# The spillover effect of neighboring port on regional industrial
diversification and regional economic resilience

Jung-In Yeon<EMAIL_ADDRESS>Sojung Hwang<EMAIL_ADDRESS>Bogang
Jun<EMAIL_ADDRESS>Sustainable Growth Initiative (SGI), Korean Chamber
of Commerce and Industry, Seoul, South Korea  Department of Economics, Inha
University, Incheon, South Korea  Department of Data Science, Inha University,
Incheon, South Korea

###### Abstract

We examine the spillover effect of neighboring ports on regional industrial
diversification and their economic resilience using the export data of South
Korea from 2006 to 2020. First, we build two distinct product spaces of ports
and port regions, and provide direct estimates of the role of neighboring
ports as spillover channels spatially linked. This is in contrast to the
previous literature that mainly regarded ports as transport infrastructure per
se. Second, we confirm that the knowledge spillover effect from neighboring
ports had a non-negligible role in sustaining regional economies during the
recovery after the economic crisis but its power has weakened recently due to
a loosened global value chain.

###### keywords:

Economic Development , GPNs , Relatedness , Spillovers , Industrial
Diversification

††journal: arXiv

##  1 Introduction

Economic development has been widely examined in various research streams in
social science from the era of mercantilism to the recent globalized world.
Over the history of capitalism, the detailed strategies and policies for the
economic development have been evolved over time as the answer of the demand
of the times. From the fall of the Iron Curtain and the ”end of history” in
the 1990s, the second wave of globalization became a global phenomenon. This
new global regime changed the battleground for the economic development of
firms, sub-national regions, and states. From the mid-2000s, the main drivers
of the industrial transformation of a state was not the domestic factors
including state-led policies, but the factors associated with complex global
production networks (GPNs). With this observations, scholars in economic
geography examined how regional actors and resources co-evolve with the
globalized world, show the strategic coupling with plug into the GPN, and in
the long run, result in the economic development of region[17, 55, 56].

[55] explained how the industrial structure of a nation could be upgraded in
the recent economic globalization and cross-border production by looking at
three East Asian countries, which are Taiwan, South Korea, and Singapore.
Regarding the economic development of those nations in the 1970s and the
1980s, as [5, 54, 20] showed, the role of state for industrial transformation
was crucial and the domestic factors on the development that were associated
with a legacy of Friedrich List played a critical role for the development
[35]. However, [55] observed that from the 2000s, economic globalization
dimmed the role of state in the industrial transformation, and transnational
economic actors such as a globally significant lead firm and geographically
dispersed network of overseas affiliates gave more significant effects on the
industrial upgrading of a nation. He conceptualized this co-evolutionary
mechanism of economic development as strategic coupling. The term ”strategic”
carries that his basic analytical significance is on firm-specific initiative,
while the term ”coupling” emphasizes that the industrial transformation should
be examined as the dynamic relational process. Since [55] regarded firm as a
basic analytical unit, this concept allowed the micro perspective in the
analysis of the development. At the same time, since this GPN research
ultimately examines the economic performance at the national level, it also
allows the lens with macro perspective.

However, as [18] mentioned, more lens to see the interaction between the
region and the GPN is required. In this regard, [56] attempt to integrate two
research streams, which are the evolutionary economic geography (EEG) and the
global production networks (GPNs), by using one of their key concepts: related
variety in EEG and strategic coupling in GPNs. He argued that although his
endeavor of bridging the two concept directed from the GPNs to the EEG
perspective, more endeavor with the opposite direction was required.

Recent research on related variety in EEG, together with those of the economic
complexity, accumulated the evidence of the role of related variety at various
levels, such as a firm, a sub-national region, or a country, for various
domains, such as research, product, or industry. Those empirical evidences
were recently formalized as the Principle of Relatedness [29]. While in the
initial work on related variety of [22], the entropy measure the related
variety, the density measure has been more used to measure the related variety
following the seminar work of [31]. [31] built a product space, which is a
network structure among products, by looking at co-exporting patterns of
products in world trade, and calculated the density of related product for
each product of each country on the product space. They found that the
industrial diversification pattern exhibits path-dependency, which means that
countries are more likely to enter a new product when they already export the
related product to the new product. This finding holds at regional level.
[13], [25], and [33] found that regions in Spain, China, and Brazil are more
likely to enter a new industry when they already have the related industries.
These findings told us that existing related industries within regions are a
source of industrial diversification through inter-industry spillovers.

However, as [56] pointed out, the perspective of relatedness has a limitation
that they only focus on local capabilities for the economic development and
cannot embrace the extra-regional linkages. To mitigate the limitation, the
role of inter-regional spillovers are also examined in the industrial
diversification of region. [7] show that countries are more likely to
successfully enter a new product when they have neighboring countries that
already export that product. At regional level, [25] find that Chinese regions
successfully diversify to a new industry when the region has neighboring
regions that already have that industry.

At the same time, examination of cross-space spillover can mitigate the
limitation. Although [7] and [25] examine the inter-regional spillover in
industrial diversification, they build one general product and industry space,
respectively, and trace the product and industry trajectories on the same
space, focusing on their product/industry spillover that happen only
product/industry space. However, a new product/industry can emerge in a region
through the spillover across different dimensions, such as between technology
space and product/industry space, or skill space and product/industry space.
[16] suggest the concept of cross-space spillover to describe the endogenous
capabilities associated with two knowledge dimensions, which are in scientific
and technological knowledge within a country. They link two dimensions by
introducing a new measure, scientific-technological cross-density, and see the
effect of cross-density on the technological diversification at country level.
They find that the cross-density is a good predictor of countries entering a
new technology. In addition to the cross-space spillover across different
dimension of knowledge, cross-spillover among different types of regions can
make an effect on the regions’ industrial diversification.

As the further empirical effort to bridge the strategic coupling in the GPN
research and the relatedness from EEG and economic complexity, we study the
role of neighboring ports, where show different types of product structure, in
regions’ industrial diversification and regional economic resilience, by using
port-level export data and regional production data of Korea from 2006 to
2020. Port is a hub that connect the region to the GPN. When sub-national
level (instead of firm level) is examined regarding the effect of GPN, it is
difficult to extract the product list that is associated with the GPN. Product
list that are exporting in a certain port, however, can reveal the product
list plugging in the GPN and the knowledge associated with being connected to
the global market or the GPNs is embedded in the products at the port.
Therefore, we use the port-level export data and regional production data to
examine the spillover effect of ports on the industrial diversification of
neighboring regions by building two types of product-space, which are product
space of ports and that of neighboring port regions, and by looking at their
cross-space spillover.

Is spillover effect from a port helpful for a region that has the port to
diversify their industry? If so, between spillover from a port (source from
outside of a region) and other related industries within the region (source
from inside of a region), which one is the prime knowledge source of
industrial diversification of a sub-national region? Do the spillover effects
change during the economic crisis?

We find that the product space of ports exhibits distinctive feature that is
different from the product space of ordinary regions and countries, mainly
because of containerization. Consistent with the previous research [31, 43,
13, 25], a region neighboring the port is more likely to enter a new industry
when it already has the related industries within their region. In addition,
we find the evidence of cross-space spillover that more related products in
ports increase the probability of success in neighboring regions’ entering a
new product. Our results imply that inter-industry spillover is the prime
engine of industrial diversification in a region, and cross-space inter-
regional spillover from nearby ports catalyzes the diversification of the
region. In other words, the main drive of industrial diversification at sub-
national level can be found within the region and their plugging into the GPNs
boosts their industrial diversification. Finally, upon the recent change in
the global trade circumstances including the trade war between China and the
US, and the global pandemic crisis with COVID-19, we investigate the period-
variant spillover effect of neighboring ports and discuss the economic
resilience of port regions. In particular, we explore the role of the
spillover effects when the regional economy faces an exogenous economic shock,
such as the financial crisis in 2008. Our estimates for the years of the
crisis show that strong inter-industry spillover helps port regions to sustain
their industrial diversification, while the effect of cross-space spillover
from neighboring ports becomes insignificant. However, during the recovery
periods, cross-space spillover from nearby ports increases the probability of
success in port regions’ entering a new industry, which verifies having a port
nearby positively contributes to the regional resilience. Our results imply
that regions’ connecting the GPNs benefits their industrial diversification
but for the sustainable industrial diversification, the main source should be
within the regions.

