Geographical indication and wine exports. An empirical investigation considering the major European producers

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Food Policy 46 (2014) 22–36

Contents lists available at ScienceDirect

Food Policy journal homepage: www.elsevier.com/locate/foodpol

Geographical indication and wine exports. An empirical investigation considering the major European producers Mariarosaria Agostino ⇑, Francesco Trivieri Department of Economics, Statistics and Finance, University of Calabria, Italy

a r t i c l e

i n f o

Article history: Received 2 August 2012 Received in revised form 10 January 2014 Accepted 4 February 2014

Keywords: Wine exports Geographical indication European Union Gravity model

a b s t r a c t Adopting a gravity framework and using data from 1995 to 2009 for France, Italy and Spain, we investigate whether the designation of the production area has a positive pay-off in terms of greater export values, volumes and presence in different export markets. We ﬁnd that quality wines produced in speciﬁed regions (QWPSR) are associated with higher exports values, while higher export volumes tend to materialize only towards high-income destination markets. Besides, the geographical designation appears increasing the extensive margin of trade. Therefore, QWPSR may represent a strategic tool for differentiation granting competitiveness in both traditional and less habitual markets. Not all producers, yet, seem to have beneﬁted to the same extent from the geographical designation, raising the question of what harmonizing and/or promotional strategy should be adopted to enhance the effectiveness of the quality wine protection system. Ó 2014 Elsevier Ltd. All rights reserved.

Introduction The European Union protects agricultural products of excellence reckoning that a high quality reputation may help European producers not only safeguard their cultural identity, but also gain proﬁtability and competitiveness in a growing globalized market. In particular, the EU protection system of designations of origin (PDO) and geographical indications (PGI) represents an important policy instrument to preserve agricultural products characteristics greatly determined by geographical factors (autochthon plants or animals, local varieties, unique environment) and/or human local expertise (methods and traditions). A precursor of the PDO/PGI system has been applied to the wine sector, since the 1960s, through special provisions concerning quality wines produced in speciﬁed regions (QWPSR). To obtain the latter designation, wines have to be produced within a speciﬁc area and have to comply to detailed rules regarding, for instance, grape varieties, production methods, organoleptic characteristics, alcoholic strength, and maximum yields per hectare. Fulﬁlling such requirements implies lower ﬂexibility and additional costs of compliance, but allows producers to beneﬁt from a collective reputation,

⇑ Corresponding author. Address: Department of Economics, Statistics and Finance, University of Calabria, Cubo 1-C, Ponte P. Bucci, Rende, Italy. Tel.: +39 0984 492447; fax: +39 0984 492421. E-mail address: [email protected] (M. Agostino). http://dx.doi.org/10.1016/j.foodpol.2014.02.002 0306-9192/Ó 2014 Elsevier Ltd. All rights reserved.

which may increase the value of designated wines and make their production more proﬁtable. Although the protection of geographical designations occupies centre stage in the quality policy pursued by the European Union, much empirical investigation is still needed to assess the inﬂuence of employing geographical labels on several economic dimensions (Josling, 2006; Bramley et al., 2009). Moving from this consideration, our paper investigates whether the EU institutional designation of origin is associated to better exports performance of its leading wine producers (France, Italy and Spain), in terms of higher export values, greater export quantities and presence in different export markets. Indeed, running separate estimations on exports values and volumes allows us to deduce whether greater exports values are driven by higher prices or traded volumes. Further, to evaluate the geographical indication (GI) role in opening new trade routes, we separately analyse the impact of the QWPSR label on the extensive margin of trade. Besides, we provide empirical insights into the export patterns of EU geographically-labelled wine exploring whether the effect of the QWPSR denomination varies according to several dimensions, such as time, destination areas and origin countries. From a methodological point of view, no empirical study has so far assessed the impact of the EU quality labelling on wine export ﬂows (towards all potential trading partners), adopting a gravity framework and very disaggregated data. A few contributions (Dascal et al., 2002; Carlucci et al., 2007, 2008) adopt the gravity framework, but they do not focus on the potential impact of GI on wine exports, rather on the factors determining total (or high

M. Agostino, F. Trivieri / Food Policy 46 (2014) 22–36

quality) wine ﬂows. We apply a gravity model both to aggregate and very disaggregated exports data, the latter allowing to distinguish all (Combined Nomenclature 8, CN8) lines of quality wine produced in speciﬁed regions. According to our results, foreign consumers seem to value GI information as we ﬁnd a positive relation between QWPSR and bilateral trade values across time and destination areas. Concerning export volumes, geographical indication tends to be associated to higher bilateral ﬂows only towards high-income destination markets. Besides, we ﬁnd a positive inﬂuence of the geographical label on the extensive margin of trade. Finally, when looking at each single exporter, French geographically-labelled wines are associated to the highest increase of value and seem beneﬁting – though to a lesser extent – also in terms of higher export volumes, whilst analogous Italian and Spanish wines appear enjoying a lower value premium, and no surplus in terms of volumes. The remainder of the paper is organized as follows. Next section brieﬂy reviews the related literature, Section ‘Descriptive evidence’ provides a preliminary analysis on exports unit values, volumes and destination areas of QWPSR, Section ‘Empirical model’ illustrates the empirical questions and the model adopted. Section ‘Data’ describes the data, while Sections ‘Results’ and ‘Robustness checks’ present the results obtained. Section ‘Concluding remarks’ concludes. Related literature In what follows, after a short recall of the literature studying the relationship between trade and quality, we focus on the contributions that investigate the relation between trade and geographical indication. Indeed, although our work is directly linked to the latter strand of literature, it is also related to the more general research that addresses the role of product quality in shaping trade patterns, since geographical designations may be regarded as a certiﬁcation of quality. As concerns the latter research, since the seminal work of Linder (1961) – according to that richer countries tend to trade with each other higher quality products1 – several empirical contributions have tested the relationship between trade and quality (we refer to McPherson et al., 2001, and Crozet et al., 2012, for some reviews), producing mixed results. Analogously to the present work, some of these contributions employ the gravity framework (for instance, Hanink, 1988, 1990). Differently from our paper focusing on an observable institutional label, yet, this literature faces the challenge of measuring quality, that is an unobserved product attribute.2 According to Schott (2004), developed countries seem exploiting their endowment and skill advantages to add quality (or additional features) to the varieties that they export, thus imposing higher prices. On the base of Hummels and Skiba’s (2004) evidence, shipping costs applied on a per unit basis lead ﬁrms to export higher quality goods (consistently with an extended Alchian and Allen, 1964, hypothesis). Using data on shipments of thousands of product categories for a cross-section of countries, Hummels and Klenow (2005) ﬁnd that ‘‘richer countries export higher quantities of each good at modestly higher prices, consistent with higher quality’’ (p. 705), lending support to trade models allowing for quality differen-

1 In other words, bilateral trade is also driven by similarities in demands, in addition to differences in relative factor endowments as postulated by the traditional models of Heckscher (1919), Ohlin (1933) and Samuelson (1949). 2 Unit values, and indicators based on it, have been employed as a proxy of quality by several studies analyzing the potential inﬂuence of quality on the international trade patterns (e.g.: Aiginger, 1997, 2001; Chiarlone, 2001; Hallack, 2006; Ninni et al., 2006; Fischer, 2010). Indeed, when the level of disaggregation is high (hence products are homogeneous within the same category), and production costs are similar among the different countries considered, unit values may represent a viable proxy of product quality. For a review of the unit value advantages and potential problems, we refer to Aiginger (2001) and Fischer (2010).

