Appendix 1 - Decomposition of the World Trade Elasticity
Define the long-run world income elasticity of trade, σW, as the percentage change in total world imports (or exports) in volume terms (mW) over the percentage change in real world income (yW), where mW and yW can be interpreted as the equilibrium levels of imports and income, respectively.22 That is,
We first show that the world trade elasticity can be decomposed as a weighted average of the elasticities of different trade categories (e.g. goods and services). To keep things simple, define Z = Z1 + Z2 and ΔZ = ΔZ1 + ΔZ2, where Z = mW, yW and 1 and 2 are the two trade categories (the extension to n trade categories is straightforward and is omitted).
We can write
This decomposition indicates that the world trade elasticity can decline for two reasons. First, for given trade weights, the elasticity is lower if the responsiveness to GDP of goods trade and/or services trade decline. Second, for given goods and services trade elasticities, the world trade responsiveness to GDP is lower if the share of world imports of the trade category with lower elasticity increases over time.
We next obtain the world trade elasticity as a weighted average of the elasticity of regions’ (or, equivalently, countries’) imports to their own income. Again, for simplicity, we focus on two regions, denominated as region 1 and 2.
where σi is the elasticity of region i’s imports to its own income. This elasticity is weighted by region i’s share in world imports and by the elasticity of region i’s income to world income (i.e. the growth rate of region i relative to world growth).
This decomposition indicates that a decline of the world trade elasticity can be explained by three factors. A first element can be a decline in the elasticity of a region’s imports to its own income. A second factor can be the increasing import share of a region with lower trade elasticity. Finally, a third element is the increasing relative growth of a region with lower trade elasticity.
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International Monetary Fund;
The authors are grateful for comments from seminar participants at the IMF, the World Bank, the 2014 Villa Mondragone International Economic Seminar and the Third IMF/WB/WTO Trade Workshop, and to Erhan Artuc, Tam Bayoumi, Chad Bown, Ruo Chen, Allen Dennis, Romain Duval, Hubert Escaith, Simon Evenett, Martin Kaufman, Ayhan Kose, Pascal Lamy, Franziska Ohnsorge, Alberto Osnago, Mika Saito and especially Silvia Sgherri for many useful suggestions. Research for this paper has been supported in part by the governments of Norway, Sweden, and the United Kingdom through the Multidonor Trust Fund for Trade and Development, and by the UK Department for International Development (DFID). The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the IMF or the World Bank, or those of the Executive Directors of the IMF or the World Bank or the governments they represent, or any of the aforementioned individuals.
For example, as we show below, Chinese exporters are now using more domestically produced inputs than imported inputs; the share of Chinese imports of parts and components in total exports has decreased from 60 percent in the mid-1990s to 35 percent today.
This terminology borrows from Eric Hobsbawm’s characterization of the “long 19th century” as the period between the years 1789 and 1914.
Recursive estimation tests (not presented in the paper for brevity) confirm these results.
Note that residuals are always found to be stationary. Moreover, the Breusch-Godfrey LM test generally accepts the null hypothesis that there is no serial correlation in the residuals of the linear regression. These results further justify the use of the ECM specification. While we always perform these tests and report them in key tables, we do not discuss them in the text as findings are broadly supportive of the model specification used.
We do not have quarterly data for 1970-1990. The regression analysis using quarterly data provides results for the period 1991-2000 that are in line with the findings for the period 1986-2000. The results from the quarterly and the yearly analysis of the 2000s are substantially the same, as expected.
Formal tests confirm that there is a significant structural break between the 1990s and the 2000s (both, pre and post-Great Recession).
Several factors could in principle disproportionately affect trade. For instance, deleveraging in advanced economies impacts durable goods (that are more trade intensive) to a larger extent than non-durable goods. Confidence about future prospects may have a similar effect on trade, as it also disproportionately affects demand of durables.
The cyclical effects can be further decomposed into the two offsetting factors: the impact growth and the speed of adjustment. The first term is given by
These numbers are the product of their share in world imports and their growth relative to world growth (see Appendix 1).
We ran country regressions both including and excluding the real effective exchange rate (REER) and results do not change much. In the tables, we report coefficients for the regressions without the REER.
Calculating these elasticities for China and the United States gives similar results, with the one exception that the elasticity of service trade in China is lower in the 2000s than in the long 1990s.
While the focus here is on the long-run trade elasticity, one should also expect a discrepancy between the short and the long-run dynamics of trade and income data. The persistently high short-run elasticity for the 2000s documented in Section III may reflect the fact that the impact of a GDP shock is larger in a world where global supply chains are more developed. The literature on the trade collapse discussed several mechanisms through which vertical specialization may increase the short-term responsiveness of trade to GDP -e.g. if expenditure declines more in vertically specialized sectors (Bems et al., 2011), if there are inventory –also called bullwhip- effects (Altomonte et al., 2012), or if there is re-nationalization of production chains (Buono and Vergara-Caffarelli, 2013).
Similarly, we find evidence that the elasticity of durables trade has decreased from 2.7 in the long 1990s to 0.8 in the 2000s. This is consistent with the changing structure of global supply chains, which are more concentrated in complex goods such as durables relative to non-durable sectors (Ferrantino and Taglioni, 2014). For brevity, these regression results are not reported.
WTO (2014 a and b) also investigates the evolution of China’s position in global value chains. They find that foreign inputs contained in China’s exports increased by 13.8 percent between 2000 and 2009 (“Evolution of GVC participation and its components, selected Asian economies,” in WTO 2014b) and that between 1995 and 2008 China’s position becoming more downstream (Figure C.7 in WTO 2014a). The apparent difference from our conclusions arises first of all because the WTO figures include imports not just of parts and components but also of fuel and raw materials. Since the prices of commodity inputs increased significantly in the 2000s, lumping together components and raw materials may create the impression that China’s exports became more import dependent even though it was reducing its reliance on imported parts and components. Despite this difference, the data on which the WTO charts are based, and to which we were given access, show that in fact China became more upstream since 2005 and that foreign inputs contained in exports actually declined by 3 percent after 2005.
These changes do not mean that China is turning its back on globalization. As discussed in Kee and Tang (2014), the enhanced availability of inputs domestically is in part linked to growing foreign direct investment in these industries. Moreover, there may be a geographical dimension to these changes, with China’s coastal regions beginning to source relatively more from the Chinese interior, because transport and communication costs have declined more sharply with the interior than with the rest of the world. Trade integration may now be taking the form of greater internal trade than international trade, which is captured by official statistics.
Data on world domestic value added and foreign value added in gross exports from the OECD-WTO dataset are only available starting from 1995 and for selected years. Here we use a time series that was created by Duval et al. (2014) by interpolating the OECD-WTO data. Further details can be found in the technical appendix of Duval et al. (2014).
Boz et al. (2014) provide evidence consistent with this hypothesis. They use the model estimates of Bussiere et al. (2013) that are based on an “import-intensity adjusted” demand, which gives higher weight to components of demand with higher import content such as investment. They show that the model predictions are close to actual trade growth for a set of advanced economies during the global trade slowdown.
The Global Trade Alert initiative considers a broader range of policy instruments relative to the WTO and has documented a larger number of protectionist measures since the crisis (Evenett, 2014). This count, however, includes both trade restrictive and trade promoting measures, particularly export subsidies and various forms of fiscal incentives to exporting firms, with potentially contrasting effects on the volume of trade.
As discussed in the paper, however, a slower pace of trade liberalization in the 2000s relative to the long 1990s may contribute to explain the elasticity in the latter period.
Differently from the main text, we use subscripts to differentiate aggregate variables from their components.