Bio-based and Applied Economics BAE Bio-based and Applied Economics 12(2): 127-139, 2023 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13628 Copyright: © 2023 F. Pagliacci, F. Fasano. Open access, article published by Firenze University Press under CC-BY-4.0 License. Firenze University Press | www.fupress.com/bae Citation: F. Pagliacci, F. Fasano (2023). Does the presence of inner areas mat- ter for the registration of new Geo- graphical Indications? Evidence from Italy. Bio-based and Applied Econom- ics 12(2): 127-139. doi: 10.36253/bae- 13628 Received: August 30, 2022 Accepted: February 27, 2023 Published: August 05, 2023 Data Availability Statement: All rel- evant data are within the paper and its Supporting Information files. Competing Interests: The Author(s) declare(s) no conflict of interest. Editor: Fabio Bartolini, Emilia Lamo- naca. ORCID FP: 0000-0002-3667-7115 Paper presented at the 11th AIEAA Conference Does the presence of inner areas matter for the registration of new Geographical Indications? Evidence from Italy Francesco Pagliacci, Francesco Fasano* Dipartimento del Territorio e Sistemi Agro-forestali (TESAF), Università di Padova, Italy *Corresponding author. E-mail: fasanofrancesco@ymail.com Abstract. Remote areas have been progressively obtained greater attention. Since 2014, the Italian National Strategy for Inner Areas has tackled remote areas with the aim of promoting local development. A tool to foster economic development in these areas is valorisation of those high-quality agri-food products that are characterised by unique features, through the use of geographical indications. This study addresses this topic, by considering the geographical indications registered in Italy since 2014. The study considers municipality-level (LAU2) data, taking the number of geographical indi- cations that each municipality is eligible to produce as a dependent variable. Hurdle models are used to assess the effect of inner areas and other covariates (i.e., agricul- ture and food industry features, socio-economic characteristics, regional settings). The results suggest that geographical indications still represent a sort of untapped resource across inner areas, even when controlling for regional settings across Italy. Thus, a more effective policy intervention is requested. Keywords: geographical indications, inner areas, rural development. JEL Codes: Q18, R50. HIGHLIGHTS: – GI registration can promote economic development in inner areas. – Degree of remoteness negatively affects GI registration in Italian munici- palities. – Socioeconomic features of agriculture and regional differences also play a role. – Policymakers should favour GI registration in inner areas. 1. INTRODUCTION Due to its geographical characteristics, Italy shows large heterogeneity in terms of landscape and territory composition, turning into different con- ditions of accessibility to essential services, which represents a critical issue http://creativecommons.org/licenses/by/4.0/legalcode 128 Bio-based and Applied Economics 12(2): 127-139, 2023 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13628 Francesco Pagliacci, Francesco Fasano when considering territorial imbalances (Christaller, 1933; Bonifazi and Heins, 2003; Barca et al., 2014; Man- tino, 2021). Thus, some municipalities, placed at further distance from major urban poles, suffer from socioeco- nomic marginalisation and underdevelopment, in com- parison with larger urban areas (Bertolini and Pagliacci, 2017; SVIMEZ, 2019; Iammarino et al., 2018; De Renzis et al., 2022). For this reason, in 2014 a specific national- level strategy was included in the National Reform Pro- gramme in Italy. The National Strategy for Inner Areas (hereinafter, NSIA), supported with public funds, has targeted inner (i.e., remote) Italian municipalities, with the aim of reversing depopulation trends and socioeco- nomic remoteness. To achieve this goal, inner areas have been supported in the capitalisation of the existing local assets and resources, through the activation of specific place-based policy measures (Barca et al., 2014). Among the available local resources inner areas can capitalise on, localized agri-food systems (LAFSs) can play a pivotal role (Arfini and Mancini, 2018). As other remote regions, inner areas are rich in high-quality and traditional agri-food products, whose value is based on the link between territorial features and production techniques (Barca et al., 2014). In particular, some of the pilot inner areas identified by the NSIA have already implemented some measures aimed at the valorisation of agro-food local products through the recognition of new Geographical Indications (GIs). Originally introduced in 1992 in the EU, GIs are currently regulated under the EU Regulation 1151/2012. GIs stress the unique characteristics of the agro-food (and wine) products they protect, being a strategic tool to increase the income of the producers (Cei et al., 2021; Crescenzi et al., 2022). With 315 registered agro-food GIs and 526 registered wine GIs, Italy is the forerun- ner in the EU for GI registration. In terms of value- added, agro-food GIs amount to €7.97 billion, while wine GIs amount to €11.16 billion (ISMEA Qualivita. 