Spatial Network Structure of Global Logistics Service Trade
Tao Luo
School of Economics and Management, Guangdong Construction Polytechnie, 638 Guanghua Second Road, Guangzhou,
China
Keywords: Spatial Network Structure, Social Network Analysis, QAP, Distance.
Abstract: Research purpose: Logistics service trade is an important part of international service trade, but the spatial
network structure of logistics service trade and its influencing factors is not clear. Research methods: This
paper uses the WIOD world input-output table, constructs a world logistics service trade matrix, establishes
relational data, and uses social network analysis to characterize. On this basis, it uses QAP regression methods
to study the factors affecting trade in logistics services. Study found: The logistics service trade network has
a small-world network effect. The overall cyberspace structure of global logistics service trade is getting
closer and closer. Developed countries are at the core. China's status in developing countries is gradually
rising. The network density of the global logistics service trade network shows an upward trend, but the
change of the network is a gradual process. The control of each country in the global logistics service trade
network is declining. Economic distance, geographical distance, proximity and trade distance have significant
effects on global logistics service trade.
1 INTRODUCTION
This paper attempts to analyse the spatial network
structure of global in logistics services trade and its
influencing factors. The development of the logistics
industry has significantly reduced the cost of
international trade, increased the efficiency of global
trade, reduced the impact of geographical distance on
global trade, and connected the various links of the
global value chain. It can effectively facilitate the full
integration of goods, technology, knowledge and
services across borders. Developed countries not only
occupy the middle and high end of global R&D and
design value chains, but also dominate in logistics
services. The World Bank report "Connecting to
Compete: Trade Logistics in the Global Economy
2018" shows that developed economies continue to
lead in global trade logistics and that most countries
in the world are actively developing innovative
logistics services. Logistics services are not only an
important component of global trade, but also an
important part of international trade and a highly
profitable link in the global value chain. Therefore,
logistics services trade plays an important role in
international trade in services and is a further focus of
international trade in services.
Compared to other productive services, logistics
services trade is more widespread, extensive and
intimate. The links are no longer mere geo-
adjacencies, but rather a multi-threaded and complex
network structure. The spatial network structure of
logistics services trade provides a better measure of
the position and evolution of logistics services in
global trade across countries. Spatial network
structure characteristics can reflect the comparative
advantage of countries in logistics services trade.
Most studies have focused on producer services trade.
Few studies have been conducted on the sub-sectors
of logistics services trade. There are no systematic
answers to the characteristics of the vertically linked
network structure of logistics services trade and the
factors influencing it. Logistics services trade is very
different from other productive service industries.
Using typical studies of other productive service
trades is not conducive to an accurate understanding
of the changing characteristics of logistics services
trade and its influencing factors. The article uses the
world input-output tables provided by the WIOD
database to construct a logistics service trade matrix
and uses social network analysis to describe the
characteristics of the spatial network structure of
logistics service trade. To solve the problem that
traditional statistical methods cannot quantitatively
Luo, T.
Spatial Network Structure of Global Logistics Service Trade.
DOI: 10.5220/0011187700003440
In Proceedings of the International Conference on Big Data Economy and Digital Management (BDEDM 2022), pages 483-489
ISBN: 978-989-758-593-7
Copyright
c
2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
483
study "relational" data, the QAP regression analysis
method is used to study the influencing factors of the
vertical network structure of logistics service trade.
2 LITERATURE REVIEW
Logistics services exist in the production, marketing,
consumption and recycling of global value chains
(Bai, 2010). Logistics services are an important
component of the global value chain, connecting all
aspects of the value chain. Logistics service providers
are required in the global value chain to provide
services beyond "basic" production (Bair, 2005).
Microscopically, leading companies in the global
value chain can outsource non-core services such as
logistics services to focus on their core competencies,
optimize their internal value chains and industry
chains, and thereby enhance their competitiveness
(Memedovic, Ojala, Rodrigue, et al, 2008).
Macroscopically, fierce competition has reduced
corporate profit margins. Logistics services are the
result of an effective division of labor in the value
chain, which contributes to the rationalization of
industrial division of labor and industrial structure,
and improves the production efficiency of complex
production networks in global value chains, which in
turn can enhance the overall economy. Innovation
and competitiveness (Rodrigue, 2010). Research in
the apparel industry shows that the development of
logistics and supply chain management helps to the
development of the apparel industry in the global
value chain (Cammett, 2013). The above research has
shown that logistics services are the result of the
international division of labor and an important
subsystem of the global value chain, connecting all
aspects of the global value chain. Logistics services
can improve the core competitiveness of enterprises
in the global value chain on a micro level. They can
enhance the innovation and competitiveness of the
economy on a macro level, promote the progress of
global value chains, and form a complex global value
chain production network.
