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TRANSCRIPT
Cultural demand of adolescents in Helsinki 2011
Seppo Suominen, Haaga-Helia University of Applied Sciences
Hietakummuntie 1 A, FIN-00700 Helsinki, Finland
Abstact
The activity to visit different cultural activities of the adolescents living in Helsinki is analysed. Visiting
movies at the cinema was the most important or the most general in 2011. The opera or the ballet and art
exhibitions are least visited. Music concerts and theatre visits are between these extremes. Previous studies
have revealed that the gender, age and educational level are very important determinants of cultural demand.
Moreover, consumer’s wealth or earnings have an influence on the participation decision. Using the data of
adolescents the consumer’s wealth or earnings are irrelevant, however their parents’ (or household’s)
incomes or wealth are used as proxy for consumer’s incomes to explain cultural demand. About 13 percent
of adolescents in Helsinki can be classified as immigrants or relevant. They or their parents were not born in
Finland. The role of immigration on the cultural demand is studied in this paper.
The data collected in 2011 covers pupils aged 11 – 16 and students aged 16 – 18. Especially the older
students in the sample have the possibility to work during their leisure time. It seems that wage incomes have
an impact on theatre and opera/ballet attendance while adolescents’ wage incomes have any impact on
cinema, concert or art exhibition visits. Age, gender, the number of siblings and mother’s education have on
impact on cultural demand. The ability to speak (ethnic origin) Somali or Arabic have a positive impact on
cultural demand while those who have an Estonian or Russian origin do not differ from original population
(Finnish or Swedish).
All estimations use seemingly unrelated regression (SURE) analysis. The model is a standard constant
elasticity of substitution (CES) function, which is used to derive the necessary equilibrium conditions and
justify the model form.
Keywords
Adolescents, Helsinki, Immigrants, Cultural participation
Word count: 7933
Introduction
In Finland, the municipalities are partially responsible for the public financing of the culture and for the
cultural services. The services are produced in the municipalities or they are arranged in cooperation with
others or they are bought from the market. The cultural services of municipalities are financed with the sales
revenues, with their own tax revenues and state subsidises of municipalities (Ruusuvirta and Saukkonen
2014). The net operating costs (revenue - cost)1 vary from about 100 € to about 200 € per inhabitant of a
municipality in the biggest Finnish cities in 2013. Due to this large variation in cultural budget it is useful to
know how this amount of money is spread across different groups. Ruusuvirta and Saikkonen (2014) define
the cultural activity of a municipality as follows: cultural activity refers to operation which takes place in
libraries, the art institutions and culture institutions, culture houses and cultural centres and art educational
institutions and furthermore, the general culture activity that has been arranged, produced or ordered by the
municipality or other branch of administration but it does not include private theatres or cinemas outside
municipality or state subsidies. In general, we can divide the cultural consumption into different groups
based on age or other socio-economic criteria. This segmentation partially reveals the incidence of
subsidises. However, this analysis must be carried out with caution since simultaneously we should know
what the tax burden of each socio-economic segment is. It is well known that women and highly educated
are more active participating in various cultural events (Kulttuuripuntari 1999, Kulttuuri ja viestintä 2001:5,
Suominen 2013, 88). Due to progressive income taxation, high income earners pay a larger share of the
taxes, therefore a straightforward analysis of the incidence of subsidizes that does not consider both the
participating and tax burden might be misleading.
In Finland men and people with lower educational level are typically more interested in sports related issues,
however with one disparity in gender: men more often go to see a sports event and women are more active
exercisers (Suominen 2013, 55). In general, this (women more active) is exceptional since worldwide men
are more active exercisers (Downward 2007 or Cabane and Lechner 2014). Therefore, we have a reason to
1 Operating costs = operation expenses + depreciation and decrease in value + imputed cost, in which operation expenses = staff expenses + purchases of services + materials and equipment and goods + rent + allowance + other expenses. Operating revenue = proceeds and imputed revenue, in which proceeds = sales revenue + dues + subsidies and allowance + rent + other revenue.
assume that nationality has an impact on leisure activities, which are related to many individual socio-
economic factors as Virtanen (2007) shows. Since in Finland, the capital region the share of people whose
mother tongue is not Finnish, Swedish or Sami (most often spoken in northern Finland, Lapland) is about 12
- 13 %, it is interesting to study what is the role of foreigners (mother tongue is not any of the three
mentioned) on cultural activities. Moreover, it is well known that the cultural participation of an adolescent
is correlated with their parents’ educational level and cultural taste (van Eijck 1999, Nagel and Ganzeboom
2002). In this study, the data was collected in 2011 about the leisure activities of adolescents in Helsinki
(Keskinen and Nyholm 2011). With this data, it is possible to study the cultural participation or activities of
those whose native tongue is not Finnish, Swedish or Sami, i.e. some foreign language or simply those who
have an immigrant family background and compare the cultural participation of these with native Finns. The
aim of this study is to investigate the leisure activities of adolescents in Helsinki and find out whether are any
differences based on native tongue.
Literature
Most of the studies on leisure time cultural activity report frequencies. One of the first is Bruun’s (1952)
study. In 1951, the youth spent about half of their leisure time outside their home. In the sample (n = 897) the
questions were about how young people had visited religious services, cinema, dance, their friends or sport
events. Cinema was popular, since almost 20 % responded that they saw a movie at the cinema during last
month at least five times and about 85 - 90 % at least once. Young men were more interested in cinema than
young women. During last month about 15 % had visited while dance was substantially more popular (about
35 - 40 % - men more active). Theatre or opera attendance differences across gender, mother tongue and
between clerical staff and pupils were significant.
The 1981 survey (Leskinen 1984) on leisure activities of the youth in Helsinki is more detailed than the 1951
survey. Leskinen shows that among 10 - 15 years old there are substantial differences across girls and boys.
Girls sing more actively while boys are more interested in engines and technology. The 1981 study includes
a large survey on sport activities. During the winter, indoor ball games were important although the
questionnaire emphasized that school sports should be eliminated. Bicycling, swimming and walking were
the most often mentioned summer habits. Among the 10 - 15 -year-old cinema was much more common than
sports events, dance or disco.
Siurala (1991) raises computer games and programming into the classification of creative hobbies of
adolescents. Boys were more active in gaming and programming than girls. Siurala notices that the structure
of creative hobbies was rather stable from 1982 to 1990. The share of those that do not have any hobbies was
stable as well as the structure of different hobbies. A substantial gender difference was present as before.
