August 2015
POST-COMPULSORY EDUCATION: WHAT
ARE THE BENEFITS?
A COMPARATIVE STUDY OF THE SOCIAL OUTCOMES
OF EDUCATION IN ENGLAND AND GERMANY
EMMA SALTER, ANGELIKA KUEMMERLING, ROD BOND AND RICARDO
SABATES
Post-compulsory education: what are the benefits? A comparative study of the
social outcomes of education in England and Germany
Table of Contents
Section 1 : Introduction .......................................................................................................................... 1
Theoretical framework ....................................................................................................................... 2
Rationale for the comparative study .................................................................................................. 3
School system ................................................................................................................................. 4
Vocational education and training .................................................................................................. 5
Labour market ................................................................................................................................. 6
Prior research: health and civic participation ..................................................................................... 6
Health and wellbeing ...................................................................................................................... 6
Civic participation ........................................................................................................................... 7
Research questions and hypothesis.................................................................................................... 7
Section 2 : Methodology ......................................................................................................................... 8
The datasets ........................................................................................................................................ 8
Our sample .......................................................................................................................................... 8
Sample characteristics .................................................................................................................... 9
Measurement of key variables ......................................................................................................... 11
Independent variable: post-compulsory education ..................................................................... 11
Dependent variables ..................................................................................................................... 13
Background variables .................................................................................................................... 14
Analytical strategy............................................................................................................................. 15
Multiple imputation ...................................................................................................................... 15
Linear panel model ....................................................................................................................... 15
Latent growth model .................................................................................................................... 16
Section 3 : Descriptive statistics............................................................................................................ 17
Descriptive analysis of educational categories ................................................................................. 17
Prior education.............................................................................................................................. 17
Sex ................................................................................................................................................. 17
Parental education ........................................................................................................................ 18
Descriptive analysis of outcomes...................................................................................................... 19
Health and wellbeing .................................................................................................................... 19
Self-rated health ....................................................................................................................... 19
Smoking..................................................................................................................................... 19
Life satisfaction ......................................................................................................................... 20
Civic participation ......................................................................................................................... 20
Political interest ........................................................................................................................ 20
Voting ........................................................................................................................................ 21
Section 4 : Results ................................................................................................................................. 23
Linear panel models .......................................................................................................................... 23
Health and wellbeing .................................................................................................................... 23
Self-rated health ....................................................................................................................... 23
Smoking..................................................................................................................................... 24
Life satisfaction ......................................................................................................................... 25
Civic participation ......................................................................................................................... 27
Political interest ........................................................................................................................ 27
Voting ........................................................................................................................................ 29
Latent growth models ....................................................................................................................... 31
Health and wellbeing .................................................................................................................... 31
Self-rated health ....................................................................................................................... 31
Smoking..................................................................................................................................... 33
Life satisfaction ......................................................................................................................... 34
Civic Participation ......................................................................................................................... 36
Voting ........................................................................................................................................ 36
Political interest ........................................................................................................................ 38
Section 5 : Discussion and conclusion................................................................................................... 41
Health and wellbeing ........................................................................................................................ 41
Civic Participation ............................................................................................................................. 42
Conclusion ......................................................................................................................................... 43
Acknowledgements........................................................................................................................... 43
Section 6 : References ........................................................................................................................... 44
Appendix A
....................................................................................................................................... 47
List of Tables
Table 2-1: Key characteristics of the final BHPS sample (n=732) ......................................................... 10
Table 2-2: Key characteristics of the final SOEP sample (n=878) ......................................................... 11
Table 4-1: Linear panel results for self-rated health (England) ............................................................ 23
Table 4-2: Linear panel results for self-rated health (Germany) .......................................................... 24
Table 4-3: Linear panel results for smoking (England).......................................................................... 24
Table 4-4: Linear panel results for smoking (Germany) ........................................................................ 25
Table 4-5: Linear panel results for life satisfaction (England) ............................................................... 26
Table 4-6: Linear panel results for life satisfaction (Germany) ............................................................. 26
