1 Introduction

In the last four decades, international trade has become increasingly characterized by international production fragmentation (IPF). International product fragmentation means that the production of goods is split across several countries. A large body of literature has documented the large extent of international product fragmentation.1 In particular, Marcel P Timmer et al. (2015) documented that the foreign content incorporated in goods increased by 85 perc. between 1995 and 2005.2

Before international product fragmentation, goods were mainly produced within one country and therefore the value of bilateral trade flows (gross exports) was a good measure of the contributions of domestic inputs and of domestic production factors. However, with international product fragmentation goods are produced in several stages in multiple countries. Consequently, gross exports also include foreign content and may not correctly capture the contribution of domestic production factors to final output. Indeed, it has been documented in a large literature that the distribution of who produces for whom in the world economy differs strongly when we look at value-added trade instead of gross exports.3 To give an example, Koopman, Wang, and Wei (2014) show that the trade surplus of China with the USA is reduced by 41 perc. based on value-added trade compared to gross exports.

The international production fragmentation literature documented that the volume of trade flows changes significantly when we focus on the inferred domestic content instead of the observed gross exports. Consequently, the pattern of specialization of countries may also be sensitive to looking at value-added trade instead of gross exports. The seminal work of Ricardo showed that a country has a comparative advantage in producing and exporting those goods for which it has a relatively lower production cost compared to its trade partners.4 Gross exports include foreign contributions to the relative production costs. The pattern of revealed comparative advantage on the basis of gross exports may differ from the pattern of revealed comparative advantage based on the domestic content of trade. The first objective of this thesis is to evaluate whether the pattern of revealed comparative advantage changes significantly, when it is computed on the basis of domestic factors only.

A theoretically sound approach is necessary to analyze the pattern of revealed comparative advantage. The seminal contribution of Costinot, Donaldson, and Komunjer (2012) showed that the ranking of relative exports maps to the underlying ranking of relative productivity in a setup with multiple countries and multiple industries. Further, the authors showed that the pattern of revealed comparative advantage could be retrieved from the bilateral trade matrix. Specifically, they showed that a two-step estimation procedure allows obtaining the pattern of revealed comparative advantage.

To compute the pattern of revealed comparative advantage, I use two sources of value-added trade and gross exports data, namely the WiOD and the TiVA.5 The TiVA data includes 61 countries of the following groups: the OECD, the EU 28, the G20 and most of East and South-East Asian countries and several South American countries. The WiOD includes the countries of the EU 27 and 14 other major economies. Both datasets provide information on the primary, manufacturing and service industries according to the ISIC Rev. 3.1 industry classification. I assess the degree of divergence of revealed comparative advantage rankings obtained on the basis of gross exports and of value-added trade with two measures of association: Spearman’s \(\rho\) and Kendall’s \(\tau\).

My main results are as follows. Regarding the stability of the ranking of revealed comparative advantage, I find that the stability depends on the way I define value-added trade. There are two main definitions of value-added trade. Backward value-added trade extracts the contribution of the domestic supply chain to gross exports. Forward value-added trade extracts the domestic factor content of trade. I find that the pattern of specialization is substantially unchanged when I compare backward value-added trade to gross exports. But there are divergences when I compare gross exports to forward value-added trade.

I interpret my results as follows. The similarity of the ranking obtained for gross exports, and backward value-added trade indicates that the foreign content does not substantially change the pattern of relative production costs. The contribution of the domestic supply chain is sufficient to predict the ranking of comparative advantage. The dissimilarity of the ranking for gross exports and forward value-added trade trade indicates that comparative advantage according to the factor content of trade differs from comparative advantage according to the domestic supply chain.

Overall, I conclude that the pattern of comparative advantage based on gross exports captures the domestic content of trade. However, it does not capture very well the pattern of comparative advantage associated with the factor content of trade.