2022). Among other goals (e.g., addressing the problem of asymmetric information between consumers and pro- ducers) (Cei et al., 2018), GIs can have positive economic effects for the involved territories, eventually favour- ing population and economic growth (Crescenzi et al., 2022). To this regard, registering new GIs really repre- sent key opportunity for inner areas. In particular, this study explores if this opportunity is actually exploited by Italian inner areas. It adopts a territorial approach, considering the agro-food GIs regis- tered in Italy from 2014 onward (i.e., after the introduc- tion of the NSIA) and the set of the municipalities (i.e., LAU2 areas) that are included within the boundaries of their eligible areas. By referring to municipality-level data, the analysis aims to investigate whether both ter- ritorial and socioeconomic features (e.g., characteris- tics of the agricultural sector and food industry; socio- economic characteristics; regional settings and quality of the public governance) matter in the process of new GI registration, with a particular interest on the role of inner municipalities. This paper aims to contribute to the rather scant literature that quantitatively addresses the drivers of GI registration at territorial level (Crescenzi et al., 2022; Vaquero Piñeiro, 2021; Resce and Vaquero Piñeiro, 2022; Cei et al., 2021). However, compared to previous stud- ies, its novelty is twofold. Firstly, it explicitly addresses the role of inner areas, as defined and mapped by the NSIA (Barca et al., 2014), while previous paper mostly addressed rural areas (e.g., Vaquero Piñeiro, 2021). Sec- ondly, its empirical strategy is grounded on the use of hurdle model, which properly handles skewed data with many zeros and admits different underlying processes to explain the zero values (i.e., registering no GIs at all at municipality level) and the positive values (Mullahy, 1986). The rest of the paper is structured as it follows. Sec- tion 2 discusses the theoretical background, with an overview on both inner areas and the concept of GI. Section 3 describes data and the adopted method. Sec- tion 4 shows the results of the analysis, discussing them in comparison with previous studies. Section 5 con- cludes the work, with possible policy implications. 2. THEORETICAL BACKGROUND This section aims to introduce some of the key con- cepts used in the analysis. Firstly, the characteristics of inner areas, as described and referred to by the NSIA, are introduced; then, the GIs, and their role for inner areas’ development are described. 2.1 The National Strategy for Inner Areas The NSIA represents an innovative place-based policy, aimed at promoting territorial development and cohesion in Italy. Launched in 2014 by the Italian gov- ernment, it represents a nation-wide support scheme aimed at addressing remote areas’ main problems, such as: remoteness, underdevelopment, marginalisation, low level of education and employment, depopulation trends (Colucci, 2019; SVIMEZ, 2019; ISTAT, 2019). More in general, it aims to reduce urban-rural disparities (Barca et al. 2014; Lucatelli 2016; Urso 2016; De Renzis et al., 2022). 129Does the presence of inner areas matter for the registration of new Geographical Indications? Evidence from Italy Bio-based and Applied Economics 12(2): 127-139, 2023 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13628 Firstly, the NSIA contributes to the mapping of the Italian municipalities with the aforementioned char- acteristics of inner areas. A peripherality indicator – expressed as the travel-time distance from the nearest urban centre providing essential services (i.e., health, education, and transportation services) – is used to define them (De Renzis et al., 2022). In particular, a 6-class taxonomy is produced, distinguishing: urban poles (A), intermunicipal poles (B), belt areas (C), inter- mediate areas (D), peripheral areas (E), ultraperipheral areas (F). Classes D-F are generically labelled as ‘Inner areas’ (Bertolini and Pagliacci, 2017). Secondly, since 2014 the NSIA has supported (and funded) the implementation of local development pro- jects, based on more integrated approaches, to overcome the traditional weakness of project management in these areas (Lucatelli, 2016), and to reinforce local territorial identities (Capello, 2018). In particular, 72 pilot areas – involving at least one inner municipality – were select- ed on a regional basis, each of them being requested to develop its own strategy through a Project Framework Agreement. According to it, several types of local inter- ventions were suggested as tools to promote development processes. They have involved land management, terri- torial safeguarding, promotion of natural and cultural assets (i.e., through sustainable rural tourism), agricul- tural activities (Bertolini and Pagliacci, 2017). However, to be successful, each of these interventions must capi- talise on the local specificities and local resources of the involved areas, i.e., some “latent development factors” (Barca et al., 2014: 40). Among the existing available local resources that deserve valorisation, Arfini and Mancini (2018) suggest the importance of LAFSs. In particular, valorisation of traditional high-quality agri-food products – through local participation and close cooperation among eco- nomic agents – can represent a valuable opportunity for local development across inner areas, as explicitly emphasized by the NSIA (Barca et al., 2014). Thus, it is not a case that some of the pilot areas (e.g., Alto-Medio Sannio, in Southern Italy, and Valchiavenna, in North- ern Italy) have implemented their local strategies with a focus on the valorisation of agro-food products through the recognition of GIs (Agenzia per la Coesione Territo- riale and Regione Molise, 2021; Agenzia per la Coesione Territoriale and Regione Lombardia, 2017). 