D8.2 Overall impact of the Innovation Union progress as measured in the IU scoreboard

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1 D8.2 Overall impact of the Innovation Union progress as measured in the IU scoreboard Deliverable: D8.2 Overall impact of the Innovation Union progress as measured in the IU scoreboard Author(s): Pierre Le Mouël, SEURECO Version: FINAL Quality review: ISINNOVA Date: Grant Agreement N : Starting Date: 01/01/2015 Duration: 36 months Coordinator: Bart Verspagen, UN-MERIT b.verspagen@maastrichtuniversity.nl Page 1 of 64 This project has received funding from the European

2 Table of Contents 1. Introduction The European Innovation Scoreboard Situation in 2015: Country ranking Evolution from 2008 to The contribution of R&D expenditures Focus on the evolution of R&D expenditures Private R&D expenditures Public R&D expenditures Total R&D expenditures Input to NEMESIS Private R&D expenditures Public R&D expenditures Other methodological issues The reference scenario Important R&I mechanism of NEMESIS for the simulation The results at EU macro level The results for EU GDP The impacts on employment at EU level The results at EU sectoral level Sectoral Value Added Sectoral Employment Trade balance The results at national level Impact on GDP Impact on intra-eu trade balance Contributions to GDP increase in Impacts on total employment in Conclusion References Appendix: The advantages of using the NEMESIS model to represent the Innovation Union The past experience of the NEMESIS model for EU R&I policies assessment NEMESIS and the other model available for EU R&I policy assessment The key innovation mechanisms of NEMESIS A Fully Endogenous Growth approach (Ha & Howitt, 2007) Multi-dimensional innovations Innovation services and complementarities Page 2 of 64 This project has received funding from the European

3 Process and product innovations Page 3 of 64 This project has received funding from the European

4 1. Introduction D 8.2 Overall impact of the Innovation Union The European Innovation Scoreboard 1 (EIS) is an instrument introduced in 2001 by the European commission to provide a comparative assessment of the innovation performance of EU Member States. Together with the Regional Innovation Scoreboard and the pilot European Public Sector Innovation Scoreboard (launched in 2013), it forms a comprehensive benchmarking and monitoring system of research and innovation trends and activities in Europe. The last edition of the EIS (2016), does not highlight many changes in the country ranking during the time span that starts from the financial crisis (2008) onward. Only Croatia and Latvia moved up to moderate innovators and Cyprus moved down to moderate innovator. The evolution across countries is nevertheless significantly differentiated. As a whole, the innovation performance of the EU increased by 5.3%, but the spectrum of individual evolutions ranges from -26.9% in Romania, the less performing country in 2015, to +31.5% in Latvia, a very moderate innovator compared to the EU average. Most countries have anyway exhibited positive trends with improvements higher than 5% for 16 countries, between 0% and 5% for 6 countries and negative evolutions only for 6 countries. While the EIS includes 25 different indicators (see section 3), we will focus in this report on the evolution of two of them that are the trends in the R&D expenditures, public and private, in percentage of GDP. These public and private R&D intensities are acknowledged as essential for making the transition to a knowledge based economy and for driving the innovation performance of the different countries. At the difference of the EIS that doesn t propose a detailed analysis of the evolutions of R&D intensities, we decompose the evolution of the private R&D intensity between the 29 private economic activities included in the NEMESIS model, and the one of the public R&D intensity between 14 different socio-economic objectives (e.g. Agriculture, Energy, Environment, Health, Education,...). We then introduce these data in the Macro sectoral model NEMESIS that we simulate to analyse how the evolutions of the public and private R&D intensities, in the different EU countries, between 2007 and 2015, have modified their innovation performance and have impacted on the level of employment and other key economic indicators such as GDP, investment, consumption, exports, and imports. This report provides in this way a complement to the EIS by analysing more precisely the evolution of R&D expenditures on the period and by introducing in the analysis economic indicators at different levels: macro, sectoral, national and European. After a presentation of the results of the EIS 2016 in section 3, section 4 details the evolution of the R&D intensity of the different countries in the period Section 5 then explains the methodology adopted to make use of R&D data in the NEMESIS model, and section 6 describes the pathways by which these data will influence the economic performance of the different 1 The EIS took this new name in It was called from 2010 to 2015 Innovation Union Scoreboard and between 2001 and 2009 the European Innovation scoreboard.. Page 4 of 64 This project has received funding from the European

5 countries and sectors, including the description of the indicators calculated by the model. Sections 7 through 9 then present the simulation results at the macro, sectoral and national level. Section 10 finally draws conclusions. Page 5 of 64 This project has received funding from the European

6 2. The European Innovation Scoreboard The European Innovation Scoreboard (EIS) measures the innovation performance of the European Union based on three main types of indicators and eight innovation dimensions, for a total of 25 different indicators The Enablers indicators capture the main drivers of innovation performance external to the firm and differentiate between three innovation dimensions: (1) The Human resources dimension, which includes three indicators and measures the availability of a high-skilled and educated workforce, (2) The Open, excellent and attractive research systems dimension, which includes three indicators and measures the international competitiveness of the science base, and (3) The Finance and support dimension, with two indicators that measure the availability of finance for innovation projects and the support of governments for research and innovation activities. 2. Firm activities indicators capture the innovation efforts at the level of the firm and differentiate between three innovation dimensions: (4) The Firm investments dimension, with two indicators that cover both R&D and non-r&d investments, (5) The Linkages & entrepreneurship dimension, which includes three indicators and measures entrepreneurial efforts and collaboration efforts among innovating enterprises (including relationships with the public sector) and (6) The Intellectual assets dimension, addressing different forms of Intellectual Property Rights (IPR) generated as a throughput in the innovation process. 3. Outputs indicators capture the effects of enterprises innovation activities and differentiate between two innovation dimensions: (7) The Innovators dimension, which includes three indicators and measures the number of enterprises that have introduced innovations for the market or within their organisations, covering both technological and non-technological innovations, (8) The Economic effects dimension, which includes five indicators and captures the economic success of innovation in employment, exports, and sales. 3.1 Situation in 2015: Country ranking The relative innovation performance of Member States is summarized by a composite indicator (Figure1, the Summary Innovation Index (SII), which averages the three main types of indicators listed above (Enablers, Activities, Outputs) 2 See, European Innovation Scoreboard Methodology report Page 6 of 64 This project has received funding from the European

7 Figure 1: Relative innovation performance of Member States measured by the SII for Innovation leaders Strong innovators Moderate innovators Modest innovators v v Romania Bulgaria Croatia Latvia Lithuania Poland Slovakia Hungary Spain Greece Portugal Italy Czech Republic Malta Estonia Cyprus Slovenia EU France Austria Luxembourg United Kingdom Belgium Ireland Netherlands Germany Finland Denmark Sweden Source: Based on EIS 2016 data Four categories of innovators are distinguished: (1) Innovation leaders, with a SII at least 20% above the EU average (Denmark, Finland, Germany, the Netherlands, and Sweden). (2) Strong innovators, with a SII between 90% and 120% of the EU average (Austria, Belgium, France, Ireland, Luxembourg, Slovenia, and the UK). (3) Moderate innovators, with a SII between 50 % and 90 % of the EU average (Croatia, Cyprus, Czech Republic, Estonia, Greece, Hungary, Italy, Latvia, Lithuania, Malta, Poland, Portugal, Slovakia, and Spain). (4) The modest innovators with a SII below 50% of the EU average (Bulgaria and Romania). The EIS 2016 does not show important changes in this performance group memberships, compared to the 2015 report. Only Latvia has moved up to the moderate innovators while the Netherlands joined the Innovation leaders. Page 7 of 64 This project has received funding from the European

8 Table 1: Values of indicators for the eight innovation dimensions for 2015 Summary Innovation Index Source: Based on EIS 2016 data Human resources Enablers Firms activity Outputs Research systems Finance and support Linkages & Firm entrepreneu investments rship Intellectual assets Innovators Romania Bulgaria Croatia Latvia Lithuania Poland Slovakia Hungary Spain Greece Portugal Italy Czech Republic Malta Estonia Cyprus Slovenia EU France Austria Luxembourg United Kingdom Belgium Ireland Netherlands Germany Finland Denmark Sw eden Economic effects When looking more closely at the values of the eight categories of indicators that compose the SII (Table 1), we see that the countries classified as modest and moderate innovators generally exhibit the lowest scores for all indicators (lower than 0.4 red diamonds or between 0.4 and 0.6 yellow triangles). Conversely, countries classified as strong innovators or innovation leaders generally score above 0.6 (green circles) or close to 0.6 for all indicators. The only exception is for the Human Resources indicator that measures the share of high-skilled and educated workers, even though on average the best performing countries have higher scores than the less performing ones. Table 2: Linear correlation across countries between the eight categories of innovation indicators for 2015 Summary Innovation Index Source: Based on EIS 2016 data Human resources Research systems Finance and support Firm investments Linkages & entrepreneurship Intellectual assets Innovators Summary Innovation Index Human resources Research systems Finance and support Firm investments Linkages & entrepreneurship Intellectual assets Innovators Economic effects Economic effects Page 8 of 64 This project has received funding from the European

