Crisis severity and the international trade network - Marianna Endrész; Frauke Skudelny (European Central Bank (ECB), 2016)
In this paper we analyse the role of the international trade network for the strength of the global recession across countries. The novelty of our paper is the use of value-added trade data to capture the importance of trade network structure. We estimate with BMA techniques how far network indicators measuring interlinkages in terms of value added trade has explanatory power both for the length and the depth of the recent crisis once we control for pre-crisis macroeconomic fundamentals. Our main findings are that the macroeconomic control variables with the strongest explanatory power for the length and the depth of the crisis are the growth rates of credit and of the real effective exchange rate in the pre-crisis period and, though to a lesser extent, GDP and inflation growth over the same period and pre-crisis foreign exchange reserves. Government debt, the GVC participation index and net foreign assets have very little explanatory power in the BMA estimations. The models' performance increases when we introduce interaction terms of credit growth with other vulnerability measures. The results demonstrate that the coincidence of vulnerabilities matters a lot. Credit growth deepens the crisis mainly if accompanied with pre-crisis GDP growth or low reserves, while the crisis tends to be longer if credit growth has led to large leverage or the accumulation of net foreign liabilities. Finally, we find evidence that value added trade linkages have an impact on the severity of the crisis. While the increasing connectivity or openness of the country makes the crisis longer, the same characteristics of the neighbours makes it also deeper. The tendency to interact with already connected countries lowers or increases the impact of the crisis depending on the position of the country. Altogether we have mixed results on the direct trade channel, but we demonstrate the importance of network structure beyond the countries' own openness. In addition, we are also able to improve results by using gross value added instead of gross trade data.
Keywords: F14; C45; C52; C67; 330; Bayesian model averaging; crisis indicators; network indicators; value added trade; WIOD