Ports as catalysts: spillover effects of neighbouring ports on regional industrial diversification and economic resilience

ABSTRACT Recognising the intricate link between ports and regional economies, this study investigates the spillover effects of neighbouring ports on regional industrial diversification and economic resilience. Analysing South Korea’s 2006–20 export data from port and neighbouring port regions, it uncovers the unique feature of ports as a distinctive knowledge source within their port regions, mainly attributable to the respective logistic and trade systems governing similar product groups. The paper confirms that ports facilitate industrial diversification through spillover effects when it is related with the regional industries. Emphasising ports’ role in strengthening economic resilience, it highlights their significance in nurturing emerging industries post-crisis.


INTRODUCTION
Economic development has been widely examined in various research streams in social science, from the era of mercantilism to the recent globalised world.The second wave of globalisation that followed the fall of the Iron Curtain altered the dynamics of economic development at various scales: from individual firms to subnational regions and states.From the mid-2000s, it was global production networks (GPNs), rather than domestic factors or state-led policies, that became principal drivers of state-level industrial transformations.Scholars have since delved into the co-evolution of regional actors, resources and globalisation (Coe & Yeung, 2015;Yeung, 2016Yeung, , 2021)).However, recent challenges, including the COVID-19 pandemic and increased geopolitical tensions, have brought global production sharing and trade to a critical juncture.Current priorities such as regional security and resilience threaten to disrupt global value chains (GVCs), and, consequently, the cross-border flow of commodities, knowledge and people (World Bank, 2020).With GVCs weakening, regions, especially those heavily involved in GPNs, must adapt by reconfiguring their industrial structures in response to the changing global market.The crucial questions are: How can these regions modify their industrial structures to ensure survival and promote economic development?How can they successfully diversify their industries by leveraging their capabilities?
To obtain answers, we look at port and port regions.Having a port nearby is a critical geographical factor for regions' economic development because maritime transport through ports represents the major transportation mode in world trade upon the pervasiveness of GVC (Amador & Cabral, 2016).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 (Bottasso et al., 2018;Ducruet & Itoh, 2016).Although the role and status of maritime transport have also been adjusted in international circumstances, ports still are a facilitator to monitor the changes in global demand and help regional economies adapt to the changes (Chacon-Hurtado et al., 2020;Merk, 2013).Accordingly, scholars in economic geography and regional studies have studied the interdependency of ports and port regions, focusing on the topics of regional development and transport geography (Bottasso et al., 2018;Márquez-Ramos, 2016;Munim & Schramm, 2018;Qi et al., 2020;Zhao et al., 2020).Their research investigated the contribution of ports to regional economic performances as a quantitative growth driver, mainly based on aggregated measures for port infrastructural quality or port throughput volume.However, as a qualitative growth driver, the spillover effect of ports on the regional economic diversification and development of their neighbouring regions is rarely examined (Ducruet & Itoh, 2016).Therefore, this study revisits the undisclosed role of neighbouring ports and their knowledge spillover effects on the industrial diversification of regions as one of the critical knowledge sources in their reconfiguring local productive structures.
To measure the industrial structure of ports and port regions, we apply the analytic framework of economic complexity, which is recently used in the strand of evolutionary economic geography (EEG) and regional studies.In the seminar work of Hidalgo et al. (2007), they build the product space, which is the network of products, by looking at co-exporting patterns of products in world trade.They find that each country makes an industrial footprint on the product space.Industrial diversification of countries exhibits path dependency, which means a country is more likely to create a new product close to the product currently produced in that country.This finding holds at a regional level.Boschma et al. (2013), Gao et al. (2021) and Jara-Figueroa et al. (2018) find that regions in Spain, China and Brazil are more likely to enter a new industry when they already have related industries locally.These findings indicate that existing related industries are the sources of industrial diversification through inter-industry spillovers.
