An Evolutionary Analysis of Industrial Districts: The Changing Multiplicity of Production Know-How Nuclei

Marco Bellandi, L De Propris and Erica Santini

Cambridge Journal of Economics, 2019, vol. 43, issue 1, 187-204

Abstract: This paper deals with the evolution of place-based division of labour, considering in particular the dynamics of differentiated pools of knowledge that can be embedded in local production systems such as industrial districts. Drawing on conceptual framework of Marshallian industrial districts, the paper develops a model that links knowledge dynamics to possible tendencies towards either industrial progress, lock-in, transition or decline of such systems. The model is complemented by a related new statistical method that allows us to analyse the evolution of the multiplicity of production know-how nuclei inside industrial districts. The methodology is applied to two cases: the ceramics district of Stoke-on-Trent (UK) and the textile district of Prato (ITA) over the period 2005–2013.

Keywords: Industrial District; Productive Knowledge; Local Change; Prato textile district; Stoke-on-Trent ceramics district (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://hdl.handle.net/10.1093/cje/bey012 (application/pdf)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:cambje:v:43:y:2019:i:1:p:187-204.

Ordering information: This journal article can be ordered from
https://academic.oup.com/journals

Access Statistics for this article

Cambridge Journal of Economics is currently edited by Jacqui Lagrue

More articles in Cambridge Journal of Economics from Cambridge Political Economy Society Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK.
Bibliographic data for series maintained by Oxford University Press ().