2.2 GIs and inner areas GIs are distinctive signs used to identify a prod- uct whose quality, reputation and traditional produc- tion techniques relate to its geographical origin (OECD, 2000; Cei et al, 2018). After having originated in Medi- terranean Europe (Cei et al., 2021; Crescenzi et al., 2022), in 1992 they were introduced in the EU. Cur- rently, they are regulated under the EU Regulation 1151/2012, hence representing one of the main elements of the EU quality policy (European Commission, 2012; Resce and Vaquero Piñeiro, 2022). GIs stress the unique characteristics of the products they protect, also address- ing the problem of asymmetric information between consumers and producers (OECD, 2000; Cei et al, 2018), and affording a product protection against conflictual uses, frauds and fake imitations (EUIPO, 2017; Wirth, 2016; Crescenzi et al., 2022). As part of the high-quality schemes, GIs represent one of the pillars of the EU agri- cultural and food policy. For 30 years, registered GIs have steadily increased in number: in 2022, and only considering agro-food GIs, there were 1,463 registered GIs in the EU (+ 50% from 2010, according to AND- International (2019)), suggesting the ever-growing EU attention to those quality labels (Cei et al. 2021). GIs not only prevent frauds and fake imitations. They also represent strategic tools to increase produc- ers’ income and to promote development in the territo- ries where GI production occurs (Gangjee, 2017; Cei et al., 2021; Resce and Vaquero Piñeiro, 2022; Török et al., 2020). With regard to single producers, the price premium recognised to a GI can compensate not only the greater costs of the GI certification but also a weakness of local farmers in successfully participating in the globalized economy, hence working as a collective property right (Bojnec and Ferto,2015; Crescenzi et al., 2022). Moreover, GI implementation is proved to positively affect also the broader local communities, and the territories involved, in terms of value distribution (Belletti and Marescotti, 2017), socio-economic and environmental sustainability (Belletti et al., 2015; Cei et al., 2018), rural development (Vaquero Piñeiro, 2021), and population growth (Cres- cenzi et al., 2022). Given such a positive impact, they are attractive for those remote areas, looking for a “new rural development paradigm” (Ilbery and Kneafsey, 1999; Marsden, 1998). Actually, the link between GIs and the place in which they are made suggests that geographi- cal factors – e.g., climate, soil, biodiversity – play a role together with the human factor in assuring product qual- ity (van Leeuwen et al., 2018). Such a link is stronger for Protected Designation of Origin (PDO) than for Protected Geographical Indication (PGI)1, but in both cases GIs rep- resent an effective way to preserve local cultural heritage (European Commission, 2020). 1 In the case of PDOs, every part of the production, processing and preparation process must take place in the defined region. In the case of PGIs, at least one of these stages must take place in the defined region. 130 Bio-based and Applied Economics 12(2): 127-139, 2023 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13628 Francesco Pagliacci, Francesco Fasano In more general terms, registering a new GI can be considered as a “collective” production process (Teil, 2012: 497), turning into a “type of collective property” (Barham, 2003). Due to the length and the cost of the application procedure (Cei et al., 2021), the whole local- level community must be actively involved in this pro- cess (Prévost et al., 2014), which must be driven by the interests of multiple stakeholders, including local poli- cymakers, local communities, agri-food producers, and even marketers and consumers (Castellò, 2021). Such a collective organization is crucial not only for the initial registration of a GI but also for its ongoing management over time (Reviron and Chappuis, 2011), for example in the case of non-minor amendments involving changes in the boundaries of the production area (Landi and Stefani, 2015). Mantino and Vanni (2018) also show the importance of the support from local administrations and local politics, by means of two case studies from Northern and Southern Italy. Thus, it is clear that, when analysing the process of registration of new GIs, several factors play a role. Actu- ally, analysing the main conditions that favour GI regis- tration is complex, due to little availability of economic data on GIs at the local level. Because of these limita- tions, previous studies addressing this nexus were mostly qualitative (see, for example, Torok et al., 2020; Bonanno et al., 2019). However, they all confirm that socio-eco- nomic determinants (e.g., infrastructure endowment and consumer demand), dynamism of the local agri-food sec- tor, and favourable institutional context all matter (Huys- mans and Swinnen, 2019; Meloni and Swinnen, 2018; Vaquero Piñeiro, 2021; Resce and Vaquero Piñeiro, 2022). Also, farmers’ characteristics matter for GI registration, and in particular: famers’ education level (Marongiu and Cesaro, 2018), and propensity to cooperate (Charters and Spielmann, 2014; Cei et al., 2021; Vaquero Piñeiro, 2021). Lastly, also pre-existing experience in GI regis- tration matters: traditional GI regions tend to be more active in new GI registration, thanks to accumulation of skills among producers and improved institutional capacity (Cei et al., 2021; Tregear et al., 2016; Huysmans and Swinnen, 2019). Also, Kizos et al. (2017) claim that implementation of GIs in those territories having expe- rienced GIs registration for decades is even more devel- oped thanks to the presences of consortia and pre-exist- ing collective actions. All these elements can be grouped under the gen- eral (albeit rather fuzzy) definition of social and territo- rial capital, whose importance