However, as Yeung (2021) pointed out, the perspective of relatedness has a limitation in that they only focus on local capabilities for regional economic development and cannot embrace the extra-regional linkages.To mitigate the limitation, the role of interregional spillovers is also examined in the industrial diversification of the region.Bahar et al. (2014) show that countries are more likely to successfully enter a new product when they have neighbouring countries that already export that product.At the regional level, Gao et al. (2021) discovered that Chinese regions can successfully diversify into a new industry if neighbouring regions already have experience in the very industry.
At the same time, an examination of cross-space spillover can mitigate the limitation.Although Bahar et al. (2014) and Gao et al. (2021) examine the interregional spillover in industrial diversification, they build one general product/industry space, respectively, and trace the product/industry trajectories on each space, focusing on its spillover benefit that is created only in the respective product/industry space.However, a new product/industry can emerge in a region through spillover across different dimensions, such as between technology space and product/industry space or skill space and product/industry space (Catalán et al., 2020;Eum & Lee, 2022).Catalán et al. (2020) suggest the concept of cross-space spillover to describe the endogenous capabilities associated with two knowledge dimensions, which are 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 technological diversification at the 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 dimensions of knowledge, in sum, it suggests that the cross-spillover among two spatial units representing distinctive types of knowledge can exist and affect regional industrial diversification.
As the further empirical effort to bridge the detailed understanding of ports' economic role to the economic geography and regional studies, and the relatedness measure from economic complexity, we study the role of neighbouring ports, which show different types of network structure of products, in regions' industrial diversification and regional economic resilience, by using port-level export data and regional production data of Korea from 2006 to 2020.The port, a distinct geographical spot, is a hub that connects the region to the GPN.When the subnational level (instead of the firm level) is examined regarding the effect of GPN, it is difficult to extract the list of products that is associated with the GPN.Products that are exported in a certain port, however, can reveal the list of products 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 neighbouring regions by building two types of product space, which are the product space of ports and that of neighbouring port regions, and by looking at their cross-space spillover.
Is the spillover effect from a neighbouring port helpful for a region that has the port to diversify its 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 subnational region?Are the spillover benefits disturbed during the economic crisis?
We find that the product space of ports exhibits a distinctive feature that differs from the product space of ordinary regions and countries, mainly because of the logistic and trade systems governing individual products.Consistent with the previous research (Boschma et al., 2013;Gao et al., 2021;Hidalgo et al., 2007;Neffke et al., 2011), regions with neighbouring ports are more likely to enter a new industry when they already have related industries locally.In addition, we find evidence of cross-space spillover from neighbouring ports that the probability of success in neighbouring regions to develop a new product is promoted when the port also has highly related products to the new one.Our results imply that inter-industry spillover is the prime engine of industrial diversification in a region, and cross-space interregional spillover from nearby ports catalyses the diversification of the region.In essence, the primary catalyst for industrial diversification at the subnational level originates from within the region itself, and this diversification process is further amplified when regions strategically plug into GPNs.
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 neighbouring 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 within regional industrial structure helps port regions to sustain their industrial diversification, while the effect of cross-space spillover from neighbouring ports becomes insignificant.However, during the recovery periods, cross-space spillover from nearby ports increases the probability of success in port regions to enter a new industry, verifying a neighbouring port positively contributes to economic resilience by sustaining regional economies.
The remainder of this paper is organised 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 the results.Section 5 concludes.

