The Competitiveness of Nations

in a Global Knowledge-Based Economy

Harry Hillman Chartrand

April 2002

Organization for Economic Co-Operation and Development

THE KNOWLEDGE-BASED ECONOMY

Paris 1996

 

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SUMMARY

I. THE KNOWLEDGE-BASED ECONOMY:
        TRENDS AND IMPLICATIONS

A. Introduction

B. Knowledge and economics

C. Knowledge codification

D. Knowledge and learning

E. Knowledge networks

F. Knowledge and employment

G. Government policies

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II. THE ROLE OF THE SCIENCE SYSTEM IN
        THE
KNOWLEDGE-BASED ECONOMY

A. Introduction

B. Knowledge production

C. Knowledge transmission

D. Knowledge transfer

E. Government policies

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III. INDICATORS FOR THE KNOWLEDGE-BASED ECONOMY

A. Introduction

B. Measuring knowledge

C. Measuring knowledge inputs    

D. Measuring knowledge stocks and flows

E. Measuring knowledge outputs    

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F. Measuring knowledge networks

G. Measuring knowledge and learning

H. Conclusion

References

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III. INDICATORS FOR THE KNOWLEDGE-BASED ECONOMY

A. Introduction

Economic indicators are measures that summarise at a glance how an economic system is performing. Since their development in the 1930s, and particularly after World War II, the national accounts and measures such as Gross Domestic Product (GDP) have been the standard economic indicators of the OECD countries. Based on detailed censuses that survey economic activity at the establishment level, they measure broad aggregates such as total production, investment, consumption and employment and their rates of change. These traditional indicators guide the policy decisions of governments and those of a broad range of economic actors, including firms, consumers and workers. But to the extent that the knowledge-based economy works differently from traditional economic theory, current indicators may fail to capture fundamental aspects of economic performance and lead to misinformed economic policies.

The traditional economic indicators have never been completely satisfactory, mostly because they fail to recognise economic performance beyond the aggregate value of goods and services. Feminists challenge the concept of GDP because it fails to take into account household work. Environmentalists maintain that traditional indicators ignore the costs of growing pollution, the destruction of the ozone layer and the depletion of natural resource endowments. Social critics point out divergence between traditionally measured economic performance and other facets of human welfare. In response to these criticisms, work is proceeding on extending censuses to include a set of household activities, such as cleaning, food preparation and child care. Attempts are being made to “green” the national accounts through indicators which track depletion of forests and minerals, and air and water pollution. Novel indicators have also been proposed to measure social welfare more directly, taking into account crime rates, low-income housing, infant mortality, disease and nutrition.

Measuring the performance of the knowledge-based economy may pose a greater challenge. There are systematic obstacles to the creation of intellectual capital accounts to parallel the accounts of conventional fixed capital. At the heart of the knowledge-based economy, knowledge itself is particularly hard to quantify and also to price. We have today only very indirect and partial indicators of growth in the knowledge base itself. An unknown proportion of knowledge is implicit, uncodified and stored only in the minds of individuals. Terrain such as knowledge stocks and flows, knowledge distribution and the relation between knowledge creation and economic performance is still virtually unmapped.

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B. Measuring knowledge

The methodology for measuring GDP and most other macroeconomic indicators is specified by the United Nations System of National Accounts, which are structured around input-output tables that map intersectoral transactions. In the national accounts framework, the gross output of each establishment is measured by its market value and summed across sectors and/or regions. Net output by sector or region is obtained by subtracting out intermediate purchases. National GDP is the sum of

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net outputs across sectors and regions. To the extent that input-output proportions are stable, this double-entry framework translates input statistics into output indicators. Thus employment, strictly speaking an input, can also be interpreted as an indirect indicator of the level of national output.

In the knowledge-based economy, problems emerge with the conceptual framework of the national accounts. Not least is the issue of subsuming knowledge creation into a measurement system designed for traditional goods and services. The pace of change complicates the task of measuring aggregate output and raises questions about the use of input measures as output indicators. Factors which are not sufficiently incorporated into the national accounts framework include qualitative changes in products, the costs of change and rapid product obsolescence.

