Selected Research Papers in Social Change, Education, Labour Market, and Criminology Volume II

4 Summary and suggestions

Educational success and social background are still closely related to each other. The Austrian educational system has not been able to adjust the educational demands to the difficult social and cultural backgrounds of many youth, which is why many of them are “left behind.” The social “elevator effect” in the context of educational participation does not apply to everybody. And just because the educational participation rate has risen in recent decades, it cannot be ignored that around 10% of 18- to 24-year-olds in Austria are still leaving the education system after compulsory school.

The problems (unemployment, risk of poverty, precarious working situations, etc.) that are faced by educationally disadvantaged youth are reproduced and stabilized by two interlocking elements: the structural discrimination against youth from weaker social milieus within the education system and its reproduction as well as the structural barriers of the labor market, whereby educationally disadvantaged groups are systematically excluded. That means that the historically composed structure of the Austrian educational system (early selection, half-day schooling, focus on deficits instead of talents, etc.) as well as the labor market (system of recognized occupation requiring formal training, strong trade unions, and so on) are in total major reasons for the quantitative discrimination of educationally disadvantaged persons in (further) education and on the labor market.

The study results show one more time that in Austria the group of educationally disadvantaged youth is not created by accident. The special groups concerned are:

• Youth from educationally disadvantaged homes • Youth with migration backgrounds • Youth who grow up in urban areas

Furthermore, it appears that the attribute “being educationally disadvantaged” has extensive implications on all aspects of living for the person concerned – whether it concerns satisfaction with life and work situation, support from the social environment, experiences with school and studying, the information aspect regarding vocational orientation, or opportunities and positions on the labor market. Educationally disadvantaged youth – compared to those who are not – are lacking in all those aspects.

From the point of view of education and inequality research, it would be necessary to shift the differentiation of the Austrian educational system (nationwide) to a later point in the educational career – as is common in the rest of Europe – and to establish obligatory full-time schooling in the field of compulsory education (Bacher, 2007, 8). These two measures could greatly enhance equality regarding (educational) opportunities. Furthermore, it is essential to create a good climate in schools to offer positive learning experiences for all pupils. There need to be attempts to combat fears regarding school and exams, as well as individual assistance and support in the event of deterioration of performance. Considering the social transformation processes, schools should focus on learning in the sense of lifelong learning.

Educationally Disadvantaged Youth in Austria 92

The distribution mechanism of the labor market should also be kept clearly in mind. Here structural and widespread interventions are needed as well. The right to qualified work and corresponding training for youth must be discussed in broad public debate. It is also necessary that the young get the feeling that they are needed and that they can contribute a worthwhile share. Furthermore, new and more vocational training/jobs at a lower qualification level are required.

From the point of view of youth research (Kahlert/Mansel, 2007; Großegger, 2005), the transition from the educational system to the labor market is seen occasionally as a quite problematic/unstable status passage for youth, because it is an essential step toward adult status. From that perspective, experts and practitioners support the idea of institutionalized support. This linking-up institution, “Transition Education system – Labor market,” should on the one hand be a general consulting and information service for youth facing the transition, and on the other hand it should be a form of support for youth who have difficulties with that transition. One focus would be on educational and vocational guidance, orientation, and information; another focus would be to support endangered youth through individual coaching (“Clearings”). This does not mean that this institution has to accomplish all these tasks, but rather it should be achieved through efficient delegation and networking with other institutions. The basic idea came from the repeated claim to cross-link all projects and institutions in this context – one institution as a starting point for all young people.

References

Bacher, J. (2007): PISA 2003: Auswirkungen früher Bildungsentscheidungen und differenzierter Schulsysteme auf Testleistungen und Chancengleichheit. http://www.soz.jku.at/Portale/Institute/SOWI_Institute/Soziologie/aes/content/e50/e 1512/e3844/StellungnahmeBacherNeuwirth(Version5)_ger[1].pdf [07.08.2010].

Bauer, F., & Kainz, G. (2007): Benachteiligung von Kindern mit Migrationshintergrund beim Bildungszugang. WISO/4.

Blumberger, W., Niederberger, K., & Affenzeller, S. (2004): LehrabsolventInnenBefragung in Oberösterreich. Linz: unveröffentlicht. Boss-Nünning, U., & Granato, M. (2008): Integration junger Menschen mit Migrationshintergrund: Ausbildungschancen und Ausbildungsorientierung. In K. Bade, M. Bommes, & J. Oltmer, Nachholende Integrationspolitik - Problemfelder und Forschungsfragen (pp. 57-90). Bad Iburg: Grote Druck.

Boss-Nünning, U., Bernardo, d. L., Rimbach, B., & Wohlbeck, I. (2008): Zusammenarbeit mit zugewanderten Eltern - Mythos oder Realität? Essen: Druckmeister.

Ecarius, J. (2010): Jugend und Familie. In Krüger, H.H. & Grunert, C., Handbuch Kindheits- und Jugendforschung (pp. 569-594) Wiesbaden: VS Verlag für Sozialwissenschaften.

Fuhs, B. (2010): Kindheit und mediatisierte Freizeitkultur. In Krüger, H.H. & Grunert, C., Handbuch Kindheits- und Jugendforschung (pp. 711-726). Wiesbaden: VS Verlag für Sozialwissenschaften.

Großegger, B. (2005): Jugend und Beschäftigung. Wege in die Arbeitswelt: Eine Problem- und Bedarfsanalyse aus Sicht von Jugendlichen, jungen Erwachsenen und ExpertInnen. Wien : im Auftrag des BmSGK.

Educationally Disadvantaged Youth in Austria 93

Kahlert, H. & Mansel, J. (2007): Bildung und Berufsorientierung von Jugendlichen in Schule und informellen Kontexten. In Kahlert, H. & Mansel, J., Bildung und Berufsorientierung (pp. 7-18). Weinheim/ München: Juventa Verlag.

