Assessing Smart Specialisation: Monitoring and Evaluation Systems
Our analysis aims to evaluate the efficiency and effectiveness of monitoring and evaluation systems of national and regional authorities implementing Smart Specialisation strategies. In addition, based on the literature review and on the evidence gathered by our research project, this publication draws some policy lessons with reflections for the 2021-2027 European Union Cohesion policy as regards to monitoring and evaluation.
Besides providing a literature review on monitoring and evaluation of Smart Specialisation, this publication offers an overview of a research project run by the Smart Specialisation p latform to gain insight on the Smart Specialisation policy experience across the EU in its 7th year of implementation. In particular, this project has analysed whether the principles of Smart Specialisation as regards to monitoring and evaluation hold true in practice from the experiences gained during the 2014-2020 programming period. Thus, our analysis aims to evaluate the efficiency and effectiveness of monitoring and evaluation systems of national and regional authorities implementing Smart Specialisation strategies. In addition, based on the literature review and on the evidence gathered by the p roject, this publicationdrawssomepolicylessonswithreflectionsforthe2021-2027European Union Cohesion policy as regards to monitoring and evaluation.
The importance of assessing the experience of the 2014-2020 programming period and the approach adopted by national and regional authorities in charge of Smart Specialisation derives from the consideration that Smart Specialisation has been the largest place-based policy experiment attempting to boost economic growth through prioritisation of research and innovation domains and through diversification. Smart Specialisation has been defined as an ex-ante conditionality for using European Development Funds (ERDF) under Thematic Objective 1 (research and innovation). Over 120 Smart Specialisation strategies have been implemented during the 2014-2020 programming period, having had guided the investment of over EUR 40 billion from ERDF (over EUR 65 billion including national co- financing).
Various sources of primary information have been used to perform this analysis: a survey addressed to S3 implementing authorities, analysis of implementation measures and case study reports. Out of the 120 existing Smart Specialisation strategies, the survey has been filled out by 79 national or regional implementing authorities from nineteen countries while the case studies cover thirteen regional and 4 national strategies and their implementation practices. Four main themes have been explored besides this publication: impact of smart specialisation on the governance of research and innovation policy systems (Guzzo and Gianelle), entrepreneurial discovery process (Perianez-Forte and Wilson, 2021) and policy implementations (Gianelle et al., 2021).
From the evidence on monitoring and evaluation, we could deduct that Smart Specialisation represent a cultural change for most regions, whether developed and already well acquainted with regional innovation policy practices or lessdeveloped with lower innovation performance. Still, the practice of policy monitoring and evaluation continues to lag behind, which in turn limit learnings and an updated strategy that is based on S3 policy outcomes and impact. It is necessary to identify a dedicated team responsible for S3 monitoring and evaluation within the public administration (equipped with adequate human and financial resources), in order to have an evaluation of the S3 results and the effectiveness of the policy intervention logic. In order to support evaluation activities, it is important to collect data relating to the behaviour of innovation actors, even those not represented in regional calls. While in vie w of the next programming period, it is necessary to make use of analytical and informative tools (big data, web semantics, etc.) able to provide different kind of data and faster return.