Geographical clustering and the effectiveness of public innovation programs

Dirk Crass, Christian Rammer and Birgit Aschhoff ()
Additional contact information
Birgit Aschhoff: Landesbank Baden-Wuerttemberg

The Journal of Technology Transfer, 2019, vol. 44, issue 6, No 6, 1784-1815

Abstract: Abstract The paper analyses how geographical clustering of beneficiaries might affect the effectiveness of public innovation support programs. The geographical proximity of firms operating in the same industry or field of technology is expected to facilitate innovation through knowledge spillovers and other localization advantages. Public innovation support programs may leverage these advantages by focusing on firms that operate in a cluster. We investigate this link using data from a large German program that co-funds R&D projects of SMEs in key technology areas called ‘Innovative SMEs’. We employ three alternative cluster measures which capture industry, technology and knowledge dimensions of clusters. Regardless of the measure, firms located in a geographical cluster are more likely to participate in the program. Firms being part of a knowledge-based cluster significantly increase their chance of receiving public financial support. We find no effects, however, of geographical clustering on the program’s effectiveness in terms of input or output additionality.

Keywords: Geographical clustering; Effectiveness of public programs; Innovation (search for similar items in EconPapers)
JEL-codes: C35 H50 O31 O32 O38 R59 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10961-017-9584-x Abstract (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Geographical clustering and the effectiveness of public innovation programs (2016)
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:kap:jtecht:v:44:y:2019:i:6:d:10.1007_s10961-017-9584-x

Ordering information: This journal article can be ordered from
http://www.springer. ... nt/journal/10961/PS2

DOI: 10.1007/s10961-017-9584-x

Access Statistics for this article

The Journal of Technology Transfer is currently edited by Albert N. Link, Donald S. Siegel, Barry Bozeman and Simon Mosey

More articles in The Journal of Technology Transfer from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().