IAME 2020 CONFERENCE | HONG KONG
Paper ID 184

Naohiro Nishitani
Central Consultant Inc.
nnishitani@central-con.co.jp
Yasuhiro Akakura
National Institute for Land and Infrastructure Management, MLIT
and Kyoto University
akakura-y83ab@mlit.go.jp
Tomoko Sasaki
National Institute for Land and Infrastructure Management, MLIT
sasaki-t92y2@mlit.go.jp
Hirohito ItoCentral Consultant Inc.
hito@central-con.co.jp

Estimation Method of Port Cargo Volume Considering
Changes in Social and Economic Conditions using a
Dynamic SCGE Model
Abstract
A large amount of investment is channeled into the development of ports, which are vital tointernational trade. It is essential to make decisions based on estimates of the amount of
infrastructure development required for future trade. Therefore, a highly accurate estimationof future port cargo volume based on potential changes in various social and economic
conditions is required.
For this reason, the purpose of this study is to establish a port cargo volume estimation
method that reflects changes in various conditions. This study utilized a spatial computable
general equilibrium (SCGE) model, which can estimate the economic impact caused bychanges in GDP, population, labor, tariff rates, transport costs, and the real effective
exchange rate.
In this estimation, the port cargo volume in 2011 was set as the starting point and changes insocial and economic conditions between 2012 and 2017 were used as shocks to the model. As
a result, the estimated value accurately reproduced Japan’s actual port cargo volume in 2017.
These results indicate that future volumes can be projected using this method and that we will
be able to utilize the estimation result as an index for judging the pace of future port
development in Japan.
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Estimation method of Port Cargo Volume Considering Changes
in Social and Economic Conditions using Dynamic SCGE Model
Paper ID 184
Keywords: port investment, maritime trade, marine transportation cost, ship type, tariff rate,
Dynamic GTAP model
1. Introduction
A large amount of investment, including subsidies, is spent on the development of ports,
which are a part of the social infrastructure vital to international trade. It is essential to makewell-informed decisions regarding future investment that are based on accurate estimations of
the infrastructure development required for future trade. Overdevelopment reduces port
profitability, while a lack of development leads to inefficiency due to chronic congestion.
Therefore, it is necessary, from the standpoint of facilitating trade and accelerating economic
activity, to accurately estimate future port cargo volume based on changes in various socialand economic conditions.
Based on this background, the purpose of this study is to establish a port cargo volume
estimation method that reflects changes in social and economic conditions. The purpose ofthis paper is to report the results of this research.
In previous estimations of port and facility plans, statistical methods (e.g., various regression
methods) based on historical data were often used to estimate port cargo volume andcontainer throughput. This method had a problem in that it is greatly affected by thecharacteristics of the historical data. In this context, previous studies developed a tradecoefficient prediction model based on input-output analysis. However, there was a problem inthat the future social structure (e.g., declining population and labor) of Japan was not
explicitly considered by the model. To overcome these problems, this study utilizes a SCGE
model, which is used globally to calculate the impact of international trade policy. Thismodel is structured to reflect changes in the gross domestic product (GDP), the populationand the number of workers, tariff rates by free trade agreements (FTAs) and economic
partnership agreements (EPAs), transport costs, and the real effective exchange rate. Since
the SCGE model is a long-term model and don’t have monetary units, there are problems inthat the nominal exchange rate cannot be directly handled, and the calculation results areaffected by the data structure used for the initial equilibrium.
The SCGE model is a model that can analyze the influence of economic entities such as
households and producers on the market through multiple regions and sectors and can also
analyze ripple effects on other sectors. In addition, since the dynamic SCGE model is used inthis study, it is possible to consider the pace of the accumulation of capital. Moreover, since
the model used in this study can explicitly handle the international transportation sector, thedecrease in marine transportation costs due to the increase in ship sizes of many ship types(container ship, bulk ship, tanker, etc.) in the sector was also considered.
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Estimation method of Port Cargo Volume Considering Changes
in Social and Economic Conditions using Dynamic SCGE Model
Paper ID 184
In the estimation of port cargo volume, the 2011 volume was set as the initial equilibrium(starting point) and changes in GDP, population, labor, tariff rates, exchange rate, and marinetransport margins from 2012 to 2017 were used as shocks to the model. As a result, theestimated value accurately reproduced Japan’s actual port cargo volume in 2017.
This result indicates that the future port cargo volume can be estimated using this method andthat there is a possibility that the estimation result can be used as an index for judging thefuture development pace of ports in Japan.
This paper is organized as follows. Section 1 is an introduction. Section 2 is a literature
review. Section 3 describes the details of the model used in this study, the data used, and theshocks input into the model. Section 4 shows the results of the calculations of import andexport cargo volume by sector and by partner country. Section 5 discusses the results andSection 6 concludes.
2. Literature Review
There are existing data that forecasts trade values or port cargo volumes as follows. The
World Trade Organization (WTO) provides forecasts of worldwide trade values for thefollowing year regularly. The WTO (2018) also provided forecasts of real trade growth bycountry and region through the year 2030. Sea Europe (2018) provided forecasts of world
trade volume by type of cargo through 2035. Drewry (2019) periodically forecasts up to five
years of port cargo volumes. In Japan, the Japan External Trade Association (JFTC, 2019)
estimates Japanese trade value by commodity for the following year and Nittsu ResearchInstitute and Consulting (2019) also forecasts trade value and cargo volume by transportmode for the following year.
In the academic field, from a methodological viewpoint, the estimation methods used in theliterature include the gravity model, econometric models using regression analysis and timeseries techniques and data, and models that take into account the structure of the economy
