Endogenous technology cycles in dynamic R&D networks

Michael D. König; Tim Rogers

doi:10.1016/j.euroecorev.2023.104531

Endogenous technology cycles in dynamic R&D networks

Michael D. König, Tim Rogers

Research output: Contribution to journal › Article › peer-review

1 Citation (SciVal)

Abstract

We study the coevolutionary dynamics of knowledge creation and diffusion with the formation of R&D collaboration networks. The novel examination of a large R&D collaboration network over several decades reveals a pronounced oscillatory (cyclical) pattern in the R&D collaboration intensity, which is not captured by existing theoretical studies. Here, we propose a new model of R&D network formation in which firms form R&D collaborations with others possessing a complementary portfolio of technologies. Innovations and knowledge spillovers alter the composition of these portfolios over time, leading to changes in the network of R&D collaborations. We show that our model is not only able to explain the emergence of oscillatory dynamics in R&D networks, but also has important policy implications. First, we demonstrate that there exists a critical threshold level for spillovers between R&D collaborating firms that must be exceeded for R&D collaborations to effectively contribute to knowledge creation in the economy. The threshold indicates that policies promoting collaborative R&D can only be successful in fostering innovations if they are substantial enough so that spillovers are above the threshold. Second, policies strengthening competition in R&D networks are found to promote oscillatory fluctuations, potentially destabilizing the network.

Original language	English
Article number	104531
Number of pages	26
Journal	European Economic Review
Volume	158
Early online date	9 Jul 2023
DOIs	https://doi.org/10.1016/j.euroecorev.2023.104531
Publication status	Published - 30 Sept 2023

Bibliographical note

Funding Information:
We would like to thank Andrea Montanari for numerous comments, guidance and support, and Adrian Etter for the excellent research assistance. Moreover, we would like to thank Paolo Pin, Fernando Vega-Redondo, Jan Eeckhout, Thomas Chaney, Kiminori Matsuyama, Chad Jones, Philippe Aghion, Matt Jackson, Francis Bloch, Armin Schmutzler, Frank Page, Andrei Levchenko, Tim Hellmann, Douglas White, David Dorn, Woody Powell, John Hagedoorn, Michelle Sovinsky, Samuel Kortum, Fabrizio Zilibotti, Pradeep Dubey, Yair Tauman, Hans Gersbach, Maik Schneider and seminar participants at the Universities of Zurich, Bocconi, Bath, Stanford, ParisTech and Stony Brook for helpful comments. Michael König acknowledges financial support from Swiss National Science Foundation through research grants PBEZP1–131169 and 100018_140266. Tim Rogers gratefully acknowledges the support of the Royal Society. The authors further acknowledge the University of Zurich S3IT: Service and Support for Science IT, for providing the support and the computational resources that have contributed to the research results reported in this publication. URL: http://www.s3it.uzh.ch.

Keywords

Competition
Innovation
Network formation
R&D networks
Technology cycles

ASJC Scopus subject areas

Finance
Economics and Econometrics

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10.1016/j.euroecorev.2023.104531Licence: CC BY

Cite this

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title = "Endogenous technology cycles in dynamic R&D networks",

abstract = "We study the coevolutionary dynamics of knowledge creation and diffusion with the formation of R&D collaboration networks. The novel examination of a large R&D collaboration network over several decades reveals a pronounced oscillatory (cyclical) pattern in the R&D collaboration intensity, which is not captured by existing theoretical studies. Here, we propose a new model of R&D network formation in which firms form R&D collaborations with others possessing a complementary portfolio of technologies. Innovations and knowledge spillovers alter the composition of these portfolios over time, leading to changes in the network of R&D collaborations. We show that our model is not only able to explain the emergence of oscillatory dynamics in R&D networks, but also has important policy implications. First, we demonstrate that there exists a critical threshold level for spillovers between R&D collaborating firms that must be exceeded for R&D collaborations to effectively contribute to knowledge creation in the economy. The threshold indicates that policies promoting collaborative R&D can only be successful in fostering innovations if they are substantial enough so that spillovers are above the threshold. Second, policies strengthening competition in R&D networks are found to promote oscillatory fluctuations, potentially destabilizing the network.",

keywords = "Competition, Innovation, Network formation, R&D networks, Technology cycles",

