Value-added in a Computable General Equilibrium model with oligopolistic competition: drivers and implications of sourcing by agent in the MIRAGE-e model

Cecilia Bellora and Jean Foure

No 332942, Conference papers from Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project

Abstract: The dramatic increase in merchandise world trade since the early 90s has been partially driven by the development of global value chains. Production processes have been fragmented and each stage localized according to comparative advantages. These global value chains are now more and more analysed and the localisation of value added is taken into account by policymakers to determine trade policies. To be fully consistent with the different initiatives that have been launched in order to improve the available statistics on trade and to take full advantage of the refinement of Input Output (IO) databases in policy analysis, general equilibrium models should adapt their modelling assumptions, in particular on trade behaviours, and their outputs. For instance, demand for imports can be differentiated by agent (final consumer or firms), allowing to account for the differentiated tariffs they face due to the aggregation of different tariff lines. We therefore develop a new version of the multi-country, multi-sector global computable general equilibrium model MIRAGE that integrates these features.They allow to trace foreign value added both in trade flows, domestic consumption and domestic production. Using the MAcMap HS-6 database on tariff protection, the Kee et al. (2009) database on ad valorem equivalents of non-tariff measures and the ImpactEcon MRIO version of the GTAP database developed by ImpactEcon, we run a simple policy simulation (uniform reduction in worldwide tariffs) in presence of oligopolistic competition and then provide a detailed analysis of the results, comparing them with the output of the standard version of the model, and identifying the drivers of the differences. We also apply the decompositions developed by Koopman et al. (2014) and Wang et al. (2013) to better take advantage of the new features of the model.

Keywords: Research; Methods/Statistical; Methods (search for similar items in EconPapers)
Date: 2018
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