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Trade in the enlarged European Union: a new approach on trade potential

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Abstract

This paper focuses on the trade potential of manufactured exports from countries belonging to the enlarged EU (EU25) to groups of countries of that economic area in 2002. We note that previous results on trade potential, based on the estimation of a gravity model, may be invalid. Thus, we propose a correct approach based on the Poisson Pseudo-Maximum Likelihood estimator and the calculation of confidence intervals with the Delta method. The gravity model includes fixed effects to capture bilateral trade specificities between country groupings. We conclude that CEEC as a group had apparently exhausted the possibilities for export expansion in the EU25, unless dynamic changes were to take place. However, several of the remaining EU25 countries had not yet reached their export potential to the EU25 markets, including to the CEEC as a group.

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Notes

  1. Note that other authors have assessed the trade potential of the CEEC and the EU members by including the CEEC in the sample of countries under consideration (in-sample approach) as the period of their analysis approached the full adoption by the CEEC of the EU trade rules (see, for instance, Baldwin 1994; Nilsson 2000; or Caetano and Galego 2005), while other authors have opted for excluding the CEEC from the regression analysis (such as, for instance, Wang and Winters 1992; Hamilton and Winters 1992; or Brülhart and Kelly 1999).

  2. See, for instance, among the pioneers, Wang and Winters (1992), Hamilton and Winters (1992) and Baldwin (1994).

  3. For cross-section gravity modelling see, for instance, Brülhart and Kelly (1999) or Nilsson (2000). For panel data modelling, see, for instance, Baldwin (1994), Gros and Gonciarz (1996), Mátyás (1997), Egger (2000), Egger (2002), Egger and Pfaffermayr (2003), De Benedictis and Vicarelli (2005) and Cheng and Wall (2005).

  4. An exception is Helmers and Pasteels (2005).

  5. This was also the option of Helmers and Pasteels (2005) in trade potential calculations.

  6. Related to the fact that, in trade between countries i and j, if country i has high barriers with other partner countries, this will reduce the relative price of country j goods and hence, increase imports of i from j.

  7. Rose (2000) estimated that the volume of trade between countries with a common currency is more than three times greater than between countries with different currencies. However, some criticism has been directed at this finding, which has been considered to be overestimated (see, for instance, Yeyati 2003).

  8. See for instance, Kandogan (2004) for a presentation of the “augmented” version.

  9. For instance, if v ij is log-normal with variance \(\sigma^{2}_{ij}=\sigma^{2} \ {\rm exp}(z_{ij})\) then:

    \(E\left( {\log \left. {v_{ij} } \right|z_{ij} } \right)=-\left( {1 \mathord{\left/ {\vphantom {1 2}} \right. \kern-\nulldelimiterspace} 2} \right)\log \left\{{1+\sigma ^2\,\exp \left( {z_{ij} } \right)} \right\}\)

  10. Among others, Dhar and Panagariya (1999) argued that total trade should not be the dependent variable, since it imposes equality of coefficients on imports and exports, a criticism that is widely accepted.

  11. We have also estimated the equation for the export flows but, as expected, the results do not fully coincide. This may be due to the possible inaccuracies in export data and also to the fact that exports and imports are computed differently and the differences between cif (for imports) and fob (for exports) may vary according to each specific flow.

  12. Many authors use GDP per capita together with GDP. We have opted to estimate the effects of GDP and population separately in order to simplify their interpretation. Indeed, the alternative option implies that not only is the coefficient of population restricted to be a function of the coefficient of the GDP, but also the effect of the latter is not directly read by only one coefficient.

  13. This is a common variable in studies that include the CEEC. See, for instance, Beers and Biessen (1996).

  14. Nonetheless, Baldwin (1994), Bayoumi and Eichengreen (1997), Beers and Biessen (1996), Boisso and Ferrantino (1997) and Paas (2002), among others, make use of income at PPP.

  15. Some authors, such as Christie (2002), substitute the capital by a major city that seems to be closer to the country’s economic centre of gravity. In this respect, we opted to consider Amsterdam, rather than Den Haag, as the Dutch capital.

  16. Also known as “as the crow flies”, which is technically defined as the great-circle distance between the two latitude-longitude combinations.

  17. Christie (2002), attempted to take account of border waiting times by making use of a transport time matrix between the main transport nodes of the CEEC, but concluded that this specific variable does not clearly outperform the traditional distance measure.

  18. See Martínez-Zarzoso and Nowak-Lehmann (2003) for an inclusion of infrastructure facilities endowments (namely, in terms of road, rail, maritime and air infrastructures) in a gravity model.

