Introduction
When talking about performing arts, it feels almost compulsory to begin with Baumol and Bowen (1966), who introduced the theory and empirical framework for conducting research in performing arts sector. Costs disease, the gap between the increase in spending of artistic organizations in public performing arts and low increase of labor productivity sector was a frequent argument for necessity of governmental subsidies. Baumol and Bowen (1966) argued that within the increase of the relative cost and price, the demand for performing arts would decline. Since the great contribution of Baumol and Bowen in 1966 there has been a progress in empirical studies and enlargement of scope on the subject in cultural economies (Blaug, 2001). The assumption of Baumol and Bowen (1966) where the demand for performing arts is price- elastic and income-inelastic has been studied ever since, with different, often contradicting results. Many studies argue that theater is a standard case of inelastic and luxury good, where audience demonstrates little sensitivity to price (Heilbrun and Gray 2001; Levy-Garboua and Montmarquette 2003). (Grisolía & Willis, 2012,p.118).
In the times when subsidies for performing arts in Europe are diminishing and artistic institutions are facing the decrease of the audience, the studies of demand for theater, dance, opera and concerts should gain even more relevancy and importance. Understanding the socio-economic factors influencing demand such as people’s preferences, audience profiles, tastes and behavior can be seen as crucial for marketing strategies, writing subsidy request and for policy making objectives.
This essay will look at 4 studies that explore various aspects of demand in European theaters. It will analyze the econometric study by Zieba (2009) on full income and price elasticities for German public theaters; look at how the distance influences the demand in case of Dutch theaters has been examined by Langeveld & Stiphout (2013); review the paper on preferences of audience by Grisolia & Willis, 2012 and consumption motives by De Rooij & Bastiaanen (2017).
Price and income elasticities by Zieba (2009)
Seaman (2006) gives a review of 44 econometric studies that have been done on demand in performing arts, from which 29 tackle price and income elasticities issues. The great number of studies (12 out of 29) argues that the demand for performing art is price inelastic (e.g., Moore, 1966; Gapinski, 1986; Luksetich and Lange, 1995) and 4 strongly confirms the price elastic demand (e.g., Pommerehne and Kirchgassner, 1987; 1996; Schimmelpfennig, 1997). The empirical studies usually focus on the 3 elements of demand in performing arts: “the level of participation, the characteristic of participation and the factors that influence participation” (McCarthy, 2001, p.18).
Zieba (2009) sees the demand for the performing arts as equivalent to theater attendance and focuses on the factors: ticket price and income that influence participation.
Zieba (2009) aims to obtain reliable and complete estimates of price and income elasticities of demand for the performing arts in case of German public theater. Using a large data set, Zieba (2009) attempts to analyze the subject in an econometrical way and to confirm previous findings of the research by Withers (1980). The data set consists of 178 theaters, from which 105 theaters are situated in West Germany gathered over a period of 40 theater seasons (1965/1966–2004/2005) and 73 theaters in East Germany collected over a period of 15 years (1990/1991–2004/2005). Zieba derived data from theatre statistic reports, which served as a main data source. The rest of the needed data was obtained from the Federal and Regional Statistical Offices. However, it is worth noticing, that the data for East Germany for years 1965/1966–1989/1990 are missing. The results might not be entirely representative for the whole population of Germany for the researched time period.
Zieba (2009) introduced following variables: theater attendance, theater ticket price, theater ticket price, quality indicators and concert ticket price. The dependent variable is the total attendance per theatre, divided by the relevant population size. Building on Werc & Heyndels (2007), Zieba (2009) describes quality through the use of three indicators: the first one is as a ratio of guest performances over the total performances, the second one is the quality of the artist, and the third the quality of the costumes and design. The choice of quality indicators remains always questionable. The quality judgement is an important factor influencing decision making, and although as Throsby (1990) argues it can be determined objectively to some degree, there will always remain a subjective component to it.
Furthermore, two ‘time-allocation’: price of leisure time and full income variables are presented. For Zieba price of leisure time is important variable, that’s needed to take into an account when measuring price and income elasticities of theater demand.