for agricultural and rural development has been largely emphasized over time (Putnam et al., 1994; Capello, 2018; Rivera et al. 2019; Cortinovis et al. 2017; Pagliacci et al., 2020). When considering the aforementioned territorial and socioeconomic characteristics, remoteness cannot be ignored as a major driver, due to the specificities that characterize inner areas. Indeed, when considering GI registration in inner municipalities, contrasting findings emerge. These areas are endowed with some crucial fac- tors, but can lack some others. At EU level, many studies have claimed that GI registration represents an econom- ic opportunity largely exploited by remote and other less favoured areas (Parrott et al., 2002; Santini et al.; 2015; van de Pol, 2017; Cei et al., 2021). However, in the case of Italy, a positive nexus between GIs and inner areas is less obvious. According to Marongiu and Cesaro (2018), Italian farmers located in the less favoured areas (i.e., remote and inner regions, among other) are less likely to engage in GI schemes than those located close to the flatlands, hence benefitting from a larger infrastructure endowment. Similarly, Vaquero Piñeiro (2021) claims that the Italian food PDOs with the highest revenues come from those municipalities that show better socio- economic conditions, a more diversified economy and a more competitive agri-food sector. 3. DATA AND METHODS This section aims to discuss the data adopted into the analysis together with the suggested method. 3.1 Data This study considers all the agro-food GIs (both PDOs and PGIs) that have been registered in Italy, since 2014, i.e., the year of introduction of the NSIA. Specifi- cally, the study takes into account all GIs registered in both northern and southern Italy, regardless of the extent of the territory specified by each GI’s Product Specification (i.e., considering both GIs produced in only a few municipalities and those produced in entire regions2), in order to have a more general overview of the possible different factors playing a role in new GI registration. However, this study only considers agro-food GIs, excluding wine GIs. Two main reasons drive this choice. Firstly, previous studies tackling GIs and their territorial distribution have favoured wine GIs more than food GIs 2 Despite its focus, this analysis also includes the GIs produced over entire regions. Actually, although inner municipalities usually play a limited role in the decisions to register new large-scale GIs, however their inclusion within the boundaries of the area of production can still represent an important decision, eventually prompting local economic development. 131Does the presence of inner areas matter for the registration of new Geographical Indications? Evidence from Italy Bio-based and Applied Economics 12(2): 127-139, 2023 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13628 (see for example Resce and Vaquero Piñeiro, 2022; Cres- cenzi et al., 2022). Secondly, an analysis on agro-food GIs – which include very different products (e.g., fruits and vegetables, cheeses, meat-based products) – can shed light on a broader set of territorial and social determi- nants of the degree of protection sought through GI labels, hence favouring more generalisable findings. In total, we consider 56 GIs, of which 15 are PDOs and 41 are PGIs. As shown in Table 1, 10 different prod- uct categories are included. Most of the GIs under analy- sis are fruit, vegetables and cereals. However, when con- sidering PDOs only, most of them are cheeses. For each of them, we retrieved the list of municipalities included within the boundaries of the eligible area of production according to each single GI’s Product Specification (as retrieved by the eAmbrosia dataset3). Considering the agro-food GIs registered in the 2014-2022 period, there are 4125 Italian municipalities (out of 7926) that are eligible for the production of at least one of them. In particular, some municipalities in Tuscany and in Apulia are eligible to produce even four different newly registered GIs (Figure 1). GI eligibility at municipality level can be jointly analysed with the Italian inner municipalities (Table 2). On average, 46.8% of the Italian municipalities are included in the production area of none of the GIs regis- tered in the period 2014-2022. However, this share is the largest in the intermunicipality poles (B) and belt areas (C), i.e., across some types of non-inner areas. Converse- ly, it is definitely lower in type D, E, and F municipali- ties. These results seem suggesting that inner areas are more likely to adopt new GIs than non-inner areas. 3 Available at https://ec.europa.eu/info/food-farming-fisheries/food- safety-and-quality/certification/quality-labels/geographical-indications- register/. 3.2 Methods To assess the role of the drivers that may affect the number of newly registered GIs at municipality level, the following empirical strategy is adopted. As a dependent variable, the number of agro- food GIs registered in the 2014-2022 period, by Italian municipality, is a count variable. It is not normally dis- tributed, as it includes many zero observations (46.8% of the total observations). In this case, it is common to adopt a count regression approach. The basic distribu- tion for a count variable is a Poisson distribution, with the conditional mean (the mean of the outcome variable Table 1. Number of GIs, by type (PDO and PGI) and product cat- egory.   