and industry.
According to Andersson et al (2008), gravity models have been an important tool forstudying trade based on distance between any two countries for a considerable length of time.
Earlier work during the 1960s, such as that of Isard (1960), Tinbergen (1962), Pöyhönen(1963), Leontief and Strout (1963) and Linneman (1966), represent the classical studies in thefield. The relationship between international trade and trade costs has traditionally been
estimated using gravity models of trade, which relate bilateral trade flows to the incomes andpopulations of trading partners and the geographical distance between them. Among more
recent research, Bensassi (2014) focused on the use of more accurate proxies for transport
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Estimation method of Port Cargo Volume Considering Changes
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Paper ID 184
costs, such as freight rates, infrastructure, or customs procedures. Limao and Venables (2001)
analyzed the dependency of trade and transport costs on geographical and infrastructure
variables and estimated the elasticity of trade with respect to transport costs. Wilmsmeier andHoffmann (2008) confirmed the general positive correlation between distance and freightrates in principle. However, statistically speaking, distance explains only one-fifth of thevariance in the freight rate. In the gravity model, it is estimated considering the positional
relationship and other indicators related to trade.
Among the econometric analyses, Seabrooke et al (2003) identified the economic indicators
affecting cargo throughput in Hong Kong and carried out a regression analysis to predictcargo growth. Zhang et al (2005) used regression analysis to examine the contribution of
growth— namely, gross industrial product value and foreign direct investment into theregion—to container throughput in the Pearl River Delta region of China. Veenstra andHaralambides (2001) contributed to the literature of forecasting seaborne trade flows byshowing that the multivariate autoregressive time series model can be used to produce longterm predictions. Rashed et al (2017) applied different univariate time series approaches, theautoregressive integrated moving average (ARIMA) model, the ARIMA-intervention model,
and the ARIMAX model with leading economic indicators. Hoffmann et al (2019)considered five separate components of the effects on trade of sailing distances and direct(air) distances and the GDP of 142 trading partners. They then applied the quasi-maximum
likelihood method to estimate the parameters of a dynamic panel data model. The above
survey of the literature demonstrates that econometric analyses are mainly based on historicaltime series data.
The gravity model is a method that is easy to intuitively understand because positional
relationship (distance), which is an index that is easy to imagine, is set as the main variable.
Although there are various mathematical calculation methods, econometric analysis is also
intuitively acceptable because it is based on past performance. However, these calculation
methods cannot take into account changes in economic and industrial structures. A
computable general equilibrium (CGE) models are excellent models for considering thestructure of the economy and industry. The SCGE model extends the CGE analysis range
from one region to multiple regions.
The CGE and SCGE models are used as an important tool in policy analysis. These models
have been widely adopted by many countries and international organizations, such as theWorld Bank and the International Trade Organization. In previous times, to analyze theimpact of differential income tax policy on the United States (US) national economy, Shoven
and Whalley (1972) developed a CGE model. Dixon and Rimmer (1998) applied the
MONASH model to the Australian economy. Recently, the SCGE model has been more
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Estimation method of Port Cargo Volume Considering Changes
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Paper ID 184
widely used for policy analyses of various aspects of national and regional economies.
Devarajan et al (2011) used a CGE model to study South African tax policies. Naranpanawa
and Arora (2014) built a single-country multi-regional SCGE model to study the link between
trade liberalization and regional disparities in India. Zhang et al (2018) analyzed, under theBelt and Road initiatives, the impact of a further reduction in the Chinese import tariff rate on
major economic indicators using a CGE model.
Looking at the application of the SCGE models on cargo volume prediction, Lee et al (2011)
estimated the seaborne cargo volumes resulting from an FTA between Taiwan and China. A
similar study exists in Japan by which the change in trade volume due to the effects of EPAs
and port policy was calculated (Higaki et al, 2008, Kadono et al, 2005)
The contribution of these studies to the literature is the further development of an estimation
method for port cargo volume utilizing a dynamic SCGE model. Many previous studies havefocused on calculating trade value with the CGE and SCGE models as an indicator for theevaluation of policy. A few studies have used the SCGE model to estimate the volume oftrade; however, there are no examples that have utilized a dynamic SCGE model to estimate
trade volume. The greatest feature of the dynamic SCGE model, especially as compared withthe static SCGE model, is its reproduction of the pace of economic change. Static SCGE
models basically do not have any temporal scale. On the other hand, the dynamic SCGE
model does exhibit instability in terms of its convergent calculations, so more proficiency inits calculation settings, procedures, and model parameters, etc. is required. The proceduredeveloped in this study is unique for considering various socio-economic factors inforecasting.
3. Methods
3.1 Calculation Model
The CGE model enables a multi-sectoral analysis of the mutual influences of economic
agents, such as households and producers, on markets. The CGE model allows an analysis of
the ripple effects on other sectors that cannot be handled by partial equilibrium analysis,
which only analyzes the mutual influence of economic agents in a single sector. The SCGE
model extends the scope of analysis to multiple regions. With the static CGE/SCGE model, it
is difficult to clearly incorporate real economic activity such as savings, investment, andcapital accumulation into the model. However, a dynamic CGE/SCGE model has theadvantage in that items that are difficult to capture with a static CGE/SCGE model can beclearly incorporated. In addition, if it is a dynamic CGE/SCGE model, it is possible toconsider calculation results in each term (e.g. result of two year-by-year) up to the final result.

