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The impact of classes of innovators on technology, financial fragility, and economic growth

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  • Stefania Vitali
  • Gabriele Tedeschi
  • Mauro Gallegati

Abstract

In this article, we study innovation processes and technological change in an agent-based model. By including a behavioral switching among heterogeneous innovative firms, the model is able to replicate, via simulations, well-known industrial dynamic and growth type stylized facts. The main original element of the model is that firms are allowed to endogenously change among three different classes, namely, single innovators, collaborative innovators, and imitators. Moreover, our analysis focuses on the impact of these three innovation categories on micro, meso, and macro aggregates. We find that collaborative companies are those having the highest positive impact on the economic system. Furthermore, we have paid particular attention to the role of credit market in promoting smart growth. For this purpose, we analyze the role of banks as sources of external funds for innovative entrepreneurs. Our results suggest a trade-off between short-term profit maximization and long-term efficiency, which prevents banks to foster investment in R&D and technological progress. The model is then used to study the effect that different innovation policies have on macroeconomic performance. Copyright 2013 The Author 2013. Published by Oxford University Press on behalf of Associazione ICC. All rights reserved., Oxford University Press.

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  • Stefania Vitali & Gabriele Tedeschi & Mauro Gallegati, 2013. "The impact of classes of innovators on technology, financial fragility, and economic growth," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 22(4), pages 1069-1091, August.
    • Handle: RePEc:oup:indcch:v:22:y:2013:i:4:p:1069-1091
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    Cited by:

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    2. D’Orazio, Paola & Valente, Marco, 2019. "The role of finance in environmental innovation diffusion: An evolutionary modeling approach," Journal of Economic Behavior & Organization, Elsevier, vol. 162(C), pages 417-439.
    3. Caiani, Alessandro & Russo, Alberto & Gallegati, Mauro, 2016. "Does Inequality Hamper Innovation and Growth?," MPRA Paper 71864, University Library of Munich, Germany.
    4. Fierro, Luca Eduardo & Caiani, Alessandro & Russo, Alberto, 2022. "Automation, Job Polarisation, and Structural Change," Journal of Economic Behavior & Organization, Elsevier, vol. 200(C), pages 499-535.
    5. Gabriele Tedeschi & Stefania Vitali & Mauro Gallegati, 2014. "The dynamic of innovation networks: a switching model on technological change," Journal of Evolutionary Economics, Springer, vol. 24(4), pages 817-834, September.
    6. Alberto Russo, 2014. "A Stochastic Model of Wealth Accumulation with Class Division," Metroeconomica, Wiley Blackwell, vol. 65(1), pages 1-35, February.
    7. Ruggero Grilli & Gabriele Tedeschi & Mauro Gallegati, 2015. "Markets connectivity and financial contagion," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 10(2), pages 287-304, October.
    8. Alessandro STERLACCHINI, 2012. "Patent Oppositions as Competitive Tools: An Analysis of the Major Players in the European Market of White Goods," Working Papers 374, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    9. Lenzu, Simone & Tedeschi, Gabriele, 2012. "Systemic risk on different interbank network topologies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(18), pages 4331-4341.
    10. Colasante, Annarita, 2016. "Evolution of Cooperation in Public Good Game," MPRA Paper 72577, University Library of Munich, Germany.
    11. Eralba CELA & Tineke FOKKEMA & Elena AMBROSETTI, 2012. "Links Between Transnationalism Integration and Duration of Residence: The Case of eastern European Migrants in Italy," Working Papers 386, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    12. Colasante, Annarita, 2017. "Selection of the distributional rule as an alternative tool to foster cooperation in a Public Good Game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 482-492.

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    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E6 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook
    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights
    • O4 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity

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