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tiation. Hallack (2006) proposes to test the impact of quality, operating on the demand side, focusing on sectorial trade and allowing for an interaction between the importer per capita income and a measure of exporter quality (price indices at the sectoral level, retrieved from export unit values). A more recent strand of the empirical literature tests the link between trade and quality building on models of ﬁrms heterogeneity à la Melitz (2003), and employing ﬁrm-level data (e.g.: Verhoogen, 2008; Iacovone and Javorcik, 2008; Manova and Zhang, 2009; Crozet et al., 2012). Among the others, Crozet et al. (2012) ﬁnd that champagne producers characterized by higher quality rating tend to export to a larger set of destination markets, export more, and impose higher prices. Turning to the relationship between trade and geographical indication, geographical designations may alleviate asymmetric information problems between wine consumers and producers, as the geographical origin of wines – translating into peculiar characteristics – represents a trust or credence quality attribute (most consumers cannot possibly deduce the product origin and related quality even after having consumed). Under these circumstances, the reputation of the sellers might represents a key signal, that may be particularly important for foreigner consumers. According to Josling (2006), yet, the trade inﬂuence of a GI system depends on the ability of national policy to offer a proper level of protection and information. To prevent trade distortion, domestic legislation needs to neither over-protect nor under-protect consumers.3 The same author emphasizes that much empirical work needs to be done to extend our knowledge on the economic inﬂuence of employing geographical labels in a global market. In particular, in spite of its potential role, no empirical study has so far assessed the impact of the EU quality labelling on wine export ﬂows (towards all potential trading partners), adopting a gravity framework and very disaggregated data. A few studies adopt the gravity framework, but they do not focus on the potential impact of GI on wine exports, rather on the factors determining total (or high quality) wine ﬂows. Among the others, estimating two gravity equations, Dascal et al. (2002) ﬁnd that (the volume of) both EU exports and imports of wine are positively associated to the GDPs per capita of the importer and the exporter countries. Besides, the remoteness between countries seems to affect exports positively and imports negatively, analogously to the depreciation of EU currencies and the production of wine in the EU. Finally, while traded volumes seem not very sensitive to the unit prices of wine, they are responsive to EU integration. Carlucci et al. (2007) estimate an extended gravity equation to investigate which factors inﬂuence the exports value of QWPSR from Italy to its largest commercial partners. Using data for the period 1995–2005, they claim a positive effect of the EU membership and the EU enlargement. Further they argue that the WTO agreements at the end of Uruguay Round Negotiations (signed in 1994) seem to have exerted a positive inﬂuence on Italian high quality wine exports. Finally, the authors argue that Italian producers should try to take advantage of demand effects, penetrating those countries with high income growth rates. To edge the risk connected to a lower long run stability of these economies, yet, Italian producers should also target countries with moderate but stable income growth rates. Carlucci et al. (2008) consider separately the total exports of Italian high quality wine and table wine to its largest commercial partners, seeking to identify the main growing markets where

3 Incidentally, different countries advocate different systems to protect geographical origin. An intense debate is still on going in the international arena between European Union and United States and their ‘‘allies’’ (developing countries and emerging producers, respectively). Whilst the EU asks a higher protection of all GI products (not only wines and spirits), and support the creation of a multilateral register of geographical indications enforceable in all countries under the TRIPS agreement, the US prefers to protect GIs by trademark laws, as a form of private right.

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participants in the wine supply-chain should concentrate communication and promotional efforts. According to their gravity estimates, while Anglo-Saxon and Latin-American countries are associated to higher exports of both high and table wine, the Indian market is associated negatively to both kind of Italian wine exports. Further, China and Eastern-European countries seem to appreciate the cheaper category of wine but not the higher one. The authors conclude that Italy should privilege the production of high quality wine, especially in the southern regions where favourable factors, such as climate and expertise, could guarantee the production of outstanding wines. As far as other agricultural products are concerned, in Vlontzos and Duquenne’s (2007) view the PDO label, which characterizes Greek Feta since 2007, could be useful, but ‘‘is not enough on its own to improve the product’s competitiveness on an international basis’’ (p. 338). However, since they estimate a gravity equation to explain the export ﬂows of Greek Feta cheese over the period 1990–2004, they do not test this hypothesis. Using ﬁrm-level data, Curzi and Olper (2011) try to disentangle the inﬂuence of productivity from that of quality on the export behaviour of Italian food ﬁrms. They show that ﬁrms producing typically ‘Made in Italy’ foods seem to display a lower export intensity towards low income destination areas, total factor productivity being equal. The same does not hold true for ﬁrms producing DOPs. Thus, the authors conclude that the latter geographical indication does not seem to offer a competitive advantage to Italian food ﬁrms. Descriptive evidence This section provides some preliminary statistics concerning the QWPSR exports of the leading European producers we consider. Focusing ﬁrst on exports volumes, we look at the trend followed by the annual ratio of total exports of QWPSR to total exports of wine (both QWPSR and table wine) for each exporter over the period 1995–2009 (Fig. 1). The French share is the highest, oscillating around 60%. The other two countries’ shares display different patterns. In the ﬁrst years, the Italian QWPSR ratio is stable around 50% while the Spanish one is declining from 40% towards 30%. Since 2002 the Italian share ﬂuctuates between 40% and 50%, by contrast the Spanish ratio increases, reaching about 40% in 2009, tending to converge to the Italian ratio. Turning to values, we compute the average unit value of QWPSR and that of table wine, for each year and each origin country. As Figs. A1–A3 and Tables A1–A3 (in the Appendix) illustrate, for each exporter and sample year (except 1998 in the Spanish case) the average unit value of QWPSR appears signiﬁcantly higher than

the corresponding value of table wines. Next, comparing the three exporters condition, French wines display the highest (average) unit values for QWPSR and, except for 1998, also for table wines. In particular, the gap between French QWPSR and the Italian and Spanish counterparts appears often noticeable, being French QWPSR characterized, on average, by a surplus over Spanish (Italian) QWPSR of 75% (49%), which tends to further increase in the last sample years. When considering table wines, the average differences between French unit values and those of Spain and Italy are about 35% and 24%, respectively, and again these differences tend to rise in the ﬁnal part of our sample. Summarizing, QWPSR seem characterized by a substantial value premium for all countries we consider, and French wines are those commanding the highest prices. A caveat seems appropriate here: the value premiums we ﬁnd associated to the category of QWPSR may reﬂect, to some extent, higher costs of production due to the fulﬁlment of geographical designation requirements. Hence, one cannot deﬁnitely conclude that exported QWPSR are on average more proﬁtable than table wines.4 However – even though lacking information on these additional production costs – we can plausibly assume that they are on average comparable for the three exporters, thus they cannot fully account for the large difference between the French (average) unit value of QWPSR and that of the Italian and Spanish homologous wines. In other words, if production costs are homogenous, French wines seem much more proﬁtable than the Italian and Spanish equivalent products. Besides, we explore the directions that QWPSR exports tend to follow. First, for each exporter, we look at the shares of QWPSR imported by the top ten destination countries. As Table A4 shows, Germany, Great Britain and USA tend to occupy the ﬁrst three positions for all three exporters, sometimes changing order over time. Overall, European countries together to USA, Canada and Japan are predominant. The other extra-European countries (Mexico for Spain, and Singapore for France) never occupy top positions and appear only in the last period (2003–2009). Moreover, the sum of the top ten individual shares (the concentration of QWPSR exports) appears high and slightly declining over time for all exporters. Italy displays the highest overall concentration in each intervals considered (going from 91.62% to 87.50%). Second, we consider the shares of QWPSR imported by nine different world regions, which are also considered in one of our estimation (Section ‘Results’). Table A5 displays that, in all cases, European and Anglo-Saxon extra-European countries tend to absorb the major shares of QWPSR, always summing up to more than the 80% of the geographically indicated exports. By contrast, Middle East and North Africa, and South Asia countries always occupy the last two positions, with limited percentages within a range of 0.01–0.29%. The shares of destination areas including emerging economies (such as China, Brazil, and India) tend to increase, but still remain limited. Thus, a great challenge ahead wine producers seems trying to expand their exports to new destinations areas, employing appropriate strategies to conquer consumers that may be not acquainted to the wine culture.

Empirical model This paper intends to provide evidence on the trade implications of the geographical designation for the greatest European wine producers (France, Italy and Spain). To this aim, ﬁrst, we analyse whether QWPSR perform better in terms of trade values. Several empirical studies support the expectation that geographical indications may positively inﬂuence the price that consumers are

Fig. 1. Annual ratios of total exports of QWPSR to total exports of wine – export quantities.

4 More generally, it is not clear if the added value of products protected by a geographical indication is comparable to that of the corresponding standard products. On this topical issue, the European Commission has currently called for a study examining several sectors among which that of wines (EUCOM, 2012).

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incline to pay (e.g.: Combris et al., 1997; Schamel, 2006; Benfratello et al., 2009; Altomonte et al., 2010; Brentari et al., 2011). More speciﬁcally, Schamel (2006) highlights the inﬂuence that, in a global wine market, regional reputation may have on price, concluding that export quality controls combined to marketing strategies valorising the origin can beneﬁt laggard regions of both emerging and traditional producer countries. Put in a nutshell, geographical indications, guaranteeing a certain quality in the eye of consumers, should differentiate the products bearing the relative logo, making consumers more willing to pay higher prices. Therefore, producers may become less affected by price competition. In light of these considerations, one may expect a positive association between exports values and QWPSR, driven by prices systematically higher for wines with geographical designation.5 Second, we separately examine the impact of QWPSR label on trade volumes, to discern the role of prices from that of sales. Indeed, a better performance could be due to either higher prices or sales, or to both of them. European producers have to compete on the international wine market with emerging producers (the so called ‘‘New World’’ countries, such as Australia, South Africa, US and Chile) that exploit scale economies, heavily invest in promotion and gain notoriety also for the volumes they are able to place on the markets. By contrast, the supply of European producers is more fragmented.6 Hence, they cannot gain notoriety of their brands pursuing the aforementioned strategies, and should greatly beneﬁt from a collective reputation offered by public regulations introducing a credible certiﬁcation of quality. Indeed, traditional winemakers could earn profits producing smaller amounts of certiﬁed products of higher quality. Third, we investigate whether QWPSR may open new trade routes. As a matter of fact, geographical indications on labels may represent a key to open modern and/or long distance markets (Belletti et al., 2007), cleaning the latter from non-authentic origin products. An institutional guarantee is important, for both consumers and producers, to defend products from geographical name abuses – particularly relevant on international markets, where consumers are less informed on the characteristics of speciﬁc products coming from distant regions, and therefore is much easier to confound them. We assess the impact of the QWPSR label on the extensive margin of trade, by estimating a Probit model where the probability of trading is a function of the same explanatory variables employed in the gravity equation. Furthermore, we analyse the relationship between geographical designation and exports of wine in an evolving and multifaceted international environment. Initially, we investigate whether this relation has changed over time. On one hand, based on a documented growing appreciation for quality wines, one may expect an increasing positive inﬂuence of the geographical denomination on the exports performance of European producers, if geographical labels are trustworthy in the eyes of consumers.7 On the other,

