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

Abstract

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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Notes

  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.

References

  1. Allard C (2009) Competitiveness in Central-Europe: what has happened since EU accession? Working paper, IMF

  2. Armington PS (1969) A theory of demand for products distinguished by place of production. IMF Staff Pap 16:159–178

    Article  Google Scholar 

  3. Balassa B (1965) Trade liberalisation and revealed comparative advantage1. Mancheser Sch 33:99–123

    Article  Google Scholar 

  4. Barro RJ (1991) Economic growth in a cross section of countries. Q J Econ 106:407–43

    Article  Google Scholar 

  5. Bayoumi T (1998) Estimating trade equations from aggregate bilateral data. CEPR Discussion Papers 1970, C.E.P.R. Discussion Papers

  6. Bems R, Johnson RC (2017) Demand for value added and value-added exchange rates. Am Econ J Macroecon 9:45–90

    Article  Google Scholar 

  7. Benkovskis K, Wörz J (2012) Non-price competitiveness gains of Central, Eastern and Southeastern European countries in the EU market. Focus Eur Econ Integr 3:27–47

    Google Scholar 

  8. Benkovskis K, Wörz J (2016) Non-price competitiveness of exports from emerging countries. Empir Econ 51:707–735

    Article  Google Scholar 

  9. Bobeica Elena CS, Tkačevs O (2016) The role of price and cost competitiveness for intra- and extra-euro area trade of euro area countries. Working Paper Series 1941, European Central Bank

  10. Brock WA, Durlauf SN (2001) Growth empirics and reality. World Bank Econ Rev 15:229–272

    Article  Google Scholar 

  11. Ca’Zorzi M, Schnatz B (2007) Explaining and forecasting euro area exports: which competitiveness indicator performs best? Working Paper Series 0833, European Central Bank

  12. Christodoulopoulou S, Tkacevs O (2013) Is there any preferred competitiveness indicator in explaining foreign trade in euro area countries? mimeo, ECB

  13. Ciccone A, Jarocinski M (2010) Determinants of economic growth: will data tell? Am Econ J Macroecon 2:222–46

    Article  Google Scholar 

  14. Danquah M, Moral-Benito E, Ouattara B (2014) TFP growth and its determinants: a model averaging approach. Empir Econ 47:227–251

    Article  Google Scholar 

  15. Delgado M, Ketels C, Porter ME, Stern S (2012) The determinants of national competitiveness. NBER Working Papers 18249, National Bureau of Economic Research, Inc

  16. di Mauro F, Lopez-Garcia P (2015) Assessing European competitiveness: the new CompNet microbased database. Working Paper Series 1764, European Central Bank

  17. Doppelhofer G, Weeks M (2009) Jointness of growth determinants. J Appl Econom 24:209–244

    Article  Google Scholar 

  18. Durlauf SN, Johnson PA, Temple JR (2005) Growth econometrics. In: Aghion P, Durlauf S (eds) Handbook of economic growth, Chap 8, vol 1. Elsevier, Amsterdam, pp 555–677

    Google Scholar 

  19. European Commission (2014) European competitiveness report 2014. Helping firms grow. Technical report, European Commission

  20. Feldkircher M, Zeugner S (2009) Benchmark priors revisited: on adaptive shrinkage and the supermodel effect in Bayesian model averaging. IMF Working Papers 09/202, International Monetary Fund

  21. Fernandez C, Ley E, Steel MFJ (2001a) Benchmark priors for Bayesian model averaging. J Econom 100:381–427

    Article  Google Scholar 

  22. Fernandez C, Ley E, Steel MFJ (2001b) Model uncertainty in cross-country growth regressions. J Appl Econom 16:563–576

    Article  Google Scholar 

  23. Giordano C, Zollino F (2016) Shedding Light on Price- and Non-price-competitiveness Determinants of Foreign Trade in the Four Largest Euro-area Countries. Rev Int Econ 24:604–634

  24. Jeffreys H (1961) Theory of probability, 3rd edn. Clarendon Press, Oxford

    Google Scholar 

  25. Joseph A, Osbat C (2016) How you export matters: the disassortative structure of international trade. Working Paper Series 1958, European Central Bank

  26. Karadeloglou P, Benkovskis K, Force CT (2015) Compendium on the diagnostic toolkit for competitiveness. Occasional paper, European Central Bank

