Very preliminary draft September 2009

Analysing the effectiveness of the EBA initiative

by using a gravity model

FRANCESCO AIELLO AND PAOLA CARDAMONE (f.aiello@unical.it – p.cardamone@unical.it)

University of Calabria Department of Economics and Statistics I-87036 Arcavacata di Rende (CS), Italy

Abstract This paper provides an empirical assessment of the effectiveness of the EBA initiative launched by the EU in 2001. The period under scrutiny is 1995-2006. With respect to the related literature, we improve the reliability of results by giving more attention to the econometric setting and to the measurement of the preferential treatment. Furthermore, the analysis is carried out by considering five agricultural products, which meet three selection conditions related to the export intensity of EBA countries, to the real tariff advantages of EBA and to the intra-year distribution of EU tariffs. While outcomes are very sensitive to the methods used in the estimations, the preliminary results show robust evidence of a positive impact of EBA in increasing the exports of crustaceans. JEL codes: F10; C33. Keywords: preferential trade agreements; EBA initiative; gravity

model; panel data.

1. Introduction The aim of this paper is to assess the effectiveness of the Everything But Arms (EBA)

initiative in increasing the exports from developing countries (LDCs) to the European Union

(EU). The empirical analysis is carried out by estimating a gravity model for five selected

agricultural products imported by EU-15 over the period 1995-2006.

The EBA agreement was introduced by the EU in 2001 in order to provide tariff free

and quota free access to all EU imports from 50 LDCs1

1 The implementation of EBA in the case of two European sensitive products, rice and sugar, should

be completed in 2009.

2

Even though EBA introduces a total removal of trade restrictions, its effectiveness is

not assured. Indeed, the EBA countries (henceforth EBAs) which are also part of the

agreements in favour of African, Caribbean and Pacific (ACP) countries (Lomè and Cotonou

agreements) prefer to export under these arrangements rather than under EBA. Some authors

(i.e, Brenton 2003; UNCTAD 2003) explain this evidence by arguing that the origin rules

required for ACPs are far less restrictive under these agreements than under EBA. Moreover,

for the group of ACPs, EBA does not introduce sharp improvements regarding entry into the

EU market because the tariffs established under the Cotonou Agreement were already very

low for a large amount of commodities and the tariff gain associated with the new initiative

was non existent for the goods which already enjoyed duty-free entry into the EU.

In order to provide further evidence on the impact of EBA on EU imports, we use a

gravity model, which, in its basic form, predicts trade flows as a function of the size of the

trade partners and the geographical distance between them.

In the literature there are only two contributions which use a gravity model to explicitly

analyse the impact of EBA (Pishbahar and Huchet-Bourdon, 2008; Persson, 2008). These

studies share the use of aggregated data, i.e. total exports from LDCs to the EU, and the use of

a dummy variable, equal to 1 if the country benefits from EBA and 0 otherwise, as a proxy of

the preferential policy. From an econometric point of view, Pishbahar and Huchet-Bourdon

(2008) use the OLS estimator, while Persson (2007) considers the Heckman (1978) procedure

in order to take account of zero trade flows. Both works show that EBA is not effective in

increasing exports from beneficiary countries to the EU.

This paper aims at improving the reliability of the results by giving more attention to

the empirical and analytical setting regarding the use of disaggregated data at HS8 level, the

measurement of preferential margins and the econometric estimators.

With regards data disaggregation, the impact of EBA has been assessed by using total

trade flows between countries. However, the considering of total exports is not adequate for

evaluation of the impact of a specific trade policy – trade preferences – which is applied at

product level. Indeed, the objective of PTAs is not to influence beneficiaries’ total trade, but

to alter the incentives for developing countries to export more in specific sectors (those in

which preferences are granted). Hence, evidence based on disaggregated data is needed to

assess the impact of PTAs. Finally, by using data at HS8 level we overcome the caveats to use

aggregations of tariffs, which cannot be simply determined as a weighted sum of tariffs

applied at commodity level (Cipollina and Salvatici, 2008; Anderson and Neary, 2005).

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In this paper we focus on a group of products which have been selected after satisfying

three conditions. The first condition refers to the opportunity to work by considering a list of

products which gauge the export capability of EBA countries before 2001. To this end, we

classify all EBAs’ goods by their export shares in the world market in 2000, that is before

EBA was in force. The rationale underlying this hypothesis is that, in the very short run, in

other words when no radical change in the production and export structures of EBA countries

is required, a total removal of trade tariffs determines an effect which can only be picked up

in empirical analysis if the preferred countries were able to export before EBA, i.e. when

facing trade restrictions adopted by importing countries. The second condition is that the GSP

tariffs applied by the EU to its imports are positive. This ensures that EBA does introduce a

real tariff gain to the exports from LDCs to the EU market. In other words, it would seem to

be pointless to evaluate EBA by considering individual products with respect to which the EU

already guarantees free access under GSP. Finally, we exclude products with intra-year

variability of tariffs because, in such a case, seasonality should be taken into account by using

monthly data on exports and tariffs (Cardamone, 2009). However, this approach could give

rise to problems regarding the presence of too many missing values with respect to monthly

LDC exports. The products which satisfied all three of these conditions are cloves, vanilla

beans, coffee, crustaceans and molluscs.

The second innovation of the paper regards the variable used to measure the trade

preferences. As proxy of the preferential treatment granted by the EU under different trade

agreements, we explicitly consider the margins of preferences rather than the dummy

variables as in Pishbahar and Huchet-Bourdon (2008) and Persson (2008). This is because a

quantitative measure of preferential treatment overcomes the two main caveats of dummies.

The first caveat refers to the fact that dummies cannot discern between the different

preferential trade policy instruments (preferential tariff margins, preferential quotas, reduced

entry prices), while the second limitation is that dummies do not measure the level of trade

preferences (i.e., if we considered dummies, we would assume that the level of trade

preferences under EBA would be the same as those under Euro-Mediterranean Agreements).

Finally, the econometric method we employ controls for heterogeneity, endogeneity

and sample selection. While a heterogeneity bias would be due to the likely correlation

between specific country-pair fixed effects and regressors, the endogeneity could arise

because of the simultaneity between the dependent variable (EU imports) and regressors, in

particular PTA variables and sample selection could be the result of excluding zero-trade

observations. In more detail, we first perform a Davidson-Mackinnon endogeneity test. Since

4

we reject the hypothesis of endogeneity of regressor, we adopt the Wooldridge (1995)

procedure which controls for heterogeneity and sample selection bias at the same time.

