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Trade Openness, Transport Networks and the Spatial Location of Economic Activity

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Abstract

This paper introduces a multi-country multi-regional model that allows the evaluation of the effects of trade openness in the distribution of economic activity across regions within countries. Relying on the agglomeration and dispersion forces characterizing the analytical framework of the New Economic Geography and New Trade Theory (neg/ntt) literature, we consider a general model with two differentiated sectors in terms of preferences, technologies and transport costs, allowing for any feasible world trade network topology where trade frictions are both transport and non-transport related (tariffs). We study systematically the critical thresholds that characterize the long run equilibria of economic activity. As benchmark simulations we choose two opposed domestic network topologies characterizing a homogeneous space and a heterogeneous space with some regions enjoying locational advantages. Our findings show that trade openness changes locational patterns in favor of better located regions with respect to the new world topology, which nevertheless may result in larger or lower spatial equality depending on the initial distribution of the economic activity. These results entail important implications in terms of transport infrastructure (accessibility) and trade (commercial agreements) policies, as both are interrelated when policy makers set regional equality goals.

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Notes

  1. In this reorganization of economic activity, as we study here, the existent layout of transport infrastructure between and within countries plays a key role. At country level, and relying on the stochastic production frontier approach, Coto-Millán et al. (2016) report the positive effects of transportation network improvements on economic growth, brought about by better logistics and information and communication technologies ICTs. More generally, the underlying growth mechanisms of transportation networks are explored by Xie and Levinson (2009), who review the progress that have been made in developing and analyzing effective network growth models.

  2. At the urban level, Blumenfeld-Lieberthal (2009) study the relevance of the network topology in explaining economic links between cities. In the same vein (Reggiani and Martín 2011) discuss the concept of accessibility and how it affects land use planning and spatial interaction.

  3. Indeed, the classical neg and ntt frameworks assume that the constant returns to scale sector produces a homogeneous good that can be traded costlessly. However, several authors have shown that these assumptions are far from innocuous. E.g., from an empirical perspective (Davis 1998) discusses how they are at odds with reality, while theoretically showing that in the case of an open economy model, composed of only two countries, and without considering the regional dimension (a ntt model), if both sectors are subject to the same transport costs, the “home market” effect vanishes. In the case of a closed economy, Helpman (1998) and Hadar (1996) and Fujita et al. (1999) show that the spatial configuration of the industrial activity is significantly altered when the second sector produces a differentiated product subject to costly trade.

  4. For the sake of simplicity, in our simulations we consider that the technological parameters F and c are also the same across regions and countries. This assumption could be relaxed to explore the effects of different sectoral productivities on the spatial location of economic activity.

  5. Therefore, our transport cost metric δ corresponds to an exponential network metric as in Behrens (2005) and Behrens et al. (2007b) or Barbero and Zofío (2016). Note, however, that the spatial equilibrium presented below in section 2.3.1 is sensitive to scale changes in the distances between regions; i.e., rescaling the distance matrix by a scalar α > 0 results in alternative spatial equilibria by increasing or reducing transport costs, and therefore favoring dispersion (α > 1) or agglomeration (α < 1), respectively. An alternative scale-invariant definition of network distances that bounds them in the interval (0, 1] can be obtained by normalizing distances by the maximum distance in the network. Other possibilities that bound distances in the above interval–and render alternative topologies comparable–circumscribe them within a given extension–e.g., a circle of radius 1, Barbero and Zofío (2016), or take the inverse of r(ik, jl) > 1. Also, the role that variable returns in the transport sector play in the spatial equilibria of neg models (either increasing, constant or decreasing) has been extensively studied in the literature, e.g., Picard and Tabuchi (2010).

  6. See Ducruet and Beauguitte (2014) for a review on how complex network research has been integrated into urban and regional economics. Also, based on the concept of spatial interaction, Caschili et al. (2015) study the effect on trade flows of the complex interaction between existing economic horizontal and vertical networks such as global supply chains, foreign direct investments and international bilateral agreements, as well as non-economic components (i.e., cultural ties, spatial barriers and existing infrastructure).

