Reframing models of arts attendance: Understanding the role of access toa venue. The case of opera in London

Orian Brook∗

School of Geography and Geosciences, University of St Andrews, St Andrews, UK

Arts attendance in England has, in recent decades, been the subject of several surveys focusingon how individual factors such as socio-economic status, education, ethnicity and age influenceattendance. These surveys have been used to create small area estimates of arts attendance. Butother studies of the use of public facilities suggest that access to a venue would be highlypredictive of attendance. This paper compares administrative data on opera attendance inLondon with small area estimates of opera audiences, and finds a systematic geographic biasin the errors of the predictions, related to a lack of information about the location of venues.It demonstrates that a model using 2001 Census data and a simple accessibility index betterpredicts attendance, and more accurately locates audiences. It concludes that, by focusing onindividual-level explanations in order to understand cultural engagement, funders havefailed to examine the effect of their own investment.

Keywords: accessibility index; opera; administrative data; segmentation; small area estimates

Introduction

The factors that influence whether individuals attend the arts have been the subject of a consider-able amount of policy research in the UK, particularly during the last decade. The focus has beenon how individual demographic and socio-economic characteristics influence engagement incultural activities. But none of this research has looked at whether living close to an arts venueinfluences individuals’ attendance. As the subsidy of specific venues constitutes a substantialpart of the funding of culture by central government, this omission is paradoxical: policy-makers have not considered the potentially important effect of their own policies on drivingarts attendance. Moreover, academic literatures about users of public and commercial facilitieshave suggested that distance would be highly influential on attendance.

A central plank of policy research has been the commissioning of large-scale surveys; the datacollected have been combined with geo-demographic segmentations to model the number ofattenders to each art form at a local level (ACE, 2008a) – a type of spatial modelling knownas small area estimates. This spatial modelling of audiences is questionable, when the researchhas failed to account for the spatial distribution of venues both theoretically and methodologi-cally: theoretically in the content of the surveys, and methodologically when modelling thespatial distribution of audiences but not to venues. A potential consequence of this approach isthat it risks confounding the effect of access to a venue with other variables. But, more critically,it frames arts attendance as being exclusively a matter of an individual’s taste and/or social pos-ition, and any decision to attend as independent of the policy context in terms of the supply ofcultural facilities.

# 2013 Taylor & Francis

∗Email: orian.brook@st-andrews.ac.uk

Cultural Trends, 2013Vol. 22, No. 2, 97–107, http://dx.doi.org/10.1080/09548963.2013.783175

This paper explores the theoretical and methodological problems with the current aspatialunderstanding of drivers of arts attendance by comparing Arts Council England’s (ACE’s)small area estimates for opera attenders to the observed location of opera attenders accordingto administrative (box office) data. It shows that the predictions are compromised by not account-ing for venue locations; that they overestimate audience levels in areas that are far from venues,and underestimate them in areas that are close to venues. Furthermore, it shows that geo-demo-graphic segmentations, such as Mosaic and ACORN, while useful for summarising and compar-ing audience characteristics, are not the most effective way of predicting audiences in small areas.

Background

Policy understanding of drivers of arts attendance has been based on a series of surveys developedover the last 26 years, since ACE began to commission questions about attendance at key artforms in the Target Group Index (TGI) omnibus survey.1 Rates of attendance at key art formsare reported alongside demographic and lifestyle characteristics of each art form audience(ACE, 2010). This analysis is then used (see box) to estimate the number of attenders to eachart form per postcode sector (ACE, 2011a), which have been used by venues to locate potentialaudiences (Hildrew, 2008) and by funders to understand local demand for cultural services (BakerRichards Consulting, 2007).

