Post on 04-Jan-2016
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
Background
© Simon Jay Price
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
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?
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
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?
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
London venues who provide data
Albany, DeptfordAlmeida TheatreartsdepotBarbican CentreBattersea Arts CentreBush TheatreCroydon ClocktowerDrill Hall English National BalletEnglish National OperaGreenwich Theatre
Royal CourtRoyal Festival HallRoyal Opera HouseSadler's Wells Shakespeare's GlobeSoho TheatreTheatre Royal, Stratford EastWatermans
Hampstead TheatreLondon Philharmonic OrchestraLondon Symphony OrchestraLyric HammersmithNational TheatreOpen Air TheatrePhilharmonia OrchestraThe PlacePolka TheatreQueens Theatre, Hornchurch Royal Albert Hall
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
Research
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
Population dataDriven 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
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)
Culture Accessibility Index (all artforms)
Children/Family Events Accessibility Index
Opera Accessibility Index
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
Cultural Commuting Index
Royal Court Commuting Index
Theatre Royal Stratford East Commuting Index
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
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%)
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
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
Proportional Accessibility to TRSE
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
orian.brook@st-andrews.ac.uk