Finally, we estimate a negative binomial model in order to take into account country-pairs not

trading and heteroskedasticity of the error term of the multiplicative gravity specification

(Santos Silva and Tenreyro, 2006). We do not employ the Poisson model, as in Santos Silva

and Tenreyro (2006), because of the restrictive assumption required by the Poisson

distribution, i.e. identical mean and variance.

With regards the results, we find mixed evidence. For four products (cloves, vanilla,

molluscs and coffee) the analysis shows that EBA is not effective, while it exerts a positive

and significant influence on the exports of crustaceans from LDCs-ACPs to the EU.

The paper is organized as follows. Section 2 presents an exploratory analysis of

exports from LDCs, section 3 introduces the gravity model and the estimation methods, while

section 4 reports the estimated results. Finally, section 5 concludes.

2. The exports of EBA to EU: a brief descriptive analysis

After the setting up of the EBA, LDCs were expected to react to the new incentives by

increasing, ceteris paribus, their market shares within the EU. Table 1 reports EBAs’ market

shares observed for three levels of data aggregation (total exports, total agricultural trade and

29 HS2-digit products) within the EU as a whole over the period 1998-2007.

With regards EBA total exports, what clearly emerges is that the market shares show

an increasing trend from 1998 to 2005, with a substantial shift in 2002, while more recently

(2005-2007) they have been stable around a value of 0.18%. This evidence indicates that the

relative importance of EBAs as suppliers of EU-27 market has increased enormously over the

period under scrutiny, passing from 0.038% in 1998 to 0.184% in 2007. The increase in the

market share is even more relevant when considering the EU market for agricultural imports

in which EBAs’ shares were 0.089% in 1998, 0.16% in 1999 and increased to 0.4%, on

average, over successive years (table 1).

A look at the 2-digit agricultural data reveals that the resulting picture is extremely

confused, in the sense that only seven categories of products (cereals, cereal-flour-starch-milk

preparation and products, cocoa, lives trees, edible vegetables, tobacco, edible fruit-nuts-peel

of citrus-melons) registered a clear increase in market shares after 2001, while the market

shares declined for other sectors (animal and vegetables fats, residual-wastes of food industry-

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animal fodder, vegetables-fruit-nut, meat, meat-fish- and seafood preparations, lac-gums-

resins-vegetable saps and extracts) and for all the remaining sectors the pattern is zig-zag (i.e.,

milling products, sugar and sugar preparations, and live animals).

Although this examination helps understand the overall changes which have occurred

in the relative capacity of EBAs to enter the EU, it does not lead to any conclusions regarding

the role of EBA. This is because the HS2-digit categories of products we consider in table 1

are still too wide in the sense that each digit covers a large number of products which are very

different from each other in many ways. The main difference we refer to regards the extent of

the preferential treatment that EBA enjoyed in exporting to the EU. Since this tariff advantage

is calculated at a very detailed level of data aggregation (the digit used by the EU to fix trade

restrictions is HS10), the 2-digit trade statistics may hide product-specific behaviour which

we are interested in for evaluation of the potential role of EBA. Again, we know that the EBA

coverage in terms of preferential treatment is at the maximum level (all goods, except for

arms, have free-access to EU), but there is a great difference within each 2-digit agricultural

sector when comparing EBA with GSP and ACP tariffs. Indeed, the real tariff gain associated

to EBA exists only if the preferential tariffs applied under GSP and ACP agreements are

positive and this occurs to a very different extent from one sector to another.

Based on these arguments, we proceed by identifying a sample of products which will

represent the baseline of the analysis. The selection was made by imposing three conditions

which refer to the export intensity of EBAs at product level, to the “size” of the preferential

treatment given by EBA and to the absence of intra-year variability of EU’s import tariffs.

As for export intensity, we order the products in terms of EBAs’ world export shares

in 2000. This ranking allows us to indentify a list of commodities, with respect to which, EBA

countries exhibited a certain degree of market competitiveness before 2001. The second step

of the selection regards the opportunity to consider the products with a real tariff advantage

and, in this sense, we restrict the sample to goods with a positive preferential tariff under the

GSP regime, the most general, world-wide preferential scheme implemented by the EU.

Finally, in order to avoid all the empirical issues related to the use of monthly data (for

instance those due to the large amount of missing values, the size of the dataset and the

method of dealing with seasonality), we remove all the commodities with a tariff calendar. To

this end, we check, year-by-year, product-by-product and for each trade agreement, the tariffs

applied by the EU and choose commodities facing the same level of duties, whatever the

month. The selection produces a sample composed of the following five products: cloves,

vanilla beans, coffee, crustaceans and molluscs.

6

For each commodity, figures 1 and 2 show the absolute values and the market shares

of EBAs’ exports to EU over the period 1995-2006. The five selected products exhibit very

different patterns. For instance, the total EU imports of cloves and vanilla from EBAs greatly

increased immediately after 2000, but they suffered a sharp reduction in 2001 and 2003,

respectively. However, with respect to total EU clove imports, those from EBAs decreased,

on average, over the period 1995-2006, while EBAs export-shares of vanilla beans alternate

decreasing to increasing annual changes (around an average share of 0.7%).. With regards

coffee, the time-series of EU imports from EBAs (both in absolute and relative terms) is

relatively stable, except for an unusual annual change between 1999 and 2000. Finally, EU

imports of molluscs and crustaceans from EBAs increased until 2000 and decreased after

2002 and 2003, respectively. The same applies for their export-shares (figures 1 and 2).

Another important issue to be addressed concerns the level of tariffs that countries face

when exporting the 5-sample products to the EU market. Figures 3-7 display the level of

import tariffs under the four main EU preferential trade agreements, namely the ordinary

GSP, the preferences granted to ACPs, the EBA and the average tariffs of Regional Trade

Agreements (henceforth RTA) which the EU makes with several developing countries (see §

3 on the RTA which we include in the analysis).