Table 4-7: Linear panel results for political interest (England) ............................................................. 27
Table 4-8: Linear panel results for political interest (Germany) ........................................................... 28
Table 4-9: Linear panel results for voting (England) ............................................................................. 29
Table 4-10: Linear panel results for voting (Germany) ......................................................................... 30
Table 4-11: Piecewise model of trajectories of self-rated health for age 21 to 35 (England) .............. 31
Table 4-12: Effect of type of post-compulsory education and background controls on changes in selfrated health (England) .......................................................................................................................... 32
Table 4-13: Piecewise model of trajectories of self-rated health for age 22 to 36 (Germany) ............ 32
Table 4-14: Effect of type of post-compulsory education and background controls on changes in selfrated health (Germany) ........................................................................................................................ 33
Table 4-15: Piecewise model of trajectories of smoking for age 21 to 35 (England) ........................... 33
Table 4-16: Effect of type of post-compulsory education and background controls on changes in
smoking (England)................................................................................................................................. 34
Table 4-17: Single linear growth model of trajectories of smoking for age 25 to 36 (Germany) ......... 34
Table 4-18: Piecewise model of trajectories of life satisfaction for age 21 to 35 (England) ................ 35
Table 4-19: Effect of type of post-compulsory education and background controls on changes in life
satisfaction (England)............................................................................................................................ 35
Table 4-20: Piecewise model of trajectories of life satisfaction for age 21 to 36 (Germany) .............. 36
Table 4-21: Effect of type of post-compulsory education and background controls on changes in life
satisfaction (Germany) .......................................................................................................................... 36
Table 4-22: Change in voting over three general elections (England) .................................................. 37
Table 4-23: Effect of type of post-compulsory education and background controls on changes in
voting (England) .................................................................................................................................... 37
Table 4-24: Unconditional piecewise model of political interest (England) ......................................... 38
Table 4-25: Effect of type of post-compulsory education and background controls on interest in
politics (England) ................................................................................................................................... 39
Table 4-26: Unconditional piecewise model of political interest for age 21 to 36 (Germany)............. 39
Table 4-27: Effect of type of post-compulsory education and background controls on changes in
political interest (Germany) .................................................................................................................. 40
Table A-1: Linear panel model results for self-rated health (England) ................................................. 48
Table A-2: Linear panel model results for self-rated health (Germany) .............................................. 49
Table A-3: Linear panel model results for smoking (England) ............................................................. 50
Table A-4: Linear panel model results for smoking (Germany) ........................................................... 51
Table A-5: Linear panel model results for life satisfaction (England) ................................................... 52
Table A-6: Linear panel model results for life satisfaction (Germany) ................................................. 53
Table A-7: Linear panel model results for political interest (England) ................................................. 54
Table A-8: Linear panel model results for political interest (Germany) ............................................... 55
Table A-9: Linear panel model results for voting (England).................................................................. 56
Table A-10: Linear panel model results for voting (Germany).............................................................. 57
Table A-11: General election year by age and year of birth (n in parentheses) (England) ................... 58
Table A-12: Number of participants for political interest by age and year of birth (England) ............. 59
List of Figures
Figure 1-1: Three capitals framework ..................................................................................................... 3
Figure 1-2: The German school system: a simplified diagram ................................................................ 4
Figure 1-3: The English school system: a simplified diagram ................................................................. 5
Figure 2-1: The ages of our sample over the course of the survey (BHPS)............................................. 9
Figure 2-2: How the education trajectories evolve over time .............................................................. 12
Figure 2-3: Proportion in each educational category in final samples ................................................. 13
Figure 2-4: Model 1 ............................................................................................................................... 15
Figure 2-5: Model 2 ............................................................................................................................... 15
Figure 2-6: Model 3 ............................................................................................................................... 16
Figure 3-1: Characteristics of trajectories by young people’s prior education..................................... 17
Figure 3-2: Characteristics of trajectories by young people’s sex ........................................................ 18
Figure 3-3: Characteristics of trajectories by highest parental education............................................ 18
Figure 3-4: Proportion of people reporting good or very good health in England (n=732) and
Germany (n=878) .................................................................................................................................. 19
Figure 3-5: Proportion of people who smoke in England and Germany .............................................. 20
Figure 3-6: Mean life satisfaction reported by people in each educational category in England and
Germany................................................................................................................................................ 20
Figure 3-7: Proportion of people who reported a high level of political interest in England and
Germany................................................................................................................................................ 21
Figure 3-8: Proportion of people who vote in England and Germany .................................................. 21
Figure A-1: UK policy interventions affecting youth transitions in the late 1980s and the 1990s ....... 47
Post-compulsory education: What are the benefits? A comparative
study of the social outcomes of education in England and Germany
Section 1 : Introduction
Since the time of Aristotle, education has been viewed as a public good; he stated that the education
of young people benefits society as a whole, both morally and intellectually (Aristotle, 1996). In
recent years educational researchers, sociologists and politicians have increasingly paid attention to
the measurable outcomes of education, in order to gain a broader understanding of the benefits of
education. Initially, this research was predominantly found in the field of economics with a focus on
the returns to education in terms of job opportunities on the labour market, career opportunities
and wages (Konsortium Bildungsberichterstattung, 2006; Reinberg and Hummel, 2007; Spangenberg
et al. 2012) as well as cost-benefit analyses of undertaking more years of education (OECD, 2012);
particularly relevant in societies where there are significant costs associated with pursuing further or
higher education. More recently, however, there has been increased interest in the non-economic or
social benefits of education either in staying on in education (OECD, 2009; BIS, 2013), or of returning
to education in adulthood (Bynner and Hammond, 2004). Increased levels of education are
associated with reduced crime rates (Sabates, 2008; Lochner, 2011), to greater happiness and
wellbeing (McMahon, 2010), improved health (Feinstein et al, 2006; Lundborg, 2008), and a more
civically-engaged and tolerant society (Bynner and Egerton, 2001).
In sum, the body of existing research provides convincing proof that education has both economic
and non-economic benefits to society. However, as identified by Heise and Meyer (2004) there are
gaps among this research:
“measurements of education and training tend mainly to be limited to the level of graduation
and the number of years in schooling or training. This creates a considerable gap in the field
of life-course research, because too few cross-European comparisons can be made about
differences in the type and quality of education and training and of qualifications and skills.”
