an estimation of the international container shipping ... - CiteSeerX

Journal of the Eastern Asia Society for Transportation Studies, Vol. 6, pp. 920 - 935, 2005

AN ESTIMATION OF THE INTERNATIONAL CONTAINER SHIPPING TRANSPORT VOLUMES AMONG ASIAN COUNRTIES BY GLOBAL TRADE ANALYSIS PROJECT MODEL AND ITS APPLICATIONS TO FTA AND TRANSPORT IMPROVEMENT SCENARIOS Liqiang MA M, Eng., Graduate Student Dept of Civil Engineering University of Tokyo Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8656 Japan Fax: +81-3-5841-8507 E-mail: [email protected]

Ryuichi SHIBASAKI Dr. Eng., Researcher Port Systems Division Port and Harbor Department National Institute for Land and Infrastructure Management Ministry of Land, Infrastructure and Transport, 239-0826 Japan Fax: +81-468-42-9265 E-mail: [email protected]

Takashi KADONO B. Eng., Director Port Systems Division. Port and Harbor Department National Institute for Land and Infrastructure Management Ministry of Land, Infrastructure and Transport, 239-0826 Japan Fax: +81-468-42-9265 E-mail: [email protected]

Tomoki ISHIKURA Dr. Eng., Researcher Airport Planning Division Airport Department National Institute for Land and Infrastructure Management Ministry of Land, Infrastructure and Transport 239-0826 Japan Fax: +81-468-42-9265 E-mail: [email protected]

Hitoshi IEDA Dr. Eng., Professor Dept of Civil Engineering University of Tokyo Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8656 Japan Fax: +81-3-5841-8507 E-mail: [email protected] Abstract: While the regional economy integration has recently attracted much interest, its impact on the rapidly developing international marine transport industry is a major concern. This paper proposes a method for estimating the international container cargo OD flows under various hypothetical FTA memberships and transport technical progress by incorporating the GTAP model, which uses the applied general equilibrium theory to conduct the quantitative analyses of international trade issues. The effects under the FTA scenarios and transport technical progress scenarios on the selected countries’ economy, social welfare, trade amounts and container OD flows are discussed. Since this method provides an approach for estimating the OD flows on a national wide level, it can be utilized for assessing the policy effects on international trade and transportation. Therefore, it is also an important step for demand forecasting such as the volume of container handling and transshipment on the international container shipping transport networks. Key Words: container cargo, OD flow, GTAP model, FTA, transport technical progress 1. INTRODUCTION Globalization and regionalism are the major trends in today’s global economy. As the world 920


economy becomes more integrated, the development of international trade is greatly stimulated, and marine transport as a major transport mode, is experiencing the prevailing development. There is an increasing demand for quantitative analyses of the impact of economy integration policy issues on the international marine transport. So far, numerous studies have been conducted on the impact of economy integration policy issues on other industries (Nakajima 2002), but almost none have been done international marine transport. Therefore, a model was developed by integrating a transport network model with an international trade model. The conceptual framework based on this ideal is shown in Figure 1 as follows. I n t’ l B ila te ra l I -O T a b le S o c ia l a n d E c o n o m ic D a ta f o r e a c h R e g io n

D o m e s tic M u lti- R e g io n a l I -O T a b le

In t’ l M u lti-R e g io n a l I- O T a b le

T ra n sp o rt V a ria b le s not changed by N e tw o rk M o d e l (L e n g th , C a p a c ity , e tc .)

T r a d e E s t im a t io n S u b -M o d e l (C G E M o d e l)

- V a lu e s per T E U - C o n ta in e r iz a tio n R a te

T r a d e V a lu e b y R e g io n

C o n ta in e r O D V o lu m e E s tim a tio n S u b -M o d e l

I n t’l C o n ta in e r O D b y R e g io n

P o lic y fo r In t’l T ra n s p o rt I n fr a s tru c tu r e In v e s tm e n t

I m p ro v e m e n t o f P r o d u c tiv ity a n d T e c h n o lo g y o f T r a n s p o r t S e c to r , e tc .