The first outcome is that GSP duties are higher than those applied under the other

preferential schemes, whatever the product. While this is, obviously, the result of the tariff

triggered criterion we introduce in selecting the products, it provides a measure of the relative

tariff advantage that, for example, LDCs would gain if they exported under the EBA regime

instead of reverting to GSP. Another finding regards the fact that the duties levied on EU

imports from ACPs are zero for all the products concerned. This means that when exporting

the 5-selected products to the EU, the 36 EBA countries which are also part of the Cotonou

Agreement2 do not obtain any advantage from the new scheme. Furthermore, in the cases of

vanilla, coffee and cloves (except 2000), there is no substantial difference between EBA or

ACP tariffs and those applied with the RTAs. All of them are roughly around zero. Finally,

2 Benin, Burkina Faso, Burundi, Central African Republic, Chad, Comoros, Congo Dem. Rep, Ivory

Coast, Eritrea, Ethiopia, Gambia, Ghana, Guinea, Guinea Bissau, Haiti, Kenya, Liberia, Madagascar, Malawi, Mali, Mauritania, Mozambique, Niger, Nigeria, Papua New Guinea, Rwanda, Sao’ Tomé and Principe, Senegal, Sierra Leone, Solomon Islands, Somalia, Tanzania, Togo, Uganda, Zambia e Zimbabwe. The EEC and the ACP countries signed their first agreement in 1969 during the Yaoundé Convention. In 1975, the Yaoundé agreement was replaced by the Lomé Convention, followed by the Cotonou Partnership Agreement in 2000. The latter agreement was replaced by the Economic Partnership Agreement (EPA) in 2008.

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with regards the exports of molluscs and crustaceans, the EBA initiative attributes an effective

tariff advantage with respect to the developing countries which have signed an RTA.

The main lesson that can be learnt from figures 3-7 is that, as far as the five selected

sectors are concerned, the 36 EBAs which also belong to the ACP agreements do not observe

any tariff advantage with respect to their previous status and, in this respect, it is reasonable to

argue that their capacity to enter EU markets does not change because of EBA. At the same

time, it is likely that EBA will exert a certain effect in favour of the non-ACP EBA countries

because the import tariffs they currently face are zero, i.e. less than the GSP duties they would

have to pay without the EBA initiative.

3. Empirical setting: the gravity model and the estimation methods

In order to assess the effectiveness of the EBA initiative we estimate the following

multiplicative and log-linearised gravity equations, respectively, over the period 1995-2006:

)exp(

)()()/()/(

4321

4321

sjt

sjt

sjt

sjt

sijt

sijt

jtitjtitsijt

EBARTAACPGSPu

POPPOPPOPGDPPOPGDPX

ββββααα

αααα

+++++++

⋅⋅⋅⋅=

[1]

sijt

sjt

sjt

sjt

sjt

jtitjtitsijt

sijt

uEBARTAACPGSP

POPPOPPOPGDPPOPGDPX

+++++

++++++=

3321

4321 )ln()ln()/ln()/ln()ln(

ββββ

ααααααα

[2]

where subscript i refers to the individual EU-15 importers (i=1,...,15), j to the exporters

(j=1,...191), t to the year (t=1995,...,2006), and s indicates the agricultural commodities, at

HS8-digit level, which are included in the five groups of products we select at HS4-digit

agricultural level,3 X is the EU’s import flow, GDP is Gross Domestic Product, POP is the

population. αsij indicates the commodity-country pair fixed effects, while us

ijt is the error

term.4

3 There is just one commodity at HS8-digit level within the HS4-digit of cloves and vanilla beans,

while there are seven at HS8-digit level within the HS4-digit of coffee, thirty-one in the group of crustaceans and, finally, thirty-two products in the aggregation of molluscs.

4 The gravity model specification used does not incorporate the country-pair background based on observable factors (distance, common border, common language, the number of landlocked

8

The GSP variable is the preferential margin granted to developing countries eligible

for GSP treatment when they export the s-th product to the EU. Similarly, the ACP and EBA

variables represent the margin of preference in favour of ACPs and EBAs, respectively. The

RTA variable indicates the margin of preference calculated by considering the applied

preferential tariffs of the bilateral trade agreements that EU signs with several countries.5 For

each preferential variable (GSP, ACP, EBA and RTA) and each tariff-line, the preferential

margin is given by the difference between the MFN duty and the preferential duty granted

under any specific trade arrangement.

Data on EU imports are from COMEXT.6 Inward processing imports are subtracted

from total imports in order to take into account imports entering the EU to be processed and

re-exported merely to benefit from tariff exemption. The set of importing countries is

comprised of the EU-15 member states, while there are 191 exporters, i.e. all the countries for

which trade statistics are available. As far as the explanatory variables are concerned, data on

GDP and population are from the World Development Indicators 2008. All data in values are

in constant 2000 Euros.

The preferential variables GSP, ACP, RTA and EBA are determined using the dataset

DBTAR (Gallezot, 2005) for the period 2001-2004, while for the years 1995-2000, 2005 and

countries in the pair, past colonial ties) which can be handled using a set of dummy variables. This exclusion is due to the fact that the dummies relating to the above mentioned variables are time invariant and, thus, will be dropped from the estimation when the country fixed effects are supposed to be the pair of trading countries (see § 3).

5 The agreements included in the analysis are those with the Mediterranean Countries (Cyprus, Algeria, Egypt, Israel, Jordan, Lebanon, Morocco, Tunisia, Turkey, Palestinian Authority), Andorra, Switzerland, Romania, Bulgaria, South Africa, Mexico, Macedonia, Croatia, Chile, Estonia, Hungary, Iceland, Norway, Poland, Slovakia, Slovenia, Czech Republic, Latvia, Lithuania, Bosnia Herzegovina, Serbia and Montenegro, Albania. The Euro-Mediterranean Partnership (Barcelona Process) started in 1995 and involves 25 EU states and 10 Mediterranean countries (Algeria, Egypt, Israel, Jordan, Lebanon, Morocco, Palestinian Territories, Syria, Tunisia, Turkey), with Libya having observer status since 1999. The Euro-Mediterranean Partnership is comprised of two complementary dimensions: a) a bilateral dimension: the EU carries out a number of activities bilaterally with each country; b) a regional dimension: regional dialogue represents one of the most innovative aspects of the Partnership, covering, at the same time, political, economic and cultural fields (regional co-operation).