Lentner, M., & Niederberger, K. (2009): Bildungsferne Jugendliche. Linz. http://www.ibe.co.at/nc/forschung/projektsuche.html?tx_ibeprojekt_pi1%5Bitem%5 D=162&tx_ibeprojekt_pi1%5Bschlagwort%5D=bildungsferne&tx_ibeprojekt_pi1%5 Bprojektleiter%5D=0&tx_ibeprojekt_pi1%5Bsuchanfrage%5D=1 [30.03.2011]. Niederberger, K., & Ratzenböck-Höllerl, I. (2007): Bedarfsdeckung eines Angebotes für benachteiligte Welser Jugendliche. Linz: unveröffentlicht.

Ribolits, E. (2008): Wer bitte sin hier die Bildungsfernen? In Schulheft 131: Schriftlos = sprachlos? Alphabetisierung und Basisbildung in der marktorientierten Gesellschaft, 03/2008 (pp. 1-6). Schoefield, J. W. (2006): Migrationshintergrund, Minderheitenzugehörigkeit und Bildungserfolg. Aki-Forschungsbilanz 5.

www.wzb.eu/ZKD/AKI/files/aki_forschungsbilanz_5.pdf [25.03.2010]. Schroeder, J. (1998): Die Hauptschule eine Migratenschule? In D. Bronder, H.-J. Ipfling, &

K. Zenke, Handbuch Hauptschulbildungsgang (pp. 102-111). Bad Heilbronn: Klinkhard. Sepp, R., Osterkorn, M., & Stadlmayr, M. (2009): Live&Work - Jugendliche am Übergang Schule-Beruf-eigenverantwortliches Erwachsenenleben. Linz: unveröffentlicht.

Specht, W. (1997): Autonomie und Innovationsklima an Schulen. Graz: Dorrang. Stadlmayr, M., Lentner, M., & Niederberger, K. (2009): Evaluierung der

Berufsausbildungsassistenz in OÖ. www.ams-forschungsnetzwerk.at/ deutsch/publikationen/BibShow.asp?id=5490&sid=84361277&look=0&stw=berufsa usbildungsassistenz+2009&gs=1&Ing=0vt=0&or=0&aktt=0&zz=30&mHlld=0&mMll d=0&sort=jahrab&Page=1 [23.03.2010].

Steiner, M. & Wagner, E. (2007): Dropoutstrategie. Grundlagen zur Prävention und Reintegration von Dropouts in Ausbildung und Beschäftigung. Wien: im Auftrag des BmUKK.

Thole, W. (2010): Jugend, Freizeit, Medien und Kultur. In Krüger, H.H. & Grunert, C., Handbuch Kindheits- und Jugendforschung (pp. 727-764). Wiesbaden: VS Verlag für Sozialwissenschaften.

Educationally Disadvantaged Youth in Austria 94

Daniela Wetzelhütter

Youth Unemployment in Finland Factors Explaining High Youth Unemployment Rate

1 Introduction

Youth unemployment is a contemporary cross-national issue. In this regard, the high unemployment rate in Finland is often highlighted within a European comparison, especially in connection with the educational system. For instance, the Austrian newspaper “News” (2005) reported that the “PISA Winner” Finland quoted a 20.7% youth unemployment rate compared to e.g. 10.5% in Austria or the EU average of 18.2%. Currently, the online version of the “Frankfurter Allgemeine - FAZ.NET” (2010) referred to Finland as the “PISA World Champion” which had to combat a high youth unemployment rate of 23.5%, even though good education had always been a guarantor of success in the work world. Accordingly, “heise online” (2010) pointed out that good education was not for a guarantee of getting a job because Finland faced a high rate of 23.7% youth unemployment while low rates in the Netherlands (7.3%), Austria (10%) and Germany (10.2%) were recorded (in February).

Actually even the Finnish Embassy in Austria deals with this topic. The unemployment registration system and the high proportion of graduates are mentioned as possible explanations for the comparatively high rate (Botschaft von Finnland; Wien, 2008). Incidentally, Bacher (2006) has already suggested the consideration of decisive factors for studies based on cross-national comparisons. In detail, economic developments, country-specific educational systems as well as the registration practice are identified as relevant for differences between countries.

This paper describes these factors in more detail. It is based on my diploma thesis “Youth Unemployment in Finland & Workshops as Labour Market Programme for the Integration based on the Helsinki Metropolitan Area” (Wetzelhütter, 2010). The results can be summarised as follows: 1. The high unemployment rate of Finnish youth can be mainly explained by the fact that students searching for a job are counted as unemployed. 2. The continuous expansion of the education system results in less labour market potential and therefore higher unemployment rates. 3. An economic boom triggers decreasing unemployment, while an economic depression affects the unemployment level negatively, although governmental reactions reduce the impact.

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2 Measurement practices

Different databases can be used to analyse the situation of unemployed young

people in Finland. Two main datasets are:

• Labour Force Survey (LFS; Eurostat, 2009a; Statistics Finland, 2010). This survey provides comparable data about the unemployment situation covering all 27 EU members as transnational subjects. In respect of the selection procedure, the sample is basically drawn as a stratified random sample.

• Unemployment registration. The second source of unemployment rates is constituted by the official records of the registration-based unemployment figures, as total statistics, published by the Ministry of Employment and the Economy (2011). Apart from the different collection methods, youth unemployment is measured

differently in the two databases:

• According to the Employment Service statistics, an unemployed person is “a jobseeker who does not have work and is available for full-time work or who is waiting for an agreed job to begin. A jobseeker who can only accept employment after a certain period of time or who is only looking for work in which the working time is shorter than half of the normal working time in the industry is not classified as unemployed. […] Recipients of unemployment pension or full-time pupils or students are not classified as unemployed jobseekers [...]” (Ministry of Employment and the Economy, 2011; p.2).

• This means that e.g. full-time students are excluded, even if they are searching for a job. In this regard, Statistics Finland (2009a) underlines that the statistic of the Employment Service “is based on legislation and administrative orders which make the statistical data internationally incomparable”. Moreover, “an unemployed person is not expected to seek work as actively as in the Labour Force Survey” and “there are differences also in the acceptance of students as unemployed”.