T ra n sp o rt T e c h n o lo g y R en e w a l S u b -M o d e l T ra n s p o rt T e c h n o lo g y c h a n g e d b y N e tw o r k M o d e l ( T i m e , F r e q u e n c y , e tc .) M a r itim e / L a n d T ra n s p o r t F lo w o f In te rn a tio n a l C o n ta in e rs

A v a ila b le D a ta

N e e d to E s tim a tio n

I n t’l C o n ta in e r F lo w E s tim a tio n S u b -M o d e l ( N e t w o r k A s s ig n m e n t M o d e l)

A m o u n t o f C o n ta in e r s , T ra n s s h ip m e n t, e tc .

Figure 1. Trade-Freight Integrated Forecasting System As part of the above research, this study is shown within a thick dotted line area in Figure 1. In detail, the objective of this study was to estimate the container OD flow changes under the various regional economic integrations, particularly in the Eastern Asian regions, because Free Trade Agreements (FTA) among these countries have recently attracted much interest. For example, a Japan-Singapore FTA has already established, the more bilateral FTAs are under discussion now, however, which is better - a bilateral and multilateral FTA? What is the difference in their impact of them on international trade and transport? Above all, what are the quantitative effects of these policies on international transport? An attempt is made to answer these questions in this paper. In this paper, first, the proposed method and data preparation are described, and then the two applications of this method are introduced, one is FTA scenario analysis by assuming various hypothetical FTA memberships and tariff settings. The other application is conducted to simulate the impact of the transport technical progress on the each country’s economy, trade and container transport flows. Then effects between FTA and transport technical progress on container transport flows were compared and examined. 2. METHOD DESCRIPTION AND DATASET PREPARATIONS 2.1 Introduction of the GTAP Model The GTAP (Global Trade Analysis Project) model was developed by Hertel et al. in 1997. The 921


GTAP model was incorporated into the method we proposed in order to deal with multicountry, economy-wide issues. GTAP is a multi-regional AGE (Applied General Equilibrium) model, which builds on the theories of consumer and producer behavior developed in the study of microeconomics by examining how the interactions of economic agents determine equilibrium in the markets for all goods simultaneously. It can analyze the complex relationships such as the multiple links between domestic input and output markets, international trade and factor movements between countries and regions. By using the AGE model, the GTAP model captures world economic activity in 57 different industries of 66 countries and regions. Its database consists of bilateral trade, transport, and protection matrices that link country/regional economic databases. It can conduct nonlinear simulations of the standard model in which changes in policy, technology, population and factor endowments can be examined. Its outputs include a complete matrix of bilateral trade, activity flows (and percentage changes) by sector and region, private and government consumption, regional welfare, and a variety of summary variables. In this study, by changing various policy shocks in the GTAP model, new outputs can be obtained of the matrix of commodity-based bilateral trade monetary-based amount flows among the targeted countries and corresponding GDP changes. Until this research did, the available GTAP database is version 5.4, which is the 1997 Datasets respectively for each country. The same calculation will do on the forthcoming new database. 2.2 Method Description Estimation of the international container shipping OD flows under the regional economic integration has recently become a concern; however, the outputs of GTAP model are only of the matrix of commodity-based bilateral trade volume in monetary-base; therefore, it still needs an approach to transform into the container flows. First, Modal Split: International trade commodities are carried by different transport modes. There is an international transport sector in the GTAP model, which provides the services that account for the difference between FOB and CIF values. This international transport sector consists of air transport, marine transport and land transport. Therefore, the modal share of marine transport must be determined. Due to the low validity of the modal share in the GTAP, which is the United States based dataset, in this study, modal share was derived from the share of trade amounts carried by each transport modal originating from or destined for Japan, using data obtained from the 1997 trade statistics of the Ministry of Finance, Japan. Second, Transform Monetary Units to Freight-Ton (FT): By using each commodity’s unit price (USD/FT) and its trade amount by marine transport, the FT-based weight can be obtained. The commodity unit price is derived from the 1996 Port Statistics Yearbook of Japan. Third, Containerized Commodity Ratio: Not all commodities for marine transport can be carried by containers; therefore, the containerized ratios of the different commodities must be examined. Because we were unable to obtain all containerized ratios for each commodity on each route, Japanese commodity-based containerized ratios were used, which were obtained from the data of the 1997 Survey Report of International Container Cargo Flow. Fourth, TEU Transformation: It is difficult to identify the number of tons of one TEU cargo 922


