Abstract
Supercedes 15-03 We employ a unique data set to examine the spatial clustering of private R&D labs. Instead of using fixed spatial boundaries, we develop a new procedure for identifying the location and size of specific R&D clusters. Thus, we are better able to identify the spatial locations of clusters at various scales, such as a half mile, 1 mile, 5 miles, and more. Assigning patents and citations to these clusters, we capture the geographic extent of knowledge spillovers within them. Our tests show that the localization of knowledge spillovers, as measured via patent citations, is strongest at small spatial scales and diminishes rapidly with distance.
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