• Consequently, regarding the “Labour Force Survey”, the unemployed are, as defined by Eurostat (2009b), persons “who were without work during the reference week, but currently available for work” and “who were either actively seeking work in the past four weeks or who had already found a job to start within the next three months”.

• As a result, students are not excluded and therefore may cause differences in general and seasonal effects in particular. This means that, in the “Labour Force Survey”, full-time students can be considered as unemployed if they state being without work but available for work and actively seeking work in the past four weeks – even if they are just looking

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1for a part-time job. However, those students are not registered as unemployed by the Employment Service.

Especially this last point causes large differences. Figure 1 shows the number of unemployed youth (under the age of 25) of the LFS in contrast to the registered ones of the Employment Service statistics, concerning the time frame from 1991 to 2009.

Figure 1: Unemployed jobseekers2 of the Employment Service statistics vs. average of unemployed in the LFS (Age group 15-24), in thousands

Own illustration; Source: Employment Service Data: Ministry of Employment and the Economy

(2010), LFS Data: Eurostat (2010)

Obviously, while the number of the registered unemployed has continuously 3declined (1994 to 2008), the difference between the LFS and the registered unemployed has increased, especially since 1996/1997. Furthermore, the LFS has counted more than twice as many unemployed people since 2005. In this regard, a regulation, in force since 1996/1997, defines that young people (18–24 years old) “without vocational education cannot receive labour market support if they do not participate in education or ALMP measures. [...] In practice, the authorities are

1 In this regard, the situation in Austria is similar. For instance, Statistik Austria (2008) mentioned that differences between the AKE (Arbeitskräfteerhebung according to the labour force definition) and the official statistics of the AMS (employment office in Austria) can occur through e.g. students or persons in training programmes. Indeed students or persons in training programmes are not registered as unemployed by the AMS but may state to be unemployed in the labour force survey if they were contacted by the AMS. However, the difference in the two statistics is smaller in Austria than in Finland.

2 Excluding lay-offs

3 Active Labour Market Programme

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able to provide education or training for all young people” (OECD, 2001, p.354). This contributed to the fact that the proportion of registered youth unemployment decreased in Finland by more than 50% from 1994 to 1999 while the LFS still counted 21.5% in 1999 because of the high number of full-time students who reported their willingness to work (OECD 2001). Expressed in figures, the counted 21.5% are about 70,000 young unemployed people (figure 1). In 2002, still more than 50% of the (counted in the LFS) unemployed youth under 25 were full-time students, without whom the number would have been 11.5% unemployed and therefore the unemployment rate would be under the EU average of 14.6% (Ministry of Labour, 2003). Another example, including a seasonal effect, would be that more than 80% of the unemployed between 15 and 25 years old were students in May 2008 (Statistics Finland, 2008). Finally, in 2009 the LFS still counts twice as many (69,400) young unemployed people as the registered ones (33,800), which can be seen in Figure 1.

At that time, the high levels were attributed to the financial crisis, since the number of unemployed had increased by about 13,000 young people from 2008 to 2009.

To sum up, the above-mentioned illustrations underline the importance of measurement practices. In this context, as pointed out before, the high number of students drastically influences the number of unemployed and the unemployment rate. In this context, it is also important to distinguish in respect of analysing unemployment figures in absolute terms or the unemployment rate. The unemployment rate is typically defined as:

Unemployment Rate = Number of Unemployed Persons / Labour Force4 Based on this, even if the number of unemployed persons is declining, the unemployment rate can increase if the labour force decreases as well. In particular, it is important to make this distinction when the influence of educational expansion on youth unemployment is analysed, since more students also means a smaller labour force.

3 Influence of educational expansion on youth unemployment

The Finnish educational system (Figure 2) is constituted by a nine-year com-pulsory basic education. This comprehensive schooling was gradually introduced in the 1970s (Statistics Finland, 2007a).

Statistically, 64,700 pupils completed comprehensive school in 2008. The transition phase to secondary education is accompanied by guidance and counselling as well as a nationwide online application system. As a result, 50% of

4 Labour force = Number of unemployed persons plus number of employed persons

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the pupils transferred to general upper secondary school, 42% to vocational upper secondary-level education and 2% continued a 10th grade for improvement. Only 6% did not continue and 0.3% (~ 200 pupils) did not complete the comprehensive school (Ministry of Employment and the Economy et al., 2009). Based on these figures, drop-outs do not seem to be a burden for Finland. The proportion of students continuing an upper secondary vocational education increased from 36% in 2000 to 42% in 2008, while the proportion of students in upper secondary general education decreased from 54% to 51%. Students who attended the 10th grade counted between 2% and 3%, whereas the proportion of comprehensive school graduates who did not continue their studies ranged between 5% and 7% from 2000 to 2008 (Statistics Finland, 2009b). Therefore, the transition from basic to upper secondary education seems to be institutionally organised to its best.

Figure 2: Education system, Finland

Source: Finnish National Board of Education (2005)

Considering a longer time frame, the matriculation examination improved from 4,000 pupils in 1950 to 33,000 pupils in 2006, although the number of pupils gaining an upper secondary education has recently decreased because of smaller age groups (Statistics Finland, 2007a). All in all, 32,900 matriculation examinations were counted in 2008 (Statistics Finland, 2009b). In contrast, vocational education

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expanded from 20,000 in 1940 to 250,000 students/trainees in 2005 (Statistics Finland, 2007a). In this respect, the extension of apprenticeship places and the competence-based qualification, which focus especially on adults, should be noted since it is not distinguishable based on the available figures.

Regarding the increasing number of students, a tertiary education is provided through 20 universities and 28 polytechnics (Finnish National Board of Education, 2010). In detail, 132,783 polytechnic students were counted, while 176,061 students attended university in Finland in 2005 (Statistics Finland, 2007c). In this context, the increasing effect of introducing polytechnics can be seen in the following figure.
Figure 3: Proportion of students in polytechnics and universities

Source: Statistics Finland (2007a)

All in all, the proportion of completers of the upper secondary or the tertiary level increased rapidly from 50% in 1975 to nearly 85% in 2005, according to the figures of the 25–34 age group (Statistics Finland, 2007a). This reinforces at least the mentioned extension of the education system.