9 These observations are confirmed by the values of the linear correlation coefficients between the different indicators calculated across countries (Table 2). The strong correlation of the individual indicators with the SII, generally higher than 0.66 (green circles) with several even higher than 0.8, confirms that countries with high SII values generally exhibit high scores for all the indicators and conversely. The exceptions concern the Human capital indicators (correlation coefficient 0.51) and the Firms investments indicator (correlation coefficient 0.47). In particular, this last result is more surprising as Firms investment covers two Enablers that measure the diffusion of new production technologies and ideas: Private R&D investment in % of GDP (from EUROSTAT) and firms investments in other innovation expenditures in % of turnover (from CIS: Community Innovation Survey), such as the purchase of innovative equipment and machineries, of patents and licenses. The reason may be that R&D investments mainly concern industrial firms or near to science sectors, such as chemicals and electronics, whereas the EIS covers all types of innovations (technological and non-technological such as organisational innovations) by all sectors (industry, services and agriculture). The correlation between the other innovation dimensions is confirmed as very strong, higher than or close to 0.66, with few exceptions. 3.2 Evolution from 2008 to 2015 Turning now to the evolution between the 2008 financial crisis and 2015, Table 3 shows the dynamics (in % change) of the SII and of the 8 categories of indicators between these two dates. While few changes occurred over the period in the country ranking (only Croatia and Latvia moved up to moderate innovators and Cyprus moved down to moderate innovator), the evolution between countries is highly contrasted. As a whole, the SII increased by 5.3%, but the spectrum of individual evolutions ranges from -26.9% in Romania, the less performing country in 2015, to +31.5% in Latvia, still a very moderate innovator compared to the EU average. Most countries have experienced a positive dynamic, with improvements above 5% for 16 countries (green circles), with other 6 countries exhibiting variations between 0% and 5% (yellow triangles), while negative variations have been reported only for 6 countries (red diamonds). Page 9 of 64 This project has received funding from the European

10 Table 3: Evolution of SII and of the eight innovation indicators categories between 2008 and 2015 (in %) Summary Innovation Index Source: Based on EIS 2016 data Human resources Enablers Firms activity Outputs Research systems Finance and support Firm investments Linkages & entrepreneurship Intellectual assets Innovators Economic effects Romania -26.9% 56.1% 16.2% -76.8% -74.0% -72.1% 10.2% -39.7% -27.0% Bulgaria 10.2% 27.8% -48.7% -67.0% -7.7% -46.8% 108.7% 19.5% -0.5% Croatia -6.2% 15.0% 24.3% 24.6% 14.4% -30.2% -3.2% -36.3% -18.8% Latvia 31.5% 74.0% 97.6% 56.0% 18.9% -24.9% 15.2% 33.7% 14.6% Lithuania 18.0% 33.1% 63.2% 103.9% 75.3% -38.4% 49.2% -51.3% -15.4% Poland 0.7% 11.8% 22.9% 28.0% 27.4% -60.7% 51.1% -39.5% -9.3% Slovakia 10.2% 43.9% 26.2% 223.8% -41.1% -18.2% 30.3% 0.4% 2.1% Hungary 2.8% 37.9% 3.6% -1.0% 32.0% -11.6% -3.4% -2.1% -6.5% Spain -5.2% 51.7% 3.6% -27.1% -31.6% -22.6% 5.1% -38.7% 1.7% Greece -1.5% 32.4% 29.6% 22.6% 22.2% -16.4% 34.3% -17.5% -27.8% Portugal 6.5% 29.3% 62.1% -5.2% -28.6% -2.4% 15.1% -10.6% -0.9% Italy 11.2% 33.9% 47.9% -11.1% 4.9% 5.0% -1.0% 10.0% 12.0% Czech Republic 5.1% 16.3% 46.5% -30.7% 6.2% -8.1% 18.4% 1.8% 10.1% Malta 27.8% 138.1% 9.6% 111.7% 16.3% 17.1% 119.5% 56.2% -16.4% Estonia 7.7% 30.1% 56.6% 118.6% -6.6% -28.6% 48.8% -29.5% -8.4% Cyprus -3.9% 20.6% 80.4% -5.0% -70.3% -30.9% 8.9% -9.1% 3.9% Slovenia 8.6% 53.8% 34.9% -18.8% -27.8% 0.9% 15.7% -6.0% 3.1% EU 5.3% 26.9% 14.8% -6.5% 16.1% 1.8% 0.4% -9.2% 7.0% France 5.4% 14.4% 14.7% -7.1% 2.7% -6.5% -1.5% 14.1% 6.5% Austria 1.5% -1.6% 36.1% 26.0% 16.5% -15.1% 2.4% -9.0% -4.3% Luxembourg -5.4% 8.3% 96.6% -43.0% -72.8% -14.9% 6.7% -23.6% -1.0% United Kingdom 14.7% 35.4% 13.8% -26.0% 2.2% 41.3% 3.3% 39.2% 11.3% Belgium 6.7% 13.1% 34.8% -21.1% 31.4% 8.5% -0.6% -14.3% 10.5% Ireland 4.2% 15.3% 23.4% -37.7% -27.2% 4.5% -1.1% 3.3% 14.2% Netherlands 15.1% 32.6% 19.2% -3.0% 3.9% 23.2% 3.5% 23.3% 14.6% Germany 1.2% 20.3% 6.8% 14.4% 33.5% -3.3% -7.3% -12.5% -3.2% Finland -2.0% -2.4% 53.9% -9.2% -18.2% -23.4% 11.7% -9.5% -1.8% Denmark 12.3% 74.1% 19.2% 3.0% -0.7% 8.8% -5.9% -9.9% 30.8% Sw eden 1.0% 5.0% 44.6% -17.9% -1.8% -11.6% -2.9% -5.6% 3.5% For most countries, the SII index was pushed up by the rise of two Enablers, Human resources and Research systems, which include indicators such as the numbers of scientific publications and of doctoral graduates. As for the other groups of innovation indicators, the Outputs indicators group features noteworthy improvements of Intellectual assets in most countries ranked as modest and moderate innovators, although this indicator shows a great variability among innovations leaders and strong innovators. The contrary is observed for Economic effects indicators, which increased in most innovation leaders and strong innovators countries, while decreasing for most of the low ranked countries. In more detail, Table 4 shows the linear correlations between the SII and the eight categories of innovation indicators over the period Despite the increase in most countries of the Indicator Human Resources, which pushed up the SII at EU-28 level, it was in fact only barely correlated (positively) to the evolution of the SII (correlation coefficient 0.08) as well as to all other innovation categories (correlation coefficient between for Firms investment - and for finance and support -). All other indicators are quite strongly positively - correlated with the SSI (correlation coefficient between 0.33 and 0.66, in yellow triangles). Page 10 of 64 This project has received funding from the European

11 Table 4: Linear correlation across countries between the evolution of the eight categories of innovation indicators on the period Summary Innovation Index Source: Based on EIS 2016 data Human resources Research systems Finance and support Firm investments Linkages & entrepreneurship Intellectual assets Innovators Summary Innovation Index Human resources Research systems Finance and support Firm investments Linkages & entrepreneurship Intellectual assets Innovators Economic effects Economic effects Among the eight innovation dimensions, the strongest positive correlations are found between Linkage and entrepreneurship one on hand and Innovators and Economic effects on the other, with correlation coefficients respectively at 0.62 and Other strong correlations (> to 0.25) are found between: Human resources and: Finance and Support (0.32) - Intellectual assets (0.46) - Innovators (0.42); Finance and support and: Firms investments (026) - Intellectual assets (034). Surprisingly, the Research systems group of indicators, which in 2015 was strongly correlated with the other groups of indicators and the country ranking, appears to be less correlated to the other, in most of the case with inverse correlations, with reference to the evolution from 2008 to 2015 of performance indicators. This could mean that the range under examination ( ) is too short to show clear trends, and that the economic crisis may have contributed to make correlations less significant. 3.3 The contribution of R&D expenditures It is commonly acknowledged 3 that in knowledge based economies R&D expenditures, public and private, are among the major drivers of economic growth. Although the EIS 2016 doesn t focus on the specific influence of the R&D intensity on the relative innovation performance of EU countries, it nevertheless recognises that As such, trends in the R&D expenditure indicator provide key indications of the future competitiveness and wealth of the EU. Research and development spending is essential for making the transition to a knowledge-based economy as well as for improving production technologies and stimulating growth. This statement is confirmed by Figure 2 that shows a strong correlation between the level of the public, private and total R&D intensity in % GDP at country level, and their innovation performance ranking for 2015, as captured by the SII. The linear correlation coefficients across countries between the total R&D intensity and the value of the SII for 2015 (Table 5) is indeed equal to Its value would even exceed 92% if we remove 3 e.g. OECD Knowledge based economy, Paris, Page 11 of 64 This project has received funding from the European

12 from the calculation the very small EU countries (Malta, Cyprus, Estonia, Latvia, Lithuania, Slovenia, Croatia, Bulgaria and Luxembourg), that account together for less than 2% of EU GDP and R&D expenditures, and Ireland, which significantly revised (upwards) its GDP figure for For public and private R&D intensities, the correlation coefficients with the SII are respectively 0.85 and The private R&D intensity is better correlated with the different innovation indicators, with coefficients often higher than 0.66 (green circles) or at least than 0.5. The strong correlation of the private R&D intensity with the EIS innovation indicators is sometimes trivial, as in the case of Firm investments, whose value already includes the influence of private R&D intensity, as well as for other important research throughputs such as Intellectual assets categories, which relies on the number of patent applications. Figure 2: Country raking (SII) and relative R&D intensity in % GDP in Innovation leaders Strong innovators Moderate innovators Modest innovators Sweden Denmark Finland Germany Netherlands Ireland Belgium United Kingdom Luxembourg Austria France EU Slovenia Cyprus Estonia Malta Czech Republic Italy Portugal Greece Spain Hungary Slovakia Poland Lithuania Latvia Croatia Bulgaria Romania SII (right axe) Public R&D intensity Private R&D intensity Total R&D intensity Source: Based on EIS 2016 data and EUROSTAT (for R&D intensities) Table 5: Linear correlation across countries between R&D intensity the eight categories of innovation indicators for 2015 Summary Innovation Index Human resources Research systems Finance and support Firm investments Linkages & entrepreneurship Intellectual assets Innovators Economic effects Public R&D intensity Private R&D intensity Total R&D intensity Source: Based on EIS 2016 data and EUROSTAT (for R&D intensities) Page 12 of 64 This project has received funding from the European