Heise and Meyer 2004; p.351
The majority of reports have taken as their independent variable the number of years spent in
education, or the highest level of educational qualifications obtained, comparing for instance in
Education at a Glance, the differences between those who have completed tertiary education, upper
secondary and below upper secondary (OECD, 2009). Less research has focussed on the type of
education being pursued, for example, whether vocational education brings as many advantages as
an academic course of study. The European Centre for the Development of Vocational Education
(CEDEFOP) has commissioned a number of reports into similar analysis across Europe with a focus on
vocational education and training, whether initial or continuous. Sabates et al (2010), using the
European Community Household Panel data found that there were social benefits of Initial vocation
education and training (IVET) and continued vocational education and training (CVET) but that these
were dependent on the type of VET system in a wider social context. A further study by CEDEFOP
found that there were a range of social benefits to participation in vocational education and training,
among them reduced uptake of unhealthy behaviours like drug taking and smoking, increased social
integration and cohesion (primarily via labour market participation and improved wellbeing and selfesteem (CEDEFOP, 2011). To date, although the empirical literature has shown that educational
qualifications are linked to improved health and more civic participation, very few studies have
focused on differences between academic and vocational qualifications in achieving these outcomes.
1
This study aims to fill the knowledge gap regarding the kind of education that matters for achieving
social benefits. The work draws heavily on research funded by the Department of Education at the
Centre for Research on the Wider Benefits of Learning, from which important theoretical models for
understanding the social benefits of education were developed as well as empirical evidence
(Schuller et al., 2004; Feinstein et al., 2006). From that work, a collaborative project emerged
funding by CEDEFOP where the social benefits of vocational education and training in a European
contexts were investigated by members of the research team (Sabates et al., 2010; Sabates, Salter
and Obolenskaya, 2012). This body of evidence enabled the research team to generate testable
hypotheses which are presented in this research report.
The report is structured as follows. In this section, we first outline the theoretical framework which
underpins the research before discussing the rationale for the comparative nature of the study. We
then review existing research on the topic and set out our research questions and hypotheses.
Section 2 introduces the datasets, the key variables of interest and the analytical strategy, followed
by descriptive analyses of the data in Section 3. Section 4 presents the results of our analytical
models, which are then discussed in Section 5 together with conclusions and policy implications
arising from the study.
Theoretical framework
Although, as shown, there has been a substantial body of research developing about the benefits of
education, there is not a consensus on the mechanisms through which such benefits occur or
whether education per se is responsible for positive outcomes or whether the type of education
matters. Theorists in a number of disciplines have grappled with how education benefits an
individual and through them a society. Developing the idea of economic capital, economists and
educationalists have conceptualised additional types of capital as mechanisms through which
education is beneficial. Social capital, in relation to education, has been used to describe both the
networks and relationships which are formed in the learning context (Coleman, 1998) and the
capabilities that a student gains through their education which can be applied to other areas of their
lives, for example, engaging in their community or wider society (Schuller et al., 2004). Social capital
is expected to increase in all educational contexts as relationships are fostered between a student
and their peers and mentors, enlarging people’s social networks. Human capital refers to the
attainment of qualifications which have a transactional value in the labour market (Côté, 2005).
Since human capital relates to the acquisition of specific skills and knowledge which have value in
wider society, education in any form has the potential to increase human capital, which may result in
increased social status. However, in an age of increased credentialism, with more graduates than in
any other period, and rises in graduate underemployment and unemployment (Schuller et al. 2004)
human capital gained through education does not necessarily translate into economic opportunities
(Wyn and Dwyer, 2002). Simultaneously, those without such credentials have even fewer
opportunities open to them due to their lack of observable human capital (idib.).
Identity capital refers to the self-esteem and self-concept that education can engender in an
individual. It has been argued that work-related training has a specific role to play in increasing
identity capital, since it helps to foster an identity which is connected to the work role (Colley et al.,
2003). In this sense, it can be assumed that the German dual system of vocational education and
training which has a longstanding tradition and close links between the workplace and the training
content would increase a German trainee’s identity capital more than that of their English vocational
2
counterpart, since the majority of vocational education and training takes place outside of the
workplace in a Further Education setting. In comparison to their English counterparts, researchers
found a more developed sense of identity and of citizenship in German apprentices, along with
greater community engagement and a higher interest in politics and increased life satisfaction
(Fouad and Bynner, 2008; Bynner, 2011).
Schuller et al (2004) developed these three concepts of capital into a “three capitals framework”,
which for the first time provided a way of visualising the interconnections between the three types
of capital and situated a range of social benefits within it (see Figure 1-1).
Figure 1-1: Three capitals framework
Source: Schuller et al., 2004
Rationale for the comparative study
Post-compulsory education has different meanings and is organised in different ways across
countries. Depending on the role post-compulsory education plays in a society and the way it is
structured, the outcomes for the individual may differ. Therefore, the current study investigates the
benefits of post-compulsory education in two different educational contexts: England and Germany.
Germany and England are often used in comparative analysis, since they differ from each other in
three important areas relating to education and the labour market and the transitions between the
3
two, namely the school system, the vocational education and training system and the organisation
of the labour market. We shall look at each of these in turn.