6 The Comext dataset provides data expressed in CIF value. Thus, in order to transform data to FOB, we compute the CIF/FOB ratio and follow the IMF Direction of Trade Statistics (DOTS) procedure. For this calculation data are from Comtrade and given that this source provides yearly data at HS6 level, we assume that CIF/FOB ratios do not differ if we move from HS6 to HS8 commodity lines.

9

2006, they are calculated by extracting the data on tariffs from TARIC

(http://ec.europa.eu/taxation_customs/dds/tarhome_en.htm).

The overlapping of preferences is taken into account by assuming that, if a country

benefits from GSP and ACP agreements, the trade flows entering the EU market under the

ACP regime will be considered. Similarly, if a country benefits from GSP and RTA, then the

imports to the EU market under the RTA will be considered. These choices are based on two

arguments. The first refers to the fact that GSP tariffs, for the five products considered in this

paper, are generally higher than the preferential tariffs established in favour of ACP and RTA

countries. The second consideration to be made is that RTA and ACP agreements introduce

rules of origin wich are much more less restrictive to satisfy than those involved with GSP

and, hence, exporting countries would prefer not to use a GSP scheme even if the condition of

equal tariffs were met.

We run regressions [1] and [2] using two different specifications of the model. In the

first estimation we worry about the overlapping between EBA and ACP agreements. This

issue is addressed in an alternative specification of the gravity model where the overlapping

between ACP and EBA is dealt with as follows. The ACP variable is defined as the

preferential margin enjoyed by the ACP countries which do not benefit from EBA. This group

is comprised of all the ACP countries up until 2000 and only the ACP-non LDC countries

after 2001, when EBA came into operation. For ease of exposition we label this variable

ACP_only. The EBA variable indicates the preferential margin for EBA countries which are

not part of the Cotonou Agreement (we refer to these countries as EBA non-ACPs). Finally,

we include in eq. [1] and [2] the preferential margin for the 36 EBA ACP countries,

determined as the maximum preferential margin between ACP and EBA (we name this group

as ACP&EBA). 7

With regards to the econometric methods used to estimate equations [1] and [2], it is

worth noticing that the use of a gravity equation to explain trade flows suffers from three

main potential sources of bias which are related to country-pair heterogeneity, endogeneity

and the sample selection of trading countries.

Heterogeneity may be due to observable and non-observable factors specific to each

country-pair. From an econometric perspective, the omission of such factors leads to a mis-

specification of the gravity equation, and could produce biased and/or inconsistent estimates.

7 It must be pointed out that the preferential margins under EBA and ACP schemes are the same (for

the five products duty-free access is granted by both the considered schemes, see figures 3-7).

10

To take account of country-pair individual effects, we include country-pair specific dummies

in the gravity equation, i.e. we decompose the error term of equation [1] as: sijt

sijt

sijt u++= ααε , where tα indicates time fixed effects, s

ijα indicates time-invariant

commodity-country pair fixed effects and sijtu is the idiosyncratic error term. As for the

endogeneity of regressors, PTA variables could be simultaneously determined through trade

flows. In fact, it is not univocally agreed whether countries trade more because they are in a

PTA or they belong to a PTA because they already traded relatively more with each other

than with third countries. Thus, we perform a Davidson-Mackinnon (DM) endogeneity test,

which compares OLS and IV estimations in a panel framework.8 As can be seen in table 2,

the p-values of the DM test allow us to reject the hypothesis of endogeneity of the

preferential variables in all estimations.

With regards the sample selection, the general approach is to treat zero-trade flows as

missing values. However, this approach could produce biased estimates. Indeed, a sample

selection might be made because of the fact that the process underlying the decision to export

might be correlated with the gravity equation used to model the exports: estimates which

disregard this correlation are biased (Heckman, 1978; Wooldridge, 2002). Thus we perform

the test suggested by Wooldridge (1995) in order to verify the presence of non-random

selection bias. This procedure “can be viewed as an extension of Heckman’s (1979) procedure

to an unobserved effects framework” (Wooldridge, 1995: 124) and controls for unobserved

heterogeneity by adopting a fixed effects (FE) model. Thus, it is possible for unobserved

components to be correlated with the explanatory variables. Moreover, idiosyncratic errors may

have serial dependence of unspecified form. The Wooldridge (1995) procedure requires the

modelling of two different, but potentially correlated, processes. As an initial step, we model

the selection process, which determines the decision about whether to export or not, through a

probit equation, where the dependent variable is coded one if country j exports to country i,

and zero otherwise. As a second step, in the primary process (i.e. the gravity equation), we

8 In performing the endogeneity test we consider just one preferential variable which includes all

preferential schemes. Overlapping is treated in the following way: if a country benefits from GSP and EBA, the latter is the scheme considered. If a country benefits from GSP and ACP or GSP and RTA, then ACP and RTA are the agreements considered in the computation, respectively. The logarithm of aid received by the exporting country is the variable used to instrument the preferential variable. We have verified that the endogenous variable is strongly correlated with the instrument, even after sifting out the other exogenous variables in the equation, in order to meet the “order conditions” (Wooldridge, 2006).

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augment the regressions by the inverse Mills ratio (IMR or lambda) retrieved from the probit

estimates and we test the significance of the IMR coefficient.9 As is displayed in table 2, the

latter is only significant in the estimation regarding coffee exports and, therefore, in this case,

the estimates to be considered as reliable are those obtained by using the Wooldridge (1995),

while in the other four cases the best-performing estimator is the LSDV.

Finally, we take into account the arguments put forward by Santos Silva and Tenreyro

(2006) according to which a multiplicative gravity specification is more appropriate than the

log-linear one. These authors show that because of the heteroskedasticity of the error term of

the original multiplicative specification, the log-linearisation of the gravity equation changes

the “properties of the error term in a nontrivial way” (Santos Silva and Tenreyro, 2006: 644)

and, as a result, the statistical independence between the error term and the independent

variables is violated leading to inconsistent estimates. Their conclusion is also supported by

Westerlund and Wilhelmsson (2006). Thus, we estimate a negative binomial model with

fixed effects on the multiplicative gravity specification.

4. Estimation Results

Table 2 presents the estimates obtained by considering the log-linear specification of the

gravity model (eq. 2). The first thing to emerge is that the coefficient of the exporter

population has not always got a positive effect on EU imports while the coefficient of the

9 The augmented regressions are estimated on the sample of positive export flows. It is worth

mentioning that the regressors included in the probit equation are the variables of the gravity equation. In fact, we find that exclusion restrictions are difficult to justify either on theoretical or empirical grounds. Therefore, as the Inverse Mills ratio is a nonlinear function of regressors, the non-linearity of lambda is the sole source of identification in the procedure adopted to control for selection bias.