5The consequences of this trend are noticeable through a reduced labour force potential. This in turn results in a proportionally higher unemployment level – counting jobseeking students as unemployed even enhances the effect. Table 1 shows that the Employment Service (which does not consider full-time students, as described in section 2) registered 8.3% (8,300) unemployed jobseekers in the 15–19 age group based on 100,000 persons defined as labour force compared to 11.5% (25,500) unemployed 20–24-year-old jobseekers based on 222,000 registered persons as labour force in this age group.

5 Laid Offs excluded

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Table 1: Unemployed youth according to age groups – Employment Service statistics

Registered unemployed 15–19-year-old unemployed 20–24-year-old unemployed Total

Absolute 8,300 25,500 33,800 Percentage 8.3% 11.5% 10.5%

Source: Ministry of Employment and the Economy (2010)

This implies that the smaller work force of the younger age group (100,000) in contrast to the second age group (220,000) influences the unemployment rate negatively. Calculating the respective unemployment rate in relation to the entire age-population, 8,300 unemployed 15–19-year-olds account for 2.6%, compared to 7.9% in the older age group. With regard to a total of 33,800 unemployed young people, the younger age group accounts for 25%.

6Including jobseeking students, the data of the LFS even suggests with an unemployment rate of 31.6% among 15–19-year-olds compared to 17.0% among 20–24-year-olds in 2009 (Ministry of Employment and the Economy, 2010) that the younger ones are mainly affected. Based on this, the increased effect of (jobseeking) students on the unemployment rate caused by a reduced workforce is comprehensible, especially since the younger ones are more likely to be integrated in school. Focusing on the integration of the young population through education or employment may again reflect the influence of a high proportion of students. As published by the OECD (2007), counting all young people who are not integrated, 2% of the 15–19-year-olds and 7% of the 20–24-year-olds became visible as excluded persons in 2005. By comparison, the dual educational system in Austria results in a higher proportion of excluded youth in the first age group (4.2%) and fewer excluded youngsters (4.6%) in the second (ibid). This may partly be explained by an early occupational entrance (at the age of 15), which is more common in Austria. Schneeberger and Nowak (2008) mentioned that 41% of the 15-year-olds started an apprenticeship in 2007 in Austria. Subsequently, more young people at this age are facing the risk of becoming unemployed and fewer youths are (safely) integrated through school-based education. Therefore, solely the unemployment rate is often not sufficient.

6 The dual education system characterises the transition from lower to upper secondary level through two main options – a school-based education (academic secondary school, higher technical and vocational college, intermediate technical and vocational school) or the entry into working life as an apprentice. For the second option a pre-vocational year bridges the last year of compulsory schooling (if required) before the entry into working life can be made (Statistik Austria, 2011).

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4 Dependencies on the economic crisis

Of course, the unemployment level depends on the economic situation. Starting 7with the oil crisis in the mid-1970s, Reija, Sanatamäki-Vuori and Standing (1990) present a rapid rise from under 10% (15–19-year-olds) and from under 5% (20– 24-year-olds) at the beginning of the 1970s to a 20% and 12%, respectively, peak unemployment rate in 1978. As governmental reactions, an extension of comprehensive schooling (additional 10th class) in 1977 and the introduction of employment subsidies in 1978 and of a youth guarantee scheme8 in 1981 were initialised (ibid). Subsequently, the unemployment figures recovered, also because an economic upswing took place (Helve, 2000). Again a new recession followed in the 1990s, as visible in Figure 4. This economic downturn has several causes. The deregulation of the financial market, the revaluation of the Finnish Markka9as well as the almost completely stopped eastern exports because of the collapse of the Soviet Union was mentioned in this context (Statistics Finland, 2007b). Accordingly, it is noticeable that the proportion of unemployed young people rapidly increased from less than 10% in 1990 to almost 30% in 1992. Moreover, in comparison with the other age groups, the youth (15–24) are the most affected since their unemployment level is and stays higher than among the other age groups (Statistics Finland, 2010). In this context, Hummeluhr (1997) underlined that the previously mentioned youth guarantee was withdrawn as a consequence of the drastic situation in 1992. Therefore, new measures were required in addition to the continuing basic measures (e.g. subsidised work places). As a matter of fact, new labour market measures were implemented. Helve (2000) describes the programme “Alternatives to Unemployment” as a suggestion for extending education and apprenticeship training places as well as subsidised workplaces between 1994 and 1996. Even though the implementation of these measures was incomplete, the unemployment rate of young people decreased. Furthermore, measures for reaching the “Aim 3”, specified by the ESF (European Social Fond) as facilitation to integrate the youth at risk of exclusion, were planned. Therefore, an increase of 2,100 six-month workshop places, 650 apprenticeship positions and 5,200 places in further occupational education was projected between 1995 and 1999 (ibid).

10Apart from a slight increase in youth unemployment beginning in 2001, which can be attributed to the downturn of the telecommunication industry, a continuous decline is evident until 2008. The declining number of unemployed young people was accompanied by the reduction of supportive measures. For instance, 350 youth workshops were available in 1993/1994 while just 236 workshops were

7 “The basic idea behind the guarantee was [...] to offer all young people a place in the upper secondary education [...] or to provide them with an ‘ordinary” job’” (Hummeluhr 1997, p.14).

8 The currency at the time.

9 As mentioned before, Bacher (2006) has already described that such a rapid rise made the comparison to Austria problematic, as this country experienced a continuous decrease.

10 E.g. the company “Nokia” also had a positive impact on the Finnish economy since it had been one of the most important high-tech companies in the 1990s until the downturn of the telecommunication industry affected the company in 2001 (Ali-Yrkköö and Hermans 2004).