5

On the other hand, as EUCOM (2004, 2006) highlight, some table wines (especially table wines with a geographical designation) can be equivalent to quality wines produced in speciﬁed regions in terms of price (and quality), while some QWPSR may be sold at lower prices due to disposal difﬁculties. 6 In the Italian case ‘‘the ﬁrst 100 producers represent about 30% of total production. The rest is made up of small, scattered wine makers that sometimes form consortia (cantine sociali or consorzi) for the distribution of their products. Such fragmentation means that only a few producers have a scale of production that makes marketing strategies cost effective. The medium to small producers have to rely on reputation and word of mouth to promote their products’’ (Brentari et al., 2011, p. 725). 7 According to Malorgio et al. (2007), reporting opposite consumption trends for quality and table wines in the EU, the European consumer is more quality than quantity-oriented. More generally, the world-wide demand for high quality wine tends to increase, whilst the total consumption of wine tends to diminish. Changing needs and habits around the world are making ‘‘sensorial pleasure, symbolic value and psychological attitudes (. . .) the most important determinants for wine consumption (Carlucci et al., 2008, p. 1).

EUCOM (2004) puts forward a decreasing signalling power of the EU quality differentiation regime, caused by two main reasons. First, since the 1990s, new emerging producers have increasingly inﬂuenced the consumer perception of the price-quality ratio by well-organized marketing campaigns and friendly packaging. Second, in some regions, several producers have adopted table wine indication to escape more restrictive rules, and have more freedom to experiment new grape varieties and innovative winemaking techniques. As a result, whilst trademarks are increasingly successful, the traditional quality ranking system may be less reliable for consumers. Then, we investigate whether the association between geographical designation and wine exports is different across destination macro-areas, as not only economic but also historical and cultural factors may inﬂuence wine consumption. For instance, European colonial heritage and high ﬂows of emigrants from the old continent have heavily affected the culture of countries such as USA and Australia, while large Asian areas have been more insulated from the European inﬂuence. Nowadays, yet, millions of ‘‘upper class’’ people in the Asian emerging economies are incline to emulate the occidental lifestyle. Thus, they could appreciate quality wine as a symbol of that lifestyle. A strand of the literature (mentioned in the review section) seeks to identify the main growing markets where participants in the wine supply-chain should concentrate communication and promotional efforts (Carlucci et al., 2007, 2008). The results provided (for instance, Carlucci et al., 2008, ﬁnd that China and Eastern-European countries seem to appreciate the cheaper category of wine but not the higher one), yet, appear likely conditioned by the omission of zero trade ﬂows, and the empirical method adopted. Finally, we investigate whether the origin country may be relevant. The GI legislation of wine is part of the EU Common Market Organization for wine, coexisting with domestic rules of producer countries. Besides, the name of the country of origin could represent a sort of ‘‘brand’’, with an associated reputation, hence an additional signal ‘‘in the eyes’’ of consumers, which could either reinforce or weaken the geographical label inﬂuence.8 To carry out our investigation, we adopt a gravity model explaining the ‘‘normal’’ or ‘‘expected’’ bilateral trade between countries. Our estimating equation (in log form) is:

WINE EXP ijkt ¼ b0 þ b1 QWPSRkt þ b2 GDP IMP jt þ b3 PRODUCTION it þ b4 POP IMP jt þ b5 DISTANCEij þ b6 CONTIGUITY ij þ b7 LANGUAGEij þ b8 RTAijt þ b9 WTOijt þ b10 CURRENCY ijt þ

X

ut T t þ lijt

ð1Þ

t

where WINE_EXP is either the value or the quantity of the bilateral export ﬂow of line k, deﬁned at the CN8 level, from exporter i (France, Italy or Spain) to importer j in period t; QWPSR is a dichotomous variable, coded one if the k line is QWPSR in period t, and zero otherwise; GDP_IMP is the importer’s gross domestic product, while PRODUCTION is the exporter’s share of QWPSR (table wine) to total production of wine for all lines of QWPSR (table wine). Since our model explains disaggregated wine export ﬂows, the source country GDP appears less appropriate to gauge the production capacity of the speciﬁc sector considered (Dascal et al., 2002; Garcia-Alvarez-Coque and Martì-Selva, 2006; Agostino et al., 2010; Cipollina and Salvatici, 2011). Nonetheless, results 8 Schamel (2010), for instance, points out a generic reputation problem for Australia in the US market. This problem, ascribed to the high volumes exported, seems driving a lower quality premium for Australian top class wine, when compared to that reached by European wines of the same category.

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are unaltered when, adopting the Baier and Bergstrand (2009) speciﬁcation, we employ the exporter countries GDP (see Section ‘Robustness checks’). POP_IMP is the importer’s population; DISTANCE is the distance, in kilometres, between the capitals of the exporter and the importer; CONTIGUITY, LANGUAGE, RTA, WTO and CURRENCY are all dichotomous variables, that indicate bilateral characteristics. The ﬁrst of them is coded 1 if exporter and importer share a common border, and zero otherwise; the second is equal to 1 if they share the same ofﬁcial primary language, and zero otherwise; RTA, WTO and CURRENCY are equal to 1 if, at time t, countries i and j have a regional trade agreement in force, both are WTO members, share a common currency, respectively, and zero otherwise.9 Finally, T is a set of time ﬁxed effects, and lijt is the error term. The description of the variables employed in the estimations, and some summary statistics, are reported in Table A6 (in the Appendix). As far as the econometric methodology is concerned, we adopt the BVOLS (Bonus Vetus OLS) estimator, suggested by Baier and Bergstrand (2009), to make gravity equations consistent to the Anderson and van Wincoop’s (2003, 2004) model. According to this model, relative trade barriers cannot be neglected as determinants of bilateral ﬂows. In fact, bilateral trade patterns depend not only on bilateral costs, but also on trade costs inﬂuencing the origin country’s exports to all other markets and on trade costs affecting the destination country’s imports from all other markets. A solution commonly used in panel data analyses to account for these ‘‘multilateral resistance’’ effects consists of adding to the gravity equation a set of importer-year and exporter-year dummies variables. When dealing with large datasets as ours, yet, one should consider thousands of dummies, that render the estimation cumbersome or unfeasible. The same problem is encountered by Liu (2009), and Head et al. (2010). Besides, Clark et al. (2004) and Liu (2009) suggest that the inclusion of a large number of country-year dummies could imply a problem of overcorrection. The BVOLS methodology allows to overcome this complication, by applying a ﬁrst-order Taylor expansion to the multilateral resistance (MR) terms of the Anderson and van Wincoop (2003, 2004) model, as follows:

MR X ij ¼

Nk X GDPk k¼1

X X Nk X Nm Nm GDPm GDPk GDPm X ik þ X mj X km GDPw GDPw GDPw GDPw m¼1 k¼1 m¼1 ð2Þ

where i is an exporter and j an importer; Xij are observed proxies of bilateral trade costs (all gravity variables with bilateral variability), indexes k and m represent countries partners of i and j, respectively.10 The gravity equation can be estimated by using the ‘‘good old’’ OLS if each of the gravity variables with bilateral variability (in our case DISTANCE, CONTIGUITY, LANGUAGE, RTA, WTO and CURRENCY) is transformed by subtracting the theoretically motivated exogenous terms (2). Besides, as robustness check, we employ the PPML estimator, proposed by Santos Silva and Tenreyro (2006), to address the problem of heteroskedastic and non-normal residuals in gravity regressions. This method also allows to account for the presence of zero 9 In Eq. (1) we omit the exporter’s population, being highly correlated to the production variable, and a dummy variable indicating if colonial links ever existed between each pair of countries as correlated to LANGUAGE (a correlation matrix is available on request). However, if we include both these variables, our results are not substantially affected. 10 The ﬁrst term on the right hand side is the GDP-weighted average (subscript w stands for world) of the trade costs that exporter i faces across all importers k, the second term is the GDP-weighted average of the trade costs that importer j faces across all exporters m, the last term is the GDP-weighted average trade cost. In our panel analysis, we compute (2) on an annual basis. In the case of time-invariant bilateral variables, the time variation of the multilateral terms is generated by the changing GDP-weights, and by the facts that importers may change over time.