The remainder of this paper is organized as follows. Section 2 briefly reviews
the recent regional studies based on relatedness and the contribution of ports
to elaborate our research problems. Section 3 details the data and research
methodology, especially the process of building separate product spaces,
calculating two types of product relatedness, and deriving our empirical
specification. Section 4 presents our results and Section 5 presents the
conclusion.

##  2 Literature review

###  2.1 Relatedness and knowledge spillover

While the concept of variety and their role in economic development had been
ignored in the mainstream economics, scholars in evolutionary economics have
paid their attention to the role of variety in the structural change and the
economic development [46, 52, 50, 51]. In this stream, the degree of
relatedness driving knowledge spillover has been highlighted as a determinant
of regional growth paths in the field of evolutionary economic geography and
regional study [10, 27, 29, 44]. [22] explained the regional economic growth
by using the concept of related variety that measured by Shannon’s entropy and
found that the positive and significant effect of related variety on regional
economic growth.

In parallel, the literature on economic complexity has further developed this
concept with empirical methodology from network science [31]. As a pioneering
work, [31] calculated proximity between products based on the probability that
a country exports both products in tandem by using world trade data,
suggesting the idea of the product space as a network representation of
proximity. For example, the proximity between a shirt and socks are closer
than that between a shirt and a car. In addition, they proposed a measure of
product relatedness of each product for country as the average proximity of
the product based of the country’s current industry structure. For example,
when a country tries to enter a car industry, the probability of success can
be higher when the country already has the related product of the car, such as
tire or engine industry, rather than when the country only has textile and
food industry. They found that the product relatedness works as a good
predictor for their future industrial structure implying the path-dependency
characteristics of industrial diversification. Owing to its methodological
intuitiveness and flexibility, a growing number of recent studies utilized
this framework to confirm that the relatedness between industries,
occupations, and patents possibly measures their localized knowledge and
capabilities, and identifies with the channels of knowledge spillovers [43,
13, 38, 21, 11, 58, 33]. The growing evidences by expanding literature,
exploring the regional diversification and development as a function of the
density of related activities within a region, has become formalized as
empirical principle called the Principle of Relatedness [29].

As [12] pointed out, the studies above mentioned tend to focus more on the
inter-industry spillover within a region—a kind of Jacobs externalities
between industries—as if a region is a geographically self-contained entity.
Of importance, however, the knowledge spillover has the spatial dimension [6]:
the interconnections with geographical neighbors provide access to new
economic knowledge, thereby exerting knowledge spillover. In this regard, [7]
explained the knowledge spillover from neighbors by showing that the
probability of a country developing a comparative advantage in an industry
increases if a neighboring country has a comparative advantage in that same
industry. Similarly, [12] suggested the neighbors sharing borders as the
source of regional diversification at the sub-national level, demonstrating
the specialization pattern of geographical neighbors shapes the local
capability to develop new industries in a region. In a recent paper, [34]
pointed out that knowledge spillover from neighboring exporters is a
significant predictor of increases in bilateral trade flows. In the similar
context, [25] showed that inter-regional spillover played a significant role
in the industrial diversification of China’s provinces.

In sum, the previous literature devoted academic efforts to analyze the inter-
regional spillover from geographical neighbors as a function of physical
distance and a direct link of the same industry itself. However, all
geographical neighbors at the same distance is not homogeneous. For example, a
port can make a different effect from an ordinary region that is not a port,
since a port is a gateway so that products and industries nearby the port can
be connected to the global market. Therefore, research that examines a inter-
regional spillover with considering this heterogeneous feature of geographic
neighbors.

Meanwhile, the recent studies on multi-dimensional network spaces within a
region have hinted at the empirical strategy of analyzing the spatial link
between two different networks of related industries in a region and its
neighbor. [33] decomposed the related knowledge that workers bring into
pioneer firms into two different dimensional spaces—the network of related
industries and the network of related occupations. Then constructing the two
relatedness indicators capturing industry and occupation knowledge, they
estimated the spillover effect from different knowledge types on the survival
rate of the firms within a region through logistic regression on the two
relatedness indicators. In a recent paper, [16] formalized the concept of a
bi-layered network to represent the interactions between scientific and
technological knowledge and capabilities at the country level by suggesting a
modified measurement called the scientific and technological cross-space. The
point is that [33] considered two exogenous types of knowledge and applied two
relatedness indicators in a regression model of spillover separately, while
[16] considered two endogenous types of knowledge and suggested a unified
relatedness indicator of the interconnected space.

###  2.2 Port and regional development

Undoubtedly, integrating transport and regional development is important in
the literature of economic geography [23]. In particular, as maritime
transport through ports has become the major transportation mode in
international trade after the rise of GVC [4, 32], recent studies on port and
port regions have renewed the significance of the interdependence between
transport and the economic performance of regions [14, 19, 42, 57, 47].
Starting from the idea of ports providing a comparative advantage to the
economic activities of neighboring regions [24], scholars tried to prove that
ports function as an important infrastructure endowment to promote
international trade and investment and facilitate regional development. [57],
for instances, argued the positive impact of port comprehensive strength on
economic growth in local and neighboring cities in general but the regional
differences in its spatial spillover affect either. [47] probed the spatial
spillover effects of logistics infrastructures including ports both at the
national and regional levels in economic development, finding the positive
dependence at the national level but the indecisive results in the regional
level.

Most of these recent studies found evidence that the improvement in port
infrastructural quality and logistics performance has in general a positive
effect on regional economic growth, although the interdependence between port
and regional activities may vary over time and space. In other words, existing
studies have produced ambiguous results on spatial spillover from neighboring
ports or only explained a broad correlation of regional growth with
neighboring ports but still limited to understanding the underlying dynamics
of mutual linkages. One main reason of this limitation is that most empirical
studies investigate the influence of ports as aggregated measures of port
activities or infrastructural characteristics, such as annual port traffic or
throughput volume or the efficiency of port infrastructure ([19].

A Port, however, is a window for a neighboring region that allows the
interaction between global market and regional production. Since the list of
products at a port is not just a piled products but a product, in which a
knowledge for being connect to the global market is embedded. In this regard,
a port is also a knowledge hub through which information and knowledge
associated with the global market or the GPNs embodied in commodity flow [14,
19, 42, 57, 47]. With consideration of a port as a knowledge hub, [19]
investigated the links between commodity types of port traffic and economic
activity types in port regions. Although their study succeeded in embracing
the contribution of neighboring ports as sources of knowledge spillover, it
didn’t link the product structure of port and that of a neighboring region at
product level, mainly because of data availability.

Based on the literature review on inter-industry spillovers from related
industries and on influences of nearby ports, there is the discrepancy between
the practical importance and academic endeavors in looking at the two
spillover channels in regional diversification by integrating ports as a
source of knowledge. This study, therefore, explores the following questions
to fill the gap in the previous literature: how do regions acquire the
knowledge and capability that need to sustain and diversify their economic
activities? Specifically, how do the international flows of commodities
through neighboring ports contribute to regional diversification and
resilience? Between spillover from a port (source from outside of a region)
and other related industries within the region (source from inside of a
region), which one is the prime knowledge source of industrial diversification
of a sub-national region? Our expected contribution to the literature on
economic geography and related diversification concerns the role of
neighboring ports as a special case of neighbors exerting inter-regional
spillover based on their own product spaces. Also, our research can nuisance
to bridge the literature on the GPNs and that on the principle of relatedness
regarding the economic development of sub-national regions.

##  3 Methodology

###  3.1 Spatial unit of analysis: port and port region

A port can be defined as a maritime facility that has wharves or loading
areas, where ships load and discharge cargo. This study considers 40 export
ports of Korea that are authorized by relevant regional offices, except for
several tiny coastal ports, fishing ports, and the subject as a sum of all the
new ports’ export value. With situating on a sea coast, its pier length is,
for example, around 27,407m in the case of Incheon Port, which is one of the
biggest ports in Korea.

We define a port region as an si-gun-gu level administrative district that has
the port in their area. The average area of port regions in our analysis is
around 625 $km^{2}$ and the export production in 26 port regions accounts for
56.4 % of the total and deals with 1,224 of all 1,241 commodities. Figure 1
shows the sample scope of analysis in this study. In the figure, black circles
represent ports and shaded areas show the localization of port regions within
the sample.

