The Innovation Output Indicator 2019

This report presents the 2019 update of the Innovation Output Indicator (IOI), which is a composite indicator published every year by the European Commission since 2013 aiming to quantify the extent to which ideas for new products and services carry an economic added value and are capable of reaching the market. A novelty of this report is a special focus on radical innovator companies in Europe, referred to as "global innovation champions" (GICs). The dispersion of a relatively small number of exporter companies that introduced a “world-first” product innovation deserves particular attention. While small- or medium-sized radical innovator enterprises in Europe are embedded in global value chains, they often remain “hidden champions” for innovation policy makers and are typically the object of selected case studies in reason of limitations in the granularity of reliable data sources. The special focus of this report aims to quantify and characterize them for a relatively large number of countries. The report presents the latest figures for the underlying indicators and composite index for 40 countries – European Union Member States and selected EFTA, OECD and emerging economies. In this edition, 2 scores are computed for the European Union, one for a bloc of 28 countries alongside with estimates where the United Kingdom is excluded. The four components of the IOI provide a benchmark for countries and the European Union as an aggregate in terms of patent-based technological innovation, skilled labor force feeding into the economic structure of a country, competitiveness of knowledge-intensive goods and services, as well as employment in fast-growing enterprises in innovative sectors. The methodology is unchanged with respect to the refinements introduced in the 2017 editions. Composite results show that the EU (using both aggregates) is outperformed by the US. There is some evidence of convergence, the gap between the EU with respect to the US as well as Israel and Japan has somewhat declined since 2011. Nevertheless, additional efforts are needed for the EU to catch up with Israel and Japan. When comparing European countries, we notice that Ireland, Sweden, and the UK are among the leaders in terms of innovation output, whereas Lithuania, Croatia and Romania are at the end of the ranking. The analysis shows the importance of benchmarking a country’s performance not only according to its composite scores, but also according to the various components. Most notably, the multivariate analysis on the relationship between the component indicators indicates that the component measuring employment in fast-growing enterprises in innovative sectors (DYN) shows a weak, positive association with the rest of the components and, as a consequence, with the IOI aggregate index. This suggests that innovation performance of countries is constituted by at least two rather distinct dimensions. The first one is related to the performance of the technology- and knowledge-based economy (development of new technology, strength of sectors relying on highly-skilled workers, and exports in sectors close to the innovation frontier). The second one concerns entrepreneurship and business dynamism in innovative sectors. Strong performance in one of these two dimensions does not automatically imply strong performance in the other, suggesting that innovation policy should carefully monitor and foster the development of both in their own merits.JRC.I.1-Monitoring, Indicators & Impact Evaluatio

Composite Indicators of Research Excellence

This report on Research Excellence is the deliverable of the third work package (WP3) of the feasibility study ‘ERA MONITORING’, financed by DG RTD. The objective of the work package was to explore the possibility to develop a composite indicator of research excellence in Europe, in coherence with the orientations of the EU 2020 strategy and the Innovation Union initiative. The study built on the theoretical framework proposed by the 2011 report of the Expert Group on the Measurement of Innovation ‘Indicators of Research Excellence’, co- authored by Rémi Barré (CNAM, France), Hugo Hollanders (UNU-MERIT, The Netherlands) and Ammon Salter (Imperial College, UK). We proposed three alternative conceptual frameworks of research excellence with different underlying indicator structures, and tested their statistical coherence. In the first theoretical framework, we aimed to follow as closely as possible the Expert Group recommendation of 6 dimensions. In the second framework, we tried to consider only two dimensions (basic and applied science), also based on the Expert Group report. The third framework was derived from the data and three dimensions were identified directly from a principal component analysis.JRC.G.3-Econometrics and applied statistic

The seductive power of Irish rain. Location determinants of foreign R&D investments in European regions