trade ﬂows.11 Incidentally, in the PPML estimations, we include exporter and importer ﬁxed effects. Finally, to separately analyse the inﬂuence of QWPSR label on the extensive margin of trade, we adopt a Probit model linking the probability of exporting wine to the same gravity explanatory variables described in Table A6 (in the Appendix). Data Information on both bilateral exports of wine, deﬁned at the CN8 level (the minimum disaggregation that allows to disentangle table wines from QWPSR), and production of wine is drawn from the Eurostat database. Before 2010, the combined nomenclature allows to distinguish QWPSR from all other table wine, without discerning table wines with geographical indication.12 Besides, at the CN8 disaggregation level, COMEXT database only reports positive trade ﬂows, missing observations being zero trade ﬂows (the very few observations reported as equal to zero are not real zero ﬂows, rather they represent negligible ﬂows, below the quantity of 100 kg). Thus, we expand the original data by adding observations equal to zero when the product lines are missing.13 Not-recorded data are also treated as zeros in Coe et al. (2002), Santos Silva and Tenreyro (2006), Felbermayr and Kohler (2006), Aiello et al.(2010) and Agostino et al.(2010). We do not consider wines held in containers greater than 2 l, as they may present different export patterns, being different in terms of logistics and insurance issues, and destined to different segments of the chain production. Data on GDP, population, distance, contiguity, language, colonial links, common currency and the variables RTA and WTO are from the CEPII Gravity Dataset, provided by Head et al. (2010). Since the CEPII data are from 1948 to 2006, we update the variables of our interest until 2009. To this aim, data on GDP and population have been drawn from the World Bank dataset, while distance, contiguity, language, and colonial links variables have been imputed taking their values at 2006. Information on RTAs and WTO membership come from the WTO Regional Trade Agreements Information System (RTA-IS) and WTO (website section) Members and Observers, respectively. Finally, the currency variable has been updated using information drawn from the European Union website. 11 The omission of these observations, a common practice in the earlier literature adopting a gravity framework (for instance, Oguledo and MacPhee, 1994; Nilsson, 2002; Rose, 2004), leads to neglect the occurrence of new trade relationships (e.g.: Helpman et al., 2008; Piermartini and Teh, 2005). In our analysis, while the BVOLS estimations focus on positive trade ﬂows (the intensive margin of trade), the PPML estimations are based on both zero and non-zero trade ﬂows (hence accounting for both the intensive and extensive margin). It is worth mentioning that, recently – in response to remarks raised by Martínez-Zarzoso et al. (2007) and by Martin and Pham (2008) – Santos Silva and Tenreyro (2011) provide simulation evidence that the PPML estimator performs very well even when the proportion of zeros is large (as in the present study). 12 Indeed, following regulation n. 816/1970, the label of table wine can carry or not a geographic designation. To give an example, IGT (Indicazione Geograﬁca Tipica) and Vin de Pays wines are, respectively, Italian and French table wines with a geographic indication on the label. Evidently, the GI legislation of wine, has been heavily conditioned by pre-existing domestic rules of traditional producers. Some countries (such as Italy and France) have their own version of QWPSR (DOC/DOCG and AOC respectively). Recently, the Council Regulation EEC n. 861/2010 has extended the PDO and PGI categorizations to the wine sector, from 2011 (January, the 1st). More precisely, the PDO (or PGI) logo can be added to or replace the national designations on the label. 13 The latter imputation needs to be extremely careful, due to the complexity of the successors and predecessors system of the Combined Nomenclature (CN) codes. As a matter of fact, the CN codes are not immutable, some of them are replaced by others overtime, therefore we proceed year by year, to avoid imputing zero to codes that are not contemplated by the nomenclature in certain years. Incidentally, it is also worth mentioning that some codes have been reused, meaning that the same code may have a different content over time. This information is not correctly displayed when extracting the data (as only the current label of each code is reported and not the old ones). Thus, to correctly generate our key variable (QWPSR), we consult the code history to check whether the product description has been modiﬁed over time.

27

M. Agostino, F. Trivieri / Food Policy 46 (2014) 22–36 Table 1 Estimation results (1995–2009). NC8 disaggregation level

QWPSR GDP_IMP POP_IMP PRODUCTION DISTANCE CONTIGUITY LANGUAGE RTA WTO CURRENCY Observations R2 Model test Log pseudolikelihood

Bilateral total ﬂows (QWPSR and table wine)

BVOLS_V Column 1

BVOLS_Q Column 2

PROBIT Column 3

BVOLS_V Column 4

BVOLS_Q Column 5

PROBIT Column 6

0.670*** 0.000 0.689*** 0.000 0.338*** 0.000 0.015*** 0.000 0.162*** 0.005 0.485** 0.027 0.795*** 0.000 0.158 0.157 0.094 0.335 0.247* 0.063

0.206* 0.076 0.600*** 0.000 0.246*** 0.000 0.004 0.124 0.302*** 0.000 0.292 0.197 0.730*** 0.000 0.163 0.156 0.238** 0.018 0.026 0.840

0.363*** 0.000 0.268*** 0.000 0.573*** 0.000 0.010*** 0.000 0.350*** 0.000 0.071 0.513 0.501*** 0.000 0.061*** 0.007 0.027 0.361 0.006 0.837

1.098*** 0.000 1.139*** 0.000 0.537*** 0.000 0.028*** 0.000 0.225* 0.059 0.339 0.346 1.596*** 0.000 0.041 0.842 0.522*** 0.003 0.935*** 0.000

0.315*** 0.001 1.005*** 0.000 0.416*** 0.000 0.010*** 0.000 0.331*** 0.009 0.668 0.109 1.440*** 0.000 0.274 0.201 0.583*** 0.001 0.547 0.100

0.452*** 0.000 0.356*** 0.000 0.154*** 0.000 0.010*** 0.000 0.031 0.683 0.377 0.450 1.241*** 0.000 0.492*** 0.002 0.411*** 0.000 0.106 0.664

94,430 0.201 93.35 0.000

82,269 0.2 61.59 0.000

448,920

12,059 0.595 95.6 0.000

11,500 0.478 53.6 0.000

14,624

161172 0.000 1.8E+05

515.60 0.000 5.0E+03

The p-values of the tests are reported in italics. Constant and time dummies always included but not reported. BVOLS_V(Q) stands for Bonus Vetus OLS when wine export ﬂows are in value (in quantity). QWPSR is a dummy variable coded 1 for bilateral ﬂows of Quality Wines Produced in Speciﬁed Regions; GDP_IMP is the gross domestic product of the importer; POP_IMP is the population of the importer; PRODUCTION is the annual exporter’s share of QWPSR (table wine) to total production of wine, for all lines of QWPSR (table wine); DISTANCE is the distance between the capitals of the exporter and the importer; CONTIGUITY is a dummy variable coded 1 if exporter and importer share a common border; LANGUAGE is a dummy variable coded 1 if exporter and importer share the same ofﬁcial primary language; RTA is a dummy variable coded 1 if exporter and importer have a regional trade agreement in force; WTO is a dummy variable coded 1 if exporter and importer are WTO members; CURRENCY is a dummy variable coded 1 if exporter and importer share a common currency. Variables GDP_IMP, POP_IMP, and DISTANCE are in natural logarithms. When considering quantities (columns 2 and 5), GDP_IMP is the importer’s GDP deﬂated by the US deﬂator (World Bank WDI, base year 2005). Model test is the F-test (Wald test) of joint signiﬁcance of all explanatory variables in the BVOLS (Probit) estimations. *** Statistical signiﬁcance at the 1% level. ** Statistical signiﬁcance at the 5% level. * Statistical signiﬁcance at the 10% level.

By matching Eurostat and CEPII information, we obtain 613,800 observations on 211 importing countries, and the tree exporters considered (France, Italy and Spain). Despite this dataset spans the years 1988–2010, we limit our analysis to the 1995–2009 interval to avoid the main nomenclature amendments occurred in 1995 and 2010. To give an example, QWPSR lines are 25% of the total number of lines from 1998 to 1994, becoming 60% of the total lines from 1995 onwards (see Table A7 in the Appendix for details on the number of quality wines and that of total lines, by year and exporter). Rather than to an extension of the exported varieties, this raise is due to a modiﬁcation of the combined nomenclature, leading to a ﬁner classiﬁcation of quality wines. Wines generically classiﬁed as produced in speciﬁc regions until 1994, since 1995 are distinguished in separate lines according to the region they originate from.14 Finally, in a robustness check of our results, we employ data on a tariff measure (TARIFF), drawn from the TRAINS (Trade Analysis and Information System) database.

