The Geography of Unconventional Innovation

Bibliographic Data

ID9708568
AuthorsEnrico Berkes (0000-0001-5117-9918, The Ohio State University), Ruben Gaetani (0000-0002-7842-8336, University of Toronto, corresponding author)
Year2021
Volume131
Issue636
Pages1466-1514
Publication date2021-05-04
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueThe Economic Journal (JOURNAL)
Journal identifiersISSN: 0013-0133 • E-ISSN: 1468-0297
PublisherOxford University Press (PUBLISHER • GB)
DOI10.1093/ej/ueaa111
OpenAlexW2261335618
LanguageEN
Citations received23
References cited36

Using a newly assembled data set of US patents, we show that innovation activity is less concentrated in high-density locations than commonly believed. Yet, inventions based on atypical combinations of knowledge are indeed more prevalent in high-density urban centres. To interpret this relation, we propose that informal interactions in densely populated areas help knowledge flows between distant fields, but are less relevant for flows between close fields. We build a model of innovation in a spatial economy that endogenously generates the pattern observed in the data: specialised clusters emerge in low-density areas, whereas high-density locations diversify and produce unconventional ideas

Data mining · Economic geography · Field (mathematics · Geography · Relation (database · Set (abstract data type · Computer Science · Firm Innovation and Growth · Innovation Diffusion and Forecasting · Mathematics · Regional Economics and Spatial Analysis

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