Close to You? Bias and Precision in Patent-Based Measures of Technological Proximity
AbstractPatent data have been widely used in research on technological innovation to characterize firms' locations as well as the proximities among firms in knowledge space. Researchers could measure proximity among firms with a variety of measures based on patent class data, including Euclidean distance, correlation, and angle between firms' patent class distributions. Alternatively, one could measure proximity using overlap in cited patents. We point out that measures of proximity based on small numbers of patents are imprecisely measured random variables. Measures computed on samples with few patents generate both biased and imprecise measures of proximity. We explore the effects of larger sample sizes and coarser patent class breakdowns in mitigating these problems. Where possible, we suggest that researchers increase their sample sizes by aggregating years or using all of the listed patent classes on a patent, rather than just the first.
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Date of creation: Aug 2007
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- Benner, Mary & Waldfogel, Joel, 2008. "Close to you? Bias and precision in patent-based measures of technological proximity," Research Policy, Elsevier, vol. 37(9), pages 1556-1567, October.
- O31 - Economic Development, Technological Change, and Growth - - Technological Change; Research and Development; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-08-27 (All new papers)
- NEP-INO-2007-08-27 (Innovation)
- NEP-IPR-2007-08-27 (Intellectual Property Rights)
- NEP-KNM-2007-08-27 (Knowledge Management & Knowledge Economy)
- NEP-TID-2007-08-27 (Technology & Industrial Dynamics)
- NEP-URE-2007-08-27 (Urban & Real Estate Economics)
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