Monday, September 25, 2006

Plenary session

• Enriching the indicator base for the economics of knowledge
• Innovation indicators: Any progress since 1996?

Enriching the indicator base for the economics of knowledge

Biographical Note

Professor D .Foray holds the Chair of Economics and Management of Innovation at the Ecole Polytechnique Fédérale de Lausanne (EPFL). He is also the Dean of the Collège du Management de la Technologie (CdM) at EPFL.

Professor Foray is currently member of the expert group "Knowledge for growth" which is a group of prominent economists established to advise the President of the European Commission on R&D policy and economic growth.

Previously he was Research Director at the Centre National de la Recherche Scientifique (CNRS) and Professor at the Institut pour le Management de la Recherche et de l'Innovation (IMRI) of the University of Paris-Dauphine (from 1993 to 2000), and then Principal Analyst at the Organization of Economic Cooperation and Development (OECD) from 2000 to 2004.

D. Foray received his Ph.D in 1984 and his "habilitation" in 1992 from the University Lumière of Lyon. He received the distinction of outstanding research 1995 (médaille du CNRS).

Academic specialties: Economics of innovation and of knowledge, economic and technology policy for the new (knowledge-based) economy.

Abstract

In this paper, I will address the issue of indicators development that provides a broad and deep measurement base on which research in the "economics of knowledge" can prosper. Economics of knowledge is now exactly at the point where it can become a strong empirically disciplined science depending upon whether enough progress can be made on developing the underlying data and ensuing indicators. I will show that the economics of knowledge is at a crossroads by using the development of knowledge management indicators as an example. The quest for a broader and more systematic empirical material relating to knowledge management is proceeding from step one to step four, that is the scientific approach is moving from a high level of abstraction towards linking this abstraction back to practice:

• The economics of knowledge has now assembled enough stylized facts (on learning by doing, weak persistency, information stickiness, importance of codification) so that a case can be made about the private value of knowledge management investments for firms.
• Under the intellectual leadership of Statistics Canada, it has been demonstrated that it is possible to produce aggregate measures about knowledge management, in spite of the great difficulties in collecting data which are rather unconventional for economists.
• Starting from this measurement base, a few economists including Elisabeth Kremp and Jacques Mairesse have tried to answer the big questions: Does knowledge management really matter? Is there any relation between knowledge management intensity, innovation performance and labour productivity?
• Unless the previous questions are answered in the affirmative, there is no point in proceeding toward the fourth step; the usual prescriptions of economics involving the manipulation of incentives and inputs to achieve particular goals.

Although a lot of progress has been made – the economics of knowledge is proceeding quite successfully along this trajectory – its future as a strong empirical discipline is still uncertain. There are, actually, a lot of failures on the market for indicators and the demonstration that the questions are relevant (steps 1 and 3) and aggregate measures are possible (steps 2 and 3) is by no means sufficient to impose internationally, new indicators and therefore routinize the data collection. The challenge is still ahead and there is a need for stronger political commitment as well as stronger interactions between statisticians, economists, policy makers and the business sector to enlarge the scope of empirical material that economists will come to regard as legitimate and perhaps even routine in applied research.

Innovation indicators: Any progress since 1996?

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Biographical Note

Anthony Arundel is a Senior Researcher at the Maastricht Economic Research Institute on Innovation and Technology (UNU-MERIT) of the University of Maastricht. His research focuses on innovation surveys and the use of survey data to develop indicators for policy analysis.

Abstract

Since the first Blue Sky conference in Paris in 1996, we have seen increasing attention paid to the concept of the knowledge economy, a growing awareness that China and India will be competitors not just in low-cost manufacturing but in innovation, and the institutionalization of innovation surveys, particularly in Europe where the Community Innovation Survey (CIS) is now implemented every two years in all EU-25 member states. Under these conditions, one would think that the policy community would have an enlarged array of innovation indicators with which to assess policy and the ability of national innovation systems to respond to the challenges of the knowledge economy and the globalization of innovative activities. Unfortunately, this hasn't happened. The European policy community still relies almost entirely on long-established indicators for R&D, patents and scientists and engineers. These are excellent indicators for measuring creative R&D within the manufacturing sector, but they are insufficient for capturing innovation as a process of diffusion, the development of distributed knowledge bases that are an essential feature of the knowledge economy, and the inexorable increase in the economic importance of the service sectors.

This paper addresses what has gone wrong and proposes solutions. First, the paper examines why the European policy community still focuses on a limited range of indicators and why these indicators are inadequate for current needs. Part of the problem is due to the Lisbon and Barcelona councils' single-minded focus on R&D as the main measure of innovation. This has been a major block to progress that has created five lost years of opportunity to build on the increasing reliability of CIS data to develop robust indicators of innovation as a diffusion process that could complement R&D indicators.

Second, the CIS has contributed far more to academic research on innovation than it has to policy development. Part of the reason is a failure to fully develop and exploit indicators based on the CIS results. In order to address this issue, the recently released micro-aggregated CIS-3 data are used to construct several indicators for non-R&D based innovation, particularly for firms that innovate through diffusion. This requires linking data on innovation expenditures with the different knowledge sources that non-R&D innovators use. These indicators are correlated with a separate set of indicators on 'innovation modes', or how firms innovate, with all analyses conducted separately for the manufacturing and services sectors. The purpose is to uncover differences in the use of distributed knowledge bases by innovative firms that perform R&D versus those that do not.

Third, new indicators must be relevant to policy in order to be adopted by the policy community. The final section of this paper discusses the relevance of new indicators of innovation diffusion to a range of policies currently in use in Europe, including programmes to encourage the commercialization of publicly-funded research, manufacturing extension services, and procurement.