How to measure the logistics service network
with the appropriate method is an issue we must
solve. Previous studies have concentrated on
micro-enterprise logistics operations networks or
product distribution networks (MD, Haijema,
Bloemhof, et al, 2015). Less discussion of logistics
service trade networks from a global perspective.
Moreover, existing research mainly adopts
geography methods, ESDA spatial analysis
techniques or regression analysis methods. These
methods form the basis for quantitative research on
this issue. However, traditional statistical methods
mainly deal with "attribute" data, and cannot process
data with obvious "relationship" characteristics.
Therefore, existing research lacks quantitative
research on the spatial network structure and
evolution of logistics service trade, especially the
lack of quantification of the space network from the
network "relationships".
Social network analysis is just an effective
method to study "relationship" data. It has been
implemented in logistics network research and
provides new methods and ideas for researching
logistics networks. The first use of social network
analysis methods to study logistics networks, and
believe that the various nodes of logistics trade and
transportation can be continuously optimized in the
network (Phillips, Phillips, 1998). Social network
analysis has changed the way of relying on surveys in
the field of logistics and supply chain management.
In particular, it can study the model of binary
relationship in logistics and supply chain, and catch
up with the lack of attention to traditional research
methods relationship" (Carter, Ellram, Tate, 2010).
Therefore, based on the relationship data and
network perspective, using the logistics service
export data of 43 major economies (including China
Taiwan) provided by WIOD in 2000-2014, the
logistics service trade matrix is constructed, and its
spatial network structure is analyzed by means of
social network analysis. And the influencing factors
were studied. This study reflects the overall
characteristics and evolution of the spatial network of
logistics service trade by measuring network density
and the overall network structure. Through central
analysis, the status and role of each economy in the
spatial network of logistics service trade are
examined. Finally, QAP regression analysis is
utilized to study study the impact of economic
distance, geographical distance, proximity and trade
distance on the spatial network of logistics service
trade.
3 RESEARCH METHODS AND
DATA
3.1 Model Building
The logistics input service network model is
constructed by using the world input-output table
provided by the WIOD database. According to the
gravity model, the two basic factors affecting
international trade are economic size and
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484
geographical distance. The efficient scale is
positively related to international trade and has a
negative correlation with geographical distance.
Therefore, the factors affecting logistics service trade
choose GDP, geographical distance, and proximity as
variables. Considering the demand for logistics
services, influenced by the scale of global trade, the
value added of trade is added as a variable to the
model. The model is built as following:
Logtra=f (GDP, Dist, Contig, VAX) (1)
In equation (1), Logtra represents the logistics
service trade matrix. GDP represents economic
distance. Dist represents geographical distance.
Contig represents proximity. VAX represents trade
distance.
3.2 Variable Selection and Meaning
The equations are an exception to the prescribed
specifications of this template. You will need to
determine whether or not your equation should be
typed using either the Times New Roman or the
Symbol font (please no other font). To create
multileveled equations, it may be necessary to treat
the equation as a graphic and insert it into the text
after your paper is styled.
Logtra means logistics service trade. The WIOD
database provides a world input-output table built by
the world's major economies, using public data from
the logistics industry code H49-H53 to merge, and 43
countries (or regions) as nodes (remove ROW data).
Logistics service trade matrix. Because the
determinants of the international competitiveness of
the production services sector is mainly intermediate
needs and technical levels (Carter, Ellram, Tate,
2010). The logistics service trade matrix constructed
is a power matrix. According to the social network
analysis, the UCINET6.212 software is used to
convert the appropriate matrix into a weightless
binary matrix.
GDP means Economic distance. Economic
distance is characterized by GDP differences.
Select the GDP data of each country provided by the
IMF to construct a GDP difference matrix. With each
country as a node, the GDP difference matrix is
constructed by subtracting the GDP of other countries
of the GDP of one country. That is, the GDP of
country i is subtracted from the GDP of country j,
which is 0 on the diagonal. The calculation method is
presented in Equation 2.