From the 1960s, the Korean government strategically invested to develop ports
associating with the export-oriented development plans. Considering that Korea
is located at the center of Northeast Asian economies with its function as a
logistics hub in the region, the role of ports has been crucial in the
development strategies of Korea [36]. Thanks to the growth potential with its
location and the second wave of globalization, Korean ports have been growing
over time and for example, around 13 million tons of cargo was transported
through ports in Korea only in November 2021. As shown in Figure 1, moreover,
the port regions in our analysis are geographically scattered over all three
coasts in the Korean peninsula. Busan port, which locates in the Southern
coast of Korean peninsula, has been regarded the representative gateway of
Korean product towards the global market, but other ports such as Incheon port
in the Western coast or Ulsan and Pohang port in Eastern coast are also
handling more or similar massive quantity of cargo. In addition, according to
[40], the concentration of bulk cargos is decreasing over time in Korea,
implying that the distribution of ports is not that skewed in Korea.

There might be a potential concern that a region with the free trade zone
shows distinctive feature and should be treated differently. However,
according to [45], upon the second wave of globalization, the benefits of free
trade zone including tax benefit and low tariff decreased for firms that
locate in the zone. Thus, considering that our data is made after the 2000s,
we didn’t distinguish the regions with the free trade zone from regions
without them.

Figure 1: The scope of analysis, 40 ports and 26 port regions in Korea (see
Table A1 for further detail on the sample)

###  3.2 Data

We use two sets of export data for 2006 to 2020 in Korea: port data and
regional data. Port data consists of the annual freight shipment value for
each product at each port, including information on destination countries.
Regional data are defined at the city or county level in Korea and consists of
the annual production value of each product in each region shipped to each
port. The data are extracted from Trade Statistics Service by Korea Customs
Services (TRASS) Trade Statistics Service, which provides detailed trade
statistics based on the Harmonized System (HS) four-digit aggregation (rev.
2017) for 1,241 commodities. For port data, the original sample of regional
data consists of 160 cities and counties in Korea. Among them, we extract the
sample of this study based on the location of ports: 26 regions out of the
original sample are classified as port regions and matched with neighboring
ports [15, 19].

We also use the data on the Product Complexity Index (PCI) from MIT’s
Observatory of Economic Complexity, measuring the knowledge intensity of
products using world trade data [53, 30]. The PCI ranks not only the
sophistication but also the diversity of the knowledge that is required to
produce a product. For example, the PCI of a car is higher than that of a
textile. The PCI data that we use distinguishes 1,060 products based on the HS
four-digit classification (rev. 2002), so we use the conversion table from the
UN Trade Statistics to convert and combine the HS 2002 codes into the HS 2017
codes of our sample.

###  3.3 Product relatedness in production and transport

How do neighboring ports affect the regional diversification of a port region?
Specifically, does the knowledge embodied in the commodity flows of
neighboring ports influence the development of new industries in a port
region? To explore our research question, the analytic framework of the
Principle of Relatedness is applied in this study [29]. The Principle of
Relatedness is an outcome-based approach regarding the spatially concentrated
knowledge and the likelihood that a region enters an economic activity related
to a new product (i.e., the development of a new industry), based on the co-
occurrence of economic activities related to ”similar products”. For its
methodological means, first, product space is introduced, which is a network
mapping the co-occurrence pattern of all products in a single space by
calculating the proximity between products traded in an economy or a world.
Second, product relatedness is brought in as a measure of capturing the
knowledge and capability of a region to implement economic activities related
to a specific product, depending on the number of related activities present
in that location.

This study concerns two decomposed activities of export: producing commodities
in regions and transporting them through ports. Therefore, when following the
Principle of Relatedness in this study, the most important starting point is
to define what similar products are in each case. For regional data, similar
products represent two products sharing production capabilities as both tend
to be produced nearby in a region. For port data, similar products refer to
the products that are likely to be transported at the same port based on
similar infrastructure, institutions, and technology. Consequently, we should
draw separate product spaces in production and transportation, computing the
proximity for port and regional data following [31]. Here, the proximity
between products in regional production $\phi$ or port activities $\Phi$ is a
proxy of their similarity, measuring the shared knowledge and capabilities to
produce or transport those products.

Mathematically, the proximity between products corresponds to the minimum of
the pairwise conditional probability that a location exports both products
with a revealed comparative advantage (RCA). Here, the $RCA$ of a region $r$
or port $p$ in a product $i$ measures whether $r$ or $p$ exports more of the
product $i$ than the average, as a share of its total exports of Korea. By
aggregating observations of the original data for 1,241 products from 2006 to
2020, we calculate the RCA following [9]:

$RCA_{r,i}=\left.\frac{x_{r,i}}{\sum_{i}{x_{r,i}}}\middle/\frac{\sum_{r}{x_{r,i}}}{\sum_{r,i}{x_{r,i}}}\right.\quad\textrm{and}\quad
RCA_{p,i}=\left.\frac{X_{p,i}}{\sum_{i}{X_{p,i}}}\middle/\frac{\sum_{r}{X_{p,i}}}{\sum_{p,i}{X_{p,i}}}\right.$
(1)

where $x_{r,i}$ and $X_{p,i}$ are matrices summarizing the export value (in US
dollars) of a region $r$ and port $p$ in a product $i$. $RCA_{i}$ allows us to
say that a region $r$ or port $p$ has the comparative advantage in a product
$i$ (i.e., the industry $i$ is active) only when $RCA_{i}$ is greater or equal
to 1.

Figure 2: Product space of Regions (A) and Ports (D) in period 2006-2020. B
and C are ego networks of the product of four-digit HS code 8541, which is
diodes, transistors, similar semiconductor devices. The node size represents
the export value of the product in a relative scale, and the node color shows
its classification as originally proposed by [39] and revised by [31]. By
comparing the two different ego networks (B and C) of the same product (8541),
we can indicate that the product space of region and of port show different
structure. Table 1: Most similar products to the product of HS code 8541
(diodes, transistors, similar semiconductor devices) by production proximity
(top) and by transport proximity (bottom)

Production proximity (in port regions)
---
HS section | HS code | Description | Proximity
6 | 3707 | Chemical preparations for photographic uses | 0.72
16 | 8542 | Electronic integrated circuits | 0.69
18 | 9002 | Lenses, mirrors and other optical elements | 0.69
16 | 8534 | Circuits; printed | 0.68
13 | 7011 | Glass envelopes and glass parts | 0.67
15 | 7413 | Copper; wire and the like | 0.67
16 | 8443 | Printing machinery | 0.67
16 | 8523 | Discs, tapes, smart cards | 0.66
6 | 2931 | Other organo-inorganic compounds | 0.66
16 | 8525 | Transmission apparatus for radio-broadcasting or television | 0.66
16 | 8532 | Electrical capacitors | 0.66
6 | 3208 | Paints, varnishes | 0.65
6 | 2810 | Oxides of boron | 0.64
18 | 9031 | Measuring or checking instruments and machines | 0.64
6 | 2942 | Organic compounds | 0.63
|  |  |
Transport proximity (in port activiteis)
HS section | HS code | Description | Proximity
1 | 505 | Skins and other parts of birds with feathers, down | 1.00
2 | 1003 | Barley | 1.00
2 | 1007 | Grain sorghum | 1.00
2 | 1008 | Buckwheat, millet and canary seeds; other cereals | 1.00
4 | 2204 | Wine of fresh grapes | 1.00
5 | 2602 | Manganese ores and concentrates | 1.00
6 | 2829 | Chlorates and perchlorates | 1.00
7 | 3921 | Plastic plates, sheets, film, foil | 1.00
8 | 4115 | Composition leather with a basis of leather | 1.00
10 | 4805 | Uncoated paper and paperboard | 1.00
11 | 6105 | Shirts; men’s or boys’, knitted or crocheted | 1.00
12 | 6505 | Hats and other headgear | 1.00
15 | 7607 | Aluminum foil | 1.00
16 | 8529 | Transmission apparatus | 1.00
17 | 8711 | Motorcycles and cycles | 1.00
1 For the production proximity, top 15 pairs of the total are presented.
2 For the transport proximity, 15 among 122 pairs with $\Phi=1$ are selected
at random since there are too many pairs with $\Phi=1$.

