What drives export market shares? It depends! An empirical analysis using Bayesian model averaging


What drives external performance of countries? This is a recurring question in academia and policy circles. The factors underlying export growth are receiving great attention, as countries endeavoured to grow out of the crisis by increasing exports and as protectionist discourses take foot again. Despite decades of debates, it is still unclear what the common characteristics of countries that have a very good external performance are and, importantly, which ones policy makers can influence. We use Bayesian model averaging in a panel setting to investigate variables related to export market shares in 25 EU countries, considering a wide range of traditional indicators along with novel ones developed within the CompNet Competitiveness Research Network. We find that export market share growth is linked to different factors in the old and in the new member states, with one exception: for both groups, competitive pressures from China have strongly affected export performance since the early 2000s. In the case of old EU member states, investment, quality of institutions and available liquidity to firms also appear to play a role. For the new EU member states, labour and total factor productivity are particularly important, while inward FDI matters rather than domestic investment. Price competitiveness does not seem to play a very important role in either set of countries: relative export prices do show correlation with export performance for the new member states, but only when they are adjusted for quality.

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  1. 1.

    Real effective exchange rates that use consumer prices as deflator ensure comparability across countries, but contain an important share of non-traded consumption goods and services; producer price-based real effective exchange rate mainly considers tradable goods prices, but the underlying price measures are not fully comparable across countries (due to differences in composition and compilation); unit labour costs-based deflators have the drawback that they consider only one cost component and disregard capital-related and other costs, e.g. energy and commodities; the GDP deflator has also been considered in computing real effective exchange rates, but the underlying statistics can be subject to relatively large revisions.

  2. 2.

    Hence, we narrow down our definition to the international trade dimension, defining competitiveness as a measure of a country’s advantage or disadvantage in selling its products in international markets. See the OECD’s definition of competitiveness in international trade: http://stats.oecd.org/glossary/detail.asp?ID=399.

  3. 3.

    Arguably, export performance in goods and in services may be driven by different factors. However, separate data on services exports are not as available and reliable, so we look at total exports. Furthermore, the use of nominal export markets shares (instead of real market shares or real export growth) has a long tradition in the literature; see, e.g. the work of Armington (1969).

  4. 4.

    See, e.g. Durlauf et al. (2005), Moral-Benito (2012) and Danquah et al. (2014).

  5. 5.

    Our measure of tax burden, or ratio of tax collection against the national GDP, follows the definition used in the Stability and Convergence Programmes submitted by the EU member states.

  6. 6.

    The old EU member states group includes Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Netherlands, Portugal, Sweden, Spain and the UK. The new EU member states group includes Bulgaria, Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia and Slovenia.

  7. 7.

    The “random \(\theta \)” prior defines the hyper-parameters mean (a) and variance (b) of the beta distribution as follows: \(a=1\) and \(b=(K-E(\bar{m}))/E(\bar{m})\).

  8. 8.

    Computations are performed using the R-package BMS (Feldkircher and Zeugner 2009). Each sampling chain consists of one million iterations using 500,000 burn-in points.

  9. 9.

    Both effects are more clear-cut for old member states.

  10. 10.

    Inward FDI serves as an important source for domestic investments in new EU member states. Overall, the ratio of inward FDI to total gross fixed capital formation was close to 25% between 2002 and 2011, with especially high numbers observed for Hungary, Bulgaria and Estonia. Although the peak of FDI importance occurred just before the financial crisis (in 2007–2008), the share of FDI inflows in total investments remains high afterwards (close to 15%).

  11. 11.

    Red indicates a negative posterior mean and blue a positive one.

  12. 12.

    We also performed further robustness checks focused on measures of price competitiveness, specifically REERs calculated using value added weights and weights that take into account bilateral imports of intermediate goods. We find that the ordering of the top variables does not change and, maybe more indicatively, the PIP of these harmonized competitiveness indicators are very similar to those of the REER deflated by CPI both for new and old member states. Tables are available upon request.

  13. 13.

    Note that an alternative jointness measure is presented by Doppelhofer and Weeks (2009). We restrict our analysis to the jointness statistic by Ley and Steel (2007) as their measure is also well defined for cases where one of the regressors is included in all or none of the models.

  14. 14.

    The jointness analysis in this section refers to a model with country fixed effects only. Jointness results for our other model specifications are available upon request.

  15. 15.

    Note that it is not uncommon to find a high share of regressor pairs to display some degree of disjointness. For example, Ley and Steel (2007) find shares of 92.3% and 99.1% for the two data sets they analyse.

  16. 16.

    These indicators use detailed six-digit product level data (HS classification, about 5000 product categories), using as raw data the UN Comtrade database compiled by the UN Statistics Division. Such a high level of disaggregation allows going beyond the simple analysis of aggregate costs and market shares and gives an opportunity to assess important aspects as competitive pressures.


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Correspondence to C. Osbat.

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The authors would like to acknowledge helpful comments from two anonymous referees, from Marek Jarocinski (ECB) and Enrique Moral-Benito (Bank of Spain). We would also like to thank the participants in the ESCB Competitiveness Research Network meetings, where previous drafts of the paper have been presented. The opinions expressed here are the authors’ and they do not necessarily represent the views of the European Central Bank, European Commission or Latvijas Banka.

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Benkovskis, K., Bluhm, B., Bobeica, E. et al. What drives export market shares? It depends! An empirical analysis using Bayesian model averaging. Empir Econ 59, 817–869 (2020). https://doi.org/10.1007/s00181-019-01727-z

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  • Export shares
  • Competitiveness
  • Bayesian model averaging

JEL Classification

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  • C51
  • C55
  • F14
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