EU11 countries restructured their innovation systems gradually, with some delay compared with the speed of economic transition in this region. The results of innovation system restructuring in EU11 countries, in combination with convergence processes, can be analyzed by comparing changes in the Summary Innovation Index (SII) with changes in real GDP per capita. This is illustrated in Figure 8, where the convergence (divergence) of real GDP per capita relative to the EU28 average (in p.p.) during the 2004–2013 period is marked on the horizontal axis, while the vertical axis shows the change in the Summary Innovation Index (SII) in relation to the EU28 average for the same period.
The convergence of GDP per capita was observed in all of the EU11 economies (excluding Slovenia) and was accompanied by slower changes in the innovativeness of these economies as expressed with the SII. However, the rate at which the SII changed in 2004–2013 in most EU11 countries was faster than the EU28 average. This trend was also present in Poland, while Slovakia and Hungary were the only EU11 economies that saw a convergence in terms of the SII in relation to the EU28 average. In the case of Slovenia, the process of catching up to the EU average in terms of innovation was accompanied by a divergence in terms of real GDP per capita (Figure 8). As far as Poland is concerned, in the 2004–2013 period, the country failed to complement the growth of real GDP per capita by catching up with the EU average—let alone EU innovation leaders—in terms of innovativeness. What are the causes of this negative trend? This question can be answered on the basis of a comparative analysis of the indicators incorporated in the SII. This analysis will show areas where the restructur‑ ing of the national innovation system was not completed. Table 5 gives an overview of the development of science and innovation in Poland compared with the other EU11 economies.
Figure 8
Changes in the Summary Innovation Index (SII) and changes in real GDP per capita (in PPS) in relation to the EU28 average levels, 2004–2013 (EU28=100; percentage points)
Source: Authors’ elaboration based on Eurostat data and on Innovation Union Scoreboard 2013, European Commission, 2013.
The main indicator describing R&D from the input side is R&D intensity, under‑ stood as the ratio of R&D expenditures to GDP, and its changes over time. In the first decade of the 21st century in Poland, this relationship was well below the EU average. For example, in 2012 the figures were 0.90 % vs. 2.06 %. On the positive side, the Polish indicator grew twice as fast (1.6 %) as the rate in the EU as a whole (0.8 %). In 2012, only two EU11 economies, Slovenia and Estonia, had a higher level of R&D expenditure than the EU average. In 2000–2011, R&D expenditure growth in these two economies was in the double digits. In terms of the intensity of R&D expenditure in the EU11 group, the Czech Republic and Hungary stand out, with 1.88 % and 1.30 % respectively in 2012. R&D expenditure in other EU11 economies did not break the 1% of GDP threshold. The low level of R&D expenditure in Poland is also reflected by the per capita value: in 2012, R&D expenditure per capita in Poland, at €73.6, was one‑seventh of the EU average of €516.2 (GUS, 2013, p. 54).
One of the problems in Poland is the structure of R&D expenditure inherited from a centrally planned economy, with the government sector in the dominant position. Only four EU11 economies—Estonia, Hungary, Slovenia, and Lithuania—were able to significantly restructure their R&D expenditures toward a growing role for the non‑government sector (Figure 9).
Table 5
Overview of research and innovation performance: Poland and other EU11 countries compared R&D intensity index, 2012 Population aged 25–64 years having completed at least upper secondary education HRST with tertiary education in science, mathematics and computing Excellence in S&T index, 2010 Inde x of economic impact of innovation 2010–2011 Knowledge‑intensity of economy 2010
value growth rate (2000– 2011)
(%)
% of total in 2012 change since 2004 in
p.p.
as a
% of active
population in 2012 change since 2004 in
p.p.
