[Kirjoita yrityksen nimi]
Essays on cultural
economics
[Tiedoston alaotsikko]
seppo
[Valitse pvm.]
Contents
0 Introduction ......................................................................................................................................................... 6
0.1 Essay 1: Critics review or preceding week’s admissions explaining movie admissions ........................ 10
0.2 Essay 2: Demand for ice hockey, the factors explaining attendance of ice hockey games in Finland .. 11
0.3 Essay 3: Fan loyalty in Finnish ice hockey .............................................................................................. 15
0.4 Essay 4: Spectators of performing arts – who is sitting in the auditorium?.......................................... 16
0.5 Essay 5: Are the spectators of performing arts and the spectators of the movies the same? ............ 20
0.6 Conclusions ............................................................................................................................................ 23
1 Critics´ reviews or preceding week’s admissions explaining movie admissions ....................................... 29
1.1 Introduction ........................................................................................................................................... 29
1.2 Literature review ................................................................................................................................... 30
1.3 Empirical model and variables............................................................................................................... 34
1.4 Estimation and results ........................................................................................................................... 38
1.5 Conclusions and suggestions ................................................................................................................. 45
2 Demand for ice hockey, the factors explaining attendance of ice hockey games in Finland ................. 56
2.1 Introduction ........................................................................................................................................... 57
2.2 Literature ............................................................................................................................................... 60
2.3 A model explaining attendance ............................................................................................................. 64
2.4 Estimation .............................................................................................................................................. 68
2.5 Conclusions and suggestions ................................................................................................................. 71
3 Fan loyalty in Finnish Ice Hockey .................................................................................................................. 87
3.1 Introduction ........................................................................................................................................... 87
3.2 Fan loyalty or brand loyalty – stochastic frontier analysis - .................................................................. 89
3.3 Stylized facts: Finnish Ice Hockey .......................................................................................................... 91
3.4 Estimation and results ........................................................................................................................... 96
3.5 Conclusions .......................................................................................................................................... 101
4. Spectators of performing arts – who is sitting in the auditorium? .......................................................... 108
4.1 Introduction ......................................................................................................................................... 108
4.2 Method and sample ............................................................................................................................ 112
4.3 Conclusions and evaluation ................................................................................................................. 120
5. Are the spectators of performing arts and the spectators of movies the same? .................................. 141
5.1 Introduction ......................................................................................................................................... 141
1
5.2 Literature review ................................................................................................................................. 146
5.3 The method and sample ...................................................................................................................... 149
5.4 Results ................................................................................................................................................. 154
5.5 Conclusions .......................................................................................................................................... 161
2
Tables
Table 1: Sports consumption in Finland 2007 ................................................................................................ 11
Table 2 Attendance popularity and correlation among adult population in Finland. 2005-2006. ............ 12
Table 3 (Table1.1) Overview of top 10 films in 2003 in Finland, source: Finnish Film Foundation ...... 35
Table 4: (Table 1.2) Descriptive statistics and sources of variables, * weekly, ** non-zero observations
................................................................................................................................................................................. 38
Table 5: (Table 1.3) Fixed Effect and Random Effect Models (Park 2008) ................................................ 39
Table 6: (Table 1.4) Estimation results, full sample, n = 1060 ...................................................................... 40
Table 7: (Table 1.5) Estimation results, all movies with previous admission in Helsinki, n = 515 .......... 42
Table 8: (Table 1.6) Estimation results, all movies critically reviewed and with previous week’s Helsinki
admission, n = 205 ............................................................................................................................................... 43
Table 9: (Table 1.7) Estimation results, all movies critically reviewed and with previous week’s Helsinki
admission, n = 205 ............................................................................................................................................... 44
Table 10: (Table 1.8) Distributors’ premieres in 2001 – 2003........................................................................ 51
Table 11: (Table 1.9) Descriptive statistics for critical review rank (scale 1 – “top” to 10 – ”lowest”)... 52
Table 12: (Table 1.10) Duration of movie run, quantiles ............................................................................... 53
Table 13: (Table 1.11) Estimation results, n = 345 ......................................................................................... 54
Table 14: (Table 1.12) Estimation results, all movies critically reviewed and with previous week’s
Helsinki admission, n = 205................................................................................................................................ 55
Table 15: (Table 2.1) Regular season 2007 – 2008 average attendance and capacity statictics, source:
Jääkiekkokirja 2007-2008 ja Jääkiekkokirja 2008-2009 .................................................................................... 80
Table 16: (Table 2.2) Variables, measurement, source and expected sign ................................................... 81
Table 17: (Table2.3) Variables, means, standard deviations and correlation matrix. ATT = attendance,
Price (€), Dist = distance between home team’s and visitor’s stadiums along road (km), Temp = max
tempature Unempl = monthly regional unemployment rate(%), HomePop = home town population,
VisiPop = vistor’s town population, HPoint = points per game, home team, before the game VPoint =
visitor’s points per game, before the game, HomeG = number of games, home team, before the game,
VisiG = number of games, visitor, before the game , Last3H = points from 3 last games, home team,
Last3V = points from 3 last games, visitor. The number of observations = 392. ..................................... 82
Table 18: (Table 2.4a) Some estimation results ................................................................................................ 83
Table 19: (Table 2.4b) Some estimation results ............................................................................................... 84
Table 20: (Table 2.4c) Some estimation results ................................................................................................ 85
Table 21: (Table 3.1) Average attendance statistics ......................................................................................... 92
Table 22: (Table 3.2) Variables, means, standard deviations and correlation matrix.................................. 94
Table 23: (Table 3.3) OLS results, dependent variable is log(Attendance), n = 406 .................................. 96
Table 24: (Table 3.4) Estimation results, dependent variable is log(Attendance), n = 406 ....................... 98
3
Table 25: (Table 2.5) Inefficiency scores of teams ........................................................................................ 100
Table 26: (Table 2.6) Correlation matrix of selected variables ..................................................................... 101
Table 27: (Table 2.7) Average attendance, home games, regular seasons .................................................. 105
Table 28: (Table 4.1) Culture and physical education hobbies 1981, 1991 and 1999 ............................... 126
Table 29: (Table 4.2) Kulttuuripuntari (culture barometer) 1999: ............................................................... 127
Table 30: (Table 4.3) Suomen Teatterit (Taloustutkimus), survey on visits to theatre, opera or ballet
during the past 12 months, years 1985, 1998, 2001, 2004 and 2007 ........................................................... 128
Table 31: (Table 4.4) Eurobarometer 56.0: August-September 2001, n = 1024. ...................................... 130
Table 32: (Table 4.5) ISSP 2007, ”How often in your leisure do you go to concerts, exhibitions, theatre
etc.?” ..................................................................................................................................................................... 131
Table 33: (Table 4.6) Visitor density, ANOVA (significance in parenthesis) ............................................ 132
Table 34: (Table 4.7) Visitor density, Anova and Manova, Women and Men separately ........................ 134
Table 35: (Table 4.8) Multivariate logit analysis ............................................................................................. 135
Table 36: (Table 4.9) Multivariate logit analysis ............................................................................................. 136
Table 37: (Table 4.10) ISSP 2007, ”How often on your leisure do you go to see sport events on the
location (ice hockey, football, athletics, motor racing etc.)? n = 1355 ....................................................... 137
Table 38: (Table 4.11) Multivariate logit analysis ........................................................................................... 138
Table 39: (Table 4.12) Bivariate probit analysis,............................................................................................. 139
Table 40: (Table 4.13) Bivariate probit analysis,............................................................................................. 140
Table 41: (Table 5.1) Spectators of movies at the cinema and performing atrs (concert, theatre, art
exhibition) in Finland, recent surveys .............................................................................................................. 166
Table 42: (Table 5.2) descriptive statistics of age-group and education variables ..................................... 167
Table 43: (Table 5.3) Average monthly household and personal gross incomes ...................................... 168
Table 44: (Table 5.4) Descriptive statistics of some explanatory variables ................................................ 169
Table 45: (Table 5.5) Bivariate probit analysis ................................................................................................ 170
Table 46: (Table 5.6) Bivariate probit analysis ,.............................................................................................. 173
Table 47: (Table 5.7) Multinomial logit (MNL) analysis ............................................................................... 176
Table 48: (Table 5.8) Multinomial logit (MNL) analysis, .............................................................................. 180
4
Figures
Figure 1: Value added of culture in 2007, EUR/capita in NUTS3 and Capital regions in Finland ......... 18
Figure 2: (Figure 2.1) Actual attendance for HIFK (Series1) and Model 6 (Series2) ................................. 86
Figure 3: (Figure 2.2) Actual attendance for HIFK (ADM), Model 6 and simulations 1 and 2 ............... 86
Figure 4: Nuts areas ............................................................................................................................................ 154
Figure 5: (Figure 5.1) Direct and indirect marginal effect of age-cohorts on highbrow art consumption
............................................................................................................................................................................... 158
Figure 6: (Figure 5.2) Direct and indirect marginal effects of education on highbrow art consumption
............................................................................................................................................................................... 158
5
0 Introduction
Stigler and Becker (1977) introduced the concept of cultural capital developed by the aggregation of
past consumption of cultural goods. The accumulation function can be considered to be similar to that
of human capital, i.e. formal education. Part of culture consumed can be considered as investment in
the future cultural capital. In other words, not all cultural spending is consumed within a year, whereas
can be consumed and accumulated over a longer period. Cultural behaviour is determined by the
consumer’s budget, time, social, physical constraints and formal education (Frey 2000). A central
feature is also the variety in cultural consumptions and its accumulation. People with higher education
have on average less leisure than those with lower education. At the same time the higher educated
have a bigger variety at leisure and therefore also in cultural consumption (Ruuskanen 2004, 136).
A large amount of economic and sociological research has been done to classify different cultural
consumption patterns. The economics of cultural consumption has traditionally focused on explaining
attendance figures and studying the socioeconomic characteristics of the audience. Audience and
participation surveys often argue that performing arts audiences consist of relatively wealthy citizens
while the audiences of sport events and cinemas are different. However, cultural consumption is not
just about going to see art exhibitions, opera or theatrical performances. Some of the consumers prefer
sport events and films. Sport events, especially football and ice hockey matches, are favoured by
middle-class males and cinema lovers are young students.
Cultural consumption is thus connected with leisure activities of consumers. They might choose to go
to the cinema, go to an ice hockey match, go to the opera or theatrical performances, etc. depending on
their preferences and the amount of leisure time and incomes. Time constraints are related to (i) the
place of residence, (ii) to the leisure time. In Finland art institutions, like opera and theatre houses, are
located in bigger cities, but there are some theatre groups making tours in the countryside. Still the
place of residence is a very important factor to explain consumers’ cultural participation decisions.
Several studies have shown that there is a substantial seasonal variation in leisure time use. During the
winter, leisure is more sports oriented while during summer more socially oriented (Niemi and
Pääkkönen 1992). Men’s ice hockey is the most popular sport in terms of total attendance. The regular
season in the highest league in Finland begins in September and ends in March. After that there are
some play-off matches in March and April. Also the movie attendance statistics in the first essay show
that the summer is the weakest time by attendance. Most citizens have their holidays in summer, but
they do not seem to go to cultural or sport events even if there was plenty of leisure.
6
The socio-economic status of the consumer clearly has an effect on leisure activities. The unemployed
have more leisure but less income. Ruuskanen (2004) has shown that both net wage and the yearly
income of the spouse have a negative effect on the joint time spent together, the number of children
reduces joint leisure time of spouses, and university education increases the time spent together in
leisure. Both the age and health situation of consumers have an impact on the leisure time and how
active the leisure is (Piekkola and Ruuskanen 2006). Both unemployed and employed older men are
more active in leisure if they consider themselves healthy, but the relation is not so obvious for younger
men. Older women are more active during their leisure than younger women. Taking care of small
children does not restrict any more. Also teenaged girls are more active in participation in cultural
activities except the movies (Pääkkönen 2010, 234) although the amount of leisure is lower for girls
than for boys.
Limited leisure time restricts and these cultural events are substitutes to some extent. However, are art
exhibitions, opera or theatrical performances substitutes or rather complements? Budget constraints
can limit the participation so that consumers can only choose one cultural event and therefore different
events are substitutes. But, on the other hand, some culturally oriented consumers can accumulate
positive experiences and these can induce further consumption. In this case cultural events are
complements. Complementarity is also likely to vary depending on the socio-economic class.
Ruuskanen (2004) has shown that skilled workers are more engaged in several types of activities while
the time use of low-educated is more monotone. The sociology of cultural participation has shown that
consumers can be classified into three groups: omnivore, paucivore and inactive (Alderson, Junisbai
and Heacock 2007). The omnivores are active in all cultural consumption, from cinema to classical
music. The paucivores go to see all kinds of cultural activities but less than the omnivores. To the
omnivores cultural events are complements.
Irrespective of the cultural events being complements or substitutes, the quality of the event is
important from the viewpoint of enjoying. Advertising provides direct information about the
characteristics of products with search qualities, their main attributes can be determined by visual or
tactile inspection (e.g. clothes) or by a test drive or trial (car). Advertising may convey hard facts, vague
claims or a favourable impression of a product. The informational content of advertising depends on
whether consumers can determine the quality of that product before buying. If the consumer can value
a product’s quality by inspection before buying it, the product has search qualities or the product is a
search good. However, if the consumer must consume the product to determine its quality, the product
7
has experience qualities or the product is an experience good (Nelson 1970). Experience goods must be
consumed before their quality can be determined (e.g. processed foods, software programs, and
gymnastic exercises). The early writers in the 1950’s considered advertising as being manipulative
(Kaldor 1950) and therefore it reduces competition and welfare since advertising persuades consumers
to purchase more heavily advertised products even though there is no quality difference between
otherwise equal or comparable products. The price of the highly advertised products rises and therefore
the advertising serves as an entry-deterring mechanism. If advertising is predatory, the incumbent firm
is capable of creating an entry-deterring strategy (Cubbin 1981). More recent authors propose that
advertising serves as a tool for transmitting information from producers to consumers about
differentiated brands and therefore reducing the search costs and increasing welfare (Benham 1972).
Nelson (1974) argues that producers of high-quality experience goods can spend more money on
advertising because first-time consumers are more likely to be satisfied with the quality and will make
repeat purchases, than with low-quality experience goods. Consumers are not dependent on the
information received through producers’ advertising when they buy search goods since they receive
that information by inspection or trial. So the effects of advertising vary between search goods and
experience goods and there is more intensive advertising with experience goods. On the other hand,
Schmalensee (1978) argues that low-quality brands are more frequently purchased and low-quality
producers advertise more intensively. The recent rise of social media has substantially changed the
media usage of advertising campaigns. Consumers trust more on recommendations from other
consumers, e.g. word-of-mouth or blogs than on paid advertising (Viljakainen, Bäck and Lindqvist 2008
or Karjaluoto 2010).
Producers (distributors, importers) can use other means to signal about the quality of their products,
not just advertising but also product labelling or branding, reputation, guarantees or expert ratings.
Some fruits and vegetables are sold without a brand name. Consumers might assume that a banana is a
banana and there is a little variation between producers or countries of origin. However, there are
strong and universal brands also, like Chiquita and consumers attach this brand and trustworthy quality.
If a large proportion of sales is generated by customers who do not repeat their purchases, like tourists,
the reputation of a shop matters less since few customers are familiar with the shop’s reputation
(Carlton & Perloff 1990, 530). To the contrary consumers who repeat purchases are willing to
repurchase cultural and other goods if their past experiences are positive and producers’ signals have
less importance. One essay shows evidence that especially committed ice hockey fans repeat their
purchases and regularly go to the ice hockey stadium. The impact of advertising on the match
attendance is minor.
8
Different terminologies have been used to rank tastes, like: highbrow – middlebrow – lowbrow, or high
– popular, or legitimate – vulgar. This division has been used frequently in the sociology of cultural
consumption. With Swedish data Bihagen and Katz-Gerro (2000) show that women are more active in
highbrow consumption (opera, dance or theatrical performances) and men in low, like watching
television (entertainment, sport). Highbrow television (documentary, culture, news) and lowbrow
culture (films) are less connected to gender and formal education, but Warde and Gayo-Gal (2009)
show that these are strongly related to age. The omnivore group is associated with legitimate taste that
is aesthetically the most valuable. Omnivorousness increases with age up to around 50 and strongly
diminishes among those over 70.
In Finland, the economics of culture has been less studied. There are a few surveys on the cinema
spectators (Suomalaisen elokuvan markkinat ja kilpailukyky 1999, Kotimaisen elokuvan yleisöt –
tutkimus 2010), theatre and opera audiences (Kivekäs 1991, Suomalaisten teatterissa käynti 2007,
Mikkonen and Pasanen 2009), audiences of sport events (e.g. Kansallinen liikuntatutkimus 2010) and a
substantial amount of sociological studies on the cultural consumption (e.g. recently Virtanen 2007 or
Purhonen, Gronow and Rahkonen 2010). Most surveys present descriptive statistics of the audience,
but there are virtually no studies that use more advanced econometric methods. Using frequency and
contingency tables, the analysis of variance and logistic regression methods Virtanen (2007) showed
that education, age and socio-economic status have important explanation power in highbrow cultural
consumption in the whole European Union area. However, these variables can explain only 10 – 15 per
cent of the variation in consumption choices. Purhonen, Gronow and Rahkonen (2010) showed using
logistic and Poisson regression analysis that regardless of how omnivorousness is operationalized,
different socio-economic variables are better to explain literature taste than musical taste. The socioeconomic variables are gender, age group, education and the place of residence. Income level is not
significant.
The purpose of this study is to use advanced econometric methods to explain cultural consumption
choices in Finland and to get more information on this topic. The study is a combination of five
separate papers in cultural economics. The cultural capital related to movies and attendance to cinema
audience is the topic in the first study. Conventionally it is argued that the biggest group in the cinema
audience consists of young people of age 15 – 24 (Suomalaisen elokuvan markkinat ja kilpailukyky,
1999, 89). What is the role of public information on the decision to go to the cinema? Will they read
critical reviews from the newspapers before they make the decision between different movies in the
9
repertoire? The second and the third essay are studying the audiences of the ice hockey matches in the
men’s champion league (Sm-liiga). What is the role of the winning percentage of the home team and of
the visitor team on the attendance? Since typically the audience is male dominant who read carefully the
sport pages in the newspapers where the series situation is published, that information might have an
important impact on the attendance figures. The fourth and fifth essays are studying what is the
composition of the audiences of highbrow arts in relation to cinema and sports. Are these audiences
different and how? The essays draw a picture of omnivore, paucivore and inactive consumers and
especially on how sensitive the omnivore consumption patterns are to the various background variables
such as age, education and gender. In the concluding chapter we also draw some tentative conclusions
on how inactive people can be encouraged to consume or invest more in cultural capital.
0.1 Essay 1: Critics review or preceding week’s admissions explaining movie
admissions
The first essay considers movie attendance in Finland in 2003 explaining the number of spectators of
the 20 most popular films in each week. The total number of films on distribution was 225 but with
only 177 premieres. Many films had the first evening during preceding December in 2002 since it is
widely known that the Christmas season is top time. Seasonal variation is high, during summer the
attendance figures are the lowest. Variation across films is also large. The aggregate attendance in 2003
was roughly 7.7 million and the top 10 got roughly 42 % of the spectators. Since both the weekly (time
series) and movie specific (cross-section) variation is substantial, conventional regression methods are
unsuitable. A panel data analysis enables regression analysis with both time-series and cross-sectional
dimension.
In 2003 the average duration of movie runs in Finland was four months for the top 10 films and
roughly one month for the median film in respect of the spectator number. Hence, spectators have had
enough time to reveal the necessary information on the quality of the film from various sources. The
essay studies the role of word-of-mouth and critical reviews in explaining movie attendance. Critical
reviews are published in the weekly magazine supplement ‘Nyt’ for the newspaper that has the largest
circulation in Finland, Helsingin Sanomat. World-of-mouth is measured by the previous week’s
attendance figure at the cinemas in Helsinki. Since more than a fourth of young audience (age-group:
15-24) are heavy users and since they read less newspapers than older citizens in Finland, the role of
critics review is probably lower than the role of word-of-mouth. Consumers in general rely more on the
word-of-mouth than on other forms of information (Viljakainen, Bäck and Lindqvist 2008, 25). The
10
first essay verifies that when the world-of-mouth is taken into account, the critics review is not a
significant variable to explain movie attendance. Since admission figures are typically the highest during
the first weeks, a variable “weeks since released” is used to control this peak. The analysis shows that it
is significant, as well as the price variable. The price elasticity of weekly movie admission is roughly -1
which shows some monopoly pricing potential. Panel data analysis also indicates that the fixed effects
model is the most suitable for explaining weekly movie admissions in Finland in 2003.
0.2 Essay 2: Demand for ice hockey, the factors explaining attendance of ice
hockey games in Finland
The second essay studies the ice hockey match attendance during the regular season 2007 – 2008 in
Finland. The ice hockey league, ‘SM-liiga’ is the most important live cultural event or series of cultural
events in terms of attendance per event. Yearly movies attendance is three times bigger compared to
the ice hockey matches but these and opera or theatrical performances are live events. At the opera and
theatre there is a manuscript that they follow and thus the possibility of surprises is smaller, but a match
has more uncertainty. The home team might win or lose the match depending on the quality of the
team and the visitor among others. The regular season usually begins in September and ends in the
following March. The number of regular season matches was 392. The total attendance was 1,964,626
i.e. 5,012 per match. Besides these matches there were play-off matches in March and April, but these
matches were left out from the examination due to the different nature of these events. The Finnish
data on the recent International Social Survey Programme (ISSP 2007) reveals that almost 40 % of the
population never goes to see a sports activity (ice hockey, football, athletics, motor racing etc.), less
than 8 % attends several times a month and the rest (i.e. more than 50 %) occasionally. The same
survey also shows that physical exercise (active sport consumption) is more common than passive sport
consumption (table 1: Sports consumption in Finland 2007).
Table 1: Sports consumption in Finland 2007
Daily
How often do you
Several times
Several times
Occasionally
Never
Total, n
a week
a month
4 (0.3%)
17 (1.3%)
82 (6.2%)
691 (52.3%)
526 (39.8%)
1320
1 (0.1%)
5 (0.7%)
38 (5.1%)
327 (44.2%)
369 (49.9%)
740
attend a sports
activity?
How often do you
11
attend a sports
activity? (Female)
How often do you
3 (0.5%)
12 (2.1%)
43 (7.6%)
358 (63.3%)
150 (26.5%)
566
301 (22.6%)
546 (41.0%)
272 (20.5%)
183 (13.8%)
28 (2.1%)
1330
189 (25.5%)
308 (41.5%)
144 (19.4%)
92 (12.4%)
9 (1.2%)
742
106 (18.5%)
230 (40.1%)
127 (22.2%)
91 (15.9%)
19 (3.3%)
573
attend a sports
activity? (Male)
How often do you
exercise sports?
How often do you
exercise sports?
(Female)
How often do you
exercise sports? (Male)
Source: ISSP 2007. Own calculations.
There is a significant difference between genders so that males are more active in passive sport
consumption (attendance, Mann-Whitley U-test, z= -8,430, sig. = 0,000), while females are more active
exercisers (Mann-Whitney U-test, z=-3,858, sig=0,000). Active and passive (attendance) sports
consumption are only slightly positively correlated (Kendall’s τ = 0,054, n = 1314, sig. = 0,028). There
is also a negative relationship between age and passive sports consumption (Spearman’s ρ = -0,182, n =
1265, sig. = 0,000). For female the negative relationship is somewhat stronger (Spearman’s ρ = -0,193,
n = 724, sig. = 0,000) than for male (Spearman’s ρ = -0,179, n = 540, sig. = 0,000). Another survey
(Liikuntatutkimus 2005-2006, Sport Survey: Adult Population) on adult population sport consumption
– both active and passive – in Finland was carried out a few years ago1. The sample size was 5510. In
this survey 44% responded that they had not attended any sports event between February 2005 and
January 2006. The most popular sports in terms of attendance were ice hockey (25.5%), football
(16.9%), athletics (10.6%), skiing (6.5%) and Finnish rule baseball (5%). The largest positive correlation
is between ice hockey and football attendance. Attendance and income level (8 categories from the
lowest to the highest) are not correlated (not reported here).
Table 2 Attendance popularity and correlation among adult population in Finland. 2005-2006.
Popularity
Ice Hockey
Football
Athletics
Skiing
F Rule
Baseball
Ice Hockey
25.5%
1
F: 14.6%
1
Recent (February – March 2007) Eurobarometer 67.1 reports that almost 56 % in the sample (n = 1054 in Finland)
had not attended any sport event during the last 12 month period. The figure was lower for male (44%) than for
female (65%).
12
M: 36.4%
Football
Athletics
16.9%
0.323 (0.000)
1
F: 11.0%
F: 0.353 (0.000)
M: 22.8%
M:0.193(0.000)
10.6%
0.093 (0.000)
0.123 (0.000)
F: 9.9%
F: 0.133 (0.000)
F: 0.156 (0.000)
M: 11.3%
M: 0.031
M:0.074 (0.000)
1
(0.108)
Skiing
6.5%
0.009 (0.517)
0.022 (0.110)
0.147 (0.000)
F: 6.3%
F: 0.002 (0.909)
F:0.019 (0.315)
F:0.150 (0.000)
M: 6.6%
M: 0.015
M: 0.024 (0.216)
M: 0.143
(0.431)
F Rule Baseball
1
(0.000)
5.0%
0.098 (0.000)
0.056 (0.000)
0.056 (0.000)
0.014 (0.295)
F: 3.9%
F: 0.085 (0.000)
F: 0.050 (0.009)
F: 0.053 (0.006)
F: 0.024 (0.212)
M: 6.1%
M: 0.096
M: 0.049 (0.010)
M: 0.058
M: 0.001 (0.942)
(0.000)
1
(0.002)
Observations 5510. Significance in parenthesis. Legend: F = female n = 2754, M = male n = 2756
Tables 1 and 2 indicate that more than half of the adult population (age between 15 and 74 in ISSP
2007) have attended a sport activity and for half of these an ice hockey match has been that event.
The sociology of sport consumption has revealed that there are substantial motive differences between
genders. A well-known classification is Sport Fan Motivation Scale (SFMS) by Wann (1995). There are
eight motives: eustress (i.e. the need for positive stress), self-esteem (i.e. the desire to maintain a
positive self-concept through team success), escape (i.e. sport as diversion from bored everyday life),
entertainment, economic (i.e. gamble on the events), aesthetic (i.e. sport as an art), group affiliation (i.e.
belongingness need), and family (i.e. opportunities to spend time with family). Wann conducted a
quantitative examination with a 23-item Likert scale questionnaire. Using confirmatory factor analysis
the above mentioned eight internally consistent, reliable and criterion valid motives were found. The
original sample consisted of primarily of university college students. Several studies, however,
confirmed the results (e.g. Wann, Schrader & Wilson 1999, Wann, Royalty & Rochelle 2002, Wann,
Robinson, Dick & Gillentine 2003, Ridinger & Funk 2006, Wann, Grieve, Zapalac & Pease 2008 or
Koo & Hardin 2008). Eustress, self-esteem and group affiliation motives were more associated with
team and aggressive sport type (e.g. football, ice hockey) rather than individual and nonaggressive sport
type. On the other hand, aesthetic motive was associated with individual and nonaggressive sport type
(e.g. figure skating, tennis). Wann, Schrader & Wilson (1999) also classify sport spectators as
intrinsically or extrinsically oriented. Fans that enjoy sport because of its aesthetics and artistic
movement (intrinsic) may not bother of their favorite team’s or individual’s poor performance since the
aesthetic performance of the event is present regardless of the outcome. On the other hand extrinsic
fans (self-esteem, economic motives) could find it unpleasant to watch their favorite team’s games
13
unless the team is victorious. Self-respect and self-fulfillment are more associated with women’s team
spectators (Kahle, Duncan, Dalakas & Aiken 2001) while self-indulgence is more men’s team
spectators’ attribute. The opportunity to spend time with family or sense of belonging or socialization is
attributes associated with women’s sport spectators (Kahle, Duncan, Dalakas & Aiken 2001 or Ridinger
& Funk 2006). Females seem to be more sport fans for social reasons (Dietz-Uhler, Harrick, End &
Jacquemotte 2000), while males are more likely to be fans because they play sports and want to acquire
sport information (e.g. read sport pages in newspapers).
The second essay (”Demand for ice hockey, the factors explaining attendance of ice hockey games in
Finland”) particularly studies among others the effects of public information on the ice hockey
attendance figures. Since men typically read the sport pages of newspapers, the home team’s
performance is well known. The performance is operationalized as the points per game measure
(success). There are four alternatives: a win within the normal playing time (60 minutes) produces 3
points, an win within extension time (> 60 min) or a penalty shot win produces 2 points, a lost within
extension time or after penalty shots produces 1 point, and a lost within normal playtime gives 0.
The results indicate that both the population of the town of the home team and of the visitor have a
statistically significant effect on attendance. The distance between home team’s town and visiting
team’s town is also significant, i.e. local games have a bigger attendance than other games. The demand
is not elastic with respect to the ticket price. Loyal supporters have a season ticket, but the share of
season ticket holders in the audience is not known. Success of both the home team and the visitor has
an effect: home team’s success with a positive and visitor’s with a negative coefficient. The number of
plays already played has a negative effect. Weekday effect is important: the attendance is bigger during
Saturdays. Also the day temperature has an effect: the colder, the bigger attendance. That effect is small
but still statistically significant. The local unemployment rate has no effect, and the success factor of the
last three games (the form guide) does not seem to explain as well as the success factor of all games
played.
With caution it can be argued that ticket price has a slight effect on attendance, since demand seems to
be inelastic. However, the price variable is not the actual average price since this data was not available.
The price variable used in the estimations is the ticket price to the best seats. As the season goes on and
more games have been played, attendance seems to diminish but the estimated coefficient is low even
though significant. Team’s success seems to attract a bigger attendance, while visitor’s success has the
opposite effect. Spectators are willing to see live game in the stadium if they expect that home team will
14
win the game. The local unemployment rate has no effect on attendance, while weather conditions
measured by the outside temperature are a significant variable. Colder weather attracts more spectators.
However, the estimated coefficient is minor but significant. The estimation results reveal that the
models can explain about two thirds of actual attendance based on the coefficient of determination.
0.3 Essay 3: Fan loyalty in Finnish ice hockey
Fan loyalty is the topic of the third essay. Sport has become more professional over the years. Sport
managers view their teams or leagues as brands to be managed. A product or service is considered as a
brand if the name, logo, sign or slogan increases the value of that product or service. The psychological
aspect in the consumer’s mind, the brand image, consists of all information and associations with a
product or service. The third essay studies fan loyalty in Finnish men’s ice hockey during the regular
season 2008-2009 using stochastic frontier analysis. Most teams in the highest ice hockey league are
local monopolies, but there are two teams in Helsinki which might be substitutes since the distance
between their stadiums is less than 3 km. Moreover, there is one team in the neighbouring city, Espoo,
whose stadium is at a distance of about 13 km from the previous. In addition there are two teams in
Tampere with a shared stadium. However, some teams are local monopolies and some teams meet
higher competition. Therefore brand loyalty or fan loyalty might differ according to the competitive
position, and the aim of this research is to study the relationship between the fan loyalty and the
competitive position of teams. The competitive position is defined here as the geographical distance
between teams’ stadiums.
The teams in the champion league generally raise funds from not just gate revenues but also from
merchandise sales, the sales of broadcast rights and commercial sponsorships. Loyal fans use various
fan products such as fan shits and scarves. Broadcast rights are usually sold by the league association
and the broadcast revenue is shared among the teams. Sponsorship revenue depends on the popularity
of the team, which in turn is associated with larger market base, i.e. larger home town population.
Depken (2000, 2001) measures fan loyalty by efficiency score in stochastic frontier analysis, Winfree,
McCluskey, Mitterhammer and Fort (2004) by the permanency of successive years’ attendance and
Brandes, Franck and Theiler (2010) by mean match tickets per market size. Also direct surveys to get
self-revealed levels of fan loyalty have been used. Wakefield and Sloan (1995) show that fan loyalty
increases home game attendance. The third essay follows Depken and uses a panel data of Finnish
men’s champion league ice hockey attendance during the regular season 2008-2009 with stochastic
15
frontier analysis. There were 406 games played during that season beginning in September 2008 and
ending in March 2009.
The explanatory variables used in this study are conventional and consistent with other studies (for a
review, see Borland and MacDonald 2003 or Simmons 2006): home town population, visitor’s town
population, distance between teams’ home stadiums, the winning percentage of the home team and of
the visitor team, the game round, and the local unemployment rate. The time specific variables are
weekday dummies and the outside temperature. The error term has two components ε*i = εi – ln(λi) in
which εi is the random error term that captures noise as well as team and time-specific unobserved
heterogeneity (Greene 2005).The inefficiency term λi in the stochastic frontier is time invariant and
team specific. Two possible distributions have frequently been used (see Greene 2008, 538): the
absolute value of a normally distributed variable (“half-normal”) and an exponentially distributed
variable. The distributions are asymmetric. However, the problem with stochastic frontier analysis is
that the error term distribution assumption has its effects on the magnitude of the measure of the fan
loyalty. If the team specific term is fixed, one of the teams is considered strong (as 100 % strong) in
the sense of fan loyalty. Fans are committed. The fan loyalty of the other teams is relative to the bestpractice team(s) in the sample (cf. Last and Wetzel 2010).
