D4.4 GVCs in a CGE model


D4.4 GVCs in a CGE model

Executive Summary

The main objective of this deliverable is to conduct a trade policy experiment and use this experiment to test the GVC module created under task T4.5. We run this policy experiment with two versions of the Modular Applied GeNeral Equilibrium Tool (MAGNET) – the standard version and the GVC version. We compare simulation results from the two different model versions to see whether the same policy change produces similar results in the two model versions or to what extent these results differ. For this modelling exercise, we employ the MAGNET CGE model, which has been
upgraded to become compatible with GTAP model version 7 and its database version 10.1 with 2014 as the base year. We prepare a model aggregation which contains 51 commodities/sectors and 16 world regions. 

The policy experiment we conduct reflects a hypothetical decoupling between the EU and China, with policy changes implemented from the EU perspective such that China’s export to the EU market would become more difficult and costly. To simplify this experiment, we do not consider potential retaliatory measures implemented by China, which will naturally complicate the situation and analysis. The experiment targets the EU’s products with an above-average import share from China implying that a total of 17 commodities which exceed the average import share of 14% from China will be targeted. For these targeted commodities, the EU’s import share from China will be halved to reflect a trade measure imposed by the EU which is implemented as a non-tariff measure in terms of an iceberg trade cost. The iceberg trade cost is imposed at the importerspecific GTAP trade level rather than the agent-specific MRIO trade level to make the two model versions comparable.

Given the parameter values, the two model versions’ results are similar and consistent across major indicators. With the imposed import shock, the EU’s domestic production and import (from exporters other than China) for the targeted commodities will increase while the EU’s export will decline to meet domestic demand. The EU’s overall import, export and production in non-targeted commodities also decline due partly to a supply chain effect reflected through input-output linkages, and partly to a resource reallocation to boost the domestic supply of the targeted commodities.

Both model versions project that the EU may experience a slightly greater decline in GDP than China, mainly because China’s share of the EU’s import in the targeted commodities is greater than the EU’sshare of China’s export in these commodities. The import to production ratio in the EU is also higher than the export to production ratio in China for these commodities. 

This makes it relatively harder for the EU to absorb the import shock domestically and via reshoring.
Global supply chains were reshuffled in response to this trade shock, with similar projections in both model versions, though only the GVC version allows for a proper assessment of the changes in the value-added structure of gross trade flows. While benefiting from China’s expansion in the export and the EU’s expansion in the import of the targeted commodities, regions differ in their respective production responses as this depends on their trade ties with the EU and China. Some exporters may benefit from increased demand from the EU; others may be hurt by the increased competition from Chinese exports displaced from the EU market. 

Our GVC analysis shows that the decrease in the EU’s imports from China affects both the value-added originating from China and the EU’s domestic value added re-imported from China. The EU’s export of the targeted commodities also declines in response to the import shock. The value added decomposition suggests that this export decline is more than proportionally driven by the input-output induced supply chain effect – less imported input causes less exported output, and this effect dwarfs the direct trade-off between exports and domestic demand.