Developing small area estimates

There are various approaches to creating small area estimates; the method used for the TGIestimates is not published. In principle, a model is built which the researchers believe bestexplains membership of the population of interest (here, opera attenders) using the dataavailable (the Taking Part and TGI surveys, and other data held by CACI). The variablesused should be available both for the population of interest and at the small area level.Those variables that are most significant in predicting the population of interest nationallyare then chosen and the effect that they have on opera attendance nationally is applied at thelocal level.In the case of Arts audiences: insight, some information about the estimation method is

published (ACE, 2008c). The national model was a segmentation, which means that thesmall area estimation had an extra step: membership of the segments was determined byresponses in Taking Part to questions about cultural attendance and reasons for attendingor not (ACE, 2011b). The profile of the membership of each segment was then analysedusing their demographic characteristics and consumer behaviour (according to TGI). Dis-criminant analysis was used to identify the most significant characteristics, and the prob-ability of belonging to each of the 13 segments was then calculated for residents of eachpostcode in England.

An increasing emphasis on evidence-based policy and a broader definition of cultural activi-ties motivated a new series of enquiries into cultural engagement, culminating in the Taking Partsurvey. A continuous survey with an initial sample of c. 28,000 adults, it collects rich data on cul-tural and sporting attendance, participation and attitudes (Bunting et al., 2007). The data collectedhave also been used in a “statistically based fusion” with TGI data to create Arts audiences:insight, a segmentation of the population based on their consumption of and attitudes toculture (ACE, 2008b), which has in turn been used to model estimates of attendance to each

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art form in Census Output Areas (OAs) (see box). However, none of this research has consideredthe influence on attendance of having access to an arts venue.

In the case of other public amenities, studies tend to find that distance from the facility has astrong impact on levels of usage and/or the profile of users, for example of open spaces(Giles-Corti et al., 2005), museums (Boter, Rouwendal, & Wedel, 2005) and libraries (Park,2011). Geographic distance from venues is of course a relatively crude proxy for a number offactors, both physical and psychological,2 which might constitute access to them. Availabilityof public transport, commuting patterns and other regular travel behaviours (shopping orschool runs) will influence attitudes to routes and destinations, and population subgroups makevarying uses and conceptualisations of geographic areas. Some of these factors can be difficultor impossible to account for3 but it is striking that the simple empirical distance measure is, none-theless, often found to be highly significant.

Only recently, with the culture and sport evidence (CASE) programme commissioned by theDepartment for Culture, Media and Sport, has the issue of the supply of culture as well as demandfor it been raised. Part of the programme involved a scoping study for the creation of a culturalassets database, which acknowledged that there has been

less of a culture of thinking spatially in terms of investment, as policy has hitherto concentrated onissues of supply (e.g. producing work and exhibiting and conserving collections) rather thandemand (e.g. how to equitably and efficiently serve demand across particular geographies). (BOPConsulting, 2009)

Existing asset lists are problematic in that they are each incomplete. But, building a comprehensiveasset list is expensive and the demand for it is not high, given the cost of creation (BOP Consulting,2009, p. 59). There is, of course, a circularity to this situation, in that the demand for cultural assetdata does not exist in part because policy-makers have not thought it relevant, and because the datado not exist, it is not possible to demonstrate whether it is relevant. Indeed, the report notes that dataon sports and heritage assets exist because its use is embedded within the practice of Sport Englandand English Heritage (BOP Consulting, 2009, p. 58). The resulting toolkit for people wishing tobuild their own database of cultural assets (Evans & Foord, 2010) was identified in the report asboth the least cost option and the one which carried the least prospect of success.

When CASE came to create robust statistical models of drivers of cultural engagement (EPPICentre & Matrix Knowledge Group, 2010) in order to account for access to arts venues, multilevelmodels were created with variables on arts provision based on ACE’s Regularly Funded Organ-isations within the local authority (LA). They found no relationship between their measure ofsupply and arts engagement, but acknowledge their measure of supply was weak (Marsh et al.,2010, p. 76). It is indeed questionable that the level of funding by ACE within an LA is agood measure of access to the arts, as it would not capture commercial and locally funded artsfacilities. Moreover, a multilevel structure requires that people only attend facilities withintheir LA, which is not the case.