The fixed effects model is here more plausible since it captures both the relevant explanatory variables
for attendance and the inefficiency scores. The estimated coefficients of the explanatory variables are
in line with those reported in the previous literature. Since the team loyalty scores are correlated with
the distance measure, the fans are more committed to ice hockey and not to a particular team. The
brand of ice hockey is stronger than the brand of an individual team. This is consistent with the results
of Bauer, Sauer and Exler (2005) that show that non-product-related attributes (e.g. stadium and
regional provenance) are more important for fan loyalty than product-related attributes like players,
success, and general team performance. It is clear that fan loyalty offers opportunities for monopoly
pricing.
0.4 Essay 4: Spectators of performing arts – who is sitting in the auditorium?
The fourth essay examines the performing arts audiences using a bivariate probit and multivariate logit
analysis. According to the statistics, around 5 per cent of the Finns go to see performing arts (art
exhibition, opera or theatrical performances) diligently and roughly 80 per cent occasionally (ISSP
16
2007). Audience and participation surveys argue that participation is segmented. Highbrow
consumption is related to gender, age and formal education. Women are more active in highbrow art
consumption, while men favour sports. The purpose of the fourth essay is to analyse differences in the
visitor density in more detail. Can differences be observed between the regions when, for example, the
effect of the educational background is taken into account? A bivariate probit model is useful because it
estimates simultaneously two equations in cultural participation decisions. It also allows to study
whether there is significant correlation between the equations’ random disturbances. With this method,
the principal characteristics of the performing arts and the sport events audiences can be identified.
Using Finnish data a study like this has not been conducted earlier.
The ISSP 2007 survey was carried out between 18th September and 11th December 2007 through a 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. The cultural
participation questions in the ISSP survey were: “How many times in the past twelve months have you
seen an art exhibition, opera or theatrical performance?” and “How many times in the past twelve
months have you been attending a sport event (ice hockey, football, athletics, motor race, etc.)?” Five
alternatives were given: ‘Every day’, ‘Several times a week’, ‘Several times a month’, ‘Less often’ or
‘Never in the last twelve months’. 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 sports (lowbrow).
The first step in the essay is to use the multivariate analysis of variance (MANOVA) to simply compare
the variance between the sample means explained by explanatory variables.
There are differences in the highbrow visitor density of across separate groups: “often”, “less often”
and “never”. The results of the multivariate analysis of variance show that gender, age, education and
the place of residence are significantly different across separate groups. However, the multivariate
analysis of variance only reveals that there are differences, but it does not show the direction of the
effect, i.e. it does not show whether for example women are more active than men in highbrow
performing art consumption. The multinomial logit model (MNL) is the second step in the essay to
find out what is the direction of the explanatory variables on art consumption. The explanatory
variables in MNL are the following: gender, classified age, education and the classified place of
17
province. The classification is needed since there are good reasons to assume that the effect of age is
not linear. On the contrary previous studies have shown that middle-aged people are most active
performing art consumers. According to the Statistics Finland, the economy of culture (value added per
capital in 2007) is highly concentrated (Figure 1).
Figure 1: Value added of culture in 2007, EUR/capita in NUTS3 and Capital regions in Finland
Source: Statistics Finland
The metropolitan area has clearly the highest value added of culture per capita and 52) % of the culture
labour is located there. In the MNL analysis one region must be considered as the reference value and
the effects of region variables are relative to this reference region. The reference values in the MNL
analysis are the following: Rural area exemplified by Northern Finland (FI1A according to the NUTS-2
classification), pupil or student (education 1), young person (age: 15-24). The region variables are
18
mainly compatible with the NUTS-2 classification except for the provinces of Uusimaa and
Ahvenanmaa.
The results of the MNL show that the ones that “often” go to performing art performances or
exhibitions have graduated from the upper secondary school (edu5) or have a bachelor’s degree
(university of applied sciences, edu7 or university, edu8) or have a master’s degree (edu9). Middle-aged
people (age between 45 and 54 or between 55 and 64) go most diligently. Gender is important: women
are more active than men. The above mentioned socio-economic variables, college level education
(edu6) and somewhat younger age (age between 35 and 44) are significant to classify “less often” group
from other visitor density groups. Regional differences are significant. The citizens of the province of
Uusimaa or the region of Eastern Finland are the most active. A conclusion from the MNL models is
that a crucial feature to classify into not attending and attending groups is at least upper secondary
school. Furthermore, the separating feature between less often and often groups is at least a bachelor’s
degree and 45 year age. Women on average are more active in highbrow art consumption. Furthermore,
the essay studies what the roles of gender and other socio-economic variables are in sport events’
attendance.
The visitor density of sport events attendance is also investigated using a MNL model. Following the
participating arts model, the sports events model has three groups: “often”, “less often” and “never”.
Gender separates, but men are significantly more active than women. This result is in line with the
participation motive models (Wann 1995) and with the statistics of the most popular sport events. Ice
hockey and football are the most popular sports in terms of attendance and both could be classified as
aggressive. A low education level (elementary school, edu2 or comprehensive school, edu3) is typical
for those that are the most active and age less than 45. The results are mainly contrary to the
performing arts participation results. However, the performing arts visitor density is added as an
explanatory variable; it has a positive coefficient meaning that these two cultural segments have a
common feature. Those that are active in highbrow art consumption are also active in sport event
consumption. This is especially true for those that are “less often” goers. High education seems to be
the common feature. There are no regional differences in sport consumption. The findings are
consistent with the time-use survey evidence that highly-educated perform more activities and these
include the consumption of cultural capital (Ruuskanen 2004).
Since there is a common factor in both participating arts (art exhibition, opera and theatrical
performances) and sport events consumption, the bivariate profit model must be used to study the
19
participation equations simultaneously. The fundamental difference between the multinomial logit
models and bivariate probit models is to assume that the error terms of the two explanatory models are
correlated. One model is estimated for highbrow (ballet, dance performance, opera) and the other for
sports (lowbrow). The multinomial logit model estimates only one equation to explain cultural
consumption, but it allows more than two categories (‘often’, ‘less often’ and ‘never’), while the
bivariate probit model assumes that there is a binary variable to be explained. If the disturbances of the
bivariate equations are correlated, both the direct marginal effects and the indirect marginal effects can
be evaluated. The general specification for a two-equation model assuming the binary choice is (Greene
2008, 817):
𝑦1∗ = 𝑥1′ 𝛽1 + 𝜀1 ,
𝑡ℎ𝑒𝑛 𝑦1 = 1 𝑖𝑓 𝑦1∗ > 0, 𝑎𝑛𝑑𝑦1 = 0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
𝑦2∗ = 𝑥2′ 𝛽2 + 𝜀2 ,
𝑡ℎ𝑒𝑛 𝑦2 = 1 𝑖𝑓 𝑦2∗ > 0, 𝑎𝑛𝑑 𝑦2 = 0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
𝐸[𝜀1 |𝑥1 , 𝑥2 ] = 𝐸[𝜀2 |𝑥1 , 𝑥2 ] = 0
𝑉𝑎𝑟[𝜀1 |𝑥1 , 𝑥2 ] = 𝑉𝑎𝑟[𝜀2 |𝑥1 , 𝑥2 ] = 1
𝐶𝑜𝑣[𝜀1 , 𝜀2 |𝑥1 , 𝑥2 ] = 𝜌
The marginal effects of each explanatory variable are more reasonable since both the direct marginal
effect and the indirect marginal effect can be estimated. Since education for example has an effect on
both cultural segments (arts and sports), the indirect effect reveals whether these cultural segments are
substitutes or complements. If the direct marginal effect of (say) master’s degree education (edu9) is
positive for arts and indirect marginal effect is negative, the arts and sports consumption are substitutes
for this socio-economic group. The results of the bivariate probit model confirm the effects of gender,
education and age. Women are active in highbrow consumption and men in sport events consumption.
Direct marginal effects of the education are significant if the education level is equal to or higher than
upper secondary (college, a bachelor’s or master’s degree). The threshold age is 35. People older than
35 prefer arts and they diminish sport events consumption. The indirect marginal effects of education
levels 6, 7 and 9 (a college diploma, and a bachelor’s degree from a university of applied sciences or a
master’s degree) reveal that these citizens consider arts and sport events as substitutes. The correlation
coefficient ρ of the error terms of the equations is 0.382 showing that the audiences of arts and sports
have a common feature.
0.5 Essay 5: Are the spectators of performing arts and the spectators of the
movies the same?
20
The fifth essay uses a similar framework than the fourth essay, but the comparison is made between
performing arts and cinema, and the effects of household incomes and family background have been
added as explanatory variables. The marginal effects of the socio-economic variables on the performing
art consumption in the multivariate logit model are examined using the ISSP 2007 survey data.
The 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 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 people having a bachelor’s degree from a
polytechnic (university of applied sciences) are somewhat younger than persons having a college
diploma. People less than 50 –years old on average have a (better and) longer education than people
older than 50. Age and education are related with the 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). 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 is 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.
The results of the bivariate probit analysis when the age-cohort 50-54 and elementary school (edu2) are
considered as reference value (i.e. the constant in the equation) show that the two spectator groups are
not independent since the correlation coefficient of the error terms ρ = 0.625. Hence the hypothesis
that the spectators of movies and arts belong to independent groups can be rejected. There are
common characteristics, a common background which could be called an intrinsic culture orientation.
If a person likes art exhibitions, opera and theatrical performances, she also likes to see movies at the
cinema and vice versa, given that the institutions in the region offer these events. 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: females 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 on
the cinema. These leisure time activities are to some extent substitutes. Marital status matters: married
or common-law married citizens go more often to see highbrow art than single people.
21
If the effect of age on cultural consumption is relative to the age cohort 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 cohort. 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. Omnivores have a higher level of
education. 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. The household incomes (or personal incomes, not
reported here) are not significant.
The age cohorts 30-34 and 35-39 are most omnivore, but this indication is unreliable to some extent.
The results of the MNL analysis confirm the importance of gender. Females 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 single or living in common-law marriage. Married people 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 agecohorts (55-59, 65-69 and 70-74) are most active. The oldest seem to strongly classify into totally notgoing and actively going groups, but the probability of belonging into ‘less often’ –group is the lowest.
Education is very important to classify performing arts consumption.
22
0.6 Conclusions
The essays have added information on the economics of culture in Finland. The sophisticated methods
have been used. The role of public, non-advertising information on the cultural consumption decisions
has been underlined. Essay 1 gives evidence that critical reviews published in the newspapers do not
have an impact on movie admissions when the world-of-mouth is taken into account. In the ice hockey
case, considered in essays 2 and 3, however, the public information in the form of series situation or
the winning percentage of the team has an impact on the attendance figure. Furthermore, there is
some evidence in essay 3 that ice hockey fans are more committed to the sport (species) than to the
team. The brand of ice hockey is stronger than the brand of an individual team.
It is reasonable to assume that the marginal costs of most cultural events are almost zero, and the
producers or distributors should maximize revenues. The cinema ownership in Finland is very
concentrated and this leads to strategic behaviour. Essay 1 shows that movie attendance has price
elasticity minus one, which as such follows the optimal pricing rule of monopolies. Movies are the most
homothetic product in cultural capital, at least in respect of the most popular movies, though the
number of annual attendees varies to significantly greater degree than in ice hockey or in highbrow
culture. In the latter case, the policy is usually to satisfy all supply by means of special discounts e.g. in
last minute reservations.
Essay 1 also shows that a wide release with extensive advertising should be used with mainstream films.
Since the word-of-mouth is important the bad experiences of low quality films has a smaller effect on
attendance. A hit-and-run strategy should be used with lower quality films while a platform release with
a small number of initial screens should be favoured with high quality films. This is compatible with the
results of Schmalensee (1978) who argue that low quality products should be advertised more
intensively. The live opera performances from the New York Metropolitan Opera that could be seen in
the biggest towns’ cinemas in Finland were launched using a platform release. During the first year only
live performances were offered but during the second season also encore performances were given
some days later. Since the performing art audiences are highly educated the ticket prices for these
cinema opera performances are substantially higher than the normal cinema tickets.
23
The audience composition can be studied using bivariate probit analysis. This analysis is an important
method to classify audiences of different cultural events and simultaneously to classify consumers into
omnivore and other groups. Essay 4 indicates that the time constraints on leisure activities are
connected with the number of children in the family. Formal education is an important factor to
classify consumers into different groups. Highly educated are more active in highbrow consumption,
but according to essay 4 they go less often to sport events. However, the omnivorousness increases
with formal education. Overall, essays 1 through 5 reveal that gender differences are important in both
the performing art consumption and the sport consumption.
Essay 4 shows that sport consumption has similar types of characteristics although it is less elitist and
typically favoured by men. 25% of total population attend at least once a year in the ice hockey match
and the preferences are fairly price inelastic. Fans are loyal to the local ice hockey team, also to a large
extent irrespective of its success or failure. The latter results only in limited substitution of a less
successful team with the one with better recent performance. Fans are still loyal to ice hockey and not
to the team. All this offers opportunities for monopoly pricing and we have observed in recent years
substantial increase in the ticket prices.
Intellectual assets including cultural capital are highly agglomerated in the greater Helsinki area
(Helsinki, Espoo and Vantaa): 52% of cultural capital and according to a recent study 48% of intangible
capital is located in the capital region with a population share of 30% (Piekkola, 2011). These areas also
have on average more educated inhabitants with human capital. Cultural capital has been shown to be
sensitive to the human capital of the attendees. In essays 4 and 5 highly skilled are typically more active
than low educated and are engaged in several types of activities including cultural events.
One can also categorize the cultural capital in terms of the degree of experience consumption. Movies
stand out again as the cultural capital with the least surprise content and hence most close to experience
consumption. Highbrow cultural capital is less frequent and includes the biggest unknown element,
although in certain dimensions sports are the most unpredictable.
All these findings are of big importance in the evaluation of cultural policy and subsidies for cultural
consumption. The inelastic part is quite insensitive to subsidies and it is expected that rather high share
of subsidies in the form of cultural spending voucher (kulttuuriseteli) benefit the price elastic cultural
activities such as theatre. Both the state and local authorities subsidy production of highbrow cultural
events but they should reallocate the policy towards consumption in terms of cultural spending voucher
24
subsidies and diminish direct production related subsidies. The institutions should also offer last minute
places to low-income students with reasonable discounts. It is also noteworthy that price elasticity is
likely to be lower for high educated that can better afford allocate their time to several activities. On the
other hand, highly educated people are more time constrained and do more voluntary work.
The study also shows clear substitutability between highbrow cultural capital and movies. Cultural
capital is highly concentrated in the greater Helsinki area and there is every reason to believe that a
fairer regional distribution will lead to much greater demand for highbrow cultural capital. Cultural
capital policy is important regionally also because it has been shown that older people consume it less
and hence the demand for cultural capital can be subject to dramatic changes in the rapidly ageing
areas, many of which are located in rural areas in eastern and northern Finland. The substitutability
between highbrow cultural consumption and sport events attendance is less low; this explains why
sport events demand falls into inelastic part while the movie attendance has price elasticity minus one.
Is cultural capital enjoyment or long-term investment? This study shows in many respects low price
elasticity and hence the importance of non-monetary reasons for cultural capital consumption. Clearly
price mechanism has only limited role or can lead easily to monopoly pricing rules, where our price
elasticities show some evidence in movie attendance. This is supported by the surprisingly limited role
that critics have on the consumer decisions. It is also noteworthy that the price elasticity in Finnish
movie consumption has been found to be lower than that observed in the Great Britain or the United
States (Davis 2002, 2006).
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28
1 Critics´ reviews or preceding week’s admissions explaining movie
admissions
1.1 Introduction
Critics and their opinions or critical reviews have been shown to have an impact on movie admissions.
Critics or reviewers are typically invited to an early screening of the film and write critics before the film
opens to the public. This information is important for many experience goods, like restaurants,
theaters, books, movies. Other information is also available after the first night. Word-of-mouth has
been recognized as one of the prime resources of information transmission. It is natural that critical
reviews have an impact on premiere weekend’s movie admissions, while word-of-mouth is more
important to explain overall (long run) admissions (Basuroy, Desai & Talukdar 2006).
The star power of actors, director power and awards or nominations for awards are movie-related
characteristics that have been shown to have an impact on movie admissions (Hennig-Thurau,
Houston and Walsh 2007). Production budget seems to correlate with opening weekend screens and
post-filming actions like advertising have been found to bring about success to theatrical box office
revenue (Hennig-Thurau, Houston and Walsh 2006). Conventional economics postulates that price
affects demand, however, in the movie admission or movie box office literature the effect of price has
been neglected. One possible explanation for this shortage is that theaters seem to use uniform pricing
(Orbach and Einav 2007). There are several possible explanations for using uniform pricing. Demand
uncertainty might result in uniform pricing. Consumers might assume that prices reflect quality; low
prices for low quality movies and high prices for high quality movies. To avoid this signaling,
distributors choose uniform prices. Another explanation is that selling packages of several tickets would
require monitoring mechanisms to prevent using low-price tickets and watching high-priced movies.
Therefore, uniform pricing is often used. But in Finland movie theater tickets are not totally uniform.
Typically the ticket price for children and conscripts is lower and also during weekdays prices might be
lower than weekends. A single ticket is cheaper within a package of several tickets; therefore, the actual
average price of a display is not uniform. However, a large portion of distributors’ profits come from
adjunct sales (e.g. popcorn, confectionery), and the price setting has minor importance (Chen 2009). It
is also true during the last decade that the role of video (DVD, Blu-Ray) rental has increased the share
in film producers’ profits.
The objective is to study what the relevance of critical reviews and word-of-mouth is in explaining
movie admissions. The contribution of this study is that Finnish weekly panel data is used to evaluate
the role of critical reviews in weekly movie admissions with having other control variables, like the
number of screens, the ticket price and so on. With the panel data, conventional regression analysis
29
cannot be used since the results might be biased. The benefits of using the panel data are that (1)
individual heterogeneity can be controlled, (2) estimated parameters are more efficient, and (3) with the
panel data the dynamics of adjustment can be studied better (Baltagi 2008, 6-7). The panel data suggests
that individuals or movies are heterogeneous. Time-series and cross-section studies that do not control
heterogeneity might yield biased results.
The variables in the panel data of this study are partially conventional and partially new. Critical reviews
published in newspapers and word-of-mouth measured as previous week’s admission in Helsinki
theaters are among the conventional variables. Weekly ticket price measured as the ratio of box office
revenue and admissions is the new candidate to explain weekly admissions. The results of this study
indicate that when word-of-mouth is taken into account, critical reviews do not seem to significantly
explain weekly movie admissions. Since admission figures are typically highest during the first weeks, a
variable “weeks since released” is used to control for this peak. The analysis shows that it is significant,
as well as the price variable. The price elasticity of weekly movie admission is roughly -1. The panel data
analysis also indicates that the fixed effects model is the most suitable for explaining weekly movie
admissions in Finland in 2003.
The article continues with a literature review, the presentation of the empirical model and variables.
This is followed by an analysis of why panel data models have been used. Section 4 presents estimation
results, and section 5 concludes.
1.2 Literature review
The correlation analysis of Eliashberg and Shugan (1997) has been very influential in explaining critical
reviews on movie admissions. Critics could act as opinion leaders (influencers) who are considered
more experienced and having more knowledge of the quality of movies. On the other hand, critics
could act merely as predictors without any impact on early box office revenue. Influencers have an
impact on early box office revenues, while predictors have an impact on overall box office revenues.
The impact of critic reviews has been found positive in many studies. Basuroy, Desai and Talukdar
(2006) also consider the impact of consensus on critics’ opinion (from Variety magazine) on movie
admissions.
Eliashberg and Shugan (1997) assess only the aggregate impact of critics, while Boatwright, Basuroy
and Kamakura (2007) also consider individual critics reviews published in newspapers and magazines
with large circulation, like Entertainment Weekly, LA Times, Chicago Tribune or New York Times.
TV shows have an even bigger audience, but Reinstein and Snyder (2005) do not find a significant
impact of critics on movie admissions when
such reviews are revealed in television talk shows
30
(national, USA). A big majority of studies on movie admissions have been carried out with US data but
there are interesting examples with other countries. Elberse and Eliashberg (2003) estimate demand
(box office revenue) and supply (number of screens) equations for several countries: the USA, France,
Germany, Spain and the UK. Critical reviews have a significant and positive impact on premiere week’s
demand in the USA and UK but a negative impact on first week’s supply. The impact is not significant
for France, Germany or Spain.
With Dutch data Gemser, Van Oostrum and Leenders (2007) show that the number and size of film
reviews have an impact on art house premier week’s revenue and also overall box office revenue, while
the same impact for mainstream movies is valid only for overall or long run box office revenue. On
the other hand, Hennig-Thurau, Houston and Walsh (2006) present results that indicate a positive
impact of critical reviews on short- term theatrical box office but not on long- term box office nor
video rental revenues of movies released during 1999-2001 in the USA. Recently Elliott and Simmons
(2008) showed that higher average critic ratings are associated with greater box office revenues and
increased advertising in the UK. They also remark that advertising is greater for films with higher US
opening revenues and higher budgets.
Hence the influence of film reviews is supported in many studies, but d’Astous, Carú, Koll and Sigué
(2005) argue that the influence depends strongly on cultural dimension. Using Hofstede’s (1984)
theoretical framework in predicting consumers’ movie attendance they show that based on differences
in power distance between Austria, Canada, Colombia and Italy, Austrian and Canadian moviegoers are
more susceptible to value-expressive social influence than Colombian or Italian audience. The impact
of consultation on film reviews is stronger among Austrian and Canadian moviegoers. Consistent with
the level of uncertainty avoidance, Canadian audience appreciates more movie genres than Austrian,
Colombian or Italian audience. Consumers with higher uncertainty avoidance are more brand loyal. In
addition, d’Astous, Colbert and Nobert (2007) propose that moviegoers may be influenced by the
movie’s country of origin when they search for information about new movies. It is also true that
critical reviews might be biased. Ravid, Wald and Basuroy (2006) propose that several critics are
significantly affected by the film distributor’s identity. High budget films seem to get more reviews, but
these reviews are worse than average, also films with star decoration tend to get more reviews with
positive assessment.
A hypothesis can be set as a summary: positive critic reviews have a positive effect on the spectator
number.
Word-of-mouth (WOM) has also a powerful effect on movie admissions. Basuroy, Desai and Talukdar
(2006) measure WOM as the cumulative number of screens since its release and they find a positive
effect. WOM incorporates three effects: valence (positive, neutral, negative), volume of mouth-tomouth discussion and persuasiveness of WOM generated. Neelamegham and Chingagunta (1999) on
31
the contrary find no significant results between weekly revenue and WOM measured as cumulative
viewership and they argue that cumulative viewership is not a good proxy for WOM. Elberse and
Eliashberg (2003) used previous week’s average revenue per screen as a proxy for WOM and they find
significant positive results. Liu (2006) proposes that the volume of WOM (from Yahoo Movies Web
site) offers significant explanatory power for both weekly and overall box office revenue, but the
valence of WOM (measured as percentages of positive and negative messages) is not significant. WOM
is more trustworthy than advertising or critical reviews since it comes from other moviegoers. Recently
Duan, Gu and Whinston (2008) show that box office revenue of a movie and online WOM valence
(measured on a daily basis from three web site sources: Variety.com, Yahoo!Movies and
BoxOfficeMojo.com) have a significant impact on WOM volume which in turn leads to higher box
office revenues. Moul (2007) proposes that WOM accounts for 10 % of the variation in the consumer
expectations of movies, while distribution related effects, like the number of screens, release time and
movie fixed effects, like star power, production budget comprise the great majority of observed
variation in movie admissions.
The second hypothesis is therefore: Word-of-Mouth has a powerful effect on admissions.
The information flow through WOM affects also supply. The number of screens must adapt as
demand develops dynamically. The prior screen decisions made before the actual release must be
adjusted as the attendance number is known during the first weeks after premiere. The demand –
supply dynamics in the movie industry, however, is subject to high uncertainty (De Vany and Walls
1996) but DeVany and Lee (2001) show that WOM can be a credible means to share information about
good and bad movies.
Movie related elements like the star power of actors (Bagella and Becchetti 1999, Neelamegham and
Chintagunta 1999, Walls 2005, Elberse 2007 or Meiseberg, Erhmann and Dormann 2008), director
power (Bagella and Becchetti 1999 or Jansen 2005) or awards/nominations (Deuchert, Adjamah &
Pauly 2005) seem to correlate with higher box office revenue or movie admissions but the evidence is,
however, mixed (see e.g. Elberse and Eliashberg (2003), Hennig-Thurau, Houston and Walsh (2006) or
McKenzie 2009). Bagella and Becchetti (1999) show that the star power of actors and directors have a
positive impact on admission but, on the contrary, McKenzie (2009) reports the insignificance.
Deuchert, Adjamah and Pauly (2005) prove that nominations generate extra income, while awards do
not have this effect. On the other hand, Lee (2009) has recently proved that there is a negative
relationship between drama awards and box office revenues as the cultural distance between the USA
and the country where the movie is shown grows. There is also strong evidence for a relationship
between weekly revenues, opening week revenues or cumulative revenues and the number of screens
(Elberse and Eliashberg 2003). Sequels also seem to collect a greater admission figure than
contemporaneous non-sequels (Basuroy and Chatterjee 2008).
32
Movie distributors seem to release more hits (blockbusters) during high season, like the beginning of
summer and during the Christmas holiday season. Collins, Hand and Snell (2002) show that action,
adventure, horror or romantic comedy movies are more often blockbusters than other genres of
movies. Einav (2007) proposes that roughly two-thirds of the seasonal variation can be explained by
underlying demand. The rest i.e. a third is associated with the number and quality of movies. Einav
also shows that wide release is often associated with heavy advertising, while word-of-mouth is more
important in platform release. Wide release begins with a large number of screens with extensive
national advertising. But only few widely released films are successful so that they are running many
weeks (DeVany and Walls 1997). Platform release begins with a small number of initial screens and
expands to additional screens and also to rural areas. Typically the movies with platform release cannot
be classified as mainstream movies, or actors are not well known stars. The production budget of a
movie or prior advertising also seem to correlate with the number of premiere week screens (Elberse
and Eliashberg 2003).
Only few studies have considered the role of the price of the ticket. Davis (2002) estimates that the
theater price elasticities of demand are about -3. The six theaters in the sample displayed different
number of movies ranging from 2 to 9 during a six-week period. Davis (2006) presents also similar
consumer price sensitivity results. In Dewenter and Westermann (2005) the price elasticity is about -2½
with German long-term (1950-2002) annual data.
Several different methods have been used to study the relationship between box office revenue or
movie admission and the explanatory variables, like correlation analysis (Eliashberg and Shugan (1997)),
OLS, or partial least squares (Hennig-Thurau, Houston and Walsh (2006)).
Elberse and Eliashberg (2003) used OLS, 2SLS and 3SLS to explain the supply of movies (screens as
dependent variable) or demand for movies (revenues as dependent variable) with various predictors
(budget, stars, director, advertising expenditure, reviews, etc.). Both 2SLS and 3SLS take into account
the endogeneity and simultaneity of screens and revenues. OLS is inconsistent since the endogeneous
variable screens used as explaining variable in the revenues equation is correlated with the error term of
the same equation. Such correlation may occur when the dependent variable causes at least one of the
regressors ("reverse" causation), when there are relevant explanatory variables which are omitted from
the model, or when the covariates are subject to measurement error. Since the error terms across
equations may be correlated, a 3SLS method is more efficient than 2SLS. Elliott and Simmons (2008)
also use 3SLS method to estimate simultaneously supply (opening screens), advertising and demand
(total revenue).
Recently Einav (2007) estimated a nested logit demand model for weekly market shares for movies.
Nested logit is a suitable method to assort two or more choice problems. With this model Einav
distinguishes seasonality (first level: to go to a movie) and the quality of a movie (second level: to
33
choose between different movies). Therefore, the second level endogeneous variable is the market
share of each movie. Also Ainslie, Drèze and Zufryden (2005) have estimated the market share of a
movie using a random effects logit model with a gamma diffusion pattern. As consumers make the
decision to see a movie, the time to decide and the time to act is derived from gamma distribution.
They show that the impact of screens on movie sales may be lower than previously thought because
screens act as a proxy for seasonality. Another interesting model is presented by Neelamegham and
Chintagunta (1999). They use a Poisson count data model with the number of screens, distribution
strategy, genre of a movie and stars explaining movie admissions. They find that the number of screens
is the most important factor on admissions. An interesting model to predict box office success with
neural networks is presented by Sharda and Delen (2006). Their neural network approach is suitable to
classify movies into nine different categories ranging from flop (box office revenue less than USD 1
million ) to blockbuster (revenue more than USD 200 million).
Davis (2002) uses the error components model (ECM) with unbalanced panel data. The data consists
of sales, price and theater characteristics for six movie theaters and for a six-week period. A
multinomial logit of demand for theaters is estimated and both own and cross price elasticities are
reported. Theater demand is rather price sensitive, cross price elasticity between theaters not in the
same group is practically zero but within the group cross price elasticities are positive and rather large.
Recently Davis (2006) showed using the generalized method of moments (GMM) and a multinomial
logit (MNL) demand model that low cross price elasticities between theaters is associated with (high)
travel costs.
As a summary of the theoretical and previous empirical literature, the following equation is reasonable:
admission = f(critics, WOM, Z)
in which Z includes the other explanatory variables. The main focus is the role of critical reviews
(critics) and Word-of-Mouth (WOM). What is the importance of critical reviews as the WOM is taken
into account?
1.3 Empirical model and variables
The empirical study focuses on assessing the effects of various factors on weekly movie admissions in
Finland in 2003. The Finnish Film Foundation (FFF) collects data from various distributors and
importers. In 2003 the total number of films in distribution was 225 with only 177 premieres. Only 14
premieres were domestic, but the share of domestic movies in total admission was about 22%.
Domestic film “Bad Boys – A True Story” got the biggest admission number: 614097 with roughly €
34
4.4 M total box office revenue. The ultimate week was the last week (53 rd. i.e. Friday 26th December
2003 to Thursday 1st January 2004) when the top 20 movies collected 296495 admissions. The lowest
figure was 48135 at the end of June. During the ultimate week “Lord of The Rings: Return of The
King” had 165502 admissions in 68 screens and “Underworld” was the last on the top 20 list with an
audience of 606 in 2 screens. The median weekly admission was 138361 and the median screen number
was 368 in 2003. Table 1 presents an overview of top 10 films in 2003 in Finland. The sample in this
study has 1060 observations, there were 53 weeks with 20 biggest admission movies.
Table 3 (Table1.1) Overview of top 10 films in 2003 in Finland, source: Finnish Film Foundation
Original title of the film
Release
Screens
date
Total gross
Admissions
box office
Country
Distributor
of Origin
Bad Boys – A True Story (local)
17.1.2003
55
4413507
614097
Finland
BVI
Lord of The Rings: The Two Towers
18.12.2002
58
3610000
467644
USA
SF/FS
Lord of The Rings: Return of the King
17.12.2003
68
3060269
355739
USA
SF
The Matrix – Reloaded
21.5.2003
55
2364215
334206
USA
SMD
Bruce Almighty
25.7.2003
32
2103080
279485
USA
SF
Johnny English
11.4.2003
45
1912100
260643
UK
UIP
Sibelius
12.9.2003
50
1885625
257031
Finland
BVI
Pirates of The Caribbean
29.8.2003
44
1865774
245252
USA
BVI
Piglet’s BIG Movie
29.8.2003
48
1398415
228421
USA
BVI
Helmiä ja sikoja (local)
29.8.2003
40
1586939
213385
Finland
Nordisk Film
Previous empirical evidence (good surveys: Hennig-Thurau, Walsh and Wruck 2001 and Eliashberg,
Elberse and Leenders 2006) has shown that the demand for movies is determined by several factors.
On the supply side, the number of screens is probably the most important factor. Once the movie
production has been completed it is ready for distribution. The launch stage includes both the physical
distribution of the prints to the theaters and the marketing activities. Einav (2007) points out that a
wide release is associated with heavy advertising, while platform or narrower release is more often
associated with information diffusion through word-of-mouth (WOM).
It is here merely assumed that the number of screens is positively associated with movie admissions.
Weekly movie admissions and the number of screens (“prints this week”) were collected by FFF which
is the source of the data. Prints this week can include several showings during that week, typically there
are some showings during the weekends, e.g. one at 3 p.m., the second at 6 p.m. and the last at 9 p.m.
