AuthorSerbanica, Cristina
  1. Introduction

    Since the early 1990s, a large body of literature has stressed the importance of regions as key drivers of innovation and the concept of regional innovation systems (RIS) has evolved into a widely used framework for innovation policy. Cooke, Gomez Uranga and Etxebarria (1997, p. 480) define regions as 'territories smaller than their state possessing significant supra-local governance capacity and cohesiveness differentiating them from their state and other regions'. Indeed, in many countries across the globe, the process of decentralization has strengthened considerably the local decision competencies and responsibilities. As long as such regions have autonomous spending capacities and decision-making powers, it seems natural for them to develop innovation policies at regional level.

    The importance of regions for innovation policy is also prompted by those theories that feature the role of geographical proximity in facilitating local interactions and the diffusion of tacit knowledge, which is usually spatially bounded (Asheim and Gertler, 2005). At the same time, the focus on regions as a locus for innovation is motivated by the existence of large disparities between the countries, but also between the regions within a country, and the need to adopt differentiated policy interventions. This approach is strongly acknowledged and encouraged at the European Union (EU) level, whose innovation agenda is strongly intertwined with the EU Cohesion/ Regional policy. The European ambition is to close the innovation divide between the innovation leaders and the modest innovators and to reduce regional inequalities. As such, within the 2014-2020 programming period, the EU regions are called to develop smart specialization strategies (RIS3), as ex-ante conditionality for receiving EU funding for research and innovation.

    A distinctive feature of smart specialization is its 'place-based' character underlining the need 'to develop and implement innovation strategies that take into proper account the regional features' (Foray et al., 2012). In fact, the use of different classification schemes that consider simultaneously the innovation potential and the regional features is highly recommended when elaborating the smart specialisation strategies.

    So far, several classifications have been developed to capture both the regional innovation performance and other relevant territory-specific factors. For example, Muller et al. (2006) have created a regional typology of innovation capacities in the new member states and candidate countries along five dimensions: knowledge creation, absorptive capacity, diffusion capacity, demand and governance capacity. Dory (2008) has investigated two dimensions of the regional techno-economic systems, namely (i) the knowledge creation and absorption capacity and (ii) the economic structure and industrial specialisation, based on which he identified seven regional groupings with significant structural similarities. Navarro et al. (2008) have used 21 indicators reflecting the knowledge-generating inputs, the regional structural characteristics, the innovation output and the economic output and identified seven types of regional innovation systems across the EU-25. In a similar vein, Marsan and Maguire (2011) have created a categorisation of OECD regions using innovation-related variables (e.g., R&D inputs, linkages, innovation outputs and economic outcomes) and obtained a set of eight regional groupings. Capello and Lenzi (2013) have also created a taxonomy of European regions based on a list of indicators that cover the complex knowledge-innovation chain. Not least, the European Innovation Scoreboard (EC, 2019a) and the European Regional Innovation Scoreboard (EC, 2019b) distinguish between four different innovation performance groups and provide assessments of contextual data, to illustrate the potential impacts of structural differences on innovation performance.

    Within this context, the aim of our paper is to examine the territorial innovation patterns in Romania, a post-communist country that joined the European Union in 2007 and is now a 'modest innovator' according to the European Innovation Scoreboard (EC, 2019a). Our main assumption is that there is large heterogeneity in the sub-national innovation patterns that is not captured in the existing typologies developed for the NUTS2 regions. The analysis is further justified by the fact that the NUTS2 divisions in Romania are not 'de facto' regions, as they do not actually have an administrative status. Consequently, we think that exploring the innovation patterns at a more granular level, namely at the NUTS3 ('county') level, gives a more nuanced perspective on innovation and structural conditions and can better inform the smart specialisation agenda.

    To our knowledge, there is no similar investigation of the territorial innovation patterns in Romania, which may be explained, to a certain extent, by the limited availability of data. The rest of the paper is organized as follows: Section II introduces the conceptual framework and the variables used in the analysis; Section III is concerned with the methodology employed by the study to produce the categorization of the Romanian NUTS3 regions. The findings and the policy implications are presented in Section IV and the conclusion is reported in Section V.

  2. Conceptual framework

    The conceptual framework of our study is adapted from the European Innovation Scoreboard (EIS) and the European Regional Innovation Scoreboard (RIS) and relies on secondary data available for the NUTS3 level (Table 1; Annex 1). To produce the envisaged categorisation, we use a two-step factor analysis, as a data reduction method, combined with hierarchical cluster analysis.

    In a first step, we investigate the territorial innovation performance based on 15 variables grouped into four dimensions. The 'Human Resources for R&D' dimension captures the availability of doctorate and post-doctorate graduates and the supply of R&D personnel, which are core inputs for the R&D-based innovation. The 'R&D Investments' dimension includes four variables that measure the total R&D expenditures and the capacity to attract R&D funds on a competitive basis from the National R&D Plan and the European Framework Programme (FP7); as regards the FP7 data, we separate the funds granted to the public actors (universities, research organisations and governmental organisations) and to the private sector (companies and private non-profit organisations), to keep up with the EIS/ RIS framework that differentiates between public and private R&D. The 'Intellectual Assets' dimension captures the intellectual property (IP) activity in the form of Patent applications, Trademark applications and Design Applications; in this respect, we consider simultaneously the IP applications at the European Patent Office (EPO), the EU Intellectual Property Office and The (Romanian) State Office for Inventions and Trademarks (OSIM), as we have observed some territorial differentiations in the IP application behavior at the European vs. the national level. Finally, the 'Innovation Impacts' dimension captures the employment impacts, namely the Employment in High and Medium High Tech Manufacturing (HMHTM), the Employment in Knowledge Intensive Services (KIS) and the sales impacts, measured in Exports of medium and high-tech products.

    Structural conditions are supposed to influence the current values and trends of innovation variables. In order to capture the regional structural features, we use 17 variables grouped into four dimensions. The first dimension covers the 'Economic performance' measured in GDP per capita (which is also a proxy for the demand for innovation), the change in GDP and the capacity to attract foreign direct investments (FDI) and European Structural and Investment (ESIF) Funds. The shares of employment in manufacturing and services are further used to reflect the 'Economic structure', together with two variables that capture the so-called 'social filters' (Rodriguez-Pose and Crescenzi, 2008), namely employment in agriculture and the share of population with tertiary education. At the same time, 'ICT adoption' is used as a proxy for digitalisation. Three socio-demographic variables are included in the analysis under the 'Demography' dimension, namely population size, population density and population change, as they are supposed to have a significant influence on the innovation performance. Finally, trade openness, company size, employment in foreign enterprises, the share of high growth enterprises and enterprise birth rates are further used to illustrate the 'Business and entrepreneurship' dimension.

    A database was created with the selected variables measured as average over a five-year period (2014-2018) at the NUTS3 level (41 units), plus Bucharest Municipality; where data for the whole period were not available, the missing values were replaced with values of the last available year (Annex 1).

  3. Research method and results

    To produce the categorisation of the NUTS3 counties, we use a two-step factor analysis combined with hierarchical cluster analysis, in order to find a reduced number of independent latent variables and create a hierarchy of territorial innovation patterns. In a first step, the counties are categorized according to their innovation performance (Factor Analysis I) and the synthetic factors resulting from this iteration are further combined with the variables describing the structural conditions (Factor Analysis II). Similar to Muller et al. (2006), we apply this strategy to place superior weight on innovation performance in defining the typological distinctions.

    In the preparation phase, we have tested the linearity and factorability, which are key assumptions in factor analysis. The data was screened for outliers (the scores higher or lower than the mean plus three standard deviations) and the capital...

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