The second objective of this thesis is to assess the contribution of different countries to the propagation of shocks in the international trade network. A large literature has addressed this question on the basis of gross trade flows, but given the extent of international production fragmentation, it is important to construct this analysis on value-added trade rather than gross exports. The shift of focus is necessary because I want to correctly capture how shocks to domestic production factors diffuse through the international trade network.

A sound approach is necessary to assess the centrality of countries in the propagation of shocks through the network. Different metrics have been proposed to assess network centrality.6 I follow the approach of Acemoglu et al. (2012), who have shown that eigenvector is the theoretically sound approach.7 Acemoglu et al. (2012) analyze the centrality of different industries in the US economy. The unit eigenvector ranks every node according to its centrality in the national network. Elements of the eigenvector capture the contribution of each node when an extra dollar is added to the network (Spizzirri 2011).

I adapt this approach to my dataset that contains many countries and many industries. First, to illustrate the concepts of the international trade network and eigenvector centrality, I compute the unit eigenvector for total value-added, where each country is a node. Second, to analyse the variability of the ranking of countries according to their eigenvector centrality, I compute the unit eigenvector separately for each sector, where each country is a node and I obtain as many eigenvectors as there are sectors.

My results are as follows. First, I find that the ranking of countries according to eigenvector centrality highlights a core-periphery structure. The core consits of seven countries, of whom four are European economies: France, Germany, Great Britain, and Italy. The remaining countries of the core group are China, Japan, and the USA. Second, I find that the rank positions of the countries in the core vary noticeably across sectors. Third, I find that for any pair of countries, the ranking of relative eigenvector centrality maps into their pattern of revealed comparative advantage. This result is new in this literature.

I interpret the findings as follows. First, the variation of the rank positions of the most central countries at the country-industry level may indicate that countries’ sectoral ability determines their network centrality.
Second, the mapping of relative eigenvector centrality to the pattern of comparative advantage indicates that relative centrality may pick up relative sectoral ability. According to the theory of comparative advantage, an industry which is relatively more productive will contribute relatively more to the world production.
According to network theory, an industry with relative higher network centrality is relatively more important in terms of how many dollars it contributes to the total value of the network. Consequently, it may be expected that both measures capture the ranking of relative sectoral ability of any given pair of countries.

This thesis contributes to the literature that studies the pattern of revealed comparative advantage. In this literature, Costinot, Donaldson, and Komunjer (2012) derived a methodology to obtain the pattern of revealed comparative advantage and computed it on a dataset with 13 manufacturing industries in 21 countries for 1997. The characterization of structural revealed comparative advantage was extended to a larger dataset with 1018 products, 20 exporting countries and 70 importing countries in the period from 1995 till 2010 by Leromain and Orefice (2014). Both papers computed the revealed comparative advantage indicator on the basis of gross exports. I contribute to this literature by quantifying the degree of divergence between the pattern of revealed comparative advantage based on value-added trade and on gross exports for fifty-six countries in 2005. I further contribute by showing that the pattern of revealed comparative advantage obtained by implementing the methodology of (Costinot, Donaldson, and Komunjer 2012) can be alternatively obtained by constructing the ranking of relative sectoral centrality for any pair of countries. The latter approach is simpler because the ranking is directly obtained from value-added trade and does not rely on estimation.

This thesis is not the first to investigate the impact of international product fragmentation on countries’ specialization in world trade. Previous studies used the ad-hoc Balassa-Samuelson measure of revealed comparative advantage. In particular, two recent contributions compared the pattern of revealed comparative advantage based on value-added trade in line with the factor content of trade (forward value-added) and gross exports.
Wang, Wei, and Zhu (2013) examined the time profile of the pattern of revealed comparative advantage of the USA and China in the period from 1995 till 2011 for one manufacturing industry and one service industry. Koopman, Wang, and Wei (2014) compared the pattern of specialization for one manufacturing industry and one service industry in 2004. Both studies found that the pattern of specialization was significantly altered by switching to value-added trade.
On the other hand, Baldwin and Lopez-Gonzalez (2014) found that the pattern of specialization for eight manufacturing industries in thirty-eight countries was not altered significantly by changing from gross exports to backward value-added trade. I contribute to this literature by switching from the ad-hoc measure of revealed comparative advantage to the structural measure derived by Costinot, Donaldson, and Komunjer (2012).