Hence the number of screens underestimates the actual showings.
Expert reviews or ratings (critical reviews) and previous week’s movie admission (WOM) can convey
some information about the quality of a movie. Critical reviews can influence consumers in their
selection process. This is the influence effect. On the other hand, reviews can forecast whether the
film becomes a success or not. This is the prediction effect of critical reviews (Eliashberg and Shugan
1997). Different proxies have been used to measure WOM in the literature. In this study critics’ reviews
have been published weekly on Fridays in “Nyt”, which is a supplement to Helsingin Sanomat that has
35
the largest newspaper circulation in Finland. In 2003 the subscription number was about 420,000, i.e.
almost every twelfth Finnish citizen receives this newspaper home delivered. There are five reviewers
that independently judge films in other newspapers than Nyt which simply collects and republishes
these reviews. Three are Finnish and their critics are published in different newspapers and magazines:
Helena Ylänen (Helsingin Sanomat), Antti Lindqvist (TV-maailma), and Tapani Maskula (Turun
Sanomat). Helena Lindblad publishes her critics in Sweden (Dagens Nyheter) and Derek Malcolm in
the UK (Guardian). Their judgement is published as stars ranging from 5 (superior) to 1 (loss of time).
The average number of stars is published weekly and films are in descending order. The most liked film
is on the top of the table and the least liked film is on the bottom. Each week 10 movies are valued.
For 43 movies the stars indicator is shown only once but there are movies for which the stars indicator
is published in more than ten succeeding Nyt2. 133 movies were critically reviewed in Nyt. Ylänen
reviewed 65, Lindqvist 118, Maskula 105, Lindblad 77 and Malcolm 75. But in the panel sample (20 top
movies, 53 weeks, i.e. 1060 observations) there are e.g. 211 non-zero observations of Ylänen’s critical
reviews. The average value of critical reviews is used as an explaining variable in the estimations.
Word-of-mouth is also based on tables printed in Nyt. The previous week’s top 10 admission figures at
theatres in Helsinki are listed on the same page as critical reviews. Typically the share of theatres in
Helsinki in total admissions is about 35-40 %.3 Both the actual number of admissions and ranking
from 1 to 10 is printed. The film with the biggest admission in Helsinki theatres is ranked as number 1,
and so on. Since that information is on the same page as critical reviews, both of these variables are
used to explain next week’s movie admissions in whole Finland.
The proxy for word-of-mouth in this study (previous week’s attendance in Helsinki) has a connection
to what have been used elsewhere: cumulative number of screens since its release (Basuroy, Desai and
Talukdar 2006), cumulative viewership (Neelamegham and Chingagunta 1999), and previous week’s
average revenue per screen (Elberse and Eliashberg 2003). Herr, Kardes & Kim (1991) or Grewal,
Cline & Davies (2003) show that anecdotal information presented in a face-to-face manner (vivid
WOM) has a greater impact on product judgments than the same information presented in printed
form (e.g. advertising, critical reviews)4. In this study it is assumed that previous week’s attendance in
Helsinki theaters is a suitable measure for vivid WOM.
2
Descriptive statistics for critical reviews is given in the appendix (table 2). It reveals that the critics of many “lower quality”
is published only once or twice since the mean of critical review rank is decreasing in time (weeks).
3
In 2005 three important cities, Helsinki, Tampere and Turku had a 56% share in total admissions and a 57 % share in
gross box revenue. Source: European Cinema Yearbook 2006
4
On the importance of WOM vs. public information, see Hidalgo, Castro & Rodriguez-Sickert (2006)
36
Seasonal variation is very important since many blockbusters are released during the high season. The
highest movie admission month in Finland has been January during a five-year period from 2003 to
2007 and June has been the lowest.
The weekly admission number is shown in appendix in figure 1. It reveals that the Christmas season
and the end of May (the school year end) and late July/early August (the summer holiday end) are the
peaks in movie admission. A proxy variable for seasonal variation is the number of all screens for all
movies that week. Admission is highest typically during the first weeks for blockbusters (e.g. Ainslie,
Drèze and Zufryden 2006). The life cycle of sleeper movies is different since demand peaks later; weeks
4 and 5 from the release demand is highest. The mean duration of a movie run is typically 7 to 10
weeks in Western countries (Neelamegham and Chintagunta 1999, table 1). A control variable to take
the life cycle effect into account is needed: weeks since released. The median duration run of films with
the biggest admission number in Finland is 17 weeks for the ultimate top 10 (1st to 10th) and roughly 10
weeks for the following 3 quantiles (from 11th to 40th)5.
Descriptive statistics and the hypothesis (expected signs) are summarized in table 2. The sample
consists of 53 weeks with 20 top movies each week. The price variable is simply box office
revenue/admission which takes into account both the difference between the price of using packages
of several tickets and normal tickets as well as children/conscripts’ lower prices compared with normal
prices.6 For some cases, especially among the lowest box office films, revenue data was not available
and some approximation was needed. Either previous week’s revenue was used or revenue was set
lower than the lowest reported revenue. Only less than 10 films the revenue data were missing and
therefore price variables are approximated. Since all the films in the sample have not been critically
evaluated or listed on Helsinki top 10, there are lots of zero observations. For the entire sample a
dummy variable “not critically reviewed” (NOTCR) or “not top10” (NOTHK) is used. Otherwise the
logarithmic values of the variables are used and therefore the estimated parameters are elasticities.
5
See appendix 3.
6
The percentiles (min, 10th, 20th, .., med, 60th, 70th, … max) in the price variable are: 1 – 5,95 – 6,52 – 6,83 – 7,07 – 7,27
(med) – 7,42 – 7,56 – 7,66 – 7,79 – 10,47 (max).
37
Table 4: (Table 1.2) Descriptive statistics and sources of variables, * weekly, ** non-zero observations
Variable
Mean
Median
sd
min
max
valid
source
6783,97
2240
14003,4
65
165502
1060
FFF
Screens (SCR)
17,10
10
15,33
1
70
1060
FFF
+
All Screens (ALLSCR)
341,94
368
72,92
176
471
1060
FFF
+
Box office revenue (BOR)
50005
15825
109700
390
1165814
1060
FFF
Price = BOR/Admission (PRICE)
7,04
7,27
0,88
1,00
10,47
1060
Critical reviews, average (CA)
2,83
3
0,90
1
5
133*
Nyt
+
Critical reviews, average (CA)
0,96
0
1,48
0
4,7
1060
Nyt
+
observations
Weekly Admission
Critical reviews, average (CA)
WOM (previous week’s admission in
expected
sign
-
2,98
3
0,87
1
4,7
340**
Nyt
+
2391,12
1500
2606,40
239
21271
520**
Nyt
+
1173
0
2181,63
0
21271
1060
Nyt
+
5,44
5
2,86
1
10
520**
Nyt
-
2,67
0
3,38
0
10
1060
Nyt
-
8,25
5
8,73
0
56
1060
FFF
-
Helsinki) (HKIADM)
WOM (previous week’s admission in
Helsinki) (HKIADM)
WOM (previous week’s admission in
Helsinki, rank) (TOP10)
WOM (previous week’s admission in
Helsinki, rank) (TOP10)
Weeks since released (WEEKSREL)
1.4 Estimation and results
Since the data has both time-series (weekly) and cross-sectional (different movies) dimension,
conventional regression analysis cannot be used. Panel data analysis enables regression analysis with
both time-series and cross-sectional dimension. Panel data can have group effects (movies), time effects
or both. Panel data models estimate fixed and/or random effects models using dummy variables. The
core difference between the fixed and random effect models lies in the role of dummies. If dummies
are considered as a part of the intercept, it is a fixed effect model. In a random effect model, the
dummies act as an error term7. The fixed effect model examines movie differences in intercepts,
assuming the same slopes and constant variance across the movies. Fixed effect models use least square
dummy variables (LSDV), within effect, and between effect estimation methods. Thus, ordinary least
squares (OLS) regressions with dummies, in fact, are fixed effect models. The random effect model, by
contrast, estimates variance components for groups and error, assuming the same intercept and slopes.
The difference among groups (or time periods) lies in the variance of the error term. This model is
estimated by generalized least squares (GLS) when the variance structure among genres is known. The
feasible generalized least squares (FGLS) method is used to estimate the variance structure when the
variance structure among genres is not known. Fixed effects are tested by the F test, while random
7
Hun Myoung Park: Linear Regression Models for Panel Data Using SAS, STATA, LIMDEP, and SPSS.
http://www.indiana.edu/~statmath/stat/all/panel/panel.pdf accessed 5th February 2008
38
effects are examined by the Lagrange multiplier (LM) test (Breusch and Pagan 1980). If the null
hypothesis is not rejected, the pooled OLS regression is favoured. The Hausman specification test
(Hausman 1978) compares fixed effect and random effect models. Table 3 (Park 2008) compares the
fixed effect and random effect models. Group effect models create dummies using grouping variables
(movie). If one grouping variable is considered, it is called a one-way fixed or random group effects
model. Two-way group effect models have two sets of dummy variables, one for a grouping variable
and the other for a time variable.
Table 5: (Table 1.3) Fixed Effect and Random Effect Models (Park 2008)
Functional form
assuming νit ~
Fixed Effect Model
Random Effect Model
yit = (α+μi)+Xit’β + νit
yit = α+ Xit’β +( μi+ νit)
Varying across groups (movies)
Constant
IID(0,σν2)
Intercepts
and/or times (weeks)
Error variances
Constant
Varying across groups
and/or times
Slopes
Estimation
Hypothesis test
Constant
Constant
LSDV, within effect, between effect
GLS, FGLS
Incremental F test
Breusch-Pagan LM test
The least square dummy variable (LSDV) model, however, becomes problematic when there are many
groups or subjects in the panel data. If the total number of periods is fixed and the total number of
observations is vast, only the coefficients of regressors are consistent. The coefficients of dummy
variables are not consistent since the number of these parameters increases as N increases (Greene
2008, 197). This is the so called incidental parameter problem. Too many dummy variables may weaken
the model for adequately powerful statistical tests. Under this circumstance LSDV is useless and
another method might be used: the within effect model which does not use dummy variables but uses
deviations from group means.
The estimation results for the full sample with three different models are presented in table 4:
conventional regression (OLS) analysis, fixed effects model and random effects model with all
explanatory variables.
39
Table 6: (Table 1.4) Estimation results, full sample, n = 1060
Model
OLS
without
group dummy
LSDV,
Fixed
effects
Random effects model
model (FEM)
(REM)
0,780
0,899
0,862
(0,021)***
(0,030)***
(0,240)***
0,314
0,139
0,238
(0,076)***
(0,089)
(0,079)**
0,042
-0,088
0,001
(0,125)
(0,112)
(0,108)
-0,378
-0,711
-0,619
(0,022)***
(0,027)***
(0,023)***
0,177
0,081
0,091
(0,025)***
(0,022)***
(0,021)***
-0,253
-0,184
-0,218
(0,049)***
(0,041)***
(0,040)***
0,250
-0,025
0,096
(0,099)**
(0,100)
(0,093)
-0,095
-0,345
-0,249
(0,139)
(0,129)**
(0,123)*
0,551
0,096
0,085
(0,234)**
(0,205)
(0,197)
variables
Log SCR
Log ALLSCR
Log PRICE
Log WEEKSREL
Log HKIADM
Log TOP10
Log CA
NotCR
NotHK
Constant
4,10
5,42
(0,523)***
(0,531)***
Depending variable is log of weekly admissions, n = 1060
Standard deviations in parenthesis
Adjusted R-sq
0,811
0,897
F-test
507,55***
61,39***
Diagnostic LL
1777,81***
2576,50***
0,784
Test statistics for the Classical Model
Constant term
Log Likelihood
LM test vs. Model (3)
only (1)
= -1720,44
395,01***
LL = -1461,90
Hausman test (FEM vs.
Group
effects
only (2)
X–
REM): 122,39***
variables
LL = -831,54
only (3)
X-and
group
LL = -432,19
effects (4)
Hypothesis
tests
(2) vs. (1)
LR test
F test
517,09***
3.99***
(3) vs. (1)
1777,80***
507,55***
(4) vs. (1)
2576.50***
61,38***
(4) vs. (2)
2059,41***
601,84***
(4) vs. (3)
798,69***
7,07***
The test statistics indicate that fixed effects model is favoured. The number of screens, weeks since
released, last week’s admission in Helsinki theatres, movies shown in Helsinki top 10 listing and a
dummy variable “not critically reviewed” are significant and correctly signed variables to explain weekly
40
movie admissions. In Helsinki top 10 listing the movie with the biggest previous week’s admissions is
numbered as 1, the movie with the second biggest admission is numbered as 2, and so on up to 10.
Hence TOP10 variable should get a negative coefficient. Since the model is log-linear, other than
dummy parameters are elasticises. Each movie has a different intercept (not shown). Since the other
dummy variable “not top 10” (NOTHK) is not significant, another estimation is carried out beginning
with the second week since released (i.e. NOTHK = 0). The sample size is now significantly lower,
there are 515 observations.
41
Table 7: (Table 1.5) Estimation results, all movies with previous admission in Helsinki, n = 515
Model
OLS
without
group dummy
LSDV,
Fixed
effects
Random effects model
model (FEM)
(REM)
0,613
0,665
0,710
(0,031)***
(0,043)***
(0,032)***
0,177
-0,034
0,167
(0,112)
(0,111)
(0,098)*
-0,773
-0,408
-0,511
(0,295)**
(0,245)
(0,232)*
-0,169
-1,006
-0,713
(0,043)***
(0,050)***
(0,041)***
0,487
0,077
0,148
(0,057)***
(0,042)
(0,041)***
-0,160
-0,235
-0,268
(0,082)
(0,061)***
(0,058)***
0,444
0,056
0,242
(0,104)***
(0,088)
(0,082)**
0,422
0,030
0,162
(0,143)**
(0,108)
(0,103)
variables
Log SCR
Log ALLSCR
Log PRICE
Log WEEKSREL
Log HKIADM
Log TOP10
Log CA
NotCR
Constant
3,73
6,757
(0,712)***
(0,652)***
Depending variable is log of weekly admissions, n = 515
Standard deviations in parenthesis
Adjusted R-sq
0,837
0,946
F-test
332,20***
81,03***
Diagnostic LL
943,96***
1638,67***
0,777
Test statistics for the Classical Model
Constant term
Log Likelihood
LM test vs. Model (3)
only (1)
= -781,75
167,74***
LL = -554,44
Hausman test (FEM vs.
Group
effects
only (2)
X–
REM): 151,36***
variables
LL = -309,78
only (3)
X-and
group
LL = 37,57
effects (4)
Hypothesis
tests
(2) vs. (1)
LR test
F test
454,62***
5,45***
(3) vs. (1)
943,95***
332,19***
(4) vs. (1)
1638,67***
81,02***
(4) vs. (2)
1184,05***
448,27***
(4) vs. (3)
694,71***
10,76***
Fixed effects model is favoured, and screens, weeks since released and movie shown in Helsinki top 10
lists are significant, but the only dummy variable “not critically reviewed” is not significant. Therefore a
new estimation is carried out without dummy variables.
42
Table 8: (Table 1.6) Estimation results, all movies critically reviewed and with previous week’s Helsinki admission, n = 205
Model
OLS
without
group dummy
LSDV,
Fixed
effects
Random effects model
model (FEM)
(REM)
0,647
0,531
0,775
(0,053)***
(0,095)***
(0,051)***
0,028
0,072
0,171
(0,172)
(0,174)
(0,146)
-0,361
-1,123
-1,133
(0,522)
(0,373)**
(0,339)**
-0,275
-1,134
-0,851
(0,075)***
(0,068)***
(0,060)***
0,621
0,163
0,248
(0,085)***
(0,054)**
(0,051)***
0,091
-0,004
-0,000
(0,133)
(0,856)
(0,080)
0,380
0,133
0,196
(0,113)**
(0,114)
(0,097)*
variables
Log SCR
Log ALLSCR
Log PRICE
Log WEEKSREL
Log HKIADM
Log TOP10
Log CA
Constant
2,519
6,994
(1,214)*
(0,950)***
Depending variable is log of weekly admissions, n = 205
Standard deviations in parenthesis
Adjusted R-sq
0,852
0,966
F-test
169,39
89,70***
Diagnostic LL
399,47
779,77***
Test statistics for the Classical Model
Constant term
Log Likelihood
LM test vs. Model (3)
only (1)
= -325,59
60,44***
LL = -151,72
Hausman test
Group
effects
only (2)
X–
REM): 78,36***
variables
LL = -125,86
only (3)
X-and
group
LL = 64,28
effects (4)
Hypothesis
tests
(2) vs. (1)
LR test
F test
347,74***
10,68***
(3) vs. (1)
399,47***
169,39***
(4) vs. (1)
779,76***
89,70***
(4) vs. (2)
432,02***
141,44***
(4) vs. (3)
380,29***
12,31**
43
(FEM
vs.
Table 9: (Table 1.7) Estimation results, all movies critically reviewed and with previous week’s Helsinki admission, n = 205
Model
OLS
without
group dummy
LSDV,
Fixed
effects
Random effects model
model (FEM)
(REM)
0,631
0,528
0,775
(0,050)***
(0,092)***
(0,049)***
-0,284
-1,058
-0,988
(0,505)
(0,329)***
(0,313)**
-0,268
-1,139
-0,860
(0,073)***
(0,066)**
(0,058)***
0,586
0,166
0,252
(0,058)***
(0,038)***
(0,036)***
0,381
0,136
0,205
(0,111)***
(0,112)
(0,096)*
variables
Log SCR
Log PRICE
Log WEEKSREL
Log HKIADM
Log CA
Constant
2,977
7,677
(1,064)**
(0,715)***
Depending variable is log of weekly admissions, n = 205
Standard deviations in parenthesis
Adjusted R-sq
0,853
0,967
F-test
238,43***
93,69***
Diagnostic LL
398,64***
779,51**
0,802
Test statistics for the Classical Model
Constant term
Log Likelihood
LM test vs. Model (3)
only (1)
= -325,59
60,84***
LL = -151,72
Hausman test
Group
effects
only (2)
X–
(FEM
vs.
REM): 79,72***
variables
LL = -126,27
only (3)
X-and
group
LL = 64,15
effects (4)
Hypothesis
tests
(2) vs. (1)
LR test
F test
347,74***
10,68***
(3) vs. (1)
398,63***
238,43***
(4) vs. (1)
779,50***
93,69***
(4) vs. (2)
431,76***
200,62***
(4) vs. (3)
380,86***
12,53***
The estimation results in table 6 and 7 indicate that word-of-mount measured as last week’s admission
in Helsinki theatres seem to explain movie admissions, but critical reviews published in newspaper
“Nyt” is not significant in fixed effects model that is favoured (Hausman test). Movie admission is price
sensitive with approximately -1 price elasticity. Test statistics for the classical model indicate that
conventional regression analysis (OLS) without group dummy variables is not suitable for explaining
weekly movie admissions. The t-statistics for critical reviews variable that illustrates significance is
misleading due to misspecified model.
44
With the Finnish data, movie admission is inelastic with respect to number of screens. The screen
variable does not take into account the number of actual seats in the hall. Blockbusters with a vast
admission are shown in larger auditoriums and with more daily showings than arts movies. Increasing
the number of screens is not as flexible as increasing daily showings if the movie turns out be a
blockbuster. If the number of screens is still increased, these are probably with lower number of actual
seats and therefore the relative admission increase is lower, and that might explain the inelasticity.
Two important hypotheses were imposed. Positive critical reviews should have a positive impact on
movie attendance, but the results indicate that this is not true. On the contrary, when the Word-ofMouth (second hypothesis) is taken into account critics do not explain attendance.
1.5 Conclusions and suggestions
In the movie admission or movie box office literature the importance of word-of-mouth has been well
documented. Word-of-mouth has a positive effect on movie admissions (Elberse and Eliashberg 2003,
Basuroy, Desai and Talukdar 2006, Liu 2006, Moul 2007, Duan, Gu and Whinston 2008). The evidence
on the impact of critical reviews on movie admissions is mixed. Eliashberg and Shugan (1997) argue
that critics could act as influencers or predictors. Influencers can predict opening box office revenue,
while predictors can classify films either to successful or not-successful films in terms of revenue in the
longer term. Hence the impact of critical reviews is not uniform. Some predict well short- term revenue
and some better long- term revenue. Not only the existence of reviews but also the variation or
consensus of critics can have an impact on admission (Basuroy, Desai and Talukdar 2006). The impact
is also different depending on genre (Gemser, van Oostrum and Leenders 2007), country of origin
(d’Astous, Colbert and Nobert 2007, King 2007) and cultural dimension (d’Astous, Carú, Koll and
Sigué 2005). Critical reviews may be biased towards distributor’s identity (Ravid, Wald and Basuroy
2006). This study shows with weekly Finnish data and using panel data estimation methods that wordof-mouth has a significant impact on movie admissions, while critical reviews have not. The critical
review variable is the average value of five independent critics published in newspaper Nyt. The impact
of an individual critic’s reviews has not been tested in this study and it needs to be done in the future.
Are there differences among different genres? Are action movie lovers (younger and) less liable to rely
on critical reviews and more liable to rely on word-of-mouth than drama and/or romance audience?
Collins and Hand (2005) show with the UK data that richer and younger people are most likely to go to
the movies, also the residential neighborhood matters.
An important implication for movie distributors in Finland is that they should use a wide release
strategy when the expected WOM is negative. In many cases, the release weekend is later than it is in
45
larger and English spoken countries. Hence there is some knowledge about the WOM in other
countries. With the wide release strategy, this negative WOM has less influence since the strategy puts
more weight on the first week and the WOM has less circulation time. On the contrary, if the expected
WOM is positive, movie distributors should use platform release with a small number of initial screens
and expanding later.
The star power of actors, director power or awards or nominations for awards have not been tested
with the Finnish data since the share of domestic films in 2003 was only 14 % in premieres or 22 % in
total admissions. The biggest admission film in 2003 was domestic and several main actors had
received Jussi Awards some years before. Jussi Award is the most important Finnish award. It remains
an open question whether these awards or well-known actors have had any impact on admissions or
box office revenue.
The role of theater ticket price has been missing in international movie admission literature. Although
the variation in prices is rather small, this study shows that movie admission is price sensitive. Davis
(2002) showed that the theater demand is elastic with respect to price (about -2,3 to -4,1). With the
Finnish data, movie demand is roughly unit elastic. Conventional regression (OLS) analysis does not
bring about significant and reasonable price elasticity estimates. Only panel data methods, especially
fixed effects models are suitable for producing proper estimates.
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Meiseberg, Brinja, Erhmann, Thomas and Dormann, Julian (2008): We Don´t Need Another Hero –
Implications from Network Structure and Resource Commitment for Movie Performance. Schmalenbach
Business Review 60: 74-98
Moul, Charles C. (2007): Measuring Word of Mouth’s Impact on Theatrical Movie Admissions. Journal
of Economics & Management Strategy 16: 859-892
Neelamegham, Ramya and Chingagunta, Pradeep (1999): A Bayesian Model to Forecast New Product
Performance in Domestic and International Markets. Marketing Science 18: 115-136
Nelson, Philip (1970): Information and Consumer Behavior. Journal of Political Economy 81: 311-329
Orbach, Barak Y. and Einav, Liran (2007): Uniform prices for differentiated goods: The case of the
movie-theater industry. International Review of Law and Economics 27: 129-153
Park , Hun Myoung: Linear Regression Models for Panel Data Using SAS, STATA, LIMDEP, and
SPSS. http://www.indiana.edu/~statmath/stat/all/panel/panel.pdf accessed 5th February 2008
Ravid, S. Abraham, Wald, John K. and Basuroy, Suman (2006): Distributors and Film Critics: Does it
take two to Tango? Journal of Cultural Economics 30: 201-218
Reinstein, David and Snyder, Christopher M. (2005): The Influence of Expert Reviews on Consumer
Demand for Experience Goods: A Case Study of Movie Critics. The Journal of Industrial Economics 53: 2752
Sharda, Ramesh and Delen, Dursun (2006): Predicting box-office success of motion pictures with
neural networks. Expert System with Applications 30: 243-254
Walls, W. David (2005): Modeling Movie Success when ‘Nobody Knows Anything’: Conditional StableDistribution Analysis of Film Returns. Journal of Cultural Economics 29: 177-190
Data sources: Finnish Film Foundation (www.ses.fi); Helsingin Sanomat, Nyt – available at Päivälehden
museo, Ludviginkatu 2-4, Helsinki, Finland (www.paivalehdenmuseo.fi)
Estimation method: LIMDEP - NLOGIT 4.0 (www.limdep.com)
50
Appendices
Figure 1.1: Weekly Total Admission, Years 2003 to 2007
350000
300000
250000
week
Admis03
Admis04
Admis05
Admis06
Admis07
200000
150000
100000
50000
0
1
4
7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52
Table 10: (Table 1.8) Distributors’ premieres in 2001 – 2003
Distributor
2001
2002
2003
Columbia Tristar Egmo
27
27
28
Terminator 3, Charlie’s Angels, Bad Boys 2
FS Film
28
28
26
Lord of The Rings: The Two Towers, Lord of The Rings: Return of The King
Buena Vista
12
20
24
Bad Boys – A True Story, Sibelius, Pirates of The Caribbean
Scanbox
6
16
19
The Hours, The Human Stain, A la Folie
Sandrew Metronome
26
25
19
The Matrix Reloaded, The Matrix Revolutions, Harry Potter and The Chamber…
Cinema Mondo
19
17
16
The Pianist, Spirited Away, Stupeur & Treblements
Kamras Film Group
10
15
12
Good Bye Lenin, Nirgendwo in Africa, Cidade de Deus
UIP
20
17
12
Johnny English, Ring, Catch Me If You Can
Future Film
9
9
11
Swimming Pool, Evil Dead, Les vacances M Hulot
Senso Films
9
11
4
L’Ultimo bacio, Movern Callar, Last Orders
5
7
6
Bella Martha aka Mostly Martha, Lejontämjaren, Pure
171
192
177
Rest;
Kinoscreen,
Rapid
examples in 2003 (or late 2002)
Eye Movie, Finnkino
All premieres
51
Table 11: (Table 1.9) Descriptive statistics for critical review rank (scale 1 – “top” to 10 – ”lowest”)
Variable
Mean
Median
sd
min
max
valid observationms
source
notes
Critical review, rank,
6,92
8
2,57
1
10
133
Nyt
43 films are reviewed
6,11
6,5
2,50
1
10
90
Nyt
Critical reviews (index: 1 to 5)
1st occurrence, display
Critical review, rank,
only once
2nd display
Critical review, rank,
is shown twice for 27 films
5,75
6
2,66
1
10
63
Nyt
5,33
5
2,96
1
10
51
Nyt
5,14
4
2,93
1
10
37
Nyt
4,67
4
2,90
1
10
27
Nyt
3,90
3
2,85
1
10
19
Nyt
3,88
3
2,87
1
10
16
Nyt
2,75
2
1,93
1
7
12
Nyt
3,4
2
2,46
1
8
10
Nyt
3
3
1,58
1
5
9
Nyt
3rd display
Critical review, rank,
4th display
Critical review, rank,
5th display
Critical review, rank,
6th
display
Critical review, rank,
7th
display
Critical review, rank,
7th
display
Critical review, rank,
8th display
Critical review, rank,
9th display
Critical review, rank,
10th display
11 weeks: 1 film, 12 weeks: 2 films
14 weeks: 3 films, 15 weeks: 1 film
18 weeks: 1 film, 20 weeks: 1 film
52
Table 12: (Table 1.10) Duration of movie run, quantiles
Variable
Top 10, duration
Mean
Median
Screens, five first
Screens, first
Screens, second
Screens, third
weeks, mean
week, mean
week, mean
week, mean
17,3
17
44,5
29,8
46,1
49,3
13,8
10,5
39,0
31,6
43,2
45,5
13,9
10,5
30,1
28,7
34,1
33,2
10,9
9
28,3
25,6
31,2
30,2
7,8
7,5
21,8
17,4
24,2
27,7
10
10,5
12,3
9,9
13,6
13,4
6,6
6,5
8,2
8,9
9,3
7,9
5,6
5
8,2
10,0
11,7
8,9
5,3
5
3,6
4,7
4,8
3,4
3,4
3,5
4,0
6,1
5,1
4,7
4
4,5
4,5
5,8
5,4
5,0
3
3,5
2,0
3,4
2,9
1,8
1,5
2
2,3
6,3
4,9
0,5
of movie run, weeks
Films 11-20, duration
of movie run, weeks
Films 21-30, duration
of movie run, weeks
Films 31-40, duration
of movie run, weeks
Films 41-50, duration
of movie run, weeks
Films 51-60, duration
of movie run, weeks
Films 61-70, duration
of movie run, weeks
Films 71-80, duration
of movie run, weeks
Films 81-90, duration
of movie run, weeks
Films 91-100, duration
of movie run, weeks
Films 101-110, duration
of movie run, weeks
Films 111-120, duration
of movie run, weeks
Films 121-130, duration
of movie run, weeks
53
Table 13: (Table 1.11) Estimation results, n = 345
Model
OLS
without
group dummy
LSDV,
Fixed
effects
Random effects model
model (FEM)
(REM)
0,955
0,691
0,929
(0,034)***
(0,068)***
(0,038)***
0,131
0,119
0,093
(0,141)
(0,169)
(0,139)
1,174
-0,942
-0,249
(0,454)*
(0,381)*
(0,340)
-0,471
-1,079
-0,893
(0,043)***
(0,048)***
(0,041)***
0,263
0,123
0,116
(0,079)**
(0,057)*
(0,052)*
0,018
0,050
-0,009
(0,119)
(0,092)
(0,083)
0,374
0,123
0,195
(0,111)****
(0,127)
(0,104)
2,009
0,985
0,859
(0,762)**
(0,579)
(0,522)
variables
Log SCR
Log ALLSCR
Log PRICE
Log WEEKSREL
Log HKIADM
Log TOP10
Log CA
NotHK
Constant
1,210
6,214
(1,161)
(1,020)***
Depending variable is log of weekly admissions, n = 345
Standard deviations in parenthesis
Adjusted R-sq
0,838
F-test
217,86***
Diagnostic LL
628,75***
Test statistics for the Classical Model
Constant term
Log Likelihood
LM test vs Model (3)
only (1)
= -589,43
118,15***
LL = -312,10
Hausman test (FEM vs
Group
effects
only (2)
X–
REM): 103,30***
variables
LL = -275,05
only (3)
X-and
group
LL = 0,667
effects (4)
Hypothesis
tests
(2) vs (1)
LR test
F test
554,65***
10,61***
(3) vs (1)
628,74***
217,85***
(4) vs (1)
1180,21***
70,22***
(4) vs (2)
625,56***
155,19***
(4) vs (3)
551,46***
10,15***
54
Table 14: (Table 1.12) Estimation results, all movies critically reviewed and with previous week’s Helsinki admission, n = 205
Model
OLS
without
group dummy
LSDV,
Fixed
effects
Random effects model
model (FEM)
(REM)
0,642
0,740
0,866
(0,050)***
(0,103)***
(0,052)***
0,052
-0,976
-0,880
(0,529)
(0,313)**
(0,301)**
-0,284
-1,150
-0,959
(0,076)***
(0,059)***
(0,054)***
0,545
0,125
0,184
(0,060)***
(0,034)***
(0,033)***
variables
Log SCR
Log PRICE
Log WEEKSREL
Log HKIADM
Constant
3,129
8,167
(1,117)*
(0,665)***
Depending variable is log of weekly admissions, n = 205
Standard deviations in parenthesis
Adjusted R-sq
0,841
0,971
F-test
268,51***
74,02***
Diagnostic LL
376,61***
792,65***
0,777
Test statistics for the Classical Model
Constant term
Log Likelihood
LM test vs Model (3)
only (1)
= -322,30
72,04***
LL = -141,72
Hausman
Group
effects
only (2)
X–
variables
LL = -133,99
only (3)
X-and
group
LL = 74,02
effects (4)
Hypothesis
tests
(2) vs (1)
test
REM): 70,56***
LR test
F test
361,16***
11,69***
(3) vs (1)
376,60***
268,51***
(4) vs (1)
792,65***
106,18***
(4) vs (2)
431,48***
255,71***
(4) vs (3)
416,04***
15,62***
55
(FEM
vs
2 Demand for ice hockey, the factors explaining attendance of ice hockey
games in Finland
The study focuses on season 2007 – 2008 ice hockey league games in Finland. The aim is to explain
factors affecting attendance. During the season there were 392 games played excluding playoff games.
The total attendance number was 1,964,626. Both the population of the home team and the visiting
team has a statistically significant effect on attendance, as well as distance between home town and
visitor’s town. Local games have a bigger attendance than other games. The demand is not elastic with
respect to the ticket price. Success of both the home team and the visitor has an effect: home team’s
success with a positive and visitor’s with a negative coefficient. The number of plays already played has
a negative effect. Weekday effect is important: the attendance is bigger during Saturdays. Also the day
temperature has an effect: the colder, the bigger attendance. That effect is small but still statistically
significant.
