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Are the spectators of performing arts and the spectators of the movies the same?

Seppo Suominen

Haaga-Helia University of Applied Sciences

Malmi campus, Hietakummuntie 1 A, FIN-00700 Helsinki, Finland

e-mail: [email protected]

Introduction

The purpose of this paper is to study performing arts consumption and movies at the cinema consumption using ISSP 2007 survey data. A number of different socioeconomic variables are used to explain cultural consumption. The bivariate probit approach to studying performing arts and movies at the cinema consumption in bundle is useful because it reveals substantially new evidence on the average profile of culture consumption. It is expected that female go more often to art exhibition, opera or theatrical performances and this was supported. The results of the bivariate probit analysis also reveal that gender is important to explain also movie attendance. Female go more often to see movies at the cinema. There is a significantly positive correlation between these two audiences indicating that there is a common background between both groups. The approach also allows finding the other relevant socioeconomic characteristics explaining cultural consumption. However, bivariate probit approach classifies consumption into two categories: yes or no. Roughly 5 percent of the consumers in the sample could be classified as heavy users and another approach must be used to study three groups: heavy, occasional and not at all. A multivariate logit analysis is one approach to classify these groups. Using both bivariate probit analysis and multivariate logit analysis results in new evidence in cultural consumption. It is widely known that gender, age and educational level are significant variables to explain cultural consumption. It is shown with Finnish ISSP 2007 data that besides these variables, also educational level of the spouse and the number of children significantly classify cultural consumption. Naturally the place of residence matters since in Southern and Western Finland the residential density is higher and there are more cultural institutions than elsewhere in Finland. There is only one permanent opera in Helsinki but also some opera associations that are more provisory and have performances outside Helsinki. The theatre institutions are located mostly in bigger cities but the number of traveling theatre groups makes is possible that citizens in the countryside can go to see performing arts.

Recently roughly 60 percent of the adult population (age between 15-79) in Finland have seen a movie at the cinema during the last year (Kotimaisen elokuvan yleisöt – tutkimus 2010). Ten percent of the adult population are heavy users: they go to the cinema one to three times a month. More than a fourth of young audience (age: 15-24) are heavy users. However, the results of that survey may be misleading since the interviews were made during January – February 2010 and it is well known that the Christmas season is the prime time. Another recent study (ISSP 2007)[footnoteRef:1] reveals that only 1.9 % of the population are heavy users and 17.9 % have not seen a movie at the cinema during the last year. The figure is comparable with the spectator number of performing arts (concert, theatrical performance, art exhibition) where the corresponding numbers are: 5.9 % are heavy users and 15.7 % have not been at all. A third recent survey (2006)[footnoteRef:2] claims that 3 % are heavy users and 45 % have not seen a movie at the cinema at all during the last year. This survey is based on interviews made during March – June 2006. The figures in the European Cultural Values study are somewhat different as seen in table 1. Hence, it seems that the timing of the interviews has a big impact on the results. [1: International Social Survey Programme 2007, sample size 2500 with 1354 valid results, respondents age between 15-74, interviews made between 18th September – 11th December 2007] [2: Adult education survey 2006, sample size 6800 with 4370 valid results, respondents age between 18 and 64, interviews made between May – June 2006 ]

(Table 1 about here)

Studies related to the spectators of performing arts are rather common: female go more often and the audience is composed of middle-aged with high educational and income levels (Baumol and Bowen 1966, Liikkanen 1996, Kracman 1996, Bihagen and Katz-Gerro 2000, Borgonovi 2004, Seaman 2005, Montgomery and Robinson 2006, Vander Stichele and Laermans 2006). Spectators of movies in cinemas are usually young students but there are no gender differences (Austin 1986 or F & L Research 1999). Recently Redondo and Holbrook (2010) show that the family-audience profile (i.e. middle-aged with children) and the family-movie profile (various genres) are strongly associated while young men seem to favour action, mystery, thrills and violence genres. It is also known that young males prefer action and excitement on the screen and women tend to favour emotional dramas (Kramer 1998). Typically the ticket price is substantially higher for performing arts than for movies and this may explain the difference between age-groups: e.g. in 2009 the average movie ticket price was 8.3€ in Finland and the ticket revenue per spectator was 32.62€ in Finnish national opera. In 2009 the average ticket price in Finnish big- and medium-size theatres was 16.21€ in top 30 theatres[footnoteRef:3]. The performing arts are heavily subsidized by the state (ministry of education: state aid) and municipalities since the share of the ticket revenues was only 15 % for the Finnish national opera and 20 % for the top 30 theatres[footnoteRef:4]. [3: According to ticket revenue in top 10 theatres the unweighted mean was 19.48€, in the next 10 (11th – 20th) 14.07€ and the next 10 (21st – 30th) 15.10€. The weighted average price in big- and medium-size theatres was 18.63€] [4: These numbers have remained fairly stable recently: e.g.in 2007 the average movie ticket price at the cinema was 7.8€, in the Finnish national opera 33.19€ and 17.63€ in big- and medium-size theaters. ]

In 2007 there were 316 cinema screens in Finland[footnoteRef:5]. The number of films in spreading was 410 and there were 163 premieres. The total number of spectators was 6.5 Million (i.e. 1.2 per capita). Correspondingly there were 46 drama theatres subsidized by law with 12361 performances and 2446500 spectators, 16 summer theatres with 821 performances and 351473 spectators and 51 theatre groups outsize the law subsidies with 4139 performances and 465997 spectators. Overall this means 103 theatres and 16695 performances and 3066530 spectators, i.e. 184 spectators per performance or 0.57 per capita. Moreover, Finnish national opera[footnoteRef:6] and other operas (13 local operas with only few performances) had 285 performances with 182728 spectators (641 per performance). Furthermore, 39 dance theatres (including National Ballet) gave 2377 performances with 523620 spectators (220 per performance).[footnoteRef:7] The total number of different plays performed in the drama theatres during the season 2006 – 2007 was 357 and there were 118 premieres. A large majority (203/357) of the plays were written by a Finnish writer (e.g. Saisio, Nopola, Wuolijoki, Krogerus.). English (e.g. Shakespeare, Pownall, Russell), American (e.g. Woolverton, Quilter, Williams), Swedish (e.g. Nordqvist, Lindgren), French (e.g. Duras, Molière) and Russian (e.g. Gogol, Tshehov) plays were the most performed foreign ones. Practically all dance theatre performances except The Finnish National Ballet were of domestic origin whereas the ballet and opera plays were mostly of foreign origin. In top 10 towns according to the movie spectator number the admissions per capita for movies and drama theatre performances[footnoteRef:8] are highly correlated (0.81) hence the supply conditions for both cultural events are fairly equal. Urban citizens have better access both to the cinema and to the theatres and concerts than people living in the rural areas. [5: Top towns based on admissions 2007. Source: The Finnish Film Foundation, Facts & Figures 2008. www.ses.fi] [6: The main stage of The Finnish National Opera was closed 6 months in 2007 due to renovation.] [7: 16 Circus companies had 804 performances with 279544 spectators. ] [8: Top towns based on movie admissions 2007: * some smaller drama theatres regularly made tours

]

However, it is not known whether the spectators of movies and performing arts are the same. Especially middle-aged high-income highly educated women seem to favour performing arts. Are they also movie lovers? A bivariate probit model is a nice method to study this question since the model enables to evaluate the marginal effects, both direct and indirect. In the table 1 four recent surveys have been compared. The International Social Survey Programme (ISSP 2007) study is most useful since it the variables in that study are suitable.

Literature review

This paper is closedly related to the sociological literature of performing arts participation. The classical work is Bourdieu (1979). The relation of social positions and cultural tastes and practices are structurally invariable. There are two interrelated spaces: the space of social space (positions) and the space of lifestyles. The social space has three dimensions: economic, social and cultural capital. Bourdieu argued that there is a structural correspondence between social space and cultural practices and the habitus serves as a mediating mechanism. Therefore the tastes, knowledge and practices are class-based. The “highbrow” cultural consumption is typical for the dominant classes. Bourdieu argued that cultural capital or social statuses are symptoms of social exclusion, cultural dominance and inequality. Bourdieu’s claims have been criticized substantially since the taste of the dominant class has lost its exclusiveness (Purhonen, Gronow and Rahkonen 2010). The dominant class has changed its cultural participation pattern. They are more omnivore. The audience segmentation has changed from elite and mass to omnivore and univore (Peterson 1992, Peterson and Kern 1996). In the European context Finland and Nordic countries in general are the leading countries in the proportion of omnivores in the population (Virtanen 2007).