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provided in 2002 (Ministry of Education, 2004). In this regard, the number of workshop participants decreased from 12,000 young people in 1997 (OECD, 1999) to 7,000 in 2002 (Ministry of Education, 2004). However, an increasing unemployment rate is once again noticeable at the end of this decade. This can be attributed to the financial crisis. Once again the workshop places increased, as Walldèn (2009) mentions, to 11,000 by 2009. Concerning the growing numbers of workshop places it has to be emphasised that young unemployed people who are not available for work as defined by Eurostat (2009b) are not considered as unemployed. This suggests that the more young people participate in activation measures, the less likely it is that they become unemployed. Without those activation measures the unemployment rate is expected to be higher. Otherwise, these places are reduced in the event of decreasing unemployment. This means that labour market measures initiated in times of an economic crisis slow down the boost, but do not avoid a high level of youth unemployment when the economy recovers since the labour market measures are reduced at this point in time.

As interim conclusion, the shown dependency of the unemployment level on the economic situation is evident, although the rates reflect the economic changes with a time lag. This is related to the labour force policy, since labour market measures (e.g. extending education and training opportunities) inhibit a stronger rise in unemployment while the youth unemployment level remains in force since the measures get reduced in the case of decreasing unemployment. Figure 4: Unemployment rates and economic growth, 1990–2009, %

Own illustration; Source: LFS Data: Eurostat (2010); Economic Growth: Eurostat (2011)

Youth Unemployment in Finland 103

5 Conclusion

In conclusion, this paper intended to point out decisive criteria (measurement practices, educational expansion, economic changes in connection with governmental reactions) for the interpretation of youth unemployment. In detail:

• The proportion of unemployed young people differs depending on the respective source. The measurement practises concerning full-time students are mainly responsible for that. For example, the consideration of jobseeking students contributes to about twice as much unemployed counted by the LFS (69,400) compared to the registered unemployed (33,800).

• The educational expansion increases the number of young people (15–19year-olds) integrated through school but also leads to less labour market potential. This fact raises the unemployment level, as visible through 8,300 15–19-year-olds accounting for 8.3% unemployed compared to 25,500 accounting for 11.5% among 20–24-year-olds, measured by their respective labour force.

• The economic influences become noticeable by the changing unemployment rates (Figure 4). Even though the effect is perceivable with a lag of time – the unemployment rates react relatively strongly in the event of a downturn, whereas an economic recovery indicates a reduced effect. In this connection, labour market measures counteract high unemployment rates, but get reduced in times of an economic upswing, which slows down the impact in both cases. All in all, the above-mentioned facts may not fully clarify the high amount of unemployment, but it implies that the employment situation in Finland cannot be explained simply by calculating the unemployment rates. We propose to use the OECD-index of excluded young people (OECD 2007) in addition.

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Youth Unemployment in Finland 105

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All links checked on October 14th, 2011.
Youth Unemployment in Finland 106

IV. Criminology

107

108

Helmut Hirtenlehner / Barbara Starzer / Christoph Weber
Patterns of Stalking Victimization: A Behavioral Typology
1 Introduction

1.1 The problem of stalking

Endless letters, phone calls, and e-mails, messages left on the car or the front door, repeated ambushes and pursuits, threats and proclamations of love, gifts, and violent behavior – the question of what stalking exactly is cannot be resolved with a simple answer. In essence, it most certainly involves a one-sided craving for contact: One person wants the other to share feelings of closeness with them and spend time thinking about them; the other has no desire for either a relationship or contact. While one vehemently wishes to establish or continue sharing an affiliation with the other and in doing so fails to acknowledge or ignores rejection, the other proves incapable of preventing the persistent attempts to initiate an exchange. On a very general level, one can differentiate between two basic forms of stalking: one that is characterized by one-sided endeavors to establish a relationship, and one in which the sought after termination of a relationship is undermined by continued bids to institute contact. The first case pertains to the fabrication of a previously nonexistent relationship through the use of socially inadequate means, while in the other the refusal to accept the termination of a previously existent relationship takes center stage (Hoffmann 2006: 1 ff). Pursuit by vindictive persons or those prepared to commit acts of (sexual) assault completes the palette of formats which stalking can assume. These types of criminals do not fuel their obsessions out of fondness for the victim, but are rather impelled by hostile motives (Mullen et al. 2000).

Formal definitions of stalking can be differentiated according to whether the determination of the phenomenon is based exclusively on the behavioral level, or if they also take into account the effects these experiences have on the victim. Pathè and Mullen (1997: 12), for example, define stalking as a constellation of behaviors in which one individual inflicts on another unwanted intrusions and communications over a certain period of time. This definition makes clear that activities designated as stalking generally comprise a range of different behaviors which occur continuously (over a long period of time) and repeatedly (with a certain frequency), and are directed towards a specific person who neither wishes for nor welcomes these advances (see also Spitzberg/Cupach 2007; Löhr 2008). The goal is to come into contact with the victim, whereby the perpetrators have no qualms about overstepping social boundaries. Due to their violation of social conventions, the petitions for contact made by the offender become burdensome; thus the attempted courtship assumes the form of harassment. The “relationship applicant“ shows him/herself to be unpredictable, which in turn makes this person

Patterns of Stalking Victimization: A Behavioral Typology 109

appear to be threatening. The capacity of the behavior to trigger fear and concern in a rational person is, therefore, included in some definitions of stalking (Spitzberg 2002; Tjaden/Thoennes 1998).

As already mentioned, stalking is seldom limited to a single type of behavior. The full severity of the damages suffered by the victim is usually determined by the combined total of the experiences encountered. While an isolated phone call or a solitary “chance encounter” would certainly be considered socially appropriate, discomfort only tends to arise in individuals when these incidents start to accumulate. It is precisely these cumulative effects of insistent efforts to establish contact that specific anti-stalking legislation ties up to: The criminalization of persistent pursuit and harassment substantiates an attempt to close a legal loophole. While protection against some offensive behaviors, which can be components of stalking (physical assault, dangerous threats, breaking into the place of residence, and damage to the victim's property, etc.), is often provided for by specific legal provisions, the damages resulting from the accumulation of individually unobjectionable acts remained without relevance under criminal law for a long period of time.