Using $RCA_{i}$ and $RCA_{j}$, we can create 1,241 x 1,241 matrices of the
proximity between products $i$ and $j$ for a region $r$ ($\phi$, production
proximity) and a port $p$ ($\Phi$, transport proximity), respectively:

$\phi_{i,j}=\frac{\sum_{r}{M_{r,i}M_{r,j}}}{max\\{k_{r,i},k_{r,j}\\}}\quad\textrm{and}\quad\Phi_{i,j}=\frac{\sum_{p}{M_{p,i}M_{p,j}}}{max\\{K_{p,i},K_{p,j}\\}}$
(2)

where $\phi_{i,j}$ and $\Phi_{i,j}\in[0,1]$; $M$ is a binary matrix of whether
$r$ or $p$ has the comparative advantage in a product or not, and $k$ and $K$
are variables of the ubiquity of a product as the number of regions or ports
having the comparative advantage in a product (i.e., $k_{r,i}=\sum{M_{r,i}}$
and $K_{p,j}=\sum{M_{p,i}}$ ). The proximity $\phi_{i,j}$ or $\Phi_{i,j}$ is
one when products $i$ and $j$ always co-occur in the export data, while
$\phi_{i,j}$ or $\Phi_{i,j}$ close to zero indicates no co-occurrence between
products due to dissimilarity.

Table 1 shows an example of the most similar products to the product HS code
8541 (diodes, transistors, similar semiconductor devices) in terms of
production proximity $\phi$ and transport proximity $\Phi$. As we expect,
$\phi$ and $\Phi$ measure the different types of product similarity in
production and transport. The top 15 products of production proximity around
the product 8541 depict domestic value chains of producing semiconductor
devices, including 6 products in the same family of industry and 9 products
related to raw materials and machinery. Most similar products by transport
proximity, on the other hand, vary from animal products to motorcycles. This
result mainly comes from the containerization of semiconductor devices as
general cargo. As shown in Table A2, the containerization ratios of
semiconductor devices and other proximate products have almost 1 in the case
of Korean maritime transport. We can also see this distinct characteristics of
transport proximity from Figures 2. Figures 2 A depicts the product space of
port regions, while Figures 2 B depicts that of ports. The example of product
HS code 8541 tells us that the way of export including containerization gives
effect on which port a product heads to export. Further information on two
product spaces can be found in Appendix.

Finally, we compute the product relatedness by year as the average proximity
of a new potential product (i.e., a new industry) to the current structure of
active export activities in a location [31, 26]. Using two proximity matrices,
we define two measures of product relatedness: the product relatedness of a
product $i$ in a region $r$ in year $t$ $\omega_{r,i}^{t}$ and the product
relatedness of a product $i$ in a port $r$ in year $t$ $\Omega_{p,i}^{t}$ as
the following equation:

$\omega_{r,i}^{t}=\frac{\sum_{j}{M_{r,i}^{t}\phi_{i,j}}}{\sum_{j}{\phi_{i,j}}}\quad\textrm{and}\quad\Omega_{p,i}^{t}=\frac{\sum_{j}{M_{p,i}^{t}\Phi_{i,j}}}{\sum_{j}{\Phi_{i,j}}}$
(3)

where $M^{t}$ is 1 when $RCA^{t}$ is greater and equal to 1 in year $t$. The
product relatedness $\omega_{r,i}^{t}$ or $\Omega_{p,i}^{t}$ is a value
between 0 and 1, where $\omega_{r,i}^{t}$ or $\Omega_{p,i}^{t}$ close to one
indicates that a region $r$ or port $p$ has abundant knowledge and capability
of production or transport related to product $i$ in year $t$.

Back to our research question, the product relatedness in port activities
$\Omega_{p,i}^{t}$ could be an explanatory variable to examine the spatial
link between regional diversification and the knowledge embodied in the
commodity flows of neighboring ports. For this purpose, we combine the port
data and the selected regional data for port regions to create the port-region
matched data based on Table A1. After matching, we rule out observations with
missing values of $\Omega_{p,i}^{t}$ mainly generated because the port does
not deal with some products manufactured in the port region11endnote: 1To
compensate for the loss of information caused by ruling out such missing
values, we consider the number of ports where each region ships each product
as a control variable to the regression model.. For the sample of 26 port
regions, Table A3 presents summary statistics of the original regional data
and port-region matched data.

###  3.4 Econometric model

With the sample of 26 port regions, we conduct a multivariate probit
regression to estimate whether product relatedness predicts increases in the
probability of developing a new industry. We apply a two-way fixed-effect
regression including year dummies and region dummies, with a standard error
term allowing for within-cluster correlation of products to be robust under
any product-specific characteristics22endnote: 2Although a three-way fixed-
effect regression model, including product-fixed effect term as well, would be
an good alternative in this study [34, 16], we choose a two-way fixed-effect
model due to the limitation of the number of observations. Instead, we include
the ubiquity k calculated from the product space and the product complexity
index derived from the world trade data to control product characteristics of
importance in this context and use a standard error term allowing for within-
cluster correlation of products to be robust.. For the model, we follow the
tradition of relatedness literature [7, 12, 25, 28] and design the models for
a two-step estimation by considering the spatial link between the ports’ and
port regions’ product spaces. The first step is to estimate the effect of
product relatedness in regional production ($\omega$) on developing a new
industry, indicating knowledge spillover in own product space. In the second
step, the model estimates the additional contribution of product relatedness
in port activities ($\Omega$) to developing a new industry using the port-
region matched data. This contribution represents the knowledge spillover that
occurs when two product spaces with different types of knowledge are spatially
connected (i.e., cross-space). If $\Omega$ shows positive dependence, we call
this effect the cross-space spillover in this study.

Formally, our model for the first step is given by Eq.4:

$S_{r,i}^{t+2}=\beta_{0}+\beta_{1}\omega_{r,i}^{t}+\beta_{2}k_{r,i}^{t}+\beta_{3}TRM_{r,i}^{t}+\beta_{4}PCI_{i}^{t}+\mu_{t}+\mu_{r}+\varepsilon_{r,i}^{t}$
(4)

where the dependent variable $S_{r,i}^{t+2}$ is a binary variable of whether a
region jumps to the new industry after two years, used to be no comparative
advantage in that industry before. Here, to avoid noises of temporary jumps,
we restrict jumps subject to the forward and backward conditions [7, 25]: a
jump needs to keep comparative advantages for two years more after the year
$t+2$ (i.e., $M_{r,i}$ from $t+2$ to $t+4$ has to be 1), and a jump also
satisfies $M_{r,i}=0$ for a further two years before the year $t$33endnote:
3For the period 2008-2016, we apply both the forward and backward conditions,
but we only apply the forward condition for 2017-2018 and the backward
condition for 2006-2007 due to the data limitation.. In this model, the main
explanatory variable $\omega$ indicates how much a region has knowledge of
similar products around a product $i$ in year $t$, and $k_{r,i}^{t}$
represents the ubiquity of a product $i$ in year $t$. $TRM_{r,i}^{t}$
represents the total number of ports where each product in each region is
shipped by year, and $PCI_{i}^{t}$ represents the product complexity index for
a product $i$ in year $t$. $\mu_{t}$ and $\mu_{r}$ are fixed-effect terms to
control omitted year-specific and region-specific variable bias, and
$\varepsilon_{r,i}^{t}$ is the error term.

In the second step, two main variables derived from the product space at ports
are added to the previous model: the product relatedness and ubiquity in port
activities, $\Omega_{p,i}^{t}$ and $K_{p,i}^{t}$. The final model of our
interest is shown as below:

$S_{r,p,i}^{t+2}=\beta_{0}+\beta_{1}\omega_{r,i}^{t}+\beta_{2}\Omega_{p,i}^{t}+\beta_{3}k_{r,i}^{t}+\beta_{4}K_{p,i}^{t}+\beta_{5}TRM_{r,i}^{t}+\beta_{6}PCI_{i}^{t}+\mu_{t}+\mu_{r}+\varepsilon_{r,p,i}^{t}$
(5)

where $\beta_{2}$ is an estimator of cross-space spillover from neighboring
ports to a port region, and $\beta_{1}$ is an estimator of knowledge spillover
within the regional product space.