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Estimation method of Port Cargo Volume Considering Changes
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Paper ID 184
The SCGE model used in this study is "Dynamic GTAP Model (GDyn)" developed by
Global Trade Analysis Project (GTAP). GDyn was developed as an extension of the standard
GTAP Model for the purpose of comparative static analysis.
The GTAP Model—developed by the World Trade Analysis Center, which was establishedin 1992 and led by Professor Hartell of Purdue University in the US—was created to evaluate
the impact of international trade on countries around the world. The GTAP Model is an
SCGE model widely used for the evaluation of policy. Currently, in addition to the GDyn
which was extended to dynamics, the GTAP-E model incorporates the relationship between
the environment and energy into the standard GTAP Model in order to evaluate greenhousegas reduction policies, and the GMig2 model allows for the migration of labor.
The GDyn can explicitly account for the dynamic effects of changes in production factors(such as population and labor), investment, and capital accumulation over time. A number ofprerequisites—such as a completely competitive market, production technologies of constantreturns, and differentiation of tradable commodities by production area (Armington
assumption); as well as frames, such as an accounting definition that describes the generalequilibrium, equations based on the optimal behavior of consumers and producers, andmarket equilibrium—which are features of the standard GTAP Model (general SCGE modelfeatures), are also inherited by the GDyn. The GDyn introduces international capital transfers
between regions through foreign direct investment; capital accumulation and capital income,which generated by international capital transfers are endogenous in the model. Figure 1
shows the whole structure of GDyn.
t+1 termt termInvestment

Investment
Area A

Investment
CapitalincomeSavingArea D
Area CArea B

International
investment trust

Dividend
Local household
budgetGovernment

Privateconsumption

Private household
budgetPrivate domestictransaction

consumption

IncomeGovernment

Investment

Government domestictransaction

Producer

HomelandInter-producer
transactions

Local household
budgetForeignnationality

Private household
budgetGovernment

Government import

Producer import

Producer export

Import andexport value.
Transport cost

Producer

Private import

Transport service

International transport

Payer

RecipientFigure 1 – Structure of Dynamic GTAP Model (GDyn).

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Estimation method of Port Cargo Volume Considering Changes
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Paper ID 184
The structure of production in the GDyn is illustrated in Figure 2.
Quantity of Output QOjr
Leontief
Intermediate Input QFijr

Surplus Value QVAjr

CES
Land

Labor Capital
QFEijrDomestic QFDijr

Foreign QFMijr
CESCountries/Regions QXSijr
Figure 2– Production Functions of the GTAP Model.

In Figure 2, intermediate input of i commodity of j sector in r country: QFijr is expressed as acomposite of domestic and international goods using a constant elasticity of substitution(CES)-type production function as shown below in Formula (1):σ Di=
QFijr etafaijr1−σ Di1−σ Di 1−σ

 Di
σ Di
σ Di
+
d
QFD
d
QFM
 Dijr

ijr
Mijr
ijr



(1)

where, QFDijr, QFMijr: international and domestic intermediate input of j sector in r country,
dDijr, dMijr: international and domestic share of i goods in r country, j sector, σ Di: CES
between international and domestic goods. In the same way, input of intermediate goods :QFMijr is expressed as a composite among international goods by a CES-type production
function. The CES of the GTAP Model is set by sector and is common among
countries/regions.
3.2 Usage Data
In this paper, the GTAP database, specifically Version 9 for the year 2011, was used as theinitial equilibrium data. Changes in population, labor, tariffs, international maritime transport
costs, and the real effective exchange rate from 2012 through 2017 are the shocks to themodel. The rate of change in Japan’s import/export volume from 2011 to 2017 was calculated.
The cargo volume in 2017 is estimated by applying the calculated rate of change inimport/export volume to Japan’s import/export cargo volume in 2011.
For the purpose of estimating Japan’s import/export trade volume and considering mattersthat may have a significant impact on Japan’s imports and exports the original data of 141
countries were aggregated and 40 countries/regions were set as shown in Table 1, by

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Estimation method of Port Cargo Volume Considering Changes
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Paper ID 184
considering the amounts of trade, concluding or scheduling economic partnerships, andanticipated future expansion of trade.
Table 1 – Setting Countries and Regions.
Area
Northeast Asia

