In recent years, European Union decision-makers have discussed which kind of policies should be set up in order to stimulate structural changes within European Union regions. Before the economic crisis begun in 2008, sector – neutral policies dominated the policy-making process in that they guarantee the neutrality of governments and prevent the latter from picking out projects based on preferred sectors (cf. Trajtenberg, 2012, p.1429), those that already represent the most important regional sources of economic growth and employment. In light of the recent crisis, these policies demonstrated not to work in less advanced and industrial transition regions (cf. Muscio, Rivera Leon, & Reid, 2015, p.1429). Thus, decision – makers reversed their approach by adopting non – neutral policies, also known as “Smart specialization”, that lead local governments to play as a collective player within their regions while looking for new areas of development (cf. Foray, 2016, pp.1431). The necessity to empower regions’ flexibility rely on the fact that, despite many factors shaping companies’ conduct remain national – specific, firms still have different opportunities for innovating at the local ground (cf. Rafiqui, 2010, p.323). Regional governments are therefore responsible for applying the European general framework to their specific context. However, it is interesting to understand the way this outline can be properly applied at local areas, considering the variety that characterizes European regions, both in terms of innovativeness and socio-economic development. The purpose of this paper is to thoroughly investigate the effectiveness of the fitting of S3 policies at regional levels.
In the first part of this paper S3 policies will be presented, followed by the introduction of RV and its application at regional levels in the second and third paragraph. Afterward, before the conclusion S3’s shortcomings will be shown.
S3, being a non-neutral policy, is a place-based approach characterized by the recognition of strategic fields for intervention based both on the investigation of regional competitive advantages and on an EDP with extensive stakeholder participation (cf. European Commission, 2018). Creating or rebuilding European value chain (cf. European Commission, 2018) and making EU regions more competitive and resilient to globalization (cf. European Commission, 2017) by generating knowledge-driven growth are S3’s main goals. Being a place-based approach implies that S3 builds on the resources and capabilities accessible to regions and the Member States and on their particular socio-economic conditions in order to recognize inimitable opportunities for progress and growth (cf. European Commission, 2018). This localism is favored by the bottom-up approach which is implemented to bring together local actors, especially entrepreneurs since they build on relevant know-how and expertise present in their companies and embedded therefore in local contexts (cf. Asheim et al., 2011, pp.895). This is the way in which S3 aims at enhancing knowledge-driven growth. In addition, S3 requires regions to set up a sound monitoring and evaluation system (cf. European Commission, 2018) in order to keep the structural change tracked and controlled. After 2020, once less developed regions have finished the trial period, S3 will become a precondition to receive EU Regional development funds (cf. European Commission, 2017).
EU commission offers its tailor-made support to regions implementing S3’s two pilot actions, embeddedness and connectedness (cf. Camagni/Capello, 2013, pp.357), in order to help them fruitfully restore their innovation systems (cf. European Commission, 2018). On one hand, embeddedness indicates the process through which regions face specific problems, like deindustrialization, with the goal of improving knowledge generation. On the other one, connectedness refers to the creation of European value chain (cf. European Commission, 2018), favoring inter-regional contaminations that can potentially lead to knowledge spillovers and “avoid [innovation] lock-in with respect to local historical specializations” (Camagni/Capello, 2013, p.360). The first pilot action is accomplished through the EDP while the second one is carried out with the establishment of thematic partnerships that facilitate knowledge exchange and the achievement of structural change.
To stress it deeply, a S3 field “is about being able to effectively match knowledge domains with market potentials” (European Commission, 2018). Thus, it expects companies operating in different sectors within the same region to create a common ground where different knowledge domains can be reassembled to realize regional competitive advantage. The reassembly of the know-how rooted in different sectors (cf. European Commission, 2018) represents S3’s novelty that eventually introduces the topic of RV.
