the geography of the uk's creative and high–tech economies

1 SUMMARY: THE GEOGRAPHY OF THE UK’S CREATIVE AND HIGH-TECH ECONOMIES

SUMMARY

Hasan Bakhshi, John Davies, Alan Freeman and Peter HiggsJanuary 2015

THE GEOGRAPHY OF THE UK’S CREATIVE ANDHIGH–TECH ECONOMIES


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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.


1. INTRODUCTION

The 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.

THE GEOGRAPHY OF THE UK’S CREATIVE ANDHIGH–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)

TABLE 2 HIGH–TECH ECONOMY EMPLOYMENT BY CATEGORY (2013)

*Numbers may not add up exactly to total because of rounding

Creative Industries Non–Creative Industries All Industries*

Creative 890,000 907,000 1,798,000 Occupations

Non–creative 818,000 28,028,000 28,845,000 Occupations

All Occupations 1,708,000 28,935,000 30,643,000

High–tech Industries Non–High–tech Industries All Industries

STEM 825,000 806,000 1,631,000 Occupations Non–STEM 1,552,000 27,460,000 29,012,000 Occupations

All Occupations 2,377,000 28,266,000 30,643,000


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.


under 0.8

0.8 to < 1

1 to < 1.2

over 1.2

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

FIGURE 2 GEOGRAPHICAL DISTRIBUTION OF EMPLOYMENT IN THE HIGH–TECH ECONOMY, UK, AVERAGE 2011–2013 (LOCATION QUOTIENTS)


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

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.

Industry Range Standard Gini coefficient deviation Agriculture 9.04 1.81 0.60

Finance and insurance 3.14 0.51 0.32

Creative 2.47 0.41 0.24

Information and communication 2.31 0.44 0.28

Real estate 2.19 0.37 0.21

Production 1.86 0.34 0.17

High–tech 1.80 0.30 0.17

Professional and support 1.47 0.22 0.13

Construction 0.94 0.17 0.09

Arts and other 0.83 0.17 0.10

Distribution, transport, hotels and restaurants 0.67 0.12 0.06

Government, health and education 0.56 0.10 0.06

Measures of dispersion


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

Cre

ati

ve

eco

no

my e

mp

loym

en

t (A

ve

rag

e 2

011

-20

13)

200,000

150,000

100,000

50,000

0

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6

Creative economy location quotient (Average 2011–2013)

Brighton and Hove

Buckinghamshire

Oxfordshire

Outer London - South

Surrey

Cambridgeshire

BerkshireHertfordshire

Milton Keynes

Bristol

Edinburgh

Hampshire

Northern Ireland

Essex CC Kent CC Greater Manchester - South

West Sussex

Top quartile creativeeconomy employment

Outer London - East and North-East

Inner London - East

Inner London - West

Outer London - West and North-West

Top quartile creative economy location quotient


FIGURE 4 HIGH–TECH ECONOMY HOTSPOTS

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)

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.

Hig

h-t

ech

eco

no

my e

mp

loym

en

t (A

ve

rag

e 2

011

-20

13)

120,000

100,000

80,000

60,000

20,000

40,000

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)

Berkshire

Aberdeen Cambridgeshire

Outer London - West and North-West

Hampshire

Surrey

Hertfordshire

Outer London - South

Inner London - West

Northern Ireland

Outer London - East and North-East

Kent CC

Essex CC

Greater Manchester - South

Cheshire CC

Buckinghamshire CC

Derby

Edinburgh

West Sussex Greater Manchester - North

Top quartile high-techeconomy location quotient

Norfolk Suffolk

Staffordshire CC North and North-East Somerset

Halton and Warrington

Swindon

Warwickshire

Inner London - East

LancashireOxfordshire

West Cumbria

LeedsTyneside

Top quartilehigh-techeconomy

employment

Creative 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.

High–tech economy hotspots

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.


3. POLICY IMPLICATIONS

This 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


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).


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.

http://epp.eurostat.ec.europa.eu/portal/page/portal/nuts_nomenclature/introduction

http://epp.eurostat.ec.europa.eu/portal/page/portal/nuts_nomenclature/introduction

http://www.nesta.org.uk/blog/ps200-million-programme-develop-uks-creative-clusters

http://www.nesta.org.uk/blog/ps200-million-programme-develop-uks-creative-clusters


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the geography of the uk's creative and high–tech economies