Methodology

In order to explore the impact of this omission, this paper makes use of administrative data collectedby The Audience Agency (TAA). Administrative data are increasingly being used for social scienceand policy research, due to the scale and detail which it is often not possible to match through tra-ditional survey methodologies (Savage & Burrows, 2007). Moreover, it avoids issues such as thenon-response bias and telescoping which are known to affect survey response, including surveysinto arts attendance behaviour (Roose, Lievens, & Waege, 2007). Venue box offices of necessity

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collect data which are both detailed and accurate, and cultural economists have started to use thisdata to understand markets and the attractiveness of venues (Boter et al., 2005; Willis, Snowball,Wymer, & Grisolı́a, 2012). TAA has collated box office data from 38 arts venues in London,and a subset of this data (transactions for opera performances) is used to provide an observationof the number of households attending opera in small areas, for comparison with modelled esti-mates. The location of each venue presenting opera and the number of opera tickets they soldare also used to create an accessibility index, which combined in a model with data from the2001 Census will be compared with other segmentations commonly in use.4

Audiences for opera were chosen for analysis for two reasons. First, funding for opera ismostly concentrated on performances in a few large venues that are expected to have broad geo-graphical reach, so one might expect distance to be a relatively less significant barrier to attend-ance; moreover, it is a genre with a reputation for audiences which are socially stratified (Chan,Goldthorpe, Keaney, & Oskala, 2008), which would again suggest that the majority of the vari-ation in attendance should be explained by socio-economic and demographic factors rather thandistance. Second, data collected by box offices for opera are relatively complete as attendances areusually booked in advance;5 and TAA’s data are comprehensive, as there are no major venues pre-senting opera whose data are not held. Therefore, there should be agreement between the mod-elled attendance according to surveys and observed attendances at opera.

London itself is not of course representative of the rest of the country, particularly in its accessto cultural facilities: all parts of London might be said to have good access to opera. Rather thanarguing that opera and London are typical examples generalisable to other art forms and regions,we contend that this is a case where one might expect to find accessibility having relatively littleimpact on attendance: if an effect is found here, then even more one might expect to find one inother art forms and locations.

Attendances between July 2004 and June 2006 were selected at the Almeida, Artsdepot, theBarbican, Cadogan Hall, English National Opera, Greenwich Theatre, Hackney Empire, PeacockTheatre, Royal Albert Hall, Royal Opera House, Sadler’s Wells and the Southbank Centre. As theadministrative data set was to be compared with the Taking Part survey, it was edited so that defi-nitions matched as far as possible. Corporate, press and group bookings were removed, as TakingPart asks people about attendances which were not undertaken as part of their job. Data werelimited to attendances from within London so that we could be more confident that residentswere not attending venues whose box office data we did not have. This left attendances fromalmost 100,000 unique households (matched across venues, so that if a household attendedmore than one venue they were counted only once).

For modelling purposes, the booking records were aggregated via their postcodes to CensusOAs, each of which contains approximately 125 households. This created a count of householdsper OA that attended opera. There are 24,141 observations (one per OA). Other variables aboutthe OAs were then appended, including variables from the 2001 Census,6 chosen because of the lit-erature on drivers of cultural attendance (Bunting et al., 2007): the proportion of residents in eachsocio-economic classification, age, ethnic and religious group; the proportion of households withaccess to a car; the proportion of adults that are full-time students, and that have each of fourlevels of qualifications, from General Certificate of Secondary Education (GCSE) to degree or above.

From the Mosaic and ACORN geo-demographic segmentations, the proportion of households(Mosaic) or adults (ACORN) belonging to each of their most detailed “type” segments7 in theOA was appended, as was the median income from Experian. From the ACE modelling, theproportion of adults belonging to each of the Arts audiences: insight segments was added. Inorder to compare the original TGI-based predictions of attendance, a further file was created,aggregating the box office data at the postcode sector level and appending the predictednumber of opera attenders according to the TGI modelling (Table 1).

