1 SUMMARY: THE GEOGRAPHY OF THE UK’S CREATIVE AND HIGH-TECH ECONOMIES
SUMMARY
THE
GEOGRAPHY
OF THE UK’S
CREATIVE AND
HIGH–TECH
ECONOMIES
Hasan Bakhshi, John Davies, Alan Freeman and Peter Higgs
January 2015
2 SUMMARY: THE GEOGRAPHY OF THE UK’S CREATIVE AND HIGH-TECH ECONOMIES
ACKNOWLEDGEMENTS
The authors would like to thank Douglas Cameron and Tom Knight of the Department for
Culture, Media & Sport for their assistance in producing the data tables and figures in this
report, and Mark Spilsbury and Derek Bosworth for their comments on an earlier version of
the research report which this briefing summarises.
Nesta is an innovation charity with a mission to help people and
organisations bring great ideas to life.
We are dedicated to supporting ideas that can help improve all our lives,
with activities ranging from early–stage investment to in–depth research
and practical programmes.
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Registered as a charity in Scotland number SCO42833. Registered office: 1 Plough Place, London, EC4A 1DE.
www.nesta.org.uk
©Nesta 2015
3 SUMMARY: THE GEOGRAPHY OF THE UK’S CREATIVE AND HIGH-TECH ECONOMIES
THE GEOGRAPHY OF
THE UK’S CREATIVE AND
HIGH–TECH ECONOMIES
1. INTRODUCTION
T
he creative and high–tech industries are at the heart of the policy debate on the
future of the UK economy. They are engines of job growth, employing highly–
skilled workers that are less vulnerable to being displaced by technological
change.1 They are widely held to be more innovative and to enjoy faster productivity
growth than other sectors, and are therefore good for the UK’s international
competitiveness and exports.2 There is evidence that they support growth in the wider
economy, for example by creating knowledge spillovers which drive innovation and
growth in other sectors.3
However, it has not always been clear how these two key parts of the UK economy are defined,
which has impeded a full understanding of their economic importance and the formulation of
policies at a national and sub–national level that relate to them.
The report undertakes a rigorous treatment of the creative and high–tech industries, and it looks
at their size, recent growth rates, intersection and geographic distribution around the country at
a regional and sub–regional level.4
It proposes classifications for these industries that are nested within classifications for the wider
creative and high–tech economies, recognising the fact that some creative and high–tech activity
takes place outside the respective industries. The approach we take to identifying industries
as high–tech is an extension of the Dynamic Mapping methodology, which we first developed
in 2013 to classify and measure the creative industries (and which has since been adopted by
government for the production of the UK’s official creative industries statistics).5
The Dynamic Mapping methodology showed that the defining feature of the creative industries
is that they employ a very high proportion of their workers in creative occupations. This report
applies the Dynamic Mapping approach to the distribution of STEM (Science, Technology,
Engineering and Mathematics) occupations in UK industries to examine those that are
particularly intense in STEM skills, allowing high–tech industries to be identified. This enables
these two key segments of the UK’s economy to be compared on a consistent basis.
4 SUMMARY: THE GEOGRAPHY OF THE UK’S CREATIVE AND HIGH-TECH ECONOMIES
2.KEY FINDINGS
The UK’s creative and high–tech economies are major employers
• There were 2.6 million jobs in the UK’s creative economy in 2013, consisting of 1.7 million
jobs in the creative industries (890,000 in creative occupations and 818,000 working in
other roles) and 907,000 jobs in creative occupations outside of the creative industries.
• There were 3.2 million jobs in the UK’s high–tech economy in 2013. This consisted of 2.4
million jobs in high–tech industries (825,000 in STEM occupations and 1.6 million in other
roles) and 806,000 jobs in STEM occupations outside of the high–tech industries.
Tables 1 and 2 show how employment in the creative and high–tech economies is divided among
their different components.