Southeast Asia

Other Asia

North America

South America

EuropeOceania

Countries / Regions

Code
1 JPN

Japan

2 CHN

China

3 HKG

China, Hong Kong SAR

4 TWN

China, Taiwan Province of China

5 KOR

Republic of Korea

6 xneaRest of Northeast Asia

7 IDN

Indonesia

8 MYS

Malaysia

9 THA

Thailand

10 SGP

Singapore

11 PHL

Philippines

12 VNM

Viet Nam

13 BRN

Brunei Darussalam

14 LAOLao People's Democratic Republic

15 KHM

Cambodia

16 xseaRest of Southeast Asia

17 INDIndia

18 xsasRest of South Asia

19 SAU

Saudi Arabia

20 ARE

United Arab Emirates

21 xmeRest of Middle East

22 USA

United States of America

23 CAN

Canada24 MEX

Mexico25 BRA

Brazil26 CHLChile

27 PAN

Panama28 xsamRest of South America

29 DEU

Germany

30 FRA

France31 GBR

United Kingdom

32 NLD

Netherlands

33 RUS

Russian Federation

34 xneeRest of Northern and Eastern Europe

35 ITA

Italy

36 ESPSpain

37 xeweRest of Southern and Western Europe

38 OCEANIA Oceania

Africa39 AFRICA

AfricaROW

40 ROW

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Estimation method of Port Cargo Volume Considering Changes
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Paper ID 184
The GTAP’s 57 sectors have been consolidated into 36 sectors, as shown in the table 2. The
table also shows the types of vessels by sector, as set forth below in Section 3.3.4,“International sea transportation costs.”
Table 2 – Setting of sectors used in the calculation and corresponding ship types
GTAP
DescriptionNo.
1 Paddy rice
2 Wheat
3 Cereal grains nec
4 Vegetables, fruit, nuts
5 Oil seeds
6 Sugar cane, sugar beet
7 Plant-based fibers
8 Crops nec
9 Bovine cattle, sheep and goats, horses
10 Animal products nec
11 Raw milk
12 Wool, silk-worm cocoons
13 Forestry
14 Fishing
15 Coal16 Oil
17 Gas18 Minerals nec
19 Bovine meat products
20 Meat products nec
21 Vegetable oils and fats
22 Dairy products
23 Processed rice
24 Sugar
25 Food products nec
26 Beverages and tobacco products
27 Textiles
28 Wearing apparel
29 Leather products
30 Wood products
31 Paper products, publishing
32 Petroleum, coal products
33 Chemical, rubber, plastic products
34 Mineral products nec
35 Ferrous metals
36 Metals nec
37 Metal products
38 Motor vehicles and parts
39 Transport equipment nec
40 Electronic equipment
41 Machinery and equipment nec
42 Manufactures nec
43 Electricity
44 Gas manufacture, distribution
45 Water46 Construction
47 Trade48 Transport nec
49 Water transport
50 Air transport
51 Communication
52 Financial services nec
53 Insurance
54 Business services nec
55 Recreational and other services
56 Public Administration, Defense, Education,
Health
57 Dwellings

AggregatedAggregated itemNo.
1
Paddy rice
2
Wheat
3
Cereal grains nec
4
Vegetables, fruit, nuts
5
Crops nec

Aggregated
code
pdr
wht
gro
v_f
ocr_n

Container
General Cargo
Dry Bulk Carrier
Container
Container

Vessel type6

Animal products nec

ap_nContainer

7
8
9
10
11
12
13

Forestry
Fishing
Coal
Oil
Gas
Minerals nec
Meat products nec

frs
fsh
coa
oil
gas
omn
omt_n

General Cargo
General Cargo
Dry Bulk Carrier
Crude Oil Tanker
Gas Tanker
Ore Carrier
Container

14
15

Vegetable oils and fats
Food products nec

vol
ofd_n

General Cargo
Container

16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33

Beverages and tobacco products
TextilesWearing apparel
Leather products
Wood products
Paper products, publishing
Petroleum, coal products
Chemical, rubber, plastic products
Mineral products nec
Ferrous metals
Metals nec
Metal products
Motor vehicles and parts
Transport equipment nec
Electronic equipment
Machinery and equipment nec
Manufactures nec
Construction, electricity

b_t
tex
wap
lea
lum
ppp
p_c
crp
nmm
i_s
nfm
fmp
mvh
otn
ele
ome
omf
cde_n

Container
Container
Container
Container
Container
Container
Oil Products Tanker
Container
Container
General Cargo
Container
Container
Container and PCC
General Cargo
Container
Container
Container-

34

Transport, communication, trade

ttt_n

-

35

Finance, insurance, service

fis_n

-

36

Administration, Defense, Education

pde_n

-

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Estimation method of Port Cargo Volume Considering Changes
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Paper ID 184
3.3 Setting the Shock Applied to the Model
Changes in population, labor, tariffs, international maritime transport costs, and the realeffective exchange rate from 2012 to 2017 were given as shocks to the model.
If a shock has an abnormal value, the risk that the simulation does not converge increases.
For this reason, to flatten shocks, shocks are aggregated for three years and given as one term.
3.3.1 Population and Labor force
The population change rate in the calculated countries/regions was set from the United
Nations (UN) population estimation database “World Population Prospects (WPP): The 2017
Revision.”
Changes in labor were set as changes in the working-age population from the UN population
estimation database “World Population Prospects (WPP): The 2017 Revision.”
3.3.2 GDP
Changes in the GDP growth rate of calculation countries/regions were set from theInternational Monetary Fund database “World Economic Outlook Database 2019 April.”
GDP is an endogenous variable by default in the GDyn but is replaced with an exogenous
variable, which is the total factor productivity of each country, in order to give the rate ofchange in GDP as an exogenous shock.
3.3.3 Tariff rate
Since the initial equilibrium data is set to 2011, changes in the tariff rate by economic
partnerships went into effect between 2011 and 2017, such as the Japan-India EPA, werecountries as shown in the Table 3 (JPN-IND cell).
The calculation process for tariff reduction is as follows.
(1) Compile import value of each commodity by harmonized system (HS) code to importing
countries/regions in 2011 and 2017 using the UN’s “Comtrade Database.”(2) Compile the tariff rates of each commodity to importing countries/regions in 2011 and2017 using the “Market Access Map (MAcMap)” of the International Trade Center.
(3) Calculate tariff amounts for each commodity in 2011 and 2017 based on the compiled
import values and tariff rates (HS codes are HS 2007 in 2011 data and HS 2017 in 2017 data).
(4) Aggregate import values and tariff amounts in 2011 and 2017 for each of the 36 sectors
and recalculate the tariff rates for each sector.

