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The breadth of preferential trade agreements and the margins of exports

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Abstract

The paper uses recently available data on the core economic provisions of PTAs to identify which (types of) provisions seem to promote bilateral exports and the intensive and extensive margins of exports. Our evidence suggests that measures applied at the border on a preferential basis tend to expand existing trade relationships rather than generate new trade relationships, while measures applied behind the border do the opposite. The effects of measures applied on an MFN basis depend on the threshold used to define the intensive margin. Individual provisions fall into two categories, those that have consistent effects across both margins regardless of the threshold, and those that can have opposing effects on the two margins depending on the threshold. The study finds evidence that PTA effects strengthen through time.

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Source: Updated from Falvey and Foster-McGregor (2018)

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Notes

  1. In what follows we take Preferential Trade Agreements (PTAs) to mean any preferential access for members of such an agreement.

  2. This may not be coincidental. Falvey and Foster-McGregor (2018) find a trade-off between the likelihood of a country-pair forming a PTA and the breadth of any PTA they do form. Thus, more distant countries are less likely to form a PTA, but if they do it is likely to be broader; while sharing a common language or a common border makes a PTA more likely, but it will be narrower in scope on average. Kohl et al. (2016) draw similar conclusions, further noting that the breadth of PTAs increases with the level of development or income of the participants and the number that are WTO members, and that interregional agreements tend to be broader than regional agreements.

  3. See Cipollina and Salvatici (2010), World Bank (2005) for meta-analyses of the trade effects of PTAs. World Bank (2005), for example, considers 362 estimates of a PTA dummy from 17 studies that cover different PTAs, time periods and equation specifications. One-third of the estimates are statistically insignificant, over 10% are negative and significant, and only just over 50% are positive and significant. The mean estimate is 0.79 but the standard error is 1.3.

  4. Studies have also attempted to examine the potential trade diversion effects of PTAs by including binary variables that take the value one if only one member of a country pair belongs to a PTA (see for example Frankel et al. 1996). The results of such studies are also mixed depending upon the sample, the time period, the specification of the gravity equation and the particular PTAs considered.

  5. They consider separately the effects of one-way preferential agreements, two-way preferential agreements, free trade agreements, and (collectively) customs unions, common markets and economic unions on aggregate trade flows, and trade at the extensive and intensive margins. See also Vicard (2009).

  6. See Head and Mayer (2014; Sect. 5.3) for a fuller discussion. Unfortunately, these two components of the intensive margin cannot be isolated algebraically in general; but Head and Mayer (2014) illustrate how they can be isolated, interpreted and estimated in the context of heterogeneous firm trade models.

  7. Average exports fall because the average exports of continuing exporters fall and because it is likely that the average exports of the new exporters are smaller than previous exports of the incumbents, at least in the short run.

  8. Resources may also be drawn from firms producing solely for the domestic market.

  9. Fernandes et al. (2019) focus on firms, defining the extensive margin as the number of exporting firms and the intensive margin as the average size of an exporter. They find that variation in exports is about evenly split between the two margins.

  10. Feenstra and Kee (2008) show that the variety of exports is also related to country productivity.

  11. Falvey and Foster-McGregor (2018) used the 2010 data to separate our PTA country pairs into 4 strata based on the ‘depth’ of their PTAs. They then calculated the expected probability of each provision appearing in a PTA between a country pair located in each stratum and subtracted it from the actual frequency with which that provision appears in that stratum. This gave a measure of ‘provision intensity’ for each of the strata. If the actual frequency exceeds the expected, then the PTA country pairs in the corresponding stratum are ‘intensive’ in that provision. They found that the lowest stratum was intensive in agricultural and industrial tariff liberalisation and produced no significant increases in trade flows. The highest stratum, which also produced no significant trade increase in trade flows, was intensive in provisions relating to IPRs, the movement of capital, investment, TRIPS, GATS, TRIMs, state aid, state enterprises and technical barriers to trade. The middle strata, where the positive effects for trade were found, were intensive in provisions relating to countervailing measures, antidumping, export taxes and customs—i.e., provisions directly relating to goods trade flows.

  12. For more details on the structural gravity model, see for example Baier et al. (2017).

  13. Alternative approaches include Heckman control functions (Baier and Bergstrand, 2002; Magee, 2003; Baier et al., 2008), instrumental variables (Egger et al., 2011) and matching econometrics (Baier and Bergstrand, 2009; Egger et al., 2008; Falvey and Foster-McGregor, 2018). Note that the inclusion of country-pair fixed effects means that it is not possible to estimate coefficients on time-invariant country-pair specific variables such as distance, common language and contiguity that have been commonly included in the gravity model.

  14. Indeed, this is an alternative definition of the intensive and extensive margin, with the extensive margin being the development of new trading relationships and the intensive margin the intensity of existing trading relationships.

  15. Other approaches are discussed in Frankel (1997) and include the use of Tobit estimation or using \(\mathrm{ln}({EXP}_{ij}+1)\) as the dependent variable. These approaches are likely to lead to inconsistent parameter estimates, however.

  16. While both the modified Heckman and PPML approaches have come in for some criticism, the Santos and Tenreyro (2006) approach is slightly easier to implement and also controls for heteroscedasticity which is inherent in the approach.

  17. We drop time subscripts where they are not necessary for the explanation. It should be kept in mind however that, unless otherwise stated, these variables are calculated for each year in the sample.