PDO PGI Total Fruit, vegetables and cereals 4 16 20 Cheeses 8 1 9 Bread, pastry, cakes, … 1 7 8 Oils and fats 6 6 Meat products 6 6 Pasta 3 3 Other products of animal origin 1 1 Fresh meat (and offal) 1 1 Chocolate and derived products 1 1 Fresh fish, molluscs, and crustaceans 1   1 Total 15 41 56 Figure 1. Number of registered GIs (2014-2022), by municipality. Table 2. Share of municipalities with no registered GIs, by type of inner-area municipality, out of the number of municipalities in each type of inner-are   Value (%) A – Urban poles 41.9 B – Intermunicipality poles 59.0 C – Belt areas 54.1 D – Intermediate areas 40.4 E – Peripheral areas 42.0 F – Ultraperipheral areas 32.9 total average 46.8 Source: own elaboration. 132 Bio-based and Applied Economics 12(2): 127-139, 2023 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13628 Francesco Pagliacci, Francesco Fasano Y given the values of the predictor variables X) being equal to the conditional variance (Cameron and Trive- di, 2013). However, given the clear stack of zero values in the dependent variable in this case, Poisson models tend to show poor fitting. Thus, the hurdle model, i.e., a modified count model, can help (Mullahy, 1986). It is a two-part model. Firstly, the zero hurdle part is adopted to model a right-censored outcome variable indicating municipalities being eligible for not even a single GI (Y = 0) or with at least one of them (Y = 1, where all values larger than 0 are censored, i.e., are fixed at 1). The sec- ond part is a truncated count, which is adopted to model the exact number of GIs for those municipalities that are eligible for producing at least one of them (municipali- ties with Y > 0). The hurdle model is also based on the idea that dif- ferent underlying processes – driven by different sets of regressors – can explain either the zero values and the positive values of this variable. If a municipality does not produce a single GI, then the threshold (i.e., the ‘hurdle’) to the truncated count part is not crossed, and a zero value is observed. Otherwise, the hurdle to the truncated count part is crossed, and any given number can be observed. In the case of GI registration, it might be expected that those municipalities that have already been included in the area of production of one GI, might benefit from further facilitation in registering addition- al GIs, compared to other municipalities (see Cei et al., 2021; Kizos et al., 2017). Dealing with the research question of this study, the hurdle model combines a binomial probability model – governing the binary outcome of whether a count vari- able has a zero or a positive value – with a zero-truncat- ed Poisson count-data model, for those observations that cross the hurdle (Y >0). Formally, we have (Zeileis et al., 2008): (1) Where the model parameters are estimated by Max- imum Likelihood, and where the specification of the likelihood has the advantage that the count and the hur- dle component can be maximized separately (Zeileis et al., 2008). The corresponding mean regression relation- ship is given by using the canonical log link, resulting in a log-linear relationship between mean and linear pre- dictor (Zeileis et al., 2008): log μi = xiT β + log(1 – fzero(0;zi,γ)) – log(1 – fcount(0;xi,β)) (2) With regard to the empirical strategy implemented here, different models, including different sets of regres- sors, grounded on the literature review carried on in Section 2, are used. Model 1 focuses on the role of inner areas, refer- ring to the 6-class taxonomy of the Italian municipalities produced by the NSIA. Model 2 considers the character- istics of the agriculture sector (i.e., utilised agricultural area per inhabitant, share of cooperative agricultural holdings out of the total, share of agricultural holders aged 40 years and less out of the total, share of agricul- tural holders having achieved tertiary education) and of the food industry (i.e., share of employment in food industry of the total manufacturing industry employ- ment). Moreover, the share of agricultural holdings being already involved in PDOs or PGIs production (considering 2010 Census data) is included as a proxy of any pre-existing experiences in GI registration. Model 3 includes socio-economic characteristics of the munici- pality, addressing average per capita income (in 2014) as a proxy for the local-level socioeconomic dynamism, and share of electoral turnout in the EU 2014 vote, as a more general proxy for social capital at local level4. Last- ly, Model 4 is the most comprehensive model, includ- ing all the aforementioned covariates. Lastly, it can be noticed that in all the Models 1-4, a categorical variable distinguishing the Italian Macro-regions (i.e., North- West, North-East, Centre, South and the Islands) is also included. Such a variable is important to address differ- ent regional settings. Indeed, Italian macroregions large- ly differ in terms of climatic conditions, characteristics of the agricultural sector and of the supply chains, and institutional settings (eventually affecting overall gov- ernance and politics quality). This categorical variable is expected to control for all these aspects. For each of the aforementioned regressors, Table 3 provides variable specification as well as data source. 