LITERATURE REVIEW
2.1.Port and regional development Undoubtedly, integrating transport and regional development is important in the literature of economic geography (Fujita et al., 1999).In particular, as maritime transport through ports has become the major transportation mode in international trade after the rise of GVC (Amador & Cabral, 2016;Jacobs et al., 2010), recent studies on ports and port regions have renewed the significance of the interdependence between transport and the economic performance of regions (Bottasso et al., 2018;Ducruet & Itoh, 2016;Moura et al., 2019;Qi et al., 2020;Zhao et al., 2020).Starting from the idea of ports providing a comparative advantage to the economic activities of neighbouring regions (Fujita & Mori, 1996), scholars tried to prove that ports function as an important infrastructure endowment to promote international trade and investment and facilitate regional development.(Zhao et al., 2020), for instances, argued the positive impact of port comprehensive strength on economic growth in local and neighbouring cities in general but the regional differences in its spatial spillover affect either.Qi et al. (2020) 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 neighbouring ports or only explained a broad correlation of regional growth with neighbouring 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 (Ducruet & Itoh, 2016).
A port, however, is a window for a neighbouring 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 (Bottasso et al., 2018;Ducruet & Itoh, 2016;Moura et al., 2019;Qi et al., 2020;Zhao et al., 2020).Considering a port as a knowledge hub, Ducruet and Itoh (2016) explored the connections between port traffic commodity types and economic activities within port regions.Despite their success in acknowledging neighbouring ports' contributions to knowledge spillover, the study did not establish a product-level link between the port's network structure of product and that of the adjacent region, primarily due to data limitations.Our research aims to bridge this gap by associating the network structure of port's product with that of its neighbouring region.
Figure 1 illustrates the conceptual framework of this study.As shown, the port serves as a window for its neighbouring region, connecting it to GPNs.In order to examine the interaction between the port and port regions, and to understand the role of knowledge spillovers between them in facilitating the emergence of new products in the region, we construct two types of product spaces.These are the product space of the port region and that of the neighbouring port, which will be elaborated upon in the methodology section.