for each commodity. In this study, the value of 19.0 Freight-Ton/TEU was used, which was taken from the Description Handbook of Port Investment Evaluation, published by the Port Investment Evaluation Committee of Japan. Since the reference materials have a commodity categories code system that differs from the GTAP codes, they had to be categorized again according to the GTAP code. For the modal share, the HS code (Harmonized Commodity Description and Coding System) was used, which has 97 categories. For the containerized ratio, the port statistic code system was used, with 54 categories. These trade amounts of commodities were aggregated again according to the GTAP model, and then the GTAP was used to calculate the commodity-based matrix of trade flow among the selected countries. Using the reorganized modal share and containerized ratio of each commodity to obtain its container OD flow, the aggregate of all commodities’ container OD flows was calculated. 2.3 Dataset Preparation In this study, GTAP version 5.4 database were used, which is the 1997 Datasets respectively for each country. Thirty countries and regions were selected in order to apply the calculation data for our international transport network model (Shibasaki, Ieda, et al, 2004). First, the GTAP model was used to calculate the GDPs and trade amounts of all these countries, and then the outputs were aggregated according the main seven regions shown in Table 1. The GTAP has 57 industry sectors; 19 non-tradable industries such as service industries were removed when aggregating the calculation results. The remaining 42 industries (refer to the Appendix) were examined in order to improve the calculation accuracy and identify the containerized volume in detail. Table 1 Classification of Regions No. Code 1 idn 2 mys 3 phl 4 sgp 5 tha 6 vnm 7 chn 8 jpn 9 kor 10 M 11 E 12 can 13 usa 14 mex 15 aus

Description Indonesia Malaysia Philippines Singapore Thailand Vietnam China Japan Korea Mediterranean Countries EU Canada USA Mexico Australia

Region No. Code Description 16 nzl New Zealand 17 hkg Hong Kong 18 twn Taiwan ASEAN 19 inbg India, Bangladesh 20 lka Sri Lanka 21 xsa Rest of South Asia China 22 xcm Central America, Caribbean Japan 23 per Peru Korea 24 chl Chile 25 xap Rest of East Southern America EU 26 wsa West Southern America 27 rus Russia NAFTA 28 xme Rest of Middle East 29 ba Black Africa ROW 30 xrw Rest of the World

Region

ROW

(Note: Among the ASEAN countries, GTAP model only has the databases on Indonesia, Malaysia, Philippines, Singapore, Thailand and Vietnam.)

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Table 2 Basic Information of the Selected Countries in 1997 Country Japan China Korea Brunei Cambodia Indonesia Lao, PDR Trade Amount 759,880 325,066 280,780 4,670 NA 95,137 1,065 GDP 4,212,258 898,244 476,487 5,422 3,063 215,749 1,747 rate 0.18 0.36 0.59 23.35 NA 0.44 0.61 Country Malaysia Myanmar Philippines Singapore Thailand Viet Nam ASEAN Trade Amount 157,771 2,903 63,499 257,433 120,282 20,777 723,862 GDP 100,169 31,002 82,239 96,318 150,617 27,609 713,935 rate 1.58 0.09 0.77 2.67 0.80 0.75 1.01 (Note: Rate = Trade Amount/GDP; Source: United Nations Yearbook 1999; Unit: Million USD)

3. APPLICATIONS 3.1 Scenario Settings As the applications of the above method, the effects of FTA and transport technical progress scenarios on the selected countries’ economies, bilateral trade amounts and container shipping transport flows were examined and discussed. In order to refer the results of two applications easily, the results were put together according to the corresponding problem categories. Six combinations of FTA memberships were considered, as shown in Table 3. The FTA Scenarios were examined under the various tariffs shown in Table 4. The tariff reduction is from 20% Off to 100% Off. It can be set in the GTAP by changing the shock variable tms (change in tax on imports of i from region r into s) In order to draw a comparison with the results of the FTA scenarios, the transport technical progress scenarios were set to the same country combinations according to the corresponding FTA countries’ combinations. In the GTAP model, the transport technical progress of a country means the ratio of the transportation cost reduction between the country and all its trade partner countries. For example, corresponding to FTA1, for PRO1 it was considered that the transport technology between Japan and all its trade partner countries was improved; meanwhile, the same change occurred between Korea and all its trade partner countries. Furthermore, the progress ratios were set from 50% down to 50% up as shown in Table 5. In the GTAP model, the shock variables were expressed as atf (tech change shipping from region r) and ats (tech change shipping to region s). For the transport technical progress scenarios, due to time constraints, only four scenarios have been calculated so far.