Drawing, painting and classical music were the hobbies of girls while rock music instruments and computers
were the ploys of boys. During two decades from 1980 to 2000 the structure of the hobbies among
adolescents in Helsinki was rather stable (Keskinen 2001). The survey of 2000 (Keskinen 2001) classifies
hobbies into four groups: traditional creative hobbies, reading, computer related hobbies and games. Partially
computed related hobbies and games can be classified as creative hobbies. Among sports swimming and
dance had become more popular than they were earlier. Football was the most important for boys, floorball
was the second in order, and basketball, badminton and ice hockey were next. Dance and gymnastics were
more favoured by girls than by boys. Cinema hold a strong position among hobbies. More than half of the
youth in Helsinki had seen a movie at the cinema once a month in the spring of 2000. Summer job that had
become more common since the 1980’s and that has had a substantial impact on hobbies. The supply of
summer jobs was generous and even as young as 13 years old could get a summer job (Keskinen 2001). The
survey of 2000 did not contain any questions about theatre or opera. A nationwide survey on leisure
activities of the youth in 2009 (Myllyniemi 2009) does not have anything on cinema, theatres etc. while a
nationwide survey in 2013 (Myllyniemi and Berg 2013) combines movies, theatres, concerts and art
exhibitions. The most common was cinema (and theatres, concerts and art exhibitions) but if the regularity of
the activity is considered then console and computer games and reading were more common. In the 2013
survey, there was a statement concerning the image of sport: “sporty active people are in general more
active”. Somewhat less than 50 % of the respondents agreed with the statement while more than 50 %
rejected that argument. The person’s education is related with the sport activity. Those with higher education
or those who are studying for a high education are more active sport exercisers. The sport activity of a young
person is higher if her/his mother has a high education. The share of not active sport exercisers is large if the
household’s incomes are low. The most top-rated modes among children and young (age 7 - 29) were
jogging, gym, cycling and walking and only after those the team sports, like floorball and football were
mentioned. Boys favoured football, floorball and ice hockey while the top-rated modes of girls were jogging,
walking, riding and dance. The cultural activities among the youth in Helsinki were very similar as they were
in south-western Finland, Turku area (Haanpää, Ehrs, Tiensuu-Tsiopoulos, Kaljonen and Lagström 2009 or
Löfblom 2013).
The surveys made by the Statistics Helsinki or nationwide reports typically present participation frequencies.
However, the participation data in relation to socio-economic variables using a multivariate analysis has been
almost totally neglected. Without a model and a multivariate analysis, a detailed description of the
phenomenon is incomplete. Virtanen (2007) used the analysis of variance and logistic regression analysis in
explaining cultural consumption across European Union countries. She showed that the education, age,
nationality, gender and profession of a person have an impact on the choice of cultural consumption. The
focus in Virtanen’s study was not in the choices of young people. Willekens and Lievens (2014), van Steen,
Vlegels and Lievens (2015) have similar results. Immigrants seem to participate less than the original
population into sports associations and clubs and local activities in Ireland (Coughlan, Doherty, O’Neill and
McGuire 2014). However, there was no difference in the cultural club participation if the immigrant speaks
English well. If the language skills were not good, the cultural participation would be lower even when the
factors have been considered. The other factors in the study were education, gender, marital status, the place
of television and console in the bedroom.
This study uses the same data as Keskinen (2012). Movies at the cinema were the most popular mode as
expected in 2011. 59 % of the 11 - 15 years old living in Helsinki had seen a movie at the cinema once a
month. The share is bigger than it was in 1982 (54 %) or 1990 (44 %). In 2011, the share of those who had
visited theatre was 7 %, art exhibition 6 %, a concert 8 % and opera or ballet only 4 %. The popularity of
those last mentioned (concert, opera or ballet) has somewhat increased since 1982 or 1990 while the
popularity of theatre or art exhibition had lowered.
The next chapter presents a model which is used to study the demand for cultural events and the factors
explaining it among the youth in Helsinki and compare the possible differences between youth of immigrant
background and youth of original population (whose mother tongue is Finnish, Swedish or Sami). The
factors that should have an impact on the demand are the following: parents’ (father and mother) education,
the age of the respondent, mother tongue, gender, a variable measuring wealth of the household and the
employment status of the respondent.
Model
The demand for the cultural events of the youth in Helsinki is modelled by using a constant elasticity of
substitution (CES) function. García, Lera-López and Suárez (2011) used that model to estimate the time used
for sports in Spain. The same model is used also in this study. The preferences of the young consumers are
indicated with the time used into different leisure activities, like movies at the cinema le and theatre
attendance lt and the net incomes, m. CES function is convenient due to its handy marginal rate of
substitution features. Let us assume that the consumer has the following maximization problem:
(1 ) maxm, l1 ,lsU (m ,le ,lt )=[m−ρ+β le
−ρ+γ lt−ρ ]
−1ρ subject ¿m=w (T−le−lt )+ y ¿
Where U denotes utility, ρ is a useful parameter to express marginal rate of substitution2. β and γ are
positive parameters, w denotes the hourly wage, T is time available (i.e. 168 hours per week). When the
optimization problem is solved, the demand functions of the movie demand (le) and theatre demand (lt) can
be obtained. In the interior point solution, the marginal rate of substitution of the net income (m) and demand
for movies and theatre equal the hourly wage (w).
2 σ= 11−ρ
is the marginal rate of substitution, ρ ≥−1
(2 ) MRSm, le=
∂ U∂ le
∂ U∂ m
=β ( ml e
)1+ρ
=w
(3 ) MRSm ,lt=
∂U∂ lt
∂U∂ m
=γ ( mlt
)1+ρ
=w
The equilibrium conditions (2) and (3) show that the marginal rate of substitution between any two
components does not depend on the third in the CES function. After some manipulation two estimable
functions are easily derived.
(4 ) log(mle )= 1
1+ρ logw−1
1+ρ logβ
(5 ) log( mlt )= 1
1+ρ logw−1
1+ρ logγ
The unobservable and observable factors that have an impact on the demand for leisure activities of the
young people in Helsinki can be considered as follows:
(6 ) β=e(Zeθe +φe)
(7 ) γ=e(Z t θt+φ t)
where Ze and Zt represent various socio-economic factors that have an impact on utility and leisure activities
and φe and φ t are unobservable random variables. If is it assumed that φe and φ t are two-dimensionally
standard normally distributed, a linear demand system for leisure activities can be estimated with seemingly
unrelated systems (SUR) method. The following equations are estimated.
(8 )log( mle )= 1
1+ρ logw−1
1+ ρ (Ze θe+φe)
(9 ) log( mlt )= 1
1+ ρ logw−1
1+ ρ (Z t θt+φt)
In both equations above the demand for movies and theatre is in the denominator of the variable to be
explained, thus the parameter estimates must be interpreted conversely: a positive parameter denotes that the
factor diminishes the demand and vice versa. Only some socio-economic variables that have been shown to
have an impact (Virtanen 2007, Willekens and Lievens 2014, Coughlan, Doherty, O’Neill and McGuire
2014, van Steen, Vlegels ja Lievens 2015) on the leisure activities are available: gender, mother tongue (“can
you speak or write some other language?”), age, education, the birth country of father and mother, the
education of father and mother, and the number of siblings. The incomes of the respondent or the household
of the respondent is not known, therefore three alternative proxies are used to measure incomes. Table 2
presents the variables used in this study.
The equations (8) and (9) have two leisure activities, movies at the cinema and theatre. However, the data
available (FSD2794 Nuoret Helsingissä 2011) contains more suitable leisure activities, like participation or
attendance at the opera or ballet, a concert or art exhibition. Figure 1 shows the questionnaire (in Finnish)
Figure 1: Question 18 in the questionnaire.
