Pulling back the curtain on ‘behind the border’ trade costs: The case of EU-US agri-food trade


Ana I. Sanjuán,

Unit of Agro-Food Economics and Natural Resources, Centre for Agro-Food Research and Technology (CITA), Instituto Agroalimentario de Aragón (IA2), CITA-Universidad de Zaragoza, Avda. Montañana 930, 50059, Zaragoza, Spain.

George Philippidis,

Unit of Agro-Food Economics and Natural Resources, Centre for Agro-Food Research and Technology (CITA), Instituto Agroalimentario de Aragón (IA2), CITA-Universidad de Zaragoza, Avda. Montañana 930, 50059, Zaragoza, Spain.

Aragonese Agency for Research and Development (ARAID), Zaragoza, Spain.

and Helena Resano

University of Zaragoza, Instituto Agroalimentario de Aragón (IA2), CITA-Universidad de Zaragoza, Faculty of Veterinary Sciences, Dept. Agriculture and Agricultural Economics. Zaragoza, Spain.


With the rise of anti-free-trade sentiment on both sides of the Atlantic, there is a growing urgency by trade negotiators to conclude the Trans-Atlantic Trade and Investment Partnership (TTIP) negotiations. The harmonisation of non-tariff restrictions is a key component of the talks, whilst global modelling databases typically lack a price compatible representation of these measures, which lends a degree of bias to ex-ante modelling assessments. In the gravity literature, there is (limited) evidence of non-tariff ad-valorem equivalent (AVE) estimates of agriculture and food, although disaggregated agri-food activities and/or bilateral EU-US route specific estimates are still in relatively short supply. Using panel data, this study consolidates both of these issues, whilst also proposing an ‘indirect’ gravity method as a basis upon which to provide econometric non-tariff AVE estimates compatible with the degree of sectoral concordance typically found in global modelling databases. On a general note, the results revealed the presence of significant 'behind the border' trade costs on both sides of the Atlantic, which exceed their tariff counterparts. Using simple aggregated averages, our estimates are comparable with ‘direct’ gravity method studies. Furthermore, rigorous qualitative and quantitative comparisons on a sector-by-sector basis showed that a number of bilateral non-tariff AVEs are also found to be plausible, although in some cases, with recourse to relevant policy documents and expert opinion, it is debatable whether the EU or the US is more restrictive. Further work could focus on refining the sector specificity of each gravity equation to improve the model’s predictive capacity. .

Additional key words: non-tariff trade costs; gravity equation; Trans-Atlantic Trade and Investment Partnership

Abbreviations used: AVE (Ad Valorem Equivalent); BRICs (Brazil, Russia, India, China); EP (European Parliament); CEPII (Centre d’Études Prospectives et d’Informations Internationales); HLWG (High Level Working Group); IPR (Intellectual Property Rights); KNO (Kee, Nicita and Olarreaga); NAFTA (North American Free Trade Area); NTM (Non-Tariff Measure); PTA (Preferential Trade Agreement); RTA (Regional Trade Agreement); SPS (Sanitary and Phyto-Sanitary); TBT (Technical Barriers to Trade); TPP (Trans-Pacific Partnership); TTIP (Transatlantic Trade and Investment Partnership).

Authors’ contributions: Conceived and designed the experiments, and interpretation of data: AIS and GP. Statistical analysis: AIS and HR. Wrote the paper and critical revision of content: GP, AIS, HR.

Citation: Sanjuán, A. I.; Philippidis, G.; Resano, H. (2017). Pulling back the curtain on ‘behind the border’ trade costs: The case of EU-US agri-food trade. Spanish Journal of Agricultural Research, Volume 15, Issue 2, e0110. 10021

Supplementary material Supplementary material (Tables S1) accompanies the paper on SJAR’s website.Received: 31 May 2016 Accepted:

Copyright © 2017 INIA. This is an open access article distributed under the terms of the Creative Commons Attribution (CC-by) Spain 3.0 License.

Funding: European Commission, Joint Research Centre (JRC.D.4, Sevilla), Spain.

Competing interests:The authors have declared that no competing interests exist

Correspondence should be addressed to George Philippidis: .





Material and methods







Over the last two decades, the political and economic landscape has realigned, in large part, due to the steady emergence of the ‘BRICs’ (Brazil, Russia, India and China) and, more recently, the fallout from the financial crisis which has saddled many western economies with heavy national debts, high unemployment and sluggish growth. From the perspective of international trade, both factors have impacted not only on the format of trade promotion, but also on the nature of how trade relations are governed between partners. In the former case, there was a time when multilateral and regional trade agreements (RTAs) appeared to act as complements in promoting liberalisation. For example, in the 1990s, as the Uruguay Round was coming to fruition the United States (US) co-signed the North American Free Trade Area (NAFTA) agreement. Similarly, in the early 2000s, China’s accession to the World Trade Organisation (WTO) was accompanied by European Union (EU) enlargement. With the exception of the Bali agreement on streamlining trade facilitating customs controls and red tape, the Doha negotiations have largely failed, in large part due to the defensive posture of post-crisis Western governments as well as the newly strengthened developing country lobby with designs on significant improvements on market access (especially in agricultural and food products). In the ensuing period, the US actively pursued strategic ‘second best’ preferential trade agreements (PTAs), whilst also playing an active role in the on-going Trans-Pacific Partnership negotiations (TPP)1 . For its part, the EU has forged a similar path.

1The TPP involves Australia, Brunei, Canada, Chile, Japan, Malaysia, Mexico, New Zealand, Peru, Singapore and Vietnam. Interestingly, China is not party to these negotiations. In a departure from previous US external trade policy, the current US administration unilaterally pulled out of the TPP.

Turning to the second issue, the rules governing the nature of trade promotion have also evolved. Traditional impediments to trade, such as tariffs, continue on a downward path. In part, this is credited to the effectiveness of the WTO’s monitoring and arbitration mechanism, but it is also related to the continued rise of covert ‘behind the border’ protectionism in the form of industrial policy, export credits or government subsidies; or other not so covert measures such as, for example, health, safety and technical standards; labour and environmental protection laws; treatment of foreign investors; intellectual property rights etc. Typically, it is the developed countries, with more sophisticated institutional capacity, which more vigorously implement the latter form of non-tariff measures (NTMs). Taking a cynical view, even non-covert NTMs may be used as a political tool to deliberately impose a barrier to trade, although it has been pointed out, especially in the domain of agri-food (OECD, 2011), that trade restricting NTMs also pursue legitimate welfare improving objectives such as the lowering of negative externalities (e.g., reduced risk of pest or diseases, improved animal welfare) or even reduced information asymmetry (e.g., food labelling).