The unemployment rate has no effect, and the success factor of the last three games does not seem to
explain, neither does the success factor of all games played.
Keywords: Ice hockey, Attendance, Finland, Temperature
56
2.1 Introduction
This paper uses regular season 2007 – 2008 Finnish ice hockey attendance figures to examine the
explaining determinants. Simple economic theory suggests that the demand for attendance should
depend on the ticket price of the game and travel costs, the incomes of spectators, the prices of
substitute goods, and market size (Simmons 2006). Usually market size is measured by local population.
There is a wide range of literature on attendance of sports events but not any with Finnish ice hockey
data. A recent sport attendance survey8 – both active consumption (participation in sport competitions
or being a member in a sport or gymnastic club) and passive consumption (attendance) reveals that the
most popular sports by attendance were ice hockey (25.5%), football (16.9%), athletics (10.6%), skiing
(6.5%) and Finnish- rules baseball (5%). In this survey 44 % responded that they had not attended any
sports event between February 2005 and January 2006. A key contribution of this paper is to show that
both the market size (town population) of the home and the visiting teams have an impact on
attendance. Previous success measured as points per game from the beginning of the season is better to
explain attendance than points per game from three last games (the form guide), and temperature also
matters although the games are not played outdoors.
There are a few studies that have compared the attendance of sport activities between men’s and
women’s games or between genders. Most studies show that there are more male spectators than
female (see Vuolle, Telama & Laakso 1986, Gantz & Wenner 1991, Zhang, Pease, Hui & Michaud
1995, White & Wilson 1999 or Thrane 2001). Women seem to favour women’s games and men favour
men’s games. (Kahle, Duncan, Dalakas & Aiken 2001). The sociology of sport consumption has
revealed that the motives for attending women’s games and men’s games differ. Typically, the aesthetics
of the game or competition is more important for women’s team spectators and for female spectators
(Ridinger & Funk 2006) ,while e.g. tracking statistics is more important for men (Fink, Trail &
8
Liikuntatutkimus 2005-2006, Sport Survey: Adult Population
57
Anderson 2002). The relationship between gender and active sport consumption, i.e. participation in
sport competitions or being a member in a sport or gymnastic club, reveals only minor differences in
Finland. Both genders are as active, but women seem to favour more clubs of commercial purposes
(e.g. gym with aerobics), while men are more often members in sports associations that play games
(Kansallinen liikuntatutkimus 2005-2006). Gymnastics at home and within a gymnastic association have
been typically female, while fishing and hunting have been male sport activities (Marin 1988).
The relationship between gender and passive sport consumption, i.e. attendance at games, has been less
studied. If there are gender differences across games and if women spectators have different motives to
attend sport activities, the factors explaining attendance should be different. Since men use more time
in tracking statistics and reading about sports in daily newspapers (Dietz-Uhler, Harrick, End &
Jacquemotte 2000), a team’s winning percentage or other previous performance measure of the team
should be less important to explain women’s teams’ attendance figure. There are also differences
between the importance of ticket pricing, friend influence and family involvement in women’s and
men’s (basketball in the USA) games (Fink, Trail & Anderson 2002); hence the price elasticity of
demand should differ. Women’s games should be more ticket price sensitive.
There are 14 teams playing at the men’s highest level in the Finnish ice hockey league. The regular
season 2007 – 2008 was a four-fold series, i.e. 52 games per team, and teams located in Helsinki
(HIFK and Jokerit) played extra four mutual games; two at home stadium and two at visitor’s stadium.
In addition to that, the remaining 12 teams played extra four games in the subdivisions of three teams.
The subdivisions were 1) Blues (home city: Espoo), Pelicans (Lahti), SaiPa (Lappeenranta), 2) HPK
(Hämeenlinna), Ilves (Tampere), Tappara (Tampere), 3) JYP (Jyväskylä), KalPa (Kuopio), Kärpät
(Oulu) and 4) Lukko (Rauma), TPS (Turku), Ässät (Pori)9. Altogether each team played 28 home games
and 28 games as visitor (Jääkiekkokirja 2008 - 2009, 55). The first regular season games were played in
9
The distance between the cities in these subdivisions are 1) Espoo – 114 km – Lahti – 152,5 km – Lappeenranta – 235,5
km – Espoo, 2) Hämeenlinna – 79,8 km – Tampere, 3) Jyväskylä – 148,9 km – Kuopio – 289,2 km – Oulu – 341,2 km –
Jyväskylä, 4) Rauma – 87,4 km – Turku – 135,9 km – Pori – 50 km – Rauma.
58
September 2007 and the last in March 2008. After that some teams continued their games in play-offs
and the champion (Kärpät) was known in mid- April.
Jokerit from Helsinki got the biggest average attendance (8591 per game), while the lowest figure was
for HPK (3281 per game). Jokerit has the biggest stadium (Hartwall Areena) in terms of capacity. The
number of seats was 13506, while in Hämeenlinna (HPK) the number of seats was only 3214 but with
1786 standing places – so altogether 5000 places. Table 1 summarizes some statistics for the average
attendance of each team during the regular season 2007 – 2008.
(Table 1 about here)
Naturally bigger cities like Helsinki, Espoo, Tampere or Turku have a bigger attendance potential, but
this does not explain enough the variation in attendance. It is also true based on the coefficient of
variation that attendance variation is much higher for Jokerit and KalPa than for Kärpät, JYP, HPK or
Pelicans.
Altogether, in regular season the number of games was 14 x 28 = 392 and the total attendance was
1964626, i.e. 5012 per game. Other cultural events, like theater, gathered a bigger admission in 2007:
about 2.7 million but with a bigger number of total presentations (about 13000) which equals 207 per
presentation. The Finnish national opera sold 162555 tickets to 198 presentations (about 820 per
presentation). In the highest football league, Veikkausliiga games, the admission number was 541612
with 182 games (2976 per game). Hence, in terms of cultural events attendance in live performances,
ice hockey was the most important. However, going to the movies was even more important since the
total admission number was about 6.5 million in Finland (population 5.3 M) but there were not live
performances (Statistical Year Book 2008 Finland and www.veikkausliiga.fi). According to statistical
surveys made by national sports associations (SLU, Suomen Liikunta ja Urheilu, published in Statistical
59
Year Book 2008, Finland), exercising ice hockey is not as usual as football, hence one might assume
that attendance in football games should be higher than ice hockey. However, the aim of this study is
not to compare different sports but to explain ice hockey games attendance, especially the role of
tracing statistics measured by team performance is the core of this study. Newspapers and sport news
on television reveal this information and since most teams have typically three games per week, this
team performance information is revealed three times a week during the regular ice hockey season.
The results indicate that both the population of the home team and the visitor has a statistically
significant effect on attendance as well as the distance between home town and visitor’s town. Local
games have a bigger attendance than other games. The demand is not elastic with respect to the ticket
price. The success of both the home team and the visitor has an effect: home team’s success with a
positive and visitor’s with a negative coefficient. The number of plays already played has a negative
effect. Weekday effect is important: the attendance is bigger during Saturdays. Also the day temperature
has an effect: the colder, the bigger attendance. That effect is small but still statistically significant. The
unemployment rate has no effect, and the success factor of the last three games does not seem to
explain or the success factor of all games played.
2.2 Literature
There is a wide range of literature on attendance in cultural events. An important and influential study
explaining movie attendance by Eliashberg and Shugan (1997) showed that attendance and the number
of screens are highly correlated. A bigger number of screens is associated with movie attendance.
Critics’ reviews published in media, like newspapers or magazines, have been shown to have an impact
on movie attendance. Also spontaneous dispersal, like “word-of-mouth” is an important factor to
explain movie attendance (Elberse and Eliashberg 2003).
The literature explaining attendance in sport events, especially in the USA, is also wide starting with
Demmert (1973) and Noll (1974). Conventional economic theory assumes that demand base measured
60
as the incomes of the relevant market population and market size (population) should have an impact
on attendance. Teams from bigger cities should have bigger attendance if the venue capacity allows it.
Many teams are local monopolies with almost zero marginal costs of attendance. Hence maximizing
profits equals maximizing revenues, and the outcome should be to set ticket prices high enough to
ensure unitary price elasticity. Most studies still reveal that sporting events are priced in the inelastic
range (Krautmann & Berri 2007).
Coates and Harrison (2005) studied baseball (MLB) attendance with a panel data throughout the years
1969 – 1996. The team’s home town population as well as winning percentage is a positively significant
variable to explain attendance, while the ticket price is negatively associated. Different, alternative ticket
prices, like “gate” measured as the ratio of total box-office income to total attendance or “seat”
measured as weighted average of different category seat prices have been used. Regardless of which
price measure is used, attendance is price inelastic. Incomes in home town do not seem to be
statistically significant. Also Coates and Humphreys (2007) have similar results. Elsewhere Depken
(2000) shows that attendance at MLB baseball is positively significant with the incomes of the home
town and team’s payroll. Kahane and Shmanske (1997) report similar results and show also that
changes in teams’ structure (scorecard) has a negative effect on attendance. Relatively big scorecard
changes between seasons diminish attendance. The distance between home town and visitor’s town has
a significant negative effect on attendance in MLB baseball (Knowles, Sherony and Haupert 1992). The
further away a visitor comes, the fewer spectators the game attracts. During weekends there are more
spectators than during weekdays. With MLB baseball McDonald and Rascher (2000) show that sales
promotion has a positive but diminishing effect on attendance. Sales promotion results in larger
attendance, but excessive promotion are probably too expensive in terms of profitability. The
competitive balance of the league is important for attendance (Schmidt and Berri 2001). If some teams
“always” win and some “always” lose the games, spectators’ motivation to attend falls. Occasionally
there have been strikes or lock-outs in baseball, which has not had any significant and long-term effect
on attendance (Schmidt and Berri 2002). During the season 1994 – 1995 even a six-month strike did
61
not have any long-term effects on attendance. Fans returned after the pause. This result is valid in
baseball but also in football (NFL) and ice hockey (NHL) as shown by Schmidt and Berri (2004).
Attendance is rather price inelastic in other sports – not just baseball, like football (NFL, Depken
2001), basketball (NBA, Coates and Humphreys 2007), Australian rules football (Borland and Lye
1992), and English rugby football (Carmichael, Millington and Simmons 1999), Spanish football (Garcia
and Rodriguez 2002). Teams seem to lower ticket prices to the inelastic range of demand to increase
non-ticket revenues, like intermediate time refreshment sales or broadcasting revenues (Fort 2004 or
Krautmann & Berri 2007).
On the other hand there is some evidence of both price elastic and price inelastic demand in British
football (Simmons 1996). Simmons argues that there is a remarkable difference between season ticket
holders and occasional spectators that buy the ticket at the gate. Season ticket holders have lower price
sensitivity than occasional spectators. Negative income elasticity or positive effect of unemployment
rate on attendance has been found in several studies: Baimbridge, Cameron and Dawson (1996) with
British football; Borland and Lye (1992) with Australian football; Falter and Perignon (2000) with
football in France but also a positive coefficient with home town incomes in some sports, like
basketball in the USA (NBA, Coates and Humpreys 2007) or baseball in the USA (MLB, Depken 2000
or Coates and Harrison 2005) or football in the USA (NFL, Depken 2001).
Some games are played outdoors and some indoors and therefore temperature or weather conditions
might have different effects on attendance. Especially weather conditions have been shown to have an
effect on sports attendance outdoors. Baimbridge, Cameron and Dawson (1995) and Jones, Schofield
and Giles (2000) have shown that high temperature and rainless conditions have a positive effect on
rugby football attendance in the UK. Spanish football attracts a larger audience when the weather is
favorable (Garcia and Rodriguez 2002) which is valid with Australian football (Borland and Lye 1992)
62
or Finnish football (Iho and Heikkilä 2008). During spring the attendance is larger in football in France
(Falter and Perignon 2000). When it is raining or snowing the attendance is lower in college football
(National College Athletic Association, NCAA, DeSchriver and Jensen 2002). To the contrary
Carmichael, Millington and Simmons (1999) show that low temperature is associated with higher
attendance in rugby football. Elsewhere Baimbridge, Cameron and Dawson (1996) find no statistical
association between weather conditions and British football. Wilson and Sim (1995) show that at the
beginning of the season the attendance is higher than later in football in Malaysia.
Winning probability or team’s success has a positive impact on attendance (Boyd and Boyd 1998,
Burdekin and Idson 1991, Coates and Harrison 2005, Coates and Humphreys 2007, Depken 2000,
Depken 2001, Kahane and Shmanske 1997, McDonald and Rascher 2000, Simmons 1996) but also the
inverse relationship has been found (Baimbridge, Cameron and Dawson 1995).
In most cases there are more males than females in the audience. The focus on the literature survey
above is also more masculine than feminine. Most surveys carried out reveal that one of the most
important reasons to attend a live game rather than watching through some media like television or
radio is to be involved in the success, the play-off games and winning the championship. The ticket
price is not so relevant (Hansen and Gauhier 1989). Most of the games are played during weekends or
evenings and therefore entertainment is a very important motive to attend, especially for men since true
fans are male (Hall and O´Mahony 2006). For women the motive to attend is to share time with friends
and family (Fink, Trail and Anderson 2002 or Dietz-Uhler, Harrick, End and Jacquemotte 2000).
Parking space, the quality of seats and transportation possibilities in general to the stadium are more
important for female than for male. Appealing side services, like the quality of parking space, the
cleanliness of the stadium, adequate entrance hall space and eating possibilities during intermediate
times seem to increase attendance (Wakefield and Sloan 1995). Men seem to fan only one team, their
favorite, to which they are rather loyal. Peer group acceptance is important and men seem to have an
emotional attitude towards the team (Bauer, Sauer and Exler 2005). Supporters use fan shirts and
scarves. Admiration is associated with strong and enjoyable feelings (Heinonen 2005).
63
Unreasonableness and superfluity are essential. The brand equity of a team has a significant positive
impact on attendance in German football (Bauer, Sauer and Schmitt 2004). Mustonen, Arms and
Russell (1996) show that the possibilities to see proficient ice hockey and support the team are among
the most important reasons to attend, while getting together and especially game violence are far less
important motives in Finland. The two latter are more important in Canada.
The survey on the impact of temperature, winning percentage and venue quality indicate that
temperature matters, after all ice hockey is played indoors, and the relationship might not be equal to
what has been found with outdoor sports events. To the contrary: when the temperature is high
especially occasional spectators have other, substitute alternatives (outdoor activities) and that might
diminish attendance. Winning percentage and brand equity are associated and they should have a
positive impact on attendance. Since most spectators are male, venue quality should have less
importance and it is not considered as an explanatory factor.
2.3 A model explaining attendance
Based on the literature survey, the following model explaining attendance can be formulated where the
utility function uijkt of individual i to attend a home team’s j game against visitor k on time t
uijkt = α1(yit – Pjkt) + α2GSFjk + α3SSFjt + α4TSFjt + εijkt
In the model yit measures spectator’s income and Pjkt is the ticket price (α1 > 0). GSFjk (game specific
factors) describes interest towards the game that can be measured with home team’s j winning ratio as
well as visitor’s k winning ratio in previous games. Based on Bauer, Sauer and Schmitt (2004) and
Coates and Harrison (2005), it is plausible to assume that interest towards the game is higher when the
home team has won the previous games. Both points per game from the beginning of the season and
points from the last three games are suitable empirical measures for the winning ratio. The regular
season games yield points according to the following scheme: a win within normal playtime (60 min)
gives 3 points, a win within extension time (60 min +) or a penalty shot win gives 2 points, a lost within
64
extension time or after penalty shots gives 1 point, and a lost within normal playtime gives 0. Interest
towards a game is larger when home town population or visitor’s town population is higher (Coates
and Harrison 2005), while a bigger distance between home town and visitor’s town should lower
interest towards the game (Knowles, Sherony and Haupert 1992). It is plausible that local games, like
HIFK – Jokerit (both from Helsinki) or Ilves – Tappara (both from Tampere) have (almost) full house.
High unemployment rate in the region on the one hand might reduce attendance due to lower average
incomes but on the other hand especially in France attendance in football games and unemployment
rate are positively correlated.
SSFt is measuring time from the beginning of the season. In early autumn when the season begins
games have high interest since the team has new players and the lines are new (Wilson and Sim 1995).
As time goes on, this interest might diminish and hence attendance also goes down. The number of
games played since the beginning of the season is the empirical measure in this study.
Attendance is rather inelastic with respect to price pjkt . Stadia or halls have different price categories.
During the season 2007 – 2008 e.g. the ticket price of Blues’s (Espoo) home games on club seats (201206) was normally €27, on the second long side (207-211) €24, standing places (terraces) (212) €10,
gable seats lower (101-102) €18, normal seat upper (401-406) €14, disabled persons €14, conscripts and
students €10 (normal seats upper, not Blues – HIFK, nor Blues – Jokerit), boxes for box owners €14,
and children under 7 years free if they were sitting on parent’s knees. Since Espoo and Helsinki are
neighboring towns, the ticket prices for games against HIFK or Jokerit (both from Helsinki) were €2
higher. These prices were valid only when the ticket was bought in advance. When bought on entrance,
there was €1 increase. For empirical purposes the variation is very challenging and since there was no
data concerning the true distribution of seats taken, the empirical equivalent of the price is usually the
ticket price of the best seat including local game excess fees. For Blues, this price is €27 or €29 with
HIFK or Jokerit as visiting team. However, throughout the regular season, ticket prices do not vary: at
the beginning of the season and at the end of season prices remain unchanged, and there is no weekend
premium, hence pjkt = pjk.
65
The proxy for the time specific factor (TSF) is partially the weather conditions and partially weekday. A
good, sunny weather brings about a larger attendance than rainy weather in Spanish football (Garcia
and Rodriguez 2002). However, ice hockey is played indoors and weather - here: the temperature
outside – might have an opposite effect. The maximum day temperature in the nearest meteorological
observation site is used to measure the temperature. For other teams than Blues, HIFK, HPK and
Jokerit, the observation site is usually the airport of the home town. The airport in Oulu (team: Kärpät)
is located in the neighboring town, Oulunsalo and the temperature for Blues (Espoo), HIFK and
Jokerit (Helsinki) is measured at Helsinki-Vantaa Airport which is located in the neighboring town,
Vantaa. The temperature for the team of Hämeenlinna, HPK, is measured in Jokioinen which is about
50 km away from Hämeenlinna.
The weekday effect takes into account the fact that during weekends there is usually a larger attendance.
Individuals have the other option not to attend an ice hockey game and the utility for the alternative is
ui0t = α1(yit ) + α4TSFt + εi0t
Letting Uijkt = uijkt - ui0t. Individuals will choose to attend a game if Uijkt > 0.
Therefore the model to be estimated explain ice hockey games attendance (ATT) is:
(1) ATT = β1(yit – Pjkt) + β 2GSFjk + β 3SSFjt + β 4TSFjt + Φijkt
A complete listing of variables is given in table 2. Variables, except for the temperature and the
weekday, are in logarithmic form and thus the parameter coefficients in estimation results are
elasticities.
(Table 2 about here)
Descriptive statistics and correlation on variables (before taking logarithms) are shown in table 3.
66
(table 3 about here)
Roughly 31 % of the games were played on Saturdays, about 27 % on Thursdays and about 25 % on
Tuesdays. In addition to that, a few games were played on Mondays (< 3 %), Wednesdays (< 5 %),
Fridays (> 6 %) and Sundays (> 3 %). Somewhat more often there were Monday games in bigger
towns (r = 0,109) against teams from far away (r = 0,106). There seems to have been more games on
Fridays in bigger towns (r = 0,121) and that seems to have been reducing Saturday games (r = -0,128).
Otherwise the weekday variables do not seem to correlate with other variables.
The correlation matrix reveals the ticket price seems to have been higher in larger towns and it seems to
have a positive relation with attendance. The number of home team games and the number of visitor’s
games were (naturally) highly positively correlated. Points per game from the beginning of the season
(HPoint) and points from the last three games (Last3H) were also positively correlated. The regional
unemployment rate seems to have been higher in areas with smaller towns. The temperature seems to
have been lower when the number of games has increased. Probably the relation is like inverse U or
inverse J. According to long-term statistics (1900 – 2000) the temperature in Helsinki (Kaisaniemi
observation site) has been + 11,1 Celsius in September, + 6,2 Celsius in October, + 1,5 Celsius in
November, - 2,1 Celsius in December, -4,7 Celsius in January, -5,7 Celsius in February and -2,2 in
March (Ilmatieteen laitos 2009).
Since points per game (HPoint or VPoint) and the corresponding points from the last three games
(Last3H or Last3V) are strongly positively correlated and these partially measure the same for empirical
purposes, these are used as alternative measures.
67
2.4 Estimation
Conventional regression analysis is used here but the results might be biased due to heterogeneity. An
alternative for conventional regression analysis is panel data methods. The benefits of using panel data
are that (1) individual heterogeneity can be controlled, (2) estimated parameters are more efficient and
(3) with panel data the dynamics of adjustment can be studied better (Baltagi 2008, 6-7). Time-series
and cross-section studies that do not control heterogeneity might yield biased results. However, since in
this study the population variable is constant for each team, i.e. for Blues (Espoo) the population is
regardless of the game always 238047 (population 31st December.2007), the panel data estimation
method (NLogit 4.0) will not estimate the coefficient, and conventional regression analysis is used.
The first model uses the price of the ticket (LogPrice), the population in home and visitor’s town
(LogHPop and LogVPop), the distance from home team’s stadium to visitor’s stadium (LogDist),
round (LogHGame), teams’ success or winning ratio (LogHPoin and LogVPoin) and the region´s
unemployment rate (LogUnemp), maximum day temperature in home town (Temp, note: not
logarithm) and dummies for Tuesday (TU) and Thursday (TH) or Saturday (SA). All parameter
estimates except the unemployment rate are statistically significant, have the right sign and are plausible.
Model 2 is otherwise similar to Model 1 except that the weekday dummy is Saturday. In Models 3 and 4
the success variable is points from three last games (LogHLast or LogVLast). In these models visitor’s
last three games points (LogVLast) do not seem to significantly explain the attendance of the game,
while home team’s last three games points seem to explain. The Saturday effect is substantial: the
audience is about 10 – 11 percent larger than on Tuesdays or Thursdays. Other weekday dummies
(Monday, Wednesday, Friday or Sunday) are not significant (not reported here).
Models 5 - 8 do not have the unemployment rate variable but the results seem to be similar. Attendance
is fairly inelastic with respect to ticket price (price elasticity is about -0,25 … - 0,32 depending on the
model), population variables get a positive coefficient so that home town population elasticity is about
8 times as high as the visitor’s town population elasticity. Distance between the home team and the
visitor’s town seem to be significant: the longer the distance, the less attendance. However, the effect
has only minor importance since the distance elasticity is absolutely rather small. Still as the distance
68
increases from 50 km to 100 km, attendance diminishes by 2½ percent. Game round has a diminishing
effect on attendance which was also found by Wilson and Sim (1995) with Malaysian football.
Home team’s success or winning ratio (LogHPoin or LogHLast) seems to attract more spectators since
these variables get a positive coefficient regardless of the model. The visitor’s success on the contrary
seems to lessen attendance (LogVPoin), but the recent success in terms of last three games (LogVLast)
does not seem to be significant. Ice hockey audience seems to favour an assured win and not even
games. If the home team falls into a losing circle, the attendance also falls. The temperature has
interesting effects on attendance since low temperature seems to attract a bigger audience. Ice hockey is
a game played indoors, thus the effect of temperature is different than in football or other outdoors
sports.
(tables 4a,4b and 4c about here)
69
Models 5 and 6 are the most credible to explain ice hockey attendance, still the coefficient of
determination (R2) is only about 0,67. In addition model 6 seems to overestimate attendance for some
teams - Blues, HIFK, HPK, JYP, KalPa, Pelicans and SaiPa – and underestimate the rest – Ilves,
Jokerit, Kärpät, Lukko, Tappara, TPS and Ässät.
Figure 1 contains the true attendance of HIFK (Series1) and the estimate made through Model 6
(Series2). Based on figure 1 the model is not able to explain peaks and bottoms, i.e. large variation. The
actual average attendance (and standard deviation) for HIFK in regular season 2007 – 2008 was 6573
(1023), while the model 6 estimates are correspondingly 7161 (585). It is possible to make some
simulation exercises using model 6. The first simulation shown in Figure 2 as SIM1, the game days for
HIFK were postponed by one, e.g. actual Tuesday’s 11th September 2007 game HIFK vs. HPK to the
following Friday 21st September 2007 when actually there was a game HIFK vs. Blues and so on,
except that the last regular season game of 4th March 2008 (HIFK vs. TPS) was moved to 11th
September 2007. Since most of the games are played on Tuesdays, Thursdays and Saturdays, the first
simulation is trying to answer the question of what might be the result of having the “Saturday games”
on Tuesdays, and having the “Tuesday games” on Thursdays and the “Thursday games” on Saturdays.
This simulation is not accurate since there have been some games on Mondays and so on. However,
simulation 1 reveals that the average attendance (and standard variation) would be 7165 (822).
Correspondingly, the other simulation 2 is made so that the “Saturday games” have been moved to
Thursdays, and the “Tuesday games” to Saturdays and the “Thursday games” to Tuesdays. In this case,
the attendance to HIFK’s home games would be on average (std): 7169 (820). With these simulations,
the home team characteristics – population (LogHPop), success (LogHPoin), round (LogHGame), the
temperature (temp) - are assumed not to change, while the visitor’s characteristics – ticket price
(LogPrice), population (LogVPop), distance (LogDist), success (LogVPoin) are assumed to change.
70
2.5 Conclusions and suggestions
There is a rather wide variation in ice hockey game attendance between weekdays. On Saturdays the
attendance is about 10 – 11 percent higher than during other conventional playdays, i.e. Tuesdays and
Thursdays. In addition to that there is also big variation across teams: Jokerit from Helsinki got the
biggest attendance, while HPK from Hämeenlinna got the lowest average attendance. It is natural that
home town population partially explains this, since the elasticity of attendance with respect to home
town population is positive (about 0,336) but also the elasticity with respect to visitor’s town
population is positive (about 0,048). The visitor’s fans will attend the team’s away games but distance
matters. The bigger distance, the lower attendance.
With caution it can be argued that ticket price has a negative effect on attendance, since demand seems
to be inelastic. However, the price variable is not the actual average price since this data was not
available. The price variable used in the estimations is the ticket price to the best seats. As the season
goes on and more games have been played, the attendance seems to diminish but the estimated
coefficient is low even though significant. Team’s success seems to attract a bigger attendance, while
visitor’s success has the opposite effect. Spectators are willing to see a live game in the stadium if they
expect that home team will win the game. The unemployment rate has no effect on attendance, while
weather condition measured by the outside temperature is a significant variable. Colder weather attracts
more spectators. However, the estimated coefficient is minor but significant.
The effect of mass media, e.g. television has been neglected in this study. Some of the games were seen
through cable television (Pay-tv) and some through open commercial channels. In Finland, all
television owners must pay a TV fee which was during these years about € 200 – 230 per year. There is
some evidence that broadcasting through tv has a negative effect on live attendance (Baimbridge,
71
Cameron and Dawson 1995 or Carmichael, Millington and Simmons 1999). Moreover, other activities
like the premieres of blockbuster movies or concerts by famous orchestras or rock bands might lower
attendance. However, these have not been taken into account.
The estimation results reveal that the models can explain about 2/3’s of actual attendance based on the
coefficient of determination. The models do not explain whether fans are loyal to their teams. Will
they abandon the team if success is not good enough? What is loyalty, what is the effect of that on
attendance? Are the results robust with international data? These topics are among those that should be
studied. Using panel data analysis and methods is also worth considering.
72
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Statistics:
Jääkiekkokirja 2007-2008. Egmont Kustannus 2008
Official Statistics, Finland 2008
78
http://www.tutiempo.net/
http://www.tem.fi
Estimation: Nlogit 4.0 (www.limdep.com)
79
Table 15: (Table 2.1) Regular season 2007 – 2008 average attendance and capacity statictics, source: Jääkiekkokirja 2007-2008 ja
Jääkiekkokirja 2008-2009
team
Home game average
attendance (in relation to
capacity)
Variation of attendance in
home games:
Stadium capacity, seats
min – max (std)
(sitting, standing, others)
Coefficient of variation
Blues
4837 (70%)
3706 – 6530 (693,5)
6914 (5633/230/1051 boxes)
V = 0,143
HIFK
6573 (80%)
5219 – 8200 (1023,4)
8200
V = 0,155
HPK
3281 (65%)
2780 – 4395 (384,8)
5000 (3214/1786)
V = 0,117
Ilves
5914 (76%)
4584 – 7800 (1026,0)
7800 (6635/1165)
V = 0,173
Jokerit
8591 (64%)
6203 – 13464 (1890,4)
13506
V = 0,220
JYP
4054 (90%)
3347 – 4500 (343,8)
KalPa
3388 (65%)
2512 – 4911 (722,3)
Kärpät
6054 (92%)
5062 – 6614 (485,2)
Lukko
3733 (69%)
2901 – 5400 (658,8)
Pelicans
4252 (87%)
3505 – 4910 (485,2)
SaiPa
3557 (73%)
2881 – 4847 (533,4)
Tappara
5712 (73%)
4193 – 7800 (1074,1)
TPS
5978 (51%)
3919 – 8394 (1155,0)
Ässät
4234 (65%)
3287 – 6472 (828,8)
4500 (2352/2148)
V = 0,084
5225 (2767/2458)
V = 0,213
6614 (4760/1854)
V = 0,080
5400 (3386/2014)
V = 0,174
4910 (3410/1500)
V = 0,114
4847 (2810/2025/12 wheelchair)
V = 0,149
7800 (6635/1165)
V = 0,187
11820 (9042/2778)
V = 0,193
V = 0,195
80
6472 (3972/2500)
Table 16: (Table 2.2) Variables, measurement, source and expected sign
variable
measure
source
Game specific factor
home town population 31.12.2007 (logHPop)
Statistics Finland
+
visitor’s
31.12.2007
Statistics Finland
+
distance between home town and visitor’s
Stadium address
-
town (logDist)
http://www.sm-liiga.fi
town
population
expected sign
(logVPop)
distance:
http://kartat.eniro.fi
home team’s points per game (logHPoin): if
own
zero, then replaced by 0,01
Jääkiekkokirja 2007-2008
visitor’s points per game (logVPoin): if zero,
own
then replaced by 0,01
Jääkiekkokirja 2007-2008
home team’s points from 3 last games
own
(logHLast): if zero, then replaced by 0,01
Jääkiekkokirja 2007-2008
visitor’s points from 3 last games (logVLast):
own
if zero, then replaced by 0,01
Jääkiekkokirja 2007-2008
Incomes
regional unemployment rate (Unempl)
http://www.tem.fi
Season specific factor
played games since the beginning of th
own
season (logHGame) if zero, then replaced by
Jääkiekkokirja 2007-2008
calclulations
calclulations
calclulations
calclulations
calclulations
based
based
based
based
on
+
on
?
on
+
on
?
?
based
on
-
0,01
ticket price (logPrice)
ticket price, pjk
Time
specific
factor,
temperature, tempit
Time
weekday
specific
temperature at nearist observation site
Jääkiekkokirja 2007 - 2008
-
http://www.tutiempo.net/
?