  7. We note that this model embeds a standard specification assuming that the second sector produces a homogeneous good with costless trade. In that limiting case the elasticity of substitution is infinitum: σ2 = ∞ and the transport cost matrix is unitary: T2 = 1 nxm . Under these assumptions the spatial equilibrium is determined by the price and wage equations of the manufacturing sector.

  8. In Eq. 16 the boundary and normalizing conditions on the equilibrium shares hold: 0 ≤ λ1ik, t ≤ 1 and \(\sum \limits _{i}^{{n_{k}}}{{\lambda _{1ik,t}}= 1}, \quad i = 1,...,n,\ k = 1,...,m\).

  9. For the stability properties of the system of equations in Eq. 16 based on the n k × n k , k = 1,..., m, Jacobian matrices see, e.g., Commendatore et al. (2015a).

  10. This resembles the situation of Landlocked Developing Countries (LLDCs); i.e., r11 could be considered as a third country on its own, facing difficulties to develop economically through foreign trade. See the reports by the UN Office of the High Representative for the Least Developed Countries, Landlocked Developing Countries and Small Island Developing States, http://unohrlls.org/about-lldcs/.

  11. This is the case observed in some European countries like Spain, Portugal or France, as studied by Condeço-Melhorado and Christidis (2017). In these countries national border areas tend to be peripheral, and generally located in sparsely populated territories, far from large urban agglomerations. As a result of their low accessibility, higher transport costs are incurred by residents and businesses when connecting with central markets. However, trade openness requires transports investments in cross-border infrastructure, thereby reversing their initial unfavorable situation. This is what we simulate in the second world topology with the appearance of the links connecting border regions when trade openness takes places.

  12. There are other possible network configurations but most of them would be uninteresting as they do not entail a trade-off between trade-openness (trade costs) and centrality (transport costs) but simply reinforce agglomeration in one of the regions. This is the case, for example, of joining the central region with the foreign country. In that case trade opennesses does not trigger counterbalancing forces, but exacerbates the centripetal forces brought about by trade cost reductions. A similar reasonings can be applied to the union of a single region of each country; or one region of one country with two regions of the other country, i.e., any configuration that favors a single location. Therefore we believe that these polar cases serve the purpose of studying the workings of the model for relevant configuration where such trade-off exists, which can be extrapolated to other similar configurations. As previously mentioned, these network topologies resemble the geography of specific countries like Spain, where Madrid represents the central region 1 and Catalonia and the Basque Country would be regions 2 and 3, both connected to the French Border. Similarly in Italy with the Lazio (Rome) region and the northern provinces in the Po valley. Finally, all simulations have been run in MATLAB using the “fsolve” function.

  13. The location of the first sector activity in the second country: λ1l2, is also subject to the forces that shape that of the reference country; i.e., real salaries in the regions of the second country will be affected by the same trade openness process. We shall take into account these feedback effects associated to changes in the distribution of the first sector in the second country when they result in different equilibria for the location of economic activity in the first country: λ1i1. For brevity, these results are not reported, but are available upon request. Nevertheless, we highlight that the rank of the real wages among the three domestic regions does not change for the three extreme scenarios: all workers of the sector 1 are evenly spread across the foreign regions (λ1j2 = 1/3, j = 1, 2, 3); all workers of the sector 1 are equally agglomerated on the two bordering regions (λ1j2 = 1/2, j= 1, 2; and when all workers are concentrated on the farthest location (λ112 = 1). The most remarkable result is that in this last scenario the domestic salaries achieve their higher levels, while when the agglomeration is at both sides of the national border wages are lower. This might be motivated for the agglomeration costs in terms of an extreme increase of the other sector’s prices (sector 2), which becomes scarce under this agglomeration situation.

  14. In the numerical simulations, autarky results are effectively observed for ρs(ik, jl) ≥ 2. Therefore for parameter values larger than this threshold the equilibrium results do not differ, and the relevant range to study the effects of trade openness is the one presented in the Figures: [0, 2].