Results
The data was collected by the Statistics Helsinki in the spring of 2011 (14 th March – 26th May 2011). The
target group contains primary school pupils (classes 5 to 9) and secondary school, both high school and
vocational school students (classes 1 and 2). Internet with discretionary cluster sampling was used in
collecting the data (Keskinen and Nyholm 2011). The response rate in the cross-section data is 71.7 with
1433 respondents. Table 1 presents some descriptive statistics on the cultural participation of 10 – 20- year-
old boys and girls living in Helsinki. Roughly half of them have seen a movie at the cinema at least once a
month. The second most popular event was a concert and the third was theatre. Art exhibition and opera or
ballet have been least popular. There is statistically significant difference between boys and girls. Girls seem
to respond “not at all” less frequently than boys regardless of the cultural subject. On the contrary, boys seem
to respond “almost daily” or “every week” more frequently than girls.
Boys, n = 633 Girls, n = 800 Different
?
n
Alm
ost daily
Weekly
Monthly
Less often
Not at all
Data m
issingn
Alm
ost daily
Weekly
Monthly
Less often
Not at all
Data m
issing
χ2
Movies 60
9
13 33 285 261 17 24 78
9
4 45 472 257 11 11 31.467***
Σ(%) 2.1 7.6 54.4 97.2 100 0.5 6.2 49.6 98.6 100
Theatre 60
0
9 5 21 264 301 33 78
7
3 2 55 507 220 13 85.010***
Σ(%) 1.5 2.3 5.8 49.8 100 0.4 0.6 7.6 72.0 100
Opera or
ballet
60
3
10 2 9 116 466 30 78
9
5 2 20 345 417 11 99.232***
Σ(%) 1.7 2.0 3.5 22.7 100 0.6 0.9 3.4 47.1 100
Concert 60
2
7 6 48 273 268 31 78
9
2 4 54 532 197 11 73.898***
Σ(%) 1.1 2.2 10.1 55.5 100 0.2 0.8 7.6 75.0 100
Art
exhibitio
n
60
1
9 2 12 200 378 32 78
8
3 4 41 366 374 12 43.861***
Σ(%) 1.0 1.8 3.8 37.1 100 0.4 0.9 6.1 52.5 100
Table 1: How often do you go to movies, theatre, opera or ballet, concert or art exhibition? Frequencies and cumulative frequencies (e.g. 7.6 % of boys go to movies almost daily or weekly). The last column on the right contains χ2 statistics on the similarity of boys’ and girls’ visiting frequency.
Table 2 (below) presents some descriptive statistics on the variables used in the estimations. The variable to
be explained in the theoretical model has two components: the net income of the person and cultural activity
or participation. Since we have data on the cultural participation of a young person, it is reasonable to
measure incomes with a variable that is related to household or parents’ incomes. As there is no direct
measure, we must use one of the three proxies: m1 = “How would do describe your family’s economic
situation?”, m2 = “How much money do you have in your own use?” or m3 = “Does your family own a car?”.
The alternative responses and the Spearman correlation coefficients are presented in table 2. The responses
are recoded so that a large number also represents that the incomes are high: for example, m 1: 4 = “good”, 3
= “fair”, 2 = “weak”, 1 = “difficult to say”. The participation or attendance has been encoded as follows: 5 =
“almost daily”, 4 = “weekly”, 3 = “monthly”, 2 = “less often” and 1 = “never”. The recoding has been made
due to using logarithms.
Variable Median Original Remarks
m1 How would do you
describe your family’s
economic situation?
”good” 1 = good, 2 =
fair, 3 = weak,
4 = difficult to
say
Spearman correlation
ρm1 m2=0.239
ρm1 m3=0.187
ρm2 m3=0.148
m2 How much money do
you have in your own
use?
”fair” 1 = plenty, 2=
fair, 3 = less
than my peers
usually have
m3 Does your family own a
car?
”yes, one” 1 = yes, two or
more, 2 = yes,
one, , 3 = no
w Are you employed? ”no” 0 = no, 1 = yes ”yes” = 259/1433 = 18.1 %
Mother
tongue
Can you speak or write
other languages?
Swedish: 17.8 %, Russian: 3.0 %, Somali: 2.5 %, Estonian: 0.6 %,
Arabic: 1.3 %, English: 34.9 %, some else: 9.0 %
age 14
High school ”yes” = 396/1433 = 27.6 %
Vocational
school
”yes” = 38/1433 = 2.7 %
Father’s
place of birth
Finland: 87.1 %, Russia: 1.3 %, Estonia: 0.6 %, Somalia: 2.2 %, Other :8.2 %
Mother’s
place of birth
Finland: 88.3 %, Russia: 2.1 %, Estonia: 1.1 %, Somalia: 1.7 %, Other: 6.3 %
Father’s
education
University, Polytechnic: 35.1 %, College: 2.6 %, High/Vocational school: 19.4 %, Primary
school/I do not know: 42.9 %
Mother’s
education
University, Polytechnic: 38.1 %, College: 4.0 %, High/Vocational school: 18.2 %, Primary
school/I do not know: 39.6 %
Father Employed: 84.2 %, At home: 1.3 %, Unemployed: 4.9 %, Unemployed: 4.5 %, I do not know: 5.1
%
Mother Employed: 84.2 %, At home: 5.6 %, Unemployed: 3.5 %, Unemployed: 4.2 %, I do not know 2.5
%
Number of
siblings
1
Table 2: Descriptive statistics of the explaining variables
The alternative variables for incomes, m1, m2 and m3 are positively correlated after these have been recoded
and transforming into logarithms. About 18 % of the youngsters in Helsinki were employed during their
leisure. If Finnish, Swedish and English are not counted, 16 % of the respondents daily use some other
languages. The most general were Russian, Somali and Arabic. Roughly 30 % of the respondents were at
high school or vocational school. If father and mother were not born in Finland, the most often mentioned
alternatives were Russia, Somalia or Estonia. Mothers in general have a better education than fathers do.
