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We thank Ashoka Mody for insightful comments and valuable suggestions. We also would like to thank Céline Allard, Ansgar Belke, Rupa Duttagupta, Felix Huefner, Irina Tytell, and Francis Vitek, participants in the “Germany in an Interconnected World Economy” conference (Berlin, 2011) and in the Graduate Institute of International and Development Studies internal seminar (Geneva) for useful discussions and comments; and Susan Becker for excellent research assistance.
While data availability constrains the inclusion of China and other emerging countries in the sample, we indirectly attempted to test this hypothesis in an earlier version of the study by including exports to China, to developing Asia, and to the world as additional control variables. Those variables were not statistically significant and the results remained largely unchanged, suggesting that the sample countries already capture the bulk of relevant global demand shocks for the period under consideration (1975–2010).
See, for example, Helbling et al. (2007), Arora and Vamvakidis (2006), Bayoumi and Swiston (2009), and Swiston (2010). For an example of similar approaches applied to the case of China spillovers, see Arora and Vamvakidis (2010).
For the latter, see Bayoumi and Swiston (2009). This study examines the extent of spillovers across industrial regions including the U.S., the euro area, Japan, and an aggregate of small industrial countries, using VARs of growth across the four regions.
Another possibility is to use model-based simulation analysis. See, for example, the analysis based on structural estimated macro models using panel unobserved components estimation as suggested by Vitek (2009 and 2010).
Cross-border trade weights are generally used to estimate the country-specific aggregate foreign variable, although one study uses annual bank lending exposures over 1999–2007 (Galesi and Sgherri, 2009).
Alternative control variables, including the oil and non-oil commodity price indices, U.S. and German short-term and long-term interest rates, U.S. investment grade and high-yield credit spreads, German corporate bond spreads, U.S. and German real equity prices, world trade, and Asia trade, were also included as a robustness check in an earlier version of the analysis. However, none of these control variables except the U.S. credit spreads and, to a lesser extent, U.S. real equity prices were significant and their inclusion left the results unchanged. The impact of the U.S. credit spread however becomes insignificant when included in addition to the 2008–09 crisis dummy, suggesting that this variable is essentially a proxy for the global financial crisis.
The rule will depend on whether the dependent variable and the shock are a level variable or a growth rate.
This is likely to underestimate the relevance of the third country effects since it is still possible that third countries can have a positive feedback to country i and via this to country k. In practice these are very small.
For all EMU members the RoW shock is identical while for all RoW members the EMU shock is identical.
In particular, we look at the average response to an EMU and a non -EMU shock ordering once
Maybe unsurprisingly, this turns out not to hold and the aggregation bias causes responses to be more pronounced (Imbs et al. 2005). Results are discussed in more detail in the respective section.
This holds also true when controlling for re-exports.
In all cases, except for Greece, Spain, Austria, and the U.K., which also have significant exposures to the rest of the world (excluding the U.S., Japan, and Europe) and Switzerland (which has the largest single exposure to the U.S.), European developed countries are the single largest source of banking exposures.
While estimates from the smaller model are directly obtained for “EMU” and “Non-EMU” shocks, the corresponding values from the baseline VAR (large model) is obtained by weighting the responses to the single countries’ shocks which constitute the EMU and the non-EMU group in the country specific VARs.
However, it should be noted that the small country-specific VARs overestimate the impact, due to the aggregation bias which tends to increase the persistence of the shocks and thus overestimate the response.
While the subsequent deflation of the property bubble and private sector deleveraging has resulted in negative dynamic contributions of Spain to other countries’ growth during the 2008-09 global recession, we find that on average over the long run Spain has been one of the major sources of positive growth spillovers to other countries especially in Europe (see section IV.B). However, the potential positive impact of Spain in future episodes could be lower than suggested by historical results if the ongoing process of unwinding of Spain’s imbalances is protracted and undermines durably Spain’s growth prospects.
The average (adjusted) R-squared value of the reduced form equations for the baseline model is around 0.6 (0.4). Including the crisis dummy implies an increase by 0.04 in explanatory power in both cases.
While the cyclical contributions of US and Japan growth dynamics on other countries are generally small (red and green bars, respectively), both countries provide the bulk of the long-run growth rate support (gray bar).
In an earlier version of the paper, we attempted to test for the relevance of financial transmission channels by following the approach of Bayoumi and Swiston (2009), i.e. by including global financial variables (such as the US equity prices, interest rates, and US and German credit spreads) as additional control variables. The US credit spread was found to be the most significant variable, with an impact similar to that of including the crisis dummy (i.e. reducing estimated outward spillovers from the US and other large countries); however, the spread variable had not statistically significant effect once the crisis dummy is also included, suggesting that this channel of transmission is only relevant during times of crisis.
Although the VAR modeling framework does not allow testing directly for asymmetry in the pattern of spillovers, the importance of third country effects during times of financial distress could explain why negative spillovers originating during a crisis tend to be empirically larger than either positive or negative spillovers outside of crisis times: unlike “normal” spillovers, “crisis” spillovers tend to be amplified to a greater extent by confidence and asset price effects.
See Vitek (2010) for model simulation-based evidence of strong transmission of supply shocks via non-trade channels in a monetary union.
Note that this concept is not identical to whether a country is a net exporter or net importer, since it refers to the change in the net trade position rather than the level.
The relationship holds also when excluding the crisis dummy. Regressing the size of the outward spillover on a constant, the log size of the country, and the correlation between GDP growth and the external contribution to growth yields a positive significant effect for the former and a negative significant effect for the latter. Increasing the size of the country by 10 percent increases the outward spillover by 0.1 percentage points and reducing the correlation of external demand and GDP growth from +0.5 to -0.5 increases the size of outward spillovers by 0.14 percentage points, The smallest four counties are excluded from the graph. While Finland confirms the pattern, Greece, Portugal and Ireland are too small to generate significant growth spillovers.