Organization for Economic Co-Operation and Development

THE KNOWLEDGE-BASED ECONOMY

Paris 1996

F. Measuring knowledge networks

Current knowledge indicators – which are primarily measures of knowledge inputs and codified knowledge flows – are not adequate to describe the dynamic system of knowledge development and distribution which is at the heart of the knowledge-based economy. Stocks and flows of more tacit forms of knowledge, such as learning that depends on conversation, demonstration and observation, cannot be traced through these indicators. New indicators are needed that capture the innovation process and the distribution of knowledge among key actors and institutions in the economy. This essentially involves measuring “national innovation systems”, including the ability of countries and systems to distribute knowledge among different actors and institutions.

Such indicators of knowledge creation and distribution are proceeding at the level of the individual firm through the vehicle of innovation surveys. These surveys capture information about the factors affecting the propensity of firms to innovate and how knowledge and innovation are diffused in the economy. Analyses explain the propensity to innovate in terms of traditional inputs such as investments in R&D, use of skilled labour and use of new domestic and imported equipment as well as other factors such as profitability, regulatory systems and institutional networking. Surveys have focused on “geographical clustering” or the effects of geographic location and the locus of individual plants on innovation (DeBresson, 1989). They have also examined “industrial clusters” or the interlinkages between user and supplier sectors or those based on key technologies and the effect on enterprise innovation (Roelandt et al., 1995).

More comprehensive surveys, such as the Community Innovation Survey (CIS) and the PACE Project, aim at compiling complete firm-level innovation data sets. The CIS, which was implemented in 1993, covers all European Union countries and has a preliminary database of 40 000 manufacturing firms. Through this survey, data is being developed on firm expenditures on activities related to the development of new products, including R&D, training, design, market exploration, equipment acquisition and tooling-up; production and sales of incrementally and radically new products; sources of information relevant to information; R&D performance and technological collaboration; and perceptions of obstacles and stimuli to innovation. The CIS contains several questions on technological co-operation and information flows and may provide the basis for linking the general innovation performance of firms with their patterns of technological collaboration and information use.

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The PACE Project (Policies, Appropriability and Competitiveness for European Enterprises Project), which covers large R&D-performing firms in Europe, asks a similar set of questions, including the types of information required in the development and introduction of technological change. The survey asked firms about the goals of innovation, external sources of knowledge, public research, methods to protect innovations, government programmes to support innovation and barriers to profiting from innovation. Initial findings show the most important external source of knowledge to be technical analysis of competitors’ products. Joint ventures are important sources of knowledge in sectors where R&D projects are expensive and complex. In most countries, public research was considered an important part of the national system of innovation (MERIT, 1995).

Based in part on these innovation surveys, efforts are just beginning to map national innovation systems and the knowledge distribution power of economies through analysing two main flows: i) the distribution of knowledge among universities, public research institutions and industry; and ii) the distribution of knowledge within a market between suppliers and users (Smith, 1995). This systems approach provides information on flows, such as the proportion of knowledge, especially in basic science, which is transferred among researchers; the proportion of academic and public knowledge that is accessible to and used by industrial innovators; and the extent and rate of diffusion of new knowledge and technologies in industry (Table 12). Data is being collected on a national basis which allows us to measure these flows between different actors and institutions in a country’s innovation system, such as has recently been done for Norway (Smith et al., 1995).

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Indicators of interactions between the public, private and academic sectors are being explored which would measure institutional capabilities to transfer knowledge and include:

à number, specialisation and funding of co-operative research projects among universities, public research institutes and industry;

à number, specialisation and funding of university-industry research centres;

à number and technological specialisation of co-patenting and co-publication among universities, public research institutes and industry;

à personnel mobility and patterns of recruitment among universities, public research institutes and industry; and

à methods of access of firms to findings of university research, including publications, conferences, trained staff, informal contacts, temporary exchanges and contract or joint R&D. Surveys are also being implemented to measure market interactions, or the capabilities of the private sector in transferring knowledge, based on:

à research co-operation within the enterprise sector, including number and relative importance of research joint ventures, technological collaboration or large co-operation programmes;

à participation of firms in industry-wide standardisation activities and informal research networks;

à rates of mobility of researchers across firms and sectors;

à methods of access of firms to findings of other firms and sectors, including published

information, joint research, cross-licencing or purchase of licenses and patents; and

à degree of internationalisation, by examining these indicators at the international as well as the national level.

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G. Measuring knowledge and learning

The advent of the knowledge-based economy raises questions about the efficiency and equity of education and training in what must also be a “learning economy”. Economists have traditionally measured the development of human capital in terms of proxies, such as years of education or experience. Such measures do not reflect the quality of education or learning nor the economic returns to investment in education and training. The existence of a large non-formal sector in which individuals are undergoing on-the-job training poses significant measurement problems and reflects the difficulties involved in tracking more tacit forms of learning and knowledge transfer. To fill in some of these measurement gaps, the OECD has recently initiated a project to develop “human capital indicators”, aimed particularly at measuring private and social rates of return to investment in education and training.

