Table of Contents

The Handbook of Evolutionary Economic Geography

The Handbook of Evolutionary Economic Geography

Elgar original reference

Edited by Ron Boschma and Ron Martin

This wide-ranging Handbook is the first major compilation of the theoretical and empirical research that is forging the new and exciting paradigm of evolutionary economic geography.

Chapter 17: Growth, Development and Structural Change of Innovator Networks: The Case of Jena

Uwe Cantner and Holger Graf

Subjects: economics and finance, evolutionary economics, regional economics, geography, economic geography, urban and regional studies, regional economics


Uwe Cantner and Holger Graf 1. Introduction The notion of collective invention has been introduced in the literature by Allen (1983) who provides evidence from the nineteenth-century iron and steel industry, where innovative success was the result of the cooperative activities of several, different actors. Anecdotal evidence such as Allen’s or the various studies on Silicon Valley (e.g. Saxenian, 1994) or other success stories of regional innovation (e.g. Braczyk et al., 1998; Cooke and Morgan, 1994, Keeble et al., 1999) was enriched by studies on the regional dimension of knowledge flows (e.g. Jaffe et al., 1993). Insights into the process of innovation at the firm level (e.g. Kline and Rosenberg, 1986) or the national level (e.g. Lundvall, 1992; Nelson, 1993) strengthened the view that the functioning of innovation systems is the basis for innovation-based economic success. Inspired by the seminal volumes by Lundvall (1992), Nelson (1993), and Braczyk et al. (1998), a large number of investigations build on the systems view in relating innovative activities and success.1 As the systemic view of innovation is inherently dynamic and deeply grounded in evolutionary theorizing, empirical studies that take the systems view seriously should have two ingredients: heterogeneous, interacting actors and the dynamics of these interactions. Unfortunately, many of the empirical studies usually fail to account for both of these ingredients, because of the unavailability of appropriate data. In studies based on aggregate data it is easier to observe the dynamics of the system as many variables are available as time series. Many...

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