Knowledge is not a traditional economic input like steel or labour. When traditional inputs are added to the stock of economic resources, the economy grows according to traditional production function “recipes”. For example, more labour can increase GDP by an amount that depends on current labour productivity, or more steel can increase production of autos, housing or tools by predictable amounts according to the current state of the arts. New knowledge, in contrast with steel or labour, affects economic performance by changing the “recipes” themselves – it provides product and process options that were previously unavailable.

While new knowledge will generally increase the economy's potential output, the quantity and quality of its impact are not known in advance. There is no production function, no input-output “recipe” that tells, even approximately, the effect of a “unit” of knowledge on economic performance.

Knowledge, unlike conventional capital goods, has no fixed capacity. Depending on entrepreneurship, competition and other economic circumstances, a given new idea can spark enormous change, modest change or no change at all. Increased resources devoted to knowledge creation are likely to augment economic potential, but little is known as to how or how much. Thus the relationship between inputs, knowledge and subsequent outputs are hard to summarise in a standard production function for knowledge.

It is also difficult to stabilise the price of knowledge by the trial and error discipline of repeated transactions in the market. There are no company knowledge records nor census of knowledge creation or exchange. In the absence of knowledge markets, there is a lack of the systematic price information that is required to combine individual knowledge transactions into broader aggregates comparable to traditional economic statistics. In knowledge exchanges, a purchaser has to gauge the value of new information without knowing exactly what it is he is to buy. New knowledge creation is not necessarily a net addition to the economically relevant knowledge stock, since it may render old knowledge obsolete.

There are thus four principal reasons why knowledge indicators, however carefully constructed, cannot approximate the systematic comprehensiveness of traditional economic indicators:

à there are no stable formulae or “recipes” for translating inputs into knowledge creation into outputs of knowledge;

à inputs into knowledge creation are hard to map because there are no knowledge accounts analogous to the traditional national accounts;

à knowledge lacks a systematic price system that would serve as a basis for aggregating pieces of knowledge that are essentially unique;

 

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à new knowledge creation is not necessarily a net addition to the stock of knowledge, and obsolescence of units of the knowledge stock is not documented.

 

The problem of developing new indicators is itself an indication of the unique character of the knowledge-based economy. Were we faced with trivial modifications to the traditional accounting system, a few add-on measures might suffice. To fully understand the workings of the knowledge-based economy, new economic concepts and measures are required which track phenomena beyond conventional market transactions. In general, improved indicators for the knowledge-based economy are needed for the following tasks:

à measuring knowledge inputs;

à measuring knowledge stocks and flows;

à measuring knowledge outputs;

à measuring knowledge networks; and

à measuring knowledge and learning.

 

C. Measuring knowledge inputs

Students of the knowledge-based economy have to date focused on new knowledge formation or knowledge inputs. The principal knowledge indicators, as collected and standardised by the OECD, are: i) expenditures on research and development (R&D); ii) employment of engineers and technical personnel; iii) patents; and iv) international balances of payments for technology (Figure 4). Some of these activities are classified by sponsorship or source of funding (government and industry) and by sector of performance (government, industry, academia). Major emphasis has been placed on the input measures of R&D expenditures and human resources. Despite significant advances in recent years, these traditional indicators still have a number of shortcomings with respect to mapping the knowledge-based economy.

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Indicators of R&D expenditures show direct efforts to enlarge the knowledge base and inputs into the search for knowledge. Indicators relating to research personnel approximate the amount of problem solving involved in knowledge production. But only a small fraction of all inputs into knowledge creation are attributable to formal R&D expenditures and official research personnel.

Successful R&D draws on ideas from many different sources, including informal professional exchanges, users' experiences and suggestions from the shop floor. In addition, current indicators count formal R&D conducted by the public sector, academia and large manufacturing firms, and tend to understate research expenditures by small firms and service-sector enterprises. As data collection improves, the importance of the services sector to R&D and innovation is only now being fully recognised.