Column 1 of Table 1 reports the BVOLS estimates concerning the benchmark Eq. (1) when the dependent variable is expressed in nominal terms, while columns 2 shows the output when the dependent variable is expressed in quintals.15 The inference is based on robust (heteroskedasticity consistent) standard errors, which also allow for correlation of the idiosyncratic error terms at the country-pair level (i.e. we cluster observations at the latter level). Considering ﬁrst the equation on exports values (columns 1), most of the signiﬁcant standard gravity variables display the expected sign, except for the contiguity (CONTIGUITY) and common currency (CURRENCY) indicators.16 Our key variable coefﬁcient is positive and statistically signiﬁcant, QWPSR being characterized by a surplus in (expected) exports value of about 95%.17 When considering exports quantities (columns 2), the QWPSR coefﬁcient, negative and statistically signiﬁcant, implies that QWPSR display lower exports

14 To give an example, the code 22042121 – individuating QWPSR (in containers not greater than 2 l, of actual alcoholic strength equal or lower than 13%) and valid from January 1988 to December 1994 – has 16 successors from 1995, indicating quality white wines produced in 16 different regions, such as Alsace (22042111), Bordeaux (22042112), Tuscany (22042126), Valencia (22042137) and so forth. Thus, the average amount of QWPSR exports computed at the line-level is abruptly decreasing from 1995 due to the classiﬁcation revision, rather than to a decline of quality wine export ﬂows. Since the expected value of line-level amounts of quality and table wines is dependent on the number of (both quality and table) lines contemplated by the CN system, and other minor nomenclature revisions have occurred over time, we will also estimate a gravity equation on total amounts of table and quality wines.

15 We do not deﬂate nominal values to avoid the ‘‘bronze medal mistake’’ pointed out by Baldwin and Taglioni (2007). On this issue, we also refer to Baldwin et al. (2008) and De Benedictis and Taglioni (2011). 16 The expected sign of the importer population is ambiguous: on one hand, larger countries could import more because the national production is limited compared to the demand, on the other, they could import less because a developed domestic sector may be able to fulﬁl a large proportion of the domestic demand. 17 Since the dependent variable is in log form and the variable of interest is a dummy, the estimated percentage increase/decrease in bilateral exports associated to geographical indication is calculated as ½expðb1 Þ 1 100, where b1 is the estimated parameter of QWPSR.

Results

28

M. Agostino, F. Trivieri / Food Policy 46 (2014) 22–36

volumes of about 19%. Finally, when looking at the Probit estimates (column 3), the dummy of interest is positive and statistically signiﬁcant, thus QWPSR appear associated to a greater probability of trading. As the different number of observations employed in the estimations suggests, data on quantities are missing more frequently than data on values. However, using either information on values or volumes to deﬁne the dummy (dependent) variable of our Probit model does not affect our results. Hence, we report Probit results obtained employing data on values, making those based on volumes available on request. So far we have considered either the value or quantity of bilateral export ﬂows at the CN8 disaggregation level. A high disaggregation is appealing to gain efﬁciency (thanks to the large number of observations), and to account for other potential determinants of wine trade ﬂows (such as tariffs), which are not much informative for more aggregated data.18 On the other hand, as pointed out in the data section, disaggregated data are inﬂuenced by the nomenclature conﬁguration. To avoid this problem, for each pair of countries and year, we consider two broad product lines: total ﬂows of table wine and total ﬂows of QWPSR.19 Comparing the estimations concerning export values (column 4 versus column 1 of Table 1) and export volumes (column 5 versus column 2 of Table 1), our main ﬁnding is conﬁrmed in terms of sign and signiﬁcance, the magnitude of our key variable coefﬁcient tending to be higher when aggregating trade ﬂows. Finally, looking at the Probit model results (column 6 versus 3), the QWPSR estimated coefﬁcients are comparable. To summarize, according to our results, the geographical indication of wine, despite associated to lower export volumes, seems increasing traded values and generating new trading relationships. To end with, we investigate the inﬂuence of geographical designation over time, destination and origin countries. To look at the association between QWPSR and exports over time, we run Eq. (1) separately year by year, and summarize the results obtained by Figs. 2 and 3. On the y-axis we place the exponentiated coefﬁcient of the dummy of interest, namely the estimated ratio between exports of QWPSR and exports of table wine. The shaded bands corresponds to 95% conﬁdence intervals. Considering ﬁrst the results concerning export values (Fig. 2), except in 1995, the aforementioned ratio is always greater than, and statistically different from one (i.e. one is not included in the conﬁdence band). Despite undulations, the ratio tends to increase up to 2007, when it reaches its maximum value, declining in the last two years of our sample. When employing data on export volumes (Fig. 3), the exponentiated coefﬁcient of QWPSR is mostly lower than (and statistically different from) one until 2001, increasing since then and becoming not statistically signiﬁcant in the last eight years of the period. To save on space, the estimates on which Figs. 2 and 3 are based are available on request. To sum up, for most of the period we consider, QWPSR are associated to increasing surpluses in terms of value, but never in terms of volumes, even though they appear losing their volume disadvantage in the last years of our sample. Overall these ﬁndings seem not supporting the hypothesis that the traditional quality rating system has weakened its inﬂuence on consumers since the 1990s (EUCOM, 2004).

18 If we extend our gravity equation by introducing a measure of import tariff (the simple average of the Effectively Applied Tariff, at 6-digit lines, provided by the WITSTRAINS data source), our results are conﬁrmed despite the observations reduce to about 25% of the estimating sample. Incidentally, tariffs may be endogenous to trade ﬂows: an increase of import ﬂows could foster protecting policies, causing an increase of tariffs in importing countries. This possibility, yet, is negligible in the present analysis, since the Uruguay Round agreement (signed in 1994) has considerably reduced the degree of protection in the wine market. 19 Although not reported, disaggregated estimations are available on request. The relative results conﬁrm our main ﬁnding: QWPSR exports tend to be characterized by a surplus (deﬁcit) in terms of values (volumes) over time and across most destination areas, and French wines seem those beneﬁting most from the geographical label.

Fig. 2. Exponentiated coefﬁcients of QWPSR. Single year BVOLS estimations (1995– 2009), export values (shaded bands corresponds to 95% conﬁdence intervals).

Fig. 3. Exponentiated coefﬁcients of QWPSR. Single year BVOLS estimations (1995– 2009), export quantities (shaded bands corresponds to 95% conﬁdence intervals).

Next, we allow the impact of the geographical indication dummy to vary according to nine geographical world regions (WRs), described in Table A5, in the Appendix. By interacting each of the regional dummy – except that indicating WR9 (West Europe) – with QWPSR, we make West Europe the control region. Hence, for the latter the estimated inﬂuence of quality labelling is the estimated coefﬁcient of QWPSR, whilst for any other region is given by the sum of the QWPSR parameter and the coefﬁcient of the relative interaction term (INTE_WR1/8).20 Columns 1and 2 of Table 2 report these sums and the p-values of the relative t-tests. Considering ﬁrst export values, column 1 shows that the inﬂuence of the geographical label is always positive and statistically signiﬁcant. Further, East Asia and Paciﬁc high-income countries (WR3) is the area characterized by the largest increase of QWPSR value relative to the table wines one, followed by extra-European Anglo-Saxon (WR1), West Europe (WR9), and Latin America (WR5), whilst Sub-Saharan Africa (WR8) is the region displaying

20 The signiﬁcance of the sum of the QWPSR coefﬁcient and a generic interaction (INTE_WR) parameter is assessed calculating the standard error: qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ r~ ¼ v arðb~QWPSR Þ þ v arðb~INTE WR Þ þ 2cov ðb~QWPSR b~INTE WR Þ.

Table 2 Estimation results. Interacting QWPSR with exporters and world regions dummies (1995–2009).

