Uwe Cantner

Classifying technology policy from an evolutionary perspective

Asked for the most important driving forces of economic development, most economists do not hesitate to state, that it is technical progress which is the main source of quanti-ta-ti-ve and qualitative economic development generated in National Systems of Innovation (NSI). To classify and analyze NSIs the con-cepts of mission- and diffusion-oriented policy designs were introduced. Although, we suppose this taxonomy to be well suited to analyze techno-logy policy, it seems to us in its basic formulation somewhat crude, especially with respect to the supposed characteristics to assign a specific innovation system to the one or the other policy design. To surmount these shortcomings we develop a new classificatory scheme building on a questionnaire approach and suggesting four categories to spread out between the tech-nology and the economic side. This scheme allows for deeper insights and more evident com-pa-risons of different NSIs.

Change, transformation and development

The general theme of the 8th International Joseph A. Schumpeter Society Conference, held at the University of Manchester was the exploration of economic and social dynamics in relation to the innovation process and its outcomes, broadly defined. This theme is very firmly rooted in the Schumpeterian tradition in which an economic perspective is mutually embedded in a wider awareness of the role of other disciplines. Indeed since Schumpeter’s time, the degree of specialisation within the social sciences has risen many fold, new sub disciplines continue to emerge, highly specialised theoretical tools and empirical methods continue to be developed, and new fields for the study of management and business overlap with the more traditional social sciences. Consequently, there is a need for connecting principles to offset the dangers of intellectual fragmentation. Evolutionary economics and evolutionary analysis more generally, certainly provide some of these connecting principles although much of this field remains to be developed. The central ideas of variation, selection and development, applied within the instituted frame of modern capitalism, provide a powerful set of concepts to consider the interaction between economic and other forces and to focus attention on its propensity to change and transform itself from within.

Total factor productivity, the East Asian miracle, and the world production frontier.

Total Factor Productivity, the East Asian Miracle, and the World Production Frontier. — The post WWII growth of the East Asian Tiger states has stimulated the discussion about its determinants. Young and Krugman hold that high capital accumulation rather than gains in efficiency or technological progress has spurred growth. Nelson and Pack, however, have recently criticized the methods of measuring technological progress. Applying the nonparametric approach to frontier production function determination and the Malmquist index of total factor productivity change, the authors take up this criticism. They calculate productivity indicators for a sample of 18 American, Asian, and European countries. For the Tiger states, their results confirm that capital accumulation was the main source of growth in 1960-1973, whereas they find evidence for an increasing importance of efficiency improvements for the growth in 1973-1990.ZusammenfassungTotale FaktorproduktivitÄt, das ostasiatische Wunder und die Weltproduktionsgrenze. — Die Wachstumserfahrung in den so genannten ostasiatischen Tiger-Staaten nach dem Zweiten Weltkrieg, gekennzeichnet durch hohe Wachstumsraten, hat die Diskussion über die verursachenden Faktoren stimuliert. Alwyn Young und Paul Krugman vertraten die Auffassung, dass Kapitalakkumulation anstatt der Effizienzverbesserungen oder des technologischen Fortschritts das Wachstum in diesen LÄndern angetrieben hat. Richard Nelson und Howard Pack haben dagegen kürzlich die Methoden zur Messung des technologischen Fortschritts kritisiert. Durch die Anwendung des nichtparametrischen Ansatzes zur Bestimmung von Randproduktionsfunktionen und des Malmquist-Index der totalen FaktorproduktivitÄt tragen wir dieser Kritik Rechnung. Damit berechnen wir ProduktivitÄtsindikatoren für eine Stichprobe von 18 LÄndern Amerikas, Asiens und Europas. Im Fall der Tiger-Staaten bestÄtigen unsere Resultate Krugmans Hypothese für die Jahre 1960-1973. WÄhrend der Jahre 1973-1990 finden wir jedoch Evidenz für eine gestiegene Bedeutung von Effizienzverbesserungen für das Wachstum in diesen LÄndern.

Knowledge and Creative Destruction over the Industry Life Cycle: The Case of the German Automobile Industry

This paper investigates how the survival of firms over the industry life cycle is affected by different kinds of knowledge, namely post-entry experience, pre-entry experience, and knowledge acquired by innovative activity. Therefore, a statistical survival analysis is performed for the German automobile industry over the period 1886- 1939 which applies a new approach that links instrumental variable estimation with the Cox regression. The main results are that all three knowledge components exert a significantly positive effect on the survival of firms. Furthermore, innovative activity is able to compensate for lacking pre-entry or post-entry experience, completely in accord with Schumpeterian creative destruction.

Heterogeneity and Evolutionary Change - Empirical Conception, Findings and Unresolved Issues

In this position paper we deal with the conception of heterogeneity as both the force and the result of evolutionary change. We ask, how this heterogeneity can be measured empirically and how we can get a measure which allows to get a broad comparable empirical account especially on several levels of aggregation. Based on this discussion we suggest that for several questions the measures of total factor productivity (TFP) and local changes of TFP seem to be acceptable candidates for measuring heterogeneity and its dynamics. Examples out of a number of empirical investigations applying this measures show how interesting empirical facts about evolutionary change on several levels of aggregation can be detected. The paper concludes by raising a number of unresolved issues mainly related to the question about the relationship between evolutionary dynamics on several levels of aggregation.

Economic evolution, learning, and complexity

Oligopoly and Learning.- Industry Studies.- Econometric and Empirical Techniques.- Growth, Human Capital and Innovation.- Governmental Learning and Policy.