4. RESULTS AND DISCUSSION The results of the hurdle models, in each specifica- tion, are returned with regard to the coefficients of the variables (Table 4) and the estimated odd ratios (Table 5). In Model 1, the baseline odds of having a positive count (i.e., at least one eligible GI, by municipality) are 1.27. The odds are affected negatively by being either 4 Actually, EU voting does not lead to any direct economic rewards, being mostly driven by a sense of public duty (Bigoni et al., 2016; Guiso et al., 2004; Putnam et al., 1994). Moreover, one could also argue that a higher electoral turnout in the elections could refer to the presence of a higher-quality political class as well. 133Does the presence of inner areas matter for the registration of new Geographical Indications? Evidence from Italy Bio-based and Applied Economics 12(2): 127-139, 2023 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13628 an intermunicipal pole (type B) or a belt area (type C), while this odds ratio is 1.805 times higher in the ultra- peripheral municipality (type F). Controlling for MAC- RO_REG, odds ratio is 8.030 times higher in Central regions while being located in the North-East decreases it by 0.399 times. Given the response is positive (i.e., the hurdle is crossed), the negative effects played by INNER AREA are largely observed: intermediate (type D), peripheral (type E) and ultraperipheral (type F) munici- palities are associated with a smaller number of newly registered GIs. When controlling for MACRO_ REG, North-Eastern regions, Southern regions and the Islands are associated to a smaller number of GIs as well. In Model 2, the baseline odds of having a positive count are positively affected by UAA and FOOD_IND, while COOP has a negative effect, despite common expec- tations. Controlling for MACRO_REG, these odds is higher in Central regions and smaller in the North-East, as observed in Model 1. Given the response is positive, UAA and PAST GIs increase the number of registered GIs in each municipality, while YOUNG and FOOD_IND negatively affect it. With regards to MACRO_REG, same effects, as observed in Model 1, are found. In Model 3, the baseline odds of having a positive count are negatively affected by INCOME and ELEC- TION, also when controlling for MACRO_REG (whose coefficients are all significant). However, given the response is positive, both INCOME and ELECTION turns to positively affect the number of registered GIs in each municipality. In Model 4, most of previous effects are largely con- firmed. The baseline odds of having a positive count are 21.957. Compared to urban poles, all other municipality types reduce these odds, with the only exception of ultra- peripheral municipalities (type F), showing no significant effect at all. Moreover, it is also significantly decreased by COOP, while both UNIVERSITY and FOOD_ IND positively affect it. Among socioeconomic characteris- tics of the municipalities, ELECTION negatively affects it. When considering MACRO_REG, North-East is con- firmed to have a negative effect on these odds, as South and the Islands have. Conversely, being a municipality in the Centre increases the odds. Given the response is posi- tive (i.e., the hurdle is crossed), the negative effects played by INNER AREA is much broader and generalised. Inner municipalities (D-F) show a lower number of registered GIs. Conversely, UAA and PAST GIs increase the num- ber of registered GIs in each municipality (confirming the findings form Model 2), and also ELECTION turns to positively affect this number. The results about the new GI registration in Italy, in years 2014-2020, confirm most of the findings from pre- Table 3. Covariates for the analysis at municipality level. Group Label Descriptions Specification Source Year Remoteness INNER AREAS Categorical variable, reflecting inner area type of Italian municipalities (A-urban poles, B-intermu- nicipal poles, C-belt, D-intermediate, E-peripheral, F-ultraperipheral), according to the NSIA classifi- cation 6 factors Own elaboration on Barca et al. (2014) 2014 Agriculture and food industry UAA Hectares of Utilised Agricultural Area (UAA) per inhabitant (2010) Ratio Italian Agricultural Census (Istat) 2010 COOP Share of cooperative agricultural holdings out of the total Ratio Italian Agricultural Census (Istat) 2010 YOUNG Share of agricultural holders aged 40 years and less Ratio Italian Agricultural Census (Istat) 2010 UNIVERSITY Share of agricultural holders having achieved tertia- ry education Ratio Italian Agricultural Census (Istat) 2010 FOOD_IND Share of employment in food industry of the total manufacturing industry employment Ratio Italian Population and Hou- sing Census (Istat) 2011 PAST GIs Share of agricultural holdings being involved in PDOs or PGIs production in 2010 Ratio Italian Agricultural Census (Istat) 2010 Socio-econo- mic characte- ristics INCOME Average gross taxable income (thousand €), for year 2014 continuous (1000€) Ministero dell’Economia e delle Finanze 2014 ELECTION Share of electoral turnout in the 2014 EU vote Ratio Ministero dell’Interno 2014 Regional settings MACRO_REG Categorical variable, for the Italian macroregion (North-west, North-east, Centre, South, the Islands) 5 factors ISTAT 2011 134 Bio-based and Applied Economics 12(2): 127-139, 2023 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13628 Francesco Pagliacci, Francesco Fasano vious studies. Surely, geographical and territorial divides across Italy matter. The results about inner areas are somehow contrasting. Also, when controlling for other socioeconomic covariates, being an inner municipality generally decreases the chance of having a GI registered. Moreover, even when one new GI is registered, inner areas tend to be associated to a smaller number of reg- istered GIs, per municipality. In fact, this finding con- trasts with what observed by Parrott et al. (2002) and by Cei et al. (2021), who considered Gi adoption in the EU Table 4. Model estimates for the number of GIs at municipality level. Variable M1 M2 M3 M4 Count model Zero hurdle model Count model Zero hurdle model Count model Zero hurdle model Count model Zero hurdle model (Intercept) 0.380 *** 0.243 0.144 * -0.054 -0.968 *** 2.800 *** -0.355 3.089 *** (0.102) (0.154) (0.057) (0.064) (0.182) (0.221) (0.243) (0.320) INNER AREAS _ type B 0.369 * -0.898 *** 0.374 * -1.018 *** (0.154) (0.154) (0.154) (0.257) INNER AREAS _ type C -0.166 -0.389 * -0.158 -0.630 *** (0.102) (0.155) (0.105) (0.158) INNER AREAS _ type D -0.389 *** 0.070 -0.358 ** -0.360 * (0.104) (0.157) (0.110) (0.165) INNER AREAS _ type E -0.491 *** 0.082 -0.451 *** -0.438 * (0.111) (0.160) (0.119) (0.171) INNER AREAS _ type F -0.870 *** 0.590 ** -0.852 *** 0.104 (0.178) (0.198) (0.185) (0.206) UAA 0.008 ° 0.030 * 0.021 *** -0.008 (0.005) (0.013) (0.005) (0.011) COOP 0.013 -0.077 ** 0.010 -0.077 ** (0.025) (0.028) (0.025) (0.028) YOUNG -0.007 ** 0.000 -0.004 -0.004 (0.003) (0.003) (0.003) (0.003) UNIVERSITY 0.002 0.000 -0.004 0.014 *** (0.003) (0.004) (0.004) (0.004) FOOD_IND -0.002 * 0.005 *** -0.001 0.003 ** (0.001) (0.001) (0.001) (0.001) PAST Gis 0.006 *** 0.000 0.006 *** -0.001 (0.001) (0.001) (0.001) (0.001) INCOME 0.033 *** -0.116 *** 0.015 -0.109 (0.008) (0.010) (0.009) (0.011) ELECTION 0.007 *** -0.008 *** 0.006 *** -0.006 ** (0.002) (0.002) (0.002) (0.002) MACRO_REG_North-east -1.806 *** -0.919 *** -1.978 *** -0.835 *** -1.776 *** -1.002 *** -1.831 *** -1.020 *** (0.163) (0.070) (0.163) (0.071) (0.164) (0.071) (0.165) (0.074) MACRO_REG_Centre 0.040 2.083 *** 0.021 2.128 *** 0.092 ° 1.914 *** 0.089 1.809 *** (0.049) (0.114) (0.052) (0.114) (0.050) (0.115) (0.057) (0.118) MACRO_REG_South -0.258 *** 0.002 -0.283 *** 0.076 -0.051 -0.623 *** 0.003 -0.757 *** (0.056) (0.063) (0.061) (0.066) (0.074) (0.086) (0.083) (0.094) MACRO_REG_Islands -0.664 *** -0.123 -0.720 *** -0.001 -0.452 *** -0.745 *** -0.342 *** -0.928 *** (0.097)   (0.087)   (0.100)   (0.086)   (0.111)   (0.111)   (0.119)   (0.117)   Note: For count model, truncated Poisson with log link; For Zero hurdle model, binomial with logit link. For INNER AREAS: omitted type A single ‘poles’ For MACRO_REG: omitted type North-West Significance: °p<0.1; *p<0.05; **p<0.01; ***p<0.001 Source: own elaboration. 135Does the presence of inner areas matter for the registration of new Geographical Indications? Evidence from Italy Bio-based and Applied Economics 12(2): 127-139, 2023 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13628 less favoured areas. Rather, this finding is more in line with the results by Marongiu and Cesaro (2018). A pos- sible explanation for these contrasting results might lie in the different geographic areas (i.e., considering Italy only) and in the different territorial scale adopted (the municipality level, i.e., a narrower territorial area). When considering other socioeconomic and territo- rial drivers, the findings from this study seem confirm- ing previous ones. For example, the results about farm- ers’ education, proxied by the share of agricultural hold- ers with tertiary education, confirm those by Marongiu and Cesaro (2018). Conversely, cooperation in the agri- cultural sector shows detrimental effect in having at least one registered GI at municipality level. This finding is contrasting with previous results (Charters and Spiel- mann, 2014; Cei et al., 2021; Vaquero Piñeiro, 2021) and largely unexpected: lack of cooperation among farmers is usually recognised as a major issue in the registra- tion process of high-quality agri-food products, which ground on consortia for their protection and valorisa- tion (see also Fasano, 2021, for a qualitative study ana- lysing some agri-food products of the Molise region and the efforts to register new GIs in Southern Italy’s inner areas). It is not a case that improving collective actions in agriculture represents a key objective of the Common Agricultural Policy (CAP) in 2023-2027 programming period. Conversely, the positive role played by pre-exist- ing experience in registering GIs confirms the findings of Cei et al. (2021), Tregear et al. (2016), and Huysmans and Swinnen (2019). Moreover, this study seems suggest- ing that accumulation of skills among producers and improved institutional capacity is even more important in explaining the registration of more and more GIs, thus confirming the vitality of traditional GI regions. In the case of the proxies for social capital endow- ment at local level, electoral turnout in EU vote shows a significant effect, albeit with contrasting sign in either the zero-part or the count-part of the model. The fact that electoral turnout can play a positive role in explain- ing the registration of more and more GIs, when at least one is registered, can be explained by the fact that qual- ity of local political institutions also matter, with a sort of multiplying effect. Actually, a greater quality of local institutions can increase citizens’ trust in the local polit- ical class, positively affecting in turn electoral turnout, also in EU elections. The nexus between electoral turn- out, quality of institutions and GI registration is some- how consistent with the idea of GIs as collective proper- Table 5. Results of the models: odd ratios. Variable M1 M2 M3 M4 Count model Zero hurdle model Count model Zero hurdle model Count model Zero hurdle model Count model Zero hurdle model (Intercept) 1.462 1.274 1.155 0.948 0.380 16.442 0.701 21.957 INNER AREAS _ type B 1.447 0.408 1.453 0.361 INNER AREAS _ type C 0.847 0.678 0.853 0.533 INNER AREAS _ type D 0.678 1.072 0.699 0.698 INNER AREAS _ type E 0.612 1.086 0.637 0.646 INNER AREAS _ type F 0.419 1.805 0.426 1.110 UAA 1.008 1.031 1.021 0.992 COOP 1.013 0.926 1.010 0.926 YOUNG 0.993 1.000 0.996 0.996 UNIVERSITY 1.002 1.000 0.996 1.014 FOOD_IND 0.998 1.005 0.999 1.003 PAST Gis 1.006 1.000 1.006 0.999 INCOME 1.034 0.891 1.015 0.897 ELECTION 1.007 0.992 1.006 0.994 MACRO_REG_North-east 0.164 0.399 0.138 0.434 0.169 0.367 0.160 0.361 MACRO_REG_Centre 1.041 8.030 1.022 8.396 1.096 6.778 1.093 6.102 MACRO_REG_South 0.773 1.002 0.754 1.079 0.951 0.536 1.003 0.469 MACRO_REG_Islands 0.515 0.884 0.487 0.999 0.636 0.475 0.711 0.395 For INNER AREAS: omitted type A single ‘poles’ For MACRO_REG: omitted type North-West Source: own elaboration. 