School system
The school system in Germany is highly stratified, with the type of school being entered at age 10 a
strong predictor of the type of education and career that follow (see Figure 1-2). The educational
content in the Hauptschule and Realschule prepare young people for vocational training when they
leave school, while attending the Gymnasium and obtaining the Abitur at age 19 is the route for
those continuing to University1, although increasingly employers require the Abitur for entry to
apprenticeships. Whichever type of school they attend, German young people are required to
remain in the education system until at least 18. Education in Germany therefore is not considered
“post-compulsory” until the age of 18/19. An additional distinction is that people undertaking
degrees in Germany in the 1990s would graduate on average at the age of 28, far later than the
average for English graduates (OECD, 2002).
Figure 1-2: The German school system: a simplified diagram
Source: based on http://www.bpb.de/fsd/bildungsgrafik2/?1; Note: ages cited are proxies
In England in the 1990s the school system had the appearance of a more equitable system (see
Figure 1-3). Although there were public and private fee-paying schools open only to the moneyed or
(less often) the very intelligent but less well off, the majority of children were educated in state
schools, without the stratification of the German system. In this period, compulsory education
ended at age 16 when examinations called GCSEs were sat, after which (and largely dependent on
the GCSE results), pupils had a number of educational options open to them. They could continue in
school and study for A-level exams at age 18 or proceed to a further education college, where they
could study A-levels or vocational qualifications such as NVQs. Alternatively they could leave school
at 16 and seek employment, or join an apprenticeship or youth training scheme. At age 18 they
1
Even in this period of time, there has been different ways to get entry to the University but they played only
an insignificant role, which is why they are ignored here.
4
could apply to higher education institutions to study a degree (BA/BSc) or a vocational BTEC or HNC,
graduating on average at the age of 21.
Figure 1-3: The English school system: a simplified diagram
In terms of differences between the post-compulsory education in each country, an obvious
dissimilarity therefore is the age at which it commences: in England in the 1990s, post-compulsory
education started at 16, while in Germany, as discussed above, post-compulsory education begins no
earlier than age 18. As to the content and context of post-compulsory education routes in England
and Germany, there were differences in both the academic and vocational pathways. For the
academic route, the primary contrast is in the age of completion. Until the implementation of the
1999 Bologna Declaration began (Kehm and Teichler, 2006), undergraduates in Germany would
begin their studies at 19/20 but were unlikely to complete their degree until the age of 28 (OECD,
2002). In England, the majority of students began aged 18 and would graduate by the age of 21,
after three fulltime years of study. In terms of vocational education however, there are stark
differences between the two countries.
Vocational education and training
The dual system of vocational education and training in Germany is an internationally recognised
model due to its long history and tradition, its strong links between education and industry and its
formalised and well-established structure. The renowned dual-system of Germany is an example of
Berufliche Bildung, with strong links between education and industry and clear paths for young
apprentices to follow to gain accreditation in their particular field (Rauner, 2006). Vocational
education in Germany is both valued and necessary for many occupations, having a broader focus in
this respect than the English system and garnering more status as a result (Hillmert, 2002). Indeed in
the early 1990s about two thirds of school leavers (including those with an Abitur) started an
apprenticeship (BIBB, n.d.).
5
Gangl (2003) contrasts Germany’s “extensive vocational training system” in which specific skills are
gained with the UK’s “general qualifications” system. In the UK, vocational education and training
has had numerous guises since the 1980s, from Youth Training Schemes to Modern Apprenticeships,
coupled with a range of qualifications from a number of accrediting bodies and frameworks. Figure
A-1 in the Appendix details key changes to the policy environment for vocational education and
training and employment opportunities for youth through the 1990s. These multiple changes have
served to impact on the esteem in which vocational qualifications are held and to complicate the
choices young people can make. As Shavit and Müller have stated, the government’s involvement
has been less about a cohesive training system and more about attempting to reduce
unemployment (Shavit and Müller, 2000).
Labour market
There have been a number of attempts to typologies the school-to-work transitions and the labour
market contexts in Western Europe (Gangl, 2001), most often resulting in binary categories, whether
Marsden’s (1999) occupational labour market versus internal labour market, or Shavit and Müller’s
(2000) qualification spaces or organisational spaces; in both cases, the VET system in Germany
situates them in first category while the less standardised system in England, which is based more in
the classroom than in the workplace places them in the second. Gangl (2001) attempts to move
away from these dichotomies in his analysis and implements a third category which accounts for the
Southern European countries, but Germany and England remain not only as distinctly different types
but as exemplifying contrasting systems (Brzinsky-Fay, 2007).
Prior research: health and civic participation
In Western societies such as England and Germany, with the current demographic of an aging
population and increased migration the questions of improved health and civic participations are
important features which is why our research is focussed on these last two domains. In the following
we elaborate a bit more on both topics.
Health and wellbeing
Education is seen to be beneficial to health in a number of ways. Increased education increases
health literacy, that is, people’s ability to engage with doctors and other medical staff, to speak
confidently about their issues and to understand their responses, for example in taking medicine
correctly (de Walque, 1997). Participating in education can change people’s time preferences (Fuchs,
1982), planning for the future rather than living day to day which has repercussions on their health
decisions, particularly around health behaviours such as smoking and healthy eating. Analysis of the
National Child Development Study (NCDS) by Schuller et al (2004) found positive changes in health
behaviours among adults who had engaged in adult education courses compared to those who had
not.
