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Cited by 357

The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.

Localised knowledge spillovers vs. innovative milieux: Knowledge “tacitness” reconsidered

Open access
Breschi, Stefano, Lissoni, Francesco·2001·Papers in Regional Science

Abstract

Abstract. This article provides a critical discussion of the recent econometric literature on “localised knowledge spillovers” and the related notion of tacit knowledge. The basic claim of the article is that the increasing, and more or less automatic reliance of industrial geographers upon such econometric evidence and theoretical concepts to support their work on industrial districts, hi-tech agglomerations and, more broadly, local innovation systems is not well placed and risks to generate conceptual confusion and to distort research agendas. Following some recent advances in the economics of knowledge, the article also suggests that more research efforts should instead be devoted to exploring how knowledge is actually transmitted, among whom, at what distance, and on the basis of which codebooks.

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Crossref · 10.1007/pl00013627

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Metadata

Authors
Breschi, S., & Lissoni, F.
Venue
Papers in Regional Science
Publisher
Elsevier BV
Published
2001-07
Volume
80
Issue
3
Pages
255-273
ISSN
  • 0486-2902 (ISSN-L)
  • 1056-8190
OpenAlex
W2141004345
Cited by
357
References
45
FWCI
24.16
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: Localised knowledge spillovers vs. innovative milieux: Knowledge “tacitness” reconsidered. https://app.cochat.ai/papers/10.1007/pl00013627
The paper
Breschi, S., & Lissoni, F. (2001). Localised knowledge spillovers vs. innovative milieux: Knowledge “tacitness” reconsidered. Papers in Regional Science. https://doi.org/10.1007/pl00013627

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.