TABLE 1 CREATIVE ECONOMY EMPLOYMENT BY CATEGORY (2013)
Creative Industries
Non–Creative Industries
All Industries*
Creative
Occupations
890,000907,000 1,798,000
Non–creative Occupations
818,000
28,028,000 28,845,000
All Occupations
1,708,000
28,935,000 30,643,000
TABLE 2 HIGH–TECH ECONOMY EMPLOYMENT BY CATEGORY (2013)
High–tech Industries
Non–High–tech Industries
All Industries
STEM
825,000
806,000
Occupations
Non–STEM 1,552,00027,460,000
Occupations
1,631,000
All Occupations
30,643,000
2,377,00028,266,000
*Numbers may not add up exactly to total because of rounding
29,012,000
5 SUMMARY: THE GEOGRAPHY OF THE UK’S CREATIVE AND HIGH-TECH ECONOMIES
Employment in the creative and high–tech economies has been growing
much faster than the UK workforce in recent years, with the creative economy
growing particularly rapidly
• Employment in the creative economy grew on average over three times faster than the
workforce as a whole (4.3 per cent per annum (p.a.) vs 1.2 per cent p.a.) between 2011 and
2013. Employment in the creative industries grew faster still, at over four times the rate of
the workforce.
• Employment in the high–tech economy also grew faster than the workforce over this period
(2.1 per cent p.a. vs 1.2 per cent p.a.), although not as fast as in the creative economy. STEM
occupations outside the high–tech industries grew faster (3.7 per cent p.a.) than STEM
employment in high–tech industries (2.5 per cent p.a.).
There is limited ‘cross–employment’ between creative service and creative
content industries
In relative terms, creative service sectors – such as advertising and design – employ
proportionately small numbers of content professionals, and creative content sectors – like film
and performing arts – employ proportionately few service professionals. In other words, just
as the creative industries are themselves a specialised employer of creative talent, each broad
category specialises in a particular type of talent within the creative industries. This helps to
explain why different sectors within the creative industries often ‘feel’ so different despite the
fact that they all employ creative people.
Those jobs where the UK’s creative and high–tech economies meet have been
growing exceptionally rapidly
There were on average 0.87 million jobs falling within both the creative and high–tech economies
in 2011–2013.6
The intersection of the creative and high–tech economies has grown exceptionally quickly over
this period, at 8.0 per cent p.a., over six times faster than the workforce as a whole. Within this,
the three Information and Communication Technology–related industries at the intersection of the
creative and high–tech economies were even more dynamic, with employment growing at 9.6 per
cent p.a.
Geography of the creative and high–tech economies
Figures 1 and 2 show the areas of the UK where creative and high–tech economy employment
are a higher proportion of the workforce than they constitute of the UK workforce as a whole.
The darker the colour in the Figures the higher the concentration of employment.7 The measure
shown is known as a location quotient (LQ). It is defined, in this instance, as the proportion of
an area’s jobs in creative (high–tech) economy employment divided by the proportion of the
national workforce that is in the creative (high–tech) economy. An LQ > 1 means the area’s
workforce is more concentrated than the national one, an LQ = 1 means that the concentration is
the same and an LQ < 1 means that it is less concentrated.
6 SUMMARY: THE GEOGRAPHY OF THE UK’S CREATIVE AND HIGH-TECH ECONOMIES
FIGURE 1 GEOGRAPHICAL DISTRIBUTION OF
EMPLOYMENT IN THE CREATIVE
ECONOMY, UK, AVERAGE
2011–2013 (LOCATION
QUOTIENTS)
under 0.8
0.8 to < 1
1 to < 1.2
over 1.2
7 SUMMARY: THE GEOGRAPHY OF THE UK’S CREATIVE AND HIGH-TECH ECONOMIES
FIGURE 2 GEOGRAPHICAL DISTRIBUTION OF
EMPLOYMENT IN THE HIGH–TECH
ECONOMY, UK, AVERAGE
2011–2013 (LOCATION
QUOTIENTS)
under 0.8
0.8 to < 1
1 to < 1.2
over 1.2
8 SUMMARY: THE GEOGRAPHY OF THE UK’S CREATIVE AND HIGH-TECH ECONOMIES
The creative economy is particularly highly concentrated in London and the
South East
The creative economy is more unevenly distributed across the country than is the high–tech
economy. In particular, London and the South East of England account for 43 per cent of
employment in the UK’s creative economy and 31 per cent of employment in its high–tech
economy, but are only 28 per cent of the workforce as a whole.
Table 3 shows three different measures of how different industries are distributed across the
UK (as captured by how their location quotients vary across the areas in the maps). The greater
the numbers shown, the more unevenly distributed are the industries around the country. It
shows that the creative industries are one of the most unevenly distributed sectors, with only
agriculture, and finance and insurance being more unevenly distributed on all three measures.
