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The economic impact of the Russian import ban: a CGE analysis

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Abstract

The aim of this paper is to assess the economic impact of the Russian embargo from 7 August 2014 on certain agricultural food products from the EU, the USA, Norway, Canada and Australia. The effects of this economic sanction are analysed in the framework of a computable general equilibrium (CGE) model with a particular focus on bilateral and total exports, production and welfare. The detailed, based on real trade data, calibration of the model allows for an exact identification of the sectoral shares and prohibitive tariffs aggregated to match the CGE model’s sectoral level of aggregation. In addition, the paper carries on a validation exercise to compare the model’s predictions with real trade data developments. The modelling simulation results show that the impact of the ban on total exports of the EU, the USA, Norway, Canada and Australia is limited. Nevertheless at a disaggregate level there are sectors – ‘vegetables and fruits’, ‘other meat’ and ‘dairy products’ – which experience two digit percentage change declines.

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Notes

  1. See Russian government (2014.

  2. The Russian import ban was initially introduced for one year, however, the Russian government subsequently prolonged the ban two time and currently the ban is set to be in place until 31 December 2017, see Russian government (2016).

  3. For more information on the type of sanctions imposed by the EU, see European Council (2014).

  4. Detailed calculations for 2011 have been performed by the author for modelling purposes, as the latest GTAP database is based on the year 2011.

  5. Own calculations based on the Comext database, date of extraction: 27.11.2014.

  6. See GATT, Article XI for further reference.

  7. See WTO (2014).

  8. A decision on the WTO compliance of the import ban is expected in the beginning of 2016, see WTO (2015).

  9. Note that the EU sanctions against Russian had also an impact on the Russian stock returns, see Hoffman and Neuenkirch (2015). This impact however cannot be included in the CGE analysis in this paper due to modelling and database limitations.

  10. In this paper, ‘ban’ and ‘embargo’ are used as synonyms, as they are conceptually the same. The embargo, however, is in general imposed for political reasons (the US-Cuba embargo, for example) whereas a ban in response to health, environmental or health concerns, see also Panagiotou (2008).

  11. For an overview of econometric methods and importance of key explanatory variables in the econometric analysis of sanctions, see Hufbauer et al. (2007).

  12. For a more detailed description of the standard GTAP model, see Hertel (1997), Global Trade Analysis: Modelling and Applications, Cambridge University Press, Cambridge.

  13. Comext database, date of extraction, 27 November 2014.

  14. GTAP database version 9.

  15. GTAP database version 9.

  16. The fact that that extra-EU exports to Russia of the affected lines did not decline by 100% is most likely due to the fact that the whole month of August is included in the calculations above (as there is no daily Comext data) but the embargo was put in place on 7 August.

  17. As shown in Figure 1, extra-EU exports of the affected lines with and without exports to Russia surpassed pre-ban levels already in October 2014. Hence, this can be thought of as indication that the effect of the ban has faded away. Moreover, levels of the same exports values were smaller in October 2013 than in August 2013 providing an insight that its development is not due to seasonal effects.

  18. Note that it cannot be excluded that other factors such as a decrease in oil prices, shift in trade preferences etc. have impacted on EU exports of the affected products as well. Nonetheless, on the basis of these monthly statistics, it can be concluded that this decline is driven to a large extent by the Russian embargo.

  19. This ‘back of the envelope’ calculation should be regarded only as an approximation as firstly, much larger time series data is needed for disentangling the seasonal effect and secondly, in order to more precisely estimate the impact of the ban on total exports of these lines, the application of econometric techniques such as the difference-in-difference approach is required.

  20. Only the extra-EU exports were taken into account which showed a development exceeding what one would expect looking at the same destination markets in the year preceding the ban i.e. 2013.

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Acknowledgements

The author would like to thank Lucian Cernat, Fabio de Franceschi, Badri Narayanan, the 18th Annual Conference on Global Economic Analysis participants as well as colleagues in DG TRADE for valuable comments and fruitful discussions. The opinions expressed in this paper are the authors’ own and do not necessarily reflect the views and opinions of the European Commission.

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  1. Chief Economist Unit, DG TRADE, European Commission, Brussels, Belgium

    Zornitsa Kutlina-Dimitrova

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Kutlina-Dimitrova, Z. The economic impact of the Russian import ban: a CGE analysis. Int Econ Econ Policy 14, 537–552 (2017). https://doi.org/10.1007/s10368-017-0376-4

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