Agglomeration

Research and Development Laboratories in the Production Process
Abstract A large body of evidence points to relationships between investment in research and development (R&D), productivity, and economic growth. This study aims to advance the understanding of precisely how R&D investments contribute to the productivity of firms: both the firms that undertake the investments directly as well as neighboring firms that benefit from spillover effects. Combining a unique data set of R&D labs with firm-level data from Compustat and restricted-use Census data at the firm- and establishment-level, this project will first answer a series of descriptive questions about how R&D labs in the U.S. are embedded in the production structure of firms. Are locations devoted to innovation dispersed or concentrated relative to other establishments that perform other functions? Does this vary by industry or domestic ownership status? In which industries do firms tend to locate labs near their own headquarters versus their production facilities or the labs of other firms? This project will extend previous work that identifies the size and location of agglomerations of R&D labs and measures the strength of knowledge spillovers within each agglomeration. Firm-level panel regressions will be used to elucidate the impact on firm productivity of R&D labs, the characteristics of the agglomeration in which labs are located, and the position of labs within the production structure of the firm.
Localized Knowledge Spillovers: Evidence from the Spatial Clustering of R&D Labs and Patent Citationss
Abstract Buzard et al. (2017) show that American R&D labs are highly spatially concentrated even within a given metropolitan area. We argue that the geography of their clusters is better suited for studying knowledge spillovers than are states, metropolitan areas, or other political or administrative boundaries that have predominantly been used in previous studies. In this paper, we assign patents and citations to these newly defined clusters of R&D labs. Our tests show that the localization of knowledge spillovers, as measured via patent citations, is strongest at small spatial scales and diminishes with distance. On average, patents within a cluster are about two to four times more likely to cite an inventor in the same cluster than one in a control group. Of import, we find that the degree of localization of knowledge spillovers will be understated in samples based on metropolitan area definitions compared to samples based on the R&D clusters. At the same time, the strength of knowledge spillovers varies widely between clusters. The results are robust to the specification of patent technological categories, the method of citation matching, and alternate cluster definitions.
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The Agglomeration of American R&D Labs
Abstract We employ a unique data set to examine the spatial clustering of about 1700 private R&D labs in California and in the U.S. Northeast Corridor. Using these data, which contain the R&D labs’ complete addresses, we are able to more precisely locate innovative activity than with patent data, which only contain zip codes for inventors’ residential addresses. We avoid the problems of scale and borders associated with using fixed spatial boundaries, such as zip codes, by developing a new point-pattern procedure. Our multiscale core-cluster approach identifies the location and size of significant R&D clusters at various scales, such as a half mile, 1 mile, 5 miles, and more. Our analysis identifies four major clusters in the Northeast Corridor (one each in Boston, New York–Northern New Jersey, Philadelphia–Wilmington, and Washington, D.C.,) and three major clusters in California (one each in the Bay Area, Los Angeles, and San Diego).
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The Geography of Research and Development Activity in the U.S.
Abstract This study details the location patterns of R&D labs in the U.S., but it differs from past studies in a number of ways. First, rather than looking at the geographic concentration of manufacturing firms (e.g., Ellison and Glaeser, 1997; Rosenthal and Strange, 2001; and Duranton and Overman, 2005), we consider the spatial concentration of private R&D activity. Second, rather than focusing on the concentration of employment in a given industry, we look at the clustering of individual R&D labs by industry. Third, following Duranton and Overman (2005), we look for geographic clusters of labs that represent statistically significant departures from spatial randomness using simulation techniques. We find that R&D activity for most industries tends to be concentrated in the Northeast corridor, around the Great Lakes, in California’s Bay Area, and in southern California. We argue that the high spatial concentration of R&D activity facilitates the exchange of ideas among firms and aids in the creation of new goods and new ways of producing existing goods. We run a regression of an Ellison and Glaeser (1997) style index measuring the spatial concentration of R&D labs on geographic proxies for knowledge spillovers and other characteristics and find evidence that localized knowledge spillovers are important for innovative activity.
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The Geography of Research and Development Activity in the U.S.
Abstract In the U.S., metropolitan areas contain the largest concentrations of people and jobs. Despite some drawbacks, these so-called agglomeration economies also have benefits, such as the cost savings that result from being close to suppliers and workers. Spatial concentration is even more pronounced among establishments that do basic research and development (R&D). In this article, we show that geographic concentration of R&D extends beyond locations such as Silicon Valley. In fact, many types of R&D establishments are highly concentrated geographically.
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