the third international self-report study of delinquency ... · 2.3 family life, parental control...
TRANSCRIPT
The Third International Self-report Study of Delinquency
among Juveniles in Switzerland and in Indonesia
Report to the Swiss National Science Foundation
(Project 100015_138401/1)
on the Survey conducted in Switzerland
by
Martin Killias & Anastasiia Monnet Lukash
University of St. Gallen
Forschungsgemeinschaft Rechtswissenschaft
Tigerbergstrasse 21
CH-9000 St. Gallen
1st September 2015
1
Index Page
Chapter 0. Introduction
01. Background 6
02. The Swiss Report 6
03. Fieldwork in Switzerland 7
04. Acknowledgements 7
05. Abbreviations 9
Chapter 1. Juvenile Delinquency in Switzerland: An Overview
1.1. What this Chapter is about 10
1.2. Social background variables 10
1.2.1 Gender and age in our samples 10
1.2.2 Birthplace 11
1.2.3 Family constellations 14
1.2.4 Religion 14
1.2.5 Employment and economic wellbeing 16
1.2.6 Changes between 2006 and 2013 19
1.3 Association between social background and delinquency 24
1.3.1 Gender, age and delinquency 24
1.3.2 Birthplace and delinquency 25
1.3.3 Family constellation and delinquency 32
1.3.4 Religion and delinquency 33
1.3.5 Economic status and delinquency 35
1.4 Comparison between ISRD-2 and ISRD-3 38
1.4.1 Trends in delinquency 38
1.4.2 Demographic characteristics and delinquency 38
1.4.3 Economic status and delinquency in 2006 vs. 2013 40
2
1.5 Victimization and demographic background 42
1.5.1 Gender and age 42
1.5.2 Birthplace and victimization 44
1.5.3 Different family configurations and delinquency 48
1.5.4 Social standing and economic well-being 49
1.6 Conclusions: Factors of delinquency and victimization 53
Chapter 2. Family, parental control and delinquency
2.1 Overview 54
2.2 Family life and parental control across regions 54
2.2.1 Dinner with family 54
2.2.2 Parental control 56
2.3 Family life, parental control and delinquency 61
2.3.1 Family life and delinquency 61
2.3.2 Parental control and delinquency 62
2.4 Conclusions 69
Chapter 3. Attitudes towards and perceptions about the school, school
performance and delinquency
2.1 Overview 70
2.2 Descriptive Statistics 71
2.2.1 Feelings about school 71
2.2.2 Perceptions about the school environment 75
2.2.3 Performance at school and truancy 77
3.3 Association between school-related variables and delinquency 82
3.3.1 Attitudes towards school and delinquency 82
3.3.2 Problems in the school environment and delinquency 86
3.4 Main findings and discussion 90
Chapter 4. Victimization
3
4.1 Overview 92
4.2 Descriptive Statistics 92
4.2.1 The prevalence of victimization across regions 92
4.2.2 More victimizations and less reporting? 93
4.3 Association between demographic variables, victimization and reporting to
the police
95
4.3.1 Gender, victimization and reporting to the police 95
4.3.2 Birthplace, victimization and reporting to the police 95
4.3.2.1 Country of birth and victimization 95
4.3.2.2 Reporting to the police by country of birth 100
4.3.3 Religion and victimization 102
4.4 Main findings and discussion 103
Chapter 5. Leisure time, life style, peers, happiness and delinquency
5.1 Overview 105
5.2 Frequencies in the national and the regional samples
5.2.1 Leisure-time activities 105
5.2.2 Peers and friends 112
5.3 Consumption of alcohol and cannabis 116
5.3.1 Leisure-time and use of substances 116
5.3.2 Peers, friends and the use of substances 127
5.4 Computer games as other types of leisure-time activities 131
5.5 Changes in leisure-time activities between 2006 and 2013 135
5.6 Happiness, leisure-time, peers and substance use 144
5.7 Leisure-time, life style, peers, happiness and delinquency 155
5.7.1 Leisure-time and delinquency 155
5.7.2 Peer-networks and delinquency 162
5.7.3 Problem behaviour and delinquency 165
5.7.4 Are delinquents happy? 168
5.7.5 Delinquency by computer games 169
4
5.8 Summary and discussion 171
Chapter 6. Perception of right and wrong
6.1 Overview 174
6.2 Moral judgements across regions 174
6.2.1 Perception of right and wrong 174
6.2.2 Feelings of shame 177
6.3 Self-control, risky behaviour and altruistic vs. egocentric attitudes 183
6.3.1 Risk taking and accidents 183
6.3.2 Egocentrism 189
6.4 Good and bad neighbourhoods 191
Chapter 7. Delinquency over space and time in various degrees of
seriousness
7.1 Overview 199
7.2 Delinquency across Switzerland’s several regions 199
7.3 Trends in delinquency over time 200
7.4 Versatility: Are offenders specialists or generalists 203
7.5 Conclusion 205
Chapter 8. Contact with the police and perception of the image of the
police
8.1 Overview 207
8.2 Experience with and attitudes to the police 207
8.2.1 Attitudes across regions 207
8.2.1.1 The prevalence of contact with police because of illegal behaviour 207
8.2.1.2 Opinion of young people about the police 207
8.2.2 Opinion of young people about the police by respondents’ birthplace 216
5
8.2.3 Opinion of young people about the police by delinquency 221
8.3 Conclusion 230
Chapter 9. Technical report
9.1 Background 231
9.2 Sample design 231
9.2.1 Step 1. Selection of schools 232
9.2.2 Step 2. Collecting information about number of classes 232
9.2.3 Step 3. Random selection of classes 232
9.2.4 Step 4. Additional sample 232
9.3 Fieldwork 232
9.3.1. Contacting schools 233
9.3.2. Data collection 233
9.3.3. Interviews and teachers 235
9.3 Questionnaire content and development 235
9.4 Interviews and teachers 235
9.5 Computer and Internet 236
9.6 Data processing and output 237
9.7 Data weighting 237
1. Attachment 1. 239
2. Attachment 2. 241
3. Attachment 3 242
4. Attachment 4 250
5. Attachment 5 252
6. Attachment 6 252
7. Attachment 7.1-7.3 252
8. Attachment 8. 252
9. Attachments 9 252
6
Chapter 0
Introduction
0.1 Background
It is about 25 years ago when, in 1989, some 20 specialists of self-report surveys gathered in a
conference hotel in Nordwijkerhout (NL) to discuss the feasibility of an international survey of
juvenile delinquency using the method of self-reported questionnaires. The first author of this
report was invited to participate in that meeting, as a specialist of conducting international,
comparative victimization surveys. Indeed, the first international crime victimization survey (van
Dijk, Mayhew &Killias 1990) had been in the field exactly about at that time. Encouraged by the
success of that first truly international survey in the field of criminology, Dr.JosineJunger-Tas
gathered around her a first steering group called to organize what then, in 1992, became the First
International Self-reported Delinquency Survey (ISRD-1) in which 12 countries had participated
(Junger-Tas, Terlouw& Klein 1994). Switzerland had been on board and it remained so at every
sweep ever since with a national sample.
After that first attempt, it was felt that next sweeps would need to be more tightly standardized.
The Steering Committee (of which the first author has remained a member since that time)
started working on a new project from 2003. In 2006, a second survey was launched, with about
30 countries participating (ISRD-2). This larger project has led to two major publications
(Junger-Tas et al. 2010, 2012). Already during the preparation of these two volumes, a new wave
(ISRD-3) has been organized in which again some 30 countries are participating. The present
report represents, so to speak, the first results of this new initiative, given that Switzerland and
Finland are among the first to have their results ready for analysis and distribution. As with
ISRD-2, the data of ISRD-3 will be publically accessible once the main analyses have been
achieved. The data of all countries participating will be introduced into a central database located
at the University of Hamburg. Since data collection is still ongoing in many countries, complete
international analyses will be available not before 2016 at best. Since Switzerland has, through
funds made available by the Swiss National Science Foundation (Switzerland and Indonesia), the
Federal Office of Migration (Serbia) and the Jacobs Foundation (Ukraine, Armenia, Bosnia-
Herzegovina, Macedonia, Kosovo and India), coordinated the ISRD-3 in nine countries all in all,
we expect being able to present at least some comparative international data in the course of
2015 already.
0.2 The Swiss Report
The present report has been prepared keeping in mind that the first priority should be
documenting the Swiss National Science Foundation with all details about the realization of this
study that might be relevant to researchers who consider using these data for their own work. For
that purpose, as well as for the Members of the SNF Council, having a detailed overview about
what is in this project should be the priority. We have, therefore, presented several hundred
mostly bivariate Figures and Tables that document the wealth of the questionnaire used and the
7
potential of a fairly large sample. Indeed, the 8 Chapters that follow give an account of correlates
of juvenile delinquency that go beyond what has usually been communicated in research papers
on this subject. Many more Figures and Tables have been prepared but, finally, eliminated from
this report in order to keep it within reasonable size. They can be obtained from the authors on
request. This broad thematic perspective has obviously had its price in the sense that we could
not, at the same time and with the given resources, prepare multivariate analyses. Over the
upcoming months, we are going to prepare a series of conference papers and publications
devoted to several aspects of juvenile delinquency where more advanced methods of data
analysis will be used. A second compromise required to refer to the existing literature only
parsimoniously, i.e. at the end of each of the following chapters and to the extent it seemed
necessary to explain certain findings in a broader context. Without such restrictions, the present
report would have grown beyond any reasonable proportions.
The report of the project in Indonesia has been completed and presented to the Swiss National
Science Foundation. We are happy that the Indonesian research team of the University of
Surabaya had the courage and the energy to overcome all the endless obstacles (natural disasters
and political troubles) that could not be reasonably anticipated.
0.3 Fieldwork in Switzerland
The fieldwork in Switzerland was relatively demanding, since more than over 200 classes out of
more than 100 schools had to be contacted. In the end, more than 4’158 students in grades 7 to 9
have participated. Three cantons – St. Gallen, Aargau and Ticino – expressed interest in having a
deeper, more detailed analysis of the situation in their canton.
Detailed indications on sampling, weighting and other aspects of the methodology can be found
in the Technical Report (Chapter 9).
0.4 Acknowledgements
There are many people who, at some time or another over the last four years, have helped to
bring this project to a good end. Our first thank goes to the Swiss National Science Foundation
and in particular to Dr. Kathrin Weder whose support was absolutely decisive in the start and the
realization of the project. We further acknowledge the kind and generous technical support that
we received, all along this project, from other Members of the ISRD Steering Committee and in
particular from Prof.Dr. I. Haen Marshall, from Dr. D. Enzmann, Prof.Dr. M. Steketee and
Prof.Dr. J. Kivivuori. Regarding the survey in Switzerland, we received kind and efficient
support from the Direction of the Conference of Education Departments, namely former cantonal
Minister I. Chassot, Secretary General H. Ambühl and Dr. T. Mattmann-Arnold, from the many
cantonal Departments who authorized us to interview students in their schools, from nearly 100
school principals and, last but not least, from nearly 200 school teachers. We feel deeply
indebted to all these personalities and express here our sincere gratitude, including to all those
that we omit mentioning here by their names but who have in many ways contributed to the
success of this project.
8
St. Gallen/Lenzburg, 31 January 2015
Martin Killias
AnastasiiaMonnet Lukash
References
Junger-Tas J., TerlouwG.J. & Klein M.W. (1994)Delinquent Behaviour among Young People in the
Western World, Dordrecht/Boston: Kluwer 1994
Junger-Tas J., Marshall I., Enzmann D., Killias M., Gruszczynska B., Steketee M. (2010) Juvenile
Delinquency in Europe and Beyond: Results of the Second International Self-Report Delinquency
Study.Berlin/New York: Springer 2010
Steketee M., Moll M., Kapardis A. (Editors) Juvenile delinquency in six new EU member states.January
2008. Utrecht: Verwey-JonkerInstituut, 2008. 190 p. P.16
Junger-Tas J., Marshall I., Enzmann D., Killias M., Steketee M., Gruszczynska B. (2012) The Many
Faces of Youth Crime: Contrasting Theoretical Perspectives on Juvenile Delinquency across Countries
and Cultures.Berlin/New York: Springer 2012
vanDijk J.J.M., Mayhew P.&Killias M. (1990)Experiences of Crime across the World. Key findings of the
1989 International Crime Survey, Deventer/Boston: Kluwer 1990
9
0.5.Abbreviations
CH = Switzerland
DE = German speaking part of Switzerland
FR – French speaking part of Switzerland
ITA – Italian speaking part (canton of Ticino)
SG – the canton of St. Gallen
AG – the canton of Aargau
GE – the canton of Geneva
ZH – the canton of Zurich
ltp – life time prevalence
lyp – last year prevalence
lmp – last month prevalence
IV – independent variable
DV – dependent variable
*** p≤.001 (high significant)
** .001<p≤.010 (significant)
* .010<p≤.050 (nearly significant)
n.s. p>.050 (not significant)
10
Chapter 1
Juvenile Delinquency in Switzerland: An Overview
1.1.What this Chapter is about
Module 1 of the ISRD-3 questionnaire includes questions on the social background of
respondents. In this Chapter, we shall present basic demographic information on our sample,
both for the national as well as the several regional subsamples, i.e. on variables including age,
sex, migrant status, religion and its importance, living arrangement, family influence, and
ethnicity. In the second part, we shall present basic findings about delinquency and its
distribution across space and time, as well as basic associations with social background
variables, such as social wellbeing, ethnicity, religion, family constellation etc. We shall
conclude with some information about victimization.
1.2. Social background variables
1.2.1. Gender and age in our samples
Table 1.1 informs about the distribution of male and female respondents across the national and
the various regional subsamples.
Table 1.1 Gender by main sample and subsamples in %. Weighted data.
Switzer
land DE FR
ITA
(TI) SG AG GE ZH
N= 4158 2560 956 642 625 555 268 266
male 48.7 48.5 48.6 51.8 47.8 52.4 47.7 44.9
female 51.3 51.5 51.4 48.2 52.2 47.6 52.3 55.1
Gender distribution in the national and the various regional samples is fairly even. With a
proportion of about 50 per cent of boys and girls, the samples are in line with the gender
distribution in the Swiss general population aged 12-16. According to population statistics of the
Swiss Federal Statistical Office, 51.4% of the age-group 12-16 is male1.
1 Bevölkerung nach Alter und Geschlecht. Swiss Federal Statistical Office (FSO)
http://www.bfs.admin.ch/bfs/portal/de/index/themen/01/02/blank/key/alter/nach_geschlecht.html
11
Table 1.2 Age of respondents in %. Weighted data.
Age % in the main sample % in total population 2
12 6.1
13 26.1 24.8
14 31.2 24.7
15 25.6 25.1
16 9.6 25.5
17-19 1.4
The age distribution in our national sample matches fairly well the student population in 7th
– 9th
grades of secondary schools. The proportion of students below 13 and above 16 is small, since
these respondents may have experienced a somewhat unusual school career. In sum, the age and
gender distribution in our sample matches population data very closely.
1.2.2. Birthplace
Since the ISRD-3 was designed to be conducted in some 30 countries around the globe, the
instrument needed to take very different situations of migration into account. The solution
adopted was to ask about country of birth of respondents and of their mothers and fathers, rather
than about nationality in a formal sense. Since these questions were asked the same way in
ISRD-2, comparisons between 2006 and 2013 are possible.
Figure 1.1 Respondents’ birthplace by main and subsamples in %3.
2Swiss Federal Statistical Office (FSO), same source as in Footnote 1 (Tables je-d-01.02.01.02.03). Respondents of
the survey were supposed to be in the 13-15 years range. Some younger and older subjects are in our sample
because students were surveyed in classes.
3 Weighted data
13.8 10.5
21.3
13.7 14.0 11.6
26.9
9.8
2.3 2.8 1.5 1.3 3.6 4.4
1.2 2.5 3.9 2.8 5.6 6.5
4.5 3.0 7.5
1.8
7.7 4.9
14.2
6.0 6.0 4.2
18.2
5.5
0.0
10.0
20.0
30.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
Born NOT in Switzerland
Ex-Yugoslavia
West Europe
Other (countries of Asia, Africa, N. America, S. America, E. Europe, C. Europe)
12
Nationwide, 14% of respondents were born outside of Switzerland. This percentage is 11% in
German-speaking cantons and about twice that high in the Romandie (21%). The countries of
birth are predominantly located in Asia, Africa, Americas, and Central/Eastern Europe. In
comparison with 2006, fewer students were born in the countries of Ex-Yugoslavia,
Figure 1.2 Fathers’ birthplace by main and subsamples in %4.
Fathers were more often born abroad than our respondents. In the Romandie, in Zurich and
Geneva, close to, or over 50 per cent, of respondents have a father born abroad.As the following
Figure illustrates, mothers were also very often born abroad.
4 Weighted data
38.2
33.9
47.1
43.7
35.7
38.7
71.0
37.8
5.9
5.0
4.7
24.0
4.5
6.9
7.4
6.4
4.9
5.4
4.2
1.3
6.3
6.9
7.0
6.4
4.8
5.8
2.5
5.0
8.9
9.1
1.2
4.4
2.7
3.5
1.1
.7
3.8
3.3
.4
2.3
4.0
.9
10.8
5.2
.9
.7
18.5
.2
4.3
2.5
8.8
1.9
4.3
2.2
13.6
3.6
2.2
1.1
5.1
.9
1.1
2.3
6.3
.2
6.5
7.5
5.0
2.1
4.9
6.1
10.5
9.4
2.1
1.7
3.1
2.3
.6
.7
4.6
4.1
.8
.5
1.6
.5
.4
.4
1.4
.8
0.0 20.0 40.0 60.0 80.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
Eastern, Central Europe
Americas (North, South)
Asia/Oceania
Africa
West Europe
Portugal
Germany
Ex-Yugoslavia, Albania
Kosovo
Italy
Born NOT in Switzerland
13
Figure 1.3 Mothers’ birthplace by main and subsamples in %5.
5 Weighted data
39.1
34.3
50.2
38.1
39.2
39.3
65.0
36.3
2.9
2.2
2.8
12.0
3.3
3.9
1.0
2.6
3.9
4.5
3.3
1.4
5.9
6.1
3.0
4.4
5.2
6.3
2.5
5.5
9.9
9.6
1.1
5.2
2.8
3.9
.8
.7
3.9
3.1
4.7
4.5
1.0
12.2
5.7
.9
1.6
20.5
.2
4.9
2.8
10.0
1.8
6.2
2.3
13.5
1.6
3.1
1.2
7.5
2.0
1.2
2.5
7.8
1.0
6.6
8.2
3.5
2.8
4.7
7.2
4.1
10.9
3.5
2.6
5.3
4.1
1.9
2.3
12.9
3.1
1.8
1.6
2.2
2.1
1.3
.8
1.1
2.6
0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
Eastern, Central Europe
Americas (North, South)
Asia/Oceania
Africa
West Europe
Portugal
Germany
Ex-Yugoslavia, Albania
Kosovo
Italy
Born NOT in Switzerland
14
1.2.3. Family constellations
The vast majority of respondents live with (step)father and (step)mother. Roughly one in six
lives with one parent alone. Other arrangements are exceptional. There is little variation across
Switzerland.
Figure 1.4 Who brought you up by main and different subsamples in %6.
Given the international character of the ISRD instrument, it was decided to include also an item
on racial identity. Table 1.3 gives the details for Switzerland.
Table 1.3 To which group do you belong? (Minority) by main and subsamples in %7.
Switzerland
(N=4'158)
DE
(N=2'560)
FR
(N=956)
ITA
(N=642)
SG
(N=625)
AG
(N=555)
GE
(N=268)
ZH
(N=266)
European 90.9 90.9 89.9 96.3 92.9 92.3 86.5 87.1
African 1.7 1.0 3.2 .6 .8 1.4 3.9 1.4
Asian 4.1 5.6 1.5 1.4 3.2 4.0 .6 7.5
Other 3.3 2.5 5.3 1.6 3.0 2.3 9.0 4.0
This is the country specific question, where each country must find its own variant. In
Switzerland minorities were defined in the form of self-identification as European, African,
Asian, and others.
Most of respondents identify themselves as Europeans in Switzerland.
1.2.4 Religion
6 Weighted data
7 Weighted data
82.9 84.7 77.7
88.3 83.5 82.7 75.3
85.3
15.0 13.6 19.4
8.3 14.0 14.0
23.4 13.9
2.1 1.7 2.9 3.4 2.4 3.3 1.3 0.8 0.0
20.0
40.0
60.0
80.0
100.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
(step)father and (step)mother
1 of parents
other (divorces parent(s), grandparent(s), other (aunt, uncle, governante, 2 fathers, internat, social workers,sister etc.)
15
Although the interest in the questions regarding religion will be centred on international
comparisons (some Muslim and other Non-Christian countries participate in ISRD-3), the
following Figures disclose a few interesting patterns.
Figure 1.5 Religious affiliation in%8.
Most students belong to any of the Christian denominations. There is, not unexpectedly, some
variation across the country. Particularly noteworthy is the low proportion of respondents that
say belonging to the protestant (reformed) Church that used to be the dominant denomination
one generation ago. Muslims are more frequent in Zurich, Aargau and St. Gallen.
Figure 1.6 Importance of religion in %9.
Beyond formal membership in any church or religion, the instrument asked about the importance
religion plays in respondents’ every-day life. As it turns out, students saying religion to be
completely unimportant to them are more numerous than those attributing it much importance.
This holds particularly true in French-speaking cantons. Weak attachment to religion is, as the
8 Weighted data
9 Weighted data
15.0 10.7
24.8
12.9 6.1
9.6
19.1 17.7
37.8 34.2
39.9
71.0
47.9 44.0 44.7
19.9 24.8
29.2
18.0
7.8
17.0 19.9
8.2
36.5
2.2 3.3 .1 4.3 5.4 2.2 5.3 6.0 4.4 1.7
6.2 4.1 9.0 6.3 8.6 9.8 7.3
2.4
14.0 13.3 6.6
10.2 6.2 6.8 5.4 4.2 4.3 3.6
12.4 7.2
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
do not belong to a religion/religion community catholic
protestant orthodox
other christian confessions muslim
other
18.2 17.7 18.1 24.5
18.2 17.5
33.0
17.1
43.1 47.1
34.6 42.1
52.3 51.9
36.7 42.9 38.7 35.2
47.4
33.4 29.5 30.6 30.4 40.0
0.0
20.0
40.0
60.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
Very important neither/nor very unimportant
16
following Figure illustrates, particularly frequent among respondents who say to belong to the
protestant church.
Figure 1.7 Importance of religion by religious affiliation in %10
.
Religion is very important mostly for Muslims. It is also fairly important to students of Non-
Christian or “other” Christian denominations.
1.2.5 Employment and economic wellbeing
Given the high unemployment rates in several Western countries, the following items will be of
interest mostly in international comparisons.
Figure 1.8 Fathers’ employment status by main and subsamples in %11
.
The level of unemployment is very low in Switzerland and does not vary across regions. Mothers
are somewhat less employed, but still at a comparatively very high rate. Again, there is little
variation across the country.
Figure 1.9 Mothers’ employment status by main and subsamplesin%12
.
10 Weighted data
11 Weighted data
2.0
15.7 8.3
36.0 35.5
58.1
36.2
18.9
53.4 43.8 47.2
42.7 37.9 42.0
79.2
30.9
47.8
16.9 21.8
3.9
21.8
do not belong to areligion/religion
community
catholic protestant orthodox other christianconfessions
muslim other
very important neither/nor very unimportant
2.1 2.1 2.1 3.5 1.4 2.7 2.5 2.9
93.8 94.1 93.6 91.6 94.5 93.5 94.7 92.6
4.0 3.8 4.3 4.9 4.1 3.8 2.8 4.5 0.0
50.0
100.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
Yes, he is unemployed No, he is working other
17
A related question is whether or not the family has its main income from father’s and/or
mother’s earnings, or from other sources (mostly presumably social welfare).
Figure 1.10 Income: Earning, wages, salary and other source by main and subsamples in %13
.
As Figure 1.10 illustrates, respondents’ families obtain their incomes almost exclusively from
earnings. Social welfare plays only in French-speaking cantons and in Geneva a more than
marginal role.
12 Weighted data
13 Weighted data
7.5 7.1 7.7 12.0 7.7 6.5 8.5 8.4
78.4 78.2 80.3 70.4 78.1 77.5 76.6 76.5
14.1 14.7 12.0 17.6 14.2 16.0 14.9 15.1
0.020.040.060.080.0
100.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
Yes, she is unemployed No, she is working other
3.9 2.7 6.4 4.9 1.2 1.5 12.0
3.8
95.0 96.3 92.4 93.1 97.7 97.6 86.6
94.8
1.1 1.0 1.1 2.0 1.0 .9 1.4 1.3 0.0
20.0
40.0
60.0
80.0
100.0
120.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
unemployment or social welfare benefits earnings, wages, or property other
18
Figure 1.11 “How well-off is your family in comparison with others” by main and
subsamples in %14
.
By far the largest group among our respondents are those who say their family’s wellbeing to be
about average. There are, in all regions, more students who say being better off than average
families, than there are students who claim being worse off than other families.
Figure 1.12 “How much money do you have by your own”, in comparison with others in
%15
.
An additional question was about how much money juveniles have available for their own needs.
The distribution is very similar to what came out in Figure 1.10 regarding the family’s economic
situation. Also in this respect, roughly half of respondents rate themselves as average, while the
overall distribution resembles very much a Gaussian curb in all regions.
14 Weighted data
15 Weighted data
7.5 8.1 6.8 4.4 8.9 11.7
3.8 9.8
15.8 16.1 16.2 10.9
17.4 19.7 22.5
14.5 18.8 20.5
15.1 18.0
23.0 19.5
11.7
19.3
49.6 48.5 51.5 52.7
46.5 43.3
49.8 47.5
6.0 5.5 6.6 8.4 3.1 4.3 5.3 6.7
1.4 .7 2.7 3.0 .7 .9 5.4
.6 .8 .5 1.1 2.7 .4 .4 1.4 1.5
0.0
10.0
20.0
30.0
40.0
50.0
60.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
much better off better off somewhat better off the same
somewhat worse off worse off much worse off
3.9 4.2 3.5 2.9 5.0 4.2 3.0 5.7 8.8 9.5 7.7 5.2 10.0 10.0
5.5 7.3
18.9 20.5 16.2 14.4
17.8 20.1 15.5
25.4
46.5 44.7 49.1
54.0 46.5 45.8
55.1
38.8
13.9 14.5 12.2 16.5 13.8 15.3
11.0 13.1 5.6 4.9 7.3 5.5 5.0 3.6 5.1 7.5
2.4 1.7 4.0 1.4 1.9 1.0 4.9 2.3
0.0
10.0
20.0
30.0
40.0
50.0
60.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
much more more somewhat more the same somewhat less less much less
19
In order to make these items more helpful for further analysis, an index has been constructed by
collapsing, in dichotomized form, (1) the source of income of the family (unemployment or
social welfare benefits vs. earnings, wages, or property of the parents); (2) the well-off of the
family, compared to others (better/same vs. worse); (3) amount of respondent’s personal money
to spend (pocket money + presents + own earnings, etc.) in comparison with others (more/same
& less). Respondents were rated as “middle or high levels” if (1) they get money from earnings,
wages, property; (2) are better off or the same as other families; and (3) have more or the same
amount as others to spend on personal needs.
Figure 1.13 Economic level in regions and selected cantons in %16
.
As Figure 1.13 illustrates, between two thirds and three quarters of respondents rate themselves
as middle or high levels.
1.2.6 Changes between 2006 and 2013
Table 1.4 Age of respondents in % for ISRD-2 and ISRD-3.
ISRD-2 (not weighteddata) ISRD-3 (weighteddata)
12 3.2 6.1
13 20.3 26.1
14 33.0 31.2
15 30.1 25.6
16 11.7 9.6
17+ 1.7 1.4
The age distribution is almost identical in 2006 and 2013. This underlines the fact that our
sample is fairly representative for the student population in grades 7 to 9.
16 Weighted data
27.1 25.2 30.3 32.1 23.6 23.0
32.4 26.7
72.9 74.8 69.7 67.9 76.4 77.0
67.6 73.3
0.0
20.0
40.0
60.0
80.0
100.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
Low level Middle and high levels
20
Figure 1.14 Respondents’ birthplace by main and subsamples in % for ISRD-2 and ISRD-317
As Figure 1.14 shows, the proportion of students born abroad has increased in Switzerland,
particularly in French-speaking cantons. The percentage of students from former Yugoslavia has
decreased. When we consider father’s birthplace, the trend is similar between 2006 and 2013.
17 Weighted data (ISRD-3, N=4158), not weighted data (ISRD-2, N=3643).
10.9 13.8
10.2 10.5 12.4
21.3
12.6 13.7
2.8
14.0
5.0
11.6
16.5
26.9
13.7 9.8
4.4 2.3
4.7 2.8 3.5
1.5 4.2
1.3 1.9 3.6
1.8 4.4
2.7 1.2
6.7
2.5 0.6
3.9 0.6
2.8 0.9
5.6 6.5 4.5 3.0 2.2
7.5
0.8 1.8 5.9
7.7 4.9 4.9
8.0
14.2
8.4 6.0
0.9
6.0 3.2 4.2
11.6
18.2
6.2 5.5
0.0
5.0
10.0
15.0
20.0
25.0
30.0
ISR
D-2
(N
=3
'64
3)
ISR
D-3
(N
=4
'15
8)
ISR
D-2
(N
=2
'55
1)
ISR
D-3
(N
=2
'56
0)
ISR
D-2
(N
=7
99
)
ISR
D-3
(N
=9
56
)
ISR
D-2
(N
=2
93
)
ISR
D-3
(N
=6
42
)
ISR
D-2
(N
=2
24
)
ISR
D-3
(N
=6
25
)
ISR
D-2
(N
=2
20
)
ISR
D-3
(N
=5
55
)
ISR
D-2
(N
=2
24
)
ISR
D-3
(N
=2
68
)
ISR
D-2
(N
=9
80
)
ISR
D-3
(N
=2
66
)
Switzerland DE FR ITA SG AG GE ZH
Born NOT in Switzerland
Ex-Yugoslavia
West Europe
Other (countries of Asia, Africa, N. America, S. America, E. Europe, C. Europe)
21
Figure 1.15 Fathers’ birthplace by main and subsamples in % for ISRD-2 and ISRD-318
.
In general the proportion of respondents having fathers born abroad has increased from ISRD-2
to ISRD-3 in all regions. Some nationalities such as Italians have considerably decreased,
whereas Yugoslavs have remained stable. No nationality seems to dominate. In other words,
Switzerland’s foreign population has increased overall and at the same time has become more
diverse.
18 Weighted data (ISRD-2 and ISRD-3)
33.0
38.2
30.3
33.9
37.7
47.1
43.8
43.7
7.2
5.9
5.8
5.0
4.2
4.7
28.1
24.0
3.6
4.9
4.1
5.4
2.4
4.2
2.1
1.3
5.2
4.8
5.8
5.8
2.6
2.5
6.0
5.0
1.6
2.7
1.9
3.5
0.9
1.1
0.7
2.7
4.0
1.3
0.9
7.7
10.8
2.1
5.2
4.4
4.3
2.9
2.5
10.6
8.8
0.7
1.9
2.0
2.2
1.5
1.1
4.1
5.1
0.9
4.1
6.5
4.9
7.5
2.2
5.0
2.5
2.1
1.4
2.1
1.1
1.7
2.4
3.1
1.4
2.3
0.8
0.8
0.9
0.5
0.7
1.6
0.7
0.5
0.0 20.0 40.0 60.0
ISRD-2 (N=3'643)
ISRD-3 (N=4'158)
ISRD-2 (N=2'551)
ISRD-3 (N=2'560)
ISRD-2 (N=799)
ISRD-3 (N=956)
ISRD-2 (N=293)
ISRD-3 (N=642)
Swit
zerl
and
DE
FRIT
A
Eastern, Central Europe
Americas (North, South)
Asia/Oceania
Africa
West Europe
Portugal
Germany
Ex-Yugoslavia, Albania
Kosovo
Italy
Born NOT in Switzerland
22
Figure 1.16 Mothers’ birthplace by main and subsamples in % for ISRD-2 and ISRD-319
.
Compared to 2006, the proportion of respondents having a mother born abroad has increased.
Again, there is no dominating group, Switzerland’s immigrant population being fairly diverse.
Father’s employment situation has remained unchanged since 2006. Both in 2006 and in 2013,
over 90 per cent are (at least partially) employed.
19 Weighted data (ISRD-2 and ISRD-3)
33.7
39.1
31.4
34.3
40.1
50.2
36.7
38.1
4.0
2.9
3.2
2.2
3.1
2.8
13.4
12.0
3.6
3.9
4.0
4.5
2.7
3.3
2.1
1.4
5.4
5.2
6.1
6.3
2.3
2.5
7.4
5.5
2.4
2.8
3.0
3.9
0.9
0.8
0.7
0.7
3.1
4.5
1.7
1.0
8.1
12.2
1.8
5.7
4.8
4.9
3.0
2.8
10.9
10.0
3.5
1.8
2.2
3.1
1.4
1.2
5.3
7.5
0.4
2.0
4.5
6.6
5.4
8.2
2.3
3.5
2.5
2.8
2.8
3.5
2.5
2.6
3.3
5.3
3.5
4.1
1.0
1.8
1.0
1.6
1.2
2.2
1.4
2.1
0.0 20.0 40.0 60.0
ISRD-2 (N=3'643)
ISRD-3 (N=4'158)
ISRD-2 (N=2'551)
ISRD-3 (N=2'560)
ISRD-2 (N=799)
ISRD-3 (N=956)
ISRD-2 (N=293)
ISRD-3 (N=642)
Swit
zerl
and
DE
FRIT
A
Eastern, Central Europe
Americas (North, South)
Asia/Oceania
Africa
West Europe
Portugal
Germany
Ex-Yugoslavia, Albania
Kosovo
Italy
Born NOT in Switzerland
23
Figure 1.17 Percent fathers who are unemployed, in % for ISRD-2 and ISRD-320
Although the number of unemployed fathers has almost doubled between 2006 and 2013 (from
1.3 to 2.1 per cent in the national sample), the proportion of those who do not belong to the
workforce (because of illness, handicaps etc.) has remained stable. With respect to mother’s
employment situation, no change between 2006 and 2013 can be observed, with the exception of
a higher unemployment rate (that rose from 2.7 to 7.5 per cent).
Figure 1.18 Mothers’ employment rate in % for ISRD-2 and ISRD-321
As a result, the economic level (as defined above) has remained stable as well (not shown).
20 Weighted data (ISRD-2 and ISRD-3)
21 Weighted data (ISRD-2 and ISRD-3)
1.3 2.1
1.1 2.1 2.0 2.1
1.4
3.5
1.8 1.4 1.4 2.7
3.4 2.5
1.2
2.9
4.3 4.0 4.0 3.8 4.6 4.3
6.0 4.9
2.3
4.1
2.4
3.8
5.8
2.8
5.3 4.5
0.01.02.03.04.05.06.07.0
ISR
D-2
(N=
3'6
43
)
ISR
D-3
(N=
4'1
58
)
ISR
D-2
(N=
2'5
51
)
ISR
D-3
(N=
2'5
60
)
ISR
D-2
(N=
79
9)
ISR
D-3
(N=
95
6)
ISR
D-2
(N=
29
3)
ISR
D-3
(N=
64
2)
ISR
D-2
(N=
22
4)
ISR
D-3
(N=
62
5)
ISR
D-2
(N=
22
0)
ISR
D-3
(N=
55
5)
ISR
D-2
(N=
22
4)
ISR
D-3
(N=
26
8)
ISR
D-2
(N=
98
0)
ISR
D-3
(N=
26
6)
Switzerland DE FR ITA SG AG GE ZH
He is unemployed (he cannot find a job) Other (ill, handicapped, retired, other)
2.6 7.5 2.6 7.1 3.4 7.7 1.0 12.0 1.8 7.7 1.8 6.5 4.5 8.5 3.1 8.4
73.6 78.4 74.8 78.2 74.6 80.3 60.3 70.4 75.1 78.1 75.8 77.5 76.1 76.6 76.5 76.5
23.8 14.1 22.6 14.7 22.0 12.0 38.6
17.6 23.1 14.2 22.4 16.0 19.4 14.9 20.4 15.1
0.020.040.060.080.0
100.0
ISR
D-2
(N=
3'6
43
)
ISR
D-3
(N=
4'1
58
)
ISR
D-2
(N=
2'5
51
)
ISR
D-3
(N=
2'5
60
)
ISR
D-2
(N
=7
99
)
ISR
D-3
(N
=9
56
)
ISR
D-2
(N
=2
93
)
ISR
D-3
(N
=6
42
)
ISR
D-2
(N
=2
24
)
ISR
D-3
(N
=6
25
)
ISR
D-2
(N
=2
20
)
ISR
D-3
(N
=5
55
)
ISR
D-2
(N
=2
24
)
ISR
D-3
(N
=2
68
)
ISR
D-2
(N
=9
80
)
ISR
D-3
(N
=2
66
)Switzerland DE FR ITA SG AG GE ZH
She is unemployed (she cannot find a job)Wokrs (permanent job, own business, sometimes work)Other (ill, handicapped, retired, other)
24
1.3 Association between social background and delinquency
1.3.1 Gender, age and delinquency
The Figure 1.19 and Table 1.4 illustrate the distribution of delinquency across gender and age.
Figure 1.19 Delinquency (last year prevalence) by gender in %,N=4051-406822
Boys are more likely to commitoffences than girls. The difference is highest for serious and
violent offences, and weakest for shoplifting.
Table 1.4 Delinquency (last year prevalence) by age in %, N=4040-406123
Graffiti
Vandal
ism
Shoplif
ting
Burglar
y
Bicycle
theft
Motorb
ike/car
theft
Car
break
Robber
y
Theft Weapo
n
Group
Fight
Assault Drug
Dealin
g
Animal
Cruelty
11-12 3.3 5.3 7.3 0.4 1.6 0.4 3.3 0.8 4.9 6.1 6.1 1.2 0.8 3.3
13 3.9 8.6 10.8 0.7 2.3 0.2 1.3 0.7 6.6 6.6 5.7 1.6 1.4 3.6
14 5.6 7.5 10.4 0.6 4.1 0.7 1.4 0.7 7.0 8.8 5.0 2.4 4.7 3.7
15 9.5 11.0 16.2 2.1 11.5 2.8 2.9 2.0 9.1 13.1 9.6 4.6 8.3 3.4
16 11.6 14.5 17.8 3.9 13.4 2.3 3.9 2.8 9.3 16.4 15.0 6.5 13.2 3.4
17-19 7.8 10.0 16.0 2.0 5.9 3.9 2.0 0.0 16.0 10.2 8.0 0.0 14.0 0.0
p= .000 .000 .000 .000 .000 .000 .005 .001 .013 .000 .000 .000 .000 .840
22 Weighted data
23 Weighted data
9.6
13.3
16.0
2.3
9.8
2.4 3.7
2.4
10.6
16.4
11.0
4.9
7.7
5.1 4.0
5.4
9.5
0.6
3.1
0.4 0.8 0.3
4.9 3.8 4.2
1.6 3.5
2.2
0.02.04.06.08.0
10.012.014.016.018.0
male female
25
Respondents older than 14 are more likely to commit offences, the maximum being reached at
age 16. The group of 17 and older students is, beyond its small size, characterized by an
unconventional school career and, thus, not necessarily comparable to younger students. The
differences across age are significant formost offences.
1.3.2 Birthplace and delinquency
Delinquency rates by birthplace (immigrant) background can be seen in the following Figures.
Figure 1.20 Delinquency (last year prevalence) by respondents’ birthplace (dichotomized) in
%, N=4048-406424
Respondents born abroad are more likely to commit offences. The difference is, although
significant for most offences, not all too large when compared to other variables considered in
this report. Drug dealing and shoplifting are two examples where youths born abroad do not
differ significantly from juveniles who were born in Switzerland.
24 Weighted data
13.7 13.5 15.6
3.1
10.9
4.5 3.4 4.0
11.9 12.5 13.0
9.0 6.7
4.5 5.6
8.6
12.2
1.1
5.7
0.8 2.0
0.9
7.0 9.6
6.6
2.3
5.4 3.4
0.02.04.06.08.0
10.012.014.016.018.0
not in Switzerland Switzerland
26
Figure 1.21 Delinquency (last year prevalence) by mothers’ birthplace in %, N=4023-404125
Respondents whose mothers were born abroad report more offences than those, whose mothers
were born in this country.
25 Weighted data
9.4 10.8
14.3
1.5
10.0
2.4 2.8 2.2
9.8 11.0 10.8
4.6 6.4
4.1 4.9
8.2
11.4
1.2
4.1
0.5 1.7 0.8
6.1
9.2
5.1
2.2
4.9 3.2
0.02.04.06.08.0
10.012.014.016.0
not in Switzerland Switzerland
27
Figure 1.22 Delinquency (last year prevalence) by mothers’ birthplace in % (detailed),
N=4023-4041 26
If respondents with mothers born abroad commit more offences overall, Figure 1.22 shows that
there is considerable variation across countries of origin.
26 Weighted data
4.9
8.2
11.4
1.2
4.1
0.5
1.7
0.8
6.1
9.2
5.1
2.2
4.9
3.2
8.6
12.0
15.5
0.9
6.0
1.7
2.6
0.9
7.8
6.0
9.6
1.7
5.3
3.5
5.1
12.1
12.7
0.6
16.6
2.5
4.5
5.7
11.3
17.7
20.3
2.5
3.8
9.4
7.8
6.3
12.1
2.4
14.6
2.4
4.3
1.9
5.8
12.6
13.6
2.0
8.8
2.9
10.7
15.2
15.2
7.1
11.5
2.7
2.7
2.6
5.2
16.7
8.0
5.2
9.7
2.6
6.0
9.8
14.6
1.1
6.6
2.7
2.2
1.6
13.7
6.0
13.1
8.2
2.7
3.8
15.2
13.8
17.9
0.0
9.7
1.5
4.1
1.5
11.8
10.2
8.7
2.1
9.7
4.6
8.9
5.7
18.5
0.0
6.5
0.0
0.0
0.8
10.5
11.3
10.5
1.6
3.3
3.3
6.8
7.1
11.7
0.4
8.3
1.5
0.8
1.1
5.7
6.4
5.6
5.2
4.5
2.6
18.4
15.6
15.0
2.9
11.6
5.1
2.9
5.0
13.7
14.3
9.4
11.4
10.0
5.8
6.7
18.7
9.3
1.3
5.3
4.0
4.0
1.3
18.9
16.0
9.3
8.0
9.3
1.3
0.0 5.0 10.0 15.0 20.0 25.0
Graffiti***
Vandalism***
Shoplifting
Burglary***
Bicycle theft***
Motorbike/car theft***
Car break*
Robbery***
Theft***
Weapon***
Group Fight***
Assault***
Drug Dealing***
Animal Cruelty*
Eastern, Central Europe
Americas (North, South)
Asia/Oceania
Africa
West Europe
Portugal
Germany
Ex-Yugoslavia, Albania
Kosovo
Italy
Switzerland
28
Figure 1.23 Delinquency (last year prevalence) by fathers’ birthplace in %, N=3990-400527
Students, whose fathers were born not in Switzerland, report more offences. The differences are
generally larger than according to mothers’ birthplace. Again, the difference varies a lot across
countries of origin (Figure 1.24).