Relatedness and knowledge spillover
While mainstream economics has relatively overlooked the concept of variety and its role in economic development, researchers in the field of evolutionary economics have turned their focus to the influence of variety on structural change and economic development (Pasinetti, 1983;Saviotti, 1996;Saviotti et al., 2020;Saviotti & Pyka, 2004).In this stream, the degree of relatedness driving knowledge spillover has been highlighted as a determinant of regional growth paths in the field of EEG and regional study (Boschma, 2017;Hassink et al., 2019;Hidalgo et al., 2018;Oinas et al., 2018).Frenken et al. (2007) 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 (Hidalgo et al., 2007).As a pioneering work, Hidalgo et al. (2007) 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 is 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 utilised this framework to confirm that the relatedness between industries, occupations, and patents possibly measures their localised knowledge and capabilities, and identifies with the channels of knowledge spillovers (Boschma et al., 2013;Boschma et al., 2015;Felipe et al., 2014;Jara-Figueroa et al., 2018;Kogler et al., 2013;Neffke et al., 2011;Zhu et al., 2017).The growing evidence by an expanding literature, exploring the regional diversification and development as a function of the density of related activities within a region, has become formalised as empirical principle called the Principle of Relatedness (Hidalgo et al., 2018).
As Boschma et al. (2017) pointed out, the studies above-mentioned tend to focus more on the inter-industry spillover within a regiona kind of Jacobs externalities between industriesas if a region is a geographically self-contained entity.Of importance, however, the knowledge spillover has the spatial dimension (Audretsch & Feldman, 2004): the interconnections with geographical neighbours provide access to new economic knowledge, thereby exerting knowledge spillover.In this regard, Bahar et al. (2014) explained the knowledge spillover from neighbours by showing that the probability of a country developing a comparative advantage in an industry increases if a neighbouring country has a comparative advantage in that same industry.Similarly, Boschma et al. (2017) suggested the neighbours sharing borders as the source of regional diversification at the subnational level, demonstrating the specialisation pattern of geographical neighbours shapes the local capability to develop The conceptual framework of this study.We investigate the spillover effect of a port on its neighbouring region's industrial diversification by looking at the interaction between two types of product spaces: that of the neighbouring port (p) and the region (r).This interaction facilitates the emergence of a new product (i) in the region.Here, V p,i and v r,i represent the density of products related to product i in port p and region r, respectively.Note: For a more detailed explanation, see section 3.3.
new industries in a region.In a recent paper, Jun et al. (2020) pointed out that knowledge spillover from neighbouring exporters is a significant predictor of increases in bilateral trade flows.In the similar context, Gao et al. (2021) showed that interregional spillover played a significant role in the industrial diversification of China's provinces.
In summary, the previous research has primarily focused on examining interregional spillovers from geographical neighbours, based on factors such as physical distance and direct industrial linkages.However, it is essential to acknowledge that all geographical neighbours at the same distance are not homogeneous.For instance, ports can yield different effects compared with non-port regions, given that a port acts as a gateway, facilitating connections between local products and industries and the global market.As such, research examining interregional spillovers should take into account these heterogeneous characteristics of geographical neighbours.
Meanwhile, the recent studies on multidimensional network spaces within a region have hinted at the empirical strategy of analysing the spatial link between two different networks of related industries in a region and its neighbour.Jara-Figueroa et al. ( 2018) decomposed the related knowledge that workers bring into pioneer firms into two different dimensional spacesthe 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, Catalán et al. (2020) formalised 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 Jara-Figueroa et al. ( 2018) considered two exogenous types of knowledge and applied two relatedness indicators in a regression model of spillover separately, while Catalán et al. (2020) considered two endogenous types of knowledge and suggested a unified relatedness indicator of the interconnected space.
Based on the literature review on inter-industry spillovers from related industries and on influences of nearby ports, there is a discrepancy between the practical importance and academic endeavours 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 neighbouring 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 subnational region?
Our expected contribution to the literature on economic geography and related diversification concerns the role of neighbouring ports as a special case of neighbours exerting interregional 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 subnational regions.
The engineering resilience concept values a region's ability to return to its pre-shock steady state, assuming this state's stability.Conversely, the ecological concept of regional resilience underscores a system's capacity to absorb shocks, adjust, and attain a new steady state, acknowledging the potential long-term shock impacts.The distinction reflects differing views on the constancy of the steady state, with economic geographers often suggesting that such shocks can indeed reshape a region's economic structure (Boschma, 2015;Martin, 2012).This perspective aligns with the evolutionary viewpoint, emphasising a region's capacity for enduring economic development in spite of disturbances, where resilience is not only about recovery, but surpassing disturbances and potentially embarking on a new developmental path.Here, regional economic resilience is tied to the structure, performance and overall function of the economic system.This perspective highlights historical path dependency, as a region's ability to forge a new path heavily relies on its existing system capabilities.However, it's often criticised for ignoring disturbances and primarily focusing on broader economic trends (Boschma, 2015).Boschma (2015) asserts that research on related variety can bridge gaps in the evolutionary approach.A region with a high level of related variety implies a broad spectrum of related industries that could foster the development of new industries via inter-industry learning.Related variety enhances regional growth potential and reduces the risk of existing industries' exit (Frenken et al., 2007;Neffke et al., 2011).
Empirical evidence supports the role of related variety in regional economic resilience.For instance, studies show that regions with higher skill-related industries are more resilient to economic shocks and can better adapt to industry decline by repurposing and recombining existing region-specific human capital (Diodato & Weterings, 2015;Eriksson et al., 2016).This suggests a higher level of resilience in regions with more related variety.
Ports as catalysts: spillover effects of neighbouring ports on regional industrial diversification and economic resilience 985