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Estimation method of Port Cargo Volume Considering Changes
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Paper ID 184(5) Calculate the rate of change in tariff rates through 2017 using the 2011 figures as the tariff
rates for the year when the EPA came into effect.
(6) All tariff reductions relating to Japan have been calculated, while other countries havecalculated one case and applied it to all cases for practical convenience. The colored cells in
the table 3 are inputs to the model.
Table 3 – Activated Economic Partnership Agreements between 2011 and 2017.
Country /Region
JPN

JPN

CHN

HKG

TWN

KOR

IDN

MYS

THA

SGP

PHL

VNM

BRN

LAO

IND

SAU

ARE

USA

CAN

DEU

NLD

GBR

FRA

ITA

ESP

RUS

BRA

PAN

MEX

(11/８)◎＼(15/12)◎＼

HKG(14/10)

◎

＼

TWN

◎

KOR

CHL

◎

＼

CHN

(14/４)＼(15/12)

◎

◎

◎

◎

(15/12)(12/３)(15/１)(15/12)

＼

IDN

＼

MYS

◎

◎

(11/７)(12/2)

◎

＼

THA

◎

SGP

(15/11)＼(14/４)◎◎(13/9)(13/9)

＼

PHL

◎

VNM

＼

(15/12)◎◎(16/10)(14/1)

＼

BRN

＼

LAO

＼

KHM
IND

KHM

◎

◎

(11/８)(11/７)

＼
◎

SAU

＼

(13/9)◎

ARE＼(13/9)

◎

USA

◎

CAN

◎

＼

(12/３)(12/10)＼(15/１)◎(17/９)(13/４)

＼

DEU

＼

NLD
GBR
FRA

◎

◎

(15/12)(17/９)

＼
＼
＼

ITA

＼

ESP

◎

RUS

＼

(16/10)

＼

BRA
CHL

◎

◎

◎

◎

◎

(14/10)(12/2)(15/11)(14/1)

PAN

＼
◎

◎

(12/10)(13/４)

MEX＼◎(15/７)◎(15/７)＼

Legends◎ ・・・Effective, (Year / Month)・・・Effective date

Data Source: JETRO (2018).

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Estimation method of Port Cargo Volume Considering Changes
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3.3.4 International sea transportation costs
In order to set the reduction in sea transportation costs, we performed the calculation using
the following procedure.
(1) First, we set the types of vessels that carry maritime cargos in each of 36 sectors (seeTable 2) considering trade values of containerized/non-containerized cargos in Japan’s trade
statistics. For non-containerized cargo transportation, the correspondence between sectors
and ship types was set in consideration of the characteristics of cargo items (sectors) (e.g. 11.
gas: gas tanker, 12. Minerals nec: ore carrier). However, for “28. Motor vehicles and parts,” itis assumed that finished motor vehicles are transported by pure car carriers (PCC) and parts
are transported by container ship.
(2) We compiled the trends of the upsizing of vessels of each type listed in Table 2. For cargo
carrier excluding container ship, we obtained all ship data for the target ship types in 2011
and 2017 from the IHS “Sea-web Ships database” and calculated the average ship size rate ofchange for each vessel type as shown in Table 4.
For container ships, we calculated the average ship size in 2011 and 2017 using the 2011 and2017 editions of “World Container Transport and Service Status” (NYK Line) considering
container shipping routes.
(3) We calculated the sea transportation costs based on the average ship size for each ship
type from 2011 to 2017 calculated above, by using the calculation formula for sea
transportation by container ship and cargo carrier excluding container ship in the “CostBenefit Analysis Manual for Port Projects in Japan.” These results are indicated in Table 4
and 5.
Table 4 – Changes in Average Vessel Size and Transportation Cost of Cargo Carrier Excluding
Container Ship.