“The concept of RV refers to the presence of different industries between which there is [the possibility] for novel recombination of knowledge, but not to such an extent that […] collaboration between the relevant actors is impossible” (Frenken et al., 2007, p.832). In fact, if cooperation among different players was impossible, entrepreneurs would not occupy their resources in projects that do not add value and, consequently, S3 would not be implemented. However, the importance of RV is established by its significant positive relation to employment growth (cf. Oort/De Geus/Dogaru, 2015, pp.1114). In fact, the pursuit of economic development through the reassembly of several competencies embedded into regions allow neighboring employees to find a job more easily. As a consequence, an innovation positively impacts all the regional firms as the basic knowledge is now widespread over many more workers. This shows RV as a sign of Jacob externalities (cf. Castaldi/Frenken/Bart Los, 2015, p.770), that are external economies available to all regional companies stemming from a variety of actors. “Different but not completely disconnected knowledge bases” (Castaldi/Frenken/Bart Los, 2015, p.770) are hence a precondition for RV to work effectively. In addition, RV is positively associated with incremental innovation (cf. Castaldi/Frenken/Bart Los, 2015, p.776). Indeed, bringing together knowledge of related activities leads more likely to incremental innovations rather than to radical upgrading. Even though drastic transformations are more easily achieved through unrelated varieties (cf. Castaldi/Frenken/Bart Los, 2015, p.777), favoring the connection of related areas represents for politicians an easier option to be achieved.
Given the significance of RV, appropriately measure the degree of relatedness among different activities become of crucial importance. The recent literature states three possible measures of relatedness: industrial classification system, vertically related variety, regional skill-relatedness.
According to the first approach, “activities with the same two-digit code were considered to be related” (Cainelli/Iacobucci, 2012, p.258). However, this method of defining RV has many drawbacks since sharing the same two-digit code does not assure a substantial level of relatedness per se. Indeed, this measure does not consider either the opportunities for local – level outsourcing or the input-output exchanges, both of them representing a better path of investigating the relatedness across local industries (cf. Cainelli/Iacobucci, 2012, p.258). The second definition is based on the idea that related sectors share common production inputs and knowledge bases (cf. Boschma et. al, 2010, pp.517). This assumption leads vertically related variety index to compute the degree of relatedness by analyzing the “intensity of input-output exchanges” (Cainelli/Iacobucci, 2012, p. 259) between regional industries. Therefore, the higher these exchanges, the higher the local related variety index (cf. Cainelli/Iacobucci, 2012, p.256) and the lower the need for firms to vertically integrate. In accordance with this index, vertical integration depends on the extent of local vertical relatedness of sectors, rather than on variation as such (cf. Cainelli/Iacobucci, 2012, p.257). Nevertheless, vertically related variety index has the limit of focusing extensively on the industry level (cf. Dahl Fitjar/Timmermans, 2017, pp.517), partly neglecting the importance of the regional dimension. This is the reason why recent studies suggest to figure out the extent of intraregional connections by stressing the importance of regional employment, either by looking at the “co-location patterns of employment” (Delgado/Porter/Stern, 2016, pp.12), or at the regional skill-relatedness (cf. Dahl Fitjar/Timmermans, 2017, pp.517). The latter is built on the labor mobility of employees across industries operating in the same region. Not only does it perform better on the manufacturing sector, but it drives to a superior focus on human capital as it is based on the realistic assumption that workers tend to apply for companies that higher price their competencies (cf. Dahl Fitjar/Timmermans, 2017, p.520). Hence, the greater the similarities in terms of knowledge bases, the higher the labor mobility and the larger the possibility of knowledge spillovers within the same region.