value growth rate (2005– 2010)
(%)
value growth rate (2000– 2011)
(%) Bulgaria 0.64 1.06 81.0 2.4 5.1 1.5 24.65 3.40 0.23 29.45 3.65 Czech Republic 1.88 4.23 92.5 5.7 9.3 2.0 29.90 4.58 0.50 39.58 2.91 Estonia 2.18 13.31 89.8 1.4 6.7 1.4 25.85 11.70 0.45 46.48 2.94 Latvia 0.66 4.15 89.1 4.1 5.1 –3.0 11.49 –0.15 0.25 34.38 3.96 Lithuania 0.90 4.13 93.4 0.4 6.5 0.0 13.92 2.62 0.22 35.28 5.04 Hungary 1.30 4.64 82.1 2.3 7.0 2.3 31.88 2.03 0.53 50.23 1.87 Poland 0.90 1.60 89.6 3.9 9.7 8.7 20.47 4.45 0.21 31.78 1.65 Romania 0.42 2.53 75.9 2.7 8.7 –7.3 17.84 7.81 0.38 28.35 5.86 Slovenia 2.80 12.47 85.0 3.5 5.1 –0.7 27.47 3.99 0.52 45.90 4.25 Slovakia 0.82 0.41 91.7 2.1 7.2 0.8 17.73 3.85 0.48 31.64 0.07 Croatia 0.75 –2.72 79.3 0.2 6.6 1.5 12.25 2.31 0.35 n.a n.a EU28
averagea 2.06 0.80 74.2 2.1 8.7 –0.1 47.86 3.09 0.61 48.75 0.93 a if data were not available for EU28, average values for EU27 are given.
Source: Methodology and data derived from: European Commission (2013a), p. 5; data also taken from the Eurostat database.
In Poland, there was no significant change in the role of the business sector in R&D expenditures in 2004–2012; it was responsible for around 30 % of total expendi‑ ture. However, the role of foreign funds in terms of R&D expenditure increased from 5.2 % in 2004 to 13.3 % in 2012. This was chiefly due to an inflow of EU funds; their contribution increased in the first few years of Poland’s membership, reaching 5.6 % in 2006. In 2007–2009, the role of EU funds fell to 3.6 %, only to spring back to 10.9 % in 2012. Even though the number of beneficiaries using EU funds for R&D has increased continuously, these entities account for a falling percentage of all research entities, at 14.2 % in 2012, down from a high of 23 % in 2006 (GUS, 2013, p. 66). This may mean that EU funds intended for R&D are increasingly reaching beneficiaries that are the most active in applying for foreign funds.
Figure 9
Gross domestic expenditure on R&D (GERD) by source of funds in Poland and other EU11 countries: 2004 and 2012 compared
Source: Own elaboration based on Eurostat data.
In summary, Poland, like most other EU11 countries, saw some restructuring in its innovation system in terms of R&D financing in 2004–2012, but this was mainly based on an increased role of foreign funds, including EU funds. The business sector still is not the key player in financing research in the EU11. However, four of these economies stand out from this pattern: Estonia, Hungary, Lithuania, and Slovenia.
In these countries, the role of the business sector as a source of R&D financing increased. But only in Slovenia did the role of businesses in financing R&D surpass 50 % and overshoot the EU average (Figure 9).
Human capital is a key factor for the proper functioning of a national innovation system. This is a strong point for Poland and most other EU11 countries, compared with the EU average, especially as human capital indicators in most of these countries grew at a faster rate than the EU average. Poland was the fifth economy among EU11 countries in terms of the percentage of the population aged 25–64 with more than a secondary education (89.6 % in 2012), but was third (behind the Czech Republic and Latvia) with regard to the average growth of this indicator in 2004–2012. A similar trend was noted in another human capital indicator in EU11 countries in 2004–2012: the percentage of employees with a university degree in mathematics and computer science in the total working population. In Poland, the Czech Republic and Romania, this indicator was above the EU average. In 2012, it amounted to 9.7 % in Poland, while the EU average was 8.7 % (Table 5).
To sum up this assessment of restructuring in EU11 innovation systems in terms of human capital, it is necessary to note the relatively strong position of most EU11 countries, Poland in particular, and the positive changes in this position from 2004 to 2012.