In this context, the US has tried to influence the terms upon which RTAs should be negotiated and implemented, by seeking to harmonise said measures with like-minded partners with a view to promoting ‘free and fair trade’ (The Economist, 2013). In 2011, the seeds were planted for a potential EU-US Transatlantic Trade and Investment Partnership (TTIP) at a High Level Working Group meeting on jobs and growth (HLWG). A significant part of the trade negotiations is dedicated to the establishment of a set of bilateral regulatory integration rules on NTMs relating to sanitary and phyto-sanitary (SPS), technical barriers to trade (TBT) and intellectual property rights (IPR).

In the gravity literature, estimates of EU and US non-tariff ad valorem equivalent (AVE) trade costs for aggregate agriculture and food activities are available, both of a bilateral- (ECORYS, 2009; CEPR, 2013; European Parliament ((henceforth EP), 2014) and unilateral (Egger et al., 2015) nature. Moreover, EP (2014) focuses on SPS and TBT measures and provides estimates for an array of disaggregated agricultural and food sectors. More recently, Arita et al. (2015) estimate bilateral NTM costs for specific SPS and TBT measures and selected food sectors. As a first aim, this paper seeks to consolidate the literature by providing gravity based EU-US bilateral non-tariff AVE estimates for a broad selection (18) of agri-food sectors.

As an input to the policy making process, respected global modelling databases (e.g., Global Trade Analysis Project - GTAP) suffer from a dearth of non-tariff AVE information, which renders ex-ante impact assessments as rather shallow. Therefore, a second aim of this research is to address this shortcoming by proposing an alternative ‘indirect gravity’ based method as a basis upon which to readily reconcile econometric non-tariff AVE estimates with the more aggregated degree of sectoral concordance typically found in global modelling databases. The study employs a panel dataset, whilst additional statistical tests were implemented to enhance the reliability of our sector-by-sector estimates. Rigorous comparisons with relevant ‘direct gravity’ based AVE estimates show that the results are highly comparable.

Material and methodsTop

Literature review

The estimation of non-tariff trade costs in the empirical literature either uses ‘prices’ (domestic and foreign) or ‘quantities’ (trade flows), while a further sub-classification distinguishes between ‘direct’ and ‘indirect’ methods, depending on the respective explicit or implicit treatment of the non-tariff indicators (see Deardorff & Stern, 1998; Ferrantino, 2006, for surveys). A cursory examination of the literature reveals that the quantity based method appears to be the more popular, in large part due to easy access to detailed public databases of trade (Berden & Francois, 2015). Examining the price-approach, Bradford (2003, 2005) calculates (rather than estimates) the implicit non-tariff impact on prices (indirect method), whilst Dean et al. (2009) and Cadot & Gourdon (2014), employ an explicit non-tariff variable (i.e., direct method) to estimate its price rising effect.

Direct methods, in both price and quantity approaches, employ secondary data sources (both quantitative and qualitative) to construct a coverage ratio or dummy variable to indicate the degree of pervasiveness or the presence of a non-tariff restriction within the commodity of interest, which subsequently enters as an explanatory variable in an econometric model (price-dependent or gravity-type quantity-dependent). To this end, data may be taken from inventories of standards and regulations (e.g., UNCTAD TRAINS database); notifications to the WTO on the implementation of new trade regulations or complaints by traders (e.g., WTO World trade Reports). In the context of the EU-US trade relations, EP (2014) use the SPS and TBT notifications to the WTO to build a non-tariff variable that when interacted with the EU-US and US-EU) route dummy, allows for the direct estimation of bilateral AVEs of non-tariff costs. In their study, the sample includes a cross-section (year 2012) of OECD countries. Unfortunately, these methods neglect the relative importance of each measure in restricting trade, while countries which are more transparent appear as more restrictive (Chen & Novy, 2012).

Arita et al. (2015) build a non-tariff variable that collects the incidence of SPS type measures between the EU and US in those cases where official concerns have been raised by US and EU exporters. A data sample consisting of three years (2010-2012) and a selection of countries ranging between 20 and 35 (depending on the sector) is constructed to estimate directly the non-tariff impact on bilateral trade. The choice of sectors and the direction for which the AVE is estimated is contingent upon the concern raised (e.g., The EU non-tariff AVE is estimated for red and white meat, maize and soy; whilst non-tariff AVEs for fruits and vegetables are estimated in both directions). In a similar fashion, Winchester et al. (2012) narrow the focus to target specific non-tariff costs based on exhaustive databases covering (inter alia) TBT and SPS, for specific countries and sectors. A disadvantage of these databases (both global or tailored) is the limited sectorial- and country coverage, whilst the data typically refers to only a single year which precludes the use of a panel database which, from a pure econometric perspective, helps to mitigate endogeneity problems. Another method of data extraction employs questionnaire responses on traders’ perceptions of market access. ECORYS (2009) interacts this non-tariff score variable with a route dummy (i.e. from EU to US and vice versa), which feeds into a standard cross-sectional gravity equation, to estimate bilateral EU-US non-tariff AVEs for agri-food. This approach is potentially open, however, to criticisms of limited sample size and response bias.

Two more direct quantity gap studies merit note. Owing to its sector (HS6 aggregation) and country coverage, Kee et al. (henceforth KNO) (2009) is recognised as the most comprehensive source of commodity specific non-tariff AVEs. The authors employ an aggregate import equation to estimate restrictiveness indexes from a dummy variable that accounts for the presence of non-tariff barriers (SPS and TBT) to trade plus domestic support. A second study by Li & Beghin (2012) conducts a meta-analysis (27 papers are considered) of direct quantity gap estimation methods to explain the variation of trade effects of health, safety and sanitary regulations and standards. The study considers differences in non-tariff measurement, data disaggregation and size, different estimation techniques and approaches to deal with zero trade values.

In contrast to the direct approach, which can isolate the trade restrictiveness resulting from specific, or groups of non-tariff restrictions, the indirect or implicit approach is better attuned to examining the collective trade restricting impact of all trade barriers which may otherwise be hidden (Dean et al., 2009). Thus, indirect methods start by acknowledging that trade barriers imposed by the importer country cause distortions in trade, reducing import quantities (i.e. quantity-gap) and/or increasing import prices (i.e. price-gap). Indirect quantity approaches infer the non-tariff impact on trade flows from ‘border-effects’ (e.g. Chevassus-Lozza et al., 2008), ‘fixed-effects’ (Fontagné et al., 2011), or from the depth of past trade agreements (Egger et al., 2015). As an alternative indirect approach, the ‘residual gravity approach’ infers the non-tariff impact on trade from the residuals by comparing the value of observed imports constrained by trade barriers, with the expected value of imports in the absence of said trade barriers predicted by the gravity equation (Ferrantino, 2006).