Nationally and internationally, improvements in health and health behaviours have been linked to
increased years of education (Feinstein et al., 2006). Bynner and Egerton (2001) found that both
men and women graduates were twice as likely to report excellent health as those with no A-levels.
These differences remained significant but lessened when a range of socio-economic and
background characteristics were controlled for. Others, however, such as Carneiro et al. (2007) have
cast doubt on the existence of a causal relationship between education and health, finding evidence
instead of unobserved heterogeneity, where skills assessments at age 11 predicted both educational
attainment at age 16 and health and health behaviours in adulthood. Cutler and Lleras-Muney
(2010) support this argument, finding that twenty percent of the effect of education on health can
6
be explained by cognitive skills. In contrast, Elias et al (2002) discovered very little in the way of noneconomic benefits of education, and found any protective influence on ill-health to have declined
over the course of the 1990s. They conclude that the increased participation in higher education has
marginalised the positive effects seen in the early 1990s.
Civic participation
In a society that is undergoing demographic and/or socio-cultural change, cohesion of the different
groups in that society is crucial. One way of achieving this cohesion can be via civic participation,
political interest, voting, volunteer work and so one. A number of suggestions have been made
about the role education can play in enhancing civic participation. Education can change an
individual’s social capital by introducing them to new peer groups and their associated networks:
“human capital as…social capital” (Glaeser, Ponzetto and Shleifer, 2007; p.78). Alternatively, skills
and knowledge acquired through education (such as historical or cultural knowledge and critical
thinking) may increase the quantity or quality of civic participation (Milligan, Moretti and
Oreopoulos, 2003). In addition, in many countries, education systems themselves are structured
with the aim of imparting democratic values and socialising children and young people (Glaeser,
Ponzetto and Shleifer, 2007). Bynner and Egerton (2001) assess the effect of higher education on
non-economic outcomes using the National Child Development Study (NCDS). They found that male
graduates were nearly three times as likely to be an active member of a voluntary organisation than
males with less than A level education, even when a controls were fitted to the model, and female
graduates were more than twice as likely than a woman with GCSE level education to be actively
involved in a voluntary organisation. Both men and women graduates were more likely to vote than
those with below A-level education, but mature graduates were even more likely to vote.
Easterbrook et al. (2015) in an analysis of three national datasets, found a stronger education effect
for political outcomes than for health and wellbeing.
Research questions and hypothesis
Against this background our research focussed on two main questions and associated with these on
the following hypotheses:
1. What is the role of post-compulsory education in the formation of social outcomes in
adulthood?
o Hypothesis 1: Academic education in both countries will lead to better outcomes
than for those who left education
2. Do different national educational systems result in different outcomes?
o Hypothesis 2:Due to the higher status of vocational education in Germany (its links
to the labour market, the increased identity and social capital that it brings)
compared to the status of vocational education and training in England, the effect of
academic and vocational trajectories on social outcomes in Germany will be more
similar than in England.
o Hypothesis 3: Young people in Germany are kept in compulsory education for longer
(until age 18) so enter post-compulsory education at a later age. This would lead us
to expect to find fewer effects of post-compulsory education on outcomes in
Germany than in England
o Hypothesis 4: Young people (or their parents) in Germany have to decide on the
educational route they will take from a far earlier age than in the UK; this is why we
expect parental background to have more influence in Germany
7
Section 2 : Methodology
The datasets
For the purpose of the analysis we used two household panel surveys: the German Socio-Economic
Panel (SOEP) and the British Household Panel Survey (BHPS). The SOEP has been running from 1984
to present, with information collected annually. In the first year, 13,361 individuals from 5,969
households were interviewed (Kroh and Spiess, 2008). The BHPS has been running from 1991 to
present, with information collected annually. 10,264 individuals from 5,505 households were
interviewed in the first year (Taylor et al., 2010). All adults in the households in both surveys have
since been included in the study on an annual basis to the present day, with new adult members of
the household included in the studies, including children in the households when they turn 16.
Questions are asked on a range of topics, from education to job histories, information on health to
views and attitudes, with many repeated every year (or wave), and others in alternate or selected
waves. One of the many advantages of using these data is that we are able to follow the educational
histories of young people year by year and then link these to their later outcomes.
Our sample
For the purposes of the research, we needed to identify a cohort of young people for whom we had
information on educational and social background, detailed information on post-compulsory
education and who would be present in the sample into adulthood, in order to investigate their
outcomes at the latest time point.
The BHPS and the SOEP are structured by wave, the year in which interviews took place. However,
for our purposes the data must be structured by age, since we are interested in how participants
change as they get older, rather than how people of different ages change by year of interview. In
structuring the data by age, however, we need to take care with the amount and pattern of missing
data since the number of people providing data at each age will, of course, depend on how old they
were in 1991 when the study began.
We therefore decided to confine the two samples to those who were present in the first wave of
interest, 1991. Those who had joined the sample in later years would have been interviewed a fewer
number of times and hence would not have provided data at the older ages. Moreover, they would
have been growing up at a different stage in history. The period 1991 – 2010 is one of rapid change
in educational and employment opportunities, particularly in the UK, and by confining our sample to
a single cohort, we ensure that they all will have experienced much the same external environment.