The omnivorousness in cultural taste has been measured the number of cultural participation areas and/or by number of genres in one specific area. A person is omnivore if she has seen a ballet, a theatre performance, a movie at the cinema, reads books, goes to a sport event and so on. Correspondingly she is univore If she prefers e.g. only sport events and is active in that field but not in the other areas of culture (Sintas and Álvarez 2004, Chan and Goldthorpe 2005). On the other hand she is omnivore if she reads books of different genres: thrillers, scifi, fantasy, romances, biographies, modern literature, classical literature, poetry, plays, religious books, leisure books (Purhonen, Gronow and Rahkonen 2010). Omnivores have a high probability of participating in everything, from the unpopular (e.g. classical music) to the popular (e.g. cinema attendance) whereas paucivores engage in intermediate levels of cultural consumption across a range of activities and inactives have a low probability of participating in any of the activities (Alderson, Junisbai and Heacock 2007). Omnivores have usually higher levels of education and higher incomes than univores (Chan and Goldthorpe 2005). Using a multinomial logit analysis Alderson, Junisbai and Heacock (2007) show that social status, having a bachelor’s degree and family incomes significantly classify inactive and the two other groups (omnivore and paucivore), having a graduate degree classifies omnivores and the other groups (paucivore and inanctive). Age is important to categorize paucivore from omnivore and inactive. Unexpectedly gender is not a significant variable to classify. The omnivore consumption pattern is typical among the upper social classes, univore among the upper-middle and middle classes and fragmental consumption among the lower social classed (Sintas and Álvarez 2004).

The sociology of cultural participation has shown than consumers can be classified into three groups: omnivore, paucivore and inactive (Alderson, Junisbai and Heacock 2007). The omnivore group is active in all cultural consumption, from cinema to classical music. The concept of cultural capital is associated with the lowbrow/highbrow consumption styles. Arts consumption is a form of cultural capital (DiMaggio 1987). Cultural capital is the accumulated amount of past consumption of cultural goods and the initial endowment of cultural capital (Stigler and Becker 1977). The accumulation function is related to human capital, i.e. formal education. The human capital argument is based on the idea of cultural behavior is constrained somehow, i.e. differences is cultural consumption is related to differences in cultural capital endowments, differences in budget, time, social and physical constraints (Frey 2000). Since cultural capital endowment is related both to formal education and age, these are proper explanatory variables. Moreover, it has been shown that gender and marital status are important to explain cultural consumption. Time constraints are related to place of residence (province) and finally budget constraints is measured by incomes (c.f. Ateca-Amestoy 2008). However, there is some evidence showing that economic wealth (net incomes, material wealth) is not a significant variable explaining cultural participation (c.f. Vander Stichele and Laermans 2006).

Alderson, Junisbai and Heacock (2007) argue that gender is not a significant variable to classify cultural consumption pattern classes (in the USA, 2002) but Bihagen and Katz-Gerro (2000) show with Swedish data (1993) that gender is important. Women are more active in highbrow consumption (opera, dance performance, theatrical performance) and men in lowbrow television (entertainment, sport) watching. Highbrow television (documentary, culture, news) and lowbrow culture (movies, rent a video) are less connected to gender, class and education but these are strongly related to age. Younger seem to favour lowbrow culture and older highbrow television watching. Lizardo (2006) shows using cluster analysis with pooled data from the 1998 and 2002 United States General Social Survey that four genres fall on to the highbrow cluster: arts consumption, going to the ballet, going to a theater and attending a classical music or opera concert. The lowbrow cluster consists of going to a popular music live concert, going to see a movie in cinema or reading a novel, poem or play. Gender matters but only with those that are active in the labour force. Among those that are not active in labour force, there is no gender difference in highbrow cultural consumption. Purhonen, Gronow and Rahkonen (2009) present similar results with Finnish data. Warde and Gayo-Cal (2009) find also mixed evidence concerning the gender effect on omnivorousness with British 2002-2003 survey data. Women seem to be more active in ‘legitimate’ culture. Different terminologies have been used to rank tastes, like: highbrow – middlebrow – lowbrow, or high – popular, or legitimate – vulgar. Bourdieu defines legitimate as being connected with dominant classes, powerful social groups and it is aesthetically the most valuable. The top quartile omnivores are associated with legitimate taste while the lowest quartile in omnivorousness least related with legitimate cultural consumption. Omnivorousness increases with age up to around 50 and strongly diminishes among those over 70 (Warde and Gayo-Cal 2009). The family background as a whole matters since parents’ cultural participation seem to be related with cultural consumption (van Eijck 1997) while participating into a culturally orientated course at school does not have any or only slight impact on cultural consumption (Nagel, Damen and Haanstra 2010).

The method and sample

The ISSP 2007 survey was carried out between 18th September and 11th December 2007 through mail questionnaire in Finland. The ISSP is a continuous programme of cross-national collaboration on social science surveys. The surveys are internationally integrated. In Finland the ISSP surveys are carried out together by three institutions: Finnish Social Science Data Archive, The Department of Social Research at the University of Tampere and the Interview and Survey Services of Statistics Finland[footnoteRef:9]. The other surveys mentioned in table 1 did not collect e.g. marital status which has been shown to have an impact on the attendance of cultural events (Upright 2004, Frateschi & Lazzaro 2008). [9: http://www.fsd.uta.fi/english/data/catalogue/series.html#issp, cited 24.9.2010. The observation unit is a person 15-74 – years old, the sampling method is a systematic random sample from the population register, the sample size was 2500 but the 1354 answers were obtained, in other words answer per cent was 54,2%. The index terms of ISSP 2007 are: use of time, physical condition, hobbies, organisations, board games, physical education, holiday, games, social relations, sports, leisure. Among others gender, year of birth, size of the household, education, participation in the working life, profession, source of livelihood or branch, regular weekly working hours, professional station, employer (the private/public sector), the membership of the trade union, voting behaviour, religiousness, incomes and residential were collected as background information.]

The cultural participation questions in The ISSP survey were: “How many times in the last twelve months have you seen an art exhibition, opera or theatrical performance?” Or “How many times in the last twelve months have you been to the cinema?” Five alternatives were given: ‘ Every day’, ‘Several times a week’, ‘Several times a month’, ‘Less often’ or ‘Never in the last twelve months’. A conventional method to study this is to use some discrete choice model, like probit or logit. A Poisson model is more suitable to study count data which is not the case here. The normal distribution for the binary choice (no = 0 / yes = 1) has been used frequently generating the probit model.

The function is the commonly used notation for the standard normal distribution (Greene 2008, 773) and x is a vector of explanatory variables and β is the corresponding vector of parameters. The logistic distribution which is mathematically convenient has been very popular.

The function is the logistic cumulative distribution function. If the responses are coded 0,1,2,3 or 4 (‘ Every day’, ‘Several times a week’, ‘Several times a month’, ‘Less often’ or ‘Never in the last twelve months’) the ordered probit or logit models have been very common. The models begin with y* = x’β + ε in which y* is unobserved and ε is random error. The discrete choices y are observed by the following way:

y = 0, if y* ≤ 0

y = 1, if 0 < y* ≤ µ1

y = 2, if µ1 < y* ≤ µ2

y = 3, if µ2 < y* ≤ µ3

y = 4, if µ3 ≤ y*

The µ’s are unknown parameters to be estimated with β. If ε is normally distributed with zero mean and variance equal to one [ε~N(0,1)] the following probabilities ensue (Greene 2008, 831-832):

The parameters of the multivariate probit model, β’s are not necessarily the marginal effects that describe the effects of the explanatory variables on cultural participation since the model is not linear. The multivariate probit model is useful to evaluate the cultural participation and influences of different explanatory variables. However, it is widely known that the categories “every day” or “several times a week” or “several times a month” get a small number of respondents and it is reasonable to combine these categories with “less often” (e.g. Vander Stichele and Laermans 2006). One step further is to assume that the error terms of two explanatory models are correlated. One model is estimated for highbrow (ballet, dance performance, opera) and another for cinema (lowbrow). If the disturbances are correlated, both the direct marginal effects and the indirect marginal effects can be evaluated. With this method the omnivore group of people can be found. The general specification for a two-equation model assuming binary choice is then (Greene 2008, 817)

If ρ equals zero, the two spectator groups are independent, and two independent probit models could be estimated and it could be claimed that the highbrow attenders are different from cinema attenders (Prieto-Rodríguez and Fernández-Blanco 2000).

Naturally consumption depends on the ticket price but since data available does not include price variable, it is not considered here.