The emergence of specific anti-stalking provisions originated in the USA. Triggered by the murder of the young actress Rebecca Schaeffer by an obsessive admirer, and the slayings of several other women by their former partners, the state of California adopted the first anti-stalking law in 1990 (Tjaden 2009). Within in a few years similar laws followed in the other U.S. states and a number of other countries, like, for example, Great Britain and Germany (De Fazio 2009; Löhr 2008; Spitzberg/Cupach 2003). Austria enacted a stand-alone stalking norm in 2006 (Mitgutsch 2009).

1.2 Previous research

Spurred on by activities in the legislature and the media attention which was 1increasingly being accorded the issue, scientific research into obsessive pursuit and harassment was initiated in the mid-1990s2. While prior to the year 1995 few scientific publications addressed the subject of “stalking”, since then the number of publications relevant to this topic has shot up drastically (Cupach/Spitzberg 2004; Spitzberg 2002). The late start is responsible for the fact that stalking research is still far away from a phase of consolidation. The young discipline still finds itself at a stage of phenomenological stocktaking of its subject. At such an early stage, the identification and classification of different manifestations of the phenomenon under investigation are of particular significance. Very much along these lines, current scientific reports in this field depict numerous attempts to disclose, and systematically categorize, the different phenotypes of stalking. Typological taxonomies subdivide the mass of incidents of obsessive pursuit and harassment into homogeneous subgroups which represent the different forms stalking can

1 The terms “obsessive pursuit” and “obsessive harassment” are used interchangeably with “stalking” in the following.

2 In German-speaking countries, the scientific debate on stalking began somewhat later (Hoffmann 2006: 9 f).

Patterns of Stalking Victimization: A Behavioral Typology 110

3assume. These attempts at systematization are partially theory driven and partially based on empirical observations. The emphasis remains on the perspective of the perpetrator: Most classifications focus on the relationship constellation between the offender and the victim, or on the motivation underlying the stalking activities committed by the culprit (e.g. Meloy 1996; Mullen et al. 1999; 2000; Sheridan/ Blaauw 2006; Sheridan/Boon 2002; Zona et al. 1993). The highest level of recognition has undoubtedly been accorded the taxonomy developed by Mullen and his colleagues (1999; 2000), which was designated the “gold standard in working with stalking typologies” by Hoffmann (2006: 72, translated from original German). Typologies based purely on behavior hardly exist. The stalking tactics outlined by 4Spitzberg and Cupach (2007; see also Cupach/Spitzberg 2004; Spitzberg 2002) describe distinguishable behavioral components of stalking5. The extent to which these behavioral dimensions materialize collectively, or to which they are mutually exclusive, is not addressed by their work. Similarly Budd and Mattinson (2000) condense individual stalking behaviors into super ordinate behavioral segments, but do not further study their combined occurrence. Victim-based typologies are also rare. Previous victim-related classifications were 6predominantly oriented on the relationship which existed between the victim and the perpetrator before the stalking actually took place (Budd/Mattinson 2000; Meloy 1996; Mullen et al. 2000; Zona et al. 1993). Consequently, almost all of these typologies include categories along the lines of “former sexual partner”, “friends”, and “strangers”. Differentiations among these typologies are found primarily in the subtlety of distinction, for instance in the number of categories located along the axis “form of prior relationship”. One exception is found in the work of Johnson and Kercher (2009), who conducted a segmentation of a population of stalking victims, based on the psychological problems resulting from the perpetrator’s behaviors. Based on the results of a latent class analysis the authors were able to identify three delimitable subgroups of victims: those who had been afflicted with nearly all of the negative psychological consequences assessed (severely affected), those whose hardships were limited to anger, lack of concentration, and loss of sleep (moderately affected), and those who experienced no psychological problems apart from anger after being stalked (mildly affected).

3 Mullen et al. (1999; 2000) distinguish five groups of stalkers: “rejected stalkers”, “intimacy seekers”, “incompetent suitors”, “resentful stalkers”, and “predatory stalkers”.

4 Details on the stalking strategies defined by Spitzberg and Cupach (2007) can be found in Section 2.2.1. of this paper.

5 Budd and Mattinson (2000) opted for a variable-oriented instead of an object-oriented strategy of analysis. In their cluster analysis behaviors were classified, not persons. Their results produced six classes of behavior: “gifts and letters”, “silent phone calls”, “following”, “obscene phone calls”, “violence or threat of violence” and “sexual assault”. The subsequent assignment of victims to classes of behavior was made on the basis of the most serious type of infringement sustained.

6 For a more detailed description of the method of latent class analysis, please refer to Section 2.3.

Patterns of Stalking Victimization: A Behavioral Typology 111

1.3 Aim of the study

The objective of the current study is to partition victims of stalking into prototypes of distinguishable patterns of victimization. Using a population of more than 300 victims of stalking in Upper Austria who had reported the obsessive pursuit to the police as the data basis, an attempt is made to develop a classification of stalking profiles that reflect the different phenotypes of stalking victimization in terms of the behavioral components of the incidents. We will analyze into which delimitable victimization profiles the numerous stalking behaviors experienced by the victims combine. While Spitzberg and Cupach (2007), as well as Budd and Mattinson (2000), directed their attention to the condensation of stalking behaviors into onedimensional behavioral bundles, the following analyses will determine which observable victimization patterns can be derived from the behavioral bundles identified. To make our understanding clear: The goal of a typifying approach is to identify distinct patterns of stalking victimization which determine the empirical reality of stalking. Thus, it generates a heuristic benefit that exceeds the insights gained by purely quantifying analyses that draw only on the number of different stalking behaviors.