In order to access how changes in the financing of R&D and in human capital affect the positions of Poland and other EU11 economies in science and technology, the Excellence in S&T index will be analyzed. This index comprises four separate indicators that describe scientific publications, references to these works, grants in
per capita terms, and R&D expenditure.9
For all of the members of the EU11 group, the values of this index are significantly lower than the EU average (47.86 in 2010). However, three subgroups can be identi‑ fied within the analyzed group.
The first subgroup consists of Hungary, the Czech Republic, Slovenia, and Estonia. The best performer in the group, Hungary, had an Excellence in S&T index of 31.88 in 2010. However, its index grew at a slower rate than the EU average in 2005–2010 (2.03 % vs. 3.09 %). The Czech Republic, Slovenia, and Estonia had relatively high indices in 2010 (above 25.0). These grew at a faster rate than the EU average in 2005–2010, with Estonia recording the fastest growth, at 11.7 %.
Poland, as with Bulgaria, Romania, and Slovakia, recorded a moderate Excellence in S&T index in relation to other EU11 economies. In 2010, Poland’s index was
9 The Excellence in S&T index consists of four separate variables: 1) the share of most‑quoted scientific publications in the general number of publications where at least one author is a resident of the said country, 2) the number of reputable universities and public R&D units in the said country per 1 million inhabitants, 3) the number of patents awarded in the international PCT procedure per 1 mil‑ lion inhabitants of the said country, and 4) the general value of European grants (ECR) awarded to the said country based on R&D expenditures of its public sector and higher education units. The details of the methodology are described in: European Commission (2013), p. 321.
20.47, similar to that in Bulgaria, but higher than in Romania (17.84) and Slovakia (17.73). Significantly, the indices in all four economies grew at a faster rate than the EU average in 2004–2010.
The third and the weakest group of the EU11 economies in terms of the Excel‑ lence in S&T index is made up of Lithuania, Croatia, and Latvia. The value of the index for these economies ranged from 11.49 (Latvia) to 13.92 (Lithuania), and its growth in 2005–2010 was slower than the EU average, with negative growth in Latvia (Table 6).
Overall, Poland is outperformed by Hungary, the Czech Republic, Slovenia, Esto‑ nia, and Bulgaria in terms of Excellence in S&T in the EU11 group. Estonia stands out positively in terms of the rate at which this index grew in the studied period: its index grew almost three times as fast as the EU average. As it turns out, there is little correlation between the growth of R&D expenditure in 2000–2011 and the growth of the Excellence in S&T index. The Pearson correlation index is 0.508 (Table 6), which means that an increase in the intensity of R&D only partially translates into a rise in the Excellence in S&T index. Given the small changes in the structure of R&D expenditure in most EU11 economies, including Poland, it can be surmised that an increased involvement of the business sector is a key factor needed to improve the quality of a national science and technology system. This is confirmed by the example of Estonia. In 2004–2011, the share of the business sector in R&D expenditure in that country increased by 12 p.p., constituting over half of total R&D expenditure. During that period, Estonia’s Excellence in S&T index rose the fastest (11.70 % in 2005–2010) among all EU11 countries and far outpaced the EU average, which grew only 3.09 % from 2005 to 2010 (Table 5).
Similar conclusions can be drawn from the Index of Economic Impact of Innova‑
tion, another indicator that describes the quality of national innovation systems.10
In 2010–2011, this index for all EU11 economies was lower than the EU average (0.61). Poland’s index, at 0.21, was just over one‑third of the EU average. In the EU11 group, the following countries stand out with their Indices of Economic Impact of Innovation: Hungary (0.53), Slovenia (0.52), the Czech Republic (0.50), Slovakia (0.48), and Estonia (0.45). These countries also lead the way in the EU11 in terms of sales of innovative goods as a percentage of total sales (Figure 10). For Poland, this percentage is one of the lowest in the studied group, with a fall from 13.5 % in 2004 to 8 % in 2010.
10 The index consists of five indicators identified in the Innovation Union Scoreboard 2013. They are: 1) patents obtained via international PCT per 1 billion EURO GDP (PPP), 2) employment in knowledge‑ based manufacturing and services as a fraction of total labor employment, 3) share of exports of mid‑high and high technology in the trade balance, 4) sales of innovation new to the market and to the firms as a percentage of total sales in innovative enterprises, and 5) export of knowledge‑based services as a percentage of total service exports. Detailed information on the methodology behind the index and its components can be found in: (European Commission, 2013; 2013a).