TABLE 3 MEASURES OF THE DISPERSION OF LOCATION QUOTIENTS BY DIFFERENT INDUSTRY GROUPS, UK, AVERAGE 2011–2013
Measures of dispersion
Industry
Range Standard Gini coefficient deviation
Agriculture
9.041.81 0.60
Finance and insurance
3.140.510.32
Creative
2.470.41 0.24
Information and communication
2.31 0.440.28
Real estate
2.19 0.370.21
Production
1.86 0.340.17
High–tech
1.80 0.300.17
Professional and support
1.47 0.220.13
Construction
0.940.17 0.09
Arts and other
0.830.17 0.10
Distribution, transport, hotels and restaurants
0.670.12 0.06
Government, health and education
0.560.10 0.06
Source: Annual Population Survey 2011–2013. Apart from the high–tech and creative industries, the industries shown are
a selection of the broad industrial groups used by ONS.8 With the exception of creative and high–tech industries the
industries shown are mutually exclusive.
Note: The ‘range’ is the difference between the largest and the smallest LQ for an industry grouping across the
geographies. The ‘standard deviation’ is a measure of dispersion which is based on the square of deviations from the
average LQ. The ‘Gini coefficient’ is a measure of equality among the LQs, which is bounded between 0 (all LQs are the
same) and 1 (complete inequality).
Although the ‘Arts and other’ sector category includes a number of arts-related activities (e.g. performing arts
employment) it also includes employment relating to sporting activities, membership organisations and personal services
e.g. washing and (dry–)cleaning of textile and fur products. The measure is therefore not directly comparable to the
creative industries figure.
9 SUMMARY: THE GEOGRAPHY OF THE UK’S CREATIVE AND HIGH-TECH ECONOMIES
Creative and high-tech economy hotspots
It is possible that areas can have comparatively low creative or high–tech location quotients, but
still in absolute terms employ large numbers of people in creative or high–tech economy jobs. To
take account of this in identifying the most significant agglomerations, we use areas that are in
either the top quartile of creative (high–tech) economy location quotients or in the top quartile
in terms of creative (high–tech) economy employment.
Figures 3 and 4 plot these creative and high–tech ‘hotspots’ alongside the other areas. An area
is considered here a hotspot if it appears either above the horizontal line or to the right of the
vertical line. Figure 3 (which looks at the creative economy) includes all the areas previously
identified as creative hotspots in Nesta’s 2010 report, Creative Clusters and Innovation.9
Table 4 provides a complete list of the creative and high–tech economy hotspots, with areas that
are identified as having both high–tech and creative economy agglomerations shaded in bold.
FIGURE 3 CREATIVE ECONOMY HOTSPOTS
Top quartile creative economy location quotient
Inner London - East
Creative economy employment (Average 2011-2013)
200,000
150,000
Outer London - West and North-West
Inner London - West
100,000
Outer London - South
Surrey
Outer London - East and North-East
Hertfordshire Berkshire
Essex CC Hampshire
Kent CC
Greater Manchester - South Oxfordshire
Northern Ireland
West Sussex
Cambridgeshire Buckinghamshire
50,000
Top quartile creative
economy employment
Edinburgh
Bristol
Brighton and Hove
Milton Keynes
0
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Creative economy location quotient (Average 2011–2013)
2.2
2.4
2.6
10 SUMMARY: THE GEOGRAPHY OF THE UK’S CREATIVE AND HIGH-TECH ECONOMIES
FIGURE 4 HIGH–TECH ECONOMY HOTSPOTS
Top quartile high-tech
economy location quotient
120,000
High-tech economy employment (Average 2011-2013)
Outer London - West and North-West
100,000
Hampshire
Inner London - East
Berkshire
Surrey
Hertfordshire
80,000
Outer London - South
Essex CC Inner London - West
Outer London - East and North-East Greater Manchester - South
Northern Ireland
60,000
Kent CC
Cambridgeshire
Lancashire
Aberdeen
Oxfordshire
Greater Manchester - North
40,000
Norfolk
West Sussex
Cheshire CC
Staffordshire CC
North and North-East Somerset
Leeds
Edinburgh
Tyneside
Buckinghamshire CC
Suffolk
Warwickshire
Top quartile
high-tech
economy
employment
Halton and Warrington
Derby
Swindon
West Cumbria
20,000
0
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
High-tech economy location quotient (Average 2011-2013)
Note: These Figures exclude a small number of NUTS3 areas where the employment levels are sufficiently low that they
cannot be published due to Office for National Statistics disclosure rules. Northern Ireland is included at the NUTS1 level (in
these and Table 4) as this is the geographic level its data was analysed at.
