Abstract
We provide empirical evidence on the network structure of trade flows between European regions and discuss the theoretical underpinning of such a structure. First, we analyze EU regional trade data using Social Network Analysis. We describe the topology of this network and compute local and global centrality measures. Finally, we consider the distribution of higher order statistics, through the analysis of local clustering and main triadic structures in the triad census of interregional trade flows. In the theoretical part, we explore the relationship between trade costs and trade links. As shown by Behrens (J Urban Econ 55(1):68–92, 2004), Behrens (Reg Sci Urban Econ 35(5):471–492, 2005a) and Behrens (J Urban Econ 58(1):24–44, 2005b) in a two-region linear new economic geography (NEG) model, trade costs and the local market size determine, even with finite trade costs, unconditional autarky and unilateral trade, that is, a one-directional flow from one region to the other. Following these contributions and guided by the empirical evidence, we clarify the relationship between market competition, trade costs and the patterns of trade in a three-region NEG model. We identify a larger set of trade network configurations other the three elementary ones that occur at the dyadic level between two regions (no trade, one-way trade, reciprocated two-way trade), and relate the model with the triad census.
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Notes
- 1.
- 2.
The sub-network including links with w > 25 is still weakly connected, but not strongly connected, that is not every vertex r is reachable from every s by a directed walk.
- 3.
Networks with high modularity have dense connections between the nodes within modules but sparse connections between nodes in different modules. We use modularity to detect the community structure of the EU regional trade network. See Newman (2006) on this issue.
- 4.
The degree centrality (C r d) is classified as a local measure of centrality since it takes into consideration only the direct links of a node, its nearest neighborhood, regardless of the position of the node in the network’s structure. Contrary to the local measures, global measures of centrality uncover the effect of others at a higher level of connection, including the direct and the indirect effect of potentially all nodes in the network. In particular, the eigenvector centrality captures the idea that the more central the neighbors of a vertex are, the more central that vertex itself is. In other words, eigenvector centrality gives greater weight to a node the more it is connected to other highly connected nodes. Thus, it is often interpreted as measuring a node’s network importance.
- 5.
A crucial difference between human and, for example, knowledge capital is that the former is embodied into the owner, whereas the second is separated. In a NEG model, this difference enters into play only when factor migration is allowed. When human capital is considered, changes in real incomes alters the migration choice also via the so-called “price index effect” so that changes in local prices may affect the long-run distribution of the industrial sector. Instead, when knowledge capital is concerned, factor movements are only driven by regional nominal profit differentials. A new economic geography (NEG) model in which the mobile factor is human capital (or, alternatively, skilled labor or entrepreneurship) is known as Footloose Entrepreneur (FE) model (developed originally in Forslid and Ottaviano 2003); a NEG model in which the mobile factor is separated from the owner (such as physical or knowledge capital) is labeled Footloose Capital (FC) model (developed firstly in Martin and Rogers 1995). As mentioned above, this distinction becomes relevant moving from the short to the long-run when factor migration is allowed. Even if the basic structure of the model is equivalent for FC and FE models, we consider the factor specific to the M-sector an entrepreneur.
- 6.
A specific configuration could emerge after empirical analysis. We could have, for example, a “hub and spoke” structure by letting: T rs ≤ T rk ≤ T sk , with T rs ≠ T rk and/or T rk ≠ T sk . This would stress the locational advantage of region r (the “hub”) with respect to s and k (the “spokes”).
- 7.
- 8.
Note that analogous expressions can be obtained for s or for k by simple switching r and s or r and k.
- 9.
Notice that the assumption of identical workers population has no significant impact on the short-run analysis and it can be easily removed; whereas in the long run, it determines the regional entrepreneurial shares and, via these shares, the trade flows.
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Acknowledgements
This Chapter is based upon work from COST Action ISCH COST Action IS1104 “The EU in the new complex geography of economic systems: models, tools and policy evaluation”, supported by COST (European Cooperation in Science and Technology) www.cost.eu. We also sincerely thank the IPTS (Institute for Prospective Technological Studies) for providing the data on interregional trade in Europe, and an anonymous referee for providing constructive comments and help in improving the contents of this chapter.
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Basile, R., Commendatore, P., De Benedictis, L., Kubin, I. (2016). An Investigation of Interregional Trade Network Structures. In: Commendatore, P., Matilla-García, M., Varela, L., Cánovas, J. (eds) Complex Networks and Dynamics. Lecture Notes in Economics and Mathematical Systems, vol 683. Springer, Cham. https://doi.org/10.1007/978-3-319-40803-3_6
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