Foreign direct investments (FDI) in research and development (R&D) are important catalysts of economic development. Through a diversity of direct and indirect policy measures, governments compete in attracting such investments. Our study investigates the factors that affect the degree of attractiveness of European regions from the perspective of a company investing abroad based on evidence gathered from all the FDI in R&D realized between 2003 and 2014. We use mixed logit models to assess: i) the relative importance of the factors influencing the choice made by multinational firms about where to locate foreign R&D investments to European regions, and ii) how such influences vary according to timing, investments’ area of provenience and industry. On average, the fiscal regime and the size of destination regions as well as the sharing of a common language in the sending and receiving regions are the most important determinants. Labor costs, technological strength and R&D expenditure, especially performed by the higher education sector, are also important, yet to a lower extent. The strength of determinants still varies greatly across considered breakdowns.JRC.I.1-Modelling, Indicators and Impact Evaluatio

Composite Indicators measuring structural change, to monitor the progress towards a more knowledge-intensive economy in Europe

This report is the deliverable of the second work package (WP2) of the feasibility study entitled ERA MONITORING and financed by DG RTD. The objective of this work package is to explore the possibility to develop a composite indicator of structural change towards a more knowledge-intensive economy in Europe, coherently with the orientations of the EU 2020 strategy and the Innovation Union initiativeJRC.G.3-Econometrics and applied statistic

Composite Indicators measuring the progress in the construction and integration of a European Research Area

This report is the deliverable of the first work package (WP1) of the feasibility study entitled: ‘ERA monitoring: Composite Indicators measuring the progress in the construction and integration of a European Research Area’, financed by DG RTD. For this deliverable we developed a composite indicator to measure progress in the construction and integration of a European Research Area (ERA). The indicators required for this study and the theoretical framework have been drawn and adapted using the headline indicators proposed by the expert group report on ‘ERA indicators and monitoring’ 2009 EUR 24171 EN. The report combines economic and statistical expertise and presents a comprehensive and flexible framework for an evidence-based monitoring of progress towards the European Research Area.JRC.G.3-Econometrics and applied statistic

Update on the Composite Indicators of Structural Change towards a More Knowledge-Intensive Economy

This report aims at assessing whether the economic structure of Europe is becoming more knowledge-intensive, in comparison with other countries (EU, EFTA and non-European benchmarks US, Japan, China). This entails the measurement of key dimension of structural change with a simple policy tool. The present work builds on and updates the results of the previous Feasibility Study on the development of composite indicators of structural change (Vertesy et al., 2012). It also builds on a previous study by Malerba et al. (2011) that identified indicators measuring changes in the actual sectoral composition of the economy. In this study we construct a composite indicator on structural change at the country level, including indicators on R&D, skills, sectoral specialization, international specialization and internationalization. This composite is a supply-oriented indicator that is largely based on past performance (the outcomes of past efforts that are already measurable in terms of actual value added and employment levels in knowledge-based activities, revealed competitive advantages, supply of skilled human resources, etc.). All these indicators are related to the overall structure of the economy and are slow to change. In order to capture short-term characteristics of structural change related to the dynamics of smaller and younger firms, future research should focus on the development of a longitudinal database collecting indicators on the share of gazelles and the share of high-growth firms in terms of employment and turnover.JRC.G.3-Econometrics and applied statistic

The Adjusted Research Excellence Index 2018: Methodology Report

The Adjusted Research Excellence Index is a composite indicator selected by the European Research Area Council (ERAC) as the headline measure to monitor country performance with respect to ERA Roadmap Priority 1, ‘Effective national research systems’ (European Commission, 2017). It combines four dimensions that characterize effective research systems, in terms of scientific and technological research excellence (the “production” of high-impact publications and valued patents), and the ability of institutes to attract outstanding research grants and participate in researcher exchanges to pave the way for future excellence and develop efficient research capacity. This report describes the methodology used to compute the latest scores for 43 countries (EU Member States and Horizon 2020 Associated Countries) and the EU28 for 4 time points: 2016, 2013, 2011 and 2010. The results show that Switzerland excels in having the most effective national research system, followed by Denmark, the Netherlands, Sweden and the United Kingdom. Most of the countries show a welcome growth of the composite score over the last period, however it is important to keep in mind that growth is, to a large extent, driven by the overall increase in the value of ERC grants. Country ranks were found to be rather robust, with uncertainty in the modelling choices having only limited impact (3-4 rank positions shifts) on the ranks of the majority of the countries.JRC.I.1-Modelling, Indicators and Impact Evaluatio