Figure 1: Theoretical model guiding research

Relationship Status of Victim

Offender-VictimRelationshipAge of Victim

Stalking Profile

Consequences of Stalking for the VictimSex of Victim

Sex of Offender

In addition to the segmentation of stalking cases into different stalking profiles, the present study also illuminates the prior determinants and resulting consequences of the various victimization patterns. With respect to the determinants of the victimization profile, we will assess the explanatory power of characteristics of the victim (sex, age, and whether or not the victim shared his/her home with a spouse or partner at the time the offenses were occurring), the offender (sex), and the

Patterns of Stalking Victimization: A Behavioral Typology 112

type of pre-existing relationship between stalker and victim. Furthermore, it is assumed that the pattern of behaviors exhibited by the offender also has a specific impact on the victim's consequent wellbeing and quality of life. The influence of the victimization profile on the consequences of stalking will be differentially evaluated with regard to both the internal (psychological) and external (physical, social, and economic) effects of obsessive pursuit.

2 Methods

2.1 Sample

The basis of the study is a standardized, written survey of persons who had filled a 7criminal complaint in the federal state of Upper Austria asserting that they had been a victim of stalking8. The study population was formed from a register of stalking victims compiled and maintained by the paramount police authority in Upper Austria. The register provided information about all persons who had been victim of a stalking offense in Upper Austria reported between July 1, 2006 and October 30, 2008. Specifically speaking, a total of 643 persons affected by stalking were recorded: 121 male and 522 female. The survey was conducted by post, in the framework of two waves of mailings. Each of the two mailings was accompanied by a letter from the police authority, and included, in order to expedite response as much as possible, a pre-addressed stamped envelope for the participants to use when returning their answers. The first mailing was made on October 31, 2008, the second on November 20, 2008. In both waves of mailings the police acted as sender, because statutory data protection regulations do not allow for a direct transfer of victim data to a university institute. The questionnaires were to be completed anonymously and returned to the Institute for Criminal Sciences at Linz University.

In response to the first mailing, 174 persons sent back completed questionnaires. February 18, 2009 marked the end of the response period for the second mailing, which yielded an additional 137 responses. As such, a total of 311 completed questionnaires were delivered to the Institute for Criminal Sciences. This corresponds to a net response rate of 48.4 %.

7 Upper Austria is one of the nine Austrian federal states. With approximately 1,500,000 inhabitants, it is characterized by a healthy economic climate and a varying density of population, ranging from rural areas to mid-sized cities. The largest city is the provincial capital of Linz, with a population of 200,000.

8 Consequently, the proportion of women in the population of registered stalking victims comes to 81 %.

Patterns of Stalking Victimization: A Behavioral Typology 113

2.2 Measurement 2.2.1 Stalking strategies (classification variables)

The foundation for the segmentation to be conducted here are types of stalking behavior which might be committed by a perpetrator. A number of previous findings have established that stalking is rarely limited to a single type of unwelcome behavior, and usually comprises a combination of various forms of stalking-typical conduct (Budd/Mattinson 2000; Dressing et al. 2006; Mullen et al. 1999; Tjaden/Thoennes 1998; Voß et al. 2006; Wondrak et al. 2006). In the current investigation a total of 20 different types of behavior associated with stalking were queried. For each of these behaviors the victim was to indicate whether it surfaced at least once over the course of the stalking. Figure 2 depicts the behavioral categories measured and their frequencies of occurrence. It becomes clear that continued attempts at contact through letters, phone calls, SMS messages, e-mails, and other means of communication constitute the most widespread forms of intrusive behavior: Nine out of ten victims reported being confronted with this type of conduct. Likewise, more than half of the victims were exposed to verbal abuse and threats, surveillance and observation, as well as defamation (the malicious circulation of rumors and lies about them).

9If we count the number of different types of unwanted behaviors each victim was subjected to in the context of the crime, a typical instance of stalking proves to be phenomenologically multifarious and diverse. More than half (61 %) of the victims reported experiencing five or more different types of infringements. On average, the victims surveyed encountered six different types of stalking-related behaviors. “Specializations” by offenders in one single specific type of offensive behavior formed the exception to the rule: Only ten percent of the victims were affected by only one of the forms of conduct depicted in Figure 2. Generally this referred to continued attempts at establishing or maintaining contact with letters, phone calls, text messages, e-mails, and similar means of communication.

Table 1: Number of stalking behaviors experienced by the victim (n = 311)

Number of different behaviors Percent 1 10 %

2 10 % 3 10 % 4 10 % 5 or more 60 % Total 100 %

9 Ø = 5.90.

Patterns of Stalking Victimization: A Behavioral Typology 114

Figure 2: Frequency of specific stalking behaviors (n = 311, multiple answers possible)

0% 20% 40% 60% 80% 100

%Initiation of contact with letters, etc.

Insults or threats

Monitoring or observation

Spread of rumors or lies

Following

Intrusion of social or occupational networks

Sending of indecent messages or objects

Residential breaking and entering

Messages through third parties

Damage to personal property

Unwanted gifts

Violent attacks

Stolen mail

Move into victim's neighborhood

Ordering of unwanted goods or services

Placement of classified ads

Hacking into computers

Postings on the internet

Placement of obituary notices

Other infringements89%

63%

62%

53%

49%

47%

46%

42%

36%

21%

20%

18%

6%

5%

5%

5%

4%

4%

2%

11%

10 An analysis of the response patterns provided by the survey participants resulted in a total of 218 different stalking profiles: in other words a total of 218 different combinations of stalking-related behaviors were reported by the victims. In order to reduce the complexity of the victimization profiles, in the next step the behaviors were compressed into general stalking tactics. This was accomplished in accordance with broadly discussed works by Spitzberg and Cupach (2007; see also Cupach/Spitzberg 2004; Spitzberg 2002). Spitzberg and Cupach have extracted eight distinguishable stalking tactics from the various acts of harassment and persecution observed in a wide range of studies on stalking: (a) hyper

10 In their meta-analysis, Spitzberg and Cupach (2007) processed a total of 175 (!) studies.

Patterns of Stalking Victimization: A Behavioral Typology 115

intimacy, (b) mediated contact, (c) interactional contact, (d) surveillance, (e) invasion, (f) intimidation and harassment, (g) coercion and threat, and (h) physical aggression or violence. These stalking strategies can be outlined as follows:

(a) Hyper-intimacy addresses general courtship activities, the frequency of which is extremely exaggerated. Examples here would be the excessive bestowal of gifts and the exaggerated doing of favors. (b) The category of mediated contact comprises all attempts made on the part of the offender to come into contact with the victim by means of communication. Examples include attempts at establishing contact by writing letters, sending e-mails, or making phone calls. (c) Interactional contacts represent all activities which aim to establish direct personal contact with the victim. Examples include appearing at places where the victim is, or changing residence to be closer to the victim. Furthermore, this behavioral style also encompasses all activities aimed at establishing indirect contact through a third party. (d) Surveillance describes the systematic attempt to gather knowledge and information about the victim – preferably without the victim’s awareness. The palette of potential actions here ranges from covertly following the victim to electronic surveillance. (e) The scenario surrounding intrusion of the victim's private sphere, or invasion, addresses behaviors where either the privacy or the personal living space of the victim is violated. This category encompasses, for example, breaking into the victim’s residence or reading and stealing the victim's mail. (f) The term “intimidation and harassment” incorporates an assortment of both verbal and nonverbal behaviors which share the common objective of instilling fear into the victim, annoying the victim or endowing the victim with some sort of psychological burden. Examples include the dissemination of rumors about the victim, sending vulgar objects or leaving disturbing messages. (g) Conduct attributed to the category “coercion and threat behaviors” is differentiated from attempts at intimidation and harassment in that it entails explicitly negative consequences, or at least clearly implies them. The actions and/or threats can be directed towards the victim him-/herself or against a person close to the victim or to the victim’s property. Examples which can be mentioned here include abducting the victim or forcibly confining the victim in his/her place of residence.

(h) Stalking behavior in the form of physical aggression and violence is characterized by physical attacks – directed either towards the victims themselves or towards their loved ones. Also falling into this category are sexual assault and the deliberate destruction of property belonging to the victim.

Patterns of Stalking Victimization: A Behavioral Typology 116

Table 2: Stalking tactics and assigned behaviors Stalking tactics Item count Items

Hyper-intimacy 1 Unwanted gifts Mediated contact 1 Initiation of contact with letters, etc.

Interactional contact 3 Intrusion of social and occupational networks, messages through third parties, move into the neighborhood

Surveillance 3 Monitoring or observation, following, hacking into computers Invasion 2 Theft of mail, residential breaking and entering

Intimidation and harassment 7 Insults or threats, sending of indecent messages or objects, spread of rumors or lies, ordering of unwanted goods or services, placement of classified ads, postings on the internet, placement of obituary notices

Physical aggression and violence 2 Violent attacks, damage to personal property

Table 2 provides information on the classification of the stalking behaviors 11assessed. It becomes clear which of the observed behaviors are assigned to which stalking tactic. The relative majority of the items correspond to the strategy of “intimidation and harassment”. None of the stalking behaviors observed could be assigned to the scenario of “coercion and threat”. Although the survey did assess whether the victim had been threatened or abused, due to the lack of differentiation between the two elements, the precise classification remained questionable. Here it is assumed that verbal abuse predominates in numbers in comparison to threats, which is why the item was considered an occurrence of "intimidation and harassment". For each of the stalking tactics defined, a new indicator variable was created. The indicator variable was assigned the value 1 when at least one of the corresponding stalking behaviors became evident. Only when none of the behaviors corresponding to this stalking practice was manifest was the indicator variable coded 0. Figure 3 shows the frequency distributions of the new variables. Stalking scenarios taking the form of mediated contact and intimidation and harassment are dominant. Nine out of ten victims experienced mediated contact from their assailant; eight out of ten were affected by intimidation and harassment. Twothirds were confronted with surveillance, and more than half with attempts at establishing interactional contact. Invasion of privacy was experienced by four out of ten victims. Three out of ten had to endure physical aggression and violence. The rather low prevalence of hyper-intimacy behaviors must be interpreted against the background of measurement deficiencies: Exaggerated courtship behaviors were determined only by the unwanted delivery of flowers and gifts.

11 In that the residual category “other infringements” was a catch-all repository for an ample number of very diverse offensive behaviors, it was not taken into consideration in the construction of the general stalking tactics. Thus the final assignment process was only based on 19 items.

Patterns of Stalking Victimization: A Behavioral Typology 117

Figure 3: Frequency of general stalking tactics (n = 311, multiple answers possible)

0% 20% 40% 60% 80% 100

%Mediated contact

Intimidation and harassment

Surveillance

Interactional contact

Invasion

Physical aggression and violence

Hyper-intimacy89%

81%

66%

55%

43%

31%

20%

Measured against general stalking tactics, the number of different stalking profiles is now reduced to 58. In other words: Response patterns based on the new indicator variables occur in 58 different combinations. The complexity of the phenomenology of stalking could thus be substantially reduced.

2.2.2 Determinants of stalking profiles (predictor variables)

12As potential determinants of the pattern of victimization, five measures were taken into account: With respect to the victim, consideration was taken of age, sex, and relationship status at the point in time when the stalking took place (whether he/she was living with a spouse or partner). In addition, the sex of the offender was considered and further note was made of the type of relationship between the victim and the offender. Table 3 provides information on the measurement and distribution of the predictor variables applied. The majority of victims of stalking are women; the majority of perpetrators are men. Around four in ten stalkers were former sexual partners. Both the observed gender ratio and the fact that among obsessive persecutors one encounters numerous ex-partners from previous sexual relationships replicate a finding which often surfaces in international research on stalking (Budd/Mattinson 2000; Mullen et al. 2000; Spitzberg/Cupach 2007; Tjaden/Thoennes 1998; Voß et al. 2006).

12 Our measurement of the various consequences of stalking was inspired by the work of Voß and colleagues (2006).

Patterns of Stalking Victimization: A Behavioral Typology 118

Nearly half of the victims were sharing their home with either a spouse or lifepartner at the time of the stalking.

Table 3: Characteristics of the victim, the offender, and their relationship Count Mean / Standard deviation Age of victim in years 302 38.24 / 12.88 Sex of victim Male 43 14 % Female 261 86 % Residing jointly with spouse or