GDP
ij
=GDP
i
-GDP
j
(2)
GDP means Geographic distance. A geographic
distance matrix is constructed by selecting
geographic distance data between capitals of various
countries in the CEPII database. The geographical
distance is the farther, the higher the cost and risk of
providing logistics services between the two
countries, and the less likely it is to trade logistics
services.
Contig means Proximity relationship.
Considering the neighbor relationship on land
between countries, the neighbor relationship matrix is
constructed. "0" indicates that there is no adjacent
relationship between the two countries on the land,
and"1” indicates that the two countries have adjacent
relations on land. If the two countries have adjacent
relations on land, it is more convenient to trade
logistics services, especially between landlocked
countries and neighboring coastal countries
(Evangelista, 2000).
VAX means Trade distance. The value-added
export matrix produced in the final stage of the
WIOD database builds a trade distance matrix. The
value-added exports produced in the final stage
reflect the mutual production and value added of
trade between the two countries, which better reflects
the trade gap between the two countries, especially
the trade level between the two countries. With each
country as a node, the VAX of one country is
subtracted from the VAX of other countries to
construct a trade distance matrix. That is, the VAX of
the country i is subtracted from the VAX of the
country j, and the diagonal is 0. The calculation
method is shown in Equation 3.
VAX
ij
=VAX
i
-VAX
j
(3)
According to the international trade
gravity model,
the relationship between logistics service trade and
explanatory variables is expected as follows:
Expectation1: The economic distance is in line
with the symbol of the international logistics service
trade matrix.
Expectation2: The geographical distance and the
symbol of the international logistics service trade
matrix are negative.
Expectation3: The symbol of the proximity
relationship and the international logistics service
trade matrix is positive.
Expectation4: The trade distance and the symbol
of the international logistics service trade matrix are
positive.
Spatial Network Structure of Global Logistics Service Trade
485
3.3 Data Sources
Logistics service trade data comes from the WIOD
database. National GDP data comes from the World
Monetary Fund (where Taiwan data comes from
Taiwan's monthly statistical report). Geographic
distance and proximity data are derived from the
CEPII database. The added value export data for the
final stage production is derived from the WIOD
database. The data sources and descriptions are
shown in Table I.
Table 1: Data source description.
Va ri a bl e
symbol
Variable meaning Data source
Logtra International
Logistics Services
Trade
http://www.wiod.org
Dist Geographical
distance
http://www.cepii.fr
Contig Proximity http://www.cepii.fr
GDP Economic distance http://data.un.org/IMF
VAX Trading Distance http://www.wiod.org
4 INTERNATIONAL LOGISTICS
SERVICE TRADE NETWORK
ANALYSIS
4.1 Small World Characteristic
Logistics service trade efficiency has increased.
Cooperation has become closer and closer, and
geographical distance has been narrowed. The length
of the feature path reflects the efficiency of
information transfer for each node in the network.
The larger the value, the lower the efficiency;
otherwise, the higher the efficiency. Table 2 shows
that the length of the characteristic pathway
decreased from 1.317 in 2000 to 1.122 in 2014. The
characteristic path length is much smaller than the
network size of 43 and is also significantly lower than
the 6 steps mentioned in six-degree separation
inference (Milgram's,1967). Explain that the logistics
service trade network has obvious characteristics of
small world networks. Moreover, the length of the
feature route is declining from 2000 to 2014,
indicating that the logistics service trade has a
phenomenon of bridging and bridging. The
characteristics of the group show that the cooperation
between countries in the world is getting closer and
closer, and mutual trust is getting higher and higher.
Bridging shows that the geographical distances of
countries around the world are getting closer, and
countries are more likely to get logistics services
from other countries.
Table 2: Average Shortest Path and Aggregation Coefficient Table.
Variable 2000 2005 2010 2014
Average Shortest Path 1.317 1.171 1.127 1.122
Aggregation Coefficient 0.825 0.877 0.900 0.903
Logistics service trade is highly aggregated. The
clustering coefficient indicates the case where the
points of the network are connected to each other. The
value of the set class coefficient is [0, 1]. When the
set class coefficient is 1, it means that there is a
connection between any two points in the network.
When the set class coefficient is 0, it means that the
network does not. There are cases where three points
are completely connected to each other. Table 2
shows that the clustering coefficient of logistics
service trade increased from 0.825 to 0.903 in 2014
from 2000 to 2014, and the clustering coefficient is
getting closer to 1. It shows that the logistics service
trade network is highly concentrated, and the number
of sides is also very large. This means that in the
world, goods can be reached in a short path from one
country to another. It proves that logistics service
trade has obvious Small World Characteristic
characteristics.