The cultural consumption y* thus depends on the following variables:

y* = f(education, age, gender, marital status, province, incomes)

Since it has been shown that middle-aged are among the most active in highbrow cultural consumption, a suitable method is to classify age into age-groups. The observation unit in the ISSP 2007 survey is a person 15-74 –years old and for the purpose of this study persons have been classified into 12 subsets: 15-19 –years old, 20-24 –years old, and so on with the last consisting persons of 70-74 –years old.

(table 2 about here)

Descriptive statistics of the explanatory variables reveal that age (age-group) and education are related. Most of the youngest in the sample were pupils or students (at a comprehensive, an upper secondary, a vocational school of course or at a college) and correspondingly the oldest had a rather low education (elementary or comprehensive school). A college level education was mainly replaced by bachelor’s degree education in the early 1990’s and therefore persons having a bachelor’s degree from a polytechnic (university of applied sciences) are somewhat younger than persons having a college diploma. Persons less than 50 –years old on average have a (better and) longer education than persons older than 50. Age and education are related with household or personal incomes. Middle-aged and high-educated seem to have the highest incomes (including all social security contributions, e.g. child benefit that may explain why the age-group 30-34 has the highest incomes, see table 3). There are some differences in education between genders. Men are somewhat less educated than women.


Since the income variable in the sample includes all social security contributions (e.g. child benefit) the number of children in used as an explanatory variable. There are two different variables: the number of less than 6-year-old children and the number of 7-17-year-old children. This leads to the following relation explaining cultural consumption. Since the number of children is considered as explanatory variable, the marital status is also added.

y* = f(education, age, gender, marital status, province, incomes, number of children)

Since the cultural participation variables are recoded conversely into binary variables: Art-consumption01234 (‘every day’ = 0, ’several times a week’ = 1, ‘several times a month’ = 2, ‘less often’ = 3, ‘never in the last 12 months’ = 4) art1234_5 (‘no’ = 0, ‘yes’ = 1), some information is lost. However, the correlation of the original and the recoded variables is high: r = -0.937. Respectively the correlation of the original movie consumption variable and the recoded variable is also high: r = -0.844. The correlation of the recoded art participation and the movie consumption variables is positive: r = 0.397. Therefore there are good arguments to study these sectors of culture jointly.

In the sample there are more female (57%) than male (43%). Most are married (50%) and the two other large groups according to the marital status are: single (20%) and common-law marriage (17%). Separated or widowed are considered as the reference group (constant) in further analysis as well as Northern Finland and Ahvenanmaa.


Area1 = FI18 (Southern Finland), Area2 = FI19 (Western Finland), Area3 = FI13 (Eastern Finland), FI1A (Northern Finland) + FI20 (Ahvenanmaa –the South-Western archipelago) are considered as reference value.

Results

Table 5 presents the results of bivariate probit analysis when age-group 50-54 and elementary school (edu2) are considered as reference value (i.e. the constant in the equation). The two spectator groups are not independent since ρ = 0.625. Hence the hypothesis that spectators of movies and arts belong to independent groups can be rejected. There are common characteristics, a common background which could be called as an intrinsic culture orientation. If a person likes art exhibitions, opera and theatrical performances, she also likes to see movies at cinema. Those that are inactive and culture orientated do not go to exhibitions or performances and to the cinema. However, there are some particular effects that are related with exhibitions and performances or with movies.

The importance of gender is very strong: female are more active in both arts (highbrow) and movies. The direct marginal effect of gender (female) is positive but the indirect marginal effect is negative. Both the direct and indirect marginal effects have been reported only for the highbrow art (art exhibition, opera and theatrical performances. The negative indirect effect describes the preference of seeing a film in the cinema. These leisure time activities are to some extent substitutes. Marital status matters: married or common-law married citizens go more often to highbrow art. The coefficient of common-law marriage in the probit equation for the movies is negative indicating that they prefer more highbrow arts than movies in the cinema. Unmarried or single citizens on the contrary go to the cinema more often. The gender effect on art consumption found here is line with the results of Bihagen and Katz-Gerro (2000). Female are more omnivore compared with male who are more paucivore.


The effect of age on cultural consumption in the table 5 is relative to the age-group 50-54. All younger cohorts prefer more movies and only the oldest (70-74) seem to go less often to the cinema than the reference group. The indirect marginal effect of age on highbrow art is negative for each younger age-group. The direct marginal effects of cohorts are not significant. The results indicate that age is not a relevant variable to classify highbrow art consumption into active and inactive groups. Education seems to be very important to classify culture consumption structures. When the reference level is elementary school (edu2), citizens with any other education level are significantly more active in culture consumption, in both directions: highbrow art and movies. The highest marginal (direct + indirect) effect is for those that have the best education (edu9 = master’s degree): 0.160 = 0.195 – 0.035. However, those with bachelor’s degree (edu8) have the largest direct and largest (negative) indirect effect: 0.150 = 0.250 – 0.100. They seem to be the most omnivore group. They are most active in highbrow art consumption as well as in movies at the cinema consumption. Consumers with college level education (edu6) are third active group. The results confirm the well-known hypothesis that omnivores have higher levels of education (Chan and Goldthorpe 2005, Alderson, Junisbai and Heacock 2007). Spouse’s education in some cases is relevant to explain consumption of movies at the cinema. If the spouse has either master’s degree or upper secondary diploma, the person is more active to go to the cinema and that indirectly reduces highbrow art participation.

The effect of domicile on culture consumption is selective. In Southern and Western Finland (Area1 or Area2) people are more active in both highbrow art and movies at the cinema consumption. In the Eastern Finland (Area3) are less active in highbrow art consumption but significantly more active in movie attendance than in the Northern Finland or in the Ahvenanmaa archipelago (reference areas). Household’s size matters only indirectly to highbrow art consumption since bigger families seem to favour movies. The number of small children (less than 7) or older children (7-17) significantly reduces both culture consumption segments. Household incomes (or personal incomes – not reported here) are not significant.

Table 6 presents the results of bivariate probit model explaining simultaneously highbrow art (art1234_5) and movie (mov1234_5) consumption when age-cohort 40-45 and upper secondary school level education (edu5) are the reference values. The gender effect is similar than in table 5. Female are more omnivore. Married citizens are more active in highbrow art consumption but the dummy variable ‘common-law marriage’ is not significant. In the previous estimation (table 5) when the reference was age-cohort 50-54 and elementary school education that variable was significant. This indicates that the effect of ‘common-law marriage’ is related to age or educational level. Age-cohorts younger than 40 are significantly more active in movie consumption but there are no differences in highbrow art consumption.

Elementary education (edu2) in relation to upper secondary (graduate) level education (edu5) lowers significantly both highbrow art and movies at the cinema consumption. College (edu6) or bachelor’s degree (edu8) educated are more active in highbrow art participation. The results in the tables 5 and 6 indicate that the most omnivore citizens are those with bachelor’s degree. They go to see art exhibition, opera or theatrical performances and also movies at the cinema. Spouse’s education in relation to upper secondary level education is significantly lowering cinema activity if the spouse has either elementary school (edu2) or vocational school or course (edu4) education. The area effects as well as the family size or the number of children effects are similar in table 6 and in table 5.

The marginal effects of age-cohorts in tables 5 and 6 are different since the reference value is different: the age-cohort 50-54 in table 5 and the age-cohort 40-44 in table 6 but only the level is different. Otherwise they reveal the same information. In figure 1 there are direct (DirME5 and DirME6) and indirect (IndME5 and IndME6) marginal effects of age-cohorts in tables 5 and 6. The values are highly correlated: ρDirME5, DirME6, age = 0.958, ρIndME5, IndME6,age = 0.981. The direct and indirect marginal effects of age-cohorts are not significantly correlated. The marginal effects in tables 5 and 6 are not all significantly different from zero but still it is worth noticing that age-cohort 20-24 is the most negative attitude towards highbrow arts and they favour movies at the cinema. Figure 1 reveals that the largest amplitudes from the negative indirect marginal effect to the positive direct marginal effect is by the age-cohorts 30-34 and 35-39. The amplitude for the cohort 30-34 is (-0.073, 0.023) ↔ 0.096 and for the cohort 35-39 (-0.045, 0.058) ↔ 1.003.

Figure 1: Direct and indirect marginal effect of age-cohorts on highbrow art consumption

The age-cohorts 30-34 and 35-39 are most omnivore but this indication is unreliable to some extent. The marginal effects of education (Figure 2) are more reliable since mainly they are significantly different from zero.

Figure 2: Direct and indirect marginal effects of education on highbrow art consumption

The marginal effects of education in tables 5 and 6 are highly correlated: ρDirME5, DirME6, edu = 0.977, ρIndME5, IndME6, edu = 0.993. The direct and indirect marginal effects are highly negatively correlated (ρDirME5, IndME5, edu = -0.859 and ρDirME6, IndME6, edu = -0.884) indicating that active in highbrow art consumption are active also in cinema consumption.

