, it becomes as high as .923 in the BCC analysis. This is almost as high as the scores of Tokyo and Nagoya. Shimizu did not score poorly in the CCR analysis, but scored even better in the BCC analysis with a ten-year average of .968, which is as high as Tokyo and Nagoya. The BCC model takes into account the small- scale operations at these ports, and appraises their recent increasing demand for Asian routes.

5.3 Scale efficiency As discussed in Section 3, scale efficiency (SE) is defined as the ratio of the CCR efficiency score to the BCC efficiency score, and indicates how close the production size of a DMU is to the most productive scale. Table 2 presents the estimated values of scale efficiency of each term and the window average for each port based on the data without labor and with labor. A DMU is fully scale efficient if the SE score is equal to 1.0.

Figure 6. BCC model results / DEA efficiency score in window analysis

1.200

1.000 Tokyo Yokohama 0.800 Sh im iz u Nagoya 0.600 Yokkaichi 0.400 Osaka

DEA Efficiency Score Kobe 0.200 Kitakyushu

0.000

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

Year

The major five ports are relatively scale efficient, with SE scores of more than .9 and as high as .995 at the Ports of Tokyo and Nagoya. While Shimizu and Kitakyushu are not too scale inefficient with SE scores of .785 and .691, respectively, the SE score of Yokkaichi is extremely low at .242. It, of course, needs to improve scale efficiency by expanding its container operation. However, Shimizu, Kitakyushu, and Yokkaichi are already on the way to increasing their demand at rates higher than those of the five major ports due to the diversifi- cation of Asian routes.

5.4 Analysis with labor input As mentioned in the previous section, the DEA applications so far did not include labor, an important production factor, since container-related labor data are not available. However, the labor of container operation for each port is estimated roughly from the overall port labor data based on the value-wise shares of containerized and conventional cargoes, and additional DEA applications are formulated for extra insights. Figures 7 and 8 summarize the results of the CCR analysis and BCC analysis, respectively, incorporating labor data, and can be compared with Figures 5 and 6, respectively. The fourth input makes DEA efficiency values higher than the case of three inputs.10 It is notable that the scores of post-earthquake Kobe are higher than the without-labor case. Labor left Kobe after the earthquake. Evidently, it did not increase soon thereafter, and this resulted in an efficiency gain. Whereas the efficiency of Yokohama in the second half of the 1990s declined in the without-labor case, it is better at around .9 throughout the study period with the labor case due to less labor requirement at Yokohama. The scores of Yokkaichi and Kitakyushu are higher with labor as input than without labor because of changes in their

10 The results verify that when one indicator is added to the DEA model, it decreases the discriminatory power.

reference sets to the ports of Kobe or Yokohama, which is evaluated as more efficient, from the ports of Tokyo or Nagoya. In particular, Yokohama and Yokkaichi even become DEA efficient with the score of unity in 1999 in the CCR analysis with labor data.

5.5 Alternative window lengths Finally, we discuss window application design focusing on the window length by com- paring the analytical results of a five-year long window with those of four alternative lengths.

Table 2. Scale economies Port Term Non-Labor Labor Port Term Non-Labor Labor

Tokyo 1 0.994 0.995 Yokkaichi 1 0.161 0.635 2 0.997 0.995 2 0.175 0.640 3 0.995 0.995 3 0.211 0.645 4 0.994 0.994 4 0.262 0.653 5 0.997 0.998 5 0.311 0.696 6 0.994 0.994 6 0.367 0.717 Average 0.995 0.995 Average 0.242 0.664

Yokohama 1 0.973 0.993 Osaka 1 0.916 0.917 2 0.889 0.987 2 0.919 0.921 3 0.901 0.988 3 0.923 0.924 4 0.918 0.991 4 0.937 0.937 5 0.942 0.996 5 0.948 0.948 6 0.952 0.999 6 0.937 0.937 Average 0.929 0.992 Average 0.931 0.931

Shimizu 1 0.763 0.840 Kobe 1 0.964 0.997 2 0.728 0.864 2 0.944 0.995 3 0.740 0.893 3 0.952 0.994 4 0.791 0.921 4 0.938 0.991 5 0.817 0.941 5 0.924 0.998 6 0.870 0.955 6 0.936 0.998 Average 0.785 0.902 Average 0.945 0.995

Nagoya 1 0.997 0.997 Kitakyushu 1 0.659 0.809 2 0.995 0.996 2 0.658 0.850 3 0.992 0.995 3 0.687 0.851 4 0.994 0.996 4 0.708 0.865 5 0.994 0.997 5 0.719 0.882 6 0.997 0.996 6 0.707 0.901 Average 0.995 0.996 Average 0.691 0.861

Figure 7. CCR model results / DEA efficiency score in window analysis including the labor input