13 The correlation between public R&D intensity and SII indicator is generally weaker than for private R&D intensity 4. The reason behind this is that the eight innovation dimensions have strong links with market products, while public R&D activity, with a large number about half of its objectives focusing on the general advancement of knowledge, shows a weak and indirect link with the market dimension. 4 With the exception of Finance and support which value depends by construction on the level of the public R&D intensity in % GDP. Page 13 of 64 This project has received funding from the European

14 3. Focus on the evolution of R&D expenditures This section focuses on the evolution of R&D expenditures in the different countries between 2007 and Globally, in the period considered, based on EUROSTAT data and following the definitions of the Frascati manual 5, the R&D intensity of the EU has increased by 0.27%, with +0.18% for private R&D and 0.09% for the public one. While in this period the EU GDP level has increased only 3.4% in real terms, the danger that the economic crisis would have contributed to reduce the R&D effort of EU Member States - with some MS engaged in very strong deleveraging policies - seems to have been avoided Furthermore, we don t find clear evidence from R&D data, nor from the EIS 2016 data presented above, that the economic crisis has been re-enforcing the double Research and Innovation divide, as stated for example by R. Veugelers in The danger was actually that a reduction of Government expenditures in R&D may re-enforce the differential in the Science and Innovation performance between Eastern and Western countries, and between Northern and Southern countries. 4.1 Private R&D expenditures Table 6 displays the evolution of private R&D intensities in EU countries. Among the 28 Member States, only 6 have experienced a decrease in the R&D effort of the business sector. The greatest drops are for Luxembourg (-0.68 GDP point), Finland (-0.48 GDP point) and Sweden (-0.11 GDP point), which all countries belong to the leading group in terms of R&D intensity and ranking in the EIS. Among the countries where the R&D effort of the business sector has increased, the strongest evolutions are found in Slovenia (+0.83 GDP point), Bulgaria (+0.57 GDP point), Hungary (+0.53 GDP point), Belgium (+0.48 GDP point), Austria (+0.46 GDP point), Poland (+0.30 GDP point) and the Czech Republic (+0.29 GDP point). Of these seven countries, 5 are Eastern countries that score below the EU average in the EIS ranking. A special attention must be paid to Ireland, where the small increase in the R&D intensity (+0.05 GDP point), hides in fact a more significant increase, due to the forceful revision of the GDP for 2015 (+26.3%), in turn reflecting an accounting re-estimation of the value of financial assets in GDP. 5 Total R&D expenditures include R&D expenditures in the Public sector (GOVERD and HERD) and all R&D expenditures in the business sector (BERD). 6 Veugelers, R (2014), Undercutting the future? European research spending in times of fiscal consolidation", Bruegel Policy Contribution 2014/06, Bruegel, Brussels. Page 14 of 64 This project has received funding from the European

15 Table 6: Evolution of private R&D intensity in % GDP in EU countries, Luxembourg 1.35% 1.28% 1.30% 1.00% 0.97% 0.71% 0.69% 0.69% 0.67% -0.68% Finland 2.42% 2.63% 2.68% 2.59% 2.56% 2.35% 2.26% 2.15% 1.94% -0.48% Sweden 2.38% 2.59% 2.45% 2.21% 2.24% 2.22% 2.28% 2.11% 2.27% -0.11% Spain 0.69% 0.72% 0.70% 0.69% 0.69% 0.68% 0.67% 0.65% 0.64% -0.05% Latvia 0.18% 0.15% 0.16% 0.23% 0.19% 0.15% 0.17% 0.24% 0.15% -0.03% Cyprus 0.09% 0.09% 0.09% 0.08% 0.06% 0.06% 0.07% 0.08% 0.08% -0.02% Romania 0.22% 0.17% 0.19% 0.17% 0.18% 0.19% 0.12% 0.16% 0.21% 0.00% Malta 0.36% 0.35% 0.33% 0.37% 0.44% 0.47% 0.40% 0.41% 0.37% 0.01% Portugal 0.58% 0.72% 0.75% 0.70% 0.69% 0.68% 0.63% 0.60% 0.60% 0.02% Ireland 0.81% 0.90% 1.10% 1.10% 1.07% 1.12% 1.12% 1.09% 0.86% 0.05% Lithuania 0.23% 0.19% 0.20% 0.23% 0.24% 0.24% 0.24% 0.32% 0.28% 0.05% Denmark 1.76% 1.94% 2.13% 1.96% 1.96% 1.95% 1.88% 1.83% 1.83% 0.07% United Kingdom 1.02% 1.02% 1.03% 1.02% 1.07% 1.02% 1.06% 1.09% 1.12% 0.10% Croatia 0.32% 0.39% 0.34% 0.33% 0.34% 0.34% 0.41% 0.38% 0.44% 0.12% Italy 0.59% 0.62% 0.65% 0.66% 0.66% 0.69% 0.72% 0.76% 0.74% 0.15% Slovakia 0.18% 0.20% 0.19% 0.26% 0.25% 0.33% 0.38% 0.32% 0.33% 0.15% Greece 0.16% 0.21% 0.23% 0.24% 0.23% 0.24% 0.27% 0.28% 0.32% 0.15% EU % 1.16% 1.19% 1.19% 1.24% 1.28% 1.29% 1.30% 1.30% 0.18% France 1.27% 1.29% 1.36% 1.37% 1.40% 1.44% 1.45% 1.45% 1.45% 0.18% Estonia 0.50% 0.54% 0.62% 0.79% 1.46% 1.22% 0.82% 0.63% 0.69% 0.18% Netherlands 0.90% 0.82% 0.79% 0.83% 1.08% 1.10% 1.09% 1.12% 1.12% 0.22% Germany 1.71% 1.80% 1.84% 1.82% 1.89% 1.95% 1.90% 1.95% 1.95% 0.23% Czech Republic 0.77% 0.73% 0.73% 0.77% 0.86% 0.96% 1.03% 1.10% 1.06% 0.29% Poland 0.17% 0.19% 0.19% 0.19% 0.22% 0.33% 0.38% 0.44% 0.47% 0.30% Austria 1.72% 1.79% 1.78% 1.87% 1.84% 2.06% 2.10% 2.16% 2.18% 0.46% Belgium 1.28% 1.31% 1.31% 1.38% 1.48% 1.68% 1.72% 1.75% 1.77% 0.48% Hungary 0.48% 0.52% 0.65% 0.69% 0.75% 0.83% 0.97% 0.97% 1.01% 0.53% Bulgaria 0.13% 0.14% 0.15% 0.28% 0.28% 0.37% 0.39% 0.52% 0.70% 0.57% Slovenia 0.85% 1.05% 1.17% 1.40% 1.79% 1.95% 1.99% 1.84% 1.69% 0.83% Source: Eurostat 4.2 Public R&D expenditures The public R&D expenditures indicator illustrates a drop in the intensity for 7 countries, with the highest falls for Hungary (-0.11 GDP point), Ireland (-0.09 GDP point), Croatia (-0.05 GDP point), Slovenia (-0.05 GDP point), Bulgaria (-0.04 GDP point), Romania (-0.03 GDP point) and United Kingdom (-0.02 point). Five of these countries belong to eastern Europe, reflecting the risk of an increased European divide as underlined by R. Veugelers (2014). However, the fall of the public R&D intensity in these countries remains very limited and is largely compensated, with the exception of Romania, by the rise of the R&D intensity in the business sector. The Irish case, here again, must be considered with caution. As, on the other hand, for the countries that have experienced an increase in pubic R&D expenditure, the highest variations are found in Slovakia (+0.57 GDP point), Luxembourg (+0.38 GDP point), Denmark (+0.38 GDP point), Czech Republic (+0.35 GDP point), Estonia (+0.24 GDP point), Greece (+0.23GDP point) and Portugal (+0.22 GDP point). Of these 7 countries, five are eastern of southern European countries that rank below average in the EIS. Page 15 of 64 This project has received funding from the European