The residual approach has been more extensively applied in services sectors (e.g., Park, 2002; Francois et al., 2005; Guillin, 2013), as the direct method requires extensive databases on regulatory regimes on services which have not, hitherto, been available (Jafari & Tarr, 2014). On the other hand, in the area of merchandise trade, this method has been applied to agri-food (Philippidis & Sanjuán, 2007a, 2007b), whilst the IMF (2002) have used this same approach to estimate the trade restricting effect of all non-tariff barriers in order to calculate ‘trade potentials’ for certain groups of countries. Following this same approach EP (2014) also calculate the trade potential between the EU and US after eliminating all possible trade barriers.

A comparison between both direct and indirect approach shows that the former provides a statistical assessment of the impact of non-tariff impediments to trade through examination of the dummy coefficient. Although this is not as straightforward in the indirect approach (Dean et al., 2009), it is still possible (see next section). A further observation when inferring trade costs (i.e., AVEs) using quantities, is the sensitivity of the AVE estimate to the value of the chosen import demand elasticity and/or elasticity of substitution. The direct price gap approach gets round this problem, as it allows the direct estimation of the impact of non-tariff impediments on prices. Finally, direct and indirect econometric approaches are susceptible to misspecification bias (e.g., omitted variable bias), whilst it has been suggested that the accuracy of the non-tariff calculation in the residuals-gravity approach is potentially even more contingent upon the estimation technique and the quality of the model specification On the other hand, AVEs derived from non-tariff dummy variables depend crucially on the quality of the measurement of the non-tariff impediment under consideration (Ferrantino, 2006).

As stated in the introduction, an important aim of this research is to provide a platform upon which compatible measures of non-tariff trade costs may be implemented into modelling databases as a basis for conducting global trade impact analyses. To this end, the GTAP database is chosen with its broad, yet comprehensive, coverage of agri-food trade. Having taken the decision to employ this level of aggregation, we effectively rule out the use of non-tariff specific dummies, since at the GTAP sector concordance, at least one NTM will always be present, thereby resulting in a limited variability of observations2.

2A possible alternative would be to employ a proportion index based on the share of NTM affected HS6 lines within each GTAP sector, although even in this case, existing databases (i.e., TRAINS) do not provide a complete picture for all countries. In this respect, the EP (2014) study reports that 100% of the HS6 agricultural and food product lines are affected by at least one NTM in OCDE countries, and accordingly, a direct NTM-dummy approach is confounded with the importer fixed effect.

Consequently, an indirect or residual quantity gap approach was favoured, whilst the approach adopted here is ‘specific’, in that it estimates non-tariff impediments to trade on a bilateral basis differentiating between intra- and extra-EU (i.e., EU-US) trade routes. To provide statistical rigour often lacking in the indirect method (see discussion above), confidence intervals for bilateral non-tariff trade cost equivalents were calculated by bootstrapping, whilst pairwise t-tests for means were applied to test for statistically different AVEs across bilateral routes.

Model specification, data and estimation

In its simplest form, the gravity model posits that trade between two countries is a positive function of GDP (i.e., ‘mass’) and a negative function of trade costs (i.e., distance). Empirical applications have extended this basic premise to encompass (inter alia) preferential trade (e.g., Hayakawa & Yamashita, 2011), contiguity (e.g., Thoumi, 1989), common language and/or ex-colonial ties (e.g., Rose & van Wincoop, 2001), or even to cater for the effect of distance along different hemispheres as well as remoteness (e.g., Melitz, 2007). Other developments (e.g., Hallack, 2006) account for the so called ‘Linder’ (1961) hypothesis, which states that countries with similar per capita incomes have a greater tendency to engage in mutual trade. This is seen as a test of the monopolistic intra-industry hypothesis, whilst the polar opposite that differences in per capita incomes (which proxy for differing factor intensities) promote trade can be interpreted as support for the Hecksher-Ohlin (HO) hypothesis. This framework has also been extended to account for the role of infrastructure (Limão & Venables, 2001; Donaubauer et al., 2016) and logistics performance indicators (Martí et al., 2014).

The general theoretically-consistent gravity equation derived by Anderson & van Wincoop (2003, 2004) is formulated as follows:

where Xij are exports from country i to country j; Yi and Yj represent GDP, Yw is world GDP, tij are trade costs i.e. tij = 1 + tij where is an ‘iceberg cost’ imposed by country j on imports originating from country i; and s is the elasticity of substitution between varieties (i.e. countries). The price index variables i and Pj, denominated as ‘multilateral resistance’ terms, are a function of bilateral trade barriers (tij), and reflect the level of difficulty for country ‘i’ to engage in trade with country ‘j’, taking into account their bilateral trade barrier relative to the average trade barriers that both countries face with all their trading partners. Empirically, these unobserved terms are proxied with country specific dummies (Anderson & van Wincoop, 2004). Furthermore, provided that these variables do not change over the time horizon of the data, country-fixed effects may also capture consumers’ preferences in the importing country or the number of varieties in the exporting country (Disdier et al., 2008).

In this study, a class of Poisson3 gravity model was favoured (Santos Silva & Tenreyro, 2006, 2011) known as the Pseudo-Maximum Likelihood (PML)4 estimator (Gourieroux et al., 1984). The model assumes that the observed volume of trade between countries i and j, Xij follows a Poisson distribution with a conditional mean (µij) which is an exponential function of the explanatory variables z: µij = exp(ß’zij)5.

4Even when the dependent variable is not pure count, as it is the case of trade observations, the Poisson Maximum Likelihood estimator still provides consistent estimates (Woolridge, 2002).

5See Cameron & Trivedi (1998) for a detailed discussion of count models.

Time series trade data were obtained from UN ComTrade database (, which has been widely used in the literature (Burger et al., 2009; Fadeyi et al., 2014; Serrano et al., 2015). Thus, annual HS6 bilateral import trade values for the years 2001 to 2011, and 78 countries was collected and reconciled with the GTAP nomenclature of 18 agri-food sectors (Table 1) using WITS concordances (World Integrated Trade Solutions by WTO []).6

3The Poisson estimator maintains the model in its multiplicative theoretical form (see Eq. [1]), thereby avoiding the coefficient bias within the log-linear transformation – the functional representation of Eq. [1] when employing Ordinary Least Squares (ECORYS, 2009), or the second stage of a Heckman approach (EP, 2014).