The Innovation Output Indicator 2017: Methodology Report

This report presents the 2017 update of the Innovation Output Indicator (IOI), which is a composite indicator published by the European Commission since 2013 aiming to quantify the extent to which ideas for new products and services carry an economic added value and are capable of reaching the market. Beyond offering the latest figures for the underlying indicators and composite index, this current edition discusses the revision of the component that measures employment dynamism in fast-growing enterprises in innovative sectors. The new definition aims to simplify the interpretation of the indicator, which now compares countries’ performance in terms of the share of employment in fast-growing enterprises in innovative sectors, rather than weighting sectoral innovation coefficients with sectoral shares of employment in high-growth enterprises. The report also discusses in details how changes in the definition of this component affect the outcomes. The rest of the components, measuring technological innovation by patents, the share of highly skilled labor force feeding into the economic structure of a country, as well as the competitiveness of knowledge-intensive goods and services, remain unchanged with regards to previous editions. Composite results show that the EU is slightly outperformed by the US, while both are trailing Israel and Japan. There is no evidence of convergence, the gap between the EU with respect to the US as well as Japan has persisted over time. When comparing European countries, we notice that Ireland, Sweden, the UK and the Netherlands are among the leaders, whereas we find Croatia, Romania and Lithuania at the end of the ranking. Multivariate statistical analysis shows that it is important to benchmark a country’s performance not only according to its composite scores, but also according to the various components. Most notably, the component measuring employment in fast-growing enterprises in innovative sectors shows a weak, positive association with the rest of the components. This may be interpreted as two aspects of Schumpeterian dynamics, where R&D-based and entrepreneurship-based innovation may require specific, dedicated policies.JRC.I.1-Modelling, Indicators and Impact Evaluatio

High-growth, innovative enterprises in Europe. Counting them across countries and sectors

High-growth, innovative enterprises are a key source of business dynamics, but little is known about their actual share in the enterprise population. This is due to an inherent uncertainty in how to define the threshold that distinguishes high-growth firms from non-high-growth firms – illustrated by the lack of agreement between the definitions applied by Eurostat and the OECD. This explorative study aims to help measure the share of high-growth, innovative enterprises in the European enterprise population, test how the choice of definition affects their share. We introduce a methodology to address the uncertainty in the definition, and compute national and sectoral average scores for high-growth and innovation in order to assess their distribution across countries and sectors of economic activity. We test the impact of a number of alternative definitions on a pooled sample of 92,960 European firms observed by the 2012 wave of the Community Innovation Survey (CIS). Our finding suggests that the share of high-growth, innovative enterprises in Europe may range between 0.1 to 10%, depending on the definitions, and the outcomes are most sensitive to the growth measure (employment- or turnover-based) and threshold (absolute or relative), as well as the degree of novelty expected of the innovations introduced by firms. With the help of aggregate measures, we observe a trade-off between high-growth and innovation performance at the country-level, which disappears at the overall European sectoral level. This observation highlights the importance of structural differences across EU Member States in terms of firms’ innovation profile, size and associated high-growth performance.JRC.I.1-Modelling, Indicators and Impact Evaluatio

Innovation and Employment in Patenting Firms: Empirical Evidence from Europe

This report explores the possible job creation effect of innovation activity. We analyze a unique panel dataset covering nearly 20,000 patenting firms from Europe over the period 2003-2012. The main outcome from the proposed GMM-SYS estimations is the labour-friendly nature of innovation, which we measure in terms of forward-citation-weighted patents. However, this positive impact of innovation is statistically significant only for firms in the high-tech manufacturing sectors, while not significant in low-tech manufacturing and services.JRC.DDG.01-Econometrics and applied statistic