##  4 Results

This section provides the regression results by estimating several
specifications of Eqs.4 and 5. Based on our research questions, we divide the
sample into groups and conduct group-wise comparisons of the estimates to
verify factors to make differences in the degree of relevant spillover.
Sections 4.1 and 4.2 present the empirical evidence of two spillover channels
for regional diversification. Section 4.3 discusses the role and significance
of two spillover channels when a port region adapts to the change of trade
landscape and external shocks.

###  4.1 Knowledge spillover within the regional product space

Table 2 shows the baseline estimates of whether a region enters a new
potential industry by Eq.4, based on the local capability and knowledge of
related industries in the current productive structure. Column (1) corresponds
to the result considering all observations of the sample (see Table A5 for
more regression estimates to test the robustness of our model). First, we find
the positive dependency of $\omega$ on the development of a new industry,
confirming the knowledge spillover within the regional product space [31, 43,
25]. This also supports the path dependency theory of regional diversification
that a new industry emerges from existing industries in a region by
recombining local capabilities related to them [13]. For other estimators, we
discover that the ubiquity $k$ positively correlates with the development of a
new industry while the product complexity $PCI$ shows a significant and
negative correlation. This result suggests that the more ubiquitous and less
complex product is a good candidate for a new potential industry in a port
region. In this respect, we further investigate whether the knowledge
spillover within the regional product space varies by the knowledge intensity
of products and the industrial characteristics.

Table 2: The development of a new potential industry by knowledge spillover
within the regional product space

| (1) |  | (2) | (3) |  | (4) | (5) | (6)
---|---|---|---|---|---|---|---|---
$S_{r,i}^{t+2}$ | All |  | Low PCI | High PCI |  | Leamer
1-6 | Leamer
7,8 | Leamer
9,10
$\omega_{r,i}^{t}$ | 7.041*** |  | 6.362*** | 8.137*** |  | 5.341*** | 7.426*** | 8.475***
| (0.342) |  | (0.417) | (0.535) |  | (0.521) | (0.698) | (0.568)
$k_{r,i}^{t}$ | 0.007*** |  | 0.004** | 0.010*** |  | 0.002 | 0.012*** | 0.007***
| (0.002) |  | (0.002) | (0.002) |  | (0.002) | (0.003) | (0.002)
$PCI_{i}^{t}$ | -0.058*** |  | -0.080** | -0.032 |  | -0.077** | 0.025 | -0.045
| (0.019) |  | (0.037) | (0.054) |  | (0.039) | (0.033) | (0.041)
$TRM_{r,i}^{t}$ | 0.156*** |  | 0.182*** | 0.138*** |  | 0.178** | 0.196*** | 0.128***
| (0.021) |  | (0.041) | (0.023) |  | (0.085) | (0.028) | (0.027)
$Constant$ | -2.135*** |  | -2.028*** | -2.364*** |  | -1.681*** | -2.669*** | -2.294***
| (0.105) |  | (0.136) | (0.177) |  | (0.181) | (0.189) | (0.202)
Year dummies | yes |  | yes | yes |  | yes | yes | yes
Region dummies | yes |  | yes | yes |  | yes | yes | yes
Observations | 33,057 |  | 15,246 | 17,448 |  | 7,879 | 10,538 | 14,501
Pseudo R2 | 0.136 |  | 0.116 | 0.162 |  | 0.099 | 0.162 | 0.163
log-likelihood | -14246 |  | -7145 | -6943 |  | -4007 | -4349 | -5628
Mean VIF | 5.24 |  | 5.49 | 5.63 |  | 5.52 | 6.21 | 5.16
1 Standard errors are in parentheses.
2 *** p$<$ 0.01, ** p$<$ 0.05, * p$<$ 0.1
3 Mean VIF represents the mean of uncentered variance inflation factors (VIFs)
for all applied variables, detecting the collinearity.

To analyze the effect of $\omega$ by levels of product complexity, the sample
is divided into two groups based on the $PCI$ value: products below the sample
mean of PCI are classified in the low PCI group, and otherwise in the high PCI
group. Columns (2) and (3) report the regression estimates for each group. The
coefficients of omega between groups suggest statistical differences by the
chi-square test (the chi-square with one degree of freedom = 14.16, p =
0.0002): the effect of relatedness of high PCI products is larger than that of
low PCI products on the development of a new industry. This result proves that
the higher the knowledge intensity of the product, the more is it influenced
by knowledge spillover from related products when a region develops a new
industry [48, 49, 34].

In addition to product complexity, the degree of knowledge spillover could be
determined by the industrial characteristics such as factors of production and
technological sophistication. Although a measure of product relatedness mainly
captures product-specific factors and is partly industry-specific [28], the
development of a new industry, our interest, is often conditioned by such
industrial characteristics. Therefore, we also aim to identify the broad
pattern of the knowledge spillover by sophistication levels of industrial
production [21]. For this reason, we classify products into 10 categories of
the Leamer classification by relative factor intensities of capital, labor,
and skills required for each category, following [31]44endnote: 4According to
[31], we can apply the revised version of the classification introduced by
[39]: petroleum (Leamer 1), raw materials (Leamer 2), forest products (Leamer
3), tropical agriculture (Leamer 4), animal agriculture (Leamer 5), cereals
(Leamer 6), labor-intensity (Leamer 7), capital-intensive (Leamer8), machinery
(Leamer 9), chemicals (Leamer 10).. Considering the characteristics of 10
categories, then, we split the sample into three groups by industry
sophistication levels: the low group for primary industries and raw material
sectors, the medium group for labor- and capital-intensive industries, and the
high group for machinery and chemical sectors required high skills for
production (i.e., knowledge-based industries).

In Table 2, Columns (4) to (6) show the estimations by industry sophistication
levels. We find that the coefficients of $\omega$ increases with the level of
industry sophistication, and the differences between groups are statistically
significant at a 10% confidence level. This result confirms when a region is
specialized more in sophisticated industries (e.g., machinery and chemicals),
the current productive structure exerts a stronger spatial spillover effect to
develop a new related industry. Together with the estimates by product
complexity levels, our results extend the literature on regional
diversification on relatedness by probing that the product relatedness of more
complex products and sophisticated industries has a greater influence on the
development of new industries in a region [34].

###  4.2 Cross-space spillover from neighboring ports

In the previous section, our results explain more about the generality of
regional diversification rather than the specialty from a port region point of
view. Here, we use the sample of matching port regions with their neighboring
ports and apply Eq. 5. Table 3 reports the regression estimates of our main
interest, the contribution of neighboring ports to developing a new industry
in a port region. The first column of Table 3 shows the result considering all
observations of the matched sample (see also Table A5 for more regression
results to check the robustness of our model). The effect of $\omega$ as the
local capability to produce related products is still significant in
developing a new industry, and the effect of $\Omega$ from neighboring ports
is non-negligible either. Even under the product relatedness within its own
product space as the main spillover channel, this result confirms the
knowledge embodied in transporting goods at ports flows into its neighboring
region and contributes to the diversification of regional productive
structures. This result demonstrates the cross-space spillover: the commodity
flows of ports could function as a source of the knowledge spillover for their
port regions.