(temp)
factor,
weekday, three dummies TU (tuessay) TH
TU –
thursday), SA (saturday)
TH –
SA +
81
Table 17: (Table2.3) Variables, means, standard deviations and correlation matrix. ATT = attendance, Price (€), Dist = distance between home team’s and visitor’s stadiums along road (km), Temp = max
tempature Unempl = monthly regional unemployment rate(%), HomePop = home town population, VisiPop = vistor’s town population, HPoint = points per game, home team, before the game VPoint = visitor’s
points per game, before the game, HomeG = number of games, home team, before the game, VisiG = number of games, visitor, before the game , Last3H = points from 3 last games, home team, Last3V = points
from 3 last games, visitor. The number of observations = 392.
variable
mean
std
ATT
ATT
5014,4
1712,8
Price
25,4
4,3
Dist
245,8
154,2
Temp
4,5
5,6
Unempl
8,3
2,0
HomePop
185320
168248
VisiPop
185320
168248
HPoint
1,45
0,51
VPoint
1,48
0,53
HomeG
27,5
16,2
VisiG
27,5
16,2
Last3H
4,24
2,51
Last3V
4,42
2,52
Price
1
Dist
Temp
Unempl
HomePop
VisiPop
HPoint
VPoint
HomeG
VisiG
Last3H
Last3V
0,619
-0,074
-0,013
-0,601
0,730
0,138
0,313
0,022
0,051
0,066
0,292
0,034
1
-0,212
0,045
-0,681
0,833
0,109
0,125
-0,001
0,022
0,036
0,200
0,063
1
-0,081
0,204
-0,155
-0,154
0,065
0,071
0,027
0,023
0,012
0,054
1
-0,175
0,047
-0,001
-0,113
-0,107
-0,714
-0,714
-0,089
-0,117
1
-0,788
0,015
-0,164
0,040
0,117
0,103
-0,190
0,036
1
-0,012
0,192
-0,010
0,009
0,024
0,227
0,012
1
0,009
0,172
0,003
-0,009
0,026
0,224
1
0,224
0,120
0,128
0,644
0,086
1
0,119
0,113
0,151
0,643
1
0,998
0,106
0,131
1
0,109
0,125
1
0,099
1
82
Table 18: (Table 2.4a) Some estimation results
Model 1
Model 2
Model 3
variable
coefficient
std
t-value
P [|T|>t]
coefficient
std
t-value
P [|T|>t]
coefficient
std
t-value
P [|T|>t]
constant
4,785
0,403
11,87
0,000
4,596
0,406
11,31
0,000
4,876
0,418
11,68
0,000
LogPrice
-0,272
0,110
-2,28
0,013
-0,265
0,111
-2,39
0,017
-0,324
0,112
-2,91
0,004
LogHPop
0,355
0,028
12,84
0,000
0,360
0,028
12,89
0,000
0,365
0,028
12,89
0,000
LogVPop
0,047
0,012
3,93
0,000
0,048
0,013
3,96
0,000
0,044
0,012
3,50
0,000
LogDist
-0,037
0,009
-4,17
0,000
-0,038
0,009
-4,22
0,000
-0,036
0,009
-3,98
0,000
LogHGame
-0,032
0,013
-2,44
0,015
-0,032
0,013
-2,41
0,016
-0,026
0,011
-2,44
0,015
LogHPoin
0,083
0,017
4,79
0,000
0,0846
0,017
4,87
0,000
LogVPoin
-0,058
0,016
-3,57
0,000
-0,058
0,016
-3,52
0,000
LogHLast
0,014
0,006
2,47
0,014
LogVLast
-0,003
0,005
-0,62
0,537
LogUnempl
0,083
0,060
1,39
0,166
0,087
0,061
1,44
0,151
0,071
0,062
1,15
0,253
Temp
-0,005
0,002
-2,00
0,046
-0,005
0,002
-2,02
0,044
-0,004
0,002
-1,83
0,068
TU
-0,111
0,023
-4,88
0,000
-0,106
0,023
-4,58
0,000
TH
-0,122
0,022
-5,50
0,000
-0,114
0,023
-5,04
0,000
χ2 = 436,08
DW = 1,96
SA
N = 392
0,116
R2 = 0,677
F = 75,34
χ2 = 453,62
DW = 2,00
R2 = 0,672
0,020
F = 80,97
83
5,75
0,000
χ2 = 446,68
DW = 2,03
R2 = 0,661
F = 70,53
Table 19: (Table 2.4b) Some estimation results
Model 4
Model 5
Model 6
variable
coefficient
std
t-value
P [|T|>t]
coefficient
std
t-value
P [|T|>t]
coefficient
std
t-value
P [|T|>t]
constant
4,706
0,420
11,20
0,000
5,210
0,263
19,78
0,000
5,039
0,265
19,02
0,000
LogPrice
-0,318
0,112
-2,83
0,005
-0,265
0,110
-2,42
0,016
-0,258
0,111
-2,33
0,020
LogHPop
0,369
0,029
12,92
0,000
0,332
0,022
15,01
0,000
0,336
0,022
15,04
0,000
LogVPop
0,044
0,013
3,54
0,000
0,047
0,012
3,93
0,000
0,048
0,012
3,96
0,000
LogDist
-0,037
0,009
-4,02
0,000
-0,036
0,009
-4,06
0,000
-0,037
0,009
-4,11
0,000
LogHGame
-0,026
0,011
-2,39
0,017
-0,033
0,013
-2,51
0,012
-0,033
0,013
-2,49
0,013
LogHPoin
0,083
0,017
4,80
0,000
0,085
0,017
4,87
0,000
LogVPoin
-0,058
0,016
-3,53
0,000
-0,058
0,016
-3,49
0,001
-0,005
0,002
-2,31
0,021
-0,005
0,002
-2,33
0,020
TU
-0,110
0,023
-4,85
0,000
TH
-0,122
0,022
-5,50
0,000
0,115
0,020
5,71
0,000
LogHLast
0,015
0,006
2,70
0,007
LogVLast
-0,003
0,005
-0,60
0,549
LogUnempl
0,073
0,062
1,17
0,243
Temp
-0,004
0,002
-1,85
0,065
SA
N = 392
0,109
0,021
R2 = 0, 657 F = 75,96
5,29
χ2 = 429,83
0,000
DW = 1,98
R2 = 0, 676 F = 82,48
84
χ2 = 451,64
DW = 2,00
R2 = 0,671 F = 89,49
χ2 = 444,56
DW = 2,03
Table 20: (Table 2.4c) Some estimation results
Model 7
Model 8
variable
coefficient
std
t-value
P [|T|>t]
coefficient
std
t-value
P [|T|>t]
constant
5,240
0,271
19,28
0,000
5,080
0,273
18,62
0,000
LogPrice
-0,318
0,111
-2,85
0,004
-0,311
0,112
-2,78
0,006
LogHPop
0,345
0,022
15,44
0,000
0,348
0,023
15,46
0,000
LogVPop
0,044
0,012
3,50
0,000
0,044
0,013
3,54
0,001
LogDist
-0,036
0,009
-3,90
0,000
-0,036
0,009
-3,93
0,000
LogHGame
-0,027
0,011
-2,54
0,009
-0,027
0,011
-2,50
0,013
LogHLast
0,014
0,005
2,60
0,010
0,016
0,006
2,83
0,005
LogVLast
-0,003
0,005
-0,57
0,570
-0,003
0,005
-0,550
0,583
Temp
-0,005
0,002
-2,12
0,035
-0,005
0,002
-2,14
0,033
TU
-0,106
0,023
-4,55
0,000
TH
-0,114
0,023
-5,04
0,000
0,108
0,021
5,27
0,000
LogHPoin
LogVPoin
LogUnempl
SA
N = 392
R2 = 0,661 F = 77,39
χ2 = 434,73
DW = 1,96
85
R2 = 0,657
F = 84,17 χ2 = 428,43 DW = 1,98
Figure 2: (Figure 2.1) Actual attendance for HIFK (Series1) and Model 6 (Series2)
9000
8000
7000
6000
5000
4000
Series1
3000
Series2
2000
1000
0
Figure 3: (Figure 2.2) Actual attendance for HIFK (ADM), Model 6 and simulations 1 and 2
10000
9000
8000
7000
6000
5000
ADM
4000
Model6
3000
SIM1
2000
SIM2
1000
0
86
3 Fan loyalty in Finnish Ice Hockey
3.1 Introduction
Sport has become more professional over the years. Sport managers view their teams, leagues as
brands to be managed. A product or service is considered as a brand if the name, logo, sign or slogan
increases the value of that product or service. The psychological aspect in the consumer’s mind, the
brand image consists of all information and associations with a product or service. The quality of brand
associations is determined by the favourability, uniqueness and strength.
High levels of brand
awareness and a positive brand image should increase among others greater consumer loyalty (Keller
1993).
Brand knowledge has two dimensions: brand awareness and brand image. Furthermore brand
awareness can be classified into active (brand recall) and passive (brand recognition) awareness. (Keller
1993). Commitment is the emotional or psychological attachment to a brand. A sports consumer is
committed if he/she feels a deep and persistent attachment to his/her favourite team and resists
conflicting information or experience and the future welfare of the team is important (Bauer, Sauer and
Exler 2005). Committed fans are loyal (Bauer, Sauer and Schmitt 2005).
This study studies fan loyalty in Finnish men’s ice hockey during the regular season 2008-2009 using
stochastic frontier analysis. Most teams in the highest ice hockey league are local monopolies but there
are two teams in Helsinki which might be substitutes since the distance between their stadiums is less
than 3 km. Anecdotal evidence suggests that the fans of these teams come from different districts
partly due to different public transport to their stadiums. One is located near to a railway station that
can be reached easily from the eastern parts of Helsinki while the other is easily reached through bus
services from the western parts of Helsinki. Moreover, there is one team in the neighbouring city,
Espoo, whose stadium is at a distance of about 13 km from the previous. In addition there are two
teams in Tampere with a shared stadium. However, some teams are local monopolies, and some teams
87
meet higher competition. Therefore brand loyalty or fan loyalty might differ according to competitive
position and the aim of this research is to study the relationship between fan loyalty and competitive
position of teams. Competitive position is defined here as the geographical distance between teams’
stadiums. Consumers or spectators can be either loyal to ice hockey or to a particular team. Those
living in Helsinki or Tampere region where there are at least two teams and are only loyal to (highest
league) ice hockey have the possibility not to remain loyal to a particular team.
Teams in the highest leagues generally get revenues not just gate revenues but also from merchandise
sales, sales of broadcast rights and commercial sponsorships. Loyal fans use fan shits and scarves.
Broadcast rights are usually sold by the league association and the broadcast revenue is shared among
the teams. Sponsorship revenue is associated with larger attendance which in turn is associated with
larger market base, i.e. larger home town population. This in enables increased budgets to spend on
playing and coaching that facilitates improved team performance (Buraimo, Forrest and Simmons
2007). However, Bauer, Sauer and Exler (2005) show that among German soccer fans the success of
the team is not the central driving force of a fan’s utility opinion.
Fan loyalty can be measured as permanency of successive years’ attendance (Winfree, McCluskey,
Mitterhammer and Fort 2004), mean match tickets per market size (Brandes, Franck and Theiler 2010)
or as an efficiency score in stochastic frontier analysis (Depken 2000, 2001). Also direct surveys to get
self-revealed levels of fan loyalty have been used. Wakefield and Sloan (1995) show that fan loyalty
increases home game attendance. The novelty of this study is that a panel data of Finnish men’s
highest league ice hockey attendance during the regular season 2008-2009 will be analysed using
stochastic frontier analysis. There were 406 games played during that season beginning in September
2008 and ending in March 2009. The Finnish men’s highest league, labelled “SM-Liiga” was a closed
league with 14 teams, so the last did not drop. The best 10 teams continued in play-off games and the
champion (JYP) was known in Mid April. During the regular season all teams had 29 home games with
total 1997019 spectators, i.e. on average 4919 per game ranging from 8456 (Jokerit from Helsinki) to
3437 (SaiPa from Lappeenranta).
88
3.2 Fan loyalty or brand loyalty – stochastic frontier analysis Following Depken (2000, 2001), a panel of Finnish men’s highest league ice hockey for the regular
season 2008 – 2009 is used in the estimation. A conventional Cobb-Douglas form to explain
attendance is used:
𝛽𝑗
1
(1) 𝐴𝑇𝑇𝑖 = 𝐶[∏𝑘𝑗=1 𝑋𝑖𝑗 ]exp(𝜀𝑖 )[𝜆 ] TSV ∗
𝑖
As (1) - in which TSV* denote for time specific variables, like weekday dummy and climate conditions
(temperature) - is transformed by taking logs of both sides, we get
(2) ln 𝐴𝑇𝑇𝑖 = 𝑙𝑛𝐶 + ∑𝑘𝑗=1 𝛽𝑗 𝑙𝑛𝑋𝑖𝑗 + ∑𝑘𝑗=1 𝛾𝑗 𝑇𝑆𝑉𝑖𝑗 + 𝜀𝑖∗
In which C a constant term identical to all teams, the βj and γj are parameters to be estimated, and ε*I =
εi – ln(λi) is the error term.
The explanatory variables Xi used in this study are conventional and consistent with other studies (for a
review, see Borland and MacDonald 2003 or Simmons 2006): home town population, visitor’s town
population, distance between teams’ home stadiums, the winning percentage of the home team and of
the visitor team, the game round, the unemployment rate. The time specific variables are weekday
dummies and the outside temperature measured in Celsius. Due to the nature of these variables, they
are not transformed by taking logs. The error term has two components ε*I = εi – ln(λi) in which εi is
the random error term that captures noise as well as team and time-specific unobserved heterogeneity
(Greene 2005).The inefficiency term λi in the stochastic frontier is time invariant and team specific.
Two possible distributions have frequently been used (see Greene 2008, 538): the absolute value of a
normally distributed variable (“half-normal*) and an exponentially distributed variable. The
distributions are asymmetric. However, the problem with stochastic frontier analysis is that the error
89
term distribution assumption has its effects on the size of the fan loyalty. If the team specific term is
fixed, one of the teams is considered strong (as 100 % strong) in the sense of fan loyalty. Fans are
committed. The fan loyalty of the other teams is relative to the best-practise team(s) in the sample (cf.
Last and Wetzel 2010). The fan loyalty estimates are sensitive to sample selection criteria and outliers.
The fixed effects approach is distribution free and it allows for correlation between effects and timespecific regressors. The random effects approach maintains the original distributional assumption. With
a half-normal model the least squares is unbiased and consistent and efficient among linear unbiased
estimators while the maximum likelihood estimator is not linear but it is more efficient (Greene 2008,
539). The shortcoming with a random effects model is that it has stronger distribution assumptions that
the effects are time invariant and uncorrelated with the explanatory variables in the model (Greene
2005). Furthermore, these models tend to overestimate the disloyalty. The assumption of time
invariance is more problematic if the time series is long, however, in this study the sample consists of
game attendance during the regular season 2008 – 2009, i.e. from September 2008 to March 2009.
As the disloyalty measure approaches 0, fans are more loyal, and the other explanatory variables,
especially winning percentage and other time specific variables matter less. Teams with low levels of
fan loyalty lose more spectators as the quality of games goes down. The climate conditions, i.e. the
temperature and a worsening winning record are more relevant and the less enthusiast spectators do
not attend. There is a wide sports economics literature that use frontier models but most of these focus
on cost efficiency (for a good survey, see Barros and Garcia-del-Barrio 2008) or technical efficiency
(Kahane 2005). The output typically is related to team performance, like winning percentage and the
inputs are cost related, like wages or the number of coaches. Most of these study professional baseball
or football (soccer) in USA or UK.
90
There are few studies with ice hockey data and even less using frontier analysis (Kahahe 2005). The
pioneering attendance study of Noll (1974) has been very influential. Jones and Ferguson (1988)
showed with NHL data that home town population, winning percentage and team related attributes like
way to play, the number of stars in the team are important to explain attendance. The effect of
population incomes was negative. However, Cocco and Jones (1997) show that the effect of incomes
was positive as expected. Using frontier analysis Kahane (2005) shows that teams owned by
corporations are more efficient than team owned by individuals.
3.3 Stylized facts: Finnish Ice Hockey
There were 14 teams playing in the highest men’s ice hockey league in Finland. Three of the teams
were located in the metropolitan area of Helsinki, two from Tampere and the rest are local monopolies.
91
Table 21: (Table 3.1) Average attendance statistics
Team
Home game average
Variation of home game
Home town
Distance to
attendance, regular season
attendance: min – max (std),
population 1st
the nearest
2008-2009, n = 29
coefficient of variation
September
team, km
2008
Blues
HIFK
HPK
Ilves
Jokerit
JYP
KalPa
Kärpät
Lukko
Pelicans
SaiPa
Tappara
TPS
Ässät
4651
6324
3780
5672
8463
4016
4599
5741
3708
4081
3437
5138
5139
4110
3922 – 5722 (476.2)
240275
0.102
(Espoo)
5005 – 8200 (933.9)
571887
0.148
(Helsinki)
3205 – 5360 (525.9)
65941
0.139
(Hämeenlinna)
4197 – 7800 (936.9)
208657
0.165
(Tampere)
6283 – 13464 (1672.5)
571887
0.198
(Helsinki)
3531 – 4180 (192.3)
127186
0.048
(Jyväskylä)
3703 – 5225 (429.1)
91601
0.093
(Kuopio)
4909 – 6614 (472.4)
132726
0.082
(Oulu)
3019 – 5400 (567.8)
39757
0.153
(Rauma)
3422 – 4910 (470.8)
99816
0.115
(Lahti)
2843 – 4847 (457.0)
70267
0.133
(Lappeenranta)
3718 – 7800 (998.4)
208657
0.194
(Tampere)
3831 – 6813 (901.1)
175279
0.175
(Turku)
3001 – 6472 (723.0)
76355
0.176
(Pori)
12.6
2.6
75.5
0
2.6
147.2
148.9
289.2
50.0
75.5
152.5
0
87.4
50.0
Note: Ilves and Tappara from Tampere are using a common stadium, whereas HIFK and Jokerit from Helsinki
have separate stadiums. Distance is measured from the team’s stadium to the nearest.
92
The Coefficient of Variation of the attendance variable and the distance to the next nearest team are
negatively correlated (ρ = -0.669) which may indicate that loyalty is positively associated with the
competitive position, i.e. local monopolies have more loyal fans. However, since team performance
among others has been shown to have an impact on attendance, a stochastic frontier analysis is needed
to obtain more certain view about this proposition. Most of the games have been played on Tuesdays
(28.6 %), on Thursdays (26.1%) and on Saturdays (33%), some games on Fridays (32 games, i.e. 7,9%)
and the rest on Mondays (9), Wednesdays (5) and Sundays (4). The correlations of weekday dummies
with the other variables are negligible except that during Saturdays the attendance is bigger. The teams
from Helsinki (HIFK and Jokerit) have had the biggest attendance but it has been declining during the
last years (see Appendix 1). Only two teams were able to increase average attendance during the regular
season 2008 – 2009: HPK by 15 % and KalPa by 36 %. The spectator number of TPS (-14 %) and
Tappara (-10 %) decreased most.
93
Table 22: (Table 3.2) Variables, means, standard deviations and correlation matrix
Variable
Mean
Std
Att
Cap
Price
Dist
Temp
Unempl HomePop
VisPop
PPGH
PPGV
FGH
FGV
HomeG
Att
4918.6
1500.9
1
0.71
0.29
-0.09
-0.06
-0.47
0.75
0.09
0.08
-0.01
0.10
-0.08
0.06
Cap
7066.6
2595.0
1
0.23
-0.08
0.07
-0.65
0.70
-0.04
-0.04
0.00
0.02
-0.05
-0.00
Price
28.2
4.1
1
-0.29
0.11
-0.52
0.52
0.11
0.14
-0.05
0.02
-0.05
-0.01
Dist
253.2
155.6
1
-0.08
0.21
-0.14
-0.13
0.09
0.10
0.05
0.09
0.00
Temp
3.0
6.3
1
-0.44
0.07
-0.02
-0.09
-0.08
-0.10
-0.07
-0.83
Unempl
8.4
2.2
1
-0.71
0.05
-0.05
0.03
-0.00
0.07
0.41
HomePop
192187
166893
1
-0.07
0.12
-0.00
0.07
-0.05
-0.02
VisPop
192198
166861
1
-0.02
0.16
-0.03
0.06
0.03
PPGH
1.47
0.44
1
0.14
0.52
0.10
0.13
PPGV
1.48
0.43
1
0.07
0.44
0.07
FGH
4.39
2.44
1
0.04
0.14
FGV
4.31
2.50
1
0.08
HomeG
29.5
16.8
1
Note : Att = home game attendance, Cap = Capacity of Stadium, Dist = distance between home team’s stadium and visitor’s stadium (km), Unempl = province monthly
unemployment rate, HomePop = home town population in the beginning of the month, VisPop = visitor’s town population in the beginning of the month, PPGH =
points per game, home team, PPGV = points per game, visitor, FGH = form guide (3 last games), home team, FGV = form guide, visitor, HomeG = leg. Price is the
ticket price of the best plain seats, not box seats, it is overrated since most of the seats are cheaper. n = 406
94
The home town population is positively correlated with the attendance, the capacity of the stadium and
the ticket price and negatively with the province unemployment rate. These variables are also associated
in other respects. The key variable or the cause is the town population. The two alternative team
performance variables (for the home team: points per game, PPGH and the form guide, FGH, or the
corresponding variables for the visitor: PPGV and FGV) are correlated. The temperature is negatively
correlated with the leg, i.e. in September when the season begins the temperature is higher than in
March when the regular season ends. The unemployment rate was increasing during the season and the
variables are positively correlated.
95
3.4 Estimation and results
The model is estimated first with OLS because of comparability and then with MLE assuming that the
inefficiency term is distributed half-normal.
Table 23: (Table 3.3) OLS results, dependent variable is log(Attendance), n = 406
Variable
OLS
OLS
OLS
OLS
OLS
OLS
OLS
OLS
LnCap
0.360 ***
0.270***
0.345***
0.264***
0.349***
0.263***
0.330***
0.254***
(0.035)
(0.032)
(0,036)
(0.033)
(0.035)
(0.032)
(0.035)
(0.033)
-0.018
-0.173**
0.003
-0.138**
-0.009
-0.162**
0.008
-0.129**
(0.062)
(0.058)
(0.064)
(0.059)
(0.063)
(0.058)
(0.064)
(0.059)
-
-0.032***
-0.031***
-0.031***
-0.033***
-
-0.032***
-0.033***
0.032***
(0.006)
(0.006)
(0.006)
(0.006)
0.034***
(0.006)
(0.007)
LnPrice
LnDist
(0.006)
Temp
(0.006)
-0.003
-0.006**
-0.005**
-0.007***
-0.003
-
-0.004*
-0.007***
(0.002)
(0,002)
(0.002)
(0.002)
(0.002)
0.006***
(0.002)
(0.002)
(0.002)
LnUnempl
LnHomePop
LnVisPop
LnPPGH
LnPPGV
0.274***
0.247***
0.273***
0.243***
(0.049)
(0.050)
(0.049)
(0.050)
0.233***
0.201***
0.234***
0.205***
0.236***
0.202***
0.238***
0.207***
(0.016)
(0.010)
(0.016)
(0.016)
(0.016)
(0.015)
(0.016)
(0.015)
0.032***
0.037***
0.034***
0.038***
0.030**
0.034***
0.031**
0.036***
(0.009)
(0,010)
(0.010)
(0.010)
(0.094)
(0.010)
(0.010)
(0.010)
0.027**
0.018
0.034**
0.025*
(0.012)
(0,012)
(0.012)
(0.012)
-0.040**
-0.042**
-0.042**
-0.043**
(0,013)
(0,013)
(0.013)
(0.013)
0.004
0.002
0.005
0.004
(0.004)
(0.004)
(0.004)
(0.004)
-0.008*
-0.008*
-0.008*
-0.008*
(0.004)
(0.004)
(0.004)
(0.004)
LnFGH
LnFGV
LnHomeG
Tuesday
-0.031
-0.018
-0.042**
-0.028*
-0.028*
-0.022
-0.034**
-0.028*
(0.016)
(0,017)
(0.016)
(0.017)
(0.014)
(0.014)
(0.014)
(0.014)
-
-0.111***
-0.117***
-
0.120***
(0.017)
(0.017)
0.109***
(0.017)
Thursday
-
(0.017)
-0.116***
-0.118***
96
-
0.121***
(0,018)
(0.017)
(0.017)
(0.018)
Saturday
Constant
0.111***
0.113***
0.111***
0.109***
0.106***
(0,015)
(0.016)
(0.015)
(0.016)
1.97***
4,15***
1.99***
3.96***
2.02***
4.21***
2.08***
4.023***
(0.482)
(0.291)
(0.490)
(0.293)
(0.483)
(0.293)
(0.492)
(0.294)
R2
0.747
0.728
0.737
0.721
0.743
0.724
0.733
0.717
LogL (Χ2)
238.97
223.63
230.68
218.52
236.17
220.74
227.37
215.52
(570.14)
(539.46)
(553.54)
(529.23)
(564.47)
(533.68)
(546.94)
(523.23)
All the variables, except the day of the week and the temperature, are logarithmic. ***, **, * denote 1%,5%,10%
significance
97
The OLS estimated price elasticity is negative only if the unemployment variable is not
enclosed. These variables are highly negatively correlated and therefore the OLS estimates for
the price variable without the unemployment variable are more plausible. The home town
population coefficient is positive as expected and roughly 5 - 6 times higher than the visitor’s
town population coefficient. The distance between the towns is significantly negative. The
temperature matters even though the coefficient is tiny. The team performance measured from
the beginning of the season is more suitable than the form guide which measures the
performance of the last three games. Spectators can easily observe the points per game variable
through tracking statistics that are shown in newspapers. During the season the spectator
number diminishes since the leg variable (HomeG) is negative. The difference between Tuesday
and Thursday games is significant with the Saturday games.
Table 24: (Table 3.4) Estimation results, dependent variable is log(Attendance), n = 406
Variable
LnCap
LnPrice
LnDist
Temp
LnHomePop
LnVisPop
LnPPGH
LnPPGV
LnHomeG
OLS
SF,MLE,
SF, MLE,
fixed
random
0.264***
0.264***
0.279
(0.033)
(0.032)
(2.92)
-0.138**
-0.138**
0.325
(0.059)
(0.058)
(2.63)
-0.031***
-0.031***
-0.041
(0.006)
(0.006)
(0.032)
-0.007***
-0.007***
-0.003
(0.002)
(0.002)
(0.024)
0.205***
0.205***
0.143
(0.016)
(0.015)
(1.004)
0.038***
0.038***
0.018
(0.010)
(0.010)
(0.019)
0.025*
0.025*
-0.025
(0.012)
(0.012)
(0.057)
-0.043**
-0.043**
-0.013
(0.013)
(0.013)
(0.073)
-0.028*
-0.028*
0.003
(0.017)
(0.016)
(0.280)
98
Saturday
Constant
0.111***
0.111***
0.107**
(0.016)
(0.015)
(0.048)
3.96***
3.96
3.47
(0.293)
(4.63)
(21.2)
0.141
0.310
218.52
293.01
σ
R2
0.721
LogL
218.52
99
The stochastic frontier model (Table 4) without panel data assumption (fixed effects) yields
almost totally similar results as OLS. The similarity of the OLS and MLE estimates is not
surprising since both methods generate consistent estimates. With fixed effects model the
inefficiency of the team is relative to the best. With random effects model the inefficiency score
is E[u|e]. The inefficiency scores of the teams are listed in table 5 below.
Table 25: (Table 2.5) Inefficiency scores of teams
Team
Inefficiency, Fixed effects
Inefficiency, random
model
effects model
Blues
0,295
0.392
HIFK
0.172
0.306
HPK
0.199
0.294
Ilves
0.156
0.203
Jokerit
0.069
0.134
JYP
0.174
0.286
KalPa
0.062
0.119
Kärpät
0
0.015
Lukko
0.089
0.286
Pelicans
0.161
0.291
SaiPa
0.283
0.334
Tappara
0.209
0.318
TPS
0.278
0.376
Ässät
0.205
0.308
The inefficiency scores are positively correlated (ρ = 0.907), even the random effects model is
unsatisfactory. These inefficiency scores and the coefficient of variation presented in table 1
measuring the variation of teams’ attendance figures are associated with the distance measure
also presented in table 1.
100
Table 26: (Table 2.6) Correlation matrix of selected variables
Inefficiency. Fixed Inefficiency.
effects model
random
coefficient
of distance
effects variation
model
Inefficiency. Fixed 1
0.907
0.178
-0.371
1
0.213
-0.509
1
-0.669
effects model
Inefficiency.
random
effects
model
coefficient
of
variation
distance
1
The correlation coefficients reveal that fan loyalty measured as inefficiency scores or
attendance’s coefficient of variation is associated with the distance, i.e. competitive position of
the team: the bigger the distance, the bigger fan loyalty.
3.5 Conclusions
The purpose of this paper was to consider the relationship between fan loyalty and the
competitive position of the men’s highest league ice hockey teams in Finland using stochastic
frontier approach. Fan loyalty was measured conversely as inefficiency score of the stochastic
frontier model explaining attendance of games. This approach is useful because it reveals that
there are differences in fan loyalty and its relation with the competitive position is plausible.
101
The random effects model is unsatisfactory since the coefficients of the variables are not
significant and therefore inefficient. The fixed effects model is more plausible since it captures
both the relevant explanatory variables for attendance and the inefficiency scores.
The
estimated coefficients of the explanatory variables are in line with those reported in the
previous literature. Since the team loyalty scores seem to be correlated with the distance
measure, the fans are more committed to ice hockey and not to a particular team. The brand of
ice hockey is stronger than the brand of an individual team. This is consistent with the results of
Bauer, Sauer and Exler (2005) who show that non-product-related attributes (e.g. logo and club
colours, club culture and tradition, stadium and regional provenance) are more important for
fan loyalty than product-related attributes like players, success, general team performance.
The coefficient of variation of the teams’ attendance number and the distance measure are
more negatively correlated than the inefficiency scores obtained through the stochastic frontier
models but still the used approach is suitable to explain fan loyalty. However, a larger data is
needed to confirm the validity of the results.
References
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football Premier League with a random frontier model. Economic Modelling. 25. 994-1002
102
Bauer. Hans H.. Nicola E. Sauer & Philipp Schmitt (2005): Customer-based brand equity in the team
sport industry. Operationalization and impact on the economic success of sport teams. European Journal
of Marketing. vol. 39. No. 5/6. 496-513
Bauer. Hans H.. Nicola E. Sauer & Stefanie Exler (2005): The loyalty of German soccer fans: does a
team’s brand image matter? International Journal of Sports Marketing & Sponsorship. vol 7. issue 1. 14-22
Borland. Jeffery & Robert Macdonald (2003): Demand for sport. Oxford Review of Economic Policy.
19. 478-502
Brandes. Leif. Egon Franck & Philipp Theiler (2010): The Group Size and Loyalty of Football Fans: A
Two-Stage Estimation Procedure to Compare Customer Potential Across Teams. University of Zurich.
Institute for Strategy and Business Economics (ISU). No 126
Buraimo. Babatunde. David Forrest & Robert Simmons (2007): Freedom of Entry. Market Size. and
Competitive Outcome: Evidence form English Soccer. Southern Economic Journal vol. 74. 204-213
Cocco. Angelo & J.C.H. Jones (1997): On going south: the economics of survival and relocation of
small market NHL franchises in Canada. Applied Economics. 29. 1537-1552
Depken. Craig A. II (2000): Fan Loyalty and Stadium Funding in Professional Basketball. Journal of
Sports Economics. vol. 1. 124-138
Depken. Craig A. II (2001): Research Notes: Fan Loyalty in Professional Sports: An Extention of the
National Football League. Journal of Sports Economics. vol. 2. 275-284
Greene. William (2005): Fixed and Random Effects in Stochastic Frontier Models. Journal of Productivity
Analysis. 23. 7-32
Greene. William H. (2008): Econometric analysis. 6th Edition. Pearson International Edition
Jones. J.C.H. and D.G. Ferguson (1988): Location and survical in the National Hockey League. The
Journal of Industrial Economics. 36. 443-457
Kahane. Leo H. (2005): Production Efficiency and Discriminatory Hiring Practices in the National
Hockey League: A Stochastic Frontier Approach. Review of Industrial Organization. 27. 47-71
Keller. Kevin Lane (1993): Conceptualizing. Measuring and Managing Customer-Based Brand Equity.
Journal of Marketing. 57. 1-22
Last. Anne-Kathrin & Heike Wetzel (2010): The efficiency of German public theaters: a stochastic
frontier analysis approach. Journal of Cultural Economics. 34. 89-110
Simmons. Rob (2006): The demand for spectator sports. In Handbook on the Economics of Sport. edited by
Wladimir Andreff & Stefan Szymanski. 77-89. Edward Elgar
Wakefield. Kirk L. & Hugh J. Sloan (1995): The Effects of Team Loyalty and Selected Stadium Factors
on Spectator Attendance. Journal of Sport Management. vol. 9. 153-172
103
Winfree. Jason A.. Jill J. McCluskey. Ron C. Mittelhammer & Rodney Fort (2004): Location and
attendance in major league baseball. Applied Economics 36.2117-2124.