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importer population is significant with a positive sign only in the case of crustaceans.10

Furthermore, the GDP per capita of exporters has a significant and positive impact only on

imports of vanilla beans and molluscs, while the estimated parameter of the importer GDP per

capita when it is significant, as in the cases of coffee and crustaceans exports, is positive,

indicating that an increase in the EU’s wealth will induce an increase in the demand for these

commodities to be met on the international market. As far as preferential schemes are

concerned, it seems that GSP failed to increase EU imports of the selected commodities,

while the agreements in favour of ACPs seem to be effective only in augmenting EU imports

of crustaceans. RTA and EBA seem to have no effect on EU imports of the five commodities

under scrutiny.

Table 3 presents the results obtained when the multiplicative specification of the

gravity equation (eq. 1) is estimated by the negative binomial estimator. With respect to the

previous estimates, the results change dramatically, indicating that the inclusion of zero-trade

flows matters. In particular, the population of exporters has a positive impact on imports of

vanilla beans, crustaceans and molluscs, while importer population always has a positive and

significant effect on EU imports. The GDP per capita of exporters has a positive effect on EU

imports of coffee, crustaceans and molluscs, while the coefficient of importer GDP per capita

is positive in the case of cloves and crustaceans. As for the impact of the EU’s preferential

schemes, we find that GSP and RTA seem to be effective in increasing EU imports of all

commodities, except for vanilla beans, while the agreements in favour of ACP exert a positive

effect on EU imports of coffee and crustaceans. The findings concerning the impact of EBA

are mixed and indicate that the new initiative seems to be effective in augmenting LDC

exports of vanilla beans and crustaceans, whereas it does not encourage the exports of the

other products (table 3).

Finally, we consider the Stavins and Jaffe (1990) criterion to compare the results

obtained using different estimators. These authors use a measure of the goodness of fit to

10 It is worth mentioning that the signs expected for populations are ambiguous. Indeed, in most papers

the coefficients related to the population are expected to be positive because it is believed that larger countries trade more. However, it has been shown (Oguledo and Macphee, 1994) that if an exporter is large in terms of population, it may either need its production to satisfy domestic demand, so that it exports less. On the other hand, it may export more than a small country, as is the case when large firms achieve economies of scale. The same reasoning can be applied to the case of the importing country: if large, it may either import less because it is likely that the domestic sector finds it profitable to develop and make the country self-sufficient, or it may import more because it cannot satisfy all domestic demand with its own production (Pusterla, 2007).

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compare the forecasting accuracy of different specifications (Martinez-Zarzoso et al., 2007;

Martinez-Zarzoso and Marquez-Ramos, 2007). This index is equal to one minus the U-Theil

statistics and is based on the difference between predicted and actual values of the dependent

variable. In line with the Stavins and Jaffe (1990) criterion, in our case the estimates that

should be preferred are those obtained by using the log-linear specification of the gravity

model instead of the multiplicative form. In so doing the results to be considered are those

according to which EBA seems to be ineffective in increasing EU imports of all the five

commodities considered in this analysis.

When considering the overlapping between ACP and EBA preferential margins (tables

4 and 5), on the one hand we find that the Wooldridge (1995) selection test is only significant

in the cases of vanilla beans and coffee and, hence, on the other hand, the hypothesis of

sample selection is rejected when analysing the exports of cloves, crustaceans and molluscs.

Furthermore, it emerges that the estimated coefficients of the controlling variables (population

and GDP per capita) do not change with respect to the results obtained when the overlapping

between ACP and EBA is not addressed (tables 1 and 2). However, the estimated impact of

PTAs on EU imports is different. In particular, if we consider the log-linear model (table 3),

PTAs seem to be ineffective in increasing exports to the EU. This holds for the five products

with the exception of the estimations related to crustacean exports where we find that the

ACP&EBA countries benefit from the preferential treatment they receive from the EU.

However, when moving to the multiplicative specification of the gravity model the

results are different from the ones we previously discussed. Indeed, it is found that the

countries which are beneficiaries of the EU preferential treatment given under the GSP, the

RTAs and through the agreements in favour of ACPs, benefit when they export coffee and

crustaceans towards the EU market. In addition, the RTAs also exert a significant positive

impact on the EU imports of cloves. With regards the role of EBA, we find a positive effect

on exports of crustaceans both from LDC non-ACPs and EBA&ACPs to the EU. The

opposite holds true when considering the exports of molluscs, where the two coefficients

associated to EBA preferential treatment are negative. Finally, the preferences given to LDCs

which are also part of the Cotonou Agreement positively affect the exports of vanilla beans to

the EU.

In the same way as before, we apply the Stavins and Jaffe (1990) criterion to the

results reported in table 4 and 5 and find that, in this case too, the preferred specification is the

log-linear. Therefore, based on this evidence, we conclude by arguing that solely the exports

14

of crustaceans from LDC ACP to the EU benefit from the preferential treatment given by the

EU.

These results are poor in terms of the estimated impact of EBA and may be due to the

limited numbers of exporters in the sample of EBA countries. If we consider the sub-sample

of LDCs non-ACPs it emerges that there are very few positive observations (zero in the case

of cloves, coffee and vanilla beans exports, just one country exports molluscs to the EU and

five LDCs non-ACPs export crustaceans). Even when considering the 36 LDCs ACPs

countries there are just two exporters of cloves and vanilla beans11 and seven exporting

countries of coffee, although these trade flows regard a very restricted number of EU-15

individual importers and stand for four out of twelve years, at maximum. With regards

molluscs there are just seven LDCs ACPs which export to the EU and twenty five LDC ACPs

that export crustaceans to the EU. In brief, EBA exports to the EU are driven by only a few

exporting countries which probably benefit from EBA. However, the estimated impact of

EBA we provide refers to an “average” value which may hide the role of EBA for individual

countries.

5. Conclusions

This paper assesses the effectiveness of the EBA initiative on the LDC exports of cloves,

vanilla beans, coffee, crustaceans and molluscs over the period 1995-2006. The sample of

commodities is derived from a selection process based on three conditions concerning the

overall export capacity of EBA countries, the existence of an effective tariff gain for LDCs as

a result of EBA and the absence of intra-year seasonality in tariff levels.