Table 3: The development of a new potential industry by cross-space spillover
from neighboring ports

| (1) |  | (2) | (3) |  | (4) | (5) | (6)
---|---|---|---|---|---|---|---|---
$S_{r,p,i}^{t+2}$ | All |  | Low PCI | High PCI |  | Leamer 1-6 | Leamer 7,8 | Leamer 9,10
$\omega_{r,i}^{t}$ | 7.833*** |  | 7.258*** | 8.634*** |  | 6.318*** | 8.258*** | 8.861***
| (0.424) |  | (0.541) | (0.615) |  | (0.701) | (0.853) | (0.675)
$\Omega_{p,i}^{t}$ | 0.707*** |  | 0.701*** | 0.693*** |  | 0.769* | 1.098*** | 0.557**
| (0.175) |  | (0.224) | (0.243) |  | (0.466) | (0.354) | (0.221)
$k_{r,i}^{t}$ | 0.056*** |  | 0.037** | 0.076*** |  | 0.061*** | 0.069*** | 0.059***
| (0.012) |  | (0.015) | (0.017) |  | (0.023) | (0.018) | (0.018)
$K_{p,i}^{t}$ | 0.006*** |  | 0.004 | 0.009*** |  | 0.000 | 0.010** | 0.006*
| (0.002) |  | (0.003) | (0.003) |  | (0.004) | (0.004) | (0.003)
$PCI_{,i}^{t}$ | -0.010 |  | -0.018 | 0.115 |  | -0.040 | 0.026 | 0.045
| (0.025) |  | (0.048) | (0.073) |  | (0.053) | (0.043) | (0.055)
$TRM_{r,i}^{t}$ | 0.111*** |  | 0.116*** | 0.112*** |  | 0.104 | 0.162*** | 0.086***
| (0.022) |  | (0.040) | (0.026) |  | (0.086) | (0.030) | (0.029)
$Constant$ | -3.001*** |  | -2.893*** | -8.001*** |  | -6.168*** | -8.272*** | -2.718***
| (0.249) |  | (0.387) | (0.538) |  | (0.706) | (0.734) | (0.332)
Year dummies | yes |  | yes | yes |  | yes | yes | yes
Region dummies | yes |  | yes | yes |  | yes | yes | yes
Observations | 13,735 |  | 6,173 | 7,365 |  | 2,852 | 4,361 | 6,435
Pseudo R2 | 0.206 |  | 0.185 | 0.229 |  | 0.162 | 0.206 | 0.241
log-likelihood | -6138 |  | -2994 | -3064 |  | -1505 | -1963 | -2563
Mean VIF | 8.33 |  | 9.10 | 4.85 |  | 6.43 | 5.74 | 7.66
1 Standard errors are in parentheses.
2 *** p$<$ 0.01, ** p$<$ 0.05, * p$<$ 0.1
3 Mean VIF represents the mean of uncentered variance inflation factors (VIFs)
for all applied variables, detecting the collinearity.

Next, we analyze the dependency of cross-space spillover on the level of
product complexity and industry sophistication. Columns (2) and (3) of Table 3
report the regression results for the low and high PCI groups. Interestingly,
the estimators of $\Omega$, indicating cross-space spillover effects, are
significantly positive in both groups but with no substantial differences
between groups. This result demonstrates that the knowledge captured by the
product relatedness in port activities only has a spillover effect in a broad
sense, regardless of products’ complexities. This is quite different from the
effect of $\omega$, the influence of knowledge spillover from regional
capability in related industries. We expect this limited dependency is mainly
because the knowledge required to produce highly complex products
qualitatively differentiates from the knowledge to transport them.
Specifically, cross-space spillover plays an extra role in developing a new
industry only by providing information about whether the product is easy to
export with other commodities to the global market (i.e., a port as a global
hub of production sharing). As presented in Columns (4) to (6), we also yield
similar results and implications to the result in Table 2, by comparing the
three groups with different sophistication levels of industrial production.

The ubiquity in port activities $K$, indicating the number of ports with $RCA$
above 1 in exporting the product, also shows positive significance in the
cases of high PCI products and the industries with medium and high
sophistication levels. This result suggests that plenty of active ports
connecting global and local markets for the potential product increase the
probability of developing the new industry especially for complex and
knowledge-based products, like electronic equipment and pharmaceutical
materials. Meanwhile, the ubiquity in regional production $k$, indicating the
number of port regions with $RCA$ above 1 in producing the export, shows the
positive correlation with developing a new industry in all groups. The
universal positive correlation indicates that more ubiquitous products in port
regions are always good candidates for a new potential industry as other
successful domestic exporters are considered as the benchmark for the
industry.

###  4.3 Product relatedness as an economic resilience

Concerning ports as gateways of international trade, our next question arises:
When does the role of the knowledge embodied in exporting products through
neighboring ports stand out, especially in terms of the regional
diversification induced by the changes in the trade environment? Table 4 shows
the results divided into three periods: the period of economic crisis
(2007–2009), the period of recovery (2010–2013), and the period of post-crisis
(2014–2018). Before getting to the main point, we point out that the impact of
$\omega$ is remarkable during the recovery period. This implies that the local
production capability to related industries could serve as not just the
spillover channel but more the regional resilience to adapt to the new
circumstances following the economic crisis. Then, we discover that the
effects of $\Omega$, indicating cross-space spillover from neighboring ports,
are significantly positive only in times after the global economic crisis and
have a marginal difference in degrees between periods. This result suggests
that the product relatedness in port activities complements the economic
resilience of a port region after the economic shock, as neighboring ports
have the cross-space spillover effect to inform about potential co-exported
goods as the candidate industry.

Table 4: The development of a new industry in times of economic crisis
(2007-2009), recovery (2010-2013) and post-crisis (2014-2018)

| (1) | (2) | (3)
---|---|---|---
$S_{r,p,i}^{t+2}$ |  Economic risis (2007-2009) |  Recovery (2010-2013) |  Post-crisis (2014-2018)
$\omega_{r,i}^{t}$ | 7.467*** | 9.544*** | 7.714***
| (1.176) | (0.728) | (0.584)
$\Omega_{p,i}^{t}$ | 0.868 | 0.981** | 0.563***
| (0.600) | (0.440) | (0.177)
$k_{r,i}^{t}$ | 0.010** | 0.004 | 0.004
| (0.004) | (0.003) | (0.003)
$K_{r,i}^{t}$ | 0.019 | 0.048*** | 0.078***
| (0.021) | (0.018) | (0.016)
$PCI_{i}^{t}$ | 0.038 | 0.006 | -0.036
| (0.046) | (0.033) | (0.033)
$TRM_{r,i}^{t}$ | 0.133*** | 0.169*** | 0.071**
| (0.037) | (0.026) | (0.030)
$Constant$ | -7.027*** | -8.453*** | -2.985***
| (1.008) | (0.689) | (0.301)
Year dummies | yes | yes | yes
Region dummies | yes | yes | yes
Observations | 2,820 | 4,748 | 5,450
Pseudo R2 | 0.188 | 0.203 | 0.213
log-likelihood | -1269.959 | -2115.669 | -2543.948
Mean VIF | 7.10 | 5.20 | 9.48
1Standard errors are in parentheses
2*** p$<$ 0.01, ** p$<$ 0.05, * p$<$ 0.1
3 Mean VIF represents the mean of uncentered variance inflation factors (VIFs)
for all applied variables, detecting the collinearity.

By comparing the results of Columns (2) and (3), we find a statistically
significant difference in the coefficients of $TRM$, measuring how many ports
each product in each region is shipped to, between the recovery and post-
crisis periods: the increase of $TRM$ during the post-crisis period
(2014–2018) is less effective than that during the years right after the
crisis. This result has interesting implications in that, in general, port
regions are linked with multiple ports for shipping each product when
diversifying export destinations to multiple continents. In other words, when
developing a new industry, a port region has less incentive to target products
with various global destinations than before the crisis, proving Korea’s
export industry is also being affected by a slowdown in globalization and a
loosened GVC [8, 41] (see Table A6, applying the variable $DES$ of
representing the total number of destination continents of a product $i$ at a
port $p$, instead of $TRM$, and confirming the significantly negative impact
of $DES$ on developing a new industry only during the post-crisis).

##  5 Conclusion

The global economy is thrown into chaos due to multiple events occurring
simultaneously in the current time after the 2008 financial crisis, and all
countries and regions have been experiencing unprecedented challenges from
such a prolonged crisis. However, the world is not flat. Some regions show
discontinuity in their socio-economic features shaping economic resilience –
how much the regional economy sustains its normality despite the exogenous
shock. Having a port nearby, for instance, is the discontinuous feature
considering that ports are not only physical gateways linking the global
market with local production but also knowledge hubs through which information
and knowledge embodied in commodity flow through international production
networks. However, little attention has been paid to embracing the distinctive
characteristics of ports and ports’ activities into empirical researches in
economic complexity and economic geography. Scholars have rather studied the
inter-regional spillover effects on the industrial diversification and
economic development from their neighbors as homogeneous geographic units by
their physical distance.