27 Weighted data
8.0
10.3
14.3
2.0
10.1
2.6 2.9 2.4
10.3 10.4 11.5
5.4 6.9
3.8 5.3
8.2
11.3
1.0
4.1
0.6 1.7
0.6
5.8
9.6
4.7
1.8
4.9 3.4
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
not in Switzerland Switzerland
29
Figure 1.24 Delinquency (last year prevalence) by fathers’ birthplace in % (detailed),N=3990-
4005 28
Respondents with fathers born in Switzerland do not have the lowest rates of delinquency
throughout all offences. For example,vandalism, shoplifting, caring weapons, drug dealing and
animal cruelty are cases where juveniles born in Switzerland have fairly high rates. In general,
28 Weighted data
5.3
8.2
11.3
1.0
4.1
0.6
1.7
0.6
5.8
9.6
4.7
1.8
4.9
3.4
6.0
12.3
16.6
2.1
7.3
2.6
2.1
2.2
9.0
8.6
10.3
5.4
6.7
5.8
5.7
14.0
16.1
3.1
21.2
2.6
5.2
5.7
14.9
17.5
19.1
4.1
7.7
6.2
9.1
8.5
11.2
3.7
13.2
2.1
3.7
1.6
5.3
12.8
15.3
3.8
9.7
3.7
13.6
11.9
12.8
0.9
6.5
2.8
2.8
3.7
3.7
9.3
10.1
7.4
7.3
3.7
6.2
10.0
18.1
0.6
6.9
2.5
2.5
0.6
13.1
6.9
14.9
8.8
2.5
2.5
8.5
9.7
10.3
2.3
6.9
2.9
4.0
2.9
16.1
13.2
8.0
1.7
6.9
1.2
3.5
3.5
15.1
0.0
5.8
1.1
2.3
0.0
12.6
7.0
6.9
0.0
9.2
2.3
6.5
7.3
13.0
0.4
8.0
1.1
1.1
0.8
6.1
5.0
5.7
4.2
3.4
3.1
14.0
15.1
18.4
3.5
15.3
8.2
3.5
4.6
14.0
17.2
12.8
14.0
13.8
5.7
18.8
15.2
6.3
3.1
6.1
9.1
3.1
3.1
12.1
9.1
12.5
15.2
9.1
0.0
0.0 5.0 10.0 15.0 20.0 25.0
Graffiti***
Vandalism*
Shoplifting*
Burglary**
Bicycle theft***
Motorbike/car theft***
Car break
Robbery***
Theft***
Weapon***
Group Fight***
Assault***
Drug Dealing***
Animal Cruelty
Eastern, Central Europe
Americas (North, South)
Asia/Oceania
Africa
West Europe
Portugal
Germany
Ex-Yugoslavia, Albania
Kosovo
Italy
Switzerland
30
however, father’s origin seems to be more important than respondents’ and mother’s country of
birth. In the following Figure, we have combined the information on parental origin.
Figure 1.25 Delinquency (last year prevalence) by parents’ birthplace in %, N=3972-3991 29
Respondents with parent(s) of foreign origin are more likely to commit an offence than their
peers with both parents born in this country. Interestingly, respondents with only one parent born
abroadtend to have the highest rates of shoplifting and drug dealing.
Given the international character of ISRD-3, it was necessary to assess ethnicity also by a
question regarding racial characteristics. The following Figure shows the association and
delinquency.
29 Weighted data
8.5 9.9
13.1
1.8
10.7
3.0 3.1 2.6
9.8 10.5 11.9
5.5 6.6 4.3
7.2
12.0
17.7
1.7
8.2
0.7 2.1 1.1
10.6 11.7 8.7
3.4
7.0
2.7 4.7
7.5 10.1
0.9 3.3
0.5 1.6 0.7
5.0
9.0
4.1 1.7
4.4 3.4
0.02.04.06.08.0
10.012.014.016.018.020.0
Both parents born NOT in Switzerland 1 of parents were born in Switzerland
Both parents born in Switzerland
31
Figure 1.26 Delinquency (last year prevalence) by social groups, minorities in %, N=4043-
4064 30
Students who indicated being Africans report higher delinquency rates than their European and
Asian peers.
30 Weighted data
5.9 8.8
12.4
1.2
5.6
0.9 2.0 1.0
7.3 9.7
6.9
2.6 5.1
3.3
13.4 14.9
22.7
7.6
18.2
7.6 9.1
6.0
14.7 13.4
23.5
7.4
14.9 14.9
9.4 8.1 10.5
1.8
11.2
4.1 2.4 1.8
6.5 8.9
5.9 6.4 5.3 3.5
22.2 21.5
17.9
4.5
17.2
7.5
3.8 6.7
17.0 17.8 17.8 15.6 14.9
6.0
0.0
5.0
10.0
15.0
20.0
25.0
European African Asian None of the above
32
1.3.3 Family constellation and delinquency
The following Figure illustrates the importance of the family background. The question in the
instrument was, once more, not about formal settings, but about by whom the respondent
actually has been brought up.
Figure 1.27 Delinquency (last year prevalence) by “Who brought you up?” in %, N=4052-
406831
The findings shown in Figure 1.27 confirm the importance of being brought up by two parents,
either natural or stepparents. Children from single-parent households have slightly higher
delinquency rates. More problematic are respondents brought up in other settings.
31 Weighted data
6.2 8.5
11.3
1.2
5.8
1.2 2.2 1.2
7.4 9.5
7.0
2.7 4.7 3.6
7.4
12.9
18.4
1.9
9.2
1.8 1.9 1.9
7.9 11.4
8.9 5.6
9.4
3.3
18.6 15.1
20.9
5.8
10.5
4.7 4.7 3.5
16.5 18.8 17.9
8.2 11.8
4.7
0.0
5.0
10.0
15.0
20.0
25.0
father and mother one parent only other situation
33
1.3.4 Religion and delinquency
The following two Figures show delinquency rates by religious affiliation, and by the importance
religion plays in respondents’ every-day life.
Figure 1.28 Delinquency (last year prevalence) by religious affiliation in %, N=4021-4040 32
32 Weighted data
7.9
10.0
16.8
1.5
4.8
0.7
2.3
0.3
11.4
16.6
5.6
2.0
8.1
1.8
6.1
9.8
11.5
1.6
4.8
1.3
1.9
0.9
7.7
7.4
7.4
3.0
4.0
4.0
5.5
7.0
11.9
0.8
5.6
0.5
2.1
0.9
5.6
9.7
5.4
2.9
4.5
2.6
6.9
5.9
12.1
1.6
6.6
2.6
1.6
2.3
5.9
7.6
5.3
3.6
7.9
3.6
7.3
11.7
15.2
2.0
19.2
2.9
4.4
4.7
10.7
16.0
22.0
5.8
7.2
5.5
11.4
14.1
10.9
2.0
5.9
2.8
2.4
2.0
5.1
6.3
4.7
5.1
8.7
7.5
0.0 5.0 10.0 15.0 20.0 25.0
Graffiti*
Vandalism***
Shoplifting*
Burglary
Bicycle theft***
Motorbike/car theft***
Car break
Robbery***
Theft***
Caring weapon***
Group Fight***
Assault*
Drug Dealing***
Animal Cruelty***
other
muslim
orthodox and otherchristian confessions
protestant
catholic
do not belong to areligion/religioncommunity
34
Students who indicate Islam or religions of Asia or Africa as their affiliation have rather higher
delinquency rates. The same is true, for some offences, also for respondents who say not to
belong to any religion. In some offences, Orthodox students are also more delinquent. Catholics
and Protestants generally have the lowest delinquency rates. This result obviously can be
attributed to integration, since both Protestants and Catholics are likely from Swiss background.
Figure 1.29 Delinquency (last year prevalence) by importance of religion in %, N=4036-
405233
Most offences are reported by students for whom religion is either very important or not
important at all. This apparently paradoxical result could be related to the various attitudes of the
several religions to stealing and violence.
33 Weighted data
5.6
9.2
11.1
2.2
9.2
2.5 2.9 3.2
7.8
10.7
12.5
3.8 5.4 5.4
6.4
8.6
11.2
0.6
6.3
1.2 1.4 1.0
6.4
8.1
6.0
3.1 3.6 3.5
7.6
10.2
15.1
2.0
5.2
1.0
2.8
0.9
9.0
11.8
6.9
3.1
7.9
2.9
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
Very important Neither/nor Very unimportant
35
1.3.5 Economic status and delinquency
The following Figures illustrate the importance of several indicators of social and economic
conditions of the respondents’ families.
Figure 1.30 Fathers’ employment status in %, N=4024-4041 34
Students, whose fathers work permanently, have lower rates of delinquency. The highest
delinquency rates can be found among respondents whose fatheris unemployed. This could point
to the importance of economic strain, but might be also seen as an outcome related to other
social problems. In a country with very low unemployment rates, persons not belonging to the
workforce are likely to be confronted with other social problems, too. Similar results can be
found for mother’s employment status.
34 Weighted data
9.4 11.8
16.5
9.3
17.4
8.1 10.5 9.3 9.3
16.5
11.8
7.0
12.8
9.3 6.5
9.1
12.5
1.3
6.0
1.2 2.0 1.1
7.5 9.6
7.2
3.1 5.3
3.6
8.5 11.5 12.1
0.0
10.3
2.4 4.2
1.8
9.1
14.5 12.1
5.5 8.5
2.4
0.02.04.06.08.0
10.012.014.016.018.020.0
he is unemployed he is working other
36
Figure 1.31 Mothers’ employment status in %, N=4042-406235
Students are more likely to commit an offence if their mothers are unemployed and cannot find a
job. However, if mothers do not work for other reasons, the effect is quite different from what
has been observed for the father (Figure 1.30).
Figure 1.32 Source of income of the family in %, N=4016-4034 36
35 Weighted data
36 Weighted data
8.9 10.8
21.3
4.9
13.5
4.9 4.6 4.6
11.7 13.1
14.4
7.2 7.9 5.6 6.7
8.9
12.6
1.2
5.9
1.1 2.2 1.1
7.6 9.6
6.8
3.0 5.5
3.0 5.8
10.7 8.4
0.7
5.3
0.9 1.2 0.7
5.8
10.6
7.6
2.5 4.6
5.8
0.0
5.0
10.0
15.0
20.0
25.0
she is unemployed she is working other
10.4 9.8
19.1
5.2
11.5
7.5 6.3 6.3
14.9 18.4
14.5 15.0
9.2 8.0 6.4
9.2 12.3
1.1
6.1
1.0 2.0 1.0
7.4 9.6
7.1
2.5 5.2
3.3
0.0
5.0
10.0
15.0
20.0
25.0
other sources of income earnings, wages or property of my parents
37
The role of social problems other than just economic strain due to unemployment is underlined
by Figure 1.33. Indeed, respondents from families who receive their income mainly through
sources other than earnings or wages have considerably higher delinquency rates.
Figure 1.33 “How well-off is your family compared to others” and delinquency in %,
N=4026-404137
Students who say their family is worse and much worse off than other families have the highest
delinquency rates in general and with regard to the most serious offences in particular.
Interestingly, the difference is striking only for the extreme category (“much worse”), not really
for those who describe their family’s economic situation as just “worse” or “some worse”. It may
well be that the “much worse” category is characterized by many other social problems as well.
The relatively moderate importance of economic strain as such is further underlined by the often
also higher delinquency rates among those who say their family is “much better off”.
37 Weighted data
8.4 8.4
14.5
5.8
14.3
4.7 6.1 4.7
10.7 14.8 15.2
6.4 7.7 4.7 5.6
9.5 13.0
1.2 5.3
1.1 2.0 1.1
8.9 12.0
7.8 3.3 3.6 4.5 6.4
8.9 11.3
0.8 6.0
0.9 1.8 0.9 6.4 7.1 6.6
1.8 3.9 3.1 6.5 7.6
11.7
0.9 5.0
0.8 1.6 0.8 6.2
8.5 6.0
2.6 5.1
2.8
9.1
18.1 18.1
0.8
7.8
1.2 1.6 2.1
13.2 18.1
9.5 4.1
13.2
6.6
13.8
39.7
31.6
6.9
15.8
8.6 8.6 5.3
26.3 21.1
15.5 10.0
14.0 10.0 11.8 11.8
14.7 11.8
20.6
9.1
14.7
9.1 12.1
18.8 21.2
29.4 32.4
8.8
0.05.0
10.015.020.025.030.035.040.045.0
much better off better off somewhat better off the same
somewhat worse off worse off much worse off
38
Figure 1.34 Own money to spend in comparison with others in %, N=4026-404138
The findings presented in Figure 1.35 illustrate this further. Also when respondents are asked
about own money they have at their disposal, it is not so much the perceived inequality with
respect to others but probably the extreme situation that is associated with higher delinquency
rates. The question, again, is whether it is lack of money or the co-occurrence of many related
social problems that lead to delinquency.
1.4 Comparison between ISRD-2 and ISRD-3
1.4.1 Trends in delinquency
Trends in delinquency over all three ISRD sweeps (1992, 2006 and 2013) are presented in
Chapter 7 (Figure 7.2). Similar trends can be seen with regard to victimization (increase from
ISRD-2 to ISRD-3, Table 4.2). In this Chapter, we are looking whether the association with
some of the independent variables considered in the preceding sections has changed.
1.4.2 Demographic characteristics and delinquency
As data not shown suggest, delinquency has, since 2006, increased in about even proportions
among girls and boys. The same is true for the several age brackets (not shown). Delinquency
has in both sweeps been concentrated among those aged 15 and above, and this gap has become
slightly more pronounced in 2013.
38 Weighted data
8.3
12.2
18.5
7.9
15.7
4.6 7.2 8.6
13.1
19.0 17.6
10.8
15.5
7.8 7.5 7.2
16.1
1.4
7.2
0.8 2.8
0.8
10.0 12.8
7.5 4.7 4.7 4.7 5.2
9.7 10.4
1.1
4.6 1.3
3.3 0.8
7.5 10.2
7.2
1.8 4.6 3.8
6.8 8.7
11.5
0.8
6.5
1.1 1.3 1.1
6.3 8.0 6.8
2.8 4.9
2.9
7.0 8.6
12.5
1.4 4.1
0.9 1.4 0.5
6.4 9.5
6.1 3.0
5.0 2.7
6.8
16.7
22.2
1.8
7.7
2.3 2.3 0.9
14.9 15.4
10.0
1.8
9.0
5.0
0.0
5.0
10.0
15.0
20.0
25.0
much more more somewhat more the same somewhat less less
39
With respect to birthplace, we consider here the association between delinquency and country of
birth of the respondents (Figure 1.36).
Figure 1.35 Delinquency (last year prevalence) by respondents’ birthplace in % for 2006 and
201339
39ISRD-2 – not weighted data, N= 3648; ISRD-3 - weighted data, N=4158
7.8
8.6
9.2
12.2
0.9
1.1
1.1
2.0
0.9
0.9
7.6
9.6
7.7
6.6
1.2
2.3
2.7
5.4
6.5
11.1
4.5
12.1
0.7
7.8
0.6
11.0
1.9
9.8
6.4
12.0
17.5
17.4
3.2
5.4
1.3
5.4
18.2
8.9
9.1
15.2
4.5
3.2
0.0
1.3
0.0
0.6
17.4
10.3
9.1
10.9
8.7
11.0
13.6
6.5
12.4
16.6
9.3
17.2
1.0
1.3
0.5
2.0
0.5
4.2
6.9
13.7
14.9
13.1
1.0
9.2
3.9
7.2
0.0 5.0 10.0 15.0 20.0
ISRD-2*
ISRD-3***
ISRD-2
ISRD-3
ISRD-2
ISRD-3***
ISRD-2
ISRD-3***
ISRD-2
ISRD-3***
ISRD-2
ISRD-3***
ISRD-2***
ISRD-3***
ISRD-2**
ISRD-3***
ISRD-2**
ISRD-3***
Van
dal
ism
Sho
plif
tin
gB
urg
lary
Car
bre
akR
ob
ber
yC
arin
gw
eap
on
Gro
up
fig
ht
Ass
ault
Dru
g d
ealin
g
Other (countries ofAsia, Africa, N.America, S. America, E.Europe, C. Europe)
West Europe
Ex-Yugoslavia
Switzerland
40
Delinquency increased, over the last seven years, mostly among juveniles born in Ex-Yugoslavia
and in a residual category of “other” countries. However, it increased also among respondents
born in Switzerland. The only group with a clearly different trend are juveniles from other
Western European countries whose delinquency rate decreased for most offences.
Parents’ countries of birth are similarly associated with delinquency in 2006 and in 2013. This
holds true for fathers’ as well as for mothers’ origin (data not shown).
1.4.3 Economic status and delinquency in 2006 vs. 2013
Parental unemployment has been shown to be significantly associated with delinquency (Figures
1.18 and 1.19). The following Figure suggests that this association may have become even
stronger in 2013. Eventually, unemployment goes more along with personal and social problems
today than a few years ago.
Figure 1.36 Delinquency (last year prevalence) by fathers’ employment in % for 2006 and
2013,40
Students whose fathers are unemployed or who do not work for other reasons are more likely to
commit an offence (both in 2006 and in 2013). However, this association became more
significant in 2013. For mother’s unemployment, the association is not as strong and the increase
is less consistent since 2006 (data not shown). It could be that unemployment of fathers and
mothers has different social meanings and that it is differentially associated with personal or
social problems.
40Weighted data (ISRD-2, N=3648 and ISRD-3, N=4158)
13.6 11.8
6.8
16.5
0.0
9.3
0.0
10.5
0.0
9.3
14.0 16.5
14.0 11.8
0.0
7.0
2.3
12.8
7.6 9.1 8.7
12.5
0.7 1.3 1.0 2.0 0.8 1.1
7.4 9.6
8.5 7.2
1.3 3.1 2.7
5.3
9.4 11.5
13.4 12.1
3.4
0.0 0.7
4.2 3.4 1.8
7.4
14.5 12.8 12.1
2.7
5.5
0.0
8.5
0.02.04.06.08.0
10.012.014.016.018.0
ISR
D-2
ISR
D-3
ISR
D-2
ISR
D-3
ISR
D-2
**
ISR
D-3
***
ISR
D-2
ISR
D-3
***
ISR
D-2
**
ISR
D-3
***
ISR
D-2
ISR
D-3
*
ISR
D-2
ISR
D-3
*
ISR
D-2
ISR
D-3
*
ISR
D-2
ISR
D-3
**
Vandalism Shoplifting Burglary Car break Robbery Caringweapon
Group fight Assault Drugdealing
He is unemployed (he cannot find a job)
Wokrs (permanent job, own business, sometimes work)
Other (ill, handicapped, retired, other)
41
In this context, it is interesting to see that, according to Figure 1.37, the relationship between
economic well-being and delinquency of respondents is not as straightforward as one might
suspect. Although delinquency increased more, from 2006 to 2013, among those whose families
are less well off, the difference is not consistent for all offences nor is it systematically strong.
Figure 1.37 Delinquency (last year prevalence) by economic level in % for 2006 and 201341
This underlines, once more, the possibility that the association between parental unemployment
and delinquency is indeed mediated by higher exposure to social and/or personal problems.
41ISRD-2 – not weighted data, N= 3648; ISRD-3 - weighted data, N=4158 (4006-4021)
7.8
13.1
7.3
16.6
0.8 2.4
1.0 2.7
1.4 2.0
7.3
12.4
9.5 9.0
1.2
5.0
1.8
7.8 8.1 8.0 9.6
11.3
0.9 1.1 1.0 2.0
0.9 1.1
7.8 9.2
8.3 7.1
1.3 2.6 3.0
4.8
0.02.04.06.08.0
10.012.014.016.018.0
ISR
D-2
ISR
D-3
***
ISR
D-2
*
ISR
D-3
***
ISR
D-2
ISR
D-3
**
ISR
D-2
ISR
D-3
ISR
D-2
ISR
D-3
*
ISR
D-2
ISR
D-3
**
ISR
D-2
ISR
D-3
*
ISR
D-2
ISR
D-3
***
ISR
D-2
ISR
D-3
***
Vandalism Shoplifting Burglary Car break Robbery Caringweapon
Group fight Assault Drugdealing
Low level Middle and high levels
42
1.5 Victimization and demographic background
1.5.1 Gender and age
The ISRD is not simply a survey of self-reported delinquency, but also a victimization survey.
The following Figures show how victimization relates to gender and age.
Figure 1.38 Victimization (last year prevalence) by gender in %, N=4120-413642
Males are more often victims of delinquency than females whenever violence and aggression are
involved, such as in robbery and assault. They are also more often victims of theft and hate
crimes, probably because of their greater presence in urban night-life. Girls, in turn, become
more often victims of cyber bullying and parental assault.
Figure 1.39 Victimization (last year prevalence) by age in %, N=4111-412543
42 Weighted data
43 Weighted data
5.0 4.4
29.1
6.0 5.9
18.6
5.6 1.6 3.3
24.6
5.3 11.3
19.5
5.3
0.0
10.0
20.0
30.0
40.0
Robbery*** Assault Personaltheft***
Hate Cyberbullying***
Parentalviolence
Parentalmaltreatment
male female
3.6 2.4
26.8
4.4 7.6
20.9
4.8 3.0 3.1
22.4
5.4 7.9
20.0
4.8 3.6 3.6
26.3
4.6
8.5
16.0
4.6 2.4
4.6
30.0
6.2
10.3
20.1
5.7 5.1 4.8
32.6
9.2 8.4
22.8
8.4
1.8
7.1
25.5
5.4 3.6
20.0
16.4
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
11-12
13
14
15
16
17-19
43
Respondents become victims of all offences at all ages, except robbery and hate crimes that are
more often experienced at age 16 or older. Also victims of parental assault are more often young
men of 16 or 17. These situations may be related to domestic conflict where older adolescents
are relatively often involved – as victims, but quite often also as offenders.44
The findings in Figure 1.39 are challenging. In ISRD-2, it has often been observed that
delinquency rates in Eastern and Asian countries are extremely low, whereas victimization rates
were not. The favorite explanation had been that these juveniles may feel more inhibited to
report offences they actually had committed, than experiences as victims. Although this
possibility certainly has to be considered, it might also be that juveniles of younger ages often
experience victimizations from older peers. The results presented in Figure 1390 offer some
support to this hypothesis. Indeed, it is not very plausible why our respondents were so much
inhibited to self-report offences, and the idea that they were actually victims of somewhat older
juveniles is certainly appealing. We shall look into that when we analyze the detailed
circumstances of experienced offences, since gender, age and a few more characteristics of the
assailant have also been recorded in the questionnaire.
44 See the survey on domestic violence in the Canton of Geneva, conducted in 2012-13 by our team.
44
1.5.2 Birthplace and victimization
As mentioned above, foreign origin has been measured by recording the country of birth of
respondents and their parents. In connection with delinquency, we have seen that the fact of
having been born abroad, or of having foreign-born parents, is associated with higher rates of
delinquency (Figures 1.21-1.26). In this section, we shall look at victimization by having been
born abroad.
Figure 1.40 Victimization (last year prevalence) by respondent’s birthplace in %, N=4118-
413245
Although respondents born abroad are more likely to become victims of delinquency, the
differences are less pronounced than in Figure 1.21. The same is true when parents’ county of
birth is considered (Figure 1.42).
45 Weighted data
4.0 6.3
34.3
7.4 10.2
25.1
10.2 3.1 3.4
25.6
5.3 8.4
18.2
4.7
0.05.0
10.015.020.025.030.035.040.0
not in Switzerland Switzerland
45
Figure 1.41 Victimization (last year prevalence) by parents’ birthplace in %, N=4042-405746
Students with parents born abroad have higher victimization rates than those with both parents
born in Switzerland. As in Figure 1.25, children with one foreign-born parent seem to be more
exposed, for several types of victimizations, than those with two parents who arrived from
another country. Beyond this paradoxical finding, the differences are far more moderate in
Figure 1.41 than in Figure 1.25. Again, there is some indication that victimization and
delinquency are not as narrowly interrelated as often has been pretended. Further, offending and
victimization are by far not always intra-ethnic events. Indeed, it is not implausible that
offenders – whoever they are – may look out for victims of predatory crimes without caring
much about their socio-demographic profile.
The same observation is true with respect to countries of origin of victims. Although Swiss-born
respondents are less often victimized, the differences by country of origin are less important than
in connection with offending (see Figures 1.22, 1.24). With the exception of hate crimes, the
difference is largest with respect to maltreatment by parents.
46 Weighted data
3.7 4.3
32.3
8.0 9.9
24.4
9.6 4.2 5.8
25.9
8.6 11.3
22.3
7.2 2.6 2.6
23.8
3.1 6.8
15.1
2.4
0.05.0
10.015.020.025.030.035.0
Both parents born NOT in Switzerland 1 of parents were born in Switzerland
Both parents born in Switzerland
46
Figure 1.42 Victimization (last year prevalence) by birthplace in %, N=4116-413247
The last item on migration included in ISRD-3 concerns the racial background of the respondent.
In Figure 1.26, we have seen that delinquency is strongly associated with racial minority status.
How does this relate to victimization?
47 Weighted data
3.1 3.4
25.6
5.3 8.4
18.2
4.7 9.5
6.3
25.0
11.6 8.4
15.8
9.4 3.1 5.0
27.7
7.5 10.1
17.6
6.9 2.9
6.9
40.5
6.0 10.5
31.4
12.3
0.05.0
10.015.020.025.030.035.040.045.0
Switzerland
Ex-Yugoslavia
West Europe
Other (countries of Asia, Africa, N. America, S. America, E. Europe, C. Europe)
47
Figure 1.43 Victimization (last year prevalence) by minority in %, N=4114-412848
Although racial minorities are more often victims of all sorts of offences – particularly of hate
crimes and parental maltreatment – the differences are by and large far less pronounced than in
Figure 1.26. In other words, a racial minority status seems more important for understanding
delinquency than victimization.
48 Weighted data
3.0 3.6
25.5
5.0 8.3
17.8
4.5 5.7 7.2
42.0
16.4 19.4
34.8
20.9
3.6 7.0
39.2
14.0 11.7
30.0
15.1
8.3 3.8
41.4
6.8 12.0
32.6
13.4
0.05.0
10.015.020.025.030.035.040.045.0
European African Asian None of the above
48
1.5.3 Different family configurations and delinquency
As we have seen in Figure 1.27, delinquency varies considerably across family constellation.
Juveniles brought up by two parents are better protected than those who are living in single-
parent families or in other arrangements (home, foster parents etc.). How does this relate do
victimization?
Figure 1.44 Victimization (last year prevalence) by “Who brought you up?” in %, N=4127-
413549
Students, who were brought up by both parents, are less victimized than those who grew up in
single-parent families or in other arrangements. However, the difference is minimal between
two- and one-parent families. Even for children growing up in other settings, the difference with
respect to victimization is by far not as large as it is for delinquency (Figure 1.26). Again, we see
that victimization is not distributed exactly in the same way as delinquency, and certain variables
may be important for delinquency that are not that crucial when it comes to victimization. It
might be that victimization, to some extent, is distributed more randomly, and that victims often
had the bad luck of being at the bad moment at the bad place.
49 Weighted data
3.0 3.0
26.7
4.9 7.4
18.4
4.7 4.5 7.6
26.4
10.0 14.0
22.5
9.2
4.5
9.0
37.5
4.5
18.2
24.7
11.4
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
father and mother one parent only other situation
49
1.5.4 Social standing and economic well-being
Respondents whose father is unemployed are considerably more often victims of parental assault
and maltreatment. Presumably unemployment goes along with domestic problems as well.
Figure 1.45 Victimization (last year prevalence) by father’s employment in %, N=4093-410750
Mother’s employment status, however, is clearly less important in this respect (not shown).
Similar results are found if the family’s main source of income is considered: families supported
by welfare payments are characterized by far more parental assault and maltreatment.
50 Weighted data
8.0 5.7
23.9 20.7
10.6
39.1
11.5
3.1 3.8
26.9
5.2 8.5
19.0
5.3 4.8 4.2
30.7
6.1 9.7
15.1 7.8
0.05.0
10.015.020.025.030.035.040.045.0
he is unemployed he is working other
50
Figure 1.46 Victimization (last year prevalence) by main source of income in %, N=4109-
412251
As the following Figure illustrates, this is largely true also for families who operate under
financial strain (Figure 1.47). Compared to Figure 1.32 where respondents whose families are
“much worse off” admitted to committing far more offences than those above the bottom, the
same extreme position does not seem to be associated with victimization to the same extent,
perhaps with the exception of cyber bullying and assault. Once more, it seems that variables that
are highly correlated to delinquency are not that much related to victimization.
51 Weighted data
9.1 7.4
28.4
15.4 14.5
30.3
16.2
2.9 3.6
26.7
5.1 8.4
18.6
5.0
0.05.0
10.015.020.025.030.035.0
other sources of income (social wellfare, pension and so on)
earnings, wages or property of my parents
51
Figure 1.47 Victimization (last year) by “how well-off is your family compared to others” in
%, N=4100-410952
The same is true with respect to money respondents have at their disposal for their own needs.
Although students with much less resources than others are more often victims of all types of
offences, the association is clearly more gradual and continuous.
52 Weighted data
5.2 6.5
32.4
7.4 9.7
20.8
9.1 4.5 3.2
27.3
6.6 10.2
18.2
4.0 3.6 3.6
26.6
5.8 8.1
17.3
2.6 2.0 2.7
25.0
3.5 7.1
17.7
5.3 4.9 7.7
35.0
11.8 15.1
33.3
12.7 8.8
19.3 24.1
31.6
12.3
40.4
19.3
8.8 11.8
38.2
11.8
29.4
20.6
9.4
0.05.0
10.015.020.025.030.035.040.045.0
much better off better off somewhat better off the same
somewhat worse off worse off much worse off
52
Figure 1.48 Victimization (last year prevalence) by own money compared to others in %,
N=4090-410153
Compared to Figure 1.34, the distribution in Figure 1.48 is again far more continuous. If poor
people are far more involved in delinquency, they are not necessarily that much more often
victims of offences.
53 Weighted data
10.2 10.1
30.2
10.1 6.3
20.8
8.9
2.2 3.9
29.1
3.3
11.1
18.6
3.3 3.5 4.3
28.0
5.6 9.0
16.6
4.3 2.1 2.1
22.6
4.8 6.5
16.8
5.2 3.5 4.6
31.1
6.3 10.0
22.8
4.7 7.8 9.5
38.5
9.5
18.7
33.6
12.7
4.1 7.1
38.8
11.2 15.2
32.0
10.3
0.05.0
10.015.020.025.030.035.040.045.0
much more more somewhat more the same somewhat less less much less
53
1.6 Conclusions: Factors of delinquency and victimization
Delinquency is committed disproportionally by boys.
The age of culmination is in the range of 16 years.
The number of students born abroad, or having parents born abroad is increasing.
The number of students born in Ex-Yugoslavia has decreased since 2006, but
delinquency of this group has increased. On the other hand, students from Western
European countries have increased, but they reported fewer offences in 2013 in
comparison with 2006.
Respondents with parents born abroad are more delinquent than their peers with parents
of Swiss origin. The difference is less strong than with many other variables and not
entirely consistent across offence types.
Students brought up by two parents are least often involved in delinquency and less often
victimized.
Unemployment among mothers and fathers has increased since 2006. It is associated with
higher delinquency rates. Students from poor families are far more often involved in
delinquency. With respect to victimization, the association is less strong.
54
Chapter 2
Family, parental control and delinquency
2.1 Overview
Module 2 of the questionnaire includes an expanded measure of family life and bonding
(parental control, parents’ supervision) with lots of variables. In this chapter we discuss only
issues of dinner with family and parental control as among the most influential for juvenile
delinquency.
2.2 Family life and parental control across regions
2.2.1 Dinner with family
Figure 2.1 “How many days a week do you usually eat an evening meal with your
parent(s)?” by main and subsamples in %54
72% of respondents reported about eating an evening meal together with parents 6 times per
week or daily. This tradition is the most developed in Italian speaking (85.4%) and in French
speaking cantons (77.1%), particularly in the canton of Geneva it is 70.5%. Such level is the
lowest in German speaking cantons (68.7%), for instance in the canton of Zurich (67.1%). One
reason may be that juveniles attend in highly urbanized areas like Zurich often schools at some
distance from their homes.
More than 80% of respondents in age of 11-12 have a dinner with their families 6 times per week
or daily. This percentage is decreasing with age. For instance, 65% among the 15 years old
54 Weighted data
9.3 10.0 8.7 3.2
11.6 11.4 16.4
9.2 18.7 21.3
14.1 11.3
24.5 20.4
13.1
23.7
72.0 68.7 77.1
85.4
63.9 68.2 70.5 67.1
0.010.020.030.040.050.060.070.080.090.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
never/1-2 times a week 3-5 times a week; 6 times/week - daily
55
students eat dinner almost every day at home. By 16 years, this proportion drops to57%
(p=.000).
Among those reportingto eat dinner with their families “never or 1-2 times per week”, 7.4% have
mothers born in Switzerland, and 12.2% abroad. Among mothers born abroad, 18.2% were born
in Asia including Oceania, 20.5% in Eastern/Central Europe and 23.1% in the countries of South
and North Americas. 14.6% were born in Ex-Yugoslavia, except Kosovo. The lowest percentage
of mothers was born in Kosovo (4.4%) and Portugal (3.8%).
Figure 2.2 “How many days a week do you usually eat an evening meal with your
parent(s)?” in % for 2006 and 201355
Having dinner at home has changed little since 2006. In 2013 slightly more students than in 2006
reported having dinner with parents once or twice per week or never.
55 ISRD-2 – not weighted data, ISRD-3 - weighted data
7.0 9.3 16.9 18.7
76.1 72.0
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
ISRD-2 (N=3'643) ISRD-3 (N=4'158)
never/1-2 times a week 3-5 times a week; 6 times/week - daily
56
2.2.2 Parental control
The following Figures inform on how parents try to control their offspring.
Figure 2.3 “My parents know where I am when I go out” in %56
This is one of the best controlled fields by parents. Only 4.5% and of respondents reported that
their parents rarely know where they are when they go out. Such percentage is slightly higher in
French speaking cantons and in Zurich.
Figure 2.4 “My Parents know what I am doing when I go out” in %57
56 Weighted data
57 Weighted data
85.9 88.5 79.4
90.0 86.4 88.6
77.2
88.8
9.7 7.7 14.7
6.9 9.8 7.6 14.7
6.1 4.5 3.9 5.9 3.1 3.8 3.8 8.0 5.1
0.0
20.0
40.0
60.0
80.0
100.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
always sometimes rarely
71.4 73.6
66.0
75.3 73.0 72.2 70.0 71.0
17.9 17.1 20.2
14.6 17.2 17.8 15.3 19.6
10.7 9.3 13.7
10.1 9.9 10.1 14.7
9.5
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
always sometimes rarely
57
If parents usually know where their children spend time, they are obviously less well informed
about what they do when they go out.
Figure 2.5 “My parents know what friends I am with when I go out” in %58
Most parents know friends of their children.
Figure 2.6 “My parents know friends I am with when I go out”. In % for ISRD-2 and ISRD-
359
In 2013 compared to 2006, more parents always know whom their children spend time with
while going out. Cell phones obviously make control of the whereabouts of children much easier
for parents.
58 Weighted data
59 ISRD-2 – not weighted data, ISRD-3 - weighted data
81.9 83.7 77.7
83.4 81.2 82.3 80.2 84.6
12.1 11.5 13.7 10.9 13.3 14.1 10.9 10.0 6.0 4.8
8.6 5.8 5.5 3.6 8.9 5.4
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
always sometimes rarely
5.4 6.0
33.9
12.1
60.7
81.9
0.0
20.0
40.0
60.0
80.0
100.0
ISRD-2 (N=3'643) ISRD-3 (N=4'158)
rarely/never sometimes always
58
Figure 2.7 “If I have been out, my parents ask me what I did, where I went and who I spent
time with” by main and subsamples in %60
About one third of parents sometimes or rarely know where and whom their children spend
leisure-time with. This proportion does not vary much across regions.
Figure 2.8 "If I go out in the evening my parents tell me when I have to be back home" in
%61
Most parents always tell their children when to come home after going out.
60 Weighted data
61 Weighted data
71.2 73.8
65.1
73.5 74.0 73.3 68.1
74.7
19.1 18.5 20.9 16.5 18.1 16.9
13.0 18.1
9.7 7.7 14.0
10.0 7.9 9.8
18.9
7.2
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
always sometimes rarely
85.6 86.1 83.9 89.8
85.7 84.0 87.7 84.7
8.9 9.0 9.0 6.4 10.3 8.9 7.2
11.9 5.5 4.9 7.0 3.8 4.0 7.2 5.1 3.4
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
100.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
always sometimes rarely
59
Figure 2.9 Has to let parents know if it gets late in %62
Most of students must always call their parents when they are returning later than planned. This
percentage is the highest in Ticino. Cell-phones make keeping parents informed much easier.
Figure 2.10 Parents check on homework in %63
Parents seem to exercise less control on school homework. About half of parents do not check if
their children did their homework. This percentage is the highest in the canton of Zurich where
55.2% of parents rarely check their homework. In Romandie and Ticino, more parents always
check their children’s homework. There is almost no correlation between delinquency and
checking homework by parents.
62 Weighted data
63 Weighted data
78.4 79.5 74.1
88.9
77.0 76.8 74.8 82.3
12.9 13.0 13.9 6.0
12.4 13.7 14.8 12.2 8.7 7.5 12.0 5.1
10.6 9.5 10.4 5.5
0.0
20.0
40.0
60.0
80.0
100.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
always sometimes rarely
29.7 23.8
38.8
52.1
30.5 25.7
51.0
20.1 21.4 22.2 19.8 20.0 19.3 22.1 16.4
24.7
48.9 54.0
41.3
27.9
50.2 52.1
32.7
55.2
0.0
10.0
20.0
30.0
40.0
50.0
60.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
always sometimes rarely
60
Figure 2.11 “I tell my parents who I spend time with” in %64
This type of parental control is very different across regions. Children in Romandie keep their
parents less regularly informed about whom they spend time with. Roughly two in five students
keep their parents informed about what they during leisure-time in general, during afternoon-
hours or what they spend their money for (not shown). These habits hardly differ across regions.
Figure 2.11_1 Experiencing any of the following events in %65
French speaking cantons have one of the highest levels of negative events that were reported by
respondents.
64 Weighted data
65 Weighted data
70.4 75.8
60.1 62.1
74.5 75.8
63.5
78.8
19.8 17.6 24.1 23.3
16.6 16.7 17.6 12.0 9.8 6.6
15.9 14.7 8.8 7.5
18.9
9.2
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
always sometimes rarely
7.7 2.4 3.7 3.7 2.0 1.7 2.4 3.5
41.7 38.0
46.6 41.3 40.5 41.5
49.9 43.0
9.7 4.1 7.1 4.3 2.0 4.2 3.8 4.7
10.2 4.4
7.8 4.3 3.6 5.5 6.5 4.5
20.6 15.5
21.6 20.0 14.9 14.1
22.7 17.3
25.9 19.4
30.7
20.6 23.6 19.4
32.3
18.0
0.0
10.0
20.0
30.0
40.0
50.0
60.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
death of father/mother serious illness of parents parents alcohol/drugs
violence between parents serious conflicts bw parents sep/divorce of parents
61
2.3 Family life, parental control and delinquency
2.3.1 Family life and delinquency
Among the many variables on family life collected in module 2 of the questionnaire, taking
evening meals with parents turned out to be particularly relevant.
Figure 2.12 Delinquency (last year prevalence) by “How many days a week do you usually
eat an evening meal with your parent(s)?” in %, N=4046-406266
Frequency of having dinner with parents relates significantly to delinquency. Respondents who
rarely eat with their parents commit more offences of all types.
66 Weighted data
13.0
16.4
20.1
4.8
10.9
2.4 2.7 2.4
10.1
14.9
10.1
7.2
14.4
5.9
8.7
14.2 16.2
1.2
8.2
1.6 2.1 1.8
11.5 12.4 10.8
3.9
7.6
4.1 5.4
7.1
10.8
1.1
5.4
1.2 2.2 1.1
6.4 8.7
6.3
2.5 3.9 3.2
0.0
5.0
10.0
15.0
20.0
25.0
never/1-2 times a week 3-5 times a week; 6 times/week - daily
62
2.3.2 Parental control and delinquency
The following Figures all relate to parental control. The frequencies and the regional distribution
have been presented in the first part of this chapter.
Figure 2.13 Delinquency (last year) by "My parents know where I am when I go out" in %,
N=4050-406767
This Figure shows that respondents whose parents are rarely aware of their children’s
whereabouts are committing far more offences of all types. For instance, more than 11% of
students who rarely kept their parents informedof their whereabouts admitted having
committedburglary, in comparison to only less than 1.0% among those whose parents always
were informed about this. This difference is consistently strong for all serious offences.
67 Weighted data
4.9 7.2 9.6
0.8 4.5
0.8 1.4 0.9 5.7 7.8 5.4
2.2 3.0 3.5
13.5 16.0
24.4
2.0
14.9
3.3 5.4 2.3
14.6 20.3
15.6
8.0
17.0
3.9
25.9
34.6
45.1
11.4
23.8
7.6 10.3 8.6
29.7 30.4 29.2
14.1
31.6
4.5
0.05.0
10.015.020.025.030.035.040.045.050.0
always sometimes rarely
63
Figure 2.14 Delinquency (last year prevalence) by "My parents know what I am doing when
I go out" in %, N=4050-406868
The association is precisely the same when we look at whether or not parents are informed about
what their children are doing when going out. For the following Figures, the findings are
basically showing always the same mechanism.
