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Technological interdependencies predict innovation dynamics

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  • Pichler, Anton
  • Lafond, François
  • Farmer, J. Doyne

Abstract

We propose a simple model where the innovation rate of a technological domain depends on the innovation rate of the technological domains it relies on. Using data on US patents from 1836 to 2017, we make out-of-sample predictions and fond that the predictability of innovation rates can be boosted substantially when network effects are taken into account. In the case where a technology's neighbourhood further innovation rates are known, the average predictability gain is 28% compared to simpler time series model with do not incorporate network effects. Even when nothing is known about the future, we find positive average predictability gains of 20%. The results have important policy implications, suggesting that the effective support of a given technology must take into account the technological ecosystem surrounding the targeted technology.

Suggested Citation

  • Pichler, Anton & Lafond, François & Farmer, J. Doyne, 2020. "Technological interdependencies predict innovation dynamics," INET Oxford Working Papers 2020-04, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.
    • Handle: RePEc:amz:wpaper:2020-04
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    References listed on IDEAS

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    1. Jeff Alstott & Giorgio Triulzi & Bowen Yan & Jianxi Luo, 2017. "Mapping technology space by normalizing patent networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(1), pages 443-479, January.
    2. Hall, Bronwyn H. & Mairesse, Jacques & Mohnen, Pierre, 2010. "Measuring the Returns to R&D," Handbook of the Economics of Innovation, in: Bronwyn H. Hall & Nathan Rosenberg (ed.), Handbook of the Economics of Innovation, edition 1, volume 2, chapter 0, pages 1033-1082, Elsevier.
    3. Aghion, Philippe & Howitt, Peter, 1992. "A Model of Growth through Creative Destruction," Econometrica, Econometric Society, vol. 60(2), pages 323-351, March.
    4. Farmer, J. Doyne & Lafond, François, 2016. "How predictable is technological progress?," Research Policy, Elsevier, vol. 45(3), pages 647-665.
    5. Lorenzo Napolitano & Evangelos Evangelou & Emanuele Pugliese & Paolo Zeppini & Graham Room, 2017. "Technology networks: the autocatalytic origins of innovation," Papers 1708.03511, arXiv.org, revised Apr 2018.
    6. François Lafond & Daniel Kim, 2019. "Long-run dynamics of the U.S. patent classification system," Journal of Evolutionary Economics, Springer, vol. 29(2), pages 631-664, April.
    7. Bronwyn H. Hall & Nathan Rosenberg (ed.), 2010. "Handbook of the Economics of Innovation," Handbook of the Economics of Innovation, Elsevier, edition 1, volume 1, number 1.
    8. Taalbi, Josef, 2020. "Evolution and structure of technological systems - An innovation output network," Research Policy, Elsevier, vol. 49(8).
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    Cited by:

    1. Hötte, Kerstin & Jee, Su Jung, 2022. "Knowledge for a warmer world: A patent analysis of climate change adaptation technologies," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    2. Hötte, Kerstin & Pichler, Anton & Lafond, François, 2021. "The rise of science in low-carbon energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    3. Barbieri, Nicolò & Marzucchi, Alberto & Rizzo, Ugo, 2023. "Green technologies, interdependencies, and policy," Journal of Environmental Economics and Management, Elsevier, vol. 118(C).
    4. Nicolo Barbieri & Alberto Marzucchi & Ugo Rizzo, 2021. "Green technologies, complementarities, and policy," SPRU Working Paper Series 2021-08, SPRU - Science Policy Research Unit, University of Sussex Business School.

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    Keywords

    innovation; technology; network; forecasting; patents; spacial econometrics;
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