m = m1 m = m1 m = m1 m = m1 m = m2 m = m2 m = m2 m = m2 m = m3 m = m3 m = m3 m = m3
Movie Theatre Movie Theatre Movie Theatre Movie Theatre Movie Theatre Movie Theatre
log( mle )log(m
lt )log(mle )log(m
lt )log( mle )log(m
lt )log( mle )log(m
lt )log( mle )log(m
lt )log( mle )log(m
lt )logw -14.407
(15.471)
-32.046(*)
(17.596)
-18.657
(15.440)
-37.362*
(17.599)
-14.374
(15.469)
-32.010(*)
(17.593)
-18.620
(15.437)
-37.323*
(17.596)
-14.341
(15.465)
-31.966(*)
(17.588)
-18.581
(15.433)
-37.274*
(17.591)
Swedish 0.136
(10.621)
-11.303
(12.080)
0.187
(10.620)
-11.259
(12.078)
0.270
(10.617)
-11.966
(12.075)
Russia -23.164
(23.911)
-38.318
(27.195)
-23.251
(23.908)
-38.413
(27.191)
-23.284
(23.901)
-38.439
(27.183)
Father, Russia 59.665
(45.138)
66.610
(51.449)
59.636
(45.131)
66.580
(51.442)
59.568
(45.119)
66.509
(51.427)
Mother, Russia -37.818
(35.250)
-30.178
(40.179)
-37.899
(32.245)
-30.256
(40.173)
-37.533
(35.235)
-30.328
(40.161)
somali -83.927**
(26.358)
-
110.671***
(29.978)
-83.811**
(26.354)
-
110.558***
(29.973)
-83.846**
(26.347)
-
110.595***
(29.965)
Father, somali -25.398
(40.413)
-23.564
(46.064)
-25.375
(40.407)
-23.543
(46.057)
-25.533
(40.396)
-23.702
(46.044)
Mother,
somali
-100.845*
(45.953)
-100.078(*)
(52.378)
-101.003*
(45.946)
-100.234(*)
(52.371)
-100.952*
(45.933)
-100.185(*)
(52.356)
Estonian 45.381
(52.004)
62.275
(59.145)
45.402
(51.997)
62.301
(59.137)
45.292
(51.982)
62.185
(59.121)
Father,
Estonian
31.336
(61.628)
33.809
(70.244)
31.544
(61.618)
34.016
(70.234)
31.353
(61.601)
33.824
(70.214)
Mother,
Estonian
14.562
(44.967)
24.028
(51.254)
14.269
(44.960)
23.735
(51.246)
14.445
(44.947)
23.909
(51.232)
Arabic -67.308(*)
(35.984)
-51.355
(40.925)
-67.332(*)
(35.979)
-51.380
(40.919)
-67.389(*)
(35.968)
-51.441
(40.908)
Age 0.087**
(0.029)
0.080*
(0.033)
0.083*
(0.029)
0.076*
(0.033)
0.087**
(0.029)
0.080*
(0.033)
0.082**
(0.029)
0.075*
(0.033)
0.087**
(0.029)
0.079*
(0.033)
0.082**
(0.029)
0.075*
(0.033)
High School. -2.464
(9.489)
5.947
(10.792)
-0.814
(9.491)
7.648
(10.818)
-2.433
(9.488)
5.978
(10.790)
-0.782
(9.489)
7.679
(10.816)
-2.454
(9.485)
5.953
(10.787)
-0.801
(9.486)
7.656
(10.813)
Vocational
School
0.750
(25.517)
-17.485
(29.021)
6.254
(25.664)
-10.408
(29.252)
0.756
(25.513)
-17.491
(29.016)
6.258
(25.660)
-10.414
(29.248)
0.800
(25.506)
-17.439
(29.008)
6.293
(25.653)
-10.371
(29.239)
Siblings 6.326*
(3.052)
9.577**
(3.472)
6.725*
(3.090)
9.795**
(3.522)
6.325*
(3.052)
9.577**
(3.471)
6.729***
(3.089)
9.799**
(3.521)
6.326*
(3.051)
9.578**
(3.470)
6.731*
(3.088)
9.801**
(3.520)
Father, edu1 -1.557
(13.702)
15.339
(15.583)
0.068
(13.716)
18.264
(15.634)
-1.601
(13.699)
15.299
(15.581)
0.018
(13.714)
18.220
(15.631)
-1.532
(13.695)
15.364
(15.576)
0.087
(13.710)
18.284
(15.627)
Father, edu2 14.519
(28.759)
32.263
(32.708)
15.809
(28.783)
32.688
(32.785)
14.474
(28.754)
32.226
(32.703)
15.771
(28.759)
32.657
(32.780)
14.500
(28.746)
32.247
(32.694)
15.810
(28.751)
32.690
(32.771)
Father, edu3 -8.050
(13.184)
-1.376
(14.995)
-7.987
(13.153)
-1.482
(14.992)
-8.128
(13.182)
-1.445
(14.992)
-8.060
(13.151)
-1.547
(14.990)
-8.091
(13.178)
-1.410
(14.988)
-8.015
(13.147)
-1.504
(14.986)
Mother, edu1 13.911
(14.095)
3.730
(16.030)
14.146
(14.104)
4.061
(16.076)
13.921
(14.092)
3.735
(16.028)
14.153
(14.102)
4.063
(16.074)
13.854
(14.088)
3.670
(16.023)
14.087
(14.098)
3.99
(16.069)
Mother, edu2 32.854
(23.792)
30.620
(27.059)
35.606
(23.795)
32.768
(14.837)
32.842
(23.789)
30.601
(27.056)
35.594
(23.791)
32.749
(27.117)
32.826
(23.782)
30.586
(27.048)
35.576
(23.784)
32.732
(27.110)
Mother, edu3 24.176(*)
(13.033)
20.987
(14.823)
24.861(*)
(13.017)
22.208
(14.837)
24.173(*)
(13.031)
20.975
(14.821)
24.850(*)
(13.015)
22.188
(14.834)
24.136(*)
(13.028)
20.938
(14.817)
24.808(*)
(13.011)
22.146
(14.830)
Boy -22.810**
(8.202)
-35.322***
(9.328)
-23.592**
(8.181)
-35.151***
(9.325)
-22.837**
(8.200)
-35.346***
(9.326)
-23.619**
(8.179)
-35.175***
(9.323)
-22.799**
(8.198)
-35.305***
(9.324)
-23.588**
(8.177)
-35.140***
(9.320)
Constant -27.879**
(9.263)
-34.394***
(10.535)
-30.580***
(9.027)
-40.729***
(10.289)
-27.979**
(9.261)
-34.493***
(10.533)
-30.669***
(9.025)
-40.820***
(10.287)
-28.287**
(9.258)
-34.800***
(10.530)
-30.963***
(9.023)
-41.112***
(10.284)
R2 0.0194 0.0282 0.0178 0.0223 0.0193 0.0281 0.0178 0.0223 0.0193 0.0281 0.0178 0.0223
F 2.67*** 3.44*** 2.44*** 2.82** 2.67*** 3.44*** 2.45** 2.82*** 2.67*** 3.44*** 2.45*** 2.82***
Table 3: SUR estimation results, movies at a cinema or theatre attendance. Edu1 = High/vocational school, Edu2 = College, Edu3 = University/Polytechnic. (+), +, ++, +++ refer to 10%, 5%, 1% or 0,1% statistical significance.