QWPSR (INTE_WR1)

QWPSR * WR2

(INTE_WR2)

QWPSR * WR3

(INTE_WR3)

QWPSR * WR4

(INTE_WR4)

QWPSR * WR5

(INTE_WR5)

QWPSR * WR6

(INTE_WR6)

QWPSR * WR7

(INTE_WR7)

QWPSR * WR8

(INTE_WR8)

QWPSR * FRA

(INTE_FRA)

QWPSR * ITA

(INTE_ITA)

GDP_IMP POP_IMP PRODUCTION DISTANCE CONTIGUITY LANGUAGE RTA WTO CURRENCY Observations R2 Model test Log pseudolikelihood QWPSR + INTE_WR1 QWPSR + INTE_WR2 QWPSR + INTE_WR3

Interacting QWPSR with exporters dummies

BVOLS_V Column 1

BVOLS_Q Column 2

BVOLS_V Column 3

BVOLS_Q Column 4

1.422*** 0.000 0.3210 0.154 0.2420 0.199 0.396* 0.055 0.370** 0.025 0.0270 0.857 0.3300 0.253 0.2820 0.212 0.956*** 0.000

0.0370 0.808 0.547*** 0.004 0.1160 0.587 0.2990 0.145 0.2690 0.141 0.0060 0.973 0.4660 0.196 0.0130 0.959 1.038*** 0.000

1.144*** 0.000

1.634*** 0.000

1.480*** 0.000 1.197*** 0.000 1.026*** 0.000 0.426*** 0.000 0.0020 0.665 0.332*** 0.008 0.5310 0.180 1.317*** 0.000 0.2650 0.214 0.608*** 0.001 0.571* 0.069 11,500 0.503 52.96 0.000

1.181*** 0.000 0.575*** 0.000 0.029*** 0.000 0.586*** 0.004 0.2360 0.528 1.590*** 0.000 0.0440 0.833 0.0320 0.867 0.380* 0.068 11,147

1.050*** 0.000 0.453*** 0.000 0.012*** 0.000 0.884*** 0.000 0.1300 0.764 1.381*** 0.000 0.2000 0.354 0.0220 0.912 0.0330 0.918 10,592

2.301*** 0.000 0.667*** 0.000 1.166*** 0.000 0.549*** 0.000 0.010*** 0.000 0.227** 0.044 0.1260 0.705 1.368*** 0.000 0.0340 0.867 0.561*** 0.001 0.945*** 0.000 12,059

0.668 76.46 0.000

0.569 48.33 0.000

0.641 103.2 0.000

1.743*** 0.000 1.181*** 0.000 1.818*** 0.000

0.584*** 0.000 0.078 0.650 0.336* 0.052 29

(continued on next page)

M. Agostino, F. Trivieri / Food Policy 46 (2014) 22–36

QWPSR * WR1

Interacting QWPSR with world regions dummies

30

Table 2 (continued)

QWPSR + INTE_WR4 QWPSR + INTE_WR5 QWPSR + INTE_WR6 QWPSR + INTE_WR7 QWPSR + INTE_WR8 QWPSR + INTE_FRA QWPSR + INTE_ITA

Interacting QWPSR with world regions dummies

Interacting QWPSR with exporters dummies

BVOLS_V Column 1

BVOLS_Q Column 2

BVOLS_V Column 3

BVOLS_Q Column 4

1.052*** 0.000 1.395*** 0.000 1.092*** 0.000 1.14*** 0.000 0.466** 0.017

0.232 0.118 0.043 0.738 0.429 0.213 0.024 0.914 1.001*** 0.000 1.157*** 0.000 0.477** 0.001

0.155* 0.079 0.437** 0.015

M. Agostino, F. Trivieri / Food Policy 46 (2014) 22–36

The p-values of the tests are reported in italics. Constant and time dummies always included but not reported. BVOLS_V(Q) stands for Bonus Vetus OLS when wine export ﬂows are in values (in quantity). QWPSR is a dummy variable coded 1 for bilateral ﬂows of Quality Wines Produced in Speciﬁed Regions; GDP_IMP is the gross domestic product of the importer; POP_IMP is the population of the importer; PRODUCTION is the annual exporter’s share of QWPSR (table wine) to total production of wine, for all lines of QWPSR (table wine); DISTANCE is the distance between the capitals of the exporter and the importer; CONTIGUITY is a dummy variable coded 1 if exporter and importer share a common border; LANGUAGE is a dummy variable coded 1 if exporter and importer share the same ofﬁcial primary language; RTA is a dummy variable coded 1 if exporter and importer have a regional trade agreement in force; WTO is a dummy variable coded 1 if exporter and importer are WTO members; CURRENCY is a dummy variable coded 1 if exporter and importer share a common currency. Variables GDP_IMP, POP_IMP, and DISTANCE are in natural logarithms. When considering quantities (columns 2 and 4), GDP_IMP is the importer’s GDP deﬂated by the US deﬂator (World Bank WDI, base year 2005). In columns 1 and 2 (3 and 4) dummies for World Regions WR_1/8 (exporter countries D_FRA and D_ITA) included but not reported. World regions are: WR1 = Anglo-Saxon (extra-European); WR2 = East Asia and Paciﬁc, excluded high-income; WR3 = East Asia and Paciﬁc, highincome; WR4 = East Europe and Central Asia; WR5=Latin America and Caribbean; WR6 = Middle East and North Africa; WR7 = South Asia; WR8 = Sub-Saharan Africa; WR9 = West Europe. Model test is the F-test of joint signiﬁcance of all explanatory variables. *** Statistical signiﬁcance at the 1% level. ** Statistical signiﬁcance at the 5% level. * Statistical signiﬁcance at the 10% level.

31

M. Agostino, F. Trivieri / Food Policy 46 (2014) 22–36 Table A1 Annual means of unit value (in $) – Spain. Year

QWPSR (a)

Table wine (b)

(a) (b)

Diff. > 0 p-value

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

3.882 4.302 3.856 4.145 4.780 4.158 4.556 n.a. 5.547 6.777 6.204 6.426 7.111 7.548 8.272

3.252 3.683 3.286 4.037 3.355 3.287 3.495 n.a. 3.748 4.563 4.880 4.223 5.148 5.449 6.030

0.630 0.619 0.570 0.108 1.424 0.870 1.061 – 1.799 2.214 1.324 2.203 1.963 2.100 2.242

0.007 0.012 0.002 0.393 0.000 0.000 0.033 – 0.000 0.000 0.011 0.000 0.001 0.000 0.000

3

4

5

6

7

8

Fig. A1

1995

1997

1999

2001

2003

Table wine

2005

2007

2009

QWPSR

Our calculations on EUROSTAT (http://epp.eurostat.ec.europa.eu/newxtweb/setupdimselection.do#) data.

Table A2 Annual means of unit value (in $) – France. Year

QWPSR (a)

Table wine (b)

(a) (b)

Diff. > 0 p-value

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

7.402 7.750 6.778 7.852 7.384 6.792 6.342 6.937 8.502 9.766 10.324 12.669 13.934 15.806 15.256

4.496 4.440 3.734 3.515 4.372 3.581 3.872 3.691 5.121 6.562 6.557 8.347 6.936 9.230 9.859

2.906 3.309 3.044 4.337 3.012 3.211 2.470 3.247 3.382 3.204 3.767 4.322 6.998 6.576 5.397

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0

5

10

15

Fig. A2

1995

1997

1999

2001

Table wine

2003

2005

2007

2009

QWPSR

Our calculations on EUROSTAT (http://epp.eurostat.ec.europa.eu/newxtweb/setupdimselection.do#) data.

the lowest QWPSR surplus. It is worth noting that the positive surplus associated to QWPSR tends to be high not only in highincome destination markets (such as East Asia and Paciﬁc highincome countries, extra-European Anglo-Saxon countries and West Europe), but also for areas including emerging economies such as China (WR2), Brazil (WR5), and India (WR7), ranging from about 213% for WR7 to about 303% for WR5. Turning to the export volumes estimations (column 2), the geographical indication parameter appears mostly negative and not statistically signiﬁcant, except

for some high-income areas (WR1 and WR3), for which a positive and signiﬁcant relationship tends to emerge. To further investigate, we interact the dummy QWPSR with the exporter (Italy and France) ﬁxed effects, making Spain the control country. According to the results reported in Table 2 (columns 3– 4), Spanish QWPSR are, on average, characterized by signiﬁcantly lower export values and volumes. Looking then at the Italian case, the geographical designation effect (given by the sum of the QWPSR and the INTE_ITA coefﬁcients, reported in Table 2 continued) is also

32

M. Agostino, F. Trivieri / Food Policy 46 (2014) 22–36

Table A3 Annual means of unit value (in $) – Italy. Year

QWPSR (a)

Table wine (b)

(a) (b)

Diff. > 0 p-value

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

3.816 4.894 4.853 4.684 4.798 4.471 4.223 5.017 6.831 7.595 7.727 9.561 9.022 10.662 9.350

3.078 3.970 3.561 3.327 3.339 3.152 3.343 3.456 4.754 5.064 4.960 5.128 6.152 6.926 6.682

0.738 0.924 1.292 1.357 1.459 1.319 0.880 1.561 2.077 2.531 2.767 4.434 2.870 3.736 2.669

0.000 0.000 0.000 0.000 0.000 0.000 0.006 0.000 0.000 0.000 0.000 0.002 0.000 0.000 0.000

2

4

6

8

10

Fig. A3

1995

1997

1999

2001 Table wine

2003

2005

2007

2009

QWPSR

Our calculations on EUROSTAT (http://epp.eurostat.ec.europa.eu/newxtweb/setupdimselection.do#) data.