16 Table 7: Evolution of public R&D intensity in % GDP in EU countries, Hungary 0.46% 0.45% 0.47% 0.44% 0.43% 0.42% 0.41% 0.37% 0.35% -0.11% Ireland 0.42% 0.49% 0.51% 0.50% 0.47% 0.44% 0.44% 0.42% 0.33% -0.09% Croatia 0.47% 0.49% 0.50% 0.42% 0.41% 0.41% 0.41% 0.41% 0.42% -0.05% Slovenia 0.57% 0.57% 0.64% 0.66% 0.63% 0.62% 0.61% 0.54% 0.52% -0.05% Bulgaria 0.29% 0.30% 0.34% 0.28% 0.25% 0.23% 0.24% 0.27% 0.25% -0.04% Romania 0.30% 0.40% 0.28% 0.28% 0.31% 0.29% 0.27% 0.22% 0.27% -0.03% United Kingdom 0.58% 0.58% 0.63% 0.61% 0.58% 0.56% 0.57% 0.55% 0.55% -0.02% France 0.72% 0.74% 0.82% 0.77% 0.76% 0.76% 0.76% 0.75% 0.74% 0.02% Cyprus 0.28% 0.26% 0.30% 0.31% 0.32% 0.31% 0.33% 0.32% 0.31% 0.03% Spain 0.54% 0.59% 0.65% 0.65% 0.63% 0.60% 0.59% 0.58% 0.58% 0.04% Finland 0.91% 0.89% 1.05% 1.11% 1.05% 1.05% 1.00% 1.00% 0.95% 0.04% Italy 0.51% 0.50% 0.53% 0.52% 0.51% 0.54% 0.55% 0.57% 0.56% 0.05% EU % 0.66% 0.72% 0.72% 0.71% 0.72% 0.72% 0.72% 0.71% 0.09% Latvia 0.37% 0.44% 0.29% 0.38% 0.50% 0.52% 0.44% 0.44% 0.47% 0.10% Netherlands 0.79% 0.82% 0.89% 0.90% 0.83% 0.84% 0.87% 0.88% 0.90% 0.10% Sweden 0.87% 0.90% 1.00% 1.00% 0.99% 1.05% 1.02% 1.03% 0.99% 0.11% Belgium 0.54% 0.59% 0.65% 0.65% 0.66% 0.68% 0.71% 0.70% 0.68% 0.14% Poland 0.39% 0.41% 0.47% 0.53% 0.52% 0.55% 0.49% 0.50% 0.54% 0.15% Austria 0.71% 0.78% 0.82% 0.85% 0.82% 0.85% 0.85% 0.88% 0.88% 0.17% Lithuania 0.57% 0.60% 0.63% 0.55% 0.67% 0.65% 0.71% 0.71% 0.76% 0.19% Germany 0.73% 0.80% 0.89% 0.89% 0.91% 0.92% 0.93% 0.94% 0.93% 0.19% Malta 0.19% 0.18% 0.19% 0.24% 0.24% 0.35% 0.37% 0.33% 0.40% 0.21% Portugal 0.44% 0.60% 0.69% 0.67% 0.64% 0.58% 0.68% 0.67% 0.66% 0.22% Greece 0.40% 0.45% 0.39% 0.36% 0.43% 0.45% 0.53% 0.54% 0.63% 0.23% Estonia 0.54% 0.69% 0.74% 0.77% 0.83% 0.88% 0.88% 0.80% 0.78% 0.24% Czech Republic 0.53% 0.51% 0.56% 0.56% 0.69% 0.82% 0.87% 0.86% 0.88% 0.35% Denmark 0.75% 0.83% 0.91% 0.95% 0.97% 1.01% 1.08% 1.12% 1.12% 0.38% Luxembourg 0.26% 0.36% 0.41% 0.51% 0.50% 0.57% 0.62% 0.59% 0.64% 0.38% Slovakia 0.27% 0.26% 0.28% 0.35% 0.42% 0.47% 0.44% 0.55% 0.84% 0.57% Source: Eurostat 4.3 Total R&D expenditures Finally, the evolution of the total R&D intensity in the different EU Member States is displayed in Table 8. There are five countries where the R&D intensity has decreased in the period , namely Finland (-0.45 GDP point), Luxembourg (-0.30 GDP point), Ireland (-0.04 GDP point), Romania (-0.03 GDP point) and Spain (-0.01 GDP point). With the exception of Finland and Luxembourg, the reduction in R&D intensity was rather modest, even though in some countries, such as Spain, when GDP has fallen by -3.6% in the period, the reduction in the level of R&D expenditures in absolute terms is higher than the reduction of the R&D intensity, close to zero. For Ireland, as previously mentioned, the reduction of the R&D intensity must be carefully interpreted. On average, the R&D intensity in the EU countries has increased by 0.27 GDP point in the period with important rises of the R&D intensity in some member states. The highest increases are found in Slovenia (+0.79 GDP point), Slovakia (+0.73 GDP point), Czech Republic (+0.64 GDP point), Austria (+0.63 GDP point), Belgium (+0.63 GDP point), Bulgaria (+0.52 GDP point) and Denmark (+0.45 GDP point), with increases between 0.45 and 0.79 GDP points. Four of these countries are eastern countries and this confirms the progresses accomplished by some lagging countries, such as Slovakia. But once again, these trends must be interpreted with caution. Page 16 of 64 This project has received funding from the European

17 Table 8: Evolution of R&D intensity in % GDP in EU countries, Finland 3.33% 3.53% 3.73% 3.70% 3.61% 3.40% 3.26% 3.15% 2.88% -0.45% Luxembourg 1.61% 1.64% 1.71% 1.51% 1.47% 1.28% 1.31% 1.28% 1.31% -0.30% Ireland 1.23% 1.39% 1.61% 1.60% 1.54% 1.56% 1.56% 1.51% 1.19% -0.04% Romania 0.52% 0.57% 0.46% 0.45% 0.49% 0.48% 0.39% 0.38% 0.49% -0.03% Spain 1.23% 1.32% 1.35% 1.35% 1.32% 1.29% 1.27% 1.23% 1.22% -0.01% Sweden 3.25% 3.49% 3.45% 3.22% 3.24% 3.27% 3.30% 3.14% 3.26% 0.01% Cyprus 0.37% 0.35% 0.38% 0.39% 0.38% 0.37% 0.40% 0.40% 0.38% 0.01% Croatia 0.79% 0.88% 0.84% 0.74% 0.75% 0.75% 0.82% 0.79% 0.85% 0.06% Latvia 0.55% 0.58% 0.45% 0.61% 0.70% 0.67% 0.61% 0.69% 0.63% 0.07% United Kingdom 1.60% 1.60% 1.66% 1.64% 1.65% 1.58% 1.63% 1.65% 1.67% 0.07% France 2.00% 2.03% 2.18% 2.15% 2.16% 2.20% 2.21% 2.20% 2.20% 0.20% Italy 1.09% 1.13% 1.18% 1.18% 1.17% 1.23% 1.27% 1.33% 1.29% 0.20% Malta 0.55% 0.53% 0.52% 0.62% 0.67% 0.83% 0.77% 0.75% 0.77% 0.22% Lithuania 0.80% 0.79% 0.83% 0.78% 0.90% 0.89% 0.95% 1.03% 1.04% 0.24% Portugal 1.02% 1.33% 1.44% 1.38% 1.33% 1.26% 1.31% 1.27% 1.26% 0.24% EU % 1.82% 1.91% 1.91% 1.95% 1.99% 2.01% 2.03% 2.02% 0.27% Netherlands 1.69% 1.64% 1.69% 1.72% 1.90% 1.94% 1.95% 2.00% 2.01% 0.33% Greece 0.57% 0.66% 0.62% 0.59% 0.67% 0.69% 0.80% 0.83% 0.95% 0.38% Hungary 0.94% 0.96% 1.12% 1.13% 1.18% 1.25% 1.38% 1.34% 1.36% 0.42% Estonia 1.04% 1.23% 1.37% 1.56% 2.29% 2.10% 1.71% 1.43% 1.47% 0.42% Germany 2.45% 2.60% 2.73% 2.71% 2.80% 2.87% 2.82% 2.89% 2.87% 0.43% Poland 0.56% 0.60% 0.66% 0.72% 0.74% 0.88% 0.87% 0.94% 1.00% 0.44% Denmark 2.50% 2.76% 3.04% 2.90% 2.93% 2.97% 2.96% 2.95% 2.95% 0.45% Bulgaria 0.43% 0.44% 0.49% 0.56% 0.53% 0.60% 0.63% 0.79% 0.95% 0.52% Belgium 1.82% 1.91% 1.97% 2.03% 2.14% 2.35% 2.43% 2.46% 2.45% 0.63% Austria 2.43% 2.58% 2.60% 2.72% 2.67% 2.92% 2.96% 3.04% 3.06% 0.63% Czech Republic 1.30% 1.24% 1.29% 1.33% 1.55% 1.77% 1.89% 1.96% 1.94% 0.64% Slovakia 0.45% 0.46% 0.47% 0.61% 0.66% 0.80% 0.82% 0.88% 1.17% 0.73% Slovenia 1.42% 1.62% 1.82% 2.06% 2.42% 2.58% 2.60% 2.38% 2.21% 0.79% Source: Eurostat For example, while Bulgaria has an ambitious Europe 2020 target of 1.5 GDP point for its R&D intensity, its level in 2015 (0.95%) remains well below the EU average (2.02%). In addition, the increase in the R&D intensity of Bulgaria from 2007 (+0.52%), hides a strong decrease of the public intensity that fell from 0.4% of GDP in 2000 to 0.29% in 2007 and 0.25% in 2015, the lowest value in EU-28. The increase in the private intensity, from 0.13% in 2007 (0.11% in 2000) to 0.70% in 2015, is itself mostly due to investments by foreign pharmaceutical companies for clinical trials, which are therefore not reflected in a similar increase in the R&D personnel or in research infrastructures. These foreign investments explain notably most of the rise that occurred from 2007 onward. Page 17 of 64 This project has received funding from the European

18 4. Input to NEMESIS This section describes how data on the evolution of public and private R&D intensities have been analysed with the NEMESIS model. 5.1 Private R&D expenditures Concerning private R&D expenditures, NEMESIS being a sectoral model it has first been necessary to disaggregate all the data in order to adhere to the model nomenclature (30 sectors including the public sector, see Table 10). The data on private R&D expenditures are available in EUROSTAT in NACE rev2 (level 2), but, as shown in Table 11, many of the data were missing at such detailed sectoral level for the period 2007 to There is in fact no sectoral detail for 2015, while for the period % of the data were missing. The quality of the data is highly variable, depending on the country at hand. At one end of the spectrum, no data is missing for the Czech Republic, Cyprus or Slovenia, while, at the other end, as much as 81% of the data are missing for Luxembourg, 75% for Sweden and 73% for Greece. In general, data were however available at a more aggregated level, which allowed for adequate estimations of the missing data through simple interpolation, extrapolation and disaggregation techniques. The results are shown, after re-aggregation at EU-28 level, in Table 12. The table shows that the increase of 0.18 GDP point in the private R&D intensity at EU-28 level in the period , was caused for about GDP point (41%) by the investments made in the transport equipment sectors, for GDP point (13%) by the investments in the communications sector and for GDP point (9%) by those in the distribution sector, these three sectors accounting for about two thirds overall of the rise in the private R&D intensity over the period. The other noticeable increase concerns the market services sector ( GDP point), which includes the KIBS (Knowledge Intensive Business Services) and notably the market research services. Page 18 of 64 This project has received funding from the European