Figure 2.15 Delinquency (last year prevalence) by “My parents know what friends I am with
when I go out” in %, N=4047-406369
68 Weighted data
69 Weighted data
4.1 6.1 7.4
0.8 4.4
1.0 1.1 0.8 4.7 6.7 4.7
2.1 2.9 3.0
10.1 12.9
20.2
1.5
8.5
0.7 3.8
0.5
11.8 13.0 9.7
4.0 7.1
4.0
19.0
25.1
35.7
5.2
16.7
4.8 7.1 6.1
20.8
27.3 22.6
9.4
21.8
7.0
0.05.0
10.015.020.025.030.035.040.0
always sometimes rarely
4.4 7.3
10.0
0.9 4.7
0.9 1.5 0.9 5.5
7.8 5.4
1.8 3.5 3.2
16.3 15.5 19.6
2.9
13.0
2.9 5.1
1.8
16.0 18.0
13.8
8.2 11.5
5.5
18.4
24.1
34.4
5.7
16.4
4.1 6.1 6.1
20.0 24.6
22.5
12.7
23.2
5.1
0.05.0
10.015.020.025.030.035.040.0
always sometimes rarely
64
Figure 2.16 Delinquency (last year prevalence) by “My parents know what friends I am with
when I go out” in % for ISRD-2 and ISRD-370
The association between parents’ control about their children’s whereabouts and delinquency has
remained unchanged from 2006 to 2013. Although parents may control better than a few years
ago their children’s night-time activities, this control, probably facilitated by the widespread use
of cell phones, may remain largely limited to geographic location but not really allow parents to
control what children actually do.
Figure 2.17 Delinquency (last year prevalence) by "If I have been out, my parents ask me
what I did, where I went and who I spent time with" in %, N=4046-406471
70 ISRD-2 – not weighted data, ISRD-3 - weighted data
71 Weighted data
25.0
34.6
22.5
45.1
5.4 11.4
7.0 10.3 3.2
8.6
24.5 30.4
21.8 29.2
7.7 14.1 11.4
31.6
12.3 16.0
12.0
24.4
1.2 2.0 1.5 5.4
1.5 2.3 10.8
20.3 12.5 15.6
1.8 8.0
3.4
17.0
4.7 7.2 6.6 9.6 0.4 0.8 0.4 1.4 0.4 0.9
4.8 7.8 5.8 5.4 0.5 2.2 1.7 3.0
0.0
10.0
20.0
30.0
40.0
50.0
ISR
D-2
***
ISR
D-3
***
ISR
D-2
***
ISR
D-3
***
ISR
D-2
***
ISR
D-3
***
ISR
D-2
***
ISR
D-3
***
ISR
D-2
***
ISR
D-3
***
ISR
D-2
***
ISR
D-3
***
ISR
D-2
***
ISR
D-3
***
ISR
D-2
***
ISR
D-3
***
ISR
D-2
***
ISR
D-3
***
Vandalism Shoplifting Burglary Car break Robbery Caringweapon
Group fight Assault Drugdealing
rarely/never sometimes always
5.6
8.0
11.5
1.1
5.3
1.0 1.8 1.2
7.1 9.1
6.6
3.0 4.8
3.4
8.3
11.8
14.5
2.1
7.4
1.3 2.7
1.3
7.8
11.2
8.5
2.9
6.3 4.7
11.7 14.0
17.8
2.3
12.7
3.6 4.6
2.3
11.2
13.8 12.5
5.1
9.9
3.1
0.02.04.06.08.0
10.012.014.016.018.020.0
always sometimes rarely
65
Apparently, parents of delinquent juveniles ask less about where, with what and with whom they
had spent time. This could indicate that the lack of parental information might be related to a
lack of parental active control, or a lack of parents’ interest for what their children are doing
during leisure-time and when going out.
Figure 2.18 Delinquency (last year prevalence) by "If I go out in the evening my parents tell
me when I have to be back home" in %, N=4041-405872
Respondents whom the parents rarely or never tell the time they have to be back home are
committing more offences of all types. This again points to a possible lack of parental initiative
and involvement in how their children shape leisure-time activities.
72 Weighted data
6.0
8.5
12.0
0.9
5.4
0.9 1.8 1.0
7.3 8.8
6.2
2.5
4.8 3.4
8.1 9.4
12.8
2.5
8.3
1.7 2.2 2.5
5.8
14.2
11.4
4.7
7.2
3.3
15.8
21.0
23.2
6.9
19.7
6.9
9.2
4.6
15.7
22.9 22.5
11.4
16.0
8.2
0.0
5.0
10.0
15.0
20.0
25.0
always sometimes rarely
66
Figure 2.19 Delinquency (last year prevalence) by “If I am out and it gets late I have to call
my parents and let them know” in %, N=4045-406073
Again, juveniles who do not have to inform their parents about being late when returning home
are committing more offences of all kinds.
Figure 2.20 Delinquency (last year prevalence) by "My parents check if I have done my
homework" in %, N=4048-406574
73 Weighted data
74 Weighted data
5.0 7.0
10.1
1.0
4.9
0.9 1.8 1.1
5.9 8.6
6.2
2.2 3.9 3.1
10.6
16.1
20.2
1.5
10.3
2.5 3.1 1.3
9.9 8.3 7.5
5.6 8.2
5.0
16.2 19.8
24.2
5.3
14.2
3.3 4.5 3.3
19.7
24.2
18.9
8.7
16.4
5.8
0.0
5.0
10.0
15.0
20.0
25.0
30.0
always sometimes rarely
5.2
7.1
9.7
1.3
5.4
1.6 2.1 1.4
8.0 7.2
6.5
2.7 3.2 3.4
6.0 6.7
13.1
0.7
5.7
0.8 2.0
1.0
5.0
11.4
7.2
1.4
3.2 2.9
7.9
11.7
14.2
1.8
7.3
1.5 2.4
1.4
8.6
11.0
8.3
4.3
8.1
4.0
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
always sometimes rarely
67
Contrary to other forms of parental control, checking children’s homework is only weakly,
inconsistently and mostly not significantly related to delinquency.
Figure 2.21 Delinquency (last year prevalence) by "I tell my parents who I spent time with"
in %, N=4041-406175
There is a significant relation between delinquency (last year prevalence) and informing parents
about whom respondents spend time with.
75 Weighted data
4.3 6.6
8.7
0.6 4.1
0.7 1.3 0.7
4.7 7.1
4.8 2.1 3.3 3.5
9.6 11.9
18.5
1.8
11.5
1.9 3.5
1.5
11.9 13.9
11.1
4.1
8.5
3.8
18.3
22.9
29.0
6.3
12.6
4.5 6.3 5.3
20.7 23.0
20.5
9.8
16.8
4.1
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
always sometimes rarely
68
Figure 2.22 Delinquency (last year prevalence) by “I tell my parents how I spend my money”
in %, N=4047-406476
Parental control over financial matters is also related to delinquency, although not as strongly as
control over leisure-time in general.
Figure 2.23 Delinquency (last year prevalence) by "I tell my parents where I am most
afternoons after school” in %, N=4047-406577
76 Weighted data
77 Weighted data
4.4 6.4
8.0
0.7
4.2
1.0 1.3 0.8
5.4 7.2
5.1
1.9 2.6 3.4
7.3 9.9
17.7
1.2
8.3
1.1 2.6
1.1
8.2 10.2
7.0
3.2
7.4
3.3
14.7
19.4
23.3
4.5
12.3
3.3 5.0
3.5
15.6
20.3
17.2
8.3
14.7
4.7
0.0
5.0
10.0
15.0
20.0
25.0
always sometimes rarely
4.1 5.9
8.1
0.6
3.9
0.7 1.1 0.8
5.1 6.5
4.8 2.2 2.9 3.1
10.3
14.1
22.7
2.0
10.6
2.1 4.8
1.6
11.7 13.5
11.5
3.5
9.2
5.2
15.5
20.7 23.4
4.9
13.9
3.7 4.9 3.7
15.9
23.8
16.7
8.3
14.8
4.4
0.0
5.0
10.0
15.0
20.0
25.0
always sometimes rarely
69
Also control over afternoon-hours is somewhat related to delinquency. Respondents who always
keep their parents informed about where they spend after-school hours commit substantially
fewer offences of all sorts. This is in line with observations in other studies (e.g. Walser 2013)
that a non-trivial part of offending tends to occur in the later afternoon, after school hours.
2.4 Conclusions
Family life and parental control play an important role in students’ life. It is strongly related to
juvenile delinquency. For instance, children, whose parents do not know where and with whom
their children spend time when they go out, are more likely to commit any type of offences. At
the same time parental control like checking homework is only weakly associated with
delinquency.
Over time, parental control did not change that much since 1992 (ISRD-1). Parents of most
children still fixed hours for returning home and they were informed about their children’s
whereabouts at similar proportions. From this perspective, it is not easy to understand why
delinquency did increase that much over time, from 1992 to 2006 and from 2006 to 2013, as
shown in Chapter 7. Perhaps the relevant changes are not really measured through questions like
these, i.e. questions regarding hours and locations of going out, but more about what actually
occurs during these moments. This obviously is hard to measure, particularly since it is hard to
anticipate in advance what might be relevant in a few years.
70
Chapter 3
Attitudes towards and perceptions about the school, school performance and
delinquency
2.1 Overview
In this chapter, we shall analyse delinquency in the light of several school variables, namely
students' feelings about their school, i.e. whether they like their school, whether they
would miss their school in case they had to move, whether they like going to school in
the morning, whether they find classes interesting, whether they would miss their teacher
in case of a change, and how important it is to them what their favourite teacher thinks
about them,
how students perceive the school environment, i.e. whether they see there a lot of
stealing, vandalism, fighting or drug use, and
how students describe their own performance and behaviour at school, i.e. whether they
see themselves as good or rather poor students, whether they have repeated a grade
during their career, what their future plans are (in terms of education) and whether or not
they have engaged in truancy.
These school variables are measured by module 3 of the questionnaire.
In section 3.2, the distribution of these school variables across Switzerland’s several subsamples
will be presented. In the following section, we shall see how these several school variables relate
to self-reported delinquency. In the final section (3.4), the results will be summarized and
discussed.
71
3.2 Descriptive Statistics
3.2.1 Feelings about school
Question 3.1 (see appendix 9.1) was about the student’s attitude towards his/her school. We first
present the answers across the several subsamples.
Figure 3.1 “If I had to move, I would miss my school” by main and subsamples in %78
.
More than 80 per cent of students would miss their schools if they have to move. This proportion
holds constantacross language and regions. The attachment seems weakest among students in
Zurich.
Figure 3.2 “Most mornings I like going to school” by main and subsamples in %79
About one third of respondents do not like going to school in the morning. This proportion is
higher in French speaking cantons and particularly in the canton of Geneva than in German
speaking cantons and especially in the canton of St. Gall. The difference between Geneva and St.
Gall is rather high (43 vs. 72% liking to go to school in most mornings). Generally, the attitude
towards the school is less positive when concrete situations are being presented, as in Figure 3.2,
than in more abstract or hypothetical situations, as in Figures 3.1 and 3.2.
78 Weighted data
79 Weighted data
14.9 14.6 14.7 19.3 10.8 13.3
22.1 15.2
85.1 85.4 85.3 80.7 89.2 86.7 77.9 84.8
0.020.040.060.080.0
100.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
disagree agree
34.6 29.4 44.0 46.7
27.8 34.4
56.7
27.8
65.4 70.6 56.0 53.3
72.2 65.6
43.3
72.2
0.0
20.0
40.0
60.0
80.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
disagree agree
72
Figure 3.3 “I like my school” by main and subsamples in %80
.
As in Figure 3.1, the results do not differ much across regions in Figure 3.3. Schools seem most
popular in St-Gall and least so in Ticino.
The same differences, as in Figure 3.2., appear when the question turns to the interest students
have in following lessons (Figure 3.4). Apparently, students in Geneva and in Ticino find their
lessons less interesting than in the canton of St-Gall.
Figure 3.4 “Our classes are interesting” by main and subsamples in %81
.
The differences across regions are substantial, although nearly two students in three feel the
lessons at school are interesting.
The question whether or not students would miss their teacher in case they had to move allowed
to grade answers in six levels. Figure 3.5 gives the detailed replies. In Figure 3.6, the scope of
possible answers is reduced to a scale of three levels.
80 Weighted data
81 Weighted data
22.7 19.2 28.1 35.9
16.5 23.7 26.5 17.2
77.3 80.8 71.9 64.1
83.5 76.3 73.5 82.8
0.020.040.060.080.0
100.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
disagree agree
33.6 30.9 37.6
44.1
25.1 33.3
49.5
31.8
66.4 69.1 62.4
55.9 74.9 66.7
50.5
68.2
0.0
20.0
40.0
60.0
80.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
disagree agree
73
Figure 3.5 “If I had to move, I would miss my teacher” by main and subsamples in %82
.
Figure 3.6 “If I had to move, I would miss my teacher” (same as Fig. 3.5, reduced to 3
categories) in %.83
In all regions of Switzerland students would somewhat miss their teachers if they have to move.
Nearly one student in two would not miss their teacher in Geneva. In the other regions, however,
differences across subsamples are not as pronounced. Apparently, the popularity of teachers in
only moderately related to school attachment.
The following Figures 3.7 and 3.8 present the results concerning students’ attitudes towards their
image among teachers. Figure 3.7 gives the results in six, Figures 3.8 in three levels.
82 Weighted data
83 Weighted data
16.3 15.6 18.4
13.4 10.3
13.6
25.4
13.1 13.7 13.4 15.5
8.5
12.2 14.3
21.1
10.6
18.7 16.0
24.9
17.7 15.2
16.8
29.2
17.8
27.1 30.0
20.3
29.4
33.5
28.8
14.0
33.6
17.8 19.0
14.9
19.6 21.3 20.1
6.3
20.4
6.3 6.0 5.9
11.6
7.5 6.3 4.0 4.5
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
not at all not much only a bit somewhat quite a lot very much
30.0 28.9 33.9 21.8 22.5 27.9
46.5
23.7
45.9 46.0 45.3 47.0 48.7 45.6 43.1 51.4
24.1 25.0 20.8 31.1 28.8 26.5
10.3
24.9
0.0
20.0
40.0
60.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
I will NOT miss my teacher A bit (neither/nor) I will miss my teacher
74
Figure 3.7 “It is important for me, how my favourite teacher thinks about me” by main and
subsamples in %84
.
For most students, it is “a bit important” and “quite important” what their favourite teacher
thinks about them. In some regions, however, a sizeable proportion of students do not care what
their teacher thinks about them.
Figure 3.8 “It is important for me, how my favourite teacher thinks about me” (same as Fig.
3.7, reduced to 3 categories) in %85
Overall and especially for the main sample, the distribution is almost perfectly normal. Most
students feel that their teacher’s opinion is “a bit important” or “a bit unimportant”. Notable
exceptions are Ticino and St-Gall where students care much more about their teacher’s opinion,
and Geneva at the opposite side of the distribution.
84 Weighted data
85 Weighted data
14.8 11.7
22.6
9.8 7.2
11.2
25.8
8.2 9.5 9.2 10.1 8.7 9.0 8.3 10.1 6.7
16.2 18.6
12.1 10.4
17.0 18.2
9.6
18.6
29.5 31.2 26.7
23.1
31.8 31.8 36.3 35.6
20.2 20.4 18.7
26.8 23.3
18.3
10.6
24.7
9.8 8.9 9.7
21.3
11.7 12.3 7.5 6.1
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
totally unimportant quite unimportant a bit unimportant
a bit important quite important very important
24.2 20.9 32.7
18.5 16.2 19.5
36.0
15.0
45.7 49.8 38.8 33.4
48.8 49.9 45.9 54.2
30.0 29.3 28.4
48.1 35.0 30.6
18.1 30.8
0.0
20.0
40.0
60.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
Not at all A bit (neither/nor) Important
75
3.2.2 Perceptions about the school environment
Students have been asked whether stealing, fighting, drug use or vandalism is common or not at
their school. The results are given in the following Figures 3.9-3.12.
Figure 3.9 “There is a lot of stealing in my school” in %.
For Switzerland as a whole,one respondent in four agrees with the statement that stealing is
common at his/her school. This proportion does not differ much across regions, with the
exception of St-Gall where only 15 per cent say that there is a lot of stealing at their school.
Figure 3.10 “There is a lot of fighting in my school” in %86
The prevalence of fighting seems to vary more across regions. More than one quarter of students
say fighting is common at their school in the canton of Ticino, but only 16 and 17 per cent in
German and French speaking cantons respectively; and less than 10 per cent in the canton of St-
Gall.
With respect to vandalism at school, there is again a fairly constant proportion of students who
report this being common at their school, with Ticino and St-Gall being the exceptions.
86 Weighted data
77.6 78.2 76.4 77.3 84.9
76.3 81.4 78.7
22.4 21.8 23.6 22.7 15.1
23.7 18.6 21.3
0.0
20.0
40.0
60.0
80.0
100.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
disagree agree
82.6 82.8 83.7 73.3
93.3 81.5 87.4 82.4
17.4 17.2 16.3 26.7
6.7 18.5 12.6 17.6
0.0
20.0
40.0
60.0
80.0
100.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
disagree agree
76
Figure 3.11 “Many things are broken or vandalized in my school”by main and subsamples in
%.87
In the case of Ticino, it might be that the high prevalence of vandalism at schools is influenced
by the presumably high prevalence of graffiti and broken objects in the public space in
neighbouring Italy. However, the rather even distribution across regions may also reflect a
nation-wide policy among schools to swiftly replace or remove broken items.
87 Weighted data
82.6 82.8 83.7 73.3
93.3 81.5 87.4 82.4
17.4 17.2 16.3 26.7
6.7 18.5 12.6 17.6
0.020.040.060.080.0
100.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
disagree agree
77
Figure 3.12 “There is a lot of drug use in my school” by main and subsamples in %.88
Drug use (presumably of cannabis) seems also to vary substantially across regions. In French-
speaking cantons, one respondent in four says drug use to be common at his/her school, whereas
only 19 and 15.4 per cent say so in the German and Italian speaking cantons. Noteworthy are the
high proportions in Geneva and in Zurich (36 and 28 per cent) and the low rate in St-Gall (13.3
per cent). There is a fairly strong association between perceived drug use among fellow-students
and respondents’ own drug use (see Figure 3.26).
3.2.3 Performance at school and truancy
Students have been asked whether they see themselves as good (or rather poor) students, whether
they ever have, during their school career, repeated a grade and whether or not they have stayed
away from school during an entire day (at least) during the last 12 months without any legitimate
excuse.
Figure 3.13 “How well do you do at school?” by main and subsamples in %.89
88 Weighted data
89 Weighted data
79.4 81.0 74.9 84.6 86.7 85.3
63.6 72.1
20.6 19.0 25.1 15.4 13.3 14.7
36.4 27.9
0.0
20.0
40.0
60.0
80.0
100.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
disagree agree
8.1 7.5 9.5 7.6 6.5 8.6 3.3 5.0
41.8 43.2 38.9 40.1 39.8 40.5 38.4 39.2
50.1 49.3 51.6 52.3 53.7 50.9 58.3 55.8
0.010.020.030.040.050.060.070.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
Bad Average Good
78
One half of respondents in Switzerland identify themselves as good students. This percentage is
relatively stable across all regions. However, students in the canton of Geneva rank themselves
more often as “good students”. This strangely contrasts with the findings reported above where
students in Geneva often seem to have more problematic attitudes towards their school and
teachers.
Figure 3.14 Repeating grades by main and subsamples in %.90
The percentage of respondents who report having ever (i.e. at least once), during their career,
repeated a grade is slightly higher in French speaking cantons (20.3%) and clearly lowest in
Ticino (8.7%). This suggests that policies seem to be rather similar across Switzerland in this
respect.
90 Weighted data
84.0 85.4 79.7 91.3 85.2 83.3
71.7 87.7
16.0 14.6 20.3 8.7 14.8 16.7
28.3 12.3
0.0
20.0
40.0
60.0
80.0
100.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
no, never yes
79
Figure 3.15 Plans for future educations, after the compulsory school by main and subsamples
in %.91
Plans for future education after having finished the current (compulsory) school differ
considerably across regions and cantons. In French- and Italian-speaking cantons, higher
education (leading to University studies) clearly leads over other options, whereas nearly one in
two opts for an apprenticeship in German speaking cantons.
Immediately applying for a job after finishing the current school is not a popular option in the
country taken as a whole. Only 2.7 per cent envisage this option, with rather small differences
across regions and cantons. In Geneva, nobody has opted for this alternative, underlining the
high value attributed to higher education among students in this canton.
There are some correlations between attitudes towards school and performance at school.
Respondents who rate themselves as “good” students generally like their school more than
others, they would miss it more, like more going to school in the morning and find classes more
interesting.
91 Weighted data
2.7 3.3 1.1 4.1 4.9 3.8
19.3
3.7
37.1
43.2
27.0
18.6
46.2 45.5
38.0
14.2 15.6 10.4
19.0 17.4 18.3 13.0 13.5
31.4
24.2
44.7 45.4
20.3 19.3
52.0
29.8
1.3 1.6 1.0 0.4 1.9 1.5 1.4
13.3 12.2 15.9
12.5 9.3
11.6 15.7 13.5
0.0
10.0
20.0
30.0
40.0
50.0
60.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
looking for a job start apprenticeship
vocation school/learn trade school to prepare acad. degree (University)
other I do not know yet
80
Figure 3.16 Feelings about school, by performance as a student in %, N=4137.
On the other hand, respondents, who see their own school less positively, usually have worse
school records than their peers.
Figure 3.17 Future plans (after current school), by performance as a student in %, N=4101.92
Respondents whosee themselves as good students envisagemore often continuing school to get
higher education. On the other hand, those who see themselves as rather poor students obviously
feel less well prepared for higher education and opt, as a result, predominantly for an
apprenticeship. This was to be expected. More noteworthy (and, eventually, a source of worry)
may be those who, although rating themselves as “bad students”, say continuing to higher
education. Provided the self-assessments among these students are realistic, there may be a lot of
frustration, due eventually to parental ambitions. A further source of concern must be the fact
that nearly one in five among the “poor” students has no clear idea about how to pursue
education after the current school.
92 Weighted data
19.2
80.8
49.7 50.3 31.9
68.1
44.8 55.2
15.0
85.0
37.0
63.0
25.6
74.4
37.7
62.3
14.0
86.0
30.2
69.8
18.8
81.2
28.2
71.8
0.0
20.0
40.0
60.0
80.0
100.0
disagree agree disagree agree disagree agree disagree agree
If I had to move, I wouldmiss my school*
Most mornings I likegoing to school***
I like my school*** Our classes areinteresting***
Bad Average Good
7.3
40.0
14.2 20.6
1.5
16.4
2.6
42.7
15.6 24.8
1.4 12.9
2.0
32.1
13.1
38.5
1.2 13.0
0.010.020.030.040.050.0
looking for a job startapprenticeship
vocationschool/learn
trade
school toprepare acad.
degree(University)
other I do not knowyet
Bad Average Good
81
Figure 3.18 Perception of problems at school, by performance as a student in %, N=4126-
413393
Respondents, who see a lot of stealing, fighting, vandalized and broken items, as well as drug
use in their schools, tend to see themselves less often as “good students”. The association,
however, is not strong, particularly with respect to fighting at school, and the direction of the
causation is not entirely clear. It could simply reflect the fact that “poor students” tend to
concentrate in schools facing a lot of problems.
Figure 3.19 Staying away from school a whole day (truancy) during last 12 month by main
and subsamples in %.94
The prevalence of truancy varies somewhat across the country, although not that many students
seem to stay away for one entire school day at least over the last 12 months. Truancy is more
common in French- and Italian-speaking cantons (14.6 and 15.1 per cent, respectively) than in
German-speaking regions and, once more, in St-Gall (8.4 per cent).
93 Weighted data
94 Weighted data
71.2
28.8
76.1
23.9
76.1
23.9
68.6
31.4
76.6
23.4
82.8
17.2
82.8
17.2
80.8
19.2
79.5
20.5
83.5
16.5
83.5
16.5
80.0
20.0
0.0
20.0
40.0
60.0
80.0
100.0
disagree agree disagree agree disagree agree disagree agree
there are a lot of stealingin my school***
there is a lot of fighting inmy scool**
a lot of things are brokenor vandalized in my
school**
there is a lot of drug usein my school***
Bad Average Good
88.2 89.8 85.4 84.7 91.6 90.6
77.5 86.5
11.8 10.2 14.6 15.3 8.4 9.4
22.5 13.5
0.0
20.0
40.0
60.0
80.0
100.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
no, never yes
82
3.3 Association between school-related variables and delinquency
3.3.1 Attitudes towards school and delinquency
Given the many items on delinquency, we shall first summarize the delinquency scores by the
several independent (school-related) variables, as discussed before. Table 3.1 gives the scores
(i.e. the percentage of) respondents having admitted having committed any of the following
offences at least once over the last 12 months.
Table 3.1 Delinquency scores (last year prevalence), by attitude towards school in %,
N=4048-406795
If I have to move,
I would miss my
school
Most mornings I
like going to
school
I like
myschool
Ourclassesareinte
resting
disagree agree p= disagree agree p= disagree agree p= disagree agree p=
Graffiti 12.1 5.7 .000 10.3 4.8 .000 13.0 4.8 .000 11.5 4.2 .000
Vandalism 14.1 8.4 .000 14.2 6.6 .000 15.5 7.4 .000 15.5 6.1 .000
Shoplifting 15.4 12.2 .026 18.0 9.8 .000 19.8 10.5 .000 20.2 8.8 .000
Burglary 3.7 1.0 .000 3.0 0.6 .000 3.5 0.8 .000 2.4 0.9 .000
Bicyclethef
t 10.8 5.6 .000 9.9 4.6 .000 12.9 4.5 .000 9.3 4.9 .000
Motorbike/
cartheft 3.0 1.1 .000 2.6 0.7 .000 3.0 0.9 .000 2.3 0.9 .000
Car break 4.3 1.8 .000 4.9 0.8 .000 5.4 1.3 .000 3.4 1.6 .000
Robbery 3.2 1.0 .000 2.3 0.8 .000 3.3 0.8 .000 2.5 0.8 .000
Theft 13.7 6.6 .000 11.7 5.6 .000 12.8 6.2 .000 13.4 4.7 .000
Weapon 18.0 8.6 .000 16.0 6.8 .000 17.4 7.8 .000 13.7 8.2 .000
Group
Fight 10.5 7.0 .003 11.8 5.3 .000 13.8 5.7 .000 12.9 4.8 .000
Assault 8.1 2.4 .000 5.7 1.9 .000 6.4 2.3 .000 5.8 1.9 .000
Drug
Dealing 10.9 4.6 .000 8.4 4.0 .000 10.6 4.1 .000 8.6 4.0 .000
AnimalCru
elty 5.5 3.3 .008 5.5 2.6 .000 5.4 3.1 .001 4.8 3.0 .005
Respondents with less positive feeling about their schools report more offence(s). In many
instances, the differences are even substantial. The following Figures will make this associations
more easily visible.
95 Weighted data
83
Figure 3.20 Delinquency (last year), by performance at school in %, N=4048-406796
Delinquency (last year prevalence) as a dependent variable is significantly related to school
performance. Students, who identify themselves as “bad students”, more likely admit to having
committed, at least once over the last 12 months, any of the offences included on the self-
reported delinquency list.
Although school performance is, to some degree, related to future plans (Fig. 3.17), the
differences, in terms of offending, are not as substantial between the several educational
alternatives that respondents envisage, as one might have anticipated (Table 3.2/Figure 3.21).
96 Weighted data
14.3 17.6
20.4
4.6
12.5
4.0 4.0 3.3
10.4
14.6
10.1 7.3
12.2
4.9 7.0
9.7
15.2
1.2
7.3
0.8 2.4
0.8
8.4 9.5 8.2
3.2 6.2
3.3 5.0
7.6 9.2
1.1 4.7
1.4 1.8 1.5
6.6 9.6
6.5
2.6 4.1 3.7
0.0
5.0
10.0
15.0
20.0
25.0
Bad Average Good
84
Table 3.2 Delinquency last year prevalence by plans after compulsory school in %, N=4017-
4034.97
Lookingf
or a job
Start
apprentices
hip
Vocationschool/learnt
rade
Continue
school to
prepare
acad.
degree
(University
) Other
I do not
know yet p=
Graffiti 11.3 6.0 8.4 6.4 20.4 4.1 .000
Vandalism 8.5 9.3 11.2 8.0 18.4 7.4 .036
Shoplifting 12.3 13.8 12.6 11.4 22.4 10.5 .076
Burglary 3.8 1.1 2.3 0.8 11.8 0.4 .000
Bicycletheft 4.7 8.8 7.6 3.1 17.6 3.9 .000
Motorbike/cart
heft 2.9 1.2 1.1 0.8 9.8 1.1 .000
Car break 2.9 2.4 1.2 2.1 7.8 1.3 .022
Robbery 0.9 0.9 2.3 0.8 14.0 0.7 .000
Theft 6.6 7.7 8.1 6.8 9.8 7.4 .875
Weapon 12.3 10.4 13.3 7.7 17.6 8.5 .001
Group Fight 7.5 7.6 11.1 5.0 21.6 6.1 .000
Assault 3.8 2.7 5.8 1.8 14.3 2.6 .000
Drug Dealing 6.6 7.2 6.9 3.9 15.7 1.5 .000
AnimalCruelty 2.8 3.4 4.1 3.4 7.8 3.1 .583
The most delinquent group seem to be juveniles in the category of “other” plans, i.e. probably
juveniles without any clear plans for the future immediately following the current school.
97 Weighted data
85
Figure 3.21 Delinquency (last year) by plans after compulsory school in %m N=4017-4034.98
Students who plan to continue with higher education after the current school are consistently less
delinquent. On the other hand, those in the category of “other” plans tend to be substantially
more delinquent with respect to most offences.
98 Weighted data
11.3 8.5
3.8 4.7 2.9 2.9
0.9
12.3
7.5
3.8 6.6 6.0
9.3
1.1
8.8
1.2 2.4 0.9
10.4 7.6
2.7
7.2 8.4 11.2
2.3
7.6
1.1 1.2 2.3
13.3 11.1
5.8 6.9 6.4 8.0
0.8 3.1
0.8 2.1 0.8
7.7 5.0
1.8 3.9
20.4 18.4
11.8
17.6
9.8 7.8
14.0
17.6
21.6
14.3 15.7
4.1
7.4
0.4
3.9 1.1 1.3 0.7
8.5 6.1
2.6 1.5
0.0
5.0
10.0
15.0
20.0
25.0
looking for a job start apprenticeship
vocation school/learn trade school to prepare acad. degree (University)
other I do not know yet
86
3.3.2 Problems in the school environment and delinquency
More even than with school performance, rather strong associations with delinquency have been
observed between the perceptions of problems in the school environment, such as frequent theft,
vandalism, fighting and drug use.
Figure 3.22 Delinquency (last year) by the perception that stealing is common at school in %,
N=4042-4059.99
Respondents, who say that “there is a lot of stealing in my school”, are more likely to admit
having committed, at least once during the last year, any of the offences included on the list.This
is particularly true for burglary, motorbike/ car theft, car break, and robbery. The explanation of
the association is not straightforward since juveniles who admit to these offences may, on one
hand, concentrate in schools with a lot of offences against property; on the other hand, the
presence of many fellow-students who engaging in such offences may operate also as a
facilitator of this kind of delinquency.
99 Weighted data
5.8 8.2
11.1
1.0
5.5
0.7 1.5 0.7
6.8 8.4
6.1
2.3 4.4 3.2
9.5
13.0
18.1
3.0
9.4
3.6 4.6 3.4
10.7
15.4 12.3
6.5 9.3
5.1
0.02.04.06.08.0
10.012.014.016.018.020.0
disagree agree
87
Figure 3.23 Delinquency (last year) by the perception that fighting is common at school in,
N=4045-4061 %.100
Again, the same picture emerges. Students, who see a lot of fighting in their school, report
delinquency more often. The association is particularly strong for serious offences, such as
burglary, theft of/from cars, robbery, and assault.
100 Weighted data
5.9 8.4
11.3
1.0
5.4
0.8 1.6 0.7
7.0 8.6
6.6
2.8 4.9
3.1
10.2
13.4
19.3
3.3
11.2
3.7 4.9 4.3
10.8
16.4
11.9
5.0
8.6 6.0
0.0
5.0
10.0
15.0
20.0
25.0
disagree agree
88
Figure 3.24 Delinquency (last year) by the perception that vandalism is common at school in,
%, N=4043-4061.101
Respondents, who report that broken and vandalized items are common at their schools, are
more likely to have committed, at least once over the last year, any offence included on our list,
especially burglary, theft if/from cars, and robbery.
Figure 3.25 Delinquency (last year) by the perception that drug use is common at school in %,
N=4038-4055.102
101 Weighted data
102 Weighted data
6.0 8.4
11.3
1.0
5.4
0.8 1.6 0.7
7.0 8.6
6.6
2.8 4.9
3.1
10.2
13.4
19.3
3.3
11.2
3.7 4.9 4.3
10.8
16.4
11.9
5.0
8.6 6.0
0.0
5.0
10.0
15.0
20.0
25.0
disagree agree
5.5 7.2
10.2
0.8
4.8
0.6 1.4 0.6
6.0 8.1
5.6 2.4 3.4 3.1
11.0
17.3
22.4
3.9
12.7
4.5 5.3 4.3
13.7
17.4 15.1
6.5
14.0
5.8
0.0
5.0
10.0
15.0
20.0
25.0
disagree agree
89
Students who report that drug use is common at their schooladmit more often having committed,
at least once over the last year, any offence included on our list. The association is even stronger
than for the perception of other social problems in the school environment, particularly for drug
dealing.
Whereas Figure 3.25 is about delinquency at the individual level, we can also see how the school
environment affects behaviour among school populations. Figure 3.26 illustrates the association
between the perception that cannabis use is common at school and the percentage of students in
any of the 95 schools included in our survey that actually use cannabis.
Figure 3.26 Cannabis use (life time) by perception of drug use at school, at schools level (95
schools participating in the survey).
% w
ho
ad
mit
can
nab
is u
se (
life
tim
e p
reva
len
ce)
% who say that drug use is common in my school
Indeed, more students admit using cannabis at schools where they say drug use being common.
This supports the view that delinquency in the school environment may act as a facilitator rather
than as a simple correlate.
90
Figure 3.27 Delinquency (last year) by staying away from school a whole day (truancy) in %,
N=4045-4061.103
Students, who stay away from school for least a whole day, are far more likely to commit an
offence. The association between these two variables is even higher than for the preceding
variables. This is noteworthy particularly for burglary, theft of/from cars, robbery, assault and
drug dealing.
3.4 Main findings and discussion
French-speaking respondents have generally less positive feelings about their school.
This holds across the several measures of attachment to school, such as missing the
school or teachers in case of moving to a different area, or interest for classes. German-
speaking respondents generally see their school environment more positively, especially
in the canton of St-Gall.
Overall, however, most Swiss students have positive attitudes towards school.
Students in Ticino are the most positively attached to their schools and teachers.
Students in French-speaking cantons report more often that there are social problems at
their school, such as widespread delinquency and drug use
Although truancy is fairly rare in Switzerland as a whole, it is far more common in
Geneva and in Zurich, and rare in St-Gall.
Despite the fact that students in French-speaking cantons and particularly in Geneva are
less attached to schools and/or teachers, and in contradiction with the fact that more
students have repeated a class there, an unusually high percentage see themselves as
103 Weighted data
4.6 6.7
9.9
0.7 4.8
0.7 1.3 0.7
5.9 8.1
5.8 2.5 3.9 3.2
22.1
28.5
33.7
6.7
18.3
6.3 9.0
6.3
20.8 23.7
20.5
9.0
18.6
7.0
0.05.0
10.015.020.025.030.035.040.0
no, never
yes
91
„good“ students, in comparison with their peers in other regions. In line with this self-
assessment, more students than in any other region plan to pursue education in higher
schools leading to academic studies. At the same time, less students than in any other
region plan to start an apprenticeship after their current school.
Delinquency is related to performance at school and attitudes towards school. Students
who plan pursuing with higher education report less self-reported offences. The highest
delinquency scores can be seen among students without clear or, eventually, unrealistic
plans for the future.
Students who report having missed school over more than one entire day during the last
12 months without legitimate excuse report having committed substantially more serious
and violent offences.
There is a significant, consistent and fairly strong association between delinquency and
the perception of social problems at school. Students are more likely to admit having
committed offences who report a lot of stealing, fighting and vandalized items at school.
The association is particularly strong for the perception that drug use is common at
school. This is especially true for more serious and violent offences.
School-related variables (as those discussed here) are particularly relevant for designing
policies of prevention. Truancy, vandalism at school as well as other social problems at
schools can be dealt with, at least to some extent, by school authorities. Although our
cross-sectional analyses cannot definitively determine the causal direction of the
observed associations, their size suggest, in some instances at least, that improving school
indicators might be a promising way to prevention.
92
Chapter 4
Victimization
4.1 Overview
In the ISRD-3, as well as during ISRD-2 (in 2006), respondents were asked whether they have
been victims of several kinds. The questionnaire included 7 questions about victimization (over
the entire life time and over last year). In case of any victimization (except parental violence and
parental maltreatment) that was experienced during last 12 month, respondents were asked
whether or not the incident had beenreported to the police.
In this chapter, findings will be presentedregarding victimization and reporting to the police.
Wealso shall compare rates of victimization in 2013, with those observed in 2006 (ISRD-2).
Further, rates of reporting to the police in 2013 and 2006 will be presented.
This chapter also includes information on demographic factors influencing the risk of
victimization and the decision to report an incident to the police.
4.2 Descriptive Statistics
4.2.1 The prevalence of victimization across regions
The following Table 4.1 gives the detailed rates on how many among the respondents have ever,
during their life, become victims of the offences listed, both nation-wide and in the several
subsamples.
Table 4.1 Victimization (life-time prevalence) by main and subsamples in %.104
Switzerland
(N=4'158)
DE
(N=2'560)
FR
(N=956)
ITA
(N=642)
SG
(N=625)
AG
(N=555)
GE
(N=268)
ZH
(N=266)
Robbery 5.1 4.5 6.1 6.7 4.4 6.5 3.6 5.5
Assault 5.0 4.4 5.9 7.3 4.8 4.4 7.1 4.2
Personal theft 35.1 35.0 35.9 31.3 35.8 32.7 32.6 36.8
Hate 7.4 7.5 7.4 5.8 6.2 6.6 7.4 10.5
Cyberbullying 15.5 12.9 21.1 16.7 15.1 16.4 18.3 12.5
Parental violence 28.4 24.3 37.5 28.7 23.4 20.3 38.1 29.7
Parental maltreatment 7.9 6.1 12.2 5.0 7.8 5.3 10.8 5.8
104 Weighted data
93
Figure 4.1 gives the same information in visualized form and for the three language regions as
well as for the country as a whole. As it seems, victimization is relatively evenly distributed
across the several regions of Switzerland.
Figure 4.1 Victimization (last year prevalence) by main and subsamples in %.105
However, respondents in German-speaking cantons are slightly less often victimized regarding
most offences. The difference is particularly marked for parental violence and parental
maltreatment that is almost twice as frequent in French-speaking cantons and especially in
Geneva (as shown in Table 4.1). The differences are similarly strong for cyber bullying.
4.2.2 More victimizations and less reporting?
Last year prevalence rates of victimization in 2013 can be compared with the data for 2006 and
for Switzerland as a whole. Exceptions are hate crimes, cyber bullying, parental violence and
maltreatment by parents that were not measured in 2006, as well as bullying in general that were
not asked in 2013.
105 Weighted data
3.3 2.9 3.8 4.5 2.0
4.0 2.4 4.3 3.8 3.4 4.9 3.2 2.6 2.9 6.0
3.7
26.9 26.9 27.1 25.2 27.2 25.8 27.3 28.8
5.6 5.5 6.2 3.8 2.8
5.7 6.0 7.7 8.7 5.4
16.7
4.7 5.6 5.6
12.3
5.3
19.1 16.4
25.4
16.9 17.1 14.9
21.5 18.1
5.5 3.9
9.3
3.5 5.4 4.1
9.6
3.3
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
Robbery Assault Personal theft Hate
Cyber bullying Parental violence Parental maltreatment
94
Table 4.2 Percent victimized and reporting to the police in % for 2013 and 2006.106
ISRD-2* ISRD-3*
Victimization
(last
yearprevalenc
e)
Reported to
the police
Victimization (last
yearprevalence)
Reported to the
police
Robbery 2.3 22.3 3.3 17.4
Assault 2.4 32.4 3.8 30.6
Personal theft 22.6 32.3 26.9 22.9
Hate
5.6 15.1
Cyberbullying
8.7 15.8
Bullying 12.4 7.8
Parental violence
19.1
Parental
maltreatment
5.5
N= 3‘648 4'158
* weighted data
The prevalence of victimization has increased from 2006 to 2013 for robbery, assault and
personal theft. The increase is significant (p ≤ .000).
Reporting to the police has been asked for the last incident (in case more than one incidence has
been reported for the last 12 months). Reporting to the police is not as unusual as one might have
expected. In general, reporting to the police has slightly decreased from 2006 to 2013. For the
interpretation of official counts of offending (such as police statistics), this means that a lower
level of reporting should be taken into account and that an eventual decrease might be overstated
if lower levels of reporting are not considered.
106 Weighted data
95
4.3 Association between demographic variables, victimization and reporting to the police
4.3.1 Gender, victimization and reporting to the police
Gender is an important variable in criminology. Table 4.3 shows,for boys and girls, the
prevalence rates of victimization (both over the last 12 months and the entire life time) as well as
the proportion of offences that were reported to the police.
Table 4.3 Victimization and reporting to the policein %.107
Victimization (life time prevalence) by gender (N=4121-4136)
Robbery Assault Theft Hate Cyberbullying
Parental
violence
Parental
maltreatment
male 7.6 6.2 39.4 7.7 10.4 27.3 8.0
female 2.7 3.9 31.0 7.0 20.5 29.4 7.7
Chi-
squire .000 .001 .000 .380 .000 .133 .719
Victimization (last year prevalence) by gender (N=4129-4138)
Robbery Assault Theft Hate Cyberbullying
Parental
violence
Parental
maltreatment
male 5.0 4.4 29.1 6.0 5.9 18.6 5.6
female 1.6 3.3 24.6 5.3 11.3 19.5 5.3
Chi-
squire .000 .051 .001 .381 .000 .426 .690
Reporting to the police by gender, last year (N=129-1096)
Robbery Assault Theft Hate Cyberbullying
male 16.7 25.8 25.2 17.6 14.8
female 18.2 36.8 20.5 12.6 16.2
Chi-
squire .842 .141 .064 .288 .738
Boysare more often victims of robbery, assault and personal theftthan girls. These differences
obviously relate to different life-styles and exposure to risk. On the other hand, girls more often
than boys experience cyber bullying. Hate crimes, parental assault and maltreatment (life-time
and last year prevalence) are relatively independent of gender.