We studied the effects of inter-regional spillover of neighboring ports on the
industrial diversification of regions. First, we found that the product space
of ports shows their distinct characteristics mainly because of how each
product deals with in logistics system. This implies that similar products or
industries on the logistics side can differ from those on the production side
in terms of economic complexity. In other words, there may exist different
types of capabilities behind the product space of ports and regions. Second,
our econometric estimates show that regions are more likely to enter a new
industry when they already have the related industries in their local
productive structure, confirming [31, 43, 13, 25]. Interestingly, we also
proved that regions’ entering a new industry can be catalyzed by neighboring
ports having competitiveness in the related products to the new industry. In
addition, by splitting our sample over PCI and leamer’s classification, we
found that the effect of inter-industry spillover within the same regions
increases with product complexity and technological sophistication of the new
potential industry, while the effect of cross-space inter-regional spillover
is the strongest for the new potential industry with medium-level
sophistication(i.e., labor- and capital-intensive industries). This result
also supports the existence of different micro-channel in spillovers between
ports and regions. Finally, we confirmed that this cross-space inter-regional
spillover counts for regional economies in developing new industries during
the recovery period after the economic crisis, suggesting port activities
positively contribute to regional resilience but weakened their influences
recently due to a loosened global value chain.

However, our research is limited in figuring out the micro-mechanisms of the
two different types of spillover. Behind the inter-industry spillover within
the region, there might be the labor flow from the related industries to a new
industry [33], knowledge flows among product lines, and further social
capability [2], technological capability [37], or institutions [3] that
support the already existing related industries. On the other hand, behind the
scene of cross-space inter-regional spillovers from neighboring ports to a
region, there might be knowledge embedded commodity flows, reprocessing of
imported products, or trade information flows among traders.

Despite our limitation, our results shed light on the cross-space spillover
among different geographical dimensions and suggest the role of ports in
knowledge spillover. This research tells us that the various types of
knowledge spillover channels are engaged in regional industrial
diversification and each channel plays a role with different intensity over
product complexity and technological sophistication, suggesting that more
delicate and targeted policy is required for regional industrial
diversification.

## Acknowledgement

This project is funded by the National Research Foundation of Korea
(NRF-2022R1A2C1012895). We also acknowledge the support of the Inha
University.

###### Notes

  1. 1 To compensate for the loss of information caused by ruling out such missing values, we consider the number of ports where each region ships each product as a control variable to the regression model.
  2. 2 Although a three-way fixed-effect regression model, including product-fixed effect term as well, would be an good alternative in this study [34, 16], we choose a two-way fixed-effect model due to the limitation of the number of observations. Instead, we include the ubiquity k calculated from the product space and the product complexity index derived from the world trade data to control product characteristics of importance in this context and use a standard error term allowing for within-cluster correlation of products to be robust.
  3. 3 For the period 2008-2016, we apply both the forward and backward conditions, but we only apply the forward condition for 2017-2018 and the backward condition for 2006-2007 due to the data limitation.
  4. 4 According to [31], we can apply the revised version of the classification introduced by [39]: petroleum (Leamer 1), raw materials (Leamer 2), forest products (Leamer 3), tropical agriculture (Leamer 4), animal agriculture (Leamer 5), cereals (Leamer 6), labor-intensity (Leamer 7), capital-intensive (Leamer8), machinery (Leamer 9), chemicals (Leamer 10).

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## Appendix

Table A1: The sample information | Port | Port region
---|---|---
Coast | Code | Name | Code | Name
south | KCN | Ghmcheon | RBUS | Busan
south | PSN | Busan new | RBUS | Busan
south | PUS | Busan | RBUS | Busan
south | MAS | Masan | RCHW | Changwon
south | CHF | Jinhae | RCHW | Changwoni
south | KHN | Kohyeon | RKJE | Geoje
south | OKP | Okpo | RKJE | Geoje
south | JPH | Jangseungpo | RKJE | Geoje
south | KJE | Geoje | RKJE | Geoje
south | TYG | Tongyeong | RTYG | Tongyeong
south | CMH | Chungmu | RTYG | Tongyeong
south | SCP | Samcheonpo | RSCN | Sacheon
south | HIN | Jinju | RSCN | Sacheon
south | DBL | Daebul | RYAM | Yeongam
south | MOK | Mokpo | RMOK | Mokpo
south | YOS | Yeosu | RYOS | Yeosu
south | WND | Wando | RWND |  Wando1
south | KAN | Gwangyang | RGWY | Gwangyang
south | YOC | Yeocheon | RGWY | Gwangyang
south | SPO | Seogwipo | RSPO | Seogwipo
south | CHA | Jeju | RJEJ | Jeju
east | KAG | Gangneung | RKAG | Gangneung
east | TGH | Donghae | RTGH | Donghae
east | MUK | Mukho | RTGH | Donghae
east | BUK | Bukpyeong | RTGH | Donghae
east | SUK | Samcheok | RSCK | Samcheok
east | OKK | Okkye | RKAG | Gangneung
east | MIP | Mipo | RUSN | Ulsan
east | ONS | Onsan | RUSN | Ulsan
east | USN | Ulsan | RUSN | Ulsan
east | KPO | Pohang | RPHG | Pohang
east | SHO | Sokcho | RSHO | Sokcho
west | GIN | Gyeong-in | RICN | Incheon
west | INC | Incheon | RICN | Incheon
west | TSN | Daesan | RSSN | Seosan
west | KUV | Gunsan | RGSN | Gunsan
west | CHG | Janghang | RSEO |  Seocheon2
west | TJI | Dangjin | RTJI | Dangjin
west | PTK | Pyeongtaek | RPTK | Pyeongtaek
west | BOR | Boryeong | RBOR | Boryeong
west | TAN | Taean | RTAN |  Taean3
1,2,3 The level of these port regions is a county, called ’gun’ in Korean.
Table A2: The containerization ratio by item, the case of Korea in 2020 Item | Containerization ratios
---|---
Meat and edible meat offal | 0.99
Fish and other aquatic invertebrates | 0.86
Cereals | 0.13
Malt, starches, inulin, wheat gluten | 0.56
Animals; live | 0.85
Animal or vegetable fats and oils | 0.48
Sugars and sugar confectionery | 0.42
Meat, fish, or preparations thereof | 0.72
Cement | 0.01
Sands and earths | 0.16
Hard coal | 0.01
Bituminous coal | 0.00
Ores, slag and ash | 0.00
Salt, sulphur and others | 0.19
Crude oil and bituminous substances | 0.00
Petroleum refining | 0.01
Petroleum products of their distillation | 0.00
Fertilizers | 0.34
Inorganic chemicals and articles thereof | 0.48
Plastics and articles thereof | 1.00
Raw hides, skins, and leather | 1.00
Wood and articles of wood | 0.53
Wood charcoal | 0.87
Silk | 1.00
Scrap metal | 0.26
Iron and steel | 0.35
Copper and articles thereof | 0.95
Nuclear reactors, machinery and parts thereof | 0.90
Electrical machinery and parts thereof | 0.99
Railway, locomotives, and parts thereof | 0.34
Aircraft, spacecraft and parts thereof | 0.95
ETC. | 0.97
1Source : Author’s elaboration, using the data of the year 2020, from The
Korean Port Management Information System (PORT-MIS)
2 The containerization ratio by item is calculated as the ratio of
containerized cargo to the total export volume by 28 major ports.
Table A3: Summary statistics of variables for testing (a) inter-industry spillover from related industries within a region and (b) cross-space spillover from neighboring ports to port regions  | Variable | Observations | Mean | Std. Dev. | Min | Max
---|---|---|---|---|---|---
(a) |  $\omega_{r,i}^{t}$ |  33,057 |  0.234 |  0.229 |  0.001 |  0.903
$k_{r,i}^{t}$ |  33,057 |  25.0 |  14.2 |  1 |  72
$TRM_{r,i}^{t}$ |  33,057 |  1.4 |  0.8 |  1 |  9
$PCI_{i}^{t}$ |  33,057 |  0.240 |  0.934 |  -3.273 |  2.644
(b) |  $\omega_{r,i}^{t}$ |  13,744 |  0.346 |  0.266 |  0.002 |  0.903
$\Omega_{p,i}^{t}$ |  13,744 |  0.419 |  0.331 |  0 |  0.977
$k_{p,i}^{t}$ |  13,744 |  20.9 |  13.6 |  1 |  72
$k_{r,i}^{t}$ |  13,744 |  4.9 |  1.9 |  1 |  15
$TRM_{r,i}^{t}$ |  13,744 |  1.7 |  1.1 |  1 |  9
$PCI_{i}^{t}$ |  13,744 |  0.278 |  0.917 |  -3.273 |  2.526
Table A4: Robustness check for Eq.4