19 Table 9: Correspondence between NEMESIS sectors and the NACE rev. 2 (1/2) NEMESIS 1 Agriculture 2 Coal and Coke 3 Oil & Gas Extraction 4 Gas Distribution 5 Refined Oil 6 Electricity 7 Water Supply 8 Ferr & non Ferrous Metals 9 Non Metallic Min Products 10 Chemicals 11 Metal Products 12 Agr & Indus Machines 13 Office machines 14 Electrical Goods 15 transport Equipment 16 Food. Drink & Tobacco 17 Tex.. Cloth & Footw. 18 Paper & Printing Products 19 Rubber & Plastic 20 Other Manufactures NACE Rev.2 A01 A02 A03 Crop and animal production, hunting and related service activities Forestry and logging Fishing and aquaculture B05 Mining of coal and lignite B07 Mining of metal ores B08 Other mining and quarrying B9.9 Support activities for other mining and quarrying B06 Extraction of crude petroleum and natural gas B9.1 Support activities for petroleum and natural gas extraction D35.2 Manufacture of gas; distribution of gaseous fuels through mains D35.3 Steam and air conditioning supply C19 Manufacture of coke and refined petroleum products D35.1 Electric power generation, transmission and distribution E36 C24 C23 C20 C21 C25 C28 C26 C27 C29 C30 C10-C12 C13-C15 C17 C18 C22 C16 C31_C32 C33 E37-E39 Water collection, treatment and supply Manufacture of basic metals Manufacture of other non-metallic mineral products Manufacture of chemicals and chemical products Manufacture of basic pharmaceutical products and pharmaceutical preparations Manufacture of fabricated metal products, except machinery and equipment Manufacture of machinery and equipment n.e.c. Manufacture of computer, electronic and optical products Manufacture of electrical equipment Manufacture of motor vehicles, trailers and semi-trailers Manufacture of other transport equipment Manufacture of food products; beverages and tobacco products Manufacture of textiles, wearing apparel, leather and related products Manufacture of paper and paper products Printing and reproduction of recorded media Manufacture of rubber and plastic products Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials Manufacture of furniture; other manufacturing Repair and installation of machinery and equipment Sewerage, waste management, remediation activities Page 19 of 64 This project has received funding from the European

20 Table 10: Correspondence between NEMESIS sectors and the NACE rev. 2 (2/2) NEMESIS 21 Construction 22 Distribution 23 Lodging & Catering 24 Inland Transports 25 Sea & Air Transport 26 Other Transports 27 Communication 28 Bank. Fin., Ins. & Real Est. 29 Other Market Services NACE Rev.2 F Construction G45 Wholesale and retail trade and repair of motor vehicles and motorcycles G46 Wholesale trade, except of motor vehicles and motorcycles G47 Retail trade, except of motor vehicles and motorcycles I Accommodation and food service activities H49 Land transport and transport via pipelines H50 Water transport H51 Air transport H52 Warehousing and support activities for transportation H53 Postal and courier activities J58 Publishing activities J59_J60 Motion picture, video, television programme production; programming and broadcasting activities J61 Telecommunications K64 Financial service activities, except insurance and pension funding K65 Insurance, reinsurance and pension funding, except compulsory social security K66 Activities auxiliary to financial services and insurance activities L Real estate activities N77 Rental and leasing activities J62_J63 Computer programming, consultancy, and information service activities M69_M70 Legal and accounting activities; activities of head offices; management consultancy activities 30 Non Market Services M71 M72 M73 M74_M75 N78 N79 N80-N82 S95 S96 O P Q86 Q87_Q88 R90-R92 R93 S94 Architectural and engineering activities; technical testing and analysis Scientific research and development Advertising and market research Other professional, scientific and technical activities; veterinary activities Employment activities Travel agency, tour operator reservation service and related activities Security and investigation, service and landscape, office administrative and support activities Repair of computers and personal and household goods Other personal service activities Public administration and defence; compulsory social security Education Human health activities Residential care activities and social work activities without accommodation Creative, arts and entertainment activities; libraries, archives, museums and other cultural activities; gambling and betting activities Sports activities and amusement and recreation activities Activities of membership organisations Page 20 of 64 This project has received funding from the European

21 Table 11: Percentage of missing data for Business Enterprises R&D (BERD) in NACE rev.-2, period Observations missing Years missing Ratio Belgium % Bulgaria % Czech Republic 0 0% Denmark % Germany 78 16% Estonia % Irland , % Greece , 2009, 2010, 2012, % Spain 26 5% France % Croatia 43 9% Italie 49 10% Cyprus 0 0% Latvia , % Lithuania % Luxembourg , 2010, 2012, % Hungary % Malta 25 5% Netherlands % Poland % Portugal 89 19% Romania % Slovenia 0 0% Slovakia % Finland % Sweden % United Kingdom 45 9% EU % Source: Authors calculations from EUROSTAT Page 21 of 64 This project has received funding from the European

22 Table 12: Evolution of Business Enterprises R&D (BERD) at sectoral level between 2007 and 2015 (in % GDP) Agriculture 0.004% 0.004% 0.004% 0.004% 0.005% 0.004% 0.005% 0.005% 0.005% 0.001% 02 Coal and Coke 0.003% 0.003% 0.003% 0.003% 0.003% 0.003% 0.003% 0.003% 0.003% 0.001% 03 Oil & Gas Extraction 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 04 Gas Distribution 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 05 Refined Oil 0.003% 0.003% 0.003% 0.002% 0.004% 0.005% 0.004% 0.004% 0.004% 0.001% 06 Electricity 0.007% 0.008% 0.009% 0.008% 0.008% 0.009% 0.009% 0.009% 0.009% 0.002% 07 Water Supply 0.001% 0.001% 0.002% 0.002% 0.002% 0.001% 0.002% 0.002% 0.001% 0.000% 08 Ferrous & non Ferrous Metals 0.012% 0.013% 0.014% 0.012% 0.014% 0.013% 0.014% 0.014% 0.014% 0.002% 09 Non Metallic Min Products 0.008% 0.009% 0.009% 0.009% 0.009% 0.009% 0.009% 0.009% 0.008% 0.000% 10 Chemicals 0.139% 0.142% 0.146% 0.140% 0.141% 0.145% 0.143% 0.141% 0.139% 0.000% 11 Metal Products 0.028% 0.030% 0.036% 0.035% 0.033% 0.035% 0.036% 0.034% 0.035% 0.007% 12 Agricultural & Industrial Machines 0.110% 0.109% 0.090% 0.090% 0.095% 0.100% 0.102% 0.103% 0.101% % 13 Office machines 0.108% 0.105% 0.104% 0.100% 0.105% 0.110% 0.111% 0.110% 0.108% 0.001% 14 Electrical Goods 0.042% 0.043% 0.046% 0.043% 0.043% 0.045% 0.046% 0.045% 0.044% 0.002% 15 Transport Equipment 0.201% 0.220% 0.225% 0.235% 0.250% 0.262% 0.258% 0.274% 0.274% 0.073% 16 Food, Drink & Tobacco 0.016% 0.016% 0.017% 0.017% 0.017% 0.018% 0.018% 0.018% 0.018% 0.002% 17 Textile, Clothes & Footwear % 0.007% 0.007% 0.007% 0.007% 0.007% 0.008% 0.008% 0.008% 0.001% 18 Paper & Printing Products 0.007% 0.008% 0.009% 0.009% 0.006% 0.006% 0.006% 0.006% 0.006% % 19 Rubber & Plastic 0.019% 0.019% 0.019% 0.019% 0.020% 0.021% 0.021% 0.021% 0.021% 0.002% 20 Other Manufactures 0.029% 0.030% 0.037% 0.033% 0.035% 0.035% 0.037% 0.037% 0.037% 0.008% 21 Construction 0.005% 0.005% 0.007% 0.007% 0.006% 0.006% 0.006% 0.007% 0.007% 0.002% 22 Distribution 0.032% 0.033% 0.036% 0.042% 0.047% 0.049% 0.048% 0.048% 0.048% 0.016% 23 Lodging & Catering 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 24 Inland Transports 0.001% 0.001% 0.001% 0.001% 0.001% 0.001% 0.001% 0.001% 0.001% 0.000% 25 Sea & Air Transport 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 26 Other Transports 0.001% 0.001% 0.002% 0.001% 0.002% 0.002% 0.001% 0.001% 0.001% 0.000% 27 Communication 0.105% 0.109% 0.115% 0.117% 0.121% 0.123% 0.128% 0.129% 0.129% 0.024% 28 Bank, Finance, Insurance, Real Estate 0.026% 0.025% 0.025% 0.024% 0.024% 0.025% 0.025% 0.025% 0.025% % 29 Other Market Services 0.214% 0.220% 0.230% 0.235% 0.251% 0.249% 0.250% 0.254% 0.257% 0.044% Sum 1.127% 1.165% 1.196% 1.196% 1.250% 1.282% 1.291% 1.309% 1.304% 0.177% Source: Authors calculations from EUROSTAT 5.2 Public R&D expenditures Concerning public R&D, the main problem is that only limited information is available on the field of application of these expenditures. The only reliable information is provided by the Government Budget Appropriations or Outlays on Research and Development (GBAORD) data, which however does not match exactly the Frascati definitions for Public R&D expenditures. The GBAORD data, that are available at EU 28 level for the period , disaggregate public R&D into application fields or socio-economic objectives, as displayed in Table 13. Based on the GBOARD data, the 0.09 GDP point increase in public R&D investments over , was associated for GDP point to the civil objectives (Total civil R&D appropriations). The difference of GDP point is attributable to the fall of the R&D expenditures for Defence. Page 22 of 64 This project has received funding from the European

