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Close to You? Bias and Precision in Patent-Based Measures of Technological Proximity

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  • Mary Benner
  • Joel Waldfogel

Abstract

Patent 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.

Suggested Citation

  • Mary Benner & Joel Waldfogel, 2007. "Close to You? Bias and Precision in Patent-Based Measures of Technological Proximity," NBER Working Papers 13322, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:13322
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    JEL classification:

    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives

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