Smart Specialisation is a place-based approach to innovation policy that underpins a significant amount of EU funding. The origins of the concept lie in the transatlantic productivity gap and a concern that previous investments in Research and Innovation (R&I) had failed to deliver commercial benefits. Following more than five years of implementation, this report contributes to the evaluation of the smart specialisation approach through quantitative analysis. As part of the Stairway to Excellence project, it is one of the first to assess its impact on regional productivity, based on the case of Portugal. This is done using the country's main instrument to support corporate Research and Development (R&D) that was launched in 2007 and adapted to accommodate smart specialisation in 2014. An analysis of project characteristics reveals that during the programming period 2014-2020, financial support to corporate R&D investment aligned with S3 priorities has been more concentrated on cooperation between regions and sectors. A higher diversification of R&D and Innovation funds across sectors, regions and beneficiaries, in comparison with 2007-2013, is also observed. As more cooperation and diversification are two important features of smart specialisation, these findings suggest improved investment choices in the programming period 2014-2020. Furthermore, after controlling for the existence of potential geographical spillover effects by applying a spatial econometric analysis, the results display a positive effect on regional productivity from the R&D and Innovation subsidies over the last two programming periods. Furthermore, a higher rate of return of RDI subsidy in the second period is also observed, which suggests that smart specialisation was able to generate an additional effect in comparison with a situation without this place-based policy. Nevertheless, we also found that - in the case of Portugal - smart specialisation has only been able to generate this additional effect in regional productivity when the R&D funding instrument is combined with other types of innovation subsidies. This finding provides additional weight to the argument for broader and more integrated smart specialization policy mixes in the new programming period.

The paper aims to investigate regional participation in a new Portuguese Research and Innovation (R&I) programme (so-called 'Agendas for Industrialization') funded by the European Next Generation EU (NGEU). Using a probit model with sample selection and a novel dataset, we find that the distribution of territorial participation to the NGEU-related programme is more similar to that observed for the European Commission's R&I Horizon 2020 programme than for the R&I funds under the Cohesion Policy (2014-2020). Results also show a rural-urban divide in regional participation, which is eventually due to a lack of demand for this type of funding due to the sectoral composition of the economy in less developed regions.

Transformative Innovation Policy (TIP) has an important role in the sudden transition that our economies require to face up to today's grand challenges (climate change, sustainable development goals). In the European Union (EU), Cohesion policy funds are one of the main financing instruments to support innovation and a fair transition. This paper focuses on TIP's monitoring and evaluation (M&E). To begin with, we discuss the various degrees of sophistication that can be found in monitoring and evaluation exercises (i.e. Storey's "6 steps to heaven" scale), ranging from interviews asking recipients whether they are happy to receive funding, to full-blown causal econometric analyses. We then provide a survey of causal inference techniques that reach the 6th step on this scale, and analyse the degree of sophistication of recent EU Cohesion project evaluations. We conclude that evaluation completed by EU Member States using causal inference techniques only represents 8% of the total evaluations conducted for period 2014-2020, and this percentage is even lower when we look at innovation or environmental-related programmes. We identify some gaps in the observed M&E of EU Member States and we provide some recommendations for how to set up M&E, contrasting traditional M&E with modern M&E, and highlighting the need for real-time data. In sum, we state that M&E needs improving, and we suggest how this might be done.

The present working paper aims to describe the data sources and methods used to develop the Territorial Economic Data viewer (TEDv), as well as to explain the purpose and usefulness of the different dashboards available in the current version of the tool. Additionally, this paper includes practical examples with policy lessons that can be drawn from the available information, as well as a glossary of the indicators within the TEDv.