The estimates of the own-price elasticity are accordingly with the previous studies (Withers,1980), which means that the demand is negatively related to the theatre ticket price in case of German public theatres (Zieba, 2009) The demand is therefore inelastic and it’s possible to increase the revenues by raising the price of the tickets. According to standard demand model, the full-income elasticity (leisure time income plus disposable income) is higher than the one indicated in previous studies by Withers (1980) and confirms the results of Le´vy-Garboua and Montmarquette (2002), indicating performing arts as luxury good. The results of the alternative demand model indicate that performing arts are time-intensive in consumption and leisure time is a complement for the performing arts.
Besides the use of incomplete data and questionable quality indicators, the biggest limitation of this study is the aggregate data. Seaman (2006) argues that the results depend on the level of data aggregation. He further agrees with Felton (1992) that the majority of studies with aggregate data result in price inelasticity and when focused on individual art organization the results tend to be elastic.
Willingness to travel
Price of tickets and income are not the only factors influencing demand and being an interest of the research. To analyze the willingness to travel research can focus on several dimensions such as: time of the travel, cost of the travel, distance of the travel or mean of travel (Langeveld & van Stiphout, 2013). The main aim of the research by Langeveld & Stiphout (2013) is to examine the influence the distance has on the demand for performing arts. In other words, it measures the willingness to travel by focusing on travel distance from home address to theatre destination. The research is based on findings by Verhoeff (1993) who detected a large variability in case of distances between home addresses to chosen theaters in the Netherlands. While most studies on willingness to travel use surveys to gather the necessary data, Langeveld & Stiphout, in like manner with Zieba (2009), derived their data from theatres database. This way of gathering data is less costly and less time consuming. It is limited however, because researcher sample consists only from theater consumers. On one side, it makes the study more feasible, but on the other hand excludes audience who don’t go to theater because of too large distance. Langeveld and Stiphout (2013) used first four digits of zip codes to establish the distance which customers had to travel to see the performance. They collected 12.399 different distances from home addresses (zip codes) to selected theatres or concert halls for the season 2009/2010. The data was collected from seven theaters and one concert hall in the Netherlands. They use distance decay model analysis and descriptive statistics to showcase the findings.
The research was divided in three parts. The first part concentrated on showing the difference in the willingness to travel for different genres of performing arts for three regional theatres, where the showcased performances are comparable, but the environment of the theatres is different. It focuses on the origin of the audience of regional theatres. The second part focuses on the origin of audience but eliminates the variable: population size and examines the distance decay of few regional theatres for three large scale musicals. The third part concerns the willingness to travel to an exclusive performance and bases on the case of Circustheater in Den Haag, which showcases big musicals.
The strength of the research is that it shows real distances audience makes for specific theaters and genres and does not focus on hypothetical willingness to travel. The results of this research are not transferable to other countries. Netherland comparing with other European countries, is small, has good roads, good and accessible public transport. The research shows only differences in distance for certain theaters and genres. It would be however interesting to know what type of audience (socio-economic background) travels more and what type travels less.
Focus on audience preferences and motivations
Some researchers do focus more on the characteristics associated with participation, where sociodemographic profiles, education as one of the most common predictors of art participation are being often examined (McCarthy, 2001). There is a variety of other external attributes that play a role in performing arts participation that has been studied. One of the interesting study on theater demand is one by Grisolia & Willis (2012).
Grisolia & Willis (2012) aim is to identify market segments for theater demand in case of regional theaters in Newcastle, England. They developed a Latent Class Model (LCM), which allows for investigating different classes/segments in terms of sizes and preferences (Greene & Hensher, 2002). The interest of Grisolia & Willis (2012) goes to preferences of people attending Norther Stage in relation to other theaters (substitutes), in relation to the attributes of various productions at Northern Stage, and in relation to ticket prices. LCM allows to identify types of audience according to preferences for attributes of theatrical productions.
The data collection in this study comes from surveys in the form of self-completion questionnaires, handed by the interviewer to 600 people with the obtained result of 55% email response. The researchers employ a discrete choice experiment method. In discrete choice experiment type questionnaires, the respondents are presented with a series of scenarios and asked to choose the option that they would prefer (Greene & Hensher, 2002). It allows therefore to explore peoples ‘preferences on different aspects. The respondents choose between the options, described by a number of attributes.
Before the data collection, Grisolia & Willis (2012) identified a set of attributes, based on discussions with theatre directors and focus groups of theatregoers. The attributes/variables are: price (the only quantitative attribute), reviews, word of mouth, author, genre, repertory classification, venues. They were interested what effect each attribute has on theater demand.