4.2 Overall Spatial Network Structure
Netdraw, a visualization tool of UCINET6.212
software, is used to map the logistics service trade
network. Due to space limitations, only the logistics
service trade network structure maps for 2000 and
2014 are given. According to the network structure
chart, the following conclusions can be brought: (1)
BDEDM 2022 - The International Conference on Big Data Economy and Digital Management
486
The logistics service trade space network has obvious
structure and strict space, and all countries can carry
out logistics service trade with other countries. There
is a link between countries, indicating the existence
of logistics services trade between the two countries.
The size of the network node is precisely proportional
to the centrality of a country's trade network. The
larger the node, the higher the centrality of the
country's logistics service trade network. (2) The
relationship between countries is closer. Observing
the graph, it can be seen that 2014 was more dense
than the 2000 network. Explain that the relationship
between countries in the network is closer with time.
(3) Developed countries are still at the core of the
network. Developed countries such as the United
States, the Netherlands, France, Germany, Denmark,
the United Kingdom, and Spain are mostly at the core
of the network. (4) China gradually approaches the
center from the edge in the network. In 2014, China's
position in the network was closer to the network
center than countries such as Japan, South Korea and
Canada, indicating that China's position in the
network is gradually rising.
Figure 1: Structure of the international logistics service
trade network in 2000.
Figure 2: Structure of the international logistics service
trade network in 2014.
4.3 Overall Spatial Network Structure
The logistics links between countries are getting
closer and closer and showing a steady growth trend.
Social network analysis uses density to measure the
ratio of the total number of relationships actually
present in a network to the total number of possible
theoretical relationships. Network density can be
used to assess the tightness of connections between
nodes in a network. According to Table III, it can be
observed that the density of logistics service trade
network increased from 0.6827 in 2000 to 0.8776 in
2014, and there was no obvious sudden drop or
upward trend. It shows that the network density of
logistics service trade network shows an overall
upward trend, reflecting the increasingly precise
logistics trade between countries. It also demonstrates
that the logistics service trade between countries has
shown a steady growth state, and the relationship is
getting closer and closer.
Table 3: Global Logistics Service Trade Network Density
Table 2000-2014.
Year 2000 2005 2010 2014
Density 0.6827 0.8295 0.8726 0.8776
5 QAP REGRESSION RESULTS
AND ANALYSIS
Using the constructed model, QAP regression
analysis was carried out on the spatial network matrix
of logistics service trade and the matrix of various
influencing factors from 2000 to 2014, and the
number of random replacements was selected 10,000
time. The observation value 1806 means that the
composition between 43 countries or regions is a
matrix of 43 × 43, and the observation value is 1,806
except for the diagonal. Table
IV reports the
regression results. The adjusted R2 in 15 years is
between 0.488 and 0.556, and the significance
probability value is 0.000, indicating that the
difference in economic distance, geographical
distance, proximity and trade distance can account for
this logistics service trade. Spatial network changes
from 48.8% to 55.6%.
First of all, the economic distance has a
significant effect on the logistics service trade
network, and the symbol is positive. The logistics
services trade network has passed at least a 10%
significant test in all years except 2003. It is shown
that the size of the economy can significantly affect
the relevance of logistics service trade. The smaller
Spatial Network Structure of Global Logistics Service Trade
487
the economic distance, the higher the possibility of
logistics services trade, the higher the correlation
between logistics service trade between the two
countries. That is to say. The logistics service trade
between countries is more likely to occur between
countries with similar economic development levels.
This conclusion is in line with the gravity model. The
closer the economic distance is, the more frequent the
logistics service trade between countries and the
stronger the network relationship.
Secondly, geographical distance has a significant
effect on the logistics service trade network, and the
symbol is negative. The logistics service trade
network did not pass the significance test before
2006, but passed the significance test of at least 10%
in the years after 2006. The significance of recent
years has increased substantially, and all symbols are
negative. It shows that geographical distance has a
significant impact on logistics services trade between
countries, i.e. the greater the geographical distance
between countries, the less likely it is that logistics
services trade will take place. This phenomenon is
consistent with the relationship between the median
distance of gravity model and trade. This is explained
by the fact that the farther away the two countries are,
the higher the cost of logistics service trade will be,
and the probability of risk increases. It is more
inclined to choose countries with closer distances as
trading partners of logistics services. The less the
geographical distance, less international logistics
services trade, and the weaker the network
relationship.