FTA1 JPN-KOR PRO1 JPN, KOR

Table 3 FTA and Transport Technical Progress Scenario Settings FTA2 FTA3 FTA4 FTA5 FTA6 JPN-CHN CHN-KOR JPN-CHNJPN-ASEAN JPN-CHNKOR KOR-ASEAN PRO2 PRO3 PRO4 PRO5 PRO6 JPN,CHN, JPN, ASEAN JPN,CHN,KOR, KOR ASEAN

TAX20%OFF

Table 4 Tariff Reduction Sets TAX40%OFF TAX60%OFF TAX80%OFF

TAX100%OFF

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Table 5 Transport Technical Progress Sets TECH -50% TECH -25% TECH＋25％ TECH ＋50% 3.2 Simulation Results 1) GDP Changes under FTA Scenarios with Various Tariff Reductions From the following figures, by comparing each FTA combination under various tariff reduction settings, it can be seen who gains the most and who gains the least in each of the combinations. In particular, z The more countries joined FTA, the more benefits they will receive. The FTA among Japan, China, Korea and ASEAN gain the largest benefits. On the other hand, the countries that didn’t join the FTA will lose the benefit in almost all cases. z Whatever the bilateral or multilateral FTA, the larger the country’s GDP is, the more increasing it is in the all FTA scenario. However, Japan at FTA1 and China at FTA3 are exceptional. In both cases, Korea benefits much more than Japan and China. z GDP increases with the tariff reduction. However, when the tariff was cut 100%, the GDP decreased in most of FTA scenarios. This is because under these circumstances its import increases much faster than its export due to the different development level of some particular industries. It shows that 100% tariff reduction is not always the most profitable case for some FTA country from the viewpoint of GDP growth. z Japan’s GDP decreased when its tariff decreased 100% in all FTA scenarios. Especially in FTA1, even the GDP change becomes a minus value due to the trade deficit. In case of FTA6 with 100% tariff reduction, when examines the commoditybased import and export amount, it is seen that imports amount of agriculture products, foods and wearing apparel increases significantly. mill USD 5000 FTA1 (Japan-Korea)

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Figure 2. GDP Changes under FTA Scenarios with Various Tariff Reductions in 1997 2) GDP Changes under Transport Technical Progress Scenarios The figures shown below demonstrate the following: z

Transport technical progress has the positive impact on the GDP growth. 926


z z

z

There is no significant difference among above four scenarios in terms of the effects of transport technical progress on country’s GDP. When China and Japan both improve their transport technology, the impact of China’s GDP is much higher than Japan. Because China has higher ratio of transport cost over the trade amount. (BY GTAP calculation: China: 11.5%, Japan: 5%, Korea: 4.5%, ASEAN: 4.2%, NAFTA: 3.8%, EU: 3.6%, ROW: 5.2%). Therefore, if China can improve its transport technology, it can reduce much of its cost which would stimulate the GDP growth. The impact of Japan and Korea’s technical progress on their GDP is bigger than in the case of Japan-Korea FTA. It can be assumed that technical progress will affect the entire tradable commodity. This implies that the effect of “bilateral” FTA would be less than the effect of technical progress by the respective countries. mill USD 5000

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Effect of Transport Technical Progress of Japan and ASEAN on GDP

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Figure 3. GDP Changes under Transport Technical Progress Scenarios in 1997 3) GDP and EV In the GTAP model, economic welfare is represented as being derived from the allocation of national income between private consumption, government consumption and savings, Hertel (2001) derived welfare decomposition for the Equivalent Variation (EV) in the GTAP model. The EV of Region r is defined as: EVr=INCOMEr(ur+popr)