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A final model was developed, which used the Census variables already mentioned, the log ofthe median income score from Experian and an accessibility index for the venues presentingopera. Accessibility indices have been used to understand the relative access to transport andother public and commercial facilities. As such, these can be used to evaluate policy alternatives(Handy & Niemeier, 1997). While there are a range of methods used, they have in common thatthey calculate, from a given point (normally a residential area), the number of opportunities(stations, shops) available within a given distance, the “attractiveness” of each opportunity (thefrequency of the trains, or size of the shop) and the “cost” of reaching it – which might be dis-tance, travel time, or financial cost of the journey.

For this paper, a simple, common approach to the calculation of access to opera was adopted(Plane & Rogerson, 1994). The straight line distance between the postcode of the venue and thecentre of the OA was calculated, and the log of the distance was taken because a small change indistance has a greater effect when the starting point is a short distance. (For the same reason, thelog of the median income score was used: small changes in income make more of a differencewhere incomes are low than where they are high.) The log of the distance between each OAand venue was multiplied by the number of tickets sold by the venue. This recognises that, foropera, distance from the Royal Opera House affects one’s access to opera more than distancefrom Greenwich Theatre. The accessibility index for opera,8 and the location and size of thevenues presenting opera in London are shown in Figure 1.

Table 1. Segmentations/estimates used for analysis.

Segmentation Scale No. variables

TGI: opera index Postcode sector 1Arts audiences: insight OA 13ACORN type OA 55Mosaic type OA 51Census variables plus accessibility OA 20

Figure 1. Opera venues and accessibility index, London.

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The data sets were then used in a series of grouped logistic regression models. A logisticregression models a binary outcome variable (i.e. a positive or negative result), and reports theeffect of each independent variable on the probability of the outcome. A grouped logistic regressionmodels a count of the number of positive outcomes (households attending opera within an area)compared with the number of possible outcomes (all residential households in that area) (Baum,2008). The standard approach to comparing such models uses the deviance statistic, the equivalentof the residual sum of squares in a linear regression model (Hosmer & Lemeshow, 1989, p. 14). Thisis a measure of how closely the modelled rate of attendance corresponds to the observed rate: if thedeviance of a model with some explanatory variables is compared with that of a model with none,reduction in deviance can be used as a pseudo-R2 to compare the models.

Results

Table 2 summarises the results for each model.9 Note that all of the existing, aspatial data used topredict arts attendance explain a similar amount of deviance, between 55 per cent and 60 per cent.The model that contains Census variables plus the accessibility index explains considerably moreat 70 per cent. There are two important possible explanations for this: one is that the other pre-dictions are made using segmentations, which are essentially data reduction methods, and sim-plify the extensive data that they are built with in order to identify patterns within the data.While useful for profiling and comparing audiences, this also removes detail and therefore accu-racy. It might have been better to use the key variables that predicted the segment membership todirectly predict audiences at a local level, rather than use them to predict segment membership andthen use segment membership to predict audiences.

The other advantage that the best performing model has is that it includes the accessibilityindex. On its own, this variable does not perform strongly: a model including just the accessibilityindex explains only 20.9 per cent of deviance (a model which includes only the percentage ofadults with degree-level qualifications predicts 57.8 per cent of deviance). But, once accessibilityis added to a model with Census variables, it becomes more significant than the other variables inthe model, as can be seen in Table 3, which shows the detailed model parameters.10

The strong significance and effect of both having access to a venue and having a degree inpredicting opera attendance are notable: in both cases, a 10 per cent increase in these values isassociated with a c. 50 per cent increase in attendance. The other variables listed, although sig-nificant, had nothing like the same strength of predictive value for attendance. As might beexpected for opera audiences, the percentage population aged over 50 is strongly positively pre-dictive of attendance, whereas the percentage aged 16–29 is negatively predictive. However, thepercentage of full-time students is positive, again (with the percentage with A levels) suggestingthe strong influence of education on opera attendance. By comparison, socio-economic status andmedian income, although significant, have more modest effects.

Table 2. Comparison of models predicting attendance to opera.