1.200

1.000 Tokyo Yokohama 0.800 Sh im iz u Nagoya 0.600 Yokkaichi 0.400 Osaka

DEA Efficiency Score Kobe 0.200 Kitakyushu

0.000

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

Year

Figure 8. BCC model results / DEA efficiency score in window analysis including the labor input

1.200 Tokyo 1.000 Yokohama 0.800 Sh im izu Nagoya 0.600 Yokkaichi 0.400 Osaka

DEA Efficiency Score Kobe 0.200 Kitakyushu 0.000

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

Year

The analysis of the five-year long window is called Window Analysis WL5. The analysis of six-year long window, which gives an equally maximized number of total DMUs, is called- Window Analysis WL6. The analysis of a one-year long window is a cross-sectional application called Cross Section (WL1). The analysis of a ten-year long window gives only a single panel, and is called Simple Panel (WL10). In addition, it is also possible to analzye each port with a simple time series DEA analysis instead of a window analysis. The former analyzes the efficiency trend of each port, but not the performance relative to those of other ports. Table 3 summarizes the results of analysis with different window lengths and one time series analysis.

Table 3. CCR Results / DEA efficiency scores in various applications 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 Tokyo Cross Section (WL1) 0.991 1.000 1.000 0.972 1.000 1.000 1.000 1.000 1.000 1.000 Window Analysis WL5 * 0.882 1.000 0.999 0.772 0.830 0.997 0.983 0.992 0.976 1.000 Window Analysis WL6 ** 0.852 0.980 0.969 0.736 0.788 0.997 0.983 0.992 0.976 1.000 Simple Panel (WL10) 0.821 0.925 0.901 0.681 0.736 0.983 0.959 0.975 0.938 1.000 Time Series 0.901 1.000 0.989 0.733 0.787 1.000 0.959 0.982 0.957 1.000 Yokohama Cross Section (WL1) 0.916 0.978 1.000 0.954 1.000 0.931 0.867 0.959 0.837 0.804 Window Analysis WL5 * 0.862 0.857 0.853 0.814 0.875 0.930 0.814 0.798 0.700 0.739 Window Analysis WL6 ** 0.712 0.774 0.801 0.774 0.846 0.930 0.814 0.798 0.700 0.739 Simple Panel (WL10) 0.711 0.773 0.801 0.770 0.842 0.924 0.808 0.794 0.697 0.739 Time Series 0.937 0.994 1.000 0.958 0.963 1.000 0.884 0.874 0.757 0.815 Shimizu Cross Section (WL1) 0.607 0.696 0.846 0.829 0.787 0.716 0.807 1.000 1.000 1.000 Window Analysis WL5 * 0.541 0.650 0.763 0.781 0.682 0.716 0.768 0.906 0.909 0.957 Window Analysis WL6 ** 0.499 0.624 0.743 0.765 0.662 0.716 0.768 0.906 0.909 0.957 Simple Panel (WL10) 0.499 0.624 0.743 0.765 0.662 0.716 0.768 0.906 0.909 0.957 Time Series 0.750 0.894 0.971 1.000 0.903 1.000 0.936 1.000 0.949 1.000 Nagoya Cross Section (WL1) 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 Window Analysis WL5 * 0.919 1.000 0.939 0.989 0.873 1.000 0.984 0.824 0.823 0.832 Window Analysis WL6 ** 0.919 1.000 0.929 0.981 0.841 1.000 0.984 0.824 0.823 0.832 Simple Panel (WL10) 0.919 1.000 0.920 0.966 0.841 1.000 0.983 0.797 0.794 0.818 Time Series 0.919 1.000 0.920 0.966 0.841 1.000 0.993 0.853 0.798 0.883 Yokkaichi Cross Section (WL1) 0.144 0.138 0.159 0.193 0.275 0.282 0.246 0.451 0.429 0.421 Window Analysis WL5 * 0.124 0.127 0.141 0.145 0.231 0.278 0.205 0.323 0.308 0.382 Window Analysis WL6 ** 0.104 0.115 0.131 0.137 0.231 0.273 0.205 0.323 0.308 0.382 Simple Panel (WL10) 0.101 0.112 0.120 0.127 0.217 0.257 0.205 0.323 0.308 0.348 Time Series 0.644 0.718 0.706 0.737 0.884 1.000 0.678 1.000 0.954 1.000 Osaka Cross Section (WL1) 0.706 0.524 0.562 0.777 0.827 0.876 0.788 0.781 0.768 0.801 Window Analysis WL5 * 0.608 0.485 0.491 0.586 0.649 0.872 0.771 0.772 0.706 0.801 Window Analysis WL6 ** 0.527 0.446 0.461 0.554 0.612 0.872 0.771 0.772 0.706 0.801 Simple Panel (WL10) 0.522 0.441 0.456 0.544 0.579 0.854 0.756 0.759 0.696 0.801 Time Series 0.807 0.642 0.675 0.724 0.736 1.000 0.886 0.889 0.816 0.939 Kobe Cross Section (WL1) 1.000 0.994 1.000 1.000 1.000 0.468 0.742 0.716 0.631 0.675 Window Analysis WL5 * 1.000 0.965 0.945 0.969 0.912 0.447 0.631 0.593 0.533 0.606 Window Analysis WL6 ** 1.000 0.935 0.924 0.950 0.896 0.444 0.631 0.593 0.533 0.606 Simple Panel (WL10) 1.000 0.907 0.892 0.906 0.848 0.423 0.608 0.576 0.513 0.583 Time Series 1.000 1.000 0.995 1.000 0.983 0.531 0.789 0.738 0.621 0.692 Kitakyushu Cross Section (WL1) 0.319 0.361 0.363 0.463 0.580 0.481 0.506 0.458 0.450 0.458 Window Analysis WL5 * 0.299 0.327 0.323 0.366 0.463 0.478 0.467 0.441 0.413 0.458 Window Analysis WL6 ** 0.250 0.296 0.304 0.350 0.442 0.478 0.467 0.441 0.413 0.458 Simple Panel (WL10) 0.242 0.285 0.296 0.341 0.430 0.466 0.451 0.436 0.408 0.458 Time Series 0.522 0.618 0.662 0.765 0.927 1.000 1.000 0.959 0.900 1.000 Notes: * indicates the value of Ave. for Column in window analysis by five window length, or these values are Ave. for Column in Table 1. ** indicates the value of Ave. for Column in window analysis by six window length for comparison with Window Analysis WL5

