The aim of this article is to assess the impact of the European Union’s trade preferences on bilateral trade flows. Using highly disaggregated 8-digit import data in a theoretically grounded gravity model framework, we define an explicit measure of preferential tariff margins and use that to estimate sector-specific elasticities. From the methodological point of view, we show that the assessment of these policies’ impacts is very sensitive to the definition of the preferential tariff margin. An important by-product of our procedures is that they can be used to obtain estimates of trade elasticities of substitution, some of the most important parameters in the international trade empirical literature. Results show that actual preferential schemes or possible future policies, such as the transatlantic trade agreement between the USA and the EU (TTIP), have a significant impact on trade volumes, with large differences across sectors.
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Arkolakis et al. (2012) have recently showed that this parameter is actually one of the (only) two sufficient statistics needed to calculate gains from trade under a large set of alternative modeling assumptions.
Import quantities are scaled in order to make all the CIF prices (i.e., including transport costs) equal to 1. Such a procedure is quite common in a static context (e.g., it is used to calibrate the baseline of computable general equilibrium models) but it may become an issue in a panel context since transport costs are likely to vary across periods.
In general equilibrium modeling of international trade, treating trade costs as an additive usually leads to severe impediments in computation. Hence, trade costs are generally assumed to act multiplicatively, as in Samuelson (1952).
Under specific assumptions about the distribution of firm heterogeneity, trade elasticity could even depend only on a supply-side parameter (Chaney 2008).
In the gravity model literature, it is customary to use expenditure shares rather than levels in order to control for heteroskedasticity in the error term. However, shares present lower and upper bounds, implying that the partial effects of the explanatory variables on the conditional mean of the dependent variable cannot be constant and must approach zero as the conditional mean approaches its bounds. Santos Silva et al. (2013) show that the majority of actual estimators used by the recent gravity literature, by ignoring these double-bounds, can lead to erroneous conclusions due to serious model misspecification.
We also run estimates at the HS-2 chapter levels. Estimates vary a lot, as could be expected given the differences across chapters in terms of number of observations, preference distribution, and homogeneity of product definitions. Results are available from the authors upon request.
If the estimated coefficient was not significant, we decreased the assumed elasticity value to the lowest possible bound (i.e., 1). In all these cases, we never get significant estimates. The number of iterations required to achieve convergence never exceeds four.
Using the applied rather than bound tariff, we avoid the risk of including ‘water’ (i.e. binding overhang) in the preference margin.
This is a shortcoming of the CES functional form that does not take into account the potential competition coming from exporters facing prohibitive tariffs. Since unit values corresponding to (non-statistical) zero trade flows are not considered in the computation of the reference tariff, our preference margins may be understated; this would lead to an overestimation of the preference impact.
For a review of the (mis)use of instrumental variables in the literature, see Deaton (2010).
Other possible solutions to the endogeneity problem may be provided by matching (i.e., comparing an exporter that benefits from preferences with another that does not, despite the same ex ante probability of receiving the preference according to the observable explanatory variables) or difference-in-difference methods. In both cases, though, it is necessary to define an exporter control group; this is difficult to envisage in our case. More importantly, in these approaches, the nature of the policy shock is quite different from ours since we exploit data in cross-section without a time series trade response to changing preferences.
NTMs represent a serious challenge both for the computation of consistent AVEs (for a recent quantification, see Beghin et al 2015) and the assessment of the bilateral component of their preferential reduction.
In 1979, the waiver was replaced by the “Enabling Clause”, which provides a legal basis for granting trade preferences in favor of developing countries and allows for special treatment of Least Developed Countries (Persson 2012).
The Comext database (http://fd.comext.eurostat.cec.eu.int/xtweb/) contains Cost-Insurance-Freight (CIF) values foreign trade data distinguished by tariff regimes as reported by EU member states.
EU Preferential schemes in 2004 are: Generalized System of Preferences (GSP), including Everything But Arms (EBA), GSP-Drugs, GSP-Labor Rights schemes; Cotonou Agreement; EU-Chile Association Agreement; EU-Mexico Free Trade Agreement; Euro-Mediterranean partnership; European Economic Area (EEA) Agreement; EU-Turkey Custom Union; Trade, Development and Co-operation Agreement (TDCA) with South Africa.
For example, Inama (2003) notes that preferential treatment under the Everything But Arms initiative was requested for less than 50% of exports from non-ACP LDCs in 2001, even though this initiative offers duty-free access for practically all goods and is the best system available for these countries. Complicated and restrictive rules of origin are often highlighted as a major reason for the low utilization of preferences; Cadot et al. (2006) estimate that for the relevant EU rules of origin, administrative costs represent 6.8% of the traded goods’ value.
We run estimations for the Sections (I-IV and VI) that include 614 goods (roughly one-quarter of the total) featuring TRQs. We use the in-quota tariff when imports are lower than the quota and the trade-weighted average of the two tariffs otherwise (Raimondi et al. 2011). The impact on trade flows turns out to be either insignificant or negative.
Full regression results are available from the authors upon request.
In principle, Section XI is nearly all covered by the ATC but we do not know how quotas were managed and to what extent they had been already phased out. Section XII is also covered by the ATC, but with a much lower coverage since quota-constrained goods represent about 15% of the trade of this Section and 16% of the observations.
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This work was supported by the European Union’s Seventh Framework program FP7/2007–2011 [Grant Number 290,693] FOODSECURE—Exploring the Future of Global Food and Nutrition Security, the CGIAR Research Program on Policies, Institutions, and Markets (PIM) led by the International Food Policy Research Institute (IFPRI) and the AGRODEP Consortium. The views expressed are purely those of the authors and do not necessarily reflect the views of PIM, CGIAR, the European Commission, or the AGRODEP Consortium. It may not in any circumstances be regarded as stating an official position of the European Commission. We would like to thank two anonymous referees for helpful comments. Unfortunately, the list of colleagues with whom we discussed these issues in seminars and conferences is too long to fit on a footnote: we are grateful to each of them though.
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Cipollina, M., Debucquet, D.L. & Salvatici, L. The tide that does not raise all boats: an assessment of EU preferential trade policies. Rev World Econ 153, 199–231 (2017). https://doi.org/10.1007/s10290-016-0270-0
- Theoretical gravity model
- Preferential trade agreements
- Trade cost elasticity
- Sectoral trade flows