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International automotive production networks: how the web comes together

Abstract

This paper aims to provide empirical evidence for the connections between new trade theory and the spatial distribution of economic activities, considering the results obtained by the literature on New Economic Geography as a starting point. To do this, we apply Social Network Analysis specifically to the World Automotive Trade Network. We explore the structural features of the auto network for the years 1996 and 2009 using data on trade flows for 172 countries. Our findings suggest that the auto network has become denser, more extensive and more integrated over time, depicting a center-periphery structure in which regional clusters play a prominent role. In this configuration, strong agglomeration forces generated by companies’ desire for large and rich market access with minimum transportation costs are balanced by the search for new high-potential markets.

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Notes

  1. 1.

    Two stable spreading equilibria result from the simplest \(2\times 2\times 2\) model (Krugman 1991): agglomeration of one of the sectors in one region when transportation costs are very low, and dispersion of this sector in two regions when transportation costs are very high. In this model, Krugman basically adds the interregional mobility factor (workers and firms choose a location) to his new trade theory model (Krugman 1980). From this stylized model, different and more complex assumptions have been added. For a discussion of the core model and several of its extensions, see, for example, Ottaviano and Thisse (2004).

  2. 2.

    The directed nature of the WATN is based in the calculus of the symmetry index, \(S\), proposed in Fagiolo (2006). Since the asymmetric patterns have been statistically identified, a directed analysis of the network is necessary.

  3. 3.

    \(\hbox {w}_{\mathrm{ij}}^{\prime } (\mathrm{t})\equiv \frac{\hbox {w}_{\mathrm{ij}} (\mathrm{t})}{\hbox {w}_{\mathrm{tot}} (\mathrm{t})}\) where \(w_{ij}(t)\) are exports from country \(i\) to country \(j\) in period \(t\) and \(w_{tot}(t)\equiv \varSigma _{i}\varSigma _{j\ne i}w_{ij}(t)\).

  4. 4.

    The generic entry of the (binary) adjacency matrix \(a_{ij} (t) = 1\) if and only if exports from country \(i\) to country \(j\) reported by the importer are strictly positive in year t, and \(a_{ij} (t) = 0\) otherwise.

  5. 5.

    A more extensive and detailed description of the topological measures included in this section can be found in the seminal book by Wasserman and Faust (1994). The corresponding analytical description of this section is presented in Table 9 of the Statistical Appendix.

  6. 6.

    Note that an authoritative country may also be a hub, and vice versa. It is also important to be aware that HITS hub/authority rankings tend to be strongly correlated with the out-/in-degrees of the corresponding nodes (Benzi et al. 2013).

  7. 7.

    In particular, we use in this study the Random-walk betweenness centrality index proposed by Newman (2005) and Fisher and Vega-Redondo (2006).

  8. 8.

    A k-core is a maximal subnetwork in which each vertex has at least degree \(k\). It therefore identifies relatively dense subnetworks and thus cohesive subgroups within the whole network.

  9. 9.

    We restricted our analysis to those import flows whose values are higher than or equal to 3 % of the country’s total imports of the specific commodity considered.

  10. 10.

    The selection of the number of countries was based on the availability of data for both of the periods analyzed. See Table 7 in the Statistical Appendix.

  11. 11.

    Peneder (1999) identified five groups rather than four. The fifth group refers to marketing-driven P&C. Since, in the case of the automotive industry, this category includes only two items which account for only 0.3 % of the total trade in automotive P&C we have decided not to analyze this category separately. The mainstream-driven category refers to those items in which input combinations do not share a major reliance on any particular input factor.

  12. 12.

    No statistically significant differences were found between both networks for most of the structural indicators.

  13. 13.

    When we say Korea, we mean South Korea.

  14. 14.

    In this respect, it is important to bear in mind the explanation of the distance puzzle given in Arribas et al. (2011) from the construction of international trade integration indicators; the authors find that the role of distance in bilateral trade, on average, still matters despite the reductions in the cost of trade, although it varies across countries.

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Acknowledgments

The authors thank Rosario Gandoy, Carmen Díaz-Mora and Maximino de Cos Hernando for helpful comments. Funding from the Regional Government of Castilla-La Mancha (PPII10-0154-9251).

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Correspondence to Belén González-Díaz.

Statistical appendix

Statistical appendix

See Tables 7, 8 and 9.

Table 7 Countries included in the analysis
Table 8 Automotive commodities included in the analysis
Table 9 Definition of topological measures

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Blázquez, L., González-Díaz, B. International automotive production networks: how the web comes together. J Econ Interact Coord 11, 119–150 (2016). https://doi.org/10.1007/s11403-015-0144-x

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Keywords

  • World Automotive Trade Networks
  • New Economic Geography
  • Social Network Analysis
  • Parts and components

JEL Classification

  • F10
  • F14
  • F15