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Innovation flow through social networks: productivity distribution in France and Italy


  • T. Di Matteo


  • T. Aste
  • M. Gallegati


From a detailed empirical analysis of the productivity of non financial firms across several countries and years we show that productivity follows a non-Gaussian distribution with `fat tails' in the large productivity region which are well mimicked by power law behaviors. We discuss how these empirical findings can be linked to a mechanism of exchanges in a social network where firms improve their productivity by direct innovation and/or by imitation of other firm's technological and organizational solutions. The type of network-connectivity determines how fast and how efficiently information can diffuse and how quickly innovation will permeate or behaviors will be imitated. From a model for innovation flow through a complex network we show that the expectation values of the productivity of each firm are proportional to its connectivity in the network of links between firms. The comparison with the empirical distributions in France and Italy reveals that in this model, such a network must be of a scale-free type with a power-law degree distribution in the large connectivity range. Copyright EDP Sciences/Società Italiana di Fisica/Springer-Verlag 2005

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  • T. Di Matteo & T. Aste & M. Gallegati, 2005. "Innovation flow through social networks: productivity distribution in France and Italy," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 47(3), pages 459-466, October.
  • Handle: RePEc:spr:eurphb:v:47:y:2005:i:3:p:459-466
    DOI: 10.1140/epjb/e2005-00332-y

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    References listed on IDEAS

    1. Pammolli, Fabio & Riccaboni, Massimo, 2002. "Technological Regimes and the Growth of Networks: An Empirical Analysis," Small Business Economics, Springer, vol. 19(3), pages 205-215, November.
    2. S. Baranzoni & P. Bianchi & L. Lambertini, 2000. "Multiproduct Firms, Product Differentiation, and Market Structure," Working Papers 368, Dipartimento Scienze Economiche, Universita' di Bologna.
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    Cited by:

    1. Chakrabarti, Anindya S., 2016. "Stochastic Lotka–Volterra equations: A model of lagged diffusion of technology in an interconnected world," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 442(C), pages 214-223.
    2. Chakrabarti, Anindya S., 2015. "Stochastic Lotka-Volterra equations: A model of lagged diffusion of technology in an interconnected world," IIMA Working Papers WP2015-08-05, Indian Institute of Management Ahmedabad, Research and Publication Department.
    3. C. Guilmi & F. Clementi & T. Matteo & M. Gallegati, 2008. "Social networks and labour productivity in Europe: an empirical investigation," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 3(1), pages 43-57, June.
    4. Keiichi Kishi, 2014. "A patentability requirement and industries targeted by R&D," Discussion Papers in Economics and Business 14-27-Rev., Osaka University, Graduate School of Economics and Osaka School of International Public Policy (OSIPP), revised Oct 2014.
    5. Zilibotti, Fabrizio & König, Michael & Lorenz, Jan, 2016. "Innovation vs. imitation and the evolution of productivity distributions," Theoretical Economics, Econometric Society, vol. 11(3), September.
    6. Geraldine Henningsen & Arne Henningsen & Christian Henning, 2015. "Transaction costs and social networks in productivity measurement," Empirical Economics, Springer, vol. 48(1), pages 493-515, February.

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