Measuring Knowledge and Its Economic Effects: the Role of Official Statistics

Advancing Knowledge and the Knowledge Economy

National Academies of Science, WashingtonD.C.January 10-11, 2005

Measuring Knowledge and its Economic Effects: The Role of Official Statistics

Fred Gault

Statistics Canada

Abstract

Evidence-based policy requires timely and credible information. Statistical offices have provided such information for decades in support of fiscal, employment and industrial policies, but now, in the 21st century, the need is for new information on the knowledge products and processes that drive the economy.

This paper examines established indicators of knowledge creation (research and development), transmission (intellectual property (IP) commercialization, spin-off firms, and human resource mobility), and use (innovation and adoption of practices and technologies). It argues for moving beyond indicators of activities to the measurement of linkages between business, governments, and universities. Such indicators include measures of co-publication, commercialization of IP, foreign funding of R&D, and sources of ideas, practices and technologies for innovation. Measuring both activities and linkages is necessary for the understanding of a dynamic economic system. However, it is the measurement of the economic and social outcomes of the activities and linkages that is key to evidence-based policy making.

Indicators of knowledge creation, transmission and use are reviewed, leading to a discussion of measures of economic and social outcomes, and of gaps in both measurement and policy.

January 21, 2005

Science, Innovation and

Electronic Information Division

Statistics Canada

Ottawa K1A 0T6

CANADA

1.Introduction

This paper is about measuring knowledge and its economic effects, and the role played in this by official statistics. Knowledge is pervasive, and more and more consumption involves products of the mind, which have economic and social consequences. The paper focuses on the generation, transmission and use of knowledge, the indicators used to describe these activities, and the economic effects that they produce.

With the information, communication technology (ICT) infrastructure in place, electronic products can be traded globally and material goods can be purchased electronically from anywhere. These products embody knowledge, some more than others, and their production and trade can be monitored, using the existing statistical infrastructure, with some additional assumptions. These statistics can contribute to discussions of the knowledge-based economy (KBE).

The paper distinguishes between the KBE and ‘knowledge’ and goes on to discuss measures of knowledge activities, linkages and outcomes before placing them in an economic and social context. Some time is spent on the activity of learning as without learning through education, training, and doing, knowledge cannot flow and without flow it cannot have an economic effect. The paper ends with a discussion of the knowledge system, which consists of institutional actors engaged in the generation, transmission and use of knowledge and identifies gaps in the statistics that describe the system and the implication for evidence-base policy.

2.The KBE and Knowledge

The KBE

Before looking at the measurement of knowledge and its economic effects, the economic analysis of the KBE is examined briefly and compared with other special frameworks.

To describe the KBE, some industries can be identified as knowledge intensive and once that is done, all of the statistics available in the system of national accounts (SNA) can be made available as a special aggregation. Special aggregations have been done for some years for the energy and the tourism sectors and their definitions are found in annexes to the International Standard Industrial Classification of All Economic Activities, revision 3 (ISIC.rev.3) (UN 1990). More recently a definition of the Information and Communication Technology (ICT) sector has been added to ISIC.rev.3.1 (UN 2002), and these aggregations of four digit ISIC classes allow analysts to track the behaviour of these sectors and to compare a sector with other sectors, or four digit classes. As a concrete example, Statistics Canada publishes monthly GDP figures for the ICT sector (Statistics Canada 2004) using this aggregation.

The classification of industries as knowledge intensive, or as high-, medium- and low-tech, based on R&D intensity (Hatzichronoglou 1997), is not without its problems as agreement has to be reached on the assumptions needed to do this. Once industries are classified, firms in knowledge-intensive industries may produce products that are not knowledge intensive, with the converse being true for firms in non-knowledge intensive industries.

If the concept of the KBE is to be taken further, there must be a classification of knowledge-based products (goods or services). If there is such a classification, trade figures can be produced, and studies done of the economic and social impacts of the acquisition and diffusion of knowledge-based products. This has been done for the ICT products by the Working Party on Indicators for the Information Society (WPIIS) OECD (2002b), andHatzichronoglou (1997) has done it for high-, medium-, and low-technology. However, there is no equivalent to these internationally agreed definitions for knowledge-based industries, or products, or agreement on the economic and social surveys required to collect information on their production and use.

Knowledge

Measuring knowledge itself is more challenging, if not impossible (Foray and Gault:18). There is no unit of knowledge that corresponds to a currency unit in the SNA and there is nothing comparable to concepts of current and constant currency units which support comparisons of the economic system over time. There is also nothing comparable to purchasing power parities (PPP) that support comparisons across space.