TABLE 4 COMPLETE LIST OF CREATIVE AND HIGH–TECH ECONOMY HOTSPOTS (AREAS IN BOLD ARE BOTH HIGH–TECH AND CREATIVE HOTSPOTS)
Creative economy hotspots
High–tech economy hotspots
Bedfordshire CC, Berkshire, Birmingham, Brighton
and Hove, Bristol, Buckinghamshire CC, Calderdale,
Cambridgeshire CC, Cardiff and Vale of Glamorgan,
Cheshire CC, Devon CC, Edinburgh, Essex CC,
Glasgow City, Gloucestershire, Greater Manchester
North, Greater Manchester South, Hampshire CC,
Hertfordshire, Inner London – East, Inner London –
West, Kent CC, Lancashire CC, Leeds, Leicestershire
CC and Rutland, Milton Keynes, Norfolk, North and
North East Somerset, Northamptonshire, Northern
Ireland, Outer London – East and North East, Outer
London – South, Outer London – West and North West,
Oxfordshire, Portsmouth, Southampton, Southend–on–
Sea, Staffordshire CC, Surrey, West Sussex, Wiltshire
CC, Worcestershire, York.
Aberdeen City, Bedfordshire CC, Berkshire, Bristol,
Buckinghamshire CC, Caithness and Sutherland and
Ross, Cambridgeshire CC, Cheshire CC, Coventry,
Derby, Edinburgh, Essex CC, Flintshire and Wrexham,
Gloucestershire, Greater Manchester North, Greater
Manchester South, Halton and Warrington, Hampshire
CC, Hertfordshire, Inner London – East, Inner London
– West, Kent CC, Lancashire CC, Leeds, Leicestershire
CC and Rutland, Milton Keynes, Norfolk, North
and North East Somerset, Northern Ireland, Outer
London – East and North East, Outer London – South,
Outer London – West and North West, Oxfordshire,
Portsmouth, Solihull, South and West Derbyshire, South
Nottinghamshire, Staffordshire CC, Suffolk, Surrey,
Swindon, Tyneside, Warwickshire, West Cumbria, West
Lothian, West Sussex, Wiltshire CC, Worcestershire.
Note This table identifies agglomerations of creative (high–tech) economy activity in absolute or relative (location quotient)
terms. However, some areas may be hotspots of activity in individual creative (high–tech) sub–sectors but still not appear
if these are not sufficiently large to show up at the level of the aggregate creative (high–tech) economy. This explains, for
example, why areas like Dundee and Liverpool do not appear, despite the significance of their video games sectors.
11 SUMMARY: THE GEOGRAPHY OF THE UK’S CREATIVE AND HIGH-TECH ECONOMIES
3.POLICY IMPLICATIONS
T
his research shows that the creative economy is unusually highly concentrated in
London and the South East of England compared with other sectors. Yet, separate
studies suggest that the supply of creative talent is more evenly spread across
the UK.10 This should be of concern to policymakers charged with promoting jobs,
innovation and economic growth in the nation as a whole.
Nesta has separately argued that government should use Regional Growth Fund money to
support burgeoning creative clusters outside London and the South East of England through
a new competitive fund that LEPs and other agencies can apply for.11 The support should be
targeted – recognising that different clusters have different strengths and local needs.
Past experience has shown that policymakers should not build clusters from scratch.12 The
Dynamic Mapping approach we have employed here produces data sets that policymakers can
use to help identify areas with the potential for cluster development.13
The Standard Occupational and Industrial Classification (SOC and SIC) codes the approach
employs are the basis for official statistics in the UK and the world over, and so policymakers
must use them. At the same time, the codes – which are set very infrequently and at an
international level – can give particularly distorted pictures for the fastest changing industries.
In such cases policymakers should also explore complementary methodologies, such as those
making use of ‘big data’ methods, as Nesta is doing in other research.14, 15
To help facilitate the methodology’s application, and to improve the usefulness of the data it
makes available in general, there are a number of changes that the Office for National Statistics
(ONS) could make.