REGIONAL STUDIES
During the 2008-12 Great Recession, Cainelli et al. ( 2019) observed positive effects of technological relatedness on short-term resilience in the European Union.Similar findings were observed in Denmark's information and communication technologies sector (Holm & Østergaard, 2015).In summary, these studies imply that the industrial/technological composition or portfolio is a significant factor in a region's resilience against shocks.
Empirical evidence establishes a positive relationship between related variety and regional resilience.However, traditional measures like entropy (Frenken et al., 2007) or the Shannon index (Holm & Østergaard, 2015) may oversimplify industry-specific information by assigning a single value to each region's related variety.A relatedness measure from economic complexity can retain this information (Hidalgo et al., 2007).As described in section 2.2, this methodology assesses the density of related industries around each industry, highlighting regional potential for new development paths (Hidalgo et al., 2018).Several studies corroborate that such relatedness enhances regional economic resilience (Jara-Figueroa et al., 2018;Jun et al., 2022).
Jun et al. ( 2022) investigate the impact of related variety on the economic resilience of amenity clusters during the COVID-19 pandemic.They used a relatedness measure and discovered that stores in Seoul, which were co-located with similar amenities, showed greater survival rates during the crisis.This indicates that relatedness can act as a source of regional resilience in the face of exogenous shocks.Extending this line of research, we investigate whether related industries of a neighbouring port and the region can likewise serve as a source of regional resilience.

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 in Korea that are authorised 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 27,407 m 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 1224 of all 1241 commodities.Figure 2 shows the sample scope of analysis in this study: black circles represent ports and shaded areas show the localisation of port regions within the sample.
From the 1960s, the Korean government strategically invested to develop ports associating with the exportoriented development plans.Considering that Korea is located at the centre 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 (Jung, 2011).Thanks to the growth potential with its location and the second wave of globalisation, 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 2, moreover, the port regions in our analysis are geographically scattered over all three coasts in the Korean peninsula.Busan port, which is located on 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 on the western coast or Ulsan and Pohang port on the eastern coast are also handling more or similar massive quantity of cargo.In addition, according to Lee et al. (2014), 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 Park (2008, pp. 35-47), upon the second wave of globalisation, 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 are made after the 2000s, we did not distinguish the regions with the free trade zone from regions without them.

Data
We use two sets of export data for 2006-20 in Korea: port data and regional data.Port data consist 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 consist of the annual production value of each product in each region shipped to each port.The data are extracted from the Trade Statistics Service by Korea Customs Services (TRASS), which provides detailed trade statistics based on the harmonised system (HS) four-digit aggregation (rev.2017) for 1241 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 neighbouring ports (Bottasso et al., 2014;Ducruet & Itoh, 2016).
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 (Hidalgo & Hausmann, 2009;Simoes & Hidalgo, 2011).The PCI ranks not only the sophistication but also the diversity of the knowledge 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 1060 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.

Product relatedness in production and transport
How do neighbouring ports affect the regional diversification of a port region?Specifically, does the knowledge embodied in the commodity flows of neighbouring ports Note: a For the production proximity, the top 15 pairs of the total are presented.
b For the transport proximity, 15 among 122 pairs with F = 1 are selected at random since there are too many pairs with F = 1.
Ports as catalysts: spillover effects of neighbouring ports on regional industrial diversification and economic resilience 987 REGIONAL STUDIES influence the development of new industries in a port region?To explore our research question, the analytical framework of the principle of relatedness is applied in this study (Hidalgo et al., 2018).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 cooccurrence 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 (Hidalgo et al., 2007).Here, the proximity between products in regional production ϕ or port activities F 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 1241 products from 2006 to 2020, we calculate the RCA following Balassa (1965): where x r,i and X p,i are matrices summarising the export value (in US$) 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.
Using RCA i and RCA j , we can create 1241 × 1241 matrices of the proximity between products i and j for a region r (ϕ, production proximity) and a port p (F, transport proximity), respectively, following Pinheiro et al. (2018) and Hidalgo et al. (2007): where ϕ i,j and F i,j [ [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 = S M r,i and K p,i = S M p,i ).The proximity f i,j or F i,j is one when products i and j always co-occur in the export data, while f i,j or F 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 ϕ and transport proximity F. As we expect, ϕ and F measure the different types of product similarity in production and transport.The top 15 products of production proximity around product 8541 depict domestic value chains of producing semiconductor devices, including six products in the same family of industry and nine 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 containerisation of semiconductor devices as general cargo.As shown in Table A2 in Appendix A in the supplemental data online, the containerisation ratios of semiconductor devices and other proximate products have almost one in the case of Korean maritime transport.We can also see this distinct characteristics of transport proximity from Figure 3. Figure 3A depicts the product space of port regions, while Figures 3B depicts that of ports.The example of product HS code 8541 tells us that the way of export including containerisation gives effect on which port a product heads to export.For further information on the two product spaces, see Appendix A in the supplemental data online.
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 (Hidalgo et al., 2007;Hartmann et al., 2021).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 v t r,i and the product relatedness of a product i in a port p in year t V t p,i as the following equation: where M t is 1 when RCA t is greater and equal to 1 in year t.The product relatedness v t r,i or V t p,i is a value between 0 and 1, where v t r,i or V t p,i 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.
Returning to our research question, the product relatedness in port activities V t p,i could be an explanatory variable to examine the spatial link between regional diversification and the knowledge embodied in the commodity flows of neighbouring 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 in Appendix A in the supplemental data online.After matching, we rule out observations with missing values of V t p,i mainly generated because the port does not deal with some products manufactured in the port region. 1 For the sample of 26 port regions, Table A3 in Appendix A online presents summary statistics of the original regional data and port-region matched data.