23 Table 13: Evolution of public R&D expenditures by socio-economic objective between 2007 and 2015 (in % GDP) Exploration and exploitation of the earth 0.009% 0.011% 0.012% 0.013% 0.013% 0.014% 0.014% 0.015% 0.015% 0.006% Environment 0.016% 0.018% 0.020% 0.019% 0.018% 0.019% 0.018% 0.018% 0.019% 0.003% Exploration and exploitation of space 0.029% 0.028% 0.036% 0.036% 0.041% 0.036% 0.038% 0.037% 0.036% 0.007% Transport, telecommunication and other infrastructures 0.014% 0.024% 0.027% 0.025% 0.021% 0.022% 0.022% 0.021% 0.021% 0.007% Energy 0.020% 0.024% 0.026% 0.028% 0.028% 0.028% 0.031% 0.030% 0.029% 0.010% Industrial production and technology 0.063% 0.061% 0.069% 0.070% 0.071% 0.065% 0.066% 0.063% 0.062% % Health 0.052% 0.052% 0.057% 0.059% 0.060% 0.061% 0.065% 0.065% 0.064% 0.012% Agriculture 0.022% 0.024% 0.024% 0.024% 0.025% 0.024% 0.024% 0.023% 0.023% 0.001% Education 0.005% 0.007% 0.007% 0.007% 0.007% 0.008% 0.009% 0.010% 0.010% 0.005% Culture, recreation, religion and mass media 0.006% 0.008% 0.008% 0.009% 0.008% 0.009% 0.008% 0.007% 0.007% 0.001% Political and social systems, structures and processes 0.012% 0.020% 0.021% 0.025% 0.023% 0.023% 0.020% 0.021% 0.020% 0.008% General advancement of knowledge: General University Funds (GUF) 0.208% 0.203% 0.226% 0.227% 0.234% 0.243% 0.249% 0.250% 0.247% 0.039% General advancement of knowledge: Other sources than GUF 0.097% 0.118% 0.126% 0.124% 0.125% 0.127% 0.126% 0.130% 0.127% 0.030% Defence 0.073% 0.060% 0.060% 0.048% 0.034% 0.037% 0.033% 0.035% 0.034% % Total civil R&D appropriations 0.552% 0.598% 0.659% 0.667% 0.674% 0.678% 0.690% 0.689% 0.681% 0.128% Total R&D appropriations 0.625% 0.658% 0.719% 0.715% 0.708% 0.715% 0.723% 0.723% 0.715% 0.090% Source: Authors calculations from EUROSTAT (GBOARD) Page 23 of 64 This project has received funding from the European

24 Table 14: Link between Public R&D expenditures and NEMESIS sectors Socio-economic objectives Exploration and exploitation of the earth Environment Exploration and exploitation of space NEMESIS Sectors All grand-fathering All grand-fathering All grand-fathering Transport, telecommunication and other infrastructures Construction, Transport services, Water supply, Telecoms (21, 24, 25, 26, 27, 07) Energy Energy, Non metal. Min. products, Chemicals, Office machines, Elec. Goods, Trans. Equip. (04, 05, 06, 09, 10, 13, 14, 15) Industrial production and technology Health Agriculture Education Culture, recreation, religion and mass media Political and social systems, structures and processes General advancement of knowledge: General University Funds (GUF) General advancement of knowledge: Other sources than GUF Defence All Ind. Sectors (8 to 20) Other market serv. (29) Agriculture (01) Other market serv. (29) Other market serv. (29) Other market serv. (29) All grand-fathering All grand-fathering All Ind. Sectors and Other market serv. (8 to 20 & 29) Concerning the civil appropriations or outlays, R&D expenditures are classified according to 12 socio-economic objectives. While some objectives are general as General advancement of knowledge, some others are more content- or sector-related, such as Energy or Agriculture. However, the links between these socio-economic objectives and the economic sectors that may benefit from these investments can only be identified indirectly, by means of hypotheses relying on Page 24 of 64 This project has received funding from the European

25 the amount of expenditures in similar sectors. These hypotheses have been used in the NEMESIS model to estimate the knowledge R&D spill-overs from public research towards the private sector, in a very tentative way 7. The hypotheses linking public investment in R&D and the economic sector of NEMESIS are displayed in Table 14. When the socio-economic objectives are very general, such as General Advancement of Knowledge, the NEMESIS link follows the grand-fathering principle and all the sectors receive spillovers from public research, proportionally to their relative R&D effort in percentage of total R&D expenditure. When the socio-economic objectives are more specific, the link was made through the related sectors. 7 Another difficulty is that the governments collect the data according to the purpose and not the content, or for example, Defense R&D may concern Human Health. The precise methodology may differ also depending of the country. Page 25 of 64 This project has received funding from the European

26 5. Other methodological issues This section presents the methodology that was used to assess the economic impacts already generated and likely to be generated in the future by the increase in the R&D intensity of EU countries in the period , should this intensity remain at its 2015 level. All the results of the simulations achieved with NEMESIS are presented in comparison to a reference scenario 8 that is a projection of the future European economy. The next section describes this reference scenario and the R&D and innovation mechanisms underpinning the NEMESIS model, which are important to interpret the simulation results. 6.1 The reference scenario The reference scenario is based, for the short-medium term GDP growth projection (up to 2018), on the DG ECFIN 2017 spring forecast and then, for the long term, on a set of exogenous variables (demography, world demand, oil price, exchange rates, etc.) and economic projections similar to DG ECFIN 2015 ageing report. This baseline scenario was designed for every EU countries at a detailed sectoral level (30 sectors) and it provides results on production, employment, revenues, and competitiveness indicators at the macro and sectoral levels up to The choice to adjust the GDP projections for the EU and the different member states on the one of the DG ECFIN is that DG ECFIN projections are produced in collaboration with the statistical offices and economic forecast administrations in the different countries, and reflect therefore an in-depth analysis of GDP growth potential in the different countries, at different time horizons, including the impact, on the short to medium term trajectory, of the deleveraging policies followed by most EU countries. This choice imposes therefore strong constraints on the NEMESIS model, where the calculus of GDP is at the same time (1) the addition of its different aggregated components: public and private final consumption, total investment and net exports (exportations minus importations), and (2) the aggregation of the added values of the 30 production sectors that are included in the model. As, except for the short term (up to 2018), the DG ECFIN forecasts doesn t provide projections for the GDP counterparts, as it neither produce forecasts at sectoral level, the original contribution of NEMESIS for this forward-looking exercise in therefore to produce its own figures, for all the other variables than GDP. These include projections for GDP counterparts, sectoral evolutions (production, value-added, exports, imports, employment per skill, etc.) and numerous other indicators as unemployment rates, energy consumption and CO 2 related GHG emissions, this, for every countries and sectors represented in the model. The overall dynamic of this scenario can be split in two phases (Table 15). In the medium term, up to , Europe ends the period of crisis. After that, in the long run, the decline of the labour force, combined with a moderated labour productivity growth, leads to a lower long term growth path than just after the crisis. 8 For a detailed presentation of the reference scenario, see I3U deliverable D Page 26 of 64 This project has received funding from the European

27 D 8.2 Overall impact of the Innovation Union Table 15: Evolution of GDP in the reference scenario GDP growth and contributions to GDP growth (in %) GDP growth 1.9% Private Consumption Gross Fixed Capital Formation Trade Balance Public consumption 1.2% 0.6% -0.2% 0.3% Real GDP growth 1.9% 1.7% 1.5% Contributions to real GDP change 0.8% 0.9% 0.9% 0.5% 0.4% 0.4% 0.3% 0.2% 0.1% 0.2% 0.2% 0.2% 1.4% 0.9% 0.4% 0.0% 0.2% Source: NEMESIS model, based on DG ECFIN forecasts (2017 spring forecast and 2015 ageing report). The period between 2015 and 2025 is characterized by the revival of the internal demand and the main contributors to GDP are private consumption and gross fixed capital formation. After 2025, the trade balance is negatively impacted by the deterioration of competitiveness with an increase of real wages higher than the rise of labour productivity. This is mainly due to the reduction of unemployment and to the influence of the labour supply shortage on the long-term growth potential. Finally, in the reference scenario, the intensities of innovation assets9 are kept constant at sectoral level from 2014 to 2050 (Table 16). They decrease slightly at macro-eu level with the evolution of the sectoral composition of the economy and the increasing contribution of service industries to EU GDP up to They remain nevertheless roughly stable with a global intensity in innovation assets passing from 5.4% of EU GDP in 2014 to 5.2% in Table 16: Evolution of innovation assets in the reference scenario Private R&D expenditures Public R&D expenditures ICT investments Other Intangibles investments (Softwares Total investments in innovation assets Intensity of innovation assets in % GDP % 1.3% 1.3% 0.7% 0.7% 0.7% 1.1% 1.1% 1.1% % 0.7% 1.2% % 0.7% 1.1% 2.4% 2.3% 2.3% 2.3% 2.2% 2.2% 5.3% 5.4% 5.4% 5.4% 5.3% 5.2% % 0.7% 1.0% Source: NEMESIS model 9 In NEMESIS, innovations come from investments in R&D (Public and private), in ICT and in Other Intangibles (Softwares and Training). Technological innovations are mainly the result of R&D investment by industrial firms while organizational innovations result mainly from investments in ICT and in Other Intangible assets by all sectors. See: P., Le Mouël, B., Le Hir, A., Fougeyrollas, P., Zagamé and B., Boitier Toward a macro-modelling of European Innovation Union: The contribution of NEMESIS model, 9th conference on Model-based Evidence on Innovation and DEvelopment (MEIDE), June 2016 Moscow, Russia. Page 27 of 64 This project has received funding from the European

