METHODOLOGICAL DEBATE ON THE ‘REGIONAL INNOVATION SCOREBOARD’.
METHODOLOGICAL DEBATE ON THE ‘REGIONAL INNOVATION SCOREBOARD’.
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tant variables at scientific, economic and social level, they cannot be considered variables that can directly represent the characteristics of an innovation system. These variables are as follows: • Population with tertiary education (% of the population aged 25-34). • Continuing education (% of the population aged 25-64). • International scientific co-publications (per million population). • Most cited scientific publications (% of the region’s scientific publications among the top 10% of the most cited publications worldwide). • Digital skills (% of the population aged 25- 74).
• Process and product innovators (% of SMEs).
Indicators that characterise innovation system inputs and outputs
• Commercial and organisational process innovators (% of SMEs).
• Patent applications (% of GDP).
• Trademark applications (% of GDP).
• Industrial design applications (% of GDP).
• Sales of innovations (% of turnover).
One of the underlying assumptions in develop - ing a synthetic index such as the RIS is that the factors measured by its indicators are perfect substitutes. In terms of the RIS, it is the same for a region to have a high proportion of inno- vation SMEs and low innovation activity perfor- mance as it is for the opposite situation to be true, i.e. few innovation SMEs and high innova- tion results. However, a basic economic analy - sis would lead us to conclude that the second case involves innovation being concentrated in a few companies, while in the first scenario the situation would be more unfavourable, since it could be evidence of inefficiencies in the com - panies' innovation effort. With regards to the RIS indicators, it would appear that some regions “do more with less”, as they achieve better innovation results with an intensity/capacity similar to other regions. Some studies have analysed national innova- tion activity efficiency based on the relation - ship between two groups of EIS indicators: inputs and outputs (Barbero et al. , 2021; Zaba - la-Iturriagagoitia et al. , 2021). In this study, we will apply this approach to the RIS, in order to study the relative efficiency of the Spanish re - gions compared to all other European regions. Following the pioneering work of Edquist et al . (2018), we will define three groups of indica - tors within the set of variables in the RIS. We will call the activities and resources directed to creating innovations and introducing them in the market inputs. This group has the following
variables:
Finally, we have the other RIS indicators group; this group is made up of other factors which, despite having a direct influence on both the input and output variables, cannot be charac- terised as variables that represent innovation system performance from either of these perspectives (i.e. inputs or outputs). As argued by Edquist et al. (2018), despite being impor-
• Public R&D expenditure (% of GDP).
• Business R&D expenditure (% of GDP).
• Innovation expenditure other than R&D (% of GDP).
• Innovation expenditure per employee (euros).
• IT specialists (% of total employees).
• SMEs involved in innovation projects (% of SMEs).
• Public-private co-publications (per million inhabitants).
Outputs, meanwhile, will refer to new products, processes, designs or brands, organisational or commercial innovations, and their successful market implementation or introduction. This would include the following variables:
• Employment in knowledge-intensive activi- ties (% of total employees). • Employment in innovation SMEs (% of total employees in SMEs).
• Air pollution (concentration of fine parti - cles).
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