4.3.2 Birthplace, victimization and reporting to the police
4.3.2.1 Country of birth and victimization
Birthplace has been measured in a way that promised to work in very different countries with
most diverse patterns of immigration. For this sake, respondents have been asked in which
country they were born themselves, and in which country their mother and their father had been
born. Table 4.4 presents how many respondents (in per cent) experienced any of the listed
107 Weighted data
96
offences over the last 12 months by whether they were born in Switzerland or in any other
country.
Table 4.4 Victimization (last year prevalence) by respondents’ birthplace in %, N=4118-
4132.108
Robbery Assault Theft Hate
Cyberbull
ying
Parental
violence
Parental
maltreatment
Born
abroad 4.0 6.3 34.3 7.4 10.2 25.1 10.2
born in
Switzerland 3.1 3.4 25.6 5.3 8.4 18.2 4.7
.247 .001 .000 .049 .171 .000 .000
In Figure 4.2, the same information is presented in a visualized form, but for offences
experienced over the last 12 months.
Figure 4.2 Victimization (life time prevalence) by respondents’ birthplace in %, N=4118-
4132.109
Respondents who were born abroad experience offences of all kinds significantly more often,
than those who were born in Switzerland. This is true for the last twelve months (Figure 4.2) as
well as over the lifespan (Table 4.4). This difference may be related to different leisure-time
activities and life-styles (see chapter 5).
Whether or not the incident was reported to the police was asked for the last incident
experienced over the last 12 months.
Birthplace can also be defined in terms of parents’ country of birth. We first present the results
by mother’s birthplace.
108 Weighted data
109 Weighted data
6.0 8.2
43.6
9.8 18.3
34.7
13.0 4.9 4.5
33.7
6.9 15.1
27.4
7.0
0.010.020.030.040.050.0
not in Switzerland Switzerland
97
Table 4.5 Victimization (last year prevalence) by mothers’ birthplacein %, N=4092-4107.110
Robbery Assault Theft Hate Cyberbullying
Parental
violence
Parental
maltreatment
Born
abroad 3.9 4.1 32.0 8.4 10.8 23.5 8.7
born in
Switzerland 2.8 3.5 23.5 3.8 7.3 16.3 3.3
.070 .300 .000 .000 .000 .000 .000
Respondents whose mother was born in Switzerland are less often victims of all listed offences
with the exception of robbery and assault (where the difference does not reach statistical
significance). Interestingly, mother’s country of birth seems to influence risks of victimization
more than whether or not the respondent had been born abroad or in Switzerland. This is
particularly true for maltreatment by parents.
The results are similar when, instead of the last 12 months, the entire life-time is considered
(Figure 4.4). In this case, the differences all reach statistical significance. Proportionately, they
become even more impressive, particularly with respect to parental maltreatment.
Figure 4.3 Victimization (life time prevalence) by mothers' birthplace in %, N=4093-4108.111
These differences can be observed, in similar proportions, in the national sample as well as in the
several subsamples (not shown).
When mothers’ country of birth is considered, some relevant differences emerge. Generally
speaking, children of mothers born in Switzerland, in Germany or in any other country of
Western or Eastern Europe have lower victimization rates than respondents whose mother was
born in Southern Europe or outside of Europe. Details are presented in Figure 4.4.
110 Weighted data
111 Weighted data
6.3 5.9
40.8
11.4 18.3
34.0
12.5 4.2 4.3
31.3
4.8 13.8
24.7
4.7
0.010.020.030.040.050.0
not in Switzerland Switzerland
98
Figure 4.4 Victimization (life time prevalence) by mother’s birthplace in %, N=4097-
4107.112
The differences are particularly pronounced for cyber bullying, parental violence and
maltreatment by parents. They are visible in all linguistic regions (not shown).
Similar patterns appear when, instead of mother’s origin, the father’s country of birth is
considered.
Table 4.6 Victimization (last year prevalence) by father’s birthplace in %, N=4058-4072.113
Robbery Assault Theft Hate
Cyberbull
ying
Parental
violence
Parental
maltreatment
Born
abroad 3.8 5.3 30.1 7.9 9.7 24.0 9.3
born in
Switzerla
nd 2.9 2.8 24.6 4.2 7.8 16.1 3.1
.135 .000 .000 .000 .032 .000 .000
The same tendency can be observed if offences experienced over the entire lifespan are
considered (Figure 4.5).
112 Weighted data
113 Weighted data
4.2 4.3
31.3
4.8
13.8
24.7
4.7 10.9
5.0
34.5
8.4 14.4
26.1
14.4 8.0
3.7
40.1
13.0 13.6
30.9
12.4 6.6
3.8
35.7
11.7 14.5
28.2
10.3
1.8 6.1
31.9
9.7 9.7 16.1
4.4 7.5 6.5
34.9
9.7
25.3
38.2
12.9 7.5 6.5
45.7
7.0
20.1
39.3
10.1
2.4 3.9
44.4
13.8
24.6
42.1
13.8
5.2 5.2
45.0
15.6 19.0
39.9
16.3
7.7 11.3
46.9
7.7 11.3
43.0
16.2
2.7
9.5
50.7
16.0
36.0
21.6
9.3
Switzerland Italy Kosovo Ex-Yugoslavia, Albania
Germany Portugal West Europe Africa
Asia/Oceania Americas (North, South) Eastern, Central Europe
99
Figure 4.5 Victimization (life time prevalence) by father’s birthplace in %, N=4059-4073.114
In this case, all differences (with the exception of theft and cyber bullying) reach statistical
significance. They hold across regions and language rather consistently. Although both parents’
foreign origin seems to increase risks of victimization among their offspring, mother’s origin
seems to be slightly more important than father’s country of birth. In further analyses, we shall
try to disentangle whether this is directly related to mother’s origin. One possibility might be that
mothers of Swiss background are more successful at protecting their offspring from domestic
violence.
When we look at fathers’ country of birth, some interesting patterns emerge. As with mothers
(see Figure 4.4), respondents whose father was born abroad tend to be victimized more often,
particularly in the domain of domestic violence (Figure 4.6).
114 Weighted data
6.2 6.5
38.6
9.8 17.2
33.8
12.8
4.4 3.9
32.7
5.8
14.4
24.7
4.7
0.05.0
10.015.020.025.030.035.040.045.0
not in Switzerland Switzerland
100
Figure 4.6 Victimization (life time prevalence) by fathers' country of birth in %, N=4058-
4072115
Domestic violence seems more common in families with fathers born in Asia and Oceania,
Portugal and countries of North and South America. No clear pattern emerges for the other
offences. The picture is similar in all regional subsamples (not shown).
4.3.2.2 Reporting to the police by country of birth
For all incidents mentioned during interviews that were experienced over the last 12 months, the
respondent was asked whether or the incident had been reported to the police. In case more than
one incident occurred during the last 12 months, the question was asked in relation to the last
one. The replies will be analysed here by respondents’ birthplace.
115 Weighted data
4.4 3.9
32.7
5.8
14.4
24.7
4.7
11.3 10.5
31.7
10.9 11.7
30.3
14.7
7.1 3.0
35.5
11.6 17.2
33.8
11.1 5.6 3.6
35.7
11.2 13.3
25.0
10.3
0.9
8.2
36.4
4.5 11.0
17.3
5.5 6.2 1.9
34.6
8.0
25.3
40.1
12.3
2.8 8.5
51.1
6.2
17.5
45.1
8.6 2.2
5.6
44.4
10.0
23.9
40.2
14.9
6.8 7.5
39.1
12.0 18.4
36.7
18.7
7.1 9.3
44.7
11.6
22.1
36.8
16.1
6.1 9.1
54.8
6.5
25.8 30.0
9.1
0.0
10.0
20.0
30.0
40.0
50.0
60.0
Switzerland
Italy
Kosovo
Ex-Yugoslavia, Albania
Germany
Portugal
West Europe
Africa
Asia/Oceania
Americas (North, South)
Eastern, Central Europe
101
Figure 4.7 Reporting to the police by respondents' birthplace in %, N=130-1095.116
Reporting to the police does not differ much along birthplace. Respondents who were born
abroad report offences to the police slightly more often than those born in Switzerland. The
differences arenot significant, however.
Reporting of experiences of victimization to the police is not significantly more frequent among
respondents whose mother was born abroad. This was true for the national sample as well as for
the several subsamples.
Figure 4.8 Reporting to the police by mother' birthplace in %, N=127-1088.117
The following Figure 4.9 shows the proportion of offences reported to the police for subjects
whose father was born abroad vs. those whose father was born in Switzerland.
116 Weighted data
117 Weighted data
22.7
31.4 25.0
20.0 19.0 16.7
30.6
22.5
13.8 15.1
0.0
10.0
20.0
30.0
40.0
Robbery Assault Theft Hate Cyber bullying
not in Switzerland Switzerland
13.8
26.6 23.6
13.6 15.6 18.8
29.9
22.7 16.8 16.0
0.0
10.0
20.0
30.0
40.0
Robbery Assault Theft Hate Cyber bullying
not in Switzerland Switzerland
102
Figure 4.9 Reporting police by fathers' birthplace in %, N=127-1073.118
For most offences, reporting is slightly more likely to occur if the respondent’s father was born
abroad. The difference is not significant, however.
4.3.3 Religion and victimization
The questionnaire contained, in a form that worked in many different nations, detailed
information about respondents’ religious affiliation. This variable turned out to be associated
with risks of victimization, both nation-wide and in the three linguistic regions of Switzerland.
Given the small size of some religious groups, we consider life-time experiences only in this
section.
Figure 4.10 Victimization (life time prevalence) by religious affiliation in %, N=4096-4105.119
118 Weighted data
119 Weighted data
18.5
32.5
23.9
14.4 17.3 15.1
26.8 21.6
15.1 14.8
0.0
10.0
20.0
30.0
40.0
Robbery Assault Theft Hate Cyber bullying
not in Switzerland Switzerland
5.5 4.9
33.2
7.5
19.2
30.1
8.3 4.7 5.0
33.9
6.6
14.6
26.9
8.1 5.2 4.5
34.4
4.9
13.0
25.8
4.6 3.9 5.9
36.6
7.5 13.7
31.4
5.5 5.4 4.8
39.9
11.9 16.1
31.2
12.7 6.6 6.2
43.2
14.0
23.3
39.3
14.7
0.0
10.0
20.0
30.0
40.0
50.0
do not belong to a religion/religion communitycatholicprotestantorthodox and other christian confessionsmuslim
103
Whereas religion is not significantly associated with robbery and assault, theft of personal items,
cyber bullying and hate crimes are inconsistently related to religion. However, respondents of
Christian (i.e. protestant, catholic or orthodox/other) background experience all listed offences
slightly less often than Muslims and those without religious affiliation. The difference is, as far
as protestant respondents are concerned, particularly strong with respect to parental violence and
maltreatment.
In German-speaking cantons, the results match by and large the national pattern. In the French
and Italian-speaking regions, the differences are mostly non-significant, presumably because of
the smaller sample sizes and the small percentage of respondents belonging to certain religious
affiliations.
4.4 Main findings and discussion
The findings in this chapter can be summarized as follows:
Victimization is fairly wide-spread. When we consider incidents experienced over the life
span, rates far above what one finds in national victimization surveys of the general
population can be observed. Obviously, juveniles are far more exposed to risks of theft,
assault and even robbery than the general population.
Juveniles report a lot of cyber bullying which seems to have replaced classical bullying to
a large extent.
Violence experienced at home, i.e. probably corporal punishment is relatively wide-
spread. Roughly close to 20 per cent of respondents has experienced this. More
disturbing is the fact that 5.5 per cent report different forms of domestic assault that can
be considered as parental maltreatment.
Although one would not expect much variation within a small country like Switzerland, it
is noteworthy that such differences exist. Juveniles in the French-speaking part report
violence somewhat more often than those in the other regions. Particularly domestic
violence and parental maltreatment are more frequent in the French-speaking part. This
coincides with higher rates of domestic violence (between parents) reported by
respondents from that region (see chapter 2, Figure 2.11_1).
Offences that are typically experienced in the streets, such as assault, robbery and theft of
personal items have increased between 2006 and 2013.
Reporting to the police decreased. This should be kept in mind when police and other
official counts of offending are to be interpreted.
Girls experience street-level offences less often than boys, presumably due to their
different life-style (and, perhaps, the fact that, while going out, they are more often in the
company of boys). However, girls experience cyber bullying more often. Violence
experienced at home and theft of personal items are more evenly distributed across
gender.
104
Girls tend to report offences somewhat less often, although differences are not large.
Birthplace was measured, in the ISRD-3, in a way that promised to work during the
fieldwork in countries as diverse as Western European nations, the Americas and Asian
countries. It was decided to ask about the country of birth of the respondent and both of
his/her parents, rather than about his official nationality. In Switzerland, a very
substantial proportion of respondents had been born abroad, or have parents who were
born outside of Switzerland (see chapter 1).
In connection with victimization, the country of birth (of the respondent, his/her mother
or father) is somewhat associated with victimization. Generally, respondents born abroad,
or whose mother or father was born abroad, experience all offences somewhat more
often. For street crime, this may be due to different life-styles and in parts be related to
offending that is also more frequent among youths with origins in other countries (see
chapter 7).
The differences are particularly strong with respect to hate crimes. This is not unexpected
in the sense that the question was obviously shaped to identify incidents experienced by
respondents who were more exposed to such forms of harassment due to their origin. On
the other hand, it is interesting to observe that respondents from Swiss background report
also such incidents, although in lower proportions.
Parental (domestic) violence and, particularly, parental maltreatment are substantially
more frequent among youths from abroad. This matches findings (presented in chapter 2)
concerning the association between origin and domestic violence (between parents) that
is, again, more frequently reported by respondents whose parents were born abroad.
Interestingly, the mother’s origin seems somewhat more important in this respect than the
father’s, or the respondent’s own country of birth. Since these findings do not imply who
of the parents actually was responsible for the maltreatment, it could be that mothers born
in Switzerland are better in a situation to protect their child from domestic violence.
Reporting assault and hate crime to the police is somewhat more common among
respondents born abroad. This may reflect higher levels of seriousness of the attack. For
other offences, the differences are not large. Assault is also more often reported to the
police if parents are born abroad.
For the police, the “good news” about these findings is that reporting does not differ
much along ethnic lines. On attitudes towards the police see chapter 8.
Religion is weakly associated with victimization overall, especially with street-crime.
However, respondents of any Christian denomination and especially Protestants are less
often victims of all listed offences than those without any religious affiliation and
Muslims. The difference is largest in connection with parental maltreatment.
105
Chapter 5
Leisure time, life style, peers, happiness and delinquency
5.1 Overview
In this Chapter findings will be presented on leisure-time and other unsupervised activities, time
spent with friends (delinquents and/or non-delinquents), time spent within the family; level of
happiness, consumption of alcohol and other substances, and the relationship of all these
variables with several measures of delinquency. In the first part (section 5.2), we shall show the
frequencies of these variables in the national as well as across the regional subsamples.
Since consumption of substances in general and particularly of cannabis can be considered as
deviant behaviour, given the respondents’ age, we shall treat use of alcohol and cannabis as a
dependant as well as an independent variable. It should be noted that the sale of alcoholic
beverages to minors of 16 is illegal. For spirits, the age-limit is 18 years. In section 5.3, we shall
look at associations between alcohol/cannabis use in relation to leisure-time activities.
In section 5.4, we shall look at changes between 2006 (ISRD-2) and 2013 (ISRD-3) with respect
to several leisure-time activities.
As a truly innovative concept, the ISRD-3 instrument included questions concerning happiness.
This concept, developed by the Swiss economist Bruno Frey, has turned out to be particularly
helpful in the analysis of experiences of victimization (Staubli, Killias& Frey 2013). Happiness
can be considered as an outcome as well as a causal variable. We shall look both at how it is
associated with leisure-time activities, use of substances and delinquency (section 5.5).
In the final part (section 5.6), we shall analyse how all these leisure-time variables and happiness
are associated with the several forms of delinquency that were included in ISRD-3.
A summary and short discussion (section 5.7) will conclude this chapter.
5.2 Frequencies in the national and the regional samples
Leisure-time activities
Several questions addressed the use of leisure-time, i.e. whether respondents go out during the
week and on weekends, when they return and whom and how otherwise they spend time with.
106
Figure 5.1 “How often do you go out a week?” by main and subsamples in%.120
More than half of respondents go out during the week up to 2 times, whereas more than one third
nevergoes out. Going out varies somewhat across region. In the French speaking part, two
students in five never go out in the evening. Geneva is the region with the most active evening
and night life. Presumably, these differences also reflect opportunities since Geneva obviously
has the most extensive offer of night-time activities.
Figure 5.2 Time of coming back after going out on weekend evenings by main and
subsamples in %.121
Most students either do not go out in the evening or come back home before midnight even on
weekends. Nearly one respondent in five say returning way after midnight, i.e. during early
morning hours or the following day. It can be assumed that these students are the least controlled
by parents. In the canton of Zürich, about 30% regularly return back home after midnight or the
following day.
120 Weighted data
121 Weighted data
38.1 35.5
45.1
31.4 36.9 33.6
45.8
27.7
45.2 48.0
38.2
50.1 44.5
49.3
29.6
53.9
16.7 16.5 16.7 18.5 18.6 17.2 24.6
18.4
0.0
10.0
20.0
30.0
40.0
50.0
60.0
CH(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
never 1-2 times per week 3-6 times per week, daily
36.8 34.0
43.6 37.0
32.8 32.0
53.0
29.1
9.6 10.6 7.9 6.2 10.3 9.2
3.3
12.8
2.7 3.5 1.0 1.6 4.1 1.9 1.3 5.6
.9 .7 1.2 1.4 1.1 .5 2.3 .8
12.8 11.8 14.2 18.7
12.9 12.3 17.0
8.4
37.2 39.3
32.0 35.1
38.8 44.1
23.2
43.2
0.0
10.0
20.0
30.0
40.0
50.0
60.0
CH(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
I do not go out in the evening 00:00 - 2:45 3:00 - 6:30
7:00 - 9:30 10:00 - 19.45 20:00 - 23:45
107
Figure 5.3 Spending time with family and friendsby main and subsamplesin %.122
Nearly half of respondents spend time mostly with 1-3 friends; and nearly one in four with other
family members and about an equal proportion with a larger group. There is not much variation
across regions
Figure 5.4 Leisure time: going to coffee bars or pop concerts by main and subsamples in
%.123
More than half of respondents never go to coffee bars or pop concerts. There is not that much
variation across regions, with the exception of Zurich where more peoplego often to coffee bars
or pop concerts.
122 Weighted data
123 Weighted data
9.3 9.1 9.8 8.8 7.2 7.8 15.7
7.7
22.0 21.2 22.9 26.1 23.2 21.7 28.3
19.9
44.4 48.3
37.2 36.7 43.9
47.3
34.3
50.2
24.3 21.4
30.0 28.4 25.7 23.2 21.7 22.2
0.0
10.0
20.0
30.0
40.0
50.0
60.0
CH(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
on my own with my family with 1-3 friends with more than 4 friends
55.1 49.3
64.8 74.9
48.3 50.9
68.5
44.9 39.8
44.7
31.8 22.4
47.3 41.8
25.1
47.0
5.1 6.0 3.4 2.8 4.4 7.3 6.4 8.1
0.0
20.0
40.0
60.0
80.0
CH(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
never sometimes often
108
Figure 5.5 Leisure time: doing something creative (theater, music, drawing, writing, reading
books) by main and subsamples in %.124
In the whole Switzerland 39% of respondents said never doing anything creative, such as playing
theatre or music, drawing, writing and reading books. This percentage is the highest in Italian
speaking cantons. Otherwise, there is not much variation across regions in this respect.
Figure 5.6 Leisure time: engaging in fights with others by main and subsamples in %.125
Only a minority of respondents admit engaging, during leisure-time, often or sometimes in fights
with others. There is obviously only limited variation across regions.
124 Weighted data
125 Weighted data
39.3 37.7 41.4 47.9
37.3 39.6 41.8 34.1 36.1 36.4 35.8 34.2 36.3 38.3 41.1 38.6
24.6 25.9 22.8 18.0
26.4 22.2
17.1
27.3
0.0
10.0
20.0
30.0
40.0
50.0
60.0
CH(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
never sometimes often
87.4 87.6 87.6 84.2 89.7 86.6 84.1 84.8
9.6 9.8 8.8 11.8 9.3 10.5 9.3 11.5 2.9 2.6 3.6 4.1 1.0 2.9 6.6 3.6 0.0
50.0
100.0
CH(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
never sometimes often
109
Figure 5.7 Leisure time: doing sportsby main and subsamples in %.126
Again, very little variation across regions can be observed. However, at closer inspection, it turns
out that respondents more often say never practicing any sports in Geneva and in French-
speaking regions.
126 Weighted data
14.1 12.1 19.1
10.1 11.1 12.8
25.9
10.5
31.5 34.1 26.1 29.5 32.2 33.1
20.7
33.9
54.4 53.8 54.8 60.5 56.7 54.0 53.4 55.6
0.0
20.0
40.0
60.0
80.0
CH(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
never sometimes often
110
Figure 5.8 Leisure time: school-related studies or homework, by main and subsamples in
%.127
The highest proportion of respondents who say never studying or doing homework during
leisure-time can be found in Geneva and other cantons in the Romandie, as well as in the canton
of Zurich. Ticino and St. Gallen have the most disciplined students, apparently.
Figure 5.9 Leisure time: hanging out in shopping centers, parks, streets etc. for fun, by main
and subsamples in %.128
Hanging out in shopping centres, parks, streets etc. for fun is the least popular among
respondents in German speaking cantons and particularly in St. Gallen. On the other hand, such
unstructured leisure-time activities are fairly wide-spread in Ticino Geneva and other French-
speaking cantons in general.
127 Weighted data
128 Weighted data
11.0 10.3 13.2 6.9 8.1 8.7
16.1 11.6
54.4 54.6 54.6 51.2 54.9 58.7 47.9 47.3
34.6 35.1 32.2 41.8
37.0 32.5 36.1 41.2
0.0
20.0
40.0
60.0
80.0
CH(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
never sometimes often
34.6 39.1
27.5
18.3
44.8
34.7 29.8 30.4
39.6 41.2 35.2
45.6
35.4
42.3
33.4
47.9
25.7 19.7
37.3 36.2
19.8 23.1
36.8
21.7
0.0
10.0
20.0
30.0
40.0
50.0
60.0
CH(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
never sometimes often
111
Figure 5.10 Leisure time: doing something illegal to have fun, by main and subsamples in
%.129
Variation is less pronounced than one might have expected, although doing illegal things for fun
is more popular among respondents in Zurich and Geneva.
Figure 5.11 Leisure time: drinking alcohol or take drugs, by main and subsamples in %.130
Drinking alcohol or taking drugs during leisure-time “often” is more common in French
speaking cantons and particularly in Geneva and in Zurich, and relatively rare in the cantons of
St. Gallen and Aargau. Presumably, opportunities – i.e. the availability of substances and places
to consume – may play an important role in this respect.
129 Weighted data
130 Weighted data
73.9 72.8 74.5 84.0
73.5 72.4 78.8
71.9
21.2 22.2 20.2 13.2
22.0 21.7 12.4
22.6
5.0 4.9 5.4 2.8 4.4 5.9 8.8 5.6
0.0
20.0
40.0
60.0
80.0
100.0
CH(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
never sometimes often
80.0 79.7 79.8 86.1 86.6 82.5 85.5 75.8
15.0 15.2 15.4 10.0 10.2 14.0 10.2 15.8 5.0 5.1 4.8 3.9 3.2 3.4 4.3 8.4
0.0
20.0
40.0
60.0
80.0
100.0
CH(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
never sometimes often
112
Figure 5.12 Leisure time: frightening and annoying people just for fun, by main and
subsamples in %.131
It seems that in the Swiss context this question, designed for a multitude of cultures included in
this project, measured “teasing”, being noisy or making jokes (“Streiche”) rather than activities
that might be proxies of delinquency. Anyhow, these sorts of activities are the most common in
fairly rural areas, such as St. Gallen and Aargau, and least common in Geneva and in the
Romandie in general.
5.2.1 Peers and friends
The questionnaire included several questions regarding friends and peers. Table 5.1 gives an
overview on how many respondents say belonging to a group of friends (peers). Further, this
section of the instrument included questions regarding deviant behaviour among peers, such as
using illicit substances, shoplifting and several other offences
Table 5.1 “Some people have a friend or a group of friends to spend time with, doing
things together or just hanging out: do you belong to such a group?” by main and
subsamples in %.132
CH
(N=4'158)
DE
(N=2'560)
FR
(N=956)
ITA
(N=642)
SG
(N=625)
AG
(N=555)
GE
(N=268)
ZH
(N=266)
no 21.8 22.5 20.3 21.2 22.6 21.4 27.3 21.5
yes 78.2 77.5 79.7 78.8 77.4 78.6 72.7 78.5
Most of students have a friend or group of friends to spent time with, doing things together or
just hanging out. However, about one student in five seems to be somewhat socially isolated.
There is almost no difference across regions.
131 Weighted data
132 Weighted data
70.7 66.9 78.4 75.0
65.7 64.6
83.7 67.5
24.0 27.1 17.7 20.2
29.2 28.3 13.2
24.3
5.3 6.0 3.8 4.8 5.2 7.2 3.1 8.2
0.0
20.0
40.0
60.0
80.0
100.0
CH(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
never sometimes often
113
Figure 5.13 Having friends who use drugs by main and subsamples in %.133
In the whole Switzerland 41.6% of respondents know at least one friend who uses drugs. This
level is the highest in French speaking cantons and in Geneva where a majority of respondents
know such a friend. The lowest proportion can be found in the cantons of Ticino and St. Gallen.
Figure 5.14 Having friends who committed shoplifting, by main and subsamples in %.134
The highest proportions of respondents who say knowing at least one among their friends/peers
who had committed shoplifting can be found in Zurich and St. Gallen. Otherwise, knowing of
somebody who has committed such an offence is fairly widespread and evenly distributed across
region.
133 Weighted data
134 Weighted data
58.4 62.8
46.1
73.1 71.5 60.9
48.9 58.3
41.6 37.2
53.9
26.9 28.5 39.1
51.1 41.7
0.0
20.0
40.0
60.0
80.0
CH(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
no yes
63.8 63.6 62.0 75.8 67.6 70.7 71.7 64.0
36.2 36.4 38.0 24.2
32.4 29.3 28.3 36.0
0.0
20.0
40.0
60.0
80.0
CH(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
no yes
114
Figure 5.15 Having friends who committed burglary, by main and subsamples in %.135
Interestingly, there is far more variation with respect to knowing at least one friend among one’s
peers who had committed burglary. This is fairly common in Geneva and other French-speaking
cantons, but exceptional in other regions.
Figure 5.16 Having friends who committed robbery by main andsubsamples in %.136
Knowing somebody who had committed robbery is very uncommon in all subsamples. This
obviously reflects the fact that this offence is not frequently committed (see Chapter 7) and
perhaps even less often admitted, even among friends.
135 Weighted data
136 Weighted data
81.1 93.0
53.5
89.9 93.1 95.1
54.7
90.3
18.9 7.0
46.5
10.1 6.9 4.9
45.3
9.7
0.0
50.0
100.0
CH(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
no yes
94.6 94.7 94.2 95.7 96.0 94.0 97.4 92.6
5.4 5.3 5.8 4.3 4.0 6.0 2.6 7.4
0.020.040.060.080.0
100.0120.0
CH(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
no yes
115
Figure 5.17 Having friends who committed assault by main and subsamples in %.137
The same holds, although to a lesser extent, for assault. Again, little variation across regions can
be observed, with the exception of Zurich.
Having deviant (or law-abiding) friends obviously is one thing, and another is how intense such
bonds may be in a series of hypothetical situations, such as moving to another city.
Figure 5.18 “If I had to move to another city, I would miss my friends”, by main and
subsamples in %.138
Most of students reported that they would miss their friends “quite a lot or very much” if they
had to move to another city. There is virtually no difference across regions and cantons.
137 Weighted data
138 Weighted data
90.3 88.0 95.2 92.2 90.4 88.6 94.7
86.8
9.7 12.0 4.8 7.8 9.6 11.4 5.3
13.2
0.0
20.0
40.0
60.0
80.0
100.0
120.0
CH(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
no yes
1.2 1.4 1.1 0.6 2.3 1.1 1.3 2.2 8.6 7.5 10.3 12.0
6.8 6.0 14.2
6.4
90.2 91.2 88.6 87.5 90.9 92.9 84.5
91.4
0.0
20.0
40.0
60.0
80.0
100.0
Switzerland DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
not at all/not much abit/somewhat quite a lot/very much
116
Figure 5.19 “It is important what friend/group of friends thinks”, by main and subsamples in
%.139
For most respondents in all regions, it is quite important or very important what their friends
think about them. The highest percentage is in French-speaking cantons.
5.3 Consumption of alcohol and cannabis
5.3.1 Leisure-time and use of substances
The instrument allowed to measure with some precision the use of several substances. The
question was, in a first round, whether or not respondents ever consumed alcohol in general
(beer, wine or spirits) and cannabis (and other drugs). Whereas only a small minority indicated
using any other illegal substances, the use of cannabis and alcohol has been admitted in
sufficient proportions to warrant detailed analyses. Beyond life-time prevalence, the actual use
(i.e. over the last 30 days) has also been measured.
139 Weighted data
3.8 3.3 5.0 4.1 3.3 3.2 2.6 2.5
21.0 23.0 17.1 18.7
25.5 21.2 19.5 23.4
75.1 73.7 77.8 77.3 71.1 75.6 77.9 74.1
0.0
20.0
40.0
60.0
80.0
100.0
Switzerland DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
totally unimportant/quite unimportant neither/nor quite importamt/very important
117
Figure 5.20 Going out in the evening by substance use, life time (ltp) and last month
prevalence (lmp) in %, N=4026-4028.140
Consumption of substances (alcohol in general, beer, wine, spirits and cannabis) is associated
with going out in the evening. The association is particularly strong with respect to cannabis.
The use of legal as well as illegal substances goes along with extreme forms of going out. It is
much more common among respondents who go out more than three times a week (Figure 5.20)
and those who, on weekends, return home after midnight (Figure 5.21) or in the early morning
hours only.
140 Weighted data
55.3
34.0
10.6
47.7 39.4
12.9
41.1 43.5
15.3
45.9 41.7
12.4
42.7 44.1
13.2
26.7
52.5
20.8 17.3
57.5
25.2 21.1
54.2
24.7 15.7
54.9
29.5
14.8
50.5
34.7
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
Never 1-2timesper
week
3-6timeper
week;everyday
Never 1-2timesper
week
3-6timeper
week;everyday
Never 1-2timesper
week
3-6timeper
week;everyday
Never 1-2timesper
week
3-6timeper
week;everyday
Never 1-2timesper
week
3-6timeper
week;everyday
Alcohol (ltp) *** Beer (lmp) *** Wine (lmp) *** Spirits (lmp) *** Cannabis (ltp) ***
No alcohol/cannabis Yes alcohol/cannabis
118
Figure 5.21 Time of coming back home after going out in the evening by alcohol/cannabis,
life time(ltp) and last month prevalence (lmp) in %, N=3783-3785.141
Indeed, alcohol and cannabis use is relatively infrequent among those who never go out or who
return home before midnight, but clearly most frequent among those who return later or even
after 3 AM. Again, the difference is stronger for hard liquor and cannabis than for beer and wine.
141 Weighted data
51.2
2.8 0.9 1.1
13.0
31.1
43.9
5.3 1.5 1.3
12.8
35.3 38.1
8.6
2.2 1.1
13.2
36.8 42.4
5.3 1.0 1.2
13.1
37.0 39.5
7.3 1.5 1.2
13.3
37.2
24.0
16.0
4.2 1.0
13.1
41.6
16.7 22.0
5.6 0.6
13.7
41.4
20.6 20.6
6.1 0.7
12.4
39.6
13.8
26.2
8.3
0.7
13.1
37.8
11.3
29.1
10.0
0.5
12.0
37.1
0.0
10.0
20.0
30.0
40.0
50.0
60.0
I do
no
t go
ou
t in
th
e e
ve
00
:00
- 2
:45
3:0
0 -
6:3
0
7:0
0 -
9:3
0
10
:00
- 1
9.4
5
20
:00
- 2
4:0
0
I do
no
t go
ou
t in
th
e e
ve
00
:00
- 2
:45
3:0
0 -
6:3
0
7:0
0 -
9:3
0
10
:00
- 1
9.4
5
20
:00
- 2
4:0
0
I do
no
t go
ou
t in
th
e e
ve
00
:00
- 2
:45
3:0
0 -
6:3
0
7:0
0 -
9:3
0
10
:00
- 1
9.4
5
20
:00
- 2
4:0
0
I do
no
t go
ou
t in
th
e e
ve
00
:00
- 2
:45
3:0
0 -
6:3
0
7:0
0 -
9:3
0
10
:00
- 1
9.4
5
20
:00
- 2
4:0
0
I do
no
t go
ou
t in
th
e e
ve
00
:00
- 2
:45
3:0
0 -
6:3
0
7:0
0 -
9:3
0
10
:00
- 1
9.4
5
20
:00
- 2
4:0
0
Alcohol (ltp) *** Beer (lmp) *** Wine (lmp) *** Spirits (lmp) *** Cannabis (ltp) ***
No alcohol/cannabis Yes alcohol/cannabis
119
Figure 5.22 Spending time with friends and family by alcohol/cannabis, life time (ltp) and
last month prevalence (lmp) in %, N=4055-4058.142
Spending one’s leisure-time alone or with other family members goes along with less use of
alcohol and/or cannabis. Particularly those who spend time mostly with groups of four friends or
more tend to use substantially more hard liquor and cannabis.
142 Weighted data
11.7
32.6 39.1
16.7 11.2
26.4
42.6
19.7
9.6
23.5
43.8
23.1
10.8
26.2
43.3
19.7
10.2
24.5
44.0
21.3
7.7 14.6
48.1
29.5
5.2 11.9
48.4
34.4
7.9 12.5
48.2
31.4
5.0 9.3
47.9
37.8
4.8 8.6
46.7 39.9
0.0
10.0
20.0
30.0
40.0
50.0
60.0
on
my
ow
n
w/
fam
ily
w/
1-3
fri
end
s
w/
larg
er g
rou
p o
f fr
ien
ds
on
my
ow
n
w/
fam
ily
w/
1-3
fri
end
s
w/
larg
er g
rou
p o
f fr
ien
ds
on
my
ow
n
w/
fam
ily
w/
1-3
fri
end
s
w/
larg
er g
rou
p o
f fr
ien
ds
on
my
ow
n
w/
fam
ily
w/
1-3
fri
end
s
w/
larg
er g
rou
p o
f fr
ien
ds
on
my
ow
n
w/
fam
ily
w/
1-3
fri
end
s
w/
larg
er g
rou
p o
f fr
ien
ds
Alcohol (ltp) *** Beer (lmp) *** Wine (lmp) *** Spirits (lmp) *** Cannabis (ltp) ***
No alcohol/cannabis Yes alcohol/cannabis
120
Figure 5.23 Friends with parents of foreign origin by alcohol/cannabis, life time (ltp) and last
month prevalence (lmp) in %, N=4041-4043.143
Interestingly, there is a moderate association between having mostly or exclusively friends of
immigrant background on one hand and consumption of substances, especially of cannabis. We
suspect this to be related to a life-style that implies going out frequently and during late hours.
143 Weighted data
14.3
54.4
27.3
3.9
13.2
52.0
30.4
4.4
12.6
51.3
31.9
4.2
13.5
53.1
29.7
3.7
13.1
52.2
30.9
3.8
10.4
48.7
36.0
5.0 9.3
48.6
37.3
4.8 8.3
49.3
36.1
6.3 7.5
44.8 40.8
6.9 5.9
44.7 41.1
8.3
0.0
10.0
20.0
30.0
40.0
50.0
60.0
no
ne
at a
ll
a fe
w
man
y o
f th
em
all o
f th
em
no
ne
at a
ll
a fe
w
man
y o
f th
em
all o
f th
em
no
ne
at a
ll
a fe
w
man
y o
f th
em
all o
f th
em
no
ne
at a
ll
a fe
w
man
y o
f th
em
all o
f th
em
no
ne
at a
ll
a fe
w
man
y o
f th
em
all o
f th
em
Alcohol (ltp) *** Beer (lmp) *** Wine (lmp) *** Spirits (lmp) *** Cannabis (ltp) ***
No alcohol/cannabis Yes alcohol/cannabis
121
Figure 5.24 Leisure time: going to coffee bars or pop concerts by alcohol/cannabis use, life
time (ltp) and last month prevalence (lmp) in %, N=4031-4036.144
There also seems to be a fairly strong association between going to coffee bars or pop concerts
and the use of substances.
144 Weighted data
69.5
27.9
2.6
62.7
34.6
2.7
58.0
37.9
4.1
61.7
35.4
2.9
58.8
37.6
3.6
45.6 47.7
6.8
38.7
50.9
10.4
39.0
50.4
10.6
36.0
52.5
11.4
36.5
50.5
13.0
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
nev
er
som
eti
me
s
oft
en
nev
er
som
eti
me
s
oft
en
nev
er
som
eti
me
s
oft
en
nev
er
som
eti
me
s
oft
en
nev
er
som
eti
me
s
oft
en
Alcohol (ltp) *** Beer (lmp) *** Wine (lmp) *** Spirits (lmp) *** Cannabis (ltp) ***
No alcohol/cannabis Yes alcohol/cannabis
122
Figure 5.25 Leisure time: doing something creative (theater, music, draw, write, reading
books) by alcohol/cannabis use, life time (ltp) and last month prevalence (lmp) in
%, N=4030-4034.145
145 Weighted data
31.7
40.1
28.2
36.2 37.8
26.0
39.7 36.2
24.1
34.7 38.0
27.3
36.3 38.0
25.7
44.7
33.2
22.1
46.5
32.1
21.4
38.1 34.8
27.0
53.5
29.9
16.6
55.6
25.8
18.6
0.0
10.0
20.0
30.0
40.0
50.0
60.0n
ever
som
eti
me
s
oft
en
nev
er
som
eti
me
s
oft
en
nev
er
som
eti
me
s
oft
en
nev
er
som
eti
me
s
oft
en
nev
er
som
eti
me
s
oft
en
Alcohol (ltp) *** Beer (lmp) *** Wine (lmp) Spirits (lmp) *** Cannabis (ltp) ***
No alcohol/cannabis Yes alcohol/cannabis
123
Figure 5.26 Leisure time: studying at school, doing homework by alcohol/cannabis, life time
(ltp) and last month prevalence (lmp) in %, N=4032-4036.146
Respondents, who do not drink alcohol and who do not smoke cannabis, are more likely doing
homework and studying at school in their free time.
On the other hand, socially well accepted leisure-time activities, such as arts, reading and the like
are going along with less drinking beer and spirits as well as with less smoking cannabis. Wine
stands out as an exception, probably because drinking wine is often related to conformist
activities.
146 Weighted data
5.9
48.4 45.7
7.8
52.5
39.6
10.0
54.1
35.9
7.6
53.0
39.5
8.1
54.1
37.8
14.5
58.6
26.9
18.1
58.9
23.0 17.1
57.0
25.9 21.3
59.2
19.5
26.3
56.8
16.9
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
nev
er
som
eti
me
s
oft
en
nev
er
som
eti
me
s
oft
en
nev
er
som
eti
me
s
oft
en
nev
er
som
eti
me
s
oft
en
nev
er
som
eti
me
s
oft
en
Alcohol (ltp) *** Beer (lmp) *** Wine (lmp) Spirits (lmp) *** Cannabis (ltp) ***
No alcohol/cannabis Yes alcohol/cannabis
124
Figure 5.27 Leisure time: engaging in fights with others by alcohol/cannabis, life time (ltp)
and last month prevalence (lmp) in %, N=4030-4032.147
Aggressive behaviour is associated with drinking. Surprisingly but in line with earlier work in
connection with ISRD-2, the association is even stronger among cannabis users.
147 Weighted data
90.4
6.9 2.7
90.2
7.1 2.6
88.6
8.8 2.6
91.3
6.5 2.2
90.5
7.4 2.1
85.5
11.3 3.1
81.4
14.8
3.8
80.9
13.9
5.1
76.4
18.4
5.2
72.1
20.7
7.3
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
100.0
nev
er
som
eti
me
s
oft
en
nev
er
som
eti
me
s
oft
en
nev
er
som
eti
me
s
oft
en
nev
er
som
eti
me
s
oft
en
nev
er
som
eti
me
s
oft
en
Alcohol (ltp) *** Beer (lmp) *** Wine (lmp) Spirits (lmp) *** Cannabis (ltp) ***
No alcohol/cannabis Yes alcohol/cannabis
125
Figure 5.28 Leisure time: hanging out in shopping centers, parks, streets etc. for fun by
alcohol/cannabis use. life time (ltp) and last month prevalence (lmp) in %,
N=4034-4039.148
Hanging out in shopping centres, parks, streets and similar locations (just for having fun) goes
along with more frequent use of substances. The difference is more substantial for spirits and
cannabis.
148 Weighted data
44.2
37.0
18.9
40.2 38.4
21.4
36.3 39.0
24.7
40.0 38.7
21.3
38.3 38.8
22.9 28.1
41.4
30.4
22.6
42.3
35.1
24.8
43.2
32.0
19.0
42.3 38.6
15.6
43.9 40.5
0.05.0
10.015.020.025.030.035.040.045.050.0
nev
er
som
eti
me
s
oft
en
nev
er
som
eti
me
s
oft
en
nev
er
som
eti
me
s
oft
en
nev
er
som
eti
me
s
oft
en
nev
er
som
eti
me
s
oft
en
Alcohol (ltp) *** Beer (lmp) *** Wine (lmp) Spirits (lmp) *** Cannabis (ltp) ***
No alcohol/cannabis Yes alcohol/cannabis
126
Figure 5.29 Leisure time: doing something illegal to have fun by alcohol/cannabis use, life
time (ltp) and last month prevalence (lmp) in %, N=4028-4033.149
Doing something illegal just for fun is far more common among users of spirits and cannabis.