With respect to the literature dealing with the same issue, the role of EBA, and using

the same empirical framework, namely the gravity model, we introduce a few innovations.

First of all, in this paper, preferential trade policies are measured not by a dummy, but by a

preferential margin. Secondly, in order to avoid aggregation bias in calculating an average

measure of tariffs and with the aim of better identifying the key trade flow on which the

preferential treatment is expected to exert an impact, the paper presents an evaluation

obtained using data disaggregated at HS8 level. Thirdly, we control for country heterogeneity,

endogeneity and sample selection by adopting the LSDV model and the Wooldridge (1995)

11 Actually, we should also mention a third exporting country to the EU, the United Rep. of Tanzania,

for which we observe just a positive export value of vanilla beans to Belgium in 2000.

15

approach. Finally, the use of the negative binomial estimator allows controlling to be carried

out for non-trading countries as well as heteroskedasticity of the multiplicative gravity

specification, as suggested by Santos-Silva and Tenreyro (2006).

We also apply the Stavins and Jaffe (1990) procedure to compare results of different

estimators and, on the basis of this criterion, the estimates to be preferred are those obtained

through the log-linear gravity specification.

Overall, the preliminary results show that EBA is ineffective in increasing LDC

exports of the sample five products to the EU. A positive impact of the new EU initiative has

only been found for crustacean exports of those LDC countries which are also part of the

Cotonou agreement.

The result regarding the ineffectiveness of the EBA initiative is due to many factors

but, if the comments are restricted to the cases analysed in this paper, the evidence provided

allows us to argue that an important feature driving the results is that the five selected

products already enjoyed a duty-free status because of the Cotonou Agreement. Therefore,

EBA has not been seen as a source of further tariff gain for the exports of the 36 LDC ACP

countries. Furthermore, it can be observed that there are very few LDCs actually exporting to

the EU, at least in the cases discussed in this paper. It is possible that EBA may have had a

positive effect on an individual country basis, but this effect might not be caught by a gravity

equation, because the estimated parameters refer to the average impact of the EU policy.

The fact that only a few LDC countries export to the EU could be due to the existence of non-

tariff barriers, such as transaction costs associated with the rules of origin, administrative

compliance and sanitary and phytosanitary standards which might lessen the effectiveness of

preferential margins, especially for the smallest or poorest countries. In particular, as Bureau

et al. (2007: 196) highlighted, the main motivation for the low utilisation of preferences is that

developing countries are unable to “match the technical, sanitary, phytosanitary and

traceability requirements imposed by developed countries, and, in particular, the private

standards imposed by importers and retailers”. Indeed, producers in developed countries can

take advantage of technology whereas producers in developing or less developed countries are

often unable to satisfy the standards required by the EU private retail sector.

16

Table 1 Export market shares of EBA countries in the EU-27 market (1998-2007).

Groups of Products (2-digit) 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

HS01 Live Animals 0.008 0.006 0.004 0.123 0.010 0.019 0.035 0.283 0.001 0.021 HS02 Meat and Edible Meat Offal 0.000 0.000 0.002 0.025 0.049 0.000 0.000 0.001 0.001 0.000 HS03 Fish, crustaceans, molluscs, acquatic invertebrates 0.134 0.401 2.118 2.368 2.697 3.290 3.000 2.443 2.381 2.603 HS04 Dairy Products, Eggs, Honey Edible Animal 0.000 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.003 0.001 HS05 Products of Animal Origin 0.002 0.025 0.392 0.031 0.005 0.025 0.028 0.021 0.030 0.027 HS06 Live Trees, plants, bulbs, roots, cut flowers, etc. 0.002 0.010 0.006 0.009 0.006 0.030 0.033 0.233 0.441 0.849 HS07 Edible vegetables and certain roots and tubers 0.054 0.035 0.051 0.150 0.183 0.173 0.293 0.346 0.313 0.327 HS08 Edible fruit, nuts peel of citrus, melons 0.070 0.097 0.113 0.086 0.103 0.085 0.118 0.127 0.128 0.111 HS09 Coffee, tea, mate and spices 0.466 0.922 1.090 2.359 3.251 3.537 3.010 3.195 3.259 2.565 HS10 Cereals 0.011 0.006 0.005 0.014 0.012 0.013 0.026 0.036 0.032 0.025 HS11 Milling products, malt, starches, nulin wheat gluten 0.001 0.008 0.009 0.021 0.015 0.033 0.062 0.015 0.052 0.045 HS12 Oil seed, oleagic fruits, grain, seed, fruit, etc, 0.301 0.172 0.163 0.151 0.152 0.166 0.282 0.264 0.177 0.129 HS13 Lac, gums, resins, vegetable saps and extracts 0.347 0.467 0.410 0.962 0.775 0.667 0.467 0.430 0.530 0.583 HS14 Vegetables plaiting materials, vegetable products 0.726 0.512 0.345 1.913 2.554 3.309 3.236 2.799 2.331 2.093 HS15 Animal, Vegetables fats and oils, cleavage products, etc 0.502 0.260 0.951 0.965 0.565 0.337 0.228 0.167 0.064 0.193 HS16 Meat, fish and seafood food preparations 0.250 0.118 0.772 0.790 0.522 0.863 0.661 0.515 0.519 0.451 HS17 Sugars and sugar preparations 0.019 0.357 0.456 0.710 0.490 0.897 0.797 0.572 0.816 1.027 HS18 Cocoa and cocoa preparations 0.025 0.015 0.056 0.082 0.094 0.068 0.108 0.145 0.134 0.118 HS19 Cereal, flour, starch, milk preparations and products 0.001 0.001 0.001 0.003 0.009 0.005 0.009 0.019 0.029 0.041 HS20 Vegetable, fruit, nut etc food preparations 0.008 0.001 0.015 0.012 0.043 0.014 0.011 0.011 0.008 0.039 HS21 Miscellaneous edible prepariations 0.002 0.001 0.004 0.001 0.002 0.001 0.003 0.002 0.004 0.002 HS22 Beverages, spirits and vinegar 0.000 0.002 0.013 0.007 0.010 0.008 0.007 0.011 0.007 0.007 HS23 Residues, wastes of food industry, animal fodder 0.051 0.015 0.093 0.111 0.123 0.064 0.033 0.008 0.022 0.024 HS24 Tobacco and manufactured tobacco substitutes 0.014 1.132 1.032 0.914 0.906 0.922 0.777 0.913 1.646 1.817