Model used for prediction Per cent deviance explained

TGI (opera index) 54.9ACORN type 55.6Arts audiences: insight 60.1Mosaic type 60.5Census variables with accessibility 70.0

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We can see that including the accessibility index removes a specific bias from the errors, illus-trated in Figures 2 and 3. The predictions from the insight model were compared with theobserved number of households attending according to the administrative data, and the resultsaggregated to the larger Lower Layer Super Output Area (LSOA) area, to aid visualisation,and mapped using a cartogram11 in Figure 2. In the dark areas, the insight model predictedonly half the number of attenders found in the administrative data; in the white areas the

Table 3. Parameters, grouped logistic regression model with Census and accessibility.

Variable Coefficient Standard error z OR (%)

Accessibility index 3.77 0.09 43.86 146Log median income 0.32 0.03 12.25 103Per cent households without access to a car 20.75 0.05 215.29 93Per cent students 1.42 0.1 13.77 115Per cent aged 5–15 1.18 0.13 8.77 113Per cent aged 16–29 20.9 0.08 210.85 91Per cent aged 50–65 2.03 0.13 15.83 123Per cent aged 65–74 2.18 0.16 14.01 124Per cent White Irish 1.57 0.2 7.78 117Per cent White other 20.45 0.08 25.83 96Per cent Asian Indian 21.73 0.1 217.57 84Per cent Asian other 23.46 0.28 212.4 71Per cent Black Caribbean 0.78 0.12 6.26 108Per cent Black African 21.9 0.14 214.03 83Per cent Jewish Religion 0.97 0.06 15.07 110Per cent religion none 1.29 0.08 15.45 114Per cent NS-SeC 7 21.87 0.23 28.02 83Per cent NS-SeC 8 1.62 0.17 9.31 118Per cent qualifications level 3 (A level) 2.79 0.14 19.65 132Per cent qualifications level 4–5 (degree or above) 4.15 0.06 72.83 151Constant 29.45 0.28 233.72

Figure 2. Arts audiences: insight prediction compared to observed attendance.

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insight model predicted twice as many attenders. Dark areas are concentrated close to centralLondon, where venues are mostly located, whereas most white areas are further away. It isclear that, even within London, there is a systematic geographic bias in the errors of the predic-tions from the ACE segmentation model, which is related to a lack of information about wherevenues are located.

By contrast, in Figure 3, the predictions were made using the Census model with accessibilityindex, and compared with the administrative data in the same way. While there is still some geo-graphic bias to the errors in the predictions, it can be seen that a considerable amount has beenremoved. In fact, the number of LSOAs where the predictions are out by a factor of two isreduced in the second model by 41 per cent, from 1144 to 673.

Conclusion

This paper began by arguing that the failure to consider access to venues in understanding driversof arts attendance was an important omission both theoretically and methodologically, in thecreation of small area estimates. By comparing administrative data to existing estimates, andusing it to calculate an accessibility index, this paper has demonstrated that, in the case ofopera audiences in London, this omission is indeed significant. A simple model using secondaryand administrative data explained more attendance than the extensive modelling of the primary datainvolved in creating the Arts audiences: insight predictions. Moreover, the location of audiences ismis-specified by not accounting for the effect of distance from a venue on levels of attendance.

And if distance has a significant effect for opera attenders, where audiences are notoriouslysocially stratified, and in London, where all areas have good access to opera venues, howmuch more impact might it have for other artforms and regions? The analysis of audiences forchildren’s events in urban and rural Scotland, for example, could show an even stronger effectof distance from the venue on attendance. Using administrative data enables a detailed dissectionof arts attendance that is not always possible with a survey, for example, comparing audiences forpantomime and new writing, or those that buy cheaper and more expensive tickets. This wouldenable a more nuanced approach that might usefully inform both funding and marketing decisions

Figure 3. Census & accessibility prediction compared to observed attendance.

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at the venue level. Furthermore, there is no doubt that London is not a typical region in either itsarts provision or its population: analysis of data from one of the other administrative data poolingschemes would be highly desirable.