Figure 5.30 Leisure time: frightening and annoying people just for fun by alcohol/cannabis,
life time (ltp) and last month prevalence (lmp) in %, N=4028-4033.150
149 Weighted data
150 Weighted data
87.4
10.0 2.5
82.6
14.4
2.9
77.1
19.0
3.9
82.8
14.6
2.5
80.7
16.9
2.3
64.8
28.6
6.6
55.0
35.7
9.3
55.6
33.1
11.3
47.9 40.2
11.9
39.2 42.4
18.4
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
100.0
nev
er
som
eti
me
s
oft
en
nev
er
som
eti
me
s
oft
en
nev
er
som
eti
me
s
oft
en
nev
er
som
eti
me
s
oft
en
nev
er
som
eti
me
s
oft
en
Alcohol (ltp) *** Beer (lmp) *** Wine (lmp) Spirits (lmp) *** Cannabis (ltp) ***
No alcohol/cannabis Yes alcohol/cannabis
78.5
17.5
4.0
74.5
21.0
4.5
72.6
22.6
4.8
74.3
21.3
4.4
73.1
22.7
4.2
65.3
28.5
6.1
62.2
30.8
7.0
59.1
32.9
7.9
59.8
32.1
8.0
57.9
31.1
11.0
0.010.020.030.040.050.060.070.080.090.0
nev
er
som
eti
me
s
oft
en
nev
er
som
eti
me
s
oft
en
nev
er
som
eti
me
s
oft
en
nev
er
som
eti
me
s
oft
en
nev
er
som
eti
me
s
oft
en
Alcohol (ltp) *** Beer (lmp) *** Wine (lmp) Spirits (lmp) *** Cannabis (ltp) ***
No alcohol/cannabis Yes alcohol/cannabis
127
Frightening and annoying people just for fun is, once more, associated with use of substances
and, in particular, with consuming spirits and cannabis.
5.3.2 Peers, friends and the use of substances
Contrary to leisure-time behaviour, having ties to friends is only weakly associated with the use
of alcohol or cannabis. This holds true for missing one’s friends in case of moving to a different
city, or having a group of friends to spend time with (Figure 5.31). This means that using alcohol
or cannabis does not necessarily help to make friends.
Figure 5.31 Having a group of friends to spend time with, by alcohol/cannabis use, life time
(ltp) and last month prevalence (lmp) in %, N=4037-4043.151
On the other hand, knowing friends who use drugs is strongly associated with use of all
substances, but particularly of cannabis.
151 Weighted data
28.4
71.6
25.3
74.7
23.0
77.0
24.2
75.8
23.2
76.8
17.5
82.5
14.6
85.4
15.8
84.2
15.2
84.8
15.3
84.7
0.010.020.030.040.050.060.070.080.090.0
No groupof friends
Group offriends
No groupof friends
Group offriends
No groupof friends
Group offriends
No groupof friends
Group offriends
No groupof friends
Group offriends
Alcohol (ltp) *** Beer (lmp) *** Wine (lmp) Spirits (lmp) *** Cannabis (ltp) ***
No alcohol/cannabis Yes alcohol/cannabis
128
Figure 5.32 Having friends who use drugs by alcohol/cannabis, life time (ltp) and lastmonth
prevalence (lmp) in %, N=4056-4058.152
Students, who drink alcohol and consume marihuana, are more likely to know friends who use
drugs. This association is especially strong with cannabis use (life time prevalence).
Figure 5.33 Having friends who committed shoplifting by alcohol/cannabis use, life time
(ltp) and last month prevalence (lmp) in %m N=4056-4058.153
The role of qualitative aspects of leisure-time activities and peer networks is further underlined
by Figure 5.33 and the following figures. Indeed, respondents who know friends who have
committed offences such as shoplifting, burglary, robbery and assault, are all more likely to have
152 Weighted data
153 Weighted data
77.6
22.4
68.9
31.1
62.0
38.0
68.3
31.7
67.1
32.9
45.5 54.5
35.4
64.6
37.5
62.5
29.4
70.6
13.7
86.3
0.010.020.030.040.050.060.070.080.090.0
100.0
Nofriendsusingdrugs
Havingfriendsusingdrugs
Nofriendsusingdrugs
Havingfriendsusingdrugs
Nofriendsusingdrugs
Havingfriendsusingdrugs
Nofriendsusingdrugs
Havingfriendsusingdrugs
Nofriendsusingdrugs
Havingfriendsusingdrugs
Alcohol (ltp) *** Beer (lmp) *** Wine (lmp) Spirits (lmp) *** Cannabis (ltp) ***
No alcohol/cannabis Yes alcohol/cannabis
82.6
17.4
72.6
27.4
66.6
33.4
72.8
27.2
70.8
29.2
50.8 49.2 44.0 56.0
46.5 53.5
36.8
63.2
27.2
72.8
0.010.020.030.040.050.060.070.080.090.0
No
fri
en
ds
com
mit
ted
sho
plif
tin
g
Hav
ing
frie
nd
sco
mm
itte
dsh
op
lifti
ng
No
fri
en
ds
com
mit
ted
sho
plif
tin
g
Hav
ing
frie
nd
sco
mm
itte
dsh
op
lifti
ng
No
fri
en
ds
com
mit
ted
sho
plif
tin
g
Hav
ing
frie
nd
sco
mm
itte
dsh
op
lifti
ng
No
fri
en
ds
com
mit
ted
sho
plif
tin
g
Hav
ing
frie
nd
sco
mm
itte
dsh
op
lifti
ng
No
fri
en
ds
com
mit
ted
sho
plif
tin
g
Hav
ing
frie
nd
sco
mm
itte
dsh
op
lifti
ng
Alcohol (ltp) *** Beer (lmp) *** Wine (lmp) Spirits (lmp) *** Cannabis (ltp) ***
No alcohol/cannabis Yes alcohol/cannabis
129
used also substances. The associations are particularly strong for spirits and cannabis (see the
following Figures 5.34-5.36).
Figure 5.34 Having friends who committed burglary by alcohol/cannabis use, life time(ltp)
and last month prevalence (lmp) in %, N=4056-4058.154
Students, who have already tried alcohol/cannabis or drank alcohol last 30 days, are more likely
to know friends, who committed burglary.
Figure 5.35 Having friends who committed robbery by alcohol/cannabis use, life time (ltp)
and last month prevalence (lmp) in %, N=4056-4058.155
154 Weighted data
155 Weighted data
88.5
11.5
85.3
14.7
82.5
17.5
85.7
14.3
85.0
15.0
76.1
23.9
72.1
27.9
73.5
26.5
67.7
32.3
61.4
38.6
0.0
20.0
40.0
60.0
80.0
100.0
No
fri
en
ds
com
mit
ted
bu
rgla
ry
Hav
ing
frie
nd
sco
mm
itte
db
urg
lary
No
fri
en
ds
com
mit
ted
bu
rgla
ry
Hav
ing
frie
nd
sco
mm
itte
db
urg
lary
No
fri
en
ds
com
mit
ted
bu
rgla
ry
Hav
ing
frie
nd
sco
mm
itte
db
urg
lary
No
fri
en
ds
com
mit
ted
bu
rgla
ry
Hav
ing
frie
nd
sco
mm
itte
db
urg
lary
No
fri
en
ds
com
mit
ted
bu
rgla
ry
Hav
ing
frie
nd
sco
mm
itte
db
urg
lary
Alcohol (ltp) *** Beer (lmp) *** Wine (lmp) Spirits (lmp) *** Cannabis (ltp) ***
No alcohol/cannabis Yes alcohol/cannabis
98.0
2.0
96.7
3.3
96.1
3.9
97.3
2.7
97.3
2.7
92.4
7.6
90.2
9.8
86.9
13.1
86.9
13.1
81.7
18.3
0.020.040.060.080.0
100.0120.0
No
fri
en
ds
com
mit
ted
rob
ber
y
Hav
ing
frie
nd
sco
mm
itte
dro
bb
ery
No
fri
en
ds
com
mit
ted
rob
ber
y
Hav
ing
frie
nd
sco
mm
itte
dro
bb
ery
No
fri
en
ds
com
mit
ted
rob
ber
y
Hav
ing
frie
nd
sco
mm
itte
dro
bb
ery
No
fri
en
ds
com
mit
ted
rob
ber
y
Hav
ing
frie
nd
sco
mm
itte
dro
bb
ery
No
fri
en
ds
com
mit
ted
rob
ber
y
Hav
ing
frie
nd
sco
mm
itte
dro
bb
ery
Alcohol (ltp) *** Beer (lmp) *** Wine (lmp) Spirits (lmp) *** Cannabis (ltp) ***
No alcohol/cannabis Yes alcohol/cannabis
130
Figure 5.36 Having friends who committed assault by alcohol/cannabis use, life time (ltp)
and last month prevalence (lmp) in %, N=4056-4058.
As one can see, the association with hard liquor and cannabis use is particularly strong for
robbery which is, presumably, considered the most “criminal” offence.
96.7
3.3
93.6
6.4
92.1
7.9
94.4
5.6
94.0
6.0
92.8
7.2
86.1
13.9
83.5
16.5
80.6
19.4
78.8
21.2
72.3
27.7
70.4
29.6
0.0
20.0
40.0
60.0
80.0
100.0
120.0N
o f
rie
nd
s co
mm
itte
das
sual
t
Hav
ing
frie
nd
sco
mm
itte
d a
ssau
lt
No
fri
en
ds
com
mit
ted
assu
alt
Hav
ing
frie
nd
sco
mm
itte
d a
ssau
lt
No
fri
en
ds
com
mit
ted
assu
alt
Hav
ing
frie
nd
sco
mm
itte
d a
ssau
lt
No
fri
en
ds
com
mit
ted
assu
alt
Hav
ing
frie
nd
sco
mm
itte
d a
ssau
lt
No
fri
en
ds
com
mit
ted
assu
alt
Hav
ing
frie
nd
sco
mm
itte
d a
ssau
lt
No
fri
en
ds
com
mit
ted
assu
alt
Hav
ing
frie
nd
sco
mm
itte
d a
ssau
lt
Alcohol (ltp) *** Beer (lmp) *** Wine (lmp) Spirits (lmp) *** Cannabis (ltp)***
Cannabis (lmp)***
No alcohol/cannabis Yes alcohol/cannabis
131
5.4 Computer games as other types of leisure-time activities
This part of Chapter 5 includes some results of the module (13) that was about computer games
and its impact on delinquency. It includes a lot of variables and 7 types of computer games. We
discuss only 3 of them:
- Ego-shooter (a computer video game genre centered on gun and projectile weapon-based
combat through a first player; the player experiences the action through the eyes of
the protagonist, and in some cases, the antagonist);
- Fighting/violence (in which the player either (a) controls an on-screen character and
engages in close combat with an opponent, or (b) is involved in controversial video
games, some of them have been banned or censored because of rudeness, violence, etc.)
- Strategy (emphasizes skillful thinking and planning with strategic, tactical, and
sometimes logistical challenges).
Figure 5.36_1 “Do you play computer games?” In %156
.
The highest percentage of those, who play computer games, is among Swiss-German
respondents, particularly in the canton of Zurich.
156 Results are on the base of the available cases. The level of missing cases is between 1.0% -13.9%.
22.9 26.4 15.4 20.6
27.3 25.6 12.9
34.4
77.1 73.6 84.6 79.4
72.7 74.4 87.1
65.6
0.0
20.0
40.0
60.0
80.0
100.0
Switzerland(4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
No Yes
132
Figure 5.36_2 Ego-shooter as a favorite genre of computer games in %157
.
Genre of Ego-shooter computer video games is evenly popular in most regions of Switzerland,
except Ticino.
Figure 5.36_3 “How long do you play ego-shooter per day?” In %158
.
Again with the exception of Ticino, time invested in ego-shooter games is about equal across the
country.
157 Results are on the base of the available cases. The level of missing cases is between 19.5% -35.7%.
158 Results are on the base of the available cases. The level of missing cases is between 74.9% -92.0%.
68.6 67.4 68.1
87.0 72.6
63.6 78.0 72.3
31.4 32.6 31.9
13.0 27.4
36.4 22.0 27.7
0.0
20.0
40.0
60.0
80.0
100.0
Switzerland DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
Not quoted Quoted
20.2 19.8 21.1 21.0 24.5 21.3 18.8 24.3
69.2 71.2 65.5 62.5 67.1 65.4 67.4 67.1
10.6 9.0 13.5 16.6 8.4
13.3 13.9 8.7
0.0
20.0
40.0
60.0
80.0
Switzerland DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
never, <15 min. per day 15 min.-3 h. per day more than 3 hours per day
133
Figure 5.36_4 Fighting/violence as a favorite genre of computer game genre in %159
.
This type of games is slightly more popular in French and Italian speaking regions, as well as in
the cantons of Aargau and Geneva.
Figure 5.36_5 “How long do you play fighting/violence per day?” In %160
.
Intensive players of fighting/violence games are in German speaking cantons. It is specially
visible in the canton of Zurich.
159 Results are on the base of the available cases. The level of missing cases is between 19.5% -35.7%.
160 Results are on the base of the available cases. The level of missing cases is between 83.0% -92.4%.
81.8 83.6 79.1 76.8 84.0
78.2 77.9 88.3
18.2 16.4 20.9 23.2 16.0
21.8 22.1 11.7
0.0
20.0
40.0
60.0
80.0
100.0
Switzerland DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
Not quoted Quoted
30.4 38.2
20.6 17.3
39.0
30.3 31.9
53.6 59.2
54.0
64.7
73.9
54.9 54.7 57.8
36.5
10.4 7.8 14.7
8.7 6.1
14.9 10.3 9.9
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
Switzerland DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
never, <15 min. per day 15 min.-3 h. per day more than 3 hours per day
134
Figure 5.36_6 Strategy/puzzle as favorite genre of computer game in %161
.
Strategy/puzzle is the second popular genre of computer video games among those selected here.
It is slightly more popular in the canton of Zürich.
Figure 5.36_7 “How long do you play strategy/puzzle per day?” In %162
.
Strategy/puzzle genre of computer video games is not only the least harmful, but it also takes
less time than the two previously mentioned computer games.
More information about influence of these computer games on juvenile delinquency, see 5.7.5
“Delinquency by computer games”.
161 Results are on the base of the available cases. The level of missing cases is between 19.5% -35.7%.
162 Results are on the base of the available cases. The level of missing cases is between 77.0% -86.6%.
71.0 69.7 73.7 70.2 71.5 72.9 79.8 62.0
29.0 30.3 26.3 29.8 28.5 27.1 20.2 38.0
0.0
50.0
100.0
Switzerland DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
Not quoted Quoted
37.6 38.2 35.6 41.0 44.0
25.6 28.1 33.7
59.0 59.0 59.6 55.6 53.4 64.7 68.9 65.4
3.4 2.8 4.8 3.4 2.6 9.7
3.0 0.9 0.0
20.0
40.0
60.0
80.0
Switzerland DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
never, <15 min. per day 15 min.-3 h. per day more than 3 hours per day
135
5.5 Changes in leisure-time activities between 2006 and 2013
It has been observed in Chapter 7 that offending seems to have increased since ISRD-2
conducted in 2006. Therefore, the question remains whether this increase can be attributed to
changes in leisure-time activities such as going out in the evenings, substance use and other
possibly explanatory factors. The following Figures present the main findings regarding several
aspects of leisure-time activities.
Figure 5.37_1 Substance use in % for 2006 and 2013, last month prevalence.163
Cannabis and alcohol did not increase over the life-time. However, recent consumption of spirits
and cannabis (last month prevalence) increased in 2013 in comparison with 2006. These
substances are very popular during night-time activities. The following figure illustrates this
further and explains rising percentage of those, who go out 3-6 times per week and consume
more alcohol in general and more spirits.
163 Weighted data
39.1 38.8
16.2
7.2
39.1 34.6
25.0
10.1
0.0
10.0
20.0
30.0
40.0
50.0
Alcohol lmp Either beer or wine* Spirits* Cannabis lmp*
ISRD-2 ISRD-3
136
Figure 5.37_2 Going out in the evening (IV) and substance use (DV) in 2006 and 2013,
life time (ltp) and last month prevalence (lmp) in %, N=4039-4042.164
Use of all substances is associated with going out at night. Those who go out more frequently
also use alcohol and cannabis more often. However and as Figure 3.57a illustrates, the
association has remained stable, from 2006 to 2013, between going out and using substances, but
the level of recent use of spirits and cannabis has increased substantially.
164 ISRD-2 – not weighted data, ISRD-3 - weighted data
40.5 41.9
12.6 18.2
4.0 10.6
0.8 2.8 3.3 6.4
68.3 69.8
36.5 44.3
13.1
31.3
4.2 11.5 13.2
18.4
82.1 74.6
55.2 50.8
27.8
45.2
15.0 24.6
31.2 34.0
0.010.020.030.040.050.060.070.080.090.0
ISR
D-2
***
ISR
D-3
***
ISR
D-2
***
ISR
D-3
***
ISR
D-2
***
ISR
D-3
***
ISR
D-2
***
ISR
D-3
***
ISR
D-2
***
ISR
D-3
***
Alcohol (ltp) Beer and/or wine(lmp)
Spirits (lmp) Cannabis (lmp) Cannabis (ltp)
never 1-2 times per week 3-6 times per week, daily
137
Figure 5.38_1 Going out in the evening in % for 2006 and 2013.165
Contrary to what one might have expected, the frequency of going out has decreased rather than
increased. Particularly the group of students who never go out in the evening has substantially
increased. Several reasons may help to explain this surprising observation. It could be, as several
other surveys suggest, that more juveniles spend time nowadays on the Internet rather than in the
company with others. Unfortunately, there was no such measure in the instrument of ISRD-2.
Second, the question did not specify, in 2006, at what hour students returned home most of the
time. Given the very strong association between returning at late-hours and consumption of
substances (see above, Figure 5.21) as well as with delinquency (see below, Figure 5.69), a shift
to staying out longer may have occurred that cannot be identified with the two instruments.
Third, the questionnaires of neither of the two surveys asked more precisely what kind of activity
was the reason for going out in the evening. Given the increasing popularity of Internet and the
difficulties of many organizers of traditional leisure-time activities, such as sports, to recruit and
keep new members, the lower frequency of going out may have gone at the expense of more
traditional activities rather than late-hour outdoor and other high-risk activities. Given the
absence of data to corroborate these possibilities, we present these ideas only under the form of
speculative hypotheses.
165 ISRD-2 – not weighted data, ISRD-3 - weighted data
18.8
48.3
32.9 38.1
45.2
16.7
0.0
10.0
20.0
30.0
40.0
50.0
60.0
never 1-2 times per week 3-6 times per week, daily
ISRD-2
ISRD-3
138
Figure 5.38_2 “With whom do you spend most of your free time?” in % for 2006 and
2013.166
In 2013, more respondents spend time by their own, compared to 2006. On the other hand,
spending time with the family has become slightly less popular.
166 Weighted data (ISRD-3, N=4158), not weighted data (ISRD-2, N=3643).
6.6
25.6
43.1
24.7
9.3
22.0
44.4
24.3
0.0
10.0
20.0
30.0
40.0
50.0
by my own With my family With 1-3 friends with more than 4 friends
ISRD-2 ISRD-3
139
Figure 5.39 “Do you have friends with parents of foreign origin?” in % for 2006 and 2013.167
The proportion of students who do not socialize with peers of foreign origin has decreased, and
more respondents say having many friends with origins abroad. This certainly reflects the
increasing proportion of the immigrant population in Switzerland and particularly at schools. At
the other extreme, there are less respondents indicating that they have only friends from abroad –
a sign that “segregation” may have decreased.
Figure 5.40 Leisure time: goingto coffee bars or pop concerts in % for 2006 and 2013.168
In 2013, fewer respondents say going to discos or pop concerts in 2013, in comparison with
2006.
167 Weighted data (ISRD-3, N=4158), not weighted data (ISRD-2, N=3643).
168 Weighted data (ISRD-3, N=4158), not weighted data (ISRD-2, N=3643).
17.4
51.3
24.7
6.6 12.1
50.8
32.6
4.5
0.0
20.0
40.0
60.0
none at all a few many of them all of them
ISRD-2 ISRD-3
50.4
38.8
9.2
1.6
55.1
39.8
5.1
0.0
10.0
20.0
30.0
40.0
50.0
60.0
never sometimes often always
Going to coffee bars or pop concerts ISRD-2 Going to coffee bars or pop concerts ISRD-3
140
Figure 5.41 Leisure time: drinking alcohol and taking drugs in % for 2006 and 2013.169
Consuming substances (alcohol and/or drugs) seems to have decreased during leisure-time spent
outdoors. (A slight change in the wording of the relevant items in the instruments, from ISRD-2
to ISRD-3, should be noted.) Since, according to the direct questions regarding use of alcohol
and cannabis, the recent use (over the last 30 days) of spirits and cannabis seems to have
increased, the apparent contradiction with the findings presented here might point to a possible
shift in the use of substances to indoor settings.
Figure 5.42 Leisure time: doing sports in % for 2006 and 2013.170
Further evidence that conformist activities may have increased comes from Figure 5.42
suggesting that sports may have gained in popularity over the past years.
169 Weighted data (ISRD-3, N=4158), not weighted data (ISRD-2, N=3643).
170 Weighted data (ISRD-3, N=4158), not weighted data (ISRD-2, N=3643).
69.4
20.2
7.3 3.1
80.0
15.0 5.0
0.0
20.0
40.0
60.0
80.0
100.0
never sometimes often always
Drinking beer/alcohol/taking drugs ISRD-2 Drinking beer/alcohol/taking drugs ISRD-3
15.6
36.9 34.1
13.4 14.1
31.5
54.4
0.0
10.0
20.0
30.0
40.0
50.0
60.0
never sometimes often always
Doing sports, athletics, exercise ISRD-2 Doing sports, athletics, exercise ISRD-3
141
Figure 5.43 Leisure time: frightening and annoying people just for fun in % for 2006 and
2013.171
Harassing people (presumably in the streets) seems also to have decreased.
Figure 5.44 Having friends who use drugs in % for 2006 and 2013.172
The same proportion of respondents reported in 2013 (compared to 2006) knowing friends who
use drugs. This is not in line with the trend of self-reported (actual) drug use (see Figure 5.37_1).
171 Weighted data (ISRD-3, N=4158), not weighted data (ISRD-2, N=3643).
172 Weighted data (ISRD-3, N=4158), not weighted data (ISRD-2, N=3643).
67.5
24.7
4.9 2.9
70.7
24.0
5.3
0.0
20.0
40.0
60.0
80.0
never sometimes often always
Frightening other people just for fun ISRD-2 Frightening other people just for fun ISRD-3
59.2 58.4
40.8 41.6
0.0
20.0
40.0
60.0
80.0
ISRD-2 ISRD-3
no
yes
142
Figure 5.45 Having friends who committed shopliftingin % for 2006 and 2013.173
Fewer students report knowing friends who have committed shoplifting. Since shoplifting has
increased between 2006 and 2013 (see Chapter 7), this finding is not entirely plausible. It could
be, however, that shoplifting has become a more individual and less a group offence over time.
Figure 5.46 Having friends who committed burglary in % for 2006 and 2013.174
Again in line with self-report measures of burglary (that suggest that this offence has increased
since 2006, see Table 7.2), more students report knowing peers who had committed this.
Figure 5.47 Having friends who committed assault in % for 2006 and 2013.175
173 Weighted data (ISRD-3, N=4158), not weighted data (ISRD-2, N=3643).
174 Weighted data (ISRD-3, N=4158), not weighted data (ISRD-2, N=3643).data
175 Weighted data (ISRD-3, N=4158), not weighted data (ISRD-2, N=3643).
56.0 63.8
44.0 36.2
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
ISRD-2 ISRD-3
no
yes
90.9 81.1
9.1 18.9
0.0
20.0
40.0
60.0
80.0
100.0
ISRD-2 ISRD-3
no
yes
89.4 90.3
10.6 9.7
0.0
20.0
40.0
60.0
80.0
100.0
ISRD-2 ISRD-3
no
yes
143
Figure 5.48 Having friends who committed robbery in % for 2006 and 2013.176
Although self-reported measures of robbery and assault suggest that these offences may have
increased between 2006 and 2013, the proportion of respondents who say knowing peers who
have committed these offences is perfectly stable. Given the relative rarity of these offences,
fewer people among peers may come to know about it. Further, burglary requires often some
cooperation from others, including fences, which increases the probability that more people will
learn about it.
Overall, the results about changes in leisure-time activities do not offer plausible explanations
for increasing trends of self-reported offending (see Table 7.2). This is not the say that the self-
report measures are not “true”, but that the causes of the observed trends must be looked for in
other areas that are not reflected in the questionnaires of ISRD-2 and ISRD-3. On the other
hand, the results on peers are more mixed, some being in line with trends in self-reported
delinquency.
176 Weighted data (ISRD-3, N=4158), not weighted data (ISRD-2, N=3643).
93.1 94.6
6.9 5.4
0.0
20.0
40.0
60.0
80.0
100.0
ISRD-2 ISRD-3
no
yes
144
5.6 Happiness, leisure-time, peers and substance use
We begin the presentation of the findings on happiness with the basic frequencies in the national
and the several regional samples.
Figure 5.49 “Would you say that most of the time you have been happy during last 6
months? by main and subsamples in %.177
The level of happiness is approximately the same in different regions. The highest percentage of
(very) unhappy students can be found in French speaking cantons, the lowest in the cantons of
Zurich and St. Gallen.
Figure 5.50 Happiness by doing sports in leisure time in %, N=4096.178
Respondents who often practice sports during leisure time are more often happy than their peers.
This may be due to a better body-feeling and (perceived) higher attractiveness.
177 Weighted data
178 Weighted data
5.8 4.4 8.8 6.4 4.5 5.9
11.6
3.5
35.3 35.4 35.0 34.3 31.8 36.4
33.1 33.4
58.9 60.1 56.2 59.3
63.7 57.6 55.3
63.1
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
Switzerland DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
unhappy neither/nor happy
11.8
42.3 45.9
5.3
40.6
54.1
4.5
30.3
65.2
0.0
20.0
40.0
60.0
80.0
(very) un(very) happy neither/nor (very) happy
never sometimes often
145
Figure 5.51 Happiness by studying at school and doing homework in leisure time in %,
N=4096.179
Investing leisure-time for school-related homework and studies is only weakly associated with
happiness. Students devoting much of their leisure-time to such activities are slightly happier,
probably because they resent more satisfaction at school and in their environment.
Figure 5.52 Happiness by doing something creative in %, N=4084.180
Students who, during leisure-time, are often doing something creative (such as playing theatre or
music, or reading) are more often unhappy. Although the difference is not large, this result is
rather unexpected and, at first sight at least, hard to explain. Could social isolation be the factor
behind this association?
179 Weighted data
180 Weighted data
6.4 5.2
13.8
28.6 31.5
14.5
2.2 3.1
9.7
30.8
40.5
13.6
1.7 2.8 7.4
22.9
45.3
19.9
0.0
10.0
20.0
30.0
40.0
50.0
very unhappy unhappy more unhappythan happy
more happythan unhappy
happy very happy
never sometimes often
6.4
35.4
58.2
4.5
33.7
61.7
6.6
36.8
56.7
0.0
20.0
40.0
60.0
80.0
(very) un(very) happy neither/nor (very) happy
never sometimes often
146
Figure 5.53 Happiness by going to coffee bars or pop concerts in %, N=4085.181
On the other hand, no clear pattern emerges with respect to going to coffee bars and pop concerts
during leisure-time. Respondents who often go to such places are somewhat more often “very
unhappy”, but they are also more often “very happy”.
Figure 5.54 Happiness by engaging in fights with others in leisure time in %, N=4082.182
Respondents who said often engaging in fighting with others during leisure-time can be both
very happy and very unhappy. Overall, however, students who say never engaging in fights with
others are more often happy.
181 Weighted data
182 Weighted data
2.7 3.4
9.1
24.8
42.7
17.3
1.9 3.1
8.7
28.2
41.9
16.2
5.3 2.9
9.1
26.8
33.5
22.5
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
45.0
very unhappy unhappy more unhappythan happy
more happythan unhappy
happy very happy
never sometimes often
2.1 3.4 8.9
26.3
42.3
16.9
2.3 2.0
9.7
28.6
42.1
15.3 14.9
3.3 9.1
14.0
28.9 29.8
0.0
10.0
20.0
30.0
40.0
50.0
very unhappy unhappy more unhappythan happy
more happythan unhappy
happy very happy
never sometimes often
147
Figure 5.55 Happiness and hanging out in shopping centers, parks and streets for fun in
leisure time in %, N=4089.183
Students, who are hanging out in shopping centres, parks and streets for fun are more likely to be
(very) unhappy than their peers.
Figure 5.56 Happiness by doing something illegal for fun in leisure time in %, N=4081.184
Respondents who report doing something illegal for fun are 3 times more likely to be (very)
unhappy than their peers.
183 Weighted data
184 Weighted data
4.0
34.2
61.9
4.1
36.2
59.7
10.7
35.1
54.2
0.0
20.0
40.0
60.0
80.0
(very) un(very) happy neither/nor (very) happy
never sometimes often
4.6
34.3
61.2
7.1
39.3
53.6
17.2
31.4
51.5
0.0
20.0
40.0
60.0
80.0
(very) un(very) happy neither/nor (very) happy
never sometimes often
148
Figure 5.57 Happiness by drinking beer/alcohol and taking drugs in leisure time in %,
N=4076.185
Students who consume alcohol and drugs during their free time, are 3 times more likely to be
(very) unhappy than their peers.
185 Weighted data
5.1
34.7
60.2
7.4
36.0
56.6
11.3
41.7 47.1
0.0
20.0
40.0
60.0
80.0
(very) un(very) happy neither/nor (very) happy
never sometimes often
149
Figure 5.58 Happiness by alcohol/cannabis (life time prevalence) in %, N=4050-4051.186
When the several substances are considered individually, it turns out that there is almost no
difference, in terms of happiness, among those who admit drinking beer or wine and those who
do not. However, the differences are larger among those who use spirits and cannabis.
Figure 5.59 Happiness by frightening and annoying people in leisure time in % N=4082.187
Those who admit often frightening and annoying people during their free time are almost 3 times
more (very) unhappy than those who say never or only occasionally doing this.
186 Weighted data
187 Weighted data
3.7
31.8
64.5
5.3
34.2
60.5
5.3
34.2
60.5
4.9
33.1
62.0
5.0
34.3
60.7
7.0
37.5
55.5
6.5
37.4
56.1
6.5
37.4
56.1
7.8
41.3 50.9
8.7
40.3
51.0
0.010.020.030.040.050.060.070.0
(ver
y) u
nh
app
y
nei
the
r/n
or
(ver
y) h
app
y
(ver
y) u
nh
app
y
nei
the
r/n
or
(ver
y) h
app
y
(ver
y) u
nh
app
y
nei
the
r/n
or
(ver
y) h
app
y
(ver
y) u
nh
app
y
nei
the
r/n
or
(ver
y) h
app
y
(ver
y) u
nh
app
y
nei
the
r/n
or
(ver
y) h
app
y
Alcohol (ltp)*** Beer (lmp)* Wine (lmp)* Spirits (lmp)*** Cannabis (ltp)***
No alcohol/cannabis Yes alcohol/cannabis
5.6
34.7
59.7
4.5
37.2
58.3
13.9
31.9
54.2
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
(very) un(very) happy neither/nor (very) happy
never sometimes often
150
Figure 5.60 Happiness by group of friends to spend time with in %, N=4093.188
There is only a very week association between having friends to spend time with and happiness.
Those who say having such friends are slightly happier than those who do not. This suggests that
those who prefer spending time on their own may make a deliberate choice, or at least not feel
unhappy because of their relative social isolation.
Figure 5.61 Happiness by having friends with parent(s) of foreign origin in %, N=4112.189
Although having friends to spend time with does not seem to increase happiness, having friends
of foreign origin apparently does add to feelings of well-being. Having friends across ethnic lines
might be related to popularity among peers, but it must be admitted that this explanation remains,
for the time being and in the absence of more complete data, rather speculative.
188 Weighted data
189 Weighted data
3.7 2.9
10.7
24.8
39.5
18.3
2.2 3.3 8.5
26.7
42.4
16.9
0.0
10.0
20.0
30.0
40.0
50.0
very unhappy unhappy more unhappythan happy
more happy thanunhappy
happy very happy
no yes
5.0
33.7
61.3
7.1
38.1
54.8
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
(very) unhappy neither/nor (very) happy
none at all/a few Many of them/all of them
151
Figure 5.62 Happiness by "it is important, what my friend thinks about me” in %, N=3217.190
Respondents who attribute much importance to what their friends think about them are
somewhat more often (very) unhappy than those who do not care that much. Although the
difference is, overall, not large, stress related to the self-imposed need to meet peers’
expectations may be the driving force behind this finding.
190 Weighted data
4.9 2.8
10.2
24.9
31.6 25.6
2.0 3.4 8.4
26.7
43.6
15.9
0.0
10.0
20.0
30.0
40.0
50.0
very unhappy unhappy more unhappythan happy
more happythan unhappy
happy very happy
totally/quite/a bit unimportant a bit/quite/totally important
152
Figure 5.63 Happiness by having friends, who use drugs in %, N=4111.191
Respondents who have friends using drugs are less happy than those, who have so such friends.
Figure 5.64 Happiness by having friends, who committed shoplifting in %, N=4111.192
Respondents whose peers have committed shoplifting are less happy than those who have so
such friends.
191 Weighted data
192 Weighted data
1.2 3.1 7.1
25.1
43.8
19.8
4.5 3.5
11.7
27.6
39.0
13.6
0.0
10.0
20.0
30.0
40.0
50.0
very unhappy unhappy more unhappythan happy
more happythan unhappy
happy very happy
no yes
1.8 2.4 7.3
25.1
44.3
19.0
4.0 4.7
12.0
27.9
37.4
14.0
0.0
10.0
20.0
30.0
40.0
50.0
very unhappy unhappy more unhappythan happy
more happy thanunhappy
happy very happy
no yes
153
Figure 5.65 Happiness by having friends, who committed burglary in %, N=4110.193
Respondents whose friends have committed burglary are less happy than those who have so such
peers.
Figure 5.66 Happiness by having friends, who committed robbery in %, N=4111.194
Respondents whose friends have committed robbery are less happy than those who have so such
peers.
193 Weighted data
194 Weighted data
1.8 2.6
8.4
26.0
43.4
17.7
5.7 6.1 11.6
26.8
34.8
15.0
0.0
10.0
20.0
30.0
40.0
50.0
very unhappy unhappy more unhappythan happy
more happy thanunhappy
happy very happy
no yes
2.1 3.2 8.7
26.4
42.6
17.0 10.5
5.0
14.5
21.8 27.7
20.5
0.0
10.0
20.0
30.0
40.0
50.0
very unhappy unhappy more unhappythan happy
more happythan unhappy
happy very happy
no yes
154
Figure 5.67 Happiness by knowing friends, who committed assault in %, N=4110.
Respondents with friends who committed assault are less happy than those who have so such
peers.
Overall, it seems that happiness is not much related to conventional activities. However, it
definitively is to deviant activities, or to being integrated in deviant networks.
2.2 3.0 8.6
26.3
42.7
17.2
5.8 6.1 12.4
25.0
33.3
17.4
0.0
10.0
20.0
30.0
40.0
50.0
very unhappy unhappy more unhappythan happy
more happythan unhappy
happy very happy
no yes
155
5.7 Leisure-time, life style, peers, happiness and delinquency
5.7.1 Leisure-time and delinquency
Life-style and leisure-time activities have been identified as key variables in the explanation of
offending many years ago (Hindelang, Gottfredson and Garofalo 1978; Felson and Boba 2010).
Changes in the organisation of leisure-time and particularly of night-time activities have also
been suggested as key-factors in the explanation of changes in crime rates (Killias et al. 2012). In
the present section, we shall begin, therefore, with presenting some findings on the association
between several forms of self-reported delinquency and leisure-time activities.
Figure 5.68 Delinquency (last year prevalence) by going out in the evening in %, N=4044-
4052.195
There seems to be a fairly linear association between the frequency of going out in the evening
and all self-reported offences listed in the instrument. However, respondents who say going out
five times and more a week have far higher rates of offending. This underlines that extreme
leisure-time activities may be more important than just going out as such.
195 Weighted data
2.5 3.0 6.9
0.5 1.9 0.3 1.4 0.4
5.0 6.1 2.2 0.6 0.7
2.9 7.3
9.8 12.9
0.7
5.6
0.9 1.5 0.8
7.6 9.8
7.7
2.8 4.8 3.5
14.5
22.4 25.0
5.6
18.4
4.4 5.9 4.9
13.9
19.5 19.3
10.5
19.0
5.5
0.0
5.0
10.0
15.0
20.0
25.0
30.0
Never 1-2 times per week 3-6 time per week; every day
156
Figure 5.69 Delinquency (last year prevalence) by time of coming back home after going out
at night in %, N=3785-3793.196
This is further illustrated by Figure 5.69. Those who return after midnight, and particularly after
3 AM, have far higher rates of offending in every respect than those who never go out or who
return before midnight. This variable largely explains also risks of victimization (see Chapter 4,
e.g. Table 4.1).
196 Weighted data
2.3 2.7 6.7
0.5 2.4 0.5 1.7 0.4 4.3 6.2
2.4 1.5 1.2 2.9
15.0 19.0
21.7
2.7
16.8
5.2 4.3 5.1
14.3 18.7 17.1
6.2
14.9
5.1
18.4 23.3
34.0
4.9
17.6
4.9 5.8 5.9
20.6
30.4 28.4
11.9
17.8
7.9 9.1
18.2
24.2
0.0
6.1
0.0 2.9
0.0
6.1 9.1
0.0 0.0 0.0 0.0 5.7
9.7 9.1
0.6 3.8
0.2 0.8 0.8 5.5 6.7 4.9
2.1 3.2 3.2 6.6
10.0 12.6
1.3 6.6
0.8 1.9 0.9
7.5 10.5
7.8 2.9
6.4 4.0
0.05.0
10.015.020.025.030.035.040.0
I do not go out in the evening 00:00 - 2:45 3:00 - 6:30
7:00 - 9:30 10:00 - 19.45 20:00 - 24:00
157
Figure 5.70 Delinquency (last year prevalence) by spending time with family and friends in %,
N=4047-4065.197
Students who spend most of their leisure-time with friends have the highest rates of delinquency.
The second delinquent group includes respondents who spend leisure-time primarily by their
own. Finally, the least delinquent are those who spend a lot of time with their families.
As Figure 5.71 illustrates, it is not just spending time with friends, but rather being with larger
groups (of four or more people) that is important. Larger groups are not only larger, but often
they may be qualitatively different and entail, therefore, different kinds including more risky
kinds of activities.
Figure 5.71 Delinquency (last year prevalence) by spending time with family and groups of
friends of different sizes, in %, N=4037-4065.198
197 Weighted data
198 Weighted data
6.3 8.7 7.6
1.6 2.4 1.1 1.6 0.5
5.5
12.6
2.4 2.9 3.7 2.4 2.5 4.4 4.9
0.3 3.3
1.0 1.6 0.4
5.2 6.3 3.9
0.5 0.3 3.6
8.1 11.0
15.7
1.8
7.9
1.5 2.5 1.7
8.7 10.8 9.4
4.1 7.5
3.8
0.0
5.0
10.0
15.0
20.0
On my own With my family With group of friends
6.3 8.7 7.6
1.6 2.4 1.1 1.6 0.5
5.5
12.6
2.4 2.9 3.7 2.4 2.5 4.4 4.9
0.3 3.3
1.0 1.6 0.4
5.2 6.3 3.9
0.5 0.3 3.6
7.2 8.3
13.1
0.8
5.7
0.9 1.9 0.9
7.5 8.5 5.4
2.9 5.2 3.7
9.6
15.8
20.3
3.5
12.0
2.5 3.5 3.2
11.1
15.0 16.6
6.4
11.8
4.0
0.0
5.0
10.0
15.0
20.0
25.0
on my own w/ family w/ 1-3 friends w/ larger group of friends
158
As one would have expected, students who spend their leisure-time mostly on their own are
rarely involved in group fights. Similarly, drug dealing is next to never reported by respondents
who spend a lot of time with their families.
Figure 5.72 Delinquency (last year prevalence) among respondents who spend leisure-time
with a larger group (of 4 or more friends) by whether or not it is considered a
gang in %, N=361-364.199
As one would have expected, respondents who are spending time with a group that is considered
as a gang admit at far higher rates committing offences across almost the entire list, than those
199 Weighted data
19.4
6.8
29.0
11.2
32.3
16.4
10.3
2.1
22.6
9.1
7.7
1.0
5.2
2.6
11.0
1.4
16.8
8.9
34.2
10.8
31.0
12.3
22.6
2.5
26.0
8.1
5.8
3.2
0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0
Gang
Not gang***
Gang**
Not gang***
Gang**
Not gang***
Gang
Not gang***
Gang
Not gang***
Gang
Not gang
Gang
Not gang
Gang*
Not gang*
Gang
Not gang**
Gang***
Not gang***
Gang**
Not gang***
Gang***
Not gang*
Gang***
Not gang***
Gang
Not gang
Gra
ffit
iVan
dal
ism
Sho
plif
tin
gB
urg
lar
yB
icyc
leth
eft
Mo
tor
bik
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oto
rbik
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rth
eft
Car
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akR
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on
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Car
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wea
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nG
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pfi
ght
Ass
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Dru
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159
who say their group is not considered a gang. This suggests that the high rates of delinquency
among those spending their leisure-time with a group of four or more people are largely
attributable to the fact that, among these larger groups, quite a few can be considered as gangs.
Gang members have indeed, in Switzerland and abroad (HaymozPantillon, Maxson, Killias
2014), far higher delinquency rates than similar juveniles who happen not to belong to such a
group.
The issue of gangs has been studies in this survey in some detail (questionnaire 11.1-11.8). The
findings usually confirm earlier work done on this theme (HaymozPantillon 2010). In the
following Figures, we shall see more in detail what impact may have the kind of leisure-time
activities.
Figure 5.73 Delinquency (last year prevalence) by hanging out in shopping centers, parks,
streets etc. for fun in %, N=4028-4045.200
Respondents who often hang out in public places are at higher risk of committing offences of all
sorts. The following Figures illustrate this further.
200 Weighted data
3.7 4.6 5.8
0.9 2.3
0.6 1.3 0.7
4.8 6.5
3.2 1.1 1.4
3.4 5.6
9.5 11.8
0.7
7.0
1.5 2.1 1.2
7.7 9.9
5.9 3.2
5.3 3.7
12.5 15.4
23.4
3.5
11.0
2.2 3.7 2.5
11.5 14.9 15.9
6.2
11.9
3.7
0.0
5.0
10.0
15.0
20.0
25.0
never sometimes often
160
Figure 5.74 Delinquency (last year prevalence) by going to coffee bars or pop concerts in %
N=4024-4041.201
Students who attend coffee bars and pop concerts often rather than never or only occasionally are
more likely to commit offences of all kinds. The association is strongest with robbery, stealing
cars/motorbikes or breaking into cars, burglary, assault and drug dealing.
