Employment growth and clusters dynamics of creative industries in Great Britain

doi:10.1017/CBO9780511493386.005

4 - Employment growth and clusters dynamics of creative industries in Great Britain

Published online by Cambridge University Press: 22 September 2009

Barry Moore and

Edited by

Bernard Fingleton: Affiliation:
Reader in Geographical Economics Cambridge University
Danilo C. Igliori: Affiliation:
Affiliated Lecturer Department of Land Economy, University of Cambridge
Barry Moore: Affiliation:
Fellow in Economics in Downing College University of Cambridge
Raakhi Odedra: Affiliation:
Fello Ministry of Education, Rwanda
Karen R. Polenske: Affiliation:
Massachusetts Institute of Technology

Book contents

Get access

Summary

Introduction

The definition of creative industries has been outlined by Britain's Creative Task Force as “those activities which have their origin in individual creativity, skill and talent, and which have the potential for wealth and job creation through the generation and exploitation of intellectual property” (Department for Culture, Media and Sport – DCMS 2001)

Creative industries have been identified as contributing to over 5 percent of GDP (DCMS 2001). A selection of some of these industries, for which data are available, generates £112.5 billion a year in revenues, of which £10.3 billion is through exports. Output growth in this set between 1997 and 1998 was a strong 16 percent, compared to 6 percent for the economy as a whole (Freeman 2002).

These industries are likely to be characterized by differentiated products, niche markets, vertical disintegration, and consumer externalities. Location also has the potential to act as a brand or image. It is estimated that 50 percent of all employees in the sector are clustered in London and in the South East of England. In addition, an important share of these jobs is to be found in SMEs. Creative industries have in particular received a fresh wave of attention as “London's Core Business” (Freeman 2002). Between 1995 and 2000, they produced the second largest source of job growth, the second largest source of output, and were the third largest source of jobs in the city.

Type: Chapter
Information: The Economic Geography of Innovation , pp. 60 - 84

DOI: https://doi.org/10.1017/CBO9780511493386.005 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2007

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Book purchase

Temporarily unavailable

References

Anselin, L. and R. Florax, 1995 “Small Sample Properties of Tests for Spatial Dependence in Regression Models: Some Further Results,” in Anselin, L. and Florax, R. (eds.), New Directions in Spatial Econometrics, Berlin: Springer-Verlag: 21–74CrossRef Google Scholar

, Anselin L. and Kelejian, H. H., 1997. “Testing for Spatial Error Autocorrelation in the Presence of Endogenous Regressors,” International Regional Science Review, 20: 153–182Google Scholar

Anselin, L. and Rey, S., 1991. “Properties of Tests for Spatial Dependence in Linear Regression Models,” Geographical Analysis, 23: 112–131CrossRef Google Scholar

Antonelli, C., 1999. “The Evolution of the Industrial Organisation of the Production of Knowledge,” Cambridge Journal of Economics, 23: 243–260CrossRef Google Scholar

Begg, I. G. and D. Hodson, 2001. “The Location of Jobs in High-Technology Industries in Great Britain in the 1990s,” London: South Bank University, Mimeo

Camagni, R., 1991. Innovation Networks: Spatial Perspectives, London: Belhaven PressGoogle Scholar

Census of Population, 2001. London: ONS

Department for Culture, Media and Sport (Department for Culture, Media and Sport), 2001. Creative Industries Mapping Document, http://193.128.182.36/creative/index.html

Department of Trade and Industry (Department of Trade and Industry), 2001. Business Clusters in the United Kingdom – A First Assessment, London: DTI

Fingleton, B., 1999. “Spurious Spatial Regression: Some Monte-Carlo Results with a Spatial Unit Root and Spatial Cointegration,” Journal of Regional Science, 39: 1–19CrossRef Google Scholar

2003a. “Increasing Returns: Evidence from Local Wage Rates in Great Britain,” Oxford Economic Papers, 55(4): 716–739CrossRef

2003b. “Externalities, Economic Geography and Spatial Econometrics: Conceptual and Modeling Developments,” International Regional Science Review, 26(2): 197–207CrossRef

Fingleton, B., D. Igliori, and B. Moore, 2003. “Employment Growth of Small Computing Services Firms and the Role of Horizontal Clusters: Evidence from Great Britain 1991–2000,” in Fingleton, B. (ed.), Regional Growth in Europe, Berlin: Springer Verlag: 267–291CrossRef Google Scholar

2004. “Employment Growth of Small Computing Services Firms and the Role of Horizontal Clusters: Evidence from Computing Services and R&D in Great Britain, 1991–2000,” Urban Studies, 41(4): 773–779CrossRef

2005. “Cluster Dynamics: New Evidence and Projections for Computing Services in Great Britain,” Journal of Regional Science, 45(2): 283–311CrossRef

Freeman, A., 2002. Creativity: London's Core Business, A Report for the Greater London Authority, at http://www.london.gov.uk

Freeman, C., 1995. “The ‘National System of Innovation’ in Historical Perspective,” Cambridge Journal of Economics, 19: 5–24Google Scholar

Fujita, M. and Thisse, J. F., 1996. “Economics of Agglomeration,” Journal of the Japanese and International Economies, 10: 339–378CrossRef Google Scholar

Johnston, J., 1984. Econometric Methods, 3rd edn., New York: McGraw-HillGoogle Scholar

Keeble, D., 2000. “Collective Learning Processes in European High-Technology Milieux,” in Keeble, D. and Wilkinson, F. (eds.), High-Technology Clusters, Networking and Collective Learning in Europe, Aldershot: Ashgate: 182–198Google Scholar

Keeble, D., Lawson, C., Moore, B., and Wilkinson, F., 1999. “Collective Learning Processes, Networking and Institutional Thickness in the Cambridge Region,” Regional Studies, 33: 319–332CrossRef Google Scholar

Keeble, D. and Wilkinson, F. (eds.), 2000. High-Technology Clusters, Networking and Collective Learning in Europe, Aldershot: AshgateGoogle Scholar

Kelejian, H. H. and Prucha, I. R., 1998. “A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances,” Journal of Real Estate Finance and Economics, 17: 99–121CrossRef Google Scholar

Kelejian, H. H. and Robinson, D. P., 1993. “A Suggested Method of Estimation for Spatial Interdependent Models with Autocorrelated Errors, and an Application to a County Expenditure Model,” Papers in Regional Science, 72: 297–312CrossRef Google Scholar

Kennedy, P., 1992. A Guide to Econometrics, 3rd edn., Oxford: BlackwellGoogle Scholar

Krugman, P., 1991a. Geography and Trade, Cambridge, MA: Massachusetts Institute of Technology PressGoogle Scholar

1991b. “Increasing Returns and Economic Geography,” Journal of Political Economy, 99(31): 483–499CrossRef

1995. Development, Geography, and Economic Theory, Cambridge, MA: Massachusetts Institute of Technology

Leser, C., 1966. Econometric Techniques and Problems, London: GriffinGoogle Scholar

Maddala, G. S., 2001. An Introduction to Econometrics, New York: WileyGoogle Scholar

Marshall, A., 1920. Principles of Economics, London: MacmillanGoogle Scholar

Porter, M. E., 1990. The Competitive Advantage of Nations, London: MacmillanCrossRef Google Scholar

1998. On Competition, Boston: Harvard Business Review Press

Storper, M., 1997. The Regional World: Territorial Development in a Global Economy, New York: Guilford PressGoogle Scholar