Innovation Networks: Measurement, Performance and Regional Dimensions

The overarching research theme in this special issue is the relationship between research competencies, knowledge flows and innovation, taking a systemic or network perspective on innovation processes. It is well known that innovation is increasingly based on interaction between a variety of actors, be it through formal agreements such as cooperation in R&D, through informal collaboration or via other channels such as job mobility. Much of our knowledge on the benefits of interaction is based on studies of bilateral agreements such as joint ventures or cooperation in R&D (see Hagedoorn, 2002 for an overview), amended by findings on the existence and the effects of knowledge spillovers (Jaffe et al., 1993). However, these important contributions cannot account for the structural properties of such knowledge networks and their economic relevance on either the individual or system level. While substantial advancements have been made in relating network position to innovative or economic success on the individual level (Powell et al., 1996; Ahuja, 2000) the economic literature especially regarding the system level (regional or sectoral) is still in its infancy. Even though the number of studies taking such a network perspective is increasing, we believe that there is substantial room for important further research. Especially, this applies to the relationship between actors’ position within regional and/or sectoral networks and innovative success as well as to the influence of network structure on the innovative success of the whole system. This raises the question of how to measure performance when moving from the individual to the system level. Related to that, there are further specific challenges in the analysis of networks. In the existing literature, firm networks are mostly reconstructed

Competition in Product Design: An Experiment Exploring Innovation Behavior

We experimentally investigate competition in innovation in a patent race scenario. Pairs of subjects compete as seller firms on a duopoly market, engaging in risky search investments. Successful innovation is rewarded through temporary monopoly rents. Throughout the interaction, subjects receive feedback on own and others search success and profit margin. Partitioning subjects into subgroups of investor types reveals that the majority of subjects condition investments on the degree of competition as measured by sales shares, while for others no correlation is ascertained. Heterogeneity in individual risk attitudes and differing experiences with related search tasks may explain this finding.

Technological leadership and variety: A Data Envelopment Analysis for the French machinery industry

We investigate structure and structural change within the French machinery industry from 1984–1991 in order to detect the apparent technology leaders and to get an account of the technological variety within the sector. The theoretical background of the paper is found in modern approaches to the economics of innovation and technology, where the very nature of technological knowledge and the local character of technological change are seen as a fundamental reason for the use of different technologies and for the different performances of firms. We apply a procedure that allows us to take into account such different performances and variety, Data Envelopment Analysis (DEA). As one major result, we find several best-practice technologies as well as a measure of technical inefficiency, allowing us to classify firms with respect to their relative technical performance. Moreover, technology leaders can be assigned to specific “technology fields”. The change within and between these fields over time is investigated.

Introduction: Structure and Dynamics of Innovation Networks

The innovation system approach places interaction between heterogeneous actors at the core of a learning economy (LUNDVALL, 1992). Studies on national innovation systems strengthen the view that the functioning of innovation systems is the basis for innovation-based economic development (NELSON, 1993). Success stories of regional innovation systems (COOKE and MORGAN, 1994; BRACZYK et al., 1998; KEEBLE et al., 1999) – in particular the prominent example of Silicon Valley (for example, SAXENIAN, 1994) – highlight the beneficial effects of regional interaction and this argument has been supported by findings on the geographic dimension of knowledge flows (for example, JAFFE et al., 1993). However, recent attempts to identify channels of knowledge spillovers challenge the argument by JAFFE et al. (1993) that knowledge flows are geographically bounded. THOMPSON and FOX-KEAN (2005), for example, find no intra-national localization effects when using a finer level of technological aggregation in their sample. BRESCHI and LISSONI (2009), by introducing a measure of social proximity between inventors to the ‘experiment’ by JAFFE et al. (1993), find that social proximity explains most of the identified spillovers. Here geographical proximity merely facilitates face-toface contacts, but it is certainly not a sufficient condition for knowledge transmission (PONDS et al., 2007; D’ESTE et al., 2012). This finding is supported by other studies stressing the importance of labour mobility for knowledge flows (ZUCKER et al., 1998; ALMEIDA and KOGUT, 1999; MØEN, 2005). For a better understanding of knowledge spillovers these findings suggest to complement and enrich the geographical dimension by a closer look at social aspects when analysing how actors establish relations to others and position themselves within social networks related to innovation, so-called innovation networks (for example, POWELL et al., 1996; CANTNER and GRAF, 2006). Following this route, (social) networks are considered a vital part of an innovation system and meanwhile an increasing number of studies on networks in a regional context are being observed. For example, networks of co-invention help one understand the evolution of local clusters (FLEMING et al., 2007; FLEMING and FRENKEN, 2007) or are used to investigate university– industry relations (BALCONI et al., 2004). The structure and characteristics of clusters and regional (inventor and innovator) networks are explored in a number of recent studies that focus on a diverse sets of cases, data and empirical methods (for overviews, see TER WAL and BOSCHMA, 2009; or CANTNER and GRAF, 2011). Along with the valuable insights from these studies, new questions opened up that are related to certain characteristic structures of innovation networks and to the dynamics driving them. For example, successful regions are observed where actors frequently cooperate on a formal or informal basis in knowledge exchange and research and development (R&D) with co-located but also with external actors, while it is yet not clear how the network structures resulting from these interactions evolve or why they differ between regions. The factors responsible for the dynamics are until now not satisfactorily identified. This theme issue has collected three contributions that focus on two issues. The first regards the dynamics of regional networks in terms of actor linkages; and the second is concerned with the structural dimensions related to the complementarities and balance of intra versus extra-local linkages.