https://orcid.org/0000-0002-9702-8696
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ABSTRACT
We examine the role of population density and accessibility in shaping innovation intensity in the 32,000 lower super output areas (LSOAs) in England. Our analysis focuses on firms’ registered intellectual property – patents, trademarks and registered designs – and using spatial autoregression models suggests four key results. We find a positive relationship between population density and innovation intensity, a consistent negative relationship between longer journey times to the nearest town centre and innovation intensity, and a strong interaction effect between population density and accessibility. Finally, we find strong evidence of local innovation spillovers reflecting either competition or demonstration effects.
ACKNOWLEDGEMENTS
We are grateful to the Intellectual Property Office (IPO) for providing the patents, trademarks and design data on which this analysis is based. Valuable comments were received from two reviewers and the editor.
DATA AVAILABILITY
The statistical data used here are from the Office of National Statistics (ONS) and are Crown copyright and reproduced with the permission of the controller of HMSO and the Queen’s Printer for Scotland. The use of the ONS statistical data in this work does not imply the endorsement of the ONS in relation to the interpretation or analysis of the statistical data. The analysis upon which this paper is based uses research datasets that may not exactly reproduce National Statistics aggregates.
DISCLOSURE STATEMENT
No potential conflict of interest was reported by the authors.
Notes
2. Confusion sometimes arises between the protection offered by a design registration and that offered by a patent: a design registration protects the visual appearance of a product whereas a patent protects a technical product and how it functions.
3. Regions were then classified as ‘rural’ if more than 50% of the population lives in rural local administrative units; ‘intermediate’ if between 15% and 50% lives in rural local units; and ‘urban’ if fewer than 15% lives in rural local units. See Martinovic and Ratkaj (Citation2015) for a recent application of the OECD approach based on population density to the case of Serbia.
5. The Dijkstra and Poelman (Citation2008) typology creates five groups of NUTS-3 regions: urban regions; intermediate regions close to a city; intermediate remote regions; rural regions close to a city; and rural, remote regions.
6. Cloke (Citation1977) used population change, household amenities, population of women of working age, commuting-out pattern, in-migration, population density, population over 65, distance from 50,000 plus urban node and employment in the primary sector.
7. Perhaps more interesting is that Harrington and O’Donoghue (Citation1998) also consider the potential for a two-dimensional index of rurality differentiating between demographic and structural dimensions long in advance of similar analysis by van Eupen et al. (Citation2012).
8. The five cluster categories are named: Middle-class countryside within the urban shadow; Working class countryside within the urban shadow; Countryside outside the urban shadow; Manufacturing periphery; and Resource periphery (Hedlund, Citation2016, tab 2, p. 466).
9. Here we focus on geographical proximity, but cognitive or cultural proximity may also be important in shaping the intensity and value of interaction (Boschma, Citation2005).
10. An IP protection mechanism is assumed to exist for a particular CRN during a given year if it is available to that CRN for more than six months in that given year.
13. See https://www.nomisweb.co.uk/
14. Appendix A online also provides details of variable definitions (in Table A2) and the correlation matrix (in Table A3).
18. The correlation between the environment element of the IMD and population density (0.439) is notably higher than that between population density and any other element of the IMD (see Table A3 in Appendix A in the supplemental data online).