The results with Finnish data are in harmony with the results of Kracman (1996), Bihagen and Katz-Gerro (2000) or Vander Stichele and Laermans (2006) who show that educational level, gender and age are related with performing arts consumption. However, the effect of education is not linear. It is true that better and longer education leads to higher probability of consuming performing arts but those with master’s level arenot necessarily more active than people with a bachelor’s degree (obtained from university). If the bachelor’s degree if obtained from a university of applied sciences (polytechnic) the positive marginal effect is positive but less positive than for those with university level bachelor’s degree. Incomes do not seem to explain cultural participation but the number of children significantly reduces cultural participation, both performing arts and movies.

For the purpose of analyzing cultural participation using bivariate probit analysis the original data was recoded and reclassified into two categories: yes vs. no. However, about 5 percent of the respondents in the sample could be classified to the category ‘often’ (‘every day’ + ‘several times per week’ + ‘several times per month’) in participating performing arts. With multinomial logit analysis, the three groups can studied but the indirect effects (between performing arts and movies) that could be evaluated by using bivariate probit model could not be obtained. Still this classification into three groups is reasonable. The results of MNL analysis to explain performing arts consumption are presented in tables 7 and 8. In the table 7 the reference values of the age-cohort and educational levels are 50-54 years old and elementary school (edu2).


The results of the MNL analysis confirm the importance of gender. Female are more active to go to an arts exhibition, opera and/or theatrical performances. Both the marginal effects of the gender variable or over individuals show that females most often belong to the group ‘less often’ (occasionally). The only marital status variable to classify into three groups is ‘married’. There are no differences if the person is the person is single or living in common-law marriage. Married persons most often belong to the group ‘less often’.

The age-cohort 25-29 is most passive in going to see performing arts. Surprisingly the older age-cohorts (55-59, 65-69 and 70-74) are most active. The oldest seem to strongly classify into totally not-going and actively going groups but the probability of belonging into ‘less often’ –group is lowest. Education is very important to classify performing arts consumption. Consumers with bachelor’ degree (edu8) are the most active. The following groups in activity are those that have either college level (edu6) or a master’s degree (edu9) but the probability of ‘less often’ is bigger if the education level is college while the probability of ‘often’ is bigger if the consumer has a master’s degree. The spouse’s education is significant only when the spouse has a master’s degree. As expected in the Southern Finland (Area1) the category ‘often’ has the biggest probability since the biggest cities with the largest number of performing arts institutions are in Southern Finland. Area2 (the Western Finland) is also a significant variable to classify between ‘never’ and the two other categories ‘less often’ and ‘often’ but there is difference between these two last categories.

The results of the other MNL analysis with other reference values for the age-cohort (40-44) and educational level (edu5 = upper secondary) are presented in the table 8.


The results in the table 8 are similar than in the table 7 but it shows that there are no differences between educational levels 1 (pupil or student), 4 (vocational school), 5 (upper secondary) or 7 (bachelor’s degree, university of applied sciences, polytechnic). Interestingly if the spouse is pupil or student (spouse-edu1) the respondent most probably is active (‘often’) in performing arts consumption. As expected if the respondent has children, it significantly lowers the probability of going to an art exhibition, opera and/or theatrical performance.

Conclusions

The purpose of this paper is to study performing arts consumption and movies at the cinema consumption. A number of different socioeconomic variables are used to explain cultural consumption. The bivariate probit approach to studying performing arts and movies at the cinema consumption in bundle is useful because it reveals substantially new evidence on the average profile of culture consumption. It is expected that female go more often to art exhibition, opera or theatrical performances and this was supported. The results of the bivariate probit analysis also reveal that gender is important to explain also movie attendance. Female go more often to see movies at the cinema. There is a significantly positive correlation between these two audiences indicating that there is a common background between both groups. The approach also allows finding the most relevant socioeconomic characteristics explaining cultural consumption.

It is widely known that gender, age and educational level of the consumer have an impact on cultural consumption (e.g. Kracman 1996, Borgonovi 2004 or Montgomery and Robinson 2006). The novelty of the results here indicates that also the educational level of the spouse matters. If the spouse has high education (master’s degree), it significantly increases highbrow cultural consumption. The probability of being classified into heavy user group increases. The analysis shows that when the effects of other socioeconomic variables have been controlled, the gross income level is not significantly explaining cultural consumption. Younger people prefer movies and their incomes are typically low and this explains why incomes are not explaining movie attendance; however the effect of incomes on highbrow performing art consumption is also zero. Education matters but incomes do not. Married consumers seem to prefer highbrow arts but the more informal partnership, ‘common-law marriage’ seems to have a negative impact on movie attendance but no effect on highbrow art consumption.

The sociology of cultural participation classifies consumers into three groups: omnivore, paucivore and inactive (Alderson, Junisbai and Heacock 2007). Omnivores are active in all cultural consumption and paucivores are less active. Female age-cohorts 30-34 and 35-39 with a bachelor’s degree (university) are most omnivore and the oldest male age-cohort with the lowest education (elementary school) are most inactive.

References

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Survey

Often

Occasionally

Never

Interviews made

Sample size

Notes

Cinema

Arts

Cinema

Arts

Cinema

Arts

2006: Adult education study

8.2 %

10.5 %

46.7 %

53.3 %

45.0 %

36.2%

March – June 2006

4370

Often = more than 7 times/year

2007: European Cultural Values

3.0%

bdo1%

t3%

c3%

49%

bdo22%

t47%

c48%

48%

bdo77%

t52%;

c49%

February – March 2007

1041


2007: ISSP

1.9%

5.6 %

80.2%

78.9 %

17.9%

15.5 %

September – December 2007

1354


2010: Kotimaisen elokuvan yleisöt

10%

70%

20%

January – February 2010

504


Table 1: Spectators of movies at the cinema and performing arts (concert, theatre, art exhibition) in Finland, recent surveys.

In the European Cultural Values study: bdo = a ballet, a dance performance or an opera; t = the theatre; c = a concert

Table 2:

edu1

edu2

edu3

edu4

edu5

edu6

edu7

edu8

edu9

5.5%

10.6%

7.9%

22.1%

7.2%

24.6%

8.1%

4.1%

9.9%

age15_19

6.2%

84.0%

12.1%

age20_24

5.4%

11.6%

26.4%

age25_29

7.4%

4.3%

28.4%

13.6%

age30_34

6.0%

13.7%

age35_39

8.0%

12.9%

17.7%

14.4%

age40_44

8.7%

13.5%

17.6%

age45_49

10.0%

11.6%

12.1%

15.2%

15.4%

age50_54

8.7%

20.2%

age55_59

11.0%

19.2%

11.6%

13.5%

age60_64

11.2%

23.9%

15.1%

14.7%

13.5%

age65_69

6.4%

24.6%

age70_74

6.1%

23.9%

100%

Three largest age-groups according to the education, e.g. 84% of the youngest are pupils/students and 23.9% of the oldest have only elementary school background.

Table 2: descriptive statistics of age-group and education variables.

edu1 = pupil or student (comprehensive, upper secondary, vocational school or course, college: 5.5% in the sample are pupils or students

edu2 = elementary school

edu3 = comprehensive school

edu4 = vocational school or course

edu5 = upper secondary, secondary school graduate

edu6 = college

edu7 = bachelor’s degree(polytechnic or university of applied sciences)

edu8 = bachelor (university)

edu9 = master’s degree

Table 3:

Household incomes

Personal incomes

age15_19

2083

90

age20_24

1629

859

age25_29

3653

1948

age30_34

6400

3310

age35_39

5175

2496

age40_44

4901

2996

age45_49

5469

2663

age50_54

4911

2483

age55_59

3684

1931

age60_64

2759

1770

age65_69

2578

1687

age70_74

2291

1449

edu1

2323

134

edu2

1759

1166

edu3

2564

1382

edu4

3063

1924

edu5

3081

1374

edu6

4905

2492

edu7

5158

2764

edu8

3885

2285

edu9

7072

3579

Average monthly household and personal gross incomes (including taxes and social security contributions by age and by education groups.