Vessel type

Average vessel size
（DWT）

Sea tranportation cost
(Yen/DWT/Day)

2011

2017

2011

2017

Crude Oil Tanker

145,235

152,615

37

36

Ore Carrier

236,763

273,896

28

26

Vehicles Carrier(PCC)

64,61672,652

59

56

Dry Bulk Carrier

66,65871,716

58

56

General Cargo

5,055

4,941

281

285

Gas Tanker

26,92432,64598

88

Oil Products Tanker

13,076

12,291

151

157

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Estimation method of Port Cargo Volume Considering Changes
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Table 5 – Changes in Transportation Costs of Container Ships.
Sea tranportation cost (Yen/TEU)

Container route

EastWest

2017
91,67684,862

East Asia / North America

93,21788,222Europe / North America

80,84076,539Europe / Middle East / South Asia

151,501

140,234

East Asia / Latin America

123,444

112,316

East Asia / Oceania

115,275

104,186

East Asia / Africa

150,390

135,654

Europe / Latin America

118,754

108,997

Europe / Africa

74,86069,464

North / Latin America

152,110

145,417

Other

101,02288,730

East Asia

71,47663,09828,08024,663

East Asia / Middle East / South Asia

NorthSouth2011

East Asia / Europe-

73,534

Far East / Middle East / South Asia
Within theEurope/ Mediterranean
region(including North Africa)
Other areas

31,75629,198

175,963

159,191

Other

135,815

129,579(4) Finally, we entered the (reduction) rate of change of sea transportation costs as a shock to
the model. In the sector setting, GTAP’s transportation sectors (48.Transport nec, 49.Watertransport, 50.Air transport) were aggregated into one item (34.Transport, communication,
trade) in this study (see Table 2), so when inputting the data into the model, considering theratio of sea transportation modes as shown in Table 6.

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Table 6 – Transportation Rates by Mode for Each Commodity.
Land

Sea

Air

1 pdr

0.768

0.195

0.037

2 wht

0.275

0.643

0.082

3 gro

0.170

0.735

0.094

4 v_f

0.571

0.356

0.073

5 ocs

0.240

0.696

0.064

6 a_p

0.492

0.347

0.161

7 frs

0.285

0.673

0.042

8 fsh

0.285

0.251

0.464

9 coa

0.106

0.871

0.023

10 oil

0.117

0.857

0.026

11 gas

0.654

0.323

0.023

12 omn

0.076

0.806

0.118

13 omp

0.524

0.425

0.051

14 vol

0.324

0.653

0.024

15 ofp

0.464

0.484

0.051

16 b_t

0.395

0.549

0.057

17 tex

0.359

0.530

0.111

18 wap

0.343

0.477

0.180

19 lea

0.318

0.539

0.144

20 lum

0.437

0.534

0.030

21 ppp

0.396

0.500

0.104

22 p_c

0.115

0.866

0.019

23 crp

0.299

0.532

0.169

24 nmm

0.430

0.460

0.110

25 i_s

0.351

0.575

0.073

26 nfm

0.285

0.482

0.232

27 fmp

0.386

0.471

0.143

28 mvh

0.469

0.473

0.058

29 otn

0.265

0.428

0.307

30 ele

0.166

0.290

0.544

31 ome

0.284

0.410

0.306

32 omf

0.236

0.560

0.204

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3.3.5 Exchange rates
GDyn used in this study, like most other CGE models, is a real model, so all figures aretreated as real values with no monetary units. Therefore, there is no nominal exchange ratethat can be directly linked to those observed in the foreign exchange markets.
In the actual economy, however, it is well known that exchange rates have a significant
impact on trade value and volume. The exchange rate of Japanese yen (¥) to the US dollar ($)
has varied substantially over the long term, for example, in 2011 it was ¥80 per $1 and ¥121
per $1 in 2015. Thus, the exchange rate is one of the most important settings for estimating
port cargo volume. The GDyn includes a real effective exchange rate mechanism as themarket price index of primary factors for each region, the “pfactor” variable. We set the valueof "pfactor" to the rate of change of the real exchange rate from 2011 (initial equilibrium),with a pass-through rate of 30%.
(1) Pass-through Rate
It is generally thought that the price of traded goods varies due to fluctuations in the exchange
rate. For example, it is considered that an appreciation in the yen represents an increase in theprice of products exported from Japan to overseas markets, and, conversely, a reduction in
the price of products imported from overseas to Japan. The degree of this effect, that is, theexchange rate elasticity of the rate of change in traded goods prices, is called the pass-throughrate. If the rate of change of the exchange rate is 100% added to the price of traded goods, thepass-through rate is 100%, and, conversely, if the change in the exchange rate does not affectthe price of tradable goods at all, the pass-through rate is 0%. Many previous study haveindicated the pass-through rate have been decreased in recent years, therefore, in this study,
the pass-through rate was set to 30%, with reference to data from Japan’s Ministry of
Economy, Trade and Industry (METI) (2013).
(2) Real Effective Exchange Rate
Japan’s real effective exchange rate was set from “the effective exchange rate index” in the
Bank of Japan’s database. The nominal exchange rate refers to the exchange rate of a bilateralcurrency at the market rate. For example, $1 is expressed as ¥100, and ¥1 is expressed as$0.01. The former is called the nominal exchange rate in yen and the latter in dollars. The
nominal exchange rate includes price fluctuations, and the exchange rate that excludes theeffect of price fluctuations is called the real exchange rate. Also, in order to see thecompetitiveness of one’s own currency in the global market as a whole, it is necessary to look
not only at the bilateral relationship with a certain currency but also at the plurality ofcurrencies. The effective exchange rate is calculated by weighting the exchange rates
between all target currencies and the Japanese yen based on the importance of trade amounts.
The real effective exchange rate is an exchange rate that takes into account both the real

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exchange rate and the effective exchange rate. Trends in Japan's real exchange rates areshown in Figure 3.