Regardless of the index used to measure the relatedness, the concept of related variety has introduced disruptive changes in the way “industrial knowledge flows in regional economies” from “vertical, cumulative and sectorially – specialized ‘silos’, to horizontal
and combinatorial platforms” (Cooke, 2012, p.832). Before the implementation of S3, regional economic policies gathered many different competencies around the leading regional sector. Today, as a result of the importance gained by RV, local policymakers work to spread the characterizing regional knowledge over many industries. While the assumption that one industry is made up of different know – how looks reasonable, it cannot be said that every industry is composed of just one knowledge base. Consequently, this newness comes with the difficulty that local actors have to combine and adapt different knowledge domains in order to enhance economic development, but this process is not straightforward since knowledge is rooted into practice (cf. James/Halkier, 2014, p.832). In fact, knowledge adaptation varies across regions depending on the different local pattern of innovation, which stands for the diverse paths in performing the various phases of innovation process depending on the environment (cf. Camagni/Capello, 2013, p.364). Three principle patterns of innovation (cf. Camagni/Capello, 2013, pp. 371) can be identified: science – driven (science-based and applied science area), typical of Germany and Austria; technological – driven (smart technological application and creative diversification area), distinguishing of Italy and French Alpine regions; imitative innovation – driven, representative of new EU Member states. Moreover, the knowledge adaptation process requires regions to take advantage of a well – built “systemic” element which should expose local educational institutions and political sphere as key influencers (cf. Comunian/England, 2018, p.8). Accordingly, the attention that has been given by S3 framework to regions and their innovation systems in pursuing RV objectives can currently be better appreciated.
The importance and peculiarity of regions are additionally stressed by the “paradoxical co-existence of counter-moving dynamics” (Fromhold-Eisebith/Eisebith, 2011, p.380) between economic crisis and innovativeness. In fact, a crisis can impact innovativeness either positively or negatively while the latter can determine the affectedness of firms and regions by the crisis, making them either more robust or vulnerable. This reciprocal causal relationship entails geographical variance of impact in terms of innovative behavior, finally affirming the relevance of regional structure and systemic qualities (cf. Fromhold-Eisebith/Eisebith, 2011, pp.378). RISs can be defined as interaction networks where innovations are carried out through the connection of local players to the institutional structure. RISs are built out of two main subsystems, a knowledge infrastructure dimension and a business organization (cf. Makkonena/Rohdec, 2016, p.1628), which are addressed by S3 policies respectively with embeddedness and connectedness.
Knowledge infrastructure dimension varies according to the different regional knowledge bases and so to dissimilar regional models of innovation. Two principal knowledge bases can be outlined: analytical and synthetic (cf. Martin, 2012, pp.1422). The difference among these two types of know-how depends on the rationale for knowledge creation (cf. Asheim/Boschma/Cooke, 2011, pp.896) from which they arise and consequently on the interaction among actors involved in the process, leading finally to dissimilar knowledge infrastructures. On one hand, analytical knowledge stems from the development of new know-how about natural systems by applying scientific laws that can be easily codified. On the other one, synthetic knowledge springs from the combination of existing competencies in new ways which strongly rely on tacit mechanisms (cf. Asheim/Boschma/Cooke, 2011, pp.896). Hence, science-based capabilities need the collaboration between research units (cf. Asheim/Boschma/Cooke, 2011, pp.896) that can be even globally dispersed (cf. Martin, 2012, pp.1422), while engineering-based knowledge development necessitates interactive learning with customers and suppliers (cf. Asheim/Boschma/Cooke, 2011, pp.896), resulting in collaboration that are regionally embedded (cf. Martin, 2012, pp.1422).
Asheim suggests distinguishing between two principal types of RISs: Embedded and Networked (cf. Asheim, 2008, pp.234). Embedded RISs are based on synthetic knowledge and characterized so by industry-specific regional knowledge groundwork. Instead, networked RISs benefit from both synthetic and analytical knowledge and present territorially – embedded knowledge infrastructures (cf. Asheim, 2008, pp.234). As can be easily understood, the “systemic” element stressed by S3 policies is more effortlessly undertaken by Networked RISs since the necessity of sharing codified discoveries and long – term relationships are their major features. Conversely, Embedded RISs are not endowed with actors that have to “horizontally” share knowledge with other players, showing therefore short – term interactions (cf. Asheim, 2008, pp.234).
