TY  - JOUR
AU  - Benner,Mary
AU  - Waldfogel,Joel
TI  - Close to You? Bias and Precision in Patent-Based Measures of Technological Proximity
JF  - National Bureau of Economic Research Working Paper Series
VL  - No. 13322
PY  - 2007
Y2  - August 2007
UR  - http://www.nber.org/papers/w13322
L1  - http://www.nber.org/papers/w13322.pdf
N1  - Author contact info:
Mary J. Benner
Management Department
The Wharton School
University of Pennsylvania
Philadelphia, PA 19104
E-Mail: benner@wharton.upenn.edu

Joel Waldfogel
Frederick R. Kappel Chair in Applied Economics
3-177 Carlson School of Management
University of Minnesota
321 19th Avenue South
Minneapolis, MN 55455
Tel: 612/626-7128
E-Mail: jwaldfog@umn.edu
AB  - 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.
ER  -