In particular, a limitation of the analysis is that classification changes (from SOC2000 to
SOC2010 in the occupational codes) make it difficult to do time–series analysis. This is why the
report’s estimates relate only to the post–2011 period. Our 2013 report presents employment
estimates for the creative economy for 2004–2010, but the classification changes mean it is not
possible to splice together the series from the two studies. To help improve our understanding
of the dynamics of these important industrial sectors, the ONS should do more to ‘backcast’
industrial employment series when changes in the classifications are implemented (for example,
by re–coding past survey responses using the new codes).16 The Dynamic Mapping method
would be a particularly powerful analytical tool in these cases, as it would enable the evolving
creative and high–tech ‘intensity’ of industries to be tracked over time.
The analysis has been undertaken using the Office for National Statistics’ Annual Population
Survey (APS). Despite its large sample size, the APS is a sample survey which limits its use at
smaller geographical scales.17 The Household Census in principle provides a solution to this
problem, but in the UK does not in practice provide information on occupations and industries at
a sufficiently high resolution.
These restrictions severely limit the use of UK Census data for industrial policy analysis (this is in
marked contrast with countries like the US and Australia where the Household Census has been
used in a multitude of influential studies of occupation and industry dynamics). These limitations
should be addressed by the ONS in the context of the reforms it is introducing in the production
of the Census.18
12 SUMMARY: THE GEOGRAPHY OF THE UK’S CREATIVE AND HIGH-TECH ECONOMIES
4.FUTURE WORK
Nesta is developing the research in several ways:
• Understanding the creative economy internationally
We are applying the Dynamic Mapping approach in a number of other countries (in the first
instance, in North America and Europe) to provide estimates of the creative economy on a
consistent basis that can be benchmarked against the UK.19 This should be of great use for
policymakers and more generally will improve understanding of the international distribution
of creative economy activity.
• Identifying the UK’s creative clusters
The Creative Industries Council (CIC) in its 2014 Create UK strategy tasked its Technical
Working Group with overseeing a cluster mapping exercise for the UK’s creative economy.20
The data collected in the current research will feed directly into this work. The mapping
work will identify clusters that have the biggest potential to drive jobs and growth, and it is
intended that the CIC will work with these clusters to support and coordinate local creative
economy strategies.
• Investigating knowledge spillovers from the UK’s creative and high–tech
economies
Building on the data sets constructed in this report, we plan to use econometric methods to
explore the extent of possible knowledge spillovers in the UK from creative and high–tech
employment. This will be examined through their effects on the wages of people employed
in these sectors and in the wider economy.21 The existence of knowledge spillovers may
suggest under–investment in areas that benefit the economy as a whole, potentially
justifying policy intervention.
• Using machine learning methods to identify creative occupations
In a research collaboration with Carl Benedikt Frey and Michael Osborne of Oxford
University, we are using machine learning techniques to identify creative occupations and
the extent to which they are at risk of automation. This builds on earlier research by Frey
and Osborne to identify which US jobs are at most of risk of automation using the detailed
occupational assessments in the US O*NET surveys.22
• Mapping the geography of the UK’s creative and high–tech economies using
business registry data
We will also explore the geography of the UK’s creative and high–tech industries using
business registry (as opposed to employment) data using the classifications adopted in the
report (an update of the analysis in Nesta’s 2010 report Creative Clusters and Innovation).
13 SUMMARY: THE GEOGRAPHY OF THE UK’S CREATIVE AND HIGH-TECH ECONOMIES
ENDNOTES
1.
Frey, C. and Osborne, M. (2013) ‘The future of employment: how susceptible are jobs to computerisation?’ Mimeo: University of
Oxford. See: http://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pdf Bakhshi, H., Frey, C. and
Osborne, M. (2014) ‘Creativity versus Robots.’ Nesta blog post. See: http://www.nesta.org.uk/blog/creativity–versus–robots
2. Falk, R., Bakhshi, H., Falk, M., Geiger, W., Karr, S., Keppel, C., Leo, H. and Spitzlinger, R. (2010) ‘Innovation and competitiveness of the
creative industries.’ Vienna: Wifo.