The estimation results (table 3) indicate that being employed has a positive effect on theatre attendance (the
parameter estimate is negative: logw is approximately between -37 and -32 in different models) but not on
movie attendance. The ethnic origin matters only if the young has a Somalian background. They are more
active in going to theatre or movies than other teenagers in Helsinki. Especially, if the mother is a Somalian,
the young visits theatre or movies more often than others. Also the Arabic language knowledge increases the
possibility of cinema attendance. Having an Estonian or Russian background does not result in any
differences from natives in movie or theatre attendance. The age has a negative impact while being a student
in high or vocational school does not affect any differences. The number of siblings reduces the frequencies
of visits; hence boys and girls of a large family go less often. If mother has a university or polytechnic
degree, then the young goes less to the cinema, however the statistical significance is about 6 % (in table 3:
Mothed, edu3 with (*)). Boys attend more than girls when all other factors have been controlled. This result
is verified also with contingency table tests: in the case movies χ2 = 31.467, p = 0.000 and in the case of
theatres χ2 = 85.010, p = 0.000. This result is surprising since in general women are more active in cultural
consumption (Suominen 2013). In this sample of Helsinki boys seem to be more active in participating if
only the two choices “almost daily” and “weekly” are compared. The result that having a highly educated
mother reduces attendance can be explained by the assumption that mothers seem to put more weight on
going to school than leisure activities. χ2 statistic shows that two questions in the questionnaire and mother’s
education are interrelated. These questions are “How would do you evaluate your school achievement?” and
“Do you like to go to school?” In general, the parents’ education and school achievement of children are
strongly correlated (Häkkinen, Kirjavainen and Uusitalo 2003).
m = m1 m = m1 m = m1 m = m1 m = m2 m = m2 m = m2 m = m2 m = m3 m = m3 m = m3 m = m3
Movies Opera or
ballet
Movies Opera or
ballet
Movies Opera or
ballet
Movies Opera or
ballet
Movies Opera or
ballet
Movies Opera or
ballet
log( mle )log( m
lt )log(mle )log( m
lt )log( mle )log( m
lt )log(mle )log(m
lt )log( mle )log(m
lt )log(mle )log(m
lt )
logw -14.407
(15.471)
-24.543
(16.700)
-18.657
(15.440)
-28.213(*)
(16.626)
-14.374
(15.469)
-24.508
(16.697)
-18.620
(15.437)
-28.176(*)
(16.624)
-14.341
(15.465)
-24.466
(16.693)
-18.581
(15.433)
-28.127(*)
(16.619)
Swedish 0.136
(10.621)
0.620
(11.465)
0.187
(10.620)
0.673
(11.463)
0.270
(10.617)
0.754
(11.460)
Russia -23.164
(23.911)
-19.992
(25.810)
-23.251
(23.908)
-20.079
(25.807)
-23.284
(23.901)
-20.114
(25.799)
Father,
Russia
59.665
(45.138)
65.498
(48.607)
59.636
(45.131)
65.469
(48.601)
59.568
(45.119)
65.399
(48.587)
Mother,
Russia
-37.818
(35.250)
-36.613
(37.959)
-37.899
(32.245)
-36.397
(37.954)
-37.533
(35.235)
-36.750
(37.943)
somali -83.927**
(26.358)
-49.456(*)
(28.451)
-83.811**
(26.354)
-49.342(*)
(28.447)
-83.846**
(26.347)
-49.379(*)
(28.439)
Father,
somali
-25.398
(40.413)
-21.613
(43.519)
-25.375
(40.407)
-21.591
(43.513)
-25.533
(40.396)
-21.750
(43.501)
Mother,
somali
-100.845*
(45.953)
-102.624*
(49.485)
-101.003*
(45.946)
-102.783*
(49.478)
-100.952*
(45.933)
-102.731*
(49.464)
Estonian 45.381
(52.004)
47.336
(56.134)
45.402
(51.997)
47.357
(56.127)
45.292
(51.982)
47.245
(56.111)
Father,
Estonian
31.336
(61.628)
35.240
(66.364)
31.544
(61.618)
35.447
(66.355)
31.353
(61.601)
35.255
(66.336)
Mother,
Estonian
14.562
(44.967)
20.327
(48.422)
14.269
(44.960)
20.033
(48.416)
14.445
(44.947)
20.209
(48.402)
Arabic -67.308(*)
(35.984)
-69.635(*)
(38.841)
-67.332(*)
(35.979)
-69.661(*)
(38.836)
-67.389(*)
(35.968)
-69.720(*)
(38.714)
Age 0.087**
(0.029)
0.084**
(0.031)
0.083*
(0.029)
0.079*
(0.031)
0.087**
(0.029)
0.084**
(0.031)
0.082**
(0.029)
0.079*
(0.031)
0.087**
(0.029)
0.084**
(0.031)
0.082**
(0.029)
0.079*
(0.031)
High
School.
-2.464
(9.489)
-0.588
(10.242)
-0.814
(9.491)
0.941
(10.220)
-2.433
(9.488)
-0.558
(10.241)
-0.782
(9.489)
0.972
(10.218)
-2.454
(9.485)
-0.580
(10.238)
-0.801
(9.486)
0.951
(10.216)
Vocationa
l School
0.750
(25.517)
8.835
(27.543)
6.254
(25.664)
13.324
(27.636)
0.756
(25.513)
8.841
(27.539)
6.258
(25.660)
13.329
(27.632)
0.800
(25.506)
8.882
(27.531)
6.293
(25.653)
13.362
(27.625)
Siblings 6.326*
(3.052)
7.604*
(3.294)
6.725*
(3.090)
8.464**
(3.327)
6.325*
(3.052)
7.604*
(3.294)
6.729***
(3.089)
8.469*
(3.326)
6.326*
(3.051)
7.605*
(3.293)
6.731*
(3.088)
8.471*
(3.325)
Father,
edu1
-1.557
(13.702)
6.324
(14.789)
0.068
(13.716)
7.823
(14.770)
-1.601
(13.699)
6.278
(14.787)
0.018
(13.714)
7.772
(14.768)
-1.532
(13.695)
6.346
(14.783)
0.087
(13.710)
7.840
(14.764)
Father,
edu2
14.519
(28.759)
19.376
(31.042)
15.809
(28.783)
20.978
(30.974)
14.474
(28.754)
13.331
(31.038)
15.771
(28.759)
20.940
(30.970)
14.500
(28.746)
19.354
(31.030)
15.810
(28.751)
20.976
(30.961)
Father,
edu3
-8.050
(13.184)
-6.147
(14.231)
-7.987
(13.153)
-5.900
(14.164)
-8.128
(13.182)
-6.225
(14.229)
-8.060
(13.151)
-5.973
(14.162)
-8.091
(13.178)
-6.189
(14.225)
-8.015
(13.147)
-5.930
(14.158)
Mother,
edu1
13.911
(14.095)
14.936
(15.214)
14.146
(14.104)
14.833
(15.188)
13.921
(14.092)
14.947
(15.212)
14.153
(14.102)
14.842
(15.168)
13.854
(14.088)
14.879
(15.207)
14.087
(14.098)
14.774
(15.182)
Mother,
edu2
32.854
(23.792)
37.926
(25.681)
35.606
(23.795)
40.716
(25.623)
32.842
(23.789)
37.914
(25.678)
35.594
(23.791)
40.704
(25.620)
32.826
(23.782)
37.897
(25.671)
35.576
(23.784)
40.686
(25.613)
Mother,
edu3
24.176(*)
(13.033)
34.844*
(14.068)
24.861(*)
(13.017)
34.758*
(14.017)
24.173(*)
(13.031)
34.840*
(14.066)
24.850(*)
(13.015)
34.746*
(14.015)
24.136(*)
(13.028)
34.801*
(14.062)
24.808(*)
(13.011)
34.702*
(14.011)
Boy -22.810**
(8.202)
-32.235***
(8.853)
-23.592**
(8.181)
-33.101***
(8.809)
-22.837**
(8.200)
-32.261***
(8.852)
-23.619**
(8.179)
-33.127***
(8.808)
-22.799**
(8.198)
-32.221***
(8.849)
-23.588**
(8.177)
-33.093***
(8.806)
Constant -27.879**
(9.263)
-36.164***
(9.998)
-30.580***
(9.027)
-38.510***
(9.721)
-27.979**
(9.261)
-36.264***
(9.996)
-30.669***
(9.025)
-38.601***
(9.719)
-28.287**
(9.258)
-36.571***
(9.994)
-30.963***
(9.023)
-38.892
(9.716)
R2 0.0194 0.0217 0.0178 0.0248 0.0193 0.0217 0.0178 0.0248 0.0193 0.0217 0.0178 0.0248
F 2.67*** 2.88*** 2.44*** 3.03*** 2.67*** 2.87*** 2.45** 3.03*** 2.67*** 2.87*** 2.45*** 3.03***
Table 4: SUR estimation results, movies at a cinema and opera or ballet attendance. Edu1 = High/vocational school, Edu2 = College, Edu3 = University/Polytechnic. (+), +, ++, +++ refer to 10%, 5%, 1% or 0,1% statistical significance.