The efficiency score of Tokyo in 1991, for example, is 1.000 under WL5, but less than 1 or DEA inefficient under Simple Panel. This is because the DMU is evaluated relative to the more efficient DMUs under WL5, which sets up a larger number of DMUs. It implies that large number of DMUs stabilizes the analytical results. The choice of window length between five and six years does not make much difference, as seen in Table 3. WL1, on the other hand, maximizes the number of windows, but the number of DMUs within a window is limited and some ports stay as DEA-efficient. This is due to the lack of inter-temporal comparisons. It is, therefore, recommended that the window length be determined so as to have a large, if not maximum, number of DMUs.

6. Conclusion We have analyzed efficiency changes of the eight international container ports in Japan, namely the Ports of Tokyo, Yokohama, Shimizu, Nagoya, Yokkaichi, Osaka, Kobe, and Kita- kyushu, during the period 1990-1999 with a DEA window analysis. The outputs for evaluation are handling volumes for export and import; the inputs are number of container berths, number of cranes, and area of container terminal for the basic application, and also labor for an additional application. According to the CCR analysis, Tokyo attained high DEA efficiency scores consistently during the study period, and Nagoya performed well during the first half presumably due to increased ship size and growing import demand. The DEA efficiency scores of Yokohama, Osaka, and Kobe were low. Yokohama’s output suffered from a relatively high concentration of Asian routes, which slowed down during late 1990s, the Asian monetary crisis and smaller berth size. Kobe’s demand did not rebound soon after the 1995 earthquake. Osaka showed poor handling growth, perhaps because of small berth size and over-investment in cranes. Other than the five major ports, Shimizu scored high due to steady demand growth. Yokkaichi and Kitakyushu score low mainly due to poor scale efficiency. The incorporation of labor in the analysis improved the efficiency of Yokohama and Kobe, and, in consequence, Yokkaichi and Kitakyushu. The BCC analysis noticeably appraised Yokkaichi and to a smaller extent Shimizu, because it takes into account the small-scale operations at these ports, and appraises their recent increasing demand from Asian routes.

The author expresses his sincere gratitude to Professors Akihiro Hashimoto and Masayuki Doi of University of Tsukuba for their encouragement and invaluable advice on this research. Instructive comments from Professor Katsuhiko Kuroda of Kobe University and Professor Hisayoshi Morisugi of Tohoku University on an earlier version of the paper at the 2001 annual meeting of Applied Regional Science Conference are also appreciated. The author also thanks the two anonymous referees for useful comments. Any remaining errors belong to the author.

Final version received June 2002.

Send inquiries to Hidekazu Itoh: [email protected]

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Appendix. Time length of window We obtain below the window length, which maximizes the number of DMUs. We denote the number of organizations, number of time periods for which data are available, time length of window, and number of windows by n, k, p, and w, respectively. Here, p ≤ k and w = k- p+1. Since the number of DMUs in each window is np, the total number of DMUs, #DMUs, is expressed by:

#DMUs = npw = ()− +1 ppkn .

#DMUs is maximized when the differential of the right hand side with respect to p is set to be zero. This leads to

k + 1 p = . (A.1) 2

Substituting k= 10 for our case, p= 5. 5 . Since p should be an integer, the window length maximizing the total number of DMUs is five or six years. Both window lengths equally give 240 DMUs.

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