Abstract
Unilateral preferences aim at increasing exports from developing countries via reductions on applied tariffs and the incentives created by the preference margin. After decades of existence, the evidence as to the extent to which preferential schemes have been genuinely effective in increasing exports is mixed. This paper evaluates the impact of the European Union’s (EU) unilateral preferential regimes on the exports of developing countries using a bilateral gravity model at the product level. We use a unique dataset that allows us to determine the actual tariff rate paid by each export flow at the product level (combined nomenclature CN-10 digits) to the EU and the preferential regime of entry. This allows us to accurately specify the impact of each trade regime and to properly address the issue of utilisation and non-utilisation of trade preferences. The most important findings of the paper are that unilateral preferences have been effective in increasing exports to the EU both as a result of the direct effect of lower tariffs and positive preference margin, and because of secondary effects associated with the preference regimes; although the outcome of these secondary effects depends on the margin of trade considered.
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Notes
African, Caribbean, Pacific.
India, for example, is proposing to offer duty-free, quota-free access to 49 less developed countries.
GATT Article XXXVI.
In addition to weapons, banana and rice were excluded from EBA between 2006 and 2009, and sugar was transitioned until 2012 with minimum prices.
In this paper, the intensive margin of trade refers to changes in the level of already existing trade flows. As we only have one destination market, the EU, the extensive margin refers to new products being exported by a given country at some point during our time period to the EU, where previously that product was not being exported by that country to the EU.
Excluding the USA, Venezuela and Cuba in America and Mauritania in Africa.
While the importance of unilateral preferences varies across exporters, it is worth noting that trade under these regimes is of comparatively low importance for the EU itself. More than 60 % of total imports in the EU are in duty-free tariff lines. Around 23 % of the remaining imports face positive MFN tariffs, either because exporters are not eligible for preferences or because they do not utilise them. The share of imports using preferential regimes is only 15 %, and more than half of this utilises other preferential regimes. The de facto share of EU imports via GSP/EBA is around 5 %.
Some example of countries with significant exports to the EU and very large non-utilisation rates in 2007 (100 % of preference eligible exports exported MFN) are Bouvet Island, Iraq, Kiribati and Palau.
For an excellent exposition of the theoretical and empirical issues in gravity models, see Anderson (2011).
Cipollina et al. (2013) derive a version of the gravity model with an explicit preference margin term. However, in order to do so, a number of extreme assumptions need to be made regarding the symmetry of trade costs.
As an alternative, we also explored using a variable which ranged between 0 and 1 for each preference regime based on the share of trade for each country and product and time period which enters the EU via each regime. While the results were consistent with those reported here, these share variables are clearly the result of endogenous decisions by the exporting firms/countries and therefore determine the composition of exports entering under different regimes. Use of these shares is therefore problematic.
An oft-cited classic example is the increase in textile investment in Mauritius as a result of the US African Growth and Opportunity Act.
For each of these flows, we use the average tariff for each regime in a given year. For example, in a given year, if exports from country j using the MFN regime paid two different MFN tariffs due to changes in the MFN structure, we use the average of both tariffs. In most cases, however, there is only one tariff for regime and year. As discussed earlier, if there are flows from country j to the EU for a given product in a given year but which use different regimes (e.g. MFN, GSP, EBA), then these are captured separately.
Trade data can be noisy due to errors when inputting customs information. In order to detect extreme and unlikely flows, we calculate unit values and search for outliers by applying Hadi (1992) filter. These extreme values are then removed from the database. In addition, very low value flows, below 500 Euros, are also removed. These small flows are likely to be the result of private individuals moving goods rather than firms and therefore more likely to be subject to proportionate errors.
While we are aware of the recent literature on the survival of trade flows (Besedeš and Prusa 2006) and the fact that many trade relationships may not survive more than five years, we expect that the number of simultaneous product dropouts for all exporters in the world to the EU in a specific year to be minimal. Therefore, the risk of eliminating products not exported to the EU in one specific year is low.
The criterion is that a country should have exported at least one product to the EU in the same year. We look at each separately in order to guarantee that Eastern European EU countries enter the sample in the first period as exporters and after joining the EU are considered members and not exporters.
Only groups for which the value of trade is different from zero in at least one period are considered.
EPA regime since 2008.
Of course this raises issues of endogeneity which we acknowledge. However, as is well known, dealing with endogeneity in gravity models is difficult. In a recent paper, Egger et al. (2011) instrument for preferential trading agreements in a two-part model. In our case, there is potential endogeneity between tariffs and exports, but applying the two-part model is not possible because of the lack of appropriate instruments at the country-product-year level. While we recognise the possibility of endogeneity, we feel our methodology is the best suited given the nature of our dataset.
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Acknowledgments
We would like to thank the associate editor, one anonymous referee, Alan Winters, Barry Reilly and the participants at the CESIfo conference on “The Estimation of Gravity Models of Bilateral Trade” May 2014 for their helpful comments and suggestions. We also thank the European Commission who funded the original CARIS project on the Generalised System of Preferences.
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Appendices
Appendix 1: Methodology for the zero flows dataset
The main challenges when calculating the zero flows dataset are:
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differentiating products that disappear due to changes in classification;
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inferring tariffs for trade flows that do not occur.
Selecting products that occur all the period
We select only those product lines that are exported most of the period. This implies selecting those exported in 2002 and 2008, those in 2003 and 2008, and those exported in 2002 and 2007. In total we select 9,068 (over 19,259 products defined in some year) product lines that represent 73.31 % of value and 71.50 % of flows. Inference of tariffs for no flows
For all the zero flows, we use the following procedure. We use the complete tariff book and paste tariffs in the following order.
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First we attach country specific duties, which are the result of FTAs or specific situations
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We set tariffs to 0, when MFN rates are 0.
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We use zero tariffs for all EBA countries
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We use GSP plus tariffs for GSP+ countries
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With countries with double membership GSP and Cotonou, and in 2008 GSP and EPA, we use the minimum tariff. When both are the same, we group the country with the GSP regime.
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Remaining tariffs are set to MFN rates
We create a dummy variable which indicates whether the tariff applied belongs to the GSP/EBA regime. Since in the case of multiple preferential regimes we do not know what regime would be utilised for zero flows, the results of the coefficient on the margin decomposition needs to be interpreted with caution.
Appendix 2: Gravity model based on aggregate trade flows
See Table 7.
Appendix 3: Impact on the extensive margin: OLS estimates using the Feenstra and Kee (2004) variety index
See Table 8.
Appendix 4: Additional regressions using total trade at the 6-digit level and GDP as activity variables
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Cirera, X., Foliano, F. & Gasiorek, M. The impact of preferences on developing countries’ exports to the European Union: bilateral gravity modelling at the product level. Empir Econ 50, 59–102 (2016). https://doi.org/10.1007/s00181-015-1011-2
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DOI: https://doi.org/10.1007/s00181-015-1011-2