The LCT model identified 3 different classes of theater audience: affluent, popular and intellectual/cultural. The results of the study suggested a heterogenous effect of the attributes on consumers choices. The results provide information for policy, give practical implications for marketing and ticket pricing, which applied could bring bigger revenues for theaters. It shows that there are differences in price elasticity between 3 audience types, where those from intellectual/cultural group are price inelastic and popular group (younger audience) is price- elastic.
However the discreet choice experiment method might have some limitations. The hypothetical scenarios can have different interpretations and can differ to the answer of real experience, causing hypothetical bias.
While Grisolia & Willis (2012) aim to identify preferences of specific theater audience types, De Rooij & Bastiaanen (2017) study focuses on consumption motives. It is a mix-method study, where qualitative and quantitative techniques have been used. They aim to contribute to the improvement of categorization and conceptualization of motives that guide the performing arts consumption. The main objective of the study is to “understand and measure consumption motives in the performing arts and to explore the importance of these motives to performing arts visitors” (p.119). The authors intend to answer two questions:
“1. How can we conceptualize and categorize consumption motives of performing arts visitors?
2. How can we measure these consumption motives?”(p.119)
The qualitative part of the research allows for deeper and nuanced understanding of the problem in the given context (Bryman, 2012). Because of the exploratory character of the study and qualitative approach, the researchers expect fresh insights on the subject.
In this case, it focuses on individual motivations. The data comes from 47 semi-structured, in depth interviews with customers of Theater Tilburg. A cross-sectional method was applied, gathering data between October 2009 and May 2010. However they also applied quota sampling, which is rarely used in academic research due to possible bias in choosing a sample by the researcher (Bryman, 2012). De Rooij & Bastiaanen however argue that they use quota sampling to ensure proportional division of “customers in different categories like attendance frequency, gender, age, postal code area, and genre (last attended)” (p.121). It is however, not a randomly selected sample, it depends on the researcher, how the final sample will look like as he or she selects people who fit into chosen categories. That can serve as an argument, that the sample is not entirely representative.
Besides the semi-structured interview, they also use an association technique, widely used in commercial, marketing researchers (Malhotra, N. 2004. Marketing Research,4th ed. Hoboken,NJ: PearsonEducation International). The interviewees were asked to put presented cards with 8 consumption motives in order of the importance and explain the choices.
The result of qualitative part of the study was the eight-motives model which was further tested trough conducting a quantitative study. Main aim of the second part of the study was to develop an instrument to measure consumption motives in the performing arts.
The quantitative part involved 220 completed questionnaires, which makes 30.8 percent response rate, of which 60.9 percent were male and 39.1 percent female, with majority being 50-70 year old, highly educated. The sample was therefore not as equal as their qualitative quota sample, dominated by elderly, highly educated men.
The principal component analysis (PCA) performed in quantitative study confirmed six out of 8 motives of the qualitative study: Cultural Relaxation, Cultural Stimulation, Social Attraction, Social Bonding, Social Distinction, and Social Duty.
This mix-method study demonstrates the presence of cultural and social aspect of consumption motives. Consumption motives seem to be uncommon subject of study in the sector of performing arts, therefore the study by De Rooij & Bastiaanen (2017) explores a new territory and contributes to understanding and operationalizing the measures of audience motives.
Conclusion
4 different studies analyzed above, demonstrate the variety of aspects and factors that demand in performing arts sector includes. Different approaches demonstrate the complexity of the issue. Based on above studies theater attendance can be driven by ticket prices, income, distance, quality of performance/venue or even social components. The quantitative methods with the use of secondary data seem to be the most popular, with the wish for results, which are generalizable for bigger population. However, it is important to mention again that the more disaggregate data, the more representative results (Seaman, 2006). More localized studies are more useful for marketing and policy strategies.
European performing art sector facing difficult times of decreasing subsidies and low attendance. There is therefore a need of deeper understanding individual preferences and exploring the characteristics of theater customers. The qualitative studies could therefore be more beneficial. The sample sizes should be extended to less frequent or non-attenders of performances, people who prefer spending their leisure time in a different way. It is still visible with a naked eye that the audience of theaters belongs to the richer, better educated, whiter population.
More research for separate genres: dance, theater and music is needed.
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