  15. From a logistics perspective, Holl and Mariotti (2017) provide further evidence in favor of firms’ decision to locate in border regions as trade openness takes place. If coupled with adequate transport infrastructure, locating in the new corridors between countries greatly reduces time and distance transportation costs in supply chains. For the Spanish case, they find that firms highly dependent on efficient logistics choose locations closer to highways and railroad junctions, facilitating the creation of logistic hubs at the border, which also reduces associated transaction costs.

  16. These indirect utility values may be considered as a straightforward measure of welfare (Castro et al. 2012). However, it is clear that from a comprehensive perspective, these measures do not take into account the real wages of workers in the immobile sector, neither the likely externalities, in terms of regional inequality, that emerges from the full concentration of mobile sector in a unique location. For different definitions of welfare in the neg model see Charlot et al. (2006).

  17. As it was mentioned these results are robust to alternative distributions of the economic activity of sector 1 in the second country, that in our base simulation is set evenly across regions; i.e., λ1j2 = 1/3, j = 1, 2, 3. Real wages in all three regions as trade liberalization increases for alternative distributions of economic activity are available upon request. These alternative scenarios agglomerate economic activity either in the farthest region: λ112 = 1, or symmetrically in the border regions: λ1j2 = 0.5, j = 2, 3. Real wage patterns and the sign of the differences between regions are unaffected by these changes.

  18. We observe that the results are sensible to the value of income spent in each sector. For an even 50% share, it is the agglomerating region, regardless its location in the domestic network, the one with the highest real wages. However, when the share of income spent in sector 2 is larger that 50%,we observe an inverse result. Therefore, the results are specific depending on the spending structure in each economy.

  19. Again, these results are robust to alternative distributions of the economic activity of sector 1 in the second country, one we change the default distribution: λ1j2 = 1/3, j = 1, 2, 3. Despite the alternative distributions agglomerating economic activity either in the farthest region: λ112 = 1; one of the border regions, λ122 = 1 or λ132 = 1; or symmetrically in the border regions: λ1j2 = 0.5, j = 2, 3,real wage trends and differences are the same. Results of these simulations are again available upon request.

  20. This situation is related to the spillover effects of transport infrastructure depending on the development and population levels of the different regions. Álvarez-Ayuso et al. (2016) and Jiang et al. (2016) conclude that while the direct effect of transportation infrastructure endowments on gross regional product is positive, its indirect spillover effects are heterogeneous, since underdeveloped–and normally unpopulated–regions do not tend to profit from transportation investments, as they are incapable of drawing economic activity due to their unattractiveness resulting from narrow labor markets, lack of higher education institutions, etc. Therefore, even if border regions benefit in principle from trade openness, this is a necessary but insufficient condition to trigger the complex agglomeration mechanisms observed in the real world.

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Acknowledgements

We gratefully acknowledge financial support from the Spanish Ministry of Economics, Industry and Competitiveness in the context of the following projects: ECO2010-21643, ECO2013-46980-P and ECO2016-79650-P. We also express our gratitude to Javier Barbero for his continuous assistance and support. Olga Alonso-Villar, Eckhardt Bode and Geoffrey J.D. Hewings provided insightful comments and suggestions; as well as participants at the 8th Summer Conference of the Gresellschaft für Regionalforschung, the 12th EUREAL Workshop and the XII Summer School in Public Economics held at the International Center for Public Policy, Georgia State University.

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  1. Department of Economics, Universidad Rey Juan Carlos (URJC), Paseo de los Artilleros, s/n, 28032, Madrid, Spain

    Nuria Gallego

  2. Department of Economics, Universidad Autónoma de Madrid (UAM), Calle Tomás y Valiente, 5, 28049, Madrid, Spain

    José L. Zofío

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Correspondence to Nuria Gallego.

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Gallego, N., Zofío, J.L. Trade Openness, Transport Networks and the Spatial Location of Economic Activity. Netw Spat Econ 18, 205–236 (2018). https://doi.org/10.1007/s11067-018-9394-1

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