Russian Roulette at the Trade Table: A specific factors CGE analysis of an agri-food import ban
Dudu, H., Boulanger, P., Ferrari, E. y Philippidis, G. (2016) Journal of Agricultural Economics, v67 (2), pp272-291
In the summer of 2014 Russia imposed a ban on most agri-food products from
countries enforcing Ukraine-related sanctions against Russia. We use a specific
factors computable general equilibrium (CGE) model to simulate the short-run
impact of this retaliatory policy. The baseline is carefully designed to isolate the
impacts of the ban on the European Union (EU), Russia itself and a selection of
key trade partners. The modelling of the ban follows a novel approach, where it is
treated as a loss of established trade preferences via reductions in consumer utility
in the Armington import function. Not surprisingly, the results indicate that Russia
bears the highest income loss (about €3.4 billion) while the EU recovers part of its
lost trade through expansion of exports to other markets. An ex-post comparison
between simulation results and observed trade data reveals the model predictions to
be broadly accurate, thereby validating the robustness of the modelling approach.
In the summer of 2014 Russia imposed a ban on most agri-food products from
countries enforcing Ukraine-related sanctions against Russia. We use a specific
factors computable general equilibrium (CGE) model to simulate the short-run
impact of this retaliatory policy. The baseline is carefully designed to isolate the
impacts of the ban on the European Union (EU), Russia itself and a selection of
key trade partners. The modelling of the ban follows a novel approach, where it is
treated as a loss of established trade preferences via reductions in consumer utility
in the Armington import function. Not surprisingly, the results indicate that Russia
bears the highest income loss (about €3.4 billion) while the EU recovers part of its
lost trade through expansion of exports to other markets. An ex-post comparison
between simulation results and observed trade data reveals the model predictions to
be broadly accurate, thereby validating the robustness of the modelling approach.