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Cultural demand of adolescents in Helsinki 2011 Seppo Suominen, Haaga-Helia University of Applied Sciences [email protected] Hietakummuntie 1 A, FIN-00700 Helsinki, Finland

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Cultural demand of adolescents in Helsinki 2011

Seppo Suominen, Haaga-Helia University of Applied Sciences

[email protected]

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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