1. Demographic indicators of cultural consumption UPTAP Workshop, University of Leeds 18 March 2008 Orian Brook, Audiences London & University of St Andrews Paul Boyle & Robin Flowerdew, University of St Andrews

2. Background © Simon Jay Price

3. Why is this project important?  Mounting interest in evidence-based policy in general  Specifically in the subsidised arts sector – who benefits from the investment?  Great deal of research based on survey data  Project will enable more sophisticated and robust policy-related conclusions to be drawn from box office data collected by regional agencies

4. Theorising Cultural Consumption  Social inequality and patterns of cultural taste and consumption are the subject of a large and complex debate  Related to social class, education, ethnicity, income?  Arts Council has targets to increase participation in culture by three target groups: lower Socio-Economic groups, Black and Minority Ethnic Groups, and Disabled People  We can see that there are relationships with all these factors, but how to compare their significance?  Will doing this with BO instead of survey data tell a different story?

5. Problem of self-reported arts attendance  Cultural consumption closely tied to personal identity  Often engaged in to claim a social status (or reject one)  Reporting of cultural attendance in surveys problematic  respondents may answer according to their identity rather than their visits  Can work positively and negatively  People may claim attendance at certain cultural events that accord with their self image  Deny attendance at certain artforms if they do not represent who they are

6. Geography of arts attendance  Previous research supposes that all demographic groups have equal opportunities to attend  But we know that communities are concentrated in different areas, with different characteristics including cultural provision  How does take-up of culture compare to provision – geographically and demographically?

7. London dataset  Box Office data collected from 33 venues  Events coded into artforms  Selected only transactions <8 tickets, not free tickets  Must have valid UK residential postcode  Only from postcodes within London (c70%)  Customer records from 2005 matched at address level  ~ 350,000 households  ~ 930,000 transactions  ~ 2 million tickets sold  ~ £51 million revenue

8. London venues who provide data Albany, Deptford Almeida Theatre artsdepot Barbican Centre Battersea Arts Centre Bush Theatre Croydon Clocktower Drill Hall English National Ballet English National Opera Greenwich Theatre Royal Court Royal Festival Hall Royal Opera House Sadler's Wells Shakespeare's Globe Soho Theatre Theatre Royal, Stratford East Watermans Hampstead Theatre London Philharmonic Orchestra London Symphony Orchestra Lyric Hammersmith National Theatre Open Air Theatre Philharmonia Orchestra The Place Polka Theatre Queens Theatre, Hornchurch Royal Albert Hall

9.  What are the best geodemographic and socio-economic predictors of arts attendance? Do they vary across:  Art forms (e.g. theatre versus dance, highbrow vs popular)?  Venue locations (Urban Centre vs edge of City)  Geographical areas? (regions, and areas within London)  Availability of venues/performances?  Do the distances that people will travel to venues vary?  Has this changed over time?  Do some geodemographic classifications give better discrimination than others when analysing arts attendees? Research Questions

10. Research

11. Methodology  Counted unique addresses attending during 2005  Compared to residential addresses during 2005 according to Experian postcode directory  Provides best match to other 2005 population/household estimates at higher geography  But used NSPD allocation to output areas  Compared at OA level to census variables (other relevant geographies for other data)  Used grouped logistic regression corrected for overdispersion

12. Population data Driven by previous research and hypotheses  Ethnic Group & Born outside UK  Qualification Level  Socio-Economic Classification (NSSEC)  Age Group  Religion  Economic Activity  Limiting Long Term Illness & Health (Good etc)  Households with Children  Social Renting  Access to a Car  Plus Income Deprivation from IMD 2004

13. Culture Accessibility Index  Demographics alone doesn’t take into account variations in each area’s access to culture  Created an Accessibility Index just for the venues for which we have box office data  Based on the distance from each OA to each venue  Weighted so that being close to Greenwich Theatre isn’t as counted the same as being close to the National Theatre  In this case, weighted by number of tickets sold (with customer capture)

14. Culture Accessibility Index (all artforms)

15. Children/Family Events Accessibility Index

16. Opera Accessibility Index

17. Commuting Index  Hypothesis: commuting to an area of high Cultural Accessibility improves chances of attending, compared to working in area of low CA (although in surveys people deny this)  Commuting varies by ethnic group  Created a Commuting Index  Downloaded commuting data matrix from CIDER  Calculated % of adults in an OA that commute to each other OA  Multiplied the % by the Culture Accessibility Index for the destination OA & summed these for the OA of origin

18. Cultural Commuting Index

19. Royal Court Commuting Index

20. Theatre Royal Stratford East Commuting Index

21. How much variation in attendance can be explained? Comparing model deviance to null deviance:  54.8% explained by Arts Council targets (non-White Ethnicity, lower four NSSEC groups, LTTI)  NSSEC and Income look highly significant  55.1% explained if Income is added  70% explained by fuller range of Census variables (35 out of 54 are significant)  71.5% if Cultural Accessibility and Commuting Indices added  Only a small overall increase, but changes relative importance of variables

22. What’s important in explaining attendance?  71.5% deviance explained by 54 variables, 65% explained by just 6:  Level of degree-level qualifications by far the most important  10% increase in graduates > 39% increase in arts attenders  Cultural Accessibility and Commuting indices  % with no religion (24%) or Jewish (20%)  % households with kids +ve (9%) but aged 0-4 –ve (17%)  % FT Students +ve (26%) but aged 16-29 –ve (18%)

23. What’s not important, what’s negative, and what’s changed  Income is not significant  NSSEC 1 is barely significant and weak effect (7%), NSSECs 5-8 not significant  Chinese and Hindu are negative  Not including accessibility and commuting:  NSSEC1 looks much more important (13%)  being retired less negative

24. How do these change by artform/venue?  % households with children still important in adult events  Childrens events:  % graduates much less important (agrees with qual research)  % households with kids no more +ve, aged 0-4 now +ve too  Opera:  NSSEC1 and income not significant  % graduates even more influential (47%)  Contrasting theatres:  % graduates: 100% vs not significant

25. Proportional Accessibility to TRSE

26. Comparing Existing Classifications and Indices  Townsend deprivation (OA) – 3%  Area Classification (OA) – 23%  Indices of Multiple Deprivation 2004 (LSOA) – 47%  Mosaic (Postcode/OA) – 51%  So new model is better than any existing classification Deviance explained compared to null model

27. orian.brook@st-andrews.ac.uk