Linking RISs to the different regional patterns of innovation is a useful approach to have a comprehensive understanding of both the regional knowledge bases and the path followed by regions to improve that groundwork. Indeed, Networked RISs can be identified as those having a science-driven pattern of innovation, while Embedded RISs are characterized by the technological – driven mode of modernization.
RV is expected to be quickly and easily achieved in networked RISs where the knowledge adaptation process is relatively more moderate compared to embedded innovation provinces. In fact, while carrying out S3 strategies, science-based regions can take advantage of both a well – built “systemic” environment and collaborations that involve relatively fewer actors. On the contrary, technological – based areas cannot benefit from long – term relationship systems and have to pool more players to recombine various knowledge domains.
Given both the differences among regions, especially in terms of expertise base and knowledge infrastructure system, and S3 being a general framework, S3 guideline principles might struggle to directly affect regional development policies.
On one hand, embeddedness mainly focuses on RV as a tool to enhance regional knowledge-driven growth. However, RV only favors incremental innovation while regions should enhance their economy by benefitting from radical modernizations as well. Those drastic changes might lay the foundations for further incremental improvements based on RV (cf. Castaldi/Frenken/Bart Los, 2015, p.777). Furthermore, RV boosts the regional employment rate but only unrelated varieties are capable of increasing the productivity growth (cf. Castaldi/Frenken/Bart Los, 2015, p.776) and the latter is crucial to sustaining regional economic development going forward. Moreover, the fear of disrespecting the place-based and bottom-up approach has probably lead S3’s designers not to advise regions on how to effectively drive knowledge adaptation within their provinces in accordance with their own specific regional pattern of innovation. For example, S3 might have further recommended embedded regions to quickly collect together local companies and foster the latter to share expertise smoothly, even with those players they are not directly connected with.
On the other one, connectedness aims at creating European value chains by developing thematic partnerships. Nevertheless, despite this goal being motivated by the willingness of spreading the profits deriving from European competitive advantages over more citizens, relatedness is better measured by looking at labor mobility. Therefore, EU politicians might consider encouraging EU labor mobility rather than sticking around S3. In addition, S3 persuades regions to improve their governance and to make the environment more business-friendly (cf. European Commission, 2018) but it might not be sufficient in some regions, like in North-western Italy, that innovate despite lacking the “systemic” element deeply (cf. De Marchi/Grandinetti, 2016, p.240).
To conclude, S3 might consider to further advice regions on how to evaluate their performances in light of both different knowledge bases and patterns of innovation. For example, EU commission might suggest embedded regions to assess their performances by fostering the return to new applications and to interregional cooperation in applications. By contrast, networked regions should only focus on increasing the return to R&D investments (cf. Camagni/Capello, 2013, pp.378).
After the recent economic crisis pointed out the limits of the sector – neutral policies in revitalizing RISs, particularly those affected by industrial transition phases, EU policymakers appropriately instituted non – neutral policies. These strategies aim at creating European value chains and wisely build on RV that themselves necessitate the large involvement of local actors. Indeed, according to S3’s inventors, the connection between regional political sphere, educational institutions, and local enterprises should allow provinces to favor the reassembly of regional – embedded knowledge domains differently, supporting provincial employment and economic growth. The newness of this concept is that every regional industry needs now to draw from diverse knowledge fields which have therefore to be adapted to the different sector – specificities. However, the combining process is not straightforward and varies depending on distinct regional patterns of innovation and knowledge bases. This is expected to be more challenging in embedded RISs.
Consequently, knowledge adaptation practices should be carried out differently among various regions. This is the main reason why S3 policies might not suit well at the regional level. Moreover, S3 drives barely general suggestions that do not consider regional differences thoroughly, such as with the sound monitoring and evaluation system. In conclusion, this scheme sets up only on RV as a means to improve the employment rate and carry out incremental innovations while it does not consider adequately unrelated varieties as a way to foster radical changes. Interesting further research might investigate the coexistence between related and unrelated varieties in the development of S3 policies.
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