Index display
2010value: 100

Figure 3 – Fluctuations in Japan’s Real Effective Exchange Rate.

Source: BANK OF JAPAN (2019).
The change rate of “pfactor” as 10% of the fluctuation of the real effective exchange rate,
because the value rate of imported goods among whole trading goods in Japan is about 30%(input-output table, Ministry of Internal Affairs and Communications), and “pfactor” iscalculated by multiplying pass-through rate by rate of imported goods.
4. Calculation Results
Calculations using the GDyn yielded the percentage change in import and export volume bysector between 2012 and 2017. By applying this rate of change in imports and exports to theactual maritime cargo volume in 2011 from Japan’s port statistics, export and import volumes
by sector in 2017 were calculated. The cargo volumes in the port statistics are tabulated using
freight ton (FT), which is shipping ton established by the weight/measure method. To
calculate the actual cargo volume in 2011 and 2017, the conversion table between 81 items of
the port statistics and 36 sectors of this study was arranged based on HS codes ([36 Sectors][HS Code]-[Port Statistics 81 Items]).
Figure 4 shows the 2017 calculation results of port cargo volume, together with the 2017
results, by sector. For exports, the correlation coefficient was 0.9979. For imports, coal, oil,

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gas, and minerals had slightly higher than calculated values, but had a correlation coefficientof 0.9992. Both exports and imports were very applicable.
Figure 5 shows results by trading partner. Although the US and China was low fit, thecorrelation coefficient for exports was 0.9678. Regarding imports, the calculated value for
Africa was higher than the actual value, but the correlation coefficient was 0.9131. The
results by trading partner were also very applicable to both exports and imports.
Figure 6 shows the results of container volume. Container volume of i commodity: Vconi
was calculated by formula (2).
=
Vcon
Valli × Ri
i(2)

where, Valli: whole maritime cargo volume of i commodity, Ri: containerized rate of icommodity in FT by Japan’s port statistics. The calculation results shown in Figure 6 were in
81 items in Japan’s port statistics using containerized rate in 2011. The results of container
volume were also very applicable to both export and import.
90,000Calculated value

200,000

coal

Calculated valueoil

80,000gas
70,000mineral150,000
60,00050,000100,000

40,00030,00050,00020,00010,000

Export

Unit: thousand FTImportActual value
200,000

150,000

100,000

2017

Correlation coefficient r = 0.9992
50,0000090,000

Actual value
80,00070,00060,00050,00040,00030,000

Correlation coefficient r = 0.9979
20,0002017

10,00000Unit: thousand FTFigure 4 – Calculation Results by Sector.

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70000

Calculated value

Calculated value

250,000

60000

China
200,000
50000

40000

150,000

U.S
30000

100,000
20000

50,000
10000

Export250,000

Actual value

200,000

150,000

100,0002017

Unit: thousand FTCorrelation coefficient r = 0.9131
50,0000070,00060,000

Actual value

50,00040,00030,00020,0002017

Correlation coefficient r = 0.9678

10,00000Unit: thousand FTImportFigure 5 – Calculation Results by Trading Partner.

25,00025,000Calculated value

20,00020,00015,00015,00010,00010,000

5,000

5,000

0

0

Correlation coefficient r = 0.9918
5,000

10,00015,000

ExportActual value

20,00025,0000Calculated value0

Unit: thousand FTCorrelation coefficient r = 0.9804
5,000

10,000

Import15,000

Actual value

20,00025,000

Unit: thousand FTFigure 6 – Calculation Results of Container Volume.

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5. Discussion
The purpose of this study was to examine a method for estimating Japan’s import/export port
cargo volume, taking into account various social and economic changes. This model
estimates Japan’s import/export port cargo volume by incorporating changes into the socialand economic condition of the population, labor, GDP, tariff rates, transport margins, and thereal exchange rate into the models, which resulted in a high level of reproducibility.
These results indicated that the future port cargo volume can be estimated using this method
and that there is a possibility that the estimation result can be used as an index for judging thedevelopment pace of Japanese ports in the future. However, the estimation method should
consider various social and economic factors influencing future port cargo volume. For
example, the population has been decreasing and the aging rate is increasing in Japan. The
Japanese government now promotes the acceptance of foreign workers in various fields as acountermeasure to deficiencies in the work force. In terms of EPAs (see Table 8), the TransPacific Partnership Agreement took effect in 2018, the Japan-EU EPA came into effect in
2019, and the Regional Comprehensive Economic Partnership, that includes Japan, China,
India, Australia and ASEAN countries, is under negotiation. On the other hand, the “tradewar” between the US and China is also under negotiation and the United Kingdom (UK)withdrew from the European Union (EU) in 2020. Many of over 10,000 twenty-footequivalent (TEU) mega-container ships are still under construction and the commission ofthese ships has a cascading effect, which means the knock-on effect of enlarging ships. The
US President Trump made comments about a high dollar exchange rate many times. These
factors have an impact on trade values and port cargo volumes. The proposed method in thisstudy is able to deal with all of these issues.
However, this study did not have a long-term verification, the duration was only six years,
because of a lack of data. In order to judge the development pace of port infrastructure, longterm estimates are needed, meaning 10 to 15 years in the future. Therefore, long-term trial
estimation is still needed in order to verify and improve the accuracy of the estimation. The
accuracy of the base data is another issue. The base year of the estimations in this study was
2011, the various economic indicators in Japan were unstable due to the Great East Japan
Earthquake, and Japan’s input-output data was estimated using 2009 data. The GTAP
database Version 10 features 2014 data, so using this data could improve this problem.
The challenge in expanding this study and applying it to future estimates is in how to ensure
the certainty of each socio-economic factor used as a shock. In this study, actual values were
provided to the model as shocks. However, in future estimations, it will be necessary to set
future values such as long-term GDP, labor migration, the effects of EPAs and protectionism
on tariff rates, changes in the exchange rate, and the long-term trends of enlarging ships. In
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addition, change in energy composition is another difficult issue. Many developed countries,
such as the EU countries, are aiming to decrease their carbon dioxide (CO2) emissions
drastically by utilizing natural energy sources such as solar power, wind power, geothermal
power, and biomass. The US became the top oil producing country in 2018 by a surge ofshale oil production and has increased exports to Canada, China, and the UK, and othercountries. These changes in energy consumption directly impact port cargo volume. The
substitution of electric sources should be accounted for by the model in some way. Theseissues cannot be addressed in only one manner, thus estimation using various scenarios isrequired to utilize the estimation results in making policy regarding the ports.
Table 7 – Status of Economic Partnership Agreements at 2018
Country /Region