28 6.2 Important R&I mechanism of NEMESIS for the simulation The R&D mechanisms The endogenization of technical progress in NEMESIS is derived from the new growth theories, where innovations result from the investment in R&D 10 by private firms and from the R&D achieved by the public sector. For a country, at a sectoral level, three main phenomena are relevant for the assessment of R&D policies or scenarios: i. The R&D decisions that increase the knowledge stock of the sector; ii. iii. The knowledge externalities, that flow between sectors and countries from private-public and basic-applied research; The economic performance resulting from the innovations induced by the variation of knowledge at sectoral level. The innovations take two forms: process and product. This distinction between process and product innovation is crucial as the econometric studies show that process innovations alone have a negative, or only a slight positive impact, on economic performance and employment, whereas the impact of product innovations is always positive. Therefore, in NEMESIS, innovations enhance competitiveness through price and quality improvement. They impact simultaneously on internal demand, notably on final consumption, owing to an increased price to quality ratio, and of course on external demand through increased competitiveness. All these sectoral evolutions are articulated by the input-output tables of the model, and by the knowledge spill-overs matrices. Then, the ultimate result of all these sectoral interdependencies is to provide an impulse to the dynamics, combining 'bottom-up' and top-down. The macroeconomic feedbacks materialise through the decrease of unemployment that enhances wages, consumption, and, ultimately, prices that will reduce the competitiveness gains. These macroeconomic feedbacks are combined with the preceding bottom-up dynamic, to provide a macroeconomic track of which characteristics are 'hybrid', combining macro and bottom-up forces. The other mechanisms The current version of NEMESIS incorporates important improvements from the recent theoretical and empirical literature on economic growth. Traditionally, the endogenous technical change is implemented in the economic models through investments in R&D only, as it was described above. Nevertheless, this limits strongly the analysis. In particular, innovation in services sectors is, by this way, partially omitted and notably the role played by organizational innovations from investments in ICT and in complementary intangible assets. 10 Within DEMETER and SIMPATIC EC research projects, besides R&D, the endogeneization of technical progress in NEMESIS was extended to the role played by investments in ICT, software and other intangible investments in the innovation process of enterprises. See below. Page 28 of 64 This project has received funding from the European

29 In the new version of NEMESIS, developed in the context of the EC FP7 research projects DEMETER and SIMPATIC, innovations arise no longer from firms and public investments in R&D only, but from investments in three complementary innovation inputs: R&D, ICT and Other Intangibles (including training and software). The innovations that appear in each sector are still process 11 and product innovations. But the resulting endogenous growth comes now from the increasing returns provoked not only from R&D spillovers, but by the accumulation of three knowledge stocks that are specific to countries and sectors and to the type of investment: R&D, ICT or OI 12. These knowledge stocks are modelled as a weighted sum of the stock of assets, R&D, ICT or OI, belonging to all sectors and countries. The spread parameters used to build these stocks are calibrated using matrices based on patent citations between sectors and countries. These matrices combine the citations between patents allocated by technology classes and country with the OECD concordance table, in order to allocate these citations between sectors. Therefore, while the NEMESIS model is not the sole model used by the Commission (see di Comite and Kancs, 2015 and also the appendix) for the assessments of its R&I policies, it is the more adapted to take into account the various forms taken by innovation, as it is defined in the Oslo manual for innovation in its third edition (see OECD, 2005) that, from a technological focus, recovers many additional forms: new products (goods or services) or new processes, new marketing or organizational methods, new workplace organizations, new linkages between producers or between producers and customers, etc. These new forms of innovations, that pass mainly by investments in ICT technologies and in other intangible assets than R&D are actually not modelled at all in the other models, where innovation relies only on investments in R&D by private firms. How a shock on R&D generates impacts in the Model For the simulation, we will reproduce in the model the R&D intensity to reproduce the increase observed on the period The first consequence, as described in Figure 3, will be the raise of the knowledge of the different economic sectors in the different EU countries. 11 By process innovations we refer implicitly to all types of innovations that result in a modification/ improvement of production processes, either technological, organizational or both. 12 See Boitier, Fougeyrollas, A., Le Hir, B., Le Mouël, P. And Zagamé, P. (2015) ( The NEMESIS Model The NEMESIS Model a Description, SEURECO/ERASME Working Paper) for a more detailed description of the NEMESIS model and Le Mouël, P., Le Hir, B., Fougeyrollas, A. and Boitier, B. (2016), Toward a macro-modelling of European Innovation Union: The contribution of NEMESIS model, paper presented at the 9th Conference on Model-based Evidence on Innovation and DEvelopment (MEIDE) the June 2016 in Moscow, Russia.. Page 29 of 64 This project has received funding from the European

30 Figure 3: The impact of a rise of R&D on knowledge D 8.2 Overall impact of the Innovation Union R&D investments (private) in the sector R&D investments (private) in others sectors Knowledge matrices based on patent citations (IOM/IOM) Public R&D investments Knowledge stock from R&D investments The R&D investments of private firms will diffuse knowledge from the knowledge transfer matrices (in black) at intersectoral and international levels at the same time. Public R&D will increase the R&D used by the different sectors, following the rules described in the preceding section, and will also diffuse by the same knowledge transfer matrices than private R&D. Figure 4: The repercussion on other innovation inputs ICT investments in the sector Knowledge stock from ICT ICT investments in others sectors OI investments in the sector Knowledge matrices based on patent citations (SOU/SOU) OI investments in the others sectors Knowledge stock from OI Page 30 of 64 This project has received funding from the European

31 The rise of R&D will also have pulling effects on the other innovation inputs, that will augment and that will also provoke an increase of their respective knowledge stocks. The rise of R&D intensity - and possibly of the intensity of the other innovation inputs - will furthermore reinforce the ability of sectors to transform knowledge into new product and process innovations, and it will again impact positively on economic activity and employment, as described on Figure 5. This figure illustrates the way the rise of R&D expenditures will increase the innovations realized by each sector (s) in each country (c) and will act on the whole economic system. The innovations in each sector result from the investments in the three innovation components, as explained above, and, in addition to the own investments of the country sector (c,s), investments in innovation assets by other countries-sectors generate spillovers and increase the ability of the considered (c,s) to innovate. The combination of these two elements - the own expenditures in innovation inputs and the spillovers - generates innovations (arrows 1 and 2) which lead to an increase of the economic performance (3) of the sector in terms of productivity and to an increase of the demand through a quality effect (4). The interaction between the innovation performance of the sector and the Good and Services market (5) determines the output of the sector. And the interaction between the labour market and both activities of production and of innovation (6 and 7) leads to the determination of employment. Figure 5: The impact on economic performance and employment. Page 31 of 64 This project has received funding from the European

32 Nevertheless, as the EU countries have contrasted economic and innovation systems and have encountered contrasted evolutions of their R&D intensity on the period , the impacts in the different countries measured by NEMESIS, will be very diverse. Page 32 of 64 This project has received funding from the European

33 6. The results at EU macro level This section shows the results of the simulation that concerns with the impacts on EU economy stemming from the rise of the R&D intensity observed on the period , with the assumption that after 2015 the public and private R&D intensity in each member state remains the same, as for the levels observed in The results for EU GDP Concerning GDP, the impacts follow two main phases as illustrated by the Figure 6: 1- During the first phase, up to 2015, there are few innovations and supply side effects. The main impacts on GDP come from the demand induced by the investments in R&D that grow by GDP point between 2007 and 2015, compared to their level in the reference scenario of the model. The investments in R&D push the internal demand up, but this increase in demand determines inflationary pressures, deteriorating the external balance of the EU. GDP gains remain nevertheless positive, with % in 2015, with however one third of these gains (0.27 GDP point) resulting mechanically from the capitalization of the rise of R&D expenditure in GDP, following the new accounting rules introduced by EUROSTAT in 2014, with the transition from SEA 95 to SEA During the second phase, beginning in 2016, GDP gains amplify gradually with the appearance of innovations that diffuse on the market. All the components of GDP contribute positively to GDP gains, with an increasing contribution of external trade up to GDP gains reach 2.75% in 2030 and 3.55% in Page 33 of 64 This project has received funding from the European

34 D 8.2 Overall impact of the Innovation Union Figure 6: The impact on EU GDP (in % difference from reference scenario) Contributions to GDP increase (gap to reference simulation) 4.00% Second phase First phase 3.50% Trade Balance Gross Fixed Capital Formation Private Consumption Gross Domestic Product % % 1.66% 1.51% 1.56% 1.41% 1.31% 1.36% 1.46% 1.18% 1.24% 1.05% 1.12% 0.91% 0.98% 0.76% 0.59% 0.67% 0.84% 0.44% 0.51% % 0.95% 0.96% 0.92% 0.93% 0.89% 0.90% 0.85% 0.82% 0.84% 0.77% 0.79% 0.72% 0.67% 0.70% 0.61% 0.64% 0.55% 0.34% 0.31% 0.38% 0.46% 0.30% 0.48% 0.00% -0.02%-0.06%-0.08%-0.09%-0.10%-0.09%-0.05% 0.50% 0.44% 0.29% 0.45% 0.29% 0.38% 0.26% 0.40% 0.27% 0.27%0.22% 0.32% 0.24% 0.09% 0.12% 0.26%0.20% 0.50% 0.58% 1.00% 0.75% 1.50% 0.87% 2.00% % 0.52% 0.86% 0.86% 0.85% 0.84% 0.83% 0.82% 0.81% 0.80% 0.78% 0.77% 0.75% 0.73% 0.70% 0.68% 0.65% 0.61% 0.58% 0.53% 0.48% 0.42% 0.36% 0.29% 0.22% 0.14% 0.06% 0.00% 3.00% % Source: NEMESIS model For the year 2030, the origin of GDP gains (2.75%) can be decomposed as follows: 1.2 point from the rise of final consumption, 0.8 point from the rise of total investment and 0.75, point from the external balance surplus. 7.2 The impacts on employment at EU level The impacts on employment at EU level decompose should also be analysed along two phases (Figure 7): 1- During the investment phase, up to 2015, job creation mainly occurs in research organisations and is made possible by the increase of research investments in the public and private sectors. In 2015, the rise of the research intensity (+0.27 GDP point, compared to its level in 2007), increases employment by units in full time equivalent, compared to its level in the reference scenario of the model. The total employment creation rises by , with additional jobs created in production activities, as a result of the better macro-economic context that takes place. In fact, these new jobs are the net balance between new low skilled jobs and a reduction ( ) of the number of high skilled jobs in production activities. The reason of this reduction of high skilled labour in production is that the rise of employment in research, which concerns high skilled personnel, crowds-out high skilled labour in production. 2- During the second phase, beginning in 2016, the level of employment in research stabilises as research intensity no longer rises. High skilled employment in production re-increases progressively and high skilled labour continues to grow. In 2030, the level of employment is 2.52 million higher than in the reference scenario, of which are new jobs in the Page 34 of 64 This project has received funding from the European

