Despite a general consensus that technological innovation is necessary for long-term growth, there is no agreement on the best way to measure the knowledge economy or innovation potential and outputs (Grupp and Mogee 2004; Radosevic 2004: 642). This is evident from the emergence of various composite indicators designed to measure innovation. Composite indicators are aggregations of different indicators designed to summarise complex phenomena in a simpler construct (Grupp and Mogee 2004: 1377). This chapter looks at several indicators – the Knowledge Assessment Methodology, the Talent Index, the National Innovation Capacity index, and the European Innovation Scoreboard. Each is designed to measure not just stocks of knowledge (most often through data on enrolment in tertiary education) but also the capacity
of the economy to benefit from innovative potential. The use of composite indicators to compare and rank countries according to measurements of innovation, globalisation, international integration and transition is a useful but methodologically flawed approach. They are useful because they condense large amounts of information in easily understandable formats, but missing data, weighting and aggregation techniques, and the potential for data manipulation to support certain arguments are important issues (Freudenberg 2003: 5). The main question is whether composite indicators reflect the reality that they seek to assess and rank countries in a meaningful and accurate manner; in this case do the variables reflect the national innovation system or knowledge-based economy? To minimise these issues, the composite indicators should have a sound conceptual basis, detailed justification of the selected variables and weighting (Freudenberg 2003: 29). The section below discusses the four composite indicators included in this research, highlighting their advantages and possible limitations.
We first turn to the World Bank’s Knowledge Assessment Methodology (KAM), which is a composite indicator designed to measure a country’s (or region’s) development as a knowledge economy. The conceptual framework is described in Box 1.
The KAM comprises four thematic indicators (called ‘pillars’) of the knowledge economy. As all composite indicators should do, the KAM provides a conceptual framework that explains the phenomenon to be observed and the selection criteria behind the individual variables. The choice of indicators in the KAM is underpinned by the assumptions that successful transition to a knowledge economy involves investment in education and training, ICT, innovation capability, and an economic environment that promotes market activity. The KAM menu contains 80 individual variables, although the basic scorecard cited in this chapter uses only 14 variables (see Table 4.4) subdivided into the four thematic ‘pillars’, which are described below:
1. “An economic incentive and institutional regime that provides good economic policies and institutions that permit efficient mobilization and allocation of resources and stimulate creativity and incentives for the efficient creation, dissemination, and use of existing knowledge.”
The KAM argues that the economic and institutional regime must provide incentives for the creation and use of knowledge, based on effective competition and regulatory policies, which involves rule of law, protection of rights and the absence of corruption.
2. “Educated and skilled workers who can continuously upgrade and adapt their skills to efficiently create and use knowledge.”
The variables under this thematic indicator include adult literacy rates and enrolment in secondary and tertiary education. The KAM argues that education at all levels is important for the generation of innovation as well as the translation of foreign technologies for local use.
3. “An effective innovation system of firms, research centres, universities, consultants, and other organizations that can keep up with the knowledge revolution and tap into the growing stock of global knowledge and assimilate and adapt it to local needs.”
Pottelsberghe 2001) showing that increases in R&D expenditure lead to increases in GDP growth and productivity rates. Further, they use the publication of academic scientific papers to proxy for the stock of knowledge, based on a study by Adams (1990) which found that technical knowledge contributed to total factor productivity growth between 1952- 1980 in the USA. To indicate the level of innovation, the KAM uses the number of patents granted per capita, receipts from royalty and licence fees per capita and the number of science and engineering (S&E) articles published per million. These are also used by the United Nation’s Technology Achievement Index (TAI) for the same purposes (UN 2001; OECD 2008b). Academic scientific papers published per capita, royalty payments and receipts, and US patents obtained per capita) are useful for showing the size of R&D systems, the technological level of industry, and indicate the degree to which the country is integrated into international scientific communities (Radosevic 2010: 187-188).
4. “A modern and adequate information infrastructure that can facilitate the effective communication, dissemination, and processing of information and knowledge.”
The KAM argues that ICT has been instrumental in global knowledge transfer and also an important factor in realising productivity gains. This is measured by data on ownership per capita of telephones and computers and internet usage.
Sources: Chen and Dahlman (2005: 4-9); Radosevic (2010: 187-188)
The KAM can be criticised for the relevance of selected variables in reflecting the complexity of the knowledge-based economy. In particular, the indicators used for the education and human resources thematic indicator measure quantity of education rather than quality. As Freudenberg (2003: 9) writes: “Many indicators of performance (e.g. innovation, ICT-readiness) may be simple reflections of the level of development of a country or per capita income and highlight problems of causality.” A further issue is that comparing countries over time may reflect improvements in data and methodology rather than reflections in the country’s performance.
Finally, it could be argued that the pillars outlined in the KAM basic scorecard are too narrow to measure a knowledge economy. The KAM also uses the Human Development Index, which has itself been criticised for methodological flaws and inconsistencies (Freudenberg 2003: 8-9). Despite these reservations, the KAM is a still a useful framework for analysis and country comparisons, which indicate the general direction that a country is heading over time. It provides a valuable assessment for the level of development of incentives and opportunities for those highly skilled individuals who carry the requisite in-demand knowledge, and an environment that is likely to encourage the circulation and transfer of knowledge.
