Identifying Clusters within R&D Intensive Industries Using Local Spatial Methods
AbstractIn recent times, there has been a renewed interest in cluster policies for supporting industrial and regional development. Prominent economics like Porter and Krugman emphasise the role of clusters in regional competition and show in which way clusters can positively affect competition by increasing productivity and innovation. Because of the linkage between growth and innovation, R&D intensive industries play a crucial role in cluster development strategies. Empirical cluster research has to contribute to the understanding the process of cluster formation. In particular for developing profound clusters strategies and assessing the limits cluster policy, knowledge of existing structures and tendencies is necessary. In these strategies, high-tech and research-intensive industries play a crucial role. Audretsch and Feldman argue that industries with high innovation activity tend to cluster for exploiting benefits from tacit knowledge flows. Krugman stresses that information flows and knowledge spillovers may be sensitive to geographic impediments. Since obstacles tend to rise with increasing distance, spatial clusters may be localised. If, however, geographic barriers are less relevant, the reach of tacit knowledge flows may be much larger. For regional policy the geographical level at which clusters occur is of prominent interest. Traditional concentration indices like the Gini coefficient, Theilsâ€™s inequalitiy index or the Ellison-Glaeser index are â€˜aspatialâ€™ by construction. This means that these indices disregard relevant spatial information on the distribution of a geo-referenced variable. In particular, attribute values of adjacent regions are completely ignored. Moreover, the spatial scale of clustering formation is not taken into account. First experiences with methods of exploratory spatial data analysis (ESDA) like local Moranâ€™s I and Getis-Ord Gi statistics in pattern recognition are available. Le Gallo and Ertur (2003) utilise local indicators of spatial association to analyse the distribution of regional GDP per capita in Europe. Feser et al. (2005), Lafourcade and Mion (2007) and Kies et al. (2009) demonstrate the potential of these ESDA techniques in identifying economic clusters and spatial heterogeneity in geographical space. However, while usually local Moranâ€™s I and Getis-Ord Gi statistics are applied in detecting economic clusters, up to now spatial scan techniques are largely ignored (Kang, 2010). In this paper, advantages and pitfalls of spatial scan tests in identifying R&D clusters are examined. Some essentials in implementing spatial scan techniques in economic clusters research are worked out.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by European Regional Science Association in its series ERSA conference papers with number ersa12p232.
Date of creation: Oct 2012
Date of revision:
Contact details of provider:
Postal: Augasse 2-6, 1090 Vienna, Austria
Web page: http://www.ersa.org
Other versions of this item:
- Reinhold Kosfeld & Jorgen Lauridsen, 2012. "Identifying Clusters within R&D Intensive Industries Using Local Spatial Methods," MAGKS Papers on Economics 201214, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
- R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
- R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-10-13 (All new papers)
- NEP-CSE-2012-10-13 (Economics of Strategic Management)
- NEP-GEO-2012-10-13 (Economic Geography)
- NEP-INO-2012-10-13 (Innovation)
- NEP-SBM-2012-10-13 (Small Business Management)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Miren Lafourcade & Giordano Mion, 2005.
"Concentration, agglomeration and the size of plants,"
PSE Working Papers
- Lafourcade, Miren & Mion, Giordano, 2007. "Concentration, agglomeration and the size of plants," Regional Science and Urban Economics, Elsevier, vol. 37(1), pages 46-68, January.
- Luisito BERTINELLI & Rosella NICOLINI, 2002. "Investment decision and the spatial dimension : Evidence from firm level data," Discussion Papers (IRES - Institut de Recherches Economiques et Sociales) 2002007, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
- Glaeser, Edward L & Hedi D. Kallal & Jose A. Scheinkman & Andrei Shleifer, 1992.
"Growth in Cities,"
Journal of Political Economy,
University of Chicago Press, vol. 100(6), pages 1126-52, December.
- Edward L. Glaeser & Hedi D. Kallal & Jose A. Scheinkman & Andrei Shleifer, 1991. "Growth in Cities," NBER Working Papers 3787, National Bureau of Economic Research, Inc.
- Glaeser, Edward Ludwig & Kallal, Hedi D. & Scheinkman, Jose A. & Shleifer, Andrei, 1992. "Growth in Cities," Scholarly Articles 3451309, Harvard University Department of Economics.
- Frank Neffke & Martin Svensson Henning & Ron Boschma & Karl-Johan Lundquist & Lars-Olof Olander, 2008. "Who Needs Agglomeration? Varying Agglomeration Externalities and the Industry Life Cycle," Papers in Evolutionary Economic Geography (PEEG) 0808, Utrecht University, Section of Economic Geography, revised Apr 2008.
- Edward Feser & Stuart Sweeney & Henry Renski, 2005. "A Descriptive Analysis of Discrete U.S. Industrial Complexes," Journal of Regional Science, Wiley Blackwell, vol. 45(2), pages 395-419.
- LE GALLO, Julie & ERTUR, Cem, 2000. "Exploratory spatial data analysis of the distribution of regional per capita GDP in Europe, 1980-1995," LATEC - Document de travail - Economie (1991-2003) 2000-09, LATEC, Laboratoire d'Analyse et des Techniques EConomiques, CNRS UMR 5118, Université de Bourgogne.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Gunther Maier).
If references are entirely missing, you can add them using this form.