IDEAS home Printed from https://ideas.repec.org/p/amz/wpaper/2020-04.html
   My bibliography  Save this paper

Technological interdependencies predict innovation dynamics

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.inet.ox.ac.uk/files/Pichler-Lafond-Farmer-Technological-interdependencies-predict-innovation-dynamics.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    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. Aghion, Philippe & Howitt, Peter, 1992. "A Model of Growth through Creative Destruction," Econometrica, Econometric Society, vol. 60(2), pages 323-351, March.
    3. Farmer, J. Doyne & Lafond, François, 2016. "How predictable is technological progress?," Research Policy, Elsevier, vol. 45(3), pages 647-665.
    4. 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.
    5. 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.
    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. 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.
    8. Taalbi, Josef, 2020. "Evolution and structure of technological systems - An innovation output network," Research Policy, Elsevier, vol. 49(8).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. Hötte, Kerstin & Jee, Su Jung & Srivastav, Sugandha, 2021. "Knowledge for a warmer world: a patent analysis of climate change adaptation technologies," INET Oxford Working Papers 2021-19, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.
    4. Kerstin Hotte & Su Jung Jee, 2021. "Knowledge for a warmer world: a patent analysis of climate change adaptation technologies," Papers 2108.03722, arXiv.org, revised Apr 2022.
    5. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dechezlepretre, Antoine & Einiö, Elias & Martin, Ralf & Nguyen, Kieu-Trang & Reenen, John Van, 2016. "Do tax incentives for research increase firm innovation? An RD design for R&D," LSE Research Online Documents on Economics 66428, London School of Economics and Political Science, LSE Library.
    2. Nomaler, Önder & Spinola, Danilo & Verspagen, Bart, 2021. "R&D-based economic growth in a supermultiplier model," Structural Change and Economic Dynamics, Elsevier, vol. 59(C), pages 1-19.
    3. Ejike Udeogu & Shampa Roy-Mukherjee & Uzochukwu Amakom, 2021. "Does Increasing Product Complexity and Diversity Cause Economic Growth in the Long-Run? A GMM Panel VAR Evidence," SAGE Open, , vol. 11(3), pages 21582440211, August.
    4. James R. Brown & Gustav Martinsson, 2019. "Does Transparency Stifle or Facilitate Innovation?," Management Science, INFORMS, vol. 65(4), pages 1600-1623, April.
    5. Kancs, d’Artis & Siliverstovs, Boriss, 2016. "R&D and non-linear productivity growth," Research Policy, Elsevier, vol. 45(3), pages 634-646.
    6. Ugur, Mehmet & Churchill, Sefa Awaworyi & Luong, Hoang M., 2020. "What do we know about R&D spillovers and productivity? Meta-analysis evidence on heterogeneity and statistical power," Research Policy, Elsevier, vol. 49(1).
    7. Antoine Dechezleprêtre & Elias Einiö & Ralf Martin & Kieu-Trang Nguyen & John Van Reenen, 2016. "Do tax incentives for research increase firm innovation? An RD design for R&D, patents and spillovers," CEP Discussion Papers dp1413, Centre for Economic Performance, LSE.
    8. Pinchetti, Marco, 2020. "What Is Driving The TFP Slowdown? Insights From a Schumpeterian DSGE Model," MPRA Paper 98316, University Library of Munich, Germany.
    9. Gray, Elie & Grimaud, André, 2014. "The Lindahl equilibrium in Schumpeterian growth models: Knowledge diffusion, social value of innovations and optimal R&D incentives," IDEI Working Papers 821, Institut d'Économie Industrielle (IDEI), Toulouse.
    10. Andrew Atkeson & Ariel Burstein, 2019. "Aggregate Implications of Innovation Policy," Journal of Political Economy, University of Chicago Press, vol. 127(6), pages 2625-2683.
    11. Felix Bracht & Dennis Verhoeven, 2021. "Air pollution and innovation," CEP Discussion Papers dp1817, Centre for Economic Performance, LSE.
    12. Choi, Mincheol & Lee, Chang-Yang, 2021. "Technological diversification and R&D productivity: The moderating effects of knowledge spillovers and core-technology competence," Technovation, Elsevier, vol. 104(C).
    13. Sasso, Simone & Ritzen, Jo, 2016. "Sectoral Cognitive Skills, R&D, and Productivity: A Cross-Country Cross-Sector Analysis," IZA Discussion Papers 10457, Institute of Labor Economics (IZA).
    14. Brown, James R. & Martinsson, Gustav & Petersen, Bruce C., 2012. "Do financing constraints matter for R&D?," European Economic Review, Elsevier, vol. 56(8), pages 1512-1529.
    15. Leandro D�Aurizio & Marco Marinucci, 2013. "Italian firms� innovation strategies in 2008-2010," Questioni di Economia e Finanza (Occasional Papers) 197, Bank of Italy, Economic Research and International Relations Area.
    16. Edwin Goni & William F. Maloney, 2014. "Why don’t Poor Countries do R&D?," Documentos CEDE 011947, Universidad de los Andes – Facultad de Economía – CEDE.
    17. Raphaël Godefroy, 2010. "The birth of the congressional clinic," PSE Working Papers halshs-00564921, HAL.
    18. Burcu Fazlıoğlu & Başak Dalgıç & Ahmet Burçin Yereli, 2019. "The effect of innovation on productivity: evidence from Turkish manufacturing firms," Industry and Innovation, Taylor & Francis Journals, vol. 26(4), pages 439-460, April.
    19. Luisa R. Blanco & Ji Gu & James E. Prieger, 2016. "The Impact of Research and Development on Economic Growth and Productivity in the U.S. States," Southern Economic Journal, John Wiley & Sons, vol. 82(3), pages 914-934, January.
    20. Schubert, Torben & Jäger, Angela & Türkeli, Serdar & Visentin, Fabiana, 2020. "Addressing the productivity paradox with big data: A literature review and adaptation of the CDM econometric model," MERIT Working Papers 2020-050, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).

    More about this item

    Keywords

    innovation; technology; network; forecasting; patents; spacial econometrics;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:amz:wpaper:2020-04. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://edirc.repec.org/data/inoxfuk.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: INET Oxford admin team (email available below). General contact details of provider: https://edirc.repec.org/data/inoxfuk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.