6The GTAP sectors of raw milk and raw sugar were discarded since they are classed as non tradables. The full list of 78 countries is available from the authors upon request.

Table 1. Description of the 18 agri-food sectors

Data on ad-valorem applied tariffs were taken from versions six (Dimaranan, 2006), seven (Narayanan & Walmsley, 2008), eight (Narayanan et al., 2012) and nine of the GTAP database corresponding to the years 2001, 2004, 2007 and 2011, respectively. Population and GDP were from the World Bank (, whilst data for cultural and geographical proximities was taken from CEPII (Mayer & Zignago, 2011).

Based on a review of the literature, a comprehensive gravity specification is formulated, and a full description of the variables is presented in Table 2. The explanatory variables of infrastructure and logistics were finally discarded owing to their incomplete country and/or temporal coverage from the available sources of data7. The Poisson regression model with an exponential mean function is presented in Eq. [2]8, where the sub-index i and j refer to the exporter and importer, respectively, whilst t refers to the year:

7In particular, the coverage of the World Bank databases is limited. Thus, the length of railways is only available from 2006 onwards; data on paved roads is no longer publicly available; and the Logistics Performance Index is based on bi-annual surveys, starting from 2007.

8As a first step, we tested for possible endogeneity between tariffs and import volumes (i.e., bilateral trade may explain bilateral import tariffs). Since the Wu-Hausman test failed to reject the null hypothesis that tariffs are exogenous [F (1, 394,447) = 0.220, p = 0.642], the subsequent analysis was conducted without taking into account instruments.

Table 2. Variable descriptions in the Gravity equation

Calculating trade barrier ad-valorem equivalents

In the indirect quantity gravity approach, discrepancies between actual (AXij, i.e. variable Xij in Eqs. [1] and [2]) and predicted (PXij, i.e. E[Xij] in Eq. [2]) values of trade are taken to be indicative of trade barriers, as the prediction by the gravity equation is assumed to reflect potential trade after controlling for observed trade frictions. Given that applied tariffs are included explicitly in the model, trade barriers implied by the residuals are considered to be due to non-tariff barriers. Thus, the trade cost tij in Eq. [1] (after controlling for observed trade frictions) is assumed to reflect the NTMs:

where is the AVE of all the NTMs imposed by country j on imports originating from country i.

Following Park (2002) and Francois et al. (2005), the AVEs are calculated by trading partner averaged over all import routes. Thus, in this study, for each country j, actual and predicted imports were summed over all its trade partners: and (in our application N<=78). In order to quantify the magnitude of the trade barriers, we also followed the aforementioned authors by normalizing each observed-to-predicted trade ratio with a benchmark of the largest ratio of observed-to-predicted trade, which is interpreted as a ‘relative free-trade benchmark’ ratio (AXb/PXb)9.

9Thus, by construction, the ad valorem trade cost is zero in the country that acts as the benchmark or reference point.

Solving for the AVE of non-tariff measures ( τijNTM ) imposed by country j:

In the current study, the interest lies in the specific non-tariff restrictions between the EU and US (i.e., intra-EU; EU to US; US to EU), which in turn, requires a modification of Eq. [4]. For example, for EU imports of US goods, the AVE becomes:

where AXUS→EU(PXUS→EU) adds up observed (predicted) imports coming from the US to any country in the EU:AXUS→EU=∑kj=1AXUS→j and PXUS→EU=∑kj=1PXUS→j (k=number of Member States in the EU).10 Sectoral substitution elasticities (s) across importing sources in [4] and [5] were taken from the GTAP database (Hertel et al., 2007) and Kee et al. (2008).11 In addition to the point estimates, confidence intervals for bilateral non-tariff trade cost equivalents were calculated.12

10The composition of the EU changes over the sample period, being composed of 15 members prior to 2004, 25 countries between 2004 and 2006, and 27 from 2007 to 2011. Accordingly, the formula in [5] was calculated for each year in the sample, whilst the final estimate is the average.

11GTAP trade substitution elasticities were employed in the case of 'pdr' and 'ctl', owing to the abnormally low and high elasticity values, respectively, estimated by KNO.

12In particular, following Cameron & Trivedi (2010, Chapter 13) a non-parametric bootstrap-pair method was employed (i.e. both explanatory and dependent variable values are resampled together with replacement), clustered (i.e. resampling assumes independence of clusters of observations, where the cluster is defined by each pair of trade partners) and with, initially, 1000 replications. For each bootstrapped sample, the Poisson model was re-estimated which provided the necessary input to recalculate the bilateral AVE in Eq. [5]. Percentile bias-corrected (BC) confidence intervals were then computed (Cameron & Trivedi, 2010). The analysis was carried out employing STATA v.13.


The estimation of Eq. [2] was carried out on a sector-by-sector basis. Owing to considerations of space, further results can be found in the supplementary material (Table S1). Comparing with the relevant literature, the fit of the models, and parameter estimates were broadly in line with a priori expectations, exhibiting an acceptable level of statistical significance, whilst the relative magnitudes across explanatory variables were also consistent with previous studies.

In this section, several diagnostic comparisons were conducted to assess the plausibility of our non-tariff restriction estimates. First we compared non-tariff AVE estimates from relevant price- and quantity-gap studies in the literature. Secondly, estimated intra- and extra-EU non-tariff restrictions were compared, whilst additional match-ups were made with corresponding applied tariff AVEs from the GTAP database. Of particular interest we tested the a priori hypotheses (i) that harmonised ‘single market’ product standards and controls result in lower intra-EU non-tariff AVE estimates and (ii) that non-tariff restrictions are more trade prohibitive compared with traditional tariff measures.

Comparing country- and sector-specific AVE estimates with the literature

Making comparisons with existing literature is complicated by the choice of sectorial and country aggregation, the estimation procedure, the quantification method and years of reference in the sample. Using WTO (World Integrated Trade Solutions-WITS) concordances between GTAP and HS6 codes, KNO (2009) arithmetic average AVEs for agricultural and food imports by GTAP sector were calculated (columns 3 and 6, Table 3) based on a sample of 61 countries which are common to both KNO and the current study.13 The current study employed Eq. [4] to calculate country non-tariff AVEs which were later averaged for each GTAP sector (columns 2 and 5, Table 3).

13The full list of countries and calculations are available upon request from the authors.

