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Cited by 266

The DOI matches its Crossref record; 81 of the 81 references it cites were found in a registry or an index.

How well do patent citations measure flows of technology? Evidence from French innovation surveys

Open access
Duguet, Emmanuel, MacGarvie, Megan·2005·Economics of Innovation and New Technology

Abstract

Patent citation data are used in a growing body of economics and business research on technological diffusion. Until now, there exists little evidence on whether patent citations are a good measure of knowledge flows. Our paper assesses the legitimacy of using European patent citations as a measure of technology flows. It uses information from the Community Innovation Survey collected by the French Service des Statistiques Industrielles (SESSI), which contain firms’ responses to questions about their innovative activity. We show that patent citations are indeed related to firms’ statements about their acquisition and dispersion of new technology, but that the strength and statistical significance of this relationship varies across geographical regions and across channels of knowledge diffusion.

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Metadata

Authors
Duguet, E., & MacGarvie, M.
Venue
Economics of Innovation and New Technology
Published
2005-07
Volume
14
Issue
5
Pages
375-393
ISSN
  • 1043-8599 (ISSN-L)
  • 1476-8364
OpenAlex
W1589529420
Cited by
266
References
81
FWCI
17.47
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: How well do patent citations measure flows of technology? Evidence from French innovation surveys. https://app.cochat.ai/papers/10.1080/1043859042000307347
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Duguet, E., & MacGarvie, M. (2005). How well do patent citations measure flows of technology? Evidence from French innovation surveys. Economics of Innovation and New Technology. https://doi.org/10.1080/1043859042000307347

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.