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Close to you? Bias and precision in patent-based measures of technological proximity

机译：接近你的？基于专利的技术接近度的偏差和精度

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摘要

Patent data have been widely used in research to characterize firms' locations in technological or knowledge space, as well as the proximities among firms. Researchers have measured firms' technological or knowledge proximities with a variety of measures based on patent data, including Euclidean distances (using the technological classifications listed on patents), and overlap in cited patents. Often research has employed only the first listed patent classification in measures of proximities. We explore the effects of using the first listed patent class as well as other methods to measure proximities. 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 or a single patent class generate both biased and imprecise measures of proximity. We discuss the implications of this for typical research questions employing measures of proximity, and 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.
机译：专利数据已广泛用于研究中，以表征公司在技术或知识空间中的位置以及公司之间的邻近程度。研究人员根据各种专利数据（包括欧几里得距离（使用专利中列出的技术分类），以及所引用专利中的重叠部分），通过各种度量来测量企业的技术或知识邻近度。通常，研究在接近程度上仅采用了第一个列出的专利分类。我们探讨了使用第一个列出的专利类别以及其他方法来测量邻近度的效果。我们指出，基于少量专利的接近度度量是不精确度量的随机变量。根据专利很少或只有一个专利类别的样本计算的度量会同时产生有偏差和不精确的接近度度量。我们讨论此问题对采用邻近度量的典型研究问题的影响，并探讨更大样本量和较粗的专利类别细分所产生的影响，以缓解这些问题。在可能的情况下，我们建议研究人员通过汇总年份或对一项专利使用所有列出的专利类别（而不是仅使用第一项）来增加样本量。

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