Hence, we chose respondents born between 1970 and 1975, and therefore aged between 16 and 21
years in 19912. Since participants were interviewed annually until 2010, this provided an age range
of 16 to 40. However, we decided to ensure that each birth year cohort would provide data for each
age. Otherwise, the proportion of missing data at the younger and older ages would be high because
not all birth year cohorts would provide data. We therefore confined our attention to the age range
from 21 years (the youngest for the 1970 birth year) and 35 years (the oldest for the 1975 birth
years). Figure 2-1 gives a graphical representation of the ages of our sample over the years of data
collection with horizontal lines marking the ages at which outcome data was observed. As English
participants were not interviewed in 2009 (due to the transition from BHPS to Understanding
Society), those born in 1974 do not provide data for those aged 35 and those born in 1975 do not
provide data for those aged 34 years. In these instances, we took the data on outcomes from the
2
For SOEP the youngest age was 17 as this is the first age that respondents are interviewed.
8
year before to minimise the amount of missing data. Otherwise, each birth year cohort provides
data for each age.
Age
Figure 2-1: The ages of our sample over the course of the survey (BHPS)
45
40
35
30
25
20
15
10
5
0
Year
There were 866 participants born in 1970-5 in 1991 in the BHPS. However, 134 of those born in 1971
or later drop out before they reach 21 years, and so our sample comprises 732 participants. For
some of our outcome measures, however, the sample size is a little smaller due to some participants
not providing any data for the particular outcome. For the SOEP, respondents were also dropped
from the sample if they left the survey by the age of 21, resulting in a final sample of 878.
Sample characteristics
Key characteristics of the two samples are presented in Table 2-1 and 2-2. In the BHPS, there are
somewhat more men (55%) than women (45%), and numbers are more or less equally divided across
the years of birth. The majority are from a middle class background: over half of the sample have at
least one parent with further or higher educational qualifications. In terms of occupational class,
41% had a parent in a professional or managerial occupation, and a further 49% had a parent in a
skilled occupation. Only 10% had parents in a partly skilled or unskilled occupation. 80% were living
with both biological parents when aged 16 years. In terms of their educational background, 36% had
five or more GCSE passes at grades A-C, and 22% had a degree.
9
Table 2-1: Key characteristics of the final BHPS sample (n=732)
Year of birth
1970
1971
1972
1973
1974
1975
Total
Sex
Female
Male
Total
Highest parental
Never went to school
education
Left school no qualifications
Left school with some qualifications
Got further education qualifications
Got university/higher education
degree
Total
Highest parental
Unskilled
occupational class
Partly skilled
Skilled
Professional /manager
Total
Living with both
No
biological parents at Yes
age 16
Total
Has 5 or more GCSEs No
at grades A-C
Yes
Total
Has a degree
No
Yes
Total
Frequency
144
121
122
118
132
95
732
330
402
732
6
119
141
318
84
Valid percent
19.7
16.5
16.7
16.1
18.0
13.0
100.0
45.1
54.9
100.0
0.9
17.8
21.1
47.6
12.6
668
14
52
320
272
658
125
492
617
469
260
729
567
156
723
100.0
2.1
7.9
48.6
41.3
100.0
20.3
79.7
100.0
64.3
35.7
100.0
78.4
21.6
100.0
The German sample has an almost equal distribution between the sexes (49% men, 51% women)
and the sample is skewed towards the earlier birth years, with progressively fewer in the later birth
years. The majority of respondents’ fathers (45%) have a vocational training; the second largest
occupational category is those whose fathers have a professional or managerial occupation (27%).
Nearly a third of the sample have achieved the Abitur, indicative of the proportions of young people
who attend the Gymnasium in Germany, rather than the Realschule and Hauptschule which is
usually followed by vocational qualifications. This percentage is slightly higher than in the general
population, but is unsurprising since there is a bias in the SOEP towards the better educated and
middle-class members of the population (Becker, 2014)).
10
Table 2-2: Key characteristics of the final SOEP sample (n=878)
Year of birth
Sex
Highest parental
education
Highest paternal
occupational class
Achieved Abitur
1970
1971
1972
1973
1974
Total
Female
Male
Total
No leaving certificate
Hauptschule
Realschule
Gymnasium
Frequency
220
201
176
145
136
878
449
429
878
131
335
219
107
Valid percent
25.1
22.9
20.0
16.5
15.5
100.0
51.1
48.9
100.0
16.5
42.3
27.7
13.5
Total
Unemployed
Unskilled
Semi-skilled
Vocational training
Professional/manager/technical
Total
No
Yes
Total
792
32
35
153
363
217
800
621
255
876
100.0
4.0
4.4
19.1
45.4
27.1
100.0
70.9
29.1
100.0
Measurement of key variables
Independent variable: post-compulsory education
The first stage of our analysis was to construct the post-compulsory educational trajectories of our
sample. Using the annually updated questions on the qualifications obtained since the previous
year, these qualifications were assessed as being academic, vocational, or both of these in cases
where two different types of qualification had been obtained simultaneously. In some cases,
difficult decisions had to be made as to which category certain qualifications were placed in. For
example, teaching and nursing were deemed to be “academic” qualifications in the English dataset,
despite their being seen as vocations, since by the 1990s, it was more common for teachers and
nurses to be taking degrees or other academic courses to qualify, rather than a vocational course in
the sense of the NVQs or BTECs on offer. Likewise in the SOEP there was a change in the
measurement educational categories what complicated the allocation to categories due to
inconsistencies over time (DIW Berlin/ SOEP, 2014).