Table 3. Comparison of ad-valorem equivalents (AVE) of aggregate NTMs by sector

Examining Table 3, our average estimate (second column) for both the agri-food and agricultural composites (25% and 30%, respectively) were very similar to the corresponding KNO estimates (31% and 25% - third column), whilst in the case of the aggregate food processing sector, the results were also broadly comparable (20% vs 34% - columns 5 and 6). On a sector-by-sector basis, KNO estimates for processed food products (except beverages and tobacco) were higher than those reported with our approach, while our estimates for agricultural products were higher in six out of ten sectors. In almost half of the sectors examined, the gap in mean AVE estimates between both approaches was less than 10 percentage points, with a minimum difference of 5 percentage points for fruits and vegetables and beverages and tobacco. In the case of food processing, the largest difference can be found in processed rice (110% versus our estimate of 28%). In agriculture, the main differences occured in plant based fibres (18% versus our estimate of 82%).

As a further basis of comparison, Table 4 compares our country specific estimates (columns 2 and 6) from Eq. (4) with two quantity gap studies (KNO, 2009; EP, 2014) and the price gap approach of Bradford (2003). In the case of agriculture, our non-tariff AVE estimate for the US and EU-27 was 27% and 29%, respectively. This is broadly compatible with corresponding estimates of 22% and 27% in KNO and 33% and 44% in EP (2014). Across the same three quantity gap studies, the divergence was higher for processed food, where KNO estimates were placed around the midpoint between the lower values in the current study and upper values of EP (2014). Despite the use of a different methodology, EU non-tariff AVE estimates in Bradford (2003) converged closely with the three quantity gap studies, whilst in common with our results Bradford (2003) concludes that agricultural non-tariff restrictions are more prohibitive than those of food in both the EU and US. Remarkably, all four studies conclude that ‘behind the border’ trade costs in the EU are more prohibitive than those of the US, although this finding is not consistent with non-tariff estimates of ‘food processing’ reported by OECD (2011) (30.1% and 49.5%, for EU and US, respectively).14

14It should be noted that the aggregate NTM estimates for the EU in Table 4 from our study also reflected intra-EU trade which is typically characterised by lower NTM trade costs (see the following subsection).

Table 4. Comparison of ad-valorem equivalents (AVE) of NTMs by country

Bilateral (EU-US) non-tariff AVE estimates

In a next step, Eq. [5] was employed to calculate non-tariff AVEs on intra-EU and trans-Atlantic trade in both directions (Table 5). The point AVE estimates reported here for (trade weighted) agri-food imports to the EU from the US (35%) and to the US from the EU (27%) are lower than those reported by ECORYS (2009) (56.8% and 73.3%, respectively). Apart from the different sectorial coverage and disaggregation, differences in results are also attributed to the choice of data, econometric estimator and modelling approach. Our estimates are more in line with those of EP (2014). Thus, examining the simple average agri-food estimates in our study, we estimated AVEs of 47% and 39% for the EU and US, respectively, compared with corresponding estimates in EP(2014) of 54% and 48%.15 Encouragingly, across the three bilateral routes, AVE point estimates for the ‘agri-food’ aggregate moved within the range of 17% to 35% (Table 5), which is more in line with the value of 31% reported by KNO (see Table 3). In addition, our AVE point estimates for extra-EU imports were 38% (agriculture) and 32% (food), which compares favourably with the corresponding extra-EU estimates by KNO (Table 4) of 27% and 40%. A similar observation is true for the US’s imports.

15The calculations were based on the simple average of the AVEs for 14 agri-food sectors reported in Table 2.9 of EP (2014).

Table 5. NTM AVE estimates and AVE Tariffs for bilateral trade EU-US (95% confidence interval in parentheses)1,2,3

Examining intra-EU trade for primary agricultural commodities (Table 5), the trade weighted AVE mean estimate was 23%. Moreover, individual sector estimates ranged between 15% and 19% in the broad (heterogeneous) sectors of ‘other crops’ and ‘other grains’, to 48% in ‘cattle and sheep’ and 63% in ‘plant based fibres’ (Table 5), whilst in the case of intra-EU processed food trade, the AVEs were of a lesser magnitude and more homogeneous in magnitude across sectors. On trans-Atlantic trade routes, the US was estimated to impose AVEs as high as 91%, 82% and 71% on imports of ‘cattle meat’, ‘plant based fibres’ and ‘dairy’, respectively. In turn, the EU AVE peaks were apparent on imports of ‘dairy’ (90%), ‘cattle meat’ (71%) and ‘pig/poultry meat’ (63%).

Comparing across the three bilateral routes, with the exceptions of ‘oilseeds’, ‘cattle’ ‘wool’, and ‘beverages and tobacco’, the AVE was found to be lowest on the intra-EU trade route (as expected). In addition, in those sectors where US non-tariff AVEs are deemed higher than their EU equivalents (i.e., ‘cattle meat’, ‘processed sugar’ and ‘processed rice’), with the exception of the latter, the result was found to be statistically significant. In the case of ‘vegetables and fruit’, despite the close AVE estimates of 35-36% in both directions, a t-test of the mean bootstrapped values revealed that the US AVE was higher; a result supported by the higher upper limit within the US confidence interval (see below). In the majority of sectors (14), the results appeared to indicate that EU AVEs are equally or more prohibitive than those of the US, which further supports the observation made at the end of the previous section (Table 4). On the other hand, pair-wise t-tests showed that this finding is only supported statistically in eight of those sectors (i.e., ‘other grains’, ‘oilseeds’, ‘plant based fibres’, ‘other crops’, ‘white meat’, ‘vegetable oils’, ‘dairy’, ‘beverages and tobacco’). Moreover, when testing for differences in the trade weighted AVE means on trans-Atlantic routes for agriculture, food and agri-food categories, no statistical difference was found. Interestingly, EP (2014) also reports this same finding.

The calculation of confidence intervals helped to capture the degree of certainty behind the AVE point estimates over a time period of the sample. Inspecting the results in Table 5, EU AVE point estimates, particularly on intra-EU trade, exhibited greater accuracy than those of the US. On closer inspection of the data, there were cases of small import trade flows (owing to high self-sufficiency) accompanied by significant import volatility (particularly on (inter alia) ‘other grains’ and ‘processed sugar’ trade from the EU to the US), whilst in other sectors (e.g., vegetable oils and fats trade from the EU to the US) the presence of a structural break was observed relating to an array of indeterminate sector specific events (i.e., weather shocks, agricultural policies, data aggregation).