The KAM includes two indexes – the KAM Knowledge Index (which measures potential of knowledge generation) and the Knowledge Economic Index (which assesses the conduciveness of the environment for knowledge to be used effectively). The KAM uses the Knowledge Economy Framework comprising four ‘pillars’ associated with successful transition to a knowledge-based economy. The World Bank argues that progress in the four ‘pillars’, summarised in the following way, “are necessary for sustained creation, adoption, adaptation and use of knowledge in domestic economic production…” (Chen and Dahlman 2005: 4):
- Investment in education
- Development of innovation capacity
- Modernisation of the information infrastructure
- Creation of an economic environment conducive to market transactions
The most commonly-used mode of KAM is the ‘basic scorecard’. This provides an assessment on a scale from 1-10 of the four pillars using 14 variables, including two performance and 12 knowledge variables (the scorecard uses three variables for each pillar). The first pillar (economic incentive and institutional regime) is designed to take into account whether the environment is conducive to use knowledge effectively for economic development, while the remaining three pillars measure the potential for knowledge development according to a country’s “ability to generate, adopt and diffuse knowledge” (World Bank KAM – www.worldbank.org/kam).
Variables Normalised score23 Performance variables
Annual GDP growth (%), 2003-2007 6.28 Human Development Index, 2005 n/a
Four pillars
Economic incentive and institutional regime
Tariff and non-tariff barriers, 2009 4.76 Regulatory quality, 2007 3.63 Rule of Law, 2007 3.63
Innovation system
Royalty Payments and receipts (US$/pop.), 2007 N/a S&E* Journal articles / Mil. People, 2005 7.57 Patents granted by USPTO* / Mil. People, avg
2003-2007 4.73
Education and human resources
Adult literacy rate (% age 15 and above), 2007 5.96 Gross secondary enrolment rate, 2007 5.69 Gross tertiary enrolment rate, 2007 n/a
Information and communication technology
Total telephones per 1000 people, 2007 8.36 Computers per 1000 people, 2007 7.61 Internet users per 1000 people, 2007 5.00 Source: World Bank KAM
*USPTO = US Patent and Trademark Office S&E = Science and engineering
Taken together, Tables 4.4 and 4.5 show that Serbia is particularly weak in providing an environment to nurture and benefit from knowledge (economic incentive and institutional regime). Regulatory control and rule of law are low (both 3.63). Innovation has fallen since 1995, while education and ICT have seen small improvements. ‘Economic incentive and institutional regime’ saw a near four-fold increase from 1.04 to 4.01, though this significant improvement represents a low starting point and the process of ‘catch-up’ since 2000. In terms of regional comparisons, as Table 4.5 below shows, Serbia’s overall score is average among SEE 8 countries. The situation in education is below the regional average. In other areas, it performs
23 Normalisation is carried out on the data to allow comparison between countries through ranking. The KAM contains
raw data from 128 countries, which are ranked according to absolute values for the variables. Subsequently, countries receive a score between 0-10 that reflects their position vis-à-vis other countries (10 being the top score and 0 the lowest). The top 10 per cent of countries receive a score between 9 and 10, the next 10 per cent between 8 and 9, and so on (Chen and Dahlman 2005: 17).
better than the SEE average, although the country lags behind regional leaders.
Table 4.5 Knowledge assessment methodology basic scorecard
Country Knowledge Economy Index Economic Incentive and Institutional Regime
Innovation Education ICT
most recent 1995 most recent 1995 most recent 1995 most recent 1995 most recent 1995 Croatia 7.28 6.72 7.26 4.98 7.67 7.49 6.56 7.05 7.62 7.36 Bulgaria 6.99 6.84 7.14 5.84 6.43 7.17 7.65 7.30 6.74 7.04 Romania 6.43 5.79 6.98 5.83 5.74 4.89 6.47 6.26 6.55 6.16 Serbia 5.74 5.26 4.01 1.04 6.15 7.79 5.83 5.33 6.99 6.88 Macedonia, FYR 5.58 5.17 5.34 4.02 4.67 4.43 5.42 5.23 6.88 7.00 Moldova 5.07 5.11 4.38 3.47 4.79 4.43 6.05 7.00 5.08 5.55 Bosnia and Herzegovina 4.58 4.37 4.26 3.67 3.11 2.93 5.70 4.95 5.24 5.93 Albania 3.96 3.97 4.09 4.67 2.82 3.38 4.97 3.33 3.96 4.50 SEE avg 5.7 5.4 5.44 4.19 5.17 5.31 6.08 5.81 6.13 6.3
Source: World Bank KAM, January 2011
The KAM scorecard shows that Serbia trails SEE countries in many areas, particularly the quality of its regulatory and economic environment and the quality of the education and innovation system. There has been some improvement but Serbia has some of the lowest scores in SEE. Weakness in these areas is likely to act as a hindrance on the country’s ability to attract the highly skilled and utilise the knowledge they bring. The next sections focus on thematic aspects of the KAM in greater detail, incorporating further composite indicators that draw attention to the economic and institutional environment, the innovation system and the education sectors.