author = "K{\"o}nig, {Michael D.} and Tim Rogers",

note = "Funding Information: We would like to thank Andrea Montanari for numerous comments, guidance and support, and Adrian Etter for the excellent research assistance. Moreover, we would like to thank Paolo Pin, Fernando Vega-Redondo, Jan Eeckhout, Thomas Chaney, Kiminori Matsuyama, Chad Jones, Philippe Aghion, Matt Jackson, Francis Bloch, Armin Schmutzler, Frank Page, Andrei Levchenko, Tim Hellmann, Douglas White, David Dorn, Woody Powell, John Hagedoorn, Michelle Sovinsky, Samuel Kortum, Fabrizio Zilibotti, Pradeep Dubey, Yair Tauman, Hans Gersbach, Maik Schneider and seminar participants at the Universities of Zurich, Bocconi, Bath, Stanford, ParisTech and Stony Brook for helpful comments. Michael K{\"o}nig acknowledges financial support from Swiss National Science Foundation through research grants PBEZP1–131169 and 100018_140266. Tim Rogers gratefully acknowledges the support of the Royal Society. The authors further acknowledge the University of Zurich S3IT: Service and Support for Science IT, for providing the support and the computational resources that have contributed to the research results reported in this publication. URL: http://www.s3it.uzh.ch. ",

year = "2023",

month = sep,

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doi = "10.1016/j.euroecorev.2023.104531",

language = "English",

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T1 - Endogenous technology cycles in dynamic R&D networks

AU - König, Michael D.

AU - Rogers, Tim

N1 - Funding Information: We would like to thank Andrea Montanari for numerous comments, guidance and support, and Adrian Etter for the excellent research assistance. Moreover, we would like to thank Paolo Pin, Fernando Vega-Redondo, Jan Eeckhout, Thomas Chaney, Kiminori Matsuyama, Chad Jones, Philippe Aghion, Matt Jackson, Francis Bloch, Armin Schmutzler, Frank Page, Andrei Levchenko, Tim Hellmann, Douglas White, David Dorn, Woody Powell, John Hagedoorn, Michelle Sovinsky, Samuel Kortum, Fabrizio Zilibotti, Pradeep Dubey, Yair Tauman, Hans Gersbach, Maik Schneider and seminar participants at the Universities of Zurich, Bocconi, Bath, Stanford, ParisTech and Stony Brook for helpful comments. Michael König acknowledges financial support from Swiss National Science Foundation through research grants PBEZP1–131169 and 100018_140266. Tim Rogers gratefully acknowledges the support of the Royal Society. The authors further acknowledge the University of Zurich S3IT: Service and Support for Science IT, for providing the support and the computational resources that have contributed to the research results reported in this publication. URL: http://www.s3it.uzh.ch.

PY - 2023/9/30

Y1 - 2023/9/30

N2 - We study the coevolutionary dynamics of knowledge creation and diffusion with the formation of R&D collaboration networks. The novel examination of a large R&D collaboration network over several decades reveals a pronounced oscillatory (cyclical) pattern in the R&D collaboration intensity, which is not captured by existing theoretical studies. Here, we propose a new model of R&D network formation in which firms form R&D collaborations with others possessing a complementary portfolio of technologies. Innovations and knowledge spillovers alter the composition of these portfolios over time, leading to changes in the network of R&D collaborations. We show that our model is not only able to explain the emergence of oscillatory dynamics in R&D networks, but also has important policy implications. First, we demonstrate that there exists a critical threshold level for spillovers between R&D collaborating firms that must be exceeded for R&D collaborations to effectively contribute to knowledge creation in the economy. The threshold indicates that policies promoting collaborative R&D can only be successful in fostering innovations if they are substantial enough so that spillovers are above the threshold. Second, policies strengthening competition in R&D networks are found to promote oscillatory fluctuations, potentially destabilizing the network.

AB - We study the coevolutionary dynamics of knowledge creation and diffusion with the formation of R&D collaboration networks. The novel examination of a large R&D collaboration network over several decades reveals a pronounced oscillatory (cyclical) pattern in the R&D collaboration intensity, which is not captured by existing theoretical studies. Here, we propose a new model of R&D network formation in which firms form R&D collaborations with others possessing a complementary portfolio of technologies. Innovations and knowledge spillovers alter the composition of these portfolios over time, leading to changes in the network of R&D collaborations. We show that our model is not only able to explain the emergence of oscillatory dynamics in R&D networks, but also has important policy implications. First, we demonstrate that there exists a critical threshold level for spillovers between R&D collaborating firms that must be exceeded for R&D collaborations to effectively contribute to knowledge creation in the economy. The threshold indicates that policies promoting collaborative R&D can only be successful in fostering innovations if they are substantial enough so that spillovers are above the threshold. Second, policies strengthening competition in R&D networks are found to promote oscillatory fluctuations, potentially destabilizing the network.

KW - Competition

KW - Innovation

KW - Network formation

KW - R&D networks

KW - Technology cycles

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DO - 10.1016/j.euroecorev.2023.104531

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JO - European Economic Review

JF - European Economic Review

M1 - 104531

ER -

Endogenous technology cycles in dynamic R&D networks

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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