Proceedings of the 12th European Conference on Knowledge ...

Marta Christina Suciu et al
In previous published works (M. C. Suciu, 2001) we highlighted that investing in education provides
several opportunities for streng<strong>the</strong>ning and developing competences along formal-informal-nonformal
axis with reference to KBES.
The international organisations, such as <strong>the</strong> OECD (Blöndal, 2002) or <strong>the</strong> World Bank are interested
in managing <strong>the</strong> competences <strong>of</strong> <strong>the</strong>se assets, and particularly in education and innovation policies in
KBES. For instance, Education for Knowledge Based Economy is a program initiated by <strong>the</strong> World
Bank. The World Bank Institute, through <strong>the</strong> Knowledge for Development program, developed a
knowledge assessment methodology (KAM) and created <strong>the</strong> index for knowledge economy (KEI).
This is an aggregate indicator referring to a country’s overall preparedness towards knowledge based
economy and society. According to <strong>the</strong> KAM, <strong>the</strong> main components <strong>of</strong> a knowledge based economy
are: economic and institutional regime; education and human resources competences; dynamic
informational infrastructure; and efficient national innovation systems. With reference to <strong>the</strong> latest KEI
in 2009, on a scale from 1 to 10, Denmark ranked <strong>the</strong> most advanced knowledge-based economy
with a KEI <strong>of</strong> 9.52. Sweden and Finland were taking <strong>the</strong> next two positions in <strong>the</strong> ranking. Romania
ranked 47 out <strong>of</strong> 145 countries in 2009 (with a score <strong>of</strong> 6.43) and it showed an improvement <strong>of</strong> KEI
performance for all <strong>the</strong> dimensions analysed (table 1 below).
Table 1 KEI in 1995 and 2009
Country
/score out
<strong>of</strong> 10
KEI Economic
and
institutional
regime
Innovation Education Information
Technology and
Communication
ICT
2009 1995 2009 1995 2009 1995 2009 1995 2009 1995
Romania 6.43 5.48 6.98 5.73 5.74 4.75 6.47 6.20 6.55 n/a
Source: adapted from <strong>the</strong> World Bank Statistics, KAM, Knowledge for Development, 2010
Based on <strong>the</strong> data provided in <strong>the</strong> table above, it can be noticed that education is one <strong>of</strong> <strong>the</strong>
components which has a significant contribution to <strong>the</strong> aggregate index value (KEI in 2009 for
Romania was 6.43 and <strong>the</strong> related education component score was 6.47). This case confirms <strong>the</strong>
World Bank's KAM methodology that education and especially higher education (university studies,
master, doctoral and postdoctoral programs, etc.) became a strategic investment priority <strong>of</strong> <strong>the</strong>
country contributing to improved performance in relation to <strong>the</strong> different benchmarking methods. LLL
is a real source <strong>of</strong> sustainable competitive advantage in <strong>the</strong> long term. As we pointed out in previous
papers (Suciu, 2004, 2002) emphasizing <strong>the</strong> importance <strong>of</strong> investment in education and <strong>the</strong>
increasingly role <strong>of</strong> intangible assets and intangible asset management.
3. Short literature review on knowledge production
The importance <strong>of</strong> knowledge is acknowledged through various papers and studies on knowledge
production and its impact on growth. The production <strong>of</strong> knowledge is at <strong>the</strong> heart <strong>of</strong> R&D (research
and development) based models <strong>of</strong> endogenous growth (e.g. Romer, 1990; Grossman and Helpman,
1991; Aghion and Howitt, 1992). In <strong>the</strong>se models, new knowledge raises productivity and stimulates
sustained capital formation. Continued technological progress - enabled by a constant allocation <strong>of</strong>
resources to <strong>the</strong> ideas-producing sector – and sustained capital accumulation, toge<strong>the</strong>r, bring about
long-run growth. Growth rates are typically predicted to be proportional to <strong>the</strong> level <strong>of</strong> R&D. The
technology is assumed to progress endogenously; self-motivated, rational, pr<strong>of</strong>it-maximizing agents
who respond to market incentives undertake R&D in search <strong>of</strong> innovation. Government policies
designed to encourage R&D investment influence <strong>the</strong> long-run rate <strong>of</strong> economic growth. These
models contrast sharply with <strong>the</strong> neoclassical growth model, proposed by Solow (1956), which
assumes that technological change and hence long-run growth are exogenously determined. Romer’s
(1990) seminal work on “endogenous technical change” states that <strong>the</strong> production (flow) <strong>of</strong> new ideas
depends on <strong>the</strong> amount <strong>of</strong> human capital devoted to <strong>the</strong> ideas-producing sector and on <strong>the</strong> stock <strong>of</strong>
knowledge available in that sector. Knowledge and ideas are intangible and difficult to measure.
Patent data are widely used to proxy <strong>the</strong>m. Griliches (1990) calls patents “a good index <strong>of</strong> inventive
activity”; Eaton and Kortum (1996) approve <strong>of</strong> patent data as a widely accepted measure <strong>of</strong>
innovation. However, patents are a ra<strong>the</strong>r “noisy” measure <strong>of</strong> innovations because: (i) <strong>the</strong>y cannot
discriminate between <strong>the</strong> quality <strong>of</strong> innovations, and (ii) not all innovations are patented. Patents differ
markedly in <strong>the</strong>ir technical and economic importance. Among <strong>the</strong> thousands <strong>of</strong> patents awarded each
year, only a few may embody valuable knowledge and ideas.
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