HS29 Organic chemicals 0.000 0.000 0.000 0.000 0.000 0.001 0.000 0.002 0.000 0.000 HS35 Albuminoids, modified starches, glues, enzymes 0.000 0.000 0.000 0.000 0.001 0.001 0.000 0.002 0.001 0.001 HS41 Raw hides an skins and leather 0.038 0.055 0.061 0.755 1.233 0.874 1.089 1.138 1.286 1.331 HS50 Silk 0.001 1.564 3.425 0.005 0.013 0.005 0.005 0.002 0.010 0.010 HS53 Wool, animal hair, horsehair yarn and fabric thereof 0.098 0.224 0.098 0.228 4.067 2.572 2.834 3.216 3.270 4.149

Total Agricultural Exports (from HS01 to HS24) 0.089 0.160 0.353 0.421 0.421 0.469 0.418 0.408 0.442 0.444 Total Exports 0.038 0.050 0.083 0.105 0.196 0.196 0.225 0.199 0.170 0.184 Source: own computations on data from Comtrade (as it is on July 22, 2009).

17

Table 2 - Estimates of the log-linear specification of the gravity model. Dependent Variable: imports in logs, 1995-2006.

Cloves 0907 Vanilla beans 0905 Coffee, coffee husks and skins and coffee substitutes 0901 Crustaceans 0306 Molluscs 0307

Fixed Effects Fixed Effects Fixed Effects Wooldridge (1995) Fixed Effects Fixed Effects

GSP 0.192 (.17) -0.261 (.15) * -0.067 (.06) -0.072 (.02) *** -0.031 (.01) *** -0.009 (.02)

ACP -0.005 (.19) -0.079 (.09) -0.093 (.12) 0.017 (.01) * 0.034 (.04)

RTA 0.094 (.11) 0.022 (.1) 0.023 (.03) 0.146 (.13) 0.012 (.01) 0.010 (.01)

EBA 0.045 (.11) -0.030 (.19) 0.273 (.28) -0.187 (.16) 0.004 (.01) 0.033 (.04)

log(POP_exporter) 3.657 (5.12) 7.677 (4.1) * -16.402 (3.34) *** -14.644 (1.13) *** -0.051 (.83) 3.980 (1.77) **

log(POP_importer) 2.762 (7.74) 5.571 (11.13) 1.920 (5.33) -0.220 (4.89) 4.112 (1.7) ** -2.958 (2.21)

log(GDP/POP_exporter) 0.701 (2.08) 5.817 (2.56) ** -6.053 (1.21) *** -1.212 (2.39) -1.230 (.31) *** 1.044 (.43) **

log(GDP/POP_importer) 0.478 (2.58) -4.004 (3.55) 3.922 (1.35) *** 2.034 (1.17) *** 1.756 (.48) *** 0.458 (.63)

Constant -111.902 (172.1) -226.337 (210.79) 267.240 (105.73) ** -88.909 (88.69) -66.583 (31.85) ** -22.195 (48.37)

Observations 337 395 1448 1448 17114 7725

R-squared 0.7204 0.6717 0.6419 0.1929 0.7216 0.7032

Davidson-MacKinnon test of exogeneity

0.4757 0.0001 1.3846 0.4258 4.1588

p-value (.49) (.99) (.24) (.51) (.04)

U-Theil 0.129 0.163 0.128 0.99997 0.099 0.098 Stavins and Jaffe (1990) 0.871

0.837

0.872

0.00003 0.901

0.902

Sample selection Test Lambda significance -3.136 (6.9) 18.935 (12.79) 14.481 (6.74) ** -9.050 (9.89) -5.307 (3.68)

Note: all regressions include yearly dummies; standard errors in parenthesis (robust to heteroskedasticity). (*), (**), (***) denote statistical significance at the 10%, 5% and % levels, respectively.

18

Table 3 - Estimates of the multiplicative specification of the gravity model. Dependent Variable: imports in levels, 1995-2006.

Cloves 0907 Vanilla beans 0905 Coffee, coffee husks and

skins and coffee substitutes 0901

Crustaceans 0306 Molluscs 0307

GSP 0.1346 (.07) * 0.0254 (.07) 0.0969 (.02) *** 0.0990 (.) *** 0.0325 (.01) ***

ACP -0.2237 (.14) * 0.0838 (.05) 0.0810 (.03) ** 0.0677 (.) *** -0.0589 (.02) ***

RTA 0.1118 (.06) * -0.0209 (.05) 0.0471 (.01) *** 0.0639 (.) *** 0.0280 (.01) ***

EBA 0.0564 (.05) 0.1889 (.05) *** -0.0426 (.06) 0.0684 (.) *** 0.0051 (.01)

log(POP_exporter) -0.274 (.05) *** 0.146 (.04) *** -0.017 (.02) 0.103 (.01) *** 0.108 (.01) ***

log(POP_importer) 0.461 (.09) *** 0.494 (.09) *** 0.208 (.04) *** 0.175 (.01) *** 0.132 (.02) ***

log(GDP/POP_exporter) -0.576 (.07) *** -0.018 (.04) 0.507 (.03) *** 0.084 (.01) *** 0.056 (.01) ***

log(GDP/POP_importer) 0.217 (.13) * -0.264 (.14) * -0.415 (.05) *** 0.042 (.02) ** -0.036 (.03)

Costant -21.048 (530.7) -28.936 (695.88) -24.713 (510.65) -9.474 (.29) *** -26.576 (395.24)

Observations 1068 1350 4877 60444 26322

Wald Chi-square 261.63 199.48 613.2 1223.43 1498.75

Log-Likelihood -3569 -4450.31 -15578.3 -195922 -88806.6 U-Theil 0.99994 0.99980 0.99998 0.992 0.99999 Stavins and Jaffe (1990) 0.00006 0.00020 0.00002 0.008 0.00001

Note: all regressions include yearly dummies; standard errors in parenthesis (robust to heteroskedasticity). (*), (**), (***) denote statistical significance at the 10%, 5% and 1% levels, respectively.

