Abstract
Gravity variables play a significant role in explaining trade flows related to global value chain participation. We find evidence that cooperation costs – measured by linguistic and geographical proximity – are more relevant for trade that reflects cross-border production sharing compared to trade in final goods. Applying an augmented gravity model framework to a newly constructed dataset, we find a positive association between bilateral FDI stock and both gross bilateral trade and the bilateral import content of exports. As bilateral FDI stock affects both the volume and composition of trade flows, we conclude that foreign investors play an active role in shaping host economies’ export structure and their participation in international production networks.
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Notes
The terms global value chains, global supply chains, international production chains, international/cross-border production sharing are used interchangeably in the literature and in the present article.
In many cases, fragmenting the production process is unavoidable, notably when natural resources are involved (in that case exports are fully conditional on imports). But fragmentation and the blurring of the ‘made in’ attribute have also become a reality for most other products, which are likely to contain (directly or indirectly) some fraction of foreign value added.
Buelens and Tirpák (2017) report a stronger association between the FDI stock and GVC participation for CEEC as compared to other countries. Furthermore, sector-level FDI in the CEEC region is positively related to the import-intensity of the sector’s exports, which in turn supports the export platform FDI hypothesis.
In the wake of the global financial crisis the average share of import content of exports declined, as the 2009 global trade collapse weighed disproportionately on trade in intermediate products. As trade recovered, the import content in exports rebounded. This reflects the fact that the 2009 trade collapse resulted from a severe adverse shock to final demand, affecting in particular the production of input-intensive and more complex durable goods, for which multiple counting of trade is particularly acute. Inventory adjustments and credit supply constraints further exacerbated the drop in final demand during the crisis (see, among others, Bems et al., 2012; and Ferrantino and Taglioni, 2014).
It should be emphasised that international input–output tables are themselves estimates based on a number of assumptions and simplifications. For example, all firms in an industry are assumed to use the same input combination and thus the same technology; or multi-product firms are typically classified within the sector of primary production, which may distort the imputed industry technology.
Note that all trade flows considered (1–6) contain both domestic and foreign value added. Total trade flows (1) can be broken down into final (2) and intermediate trade (3), while trade flows (4–6) are subsets of intermediate trade (3). The \(A_{ij}\) term refers to a sub-matrix of technical (input–output) coefficients, which specify in which proportion inputs from country i enter country j’s production process.
The common language index takes into account the linguistic proximity of two languages, even if they are formally distinct. All other things equal, a higher linguistic similarity should facilitate cooperation via lower interpretation and communication costs.
In other words, it is a backward (upstream) link of country \(i\) to country \(o\), or, equivalently, the forward (downstream) link of country \(o\) via country \(i\).
Restricting the potential determinants of bilateral trade flows to country- and pair-specific variables of the two countries involved only, would disregard the fact that countries (generally) have more than one bilateral trade partner and that other bilateral trade relationships may create or divert trade – failure to do so would yield a “naïve” version of the gravity model with omitted variable and award the “gold medal” of gravity model errors (Baldwin and Taglioni, 2006).
Specifically, the transformation for the bilateral distance is given by \(\ln \,{\text{dist}}_{ij}^{ *} = \left[ {\frac{1}{N}\left( {\mathop \sum \limits_{j = 1}^{N} \ln \,{\text{dist}}_{ij} } \right) + \frac{1}{N}\left( {\mathop \sum \limits_{i = 1}^{N} \ln \,{\text{dist}}_{ij} } \right) - \frac{1}{{N^{2} }}\left( {\mathop \sum \limits_{i = 1}^{N} \mathop \sum \limits_{j = 1}^{N} \ln \,{\text{dist}}_{ij} } \right)} \right]\). The transformation for contiguity is similar.
See Electronic Supplementary Material of this article (https://doi.org/10.1057/s41294-017-0036-2).
Further results from alternative specifications, including (i) a baseline gravity model for imports estimated by PPML and (ii) baseline gravity models for exports and imports using truncated time sample, excluding the crisis and post-crisis periods are reported in the Electronic Supplementary Material to this article (https://doi.org/10.1057/s41294-017-0036-2).
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Acknowledgements
We are grateful for helpful comments and suggestions from Maja Ferjancic, Martin Schmitz, Fernando Zarzosa, the editor, Paul Wachtel, and four anonymous referees. We also thank participants at the 2015 Slovak Economic Association Meeting in Košice, the 2016 INFER workshop in Bratislava, the 2016 European Trade Study Group in Helsinki, the 2016 Vienna Investment Conference, Center for Social and Economic Research (CASE) 25th Anniversary Conference in Warsaw and an ECB seminar for useful discussions and Giovanni Palmioli for his research assistance in early stages of this project. This article should not be reported as representing the views of the European Central Bank (ECB) and/or European Commission (EC). The views expressed are those of the authors and do not necessarily reflect those of the ECB and/or the EC.
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Buelens, C., Tirpák, M. Reading the Footprints: How Foreign Investors Shape Countries’ Participation in Global Value Chains. Comp Econ Stud 59, 561–584 (2017). https://doi.org/10.1057/s41294-017-0036-2
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DOI: https://doi.org/10.1057/s41294-017-0036-2