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 and region fixed-effects, with a standard error term allowing for within-cluster correlation of products to be robust under any product-specific characteristics (see also Tables A6-A8 in Appendix A in the supplemental data online). 2For the model, we follow the tradition of relatedness literature (Bahar et al., 2014;Boschma et al., 2017;Gao et al., 2021;Hausmann & Klinger, 2007) 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 (ω) 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 (V) 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 V shows positive dependence, we call this effect the cross-space spillover in this study.
Formally, our model for the first step is given by: where the dependent variable S t+2 r,i is a binary variable.It is defined as 1 if a region jumps into a new industry, after two years, where it previously lacked a comparative advantage, and 0 otherwise.Here, to avoid noises of temporary jumps, we restrict jumps subject to the forward and backward conditions (Bahar et al., 2014;Gao et al., 2021): a jump needs to keep comparative advantages for two years more after the year t + 2 (i.e., M r , from t + 2 to t + 4 has to be 1), and a jump also satisfies M r,i ¼ 0 for a further two years before the year t. 3 In this model, the main explanatory variable ω indicates how much a region has knowledge of similar products around a product i in year t, and k t r,i represents the ubiquity of a product i in year t.TRM t r,i represents the total number of ports where each product in each region is shipped by year, and PCI t i represents the product complexity index for a product i in year t.μ t and μ r are fixed-effect terms to control omitted year-and region-specific variable bias, and 1 t r,i 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, V t p,i where β 2 is an estimator of cross-space spillover from neighbouring ports to a port region, and β 1 is an estimator of knowledge spillover within the regional product space.