Source of the statistical data: official statistics (www.stat.fi) or The Finnish National Hockey League
(www.sm-liiga.fi). Estimation made with NLogit 4.0 (www.limdep.com)
104
Table 27: (Table 2.7) Average attendance, home games, regular seasons
Jokerit
HIFK
TPS
Ilves
Tappara
Kärpät
Blues
Ässät
JYP
SaiPa
HPK
Pelicans
KalPa
Lukko
Total
2005-2006
8850
6821
6444
5867
5866
5791
5111
4183
3808
3724
3555
3455
3302
3183
69960
2006-2007
8928
6629
6441
5660
5619
5697
4763
4391
3351
3439
3556
4042
3120
3769
69405
2007-2008
8591
6573
5979
5914
5712
6055
4838
4235
4055
3558
3282
4253
3388
3733
70166
2008-2009
8456
6324
5139
5672
5138
5741
4651
4120
4016
3437
3780
4081
4599
3708
68862
2005-2006
12.7 %
9.7 %
9.2 %
8.4 %
8.4 %
8.3 %
7.3 %
6.0 %
5.4 %
5.3 %
5.1 %
4.9 %
4.7 %
4.5 %
2006-2007
12.9 %
9.6 %
9.3 %
8.2 %
8.1 %
8.2 %
6.9 %
6.3 %
4.8 %
5.0 %
5.1 %
5.8 %
4.5 %
5.4 %
2007-2008
12.2 %
9.4 %
8.5 %
8.4 %
8.1 %
8.6 %
6.9 %
6.0 %
5.8 %
5.1 %
4.7 %
6.1 %
4.8 %
5.3 %
2008-2009
12.3 %
9.2 %
7.5 %
8.2 %
7.5 %
8.3 %
6.8 %
6.0 %
5.8 %
5.0 %
5.5 %
5.9 %
6.7 %
5.4 %
0.9 %
-2.8 %
0.0 %
-3.5 %
-4.2 %
-1.6 %
-6.8 %
5.0 %
-12.0 %
-7.7 %
0.0 %
17.0 %
-5.5 %
18.4 %
-0.8 %
-3.8 %
-0.8 %
-7.2 %
4.5 %
1.7 %
6.3 %
1.6 %
-3.6 %
21.0 %
3.5 %
-7.7 %
5.2 %
8.6 %
-1.0 %
1.1 %
-1.6 %
-3.8 %
-14.0 %
-4.1 %
-10.0 %
-5.2 %
-3.9 %
-2.7 %
-1.0 %
-3.4 %
15.2 %
-4.0 %
35.7 %
-0.7 %
-1.9 %
1.
3.
2.
share. %
change%
05/06 ->
06/07
06/07 ->
07/08
07/08 ->
08/09
Medalists
2000-2001
2001-2002
1.
2.
3.
105
2002-2003
1.
2003-2004
2004-2005
3.
2.
3.
1.
2.
1.
2005-2006
2006-2005
2.
3.
3.
2.
2007-2008
2008-2009
2.
1.
1.
3.
1.
3.
2.
2.
1.
Appendix 1: Source: www.sm-liiga.fi
106
3.
Fan loyalty in Finnish Ice Hockey
Seppo Suominen
Haaga-Helia University of Applied Sciences
Malmi campus, Hietakummuntie 1 A
FIN-00700 Helsinki, Finland
e-mail: seppo.suominen@haaga-helia.fi
The study studies fan loyalty in Finnish men’s ice hockey during the regular season 2008-2009 using
stochastic frontier analysis. Fan loyalty is measured as inefficiency score of the stochastic frontier model
explaining games’ attendance. There were 14 teams playing in the highest men’s ice hockey league in
Finland with 406 games, i.e. all teams had 29 games at home stadium and 29 games as visitor. The error
term of the stochastic frontier model has two components and the other of these can be considered as
the inefficiency term or inversely as the fan loyalty term. The fan loyalty measure is reasonable and
negatively correlated with the distance between home stadium and the nearest stadium of the other
team. The distance is a proxy for local provenance or monopoly position. The fixed effects model is
plausible and the fan loyalty terms are reasonable while the random effects model is not efficient.
107
4. Spectators of performing arts – who is sitting in the auditorium?
Seppo Suominen
Haaga-Helia University of Applied Sciences
Malmi Campus, Hietakummuntie 1 A, FIN 00700 Helsinki, Finland
4.1 Introduction
Approximately 5 or 6 per cent of Finns go to see performing arts or to an art exhibition several times
per month and every sixth do not go at all. Most of Finns occasionally go10. The purpose of this study
is to find out in more detail the differences in visitor density. What kind of person is a heavy user of
performing arts (art exhibition, opera and theatrical performances) and correspondingly what kind of
people do not go at all? Are there any differences across areas or provinces when the effect of a
person’s education is taken into account?
Fairly many good reports have been drawn up on the audiences of cultural events, but a great majority
of the results have been presented as descriptive statistics and virtually there are no studies that have
used multivariate analysis. Leisure activities: Culture and sport in 1991 and 1999 (Statistics Finland
2001) clarifies among others what the activity of more than 10-year-old Finns is to go to the theatre,
10
According to a survey on cultural participation that was conducted in 1999 (“Kulttuuripuntari”), a typical opera visitor is
a 50 to 64 year- old female with university education living in Uusimaa region (in Southern Finland including the capital,
Helsinki). The opposite person is a young male with low education living in a sparsely populated area in Northern Finland.
Correspondingly an art exhibition visitor is typically a 50-64 year- old female living in the city centre and a non-visitor is
young male with vocational education and living in a sparsely populated area or an adult man with low education. A typical
theatre visitor is more than 50 year- old working female with university education, living in a city in Southern or Western
Finland, while the non-visitors are on average unemployed 20-24 year- old men without vocational education living in
Northern Finland. Kulttuuripuntari 1999, Opetusministeriön Kulttuuripolitiikan osaston julkaisusarja 9:1999, readable:
http://www.minedu.fi/OPM/Julkaisut/1999/kulttuuripuntari_1999__raportti_kulttuuripalvelujen_kaytosta_ja?lang=fi,
read 31.5.2010
108
dance performances, concerts and opera and as comparison go to sport events during the years in
question. The results (table 1) are equal to the 1999 survey (Kulttuuripuntari): women go more often
than men, highly educated are more active and the differences among provinces are substantial.
(table 1 about here)
Based on the statistics above, the theatre visitor density did not change essentially during the 1990’s
except that the youngest and citizens living in eastern Finland may have increased the visitor density.
The visitor density of the dance performances was roughly half of the theatres and no substantial
changes took place during that decade. Opera and concert performances increased especially among the
25-44 and 45-64 year-old cohort and in the Uusimaa region (both the Helsinki metropolitan area and
eastern Uusimaa province). In comparison, the spectator frequency of the sport events and
competitions decreased. The majority of the sport spectators are men. The audience survey of two
regional and occasional operas at Pori and Tampere (Kivekäs 1991) reveal that the visitors have on
average a higher education than those of the theatres but it is to some extent lower than that of the
National opera and the Savonlinna opera festival. During the autumn season in 1999 the Pori opera
was distinctively an opera of its own area, while the audience at Tampere opera was more national. The
audience of the Savonlinna opera festival has a high education: more than 55 per cent have a university
degree. There are more women than men, the 60—69 year-old cohort is the largest if the audience is
classified with ten- year spaces (Mikkonen and Pasanen 2009). By far the most of the spectators come
from other cities during the summer holidays since the opera festival is kept in July and August. The
music festival in Kangasniemi, which is similar to a music species event, has a similar audience to that
of Savonlinna opera festival (Mikkonen, Pasanen and Taskinen 2008).
109
In the early 1990’s one of the most severe economic recession in the economic history of Finland
occurred. Unemployment rose to the record and that might have had an impact on the visitor density.
When the years 1981, 1991 and 1999 are compared (table 4) it can be seen that practically all groups
(women, men, different age cohorts) had the lowest density figure in 1991. Only the pensioners did not
cut down going to the theatre, on the contrary they increased visits.
The figures in the Culture barometer (Kulttuuripuntari in table 2) are substantially higher than those
presented above. The reason for these higher figures is not known. The leisure activities: culture and
sport statistics are based on interviews on the use of leisure time made by the Statistics Finland. The
1999 figures were collected between March 1999 and February 2000, while the Culture barometer data
was collected in connection with the labour study. One commercial research institution
(Taloustutkimus) has conducted several surveys on the visitor density of theatre, opera or ballet
performances on the basis of assignments by the association of Finnish theatres (Suomen teatterit). The
sample size has been around 1000 in the surveys in 1995, 1998, 2001, 2004 and 2007 (table 3).
(tables 2 and 3 about here)
The results in table 3 show that women go more often to the theatre, opera or ballet than men,
especially the difference is biggest among those that visit twice or 3 – 5 times per year. It is noticeable
that the amount of those that visit 3-5 times per year has gradually declined throughout the years 1985
– 2007. The 45-64 year-old are the most active, especially among those that go twice or 3 – 5 times per
year. The effect of the visitor’s formal education is clear; higher education and a bigger frequency are
related. The effect is stronger among those that go to the theatre, opera or ballet more than 6 times per
year. Persons living in southern Finland go most often and those in northern Finland go least.
Furthermore, the visitor density of the people in southern Finland has been growing during the period
1985 – 2007, while people in northern Finland have decreased visitor activity. The most often
110
mentioned reasons for not going were the lack of interest (56 %) or the lack of time (26 %).
Furthermore, 12 % informed that there is no theatre on the locality.
The number of opera visitors in the Eurobarometer 56.0 –study (table 4) is substantially lower than the
figure in the Culture barometer survey (1999). The Eurobarometer study was made two years later than
the Culture barometer survey. It is not known how e.g. the composition of the sample could explain
that difference. Also the visitor density in the theatres seems to have declined: for women 68.8 % 
49.5% and for men 47.1 %  30.4%. Any macroeconomic variable, like the unemployment rate (that
declined by approximately one per cent) or the income level index (that increased by 8 per cent) cannot
explain that drop.
(table 4 about here)
On the basis of preliminary statistical examination and based on earlier studies a hypothesis can be set:
H1: the visitor density on cultural events depends on gender, person’s age and education. Furthermore,
the regional supply has an effect.
In the Helsinki metropolitan area there are more visitors than elsewhere. Women go more often than
men. The audience composition of the theatrical or opera performances and classical music concerts is
different than that of the sport events. Moreover, the composition of sport spectators also depends on
gender, person’s age and education
in contrast to the performing arts audiences. The second
hypothesis is set:
111
H2: the spectators of sport events are reversely related to the composition of performing arts
audiences.
4.2 Method and sample
The most recent data, ISSP 2007 (International Social Survey Programme) is based on a mail survey
that was carried out by the Statistics Finland Autumn 2007 (18th September – 11th December 2007).
The sample unit is a person, age between 15 and 74. The sample method was a systematic random
sample from the population register, the sample size was 2500 but only 1354 answers were returned, i.e.
the response rate was 54.2%. The key words in the ISSP 2007 are the following: use of time, physical
condition, hobbies, organisations, board games, physical education, holiday, games, social relations,
sports, leisure. As background information the following data among others was collected: gender, year
of birth, size of the household, education, participation in working life, profession, source of livelihood
or branch, regular weekly working hours, professional status, employer (private or public sector),
membership of
a trade union, voting behaviour, religiousness, income and some information
concerning the place of residence.
There are at least three suitable statistical methods that can be used with that data: 1) the analysis of
variance (ANOVA), the multivariate analysis of variance (MANOVA) or the covariance analysis
(MANCOVA), 2) the multinomial logit and 3) the bivariate probit. The analysis of variance is a suitable
method for comparing the difference of means of two groups (e.g. the heavy users and the rest). With
the MANOVA it is possible to have more than one explanatory variable (e.g. gender, income, province,
and age). In the MANCOVA the values of the explanatory variables are corrected with the information
from the covariate. The purpose of this covariate is to reduce the heterogeneity of the variable to be
explained: for example most of the audience at the opera live in the Uusimaa region, therefore it is
112
reasonable to use the place of residence as covariate. If there are many explanatory variables, both
MANOVA and MANCOVA give results that can be divided into the separate and joint effects of each
variable. The total sum of squared deviations about the grand mean is partitioned into a sum of squares
due to many sources and a residual sum of squares. However, the direction of the effect remains open,
e.g. it is not known whether higher incomes increase or decrease opera visits.
The deviation of the individual from the grand mean Xij – GM in the analysis of variance can be
divided into two parts:(𝑋𝑖𝑗 − 𝑋̅𝑗 ) + (𝑋̅𝑗 − 𝐺𝑀). The first part is the deviation of the individual from its
own group’s mean and the second part is the deviation of the group mean from the grand mean. When
the deviation is calculated to all observations, the total sum of squares ∑𝑛𝑖=1 ∑𝑘𝑗=1(Xij – GM)2 = SStotal
can be partitioned into two parts: SSwithin and SSbetween, i.e. the internal (within) sum of squares and the
sum of squares between the groups (between). When the sums of squares are divided by their degrees
of freedom (within = N – k, between = k – 1, where N is the sample size and k in the number of
groups), the mean squares are obtained. The mean squares of the parts (i.e. within and between) are
compared with the F-test.
The test statistics 𝐹 =
𝑆𝑆𝑏𝑒𝑡𝑤𝑒𝑒𝑛
⁄(𝑘−1)
𝑆𝑆𝑤𝑖𝑡ℎ𝑖𝑛
⁄(𝑁−𝑘)
is distributed according to the F-distribution. If the difference
between groups is significant, the difference can be evaluated with ή2 = SSbetween/SStotal which tells how
much of the variation of the variable to be explained can be explained by the grouping variables
(Metsämuuronen 2009, 785-789).
The second possible statistical method is a logistic regression analysis or multinomial logit. An
equation explaining the visitor density of performing arts must be formulated to find out the impact of
each explanatory variable. Furthermore, it is possible to predict behaviour because the effect and
113
direction of explanatory variables are found out. The variable to be explained is either a binary variable
(binary logistic) or multinomial but rather often also ordered variable (multinomial logistic). In the ISSP
2007 data the question is: “How often during the past 12 months on your leisure did you go to
converts, theatrical performances, art exhibitions, etc.?” The answer alternatives were: 1 = daily, 2 =
several times per week, 3 = several times per month, 4 = less often, 5 = never. When a binary logistic
method is used, the alternatives could be reclassified for example so that one alternative is a
combination of 1,2 and 3 and the second alternative is a combination of 4 and 5. If the probability of
𝑝
the first choice is p and the probability of the second is 1-p, then 𝑙𝑜𝑔𝑖𝑡(𝑝) = 𝑙𝑜𝑔 1−𝑝 = log(𝑝) −
log(1 − 𝑝) = 𝑋𝛽 + 𝑢 where X includes all explanatory variables and β is the vector of coefficients, u
is the error term. The statistical significance of β can be evaluated with a suitable test. Usually it is
assumed that the error term is distributed according to logistic (Weibull) distribution or to normal
distribution. In the last case, the model is probit. Both logit and probit give more information
compared with the analysis of variance because both the coefficients of the explanatory variables and
the direction of the effect are found out: positive or negative and its statistical significance.
One step forward is to simultaneously study the visitor density of different leisure activities, e.g.
“performing arts” on the one hand and “at the movies” or “physical exercise activity”. If the
unobserved person’s preference for performing arts is y1* and the preference for movies is y2* and the
corresponding explanation models are 𝑦1∗ = 𝑋𝛽 + 𝑢1 and 𝑦2∗ = 𝑋𝛽 + 𝑢2 where the error terms u1 and
u2 are jointly bivariate distributed N(0,1). If the u1 and u2 are linearly independent, the correlation
coefficient ρ measures the relation of different leisure activities’ visitor density. Under the null
hypothesis that ρ equals zero, the model consists of two independent probit equations (Greene 2008,
820). If the correlation coefficient equals zero, the performing arts consumption and movies at the
cinema consumption are unrelated (Prieto-Rodriguez and Fernandez-Blanco 2000). The estimation of
the equations could be based on classification y1 = (“daily” or “several times per week” or “several
114
times per month”) = 1 if 𝑦1∗ > 0 and y2 = (“less often” or “never”) = 0, if 𝑦1∗ ≤ 0. In the above
example a difference is made between “several times per month” and “less often” but this separation
point could be another. With the probit model the marginal effects of each variable could be evaluated
(Greene 2008, 821). The coefficients in the probit model are difficult to interpret since they present
what the effect of the variables is on the unobserved dependent variable y*1. However, the marginal
effects of the explanatory variables are on the observed variable y1. The total marginal effect could be
partitioned into two parts: the direct marginal effect and the indirect marginal effect. The latter part is
formed through the correlation coefficient of the error terms.
All the variables of ISSP 2007 are not used in this study. Only those that are related to the concerts,
theatre and exhibitions are used according to the purpose of the study. The questionnaire has the
following question: How often during the past 12 months on your leisure did you go to converts,
theatrical performances, art exhibitions, etc.?” The descriptive statistics are presented in table 5.
(table 5 about here)
It is most reasonable to divide from the point of view of further analysis the visit activity into three
classes: regularly (daily, several times per week, several times per month), occasionally (less often) and
never. The results of the analysis of variance are presented in table 6.
(table 6 about here)
115
The statistical programme (PASW 18) available did not conduct the multivariate analysis of variance
(MANOVA) with four explanatory variables (gender, year of birth, place of province and education).
On the basis of the results it is clear that the first hypothesis is supported: performing arts visitor
density depends on gender, person’s age and education. Moreover, the regional supply has an effect.
The variance analysis shows that every explanatory variable (gender, year of birth, place of province,
and education) would alone separate into classes: regularly, occasionally and never. The joint effect of
the explanatory variables is nearly always significant if the education variable is present. In the table 7
different genders have been examined separately. Both for women and men, education would seem to
be the crucially important variable.
(table 7 about here)
Even if the ISSP 2007 data would make it possible to use other explanatory variables, these are not
used since, based on rather high values of ή2, the variables are adequate to explain consumers’
performing arts behaviour. Any single variable alone is not good enough, but a combination of the
variables explains more.
Next the multinomial logit model results will be presented. It is assumed in the binary logit model that
the dependent variable has two classes, whose probabilities are p and (1-p). In this case, the equation to
𝑝
be estimated is: 𝑙𝑜𝑔𝑖𝑡(𝑝) = 𝑙𝑜𝑔 1−𝑝 = log(𝑝) − log(1 − 𝑝) = 𝑋𝛽 + 𝑢 where X contains the
explanatory variables and β is a vector of coefficients. In the multinomial logit there are more than two
alternatives or classes, e.g. “often”, “occasionally” and “never”. The probability of the choice “often” is
𝑃𝑟𝑜𝑏(𝑌𝑖 =′ 𝑜𝑓𝑡𝑒𝑛′|𝑤𝑖 ) = 𝑃𝑖′𝑜𝑓𝑡𝑒𝑛′ =
exp(wi βi )
𝑗
1+∑𝑘=1 𝑒𝑥𝑝(wk βk )
where “i” stands for the person’s i choice
between different alternatives k (1,…,) on the condition wi These conditions are characteristics that
have an impact on the person’s choice, like gender, year of birth or education. In the multinomial logit
model one alternative is the zero alternative and the other alternatives are compared in relation to zero.
116
The variable which describes age has been recoded more roughly due to a more rational way of
presenting the results: age15_24, age25_34, age35_44, age45-54, age55_64, age64_. Unfortunately the
place of residence (province) cannot be used since the number of observations on some provinces is
too small.
(table 8 about here)
The often visiting group has graduated from an upper secondary school, from a university of applied
sciences or from a university. Others with different educational background do not significantly belong
to the group “often”. The age cohorts 45-54 and 55-64 are the most active. The “occasionally” group
consists of those with elementary school, vocational school or comprehensive school education or they
are still at school (zero alternative, constant in the equation). Women belong significantly more often
than men to the groups “often” or “occasionally”. The results are in line with the earlier studies
(Kivekäs 1991) and support the first hypothesis: the person’s education, gender and age have a
significant effect on the performing arts visiting density. The province variable is not used in the
estimations presented in table 8 since the number of provinces is too big. To a few groups there would
have been too few observations and the results would not have been credible. Due to that the models
in the table 9 are estimated so that the provinces have been regrouped into bigger entities. The entities
have mainly been formed according to the NUTS2 classification.
(table 9 about here)
The first hypothesis is verified by the results of the multinomial logit analysis: consumption of cultural
events depends on gender, person’s age and education and there are substantial differences across
regions. The effect of gender is clear between all the groups: “often”, “occasionally” and “never”.
Women are more active. As the groups “occasionally” and “never” are compared, the effect of the
117
person’s education is significant already when the education is either upper secondary school or
vocational school. As the previous groups and the “often” group are compared, the effect of education
is significant when the education is upper secondary school or any university degree. The age cohort
45-54 is most active. The impact of the region is the following: the Uusimaa region, other southern
Finland and western Finland are different than other areas in Finland among those that “never” go to
cultural events. The null area (in constant) in the estimations is northern Finland. To conclude, it can be
argued that crucial educational level is upper secondary school. All education after the upper secondary
school seems to increase cultural consumption. The separating points between the groups
“occasionally” and “often” seems to be university degree and regionally Uusimaa region or eastern
Finland.
One must keep in mind that not all persons with a university degree have visited cultural events.
Approximately 4 per cent of those who have completed a university degree (either a bachelor’s or a
master’s degree) have not participated at all. Correspondingly 13 per cent of those belong to the group
“often”.
The second hypothesis proposes that there is a reverse relationship between cultural events and sport
events. Montgomery and Robinson (2006) show with American data (USA 2004) that these events are
exclusionary. They have a separate public. Descriptive statistics of the sport audiences is presented in
table 10.
(table 10 about here)
Since the number of those that visit “daily” or “several times per week” is so small, these groups and
“several times per month” are combined. Hence there are three groups: “often”, “occasionally” and
“never” as above was done in connection with the cultural events. The correlation of the three valued
118
(“often”, “occasionally”, “never”) participation into cultural events and participation into sports events
is 0.09.
The sport events consumption is classified into three groups: “often”, “occasionally” and “never” in
the multinomial logit model (table 11). Gender classifies into groups so that men are more active. The
most active sports events consumers are those with elementary school (edu2) or comprehensive school
(edu3) education, men and younger than 45. The results are rather opposite with the visitor density of
cultural events. However, the cultural events participation variable (cult3) has a positive coefficient in
the equations showing that the activity in the cultural events is positively related with the activity in the
sport events. These events are complements although the audiences are rather separate. The separating
point of those that have been consuming occasionally is the age cohort 35-44. After that age consumers
choose either cultural events or a passive relationship, i.e. neither going to see cultural events and nor
sport events. The behaviour is most visible among the 45-64 years-old.
(table 11 about here)
Finally the connection of culture events and sport events is examined with the bivariate probit analysis.
First the depending variable is binary such that 0 equals “daily”, “several times per week” or “several
times per month” and 1 equals “less often” or “never”. The results are presented in table 12. It must be
noticed that the depending variable values of the multinomial logit analysis are the opposite than they
are in table 12.
(table 12 about here)
The classification in table 12 shows that only some explanatory variables are significant: the age cohorts
45-54 and 55-64. They are classified significantly less often than the group “less often” + “never”. The
119
error term u1 in the cultural events participation equation and the error term u2 in the sport events
participation equation are correlated: ρ = 0.463, meaning that there is a latent visitor density factor. The
classification of the depending variables in table 13 has been formed as follows: y 1 = 0 if the response
is “daily”, “several times per week”, “several times per month” or “less often” and y 2 = 1 if “never”.
This classification (yes/no) is for the both depending variables: “How often during the past 12 months
on your leisure did you go to concerts, theatrical performances, art exhibitions, etc.?” and “How often
during the past 12 month on your leisure did you go to sport events (ice hockey, football, athletics,
motor racing, etc.?”
(table 13 about here)
The results in table 13 reveal that the effect of gender is clear: women go more often to cultural events
while men are more active sport events consumers. All education above the level 5 (5 = upper
secondary school, 6 = college, 7 = university of applied sciences, 8 = bachelor’s degree, university, 9 =
master’s degree) are statistically significant in the culture participation model. The direct marginal effect
is larger the higher the education except for the level 7 (university of applied sciences). The age cohorts
35-44, 45-54 and 55-64 are significantly more active in culture participation and less active in sport
events. The vital segregation point is around the age 35. At that age consumers choose cultural events
in the expense of sporting events. The place of residence (in comparison with northern Finland which
is the constant in the equation) is significant in the cultural participation model, while in the sporting
events model only other southern Finland and western Finland are different from the other areas.
Typically the unemployment rate in northern and eastern Finland is higher than elsewhere and this
might be the reason for the higher sporting events consumption. Moreover, the higher unemployment
in combination with the lower educational level in these regions explains that sporting events are
favoured in northern and eastern Finland.
4.3 Conclusions and evaluation
120
The purpose of this study is to analyse the properties of the audiences of cultural events with the
Finnish 2007 data. Mainly these events are concerts, art exhibitions and theatrical or opera
performances. The preliminary method is the analysis of variance (ANOVA or MANOVA). The results
indicate that gender, person’s education, age and the place of residence are important factors to classify
visitor density. By definition the analysis of variance does not expose the well-known fact that women
are more active visitors. Another method, the multivariate logit analysis is more useful since it reveals
both the direction of the effect and the statistical significance of each explanatory variable. The results
show that women are significantly more active visitors than men even when the impact of other
explanatory variables is taken into account. The other significant factors are education (higher than
upper secondary school) and age (between 35 and 64) and the place of residence (compared with the
northern Finland). The most active (“often”) group and the less active group (“occasionally”) can be
separated with the factors mentioned above: gender, education, age (in this case, often vs. occasionally:
45 – 64 years old) and Uusimaa region and eastern Finland. The first hypothesis is verified.
An alternative way to spend leisure is to go to sporting events instead of cultural (concerts, exhibitions,
performances) events. As the impact of sport consumption is taken into account, a suitable method is a
bivariate probit analysis. The results of the bivariate probit model show that the visitor density of these
two alternatives is positively correlated meaning that there is a common activity factor. No previous
studies have been made with Finnish cultural consumption data using either multivariate logit or
bivariate probit model. These models give similar results, but the latter enables evaluation of the
marginal effects, both direct and indirect marginal effects. The indirect marginal effect is significantly
negative and thus reducing the cultural events (concerts, exhibitions, performance) participation in the
following cases: education is comprehensive school (edu3), vocational school (edu4), college (edu6),
university of applied sciences (edu7) or a master’s degree (edu9). To the contrary the direct marginal
effect is positive and larger than the negative indirect effect in the following cases: education is upper
121
secondary school (edu5), college (edu6), university of applied sciences (edu7), a bachelor’s or a master’s
degree (edu8 or edu9). The second hypothesis proposed is not verified. The audiences in the cultural
events and sport events are not separate.
The results are in line with those of Borgonovi (2004), Montgomery and Robinson (2006) or Masters
(2007). Montgomery and Robinson showed with data from the USA that arts’ spectators are not young,
better educated and mostly women. A similar conclusion can be made with the Finnish data. Frateschi
and Lazzaro (2008) found out with Italian data that the spouse’s, especially husband’s high education is
an important factor to explain art (museums, concerts, theatre) consumption if they go together.
However, international comparisons must be made with caution since the international differences in
culture consumption are large (Seaman 2005 or Virtanen 2007).
The impact of regions is substantial. In this study the regions have been formed mainly based on the
NUTS2-classification. The marginal effects in the bivariate probit model are the strongest in southern
Finland: Uusimaa region (-0.149), western Finland (-0.130), eastern Finland (-0.111) and the rest of
southern Finland (excluding Uusimaa, -0.107) when the groups “never” and “yes” are compared.
References
Borgonovi, Francesca (2004) Performing arts attendance: an economic approach. Applied Economics 36: 18711885
Eurobarometer 56.0, http://www.fsd.uta.fi/aineistot/luettelo/FSD0099/meF0099.html
Fratesch, Carlofilippo ja Elisabetta Lazzaro (2008) Attendance to cultural events and spousal influences: the
Italian case. Universita degli studi di Padova, “Marco fanno” Working Paper 84
Greene, William H. (2008) Econometric analysis, 6th Edition. Pearson International Edition
122
ISSP 2007: vapaa-aika ja urheilu: Suomen aineisto [elektroninen aineisto]. FSD2330, versio 1.0 (200803-18). Blom, Raimo (Tampereen yliopisto. Sosiologian ja sosiaalipsykologian laitos) & Melin, Harri
(Turun
yliopisto.
Sosiologian
laitos)
&
Tanskanen,
Eero
(Tilastokeskus.
Haastattelu-
ja
tutkimuspalvelut) [tekijä(t)]. Tampere: Yhteiskuntatieteellinen tietoaineisto [jakaja], 2008
Kivekäs, Kyllikki (1991) Alueoopperat ja yleisöt. Taiteen keskustoimikunta, Tilastotietoa taiteesta 5A
Kulttuuri- ja liikuntaharrastukset 1991 ja 1999 (2001) Tilastokeskus. Kulttuuri ja viestintä 2001:5
Kulttuuripuntari 1999 (1991) Opetusministeriön Kulttuuripolitiikan osaston julkaisusarja 9: 1999.
http://www.minedu.fi/OPM/Julkaisut/1999/kulttuuripuntari_1999__raportti_kulttuuripalvelujen_kay
tosta_ja?lang=fi
Masters, Tristan Andrew (2007) Supply and demand of creative arts in regional Victoria, Australia. Ph.D. –
thesis. Royal Melbourne Institute of Technology
Metsämuuronen, Jari (2009) Tutkimuksen tekemisen perusteet ihmistieteissä 4. Methelp 2009
Mikkonen, Jenni ja Katja Pasanen (2009) Selvitys Savonlinnan oopperajuhlien asiakasprofiileista ja aluellisesta
vaikuttavuudesta. East side story – Puhtia itäsuomalaiseen tapahtumamatkailuun – hankkeen tutkimusraportti. ItäSuomen yliopisto
Mikkonen, Jenni, Katja Pasanen ja Heidi Taskinen (2008) Itäsuomalaisten tapahtumien asiakasprofiilit ja
aluetaloudellinen vaikuttavuus. Joensuun yliopisto, Matkailualan opetus- ja tutkimuslaitoksen julkaisuja 1.
Montgomery, Sarah S. ja Michael D. Robinson (2006) Take Me Out to the Opera: Are Sports and Arts
Complements? Evidence from the Performing Arts Research Coalition Data. International Journal of Arts
Management 8:2, 24-39
Prieto-Rodríguez, Juan ja Víctor Frenández-Blanco (2000) Are Popular and Classical Music Listeners the
Same People? Journal of Cultural Economics, 24: 147-164
Seaman, Bruce A. (2005) Attendance and Public Participation in the Performing Arts: A Review of the Empicical
Literature. Georgia State University, Working Paper 05-03, August 2005
Suomen teatteriliitto (2001) Suomalaisten teatterissa käynti. Taloustutkimus Oy
Suomen teatteriliitto (2004) Suomalaisten teatterissa käynti. Taloustutkimus Oy
Suomen Teatterit ry (2007) Suomalaisten teaterissa käynti. Taloustutkimus Oy
123
Virtanen, Taru (2007) Across and beyond the bounds of taste on cultural consumption patterns in the European
Union. Turun kauppakorkeakoulu Sarja A-11: 2007
Statistical estimations:
ANOVA ja MANOVA: PASW 18 (www.spss.com)
Multinomial logit and bivariate probit: NLOGIT 4.0 (www.limdep.com)
124
125
Table 28: (Table 4.1) Culture and physical education hobbies 1981, 1991 and 1999
Has visited during Year
the
past
Theatre
12
Dance
Concert
Opera
performance
Sport
event
months, %
Women
1981/1991/1999
52/46/47
31/22/22
40/39/42
8/6/9
-/40/30
Men
1981/1991/1999
36/28/29
19/14/16
29/27/33
4/3/5
-/57/49
age 10-14
1981/1991/1999
48/35/40
25/17/21
42/31/33
5/2/3
-/79/57
age 15-24
1981/1991/1999
46/36/31
26/21/22
48/48/52
5/3/5
-/71/52
age 25-44
1981/1991/1999
49/36/39
27/18/22
31/34/41
6/4/7
-/54/48
age 45-64
1981/1991/1999
46/43/44
26/22/21
34/32/36
8/6/10
-/36/34
age 65-
1981/1991/1999
29/31/35
19/11/9
22/22/22
4/3/4
-/14/13
Lower basic
1991/1999
28/28
13/12
18/20
2/2
28/24
Upper basic
1991/1999
36/34
19/21
41/39
3/5
63/42
Middle
1991/1999
36/34
19/18
36/39
3/6
52/41
Lower Higher
1991/1999
49/55
24/27
47/50
8/11
55/46
Higher
1991/1999
63/60
28/29
58/61
16/22
51/46
Metropolitan area
1991/1999
47/48
21/25
43/50
11/19
48/42
Rest of Uusimaa
1991/1999
33/41
18/19
34/40
4/8
46/36
Southern Finland
1991/1999
41/40
18/20
33/38
3/5
44/39
Eastern Finland
1991/1999
30/35
17/18
34/33
3/4
43/34
Central Finland
1991/1999
35/36
17/16
30/31
2/3
47/39
Nortern Finland
1991/1999
26/25
17/14
26/30
3/5
48/29
Age
education:
Region:
Sample 4677 households in year 1999
126
Table 29: (Table 4.2) Kulttuuripuntari (culture barometer) 1999:
Has visited during
Theatre
Concert
Opera
Sport event
Women
68,8
75,8
15,1
42,9
Men
47,1
65,3
8,6
62,7
age 20-24
48,4
80,1
7,5
55,0
age 30-34
52,1
71,3
9,2
55,5
age 40-44
63,8
69,7
10,7
54,7
age 50-54
62,6
66,3
17,4
42,3
age 60-64
60,2
56,8
13,1
37,0
No vocational
52,9
63,0
6,7
49,0
Vocational
49,1
63,5
6,3
54,8
University
85,0
88,9
39,3
53,2
Southern Finland
61,7
74,9
17,8
54,1
Western Finland
60,3
69,9
7,5
55,4
Eastern Finland
50,0
67,3
9,4
48,4
Oulu
49,1
67,0
8,5
51,1
Lappi
45,5
52,6
6,1
39,2
the
past
12
months, % 1999
education:
Province (etc.):
Sample size 1810 carried out in August 1999 in connection with labour study as
telephone interview
127
Table 30: (Table 4.3) Suomen Teatterit (Taloustutkimus), survey on visits to theatre, opera or ballet during the past 12 months,
years 1985, 1998, 2001, 2004 and 2007
Has visited during 1994
1998
2001
2004
2007
the past 12 months
n = 956
n = 1013
n = 994
n = 984
n = 999
once/2/3-5/6-
16/12/13/3 = 19/12/11/3 = 19/11/9/4
times = total
44
45
= 23/11/10/2 = 21/14/8/4
43
42
=
47
gender:
women: 1/2/3-5/6men: 1/2/3-5/6-
17/16/18/4 = 20/14/16/5 = 22/13/14/5 = 25/12/14/4 = 22/17/11/4 =
55
55
16/9/7/3 = 38
18/10/5/1
54
55
= 15/9/5/3 = 36
34
21/10/6/2
54
= 19/10/4/3
39
=
36
age:
15-24 y..: 1/2/3- 19/10/10/1 = 22/10/7/2
5/625-44
40
y.:
= 23/9/6/3 = 41
41
25/10/4/1
= 17/6/5/1 = 29
40
1/2/3- 19/11/10/2 = 19/11/10/3 = 22/10/9/4
43
= 26/9/8/3= 46
22/12/7/3= 44
5/6-
42
45-64 y: 1/2/3-5/6-
13/13/17/5 = 19/15/14/3 = 14/13/10/4 = 22/13/15/3 = 25/17/10/4 =
48
65-79 y: 1/2/3-5/6-
45
51
12/16/12/3 = 12/9/10/5
43
41
53
= 15/14/13/3 = 15/12/9/3
36
45
56
= 14/18/8/5
=
39
45
= 16/9/7/2 = 34
19/9/5/0 = 33
18/9/7/2 = 36
= 15/8/6/1 = 30
20/8/8/1 = 37
17/13/4/2
education
Elemen/compr.:
15/12/10/2 = 17/10/5/2
1/2/3-5/6-
39
Voc/tech/busi:
16/11/7/3
1/2/3-5/6-
37
42
= 18/11/9/1
39
Upper sec: 1/2/3- 17/14/18/2 = 26/12/9/5
=
36
= 27/8/11/5
5/6-
51
Coll/polytech:
16/13/22/3 = 20/13/17/3 = 24/17/12/4 = 36/11/12/5 = 23/24/12/3 =
1/2/3-5/6-
54
Univers:
5/6-
52
= 20/14/11/2 = 25/7/8/1 = 41
51
53
47
57
64
62
1/2/3- 24/16/15/14 = 18/16/22/12 = 19/14/17/19 = 17/21/21/11 = 29/12/11/10 =
69
68
69
70
62
Region:
Southern F: 1/2/3- 17/14/14/5 = 19/14/12/4 = 21/12/10/5 = 25/12/11/5 = 23/16/11/5 =
5/6-
50
49
48
53
55
Central F: 1/2/3- 14/13/13/2 = 19/9/9/1 = 38
15/14/9/2
5/6-
40
45
33
12/5/7/4 = 28
14/9/6/0 = 29
14/9/2/1 = 26
42
Northern F: 1/2/3- 17/9/7/1= 34
17/8/9/2 = 36
128
= 23/10/11/1 = 18/11/3/1
=
5/6-
129
Table 31: (Table 4.4) Eurobarometer 56.0: August-September 2001, n = 1024.