Table 4

female: 57 %

male: 43 %

n = 1232

marital status: single

18.3%

23.0%

20,3%

married or registered pair relation

48.6%

51.9%

50.0%

common-law marriage

17.0%

17.3%

17.1%

judicial separation*

0.3%

0.7%

0.5%

separated*

11.0%

5.2%

8.4%

widow(er)*

4.9%

1.9%

3.6%

Province: Area1

53.0%

49.3%

51.4%

Area2

25.9%

25.7%

25.8%

Area3

12.2%

13.6%

12.8%

Rest of Finland*:

8.8%

11.5%

10.0%

Descriptive statistics of some explanatory variables. * = reference groups (constant) in probit or logit analysis

Art1234_5

Art1234_5: total marginal effect

Art1234_5: direct marginal effect

Art1234_5: indirect marginal effect

Mov1234_5

gender (male=1, female=2)

0.423

(0.097)***

0.064

(0.016)***

0.074

(0.017)***

-0.010

(0.005)(*)

0.189

(0.096)*

Marital status: single

-0.004

(0.188)

0.009

(0.031)

-0.001

(0.033)

0.010

(0.010)

-0.180

(0.188)

Marital status: married

0.319

(0.203)

0.070

(0.034)*

0.056

(0.036)

0.014

(0.011)

-0.264

(0.198)

MS: common-law marriage

0.198

(0.217)

0.057

(0.036)

0.034

(0.038)

0.022

(0.012)(*)

-0.403

(0.225)(*)

age15_19

-0.038

(0.310)

-0.063

(0.053)

-0.007

(0.054)

-0.056

(0.026)*

1.027

(0.458)*

age20_24

-0.242

(0.270)

-0.087

(0.045)*

-0.042

(0.047)

-0.044

(0.018)*

0.806

(0.327)*

age25_29

0.056

(0.259)

-0.033

(0.043)

0.010

(0.045)

-0.043

(0.015)**

0.786

(0.254)**

age30_34

0.133

(0.297)

-0.049

(0.048)

0.023

(0.052)

-0.073

(0.021)***

1.321

(0.369)***

age35_39

0.331

(0.249)

0.013

(0.041)

0.058

(0.044)

-0.045

(0.015)**

0.826

(0.268)**

age40_44

0.069

(0.244)

-0.017

(0.041)

0.012

(0.043)

-0.029

(0.013)*

0.531

(0.229)*

age45_49

0.158

(0.205)

-0.007

(0.034)

0.028

(0.036)

-0.034

(0.012)***

0.626

(0.212)***

age50_54 = con

age55_59

0.064

(0.204)

0.001

(0.034)

0.011

(0.036)

-0.011

(0.009)

0.193

(0.169)

age60_64

0.035

(0.189)

-0.007

(0.032)

0.006

(0.033)

-0.013

(0.009)

0.234

(0.168)

age65_69

0.053

(0.233)

-0.006

(0.037)

0.009

(0.040)

-0.016

(0.012)

0.284

(0.212)

age70_74

-0.168

(0.236)

-0.009

(0.039)

-0.029

(0.041)

0.020

(0.011)(*)

-0.367

(0.201)(*)

edu1

0.687

(0.290)*

0.077

(0.049)

0.120

(0.052)*

-0.043

(0.023)(*)

0.783

(0.432)(*)

edu2 = con

edu3

0.424

(0.213)*

0.053

(0.035)

0.074

(0.037)*

-0.022

(0.011)*

0.395

(0.196)*

edu4

0.550

(0.167)***

0.075

(0.028)**

0.096

(0.030)**

-0.021

(0.009)*

0.386

(0.160)*

edu5

0.841

(0.221)***

0.108

(0.039)**

0.147

(0.040)***

-0.039

(0.014)***

0.714

(0.248)***

edu6

1.037

(0.191)***

0.145

(0.033)***

0.182

(0.035)***

-0.037

(0.010)***

0.675

(0.165)***

edu7

0.863

(0.258)***

0.104

(0.043)*

0.151

(0.046)***

-0.047

(0.016)***

0.865

(0.280)***

edu8

1.425

(0.463)***

0.150

(0.082)(*)

0.250

(0.081)***

-0.100

(0.037)***

1.821

(0.665)***

edu9

1.111

(0.340)***

0.160

(0.058)**

0.195

(0.062)***

-0.035

(0.014)*

0.635

(0.249)**

spouse-edu1

-0.226

(1.327)

-0.049

(0.335)

-0.040

(0.233)

-0.009

(0.112)

0.169

(2.030)

spouse-edu2 = C

spouse-edu3

-0.129

(0.257)

-0.034

(0.043)

-0.023

(0.045)

-0.011

(0.014)

0.208

(0.252)

spouse-edu4

-0.225

(0.185)

-0.039

(0.030)

-0.040

(0.032)

0.000

(0.009)

-0.004

(0.172)

spouse-edu5

0.197

(0.329)

-0.004

(0.063)

0.035

(0.058)

-0.039

(0.023)(*)

0.711

(0.414)(*)

spouse-edu6

0.030

(0.227)

-0.011

(0.039)

0.005

(0.040)

-0.016

(0.011)

0.299

(0.199)

spouse-edu7

0.135

(0.333)

0.001

(0.055)

0.024

(0.058)

-0.022

(0.018)

0.410

(0.328)

spouse-edu8

-0.333

(0.388)

-0.055

(0.074)

-0.058

(0.068)

0.004

(0.022)

-0.066

(0.407)

spouse-edu9

0.597

(0.458)

0.069

(0.079)

0.105

(0.078)

-0.036

(0.018)*

0.648

(0.317)*

Area1

0.331

(0.149)*

0.026

(0.025)

0.058

(0.026)*

-0.032

(0.009)***

0.576

(0.143)***

Area2

0.356

(0.166)*

0.025

(0.028)

0.062

(0.029)*

-0.037

(0.010)***

0.682

(0.163)***

Area3

0.176

(0.180)

0.003

(0.030)

0.031

(0.032)

-0.028

(0.100)***

0.511

(0.176)***

Household’s size

0.033

(0.062)

-0.003

(0.011)

0.006

(0.011)

-0.009

(0.004)*

0.162

(0.076)*

Children <7

-0.221

(0.098)*

-0.031

(0.017)(*)

-0.039

(0.017)*

0.007

(0.006)

-0.133

(0.111)

Children 7-17

-0.292

(0.134)*

-0.032

(0.021)

-0.051

(0.024)*

0.019

(0.009)*

-0.343

(0.154)*

Household Incomes

0.191D-5

(0.582D-5)

-0.294D-6

(0.966D-6)

0.335D-6

(0.102D-5)

-0.629D-6

(0.492D-6))

0.114D-4

(0.901D-5)

Constant

-0.759

(0.252)***

-1.033

(0.270)***

ρ = 0.625 (0.048)***

Table 5: Bivariate probit analysis , (standard error in parenthesis.). Art1234_5: 0 =’ Never in the last twelve months’, 1 = Less often’ or ‘Several times per month’ or ‘Several times per week’ or ‘Every day’ - Mov1234_5 classified in the same way.

Log Likelihood = - 985.15, AIC = 1.633, BIC = 1.953, HQIC = 1.754, (*), *, **, *** = significance level 10%,5%,1%,0,1% .

Table 5: Bivariate probit analysis , (standard error in parenthesis.). Art1234_5: 0 =’ Never in the last twelve months’, 1 = Less often’ or ‘Several times per month’ or ‘Several times per week’ or ‘Every day’ - Mov1234_5 classified in the same way.


Art1234_5

Art1234_5: marginal effect

Art1234_5: direct marginal effect

Art1234_5: indirect marginal effect

Mov1234_5


0.422

(0.092)***

0.063

(0.016)***

0.074

(0.016)***

-0.011

(0.005)*

0.196

(0.095)*


0.032

(0.182)

0.013

(0.030)

0.006

(0.032)

0.008

(0.010)

-0.141

(0.185)


0.409

(0.224)(*)

0.058

(0.038)

0.071

(0.039)(*)

-0.014

(0.014)

0.249

(0.255)


0.286

(0.239)

0.043

(0.040)

0.050

(0.042)

-0.007

(0.015)

0.125

(0.269)

age15_19

-0.014

(0.293)

-0.046

(0.050)

-0.002

(0.051)

-0.044

(0.025)(*)

0.782

(0.425)(*)

age20_24

-0.172

(0.260)

-0.064

(0.043)

-0.030

(0.045)

-0.034

(0.018)(*)

0.612

(0.324)(*)

age25_29

0.061

(0.252)

-0.019

(0.042)

0.011

(0.044)

-0.030

(0.014)*

0.534

(0.252)*

age30_34

0.109

(0.278)

-0.038

(0.046)

0.019

(0.049)

-0.057

(0.021)**

1.015

(0.361)**

age35_39

0.335

(0.238)

0.027

(0.039)

0.058

(0.042)

-0.031

(0.014)*

0.561

(0.254)*

age40_44 = C

--

--

--

--

--

age45_49

0.123

(0.195)

0.002

(0.033)

0.021

(0.034)

