Comparative Analysis of Policy-Mixes of Research and Innovation Policies in Central and Eastern European Countries

Observing the CEE members of the EU (EU10 countries) from a distance, they certainly used to share major structural similarities given their historical legacies, as well as certain ‘unifying’ effects of their transition to market economy and democracy. Yet, a closer look reveals important elements of diversity in (a) the structure of their national innovation system, (b) the direction of recent structural changes, (c) innovation performance, and (d) patterns of business-academia collaboration. Given this diversity one would assume that fairly different needs are identified in the EU10 countries, necessitating differentiated, ‘tailored’ policy responses. Yet, these countries follow the same STI policy rationale, namely the market failure argument, which itself can be seen as a unifying force. Actually, this is not unique to the EU10 countries: the science-push model of innovation is still highly influential in the STI policy circles both at the level of the EC and the member states, despite a rich set of research insights stressing the importance of non-R&D types of knowledge in innovation processes. While the innovation systems literature stresses without any hesitation that there is no single optimal policy model – on the contrary, there is a need to understand and respond to the specific characteristics and challenges in each national innovation system – a cluster analysis the national STI policy mixes clearly suggests an unexpected convergence among the EU countries. Thus, countries with different technological challenges are following similar approaches although their particular profile would require reflecting more differences. Policy learning might be welcome, indeed, but too much convergence among innovation policy mixes is likely to undermine the effectiveness of policies. Further policy conclusions, that are of particular relevance for the EU10 countries, include: i) analysts and policy-makers need to avoid the trap of paying too much attention to simplifying ranking exercises based on composite indicators, and devote their efforts to conduct thorough comparative analyses instead; (ii) new indicators that better reflect the evolutionary processes of learning and innovation would be needed to support policy-making; iii) STI policies should promote knowledge-intensive activities in all sectors, including low- and medium-technology industries and services; iv) it is a demanding task to identify what failure(s) is (are) blocking innovation processes in what part of a given innovation system, but that effort cannot be spared if the aim is to design appropriate – sound and effective – policies