Visited, %
Ballet/Dance
Theatre
Concert
Concert:
Concert:
Concert:
Sport
classical
opera
rock/pop
event
Women
17,8
49,5
41,2
30,4
15,4
27,5
30,4
Men
10,4
30,4
32,8
16,9
7,7
45,8
59,9
age: 15-24
14,8
36,3
50,8
12,6
2,1
68,8
57,8
age: 25-34
12,2
36,6
39,7
13,5
7,7
48,0
55,7
age: 35-44
21,6
41,7
43,2
25,7
13,5
41,9
45,6
age: 45-54
16,9
44,7
32,6
35,3
14,7
11,8
38,9
age: 55-64
13,2
47,8
34,8
29,2
20,8
6,3
40,0
age: 65-
8,3
41,2
25,5
44,7
25,5
0,0
23,0
Uusimaa
17,5
43,6
36,9
24,7
18,0
37,0
46,0
Rest southern F
15,0
44,9
40,6
23,1
13,4
34,8
39,9
Eastern F
11,2
35,3
34,1
25,6
7,0
32,6
45,2
Central F
11,6
43,8
35,4
30,4
8,7
43,5
49,3
Northern F
12,2
28,0
34,3
27,0
5,4
16,2
37,4
n = 382
n = 379
n = 382
age
Region:
If Concert = 1, then
http://www.fsd.uta.fi/aineistot/luettelo/FSD0099/meF0099.html
130
Table 32: (Table 4.5) ISSP 2007, ”How often in your leisure do you go to concerts, exhibitions, theatre etc.?”
Daily
Several times Several times Less often
per week
Never
Missing
per month
Frequency
0
4
71
1040
209
30
%
0
0,3
5,2
76,8
15,4
2,2
% of
0
0,3
5,4
78,5
15,8
--
Women, %
0
0,3
6,6
81,2
11,3
n = 741
Men, %
0
0,3
3,9
74,3
21,5
n = 568
0
1,1
4,5
78,7
15,7
n = 89
0
0
1,9
85,5
15,5
n = 103
0
0
0,8
88,9
10,3
n = 117
0
0
6,7
85,2
8,1
n = 135
0
0
10,2
80,8
9,0
n = 167
0
0,7
12,3
74,6
12,3
n = 130
0
0
5,2
58,6
36,2
n = 58
0
1,3
2,7
78,7
17,3
n = 75
0
0
4,2
80,2
15,6
n = 96
0
0
3,5
76,3
20,2
n = 114
0
0,8
4,7
73,2
21,3
n = 127
0
0
3,1
73,5
23,5
n = 98
responses
Women, %
15 < age <
24
25 < age <
34
35 < age <
44
45 < age <
54
55 < age <
64
65 < age
Men, %
15 < age <
24
25 < age <
34
35 < age <
44
45 < age <
54
55 < age <
64
65 < age
131
Table 33: (Table 4.6) Visitor density, ANOVA (significance in parenthesis)
Grouping
F-value (sig.)
ή2
Grouping variable
F-value (sig.)
ή2
Gender (S)
26,218 (0,000)
0,019
Year of birth(Y)
1,319 (0,055)
0,062
Province (A)
2,624 (0,000)
0,037
Education (E)
10,175 (0,000)
0,060
variable
ANOVA
MANOVA
Gender (S)
20,068 (0,000)
Gender (S)
12,695 (0,000)
Year of birth (Y)
1,366 (0,036)
Province (A)
2,612 (0,000)
S*Y
1,025 (0,426)
S*A
0,663 (0,857)
Gender (S)
25,716 (0,000)
Year of birth (Y)
1,099 (0,291)
Education (E)
9,638 (0,000)
Province (A)
1,755 (0,022)
S*E
2,115 (0,032)
Y*A
1,029 (0,363)
Year of birth (Y)
1,655 (0,002)
Province (A)
1,188 (0,260)
Education (E)
9,394 (0,000)
Education (E)
3,594 (0,000)
E*Y
1,127 (0,102)
A*E
1,054 (0,328)
Gender (S)
7,485 (0,006)
Gender (S)
11,581 (0,001)
Year of birth (Y)
1,295 (0,078)
Province (A)
1,429 (0,104)
Province (A)
1,569 (0,060)
Education (E)
3,329 (0,001)
S*Y
1,171 (0,193)
S*A
1,221 (0,231)
S*A
0,681 (0,813)
S*E
1,598 (0,121)
Y*A
1,123 (0,100)
A*E
1,144 (0,137)
S*Y*A
1,178 (0,126)
S*A*E
1,403 (0,010)
Gender (S)
15,496 (0,000)
Year of birth (Y)
1,764 (0,002)
Year of birth (Y)
1,510 (0,010)
Province (A)
1,775 (0,027)
Education (E)
8,314 (0,000)
Education (E)
4,735 (0,000)
S*Y
1,233 (0,120)
Y*A
1,162 (0,101)
S*E
1,575 (0,129)
Y*E
1,324 (0,018)
Y*E
1,226 (0,018)
A*E
1,174 (0,188)
0,130
0,089
0,374
0,688
132
0,064
0,523
0,193
0,314
S*Y*E
1,149 (0,152)
0,535
Y*A*E
0,975 (0,532)
0,861
Education: 1 = pupil, student, 2 = elementary school, 3 = comprehensive school, 4= vocational school or
course, 5= upper secondary school, 6 = college 7= university of applied sciences, 8 = bachelor, university, 9 =
master, university
133
Table 34: (Table 4.7) Visitor density, Anova and Manova, Women and Men separately
Grouping variable
F-value (sig.)
ή2
Grouping
F-value (sig.)
ή2
variable
ANOVA Men
ANOVA
Women
Year of birth
1,007 (0,465)
0,112
Year of birth
1,454 (0,017)
0,116
Province
1,870 (0,014)
0,061
Province
1,202 (0,249)
0,031
Education
6,501 (0,000)
0,087
Education
5,186 (0,000)
0,055
MANOVA
Men
Year of birth (Y)
Women
1,007 (0,473)
Year of birth 1,198 (0,169)
(Y)
Province (A)
1,208 (0,203)
Y*A
1,238 (0,066)
Year of birth (Y)
0,829 (0,807)
0,746
Province (A)
0,655 (0,861)
Y*A
1,002 (0,495)
0,621
Year of birth 2,281 (0,000)
(Y)
Education (E)
5,062 (0,000)
Education (E)
5,888 (0,000)
Y*E
1,035 (0,395)
Y*E
1,335 (0,006)
Province (A)
1,413 (0,116)
Province (A)
1,021 (0,434)
Education (E)
2,724 (0,006)
Education (E)
1,819 (0,071)
A*E
1,447 (0,006)
A*E
0,924 (0,699)
Year of birth (Y)
1,575 (0,051)
0,527
0,370
0,525
0,232
Year of birth 1,612 (0,018)
(Y)
Province (A)
2,209 (0,013)
Province (A)
0,756 (0,752)
Education (E)
3,817 (0,001)
Education (E)
3,111 (0,004)
Y*A
1,533 (0,044)
Y*A
1,098 (0,303)
Y*E
1,573 (0,051)
Y*E
1,324 (0,078)
A*E
0,944 (0,514)
A*E
0,927 (0,566)
Y*A*E
--
Y*A*E
--
0,948
134
0,885
Table 35: (Table 4.8) Multivariate logit analysis
n = 1269
’less often, occasionally’
’often’
β (s.e.)
β (s.e.)
0,643 (0,157)***
0,657 (0,253)**
school=2
-0,611 (0,375)
-0,352 (0,654)
school=3
0,124 (0,384)
0,269 (0,698)
school=4
0,154 (0,315)
0,023 (0,616)
school=5
0,768 (0,390)*
1,471 (0,672)*
school=6
1,155 (0,364)**
0,716 (0,655)
school=7
1,035 (0,430)*
1,446 (0,740)(*)
school=8
1,744 (0,779)*
2,554 (0,977)**
school=9
2,131 (0,591)***
2,894 (0,801)***
Age25_34
0,366 (0,334)
-0,418 (0,660)
Age35_44
0,604 (0,316)(*)
0,647 (0,561)
Age45_54
0,743 (0,317)*
1,558 (0,534)**
Age55_64
0,882 (0,366)*
2,112 (0,577)***
Age65_
0,479 (0,311)
0,284 (0,573)
constant
-0,330 (0,353)
-3,165 (0,686)***
Gender
LL = -794.9, AIC = 1.30, BIC = 1.42, HQIC = 1.35
Pseudo-R2 (McFadden) = 0,083
Depending variable =” How often in your leisure do you go to concerts, exhibitions, theatre etc.?”, Null
(constant) is ’never’, education = pupil of student, age = age15_24. The province variables are not used.
Gender: 1 = man, 2 = woman. ***,**,*,(*) significant at 0.1, 1, 5, 10 per cent level
135
Table 36: (Table 4.9) Multivariate logit analysis
n = 1269
’less often, occasionally’
’often’
β (s.e.)
β (s.e.)
0,645 (0,159)***
0,645 (0,255)**
school=2
-0,575 (0,381)
-0,362 (0,662)
school=3
0,084 (0,387)
0,240 (0,702)
school=4
0,199 (0,318)
0,044 (0,623)
school=5
0,703 (0,393)(*)
1,366 (0,675)*
school=6
1,164 (0,366)**
0,696 (0,660)
school=7
1,038 (0,434)*
1,408 (0,743)(*)
school=8
1,693 (0,784)*
2,482 (0,985)*
school=9
2,160 (0,594)***
2,840 (0,805)***
Age25_34
0,366 (0,338)
-0,392 (0,664)
Age35_44
0,617 (0,319)(*)
0,664 (0,567)
Age45_54
0,725 (0,319)*
1,563 (0,539)**
Age55_64
0,842 (0,254)*
2,115 (0,584)***
Age65_
0,480 (0,314)
0,320 (0,578)
Uusimaa
0,842 (0,254)***
1,045 (0,427)*
Rest southern F
0,672 (0,246)**
0,471 (0,448)
Eastern F
0,716 (0,294)*
1,142 (0,474)*
Western F
0,888 (0,260)***
0,570 (0,464)
constant
-0,976 (0,394)*
-3,801 (0,754)***
Gender
LL = -784.4, AIC = 1.30, BIC = 1.45, HQIC = 1.35
Pseudo-R2 (McFadden) = 0,095
Depending variable =” How often in your leisure do you go to concerts, exhibitions, theatre etc.?”, Null
(constant) is ’never’, education = pupil of student, age = age15_24, province = Norhern Finland - Regions: Rest
souther Finland = Itä-Uusimaa, Varsinais-Suomi, Kanta-Häme, Päijät-Häme, Kymenlaakso, Etelä-Karjala,
Ahvenanmaa. Eastern Finland = Etelä-Savo, Pohjois-Savo, Pohjois-Karjala, Kainuu. Western Finland =
Satakunta, Pirkanmaa, Keski-Suomi, Etelä-Pohjanmaa, Pohjanmaa. Norhern Finland = Keski-Pohjanmaa,
Pohjois-Pohjanmaa, Lappi, i.e. NUTS2-regions except. Ahvenanmaa and Uusimaa. Gender: 1 = man, 2 =
woman
136
Table 37: (Table 4.10) ISSP 2007, ”How often on your leisure do you go to see sport events on the location (ice hockey,
football, athletics, motor racing etc.)? n = 1355
Daily
Several times Several times Less often
per week
Never
Missing
per month
Frequency
4
17
82
691
526
35
%
0,3
1,3
6,1
51,0
38,8
2,6
% of
0,3
1,3
6,2
52,3
39,8
Women, %
0,1
0,7
5,0
43,4
49,0
1,7
Men, %
0,5
2,1
7,6
63,3
26,5
3,4
0
0
9,1
48,5
42,4
0
0
3,1
6,9
46,6
43,5
0
0
0,8
6,1
42,7
49,6
0,8
0,6
0
2,4
40,5
54,8
1,8
0
0
1,9
28,7
63,0
6,5
0
0
5,2
54,3
38,8
1,7
0
1,6
8,1
67,7
21,0
1,6
0
4,2
9,4
69,8
16,7
0
0,8
0,8
9,2
68,9
17,6
2,5
0
0,8
6,8
56,4
30,8
5,3
1,5
1,5
1,5
45,5
43,9
6,1
0,9
3,6
7,3
56,4
27,3
4,5
responses
Women, %
15 < age <
24
25 < age <
34
35 < age <
44
45 < age <
54
55 < age <
64
65 < age
Men, %
15 < age <
24
25 < age <
34
35 < age <
44
45 < age <
54
55 < age <
64
65 < age
137
Table 38: (Table 4.11) Multivariate logit analysis
n = 1269
’less often, occasionally’
’often’
β (s.e.)
β (s.e.)
Culture*
0,747 (0,145)***
1,361 (0,234)***
Gender
-0,999 (0,130)***
-1,273 (0,204)***
school=2
0,162 (0,350)
1,007 (0,564)(*)
school=3
0,628 (0,350)(*)
1,250 (0,553)*
school=4
0,489 (0,292)(*)
0,321 (0,505)
school=5
0,377 (0,335)
0,237 (0,568)
school=6
0,743 (0,301)*
0,807 (0,510)
school=7
0,862 (0,351)*
0,622 (0,587)
school=8
0,356 (0,410)
-0,159 (0,779)
school=9
0,569 (0,336)(*)
-0,250 (0,627)
Age25_34
-0,415 (0,280)
-0,091 (0,453)
Age35_44
-0,476 (0,269)(*)
-0,500 (0,440)
Age45_54
-0,797 (0,268)**
-1,188 (0,440)**
Age55_64
-1,288 (0,302)***
-1,463 (0,507)**
Age65_
-0,536 (0,271)*
-0,378 (0,440)
Uusimaa
0,183 (0,205)
-0,199 (0,352)
Rest southern F
0,342 (0,211)
0,284 (0,346)
Eastern F
0,019 (0,246)
0,559 (0,367)
Western F
0,463 (0,213)*
0,256 (0,354)
constant
1,030 (0,351)**
-0,723 (0,599)
LL = -1106.6, AIC = 1.81, BIC = 1.97, HQIC = 1.87
Pseudo-R2 (McFadden) = 0,078
Depending variable =”How often on your leisure do you go to see sport events on the location (ice hockey,
football, athletics, motor racing etc.)? Null (constant) is: education = pupil or student, age = age15_24, region =
Northern Finland. Culture* = previously defined culture participation (0=’never’, 1=’occasionally’ and
2=’often’), Gender: 1 = man, 2 = woman.
138
Table 39: (Table 4.12) Bivariate probit analysis,
Cult2
Cult2:
direct Cult2:
indirect Sport2
marginal effect
marginal effect
Gender
-0,048 (0,128)
-0,005 (0,013)
-0,006 (0,002)*
0,293 (0,106)**
school2
-0,077 (0,308)
-0,008 (0,032)
0,008 (0,006)
-0,428 (0,287)
school3
-0,095 (0,317)
-0,010 (0,033)
0,009 (0,006)
-0,463 (0,278)(*)
school4
0,006 (0,287)
0,001 (0,030)
0,000 (0,005)
-0,026 (0,252)
school5
-0,440 (0,307)
-0,045 (0,031)
0,002 (0,005)
-0,122 (0,279)
school6
0,083 (0,283)
0,009 (0,029)
0,005 (0,005)
-0,243 (0,261)
school7
-0,305 (0,320)
-0,031 (0,033)
0,003 (0,005)
-0,135 (0,280)
school8
-0,543 (0,353)
-0,056 (0,036)
0,000 (0,007)
-0,004 (0,373)
school9
-0,480 (0,310)
-0,049 (0,032)
-0,003 (0,006)
0,148 (0,304)
age25-34
0,288 (0,304)
0,030 (0,031)
0,002 (0,004)
-0,098 (0,223)
age35-44
-0,044 (0,236)
-0,005 (0,024)
-0,001 (0,004)
0,056 (0,219)
age45-54
-0,412 (0,229) (*)
-0,042 (0,024)(*)
-0,005 (0,005)
0,269 (0,228)
age55-64
-0,671 (0,250)**
-0,069 (0,026)**
-0,005 (0,005)
0,254 (0,252)
age65-
0,089 (0,246)
0,009 (0,025)
0,000 (0,004)
-0,012 (0,228)
Uusimaa
-0,216 (0,197)
-0,022 (0,020)
-0,002 (0,003)
0,088 (0,174)
Rest southern F
-0,004 (0,206)
-0,000 (0,021)
0,002 (0,003)
-0,088 (0,174)
Eastern F
-0,267 (0,227)
-0,027 (0,023)
0,007 (0,004)(*)
-0,359 (0,189)(*)
Western F
0,059 (0,216)
0,006 (0,022)
0,001 (0,003)
-0,044 (0,176)
constant
1,922 (0,375)***
0,976 (0,273)***
ρ = 0,463 (0,086)***
(standard errors in parenthesis). Cult2: ’0 = daily, several times per week or several times per month’ and ’1 =
less often or never’, Sport2 classified in the same way.
Log Likelihood = - 701,7, AIC = 1,167, BIC = 1,326, HQIC = 1,227, (*), *, **, *** = significant at 10,5,1,0.1 %
139
Table 40: (Table 4.13) Bivariate probit analysis,
Cul2
Cul2:
direct Cul2:
indirect Sport2
marginal effect
marginal effect
Gender
-0,351 (0,083)***
-0,107 (0,026)***
-0,044 (0,009)***
0,568 (0,067)***
school2
0,167 (0,194)
0,051 (0,059)
0,010 (0,015)
-0,127 (0,188)
school3
-0,120 (0,204)
-0,037 (0,062)
0,031 (0,016)(*)
-0,398 (0,191)*
school4
-0,238 (0,173)
-0,073 (0,053)
0,022 (0,013)(*)
-0,282 (0,159)(*)
school5
-0,505 (0,206)*
-0,154 (0,063)*
0,021 (0,015)
-0,266 (0,187)
school6
-0,705 (0,184)***
-0,215 (0,057)***
0,040 (0,015)**
-0,508 (0,165)**
school7
-0,661 (0,029)**
-0,202 (0,064)**
0,049 (0,017)**
-0,631 (0,192)***
school8
-1,064 (0,373)**
-0,325 (0,113)**
0,027 (0,018)
-0,352 (0,226)
school9
-1,205 (0,270)***
-0,368 (0,084)***
0,028 (0,015)(*)
-0,358 (0,186)(*)
age25-34
-0,105 (0,177)
-0,032 (0,054)
-0,019 (0,013)
0,248 (0,159)
age35-44
-0,324 (0,173)(*)
-0,099 (0,053)(*)
-0,020 (0,12)(*)
0,260 (0,152)(*)
age45-54
-0,363 (0,169)*
-0,111 (0,051)*
-0,038 (0,014)**
0,484 (0,151)**
age55-64
-0,541 (0,194)**
-0,165 (0,059)**
-0,057 (0,017)***
0,729 (0,165)***
age65-
-0,214 (0,160)
-0,065 (0,049)
-0,026 (0,013)*
0,339 (0,149)*
Uusimaa
-0,488 (0,136)***
-0,149 (0,042)***
0,012 (0,009)
-0,154 (0,112)
Rest southern F
-0,349 (0,133)**
-0,107 (0,041)**
0,019 (0,010)(*)
-0,240 (0,115)*
Eastern F
-0,364 (0,155)*
-0,111 (0,048)*
0,012 (0,011)
-0,160 (0,134)
Western F
-0,425 (0,143)**
-0,130 (0,044)**
0,024 (0,010)*
-0,312 (0,117)*
constant
0,482 (0,207)**
-1,006 (0,188)***
ρ = 0,382 (0,050)***
(standard errors in parenthesis). Cul2: ’0 = daily, several times per week or several times per month or less
often’ and ’1 = never’, Sport2 classified in the same way.
Log Likelihood = - 1404,9 AIC = 2,038 BIC = 2,183 HQIC = 2,092, (*), *, **, *** =significant at 10,5,1,0.1 %
140
5. Are the spectators of performing arts and the spectators of movies the
same?
Seppo Suominen
Haaga-Helia University of Applied Sciences
Malmi campus, Hietakummuntie 1 A, FIN-00700 Helsinki, Finland
e-mail: seppo.suominen@haaga-helia.fi
5.1 Introduction
The purpose of this paper is to study performing arts consumption and movies at the cinema
consumption using the 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 together is useful because it reveals substantially new evidence on the
average profile of culture consumption. It is expected that females go more often to art exhibitions,
opera or theatrical performances and this was supported. The results of the bivariate probit analysis
also reveal that gender is important also in explaining movie attendance. Females 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 the Finnish ISSP 2007 data that
besides these variables, also educational level of the spouse and the number of children significantly
141
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 for citizens in the countryside to go
and 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)11 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)12 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.
(Table 1 about here)
11
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
12
Adult education survey 2006, sample size 6800 with 4370 valid results, respondents age between 18 and 64, interviews
made between May – June 2006
142
Studies related to the spectators of performing arts are rather common: females go more often and the
audience is composed of middle-aged people 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) showed that the family-audience profile (i.e. middleaged with children) and the family-movie profile (various genres) are strongly associated, while young
men seem to favour action, mystery, thriller 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 € 8.3in
Finland and the ticket revenue per spectator was € 32.62 in the Finnish national opera. In 2009 the
average ticket price in Finnish big- and medium-size theatres was €16.21 in top 30 theatres13. 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 theatres14.
In 2007 there were 316 cinema screens in Finland15. 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).
13
According to ticket revenue in top 10 theatres the unweighted mean was €19.48, in the next 10 (11 th – 20th) €14.07 and the
next 10 (21st – 30th) €15.10. The weighted average price in big- and medium-size theatres was €18.63
14
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.
15
Top towns based on admissions 2007. Source: The Finnish Film Foundation, Facts & Figures 2008. www.ses.fi
Town
Helsinki
Admissions
Admissions/capita
Screens
Seats
Seats/Screens
2188094
3.84
37
7327
198.02
143
Correspondingly, there were 46 drama theatres subsidized by law with 12,361 performances and
2,446,500 spectators, 16 summer theatres with 821 performances and 35,1473 spectators, and 51
theatre groups outsize the law subsidies with 4,139 performances and 465997 spectators. Overall this
means 103 theatres and 16,695 performances and 3,066,530 spectators, i.e. 184 spectators per
performance or 0.57 per capita. Moreover, the Finnish national opera16 and other operas (13 local
operas with only few performances) had 285 performances with 182,728 spectators (641 per
performance). Furthermore, 39 dance theatres (including the National Ballet) gave 2,377 performances
with 523,620 spectators (220 per performance).17 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
Tampere
667205
3.21
17
2663
156.64
Turku
542398
3.09
17
2602
153.06
Oulu
329533
2.50
13
1762
135.54
Jyväskylä
237075
2.77
8
1097
137.13
Lahti
174234
1.75
7
1036
148.00
Espoo
170354
0.71
5
825
165.00
Pori
169364
2.22
6
748
124.67
Kuopio
123835
1.35
6
1191
198.50
Joensuu
123515
2.14
4
659
164.75
16
The main stage of The Finnish National Opera was closed 6 months in 2007 due to renovation.
17
16 Circus companies had 804 performances with 279544 spectators.
144
and drama theatre performances18 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.
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 the
variables in that study are suitable.
18
Top towns based on movie admissions 2007: * some smaller drama theatres regularly made tours
Town (m. adm.)
Theatre Adm.
Admissions/capita
Drama theatres*
Performances
Adm/Perform
Helsinki (2188094)
753233
1.33
10
3132
240.50
Tampere (667205)
325335
1.58
5
902
360.68
Turku (542398)
166442
0.95
3
991
167.95
Oulu (329533)
66725
0.51
1
351
190.10
110251
1.30
2
443
248.87
Lahti (174234)
84498
0.86
1
266
317.66
Espoo (170354)
57444
0.24
3
516
111.33
Pori (169364)
48450
0.64
1
282
171.81
Kuopio (123835)
49871
0.55
1
322
154.88
Joensuu (123515)
30986
0.54
1
280
110.66
Jyväskylä (237075)
145
5.2 Literature review
This paper is closely related to the sociological literature of performing arts participation. The classical
work is Bourdieu (1979). The relation of social positions to cultural tastes and practices is 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 the 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 according to
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
146
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), while 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 classes (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 that
cultural behavior is constrained somehow, i.e. differences in cultural consumption are 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 are 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).
147
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 being aesthetically the
most valuable. The top quartile omnivores are associated with legitimate taste, while the lowest quartile
in omnivorousness is 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).
Family background as a whole matters, since parents’ cultural participation seems to be related with
cultural consumption (van Eijck 1997), while participating in a culturally orientated course at school
does not have any or only slight impact on cultural consumption (Nagel, Damen and Haanstra 2010).
148
5.3 The method and sample
The ISSP 2007 survey was carried out between 18th September and 11th December 2007 by means of a
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
Finland19. 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).
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.
𝑥′𝛽
𝑃𝑟𝑜𝑏(𝑌 = 1|𝑥) = ∫−∞ ∅(𝑡)𝑑𝑡 = Φ(𝑥 ′ 𝛽)
19
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 response rate 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.
149
The function Φ(𝑥 ′ 𝛽) 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.
𝑒 𝑥′𝛽
𝑃𝑟𝑜𝑏(𝑌 = 1|𝑥) =
= Λ(𝑥 ′ 𝛽)
1 + 𝑒 𝑥′𝛽
The function Λ(𝑥 ′ 𝛽) 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):
𝑃𝑟𝑜𝑏(𝑦 = 0|𝑥) = Φ(−𝑥 ′ 𝛽)
𝑃𝑟𝑜𝑏(𝑦 = 1|𝑥) = Φ(𝜇1 − 𝑥 ′ 𝛽) − Φ(−𝑥 ′ 𝛽)
𝑃𝑟𝑜𝑏(𝑦 = 2|𝑥) = Φ(𝜇2 − 𝑥 ′ 𝛽) − Φ(𝜇1 − 𝑥 ′ 𝛽)
𝑃𝑟𝑜𝑏(𝑦 = 3|𝑥) = Φ(𝜇3 − 𝑥 ′ 𝛽) − Φ(𝜇2 − 𝑥 ′ 𝛽)
𝑃𝑟𝑜𝑏(𝑦 = 3|𝑥) = 1 − Φ(𝜇3 − 𝑥 ′ 𝛽)
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
150
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)
𝑦1∗ = 𝑥1′ 𝛽1 + 𝜀1 ,
𝑡ℎ𝑒𝑛 𝑦1 = 1 𝑖𝑓 𝑦1∗ > 0, 𝑎𝑛𝑑𝑦1 = 0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
𝑦2∗ = 𝑥2′ 𝛽2 + 𝜀2 ,
𝑡ℎ𝑒𝑛 𝑦2 = 1 𝑖𝑓 𝑦2∗ > 0, 𝑎𝑛𝑑 𝑦2 = 0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
𝐸[𝜀1 |𝑥1 , 𝑥2 ] = 𝐸[𝜀2 |𝑥1 , 𝑥2 ] = 0
𝑉𝑎𝑟[𝜀1 |𝑥1 , 𝑥2 ] = 𝑉𝑎𝑟[𝜀2 |𝑥1 , 𝑥2 ] = 1
𝐶𝑜𝑣[𝜀1 , 𝜀2 |𝑥1 , 𝑥2 ] = 𝜌
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
151
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.
(table 3 about here)
Since the income variable in the sample includes all social security contributions (e.g. child benefit), the
number of children is 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
152
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: Artconsumption01234 (‘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 females (57%) than males (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.
(table 4 about here)
153
Figure 4: Nuts areas
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.
5.4 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.
154
The importance of gender is very strong: females 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 see 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 in line with the results of
Bihagen and Katz-Gerro (2000). Females are more omnivore compared with males who are more
paucivore.
(table 5 about here)
The effect of age on cultural consumption in table 5 is relative to the age group 50-54. All younger
cohorts prefer 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 a 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
155
level education (edu6) are third most 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 a 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
Eastern Finland (Area3) people are less active in highbrow art consumption but significantly more
active in movie attendance than in 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. Females 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.
156
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 tables 5 and 6
indicate that the most omnivore citizens are those with a 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 effects of the area
as well as the family size or the number of children 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 has 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.
157
Figure 5: (Figure 5.1) Direct and indirect marginal effect of age-cohorts on highbrow art consumption
0.08
0.06
0.04
DirME5
0.02
IndME5
0
DirME6
-0.02
IndME6
-0.04
-0.06
-0.08
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 6: (Figure 5.2) Direct and indirect marginal effects of education on highbrow art consumption
0.3
0.25
0.2
0.15
DirME5
0.1
IndME5
0.05
DirME5
IndME6
0
-0.05
edu1 edu2 edu3 edu4 edu5 edu6 edu7 edu8 edu9
-0.1
-0.15
The marginal effects of education in tables 5 and 6 are highly correlated: ρDirME5, DirME6, edu = 0.977, ρIndME5,
IndME6, edu
edu
= 0.993. The direct and indirect marginal effects are highly negatively correlated (ρDirME5, IndME5,
= -0.859 and ρDirME6, IndME6, edu = -0.884) indicating that those active in highbrow art consumption are
active also in cinema consumption.
158
The results with the 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 education are not necessarily more active than people with a bachelor’s degree
(obtained from university). If the bachelor’s degree is obtained from a university of applied sciences
(polytechnic), the marginal effect is positive but less positive than for those with a 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 in performing arts events. With multinomial logit analysis, the three
groups can be 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 the MNL analysis to explain performing arts consumption are
presented in tables 7 and 8. In table 7 the reference values of the age-cohort and educational levels are
50-54 years old and elementary school (edu2).