In England, young people will generally have finished their engagement with post-compulsory
education by the age of 25, with most graduates leaving University at the age of 21. For this reason,
we chose to look at the educational histories of young people in England between the ages of 16 and
25. While it is possible for young people who have left school without sufficient qualifications for
further study to re-engage with post-compulsory education at a later stage, via access courses, this
information would only have been included in our sample’s educational histories if it occurred
11
before the age of 25. Due to the nature of the educational system in Germany, as discussed earlier,
we assessed the qualifications of German young people between the ages of 18 and 28, to capture
the structural differences in completion ages of tertiary education.
The annual qualification information for each individual was used to create trajectories of their
progress through post-compulsory education. Some only achieved academic qualifications, some
only vocational, others a mixture of the two and some had not attained any qualifications since the
compulsory school leaving age. Figure 2-2 shows how the respondents move into their respective
categories over time within the two datasets.
Figure 2-2: How the education trajectories evolve over time
England
Germany
100%
100%
90%
90%
80%
80%
70%
70%
60%
60%
50%
50%
40%
40%
30%
30%
20%
20%
10%
10%
0%
0%
academic
vocational
mixed
no further ed
In both datasets it can be seen that the no further education category shrinks as people attained
qualifications, and how the “mixed” category increases due to people achieving both an academic
and vocational qualification over time. It is notable how swiftly the categories ‘settle’ in the English
cohort, indicating the earlier completion age of post-compulsory education in England. In
comparison the German cohort tend to spend longer in education on average, particularly if
pursuing a University degree, as evidenced by the later emergence of the academic category.
Based on what qualifications respondents had acquired on the completion of their post-compulsory
trajectories, we placed them in one of four final categories: “academic”, for those with purely
academic qualifications, “vocational” for those with purely vocational qualifications, a “mixed”
category comprised of those who achieved both academic and vocational qualifications, no matter
in which order these were obtained; and finally a “no further education” category which consisted of
those who left the education system at 16 in England and after 18/19 in Germany, but who did not
achieve any qualifications within the subsequent period under observation3. Figure 2-3 shows the
proportion of respondents contained in each category in the two datasets. As Figure 2-3
demonstrates there are large differences in the educational trajectories in the observed cohorts
between the two countries. The importance of vocational education in Germany shows clearly with
3
the percentage in the no further education category is exceptionally high in Germany because it may contain people who
are still in education, despite our best efforts to eliminate this issue.
12
more than 60 per cent of respondents stating that they completed full vocational training after
school, whereas only 35 per cent of respondents in England have only vocational qualifications. On
the other hand nearly one in four in England followed the academic track while only 11 per cent in
Germany did so, though this may be explained by the academic category in England including those
who took A-levels, since this was after the compulsory leaving age at this time. Surprisingly similar
are the share of people with no further education after leaving school.
Figure 2-3: Proportion in each educational category in final samples
70
62
60
50
40
35
%
30
23
23
19
20
22
11
10
6
0
academic
vocational
England
mixed
no further education
Germany
Dependent variables
As has been outlined above we were particularly interested in the social outcomes of education in
the two domains of health and civic participation.
In the health domain, three measures were chosen: self-rated health, smoking and life satisfaction.
For self-rated health, at each wave of the BHPS, participants were asked, “Please think back over the
last 12 months about how your health has been. Compared to people of your own age, would you
say that your health has on the whole been...” and rated their health on a 5-point scale (Excellent,
Good, Fair, Poor, Very poor). The one exception is that the question was not asked in wave 9 (1999)
and hence is missing by design for our birth cohorts in this year. In terms of our outcome variable,
this affects the self-rated health at 25 only and for the 1974 cohort. In this case, their health from
the previous year was used instead. In the SOEP participants were asked “How would you describe
your health at present?” Answer categories were given on a 5-point scale (Very good, good,
satisfactory, poor and very poor).
Smoking was measured from yes/no answers to the question “do you smoke?” in the BHPS and
recoded from answers to the question, “Do you smoke, be it cigarettes, a pipe or cigars?” in the
SOEP. As only a few waves of data were available for the smoking measure in the SOEP, it was not
possible to measure it at ages 25 and 35 as with the BHPS. Instead, it was measured in 1999 and
2010. Possible answers differed in the two years. In 1999 the options were no, no, but I used to and
yes, while in 2010 people could only chose between yes and no. For the current research we recategorised the 1999 answers into yes and no, representing currently smoking or not smoking.
13
Overall life satisfaction was measured on a seven point scale in the BHPS in response to the
question: “How dissatisfied or satisfied are you with.........your life overall”, and on an eleven point
scale in the SOEP, responding to the question: “How satisfied are you with your life, all things
considered?” with 0 meaning not at all satisfied and 10 highly satisfied.
Civic participation was measured in two ways, political interest and voting. In both the BHPS and the
SOEP, respondents were asked, “Generally speaking, how much are you interested in politics?” on a
scale of 1 to 4 with 1 indicating very interested and 4 not at all interested. In both datasets these
were recoded so that a higher value represented higher levels of interest.