-0.019

(0.012)

0.342

(0.220)

age50_54

0.011

(0.214)

0.012

(0.035)

0.002

(0.037)

0.010

(0.011)

-0.174

(0.189)

age55_59

0.026

(0.207)

0.007

(0.034)

0.005

(0.036)

0.003

(0.010)

-0.046

(0.187)

age60_64

0.009

(0.196)

0.001

(0.033)

0.002

(0.034)

-0.001

(0.011)

0.014

(0.189)

age65_69

0.012

(0.242)

-0.002

(0.039)

0.002

(0.042)

-0.005

(0.013)

0.083

(0.234)

age70_74

-0.253

(0.236)

-0.009

(0.039)

-0.044

(0.041)

0.035

(0.013)**

-0.627

(0.218)**

edu1

0.020

(0.279)

-0.003

(0.047)

0.036

(0.049)

-0.007

(0.022)

0.119

(0.398)

edu2

-0.646

(0.208)***

-0.077

(0.036)*

-0.113

(0.037)**

0.035

(0.013)**

-0.636

(0.224)**

edu3

-0.198

(0.225)

-0.025

(0.038)

-0.035

(0.039)

0.010

(0.013)

-0.172

(0.235)

edu4

-0.065

(0.178)

-0.002

(0.031)

-0.011

(0.031)

0.009

(0.011)

-0.169

(0.201)

edu5

ccc

ccc

ccc

ccc

edu6

0.428

(0.198)*

0.067

(0.034)*

0.075

(0.035)*

-0.007

(0.012)

0.132

(0.209)

edu7

0.229

(0.253)

0.023

(0.042)

0.040

(0.044)

-0.017

(0.017)

0.308

(0.297)

edu8

0.806

(0.465)(*)

0.071

(0.081)

0.141

(0.081)(*)

-0.070

(0.038)(*)

1.256

(0.681)(*)

edu9

0.492

(0.348)

0.080

(0.059)

0.086

(0.062)

-0.006

(0.014)

0.103

(0.258)

spouse-edu1

-0.273

(1.375)

-0.029

(0.347)

-0.048

(0.241)

0.019

(0.116)

-0.341

(2.088)

spouse-edu2

0.044

(0.242)

0.038

(0.041)

0.008

(0.042)

0.030

(0.015)*

-0.540

(0.256)*

spouse-edu3

-0.156

(0.270)

-0.013

(0.046)

-0.027

(0.047)

0.014

(0.016)

-0.250

(0.294)

spouse-edu4

-0.270

(0.200)

-0.021

(0.035)

-0.047

(0.035)

0.027

(0.014)(*)

-0.476

(0.235)*

spouse-edu5

ccc

ccc

ccc

ccc

spouse-edu6

-0.012

(0.245)

0.009

(0.043)

-0.002

(0.043)

0.011

(0.014)

-0.200

(0.254)

spouse-edu7

0.072

(0.326)

0.017

(0.055)

0.013

(0.057)

0.004

(0.020)

-0.079

(0.360)

spouse-edu8

-0.374

(0.384)

-0.036

(0.073)

-0.065

(0.067)

0.029

(0.025)

-0.526

(0.436)

spouse-edu9

0.575

(0.469)

0.091

(0.081)

0.100

(0.080)

-0.009

(0.019)

0.170

(0.337)

Area1

0.359

(0.146)*

0.030

(0.025)

0.063

(0.026)*

-0.033

(0.009)***

0.593

(0.141)***

Area2

0.368

(0.164)*

0.026

(0.027)

0.064

(0.029)*

-0.038

(0.010)***

0.678

(0.161)***

Area3

0.203

(0.179)

0.004

(0.030)

0.035

(0.031)

-0.031

(0.010)**

0.556

(0.174)**

Household’s size

0.035

(0.060)

-0.002

(0.011)

0.006

(0.011)

-0.009

(0.004)*

0.153

(0.076)*

Children <7

-0.217

(0.094)*

-0.033

(0.016)*

-0.038

(0.016)*

0.005

(0.006)

-0.087

(0.104)

Children 7-17

-0.295

(0.130)*

-0.034

(0.021)(*)

-0.051

(0.023)*

0.018

(0.008)*

-0.317

(0.150)*

Household Incomes

0.278D-5

(0.563D-5)

-0.234D-6

(0.906D-6)

0.486D-6

(0.989D-6)

-0.720D-6

(0.499D-6

0.129D-4

(0.900D-5)

Constant

-0.186

(0.271)

-0.280

(0.301)

ρ = 0.631 (0.047)***

Table 6: Bivariate probit analysis , (standard error in parenthesis.). Art1234_5: 0 =’ Never in the last twelve months’, 1 = ‘Less often’ or ‘Several times per month’ or ‘Several times per week’ or ‘Every day’ - Mov1234_5 classified in the same way.


Table 6: Bivariate probit analysis , (standard error in parenthesis.). Art1234_5: 0 =’ Never in the last twelve months’, 1 = ‘Less often’ or ‘Several times per month’ or ‘Several times per week’ or ‘Every day’ - Mov1234_5 classified in the same way.


y = 1

y = 2

marginal effects: y = 0



Marginal effects averaged over individuals:

y = 0


y = 1


y = 2

Averages of Individual Elasticities of Probabilities:

y = 0


y = 1


y = 2


0.771

(0.159)***

1.156

(0.313)***

-0.090

(0.018)***

0.078

(0.019)***

0.013

(0.008)(*)

-0.098

0.076

0.023

-1.042

0.153

0.750


-0.075

(0.311)

0.497

(0.572)

0.007

(0.036)

-0.021

(0.038)

0.015

(0.014)

0.006

-0.032

0.026

0.005

-0.011

0.112


0.562

(0.343)(*)

-0.499

(0.746)

-0.061

(0.040)

0.087

(0.043)*

-0.026

(0.018)

-0.063

0.109

-0.045

-0.211

0.062

-0.454


0.275

(0.380)

0.108

(0.789)

-0.031

(0.044)

0.035

(0.047)

-0.003

(0.019)

-0.033

0.039

-0.006

-0.037

0.009

-0.019

age15_19

-0.056

(0.522)

-0.505

(1.093)

0.008

(0.060)

0.004

(0.065)

-0.012

(0.027)

0.010

0.011

-0.021

0.004

0.000

-0.028

age20_24

-0.443

(0.432)

-0.615

(0.797)

0.052

(0.050)

-0.046

(0.052)

-0.006

(0.019)

0.056

-0.046

-0.011

0.020

-0.005

-0.014

age25_29

0.166

(0.427)

-2.048

(1.201)(*)

-0.011

(0.049)

0.069

(0.055)

-0.058

(0.030)(*)

-0.007

0.108

-0.101

-0.009

0.003

-0.161

age30_34

0.244

(0.467)

0.321

(0.887)

-0.028

(0.054)

0.026

(0.056)

0.003

(0.021)

-0.031

0.026

0.005

-0.013

0.002

0.007

age35_39

0.602

(0.437)

0.544

(0.817)

-0.069

(0.050)

0.069

(0.052)

0.001

(0.019)

-0.075

0.073

0.001

-0.043

0.005

0.000

age40_44

0.029

(0.385)

-0.902

(0.942)

0.329D-4

(0.044)

0.024

(0.048)

-0.024

(0.023)

0.002

0.040

-0.043

-0.001

0.002

-0.080

age45_49

0.192

(0.359)

0.427

(0.690)

-0.023

(0.041)

0.016

(0.043)

0.007

(0.016)

-0.025

0.013

0.012

-0.018

0.001

0.025

age50_54,C

---

age55_59

-0.016

(0.333)

1.037

(0.632)(*)

-0.002

(0.038)

-0.026

(0.040)

0.028

(0.015)(*)

-0.005

-0.044

0.048

-0.008

-0.009

0.107

age60_64

-0.049

(0.329)

1.221

(0.623)*

0.001

(0.038)

-0.034

(0.040)

0.033

(0.015)*

-0.002

-0.056

0.058

-0.009

-0.014

0.128

age65_69

-0.015

(0.395)

0.476

(0.845)

-0.870D-4

(0.045)

-0.013

(0.049)

0.013

(0.020)

-0.001

-0.021

0.022

-0.000

-0.001

0.030

age70_74

-0.412

(0.385)

1.648

(0.698)*

0.040

(0.044)

-0.093

(0.047)*

0.053

(0.018)**

0.038

-0.130

0.092

0.003

-0.023

0.104

edu1

1.155

(0.493)*

1.730

(1.225)

-0.136

(0.057)*

0.116

(0.064)(*)

0.019

(0.030)