Knowledge is different from conventional economic products. It can be sold or given away, but it is still retained by its original owner, unlike a tangible good (a brick), or an intangible good (music, text, or video on a medium). Knowledge can, in some cases, be disseminated instantaneously (an encryption key), unlike goods which have to be transported, or services which have to be rendered (haircuts, computer system designs, or sessions of financial advice). In other cases, the dissemination is far from instantaneous. It takes months to become proficient in a foreign language or to able to solve a differential equation. It may take an eternity to achieve knowledge of the presence of a supreme being.

Knowledge is a complex and growing in complexity (Hodgson 2000), as is the measurement of knowledge, assuming that measurement is possible. This paper addresses knowledge that can result in economic or social outcomes, and then focuses on economic effects (Stehr 1996). Three kinds of knowledge activity are considered: knowledge generation; transmission; and, use. (Foray and David 1995). In each of these activities, knowledge can be codified and stored in documents, or embodied in a product, or tacit and present in a person, a team, an organization, a region, or a country. Moving knowledge requires the capacity to transmit it, to publish, to demonstrate, to mentor, or to teach. It also requires the capacity to absorb, or to learn, through education and training, and by doing.

Knowledge activities are more encompassing than just those associated with natural sciences and engineering, they include social sciences and humanities activities. This gives rise to a different set of policy issues that go beyond promoting the creation of new knowledge through curiosity driven research. For example, the involvement of living things in research introduces ethical issues, and some activities, such as human cloning or stem cell research raise moral issues. National security can also circumscribe the creation, transmission and use of knowledge about, for example, encryption, the artificial creation of viruses, or applications of nano-devices

Conveying a capacity for action is broader than research activities and their associated social and economic boundaries. Using a taxonomy developed by Lundvall and Johnson (1994), knowledge is conveyed in schools where students learn to ‘know what’, (the density of lead), in universities where they learn to ‘know why’ (the laws of quantum physics), in the workplace where they learn to ‘know how’ (on the job training), and, as they become part of networks, they learn to ‘know who’ (Lundvall 2000).

As this paper is about measurement of knowledge, and its economic effects, examples of knowledge activities, linkages and outcomes, and their related indicators, are presented in order to lead to a discussion of how the indicators could be used in an integrated manner to describe the generation, transmission and use of knowledge and where there are gaps in such an approach.

3.Knowledge Activities

The description of activities starts with formal knowledge generation as a result of research and development (R&D), continues with invention, innovation, adoption and diffusion of practices and technologies, and then moves to the human resources related to all of these activities. Once people are brought into the analysis the measurement problems, and the policy issues, multiply and that leads to the following section on Knowledge Linkages. The order of presentation is not meant to suggest a linear model view of knowledge activities. Each activity can, and does, occur independently, as well as in conjunction with other knowledge activities. In innovation, this is described by the ‘chain-link’ model of Kline and Rosenberg (1986).

Research and Development

This is the creation of new knowledge which may appear as a seminar, a publication, a patent, or as knowledgeable graduate students and researchers. The indicators for R&D are well established (OECD 2002) and include expenditure on the funding and performance of R&D and the human resources allocated to the doing of R&D (OECD 2004). With the increasing global nature of R&D, attention is turning to the international payments and receipts for R&D services (OECD 1990) and the role of networks, alliances and partnerships in the doing of R&D (Hagadoorn 2001).

Invention

Inventions can be an outcome of R&D, a formal process of knowledge generation, they can result from learning by doing in a production or delivery environment, and they can be influenced by suppliers and clients. Inventions may be protected by intellectual property instruments, such as a patent, a trademark, or a copyright, or by trade secrecy. Patent indicators are produced by the OECD (2004). Guidelines for developing and using patent indicators are also provided by the OECD (1994)

Innovation

For the last 20 years technological innovation has been regarded as the bringing of new or significantly improved products (goods or services) to market or the introduction of new or significantly improved processes for the production or delivery of products. This can be regarded as the use of knowledge, both internal and external to the firm, to create value. Guidelines on how to measure the activity of innovation were codified in 1992 as the Oslo Manual (OECD 1992) and revised in 1997 (OECD/Eurostat 1997). The indicators, resulting from three rounds of the European Community Innovation Survey (CIS), are published by the EU (European Communities 2004) and by individual governments, as are indicators derived from surveys in non-EU countries (Gault 2003). They include the propensity to innovate as well as a number of indicators related to sources of information, outcomes, use of intellectual property, and barriers to innovation.

