As a source of data for studying innovation, patents are really seductive. There’s nothing else quite like them. And at first glance, one of the most appealing things patents is that they cite each other. That means, patents might help us understand how knowledge spills over from one application to another, which is one of the most distinctive things about innovation, as compared to other economic activities.
But there are dangers. A citation might not mean quite what you think. This podcast looks at their shortcomings, while ultimately concluding they can still provide value, especially when they can be complemented with other sources of data.
This podcast is an audio read through of the (initial version of the) post Measuring Knowledge Spillovers: The Trouble with Patent Citations, published on New Things Under the Sun.
Articles mentioned:
Moser, Petra, Joerg Ohmstedt, and Paul W. Rhode. 2016. Patent Citations—An Analysis of Quality Differences and Citing Practices in Hybrid Corn. Management Science 64 (4) 1926-1940. https://doi.org/10.1287/mnsc.2016.2688
Jaffe, Adam B., Manuel Trajtenberg, and Michael S. Fogarty. 2000. The Meaning of Patent Citations: Report on the NBER/Case-Western Reserve Survey of Patentees. NBER Working Paper 7631. https://doi.org/10.3386/w7631
Kuhn, J., Younge, K. and Marco, A. 2020. Patent citations reexamined. The RAND Journal of Economics 51: 109-132. https://doi.org/10.1111/1756-2171.12307
Lampe, Ryan. 2012. Strategic Citation. The Review of Economics and Statistics, 94(1), 320-333. https://www.jstor.org/stable/41349178
Jaffe, Adam B., Manuel Trajtenberg, and Rebecca Henderson. 1993. Geographic Localization of Knowledge Spillovers as Evidenced by Patent Citations. The Quarterly Journal of Economics 108, no. 3: 577-98. https://doi:10.2307/2118401
Michael Roach, and Wesley M. Cohen. 2013. Lens or Prism? Patent Citations as a Measure of Knowledge Flows from Public Research. Management Science 59 (2) 504-525. https://doi.org/10.1287/mnsc.1120.1644
Younge, Kenneth A. and Jeffrey M. Kuhn. 2016. Patent-to-Patent Similarity: A Vector Space Model. SSRN Working paper. http://dx.doi.org/10.2139/ssrn.2709238
Feng Sijie. 2020. The proximity of ideas: An analysis of patent text using machine learning. PLoS ONE 15(7): e0234880. https://doi.org/10.1371/journal.pone.0234880