Above, in Table 4, the movies and opera or ballet attendance is presented using the SUR method. The results
for movies are identical to table 3. Employment of a young person has a positive impact on opera or ballet
attendance although the significance is only 10 %. However, that result is in line with results of the theatre
attendance in Table 3. On the contrary employment status has neither impact on movie attendance nor the
concert (table 5) or art exhibition (table 6) visits. The results in tables 3 and 4 indicate that money received
through the own employment of a teenager has some effect on theatre and opera or ballet visits.
m = m1 m = m1 m = m1 m = m1 m = m2 m = m2 m = m2 m = m2 m = m3 m = m3 m = m3 m = m3
Movies Concert Movies Concert Movies Concert Movies Concert Movies Concert Movies Concert
log( mle )log(m
lt )log(mle )log(m
lt )log( mle )log(m
lt )log( mle )log(m
lt )log( mle )log(m
lt )log( mle )log(m
lt )logw -14.407
(15.471)
-21.071
(16.922)
-18.657
(15.440)
-24.567
(16.853)
-14.374
(15.469)
-21.041
(16.920)
-18.620
(15.437)
-24.534
(16.851)
-14.341
(15.465)
-21.004
(16.915)
-18.581
(15.433)
-24.490
(16.846)
Swedish 0.136
(10.621)
0.921
(11.617)
0.187
(10.620)
0.980
(11.616)
0.270
(10.617)
1.056
(11.613)
Russia -23.164
(23.911)
-23.520
(26.153)
-23.251
(23.908)
-23.607
(26.150)
-23.284
(23.901)
-23.641
(26.143)
Father, Russia 59.665
(45.138)
65.043
(49.272)
59.636
(45.131)
65.014
(49.265)
59.568
(45.119)
64.947
(49.251)
Mother, Russia -37.818
(35.250)
-37.862
(38.478)
-37.899
(32.245)
-37.944
(38.473)
-37.533
(35.235)
-38.015
(38.462)
somali -83.927**
(26.358)
-45.433
(28.830)
-83.811**
(26.354)
-45.317
(28.826)
-83.846**
(26.347)
-45.354
(28.818)
Father, somali -25.398
(40.413)
-17.119
(44.114)
-25.375
(40.407)
-17.097
(44.108)
-25.533
(40.396)
-17.255
(44.096)
Mother,
somali
-100.845*
(45.953)
-98.571*
(50.162)
-101.003*
(45.946)
-98.729*
(50.155)
-100.952*
(45.933)
-98.679*
(50.141)
Estonian 45.381
(52.004)
49.083
(56.881)
45.402
(51.997)
49.105
(56.873)
45.292
(51.982)
48.994
(56.857)
Father,
Estonian
31.336
(61.628)
36.049
(67.271)
31.544
(61.618)
36.257
(67.262)
31.353
(61.601)
36.066
(67.243)
Mother,
Estonian
14.562
(44.967)
18.537
(49.085)
14.269
(44.960)
18.244
(49.078)
14.445
(44.947)
18.421
(49.064)
Arabic -67.308(*)
(35.984)
-66.662(*)
(39.358)
-67.332(*)
(35.979)
-66.685(*)
(39.353)
-67.389(*)
(35.968)
-66.744(*)
(39.342)
Age 0.087**
(0.029)
0.084**
(0.032)
0.083*
(0.029)
0.079*
(0.032)
0.087**
(0.029)
0.084**
(0.032)
0.082**
(0.029)
0.079*
(0.032)
0.087**
(0.029)
0.084**
(0.032)
0.082**
(0.029)
0.079*
(0.032)
High School. -2.464
(9.489)
-2.467
(10.379)
-0.814
(9.491)
-0.873
(10.360)
-2.433
(9.488)
-2.436
(10.377)
-0.782
(9.489)
-0.841
(10.358)
-2.454
(9.485)
-2.455
(10.374)
-0.801
(9.486)
-0.859
(10.355)
Vocational
School
0.750
(25.517)
14.284
(27.909)
6.254
(25.664)
18.637
(28.014)
0.756
(25.513)
14.291
(27.905)
6.258
(25.660)
18.642
(28.010)
0.800
(25.506)
14.333
(27.898)
6.293
(25.653)
18.676
(28.002)
Siblings 6.326*
(3.052)
3.939
(3.338)
6.725*
(3.090)
4.750
(3.372)
6.325*
(3.052)
3.938
(3.338)
6.729***
(3.089)
4.754
(3.372)
6.326*
(3.051)
3.941
(3.337)
6.731*
(3.088)
4.756
(3.371)
Father, edu1 -1.557
(13.702)
-7.567
(14.986)
0.068
(13.716)
-6.028
(14.972)
-1.601
(13.699)
-7.607
(14.984)
0.018
(13.714)
-6.073
(14.970)
-1.532
(13.695)
-7.539
(14.980)
0.087
(13.710)
-6.005
(14.966)
Father, edu2 14.519
(28.759)
14.009
(31.455)
15.809
(28.783)
15.558
(31.397)
14.474
(28.754)
13.966
(31.451)
15.771
(28.759)
15.523
(31.393)
14.500
(28.746)
13.990
(31.442)
15.810
(28.751)
15.559
(31.384)
Father, edu3 -8.050
(13.184)
-9.915
(14.420)
-7.987
(13.153)
-9.597
(14.358)
-8.128
(13.182)
-9.991
(14.418)
-8.060
(13.151)
-9.668
(14.356)
-8.091
(13.178)
-9.956
(14.414)
-8.015
(13.147)
-9.625
(14.352)
Mother, edu1 13.911
(14.095)
13.620
(15.416)
14.146
(14.104)
13.517
(15.396)
13.921
(14.092)
13.627
(15.414)
14.153
(14.102)
13.521
(15.394)
13.854
(14.088)
13.568
(14.410)
14.087
(14.098)
13.463
(15.389)
Mother, edu2 32.854
(23.792)
41.612
(26.023)
35.606
(23.795)
44.234(*)
(25.974)
32.842
(23.789)
41.595
(26.020)
35.594
(23.791)
44.219(*)
(25.970)
32.826
(23.782)
41.583
(26.012)
35.576
(23.784)
44.205(*)
(25.963)
Mother, edu3 24.176(*)
(13.033)
36.509**
(14.255)
24.861(*)
(13.017)
36.383**
(14.209)
24.173(*)
(13.031)
36.503**
(14.253)
24.850(*)
(13.015)
36.368**
(14.207)
24.136(*)
(13.028)
36.468**
(14.249)
24.808(*)
(13.011)
36.329**
(14.203)
Boy -22.810**
(8.202)
-34.151***
(8.971)
-23.592**
(8.181)
-34.921***
(8.930)
-22.837**
(8.200)
-34.180***
(8.969)
-23.619**
(8.179)
-34.951***
(8.929)
-22.799**
(8.198)
-34.141***
(8.967)
-23.588**
(8.177)
-34.917***
(8.926)
Constant -27.879**
(9.263)
-27.031**
(10.131)
-30.580***
(9.027)
-29.390**
(9.853)
-27.979**
(9.261)
-27.130**
(10.129)
-30.669***
(9.025)
-29.477**
(9.852)
-28.287**
(9.258)
-27.441**
(10.127)
-30.963***
(9.023)
-29.775**
(9.849)
R2 0.0194 0.0183 0.0178 0.0207 0.0193 0.0183 0.0178 0.0207 0.0193 0.0183 0.0178 0.0207
F 2.67*** 2.57*** 2.44*** 2.68*** 2.67*** 2.57*** 2.45** 2.68*** 2.67*** 2.57*** 2.45*** 2.68***
Table 5: SUR estimation results, movies at a cinema and concert attendance. Edu1 = High/vocational school, Edu2 = College, Edu3 = University/Polytechnic. (+), +, ++, +++ refer to 10%, 5%, 1% or 0,1% statistical significance.