Comparing between (trade weighted) non-tariff and tariff AVE’s on EU-US bilateral trade (Table 5), for the selection of agri-food products under consideration (with one exception16), NTMs (as expected) were found to be more trade prohibitive on trans-Atlantic imports in both directions. For aggregate agricultural imports, the tariff and non-tariff AVEs for the EU (US) were 3% and 38%, (2% and 30%), respectively. In the case of food, the corresponding estimates were 13% and 32% (2% and 27%), respectively. This finding reinforces the view expressed in the introduction that non-tariff restrictions have replaced tariffs as the dominant form of (agri-food) trade protectionism.

16In the case of EU imports of US processed sugar, the NTM and tariff AVEs are 13% and 19%, respectively


From a policy perspective, the credibility of bilateral non-tariff AVE estimates is very much a function of the sector under analysis. In both meat sectors, the relatively high non-tariff AVEs are entirely consistent with rigorous control standards and even import restrictions imposed by both the EU and US. In the case of ‘cattle meat’, high non-tariff AVEs in both partners is perhaps to be expected given EU import quota regulation, heavy SPS regulatory barriers and the retaliatory history of trade in this sector. Indeed, on the latter point, the long serving EU restriction on cattle meat treated with growth promoting hormones has been met by US sanctions on EU origin beef. It is only more recently, within the TTIP negotiations that the EU agreed to authorise the US’s use of lactic acid in meat preparations.

In the pig/poultry meat sector, the finding that the EU imposes higher non-tariff costs may be partly explained by its ban on the use of pathogen reduction treatments of poultry carcasses. Moreover, there has been ongoing concern within the EU regarding the compulsory usage of origin labelling and traceability for fresh and frozen poultry meat, which presents an important additional trade cost.17 In addition, the AVE estimate presented here (63%) for white meat trade from the US to EU coincides with the lowest estimate for pork reported by Arita et al. (2015). On the other hand, the EP (2014) estimate for white meat (82%) is closer to our upper limit (78%).

17Based on the willingness of the US chicken and pork industries to adapt to EU production standards, it has been suggested (Arita et al., 2014), that a relaxation of the EU’s tariff rate quota scheme would perhaps not have such a significant impact on US chicken exports, whilst in the pork sector, a clear rise in exports could be expected.

In the live ‘cattle’ (principally live bovine, ovine and equine) and ‘other animal products’ (principally live swine and poultry) sectors, the high AVEs reported for both partners (Table 5) are compatible with significant transport costs related to the application of mandatory requirements governing animal welfare. The result for ‘ctl’ concurs with the estimates from EP (2014) (38% and 22% in the EU and US, respectively), both of which fall within our confidence interval. Notwithstanding, in live cattle, there is an apparently counter-intuitive result that the intra-EU bootstrapped estimate (48% - Table 6) is statistically higher than the extra-EU and US import equivalents. In any case, it remains unclear as to whether significant live animal trade would occur under a hypothetical TTIP agreement.

On dairy trade, the twenty percentage point gap reported in Table 5 between the US and EU is questionable, despite the statistically significant difference in bootstrapped means. On the other hand, our point estimates reported in Table 5 are remarkably close to those in EP (2014) (92% and 68% for EU and US, respectively). On both sides of the Atlantic, a system of dairy tariff rate quotas (TRQs) is imposed, whilst it is true that the EU imposes considerable administrative burdens relating to (inter alia) milk quality requisites (somatic cell counts), and the usage of geographic indicators (i.e., parmesan, feta etc.) (USDEC, 2013). Notwithstanding, interstate sales of EU origin pasteurised milk products (known as ‘Grade A’ milk produce) are heavily complicated by numerous rules of compliance with US regulations. In its qualitative survey, ECORYS (2009) classifies these non-tariff restrictions as highly prohibitive to EU sales, which explains why the EU sought after an acceptance of equivalence in order to permit exports of EU Grade A milk produce to the US. In addition, from 2011 onwards, the Dairy Promotion Program (DPP) in the US has levied an additional charge on imported dairy products, which in turn finances promotion, education and research programs, although it remains unclear as to whether this measure benefits imported dairy products.

The fruit and vegetable market in both partners is also subject to strict quality control programs. This is reflected by notable AVE estimates on both sides, whilst the US AVE is found to be statistically higher, as noted above. Importantly, our AVEs are comparable to the estimates in Arita et al. (2015, Table 11, pp20) for fruits and vegetables. EU imports from third countries must comply with the harmonisation of maximum residue limits for pesticides and strict requirements regarding traceability, whilst the US also imposes stringent inspection programs. For example, the US Agricultural Marketing Agreement Act of 1937 establishes different marketing orders of particular relevance to fruits and vegetables (TRALAC, 2010).

On cereals trade, the point estimates for wheat are statistically the same, whilst for ‘other grains’, EU non-tariff restrictions were found to be more trade prohibitive. In the latter case, this result is perhaps not surprising, since US exports of ‘other grains’ to the EU (principally maize) are subject to strict monitoring for genetic modifications (USTR, 2007), whilst the EU imposes a TRQ scheme on grain imports. On EU imports of wheat, EU tolerance limits on mytoxins are lower than in the US which could be a significant hindrance to US exporters.18 For its part, US policy bestows significant behind the border competitive advantages to cereal producers in the form of marketing assistance, storage facility loans and insurance subsidies (TRALAC, 2010). In the case of oilseeds trade, the EU non-tariff AVE is considerably higher than the US equivalent which is consistent with the EU’s adherence to stringent GM regulations on imports of soybeans and linseeds.

18This also affects US exports of almonds and peanuts in the GTAP aggregate, 'vegetables, fruits and nuts'.

The fact that the quantitative results suggest that the US is the more prohibitive on imports of processed rice and sugar is also open to debate. The non-tariff restrictions on both sides reflect, in part, the quantitative restrictions under the tariff-rate-quota regime for both commodities. On the other hand, the EU once again imposes further controls on its GM sugar and rice imports, particularly in the case of the latter.19 Finally, on beverages and tobacco trade, the US imposes severe cross state retailing and distribution red tape restrictions on EU products (ECORYS, 2009), whilst the geographical indicator (particularly for wine) which receives much attention within the EU, is not given due consideration by US authorities. As a caveat, one should exercise caution when interpreting a single non-tariff AVE estimate for this broad sector (i.e., soft drinks; alcohol, wines, spirits and tobacco etc.). Similar caution should also apply in the case of the ‘other food’ processing estimates.