136 Bio-based and Applied Economics 12(2): 127-139, 2023 | e-ISSN 2280-6172 | DOI: 10.36253/bae-13628 Francesco Pagliacci, Francesco Fasano ties (Barham, 2003), which calls for a high level of social capital for their implementation. These results are somehow consistent with those about the differences observed across Italian macrore- gions. Southern Italian regions and the Islands tend to show lower propensity to register new GIs, and they are also characterised by a smaller number of registered GIs per single municipality, when at least one has been registered. As already observed, several reasons might explain these differences across Italian macroregions, including different climatic conditions, different struc- tures of the supply chains, different institutional settings and quality of the local governance. In particular, sev- eral authors have stressed the importance of this latter hypothesis. Indeed, Vaquero Piñeiro (2021), Meloni and Swinnen (2018) and Crescenzi et al. (2022) point out the role of institutional quality in GI registration. When considering single cases studies, also Mantino and Van- ni (2018) suggest the importance of the attitude of the local policy system, finding same differences when com- paring Northern and Southern regions. Overall, these results could perhaps challenge the willingness of the policymakers (both at EU and nation- al level) to provide a tool, such as GIs, to foster remote areas’ development. In particular, given the negative relationship between inner areas and the number of reg- istered GIs, the effectiveness of many of the strategies implemented at local level by the 72 pilot inner areas might seem not effective at all (see Dipartimento per le politiche di coesione, 2020). Especially across Southern Italy, promotion of agro-food quality systems is con- sidered relevant and supported by local policymakers. However, the existence of some major weaknesses in the inner areas (e.g., remoteness, scarcity of agricultural modernisation, presence of elderly farmers in the inner areas) seems to overcome any political will. Thus, in the case of Italian inner areas, not even the NSIA has been able to revert these weaknesses, hence turning into a still too limited exploitation of GI registration. 5. CONCLUSIONS AND POLICY IMPLICATIONS In order to foster socioeconomic development and agriculture diversification of inner areas (as of other marginal areas), EU quality schemes for agro-food products (and GIs in particular) are considered as a key opportunity, by both EU and national policymakers. Actually, inner areas share a large amount of natural resources as well as of traditional agro-food products, which might benefit from GI protection. In particular, this paper has contributed to the empirical debate of the territorial and socioeconomic drivers that can affect GI registration in Italy (i.e., the frontrunner country in the EU), by demonstrating which of them play the most prominent role. By considering the number of agro-food GIs registered across Italian municipalities in years 2014-2022, and by using hurdle models, this study suggests that this opportunity still represents a sort of untapped opportunity for Italian inner areas, despite the strong political commitment to promote them. Moreover, future works will try to extend these findings to other national contexts, as well as to include also the wine sector. However, it should be noticed that not even the inclusion in the area of production is necessarily a guar- antee of production exploitation of the GI for the munic- ipality itself. Actually, GI producers are free to locate in any municipalities within the boundaries of the produc- tion area, eventually favouring non-inner municipalities. Nevertheless, being included within the area of produc- tion of a GI (even in the case of larger scale GIs) might represent a key element for any communities that aim to create a collective property, as GIs are. Therefore, this inclusion represents a tool to add value to the local agro- food production. As suggested by this study, in addition to geographic remoteness, other factors might hinder this process, e.g., the lack of local-level political commit- ment, and the limited extent of social capital at local lev- el. However, further studies will also tackle the location of the producers within the boundaries of the produc- tion area, to test their effective links with inner areas. 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CAP, Farm to Fork and Green Deal: policy coherence, governance, and future challenges Annalisa Zezza Key policy objectives for European agricultural policies: Some reflections on policy coherence and governance issues Silvia Coderoni Towards a new generation of (agri-) food policies Gianluca Brunori Earliness, phenological phases and yield-temperature relationships: evidence from durum wheat in Italy Marco Tappi1,*, Federica Carucci2, Anna Gagliardi1, Giuseppe Gatta1, Marcella Michela Giuliani1, Fabio Gaetano Santeramo1 Does the presence of inner areas matter for the registration of new Geographical Indications? Evidence from Italy Francesco Pagliacci, Francesco Fasano* Learning, knowledge, and the role of government: a qualitative system dynamics analysis of Andalusia’s circular bioeconomy Antonio R. Hurtado1,2,*, Julio Berbel2