Do innovations diffuse faster within geographical clusters

Abstract There is considerable evidence to demonstrate that the diffusion of new technologies is spatially variable. This paper argues that externalities promoting the adoption of new technology are stronger at the regional level and depend positively on the proximity of early users. An empirical model of diffusion is built, including variables related to the regional density of adopters and of technologically close firms. Results support the existence of significant regional learning effects on adoption. These effects seem to be stronger at the early stages of diffusion.

[1]  C. Debresson,et al.  Networks of innovators:A review and introduction to the issue , 1991 .

[2]  John B. Meisel,et al.  A dynamic analysis of the adoption of a new technology: the case of optical scanners , 1987 .

[3]  D. Fudenberg,et al.  Preemption and Rent Equalization in the Adoption of New Technology , 1985 .

[4]  Paul L. Joskow,et al.  The Diffusion of New Technologies: Evidence from the Electric Utility Industry , 1990 .

[5]  N. Kiefer.  Economic Duration Data and Hazard Functions , 1988 .

[6]  M. Porter.  The Competitive Advantage Of Nations , 1990 .

[7]  J. Heckman,et al.  Econometric duration analysis , 1984 .

[8]  Ronald Briggs,et al.  The adoption of new technology in the American machinery industry , 1984 .

[9]  Clayton M. Christensen,et al.  Explaining the attacker's advantage: Technological paradigms, organizational dynamics, and the value network , 1995 .

[10]  G. Dosi,et al.  Technical Change and Economic Theory , 1989 .

[11]  A. Thwaites.  Some evidence of regional variations in the introduction and diffusion of industrial products and processes within British manufacturing industry , 1982 .

[12]  S. Davies.  The Diffusion of Process Innovations , 1979 .

[13]  P. Swann,et al.  Do firms in clusters innovate more , 1998 .

[14]  Paul Stoneman,et al.  Rank, Stock, Order And Epidemic Effects In The Diffusion Of New Process Technologies : An Empirical Model , 1990 .

[15]  Hans-Peter Blossfeld,et al.  Techniques of event history modeling : new approaches to causal analysis , 2001 .

[16]  James M. Utterback,et al.  The Effects of Communication on Technological Innovation , 1984 .

[17]  Daniel A. Levinthal,et al.  Innovation and Learning: The Two Faces of R&D , 1989 .

[18]  Nathan Rosenberg,et al.  On Technological Expectations , 1976 .

[19]  D.,et al.  Regression Models and Life-Tables , 2022 .

[20]  R. Baptista.  The Diffusion of Process Innovations: A Selective Review , 1999 .

[21]  M. Feldman,et al.  R&D spillovers and the ge-ography of innovation and production , 1996 .

[22]  Timothy H. Hannan,et al.  Rival Precedence and the Dynamics of Technology Adoption: An Empirical Analysis , 1987 .

[23]  Jennifer F. Reinganum.  On the diffusion of new technology: A game theoretic approach , 1981 .

[24]  E. Mansfield.  Industrial research and technological innovation , 1968 .

[25]  Paul Stoneman,et al.  Order Effects, Perfect Foresight and Intertemporal Price Discrimination , 1985, Recherches économiques de Louvain.

[26]  Everett M. Rogers,et al.  Innovation Diffusion As a Spatial Process , 1967 .

[27]  E. Glaeser,et al.  Growth in Cities , 1991, Journal of Political Economy.

[28]  Paul Stoneman,et al.  RANK, STOCK, ORDER AND EPIDEMIC EFFECTS IN THE DIFFUSION OF NEW PROCESS TECHNOLOGIES: AN EMPIRICAL MODEL , 1993 .

[29]  David F. Midgley,et al.  The effect of network structure in industrial diffusion processes , 1992 .

[30]  Maryann P. Feldman.  The geography of innovation , 1994 .

[31]  Timothy H. Hannan,et al.  The Determinants of Technology Adoption: The Case of the Banking Firm , 1984 .

[32]  W. E. G. Salter,et al.  Productivity and Technical Change. , 1961 .

[33]  Gregory Tassey,et al.  The functions of technology infrastructure in a competitive economy , 1991 .

[34]  Jennifer F. Reinganum.  Market Structure and the Diffusion of New Technology , 1981 .

[35]  S. Davies,et al.  Modelling Regional Patterns of Innovation Diffusion in the UK Metalworking Industries , 1990 .

[36]  J. Heckman,et al.  A Method for Minimizing the Impact of Distributional Assumptions in Econometric Models for Duration Data , 1984 .