19A genetically modified strain of rice known as LibertyLink rice was developed by an eventual parent company known as Bayer CropScience. The rice was genetically modified to be tolerant to glufosinate, the active ingredient in Liberty herbicide, although in 2006 it was discovered that trial tests of this rice had contaminated the US rice supply


The year 2013 signalled the opening of negotiations between the world’s two largest trading partners, the European Union (EU) and the United States (US). Unfortunately, conventional impact studies are ill equipped to assess the potential economic gains due to the lack of any coherent and consistent database relating to non-tariff restrictions. Indeed, unlike conventional tariff measures, non-tariff impediments to trade do not have a transparent price effect which can be readily inserted into an economic model and consequently there is uncertainty in policy circles regarding the real trade cost of ‘behind the border’ measures.

To empirically answer this question, the gravity model has received recognition as a vehicle for understanding the trade restrictive impact of non-tariff measures. To complement and deepen previous gravity studies in the literature, this study provides comprehensive bilateral EU-US non-tariff ad valorem equivalent (AVE) estimates for 18 disaggregated agriculture and food activities. Indeed, in comparison with ECORYS (2009) and EP (2014), it takes further steps either in terms of the methodological approach adopted; or the level of agri-food sectoral coverage; or the decomposition of NTM AVE estimates by intra-EU and trans-Atlantic bilateral trade routes. As a means to providing accessible non-tariff AVE estimates compatible with the typically more aggregated sectoral concordance found in global modelling databases, this explorative research proposes an ‘indirect’ gravity approach.

As an initial conclusion, the magnitudes of the non-tariff AVEs estimated for both partners suggests that in the ‘cornerstone’ sectors of (inter alia) meat, dairy, cereals and vegetables and fruit, substantial trade led opportunities and threats could emerge if, under the auspices of the TTIP, both partners arrive at a common terms of reference for the harmonisation of 'behind the border' measures. In an attempt to further assess the credibility of the estimates, comparisons are made employing a number of approaches. As expected, the magnitudes of the NTMs are found to be higher than those of the tariffs, confirming the expectation that non-tariff impediments have replaced tariff barriers as the main form of trade protectionism (Hummels & Schaur, 2013).

Comparing with the literature (Bradford, 2003; KNO, 2009; EP, 2014), the results are broadly comparable, although most strikingly, there is a consensus that the EU imposes more prohibitive agri-food NTMs than the US. In many sectors, the results appear to be credible (i.e., cattle meat, pig/poultry meat, fruit and vegetables, cereals). Elsewhere, the general magnitudes appear to be plausible, although it is debateable whether the EU or US AVE should be more restrictive (i.e., dairy, processed rice and sugar). For some specific bilateral routes there are some counterintuitive results coinciding with those sectors where relatively small and volatile trans-Atlantic trade flows are observed. In addition, estimation difficulties were encountered where there are (unexplained) structural breaks within the data. In recognition of these issues, a bootstrapping procedure was employed to generate confidence intervals in order to assess the reliability of each bilateral AVE point estimate. Notwithstanding, further work could be focused on improving the sector specificity of each gravity equation to better capture observed trade trends, thereby improving the model’s predictive capacity.


The authors would like to thank two anonymous reviewers for providing a thorough review on an initial draft submission. We would also like to thank the Joint Research Centre Seville (JRC) European Commission, for providing assistance on this project. Special thanks go to Robert M’barek for supporting this initiative, whilst we are also indebted to Pavel Ciaian and Ignacio Pérez Domínguez for reading and providing insightful feedback on an earlier version of this work. The views expressed are purely those of the authors and may not in any circumstances be regarded as stating an official position of the European Commission.


Anderson JE, van Wincoop E, 2003. Gravity with gravitas: a solution to the border puzzle. Am Econ Rev 93: 170-192.

Anderson JE, van Wincoop, E, 2004. Trade Costs. J Econ Lit 42: 691-751.

Arita S, Beckman J, Kuberka L, Melton A, 2014. Sanitary and phytosanitary measures and tariff-rate quotas for U.S. meat exports to the European Union. Economic Research Service, USDA.

Arita S, Mitchell L, Beckman J, 2015. Estimating the effects of selected sanitary and phytosanitary measures and technical barriers to trade on U.S.-EU agricultural trade. USDA Econ Res Serv, Economic Research Report No. 199, November.

Berden K, Francois J, 2015. Quantifying Non-Tariff Measures for TTIP. Paper No.12 in the CEPS-CTR project “TTIP in the Balance” and CEPS. Centre for European Policy Studies/Center for Transatlantic Relations. Special Report No. 116, July. [April 27, 2016].

Bradford S, 2003. Paying the price: Final goods protection in OECD countries’. Rev Econ Stat 85 (1): 24-37.

Bradford S, 2005. The Extend and Impact of Final Goods Non-Tariff Barriers in Rich Countries. In: Quantitative Measures for Assessing the Effect of Non-Tariff Measures and Trade Facilitation; Dee P, Ferrantino M (eds.). pp: 435-481. Ed World Scientific Ltd, Singapore.

Burger M, van Oort F, GJ Linders, 2009. On the specification of the gravity model of trade: zeros, excess zeros and zero-inflated estimation. Spatial Econ Anal 4(2): 167-190.

Cadot O, Gourdon J, 2014. Assessing the price-raising effect of Non-Tariff Measures in Africa. J Afr Econ 23(4): 425-463.

Cameron AC, Trivedi PK, 1998. Regression Analysis of Count Data Cambridge University Press, Cambridge, UK. 432 pp.

Cameron AC, Trivedi PK, 2010. Microeconometrics using STATA. STATA Press, Texas, US. 706 pp.

CEPR, 2013. Reducing transatlantic barriers to trade and investment – An economic assessment. European Commission Contract TRADE10/A2/A16. Center for Economic Policy Research.

Chen N, Novy D, 2012. On the measurement of trade costs: direct vs indirect approaches to quantifying standards and technical regulations. World Tr Rev 11(3): 401-414.

Chevassus-Lozza E, Latouche K, Majkovic D, Unguru M, 2008. The importance of EU-15 borders for CEECs agri-food exports: The role of tariffs and non-tariff measures in the pre-accession period. Food Pol 33: 595-606.

Dean JM, Signoret J, Feinberg R, Ludema R, Ferrantino M, 2009. Estimating the price effects of Non-Tariff Barriers. The B.E. J Econ Anal Pol, 9(1): 1-41.

Deardorff AV, Stern RM (eds), 1998. Measurement of Nontariff Barriers. University of Michigan Press, Michigan, USA, 137 pp.

Dimaranan BV (ed), 2006. Global Trade, Assistance, and Production: The GTAP 6 Data Base. Center for Global Trade Analysis, Purdue University, West Lafayette, USA.