The estimation results in table 4 and 5 reveal that the knowledge of Somali or Arabic is related to a higher
tendency to visit opera or ballet and concerts. Boys seem to have more chances to visit these cultural events
than girls when all other relevant factors have been considered. If mother has either college or
university/polytechnic education, then we see fewer visits while father’s education is not significant.
However, in the case of mother having a Somalian or Arabic background, the diminishing effect is lower
since teenagers in Helsinki with knowledge in Somali or Arabic seem to visit more often than other
teenagers. The number of siblings does not have any effect on visits to concerts while it reduces all other
cultural event participation.
m = m1 m = m1 m = m1 m = m1 m = m2 m = m2 m = m2 m = m2 m = m3 m = m3 m = m3 m = m3
Movies Art
exhibition
Movies Art
exhibition
Movies Art
exhibition
Movies Art
exhibition
Movies Art
exhibition
Movies Art
exhibition
log(mle )log( m
lt ) log( mle )log( m
lt ) log(mle )log( m
lt ) log( mle )log(m
lt ) log( mle )log( m
lt ) log( mle )log(m
lt )logw -14.407
(15.471)
-18.999
(17.271)
-18.657
(15.440)
-23.307
(17.210)
-14.374
(15.469)
-18.966
(17.268)
-18.620
(15.437)
-23.271
(17.208)
-14.341
(15.465)
-18.926
(17.264)
-18.581
(15.433)
-23.224
(17.203)
Swedish 0.136
(10.621)
2.098
(11.857)
0.187
(10.620)
2.150
(11.855)
0.270
(10.617)
2.230
(11.852)
Russia -23.164
(23.911)
-41.020
(26.693)
-23.251
(23.908)
-41.098
(26.689)
-23.284
(23.901)
-41.118
(26.682)
Father,
Russia
59.665
(45.138)
101.876*
(50.315)
59.636
(45.131)
101.832*
(50.308)
59.568
(45.119)
101.736*
(50.294)
Mother,
Russia
-37.818
(35.250)
-86.111*
(39.293)
-37.899
(32.245)
-86.172*
(39.287)
-37.533
(35.235)
-86.208*
(39.276)
somali -83.927**
(26.358)
-78.773**
(29.424)
-83.811**
(26.354)
-78.659**
(29.420)
-83.846**
(26.347)
-78.696**
(29.412)
Father,
somali
-25.398
(40.413)
-16.162
(45.048)
-25.375
(40.407)
-16.142
(45.042)
-25.533
(40.396)
-16.305
(45.029)
Mother,
somali
-100.845*
(45.953)
-109.141*
(51.223)
-101.003*
(45.946)
-109.297*
(51.216)
-100.952*
(45.933)
-109.240*
(51.202)
Estonian 45.381
(52.004)
61.608
(58.053)
45.402
(51.997)
61.625
(58.046)
45.292
(51.982)
61.506
(58.029)
Father,
Estonian
31.336
(61.628)
47.007
(68.695)
31.544
(61.618)
47.210
(68.686)
31.353
(61.601)
47.009
(68.667)
Mother,
Estonian
14.562
(44.967)
16.153
(50.124)
14.269
(44.960)
15.862
(50.117)
14.445
(44.947)
16.042
(50.103)
Arabic -67.308(*)
(35.984)
-62.600
(40.169)
-67.332(*)
(35.979)
-62.626
(40.164)
-67.389(*)
(35.968)
-62.685
(40.153)
Age 0.087**
(0.029)
0.081*
(0.032)
0.083*
(0.029)
0.077*
(0.032)
0.087**
(0.029)
0.081*
(0.032)
0.082**
(0.029)
0.077*
(0.032)
0.087**
(0.029)
0.081*
(0.032)
0.082**
(0.029)
0.077*
(0.032)
High School. -2.464
(9.489)
-5.363
(10.593)
-0.814
(9.491)
-2.984
(10.579)
-2.433
(9.488)
-5.331
(10.591)
-0.782
(9.489)
-2.951
(10.577)
-2.454
(9.485)
-5.352
(10.588)
-0.801
(9.486)
-2.971
(10.574)
Vocational
School
0.750
(25.517)
7.988
(28.484)
6.254
(25.664)
14.848
(28.607)
0.756
(25.513)
7.994
(28.481)
6.258
(25.660)
14.852
(28.603)
0.800
(25.506)
8.036
(28.473)
6.293
(25.653)
14.884
(28.595)
Siblings 6.326*
(3.052)
7.956*
(3.407)
6.725*
(3.090)
8.281*
(3.443)
6.325*
(3.052)
7.956*
(3.407)
6.729***
(3.089)
8.285*
(3.443)
6.326*
(3.051)
7.956*
(3.406)
6.731*
(3.088)
8.287*
(3.442)
Father, edu1 -1.557
(13.702)
12.554
(15.295)
0.068
(13.716)
14.641
(15.289)
-1.601
(13.699)
12.506
(15.293)
0.018
(13.714)
14.588
(15.287)
-1.532
(13.695)
12.572
(15.289)
0.087
(13.710)
14.654
(15.282)
Father, edu2 14.519
(28.759)
29.190
(32.104)
15.809
(28.783)
30.072
(32.062)
14.474
(28.754)
29.141
(32.100)
15.771
(28.759)
30.031
(32.058)
14.500
(28.746)
29.162
(32.090)
15.810
(28.751)
30.064
(32.049)
Father, edu3 -8.050
(13.184)
3.882
(14.717)
-7.987
(13.153)
3.933
(14.662)
-8.128
(13.182)
3.800
(14.716)
-8.060
(13.151)
3.856
(14.660)
-8.091
(13.178)
3.833
(14.711)
-8.015
(13.147)
3.896
(14.655)
Mother, edu1 13.911
(14.095)
10.443
(15.734)
14.146
(14.104)
11.243
(15.722)
13.921
(14.092)
10.456
(15.732)
14.153
(14.102)
11.254
(15.719)
13.854
(14.088)
10.390
(15.727)
14.087
(14.098)
11.187
(15.715)
Mother, edu2 32.854
(23.792)
31.829
(26.560)
35.606
(23.795)
35.175
(26.523)
32.842
(23.789)
31.819
(26.556)
35.594
(23.791)
35.254
(26.520)
32.826
(23.782)
31.803
(26.549)
35.576
(23.784)
35.148
(26.512)
Mother, edu3 24.176(*)
(13.033)
21.764
(14.549)
24.861(*)
(13.017)
22.812
(14.510)
24.173(*)
(13.031)
21.765
(14.547)
24.850(*)
(13.015)
22.805
(14.508)
24.136(*)
(13.028)
21.730
(14.543)
24.808(*)
(13.011)
22.765
(14.503)
Boy -22.810**
(8.202)
-34.823***
(9.155)
-23.592**
(8.181)
-35.697***
(9.119)
-22.837**
(8.200)
-34.849***
(9.154)
-23.619**
(8.179)
-35.723***
(9.118)
-22.799**
(8.198)
-34.810***
(9.152)
-23.588**
(8.177)
-35.690***
(9.115)
Constant -27.879**
(9.263)
-35.168***
(10.340)
-30.580***
(9.027)
-37.870***
(10.062)
-27.979**
(9.261)
-35.268***
(10.338)
-30.669***
(9.025)
-37.960***
(10.061)
-28.287**
(9.258)
-35.574***
(10.335)
-30.963***
(9.023)
-38.251***
(10.058)
R2 0.0194 0.0228 0.0178 0.0241 0.0193 0.0228 0.0178 0.0241 0.0193 0.0228 0.0178 0.0241
F 2.67*** 2.97*** 2.44*** 2.97*** 2.67*** 2.97*** 2.45** 2.97*** 2.67*** 2.97*** 2.45*** 2.97***