What has become clear over the last decade of formal measurement of innovation in many countries is the importance of organizational structures and management practices as well as the development of new and existing markets to the innovation process. This would have been no surprise to Schumpeter (1947), but it has taken some time for the empirical world to move from technological innovation rooted in natural science and engineering to a broader concept that includes knowledge drawn from the social sciences and humanities. The most recent revision of the OECD/Eurostat Oslo Manual incorporates these changes and will be released in 2005. This will lead to new indicators of innovation which will show how knowledge from different sources combines to add value to the firm and to have impact on businesses and people.

Use of Technologies and Practices

Innovation is classified according to its novelty. It can be a world first, a market first, or a firm first. This means that firms that adopt new technologies and practices can lay some claim to a firm first innovation. Indicators of the adoption and diffusion of technologies and practices are guides to how the economy and society are changing and to the impacts resulting from such change.

There are many indicators of the activity of adopting technologies and practices and of related activities, much as is the case for the activity of innovation. The OECD publishes indicators of the use of biotechnologies, information and communications technologies (ICTs), and of nano technologies (OECD 2003, 2002a) and of knowledge management practices (Foray and Gault, 2003)

These indicators illustrate the use of knowledge by the firm to adopt new processes in order to do better what it does.

Human Resource Development

A key to all of these activities is the human element. It is the person or, more likely, the team (Prusak 2001), that changes behaviour and creates additional value. The question is how the knowledge held by the people involved is measured. One way is to identify the percentage of the labour force with particular educational attainments on the assumption that the higher the attainment, the more likely the people are to be sources of knowledge and to be able to capture knowledge for value creation. This approach is used in the European Innovation Scoreboard (EIS) (Commission of the European Communities 2004a) which records the percentage of the labour force in the Eurostat Labour Force Survey in the age range 25-64 with tertiary education.

An attempt at codifying measures of the characteristics of people in science and technology led to the Canberra Manual (OECD/Eurostat 1995) which focussed on level of education and on occupation. This has proved difficult to apply as, outside of a population census, there are few surveys that collect data from a large enough sample to support analysis of the characteristics of people by industry, level of education, and occupation. An alternative approach, used in the EIS, is to record the percentage of science and technology graduates in the population with an age range of 20 to 29. While the characteristics of the highly qualified personnel in the population and the labour force are important indicators of the stock, the policy preoccupation has been shifting to mobility.

Mobility of people is a flow of embodied knowledge, and of absorptive capacity. This is addressed in the next section on Linkages. The attraction, and the retention, of the highly qualified are becoming more important as retirements increase because of the age distribution of the labour force, and there is concern that the production of newly qualified graduates by the higher education sector is not able to deal with the demand and that immigration cannot fill the gap as it has in the past in some countries. The market for the highly qualified has moved from overlapping local markets to a global market and this is especially true for academics. The question is how to capture this in official statistics and that is discussed in the next section.

4.Knowledge Linkages

Many of the knowledge activities in the previous section are covered by official statistics, although there are still gaps. The same can not be said for knowledge linkages which tie together knowledge activities. These ties can be bilateral one or two-way flows, or they can be multi-lateral and multi-directional. Understanding them, and providing robust indicators is another challenge which must be met if there is to be a beginning of an understanding of how this dynamic system works.

This section is divided into two subsections, one on sources of knowledge and one on networks of knowledge

Sources of Knowledge

The R&D department in a firm, a government laboratory or a university department can use and generate knowledge but for it to enable the creation of value, it must be transferred to the market directly, or indirectly. There are several ways of doing this

Intellectual property can be transferred within the firm that has generated it, licensed to an affiliated or unaffiliated firm, or to a new ‘spin-off’ firm established to develop the knowledge in the market place. In all of these cases, the knowledge flow can be one or two-way. If it is two-way, the market experience can be fed back to the knowledge creators and the knowledge can develop in response. The role of the client as a source of ideas has been developed by von Hippel (1988, 1998), but they are not the only source.

Suppliers of materials, equipment and people may be sources of ideas for knowledge activities in an organization, and ideas for doing new things do not just come from the R&D department. They may come from the production unit, if management is aware enough to harvest them, or from the providers of finance who wish to grow their investment.

In the development of official statistics on innovation and the use of technologies and practices, questions are added to surveys to identify the sources of ideas for the activity and the answers illuminate policy discussions about the purpose of government laboratories, or the role of research in universities.

Little happens without money and large firms are able to fund new activities from retained earnings or through debt. A small firm in the service sector will have limited revenue and no assets beyond the minds of its people. For it to bring new products to market or to introduce a new process for production or delivery, it needs help and that is a role for the angel investor or the venture capitalist. Financial organizations, especially if they have been working in the industry, bring to the firm knowledge about pitfalls and opportunities and the transfer of that knowledge to the firm is key to its success.