Thirdly, the proximity relationship has a
significant impact on the logistics service trade, and
the symbol is positive. Except for 2000, the proximity
relationship between the two countries has passed at
least 10% significance test, and the symbols are
positive. Explain that proximity can promote the
trade of logistics services between countries. The
main reason is that logistics service trade between
neighboring countries can make up for the lack of
logistics capacity between countries to meet the needs
of import and export. It is more reasonable to choose
logistics services from neighboring countries, which
can effectively reduce the cost and risk of logistics
service trade between countries. More international
logistics services trade between countries with
neighboring relationships, the stronger the network
relationship.
Finally, trade distance significantly affects the
logistics service trade network, and the symbol is
positive. The trade distance between countries has a
1% significant test on the impact of logistics services
trade, and the symbols are all conclusive. It shows
that the greater the trade distance between countries,
the more likely the countries are to trade logistics
services. This conclusion is consistent with
international trade theory. Because logistics services
are the cause of demand, when goods trade between
the two countries generates demand for logistics
services, the more frequent the trade of goods
between countries, the larger the scale, the greater the
demand for logistics services. Moreover, it is more
likely to be consistent with the flow of goods to trade,
that is to say, when a country exports goods, it may
also bring export of logistics services. The trade
distance is farther, the less logistics services trade
between countries and the weaker the network
relationship.
Table 4: Global Logistics Service Trade Weighted Network
Qap Regression Results.
Year 2000 2014
GDP 0.051** 0.083**
Dist -0.002 -0.046*
Contig 0.006 0.066 ***
VAX 0.742 *** 0.675 ***
R
2
0.556 0.488
Adj R
2
0.555 0.488
Probability 0.000 0.000
Number of observation 1806 1806
Number of permutations 10000 10000
6 CONCLUSIONS
As a downstream link of the global value chain,
logistics services are increasingly valued by various
countries. Developed countries not only occupy the
upstream links of global value chains, but also occupy
the dominant position of logistics services in global
trade. Utilizing the world input-output table provided
by WIOD database, constructing logistics service
trade matrix, using UCINET to study the spatial
network structure and evolution of logistics service
trade, and using QAP analysis to study the
influencing factors of logistics service trade, the
following conclusions are obtained:
First, the logistics service trade network has a
small world network effect. The length of the
characteristic route of logistics service trade has been
decreasing year by year, and the logistics service
trade between countries is increasing. The clustering
coefficient is getting bigger and bigger, and the
logistics service relationship between countries is
getting closer and closer.
Second, the overall cyberspace structure of
logistics service trade is getting closer and closer, the
BDEDM 2022 - The International Conference on Big Data Economy and Digital Management
488
developed countries are right on the core, and the
status of China in developing countries is gradually
rising. In 2014, the logistics service trade network
was more dense than the 2000 network, indicating
that the relationship between countries in the logistics
service trade network is closer. Developed countries
remain at the core of logistics service trade. China is
moved from the edge to the center in the logistics
service trade network, and its position in the logistics
service trade network has gradually increased.
Third, the network density of the logistics service
trade network is on the rise, but the change of the
network is a gradual process. Since 2000, the network
density of the logistics service trade network has
generally shown an upward trend, and the trade links
between countries in the network have become more
and more close.
Fourth, economic distance, geographical distance,
proximity and trade distance have a significant effect
on logistics service trade. The economic distance has
a significant effect on the logistics service trade
network. The closer the economic distance is, the
more noticeable the interaction of the logistics service
trade and the stronger the network relationship.
Geographical distance has a negative effect on the
logistics service trade network. The farther the
geographical distance is, the weaker the worldwide
logistics service trade network relationship.
Neighboring relations have a significant influence on
logistics service trade. The more frequent worldwide
logistics service trade between countries with
neighboring relationships, the stronger the network
relationship. The trade distance substantially affects
the logistics service trade network. The closer the
trade distance is, the more frequent the logistics
service trade and the stronger the network
relationship. In developing the economy and
increasing exports, developing countries have
gradually narrowed the gap with developed countries.
ACKNOWLEDGEMENT
This research was funded by Guangdong
Construction Polytechnie Subject (grant number
KY2021-26).
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