Figure 10
Turnover from innovation as % of total turnover
Source: Own elaboration based on Eurostat data.
There is no strong correlation between the Index of Economic Impact of Innova‑ tion and the average growth of R&D intensity in 2000–2011. The Pearson correlation coefficient between these two variables is 0.42 (Table 6), which shows that the growth of R&D expenditure is not strongly connected with the economic impact of innovation on EU11 economies. The strength of this relationship is clearly impacted by the fact that the increase of R&D expenditure in most EU11 economies is not accompanied by a new structure of research funding.
Table 6
Pearson’s correlation coefficients between variables describing performance of national innovation systems in EU11 countries
Variables correlated Index of Excellence in S&T (2010) Index of economic impact of innovation R&D intensity growth rate
(2000–2011) 0.508 0.421
Source: Own calculations in SPSS based on data from: European Commission, (2013a).
This general picture of Poland’s innovative position and changes in it since the country’s accession to the European Union needs to be supplemented by a comment on Poland’s technological profile. The main technologies in which EU members spe‑ cialize were identified on the basis of patents granted by the European Patent Office
in 2001–2010, broken down by the investor’s home country as well as the country from which the patent application originated (European Commission, 2013a). Table 7 gives an insight into Poland’s technological profile against the background of other EU11 economies.
Table 7
Hot spots in key technologies in EU11 countries
Country Hot spots in key technologies Bulgaria agriculture, nano‑ and biotechnology, ICT and energy
Croatia healthcare sector; food processing and agribusiness; energy technology; electronics and advanced materials and digital techniques Czech Republic automobiles, transport, construction, materials, energy and environment
Estonia energy, environment, food and agriculture Hungary health, environment, automobiles, biotechnology Latvia materials, health, nano‑sciences, environment, energy
Lithuania other transport technologies (other than automobiles and aeronautics), construction technologies, energy Poland food, agriculture and fisheries; energy; environment; security; ICT; materials Romania automobiles, ICT, new production technologies, nanotechnologies, and security Slovakia food and agriculture, energy, ICT, materials
Slovenia health, food and agriculture, ICT, materials, new production technologies, environment
Source: Research and Innovation Performance in EU Member States and Associated Countries. Innovation Union Progress at Country Level, Directorate‑General for Research and Innovation, European Commission 2013.
The key technologies in which Poland has technological advantages are in areas such as food, agriculture and fisheries, energy, IT, materials, the environment and security. The technological advantages of individual EU11 economies vary consider‑ ably. In many of these countries, technological advantages are found in traditional industries such as agriculture and food production (Bulgaria, Croatia, Poland, Slovakia, and Slovenia). Advantages in the automobile sector—which is classified as a mid-high technology industry—play a significant role in the Czech Republic, Hungary, and Romania. In some EU11 countries, for instance in Slovenia, the Czech Republic, Estonia, Poland, Latvia, and Hungary, environmental technologies are among tech‑ nological specializations. Some advantages are also emerging in high‑tech industries in the EU11, for example in IT and nanotechnology. Hence, there is no one model of technical specialization for all of the EU11 members, and this observation has significant implications for policy makers.
Support for innovation is especially important in the case of Poland, because this analysis shows restructuring of the national innovation system has not yet been
completed. As a result, the innovation gap between Poland and other EU economies is growing.
In this context, the question is where innovation policies should be focused so that the innovation gap can be bridged. In addition, it is worth considering whether and to what extent this gap can be narrowed through using funds for research and development from the EU budget. This question may be answered by examining the impact that EU funds set aside for R&D and absorbed by Poland have on the country’s innovativeness. Such an analysis can point to new areas of interest in Polish innovation policy, especially since the role of EU funds in Poland’s R&D expenditure has been growing since 2008. In 2012, EU funds accounted for more than 10 % of Poland’s total R&D expenditure (GUS, 2013).