RESULTS
This section provides the regression results by estimating several specifications of equations ( 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 equation ( 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 A4 in Appendix A in the supplemental data online for more regression estimates to test the robustness of our model).First, we find the positive dependency of ω on the development of a new industry, confirming the knowledge spillover within the regional product space (Gao et al., 2021;Hidalgo et al., 2007;Neffke et al., 2011).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 (Boschma et al., 2013).
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.Our initial expectation centred around a positive effect of PCI on industrial diversification.However, our result suggests that the more ubiquitous and less complex product is a good candidate for a new potential industry in a port region.The negative correlation between PCI and the likelihood of industrial diversification may be attributed to the fact that more complex industries require a higher level of technology and manufacturing capabilities in their diversification.This observation aligns with the findings of Kim et al. (2022).Using patent data, Kim et al. (2022) examined the part that related technology plays in technological diversification among firms, revealing that entry into more complex technology requires more related technologies.Balland and Rigby (2017) pointed out it as a diversification dilemma, meaning that an economic agent cannot jump into more complex activities, while these complex activities are more attractive to them.As such, Pinheiro et al. (2018) emphasised the significance of augmenting factors that could elevate the success rate of diversification, for instance, enhancing the level of regional human capital, among others.Intrigued by this result, we further investigate whether the knowledge spillover within the regional product space varies by the knowledge intensity of products and the industrial characteristics.
To analyse the effect of ω 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 1 degree of freedom ¼ 14.16, p ¼ 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 (Figure 4).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 (Jun et al., 2020;Rauch, 1999Rauch, , 2001)).
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 (Hausmann & Klinger, 2007), 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 (Felipe et al., 2014).For this reason, we classify products into 10 categories of the Leamer classification by relative factor intensities of capital, labour and skills required for each category, following Hidalgo et al. (2007). 4Considering the characteristics of 10 categories, we then split the sample into three groups by industry sophistication levels: the low group for primary industries and raw material sectors, the medium group for labour-and capital-intensive industries, and the high group for machinery and chemical sectors required high skills for production (i.e., knowledge-based industries).
Table 2, columns (4) to ( 6), shows the estimations by industry sophistication levels.We find that the coefficient of ω 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 specialised 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 (Jun et al., 2020).
Table 2. Development of a new potential industry by knowledge spillover within the regional product space.
( Ports as catalysts: spillover effects of neighbouring ports on regional industrial diversification and economic resilience 991 REGIONAL STUDIES 4.2.Cross-space spillover from neighbouring 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 neighbouring ports and apply equation ( 5).Table 3 reports the regression estimates of our main interest, the contribution of neighbouring 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 A6 in Appendix A in the supplemental data online for more regression results to check the robustness of our model).The effect of ω as the local capability to produce related products is still significant in developing a new industry, and the effect of V from neighbouring 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 neighbouring 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.
Next, we analyse 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 V, 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 ω, 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 (see also Table A6 in Appendix A in the supplemental data online for more regression results to check the robustness of our model). 5Specifically, 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.

Product relatedness as the source of 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 neighbouring 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 (2007-09), the period of recovery (2010-13) and the period of post-crisis .Before getting to the main point, we point out that the impact of ω 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 source of regional resilience to adapt to the new circumstances following the economic crisis.
Then, we discover that the effects of V, indicating cross-space spillover from neighbouring ports, are significantly positive only in times after the global economic  Ports as catalysts: spillover effects of neighbouring ports on regional industrial diversification and economic resilience 993 REGIONAL STUDIES crisis and have a marginal difference in degrees between periods.These results suggest that product relatedness of port activities can enhance regional economic resilience post-crisis by the cross-space spillover effect from neighbouring ports.In detail, the port, as a knowledge hub, supports neighbouring regions to endure economic development and surpass disturbance by introducing a new development path by providing information on logistic and trade systems about potential co-exported goods as prospective industries.
Interestingly, the catalytic role of ports during a recovery period is also evidenced by the effect of ubiquities, k t r,i and K t p,i .The ubiquity of a product at the regional level, k t r,i , is only significant in crisis, while that at the port level, K t p,i , becomes significant post-crisis, demonstrating that more ports to export a product within a country serve better conditions to de-risk the market disturbance after the crisis.This empirical finding aligns with the evolutionary resilience concept as well.
By comparing the results of columns (1) with (3), representing the changes before and after the crisis, we find a statistically significant difference in the coefficients of TRM measuring the total number of ports for each product to be shipped, not just neighbours: the increase of TRM during the post-crisis period (2014-18) is less effective in industrial diversification than during the years of crisis and recovery.This difference suggests that increasing the number of ports to transport a new potential product becomes less incentive to diversify the regional industry into the new product for post-crisis.In other words, this result implies the weakened link between broadening trade partners and industrial diversification after adapting to new trade circumstances.Considering that regions have diversified their export destinations by transporting products through many ports on different coasts, we conclude that Korea's export industry and its engagement in the GVC are also being affected and adjusted by a slowdown in globalisation and more localisation (Bailey & De Propris, 2014;Lund & Steen, 2020).

