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Preferential trade agreements and the structure of international trade

Abstract

In this paper we examine the impact of membership in preferential trade agreements (PTAs) on trade between PTA members. Rather than considering the impact of PTA membership on the volume of trade we consider the impact of membership on the structure of trade. For a large sample of countries over the period 1962–2000 we find that membership in a PTA is associated with an increase in the extent of intra-industry trade. Our results indicate that this is especially the case for PTAs formed between richer countries, with the effects of PTAs between poorer countries found to be smaller.

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Notes

  1. 1.

    In what follows we take preferential trade agreements (PTAs) to mean any preferential access for members of such an agreement.

  2. 2.

    For example, the US has signed agreements with Israel (1985), Jordan (2002), Australia (2004), Morocco (2005) and Peru (2009), while the EU has signed agreements with Turkey (1996), the Faroe Islands (1997), the Palestinian Authority (1997), Tunisia (1999), South Africa (2000), Morocco (2000), Israel (2000), Mexico (2000), Chile (2004), Algeria (2006) and Cote d’Ivoire (2008).

  3. 3.

    This number includes countries no longer in existence (e.g. Czechoslovakia, ex-Yugolslavia) along with the countries that replaced them (e.g. Czech Republic, Slovakia).

  4. 4.

    Baier and Bergstrand (2009a) adopt both approaches.

  5. 5.

    Anderson and van Wincoop (2003) note that theory indicates that trade between two countries is decreasing in their bilateral trade costs relative to the average of the costs of the two countries to trade with all their partners, rather than to absolute trade barriers. This they refer to as multilateral resistance.

  6. 6.

    They also consider an adjustment to this index that accounts for trade imbalances.

  7. 7.

    Grubel and Lloyd (1975) also showed that the level of IIT increased after the formation of the Organisation for European Economic Cooperation (OEEC) and the European Economic Community (EEC).

  8. 8.

    In the former case one is able to examine the coefficients on the level of GDP and population for the exporter and importer separately, while in the latter one considers the product of countries i and j’s the GDPs and populations. Considering these variables separately for countries i and j in this case makes little sense as the ordering of the data will determine whether a particular country is classified as the exporter or importer.

  9. 9.

    These are the Association of South-East Asian Nations (AFTA), Australia–New Zealand Trade Agreement (ANZCERTA), Asian Pacific Economic Cooperation (APEC), Andean Pact (AP), Central American Common Market (CACM), Caribbean Community (CARICOM), European Economic Area (EEA), European Free Trade Agreement (EFTA), European Union (EU), Latin America Integration Agreement (LAIA), Southern Cone Common Market (MERCOSUR) and the North America Free Trade Agreement (NAFTA).

  10. 10.

    http://www.cepii.fr/anglaisgraph/bdd/distances.htm.

  11. 11.

    http://en.wikipedia.org/wiki/Landlocked.

  12. 12.

    http://rtais.wto.org/UI/PublicAllRTAList.aspx.

  13. 13.

    The reason for considering alternative sources is that the WTO data set only includes PTAs in force, thus excluding a number of PTAs that are no longer in force, but that would have been in the period of interest, examples being the PTAs agreed between the EU-15 and Romania, Bulgaria and others in the 1990s, but which are no longer in force now that these countries are members of the EU.

  14. 14.

    A number of other approaches have been suggested including Tobit estimation and adding one to the value of trade, which then allows one to calculate the log of trade. More recently, Helpman et al. (2008) have proposed a two-stage estimation procedure. In the first stage a Probit model is estimated relating the probability of country j exporting to country i as a function of observables. Predicted components of this equation are then used in the second stage to estimate the gravity equation in log-linear form. Santos Silva and Tenreyro (2006) have proposed a Poisson Pseudo Maximum Likelihood estimation technique which is consistent in the presence of heteroscedasticity and also provides a natural way of dealing with zero observations.

  15. 15.

    See Grubel and Lloyd (1971, 1975).

  16. 16.

    The measures of IIT are calculated at the four-digit level. The paper thus uses more disaggregated data than is often used in the literature, which tends to use data at the one- or two-digit level.

  17. 17.

    Given the fact that a large number of the observations on GLI and CGLI are zero (i.e. we have a corner solution problem, see Wooldridge, 2009) we also estimate our gravity equation using a Tobit model, as done in a gravity context by Felbermayr and Kohler (2006) for example. The results we obtain using the Tobit model are consistent with those reported, and are thus excluded from the paper for brevity. These results are available upon request however. It would also be possible to adopt the approaches of Helpman et al. (2008) and Santos Silva and Tenreyro (2006) mentioned in footnote 14 to consider the issue of zero values of the IIT index.

  18. 18.

    This is calculated as (e βPTA − 1).

  19. 19.

    Since the IIT variable varies between zero and one we can interpret the coefficients on the PTA variable as the change in the share of IIT in total trade.

  20. 20.

    It would be interesting therefore to examine empirically whether the trade-creating effects of PTAs are stronger between close or more distant trade partners. As far as we know this hasn’t been attempted to date.

  21. 21.

    To be consistent with the estimates for GLI and CGLI we report the estimated percentage increase in the level of trade following membership in one of the PTAs. This is calculated as in footnote 17.

  22. 22.

    This result is consistent with Markusen (1986).

  23. 23.

    The results are consistent with the arguments of Globerman (1992) however.

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Acknowledgments

The present paper was prepared as part of the EU FP7 programme project ‘World Input–Output Database: Construction and Applications’ (WIOD). This project is funded by the European Commission, Research Directorate General as part of the Seventh Framework Programme, Theme 8: Socio-Economic Sciences and Humanities. Grant Agreement no. 225 281. The authors would like to thank two anonymous referees and the editor for valuable comments.

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Correspondence to Neil Foster.

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Foster, N., Stehrer, R. Preferential trade agreements and the structure of international trade. Rev World Econ 147, 385–409 (2011). https://doi.org/10.1007/s10290-011-0093-y

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Keywords

  • Preferential trade agreements
  • Intra-industry trade
  • Gravity equation

JEL Classification

  • F10
  • F15