JPN

CHN

JPN

＼

□

CHN

□

＼

HKG

KOR

IDN

MYS

THA

SGP

PHL

VNM

BRN

LAO

IND

▲

SAU

ARE

□

□

□

□

□

□

USA

CAN

DEU

NLD

GBR

FRA

◎

○

(18/12)(18/7)

ITA

ESP

CHL

▲

PAN

MEX

□

(17/11)□＼□(17/12)

▲

◯

▲

＼

THA

▲

○

＼

(17/11)◎○(18/12)(14/10)

▲

▲

□

◯

○

◯

(15/12)(18/3)

▲

□

＼

KHM

▲

SAU

□

□

□

▲

ARE

□

□

□

▲

◎

◯

(18/12)(18/3)

□
＼

□

□

＼

□
▲

USA

▲

□

＼

(18/12)▲▲(18/3)

▲

□
＼

◯

□

□

□

▲
◎

(18/12)

□
＼

LAO

◎

□

(18/3)＼

BRN(18/3)

▲

＼

VNM

◯

□

(18/3)

＼

SGP

IND

(18/2)

○
＼

PHL

○

□

＼

MYS

◯

□

(18/11)◯□(18/11)◯(18/11)

＼

▲
＼

DEU

＼

NLD

□
FRA

BRA

○

IDN

GBR

RUS

＼

TWN

CAN

KHM

□

＼

HKG

KOR

TWN

○

□

(18/7)○(14/10)□○(15/８)

▲
＼

□

□

□

□

□

□

□
＼
＼

ITA

＼

ESP
RUS

▲

BRA

□

▲

▲

□
○

CHL

○

＼

(18/2)

□

MEX

＼
＼

(17/12)

□

PAN

＼
□

◯

◎

◯

◯

(18/3)(18/12)(18/3)(18/11)＼◎ ・・・Effective, (Year / Month)・・・Effective date
○ ・・・General agreement (not effective), (Year / Month)・・・Agreement date
□ ・・・In negotiations▲ ・・・Conceptual phase・・・TPP

Data Source: JETRO (2018).
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6. Conclusions
The purpose of this study was to establish a port cargo volume estimation method thatreflects changes in various social and economic conditions, such as GDP, population, thetariff rate, etc.
We set a starting point of 2011 for calculations and changes in GDP population, number oflabors, tariffs, international maritime transport costs, and the real effective exchange ratefrom 2012 to 2017 were shocks to the model, and the rate of change in Japan’s import/export
volume from 2011 to 2017 was calculated. The change in tariff rates was caused by EPAs
that went into effect during the period between 2011 and 2017 and transport costs were
impacted by enlarging ship sizes corresponding to the defined sectors.
The calculation results by sector show that the correlation coefficient is 0.9979 for exports
and 0.9992 for imports. By trade partner country, the correlation coefficient was 0.9678 forexports and 0.9131 for imports. For container volume, the correlation coefficient was 0.9918
for exports and 0.9804 for imports. The estimation results had very good reproducibility toactual volumes.
This study’s estimation method is unique for utilizing a dynamic SCGE model. No previous
study has used the same method. Although dynamic SCGE models do feature instability inconvergent calculations, this study proposed a procedure for getting relevant calculations
despite this shortcoming.
These results indicate that future volumes can be estimated using this method, and that thisestimation result can be utilized as an index for judging the development pace of ports inJapan. However, this study does not have long-term verification, the duration was short term.
For judging the development pace of port infrastructure, long-term estimates are required,namely 10 to 15 years in the future. Therefore, long-term trial estimation is still needed toverify and improve the accuracy of the estimates by this model. We will tackle this issue
continuously.
Acknowledgements
The authors sincerely thank Prof. Kazutomo ABE from Tokyo Denki University, Prof.
Tomihiro WATANABE from Kyoto University and Assoc. Prof. Kazuhiko ISHIGURO from
Kobe University for their valuable advice, suggestions and in-depth discussions to advance
this study.

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