Patents, since they represent ideas themselves, are the closest to direct indicators of knowledge formation; of all the traditional knowledge indicators, patents most directly measure knowledge outputs (rather than inputs). Patent data have certain advantages in that most countries have national patent systems organised on centralised databases, the data cover almost all technological fields, and patent documents contain a large amount of information concerning the invention, technology, inventor, etc. There are several ways to analyse patent data, including categorising patents by geographic area and industrial product group. However, differences in national patenting systems introduce bias which make comparisons difficult. In general, not all new applications of knowledge are patented and not all patents are equally significant. Patents also represent practical applications of specific ideas rather than more general concepts or advances in knowledge.

The technology balance of payments measures international movements of technical knowledge through payments of licensing fees and other direct “purchases” of knowledge, and thus is more appropriately a flow measure than an input measure. But there is no claim that the technology balance of payments measures the full flow of technical knowledge between any two countries. International transfers of knowledge through employment of foreign personnel, consulting services, foreign direct investment or intra-firm transfers are important avenues of diffusion that are not factored into these indicators. International joint ventures and co-operative research agreements are also instrumental in the global diffusion of knowledge.

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D. Measuring knowledge stocks and flows

In order to improve the measurement of the evolution and performance of the knowledge-based economy, indicators are needed of the stocks and flows of knowledge. It is much easier to measure inputs into the production of knowledge than the stock itself and related movements. In the case of traditional economic indicators, the transmission of goods and services from one individual or organisation to another generally involves payment of money, which provides a “tracer”. Knowledge flows often don't involve money at all, so that alternative “markers” must be developed to trace the development and diffusion of knowledge.

Measuring the stock of physical capital available to an economy is an awesome task, so that measuring the stock of knowledge capital would seem almost impossible. Yet measuring knowledge stocks could be based on current science and technology indicators if techniques were developed for dealing with obsolescence. For example, annual R&D inputs could be accumulated for various countries and industries and then amortised using assumptions concerning depreciation rates. In this way, measures of R&D stock relative to production have been used to estimate rates of return to R&D investment. Similarly, stocks of R&D personnel could be estimated based on annual increases in researchers in particular fields, depreciated by data on personnel movements and occupational

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mobility. The patent stock might be approximated using data on use and expiration of periods of exclusive rights.

A more difficult challenge is measuring the flows of knowledge, or the proportion of knowledge stock which enters into the economy during some time period. Two proxy indicators are most frequently used to measure knowledge flows: i) embodied diffusion, or the introduction into production processes of machinery, equipment and components that incorporate new technology; and ii) disembodied diffusion, or the transmission of knowledge, technical expertise or technology in the form of patents, licences or know-how.

Overall flows of embodied knowledge, particularly embodied technology or R&D, can be measured using input-output techniques. Technology flow matrices have been constructed as indicators of inter-industry flows of R&D embodied in intermediate and capital goods. This methodology allows separation of the equipment-embodied technology used by a particular industry into the technology generated by the industry itself and the technology acquired through purchases. In this way, estimates can be made of the proportions of R&D stock which flow to other industries and the extent to which industries are sources of embodied knowledge inputs (Table 6). Analysis of embodied technology diffusion shows that inter-sectoral flows vary by country. Countries also differ in the amount of embodied technology acquired from abroad vs. that purchased domestically (Sakurai et al., 1996).

Micro-level analyses of embodied knowledge flows focus on the diffusion and use of specific technologies in different sectors of the economy – an area of analysis which needs more standardisation across countries in order to allow international comparisons. Studies attempting to compare the diffusion of microelectronics in OECD countries have encountered severe statistical problems in defining the technologies, gathering data on use and calculating the share of total investment (Vickery, 1987). Existing comparative data are sketchy; they show generally that Japan and Sweden have the most widespread use of advanced manufacturing technologies (AMT), followed by Germany and Italy who have profited from AMT in their motor vehicle and mechanical

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engineering sectors. Industry in the United States uses relatively more of other types of computer-based engineering applications (OECD, 1995b).

More is known about technology diffusion patterns in individual countries. Canadian surveys, for example, have asked manufacturing firms about their use of 22 advanced manufacturing technologies, including computer-aided design and engineering (CAD/CAE), computer integrated manufacturing (CIM), flexible manufacturing systems, robotics, automated inspection equipment and artificial intelligence systems. Approximately 48 per cent of Canadian firms use these technologies, mostly in the area of inspection and communications. The attempt to relate technology use to performance showed that technology-using firms tended to have higher labour productivity and to pay higher wages than non-users (Baldwin et al., 1995).