  18. Specifically, to construct the set of least-traded-goods from exporter i to importer j, they order the goods by their average value of trade over the first three years of the sample, and then cumulate the ordered SITC codes to form 10 sets, each representing one-tenth of total exports. The first set is constructed, starting with the codes with the smallest amounts of trade, including the zero trades, by adding codes to the set until the sum of their trade reaches one-tenth of total export value. The first set thus consists of the least-traded goods, and it is these goods that form the extensive margin for this bilateral trading relationship. See Kehoe and Ruhl (2013).

  19. Note that this is not an attempt to ‘duplicate’ the Kehoe and Ruhl (2013) approach, since bilateral-trade-relationship specific thresholds are essential to their analysis. We continue to apply a common threshold across all relationships.

  20. Hoffman et al. (2017) also draw a distinction between those provisions which are ‘legally enforceable’ and those which are not. Here we restrict attention to legally enforceable provisions.

  21. See Limao (2016) for a fuller discussion of the role of different types of policies in providing market access.

  22. Principal component analysis is a method of condensing a number of random variables into a smaller number of uncorrelated variables. Each component is a weighted sum of the variables, where weights are chosen so that the first component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible while being orthogonal to the preceding components. For an introduction to principal components and factor analysis see Kline (1994). Other studies employing this provision data have also used principal components, but typically considered all 52 provisions. For example, Orefice and Rocha (2014) investigate the effects of integration on production networks using the five provisions with the highest weights in the first component to create an index of integration agreement ‘depth’. These five provisions are TRIPs, IPR, CVM, STE and Cap. Similarly, Osnago et al. (2016) use the same index plus an alternative extended to the ten provisions with the highest weights to consider the effects of trade agreements on vertical FDI. This adds PubP, ComP, AD, Inv and SAid. Note that these ten provisions are all core provisions. Kohl et al. (2016) apply principal components separately to 13 WTO + and 4 WTOX provisions, and to 9 indicators of institutional quality (IQ).

  23. Our results also suggest that PC3 may capture the anti-trade bias of the PTA. See Sect. 3.2 below.

  24. Five out of six preferential provisions have a positive weight in PC2 and a negative weight in PC3, but the weights on the MFN provisions are approximately equally positive and negative on both components.

  25. Calculated as \({\mathrm{exp}}^{({\beta }_{1})}-1\).

  26. Martincus and Estevadeordal (2009) find evidence in support of both general and preferential trade liberalisation increasing specialisation in manufacturing production in the context of Latin American countries. The result that the positive impact of PTAs is stronger for the intensive margin is also in line with the results of Baier et al. (2014).

  27. The only reversals are for the extensive margin at the $50 k threshold.

  28. Strictly speaking we are changing both the sample (adding the zeros) and the estimator, but a restriction of the PPML estimation to the positive flows shows similar results to fixed effects OLS. Differences in the magnitudes of estimated coefficients between OLS and PPML are common in the literature, (see for example Bergstrand et al. 2015), and can be in either direction. These differences have been attributed to differences in the distributional assumptions on the error terms between PPML and OLS, and to the possibility of rising trade cost elasticities as trade increases. See Head and Mayer (2014) for a detailed discussion.

  29. All 3 cases occur in the estimates for PC3. They are the fixed weight and unweighted estimates for the intensive margin at the $50 k threshold, and the estimates for total exports at the $1 m threshold. In each case the contemporaneous effect is estimated to be positive and significant while the decadal effect is negative and significant.

  30. The exception is the five-year lag under the $1 m threshold, where only 3 of the 18 sets of estimates are fully consistent.

  31. The majority of reversals occur at the lowest threshold (with 5 provisions and 12 coefficients affected); at the measures using time-varying weights (7) rather than with fixed weights (5) or unweighted (4); and at the intensive margin (6) rather than the extensive margin (1).

  32. 63% are delayed and 37% are temporary.

  33. One may wonder why negotiators would bother including provisions that appear to have no significant effect. There are at least four possible responses: (i) negotiators are unaware of the apparent impotence of these provisions; (ii) these provisions do have significant effects on trade values at the product level, but these effects are too small to be picked up at the aggregate level; (iii) as we shall see below, these provisions can have significant positive effects on trade values at one margin but offsetting significant negative effects at the other margin; and (iii) they have significant effects on other economic interactions of interest to negotiators.

  34. Indeed, some are implausibly high, for example it seems that a PTA consisting only of an AD provision could wipe out bilateral exports if given enough time. Of course, no actual PTA has only an AD provision so that (ab)using our estimates in such a way is very much ‘predicting out of sample’.

  35. Osnago et al. (2016) go one step in this direction by showing that broader trade agreements increase vertical FDI.

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Acknowledgements

We thank two referees for useful and constructive comments.

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Correspondence to Neil Foster-McGregor.

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Appendices

Appendix A: PPML estimates

See Tables 10, 11 and 12.

Table 10 PPML regression results with a $0 trade threshold
Table 11 PPML regression results with a $50 k trade threshold
Table 12 PPML regression results with a $1 m trade threshold

Appendix B: Individual provision estimates

See Tables 13, 14 and 15.

Table 13 Fixed effects regression results with $0 threshold – individual provisions
Table 14 Fixed effects regression results with $50 k threshold – individual provisions
Table 15 Fixed effects regression results with $1 m threshold – individual provisions

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Falvey, R., Foster-McGregor, N. The breadth of preferential trade agreements and the margins of exports. Rev World Econ 158, 181–251 (2022). https://doi.org/10.1007/s10290-021-00430-5

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