(1)

where INCOMEr is the income level of Region r; ur is the percent change in per capita utility; popr is the population change rate of Region r. Figure4 and Figure 5 illustrate that in most cases, FTA and transport technical progress will improve the GDP growth and social welfare EV simultaneously. Furthermore, in the two figures, China has the smallest slopes in the liner regressions. In order to understand it, in GTAP, GDPr is defined as GDPr = INCOMEr * POPr

(2)

where POPr is the population of Region r. Therefore, ∆GDP=∆INCOMEr*POPr+INCOMEr* popr

(3)

When the change in GDP is equal, if the population and/or its change rate are larger, the income and/or its change rate will be smaller. Then, EVr will be smaller because the EV includes the term of product of income, INCOMEr, and utility change, ur, in addition to the contribution of population change. Since Chinese population and its change rate are absolutely larger, thus, with the same change of GDP, China has the smallest EV. In addition, when comparing with the changes of GDP and EV under FTA scenarios and transport technical progress scenarios, it is seen that transport technical progresses have much more effect on country’s economic welfare than country’s GDP, especially for ASEAN countries.

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Figure 5. Changes of GDP and EV under Transport Technical Progress in 1997 4) Trade Amounts As for the trade amounts under the various FTA scenarios, it was still confirmed the more countries that joined FTA, the more benefit they would obtain. On the other hand, the non929


FTA countries would lose the benefit. Furthermore, the trade amount would increase more by reducing the tariff to zero, which differs from the results for GDP change. The following Figure 6 is an example of the FTA situation among Japan, China, Korea and ASEAN under the various tariff reductions, and also shows that the trade amount increases following the tariff reduction. Meanwhile, transport technical progresses stimulate all counties’ trade amounts. In Figure 7, it can be seen that the technical progress improved China’s trade amount much more than other countries due to China having a higher transport cost share compared to its trade amount. mill USD 70,000 Tax20%OFF Tax40%OFF Tax60%OFF Tax80%OFF Tax100%OFF

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Figure 7 Trade Amount Change under PRO6 Scenarios in 1997 5) Container Volume The container shipping volume of each country can be derived from the GTAP model outputs under the various FTA and transport technical progress scenarios settings. It also confirms the above conclusions such as that Japan-China-Korea-ASEAN FTA or transport technical progress among these countries can greatly stimulate the container volumes. TEU 1,200,000 Tax20%OFF Tax40%OFF Tax60%OFF Tax80%OFF Tax100%OFF

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Figure 8 Container Volume Change under FTA 6 Scenarios in 1997 930


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Figure 9 Container Volume Change under PRO6 Scenarios in 1997 6) Container OD Flow For the international container shipping OD flow, GTAP model can calculate bilateral trade amounts, and then by using the proposed method, it can obtain the container shipping OD flow. As space is limited, here, just discuss the aggregated results of Japan-China-KoreaASEAN FTA with 40% tariff reduction and 50% transport technical progress in the corresponding countries. Base on the OD tables, as shown in Table 6 and Table 7, Japan’s import and export container OD flows can be compared in Figure 10 and Figure 11. Table 6 OD Matrix of Container Cargo Change under FTA6 with TAX40% OFF (unit: TEU) From\To

JPN CHN KOR ASE NAFTA EU ROW

JPN 0 161,156 42,471 92,651 -29,915 -28,624 -34,402

CHN 112,081 0 57,672 45,519 -13,027 -23,005 -39,441

KOR 30,704 72,668 0 14,319 -25,693 -6,600 -8,327

ASE NAFTA 60,478 -42,527 37,497 -8,438 33,932 -10,675 50,217 -13,475 -12,887 15,653 -32,841 7,937 -15,611 19,006

EU -26,073 -8,338 -12,930 -12,615 6,185 8,961 16,705

ROW -24,692 -9,545 -15,626 -17,325 4,487 4,066 10,524

Table 7 OD Matrix of Container Cargo Change under PRO6 with TECH+50% (unit: TEU) From\To