One approach to assessing social rates of return is to measure the impact of education expenditure and attainment levels in society at large on economic growth. A study of 29 countries found education accounting for up to a quarter of economic growth (Psacharopoulos, 1984). Another study of 24 countries (seven of which were OECD countries) reached similar conclusions (OECD, 1994). The finding that human capital investment can generate economic growth was shown in a study measuring the percentage of the working age population attending secondary school and the effects on productivity levels; it was found to be significant for the entire sample of countries and a sub-sample of 22 OECD countries (Mankiw et al., 1992).

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Measuring private rates of return has tended to look at changes in human skills and competencies at the individual or firm level and the impacts on firm performance. A number of studies have been conducted of the effects of on-the-job training on wages and productivity; these point to substantial positive effects on wages, typically ranging from 5 to 15 per cent, as well as positive impacts on productivity (OECD, 1996c). One analysis of a large US manufacturing firm revealed that an increase in training expenditure yielded a rate of return for the company of 20 to 35 per cent (Bartel, 1995). Other studies have found that the beneficial effects of enterprise training depend on collateral investment in technology (Lynch, 1995).

More micro-level or firm-level indicators are needed to establish linkages between enterprise training, its impact on human capital and skill formation and the effects on firm performance (Table 13). While improvements have been made in the collection of data on vocational training in enterprises, firm surveys are needed to assess firm expenditure on training by type of training (general, technical, management), by staff category (worker, researcher, manager) and by type of firm (sector, size).

A related research effort should be devoted to identifying the human resources and critical skills required by industry to better match supply and demand for human capital. Data is now being collected by the OECD on employment by industry and occupation, which may be used in the future to track shifts in employment within and among industries, examine the evolution of skilled and unskilled employment over time, and identify factors which underlie job gains and losses in particular sectors. Also relevant is how technological and organisational change at the firm level (e.g. just-in-time management, flexible manufacturing, outsourcing, downsizing, etc.) may change demand for human resources. The OECD is initiating Flexible Enterprise Surveys in various Member countries to assess what developments might be expected in labour markets with respect to qualification requirements, staff training, average tenure and patterns of employment.

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H. Conclusions

Our understanding of what is happening in OECD economies is constrained by the extent and quality of the available indicators. While advances are being made in economic theory and methodologies, these will not be fruitful unless they are applied to the right data. Traditional national accounts frameworks were designed in an earlier era when the economy was simpler and the role of knowledge and technical change was not fully acknowledged. As a result, this measurement framework is not offering reasonable explanations for trends in economic growth, productivity and employment. The contributions of R&D to productivity growth, the economic effects of the computer and information networks, the role of tacit learning and formal and informal economic interactions are among the phenomena which at present elude us.

To fill these gaps, work must first continue on improvements, extensions and new combinations of current knowledge indicators relating to R&D expenditures and research personnel, particularly to develop a clearer picture of the research and innovation role of the services sector. But indicators for the knowledge-based economy must go beyond measuring knowledge inputs to measuring stocks and flows, rates of return and distribution networks. The central role of learning also underlines the need for new indicators of human capital, training and labour requirements. Fruitful areas for further indicator development in the near term include:

à Knowledge stocks and flows – Statistical techniques could be developed to estimate knowledge stocks based on current R&D input and flow measures. Development of knowledge flow indicators would yield better measures of the R&D and knowledge intensity of industries and economies. This includes more extensive and comparable indicators of the acquisition and use of different types of technology by industry, particularly information technologies. More creative analysis of existing patent data at the national and international levels could help trace flows of disembodied knowledge.

à Knowledge rates of return – In order to assess knowledge outputs and evaluate the performance of knowledge-based economies, priority should be placed on developing improved indicators of the private and social rates of return to R&D and other knowledge inputs. This includes measuring returns to individuals, firms and societies in terms of employment, output, productivity and competitiveness, and could be based on both macro-level econometric analyses and firm-level surveys. One of the great challenges is to develop indicators and methodologies for gauging the impact of technology on productivity and economic growth.

à Knowledge networks – Given the importance of tacit as well as codified knowledge, diffusion as well as creation of knowledge, and know-how and know-who in the knowledge-based economy, indicators of the knowledge distribution power and other characteristics of innovation systems are key. Firm-level innovation surveys, as well as other measurement approaches, need to be developed to better characterise innovation processes and interactions among firms and a range of institutional actors in the economy.

à Knowledge and learning – Human capital indicators, particularly those relating to education and employment, are central measures for the knowledge-based economy. Measuring the private and social rates of return to investments in education and training will help point to means of enhancing the learning capacity of individuals and firms. Micro-level firm indicators on human resource requirements, employment and occupational mobility will help better match supply and demand for skills in the labour market.

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