Table 6: SUR estimation results, movies at a cinema and art exhibition visits. Edu1 = High/vocational school, Edu2 = College, Edu3 = University/Polytechnic. (+), +, ++, +++ refer to 10%, 5%, 1% or 0,1% statistical significance.
Table 6 presents the results of movies and art exhibition visits. In the case of art exhibitions, the employment
status of the teenager or mother’s education or the knowledge to use Arabic are not significant. Gender, age,
the number of siblings or the knowledge of Somali are significant. The effect is in line with other cultural
activities: boys seem to visit more often while age and the number of siblings have a negative impact on
visiting art exhibitions. The knowledge of Somali seems to increase art exhibition visits. However, the
findings related to Somali or Arabic must be interpreted with caution since the data has a limited amount of
those that can speak and write Somali or Arabic.
The last table 7 contains all statistically significant variables with a sign corrected so that the true effect is
seen. For example, if the teenager in Helsinki is employed, she/he has a higher probability to attend theatre
and opera or ballet (with the significant level of 10 %).
Movies Theatre Opera or ballet Concert Art exhibition
Young
employed
(+) (+)
Somali language/mother Somali
+++/+ +++/(+) (+)/+ /+ ++/+
Arabic (+) (+) (+)
Age -- - -- -- -
Siblings - -- - -
Mother has
college
education
(-)
Mother has
univ./pol.
degree
(-) - --
Boy ++ +++ +++ ++ +++
Table 7: All statistically significant variables that have an impact on cultural event participation. (+), +, ++, +++ refer to 10%, 5%, 1% or 0,1% statistical significance.
The employment status has no effect on the movie, concert or art exhibition attendance. Is it so that a young
visiting theatre and opera or ballet is also active in working during their leisure? Is it so that working enables
them to visit rather expensive theatre and opera or ballet? The ability to speak and write Somali or Arabic is
positively related to almost all cultural activities, therefore we can argue that immigration status (either first
generation immigrants, i.e. born outside Finland or second generation immigrants, i.e. parents born outside
Finland) does not reduce cultural participation. Age has negative impact on visiting activity as well the
number of siblings. Teenager with many sisters and brothers seems to have fewer possibilities to visit
different cultural events. Mother’s education (more than primary school) has a negative effect on attending
concerts, opera or ballet and movies at the cinema. Perhaps highly educated mother is reducing their
children’s leisure activities and they emphasize more school achievement.
A movie at the cinema has been during the last 60 or 70 years the most popular cultural event among
teenagers. The frequency of visits has doubled since 1951. The other cultural events or activities are far
behind movies in the popularity. Theatre, opera or ballet, concert and art exhibition have throughout the last
60 or 70 years been behind movies. Contrary to Irish results (Coughlan et al 2014) teenagers with immigrant
status do not seem to be less active participants in various cultural activities. In that respect, we see no
differences based on the ethnic background in Helsinki. The possible differences can be explained by the age
of the teenager, the number of siblings and her/his mother’s education.
Conclusions
This study investigated the cultural activity of adolescents in Helsinki. A movie at the cinema was the most
popular mode in the sample collected in 2011. Opera or ballet or art exhibitions were least visited. The
previous literature and studies have shown that gender, age, education and incomes or wealth influence
participation activity. Adolescents are still pupils or students; therefore, the education measure is their
parents’ education that seems to influence the cultural activity. In Helsinki, roughly 12 – 13 % of adolescents
have immigration status; therefore, the novelty of this study is to introduce the impact of this immigration
status on cultural participation.
The results indicate that being (part time) employed has a positive effect on theatre and opera or ballet visits
but no statistical effect on movies, concert or art exhibition participation. No immigration group has fewer
visits to these cultural events than teenagers with native Finns. On the contrary adolescents that can speak
and write Somali or Arabic seem to visit every cultural activity more often. However, the share of those is
rather small, so the result must be interpreted with caution. The age and the number of siblings seem to
reduce cultural participation during their leisure time. Moreover, if mother has a high education, then the
frequency of visits is lower. Father’s education has no statistically significant effect. Surprisingly, boys seem
to participate more than girls if all other relevant factors have been controlled.
To conclude, adolescents with the immigration background do not participate less in various cultural
activities. If they visit less often, the explanation is either age, the number of siblings or employment status,
i.e. they are not working part time.
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