These offences are typically street-crimes. They can be efficiently prevented by removing young
people from the streets, or by reducing leisure-time that is being spent in the streets. The
following Figures offer further illustration to that effect.
201 Weighted data
4.4 7.9
12.2
1.1 4.0
0.7 1.8 0.6
6.4 7.6 5.8 1.6
3.8 3.1
9.2 10.2 12.5
1.2
8.9
1.4 1.9 1.6
8.7 11.6
8.4 3.7
6.4 3.6
12.0
18.3 19.7
7.2
13.0 8.7 9.7 7.7
13.9
24.5 20.2
16.4 19.4
9.2
0.05.0
10.015.020.025.030.0
never sometimes often
161
Figure 5.75 Delinquency (last year prevalence) by doing something creative in %, N=4023-
4041.202
Students who never engage in creative leisure-time activities (such as playing theater or music,
drawing, writing, reading books etc.), are more likely to commit an offence.
Figure 5.76 Delinquency (last year prevalence) by spending leisure-time on school-related
studies or homework in %, N=4026-4041.203
Respondents who invest often leisure-time for school-related studies or homework are far less
involved in delinquent activities. The difference is again strongest for offences that are typically
committed in the streets.
202 Weighted data
203 Weighted data
8.2
13.8 16.9
2.0
10.3
1.6 2.9 2.1
9.7 12.0 12.2
3.9
9.3
4.6 5.5 7.1
10.4
0.8
4.7
1.2 1.9 0.6
7.3 8.3
4.9 2.9 3.3 2.8
6.1 5.3
9.4
1.5 2.7 1.2 1.6 1.2
5.0
9.4
3.9 2.7 3.1 3.4
0.02.04.06.08.0
10.012.014.016.018.0
never sometimes often
17.8
25.9 24.5
5.6
15.4
5.2 6.1 4.5
16.0 20.0
16.2
8.5
17.4
6.2 7.0 9.5
15.2
1.0
6.7
0.9 1.9 1.2
8.4 10.1 8.0
2.6 5.5 3.7 2.8 3.7 5.0
0.9 3.0
0.9 1.4 0.5 4.0
6.6 3.9 2.5 2.1 2.7
0.0
5.0
10.0
15.0
20.0
25.0
30.0
never sometimes often
162
A similar effect can be might be expected with respect to sports. Although not really an indoor
activity, it offers juveniles an opportunity to spend leisure-time in a structured way. The
following Figure illustrates, however, that with sports the findings are more complex and do not
allow a straightforward interpretation.
Figure 5.77 Delinquency (last year prevalence) by doing sports in %, N=4033-4049.204
Although students who say practicing sports “often” rather than occasionally or never commit
over all less offences, the differences are not as strong as in the preceding Figures and not always
significant. Group fights are even slightly more frequent among those practicing sports “often”.
This matches observations made by Walser (2013) in a similar survey in the canton of St. Gallen
where the effect of several kinds of sports has been studied in detail. As it turned out, some
sports go along with reduced delinquency, whereas other have an opposite effect, leading for
sport activities overall to an effect that is, as here, closed to zero or inconsistent at least.
5.7.2 Peer-networks and delinquency
Before we look into the effects of deviant peer activities, we begin with a look on the importance
of peer-networks as such.
204 Weighted data
7.2 9.7
16.3
3.0
7.4
1.4 1.6 1.4
9.6 10.5
6.1
2.8
10.1
3.7 5.9
10.2 12.0
1.0
5.6
1.1 1.5 0.8
5.7
9.0
5.8
1.8
5.6 3.2
7.1 8.6
12.1
1.3
6.6
1.5 2.8
1.6
8.3 10.4
8.9
4.1 4.5 3.8
0.02.04.06.08.0
10.012.014.016.018.0
never sometimes often
163
Figure 5.78 Delinquency (last year prevalence) by "I would miss my friends if I move to
another city" in %, N=3170-3182.205
Respondents who do not have strong ties with their friends and who would not miss them if they
had to move to another city are more likely to commit offences of all sorts. This association is
strongest for burglary and robbery. In other words, delinquents are less integrated in peer-
networks than more pro-social youths. It means that social networks should not be seen as a
source of problem behavior – the effects are the other way around.
Figure 5.79 Delinquency (last year prevalence) by "It is important for me, what my friend(s)
think(s)” in %, N=3169-3179.206
205 Weighted data
206 Weighted data
24.3
16.7
32.4
16.7
22.2
16.7 16.2
24.3
11.1 13.9
18.9
25.7 25.7
14.3
8.1 9.6
15.9
2.6 6.7
1.1 3.0 1.8
12.1 14.8
11.5
3.3 7.4
4.4 7.4
10.1 13.3
1.2
7.1
1.2 2.3 1.2
8.0 10.4
7.9 3.0
6.1 3.4
0.05.0
10.015.020.025.030.035.0
not at all/not much neither/nor quite a lot/very much
16.7
9.2
16.7
7.6
16.9
8.5 9.3 9.2 8.4
13.6
20.5
6.7 8.5 8.3 7.1
10.7 12.4
1.1
8.6
1.1 1.8 1.5
7.2
13.6
8.7
4.2
8.0
4.4 7.4
10.1
14.0
1.4
6.5
1.2 2.3 1.2
8.8 9.9 7.7
2.9 5.9
3.2
0.0
5.0
10.0
15.0
20.0
25.0
totally unimportant/quite unimportant neither/nor quite importamt/very important
164
Figure 5.79 confirms this impression. Students who do not care what their peers think about
them tend to commit more offences and particularly more street crimes.
Figure 5.80 Delinquency (last year prevalence) by friends with parents of foreign origin in %,
N=4038-4056207
With respect to the ethnic composition of peer-networks, two interesting observations can be
made. First, respondents with exclusively Swiss networks, or with only few immigrants among
their friends, commit clearly less offences than those whose network consists predominantly or
exclusively of immigrant youths. This reflects probably the higher delinquent involvement
among immigrant youths in general that has been observed in Chapter 7. Second, it is interesting
to note that respondents with ethnically mixed networks report clearly fewer offences than those
from ethnically homogeneous groups. Integrating juveniles from immigrant backgrounds seems,
therefore, to go along with reduced delinquent involvement.
Figure 5.81 “Do you consider your group of friends to be a gang?” by ethnic composition of
the group (i.e. having or not friends with parents of foreign origin in one’s
group), in %.208
207 Weighted data, N= 4158
208 Weighted data, N=4158
2.7 4.3 6.4
0.8 3.1
0.8 1.5 0.4
5.0 6.0 4.8 2.1 3.3 3.3 4.9
7.6 10.7
0.7 4.5
0.7 1.9 0.6
6.5 9.0
5.3 1.9
3.8 3.6
9.3 12.4
16.2
1.6
9.4
1.4 2.0 1.7
8.8 11.2 10.3
4.2 7.1
3.0
17.7 19.9
24.9
9.4
15.0
9.4 9.4 9.8
19.7 21.7
19.1
13.3
19.7
8.7
0.0
5.0
10.0
15.0
20.0
25.0
30.0
none at all a few many of them all of them
5.2
8.3
12.4
0.0
5.0
10.0
15.0
none at all a few many or all of them
165
Essentially the same can be observed in Figure 5.81. Less than 5 per cent of respondents with
ethnically homogeneous (Swiss-based) networks say their group can be considered a “gang”.
Among those with homogeneous (immigrant-based) networks, only 12 percent say their group is
a “gang”. In other words, belonging to a gang is clearly a minority phenomenon and, apparently,
only moderately related to ethnic background.
5.7.3 Problem behaviour and delinquency
In the following Figures, we look at the association between several kinds of problematic
behaviour among juveniles and delinquency.
Figure 5.82 Delinquency (last year prevalence) by engaging in fights with others in %,
N=4035-4040.209
Respondents who often engage in fights with other people are more likely to commit all types of
offences. As one might have expected, the association is particularly strong for assault and group
fights, but by no means limited to these offences. This observation points to the possibility that
frequent involvement in physical confrontations is a symptom of a broader personality disorder.
209 Weighted data
4.8 6.4 10.3
0.7 3.6 0.3 1.3 0.3
5.8 6.9 3.3 1.2 3.7 2.9
17.2
26.2 26.7
4.4
22.9
5.7 4.9 4.4
19.6
30.6 34.4
14.2 17.2
5.4
30.5
40.7 38.7
16.4
35.7
18.1 20.9 22.2 25.9
36.8
47.0
31.5 28.7 21.3
0.05.0
10.015.020.025.030.035.040.045.050.0
never sometimes often
166
Figure 5.83 Delinquency (last year prevalence) by doing something illegal to have fun in %,
N=4023-4039.210
Students who admit doing often something illegal “for fun” report more offences than their
peers. Again the difference is large for all offences. It is slightly more modest for shoplifting,
vandalism, graffiti, caring weapons and personal theft, pointing to the possibility that these
offences are somewhat more “normal”, committed by more “normal” people.
210 Weighted data
2.8 4.3 6.7 0.3 2.6 0.4 0.7 0.5
3.9 5.2 3.4 1.0 1.7 2.8
14.8 19.9
27.5
2.3
13.0
2.0 4.2 2.1
16.4 21.3
15.8
7.0 11.6
4.7
30.8
39.9 39.7
13.9
35.0
13.4 15.9 10.1
27.5 33.7 34.0
20.6
39.8
11.0
0.05.0
10.015.020.025.030.035.040.045.0
never sometimes often
167
Figure 5.84 Delinquency (last year prevalence) by frightening and annoying other people just
for fun in %, N=4022-4039.211
Those who often frighten or annoy other people for fun more often report having committed all
sorts of offences. The difference is particularly strong with respect to street offences like
robbery. The findings presented in Figures 5.83 and 5.84 underline the great importance of need
of excitement (looking for the “kick”) in the explanation of offending.
Figure 5.85 Delinquency (last year prevalence) by substance use (last month prevalence) in %,
N=4040-4048.212
211 Weighted data
212 Weighted data
5.1 5.9 8.4
0.9 3.3
0.5 1.4 0.4
5.2 6.6 4.3
1.9 3.9
1.9
9.7
16.1
22.3
1.3
12.1
2.5 3.0 1.5
12.9 16.8
12.7
4.8 8.4
6.2
15.6
24.6 26.4
9.9
21.7
7.1 10.4
12.7
17.5
26.1 28.0
14.9 16.2 15.6
0.0
5.0
10.0
15.0
20.0
25.0
30.0
never sometimes often
39.9
10.3 7.7
26.4
12.8
36.8
27.2
4.3 3.8
16.2
7.8
17.3
21.3
3.3 2.6
13.1
6.1
12.5
5.2
0.3 0.4 3.4
1.1 0.3 0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
Shoplifting*** Burglary*** Robbery*** Personal theft*** Assault*** Drug dealing***
Cannabis (last month)
Hard alcohol (last month)
Beer and/or wine (last month)
Abstinent
168
Respondents, who said having consumed alcoholic beverages or cannabis over the last 30 days,
report far more often committing offences of all sorts. Particularly impressive are the low
delinquency rates among abstinent youths. This underlines the preventive potential of policies
designed to reduce substance abuse among junior high-school populations.
5.7.4 Are delinquents happy?
Former research has shown that victims of offences tend, overall, to be less happy than people
who never have gone through this kind of experience (Staubli, Killias, Frey 2014).
The question now is whether offenders are happier than non-offenders. The following Figure
gives an answer.
Figure 5.86 Delinquency (last year prevalence) by happiness in %, N=4044-4064.213
As one can see, unhappy people commit far more often offences of all sorts. The association
holds throughout the list of offences, but it is strongest for street crime and other more serious
offences, and smallest for relatively “normal” offences. Given the cross-sectional nature of our
research design, the question of the causal order cannot be determined based on our data. It
might be that unhappy people are more prone to commit offences, and that offending is for them
a search for goods that might provide happiness. On the other hand, offending may make people
unhappy, among other things perhaps because delinquents live less stable social relationships
and may even be more isolated, as some findings presented above (Figures 5.78, 5.79) suggest.
213 Weighted data
13.5 16.5
21.7
4.3
10.0
3.4 3.5 4.8
10.4 14.3
10.8
5.0
10.3
4.9 7.2
11.3 13.8
1.3
5.7
1.0 1.9 1.0
8.9 11.1
7.5 3.6
5.4 3.4
5.6 7.4
11.0
1.2
6.5
1.3 2.3 1.2
6.6 8.8
7.2
2.8 5.3
3.6
0.0
5.0
10.0
15.0
20.0
25.0
unhappy neither/nor happy
169
5.7.5 Delinquency by computer games
Figure 5.86_1 Delinquency (last year prevalence) by playing computer games in %,
N=3924-3930 214
Respondents, who play computer games regardless its genre, are significantly more likely to
commit vandalism, personal theft and caring weapon. For the other offences, the difference is not
significant and obviously moderate at best.
Figure 5.86_2 Delinquency (last year prevalence) by playing “ego-shooter” as a favorite
genre of computer game in %, N=4053-4072 215
Respondents, who play ego-shooter games are more likely to commit almost all types of
offences.
214 Weighted data
215 Weighted data
5.0 5.6
10.7
1.2
6.4
1.4 1.6 1.9
4.8 6.2 6.4
2.2
5.4
2.4
6.9
9.8
12.8
1.4
6.1
1.2 2.3
1.1
8.3
10.7
7.4
3.3 5.4
3.8
0.02.04.06.08.0
10.012.014.0
no yes
5.3 7.7
10.0
0.9 4.3
0.9 1.7 0.7
7.1 7.2 6.1
2.2 3.2 3.8
10.7
14.7
19.2
2.4
10.3
1.9 3.4
1.9
10.8
18.5
10.3
5.8
10.3
3.7
0.0
5.0
10.0
15.0
20.0
25.0
Not quoted Quoted
170
Figure 5.86_3 Delinquency (last year prevalence) by playing “fighting/violence” as a
favourite genre of computer game in %, N=4053-4071.216
Students, who reported playing computer video games from the fighting/violence genre, are
more likely to have committed offences of all types than their peers.
Figure 5.86_4 Delinquency (last year prevalence) by playing “strategy/puzzle” as a
favourite genre of computer game in %, N=4054-4070.217
In comparison with the previous results, playing strategy/puzzle games is only moderately
associated with delinquent behaviour.
216 Weighted data
217 Weighted data
5.9 8.4
10.9
0.9
4.8
0.8 1.8 0.8
7.0 9.4
6.2
2.3 4.0 3.6
12.3
16.8
21.8
3.6
11.9
2.9 4.7
2.3
13.8 17.1
12.8
8.0
12.0
4.7
0.0
5.0
10.0
15.0
20.0
25.0
Not quoted Quoted
7.6
11.8
14.7
1.6
7.1
1.3 2.4
1.4
9.2
11.5
9.0
3.9 5.7
4.1 5.8 5.3
8.4
0.9
3.7
0.8 2.0
0.2
5.9
9.1
3.4 1.9
4.8 3.2
0.02.04.06.08.0
10.012.014.016.0
Not quoted Quoted
171
Students, who are playing computer games 3 to 5 hours per day are more delinquent than those
who play less or never, regardless the genre of game.
Figure 5.86_5 Delinquency (last year prevalence) by spending time in social nets,
N=2880-2890218
Students, who spend more time in social networks, are more likely to commit offences of all
types.
5.8 Summary and discussion
Leisure-time activities do not differ much across regions within Switzerland, although
some noteworthy exceptions appeared. In general, respondents in French-speaking
cantons tend to spend time more often in shopping centers, in the streets or parks.
Usually, respondents in larger urban areas, such as Geneva and Zurich, share this pattern.
In Zurich especially, going out in the evenings and returning home at very later hours is
particularly common.
On the other hand, students in French- and Italian-speaking cantons share more of their
leisure-time with their families. This does not necessarily point to contradictions in the
answers to our questionnaire, but could reflect the fact that whenever deviance is more
common in certain areas, a certain proportion of students in such environments stick to a
more traditional pattern of leisure-time activities.
218 Weighted data
3.9 4.3
10.4
0.1
3.2 0.8 0.4 0.0
5.3
9.6
3.3 1.4
2.9 3.5
6.6
9.2
13.1
1.2
6.2
0.7 1.9 1.0
7.3 8.6
6.7
3.0 5.2
3.2
12.8
19.2
22.8
4.2
14.6
4.4 3.9 3.1
12.5
15.4 15.1
5.1
13.9
3.2
0.0
5.0
10.0
15.0
20.0
25.0
I do not spend time in social net, less than 15 min. 15 min. - 3 h. more than 3 h. per day
172
Students in French-speaking cantons are more often doing illegal things just for fun,
while at the same time they pursue less often sports and other conventional leisure-time
activities.
Drinking and using cannabis is also more wide-spread in French-speaking cantons and
particularly in Geneva and in Zurich. In these cantons and regions, more respondents
know friends who use drugs or commit several offences. There is a clear association (at
the level of schools) between having friends who use drugs and consumption of cannabis
(see Figure 3.26).
In practically all these respects, respondents in the canton of St. Gallen tend to have the
most conventional life-styles.
Going out at night, going to coffee bars, pop concerts, as well as using alcohol have
become less popular between 2006 and 2013, whereas more students practice sports
when going out. On the other hand, respondents in 2013 spend less time with their
families, and more among them have peers who use drugs or who committed offences
such as burglary. Further, recent use of hard liquors and cannabis has increased (see
Chapter 9). This somewhat contradictory picture could mask important, but unmeasured
shifts in the organization of leisure-time, namely increasing use of Internet (and time
spent behind computers), on one hand, and a night-life that is extending more into early
morning-hours.
Peer-networks do not differ much across the country. Particularly in French-speaking
cantons, respondents care a lot about what their peers think about them. Ethnically
homogeneous peer networks seem to be less common nowadays (compared to 2006), and
more students have friends of different backgrounds. Students who have friends of
different origins among their peers express more feelings of satisfaction with life
(happiness) than others.
The level of happiness is approximately the same in different regions. Respondents with
problem behaviour (such as doing illegal things, having problematic use of substances) or
who know friends with problem behaviour are substantially less happy than others. On
the other hand, students who practice sports are somewhat happier. Although leisure-time
activities do not seem to influence a lot life satisfaction, the association is strong with
delinquent behaviour. Respondents who report having committed offenses of all sorts are
substantially less happy than those with more conventional behaviour. As for
victimization where the impact on happiness is well documented, this variable seems to
be a strong associate of delinquency.
Delinquency is strongly associated with leisure-time. Respondents who stay out beyond
mid-night, who hang out in streets and shopping malls or with problem behaviour admit
far more often having committed delinquent acts listed on our self-report instrument. On
the other hand, students with conventional leisure-time activities, such as school-related
work, creative activities (hobbies) and spending time with their families, have far higher
rates of offending. The same is true for juveniles who abstain from using substances. In
173
line with these findings and as shown in Chapter 3, students who missed school (truancy)
have far higher offending rates.
Playing computer games is not always associated with delinquency. There are
noteworthy differences by type of games, the violent games going along with stronger
associations.
All these findings point to one basic conclusion: preventive efforts should focus on
juveniles’ leisure-time activities. “Time” is the crucial factor in prevention.
Interestingly, delinquent respondents are less attached to their peers and socially more
isolated. This is encouraging in the sense that delinquent behaviour is not “normal”, but
clearly problem behaviour that is concentrated among youths with problematic leisure-
time activities.
References:
HaymozPantillon, S. (2010) Les gangs en Suisse: délinquance, victimisation et facteurs de
risque. Doctoral dissertation.University of Lausanne (School of Criminal Justice).
HaymozPantillon S., Maxson C., Killias M. (2014) “A Comparison of Correlates with Street
Gang Participation in Europe”, European J. of Criminology 11/6, 659-681
Killias M., Lanfranconi B. (2012) “The Crime Drop Discourse – or the Illusion of Uniform
Continental Trends: Switzerland as a Contrasting Case”, in J. van Dijk, A. Tseloni& G.
Farrell (Eds.), The International Crime Drop. New Directions in Research, Basingstoke
(Hampshire, UK): Palgrave-Macmillan, 268-278
Staubli S., Killias M., Frey B.S. (2014) “Happiness and victimization: An empirical study for
Switzerland”. European J. of Criminology 11/1, 57-72
Walser S. Freizeitverhalten und Gewalt bei Jugendlichen: eine situative Perspektive.
Abhandlung zur Erlangung der Doktorwürde der philosophischen Fakultät Universität
Zürich. Dissertation. Prof. Dr. Rainer Hornung und Prof. Dr. Martin Killias. Zürich,
2013. 237p.
174
Chapter 6.
Perception of right and wrong
6.1 Overview
The questionnaire contained many questions regarding moral judgements, perceptions of one’s
own reactions, egocentrism vs. altruism, hedonistic attitudes vs. risk control and neighbourhood
characteristics (particularly regarding symptoms of decay and social cohesion). In general, moral
condemnation and shame regarding offences is broad. The same is true for almost all items in
this part of the questionnaire. In this chapter, we shall focus on variables where there was no
universal consensus across regions and little variation across time (in comparison to the results
obtained in 2006).
6.2 Moral judgements across regions
6.2.1 Perception of right and wrong
Moral condemnation is almost universal. Few students feel that serious offending (violence,
robbery, racist attacks or burglary) is morally acceptable. With minor offences, such as
shoplifting, vandalism and “illegal” downloading (which, according to Swiss law, is “legal”
indeed) moral condemnation is clearly weaker, but still a majority of respondents consider this as
very or somewhat wrong. Even lying to a teacher is not approved by the majority. Although
these judgements are universal, they have some impact on delinquency, as the following Figures
show.
175
Figure 6.1 Delinquency (last year prevalence) by lying, disobeying or talking back to adults,
such as parents and teachers in %, N=4036-4055.219
Respondents who challenge adults’ authority tend to admit more often having committed all
sorts of offences.
Figure 6.2 Delinquency (last year prevalence) by purposely damaging or destroying
property that does not belong to you in %, N=4031-4046.220
Respondents, who think that purposely damaging or destroying somebody’s property is not
wrong at all, are more likely to commit an offence regardless its type.
219 Weighted data
220 Weighted data
14.4
22.8
28.8
9.7 13.4
6.9 10.1
7.9
19.5
26.4
18.5
12.1
18.1
11.1 9.2
13.7 17.0
1.7
7.9
1.1 2.4 1.1
10.1 11.9
9.0
4.0 6.5
4.0 4.6 6.2 9.5
0.4 4.0
0.4 1.3 0.6
5.2 7.3
5.0 1.4
4.6 2.5
4.7 4.7 7.6
1.0
7.1
2.3 1.8 1.5
5.7 8.2 7.5
3.6 2.8 3.4
not wrong at all a little wrong wrong very wrong
21.7
31.1
46.7
29.5
38.3
21.7 26.7 28.3
35.0
45.0
35.0 33.3 33.3
21.7 25.4 27.2 28.4
1.6
17.1
2.6 6.2 4.2
17.6 16.6 19.8
10.4
21.9
4.2 7.9
12.0 17.0
2.2
8.3
1.5 3.2 1.1
10.0 12.0 9.1
3.2 6.0 5.0 4.4 6.0
8.6
0.4 4.1
0.7 1.0 0.6 5.3
7.9 5.2
2.0 3.5 2.6
0.05.0
10.015.020.025.030.035.040.045.050.0
not wrong at all a little wrong wrong very wrong
176
Figure 6.3 Delinquency (last year prevalence) by stealing something small, such as a
chocolate bar from a shop in %, N=4035-4048.221
Respondents, who think that shoplifting is not wrong at all, are more likely to commit an offence
regardless its type. Together, the findings shown in Figures 6.1-6.3 illustrate the importance of
moral standards in shaping human behaviour. Of course, given the cross-sectional design of our
study, we cannot rule that that, actually, moral judgements are being shaped by (preceding)
deviant behaviour, rather than the other way around.
221 Weighted data
23.2
34.5
46.0
11.7
33.7
9.7 14.8
11.3
36.4 32.3
28.2
13.9
25.3
9.8 10.0
16.1
25.3
1.2
9.2
1.1 3.0 1.3
12.5 13.4 11.8
4.3 7.8
5.4 6.1 7.2 9.7
1.0 5.0
0.9 1.8 0.8 5.7
8.8 6.0
2.6 5.1 3.2 3.0 3.3 3.1
0.6 2.5 0.9 0.5 0.6 3.1
6.1 3.8 1.9 2.0 2.2
0.05.0
10.015.020.025.030.035.040.045.050.0
not wrong at all a little wrong wrong very wrong
177
6.2.2 Feelings of shame
Beyond one’s own moral judgement, the fear of moral condemnation by others, i.e. shame, is
another powerful factor in shaping human behaviour. In our questionnaire, several questions
were included on this issue. For a number of offences, respondents were asked how much they
would feel ashamed if their best friend, their teacher or their parents came to know about a
(hypothetical) offence committed by them. In general, feelings of shame are anticipated for all
these hypothetical situations. However, shame of parents and teachers is more evenly distributed
across regions than the fear of one’s best friend reaction. As we shall see later, fear of parents is
also less clearly associated with offending than the fear of friends’ reaction. In other words,
parents may be feared and shame may go along with their knowledge about one’s offences, but
they count clearly less than best friends.
As the following Figures show, the anticipation of feeling ashamed of friends is not evenly
distributed across the country.
Figure 6.4 “Imagine you were arrested by the police for committing a crime, would you feel
ashamed if your best friend found out about it” by main and subsamples in%.222
Apparently, friendship networks operate differently across the country. Fear of shame in the
hypothetical situation of being arrested by the police for a crime is stronger in German speaking
regions and particularly in the canton of St-Gall. At the other end of the distribution is Geneva.
222 Weighted data
16.5 14.4 21.4
14.3 15.3 14.3 20.9
13.2 23.2 23.0 23.6 23.8
15.8 25.5
20.8 25.2
60.3 62.6 55.0
61.9 69.0
60.3 58.4 61.5
0.010.020.030.040.050.060.070.080.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
no, not at all yes, a little yes, very much
178
Figure 6.5 “Imagine you were arrested by the police for committing a crime, would you feel
ashamed if your parents found out about it” by main and subsamples in%.223
As Figure 6.5 illustrates, parents’ reaction is well anticipated and feared, but little variation
appears across regions.
In the following figures 6.6-6.9, we shall see how the fear of shame about friends’ vs. parents’
reaction goes along with behavior, namely a more trivial offence like shoplifting and a more
serious event (being arrested by the police for a crime).
Figure 6.6 Delinquency (last year prevalence) by “Imagine you were caught shoplifting,
would you feel ashamed if your best friend found out about it” in %, N=4040-
4060.224
223 Weighted data
224 Weighted data
4.3 4.6 3.6 4.6 5.7 4.1 4.3 5.0 8.0 8.9 6.3 7.7 6.0 8.3 7.1 6.9
87.7 86.5 90.1 87.7 88.3 87.7 88.6 88.1
0.0
20.0
40.0
60.0
80.0
100.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
no, not at all yes, a little yes, very much
14.8
20.8
28.5
4.4
15.7
3.8 5.5 4.2
16.2 18.9
16.6
7.9
13.1
5.7 5.7 7.6
11.4
0.7 4.8
0.6 2.0 0.6
6.5 9.4
5.9 2.5
4.5 3.5 2.5 3.6 3.8 0.3
2.0 0.5 0.3 0.2 3.3 4.8 3.3
0.9 1.9 2.4
0.0
5.0
10.0
15.0
20.0
25.0
30.0
not at all a little very much
179
Respondents, who would not feel ashamed at all if caught shoplifting and even if their friends
find out about it, are more likely to commit an offence regardless its type. As the following
Figure 6.7 illustrates, the effect of shame is far weaker if the parents find out about.
Figure 6.7 Delinquency (last year prevalence) by “Imagine you were caught shoplifting,
would you feel ashamed if your parents found out about it” in %, N=4037-
4057.225
When we turn to a more serious event, such as being arrested by the police for a crime, the same
difference appears (Figures 6.8 and 6.9).
225 Weighted data
17.5 18.3
22.2
7.9
15.9
9.9 8.6 7.9
17.2 18.5
13.9
9.9
15.8
6.6
17.0 19.8
25.1
5.6
13.6
3.8
7.8
2.9
14.2 15.1
11.2
7.5
13.1
4.4 4.9 7.5
10.6
0.6
5.1
0.7 1.2 0.9
6.4 9.0
6.8
2.4 4.2 3.3
0.0
5.0
10.0
15.0
20.0
25.0
30.0
not at all a little very much
180
Figure 6.8 Delinquency (last year prevalence) by “Imagine you were arrested by the police
for committing a crime, would you feel ashamed if your best friend found out
about it” in %, N=4040-4060.226
Respondents, who would feel much ashamed if they were arrested by the police for committing a
crime and their friends find out, are less likely to commit an offence regardless its type.
Figure 6.9 Delinquency (last year prevalence) by “Imagine you were arrested by the police
for committing a crime, would you feel ashamed if your parents found out
about it” in %, N=4037-4057.227
226 Weighted data
227 Weighted data
19.7 23.9
29.3
6.0
19.0
6.0 6.7 5.2
18.8 23.4
21.0
10.2
19.3
6.5 7.0 10.8
15.5
1.0
7.5
0.8 3.5
1.1
8.3 11.5
8.2
3.4 6.2
4.3 3.0 4.7 7.0
0.4 2.5
0.2 0.5 0.4 4.4 5.7
3.5 1.3 1.7 2.6
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
not at all a little very much
24.7
18.5
24.7
10.9
16.2
8.0 8.0 8.6
17.8
22.1 19.1
13.8 17.9
6.9
13.5 17.4
24.1
4.6
15.6
4.6 8.3
2.8
11.3
16.9 12.9
7.0
15.0
5.5 5.2 8.0
11.0
0.7
5.1
0.7 1.4 0.8
6.9 8.7
6.4 2.4
4.2 3.3
0.0
5.0
10.0
15.0
20.0
25.0
30.0
not at all a little very much
181
Compared to Figure 6.8, those who would feel much ashamed in case their parents find out about
their being arrested by the police (Figure 6.9) are, apparently, less retained from committing
offences of any kind. This means that, although students anticipate being ashamed, they are less
influenced by their parents compared to their best friends’ reaction.
182
Figure 6.9_1 Delinquency (last year prevalence) by “what would the best friend feel if
he/she finds out that respondent sold an old cell phone to classmate as a
new one” in %, N=3978-3984.228
As in the previous Figure (6.9), students, who in case of unfair behavior anticipate criticism by
their favorite mate, are less likely to commit offences of all types than those who would expect
being admired by them.
228 Weighted data
16.2
22.8 24.3
8.6
18.6
8.6 8.6 7.4
19.3
25.6
22.4
9.7
16.9
8.3
12.4
16.0
20.7
1.4
10.4
1.4
4.3
1.4
11.3
15.8
12.2
4.5
8.4
5.2 7.2
9.1
14.3
1.3
7.0
0.8 2.0 1.1
7.6
10.5 7.7
5.0 5.6 3.5 4.4 4.8
7.9
0.1
3.0
0.0 0.8 0.0
4.9 5.5 3.2
0.4
3.9 2.2 2.1
4.3 6.0
0.4 2.9
0.4 0.5 0.5
4.1 5.4
3.6 0.8 1.2
2.7
0.0
5.0
10.0
15.0
20.0
25.0
30.0
would admire me rather admire neither/nor rather critique critisize a lot
183
6.3 Self-control, risky behaviour and altruistic vs. egocentric attitudes
6.3.1 Risk taking and accidents
Derived from the well-established concept of self-control, the questionnaire contained a series of
items regarding one’s anticipated reaction while making decisions. Most variables turned out to
vary only moderately across time and space. We shall focus here on a few that turned out to be
particularly relevant in connection with delinquency.
Figure6.10 “I act on the spur of the moment without stopping to think”by main and
subsamples in %.229
In German-speaking cantons, roughly two students in five disagree fully or somewhat to “act on
the spur of the moment without stopping to think”. In French-speaking cantons, in Geneva and in
Ticino, the distribution is more normal-shaped, as if the population there were more divided.
There is surprisingly little variation across regions within linguistic regions.
229 Weighted data
17.0 19.1
12.9 13.0
19.3 20.9
13.5 18.8
45.7 48.4
42.4
28.5
47.1 46.0 45.5 46.6
30.2 27.3
34.5
42.3
27.6 27.8 26.1 29.3
7.2 5.1 10.1
16.1
6.0 5.3
14.9
5.3
0.0
10.0
20.0
30.0
40.0
50.0
60.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
agree fully agree somewhat disagree somewhat disagree fully
184
Figure 6.11 “I am more concerned with what happens to me in the short run than in the long
run”by main and subsamples in %.230
Regarding considering the short vs. the longterm perspective, some differences across regions
become visible. Generally, German-Swiss respondents, even in urban areas like Zurich, are more
long-term oriented than French- and Italian-speaking respondents.
230 Weighted data
8.7 7.3 11.0
14.2
9.4 7.3
16.4
6.7
28.0 25.8
32.6 29.7
25.7 26.0 28.9
24.8
40.9 41.6 40.2 36.9 37.2
39.9 35.8
37.6
22.3 25.3
16.3 19.2
27.7 26.8
18.9
30.9
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
45.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
agree fully agree somewhat disagree somewhat disagree fully
185
Figure 6.12 “I like to test myself every now and then by doing something a little risky” by
main and subsamples in %.231
In line with the finding presented in Figure 6.11, German-Swiss juveniles (even in Zurich) are
less inlcined to take risks than respondents in other areas.
Figure 6.13 Having an accident that was so serious that it was necessary to see a doctor by
main and subsamples in %.232
Surprisingly, having experienced accidents of some seriousness (necessitating medical
assistance) are rather more common in German-speaking cantons than in Ticino and in the
Romandie. A possible explanation might be the lower prevalence of sports as a major leisure-
time activity in Latin cantons (see Chapter 5, Figure 5.7).
231 Weighted data
232 Weighted data
11.2 9.3
14.9 15.9
10.7 10.8
16.0
10.1
23.5 20.0
30.9
26.5
21.5 23.8
27.8
13.4
32.6 34.0
29.2
34.3 33.7 32.0
22.6
34.9 32.6
36.7
25.0 23.3
34.1 33.3 33.6
41.6
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
45.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
agree fully agree somewhat disagree somewhat disagree fully
43.5 39.0
52.3 51.0
37.2 33.8
53.4
39.4 36.8 38.3 33.7 34.1
39.5 40.8
31.3 38.0
19.7 22.7
14.0 14.9 23.3 25.4
15.3 22.6
0.0
10.0
20.0
30.0
40.0
50.0
60.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
never once more than once
186
Figure 6.14 Delinquency (last year prevalence) by “I act on the spur of the moment without
stopping to think” in %, N=4027-4050.233
Respondents, who act on the spur of the moment without stopping to think, are more likely to
commit an offence regardless its type.
233 Weighted data
13.7 15.2
20.3
4.1
12.7
3.7 4.7 3.8
12.7
15.8
13.0
7.6
10.9
6.9 5.8
9.9
13.6
0.9
6.0
0.8 1.7 0.9
7.1
10.2
7.5
3.0 5.3
3.3 5.0 5.4
8.0
0.5
3.6
0.3 1.3 0.2
5.6 5.8 4.1
1.1 3.4
2.3 3.4
6.5 6.9
2.1
5.8 3.8 3.1 2.7
7.6
12.4
7.2
2.4 3.5 3.1
0.0
5.0
10.0
15.0
20.0
25.0
fully agree agree somewhat disagree somewhat fully disagree
187
Figure 6.15 Delinquency (last year prevalence) by “I am more concerned with what happens
to me in the short run than in the long run” in %, N=4011-4036.234
Students, who are more concentrated on the short than on the long run, are more likely to commit
any type of offence.
234 Weighted data
13.3 14.4
19.5
3.4
12.4
4.5 4.5 4.0
12.4 13.8
10.2
5.6
10.7
5.9 8.2
12.2
17.8
2.3
8.9
1.6 2.9
1.2
9.8 11.3
9.6
5.1
7.9
3.5
6.4
8.9
11.5
0.7
5.4
0.8 2.0 1.0
7.1 9.3
7.2
2.6 4.3
3.3 2.6 3.7
5.4
0.7 3.0
0.6 0.9 0.8
3.6
8.2
3.9
0.9 2.4 3.3
0.0
5.0
10.0
15.0
20.0
25.0
fully agree agree somewhat disagree somewhat fully disagree
188
Figure 6.16 Delinquency (last year prevalence) by “I like to test myself every now and then
by doing something a little risky” in %, N=4022-4044.235
Students, who like doing something a little risky, are more likely to commit any type of offence.
Figure 6.17 Delinquency (last year prevalence) by “having an accident that was so serious
you had to see a doctor, such as during sports or a traffic accident” in %,
N=4045-4066.236
235 Weighted data
236 Weighted data
22.2 25.1
30.4
5.1
19.9
5.6 6.9
5.3
20.3
31.6
23.3
12.4
17.2
5.8
9.4
12.8
19.1
1.7
8.5
1.2 3.3
1.8
12.0 12.9 11.0
3.9
7.3
3.9 4.7 7.2
10.8
0.8
4.3
0.6 1.3 0.5
4.3 7.1
4.2 2.1
4.8 4.6 1.4
3.1 3.6 0.5
2.4 0.6 0.8 0.5
3.5 3.4 2.6 0.5 0.8 1.7
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
fully agree agree somewhat disagree somewhat fully disagree
5.5 7.3
11.4
1.1
5.0
0.7 1.5 0.8
7.0 8.1
5.3
2.2 3.9
2.3
6.0 8.4
11.7
1.0
5.9
1.3 2.0 1.3
6.9 8.5
7.0
2.8
6.3 4.2
10.6
15.3 17.3
2.8
10.4
2.8 4.0
2.4
10.6
16.8
13.0
6.5 7.9
5.5
0.02.04.06.08.0
10.012.014.016.018.020.0
never once more than once
189
Students, who had a serious accident with the necessity to see a doctor, are more likely to
commit any type of offence. The association between having experienced accidents and
offending has been observed in several studies over the last years (Killias/Kuhn/Aebi 2011, 248).
6.3.2 Egocentrism
Figure 6.18 “If things I do upset people, it is their problem, not mine”by main and
subsamples in %.237
Most respondents do not accept this statement. Egocentric acting is the most clearly rejected in
Italian and French speaking regions. Interestingly, the two urbanized cantons of Geneva and
Zurich are particularly different in this respect. There may be a cultural underpinning at work
that is hard to understand without further data.
237 Weighted data
15.5 16.6 13.6 12.7
18.6 15.5
10.8
15.8
25.0 28.5
18.2 19.7
25.0 27.0
16.1
27.3
34.4 33.6 36.6
30.9 33.0
36.8 34.2 32.9
25.1 21.3
31.6
36.8
23.4 20.7
38.9
23.9
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
45.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
agree fully agree somewhat disagree somewhat disagree fully
190
Figure 6.19 Delinquency (last year prevalence) by “If things I do upset people, it is their
problem, not mine” in %, N=3998-4021.238
Students, who do not care if they make upset other people by their own behaviour, are more
likely to commit any type of offence.
These and a few more questions have been asked also during the preceding ISRD-2 conducted in
2006. As figures 6.20 and 6.21 show, attitudes have remained fairly stable over time, but
students may have become slightly more egocentric.
Figure 6.20 “I try to look out for myself first, even if it means making things difficult for
other people” in % for 2006 and 2013, N=3987-4011.239
238 Weighted data
239 ISRD-2 – not weighted data, ISRD-3 - weighted data
13.5
16.9
20.9
4.2
15.9
3.5 3.9 4.5
16.0
20.5
14.9
8.5
13.1
7.0 6.6
11.6
16.1
1.2
8.4
1.4 3.0
1.1
8.4 8.7 9.0
3.3 5.9
3.1 5.9
7.5 9.5
0.9 3.3
1.0 1.4 0.7
5.5
9.7
5.0 2.1
4.2 3.2 3.7 4.3
8.3
0.5 2.9
0.4 1.6 0.4
4.7 5.2 4.2
1.1 2.0 2.4
0.0
5.0
10.0
15.0
20.0
25.0
fully agree agree somewhat disagree somewhat fully disagree
41.5 37.7
14.6
6.3
32.5
41.3
19.5
6.7
0.0
10.0
20.0
30.0
40.0
50.0
disagree fully disagree somewhat agree somewhat agree fully
ISRD-2 ISRD-3
191
Figure 6.21 “If things I do upset people, it is their problem, not mine” in % for 2006 and
2013.240
These changes seem insufficient, however, to explain the substantial increases in offending
between 2006 and 2013 (see Chapter 7, Figure 7.2). The other attitudes seem to have changed
even less.
6.4 Good and bad neighbourhoods
Several questions in our instrument referred to the neighbourhood. In this connection, it should
be kept in mind that the same questionnaire was to be applied in some 30 countries. The several
items related to neighbourhood characteristics had, therefore, to work in very different social and
economic backgrounds.
240 Weighted data (ISRD-3, N=4158), not weighted data (ISRD-2, N=3643).
27.7 32.3
23.1
16.9
25.1
34.4
25.0
15.5
0.0
10.0
20.0
30.0
40.0
disagree fully disagree somewhat agree somewhat agree fully
ISRD-2 ISRD-3
192
Figure 6.22 “There is a lot of crime in my neighbourhood” by main and subsamples by main
and subsamples in %.241
Only few respondents live in neighbourhoods where crime is fairly common. More respondents
in Geneva and Zurich report higher crime rates in their neighbourhood. This corresponds to the
observations during several crime surveys that crime is relatively evenly distributed across
Switzerland (Killias et al. 2011).
Figure 6.23 “There is a lot of graffiti in my neighbourhood” by main and subsamples in %.242
Most of neighbourhoods do not have a lot of graffiti. The highest percentage of those, who have
it is in the canton of Geneva.
