|
Mary Benner, Joel Waldfogel
NBER Working Paper No. 13322
Issued in August 2007
NBER Program(s): IO
PR
---- 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.
Would you like an annual subscription to NBER Working Papers? Click
here for more information.
You may purchase this paper on-line in .pdf format
from SSRN.com ($5) for electronic delivery.
Information for subscribers and others expecting no-cost downloads
Machine-readable bibliographic record -
MARC,
RIS,
BibTeX
| 