  19. Polak (1996) used a location index equal to the sum of all bilateral distances weighted by partners’ GDPs.

  20. Source: www.wcrl.ars.usda.gov/cec/java/lat-long.htm.

  21. See, for instance, Kösekahyaoǧlu (1994).

  22. The Delta method is used to obtain the variance of non-linear functions of parameters estimators. See Greene (2007) for details.

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Authors and Affiliations

  1. ISEG, Technical University of Lisbon, Lisbon, Portugal

    Isabel Proença & Maria Paula Fontoura

  2. Room 101, Rua do Quelhas, n.6., 1200-786, Lisbon, Portugal

    Isabel Proença

  3. Portuguese Ministry of Finance, Lisbon, Portugal

    Enrique Martínez-Galán

Authors
  1. Isabel Proença
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  2. Maria Paula Fontoura
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  3. Enrique Martínez-Galán
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Corresponding author

Correspondence to Isabel Proença.

Additional information

The authors are indebted to J.M.C. Santos Silva and to an anonymous referee for their helpful comments. Useful comments received at the fourth annual European Economic and Financial Society conference and the seventh annual European Trade Study Group conference are equally acknowledged. We express our grateful appreciation for the financial support received from the Fundação para a Ciência e a Tecnologia, under the FCT/POCTI, partially funded by FEDER and the Fundo de Apoio à Comunidade Científica.

Appendix

Appendix

1.1 A.1 Countries included in the data set

Austria, Belgium, Bulgaria, The Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, The Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden and the United Kingdom.

1.2 A.2 Variables

1.2.1 A.2.1 Dependent variable

M—Nominal Import (cif) flows of manufactured products (covering Comext’s 2-digit Combined Nomenclature, codes 16 to 98), measured in thousands of euro, 2002. Source: European Commission’s Comext Database.

1.2.2 A.2.2 Independent variables

  • DIST—Absolute Distance: the geodesic distance between capitals (in the case of The Netherlands, Amsterdam substitutes Den Haag), measured as the surface distance between two points of latitude and longitude (great circle distance), expressed in kilometers. Source: www.wcrl.ars.usda.gov/cec/java/lat-long.htm .

  • MGDP/XGDP—Importer/Exporter country’s Nominal Gross Domestic Product at Market Prices, expressed in thousands of euro, 2002. Source: Eurostat’s New Cronos Database, Nov. 24th, 2003.

  • MPOP/XPOP—Importer/Exporter country’s Population, expressed in thousands of people at the end of 2002. Source: Eurostat’s New Cronos Database, November 24th, 2003.

  • NEIGH—Neighbouring Dummy Variable: equal to one if two trading partners share a land or sea border, zero otherwise. Source: CIA’s World Factbook 2003 on www.cia.gov/cia/publications/factbook/index.html .

  • IDIOM—Common Language Dummy Variable: equal to one if two trading partners share a same official language, zero otherwise. Source: CIA’s World Factbook 2003 on www.cia.gov/cia/publications/factbook/index.html .

  • ETHN—Ethnic Dummy Variable: equal to one if there is in one of the countries an ethnic minority of the other country that represents more than 5% of total population of the latter, zero otherwise. Source: CIA’s The World Factbook 2003.

  • CCT—Commodity composition of trade (COS Variable): complementarity measure of trading structures. See formula in the text. Covering 6-digit CN yearly data of manufactured products for 2002. Source: European Commission’s Comext Database.

  • EURO—Euro Dummy Variable: equal to one if both countries involved in the trade flow share the euro as a common currency, zero otherwise.

  • RECI(−1)—Reciprocity: the opposite trade flow of the dependant variable (2-digit of the Combined Nomenclature), measured in thousands of euro. Source: European Commission’s Comext Database, 2001 and 2002.

  • GERMAN—German Dummy Variable: equal to one if one of the countries involved in the trade flow is Germany, zero otherwise.

  • MLOCK/XLOCK—Landlockedness Dummy Variable for the Importer/ Exporter country: is equal to one if the importing country has no direct connection to sea, zero otherwise.

  • Specific group-pair dummies: CEEC-CEEC; CEEC-CC; CEEC-EU11; CC-CEEC; CC-CC; CC-EU11; EU11-CEEC; EU11-CC and EU11–EU11. Equal to one if the exporting country belongs to the first group and the importing country to the second group, zero otherwise.

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Proença, I., Fontoura, M.P. & Martínez-Galán, E. Trade in the enlarged European Union: a new approach on trade potential. Port Econ J 7, 205–224 (2008). https://doi.org/10.1007/s10258-008-0033-5

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