Abstract
This study examines the trade effects of the Eurasian Economic Union on global production sharing. We use a panel dataset of bilateral exports of intermediate goods, parts and components and final assembly for 12 Eurasian countries with 28 partners for 2000–18. We estimate a gravity model using the Poisson Pseudo Maximum Likelihood method to mitigate zero trade values and heteroskedasticity issues. Our analysis provides new empirical evidence on significant net trade creation effects of 111% in intermediate goods exports due to EAEU formation. Our findings also highlight that a substantial share of the increase in intermediate goods exports originates from trade creation in final assembly exports while parts and components show net trade diversion effects. Further, a country-level analysis reveals that the trade effects of EAEU are heterogeneous across all the members, with Armenia and the Russian Federation benefiting the most and the Kyrgyz Republic benefiting the least from the EAEU formation. Our study has important policy implications on promoting production sharing in the Eurasian region and hence remains of interest to policymakers.
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Source: Author's calculations based on UN Comtrade database
Source: Author's calculations based on World Integrated Trade Solution (WITS) database
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Notes
We define intermediate goods trade as 58, 59, 61, 62, 65, 66, 69, 71, 72, 73, 74, 75, 76, 77, 78, 79, 81, 82, 84, 87, 88, and 89 of the SITC Rev.3. It is to be noted that Athukorala (2011) and Athukorala et al. (2017) define SITC 71, 72, 74, 75, 76, 77, 78, 79, 87, and 88 as the core categories where intermediate goods trade is concentrated. We expand the definition by considering other potential product categories contributing to the intermediate goods trade. The rationale behind the expansion is that the definition of parts and components at the SITC Rev.3 5-digit classification corresponds to all the 22 product categories mentioned above.
Refer to Athukorala (2011) for the list of parts and components identified from SITC Revision 3.
Analyses on Eurasia’s world trade, ECU’s world trade and EAEU’s world trade are not reported. They are available upon request.
Year 2019 is excluded from the analysis due to non-availability of data.
We use the tariff data on machinery and transport equipment to represent global production sharing. Empirical literature argues that global production sharing and related network trade is highly concentrated in the machinery sector (See Hayakawa and Yamashita, 2011).
Simple average tariff for the region is calculated from simple averages at the country level. First, we calculated each country’s average bilateral tariff for 2000–18. Next, we averaged the tariffs for each year. We have modified the definition of simple average of simple averages reported by the UNCTAD Statistics on import tariffs. See https://unctadstat.unctad.org/wds/TableViewer/summary.aspx for further details.
We replicated the analysis for parts and components and final assembly and the inferences are qualitatively similar. They are available upon request.
Yang and Martinez-Zarzoso (2014) captured trade effects using three different dummy variables, for intra-bloc trade effects, export diversion and import diversion, following the method employed in Endoh (1999). The use of three dummy variables enables us to test whether the creation of an economic integration agreement facilitated international trade among the member countries at the expense of non-member countries.
The construction of RER is detailed in the Appendix.
The data set used is available, on request, for those who wish to replicate the results of this research.
Marginal effects of PPML estimates are obtained following the formula. (exp(coefficient) − 1) *100 = Marginal effects. For instance, [exp (0.648) − 1] *100 = 91.17.
The average treatment effect of EAEU is obtained by adding all the significant coefficient values of column 1 and 4. The marginal effects are calculated using the formula mentioned in footnote 1.
We have not considered the anticipatory effects on the extra-bloc trade in the analysis.
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We gratefully appreciate the anonymous referees and the editor of the journal of Eurasian Economic Review for their constructive comments and suggestions that enhanced the quality of this paper.
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Appendix
Appendix
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1.
List of countries
Eurasian Countries: Armenia, Belarus, Kazakhstan, Kyrgyz Republic, Russian Federation, Azerbaijan, Georgia, Moldova, Tajikistan, Turkmenistan, Ukraine, Uzbekistan.
Partner countries: Austria, Belgium, Canada, China, Czech Republic, Estonia, Finland, France, Germany, Hungary, India, Italy, Japan, Korea Republic, Lithuania, Netherlands, Poland, Romania, Slovakia, Spain, Sweden, Switzerland, Turkey, Ukraine, United Arab Emirates, United Kingdom, United States, Viet Nam.
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2.
Real bilateral exchange rate construction
We obtain the nominal exchange rates in local currency per US$, period average, from International Financial Statistics of the International Monetary Fund. We use the Consumer Price Indices to adjust for domestic price fluctuations, obtained from UNCTAD, to adjust for domestic price fluctuations. Using them, we construct the real bilateral exchange rate as follows.
Following Florensa et al. (2015), we expect that an increase in this variable is indicative of depreciation in the exchange rate and improvement in the exports.
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Vasudevan, S., Babu, M.S. Global production sharing and trade effects: an analysis of Eurasian Economic Union. Eurasian Econ Rev 11, 633–665 (2021). https://doi.org/10.1007/s40822-021-00179-0
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DOI: https://doi.org/10.1007/s40822-021-00179-0
Keywords
- Eurasian economic union
- Trade effects
- Global production sharing
- Parts and components trade
- Gravity model
- Poisson pseudo maximum likelihood