Paper of the Day

Is here:

We study the location and productivity of more than 1,000 research and development (R&D) labs located in the Northeast corridor of the U.S. Using a variety of spatial econometric techniques, we 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. These findings are consistent with empirical research that suggests that some spillovers depreciate very rapidly with distance, while others operate at the spatial scale of labor markets. We 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 (Boston, New York-Northern New Jersey, Philadelphia-Wilmington, and Washington, DC) that appear to represent research clusters. We verify this conjecture using significance-maximizing techniques (e.g., SATSCAN) that also address econometric issues related to “multiple testing” and spatial autocorrelation.

We 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. We document that while locations in these clusters are often related to basic infrastructure, such as access to major roads, there is significant variation in the composition of labs across these clusters. Finally, we show that R&D labs located in clusters defined by this approach are, all else equal, substantially more productive in terms of the patents or citation-weighted patents they receive.


  1. MW says:

    Wow, there are some great maps in the back. The maps certainly support their assertion that proximity to highways matter for R&D labs. But I am not sure why that is the case.
    The only thing I can come up with is that density of STEM graduates matters and that occurs in the favorite quarter of the metro area. Highways increase this preceived density because the people that are within a half hour travel time are greater.
    That preceived density or travel density is an under discussed topic.

  2. ezra abrams says:

    as a phd scientist who has worked at several companies along route 128 in Boston, this paper represents everything that is wrong with economics.
    Maybe I really don’t get it, but why all the math ?
    wouldn’t the science thing be to develop hypotheses (drive time, availability of skilled labor pool, close to grad schools..) and then drive out to RnD facilities and talk to people and test your hypothesis ?
    who care about block level or quarter mile level model navel gazing; you are not out in the field talking to people, so what is the point ? I really just don’t get it
    In my view, the dominant reason for clustering is that the CEOs. kids are in school, and he doens’t want a long drive. since in most cases the CEO worked as a vp or something in the area, this means, the new company doesn’t move far.
    i first heard this in the 60s, in an op ed in the times, where someone calculated the change in commute time for CEOs of companies that moved their hqs out of nyc to the suburbs.
    no surprise, the drive time went down

    there is also a male macho competitiveness thing: guys who become ceos tend to be competitive; a big shiny building on hte highway is good in this game

    MW: you are building an RnD. Your people – who are hard to replace – are going to drive there. you make em spend 45 minutes on some back road, they will look for another place. There is also zoning that favors office parks off the interstate, and I think salesmenship by the developers who put up the building; it is easier to show something tht is easy to get to