JPN CHN KOR ASE NAFTA EU ROW

JPN 0 48,205 13,215 13,430 59,842 35,587 34,098

CHN 60,996 0 35,115 33,848 35,591 38,842 81,015

KOR 10,247 5,375 0 3,690 16,715 14,082 9,065

ASE NAFTA 17,261 -12,857 17,292 71,192 5,389 438 25,783 7,920 14,539 -5,821 20,633 6,440 19,555 -28,243

EU -12,765 60,219 -1,423 511 -18,683 -8,248 -28,390

ROW -60 -11,496 2,215 11,156 -3,646 18,147 -7,650

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Figure11 Container Volume Change Rates from Origin Countries to Japan at FTA6 with TAX40%OFF and PRO6 with TECH+50% in 1997 In Figure 10, the container cargo transported from Japan to China changes at the fastest rate under both tariff reduction and transport technical progress. Meanwhile, the container cargo transported from Japan to EU and NAFTA decreases in both cases. In Figure 11, the container cargo from Korea to Japan increases at the fastest rate in both change cases. Meanwhile, the container cargo from NAFTA and EU decreases under the tax reduction case, but increases in the technical progress case. However, it was found the exports from Japan to NAFTA and EU will decrease even the transport technical progress increased by 50% in Figure 10. The reason for this was found from checking the O&D flow in Table 7: NAFTA and EU imports more from China and ASEAN instead of Japan. Furthermore, by examining the container shipping flow changes by each commodity in the following Figure 12 (due to the limited space, only the top 10 fastchanging commodities are shown), it can be seen that the export of mvh (automobile) from Japan to NAFTA and EU decreased rapidly. (For the notation of commodity code, Please refer to the Appendix)

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Figure 12 Commodity-based Container Cargo Volume Change from Japan under PRO6 with TECH＋50% (Unit: TEU) 4. CONCLUSION This study proposed a method for estimating the trade volume and international container shipping transport OD flow under various changes in socioeconomic factors changes by using the GTAP model. In particular, as two applications in this study, the impacts of various regional economy integrations (FTA scenarios with different tariff reduction schemes) and transport technical progress on selected countries’ economies especially in terms of international container shipping transport OD flows were examined and discussed. This method has the following features. 1) This methodology can provide the commodity-based OD flow information among the studied countries from the viewpoints of international trade and container flows, by processing the outputs from the GTAP model, which is also one of few applications of the GTAP model at the filed of international maritime container transport network. 2) This methodology can also estimate the maritime cargo and container OD flow changes under various FTA scenarios and transport technology improvement scenarios among several combinations of Asian countries incorporated in the outputs from the GTAP model, which will be a useful tool to do the economic policy evaluations on the international maritime container transport network. 3) The result of the research will be also an important step for the demand forecasting such as the volume of container handling and transshipment on the international container shipping transport network. From the applications at this study, it was demonstrated that FTA and transport technical progress have almost the positive effect on the GDP, trade amount and container OD flows. These effects are increased by the growth of the number of FTA membership countries. Due to the difference in each country’s industry development level and structure, the GDP of some countries will decrease when it join the non-tariff FTA. This is because the import volume is higher than export volume. However, in term of the trade amount, it will increase. Consequently, the containerized goods will always increase. The change in the trade pattern 933


generates new container shipping transport OD flows. Additionally, since the calculation of the GTAP model is based on each commodity, the proposed method can show not only the change in total container cargo volume on each OD pair, but also indicate the change in the commodity–based container goods composition on the each route under these scenarios. As the future studies, the above all results should be further examined and explained theoretically based on the model structure, and the accuracy of the method should be improved. Furthermore, since this OD estimation is on a national level, consideration should be given to a method for assigning the OD flow into each port at the selected countries in case of one country has more than one port. This will be the next step in our study on the international shipping transport network.

REFERENCES Hertel, T. W. (1997) Global Trade Analysis Project. Cambridge University Press. Shoven, J.B., Whalley, J.(1992) Applying General Equilibrium. Cambridge University Press. Filippini, C., Molini, V., (2003) The determinants of East Asian trade flows: a gravity equation approach, Journal of Asian Economics, 14 (2003) , 695-711. Shibasaki, R., Kizushi, R., Ieda, H. Kadaono, T, (2004) An improved model of international maritime container cargo flow in eastern Asian considering both shippers’ and carriers’ behavior, International Association for Maritime Economists Ports and Harbors Bureau, Ministry of Transport, Japan, Survey Report of International Container Cargo Flow Ministry of Transport, Japan, Port Statistics Yearbook of Japan in 1996. Handbook of Port Investment Evaluation, published by Port Investment Evaluation committee of Japan Trade Statistics of Ministry of Finance, Japan in 1997 Nakajima, T.(2002), An analysis of the Economic Effects of Japan-Korea FTA, ERINA Discussion Paper No. 0202e

