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The design of international trade agreements: Introducing a new dataset

Abstract

Preferential trade agreements (PTAs) have been proliferating for the last twenty years. A large literature has studied various aspects of this phenomenon. Until recently, however, many large-N studies have paid only scant attention to variation across PTAs in terms of content and design. Our contribution to this literature is a new dataset on the design of trade agreements that is the most comprehensive in terms of both variables coded and agreements covered. We illustrate the dataset’s usefulness in re-visiting the questions if and to what extent PTAs impact trade flows. The analysis shows that on average PTAs increase trade flows, but that this effect is largely driven by deep agreements. In addition, we provide evidence that provisions that tackle behind-the-border regulation matter for trade flows. The dataset’s contribution is not limited to the PTA literature, however. Broader debates on topics such as institutional design and the legalization of international relations will also benefit from the novel data.

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Notes

  1. In contrast to these lists, we did not count accession agreements, and services agreements that are signed at the same time as goods agreements, as separate PTAs. This explains why our count of agreements notified to the WTO is smaller than the one indicated by the WTO itself.

  2. The additional agreements that we found were not notified to the WTO, and thus have not made it into many of the datasets on PTAs that are based on the WTO’s PTA inventory.

  3. For these other datasets, see Estevadeordal et al. (2009) and World Trade Organization (2011).

  4. In the Online Appendix, which is available at this journal’s website, we also show variation by level of development (Fig. A-2) and across time (Fig. A-1) in the inclusion in PTAs of a few key provisions.

  5. A third measure, relying on tetrachoric factor analysis and the Thurstone method to calculate factor scores, is highly and positively correlated with the two measures we present here and produces the same substantive findings (r=0.64 and r=0.63, respectively).

  6. We thus leave out all flexibility and enforcement provisions. The Online Appendix contains a list of all the variables used in the latent trait analysis.

  7. The Online Appendix discusses this approach and shows the extent to which each variable is related to the latent trait.

  8. For the following figures we rescaled the variable resulting from latent trait analysis to have a minimum of 0 (rather than a mean of 0).

  9. On the effect of PTAs on investments, see, for example, Büthe and Milner (2008).

  10. Also Baier and Bergstrand (2007) and Goldstein et al. (2007) use directed dyads. The results do not change when using undirected dyads.

  11. We rescaled the latent trait measure to have positive values for all dyads that signed a PTA.

  12. Of 617 agreements for which we have this information, 459 entered into force within a year of signature and another 81 within two years of signature. Moreover, some agreements are provisionally applied immediately after signature.

  13. If we add five-year and ten-year lags, the coefficients for PTA and the two lags are positive and statistically significant. The total effect, i.e., the sum of the coefficients of PTA and its lags, is 0.34. Results are reported in Table (Model A5) in the Online Appendix. If we add five-year and ten-year lags without including \(Depth\), the total effect of PTA and its lags is 0.50.

  14. For a similar approach, see Kuziemko and Werker (2006). We also include other leads to capture an anticipatory effect between six and ten year before the signature of a PTA as well as between 11 and 15 years before the signature of a PTA. Whereas our main results do not change, 15-year leads are statistically significant. Results are shown in the Online Appendix (Model A1 in Table A-2).

  15. Following Goldstein et al. (2007), we use arc elasticity, which is the appropriate way to calculate the effect of dummies on the response variable. The arc elasticity is defined as the elasticity of one variable with respect to another between two given points. As the two points get closer together, arc elasticity approaches point elasticity. More formally, the arc elasticity is defined as \(\frac {\Delta Trade/Trade}{\Delta PTA/PTA}\) (Goldstein et al. 2007, 47).

  16. We also implement first-differenced panel gravity equation estimates as suggested by Baier and Bergstrand (2007). Specifically, we take the first differences of \(Trade\), PTA, and \(Depth\) and regress them on it, jt, and ij dummies. Then, we get the residuals for these estimations. Finally, we run a simple ordinary least squares regression with robust standard errors in which \(Trade\) residuals are the dependent variable and PTA and \(Depth\) residuals are the independent variables. The coefficients of both PTA and \(Depth\) remain positive and statistically significant. Results are reported in Model A3 in Table A-2 in the Online Appendix.

  17. The results (available upon request) do not change if we include one-year and two-year lags of PTA and \(Depth\) in the five-year dataset. Specifically, the coefficient of these lags are positive and statistically significant.

  18. We use the command PPML, written by Santos Silva and Tenreyro (2006) in STATA 12.

  19. This specification does not allow to separate the selection effect from the firm heterogeneity effect. However, we can “obtain our key results for the intensive-margin contribution of the various trade barriers” (Helpman et al. 2008, 465).

  20. Any cumulative distribution function instead of the normal distribution would work here. We estimate a probit model following Helpman et al. (2008).

  21. Data on the variable religion come from the CIA World Factbook. This variable is positive and statistically significant in the first stage.

  22. If we use 100 bins, we obtain the same results, which are available upon request.

  23. Differently from Helpman et al. (2008), the coefficient of PTA is positive and statistically significant. That might be explained by the fact that our sample is much larger than the one of Helpman et al. (2008).

  24. Results are reported in Model A4 (Table A-2 ) in the Online Appendix.

  25. Tariff transition data might be different for country i and country j. However, since the correlation between \(Transition_{i}\) and \(Transition_{j}\) is 0.98, we take the minimum of these two values to avoid multicollinearity problems.

  26. In fact, in our dataset full free trade agreements have an average transition period of 5.7 years as compared to 1.7 years for partial trade agreements. Customs unions also have a relatively long transition period of 4.5 years.

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Acknowledgments

All authors have contributed equally to the paper. We are grateful to the NCCR Trade Regulation ( www.nccr-trade.org) for financial support and our research assistants for help in collecting the data for this paper. Special thanks go to Karolina Milewicz for her support in the larger project. Todd Allee, Richard Baldwin, Tim Büthe, Jappe Eckhardt, Jeff Kucik, Marcelo Olarreaga, Ron Rogowski, Michael Zürn and participants of the ECPR Joint Sessions of Workshops at the University of Antwerp provided helpful comments on previous versions of this paper. We also wish to thank Raymond Hicks for kindly assembling the gravity model data. For more information on the DESTA dataset, see www.designoftradeagreements.org.

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Correspondence to Manfred Elsig.

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Dür, A., Baccini, L. & Elsig, M. The design of international trade agreements: Introducing a new dataset. Rev Int Organ 9, 353–375 (2014). https://doi.org/10.1007/s11558-013-9179-8

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Keywords

  • Preferential trade agreements
  • New regionalism
  • Institutional design
  • Dataset
  • Trade flows
  • Gravity model

JEL Classifications

  • F140
  • F150
  • F5