Lee, Neil;
Nathan, Max;
(2010)
Knowledge workers, cultural diversity and innovation: evidence from London.
International Journal of Knowledge-Based Development
, 1
(1-2)
pp. 53-78.
10.1504/IJKBD.2010.032586.
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Abstract
London is one of the world's major cities and one of its most culturally diverse. A number of studies link diverse workforces and populations to levels of urban innovation, especially in global cities. While widely explored as a social phenomenon, there has been little work on the importance of London's diversity for the city's businesses. This paper uses the 2007 London Annual Business Survey to investigate, exploiting the survey's unique coverage of both workforce composition and innovation outcomes. From a cross-section of over 2300 firms, we find significant positive relationships between workforce and ownership diversity, and product and process innovation. These provide some support for claims that London's cultural diversity is a source of economic strength.
Type: | Article |
---|---|
Title: | Knowledge workers, cultural diversity and innovation: evidence from London |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1504/IJKBD.2010.032586 |
Publisher version: | https://doi.org/10.1504/IJKBD.2010.032586 |
Language: | English |
Additional information: | This version is the author-accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | cultural diversity, migration, ethnicity, cities, firms, innovation, knowledge economy, economics, urban policy, economic development, knowledge workers, knowledge-based development, London, UK, United Kingdom, workforce composition, workforce diversity, product innovation, ownership diversity, process innovation |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Centre for Advanced Spatial Analysis |
URI: | https://discovery.ucl.ac.uk/id/eprint/10175166 |
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