241 Weighted data
242 Weighted data
74.5 74.6 74.8 71.7 71.8 80.4
70.2 68.2
18.0 17.8 18.4 17.8 20.0 13.4
20.4 23.4
4.5 4.6 4.0 5.3 6.1 3.0 5.4 4.4 3.0 3.0 2.8 5.2 2.0 3.1 4.1 4.0
0.0
20.0
40.0
60.0
80.0
100.0
CH(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
1 disagree fully 2 disagree somewhat 3 agree somewhat 4 agree fully
78.7 82.1 73.3
65.3
81.5 83.0 79.5
55.2
12.5 11.1 14.5 18.5 12.5 8.6 11.7
20.6
5.6 4.2 8.0 9.9 4.0 4.8 4.7
14.1 3.3 2.6 4.2 6.3 2.0 3.7 4.1
10.2
0.0
20.0
40.0
60.0
80.0
100.0
CH(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
disagree fully disagree somewhat agree somewhat agree fully
193
Figure 6.24 “People around here are willing to help their neighbours” by main and
subsamples in %.243
Respondents in French-speaking cantons perceive less solidarity between people living in their
neighbourhood than in German-speaking regions.
Figure 6.25 “There is a close-knit neighbourhood” by main and subsamples in %.244
On the other hand, replies to the question about helping eachother out is only moderatly
associated with another aspect of solidarity, namely whether or not one’s neighbourhood is seen
as “close-knit”. In this respect, German Swiss respondents see their neighbourhood more
positively than those in French-speaking cantons.
243 Weighted data
244 Weighted data
6.7 5.0 10.2 7.8 5.3 3.4 4.3
11.1 15.3 12.7
20.7 18.9 11.5 11.0 12.2
19.2
44.2 44.8 43.2 42.9 43.7 42.0 49.9
34.5 33.8 37.6
25.9 30.5
39.5 43.6
33.7 35.2
0.0
10.0
20.0
30.0
40.0
50.0
60.0
CH(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
disagree fully disagree somewhat agree somewhat agree fully
16.7 17.6 15.4 12.2
17.1 15.8 18.3 17.7
36.5 40.2
29.8 28.6
39.6 36.6
39.1
23.8
33.6 33.1 34.2 38.0
35.1 30.6
33.8
26.4
13.1 9.2
20.6 21.2
8.2
17.0
8.9
32.1
0.0
10.0
20.0
30.0
40.0
50.0
CH(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
disagree fully disagree somewhat agree somewhat agree fully
194
Figure 6.26 Delinquency (last year prevalence) by “There is a lot of crime in my
neighbourhood” in %, N=4014-4037.245
Students, who perceive a lot of crime in their neighbourhood, are more likely to commit an
offence.
Figure 6.27 Delinquency (last year prevalence) by “There is a lot of graffiti in my
neighbourhood” in %, N=4010-4034.246
245 Weighted data
246 Weighted data
4.4 7.3
10.0
0.5 4.3
0.4 1.2 0.4
6.3 7.8 4.5
1.9 3.2 2.8
10.1 11.9
19.0
1.7
9.0
1.2 3.0 1.5
9.5 13.3
11.2
3.4
9.4 4.4
11.0
17.0 18.6
1.7
13.0
2.3 2.8 3.4
11.9
19.3 21.0
8.4 11.7
4.5
27.9 25.4
32.0
21.3
34.4
22.1 20.5 19.8 24.2
28.9
37.2
27.3 27.3
15.7
0.05.0
10.015.020.025.030.035.040.0
fully disagree disagree somewhat agree somewhat fully agree
3.9 7.4
10.1
0.4 4.6
0.5 1.0 0.5
6.1 8.6
4.9 2.1
4.0 2.9
10.1 11.0
18.6
2.2
7.7
1.0 2.6 1.6
9.8 10.8 10.9
4.4 7.6
3.2
20.4
15.5
23.0
4.4
15.9
3.5 6.2
3.6
12.9
18.7 19.1
7.1 9.8
6.2
31.1 30.3 32.6
15.9
30.3
19.7 21.8
16.8
28.2 26.7
34.4
18.9
23.7
15.3
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
fully disagree disagree somewhat agree somewhat fully agree
195
Respondents, who see a lot of graffiti in their neighbourhood, are more likely to commit an
offence.
Figure 6.28 Delinquency (last year prevalence) by “People around here are willing to help
their neighbours” in %, N=4015-4037.247
Respondents, who live in neighbourhood where people are not willing to help each other, are
more likely to commit an offence.
247 Weighted data
11.9 14.9
26.9
4.2
11.0
4.5 5.7 4.9
10.9 14.0 13.3
7.3
12.1
3.9 6.2
11.4 12.8
1.3
8.3
1.1 1.8 1.1
11.0 11.9 8.8
3.4 5.7
2.6 6.9
10.0 13.0
1.4
6.2
0.9 1.6 0.9
7.2 9.2
7.3 3.0
6.2 4.0 5.1 5.8
9.2
0.9 5.0
1.4 2.4 1.2
6.2 9.4
5.9 2.7 3.5 3.5
0.0
5.0
10.0
15.0
20.0
25.0
30.0
fully disagree disagree somewhat agree somewhat fully agree
196
Figure 6.29 Delinquency (last year prevalence) by “This is a close-knit neighbourhood” in
%, N=4006-4028.248
Students, who live in a close-knit neighbourhood, commit fewer offences. In the first situation it
can be explained by committing offence in a group, in the second one – is because of absence of
bonding with own neighbourhood. Among exceptions are vandalism and shoplifting.
Some of the variables here have also been measured in the 2006 survey. The following Figures
show how neighbourhood perception has developed over the last years.
Figure 6.30 “There is a lot of crime in my neighbourhood” in % for 2006 and 2013.249
Percentage of respondents, who declined a lot of crimes in their neighbourhoods, decreased.
248 Weighted data
249 Weighted data (ISRD-3, N=4158), not weighted data (ISRD-2, N=3643).
7.6
13.4
18.1
3.1
10.6
2.7 3.6 3.3
9.7
13.3
10.5
4.7
9.4
4.1 6.2
9.8 10.9
0.9
6.4
0.6 1.3 0.5
7.1
9.7
6.7
2.8
5.8
3.0 5.1
7.1
13.2
1.0
4.1
0.9 1.8 0.8
6.4 8.5
6.3
2.6 3.4 3.6
9.2 7.2
9.2
1.7
7.1
2.8 4.1
2.3
10.2 10.4 8.7
3.8 4.7 4.6
0.02.04.06.08.0
10.012.014.016.018.020.0
fully disagree disagree somewhat agree somewhat fully agree
62.9
24.8
8.2 4.1
74.5
18.0
4.5 3.0
0.0
20.0
40.0
60.0
80.0
disagree fully disagree somewhat agree somewhat agree fully
ISRD-2 ISRD-3
197
Figure 6.31 “There is a lot of graffiti in my neighbourhood” in % for 2006 and 2013.250
Percentage of respondents, who live in neighbourhood without graffiti, slightly increased
Figure 6.32 “People around here are willing to help their neighbours” in % for 2006 and
2013.251
Percentages of respondents, who want or do not want helping their neighbours, remained stable.
250 Weighted data (ISRD-3, N=4158), not weighted data (ISRD-2, N=3643).
251 Weighted data (ISRD-3, N=4158), not weighted data (ISRD-2, N=3643).data
72.3
17.0 6.6 4.1
78.7
12.5 5.6 3.3
0.0
20.0
40.0
60.0
80.0
100.0
disagree fully disagree somewhat agree somewhat agree fully
ISRD-2 ISRD-3
7.1
19.7
42.3
31.0
6.7
15.3
44.2
33.8
0.0
10.0
20.0
30.0
40.0
50.0
disagree fully disagree somewhat agree somewhat agree fully
ISRD-2 ISRD-3
198
Figure 6.33 “This is a close-knit neighbourhood” in % for 2006 and 2013.252
The results are somewhat difficult to interpret. On one hand, fewer respondents say there is “a lot
of crime” (or a lot of graffiti) in their neighbourhood. At the same time, judgements about
solidarity among neighbours (Figures 32 and 33) are less optimistic than in 2006. Overall, the
safest way of interpreting changes over time would be to say that they are not contributing much
to understand changes in crime rates, as observed in Chapter 7 (Figure 7.2). However,
differences in the prevalence of social problems in the neighbourhood were among the strongest
variables explaining different levels of crime rates in an international perspective, i.e. across
countries (Junger-Tas, Steketee, Jonkman 2012, 266).
References:
Killias M., Kuhn A., Aebi M. Grundriss der Kriminlogie. Eine Europpeische Perspektive. Zweite
Auflage. Bern: Stämpfli Verlag AG, 2011. 568 p.
Junger-Tas J.J.T., Steketee M., Jonkman H.The nneighbourhood context.Chapter 10.The many
faces of Youth crime.Contrasting theoretical perspectives on juvenile delinquency across
countries and cultures. New York: Springer,2012, 367 p. P. 257-284.
Killias M., Staubli S., Biberstein L., Bänziger M., Iadanza S.International Crime Victimization
Survey (ICVS) 2010/2011.
http://krc.ch/de/index.php?a=Research&b=Forschung&c=Abgeschlossene-
Forschungsprojekte
252 Weighted data (ISRD-3, N=4158), not weighted data (ISRD-2, N=3643).
9.5
25.9
41.4
23.1 16.7
36.5 33.6
13.1
0.0
10.0
20.0
30.0
40.0
50.0
disagree fully disagree somewhat agree somewhat agree fully
ISRD-2 ISRD-3
199
Chapter 7
Delinquency over space and time in various degrees of seriousness
7.1 Overview
In this Chapter, we shall first present the prevalence rates over the last year and over the entire
lifespan for the entire country as well as for the several regional subsamples. In the second part,
we shall look at trends in offending since ISRD-1 (in 1992) and ISRD-2 (in 2006). In the final
section, we shall look at the so-called “versatility”, i.e. the extent to which respondents admitting
having committed one particular offence have been involved also in other forms of delinquency.
7.2.Delinquency across Switzerland’s several regions
Table 7.1 gives an overview of how many respondents (in per cent) admitted having committed
any of the listed offence types at least once over the last 12 months.
Tabelle 7.1 Delinquency (last year prevalence) by main and different subsamples253
Switzerland254
(N=4'158)
DE
(N=2'560
)
FR
(N=956
)
ITA
(N=642
)
SG
(N=625
)
AG
(N=555
)
GE
(N=268
)
ZH
(N=266
)
Graffiti 6.7 5.1 9.3 12.0 4.6 5.3 11.9 3.8
Vandalism 9.3 7.8 12.8 7.8 6.9 8.1 15.0 7.1
Shoplifting 12.6 10.1 18.9 9.2 9.7 9.0 14.4 8.9
Burglary 1.4 1.4 1.4 2.1 1.2 1.6 1.4 1.2
Bicycle theft 6.4 7.1 5.1 4.5 7.0 6.9 3.0 5.3
Car theft 1.3 1.4 1.3 1.5 0.6 2.1 2.6 1.8
Car break 2.2 1.7 3.1 3.0 0.8 2.0 2.8 2.2
Robbery 1.3 1.4 0.9 2.1 0.5 1.4 1.4 1.8
Personal theft 7.7 5.1 13.3 7.9 5.5 5.1 13.1 3.3
Weapon 10.0 9.8 10.6 7.6 8.4 11.4 9.9 10.0
Group fight 7.5 5.8 11.2 8.1 5.5 7.4 11.0 4.5
Assault 3.2 3.4 3.1 2.1 3.3 2.1 5.9 4.1
Drug dealing 5.6 5.9 5.0 5.0 3.7 4.0 4.4 6.2
Animal
cruelty 3.6 3.9 2.7 5.3 3.5 3.4 3.6 1.4
Among the most frequent offences are vandalism, shoplifting, personal theft, caring weapons and
group fights. The highest prevalence rates can be observed in French speaking cantons including
Geneva as well as in Zurich. Shoplifting and theft of personal items are particularly frequent in
253 Weighted data 254
N=4054-4071
200
the French-speaking cantons, whereas for other offences, the differences are less pronounced and
less consistent. The canton of St-Gall stands out as the region with the lowest delinquency rates.
The same picture emerges if offending over the entire lifespan (rather than offending over the
last 12 months) is considered (Figure 7.1). With the exception of bicycle theft, stealing is more
common in the French-speaking regions, whereas German-speaking cantons tend to have higher
rates of violence including group fights.
Figure 7.1 Delinquency (life time prevalence) by language regions in %.255
Thus, the geographic distribution does not change if, instead of 12-months rates, lifespan
prevalence rates are considered.
7.3.Trends in delinquency over time
The question whether offending in general and among juveniles in particular has increased over
the last 20 years has often been a matter of debate. Before the recent drop after 2011 (whose
reasons are not entirely clear), offending in general and especially violent offences have
considerably increased in police statistics since 1990. Victimization surveys (Killias et al. 2011)
and health statistics (Killias&Lanfranconi 2012) have shown similar trends. One self-report
survey conducted in the canton of Zurich in 1998 and in 2007 has shown, however, that
offending and victimization have essentially remained stable over these years (Ribeaud& Eisner
2009). Since the International Self-reported delinquency survey has been conducted in
Switzerland in 1992, 2006 and 2013, some longitudinal comparisons are possible. It should be
taken into account, however, that not all offences were included among the list of self-reported
offences in all three sweeps of this survey. The details can be seen from Table 7.2.
255 Weighted data
16.5
7.4 10.7
1.8 1.7
8.2 3.8
13.8
8.4 8.2
2.0 1.9 6.3
4.2
23.5
5.7
16.4
1.5 1.3
12.2
3.3
11.8
5.4
10.6
2.1 2.1
8.3
2.3
0.0
10.0
20.0
30.0
Shoplifting Bicycle theft Personal theft Burglary Robbery Group fight Assault
Switzerland (N=4'158)German speaking part (N=2'560)Romandie (N=956)Tessin (Main sample + additional sample, N=642)
201
Table 7.2 Delinquency (life time and last year prevalence) in % for 1992, 2006 and2013.256
ISRD-1 *
(N=529) ISRD-2 * (N=3'648) ISRD-3* (N=4‘158)
Last year Life time Last year Life time Last year
Graffiti
8.8 6.6
Vandalism
13.4 7.8 11.3 9.1
Shoplifting 15.3 23.6 9.1 16.2 12.4
Burglary 0.6 2.0 0.9 1.8 1.4
Bicycle/moped/scooter
theft (ISRD2) 6.6 3.7
BicycleTheft (ISRD3)
7.3 6.3
Motobike/cartheft
0.8 0.4 1.6 1.3
Car break 1.0 2.7 1.0 2.7 2.2
Robbery 0.0 1.4 0.9 1.7 1.3
Personal theft (ISRD3)
10.5 7.5
Caringweapon 9.5 11.1 7.8 10.8 9.7
Group fight 10.0 15.5 8.4 8.0 7.3
Assault 0.5 2.9 1.2 3.7 3.1
Drug Dealing (ISRD2) 1.5 3.7 2.8
Drug dealing (ISRD3)
5.6 5.4
AnimalCruelty 12.2
4.2 3.5 * Weighteddata
To the extent that the several offences were measured in a comparable way over time in all
waves of ISRD, an increasing trend emerges for most offences. The exceptions are shoplifting,
where preventive measures (i.e. the increased use of modern technology) in shops may have
reversed the trend, and group fights. For all the other offences shown in Table 2, the increase is
substantial, especially for bicycle theft, assault and drug (i.e. mostly cannabis) dealing. Further
(and once more with the exception of shoplifting), the trend is consistent from 1992 to 2006 and
from 2006 to 2013.
256 Weighted data
202
Figure 7.2 Delinquency (last year prevalence), the whole Switzerland in % for 1992, 2006,
2013.257
Table 7.3 Significance of the comparison of ISRD-1, ISRD-2 and ISRD-3
ISRD-1 and ISRD-2 ISRD-2 and ISRD-3
p-value
p-value
Shoplifting .000
Shoplifting .000
Burglary .025
Burglary .002
Car break .742
Car break .000
Robbery .009
Caringweapon .000
Caringweapon .000
Group fight .007
Group fight .163
Assault .000
Assault .000
Drug dealing .000
Drug dealing .000
The reason of the controversy may be that some surveys (such as the one conducted in Zurich by
Ribeaud& Eisner 2009), inspired by the model of the Kriminologisches Institut Niedersachsen
(KFN) at Hannover, are using very broad concepts of violence. For example, roughly 20 per cent
of their respondents reported having committed bodily injury at least once over the last 12
months, whereas the ISRD measure includes only cases where the victim needed to see a doctor.
As a result, only about 3 per cent of our respondents admitted having committed this narrowly
defined offence of “assault” in 2013. Under these circumstances, variation over time is far more
likely. The same holds for victimization where our rates are also far lower than those observed
by Ribeaud& Eisner (2009). In line with what has been observed in Figure 7.2, the rates of
257 Weighted data
15.3
0.6 0.0
10.0
0.5 1.5
9.1
0.9 0.9
7.8 8.4
1.2 2.8
12.4
1.4 1.3
9.7
7.3
3.1
5.4
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
18.0
Shoplifting Burglary Robbery Cating weapon Group fight Assualt Drug Dealing
1992 (ISRD-1, N=529)* 2006 (ISRD-2, N=3'648)* 2013 (ISRD-3, N=4'158)*
203
victimization (particularly for assault and robbery) have also increased substantially between
2006 and 2013 (see Figure 4.1). The Zurich study, however, did not find any increase with its far
broader definitions of victimization.
In sum, the trends shown here are consistent with increasing rates of victimization observed in
chapter 4 (Figure 4.1). That victimization and offending show similar trends over time, is
consistent with the fact that the two phenomena tend to co-vary at the individual level (see the
following Figure 7.2_1). It is, therefore, plausible that they co-vary also over space and time.
Figure 7.2_1 Delinquency by victimization (assault) in %, N=40521-4068 258
As one can see from the Figure 7.2_1, victims of assault tend to commit far more offences than
those who never experienced intentional physical injury. The same association between
offending and victimization can also be observed for other offences. Although this connection is
well-established, it does not mean that the same factors are at work for delinquency and
victimization. In Chapter 1, we have seen several examples where variables that were strongly
correlated with delinquency are not or moderately at best with victimization (see 1.5)
7.4.Versatility: Are offenders specialists or generalists259
?
A highly relevant question is whether offending is limited to a unique type of offence or the
symptom of a broader problem. By versatility we mean that a person committing offense X has,
over the same reference period (of, say, 12 months) committed further offences (and, eventually,
how many different offences). We can express this idea either by looking at how many
258 Weighted data 259
The variable measuring the delinquency in general is made up of 14 different types of offences. The versatility
score has been calculated considering only those cases who gave a valid answer to each of these 14 variables
(offences, last year prevalence). Respondents who provided a missing value to at least one of these 14 variables are
not included into the calculation of the versatility score.
6.2 8.6 11.7
1.1 5.8
0.9 1.9 0.9
7.2 9.4 6.8
2.8 5.0 3.5
17.3
26.9
34.6
9.0
20.5
11.5 9.6 11.5
20.5 24.5 24.4
14.8
21.5
8.2
0.05.0
10.015.020.025.030.035.040.0
No assault Reported assault
204
respondents who admit having committed a certain offence have committed also other types of
offences. This is illustrated by Figure 7.3.
Figure 7.3 How versatile are juveniles who admit having committed (at least once over the
last year) each single minor property offence on our list? in %, N=4025-4026.260
Respondents reporting more trivial property offences, such as graffiti, vandalism, personal theft
and shoplifting, are mostly one-type offenders, i.e. less versatile or more “normal” juveniles.
Figure 7.4 How versatile are juveniles who admit having committed (at least once over the
last year) each single violent or serious offence on our list, in %, N=4025-
4026.261
Respondents reporting serious property offences, such as burglary, motorbike/car theft or
robbery, are more versatile. For example, two in three who admit having committed at least one
260 Weighted data
261 Weighted data
55.7 56.0
68.2
42.5
17.0
55.8
28.2 29.9 21.5
36.7
22.6 29.4
16.1 14.0 10.3
20.8
60.4
14.8
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
Graffiti *** Vandalism *** Shoplifting *** Bicycle theft *** Motorbike/cartheft ***
Personal theft***
1-3 offences 4-7 offences 8-14 offences
8.9
31.5
17.0
60.8
49.3
39.5 43.9
68.3
26.8 31.5
20.8 25.8
33.7 28.7
33.0
15.2
64.3
37.1
62.3
13.5 17.0
31.8 23.1
16.6
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
Burglary *** Car break***
Robbery*** Caringweapon ***
Group fight***
Assault *** Drug dealing***
Animalcruelty ***
1-3 offences 4-7 offences 8-14 offences
205
robbery have committed at least 8 further types of offences. On the other hand, carrying a
weapon, group fight and animal cruelty are often committed by less versatile (or more “normal”)
offenders.
We can look at versatility also from the opposite angle, namely how many low, medium or high
versatility offenders have committed at least one of the offences on our list.
Figure 7.5 How many among low/medium/high versatility offenders have committed at
least one of the following offences (over the last 12 months)? in %, N=4025-
4026.262
Most offences highly versatile offenders have committed most types of offences on our list. The
exception is animal cruelty that is committed only by half of the highly versatile offenders.
However, there are some nuances across offences. For example, robbery, burglary and theft of
cars/motorbikes is not committed systematically by highly versatile offenders, whereas
shoplifting, vandalism, drug dealing, bicycle theft, group fight and carrying weapons is
committed by over 90 percent of them.
7.4 Conclusions
Serious juvenile delinquency in Switzerland has a increased since 1992. The Romandie region
has higher rates of delinquency, along with urban areas like Zurich and Geneva. St-Gall is the
canton with the lowest rates.
Students, who report smaller number of offences, commit usually less serious offences. Overall,
offenders are generalists rather than specialists, in the sense that they commit different types of
offences. Those who commit different types of offences are consistently committing also more
serious offences. In this sense, versatility is a good proxy for seriousness of offending.
262 Weighted data
14.0 18.8 31.6
0.5 10.1
0.8 2.6 0.8 15.9 22.4
13.6 4.7 8.9 9.1
42.5 60.2 59.7
8.3
52.5
6.6 15.5
6.1
50.3 56.9 56.1
20.6
40.6
12.2
78.6 91.1 91.2
64.3
96.4
57.1 58.9 58.9
82.1 96.4 91.1
73.2
91.1
42.9
0.0
20.0
40.0
60.0
80.0
100.0
120.0
1-3 offences 4-7 offences 8-14 offences
206
References:
Killias M., Staubli S., Biberstein L., Bänziger M., Iadanza S. International Crime Victimization
Survey (ICVS) 2010/2011.
http://krc.ch/de/index.php?a=Research&b=Forschung&c=Abgeschlossene-Forschungsprojekte
Lanfranconi B., Killias M. “The Crime Drop Discourse – or the Illusion of Uniform Continental
Trends: Switzerland as a Contrasting Case”, in J. van Dijk, A. Tseloni& G. Farrell (Eds.),
The International Crime Drop. New Directions in Research, Basingstoke (Hampshire,
UK): Palgrave-Macmillan 2012, 268-278.
Ribeaud D. & Eisner M. Entwicklung von Gewalterfahrungen Jugendlicher im Kanton
Zürich.Oberentfelden: Sauerländer. 2009.
207
Chapter 8
Contact with the police and perception of the image of the police
8.1 Overview
In the ISRD-3 respondents were asked about experience of contact with police because of
doing something illegal; as well as about students’ attitude to the police, their assessment of
police’s work and image of the police. The questionnaire includes 8 questions and 10
variables (modules 7 and 10 and about the police). Only 9th grade students were asked to
answer these question.
This chapter includes analyses of demographic factors influencing attitudes to the police and
information on how different attitudes to police are associated with delinquency.
8.2 Experience with and attitudes to the police
8.2.1 Attitudes across regions
8.2.1.1 The prevalence of contact with police because of illegal behaviour
Most respondents had some police contact at least once during their life-time, but few
(roughly 8 per cent) had so over the last year. There is fairly little difference across regions.
8.2.1.2 Opinion of young people about the police
Several questions were asked regarding views about the police and police work, including
illegal or unfaithful behaviour on the side of police officers. The findings certainly do not
necessarily reflect real experiences, but give an idea about what young people of 15-16 years
think about the police.
208
Figure 8.1 When victims report an offence to the police, police treat all groups
equally,by main and subsamples, in %.263
Overall, about two in five students think the police treat everybody the same way if a victim
comes to report a crime. However, there is some variation across the country. Ticino youths
are almost unanimous at considering police behaviour as egalitarian, whereas in French-
speaking cantons, as well as in Aargau and Zurich, many students claim not being able to say.
If discriminatory behaviour is anticipated, it is so mostly against Africans, Arabs, citizens of
former Yugoslavia, or Muslims in general.
A further question concerned presumed responsiveness in case of an emergency call. As an
example, burglary was presented.
263 Weighted data
39.1 40.8
33.9
50.4 48.0
34.4 39.3
34.4 38.7 37.7 40.0
49.6
37.9
46.9
39.3 40.6
10.5 7.1
19.4
1.7 4.7
16.7 10.0 11.7 14.4
6.8 12.4 14.1
4.7
15.0
0.0
20.0
40.0
60.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
No, everybody is treated equally
I do not know
Africans, Arabs, Yugoslavians, Moslems are treated worse
Other groups of foreighners, other groups of people (youths, other religion and so on) are treated worse
209
Figure 8.2 “If burglary was committed, how quickly police would arrive?” by main and
different subsamples by main and subsamples in %.264
Most students assessed speed, with which police would arrive at the scene of burglary, as
“middle” in almost in all regions.
The next questions were about police attitudes towards young people in general.
Figure 8.3 “Would you say that police generally treat young people with respect?”by
main and subsamples in %.265
Overall, nearly half of respondents reported that police treat young people “often” or
“always” with respect. More than10 per cent say that respect for young people can (almost)
never be expected. There is some variation across the country, critical attitudes being the most
frequently expressed by students from Latin cantons including Geneva.
264 Weighted data
265 Weighted data
15.7 15.0 17.9 14.2 17.5 13.3 13.0 15.4
59.7 61.8 56.6
47.7 57.7 58.3
53.6 62.8
24.5 23.2 25.5
38.1
24.7 28.3 33.3
21.8
0.010.020.030.040.050.060.070.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
slow middle fast
12.5 10.7
16.4 13.9 12.3
7.6
17.3
10.4
30.7 26.8
40.1
29.4
20.3
27.3
46.2
26.2
36.5 37.7 35.3
26.9
32.6 30.3 30.1
47.0
12.9 14.9
8.1 12.9
18.8 16.7
6.4 9.8 7.5
9.9
0.2
16.9 16.1 18.2
6.7
0.0
10.0
20.0
30.0
40.0
50.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
(almost) never sometimes often (almost) always don't know
210
Figure 8.4 How often police explains their decisions and actions to young peopleby main
and subsamples in %.266
According to about half of our respondents, police explain their decisions and actions often or
always. Again, attitudes are more critical in this respect in Latin cantons and in Geneva.
Given the urban background of Zurich, it is remarkable that the Zurich police is rated
relatively favourably across these different dimensions.
Figure 8.5 “How you think about your duty towards the police: To what extent is it your
duty to do what the police tell you, even if you don’t understand or agree with
the reasons?” by main and subsamples in %.267
266 Weighted data
267 Weighted data
13.0 10.6
18.5 14.9
9.9 15.2
19.9
9.8
28.8 24.7
38.2
29.4
19.0
25.8
35.9
28.7 34.5 35.6 33.3
27.9 34.0
24.2
33.3 37.8
14.1 16.4
9.9 7.0
15.3 15.2 10.9
15.9
9.5 12.7
0.2
20.9 21.9 19.7
7.9
0.0
10.0
20.0
30.0
40.0
50.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
(almost) never sometimes often (almost) always don't know
13.8 13.9 13.4 16.2 18.0
15.4 16.6 12.3
33.6 32.7 36.4
29.3 30.0 33.8
31.1
38.7 36.5 31.3
49.5
30.8 29.4 27.7
52.3
30.1
16.1 22.1
0.7
23.7 22.6 23.1 19.0
0.0
10.0
20.0
30.0
40.0
50.0
60.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
my duty neither/nor not my duty I do not know
211
Roughly two in five respondents accept as their duty to do what the police tell them to do.
Interestingly, given the more critical attitudes, in Latin cantons, this proportion is highest in
French-speaking cantons including Geneva.
Figure 8.6 “Police has the same sense of right/wrong as I do” (Agree/disagree) by main
and subsamples in %.268
Overall, about half of respondents feel that the police share the same sense of right and wrong
as they do. Agreement is lower among students in French-speaking cantons and in Geneva.
Again, the contrast between Geneva and Zurich/St. Gallen is remarkable.
Figure 8.7 “Police are appreciative of how young people think” (agree/disagree) by main
and subsamples in %.269
268 Weighted data
269 Weighted data
9.0 8.5 9.8 11.3 6.7
12.5 10.5
1.9
10.9 9.6 13.9
11.3 6.9 7.8
14.5 10.6
30.3 26.6
38.6 33.3
27.0 21.9
38.8
30.0
36.9 40.5
29.6 31.8
42.6 37.5
21.7
41.3
12.8 14.9
8.1 12.3
16.8 20.3
14.5 16.3
0.0
10.0
20.0
30.0
40.0
50.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
disagree strongly disagree neither/nor agree agree strongly
12.5 12.7 12.0 12.7 10.5
14.3 11.8
7.6
19.7 18.9 20.8
24.3
7.3
17.5 16.4
22.3
33.1 31.4
37.0 33.3 32.7
28.6
36.8 35.0
26.3 27.4 24.4
22.2
33.1 33.3
20.4
25.5
8.4 9.7 5.8 7.4
16.4
6.3
14.5
9.6
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
disagree strongly disagree neither/nor agree agree strongly
212
Opinions on whether or not the police are appreciative of how young people think do not
differ much across regions.
Figure 8.8 “I support how police act” (agree/disagree) by main and subsamples in %.270
Overall, about half of respondents support how the police act. Support is lower in Latin
cantons and in Geneva, but explicit disagreement does not vary much across regions.
Figure 8.9 How often police takes bribes by main and subsamples in %.271
270 Weighted data
271 Weighted data
8.3 8.5 7.1
12.7
4.8
11.5 9.2
5.0
10.4 9.1 12.4
15.9
5.0
9.8 12.5 12.6
30.0
25.6
39.7
32.8 30.8
27.9
33.6
26.4
39.4 43.1
32.3 29.6
41.0 39.3
29.6
44.0
12.0 13.7
8.5 9.0
18.4
11.5 15.1
11.9
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
45.0
50.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
disagree strongly disagree neither/nor agree agree strongly
46.4
53.5
32.8 29.1
53.2
40.0 34.2
52.1
29.4
21.5
47.9
30.7 24.8
21.5
46.7
24.5
12.7 9.9
19.0 15.1
8.5 12.3
19.1
10.4 11.4 15.2
0.3
25.1
13.5
26.2
12.9
0.0
10.0
20.0
30.0
40.0
50.0
60.0
Switzerland(N=4'158)
DE(N=2'560)
FR(N=956)
ITA(N=642)
SG(N=625)
AG(N=555)
GE(N=268)
ZH(N=266)
never/almost never neither/nor often/very often I do not know
213
Nearly half of students say the police never take bribes. There is, however, considerable
variation across the country, with more respondents suspecting the police to take bribes in
Latin cantons.
8.2.2 Opinion of young people about the police by gender
Boys and girls do not differ much regarding perceived egalitarian vs. racist attitudes, as the
following Table 8.1 illustrates. None of the differences is significant.
Table 8.1 “When victim reports to police, police treats all groups equally” by gender in
%, N=1296.272
No, everybody is
treated equally*
I do not
know*
No, police is biased
against africans,
arabs, yugoslavians,
moslems*
Other groups of
people: foreigners,
youths, people from
other religions and so
on*
Male 39.0 35.6 12.5 12.9
Female 39.1 41.6 8.7 10.6
Chi-
Squaire .029
*These groups were obtained by recoding of the opened answer of the question 10.1 “When victims report crimes to the
police, do you think the police treat people of different races, different ethnic groups, or of foreign origin equally?”
However, as the following Figures illustrate, boys tend to be far more critical about the police
than girls.
Figure 8.10 “If burglary was committed, how quickly police would arrive?” by gender in
%, N=1233.273
272 Weighted data
273 Weighted data
12.6
64.0
23.4 19.1
55.2
25.7
0.0
20.0
40.0
60.0
80.0
slow middle fast
female
male
214
Figure 8.11 “Would you say the police generally treat young people with respect?” by
gender in %, N=1357.274
Figure 8.12 “How often the police explain their decisions and actions to young people?”
by gender in %, N=1355.275
274 Weighted data
275 Weighted data
8.0
29.5
39.1
14.7 8.8
17.1
31.9 33.7
11.0 6.2
0.0
10.0
20.0
30.0
40.0
50.0
(almost) never sometimes often (almost) always don't know
female
male
9.4
29.8
35.5
14.0 11.4
17.0
27.7
33.6
14.1
7.6
0.0
10.0
20.0
30.0
40.0
(almost) never sometimes often (almost) always don't know
female
male
215
Figure 8.13 “How you think about your duty towards the police?” by gender in %,
N=1339.276
Males do twice as often not consider it to be their duty to do what the police tell them to do
than females.
Figure 8.14 “The police has the same sense of right/wrong as I do (agree/disagree)” by
gender in %, N=1324.277
Boys and girls do not differ much in their perception of police values, but boys tend to
express more extreme judgments (in either way).
276 Weighted data
277 Weighted data
9.8
29.8
42.2
18.2 18.1
37.6
30.5
13.8
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
45.0
my duty neither/nor not my duty I do not know
female
male
6.2 9.9
32.0
41.8
10.2 12.1 11.9
28.5 31.8
15.7
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
45.0
disagree strongly disagree neither/nor agree agree strongly
female
male
216
Figure 8.15 “I generally support how the police usually act (agree/disagree)” by gender in
%, N=1315.278
Boys and girls, once more, do not differ much overall, but boys’ judgments are more extreme.
The questions on police corruption did not significantly differentiate between girls and boys.
8.2.2 Opinion of young people about the police by respondents’ birthplace
Attitudes towards the police are often a source of worry when it comes to ethnicity. In many
countries, immigrant youths are said having particularly bad views about the police. In
Switzerland, this did not hold true for the general (adult) population, as several Swiss Crime
surveys have allowed to discover (Killias, Haymoz&Lamon 2007, 92). It has not been studied
in detail, however, to what extent this holds true also for juveniles.
In the present study, ethnicity has been measured by questions concerning birthplace (country
of origin) of respondents and their fathers/mothers, rather than by asking about nationality.
This was the only way the ISRD Steering Committee could think of that might work in some
30 countries around the globe. In connection with attitudes towards the police, respondents’
birthplace turned out to be more important than the country of origin of their parents. We
shall, therefore, focus on respondent’s birthplace in this section.
Overall, respondents born abroad tend to be more critical about the police. This might to some
extent be due to higher involvement in delinquency, as shown in Chapter 7. It will be most
interesting to see, when the results from other countries become available, whether attitudes
among immigrant youths in Switzerland differ from those expressed by juveniles in other
countries.
278 Weighted data
5.6 9.6
30.7
45.9
8.3 11.1 11.3
29.2 32.5
15.9
0.05.0
10.015.020.025.030.035.040.045.050.0
disagree strongly disagree neither/nor agree agree strongly
female
male
217
Figure 8.16 “When victims report crimes to the police, do you think the police treat
people of different races, different ethnic groups, or of foreign origin
equally?” by respondents’ birthplace in %, N=1296.279
Students born in Switzerland are more likely to report that police treats all people equally.
However, the differences are not that large – Swiss respondents more often indicate “not to
know”, due probably to lack of relevant experience.
Figure 8.17 “If a violent crime or a burglary happened near where you live and the police
were called, how quickly do you think they will arrive at the scene?” by
respondents’ birthplace in %, N=1231.280
Respondents born abroad more often say the police would come slowly if a violent crime or a
burglary happened.
279 Weighted data
280 Weighted data
35.6 37.5
15.4 11.5
39.7 39.0
9.6 11.8
0.0
10.0
20.0
30.0
40.0
50.0
No, everybody is treatedequally
I do not know Africans, Arabs,Yugoslavians, Moslems are
treated worse
Other groups offoreighners, other groupsof people (youths, otherreligion and so on) are
treated worsenot in Switzerland Switzerland
19.3
53.5
27.3
15.1
60.8
24.0
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
slow middle fast
not in Switzerland
Switzerland
218
Figure 8.18 “How often would you say the police explain their decisions and actions to
young people?” by respondents’ birthplace in %, N=1354.281
Respondents born in Switzerland are more likely to report that police often and very often
explain their decisions and actions to young people.
Figure 8.19 “How you think about your duty towards the police?” by respondents’
birthplace in %, N= 1337.282
Respondents born abroad are less likely to consider having the duty to do what the police tell
them to do.
281 Weighted data
282 Weighted data
17.8
36.5
24.7
14.2
6.8
12.2
27.3
36.3
14.1 10.1
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
(almost) never sometimes often (almost)always
don't know
not in Switzerland
Switzerland
18.8
33.3 33.8
14.1 12.9
33.7 36.9
16.5
0.0
10.0
20.0
30.0
40.0
my duty neither/nor not my duty I do not know
not in Switzerland
Switzerland
219
Figure 8.20 “I generally support how the police usually act” by respondents’ birthplace in
%, N=1313.283
Students born abroad are less likely to support how the police usually act.
In many of these comparisons, the differences are, although significant, not that strong. It is,
therefore, noteworthy that several attitude questions did not produce significant differences.
This has been the case for the following items:
- “Police generally have the same sense of right and wrong as I do” (p=.540)
- “The police are appreciative of how young people think” (p=.509)
- “Would you say the police generally treat young people with respect?” (p=.377)
Regarding police corruption, it is somewhat disturbing that quite a few respondents, both
from immigrant and Swiss background, think the police were taking bribes. Given
Switzerland’s scores in all international comparisons, this comes as a surprise and presumably
shows that preconceptions in this field may be more important than concrete experience.
Unfortunately and for reasons related to the sensitive aspect of this theme in many countries,
no question as to concrete experiences with paying bribes, or being requested to do so, could
be included in the questionnaire.
283 Weighted data
10.5 7.6
37.1 34.3
10.5 7.8
10.8
28.7
40.4
12.2
0.05.0
10.015.020.025.030.035.040.045.0
disagreestrongly
disagree neither/nor agree agree strongly
not in Switzerland
Switzerland
220
Figure 8.21 “Do you think the police take bribes, and if yes, often?” by respondents’
birthplace in %, N=1339.284
Respondents born in Switzerland are more likely to think that police never or almost never
take bribes. Students born abroad have more neutral position or answer that police take bribes
often or very often. That immigrant youths are more often inclined to suspect the police to be
corrupt may, to some extent, eventually be related to their own experience in their respective
countries of origin. As Crime Surveys conducted in Switzerland allowed to discover,
corruption is, for the Swiss general (adult) population, mostly an experience lived abroad
(Killias, Haymoz&Lamon 2007, 57).
284 Weighted data
34.6 32.7
15.9 16.8
48.8
28.7
12.1 10.4
0.0
10.0
20.0
30.0
40.0
50.0
60.0
Never/almostnever
neither/nor often/very often I do not know
not in Switzerland
Switzerland
221
8.2.3 Opinion of young people about the police by delinquency (last year prevalence)
Attitudes towards the police are associated with delinquency. Given the cross-sectional design
of ISRD, we cannot say, however, whether negative feelings about the police favor
delinquency, or whether, the other way around, delinquents have less positive attitudes
towards police officers. Both explanations are, at first glance, equally plausible.
Figure 8.22 Delinquency (last year prevalence) by “Would you say the police generally
treat young people with respect?” in %, N=1347-1355.285
Respondents, who report that police never or almost never treat young people with respect,
are more likely to commit an offence.
285 Weighted data
28.0 32.1 32.7
11.4
21.1
12.7 14.5 10.2
22.9
29.3 28.3
11.4
24.6
13.2 11.1 16.4
22.5
2.7
11.4
1.5 4.1
1.0
13.0 17.9
14.3
5.8
11.6
2.6 6.5
8.7 11.7
0.8
8.3
0.6 1.0 1.0
7.3 6.9 4.9 2.2
6.1 3.0
6.9 2.9
9.1
1.1 5.7
1.7 2.3 2.3 4.6
8.6 6.9 2.9 1.7 2.3
4.9 1.0
4.0 0.0
5.9 1.0 0.0 1.0 2.9 4.0 5.9
2.0 3.9 2.0
0.05.0
10.015.020.025.030.035.0
(almost) never sometimes often (almost) always don't know
222
Figure 8.23 Delinquency (last year prevalence) by how often police explains their
decisions and actions to young people in %, N=1347-1353.286
Students, who say that police never or almost never explain their decisions and actions to
young people, are more likely to commit any type of offence. The same pattern is visible
throughout the following Figures.
286 Weighted data
25.0 28.6
26.7
12.6
19.5
10.3 12.1 9.1
21.1
32.8
24.7
12.1
28.2
11.4 11.3
16.4 19.5
1.5
10.8
1.5 2.1 1.0
11.3 13.2 12.1
3.1
10.0
2.6
7.7 9.8
15.8
1.1
9.0
0.9 2.1 1.5
7.9 8.8 7.3 3.6
5.8 2.6
4.8 4.2
11.6
1.1
7.9
2.1 4.2
1.6
9.4 11.0 10.6
4.7 3.2 4.7 6.2 2.3
5.4 1.5
6.2 2.3 2.3 0.8
3.1 3.9 3.9 1.6 3.1 1.6
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
(almost) never sometimes often (almost) always don't know
223
Figure 8.24 Delinquency (last year prevalence) by “When victim reports to police, police
treats all groups equally” in %, N=1286-1292 287
Students, who report that police treat Africans, Arabs, citizens of former Yugoslavia and
Muslims less favourably, commit more offences. For all offences, those who perceive no
discriminatory attitude on the side of the police have the lowest offending rates.
The interesting question is whether this effect is different for Swiss and for immigrant
respondents. Given that only 9th-grade students answered to these questions and that the
national sample is smaller in this chapter, we use in the following two Figures mother’s
background as a proxy for nationality. Indeed, more mothers (344) were born abroad than
fathers (325) and respondents (134).
