Using a variety of spatial econometric techniques, they find that these labs are substantially more concentrated in space than the underlying distribution of manufacturing activity. Ripley’s K-function tests over a variety of spatial scales reveal that the strongest evidence of concentration occurs at two discrete distances: one at about one-quarter of a mile and another at about 40 miles. They also find that R&D labs in some industries (e.g., chemicals, including drugs) are substantially more spatially concentrated than are R&D labs as a whole. Tests using local K-functions reveal several concentrations of R&D labs that appear to represent research clusters. They verify this conjecture using significance maximizing techniques (e.g., SATSCAN) that also address econometric issues related to “multiple testing” and spatial autocorrelation. The authors develop a new procedure for identifying clusters — the multiscale core-cluster approach, to identify labs that appear to be clustered at a variety of spatial scales. Locations in these clusters are often related to basic infrastructure such as access to major roads. There is significant variation in the industrial composition of labs across these clusters. The clusters the authors identify appear related to knowledge spillovers: Citations to patents previously obtained by inventors residing in clustered areas are significantly more localized than one would predict from a (control) sample of otherwise similar patents.