negative but lower in absolute value than in the Spanish instance (to test the signiﬁcance of the sum of the QWPSR coefﬁcient and each interaction parameter, we proceed analogously to what done for the WRs cases). Finally, considering the French case, the impact of geographical designation (sum of the QWPSR and the INTE_ FRA coefﬁcients) is positive and statistically signiﬁcant in the values case, being negative (but the lowest in magnitude) and marginally signiﬁcant for export volumes. Robustness checks In this section, we ﬁrst modify the speciﬁcation of model (1), and then adopt different estimators. To begin with, we estimate the (more parsimonious) speciﬁcation of Anderson and van Wincoop (2003), also adopted by Baier and Bergstrand (2009). Further, we assume a more general decomposition of the error term, specifying non-symmetric country-pair ﬁxed effects as suggested by Cheng and Wall (2005). Indeed, the inclusion of exporter and importer ﬁxed effects (initially suggested by Mátyás, 1997, to control for unobserved country heterogeneity and more recently employed by the literature to proxy for ‘‘multilateral resistance’’ à la Anderson and van Wincoop, 2003) represents a special case of the Cheng and Wall (2005) decomposition. Moreover, we estimate an equation including exporter-time, importer-time and product-time ﬁxed effects.21 When employing disaggregated data, to make the estimation feasible we split the sample into two parts (1995–2001 and 2002–2009). For the sake of conciseness, and also because they tend to conﬁrm our ﬁndings, we omit the results of all these sensitivity checks, making them available on request. Our main ﬁndings are also conﬁrmed when adopting the PPML estimator, with some important qualiﬁcations. When considering the benchmark Eq. (1), analogously to what observed in the BVOLS case, QWPSR are characterized by a better export performance in terms of value, but not in terms of quantity. A noteworthy differ21 Importer and product time-varying effects tend to account also for the variation in the competition that exporters face in each importer market and for each product line (over time).

ence, yet, is that accounting for both the intensive and extensive margin of trade (as we do when using the PPML estimator) leads to greater QWPSR parameters. An analogous difference in magnitude emerges when looking at the evolution over time, the different destination areas, and exporters. Furthermore, in the latter case we detect the following notable modiﬁcations: French QWPSR show a surplus not only in terms of values but also of volumes, the increase being higher for values (392%) than quantities (41%). Despite to a lesser extent, also Italian and Spanish QWPSR seem beneﬁting in terms of traded values, QWPSR being associated to raises in bilateral trade values of about 32% for Italy and about 29% for Spain. Spanish quality wines, though, are characterized by a gap in exported volumes of about 40% (thus their value surplus is relatively higher), whilst the Italian counterparts seem characterized by export volumes comparable to those of table wines. The described outcomes are consistent with the Probit results, as the PPML estimations take into account the inﬂuence of the geographical indication on the probability that two countries trade (the extensive margin of trade), which we have found positive when estimating the Probit model. Finally, as advocated by an anonymous referee, we adopt a stochastic varying coefﬁcient gravity model (Tzouvelekas, 2007). Generally, this model represents an alternative way to account for country-pair heterogeneity, allowing for parameter variation across country pairs. In our analysis, due to the disaggregation of our trade data, each couple of countries is observed several times in a year, hence we carry out an estimation allowing for countrypair-line random coefﬁcients. Concluding remarks During the last decades, the European wine makers have been challenged by emerging wine producers, both at the domestic and at the international level. Besides, they have experienced a drop in domestic wine consumption, and a worldwide growing interest in quality wines. A key competitive advantage of EU traditional producers may be that of offering superior products, thanks to ancient native varieties and particular methods selected over centuries. A

M. Agostino, F. Trivieri / Food Policy 46 (2014) 22–36 Table A4 Top ten importers of QWPSR. 1995–2002

(%)

2003–2009

(%)

France United Kingdom United States Germany Belgium Japan Switzerland Netherlands Italy Canada Denmark

18.62 16.83 13.70 10.16 6.87 6.07 5.77 3.55 3.11 2.92

United Kingdom United States Belgium Germany Japan Switzerland Netherlands Italy Canada Singapore

22.08 16.03 9.85 8.55 6.66 4.94 4.24 4.01 3.76 2.50

Total

87.59

Italy Germany United States United Kingdom Switzerland Canada Japan Denmark Netherlands France Sweden

33.35 24.24 10.31 6.40 4.63 4.62 2.29 2.08 2.03 1.67

Total

91.62

Spain United Kingdom Germany Sweden Denmark United States Netherlands Switzerland Norway France Belgium

17.84 17.38 10.90 10.16 8.37 8.25 7.15 2.98 2.32 2.16

Total

87.52

82.62

United States Germany United Kingdom Switzerland Canada Japan Denmark Netherlands France Sweden

27.80 22.45 9.79 7.36 6.80 3.75 3.25 2.29 2.13 1.87 87.50

United Kingdom Germany United States Switzerland Netherlands Sweden Denmark Mexico Belgium Canada

19.03 16.51 13.30 8.19 7.20 3.96 3.52 3.37 3.17 3.10 81.35

Our calculations on EUROSTAT (http://epp.eurostat.ec.europa.eu/newxtweb/setupdimselection.do#) data. This table reports, for each exporter, the averages of the ratios between the annual exports of QWPSR towards each top ten importer and total QWPSR exports.

geographical designation system, by representing both a mean to protect producers from unfair competition and a guarantee of certain product characteristics in the eyes of consumers, may make consumers more willing to pay higher prices, thus making producers less affected by price competition, and allowing them to bear higher costs for achieving higher quality and differentiation. In this contribution, we appraise the performance of quality wines produced in speciﬁed regions in terms of exports values and volumes, adopting a gravity model and carrying out several estimations to investigate temporal, destination, and exporters differences. Besides, we disentangle the impact of the geographical designation on the extensive margin of trade by estimating a Probit model. According to our estimates, QWPSR seem characterized by a positive and signiﬁcant difference in exports value with respect to the lower category of wine. This positive surplus tends to be greater when accounting for the occurrence of new trade routes (the ‘‘extensive margin’’ of trade), and is increasing over time, except for the last two years considered. By contrast, QWPSR display a signiﬁcant surplus in terms of exported volumes only when considering some high-income destination markets. What is more, we ﬁnd a positive association between quality wines and the probability of exporting. When analysing each origin country, French quality wines are characterized by the highest increase in terms of value compared to table wines. Italian and Spanish counterparts display lower

33

surpluses, and only when considering both the intensive and extensive margins. As concerns export volumes, while French wines seem beneﬁting from a geographical origin surplus when accounting for the creation of new trade routes, this is never the case for the Italian and Spanish ones, Spanish QWPSR being characterized by signiﬁcantly lower exported volumes than Spanish table wines. In light of our ﬁndings, international markets seem rewarding the geographical origin of wines as QWPSR appear associated to signiﬁcant value premiums especially for France. On one hand, such premiums could reﬂect higher production costs due to the fulﬁlment of geographical designation requirements, thus we cannot draw a deﬁnitive conclusion in terms of higher proﬁtability. On the other, if production costs are homogenous for the exporters we consider, the much higher surplus associated to French QWPSR cannot simply reﬂect higher costs of production. In addition, according to our results, QWPSR seem achieving value premiums also in low and middle-income destination areas, still absorbing a limited part of the overall exports of QWPSR, as both economic and cultural factors may deter wine consumption. Thus, since QWPSR are also associated to higher probability of trading, they could play a pioneering role in accessing emerging economies, where they could initially represent a status-symbol for ‘‘upper class’’ people, which are incline to emulate the occidental lifestyle. More generally, QWPSR seem enhancing the probability of entering new markets. Such a positive effect may be driven by both demand and supply factors. On the demand side, since consumers are not familiar with the characteristics of new entrant wines, the European institutional label – condensing a history of high reputation – may convey relevant information for the ﬁnal choice. On the supply side, our ﬁndings may well support the prediction that ‘‘only the best ﬁrms can proﬁtably enter the most difﬁcult markets’’ (Crozet et al., 2012, p. 24), as suggested by quality sorting models based on the heterogeneous ﬁrms hypothesis of Melitz (2003). Indeed, also Crozet et al. (2012) ﬁnd that champagne producers with higher quality rating enjoy a pay-off in terms of greater presence in export markets. Therefore, our results suggest that, thanks to the ‘‘institutionalization of quality’’, European producers could gain higher margins on international markets and expand their exports to new destinations areas. Indeed, the geographical identity of wines may represent a strategic tool for differentiation granting competitiveness in both traditional and less habitual markets. Backed by the EU geographical protection, wine makers may aim at quality and diversiﬁcation granting higher prices, rather than producing large amounts of products bearing lower prices. Such a competitive strategy appears particularly suitable for small producers, that are not able to exploit scale economies and compete through large volumes and costly promotional campaigns. What is more, the smaller volume-higher price option appears desirable for its potential positive effects on rural development and environmental sustainability. Indeed, rural communities could adopt less intensive agricultural approaches and extract rents respecting local traditions and the natural vocations of their territory. Not all producers, yet, seem to have been able to reap the potential beneﬁts associated to the geographical designation of wines, French QWPSR being associated to higher value premiums, which cannot be fully explained by higher export volumes. We believe that these ﬁndings may represent a ﬁrst step for stimulating new research projects. Further investigation could assess what drives the superior French performance. Indeed, French wines have been historically associated to the category of quality and their consumption has been worldwide synonym of richness and elegance. Besides, particular traditions, speciﬁc QWPSR regulations and a better enforcement effectiveness could lead French producers to maintain, on average, higher levels of quality, and thus higher