The thesis is organised as follows. In chapter two I present the methodology to retrieve the revealed comparative advantage from trade flows. Moreover, I discuss the results of analyzing the degree of divergence of revealed comparative advantage based on value-added trade and gross exports. In chapter three I outline the network theory of trade and the concept of eigenvector centrality. Further, I present the results on the contribution of different countries to shock diffusion on the basis of value-added trade. I also compare the ranking of relative network centrality and the ranking of revealed comparative advantage. In chapter four I conclude.

References

Timmer, Marcel P, Erik Dietzenbacher, Bart Los, Robert Stehrer, and Gaaitzen J Vries. 2015. “An Illustrated User Guide to the World Input–Output Database: The Case of Global Automotive Production.” Review of International Economics. Wiley Online Library.

Koopman, Robert, Zhi Wang, and Shang-Jin Wei. 2014. “Tracing Value-Added and Double Counting in Gross Exports.” The American Economic Review 104 (2): 459–94.

Costinot, Arnaud, Dave Donaldson, and Ivana Komunjer. 2012. “What Goods Do Countries Trade? A Quantitative Exploration of Ricardo’s Ideas.” The Review of Economic Studies 79 (2): 581–608.

Acemoglu, Daron, Vasco M. Carvalho, Asuman Ozdaglar, and Alireza Tahbaz-Salehi. 2012. “The Network Origins of Aggregate Fluctuations.” Econometrica 80 (5): 1977–2016.

Spizzirri, Leo. 2011. “Justification and Application of Eigenvector Centrality.” Algebra in Geography: Eigenvectors of Network.

Leromain, Elsa, and Gianluca Orefice. 2014. “New Revealed Comparative Advantage Index: Dataset and Empirical Distribution.” International Economics 139: 48–70.

Wang, Zhi, Shang-Jin Wei, and Kunfu Zhu. 2013. “Quantifying International Production Sharing at the Bilateral and Sector Levels.” NBER Working Papers, no. 19677. National Bureau of Economic Research, Inc.

Baldwin, Richard, and Javier Lopez-Gonzalez. 2014. “Supply-Chain Trade: A Portrait of Global Patterns and Several Testable Hypotheses.” The World Economy, 1–40.

  1. Prominent papers in this literature are Johnson and Noguera (2012), Baldwin and Lopez-Gonzalez (2014), Marcel P Timmer et al. (2015). Johnson and Noguera (2012) measure the extent of international product fragmentation between 1970-2010 with an indicator of value-added trade to gross exports. Specifically, they show that international product fragmentation grows in the 1970s, is unchanged over the 1980s and grows at an extended speed since the 1990s.↩

  2. Marcel P Timmer et al. (2015) analyzed 560 final manufacturing products in 40 countries of which 27 were European countries.↩

  3. Some references in this literature are Daudin, Rifflart, and Schweisguth (2011), Koopman, Wang, and Wei (2014), Wang, Wei, and Zhu (2013), Baldwin and Lopez-Gonzalez (2014).↩

  4. Ricardo’s model studied the case of two countries and two goods. His results were extended to the case of a continuum of goods by Dornbusch, Fischer, and Samuelson (1977) and to any number of countries by Eaton and Kortum (2002).↩

  5. Details about the WiOD are provided in Marcel P Timmer et al. (2015). The TiVA database is a joint initiative of the WTO and the OECD. Details are provided in OECD-WTO (2015).↩

  6. Some references in this literature are Katz (1953), Bonacich (1972), Freeman (1978) and Jackson and Wolinsky (1996).↩

  7. Acemoglu et al. (2012) use the notion of influence vector constructed on the basis of the alpha-centrality (see (Bonacich and Lloyd 2001)). Alpha-centrality is very closely linked to eigenvector-centrality.↩