287 Weighted data
8.6 6.5 12.1
0.8
8.2
1.2 1.8 1.4 7.4 6.8 7.2
3.0 4.2 3.4 7.6
11.0 14.2
1.6 7.4
1.6 3.8 1.6
8.8 11.0 8.0
3.4 6.0 3.2
16.2
24.3
34.6
10.4
19.4
3.7 5.2 3.7
19.1
27.9 22.1
8.9
19.9
7.4
15.1 21.1 20.4
2.6
14.5
5.3 4.6 3.3
13.1
19.9 15.8
6.6
21.6
5.9
0.0
10.0
20.0
30.0
40.0
No, everybody is treated equallyI do not knowAfricans, Arabs, Yugoslavians, MoslemsOther groups of foreighners, other groups of people (youths, other religion and so on)
224
Figure 8.25 Delinquency (last year prevalence) by “When victim reports to police, police
treat all groups equally” if mothers were born in Switzerland in %, N=732-
737.288
288 Weighted data
5.4 7.8
11.4
0.3
7.5
0.3 2.0 1.0 5.1 7.2 6.5
2.4 4.0 3.0 7.6 9.6
13.7
2.1 6.2
1.0 3.4 2.1
7.9 11.7
6.9 2.4
6.6 2.8
10.8
23.1
38.5
6.2
13.8
0.0 3.1
0.0
18.5
27.7
21.5
6.2
15.4
4.6 9.4
20.0 15.3
1.2 5.9
1.2 1.2 1.2
10.6
16.5 11.8
7.1
22.4
3.6
0.05.0
10.015.020.025.030.035.040.045.0
No, everybody is treated equally
I do not know
Africans, Arabs, Yugoslavians, Moslems
Other groups of foreighners, other groups of people (youths, other religion and so on)
225
Figure 8.26 Delinquency (last year prevalence) by “When victim reports to police, police
treats all groups equally” if mothers were born abroad in %, N=540-544.289
The pattern is basically the same for respondents born in Switzerland and born abroad.
However, seeing the police as biased against immigrants is less clearly associated with
delinquency among respondents with a foreign-born mother. It could be that seeing the police
as biased against certain ethnic groups is more related to an immigrant background as such
rather than to delinquency, as it typically is among Swiss respondents.
289 Weighted data
12.9
4.9
13.2
1.5
9.4
1.5 2.0 1.5
10.9
5.9 7.9
3.5 4.4 3.9 7.3
12.1 14.1
0.0
8.3
1.5 3.4
1.0
8.7 9.6 8.7
3.8 4.4 2.9
20.3 23.2
29.0
14.7
25.0
7.4 8.8 7.2
17.4
27.5
21.7
11.6
24.6
8.8
23.1 23.1 27.7
4.6
25.8
10.8 9.2 7.7
15.4
24.6 21.5
7.6
21.5
7.7
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
No, everybody is treated equallyI do not knowAfricans, Arabs, Yugoslavians, MoslemsOther groups of foreighners, other groups of people (youths, other religion and so on)
226
Figure 8.27 Delinquency (last year prevalence) by “Duty to do what police tells you to do
even if you do not understand or agree” in %, N=1331-1337.290
A more critical attitude towards the police goes along, once more, with higher rates of
delinquency. The same is true for the following Figures.
Figure 8.28 Delinquency (last year prevalence) by “Police has the same sense of
right/wrong as I do (agree/disagree)” in %, N=1315-1323. 291
290 Weighted data
291 Weighted data
22.5
30.9 28.6
12.7
23.3
10.6 11.6 7.7
19.2
27.1 27.8
13.1
25.4
12.0 8.9
13.6
19.1
0.9
8.2
1.1 2.7 1.3
11.6 12.2 10.7
3.8
9.3
2.0
9.4 8.0
13.1
0.6
6.0 1.6 2.3 1.8
7.8 10.9
6.6 2.7
4.7 3.3 5.1 4.7 9.3
2.3
13.9
0.9 1.9 0.0
5.6 7.0 7.4
2.3 5.6
2.8
0.05.0
10.015.020.025.030.035.0
my duty neither/nor not my duty I do not know
27.7
37.8
31.1
11.8
28.6
11.8 10.9 5.9
26.1 29.9
34.5
15.1
29.4
12.6 16.1 16.2
23.9
1.4
11.9
2.1 0.0 2.1
11.8
22.9
12.0
2.1
11.9
1.4
9.2 10.7
19.2
1.5
11.2
0.5 4.0
0.7
12.5 12.3 9.0
3.5 8.5
2.2 6.1 7.4
9.8
1.0 4.1
0.8 1.6 1.4 5.6 6.8 6.6
2.3 4.7 2.7
8.9 10.1 12.4
4.1
11.8
5.4 5.3 5.3 5.9
12.4 9.4
6.5 7.1 7.0
0.05.0
10.015.020.025.030.035.040.0
disagree strongly disagree neither/nor agree agree strongly
227
Figure 8.29 Delinquency (last year prevalence) by “Police are appreciative of how young
people think (agree/disagree)” in %, N=1305-1312.292
Figure 8.30 Delinquency (last year) by “I support how police act (agree/disagree)” in %,
N=1306-1314.293
Delinquency rates are also higher among respondents who see the police as corrupt. Indeed,
perceived delinquency among those who are in charge of maintaining public order certainly is
directly threatening the legitimacy of such an institution.
292 Weighted data
293 Weighted data
24.5 29.6 30.1
8.6
26.4
9.8 8.6 4.3
18.8
30.1 27.8
11.0
25.9
8.5 10.1
17.8 18.9
1.6
9.7
1.5 1.9 1.2
13.2 12.8 13.5
3.5
12.0
3.9 7.1 8.3
13.6
1.2
6.9
0.5 3.2 1.4
8.1 10.0 6.0
2.5 4.8
1.4
9.0 7.5
13.3
1.8
7.3
1.5 2.6 1.5
8.2 11.1
8.2 3.8 5.8
3.2
9.2 9.2
15.5
5.5
11.8
6.4 5.5 7.3 9.1 9.1 9.1 6.3 6.3
10.8
0.05.0
10.015.020.025.030.035.0
disagree strongly disagree neither/nor agree agree strongly
27.5
37.6 33.0
12.8
31.2
15.7 13.9 8.3
25.7
35.8 38.9
12.8
33.0
13.8 19.7 17.5
25.0
3.7
16.9
2.2 3.6 2.2
11.0
18.4 19.7
6.6
15.4
4.4 10.9
14.0 20.0
1.3
9.4
0.8 2.5 1.3
11.7 14.0 7.6
2.0
10.4
1.3 4.9 6.2
10.0
1.0 5.3
0.6 1.9 0.8 6.0 7.0 6.0 3.7 2.7 2.7
8.4 9.2 11.7
3.9 9.7
5.2 4.5 5.7 10.2 10.8 9.7
5.7 5.2 8.9
0.05.0
10.015.020.025.030.035.040.045.0
disagree strongly disagree neither/nor agree agree strongly
228
Figure 8.31 Delinquency by perceived police corruption (“Do you think the police take
bribes, and if yes, how often?”) in %, 1332-1339.294
Contrary to misbehavior among police officers, the feeling that their efficiency as an
organization is less than perfect is less clearly associated with delinquency, as the following
Figure illustrates.
294Weighteddata
6.3 9.5
13.3
1.8
10.6
1.5 2.9 1.6
8.9 11.3
8.9
2.3
6.8 3.7
12.4 14.0
18.5
2.0
7.6
1.5 2.8 1.0
10.7 12.8
10.2
6.1
10.7
1.8
20.7 23.7
27.2
8.9
14.3
8.9 8.3 6.5
14.6
18.8 22.0
8.2
13.1
7.6 11.2
8.5
13.7
0.0
12.5
2.6 3.3 3.3
10.5 13.6
9.2
4.6
11.1
6.5
0.0
5.0
10.0
15.0
20.0
25.0
30.0
never/almost never neither/nor often/very often I do not know
229
Figure 8.32 Delinquency (last year prevalence) by “If burglary was committed, how
quickly police would arrive?” in %, N=1225-1234.295
Students, who say that police would arrive slowly to a crime scene are more likely to commit
offences of all sorts. However, the differences are somewhat less pronounced than in the
preceding Figures. This could be related to the fact that lack of efficacy is less considered as a
kind of a “moral” defect.
295Weighteddata
10.8 14.0
19.6
2.1
13.5
2.6 5.7
2.6
12.0 13.6 13.6
4.7
13.0
3.1
8.3
12.2 15.5
1.4
8.4
1.2 2.0 1.5
9.6 11.7
8.8
3.8
7.9
3.1
11.0 8.3
13.9
2.3
9.3
2.7 3.7 3.0
9.0 10.7 12.0
3.6
7.6 5.0
0.0
5.0
10.0
15.0
20.0
25.0
slow middle fast
230
8.3 Conclusion
Attitudes towards the police are relatively positive overall. However, there are a few
remarkable regional differences. In the Romandie cantons and especially in Geneva, attitudes
are more critical towards the police than in German-speaking Switzerland. This may, to some
extent, be due to the higher proportion of immigrants in French-speaking cantons where more
than 50 per cent of parents of respondents were actually born abroad (see Chapter 1).
Noteworthy is the consistently positive view of the police in Zurich, given the highly
urbanized environment. Girls see the police more favorably than boys.
Students born in Switzerland have more positive views about the police.
Positive attitude towards the police goes along with lower rates of delinquency. It is not clear
whether delinquency is an outcome of negative feelings about the police, or whether
delinquent involvement and, therefore, probably more contacts with the police as a suspect
produce negative feelings.
References:
Killias M., Haymoz S., Lamon P. Swiss Crime Survey. Die Kriminalität in der Schweiz im
Lichte der Opferbefragung von 1984 bis 2005. Bern: Stämpfli Verlag AG, 2007. 179p.
231
Chapter 9
Technical Report
9.1 Background
The first juvenile self-reported delinquency survey conducted in Switzerland with a national
sample took place in 1992, when the country participated in the first International Self-Reported
juvenile Delinquency project (ISRD-1). The second juvenile self-reported delinquency survey
took place in 2006, where more than 3’500 male and female juveniles attending the 7th – 9th
grades in 20 cantons participated. Since then no national surveys were conducted, but some
cantonal or city surveys were carried out (e.g. in the cantons of Zürich, Vaud and St. Gallen). In
recent years most of the official measures of crime (police and court statistics) suggest that
juvenile delinquency has a tendency of decreasing that contradicts with our results (comparison
of the prevalence of the selected offending in 2006 (ISRD-2) and 2013 (ISRD-3). Thus, the
current survey is extremely important in order to assess trends in juvenile delinquency at a
national level.
9.2 Sample design
The Swiss ISRD-3 involves a national random sample of 2’854 male and female juveniles
attending the 7th – 9th grades, which in the Swiss context corresponds to pupils aged mostly 12-
16. This sampling procedure was preferred over the city-sampling procedure used in most of
the participating countries because Switzerland is a small country with approximately 8 million
inhabitants296 and does not have any large city but only medium ones (the largest city, Zürich,
has a population of 400’000 inhabitants297). The sampling was drawn out of a list given by the
Swiss Federal Statistical Office containing all school facilities from the 7th
to the 9th
grades
existing in each Swiss canton. This list also gave information about the number of students per
grade, but did not include information about number of classes per grade and type of school298. It
did not allow us to make a sample of classes at the very beginning, although the sample unit was
class.
The sampling occurred in four steps described below in the following sections.
296 Bevölkerungsstand und struktur.
Schweiz.http://www.bfs.admin.ch/bfs/portal/de/index/themen/01/02/blank/key/alter/gesamt.html 297
Statistik Stadt Zürich https://www.stadt-zuerich.ch/prd/de/index/statistik/bevoelkerung/bevoelkerungsstand.html
298The types of schools in accordance with: (1) pattern of property (Private/government); (2) pupils' capacities and
career-intentions (vary in different cantons).
232
9.2.1 Step 1: Selection of schools
For the national sample, 3’000 interviews were envisaged. Based on observations made during
ISRD-2 in 2006, we presumed that the number of students would be about 19 on average per
class. Therefore, it was planned to interview about 160 classes (3‘000 / 19 ~160). Anticipating
that about 70 percent of schools would cooperate, we decided to draw a sample of 219 classes.
The Swiss Federal Office of Statistics provided us with a list of schools that was used for the
national PISA tests. Out of these, 219 schools were randomly selected. Both private and
government schools where taken. Schools for children with special needs and small schools that
included only few students per grade were excluded because we could not guarantee the
confidentiality of responses in such settings.
9.2.2. Step 2: Collecting information about number of classes in the selected schools
All cantonal Departments of Education were contacted to obtain a complete list of classes per
selected school. Information was also obtained from School Internet sites and some school
principals. Thus, we gained a list of 2’458 classes nationwide299.
9.2.3. Step 3: Random selection of classes
Out of this list of 2’485 classes, 219 classes were randomly selected. In larger schools, two or
more classes were included due to this procedure. These 219 classes belonged to 127 schools. At
this stage, four small cantons (UR, NW, SH, JU) dropped out because none of their classes was
included in the national sample. The list of classes is given in Attachment 3.
9.2.4. Step 4: Additional sample
In the three cantons that wished to receive in-depth analysis, additional classes were randomly
selected to receive final (cantonal) samples of 550 students at least. The oversamples are as
follows:
- in the canton of Aargau, 17 classes from 11 schools,
- in the canton of St. Gallen, 26 classes from 12 schools,
- in the canton of Ticino, 22 classes from 14 schools.
9.3 The fieldwork
The fieldwork started by the end of February and ended by the end of November 2013. It was
carried out by our research group initially located at the Universtiy of Zurich (till June 2013) and
later at the University of St. Gallen (from June 2013 till now).
299decision was made 20.09.2012.
233
9.3.1. Contacting schools
We contacted the Departments of Education in all cantons (see Attachment 9) with the support of
the Conference of Cantonal Directors of Education (Konferenz der Kantonalen
Erziehungsdirektoren EDK), see Attachment 8.
In some cantons we also contacted the schools directly by sending official letters with (1) copies
of the letters from the Department of Education supporting the project; (2) letter from the
Institute of Criminology of Zürich University asking for their participation with 3 attachments
(Instructions for teachers of survey making, Research plan, Informational letter for parents) (see
Attachment 5). Our research group made the necessary support by telephone and email,
communicated with school principals and single teachers.
9.3.2. Data collection
The cooperation of schools in the main sample was about as expected. In the end, 170 out of 219
classes have participated, which leaves a class-based response rate of 77.6% percent. The
number of schools that participated is 98 out of 127 (response rate is 77.2%). The response rate
at the level of schools and classes is lower in 2013 compared to 2006 when it was 94.5%. The
higher drop-out rate can be explained by increasing reservations among school principals about
the involvement of schools in research projects.
In case of declining participation schools were replaced (the table is below) if time constraints
allowed to do so. The list of replacements is given in Table 9.1. Schools could not be replaced if
we were informed about their refusal after May 2013.
234
Table 9.1 List of substituted schools
Date Originally sampled school Substituted school Canton
26.02.2013 Schule Liestal BL Rudolf Steiner Schule Birseck BL
26.02.2013 Real, Sekundar, Berufswahlschule,
Kleinklasse der Orientierungsschule,
Neuendorf
Schulanlage, Boswil AG
26.02.2013 Kollegium Spiritus, Brig-Gris, VS Orientierungsschule, Gampel VS
28.02.2013 Bern Munzinger Ecolesecondaire, Courtelary BE
28.02.2013 Grenchen Schulhaus III Oberstufenhaus, Niedergösgen SO
09.04.2013 Oberstufenschule Aeschi-Krattigen, Täuffelen, Oberstufenzentrum BE
No school refused in the cantons of AI, LU, SG, GL, OW, BL, GR, NE, TI, AR; 100% of the
selected classes were surveyed. In the cantons of AG, FR, VD, BE, TG, SO, ZH and GE, more
than ½ of sampled classes finally participated. The lowest response rate was in VS, ZG und SZ,
where less than one third from the estimated classes participated. The most prominent reasons of
declining participation were mostly (1) a packed schedule at schools, or (2) participation in other
“similar” surveys. In case of declining participation schools were replaced (the table is below) if
time constraints allowed to do so. The canton of Basel-Stadt declined categorically taking part in
the study.
In order to calculate the response rate at the level of students, the Teachers’ Feedback Form has
been used. We have received this form from 80.0% percent of the classes included in the
national sample (i.e. leaving out cantons with oversamples), with a total of 2’386 enrolled
students, compared with 2’854 completed interviews. In all classes with this additional
information, 2’230 students were present during data collection, 160 were absent for reasons
unrelated to the survey, and 21 were present but did not respond. This leaves 2’209 completed
interviews, or a student response rate of 92.1 percent (i.e. 2’209 / 2’386).
For the national sample (i.e. without the oversamples in the cantons of SG, AG and TI), 2’854
interviews have been completed, of which 1’702 are in German (59.6%), 956 in French (33.5%)
and 196 in Italian (6.9%, not weighted data).
Including the oversamples in three cantons, 4’158 interviews have been completed.
235
9.3.3. Interviews and teachers
The survey took place in the computer rooms of the schools. To open the questionnaire each
respondent had to follow the link of the survey and enter the password as indicated in the
instructions that were sent to teachers. There were 10 links for students, on the Internet page300
:
- For German speaking schools for the 7th
-8th
grades;
- For German speaking schools for the 9th
grade;
- For French speaking schools for the 7th
-8th
grades;
- For French speaking schools for the 9th
grade;
- For Italian speaking schools for the 7th
-8th
grades (this link was deleted after completing
the study in TI to avoid confusions with the additional sample);
- For Italian speaking schools for the 9th
grade (this link was deleted after completing the
study in TI to avoid confusions with the additional sample);
- For the additional sample. For German speaking schools for the 7th
-8th
grades;
- For the additional sample. For German speaking schools for the 9th
grade;
- For the additional sample. For Italian speaking schools for the 7th
-8th
grades;
- For the additional sample. For Italian speaking schools for the 9th
grade.
Pupils had to use their computer mouse and keyboards to fill in the questionnaire. Although most
of questionnaires were filled in completely, some students did not finish for technical,
organizational or personal reasons. Nevertheless all responses reached the server automatically.
There were also links for Teachers’ Feedback Forms with the password, as indicated in the
instructions. Information from these Feedbacks allowed observing the process of the survey and
seeing the possible technical and organizational problems.
9.4. Questionnaire content and development
Instead of the paper-pencil questionnaire used in many countries participating in the ISRD3,
Switzerland used a computer-based online method through the Internet. In accordance with
experience of ISRD-2 in Switzerland (2006), there are no significant differences (Lucia,
Herrmann, Killias, 2007301
) and the computer version reduces the risk of typing errors while
entering the data collected with a paper-pencil questionnaire.
In contrast to ISRD-2, the survey took place without external supervisors or research assistants:
the study at schools was controlled by school teachers. The comparative test showed that using
300http://www.rwi.uzh.ch/lehreforschung/alphabetisch/killias.html
301Lucia, S., Herrmann, L. & Killias, M. (2007).How important are interview methods and questionnaire designs in
research on self-reported juvenile delinquency? An experimental comparison of Internet vs paper-and-pencil
questionnaires and different definitions of the reference period. J. Exp. Criminol. (3), 39-64.
236
online questionnaire with teachers as supervisors may not affect validity while making surveys
less expensive and intrusive (Walser, Killias, 2012302
).
Teachers filled out the online questionnaire-feedbacks, affording information about the course of
the survey: data and duration of the survey, number of present and absent students in class,
problems during the survey and so on. The full text of the Teachers’ Feedback is in Attachment
6.
The translations of the standardised English questionnaire into French, German and Italian were
made by our research group (text of the questionnaires in German, French and Italian are in the
Attachments 7.1-7.3.
The questionnaire in Switzerland includes 13 modules: 11 are from the core questionnaire and 2
are additional modules (“Dating control module” and “Computer games module”). The core
questionnaire is identical to the standardized one used by the rest of the countries, except the
country specific questions, needed to reflect the national situation. Among them are: Qs.1.3-1.5
to detect the birthplace of respondent and his/her parents; Q.1.8 to see the distribution of
religions appropriate for Switzerland; Q.1.10 to identify the minority group membership; Q. 1.13
to determine the source of family income; Q.5.5 to gain information on the composition of the
groups of friends with respect to migration status. To identify the class, grade, school, school
type, town and the canton of the respondent, the special questions were added with the title
“Before the beginning”.
Students of the 9th
grade were asked all questions of the questionnaire. Students of the 7th
-8th
grade did not answer the questions in module 10 regarding attitudes to the police.
9.5. Computer and Internet
During the survey, minor difficulties had to be faced due to the use of the computer
questionnaire: mostly they were related to connection problems but could be settled. One class in
TI could not complete the questionnaire; they were asked to try again, but they could not finish
the survey again. These 19 answers were deleted during the data cleaning, because they did not
include essential information. In one class, according to a teacher’s report, some students
expressed concern that our research group might inform their parents about the results of the
survey. However, they continued answering questions when teachers explained the principle of
anonymity.
302 Walser, S., Killias, M. (2012) Who should supervise students during self-report interviews? A controlled
experiment on response behavior in online questionnaires. J. Exp. Criminol. (8/1), 17-28.
237
9.6. Data processing and output
To collect the data, the online program Unipark was used. The time needed to fill out the
questionnaire did usually not exceed one lesson. Data from completed questionnaires were
entered automatically into the database, which could be then exported to SPSS Version 21.0 for
the analyses.
9.7. Data weighting
To take the size of cantons and the school grades into account, the data was weighted at the
national level for canton and school grade. Within the oversampled cantons the data was
weighted by the school grades only. Distribution of respondents by grade in the final unweighted
dataset (N=4’158) is as follows: 7th grade: 30.0%, 8th grade: 35.1%, and 9th grade: 34.9%. After
the data weighting such distribution is: 7th grade: 34.7%, 8th grade: 31.7%, and 9th grade:
33.6%. The slight imbalance in the general population is due to demographic changes within the
age-brackets included in this study303
.
No weighting for age, gender and other demographic characteristics turned out to be necessary,
given the satisfactory match between the distributions in the sample and in the general juvenile
population.
All analyses were conducted with weighted data. The following table includes the sample size
per canton before and after weighting. In each Table, the N (absolute number) of respondents on
which the (valid) percentages are based is indicated. For practical reasons, the range of the
absolute numbers (in the national sample) is indicated, rather than the N for each category shown
in the Figure separately.
303http://www.bfs.admin.ch/bfs/portal/de/index/themen/01/02/blank/key/alter/gesamt.html
238
Table 9.2 Obtained data before and after weighting by canton and grade.
General number of
students
participated in the
survey
Distribution of the obtained data
by main and additional samples Weighted data
on the
national level
Percentage of
the weighted
data on the
national level
Main sample
Additional
sample
Aargau * 555 219 336 444 10.7
Appenzell
Innerrhoden 54 54 0 7 0.2
Appenzell
Ausserrhoden 15 15 0 10 0.2
Bern 233 233 0 482 11.6
Basel-
Landschaft 99 99 0 182 4.4
Fribourg 309 309 0 180 4.3
Geneva 268 268 0 231 5.6
Glarus 79 79 0 21 0.5
Graubünden 62 62 0 66 1.6
Lucerne 183 183 0 225 5.4
Neuchâtel 101 101 0 95 2.3
Obwalden 61 61 0 7 0.2
St. Gallen * 625 103 522 268 6.4
Solothurn 120 120 0 130 3.1
Schwyz 15 15 0 27 0.6
Thurgau 93 93 0 143 3.4
Ticino * 642 196 446 219 5.3
Vaud 297 297 0 634 15.3
Valais 68 68 0 119 2.9
Zug 13 13 0 19 0.5
Zurich 266 266 0 647 15.6
Total 4158 2854 1304 4158 100.0
* the cantons that were oversampled
239
1. Attachement 1 304
.
Population in Switzerland
General population per canton (in %)
Population of secondary school
students per canton (in %,
2010/2011 academic year)
Aargau 7.8 8.1
Appenzell A. 0.7 0.8
Appenzell I. 0.2 0.3
Basel-Land 3.5 3.3
Basel-Stadt 2.4 1.9
Bern 12.4 11.7
Fribourg 3.5 4.1
Geneva 5.8 6.2
Glarus 0.5 0.5
Graubünden 2.4 2.3
Jura 0.9 1.0
Lucerne 4.8 5.3
Neuchâtel 2.2 2.3
Nidwalden 0.5 0.5
Obwalden 0.5 0.5
Schaffhausen 1.0 1.0
Schwyz 1.9 2.0
Solothurn 3.2 3.2
St. Gallen 6.1 6.6
Thurgau 3.2 3.8
Ticino 4.2 3.4
Uri 0.4 0.5
Valais 4.0 9.4
Vaud 9.1 4.0
Zug 1.4 1.4
Zurich 17.4 15.9
304ObligatorischeSchule.
Schweiz.http://www.bfs.admin.ch/bfs/portal/de/index/themen/15/03/key/blank/obligatorische_r/schuelerinnen_und.
html
240
241
2. Attachment 2
List of cantons that participate in the survey
Frequency (number
of classes) %
AG 16 7.4
AI 3 1.4
AR 1 0.5
BE 23 10.6
BL 6 2.8
BS 5 0.9
FR 18 8.3
GE 25 11.6
GL 5 2.3
GR 4 1.9
LU 10 4.6
NE 5 2.3
OW 2 0.9
SG 6 2.8
SO 12 5.6
SZ 3 1.4
TG 9 4.2
TI 10 4.6
VD 22 10.2
VS 14 6.5
ZG 4 1.9
ZH 16 7.4
Total 219 100
Cantons that do not participate in the survey: JU, NW, SH, UR
242
3. Attachment 3
Schools and classes. National sample
N
Kanton Town
(Gemeindename)
School name (Bildungsinstitution) Grade Clas
s
Number
of classes
in the
concrete
school
1
AG Aarau Oberstufenschulhaus Schachen,
Aarau
7 A 1
2 AG Aarburg Oberstufe Paradiesli, Aarburg 9 A 1
3
AG Baden Bezirksschule Burghalde,
Baden
7 G 1
4
AG Dottikon Oberstufenschulhaus Risi,
Dottikon
8 B 1
5 AG Fahrwangen Schulanlage, Fahrwangen 7 B 1
6 AG Möhlin Oberstufe, Möhlin 8 D 2
7 8 C
8
AG Neuenhof Real, Sekundar,
Berufswahlschule, Kleinklasse
der Orientierungsschule,
Neuenhof
8 F 2
9 9 F
10
AG Oberentfelden Oberstufenschulhaus,
Oberentfelden
7 B 2
11 8 B
12 AG Othmarsingen Schulanlage, Othmarsingen 9 B 1
13
AG Wohlen (AG) Oberstufe Bünzmatt 2, Wohlen
(AG)
7 A 4
14 8 D
15 9 B
16 9 E
17
AI Appenzell Sekundarschulhaus Hofwies 1 +
2, Appenzell
8 B 3
18 8 E
19 9 C
20 AR Speicher Schulhaus Zentral, Speicher 7 A 1
21 BE Aarberg Real-/Sekundarschule Aarberg 9 A 1
22 BE Aarwangen Volksschule Aarwangen 7 A 5
23 8 G
24 8 C
25 9 G
26 9 B
27
BE Aeschi bei
Spiez
Oberstufenschule Aeschi bei
Spiez
8 B 1
28 BE Belp Volksschule Belp Oberstufe 8 E 1
243
29 BE Bern Neue Mittelschule, Bern 7 A 2
30 BE 9 A
31 BE Bern Bern Munzinger 7 C 1
32
BE Biel/Bienne Ecole secondaire Châtelet,
Biel/Bienne
7 B 2
33 9 C
34
BE Biglen Sekundar- und Realschule
Biglen
8 B 1
35 BE Köniz Schule Niederwangen 9 B 1
36
BE Halse bei
Burgdorf,
Rüegsau
Sekundarstufe I Rüegsau.
Gemeindeverband
Sekundarschule - Hasle-
Rüegsau, Hasle bei Burgdorf
7 A 1
37 BE Sumiswald Schulen Sumiswald-Wasen 8 A 4
38 8 C
39 8 E
40 9 C
41 BE Tavannes Ecolesecondaire, Tavannes 8 A 2
42 9 B
43 Wattenwil OSZ Wattenwil 7 A 1
44
BL Liestal Sekundarschulhaus Burg,
Liestal
7 B 1
45
BL Münchenstein Sekundarschulhaus Lärchen,
Münchenstein
7 C 3
46 8 F
47 9 E
48
BL Pratteln Sekundarschulhaus Erlimatt 1 +
2, Pratteln
8 C 1
49 BL Zwingen Sekundarschulhaus, Zwingen 9 D 1
50
BS Basel Orientierungsschule Isaak
Iselin, Basel
8 B 1
51
BS Basel Weiterbildungsschule Holbein,
Basel
8 A 1
52
BS Basel Gymnasium am Mümsterplatz,
Basel
7 B 1
53
BS Basel Orientierungsschule
Gundeldingen, Basel
8 B 1
54
BS Basel Gymnasium Kirschgarten,
Basel
7 F 1
55
FR Avry Ecole du CO de Sarine Ouest,
Avry
7 A 2
56 8 A
57
FR Bulle Ecole du cycle d'orientation de
la Gruyère, Bulle
7 F 3
244
58 8 J
59 9 J
60
FR Estavayer-le-La Cycle d'orientation de la Broye
- Route de la Chapelle 31,
Estavayer-le-Lac
7 E 3
61 7 F
62 9 D
63
FR Freiburg Cycle d'orientation du Belluard
- Derrière-les-Remparts 9,
Fribourg
7 K 3
64 8 D
65 8 K
66
FR Freiburg Cycle d'orientation de Pérolles -
Pérolles 68, Fribourg
7 C 2
67 9 A
68
FR Kerzers Orientierungsschule -
Schulhausstrasse 11, Kerzers
9 D 1
69
FR Murten Centrescolaire/Schulzentrum
Prehl, Murten
8 D 4
70 9 A
71 9 C
72 9 D
73
GE Carouge (GE) Cycle d'orientation, Carouge
(GE)
7 H 3
74 8 F
75 9 E
76
GE Chêne-
Bougeries
Cycle d'orientation, Chêne-
Bougeries
7 B 1
77
GE Chêne-Bourg Cycle d'orientation, Chêne-
Bourg
8 D 3
78 9 A
79 9 D
80
GE Collonge-
Bellerive
Cycle d'orientation, Collonge-
Bellerive
7 I 2
81 9 E
82 GE Confignon Cycle d'orientation, Confignon 8 I 3
83 9 D
84 9 E
85 GE Lancy Institut Florimont, Lancy 7 B 2
86 9 E
87 GE Lancy Cycle d'orientation, Lancy 7 I 5
88 8 E
89 8 D
90 9 I
245
91 9 J
92 GE Onex Cycle d'orientation, Onex 8 C 3
93 9 C
94 9 D
95
GE Thônex Privésecondaireinférieur,
Thônex
7 A 1
96
GE Vernier Privésecondaireinférieur,
Vernier
7 A 1
97 GE Versoix Collège du Léman, Versoix 7 G 1
98
GL Glarus Oberstufenschulkreis Glarner
Mittelland - Schulhaus
Buchholz, Glarus
7 D 5
99 7 B
100 8 D
101 9 C
102 9 D
103 GR Chur Schulhaus Quader, Chur 7 A 1
104
GR Paspels Oberstufe - Kreisschulen
Domleschg, Paspels
8 B 2
105 9 A
106
GR Vaz/Obervaz Schulhaus Lenzerheide/Lai,
Vaz/Obervaz
7 A 1
107
LU Entlebuch Oberstufenschulhaus
Entlebuch
7 A 2
108 9 A
109 LU Hochdorf Kantonsschule Seetal, Hochdorf 7 B 2
110 8 A
111 LU Luzern SH Staffeln Littau 7 C 1
112 LU Malters SH Muoshof 3 9 F 1
113 LU Nebikon Oberstufenschulhaus Nebikon 7 B 2
114 8 A
115 LU Sursee Kantonsschule, Sursee 8 C 2
116 9 D
117
NE Colombier
(NE)
CESCOLE, Colombier (NE) 8 F 1
118
NE La Chaux-de-
Fonds
ESCF Les Forges, La Chaux-
de-Fonds
7 B 2
119 9 D
120
NE La Chaux-de-
Fonds
ESCF Les Crêtets-Bellevue, La
Chaux-de-Fonds
8 D 1
121 NE Le Landeron ESRN C2T, Le Landeron 8 B 1
122
OW Engelberg Stiftsschule (Sekundar),
Engelberg
7 A 2
123 9 B
246
124
SG Altstätten Oberstufenschulhaus Feld II,
Altstätten
8 A 1
125 SG Buchs Oberstufenzentrum Grof, Buchs 9 B 1
126
SG Neckertal Oberstufenschulhaus Necker-
Mogelsberg, Neckertal
7 B 1
127
SG Quarten Oberstufenschulhaus
Unterterzen, Quarten
9 A 1
128
SG Rapperswil-
Jona
Oberstufenschulhaus Weiden-
Jona, Rapperswil-Jona
8 A 1
129
SG Rorschacherber
g
Schulhaus Steig,
Rorschacherberg
9 B 1
130
SO Derendingen Kreisschule Wasseramt Ost -
Oberstufenschulzentrum
Derendingen/Luterbach ,
Derendingen
8 B 6
131 8 E
132 8 A
133 9 G
134 9 E
135 9 B
136
SO Egerkingen Kreisschule Gäu -
Sekundarschule Mühlematt,
Egerkingen
9 A 1
137
SO Grenchen Schulhaus an der Halde,
Grenchen
8 C 1
138
SO Grenchen Bezirkschule - Schulhaus III,
Grenchen
7 A 3
139 8 F
140 8 C
141
SO Matzendorf Kreisschule Thal - Bezirk- und
Sekundarschule, Matzendorf
8 B 1
142
SZ Einsiedeln Schuleinheit Team 7 - 9,
Einsiedeln
7 B 1
143
SZ Schwyz Schulhaus Rubiswil - Ibach,
Schwyz
8 A 2
144 9 A
145 TG Affeltrangen Affeltrangen SSG 7 A 2
146 8 B
147 TG Frauenfeld SSG Reutenen 9 A 1
148
TG Kemmental
VSG
OSA 7 D 2
149 8 B
150
TG Sulgen SSG Oberstufenzentrum Befang,
Sulgen
8 B 1
247
151
TG Wängi OberstufenshausImbach II,
Wängi
8 A 1
152 TG Weitenzelg Romanshorn-Salmsach SSG 7 A 2
153 8 A
154 TI Ascona 6613 CollegioPapio 8 B 1
155
TI Bironico CP 36
- 6804
Scuolamedia di Camignolo 8 E 2
156 9 B
157 TI Castione 6532 Scuolamedia di Castione 8 B 1
158 TI Gordola 6536 Scuolamedia di Gordola 8 C 1
159
TI Pregassona
(Lugano)
6963 Via
Terzerina
Scuola media di Pregassona.
Preganossa (Lugano)
7 C 3
160 7 B
161 9 C
162
TI Riva S. Vitale
6834
Scuola media di Riva S. Vitale 8 B 1
163
TI Via alla Roggia
–
6924 Sorengo
Scuolamedia Parsifal 9 B 1
164
VD Aigle Collège des Dents du Midi,
Aigle
7 B 2
165 9 G
166 VD Aubonne Collège du Chêne, Aubonne 8 A 2
167 9 E
168 VD Avenches Collège du Château, Avenches 7 A 2
169 7 B
170
VD Bussigny-près-
Lausanne
Tombay II, Bussigny-près-
Lausanne
7 C 1
171
VD Lausanne Ecole Nouvelle de la Suisse
romande, Lausanne
8 A 1
172 VD Lausanne La Garanderie, Lausanne 8 A 1
173
VD Le Chenit Chez-le-Maître-Le Sentier, Le
Chenit
9 B 1
174 VD Lutry Collège des Pâles 2, Lutry 9 A 1
175 VD Morges Collège de Chanel, Morges 7 A 2
176 8 D
177 VD Orbe Montchoisi, Orbe 7 C 2
178 9 E
179 VD Oron-la-Ville Collège, Oron-la-Ville 7 C 3
180 8 E
181 9 C
182 VD Pully Collège des Alpes, Pully 7 C 3
248
183 8 B
184 9 C
185 VD Vevey Collège Bleu, Vevey 8 C 1
186
VS Brig-Glis Kollegium Spiritus Sanctus,
Brig-Glis
7 A 3
187 8 J
188 9 I
189
VS Monthey Cycle d'orientation CO -
Reposieux, Monthey
7 C 3
190 8 F
191 9 D
192
VS Münster-
Geschinen
Orientierungsschule OS,
Münster-Geschinen
9 A 1
193
VS Nendaz Cycle d'orientation CO - Basse-
Nendaz, Nendaz
7 C 1
194
VS Sierre Cycle d'orientation CO -
Goubing, Sierre
9 E 1
195 VS Sion Les Creusets, Sion 7 J 5
196 8 J
197 8 B
198 9 I
199 9 D
200 ZG Menzingen Ochsenmatt II, Menzingen 8 B 1
201 ZG Zug Kantonsschule, Zug 7 J 3
202 8 J
203 9 I
204
ZH Affoltern am
Albis
Schulhaus Ennetgraben 7 B 1
205 ZH Dübendorf Kantonsschule Glattal 7 B 2
206 9 D
207 ZH Meilen Schulhaus Sekundar Allmend 9 B 1
208 ZH Nürensdorf Schulhaus Hatzenbühl 8 A 2
209 9 B
210 ZH Richterswil Schulhaus Boden 8 B 1
211 ZH Seuzach Schulhaus Halden 7 B 1
212 ZH Wald ZH Schulhaus Burg 9 A 1
213
ZH Wetzikon Schulhaus Sekundarschule
Walenbach
7 A 1
214 ZH Zürich Schulhaus Aemtler B 7 A 1
215 ZH Zürich Schulhaus Hirschengraben 8 B 1
216 ZH Zürich Schulhaus Leutschenbach 9 A 1
217 ZH Zürich Kantonsschule Stadelhofen 9 A 1
218 ZH Zürich Kantonsschule Enge 9 E 2
219 9 J
249
Classes and Schools
Number of classes per
school
Frequency
of schools
Percent Valid percent Cummulated
Percent
Valid
1.00 73 33.3 57.5 57.5
2.00 30 13.7 23.6 81.1
3.00 16 7.3 12.6 93.7
4.00 3 1.4 2.4 96.1
5.00 4 1.8 3.1 99.2
6.00 1 .5 .8 100.0
Total 127 58.0 100.0
250
4. Attachment 4.
Additional samples in Aargau (AG), St. Gallen (SG) and Ticino (TI)
AG
11 Schulen
17 Klassen
N ort Schule Klasse
1 Aarau Bezirksschule Zelgli, Aarau 8f
2 Berikon Kreisschule Mutschellen - KSM 3, Berikon 8a
3 8b
4 Döttingen Oberstufe, Döttingen 9a
5 Frick Oberstufenzentrum, Frick Sekudarschule 8d
6 Realschule 9b
7 Muri (AG) Bezirksschule, Muri (AG) 7b
8 7c
9 8b
10 Niederlenz Schulanlage, Niederlenz 9b
11 Schinznach-Dorf Bezirksschule, Schinznach Dorf 7b
12 9b
13 Spreitenbach Bezirksschule, Spreitenbach 8c
14 Villmergen Schulhaus Hof, Villmergen 7b
15 8d
16 Wohlen (AG)
Primarschule und Oberstufe - Bünzmatt 3, Wohlen
(AG) 9b
17 Würenlingen Schulhaus Dorf 1968, Würenlingen 8b
SG
12 Schulen
26 Klassen
N Ort Schule Klasse
1 Amden Realschule Amden-Weesen, Amden 7a
2 Bronschhofen Oberstufenschulhaus, Bronsschhofen 7c
3 8a
4 Diepoldsau Oberstufenzentrum Kleewies, Diepoldsau 7b
5 Ebnat-Kappel Oberstufenschulhaus Wier, Ebnat-Kappel 8a
6 Flawil Oberstufenzentrum Feld, Flawil 7d
7 8c
8 9c
9 Goldach Oberstufenschulhaus, Goldach 3fReal
10 Jonschwil Oberstufenzentrum Degenau, Jonschwil 8b
11 Mels Oberstufenschulhaus Feldacker, Mels 7a
12 7f
251
13 8a
14 8c
15 St. Gallen Kantonsschule Am Burggraben, St. Gallen 7a
16 9b
17 7b
18 Weesen Sekundarschule Weesen-Amden, Weesen
Sekundarschule, 3 Kl
(9Oberstufe)
19 Widnau Oberstufenschulhaus Gässeli, Widnau 7a
20 7e
21 8a
22 8b
23 8c
24 8d
25 9d
26 Wil (SG) Privatschule Dominik Savio, Wil (SG) 8a
TI
14 Schulen
22 Klassen
N Ort Schule Klasse
1 Agno Agno 7b
2 8c
3 Bedigliora Bedigliora 8c
4 Bellinzona Scuolaelementis&media, Bellinzona 8b
5 9f
6 Cadenazzo Cadenazzo 9a
7 Capriasca Tesserete 7d
8 Giornico Giornico 8b
9 9a
10 Giubiasco Scuolaelementis&media, Giubiasco 7a
11 9g
12 Gravesano Gravesano 7a
13 Locarno Scuolaelementis&media, Locarno 7c
14 7j
15 8d
16 9d
17 Losone Losone 8b
18 Mendrisio Scuolaelementis&media, Mendrisio 8a
19 Minusio Scuolaelementis&media, Minusio 8d
20 9c
21 Origlio Origlio 7a
22 Quinto Quinto 9a
252
5. Attachment 5.
- Example of the letter to school principal (File “Letter_School_Principal_Example”.
Folder “Attachment 5”)
- Attachment totheletter N1 “Beilage 1 Zusammenfassung Studie“ (File „Beilage 1
Zusammenfassung Studie“.
Folder „Attachment 5“)
- Attachment totheletterN2 “Instruktionen für die Durchführung der Befragung im
Klassenverband“ (File „Beilage_2_Instruktion“.
Folder „Attachment 5“)
- Attachment totheletterN3 „An die Eltern der Schülerinnen und Schüler der ausgewählten
Schulklassen“ (File “ Beilage_3_Orientierungsschreiben“.
Folder „Attachment 5“)
6. Attachment 6.
- Questionnaire for teachers (File „Feedback-Fragebogen für Lehrer“.
Folder “Attachment 6”)
7. Attachments 7.1-7.3.
- The questionnaire in German, French and Italian (files “Questionnaire German”,
“Questionnaire French”, “Questionnaire Italian”.
Folder “Attachment 7.1-7.3”)
8. Attachment 8.
- Email „Schweizerische Konferenz der kantonalen Erziehungsdirektoren“
(File „ISRD3.Brief.Information.EDK“.
Folder „Attachment 8“).
9. Attachment 9.
- Example of the letter to cantons (File “Bildungsdirektion_Brief_Beispiel”.
Folder “Attachment 9”)
- “Research plan”. Attachment to the letter in German, French and Italian (Files
“ISRD3.Research.Plan_DE”, “ISRD3.Research.Plan_DE”, “ISRD3.Research.Plan_DE”.
Folder “Attachment 9”)