-0.147

0.114

0.034

-0.046

0.016

0.046

edu2

C

edu3

0.667

(0.331)*

0.733

(0.866)

-0.077

(0.038)*

0.073

(0.044)(*)

0.004

(0.022)

-0.083

0.076

0.007

-0.040

0.013

0.018

edu4

0.869

(0.269)***

1.678

(0.665)**

-0.103

(0.031)***

0.079

(0.035)*

0.024

(0.017)

-0.113

0.071

0.043

-0.155

0.034

0.210

edu5

1.376

(0.410)***

3.184

(0.779)***

-0.166

(0.048)***

0.113

(0.051)*

0.053

(0.020)**

-0.182

0.091

0.092

-0.098

0.001

0.132

edu6

1.805

(0.321)***

2.551

(0.689)***

-0.211

(0.037)***

0.185

(0.040)***

0.026

(0.017)

-0.229

0.184

0.046

-0.400

0.028

0.205

edu7

1.392

(0.418)***

3.063

(0.862)***

-0.167

(0.049)***

0.118

(0.052)*

0.049

(0.022)*

-0.184

0.098

0.085

-0.106

0.008

0.145

edu8

2.478

(0.796)***

4.609

(1.066)***

-0.294

(0.090)***

0.229

(0.090)**

0.065

(0.022)**

-0.321

0.208

0.113

-0.100

-0.008

0.071

edu9

1.921

(0.518)****

3.733

(0.839)***

-0.229

(0.059)***

0.174

(0.061)**

0.055

(0.020)**

-0.250

0.155

0.095

-0.196

-0.010

0.166

spouse-edu1

-0.549

(0.987)

2.398

(1.516)

0.053

(0.113)

-0.128

(0.120)

0.076

(0.036)*

0.050

-0.182

0.132

0.000

-0.003

0.015

spouse-edu2

C

spouse-edu3

-0.259

(0.433)

0.694

(0.926)

0.026

(0.050)

-0.051

(0.054)

0.024

(0.023)

0.026

-0.069

0.042

0.007

-0.005

0.039

spouse-edu4

-0.363

(0.324)

0.378

(0.738)

0.039

(0.037)

-0.057

(0.041)

0.018

(0.018)

0.041

-0.072

0.032

0.047

-0.018

0.116

spouse-edu5

0.393

(0.539)

1.268

(1.083)

-0.049

(0.062)

0.024

(0.066)

0.024

(0.026)

-0.054

0.012

0.043

-0.015

0.001

0.035

spouse-edu6

0.119

(0.391)

0.613

(0.791)

-0.015

(0.045)

0.002

(0.048)

0.013

(0.019)

-0.018

-0.006

0.023

-0.018

-0.002

0.069

spouse-edu7

0.164

(0.516)

0.909

(0.993)

-0.022

(0.059)

0.001

(0.062)

0.020

(0.023)

-0.025

-0.010

0.035

-0.010

-0.001

0.041

spouse-edu8

-0.514

(0.562)

-1.171

(1.345)

0.062

(0.065)

-0.043

(0.070)

-0.019

(0.033)

0.068

-0.035

-0.033

0.013

-0.002

-0.021

spouse-edu9

1.152

(0.690)(*)

2.539

(0.967)**

-0.138

(0.078)(*)

0.097

(0.078)

0.041

(0.020)*

-0.152

0.081

0.071

-0.101

-0.010

0.099

Area1

0.533

(0.248)*

1.402

(0.609)*

-0.065

(0.029)*

0.040

(0.032)

0.025

(0.015)(*)

-0.072

0.029

0.043

-0.256

0.011

0.447

Area2

0.583

(0.273)*

1.288

(0.649)*

-0.070

(0.031)*

0.049

(0.034)

0.021

(0.016)

-0.077

0.041

0.036

-0.134

0.015

0.195

Area3

0.280

(0.299)

0.588

(0.739)

-0.033

(0.035)

0.024

(0.038)

0.009

(0.018)

-0.037

0.021

0.016

-0.029

0.007

0.046

Household’s size

0.062

(0.104)

0.157

(0.233)

-0.008

(0.012)

0.005

(0.013)

0.003

(0.006)

-0.008

0.004

0.005

-0.130

0.015

0.238

Children <7

-0.423

(0.147)**

-0.334

(0.350)

0.049

(0.017)**

-0.049

(0.019)**

0.001

(0.001)

0.052

-0.054

0.002

0.138

-0.039

-0.002

Children 7-17

-0.488

(0.213)*

-2.359

(1.038)*

0.063

(0.025)**

-0.012

(0.031)

-0.051

(0.022)*

0.072

0.017

-0.089

0.068

-0.014

-0.324

Household Incomes

0.129D-4

(0.173D-4)

-0.431D-4

(0.517D-4)

-0.128D-5

(0.025)

0.271D-5

(0.226D-5)

-0.142D-5

(0.128D-5)

0.000

0.000

0.000

-0.034

0.015

-0.199

Constant

-1.339

(0.428)***

-6.840

(1.107)***

0.175

(0.050)***

-0.025

(0.059)

-0.150

(0.035)***

Table 7: Multinomial logit (MNL) analysis, explanatory variable: y = “How many times in the last twelve months have you seen an art exhibition, opera or theatrical performance?” = 0 (never), 1 (less often) or 2 (daily, several times per week or several times per month).

McFadden pseudo R2 = 0.146, χ2 = 246.006***, AIC = 1.261, BIC = 1.569, HQIC = 1.371

(*), *, **, *** = significance level 10%,5%,1%,0,1% .

Partial derivatives of probabilities with respect of the vector of characteristics are computed at the means of the Xs. Probabilities at the mean vector are Prob(y=0) = 0.133, Prob(y=1) = 0.840, Prob(y=2) = 0.027

y = 1

y = 2





y = 0


y = 1


y = 2


y = 0


y = 1


y = 2


0.761

(0.156)***

1.126

(0.310)***

-0.090

(0.018)***

0.077

(0.019)***

0.013

(0.008)(*)

-0.098

0.076

0.022

-1.027

0.152

0.718


-0.023

(0.303)

0.547

(0.558)

0.001

(0.035)

-0.016

(0.037)

0.016

(0.014)

-0.001

-0.025

0.026

-0.003

-0.008

0.113


0.701

(0.390)(*)

-0.162

(0.836)

-0.078

(0.045)(*)

0.099

(0.049)*

-0.021

(0.022)

-0.082

0.117

-0.035

-0.274

0.067

-0.352


0.418

(0.415)

0.456

(0.868)

-0.049

(0.048)

0.046

(0.052)

0.003

(0.022)

-0.053

0.048

0.005

-0.058

0.012

0.018

age15_19

0.032

(0.497)

-0.033

(1.059)

-0.003

(0.057)

0.005

(0.063)

-0.002

(0.027)

-0.004

0.006

-0.003

-0.001

0.001

-0.003

age20_24

-0.257

(0.421)

-0.135

(0.795)

0.029

(0.049)

-0.032

(0.052)

0.002

(0.020)

0.031

-0.035

0.004

0.010

-0.004

0.003

age25_29

0.201

(0.420)

-1.884

(1.205)

-0.015

(0.049)

0.072

(0.055)

-0.057

(0.031)(*)

-0.011

0.107

-0.095

-0.011

0.004

-0.151

age30_34

0.239

(0.454)

0.350

(0.889)

-0.028

(0.053)

0.024

(0.056)

0.004

(0.022)

-0.031

0.024

0.007

-0.013

0.002

0.008

age35_39

0.640

(0.423)

0.646

(0.799)

-0.074

(0.049)

0.072

(0.051)

0.003

(0.019)

-0.080

0.076

0.005

-0.046

0.005

0.005

age40_44

C

C

C

C

C

age45_49

0.148

(0.349)

0.466

(0.668)

-0.018

(0.040)

0.009

(0.043)

0.009

(0.016)

-0.021

0.005

0.016

-0.015

0.000

0.032

age50_54

0.003

(0.356)

-0.760

(0.918)

0.003

(0.041)

0.019

(0.046)

-0.021

(0.024)

0.005

0.031

-0.035

0.001

0.001

-0.065

age55_59

-0.064

(0.348)

0.902

(0.661)

0.004

(0.040)

-0.030

(0.043)

0.027

(0.017)

0.002

-0.046

0.045

-0.002

-0.010

0.097

age60_64

-0.080

(0.0345)

1.145

(0.649)(*)

0.005

(0.040)

-0.038

(0.042)

0.034

(0.017)*

0.002

-0.058

0.056

-0.005

-0.014

0.123

age65_69

-0.057

(0.412)

0.281

(0.866)

0.005

(0.048)

-0.014

(0.051)