(table 7 about here)
The results of the MNL analysis confirm the importance of gender. Females are more active to go to an
arts exhibition, opera and/or theatrical performances. Both the marginal effects of the gender variable
159
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 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 a bachelor’ degree (edu8) are
most active. The following groups in activity are those that have either a 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 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 ( Western Finland) is also a significant
variable to classify between ‘never’ and the two other categories ‘less often’ and ‘often’ but there is a
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.
(table 8 about here)
160
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 a 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.
5.5 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 females go more often to an 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. Females 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
161
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 does not significantly explain
cultural consumption. Younger people prefer movies and their incomes are typically low and this
explains why incomes do not explain 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-cohorts with the lowest education (elementary school) are most
inactive.
162
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165
Table 41: (Table 5.1) Spectators of movies at the cinema and performing atrs (concert, theatre, art exhibition) in Finland,
recent surveys
Survey
Often
Cinem
Arts
Occasionally
Cinema
Never
Interview
Sampl
Arts
Cinema
Arts
s made
e size
53.3 %
45.0
36.2%
March
– 4370
Notes
a
2006:
8.2 %
10.5 % 46.7
Adult
%
%
June 2006
Often
=
more than
education
7
study
times/yea
r
2007:
3.0%
bdo1
49%
bdo22
48%
bdo77
February
European
%
%
%
–
Cultural
t3%
t47%
t52%;
2007
Values
c3%
c48%
c49%
1041
March
Often
=
more than
5
times/yea
r
2007: ISSP
1.9%
5.6 %
80.2%
78.9 %
17.9%
15.5 %
Septembe
r
1354
–
Often
=
more than
Decembe
12
r 2007
times/yea
r
2010:
10%
70%
20%
January – 504
Often
Kotimaise
February
more than
n elokuvan
2010
12
yleisöt
=
times/yea
r
In the European Cultural Values study: bdo = a ballet, a dance performance or an opera; t = the
theatre; c = a concert
166
Table 42: (Table 5.2) descriptive statistics of age-group and education variables
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%
age30_34
6.0%
age35_39
8.0%
12.9%
age40_44
8.7%
13.5%
age45_49
10.0%
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%
28.4%
13.6%
13.7%
11.6%
12.1%
15.2%
17.7%
14.4%
17.6%
15.4%
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.
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
167
Table 43: (Table 5.3) Average monthly household and personal gross incomes
Table 3:
Household income
Personal income
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
including taxes and social security contributions by age and
by education groups
168
Table 44: (Table 5.4) Descriptive statistics of some explanatory variables
female: 57 %
male: 43 %
n = 1232
18.3%
23.0%
20,3%
married or registered 48.6%
51.9%
50.0%
marital status: single
pair relation
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%
*
= reference groups (constant) in probit or logit analysis
169
Table 45: (Table 5.5) Bivariate probit analysis
Art1234_5
Art1234_5: total Art1234_5:
marginal effect
Art1234_5:
Mov1234_5
direct marginal indirect
effect
marginal
effect
gender (male=1, 0.423
(0.097)***
female=2)
Marital
status: -0.004
single
(0.188)
Marital
status: 0.319
married
MS:
(0.203)
common- 0.198
0.064
0.074
-0.010
0.189
(0.016)***
(0.017)***
(0.005)(*)
(0.096)*
0.009
-0.001
0.010
-0.180
(0.031)
(0.033)
(0.010)
(0.188)
0.070
0.056
0.014
-0.264
(0.034)*
(0.036)
(0.011)
(0.198)
0.057
0.034
0.022
-0.403
law marriage
(0.217)
(0.036)
(0.038)
(0.012)(*)
(0.225)(*)
age15_19
-0.038
-0.063
-0.007
-0.056
1.027
(0.310)
(0.053)
(0.054)
(0.026)*
(0.458)*
-0.242
-0.087
-0.042
-0.044
0.806
(0.270)
(0.045)*
(0.047)
(0.018)*
(0.327)*
0.056
-0.033
0.010
-0.043
0.786
(0.259)
(0.043)
(0.045)
(0.015)**
(0.254)**
0.133
-0.049
0.023
-0.073
1.321
(0.297)
(0.048)
(0.052)
(0.021)***
(0.369)***
0.331
0.013
0.058
-0.045
0.826
(0.249)
(0.041)
(0.044)
(0.015)**
(0.268)**
0.069
-0.017
0.012
-0.029
0.531
(0.244)
(0.041)
(0.043)
(0.013)*
(0.229)*
0.158
-0.007
0.028
-0.034
0.626
(0.205)
(0.034)
(0.036)
(0.012)***
(0.212)***
0.064
0.001
0.011
-0.011
0.193
(0.204)
(0.034)
(0.036)
(0.009)
(0.169)
0.035
-0.007
0.006
-0.013
0.234
(0.189)
(0.032)
(0.033)
(0.009)
(0.168)
0.053
-0.006
0.009
-0.016
0.284
(0.233)
(0.037)
(0.040)
(0.012)
(0.212)
age20_24
age25_29
age30_34
age35_39
age40_44
age45_49
age50_54 = con
age55_59
age60_64
age65_69
170
age70_74
edu1
-0.168
-0.009
-0.029
0.020
-0.367
(0.236)
(0.039)
(0.041)
(0.011)(*)
(0.201)(*)
0.687
0.077
0.120
-0.043
0.783
(0.290)*
(0.049)
(0.052)*
(0.023)(*)
(0.432)(*)
0.424
0.053
0.074
-0.022
0.395
(0.213)*
(0.035)
(0.037)*
(0.011)*
(0.196)*
0.550
0.075
0.096
-0.021
0.386
(0.167)***
(0.028)**
(0.030)**
(0.009)*
(0.160)*
0.841
0.108
0.147
-0.039
0.714
(0.221)***
(0.039)**
(0.040)***
(0.014)***
(0.248)***
1.037
0.145
0.182
-0.037
0.675
(0.191)***
(0.033)***
(0.035)***
(0.010)***
(0.165)***
0.863
0.104
0.151
-0.047
0.865
(0.258)***
(0.043)*
(0.046)***
(0.016)***
(0.280)***
1.425
0.150
0.250
-0.100
1.821
(0.463)***
(0.082)(*)
(0.081)***
(0.037)***
(0.665)***
1.111
0.160
0.195
-0.035
0.635
(0.340)***
(0.058)**
(0.062)***
(0.014)*
(0.249)**
-0.226
-0.049
-0.040
-0.009
0.169
(1.327)
(0.335)
(0.233)
(0.112)
(2.030)
-0.129
-0.034
-0.023
-0.011
0.208
(0.257)
(0.043)
(0.045)
(0.014)
(0.252)
-0.225
-0.039
-0.040
0.000
-0.004
(0.185)
(0.030)
(0.032)
(0.009)
(0.172)
0.197
-0.004
0.035
-0.039
0.711
(0.329)
(0.063)
(0.058)
(0.023)(*)
(0.414)(*)
0.030
-0.011
0.005
-0.016
0.299
(0.227)
(0.039)
(0.040)
(0.011)
(0.199)
0.135
0.001
0.024
-0.022
0.410
(0.333)
(0.055)
(0.058)
(0.018)
(0.328)
-0.333
-0.055
-0.058
0.004
-0.066
edu2 = con
edu3
edu4
edu5
edu6
edu7
edu8
edu9
spouse-edu1
spouse-edu2 =
C
spouse-edu3
spouse-edu4
spouse-edu5
spouse-edu6
spouse-edu7
spouse-edu8
171
(0.388)
(0.074)
(0.068)
(0.022)
(0.407)
0.597
0.069
0.105
-0.036
0.648
(0.458)
(0.079)
(0.078)
(0.018)*
(0.317)*
0.331
0.026
0.058
-0.032
0.576
(0.149)*
(0.025)
(0.026)*
(0.009)***
(0.143)***
0.356
0.025
0.062
-0.037
0.682
(0.166)*
(0.028)
(0.029)*
(0.010)***
(0.163)***
0.176
0.003
0.031
-0.028
0.511
(0.180)
(0.030)
(0.032)
(0.100)***
(0.176)***
Household’s
0.033
-0.003
0.006
-0.009
0.162
size
(0.062)
(0.011)
(0.011)
(0.004)*
(0.076)*
Children <7
-0.221
-0.031
-0.039
0.007
-0.133
(0.098)*
(0.017)(*)
(0.017)*
(0.006)
(0.111)
-0.292
-0.032
-0.051
0.019
-0.343
(0.134)*
(0.021)
(0.024)*
(0.009)*
(0.154)*
Household
0.191D-5
-0.294D-6
0.335D-6
-0.629D-6
0.114D-4
Incomes
(0.582D-5)
(0.966D-6)
(0.102D-5)
(0.492D-6))
(0.901D-5)
Constant
-0.759
-1.033
(0.252)***
(0.270)***
spouse-edu9
Area1
Area2
Area3
Children 7-17
ρ
=
0.625
(0.048)***
(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% .
172
(*), *, **, ***
=
Table 46: (Table 5.6) Bivariate probit analysis ,
Art1234_5
gender (male=1, 0.422
(0.092)***
female=2)
Marital
status: 0.032
single
(0.182)
Marital
status: 0.409
married
MS:
(0.224)(*)
common- 0.286
Art1234_5:
Art1234_5:
Art1234_5:
Mov1234_5
marginal
direct
indirect
effect
marginal
marginal
effect
effect
0.063
0.074
-0.011
0.196
(0.016)***
(0.016)***
(0.005)*
(0.095)*
0.013
0.006
0.008
-0.141
(0.030)
(0.032)
(0.010)
(0.185)
0.058
0.071
-0.014
0.249
(0.038)
(0.039)(*)
(0.014)
(0.255)
0.043
0.050
-0.007
0.125
law marriage
(0.239)
(0.040)
(0.042)
(0.015)
(0.269)
age15_19
-0.014
-0.046
-0.002
-0.044
0.782
(0.293)
(0.050)
(0.051)
(0.025)(*)
(0.425)(*)
-0.172
-0.064
-0.030
-0.034
0.612
(0.260)
(0.043)
(0.045)
(0.018)(*)
(0.324)(*)
0.061
-0.019
0.011
-0.030
0.534
(0.252)
(0.042)
(0.044)
(0.014)*
(0.252)*
0.109
-
0.038
0.019
-0.057
1.015
(0.278)
(0.046)
(0.049)
(0.021)**
(0.361)**
0.335
0.027
0.058
-0.031
0.561
(0.238)
(0.039)
(0.042)
(0.014)*
(0.254)*
age40_44 = C
--
--
--
--
--
age45_49
0.123
0.002
0.021
-0.019
0.342
(0.195)
(0.033)
(0.034)
(0.012)
(0.220)
0.011
0.012
0.002
0.010
-0.174
(0.214)
(0.035)
(0.037)
(0.011)
(0.189)
0.026
0.007
0.005
0.003
-0.046
(0.207)
(0.034)
(0.036)
(0.010)
(0.187)
0.009
0.001
0.002
-0.001
0.014
(0.196)
(0.033)
(0.034)
(0.011)
(0.189)
0.012
-0.002
0.002
-0.005
0.083
(0.242)
(0.039)
(0.042)
(0.013)
(0.234)
age20_24
age25_29
age30_34
age35_39
age50_54
age55_59
age60_64
age65_69
173
age70_74
edu1
edu2
edu3
edu4
-0.253
-0.009
-0.044
0.035
-0.627
(0.236)
(0.039)
(0.041)
(0.013)**
(0.218)**
0.020
-0.003
0.036
-0.007
0.119
(0.279)
(0.047)
(0.049)
(0.022)
(0.398)
-0.646
-0.077
-0.113
0.035
-0.636
(0.208)***
(0.036)*
(0.037)**
(0.013)**
(0.224)**
-0.198
-0.025
-0.035
0.010
-0.172
(0.225)
(0.038)
(0.039)
(0.013)
(0.235)
-0.065
-0.002
-0.011
0.009
-0.169
(0.178)
(0.031)
(0.031)
(0.011)
(0.201)
ccc
ccc
ccc
edu5
ccc
edu6
0.428
0.067
0.075
-0.007
0.132
(0.198)*
(0.034)*
(0.035)*
(0.012)
(0.209)
0.229
0.023
0.040
-0.017
0.308
(0.253)
(0.042)
(0.044)
(0.017)
(0.297)
0.806
0.071
0.141
-0.070
1.256
(0.465)(*)
(0.081)
(0.081)(*)
(0.038)(*)
(0.681)(*)
0.492
0.080
0.086
-0.006
0.103
(0.348)
(0.059)
(0.062)
(0.014)
(0.258)
-0.273
-0.029
-0.048
0.019
-0.341
(1.375)
(0.347)
(0.241)
(0.116)
(2.088)
0.044
0.038
0.008
0.030
-0.540
(0.242)
(0.041)
(0.042)
(0.015)*
(0.256)*
-0.156
-0.013
-0.027
0.014
-0.250
(0.270)
(0.046)
(0.047)
(0.016)
(0.294)
-0.270
-0.021
-0.047
0.027
-0.476
(0.200)
(0.035)
(0.035)
(0.014)(*)
(0.235)*
ccc
ccc
ccc
edu7
edu8
edu9
spouse-edu1
spouse-edu2
spouse-edu3
spouse-edu4
spouse-edu5
ccc
spouse-edu6
-0.012
0.009
-0.002
0.011
-0.200
(0.245)
(0.043)
(0.043)
(0.014)
(0.254)
0.072
0.017
0.013
0.004
-0.079
(0.326)
(0.055)
(0.057)
(0.020)
(0.360)
-0.374
-0.036
-0.065
0.029
-0.526
(0.384)
(0.073)
(0.067)
(0.025)
(0.436)
spouse-edu7
spouse-edu8
174
spouse-edu9
0.575
0.091
0.100
-0.009
0.170
(0.469)
(0.081)
(0.080)
(0.019)
(0.337)
0.359
0.030
0.063
-0.033
0.593
(0.146)*
(0.025)
(0.026)*
(0.009)***
(0.141)***
0.368
0.026
0.064
-0.038
0.678
(0.164)*
(0.027)
(0.029)*
(0.010)***
(0.161)***
0.203
0.004
0.035
-0.031
0.556
(0.179)
(0.030)
(0.031)
(0.010)**
(0.174)**
Household’s
0.035
-0.002
0.006
-0.009
0.153
size
(0.060)
(0.011)
(0.011)
(0.004)*
(0.076)*
Children <7
-0.217
-0.033
-0.038
0.005
-0.087
(0.094)*
(0.016)*
(0.016)*
(0.006)
(0.104)
-0.295
-0.034
-0.051
0.018
-0.317
(0.130)*
(0.021)(*)
(0.023)*
(0.008)*
(0.150)*
Household
0.278D-5
-0.234D-6
0.486D-6
-0.720D-6
0.129D-4
Incomes
(0.563D-5)
(0.906D-6)
(0.989D-6)
(0.499D-6
(0.900D-5)
Constant
-0.186
-0.280
(0.271)
(0.301)
Area1
Area2
Area3
Children 7-17
ρ = 0.631 (0.047)***
(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% .
175
(*), *, **, ***
=
Table 47: (Table 5.7) Multinomial logit (MNL) analysis
y=1
y=2
marginal
effects:
=0
gender
marginal
y
effects:
=1
y
marginal
Marginal
Marginal
Marginal
Averages of
Averages of
Averages of
effects: y =
effects
effects
effects
Individual
Individual
Individual
2
averaged
averaged
averaged
Elasticities of
Elasticities of
Elasticities of
over
over
over
Probabilities:
Probabilities:
Probabilities:
individuals:
individuals:
individuals:
y=0
y=1
y=2
y=0
y=1
y=2
-0.098
0.076
0.023
-1.042
0.153
0.750
0.006
-0.032
0.026
0.005
-0.011
0.112
-0.063
0.109
-0.045
-0.211
0.062
-0.454
-0.033
0.039
-0.006
-0.037
0.009
-0.019
0.010
0.011
-0.021
0.004
0.000
-0.028
0.056
-0.046
-0.011
0.020
-0.005
-0.014
-0.007
0.108
-0.101
-0.009
0.003
-0.161
-0.031
0.026
0.005
-0.013
0.002
0.007
0.771
1.156
-0.090
0.078
0.013
(0.159)***
(0.313)***
(0.018)***
(0.019)***
(0.008)(*)
Marital
-0.075
0.497
0.007
-0.021
0.015
status: single
(0.311)
(0.572)
(0.036)
(0.038)
(0.014)
Marital
0.562
-0.499
-0.061
0.087
-0.026
status:
(0.343)(*)
(0.746)
(0.040)
(0.043)*
(0.018)
MS:
0.275
0.108
-0.031
0.035
-0.003
common-
(0.380)
(0.789)
(0.044)
(0.047)
(0.019)
-0.056
-0.505
0.008
0.004
-0.012
(0.522)
(1.093)
(0.060)
(0.065)
(0.027)
-0.443
-0.615
0.052
-0.046
-0.006
(0.432)
(0.797)
(0.050)
(0.052)
(0.019)
0.166
-2.048
-0.011
0.069
-0.058
(0.427)
(1.201)(*)
(0.049)
(0.055)
(0.030)(*)
0.244
0.321
-0.028
0.026
0.003
(0.467)
(0.887)
(0.054)
(0.056)
(0.021)
(male=1,
female=2)
married
law marriage
age15_19
age20_24
age25_29
age30_34
176
age35_39
age40_44
age45_49
0.602
0.544
-0.069
0.069
0.001
(0.437)
(0.817)
(0.050)
(0.052)
(0.019)
0.029
-0.902
0.329D-4
0.024
-0.024
(0.385)
(0.942)
(0.044)
(0.048)
(0.023)
0.192
0.427
-0.023
0.016
0.007
(0.359)
(0.690)
(0.041)
(0.043)
(0.016)
age50_54,C
---
age55_59
-0.016
1.037
-0.002
-0.026
0.028
(0.333)
(0.632)(*)
(0.038)
(0.040)
(0.015)(*)
-0.049
1.221
0.001
-0.034
0.033
(0.329)
(0.623)*
(0.038)
(0.040)
(0.015)*
-0.015
0.476
-0.870D-4
-0.013
0.013
(0.395)
(0.845)
(0.045)
(0.049)
(0.020)
-0.412
1.648
0.040
-0.093
0.053
(0.385)
(0.698)*
(0.044)
(0.047)*
(0.018)**
1.155
1.730
-0.136
0.116
0.019
(0.493)*
(1.225)
(0.057)*
(0.064)(*)
(0.030)
age60_64
age65_69
age70_74
edu1
edu2
C
edu3
0.667
0.733
-0.077
0.073
0.004
(0.331)*
(0.866)
(0.038)*
(0.044)(*)
(0.022)
0.869
1.678
-0.103
0.079
0.024
(0.269)***
(0.665)**
(0.031)***
(0.035)*
(0.017)
1.376
3.184
-0.166
0.113
0.053
(0.410)***
(0.779)***
(0.048)***
(0.051)*
(0.020)**
1.805
2.551
-0.211
0.185
0.026
(0.321)***
(0.689)***
(0.037)***
(0.040)***
(0.017)
1.392
3.063
-0.167
0.118
0.049
edu4
edu5
edu6
edu7
-0.075
0.073
0.001
-0.043
0.005
0.000
0.002
0.040
-0.043
-0.001
0.002
-0.080
-0.025
0.013
0.012
-0.018
0.001
0.025
-0.005
-0.044
0.048
-0.008
-0.009
0.107
-0.002
-0.056
0.058
-0.009
-0.014
0.128
-0.001
-0.021
0.022
-0.000
-0.001
0.030
0.038
-0.130
0.092
0.003
-0.023
0.104
-0.147
0.114
0.034
-0.046
0.016
0.046
-0.083
0.076
0.007
-0.040
0.013
0.018
-0.113
0.071
0.043
-0.155
0.034
0.210
-0.182
0.091
0.092
-0.098
0.001
0.132
-0.229
0.184
0.046
-0.400
0.028
0.205
-0.184
0.098
0.085
-0.106
0.008
0.145
177
(0.418)***
(0.862)***
(0.049)***
(0.052)*
(0.022)*
2.478
4.609
-0.294
0.229
0.065
(0.796)***
(1.066)***
(0.090)***
(0.090)**
(0.022)**
1.921
3.733
-0.229
0.174
0.055
(0.518)****
(0.839)***
(0.059)***
(0.061)**
(0.020)**
spouse-
-0.549
2.398
0.053
-0.128
0.076
edu1
(0.987)
(1.516)
(0.113)
(0.120)
(0.036)*
spouse-edu2
C
spouse-edu3
-0.259
0.694
0.026
-0.051
0.024
(0.433)
(0.926)
(0.050)
(0.054)
(0.023)
-0.363
0.378
0.039
-0.057
0.018
(0.324)
(0.738)
(0.037)
(0.041)
(0.018)
0.393
1.268
-0.049
0.024
0.024
(0.539)
(1.083)
(0.062)
(0.066)
(0.026)
0.119
0.613
-0.015
0.002
0.013
(0.391)
(0.791)
(0.045)
(0.048)
(0.019)
0.164
0.909
-0.022
0.001
0.020
(0.516)
(0.993)
(0.059)
(0.062)
(0.023)
-0.514
-1.171
0.062
-0.043
-0.019
(0.562)
(1.345)
(0.065)
(0.070)
(0.033)
1.152
2.539
-0.138
0.097
0.041
(0.690)(*)
(0.967)**
(0.078)(*)
(0.078)
(0.020)*
0.533
1.402
-0.065
0.040
0.025
(0.248)*
(0.609)*
(0.029)*
(0.032)
(0.015)(*)
0.583
1.288
-0.070
0.049
0.021
(0.273)*
(0.649)*
(0.031)*
(0.034)
(0.016)
0.280
0.588
-0.033
0.024
0.009
edu8
edu9
spouse-edu4
spouse-edu5
spouse-edu6
spouse-edu7
spouse-edu8
spouse-edu9
Area1
Area2
Area3
-0.321
0.208
0.113
-0.100
-0.008
0.071
-0.250
0.155
0.095
-0.196
-0.010
0.166
0.050
-0.182
0.132
0.000
-0.003
0.015
0.026
-0.069
0.042
0.007
-0.005
0.039
0.041
-0.072
0.032
0.047
-0.018
0.116
-0.054
0.012
0.043
-0.015
0.001
0.035
-0.018
-0.006
0.023
-0.018
-0.002
0.069
-0.025
-0.010
0.035
-0.010
-0.001
0.041
0.068
-0.035
-0.033
0.013
-0.002
-0.021
-0.152
0.081
0.071
-0.101
-0.010
0.099
-0.072
0.029
0.043
-0.256
0.011
0.447
-0.077
0.041
0.036
-0.134
0.015
0.195
-0.037
0.021
0.016
-0.029
0.007
0.046
178
(0.299)
(0.739)
(0.035)
(0.038)
(0.018)
Household’s
0.062
0.157
-0.008
0.005
0.003
size
(0.104)
(0.233)
(0.012)
(0.013)
(0.006)
Children <7
-0.423
-0.334
0.049
-0.049
0.001
(0.147)**
(0.350)
(0.017)**
(0.019)**
(0.001)
Children 7-
-0.488
-2.359
0.063
-0.012
-0.051
17
(0.213)*
(1.038)*
(0.025)**
(0.031)
(0.022)*
Household
0.129D-4
-0.431D-4
-0.128D-5
0.271D-5
-0.142D-5
Incomes
(0.173D-4)
(0.517D-4)
(0.025)
(0.226D-5)
(0.128D-5)
Constant
-1.339
-6.840
0.175
-0.025
-0.150
(0.428)***
(1.107)***
(0.050)***
(0.059)
(0.035)***
-0.008
0.004
0.005
-0.130
0.015
0.238
0.052
-0.054
0.002
0.138
-0.039
-0.002
0.072
0.017
-0.089
0.068
-0.014
-0.324
0.000
0.000
0.000
-0.034
0.015
-0.199
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 to 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
179
Table 48: (Table 5.8) Multinomial logit (MNL) analysis,
y=1
y=2
marginal
effects:
=0
marginal
y
effects:
=1
y
marginal
Marginal
Marginal
Marginal
Averages of
Averages of
Averages of
effects: y =
effects
effects
effects
Individual
Individual
Individual
2
averaged
averaged
averaged
Elasticities of
Elasticities of
Elasticities of
over
over
over
Probabilities:
Probabilities:
Probabilities:
individuals:
individuals:
individuals:
y=0
y=1
y=2
y=0
y=1
y=2
-0.098
0.076
0.022
-1.027
0.152
0.718
-0.001
-0.025
0.026
-0.003
-0.008
0.113
-0.082
0.117
-0.035
-0.274
0.067
-0.352
-0.053
0.048
0.005
-0.058
0.012
0.018
-0.004
0.006
-0.003
-0.001
0.001
-0.003
0.031
-0.035
0.004
0.010
-0.004
0.003
-0.011
0.107
-0.095
-0.011
0.004
-0.151
-0.031
0.024
0.007
-0.013
0.002
0.008
gender
0.761
1.126
-0.090
0.077
0.013
(male=1,
(0.156)***
(0.310)***
(0.018)***
(0.019)***
(0.008)(*)
Marital
-0.023
0.547
0.001
-0.016
0.016
status: single
(0.303)
(0.558)
(0.035)
(0.037)
(0.014)
Marital
0.701
-0.162
-0.078
0.099
-0.021
status:
(0.390)(*)
(0.836)
(0.045)(*)
(0.049)*
(0.022)
MS:
0.418
0.456
-0.049
0.046
0.003
common-
(0.415)
(0.868)
(0.048)
(0.052)
(0.022)
0.032
-0.033
-0.003
0.005
-0.002
(0.497)
(1.059)
(0.057)
(0.063)
(0.027)
-0.257
-0.135
0.029
-0.032
0.002
(0.421)
(0.795)
(0.049)
(0.052)
(0.020)
0.201
-1.884
-0.015
0.072
-0.057
female=2)
married
law marriage
age15_19
age20_24
age25_29
age30_34
(0.420)
(1.205)
(0.049)
(0.055)
(0.031)(*)
0.239
0.350
-0.028
0.024
0.004
(0.454)
(0.889)
(0.053)
(0.056)
(0.022)
180
age35_39
0.640
0.646
-0.074
0.072
0.003
(0.423)
(0.799)
(0.049)
(0.051)
(0.019)
age40_44
C
C
C
C
C
age45_49
0.148
0.466
-0.018
0.009
0.009
(0.349)
(0.668)
(0.040)
(0.043)
(0.016)
0.003
-0.760
0.003
0.019
-0.021
(0.356)
(0.918)
(0.041)
(0.046)
(0.024)
-0.064
0.902
0.004
-0.030
0.027
(0.348)
(0.661)
(0.040)
(0.043)
(0.017)
-0.080
1.145
0.005
-0.038
0.034
(0.0345)
(0.649)(*)
(0.040)
(0.042)
(0.017)*
-0.057
0.281
0.005
-0.014
0.009
(0.412)
(0.866)
(0.048)
(0.051)
(0.022)
-0.540
1.308
0.056
-0.105
0.049
(0.399)
(0.708)(*)
(0.046)
(0.049)*
(0.019)*
0.124
-0.863
0.011
0.038
-0.027
(0.460)
(1.076)
(0.053)
(0.059)
(0.028)
-0.998
-2.125
0.120
-0.085
-0.035
(0.358)**
(0.741)**
(0.042)**
(0.046)(*)
(0.020)(*)
-0.276
-1.495
0.037
-0.002
-0.035
(0.359)
(0.788)(*)
(0.042)
(0.046)
(0.021)(*)
-0.074
-0.645
0.011
0.005
-0.016
(0.295)
(0.554)
(0.034)
(0.037)
(0.014)
edu5
C
C
C
C
C
C
edu6
0.868
age50_54
age55_59
age60_64
age65_69
age70_74
edu1
edu2
edu3
edu4
edu7
0.248
-0.098
0.112
-0.014
(0.344)*
(0.581)
(0.039)*
(0.041)**
(0.014)
0.417
0.584
-0.049
0.043
0.006
-0.080
0.076
0.005
-0.046
0.005
0.005
-0.021
0.005
0.016
-0.015
0.000
0.032
0.005
0.031
-0.035
0.001
0.001
-0.065
0.002
-0.046
0.045
-0.002
-0.010
0.097
0.002
-0.058
0.056
-0.005
-0.014
0.123
0.005
-0.020
0.015
0.002
-0.002
0.020
0.055
-0.138
0.082
0.010
-0.023
0.090
-0.009
0.054
-0.045
-0.003
0.003
-0.049
0.133
-0.074
-0.059
0.069
-0.034
-0.150
0.043
0.016
-0.059
0.019
-0.002
-0.097
0.013
0.014
-0.027
0.018
0.002
-0.122
-0.105
0.128
-0.023
-0.182
0.023
-0.124
-0.053
0.043
0.011
-0.031
0.004
0.017
181
edu8
edu9
spouse-edu1
spouse-edu2
spouse-edu3
spouse-edu4
(0.416)
(0.732)
(0.048)
(0.051)
(0.018)
1.515
2.190
-0.179
0.154
0.025
(0.800)(*)
(0.975)*
(0.091)*
(0.091)(*)
(0.018)
0.959
1.273
-0.113
0.100
0.012
(0.520)(*)
(0.717)(*)
(0.060)(*)
(0.060)(*)
(0.015)
-0.651
2.137
0.065
-0.140
0.075
(0.987)
(1.543)
(0.114)
(0.121)
(0.039)(*)
0.093
0.334
-0.012
0.005
0.007
(0.432)
(0.999)
(0.050)
(0.055)
(0.026)
-0.293
0.570
0.031
-0.054
0.023
(0.463)
(0.997)
(0.054)
(0.059)
(0.026)
-0.422
0.169
0.047
-0.062
0.015
(0.355)
(0.825)
(0.041)
(0.045)
(0.021)
spouse-edu5
C
spouse-edu6
0.070
0.462
-0.010
-0.001
0.011
(0.419)
(0.870)
(0.049)
(0.052)
(0.022)
0.074
0.740
-0.011
-0.008
0.019
(0.527)
(1.039)
(0.061)
(0.065)
(0.026)
-0.575
-1.252
0.069
-0.048
-0.021
(0.579)
(1.389)
(0.067)
(0.073)
(0.036)
1.134
2.515
-0.137
0.094
0.043
(0.699)(*)
(1.021)*
(0.079)(*)
(0.080)
(0.023)(*)
0.573
1.433
-0.070
0.044
0.026
(0.246)*
(0.603)*
(0.029)*
(0.032)
(0.016)(*)
0.593
1.256
-0.071
0.051
0.021
(0.271)*
(0.643)*
(0.031)*
(0.035)
(0.017)
0.322
0.630
-0.039
0.029
0.010
spouse-edu7
spouse-edu8
spouse-edu9
Area1
Area2
Area3
-0.194
0.153
0.042
-0.057
-0.001
0.024
-0.122
0.101
0.021
-0.092
0.001
0.032
0.063
-0.188
0.125
0.001
-0.003
0.014
-0.013
0.001
0.012
-0.006
0.001
0.018
0.031
-0.069
0.038
0.008
-0.005
0.035
0.049
-0.074
0.025
0.057
-0.019
0.087
-0.011
-0.007
0.019
-0.012
-0.002
0.054
-0.014
-0.018
0.031
-0.005
-0.001
0.037
0.077
-0.041
-0.035
0.014
-0.002
-0.021
-0.151
0.080
0.072
-0.100
-0.010
0.098
-0.078
0.034
0.044
-0.273
0.014
0.446
-0.079
0.044
0.035
-0.136
0.016
0.185
-0.042
0.026
0.017
-0.033
0.008
0.047
182
(0.299)
(0.733)
(0.035)
(0.039)
(0.019)
Household’s
0.063
0.151
-0.008
0.005
0.003
size
(0.105)
(0.232)
(0.012)
(0.013)
(0.006)
Children <7
-0.423
-0.443
0.049
-0.047
-0.002
(0.143)**
(0.343)
(0.017)**
(0.018)**
(0.009)
Children 7-
-0.497
-2.415
0.065
-0.010
-0.055
17
(0.213)*
(1.032)*
(0.025)**
(0.032)
(0.023)*
Household
0.152D-4
-0.299D-4
-0.160D-5
0.279D-5
-0.120D-5
Incomes
(0.177D-4)
(0.483D-4)
(0.205D-5)
(0.229D-5)
(0.125D-5)
Constant
-0.466
-4.558
(0.460)
(1.028)***
-0.009
0.004
0.005
-0.132
0.017
0.220
0.053
-0.049
-0.004
0.140
-0.037
-0.046
0.075
0.017
-0.092
0.069
-0.014
-0.332
0.000
0.000
0.000
-0.044
0.015
-0.159
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 to 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
183
184