| (1) | (2) | (3) | (4) |  | (5) | (6) | (7)
---|---|---|---|---|---|---|---|---
$S_{r,i}^{t+2}$ |  Probit, applying variables one by one |  | Probit | Logit | LPM
$\omega_{r,i}^{t}$ | 7.230*** |  |  |  |  | 7.041*** | 12.044*** | 2.077***
| (0.315) |  |  |  |  | (0.342) | (0.615) | (0.076)
$k_{r,i}^{t}$ |  | 0.015*** |  |  |  | 0.007*** | 0.013*** | 0.001***
|  | (0.001) |  |  |  | (0.002) | (0.003) | 0.000
$PCI_{i}^{t}$ |  |  | -0.074*** |  |  | -0.058*** | -0.087** | -0.011**
|  |  | (0.021) |  |  | (0.019) | (0.034) | (0.005)
$TRM_{r,i}^{t}$ |  |  |  | 0.056** |  | 0.156*** | 0.272*** | 0.041***
|  |  |  | (0.026) |  | (0.021) | (0.036) | (0.006)
$Constant$ | -1.722*** | -2.247*** | -1.747*** | -1.842*** |  | -2.135*** | -3.727*** | -0.081***
| (0.098) | (0.108) | (0.098) | (0.100) |  | (0.105) | (0.197) | (0.019)
Year dummies | yes | yes | yes | yes |  | yes | yes | yes
Region dummies | yes | yes | yes | yes |  | yes | yes | yes
Observations | 33,057 | 33,057 | 33,057 | 33,057 |  | 33,057 | 33,057 | 33,057
Pseudo R2 | 0.125 | 0.099 | 0.085 | 0.084 |  | 0.136 | 0.135 | 0.1423̂
log-likelihood | -14421 | -14860 | -15087 | -15105 |  | -14246 | -14256 | -14018
Mean VIF | 4.70 | 2.00 | 1.85 | 1.97 |  | 5.24 | 5.24 | 7.87
1 Standard errors are in parentheses
2*** p$<$ 0.01, ** p$<$ 0.05, * p$<$ 0.1
3 This is the value of R2 in the case of a linear probability model(LPM).
4 Mean VIF represents the mean of uncentered variance inflation factors (VIFs)
for all applied variables, detecting the collinearity.

Table A5: Robustness check for Eq.5 | (1) | (2) | (3) | (4) | (5) | (6) |  | (7) | (8) | (9)
---|---|---|---|---|---|---|---|---|---|---
$S_{r,p,i}^{t+2}$ |  Probit, applying variables one by one |  | Probit | Logit | LPM
$\omega_{r,i}^{t}$ | 7.818*** |  |  |  |  |  |  | 7.833*** | 13.414*** | 2.329***
| (0.384) |  |  |  |  |  |  | (0.424) | (0.775) | (0.090)
$\Omega_{p,i}^{t}$ |  | 1.370*** |  |  |  |  |  | 0.707*** | 1.248*** | 0.194***
|  | (0.233) |  |  |  |  |  | (0.175) | (0.302) | (0.044)
$k_{r,i}^{t}$ |  |  | 0.056*** |  |  |  |  | 0.056*** | 0.097*** | 0.014***
|  |  | (0.015) |  |  |  |  | (0.012) | (0.020) | (0.003)
$K_{p,i}^{t}$ |  |  |  | 0.024*** |  |  |  | 0.006*** | 0.011*** | 0.001**
|  |  |  | (0.002) |  |  |  | (0.002) | (0.004) | 0.000
$PCI_{i}^{t}$ |  |  |  |  | -0.054* |  |  | -0.010 | 0.000 | 0.001
|  |  |  |  | (0.028) |  |  | (0.025) | (0.043) | (0.006)
$TRM_{r,i}^{t}$ |  |  |  |  |  | -0.014 |  | 0.111*** | 0.197*** | 0.029***
|  |  |  |  |  | (0.026) |  | (0.022) | (0.036) | (0.006)
$Constant$ | -2.349*** | -1.856*** | -2.164*** | -2.330*** | -1.798*** | -1.805*** |  | -3.001*** | -5.197*** | -0.239***
| (0.237) | (0.230) | (0.248) | (0.240) | (0.230) | (0.232) |  | (0.249) | (0.469) | (0.029)
Year dummies | yes | yes | yes | yes | yes | yes |  | yes | yes | yes
Region dummies | yes | yes | yes | yes | yes | yes |  | yes | yes | yes
Observations | 13,735 | 13,735 | 13,735 | 13,735 | 13,735 | 13,735 |  | 13,735 | 13,735 | 13,744
Pseudo R2 | 0.193 | 0.119 | 0.116 | 0.142 | 0.113 | 0.113 |  | 0.206 | 0.206 | 0.222
log-likelihood | -6241 | -6813 | -6830 | -6629 | -6853 | -6860 |  | -6138 | -6139 | -6276
Mean VIF | 5.42 | 4.55 | 2.67 | 2.44 | 2.28 | 2.41 |  | 8.33 | 8.33 | 663.32
1 Standard errors are in parentheses
2*** p$<$ 0.01, ** p$<$ 0.05, * p$<$ 0.1
3 This is the value of R2 in the case of a linear probability model(LPM).
4 Mean VIF represents the mean of uncentered variance inflation factors (VIFs)
for all applied variables, detecting the collinearity.
Table A6: The development of a new industry in times of economic crisis
(2007-2009), recovery (2010-2013) and post-crisis (after 2014)

| (1) | (2) | (3)
---|---|---|---
$S_{r,p,i}^{t+2}$ |  Economic crisis (2007-2009) |  Recovery (2010-2013) |  Post-crisis (2014-2018)
$\omega_{r,i}^{t}$ | 6.903*** | 8.405*** | 7.243***
| (1.073) | (0.673) | (0.570)
$\Omega_{p,i}^{t}$ | 0.512 | 0.728* | 0.547***
| (0.619) | (0.420) | (0.199)
$k_{r,i}^{t}$ | 0.009** | 0.003 | 0.003
| (0.004) | (0.003) | (0.003)
$K_{p,i}^{t}$ | 0.027 | 0.062*** | 0.091***
| (0.021) | (0.018) | (0.017)
$PCI_{i}^{t}$ | 0.055 | 0.030 | -0.013
| (0.045) | (0.032) | (0.032)
$DES_{p,i}^{t}$ | 0.017 | -0.015 | -0.036**
| (0.025) | (0.019) | (0.017)
$Constant$ | -7.027*** | -8.453*** | -2.985***
| (1.008) | (0.689) | (0.301)
Year dummies | yes | yes | yes
Region dummies | yes | yes | yes
Observations | 2,820 | 4,748 | 5,450
Pseudo R2 | 0.188 | 0.203 | 0.213
log-likelihood | -1269.959 | -2115.669 | -2543.948
Mean VIF | 7.50 | 5.37 | 9.29
1Standard errors are in parentheses
2*** p$<$ 0.01, ** p$<$ 0.05, * p$<$ 0.1
3 Mean VIF represents the mean of uncentered variance inflation factors (VIFs)
for all applied variables, detecting the collinearity.

Figure A1: Product space based on the production proximity $\phi$, 2006-2020 :
(a) Network representation of all products, (b) Ego network of the product of
HS code 8541 (depth=3), (c) cumulative distribution of the production
proximity $\phi$, and (d) density distribution of $\phi$
1Product spaces are built following Hidalgo et al. [2007] and Gao et al.
[2021]. 2 The node size represents the export value of the product in a
relative scale, and the node color shows its classification as originally
proposed by Leamer [1984] and revised by Hidalgo et al. [2007] Figure A2:
Product space based on the transport proximity $\Phi$, 2006-2020 : (a) Network
representation of all products, (b) Ego network of the product of HS code 8541
(depth=3), (c) cumulative distribution of the production proximity $\Phi$, and
(d) density distribution of $\Phi$
1Product spaces are built following Hidalgo et al. [2007] and Gao et al.
[2021]. 2 The node size represents the export value of the product in a
relative scale, and the node color shows its classification as originally
proposed by Leamer [1984] and revised by Hidalgo et al. [2007]