0.009

(0.022)

0.005

-0.020

0.015

0.002

-0.002

0.020

age70_74

-0.540

(0.399)

1.308

(0.708)(*)

0.056

(0.046)

-0.105

(0.049)*

0.049

(0.019)*

0.055

-0.138

0.082

0.010

-0.023

0.090

edu1

0.124

(0.460)

-0.863

(1.076)

0.011

(0.053)

0.038

(0.059)

-0.027

(0.028)

-0.009

0.054

-0.045

-0.003

0.003

-0.049

edu2

-0.998

(0.358)**

-2.125

(0.741)**

0.120

(0.042)**

-0.085

(0.046)(*)

-0.035

(0.020)(*)

0.133

-0.074

-0.059

0.069

-0.034

-0.150

edu3

-0.276

(0.359)

-1.495

(0.788)(*)

0.037

(0.042)

-0.002

(0.046)

-0.035

(0.021)(*)

0.043

0.016

-0.059

0.019

-0.002

-0.097

edu4

-0.074

(0.295)

-0.645

(0.554)

0.011

(0.034)

0.005

(0.037)

-0.016

(0.014)

0.013

0.014

-0.027

0.018

0.002

-0.122

edu5

C

C

C

C

C

C

edu6

0.868

(0.344)*

0.248

(0.581)

-0.098

(0.039)*

0.112

(0.041)**

-0.014

(0.014)

-0.105

0.128

-0.023

-0.182

0.023

-0.124

edu7

0.417

(0.416)

0.584

(0.732)

-0.049

(0.048)

0.043

(0.051)

0.006

(0.018)

-0.053

0.043

0.011

-0.031

0.004

0.017

edu8

1.515

(0.800)(*)

2.190

(0.975)*

-0.179

(0.091)*

0.154

(0.091)(*)

0.025

(0.018)

-0.194

0.153

0.042

-0.057

-0.001

0.024

edu9

0.959

(0.520)(*)

1.273

(0.717)(*)

-0.113

(0.060)(*)

0.100

(0.060)(*)

0.012

(0.015)

-0.122

0.101

0.021

-0.092

0.001

0.032

spouse-edu1

-0.651

(0.987)

2.137

(1.543)

0.065

(0.114)

-0.140

(0.121)

0.075

(0.039)(*)

0.063

-0.188

0.125

0.001

-0.003

0.014

spouse-edu2

0.093

(0.432)

0.334

(0.999)

-0.012

(0.050)

0.005

(0.055)

0.007

(0.026)

-0.013

0.001

0.012

-0.006

0.001

0.018

spouse-edu3

-0.293

(0.463)

0.570

(0.997)

0.031

(0.054)

-0.054

(0.059)

0.023

(0.026)

0.031

-0.069

0.038

0.008

-0.005

0.035

spouse-edu4

-0.422

(0.355)

0.169

(0.825)

0.047

(0.041)

-0.062

(0.045)

0.015

(0.021)

0.049

-0.074

0.025

0.057

-0.019

0.087

spouse-edu5

C

spouse-edu6

0.070

(0.419)

0.462

(0.870)

-0.010

(0.049)

-0.001

(0.052)

0.011

(0.022)

-0.011

-0.007

0.019

-0.012

-0.002

0.054

spouse-edu7

0.074

(0.527)

0.740

(1.039)

-0.011

(0.061)

-0.008

(0.065)

0.019

(0.026)

-0.014

-0.018

0.031

-0.005

-0.001

0.037

spouse-edu8

-0.575

(0.579)

-1.252

(1.389)

0.069

(0.067)

-0.048

(0.073)

-0.021

(0.036)

0.077

-0.041

-0.035

0.014

-0.002

-0.021

spouse-edu9

1.134

(0.699)(*)

2.515

(1.021)*

-0.137

(0.079)(*)

0.094

(0.080)

0.043

(0.023)(*)

-0.151

0.080

0.072

-0.100

-0.010

0.098

Area1

0.573

(0.246)*

1.433

(0.603)*

-0.070

(0.029)*

0.044

(0.032)

0.026

(0.016)(*)

-0.078

0.034

0.044

-0.273

0.014

0.446

Area2

0.593

(0.271)*

1.256

(0.643)*

-0.071

(0.031)*

0.051

(0.035)

0.021

(0.017)

-0.079

0.044

0.035

-0.136

0.016

0.185

Area3

0.322

(0.299)

0.630

(0.733)

-0.039

(0.035)

0.029

(0.039)

0.010

(0.019)

-0.042

0.026

0.017

-0.033

0.008

0.047

Household’s size

0.063

(0.105)

0.151

(0.232)

-0.008

(0.012)

0.005

(0.013)

0.003

(0.006)

-0.009

0.004

0.005

-0.132

0.017

0.220

Children <7

-0.423

(0.143)**

-0.443

(0.343)

0.049

(0.017)**

-0.047

(0.018)**

-0.002

(0.009)

0.053

-0.049

-0.004

0.140

-0.037

-0.046

Children 7-17

-0.497

(0.213)*

-2.415

(1.032)*

0.065

(0.025)**

-0.010

(0.032)

-0.055

(0.023)*

0.075

0.017

-0.092

0.069

-0.014

-0.332

Household Incomes

0.152D-4

(0.177D-4)

-0.299D-4

(0.483D-4)

-0.160D-5

(0.205D-5)

0.279D-5

(0.229D-5)

-0.120D-5

(0.125D-5)

0.000

0.000

0.000

-0.044

0.015

-0.159

Constant

-0.466

(0.460)

-4.558

(1.028)***

Table 8: Multinomial logit (MNL) analysis, explanatory variable: y = “How many times in the last twelve months have you seen an art exhibition, opera or theatrical performance?” = 0 (never), 1 (less often) or 2 (daily, several times per week or several times per month).

McFadden pseudo R2 = 0.139, χ2 = 233.029***, AIC = 1.263, BIC = 1.579, HQIC = 1.381

(*), *, **, *** = significance level 10%,5%,1%,0,1% .

Partial derivatives of probabilities with respect of the vector of characteristics are computed at the means of the Xs. Probabilities at the mean vector are Prob(y=0) = 0.134, Prob(y=1) = 0.837, Prob(y=2) = 0.029

DirME5age15_19age20_24age25_29age30_34age35_39age40_44age45_49age50_54age55_59age60_64age65_69age70_74-7.0000000000000001E-3-4.2000000000000003E-20.012.3E-25.8000000000000003E-21.2E-22.8000000000000001E-201.0999999999999999E-26.0000000000000001E-38.9999999999999993E-3-2.9000000000000001E-2IndME5age15_19age20_24age25_29age30_34age35_39age40_44age45_49age50_54age55_59age60_64age65_69age70_74-5.6000000000000001E-2-4.3999999999999997E-2-4.2999999999999997E-2-7.2999999999999995E-2-4.4999999999999998E-2-2.9000000000000001E-2-3.4000000000000002E-20-1.0999999999999999E-2-1.2999999999999999E-2-1.6E-20.02DirME6age15_19age20_24age25_29age30_34age35_39age40_44age45_49age50_54age55_59age60_64age65_69age70_74-2E-3-0.031.0999999999999999E-21.9E-25.8000000000000003E-202.1000000000000001E-22E-35.0000000000000001E-32E-32E-3-4.3999999999999997E-2IndME6age15_19age20_24age25_29age30_34age35_39age40_44age45_49age50_54age55_59age60_64age65_69age70_74-4.3999999999999997E-2-3.4000000000000002E-2-0.03-5.7000000000000002E-2-3.1E-20-1.9E-20.013.0000000000000001E-3-1E-3-5.0000000000000001E-33.5000000000000003E-2DirME5edu1edu2edu3edu4edu5edu6edu7edu8edu90.1207.3999999999999996E-29.6000000000000002E-20.146999999999999990.1820.1510.250.19500000000000001IndME5edu1edu2edu3edu4edu5edu6edu7edu8edu9-4.2999999999999997E-20-2.1999999999999999E-2-2.1000000000000001E-2-3.9E-2-3.6999999999999998E-2-4.7E-2-0.1-3.5000000000000003E-2DirME5edu1edu2edu3edu4edu5edu6edu7edu8edu93.5999999999999997E-2-0.113-3.5000000000000003E-2-1.0999999999999999E-207.4999999999999997E-20.040.140999999999999998.5999999999999993E-2IndME6edu1edu2edu3edu4edu5edu6edu7edu8edu9-7.0000000000000001E-33.5000000000000003E-20.018.9999999999999993E-30-7.0000000000000001E-3-1.7000000000000001E-2-7.0000000000000007E-2-6.0000000000000001E-3

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