TRANSITION REPORT 2014 Innovation in Transition

Skills of the workforce

The third aspect of the business environment that constrains innovative firms particularly strongly is the availability of the right skills. In country-level regressions (such as those reported in Table 3.3) measures of human capital – including the percentage of the population that has completed secondary or tertiary education, the average number of years of schooling and the average number of years of tertiary education – are not consistently found to be significant determinants of innovation. However, a higher average number of years of university education is generally associated with a higher patent output. This weaker correlation may be due to the fact that enrolment ratio-type measures predominantly capture the quantity – rather than the quality – of education.18

A more nuanced measure of the quality of education and basic skills is available for a sample of 65 OECD and non-OECD economies, based on the Programme for International Student Assessment (PISA) conducted by the OECD. PISA is a standardised international assessment of 15-year-old students’ abilities in the areas of reading, mathematics and science. It has been conducted every three years since 2000, with a sample of schools chosen at random in each country. Higher average scores across all students in all three subjects generally correspond to a higher quality of education in a given country.

For the sub-sample of countries participating in PISA, the average scores achieved by these 15-year-old students are positively and significantly correlated with innovation, in terms of both patent output and the innovation intensity of exports (see Chart 3.11). This relationship is particularly strong for patent output (with the correlation coefficient standing at around two-thirds), highlighting the role that the quality of education plays in facilitating innovation at the technological frontier.

The effect that R&D has on innovation outcomes, which was examined earlier at the level of individual firms, can also be observed in cross-country data (see Table 3.3). Furthermore, the results of cross-country analysis reveal that the distribution of R&D spending across firms, academic institutions and government also plays an important role. Both business R&D spending and government R&D spending are associated with increases in patent output, with the impact of an additional US$ 1 of R&D spending estimated to be higher for government R&D than for business R&D. However, only business R&D appears to have a positive impact on the innovation intensity of exports. This could be because of the poor links between science and industry in transition countries (see Box 5.3).

This discussion of the links between innovation and R&D in the various sectors also highlights the complexity of the innovation process, which requires a variety of general and specialist inputs. For this reason, countries that are at a more advanced stage in their development (measured, for instance, by GDP per capita at purchasing power parity) may be better placed to innovate. The cross-country results presented in Table 3.3 confirm that rich countries do tend to patent more.

However, there does not appear to be any correlation between income per capita and the innovation intensity of output. This may be due to the fact that firms in less developed countries have become increasingly successful at adopting existing technology over the last few decades.

Overall, the various factors discussed above explain between 60 and 90 per cent of variation in innovation outcomes across countries. The analysis also suggests that, given their income per capita, economic openness, human capital, economic institutions, R&D spending and other characteristics, transition economies innovate at around or slightly above the level that would be expected of them, in terms of both patent output and the innovation intensity of their exports.19

CHART 3.11
  • Transition countries
  • Other countries

  • Transition countries
  • Other countries

Source: OECD, USPTO, UN Comtrade, Feenstra et al. (2005) and authors’ calculations.
Note: PISA scores are averages across mathematics, science and analytical reading. Data are based on the 2012 survey (or the latest survey available).