35 research sector and are high skilled jobs in production. More than 50% of the total jobs created are low skilled jobs in production (an increase of million). After 2030, the pressure on wages contributes to a progressive decrease in the number of additional jobs creations. In 2040 the total jobs created reaches million of which are in research, million are high skilled positions in production and are low skilled jobs. Figure 7: The impact on total employment in EU (difference from reference scenario in thousands) First phase Second phase Source: NEMESIS model Low skill labour(production) High skill labour (production) Total employment in Research Total employment Page 35 of 64 This project has received funding from the European

36 7. The results at EU sectoral level This section now describes in more detail the results obtained at EU sectoral level for value-added, employment and external trade balance. 8.1 Sectoral Value Added Concerning sectoral value added, different phases of impacts can also be observed (Table 17): 1. During the first years of the investment phase up to , the value-added creation occurs mainly in the service sectors and in all the sectors producing consumers goods. The reason lies in the hiring of new research personnel that increases wages and final consumption, and then pulls up the production of goods and services that compose the final consumption of households. The industrial sectors, that are also R&D intensive, see their competitiveness deteriorate on the external market, as they increase their research investments and therefore their production costs, without the appearance of sufficient process innovations to alleviate their costs and product innovations to increase their nonprice competitiveness. For example, in 2012, value added decreases by -0.4% in chemicals by -0.1% in high tech industries, while it increases by +0.4% in distribution and by +0.3% in other market services. 2. Then, after , the value-added in R&D industrial sectors grows faster than in the other sectors of the economy, which are less R&D intensive and less exposed to international competition. In 2030 value added increases by 5.3% in chemicals and 6.3% in high tech industries, while the increase is limited to 2.3% in distribution and to 2.7% in other market services. Table 17: The impacts of EU-28 sectoral value-added (in % deviation from reference scenario) Agriculture 0.1% 0.3% 0.3% 0.3% 0.4% 0.4% 0.5% 0.5% 1.0% 2.5% - Utilities 0.0% 0.1% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.4% 1.7% - Heavy Industries 0.1% 0.2% 0.1% 0.1% 0.1% 0.2% 0.2% 0.2% 1.0% 2.8% - Chemicals -0.1% -0.2% -0.3% -0.4% -0.4% -0.3% 0.1% 0.6% 2.8% 5.3% - High Tech Industries 0.0% -0.1% -0.1% -0.1% -0.1% 0.1% 0.6% 1.1% 3.5% 6.3% - Transport Equipments -0.1% -0.1% -0.1% 0.0% 0.2% 0.6% 1.5% 2.6% 7.9% 12.3% - Other Industries 0.2% 0.3% 0.3% 0.2% 0.3% 0.3% 0.4% 0.5% 1.3% 3.1% - Construction 0.2% 0.4% 0.4% 0.4% 0.4% 0.5% 0.5% 0.5% 0.8% 1.9% - Distribution 0.1% 0.3% 0.3% 0.3% 0.4% 0.4% 0.4% 0.4% 0.8% 2.3% - Transports 0.1% 0.2% 0.2% 0.2% 0.2% 0.3% 0.3% 0.4% 1.3% 3.4% - Communication 0.1% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.8% 2.3% - Bank, finance, insurance 0.1% 0.3% 0.3% 0.4% 0.4% 0.4% 0.5% 0.5% 0.8% 2.1% - Other market services 0.1% 0.3% 0.3% 0.3% 0.3% 0.4% 0.4% 0.5% 1.1% 2.7% Source: NEMESIS model Page 36 of 64 This project has received funding from the European

37 8.2 Sectoral Employment Concerning employment, the results at sectoral level are much more contrasted than for valueadded (Table 18): 1. During the first years, the employment creation concentrates in the R&D intensive sectors, where most of research employment is created. For example, of the jobs created in 2008, are in chemicals, are in high tech industries, in transport equipment, in other industries, in other market services and in non-market industries (which includes the public research sector in NEMESIS). About two thirds of employment creation occurs therefore in the sectors with R&D investments. 2. Then the creation of jobs spread more widely between sectors that are pulled up by the increase of internal and external demand. Employment increases in almost every sector until 2012, but afterwards there are decreases in some R&D intensive sectors, such as chemicals, high tech industries and transport equipment, where process innovations are important and tend to do away with jobs. In other sectors, such as heavy industries that produce intermediate goods, jobs destruction originates in this case from productivity improvements in the other sectors of the economy, which reduce their consumption of intermediate products. 3. In the long term (2030), while most of the new jobs are created are in the services sector - which currently accounts for about 70% of total employment against only 15% for industry - about 30% of total job creation occurs nevertheless in the industrial sector: out of 2.52 million. The reason is that the rise of the R&D intensity improves significantly the external competitiveness of the industrial sectors and prevents job destruction in industry, due to the competition of non-eu countries. Table 18: The impacts on EU-28 sectoral employment (in thousands and in difference from reference scenario) Agriculture Utilities Heavy Industries Chemicals High Tech Industries Transport Equipments Other Industries Construction Distribution Transports Communication Bank, finance, insurance Other market services Non-market services Total Source: NEMESIS model Page 37 of 64 This project has received funding from the European

38 8.3 Trade balance D 8.2 Overall impact of the Innovation Union Concerning trade balance, Table 19 shows a similar evolution of impacts in two phases identified with reference to the analysis at sectoral level. : Table 19: The impact on EU-28 sectoral trade balance (in billion of 2005 and in difference from reference scenario) Agriculture Utilities Heavy Industries Chemicals High Technological Industries Transports Equipment Other Industries Construction Distribution Transports Communication Bank, finance, insurance Other market services All sectors Source: NEMESIS model 1- During the investment phase up to 2015, the external competitiveness of the EU deteriorates as few innovations appear and a direct increase of imports occurs, as a result of the rise in households final consumption and investment, whereas the increase of R&D expenditure induces inflationary pressures. The external balance stays negative until 2014, with a maximum deficit of the trade balance of 13.1 billion EUR in Then, in the longer term, the appearance of innovation improves the external competitiveness of EU firms and exports increase gradually in time. In 2030, the trade balance surplus reaches 131 billion EUR. About two thirds of these gains occur in the industrial sectors, which are more R&D intensive and more open to international competition. Page 38 of 64 This project has received funding from the European

39 8. The results at national level This section details the above results for the different EU Member States. 9.1 Impact on GDP The first conclusion, illustrated In Table 20 and Figure 8, is that the impacts on GDP in the different countries are quite in line with the evolution of their R&D intensity during the period These impacts range, in 2030, from 0% in Spain, which has roughly maintained its R&D intensity constant in 2015 as compared to 2007, up to 18.5% in Slovenia, which increased its R&D intensity by 0.79 GDP point, the best performance across EU-28. To focus more closely on this outlier we must first notice that Slovenia, after five years of economic crisis, is expected to experience a steady acceleration of GDP with 2.3% growth in 2015, followed by 2.5% in 2016, and then 3% expected for It is a remarkable performance, as the country engaged, during the same period, into in a strong deleveraging policy, with a public debt that exceeded 80% of GDP. An attractive feature of the Slovenian economy is furthermore the development of its industry, that yields about one quarter of its GDP, with, from 1980, an evolution from traditional industries, such as agriculture, textile and metal products, to very innovative and high value-added sectors, such as industrial machinery, transport equipment, electrical goods, pharmaceutical and chemical industries. With this important industrial basis, Slovenia has a very high export rate that represents about 78% of its GDP, with about two thirds of its exports toward the EU internal market. Page 39 of 64 This project has received funding from the European

40 Table 20: Long term impacts on GDP per country (in % diff. from reference scenario) Evolution of GDP GDP gain in 2030 in % Increase in long term annual growth rate of GDP ( ) Increase in R&D intensity Austria 7.25% 0.14% 0.63% Belgium 5.91% 0.19% 0.63% Bulgaria 1.88% 0.24% 0.52% Cyprus 1.11% 0.06% 0.01% Czech Republic 4.70% 0.10% 0.64% Germany 3.33% 0.04% 0.43% Denmark 4.09% 0.08% 0.45% Estonia 6.99% 0.15% 0.42% Spain -0.03% -0.03% -0.01% Finland 1.28% -0.12% -0.45% France 1.83% 0.10% 0.20% Greece 5.03% 0.38% 0.38% Hungary 4.58% 0.19% 0.42% Ireland 5.17% 0.10% -0.04% Italy 2.27% 0.10% 0.20% Lithuania 2.32% 0.06% 0.24% Luxembourg 0.68% -0.46% -0.30% Latvia 0.80% -0.01% 0.07% Malta 2.88% 0.02% 0.22% The Netherlands 2.07% 0.10% 0.33% Poland 6.27% 0.13% 0.44% Portugal 2.55% 0.02% 0.24% Romania 0.45% 0.08% -0.03% Sweden 0.95% -0.04% 0.01% Slovenia 18.51% 0.50% 0.79% Slovakia 5.20% 0.11% 0.73% The United-Kindgom 1.91% 0.09% 0.07% EU % 0.07% 0.27% Source: NEMESIS model Page 40 of 64 This project has received funding from the European

41 Figure 8: Impact annual long term GDP growth rate at country (in deviation from reference scenario, at left) and increase on R&D intensity (in % GDP, right side) Source: NEMESIS model When it comes to the evolution of the R&D intensity in the business sector in Slovenia, in the period , we observe that a large part of the GDP point increase originates from the investments made by the Chemical and Pharmaceutical industries (+0.12 GDP point), the office machines sector (+0.12 GDP point), the transport equipment industries (+0.04 GDP point) as well as the communication sector (+0.08 GDP point), with the largest increase in the other services sector (+0.3 GDP point) that includes the KIBS. It should be noticed that the R&D intensity increases in all sectors, thanks to very important public support to the R&D investments of firms, re-enforced in Page 41 of 64 This project has received funding from the European

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