Information technology indicators are being developed which focus on the diffusion and use of information technologies – computers, software, networks – by businesses and households. These measures of technology flows, and factors facilitating and impeding such flows, such as pricing, give an indication of the rapid growth of the information society. For example, the OECD is compiling indicators of the number of personal computers, CD-ROMs, fax machines and modems per household in the OECD countries. Data show that the use of personal computers has more than doubled in the last decade, with about 37 per cent of US households having computers compared to 24 per cent in the United Kingdom and 12 per cent in Japan (Table 7).

The knowledge-based economy is an interactive economy at both the national and international levels as illustrated by emerging indicators of computer and communications network infrastructure. Such measures show the ratio of households and businesses with outside computer linkages, cable connections and satellite services. More work is needed on indicators by country and region of the development of the Internet, the world-wide web of computer networks; these include host penetration, network connections, leased line business access, dial-up services and price baskets.

Growth in the number of computers hooked to the Internet has been phenomenal – from 1 000 in 1984 to 100 000 in 1989 to over 4.8 million in 1995. It is estimated that the number of Internet users (as to official host connections) exceeded 30 million in 1995 (OECD, 1995b).

Flows of disembodied knowledge are most often measured through citation analysis. In scholarly journals and patent applications, it is the practice that users of knowledge and ideas cite their

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sources. This makes it possible to map the interconnections among ideas in specialised areas. For example, the Science Citation Index provides a database for exploring inter- and intra-disciplinary flows of knowledge in the realm of basic research. Attempts have been made to map the interdependence of scientific ideas using a citation index (Small and Garfield, 1985; Leontief, 1993).

In the future, computer capabilities may make it possible to scan and analyse enormous volumes of text, flagging complex similarities and differences and enabling us to identify knowledge flows beyond the areas where formal citation is practised. Others have traced the linkages among areas of applied technical knowledge through patent citations, which are considered carriers of the R&D performed in the originating industry (Table 8).

Based on a concordance of US patent classes and related research, input-output matrices have been constructed of US industry with the rows being the generating industry, the columns the user industry and the diagonal elements the intramural use of process technology. The patent data show that about 75 per cent of industrial R&D flowed to users outside the originating industry (Scherer, 1989).

Similarly, improved data on international patent citations can help track technology flows on a global basis as could further refinements of technology balance of payments measures. But while the amount of knowledge subject to formal citation requirements includes the entire content of scientific literature and all patented ideas, these areas are only a limited part of the modern economy's knowledge base.

E. Measuring knowledge outputs

The standard R&D-related measures do not necessarily show successful implementation or the amount and quality of outputs. Nevertheless, these input and flow indicators form the starting point for measuring knowledge outputs and for gauging social and private rates of return to knowledge investments. Rough indicators have been developed which translate certain knowledge inputs into knowledge outputs in order to describe and compare the economic performance of countries. These measures tend to categorise industrial sectors or parts of the workforce as more or less intensive in R&D, knowledge or information. The measures are based on the assumption that certain knowledge-intensive sectors play a key role in the long-run performance of countries by producing spill-over benefits, providing high-skill and high-wage employment and generating higher returns to capital and labour.

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For example, the OECD maintains a classification of high-technology, medium-technology and low-technology manufacturing sectors based on their relative R&D expenditures or R&D intensity (ratio of R&D expenditures to gross output). Computers, communications, semiconductors, pharmaceuticals and aerospace are among the high-technology and high-growth OECD sectors and are estimated to account for about 20 per cent of manufacturing production. Output, employment and trade profiles can be drawn for countries, based on the relative role of their high-, medium- and low-technology sectors. However, current indicators of R&D intensity are now confined to manufacturing sectors and have not been developed for the fast-growing service portion of OECD economies. Nor do these indicators take into account R&D which may be purchased from other industrial sectors, either embodied in new equipment and inputs or disembodied in the form of patents and licences. More complete indicators of total R&D intensity, including both direct R&D efforts and acquired R&D, need to be developed (Table 9).