3. Muller, K., Rammer, C. and Truby, J. (2009) The role of creative industries in industrial innovation. ‘Innovation: Management, Policy &
Practice.’ 11(2): 148–68; Bakhshi, H. and McVittie, E. (2009) Creative supply chain linkages and innovation: do the creative industries
stimulate business innovation in the wider economy? ‘Innovation: Management, Policy & Practice.’ 11(2): 169–189; Bakhshi, H., Edwards,
J., Roper, S., Scully, J. and Shaw, D. (2011) ‘Creating Innovation in SMEs: evaluating the short–term effects of the Creative Credits pilot.’
London: NESTA.
4. The analysis is based on the Office for National Statistics’ Annual Population Survey (APS) for 2011, 2012 and 2013.
5. Bakhshi, H., Freeman, A. and Higgs, P. (2013) ‘A Dynamic Mapping of the UK’s Creative Industries.’ London: Nesta.
6. These are either jobs that are both creative and STEM, or non–creative and/or non–STEM jobs in industries that are both creative
and high–tech. There are four common SOC codes that are both STEM and creative occupations: Information technology and
telecommunications directors (1136); IT business analysts, architects and systems designers (2135); Programmers and software
development professional (2136), and Web design and development professionals (2137). Three of the creative industry codes are
also classified as high–tech: Other software publishing (5829); Computer programming activities (6201) and Computer consultancy
activities (6202).
7. The analysis in the report is based on the 2003 European Union NUTS3 geographies http://epp.eurostat.ec.europa.eu/portal/page/
portal/nuts_nomenclature/introduction. Northern Ireland appears at the NUTS1 level only in the APS data analysed i.e. the spatial
resolution is Northern Ireland as a whole. This means concentrations of creative or high–tech activity at lower levels of spatial
resolution in Northern Ireland will not be identified.
8. These classifications are the broad industrial sectors used by the Office for National Statistics, and therefore aggregate a number of
industries within them. See Chapter 2 of the 2014 Blue Book for more information.
9. Chapain, C., Cooke, P., De Propris, L., MacNeill, S. and Mateos–Garcia, J. (2010),‘Creative clusters and innovation Putting creativity on
the map’, Nesta.
10. Comunian, R. and Faggian, A. (2014) Creative Graduates and Creative Cities: Exploring the Geography of Creative Education in the
UK. ‘International Journal of Cultural and Creative Industries.’ Vol.1 (2), p.18–34.
11. Nesta (2014) ‘A £200 million programme to develop the UK’s Creative Clusters.’ Nesta blog. See: http://www.nesta.org.uk/blog/
ps200–million–programme–develop–uks–creative–clusters
12. Chapain, C., Cooke, P., MacNeill, S., de Propris, L. and Mateos–Garcia, J. (2010) ‘Creative Clusters and Innovation: Putting creativity on
the map.’ London: NESTA.
13. Bakhshi, H., Hargreaves, I. and Mateos–Garcia, J. (2013) ‘A Manifesto for the Creative Economy.’ London: Nesta.
14. Mateos–Garcia, J., Bakhshi, H. and Lenel, M. (2014) ‘A map of the UK games industry.’ London: Nesta.
15. Nathan, M., Rosso, A. and Bouet, F. (2014) ‘Mapping ‘information economy’ businesses with Big Data: findings for the UK.’ London:
Nesta. Working paper No. 14/10.
16. In January 2015, the DCMS published a backcast series of employment at the level of the creative economy (industries) as a whole
going back to 1997. See DCMS (2015), ‘Creative Industries Economic Estimates, January 2015, Chapter 3’, DCMS.
17. Currently the APS dataset contains around 340,000 individuals.
18. ONS (2014) ‘Beyond 2011: ONS Response to recommendations from the Independent Review of Methodology.’ Newport: ONS.
19. Inconsistencies in the resolution at which employment data are available in different EU countries means that estimates may in some
cases need to be produced at the 3– rather than 4–digit SIC level.
20. The Creative Industries Council (2014) ‘Create UK.’ p16.
21. Bakhshi, H., Lee, N. and Mateos–Garcia, J. (2014) ‘Capital of culture? An econometric analysis of the relationship between arts and
cultural clusters, wages and the creative economy in English cities.’ London: Nesta. Working Paper No. 14/06.
22. Frey, C. and Osborne, M. (2013) ‘The future of employment: how susceptible are jobs to computerisation?’ Mimeo: University of
Oxford.
14 SUMMARY: THE GEOGRAPHY OF THE UK’S CREATIVE AND HIGH-TECH ECONOMIES
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