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Appendices Table 8 GTAP Commodity Code, Aggregated Modal share and Containerized Ratio Marine Substitution Elasticities Containerized Unit Price Transport Domestic/ Sourcing of Ratio (USD/FT) Share Imported Imports 1 pdr Paddy rice 1.000 0.027 0.1467 2.20 4.40 2 wht Wheat 1.000 0.056 0.1426 2.20 4.40 3 gro Cereal grains nec 1.000 0.124 0.0993 2.20 4.40 4 v_f Vegetables, fruit, nuts 0.836 0.496 0.4363 2.20 4.40 5 osd Oil seeds 1.000 0.498 0.8490 2.20 4.40 6 c_b Sugar cane, sugar beet 1.000 0.498 10.0172 2.20 4.40 7 pfb Plant-based fibers 1.000 0.897 0.5195 2.20 4.40 8 ocr Crops nec 0.964 0.434 1.2051 2.20 4.40 9 ctl Cattle,sheep,goats,horses 0.074 0.902 32.6194 2.80 5.60 10 oap Animal products nec 0.842 0.933 1.8822 2.80 5.60 11 rmk Raw milk 0.959 0.933 1.8822 2.20 4.40 12 wol Wool, silk-worm cocoons 0.995 0.957 0.2670 2.20 4.40 13 for Forestry 0.941 0.089 0.0945 2.80 5.60 14 fsh Fishing 0.380 0.623 4.0852 2.80 5.60 15 col Coal 1.000 0.004 0.0364 2.80 5.60 16 oil Oil 1.000 0.000 0.1141 2.80 5.60 17 gas Gas 1.000 0.002 0.0623 2.80 5.60 18 omn Minerals nec 0.927 0.013 0.0447 2.80 5.60 19 cmt Meat: cattle,sheep,goats,horse 0.965 0.904 2.0149 2.20 4.40 20 omt Meat products nec 0.989 0.903 1.8896 2.20 4.40 21 vol Vegetable oils and fats 0.991 0.608 1.4248 2.20 4.40 22 mil Dairy products 0.959 0.959 2.4760 2.20 4.40 23 pcr Processed rice 1.000 0.050 0.1411 2.20 4.40 24 sgr Sugar 0.999 0.111 0.1944 2.20 4.40 25 ofd Food products nec 0.985 0.796 1.5493 2.20 4.40 26 b_t Beverages and tobacco products 0.986 0.961 1.3429 3.10 6.20 27 tex Textiles 0.874 0.984 3.2437 2.20 4.40 28 wap Wearing apparel 0.713 1.000 1.4116 4.40 8.80 29 lea Leather products 0.679 1.000 1.5590 4.40 8.80 30 lum Wood products 0.982 0.121 0.3758 2.80 5.60 31 ppp Paper products, publishing 0.868 0.479 0.9301 1.80 3.60 32 p_c Petroleum, coal products 0.999 0.011 0.1610 1.90 3.80 33 crp Chemical,rubber,plastic prods 0.772 0.498 1.2093 1.90 3.80 34 nmm Mineral products nec 0.578 0.174 0.3778 2.80 5.60 35 i_s Ferrous metals 0.990 0.049 0.6236 2.80 5.60 36 nfm Metals nec 0.827 0.436 2.5081 2.80 5.60 37 fmp Metal products 0.826 0.835 3.0542 2.80 5.60 38 mvh Motor vehicles and parts 0.991 0.290 1.3061 5.20 10.40 39 otn Transport equipment nec 0.821 0.289 1.5646 5.20 10.40 40 ele Electronic equipment 0.374 0.810 11.8478 2.80 5.60 41 ome Machinery and equipment nec 0.647 0.829 6.6274 2.80 5.60 42 omf Manufactures nec 0.607 0.821 1.6608 2.80 5.60

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