19

Table 4 - Estimates of the log-linear specification of the gravity model with overlapping between EBA and ACP. Dependent Variable: imports in logs, 1995-2006.

Note: all regressions include yearly dummies; standard errors in parenthesis (robust to heteroskedasticity). (*), (**), (***) denote statistical significance at the 10%, 5% and 1% levels, respectively.

Cloves 0907 Vanilla beans 0905 Coffee, coffee husks and skins and coffee substitutes 0901 Crustaceans 0306 Molluscs 0307

Fixed Effects Fixed Effects Wooldridge (1995) Fixed Effects Wooldridge

(1995) Fixed

Effects Fixed

Effects

GSP -0.298 (.5) 0.177 (.16) -0.129 (1.53) -0.067 (.06) -0.009 (.04) -0.027 (.01) *** 0.055 (.04)

ACP only -0.014 (.11) -0.283 (.23) 0.420 (1.51) -0.079 (.09) -0.156 (.16) -0.006 (.01) -0.049 (.06)

RTA -0.211 (.15) 0.076 (.1) 0.031 (.44) 0.023 (.03) 0.202 (.17) -0.012 (.01) 0.026 (.03)

EBA non-ACPS 0.014 (.02)

EBA&ACP -0.088 (.06) -0.153 (.17) 0.798 (3.57) 0.194 (.27) -0.185 (.18) 0.018 (.01) * 0.039 (.04)

log(POP_exporter) 8.526 (9.8) -5.218 (11.85) 18.943 (4.08) *** -16.402 (3.34) *** -15.092 (1.38) *** -0.379 (.85) 2.949 (1.86)

log(POP_importer) 3.413 (7.5) 5.280 (10.79) -2.049 (20.14) 1.920 (5.33) 0.748 (5.49) 4.128 (1.7) ** -3.090 (2.2)

log(GDP/POP_exporter) 0.638 (2.1) 7.170 (2.26) *** -2.868 (6.2) -6.053 (1.21) *** -0.635 (2.69) -1.295 (.31) *** 0.939 (.44) **

log(GDP/POP_importer) 0.317 (2.65) -4.672 (3.37) 5.230 (1.42) *** 3.922 (1.35) *** 1.720 (1.32) 1.860 (.48) *** 0.533 (.63)

constant -205.355 (219.11) 0.684 (308.65) -114.132 (551.) 267.240 (105.73) ** -107.216 (98.11) -61.680 (31.9) ** -0.890 (48.59)

Observations 337 395 395 1448 1448 17113 7725

R-squared 0.7218 0.6739 0.3194 0.6419 0.1935 0.7212 0.7032

U-Theil 0.147 0.138 0.023 0.127 0.035 0.221 0.079 Stavins and Jaffe (1990) 0.853 0.862 0.977 0.873 0.965 0.779 0.921 Sample selection Test Lambda significance 13.634

(15.31) 22.275

(11.81) *

16.227

(7.12) ** 12.010

(18.29) -4.597

(4.05)

20

Table 5 - Estimates of the multiplicative specification of the gravity model with overlapping between EBA and ACP. Dependent Variable: imports in levels, 1995-2006.

Cloves 0907 Vanilla beans 0905 Coffee, coffee husks and skins and coffee substitutes 0901

Crustaceans 0306 Molluscs 0307

GSP 0.0418 (.07) -0.0438 (.05) 0.0969 (.02) *** 0.0453 (.) *** -0.0285 (.01) ***ACP only 0.0673 (.06) 0.0268 (.04) 0.0810 (.03) ** 0.0510 (.) *** -0.0793 (.01) ***RTA 0.1571 (.04) *** -0.0085 (.03) 0.0471 (.01) *** 0.0465 (.) *** 0.0060 (.01) EBA non-ACPS 0.0264 (.01) *** -0.1100 (.05) ** EBA&ACP 0.0339 (.06) 0.2169 (.06) *** -0.0426 (.06) 0.0564 (.) *** -0.0304 (.01) ** log(POP_exporter) -0.218 (.06) *** 0.196 (.05) *** -0.017 (.02) 0.122 (.01) *** 0.087 (.01) ***log(POP_importer) 0.459 (.09) *** 0.521 (.09) *** 0.208 (.04) *** 0.174 (.01) *** 0.133 (.02) ***log(GDP/POP_exporter) -0.516 (.12) *** 0.004 (.07) 0.507 (.03) *** 0.116 (.01) *** -0.008 (.01) log(GDP/POP_importer) 0.189 (.15) -0.253 (.15) * -0.415 (.05) *** 0.021 (.02) 0.003 (.03)

costant -24.665 (1551.02) -29.378 (393.75) -24.712 (510.59) -10.081 (.3) *** -25.739 (353.93)

Observations 1056 1350 4877 60432 24995 Wald Chi-square 260.99 215.87 613.2 1322.92 1466.82

Log-Likelihood -3567.65 -4444.14 -15578.3 -195861 -87943.7 U-Theil 0.99993 0.99980 0.99998 0.99230 0.99999 Stavins and Jaffe (1990) 0.00007 0.00020 0.00002 0.00770 0.00001

Note: all regressions include yearly dummies; standard errors in parenthesis (robust to heteroskedasticity). (*), (**), (***) denote statistical significance at the 10%, 5% and 1% levels, respectively.

21

Figuure 1 – EU imports from EBA countries of five selected HS4-digit agricultural products (1995-2006). Data are expressed at 2000 constant prices.

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22

Figure 2 – Export market shares of EBA countries in the EU market of five selected HS4-digit agricultural products (1995-2006).

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23

Figure 3 – Tariff trend for EU imports of Cloves by PTA, 1995-2006. 0

12

34

1995 2000 2005year

GSP ACPRTA EBA

Figure 4 – Tariff trend for EU imports of Vanilla beans by PTA, 1995-2006.

01

23

4

1995 2000 2005year

GSP ACPRTA EBA

24

Figure 5 – Tariff trend for EU imports of Coffee by PTA, 1995-2006. 0

510

15

1995 2000 2005year

GSP ACPRTA EBA

Figure 6 – Tariff trend for EU imports of Crustaceans by PTA, 1995-2006.

01

23

45

1995 2000 2005year

GSP ACPRTA EBA

25

Figure 7 – Tariff trend for EU imports of Molluscs by PTA, 1995-2006. 0

12

34

1995 2000 2005year

GSP ACPRTA EBA

26

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