Disdier AC, Fontagné L, Mimouni M, 2008. The impact of regulations on agricultural trade: Evidence from SPS and TBT agreements. Amer J Ag Econ 90(2): 336-350.

Donaubauer J, Meyer BE, Nunnenkamp P, 2016. A new global index of infrastructure: Construction, rankings and applications. World Econ 39: 236-259.

ECORYS, 2009. Non-tariff Measures in EU-US Trade and Investment – An Economic Analysis. Report prepared by K. Berden, J.F: Francois, S. Tamminen, M. Thelle & P. Wymenga for the European Commission, Reference OJ 2007-S 180-219493.

Egger P, Francois J, Manchin M, Nelson D, 2015. Non-tariff barriers, integration and the transatlantic economy. Econ Pol 30(83): 539-584.

European Parliament (EP), 2014. Risks and opportunities for the EU Agri-Food Sector in a possible EU-US Trade Agreement. [April 28, 2016].

Fadeyi OA, Bahta TY, Ogundeji AA, Willemse BJ, 2014. Impacts of the SADC free trade agreement on South African agricultural trade. Outlook Agr 43(1): 53-59.

Ferrantino M, 2006. Quantifying the trade and economic effects of non-tariff measures, OECD Publications. Paris, France. Working paper No. 28.

Fontagné L, Guillin A, Mitaritonna C, 2011. Estimations of tariff equivalents for the services sectors. CEPII working paper.

Francois J, van Meijl H, van Tongeren F, 2005. Trade liberalisation in the Doha development round. Econ Pol 28: 349-391.

Gourieroux C, Monfort A, Trognon A, 1984. Pseudo Maximum Likelihood Methods: Applications to Poisson models. Economet 52: 701-720.

Guillin A, 2013. Assessment of tariff equivalents for services considering the zero-flows. Wor Tr Rev 12: 549-575.

Hallack JC, 2006. Product quality and the direction of trade. J of Int Econ 68: 238-265.

Hayakawa K, Yamashita N, 2011. The role of preferential trade agreements (PTAs) in facilitating global production networks. J Wor Tr 45: 1181-1207.

Hertel TH, Hummels D, Ivanic M, Keeney R, 2007. How confident can we be of CGE-based assessments of Free Trade Agreements Econ Mod 24: 611-635.

Hummels D, Schaur G, 2013. Time as a Trade Barrier. Ame Eco Rev 103: 2935-2959.

IMF (International Monetary Fund), 2002. World Economic Outlook, September, Trade and Finance. The International Monetary Fund, Washington D.F., USA. 247 pp.

Jafari Y, Tarr DG, 2014. Estimates of ad valorem equivalents of barriers against foreign suppliers of services in eleven services sectors and 103 countries. Policy World Bank Group. Washington, DC, USA. Research working paper no. WPS 7096.

Kee HL, Nicita A, O M (KNO), 2008. Import Demand Elasticities and Trade Distortions. Rev Econ Stat 90: 666-682.

Kee HL, Nicita A, Olarreaga M (KNO), 2009. Estimating trade restrictiveness indices. The Econ J 119: 72-199.

Li Y, Beghin JC, 2012. A meta-analysis of estimates of the impact of technical barriers to trade. J Pol Model 34: 497-511.

Limão N, Venables AJ, 2001. Infrastructure, geographical disadvantage, transport costs, and trade. World Bank Econ Rev 15(3): 451-479.

Linder S, 1961. An essay on trade and transformation. Almqvist & Wiksell, Stockholm, Sweden, 167 pp.

Martí L, Puertas R, García L, 2014. The importance of the Logistics Performance Index in international trade. Appl Econ 46(24): 2982-2992.

Mayer T, Zignago S, 2011. Notes on CEPII’s distances measures: The GeoDist database. CEPII. Working Paper No 25. [May 10, 2017].

Melitz J, 2007. North, South and distance in the gravity model. Eur Econ Rev 51: 971-991.

Narayanan BG, Walmsley TL (eds), 2008. Global Trade, Assistance, and Production: The GTAP 7 Data Base. Center for Global Trade Analysis, Purdue University, USA.

Narayanan BG, Aguiar A, McDougall R (eds.), 2012. Global Trade, Assistance, and Production: The GTAP 8 Data Base. Center for Global Trade Analysis, Purdue University, USA.

OECD, 2011. The impact of trade liberalisation on jobs and growth: Technical note. OECD Publishing. OECD Trade Policy Papers, No. 107. [April 28, 2016].

Park SC, 2002. Measuring Tariff Equivalents in Cross Border Trade in Services. Korea Institute for International Economic Policy. Working Paper 15.

Philippidis G, Sanjuán AI, 2007a. An examination of Morocco’s trade options with the EU. J Afr Econ 16: 259-300.

Philippidis G, Sanjuán AI, 2007b. An analysis of Mercosur’s regional trading agreements. World Econ 30: 504-531.

Rose AK, van Wincoop E, 2001. National Money as a Barrier to International Trade: The Real Case for Currency Union. Amer Econ Rev 91(2): 386-390.

Santos Silva JMC, Tenreyro S, 2006. The log of gravity. Rev Econ Stat 88: 641-658.

Santos Silva JMC, Tenreyro S, 2011. Further simulation evidence on the performance of the Poisson pseudo-maximum likelihood estimator. Econ Lett 112: 220-222.

Serrano R, García-Casarejos N, Gil-Pareja S, Llorca-Vivero R, Pinilla V, 2015. The internationalisation of the Spanish food industry: The home market effect and European market integration. Span J Agric Re 13 (3): e0104.

The Economist, 2013. In My Backyard: Special Report-World Economy, October 12th. [April 28, 2016].

Thoumi F, 1989. Trade flows and economic integration among the LDCs of the Caribbean Basin. Soc Econ Stud 38: 215-233.

TRALAC, 2010. Determining the scope and nature of non-tariff measures prevalent in selected international markets. Trade Law Centre for Southern Africa, November.

USDEC, 2013. Dairy Groups Welcome Launch of U.S.-EU Negotiations. United States Dairy Export Council. [May 10, 2017].

USTR, 2007. National trade estimate report on foreign trade barriers. European Union trade summary. United States Trade Representative. [May 10, 2017].

Winchester N, Rau ML, Goetz C, Larue B, Otsuki T, Shutes K, Wieck C, Burnquist HL, Pinto de Souza MJ, Nunes de Faria R, 2012. The impact of regulatory heterogeneity on agri-food trade. World Econ 35(8): 973-993.

Woolridge JM, 2002. Econometric analysis of cross section and panel data, MIT Press, Boston, MA, USA. 1064 pp.