This analysis also needs to be expanded methodologically. The intention here was to show thateven a simple accessibility index could improve models of attendance. A more sophisticated approachwould be desirable, with the inclusion, for example, of commuting behaviour or access to publictransport. A disadvantage of this analysis is that it is an ecological model: households attendingmay not be representative of the areas that they live in, so that relationships at the area level maynot exist at the individual-level. Including an accessibility index in an analysis of survey datawould allow a model to be created using respondents’ individual characteristics, which could alsoexamine their motivations and attitudes to culture, not simply whether or not they attended. Itcould also include types of activities and art forms for which no acceptable administrative data exist.

The analysis also brings into question the sector’s reliance on segmentations for predictingaudience levels. It is easy to understand why segmentations are popular: they identify patternsin complex data; offer a user-friendly means to communicate audience profiles; and make profil-ing accessible to non-specialists, as it requires only a list of postcodes. However, the summarisingof data involved in creating the segmentation implies that it was relatively easy to build a simpledisaggregated model, which provides better discrimination for predicting attendance levels. It isnot that segmentations should no longer be used, but how and when they are best used mightrequire careful consideration.

In addition to the question of accuracy, there is one of accountability. The framing of artsattendance as an individual choice, or one informed by social processes, ignores the role of thefunders of such services in enabling attendance. If arts provision is thought to be a publicgood, and there is a considerable though contested body of literature on the impacts of culture(Belfiore & Bennett, 2008; Ruiz, 2004), then the role of policy-makers in providing culturalopportunities needs to be acknowledged and examined. By focusing on individual-level expla-nation to understand cultural engagement, funders have failed to examine the effect of theirown investments (and indeed disinvestments). A natural experiment, looking at the effect onarts attendance when a venue opened or closed, would be highly informative, and could poten-tially use existing administrative or survey data, or ideally, the new longitudinal data on culturalengagement, which is becoming available.

AcknowledgementsCensus boundary data 2001 were supplied by The Office for National Statistics. The author is a PhD studentsupported by the ESRC Capacity Building Cluster: Creative Industries Scotland RES-187-24-0014. Thanksto Dr Chris Dibben for comments and suggestions.

Notes1. There is a substantial sociological literature on cultural consumption and taste, and their contribution to

the development and perpetuation of social structures, using both primary data (Bennett et al., 2009;Bourdieu, 1984) and policy surveys (Chan & Goldthorpe, 2005), some of which has informed policyresearch. But it also fails to address proximity to arts venues.

2. Gould and White (1974) have explored psychological aspects of distance.3. For an example of analysis incorporating workplace access to culture, see Brook, Boyle, and Flower-

dew (2010).4. ACORN and Mosaic are both used to profile attenders or areas where they may live (Hillman, 2002).

Full details of their construction are not published, but they are both based on modelling of Census,Electoral Roll and Land Registry data along with consumer surveys.

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5. Neill and Orme (2006) found that arts attenders who did not buy their own tickets (and have their datacaptured) were similar demographically to those who bought tickets, and were likely to have been cap-tured on another occasion.

6. The Census data only offers a snapshot of the population on the day of collection, and residential popu-lations will have changed somewhat between 2001 and 2004–2006: however, it is the only source forthe detailed level required.

7. Four segments for ACORN and eight for Mosaic were excluded as they represent no or very few post-codes within London.

8. For other cultural accessibility indices, see Brook et al. (2010).9. The percentage deviance explained by the TGI model is not strictly comparable to the other models as

it uses a different geographic scale. However, larger geographic scales usually give a better model fit(Openshaw, 1983): as the TGI model uses a larger geographical scale, but explains less deviance, thanthe other models, we can be confident that it explains opera attendance less well.

10. This model was developed by first using all of the explanatory variables suggested by the literature plusthe accessibility index, and removing the least significant variables until collinearity was acceptable.

11. A cartogram is a map where geographic areas are displayed resized according to some other variables(Dorling, 2011). In this case, the number of residential households was used, so that the more denselyand sparsely populated areas are given equal visual prominence.

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