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In a similar vein, early studies in the United States constructed a statistical profile of a group of industries collectively dubbed the knowledge industries, essentially education, communications media, computers and information services. These knowledge industries were found to account for some 29 per cent of GNP and 32 per cent of the workforce in the United States in 1958 (Machlup, 1962). A later study showed that the proportion of knowledge production in the (adjusted) GNP increased from 29 per cent in 1958 to 34 per cent in 1980 (Rubin and Huber, 1984). A US government study included a similar list of sectors and added a secondary information sector which provided inputs to the manufacturing process for non-information products; the entire information sector was estimated to account for over 46 per cent of GNP in 1974, updated to 49 per cent in 1981 (US Department of Commerce, 1977).

A related methodological approach is to use employment and occupational data to categorise jobs according to their R&D, knowledge or information content. One early study used occupational classifications to assign jobs an informational component; information workers included those in the primary information sector, a large portion of the public bureaucracy and a few in remaining sectors.

According to this study, information activities accounted for 47 per cent of GNP in the United States in 1967 (Porat, 1977). Recent Canadian studies have measured the knowledge-intensity of the manufacturing and services sectors by the proportion of total weeks worked in an industry by workers with university degrees. High-knowledge sectors include electronic products, health services and business services, which were found to have expanded since the early 1970s while output in medium and low-knowledge industries has declined (Gera and Mang, 1995).

Occupational data has been used to estimate the proportion of economic effort devoted to creating, implementing and administering change. One study finds a variation among sectors in the proportion of non-production workers in total employment, ranging from as high as 85 per cent in sectors normally seen as high-technology to 20 per cent or less in slower-growth, more traditional industries (Carter, 1994). There appears to be a close connection between the proportion of nonproduction workers and the rate of change in a sector; the major function of non-production workers may be to create or react to change. In these sectors, more workers are engaged in the direct search for new products and processes, in implementing new technology on the shop floor and in opening new markets and reshaping organisations to accommodate changes in production. As a result, a growing proportion of costs are most likely the costs of change rather than the costs of production.

Indicators are needed which go beyond measuring R&D and knowledge intensity to assessing

social and private rates of return (Table 10). Rates of return are generally estimated by computing the benefits (including discounted future benefits) vs. the costs of innovation. For example, early studies of the agricultural sector showed that public research was undervalued and that private investment did not naturally respond to the prospect of large returns to scientific research. One analysis estimated that social returns of 700 per cent had been realised from US$2 million in public and private investments in the development of hybrid corn from 1910-55 (Griliches, 1958). In another, the median private return to the innovations studied was 25 per cent, while the median social rate of return was 56 per cent (Mansfield et al., 1977). A recent review of macro-level econometric studies of the United States concluded that the average rate of return to an innovation is between 20 and 30 per cent, while the social rate of return is closer to 50 per cent (Nadiri, 1993).

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The importance of both innovation and technology for productivity growth and long-term economic growth is poorly understood; indicators are needed which capture the impacts of technological progress on the economy and employment. Measuring rates of return to R&D may be particularly challenging in the services sector where productivity is especially difficult to measure.

Regression analysis can be used to estimate the returns to R&D in terms of total factor productivity growth. This is being attempted for both the manufacturing and services sectors and for performed and acquired (or embodied) R&D. On average, across ten OECD countries, the estimated rate of return of embodied R&D in terms of manufacturing productivity growth has been estimated at 15 per cent and in the services sector at over 100 per cent in the 1980s, illustrating the importance of technology diffusion (Sakurai et al., 1996).

Indicators are also being developed of rates of return to R&D expenditures and acquisitions at the firm- or micro-level. In one study, the top R&D executives of major American firms were polled about the proportion of the firm's new products and processes that could not have been developed (without substantial delay) in the absence of academic research (Table 11). Extrapolating the results from this survey to the academic research investment and returns from new products and processes, a social rate of return of 28 per cent was calculated (Mansfield, 1991). Measuring financial return to a firm's own R&D involves assessing the fraction of sales derived from new products and estimates of cost savings from new process developments. Other measures are the projected future sales and income from R&D projects in the pipeline; customer or consumer evaluation of product quality and reliability; estimates of the effectiveness of the transfer of new technology to manufacturing lines; and percentage of research project outcomes published in technical reports (Tipping et al., 1995).

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