CONCLUSIONS
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 resiliencehow 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 research in economic complexity and economic geography.Scholars have rather studied the interregional spillover effects on the industrial diversification and economic development from their neighbours as homogeneous geographical units by their physical distance.
We studied the effects of interregional spillover of neighbouring 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 the 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 (Boschma et al., 2013;Gao et al., 2021;Hidalgo et al., 2007;Neffke et al., 2011).Interestingly, we also proved that regions' entering a new industry can be catalysed by neighbouring 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 interregional spillover is the strongest for the new potential industry with medium-level sophistication (i.e., labourand 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 interregional 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 GVC.
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 labour flow from the related industries to a new industry (Jara-Figueroa et al., 2018), knowledge flows among product lines, and further social capability (Abramovitz, 1986), technological capability (Kim, 1999) or institutions that support the already existing related industries.On the other hand, behind the scene of cross-space interregional spillovers from neighbouring 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.

DISCLOSURE STATEMENT
No potential conflict of interest was reported by the authors.

FUNDING
This project was funded by the National Research Foundation of Korea [grant numbers NRF-2022R1A2C1012895 andNRF-2022R1A5A7033499].We also acknowledge the support from Inha University.NOTES 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. According to the literature (Catalán et al., 2020;Jun et al., 2020), a three-way fixed-effect regression model, including a product fixed-effect term, would be a good alternative in this study.Therefore, we also conduct a three-way fixed-effect estimation using the extended version of the original two-way fixed-effect model with the two-digit product fixed effect to control the distinct characteristics of each product group at the industry level.The corresponding results are shown in Tables A5-A8 in Appendix A in the supplemental data online.3.For the period 2008-16, we apply both the forward and backward conditions, but we only apply the forward condition for 2017-18 and the backward condition for 2006-07 due to the data limitations.4. According to Hidalgo et al. (2007), we can apply the revised version of the classification introduced by Leamer (1984): petroleum (Leamer 1), raw materials (Leamer 2), forest products (Leamer 3), tropical agriculture (Leamer 4), animal agriculture (Leamer 5), cereals (Leamer 6), labour intensity (Leamer 7), capital intensive (Lea-mer8), machinery (Leamer 9) and chemicals (Leamer 10). 5.In Table A6, column (8), in Appendix A in the supplemental data online, only with variables related to neighbouring ports shows a similar size of the estimator of ω compared with the result in Table 3 with no significant difference, suggesting that the knowledge spillover from ports is neither a competitive nor a substitutive effect to the typical production knowledge spillover within regions.

Figure 1 .
Figure1.The conceptual framework of this study.We investigate the spillover effect of a port on its neighbouring region's industrial diversification by looking at the interaction between two types of product spaces: that of the neighbouring port (p) and the region (r).This interaction facilitates the emergence of a new product (i) in the region.Here, V p,i and v r,i represent the density of products related to product i in port p and region r, respectively.Note: For a more detailed explanation, see section 3.3.

Figure 2 .
Figure 2. The scope of analysis: 40 ports and 26 port regions in Korea.Note: See TableA1in Appendix A in the supplemental data online for further detail on the sample.

Figure 3 .
Figure 3. Product space of regions (A) and ports (D) in period 2006-20.(B, C) Ego networks of the product of four-digit harmonised system (HS) code 8541, which is diodes, transistors, similar semiconductor devices.The node size represents the export value of the product in a relative scale; the node colour shows its classification as originally proposed by (48) and revised by (38).By comparing the two different ego networks (B, C) of the same product (8541), we can indicate that the product space of region and of port show different structure.

Figure 4 .
Figure 4.The different size of effect of relatedness on industrial diversification over different product complexity (A) and industrial sophistication (B).

Table 1 .
Most similar products to the product of harmonised system (HS) code 8541 (diodes, transistors, similar semiconductor devices) by production proximity (top) and by transport proximity (bottom).

Table 3 .
Development of a new potential industry by cross-space spillover from neighbouring ports.