34

M. Agostino, F. Trivieri / Food Policy 46 (2014) 22–36

Table A5 Exports of QWPSR by world region. 1995–2002

(%)

2003–2009

(%)

France West Europe Anglo-Saxon (extra-European) East Asia and Paciﬁc, high-income Latin America and Caribbean Sub-Saharan Africa East Asia and Paciﬁc, excl. high-income East Europe and Central Asia Middle East and North Africa South Asia

66.23 20.96 9.32 1.30 0.77 0.64 0.53 0.16 0.06

West Europe Anglo-Saxon (extra-European) East Asia and Paciﬁc, high-income East Asia and Paciﬁc, excl. high-income Sub-Saharan Africa East Europe and Central Asia Latin America and Caribbean Middle East and North Africa South Asia

62.70 21.17 11.25 1.35 1.09 1.08 1.03 0.29 0.12

Italy West Europe Anglo-Saxon (extra-European) East Asia and Paciﬁc, high-income Latin America and Caribbean East Europe and Central Asia East Asia and Paciﬁc, excl. high-income Sub-Saharan Africa South Asia Middle East and North Africa

62.63 29.88 4.98 1.32 0.69 0.23 0.09 0.02 0.02

West Europe Anglo-Saxon (extra-European) East Asia and Paciﬁc, high-income East Europe and Central Asia Latin America and Caribbean East Asia and Paciﬁc, excl. high-income Sub-Saharan Africa South Asia Middle East and North Africa

56.14 35.46 4.37 2.43 0.95 0.49 0.12 0.06 0.03

Spain West Europe Anglo-Saxon (extra-European) Latin America and Caribbean East Asia and Paciﬁc, high-income East Europe and Central Asia East Asia and Paciﬁc, excl. high-income Sub-Saharan Africa Middle East and North Africa South Asia

83.30 9.81 3.96 2.14 0.47 0.16 0.08 0.03 0.01

West Europe Anglo-Saxon (extra-European) Latin America and Caribbean East Asia and Paciﬁc, high-income East Europe and Central Asia East Asia and Paciﬁc, excl. high-income Sub-Saharan Africa Middle East and North Africa South Asia

73.76 16.67 5.51 2.11 1.33 0.57 0.18 0.03 0.02

Our calculations on EUROSTAT (http://epp.eurostat.ec.europa.eu/newxtweb/setupdimselection.do#) data. This table reports the averages of the ratios between the annual exports of QWPSR towards each World Region and total QWPSR exports. In this table, the categorization we adopt follows the World Bank list of economies (1 July 2011) for all world regions, except for Anglo-Saxon (extra-European), East Asia and Paciﬁc (high-income), and West Europe, which are our aggregations obtained by grouping countries (all high-income, both OECD and non-OECD) that the World Bank does not include in any World Regions. Such countries are: Australia, Canada, New Zealand, and USA (AngloSaxon, extra-European); Hong Kong, Japan, Korea Rep., Macao and Singapore (East Asia and Paciﬁc, high-income); Andorra, Austria, Belgium, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Gibraltar, Greece, Greenland, Hungary, Iceland, Ireland, Italy, Liechtenstein, Luxembourg, Malta, Monaco, Netherlands, Norway, Poland, Portugal, San Marino, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom (West Europe).

Table A6 Description of the variables used in the estimations and their main summary statistics. Variable

Description

Mean

Std. dev.

Min.

Max.

Obs.

WINE_EXP_V a WINE_EXP_Q b QWPSR

Bilateral export ﬂows of wine (at the CN8 disaggregation level), values Bilateral export ﬂows of wine (at the CN8 disaggregation level), quantities Dummy variable coded 1 for bilateral ﬂows of QWPSR (Quality Wines Produced in Speciﬁed Regions) Gross domestic product of importer Population of importer Exporter’s share of QWPSR to total production of wine (for all QWPSR lines)

305.46 640.63 0.606

5348.38 10633.03

0 0 0

796,000 872,727 1

501,660 490,380 501,660

230,875 34.91 35.05

982,401 127.86 9.63

48 0.03 17.79

14,300,000 1331.46 50.88

454,500 474,120 303,774

Exporter’s share of table wine to total production of wine (for all table wine lines) Distance between the capitals of the exporter and the importer Dummy variable coded 1 if exporter and importer share a common border Dummy variable coded 1 if exporter and importer share the same ofﬁcial primary language Dummy variable coded 1 if colonial links ever existed between exporter and importer Dummy variable coded 1 if exporter and importer have a regional trade agreement in force Dummy variable coded 1 if exporter and importer are WTO members Dummy variable coded 1 if exporter and importer share a common currency

64.95 6002 0.023 0.110 0.108 0.199 0.715 0.056

9.65

49.12 328 0 0 0 0 0 0

82.21 19,517 1 1 1 1 1 1

197,886 501,660 501,660 501,660 501,660 498,960 501,660 501,660

GDP_IMP c POP_IMP d PROD_QWPSR e

PROD_TABLE DISTANCE f CONTIGUITY LANGUAGE COLONY RTA WTO CURRENCY

e

Variables WINE_EXP_V, WINE_EXP_Q, PRODUCTION and the information to code QWPSR are drawn from EUROSTAST (http://epp.eurostat.ec.europa.eu/newxtweb/setupdimselection.do#, accessed on 21/02/2012). The others variables are from the CEPII Gravity Dataset, provided by Head et al. (2010) (available at: http://www.cepii.fr/ anglaisgraph/bdd/gravity.htm). Since the CEPII data are from 1948 to 2006, we update the variables of interest until 2009. a In thousands of current dollars. b In quintals. c In millions of current dollars. d In millions of units. e In percentage. f In kilometers. The other variables are dummies. For these latter the mean represents the percentage of observations falling into the category of one.

35

M. Agostino, F. Trivieri / Food Policy 46 (2014) 22–36

Table A7 Number of QWPSR lines and total lines, by exporters (1988–2010). Year

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

France

Italy

Spain

QWPSR number of lines

Total number of lines

QWPSR number of lines

Total number of lines

QWPSR number of lines

Total number of lines

5 5 5 5 5 5 6 36 36 36 36 36 36 36 36 36 36 37 37 37 37 37 36

20 20 20 20 20 20 21 60 60 60 60 60 60 60 60 60 60 60 60 60 59 60 67

5 5 5 5 5 5 6 36 36 36 36 36 36 36 36 36 36 36 37 37 37 37 36

20 20 20 20 20 20 21 60 59 59 60 60 60 60 60 60 60 59 60 60 60 60 68

5 5 5 5 5 5 6 30 29 29 31 33 33 35 33 33 35 36 35 36 35 37 35

20 20 20 20 20 20 21 54 53 52 54 55 55 57 55 56 56 57 57 58 57 60 67

Our calculations on EUROSTAT (http://epp.eurostat.ec.europa.eu/newxtweb/setupdimselection.do#) data. Lines are distinguished into two broad categories: Quality Wine Produced in Speciﬁed Regions (QWPSR) and table wines.

prices than the other exporter countries. Moreover, additional research could assess whether the ‘‘cohabitation’’ of European and national rules within the EU wine protection system could engender confusion rather than informing consumers (e.g.: Chiodo et al., 2011; Josling, 2006). Indeed, especially in distant areas (in terms not only of kilometres, but also of culture and alimentary habits), the name of the country of origin could represent a ﬁner signal in the eyes of consumers, reinforcing the geographical label inﬂuence, as our evidence tends to suggest. If this was the case, an effort of further harmonization of the EU protection system, combined to an incisive policy of information on the characteristics of geographically designated wines could make all traditional producers able to exploit more successfully an important collective resource. In other words, to boost the competitiveness of QWPSR the European Union should not only provide legal protection and guarantee of high quality standards, but also ﬁnance sufﬁcient market research and promotional campaigns to overcome the lack of information on EU quality wines on foreign markets. Acknowledgements We are very grateful to three anonymous reviewers, Giovanni Anania and Maurizio Agostino for their precious comments and suggestions. We also wish to thank the co-Editor in Chief Mario Mazzocchi. Any remaining errors are solely our responsibility. Appendix A See Tables A1–A7. References Agostino, M., Demaria, F., Trivieri, F., 2010. Non-reciprocal trade preferences and the role of compliance costs in the agricultural sector: exports to the EU. J. Agric. Econ. 61 (3), 652–679. Aiello, F., Cardamone, P., Agostino, M., 2010. Evaluating the impact of nonreciprocal trade preferences using gravity models. Appl. Econ. 42, 3745–3760. Aiginger, K., 1997. The use of unit values to discriminate between price and quality competition. Camb. J. Econ. 21, 571–592.

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Geographical indication and wine exports. An empirical investigation considering the major European producers

Geographical indication and wine exports. An empirical investigation considering the major European producers

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