  27. Karl A, Lenkoski A (2012) Instrumental variable Bayesian model averaging via conditional Bayes factors. arXiv:1202.5846

  28. Kass RE, Wasserman L (1994) A reference Bayesian test for nested hypotheses and its relationship to the Schwarz criterion. J Am Stat Assoc 90:928–934

  29. Koopman R, Powers W, Wang Z, Wei S-J (2010) Give credit where credit is due: tracing value added in global production chains. NBER Working Papers 16426, National Bureau of Economic Research, Inc

  30. Krugman P (1994) Competitiveness: a dangerous obsession. Foreign Aff 73:28–44

    Article  Google Scholar 

  31. Lenkoski A, Eicher T, Raftery A (2014) Two-stage Bayesian model averaging in endogenous variable models. Econom Rev 33:122–151

    Article  Google Scholar 

  32. Leon-Gonzalez R, Montolio D (2015) Endogeneity and panel data in growth regressions: a Bayesian model averaging approach. J Macroecon 46:23–39

    Article  Google Scholar 

  33. Ley E, Steel MF (2007) Jointness in Bayesian variable selection with applications to growth regression. J Macroecon 29:476–493

    Article  Google Scholar 

  34. Ley E, Steel MF (2009) On the effect of prior assumptions in Bayesian model averaging with applications to growth regression. J Appl Econom 24:651–674

    Article  Google Scholar 

  35. Ley E, Steel MF (2012) Mixtures of g-priors for Bayesian model averaging with economic applications. J Econom 171:251–266

    Article  Google Scholar 

  36. Liang F, Paulo R, Molina G, Clyde MA, Berger JO (2008) Mixtures of g priors for Bayesian variable selection. J Am Stat Assoc 103:410–423

    Article  Google Scholar 

  37. McGuirk AK (1987) Measuring price competitiveness for industrial country trade in manufactures. IMF Working Paper 87/34

  38. Moral-Benito E (2012) Determinants of economic growth: a Bayesian panel data approach. Rev Econ Stat 94:566–579

    Article  Google Scholar 

  39. Moral-Benito E (2015) Model averaging in economics: an overview. J Econ Surv 29:46–75

    Article  Google Scholar 

  40. Porter ME (1990) The competitive advantage of nations. Free Press, New York

    Google Scholar 

  41. Raftery A (1995) Bayesian model selection in social research. Sociol Methodol 15:229–272

    Google Scholar 

  42. Reinhart C (1995) Devaluation, relative prices, and international trade: evidence from developing countries. MPRA Paper 6974. University Library of Munich, Germany

  43. Reis JG, Farole T (2012) Trade competitiveness diagnostic toolkit. World Bank Group, Washington, DC

    Google Scholar 

  44. Sala-i-Martin X, Doppelhofer G, Miller RI (2004) Determinants of long-term growth: a Bayesian averaging of classical estimates (BACE) approach. Am Econ Rev 94:813–835

    Article  Google Scholar 

  45. Schwab K, World Economic Forum (2017) The global competitiveness report 2017–2018. Global competitiveness report, World Economic Forum

  46. Silgoner M, Steiner K, Wörz J, Schitter C (2013) Fishing in the same pool? Export strengths and competitiveness of China and CESEE in the EU-15 Market. Working Paper Series 1559, European Central Bank

  47. Stehrer R, de Vries GJ, Los B, Dietzenbacher H, Timmer M (2014) The world input-output database: content, concepts and applications. GGDC Research Memorandum GD-144, Groningen Growth and Development Centre, University of Groningen

  48. Verheyen F (2015) The role of non-price determinants for export demand. Int Econ Econ Policy 12:107–125

    Article  Google Scholar 

  49. World Bank (2013) Doing business 2014: understanding regulations for small and medium-size enterprises, 11th edn. World Bank Group, Washington, DC

  50. Zellner A (1986) On assessing prior distributions and Bayesian regression analysis with g-prior distributions. In: Goel P, Zellner A (eds) Bayesian inference and decision techniques: essays in honour of Bruno de Finetti. North-Holland, Amsterdam, pp 233–243

    Google Scholar 

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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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Keywords

  • Export shares
  • Competitiveness
  • Bayesian model averaging

JEL Classification

  • C23
  • C51
  • C55
  • F14
  • O52