Appendix I. Further details about the Dynamic Factor Analysis
Appendix II. Further details on the data
Appendix III. Robustness for Econometrics—Using four Periods
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We are using annual data because most of the (value added) trade variables are available at the annual or lower frequency. We also focus on the past two decades because data on many relevant variables were not available earlier for many emerging economies.
Consequently, these authors transformed the dependent BCS variable so that it is not bounded between -1 and 1.
The China supply-chain economies are identified based on the intensity of their trade linkages (in value added terms) with China. See below.
For simplicity and focus, we mainly focus here on results for output even though the model includes consumption and investment.
Note that vertical trade intensity could alternatively be defined as the ratio of (the sums of each country’s) foreign value added to (the sums of) GDP, in line with the definition of trade intensity above. However, the trade and vertical trade intensity variables would then be collinear and could not be included simultaneously in the regressions. For this reason, controlling for trade intensity, we instead assess any additional effect of vertical (versus non-vertical) trade through a variable that is the ratio of (the sums of) foreign value added to (the sums of) domestic value added. That said, alternative vertical trade variables were tried in the regressions, without any of them turning out to be statistically significant and robust.
Upstream vertical integration with China is defined as foreign value added embedded in country i’s exports that comes from China. Downstream vertical integration with China is defined as foreign value added embedded in China’s exports that come from country i. Both upstream and downstream vertical integration indicators are computed as a percent of country i’s GDP.
Given that intra-industry trade and trade specialization correlation are more structural in nature and less susceptible to reverse causality, their contemporaneous values are used.
We also do not report alternative regressions using alternative definitions for the vertical trade variable; none of these turned out to show statistically significant and robust effects.
The coefficient of the banking integration interaction term suggests that the sign of the effect of banking integration on BCS changes in crisis times, becoming positive (-0.00862 + .397 = .39).
A more parsimonious model was also estimated featuring only the interaction between trade intensity and the GFC (year 2009 time dummy). Results, in particular for interaction terms, were largely similar to those obtained when the full set of time dummies is included.
All models include controls for time trend and global shock including factors such as global oil prices and the VIX.
Even though the baseline model allows for serial correlation in the error term