Voting was measured in the general election years in both countries to minimise any bias due to
prolonged periods of recollection, with slightly different questions: “did you vote in the last
election?” for the BHPS and “would you vote if there was an election on Sunday?” in the SOEP. The
SOEP measured voting behaviour only twice, 2005 and 2009 in the relevant time period – in both
years general election took place.
With the exception of the voting variable, and the smoking variable in the SOEP, each outcome was
measured at two time points (age 25 and 35), with age 35 the latest time point where we were able
to use information from all survey participants. Thus, we were able not only to differentiate
between the immediate and longer term effects of post-compulsory education. Analyses were
conducted for the whole sample and separately by gender; the gender-disaggregated results will be
reported for all models.
Background variables
Social and educational background controls were selected based on their prior use in research, their
known influence on children’s education and their availability in both datasets. The final controls
were parental education, parental occupational class and prior education.
There were some slight variations between the measurement of these variables in the two datasets,
reflecting cultural differences and the educational context discussed earlier. For parental education,
the higher level of education was taken of the parents, or the available information if one was
missing. In England, the educational categories were: left school with no qualifications, left school
with some qualifications, has further education qualifications and has university or higher education
qualifications. In Germany, the categories were: no further education, semi-skilled, has a vocational
training, has a university degree. In the final analyses, a dichotomous variable was used for both
countries comparing further/higher education to all other categories. For occupational class, in
England, again, parental occupational class was used, taking the higher of the parents’ class, or, the
one for which we have information if there was missing information on the other parent. In
Germany, the father’s occupational class was used as the great majority of the respondents’ mothers
have been housewife and as such were not employed. In the final analysis, a dichotomous variable
was again used, comparing the professional/managerial class to all others.
In addition to these parental level variables, we controlled for the young person’s prior education.
This was based on GCSE attainment in England, whether or not the young person achieved five or
more A-Cs at GCSE. In Germany, the comparable variable was whether or not a young person
achieved the Abitur at age 19. In both cases, therefore, the prior education was measured in the
year before post-compulsory education was entered into or measured in each country. In the English
data we included whether or not the respondent was living with both biological parents at age 16, as
14
a measure of childhood stability. The comparable measure in the SOEP contained too few data to
allow for analysis.
Analytical strategy
Our research is concerned with two main questions: firstly, what short- and long-term impact does
the type of post-compulsory education have on the health and civic participation of adults and
secondly, does the educational system matter. In order to answer our research questions we
conducted two different analyses: a linear panel model and a latent growth model. The linear panel
model enables us to understand the relationships between the educational trajectories and social
outcomes at two time points in adulthood, while controlling for socio-economic factors, while the
latent growth models allow us to investigate individual changes in these outcomes over time and
whether these were different depending on type of education.
Multiple imputation
In order to deal with missing data in the two datasets, multiple imputation was used. One advantage
of multiple imputation is that we can include more variables in the model to derive imputed values
than are included in our substantive models, and therefore include variables that cause missingness
but are not of substantive interest. For instance, in the BHPS, household income and scores on the
general health questionnaire (a measure of psychological wellbeing) between ages 21 to 35 were
included as potential predictors of missingness. We imputed twenty datasets as this is deemed a
sufficient number (Enders, 2010; Shafer & Graham, 2002). Both types of models were run using
these imputed datasets, using MPlus (Muthén and Muthén, 1998-2012).
Linear panel model
Three models were run for each outcome. The key independent variable, type of post-compulsory
education, was represented by three dummy variables for academic, vocational and mixed
education. Model 1 regressed post-compulsory education on the outcome at age 35 (or the latest
time point), to see whether there were any differences at age 35 by type of education, controlling
only for year of birth.
Figure 2-4: Model 1
Post-compulsory education
Outcome at age 35
Model 2 regressed post-compulsory education on the outcome at age 35 but additionally included
the outcome at age 25 (or the earlier time point) in order to examine whether the effects of
education were visible on the outcome at an earlier time point. As with Model 1, the only control
was year of birth.
Figure 2-5: Model 2
Post-compulsory education
Outcome at age 25
Outcome at age 35
15
A third model (see Figure 2-6) included background controls to investigate their effect on any
observed relationship between post-compulsory education and the outcomes. In addition, we
tested for the significance of the indirect effects of further education on the outcome at age 35 via
the outcome at age 25 using bootstrapped standard errors (Mackinnon, 2008). In all models, the
post-compulsory education category of “no further education” was the reference category; that is
each model measured the effect of three different routes through post-compulsory education on
social outcomes compared with the effect of leaving the education system.
Figure 2-6: Model 3
Post-compulsory
education
Outcome at age 25
Outcome at age 35
Socio-demographic
factors
Latent growth model
We wanted to assess the impact of type of education on health outcomes and civic participation,
once post-compulsory education had been completed, and therefore looked for change after the
age of 25. Given that we have measures on outcome variables typically annually from age 21 to age
35, we fit a piecewise model (sometimes called a spline regression), where we are able to distinguish
differences in initial standing at age 21, change between 21 and 25 when many were undertaking
further education, and change from 25 to 35 after further education has been completed.
Three models were run for each outcome: the first was an unconditional growth model, that enables
us to see whether there are significant individual differences in initial status, change between 21 and
25, and change between 25 and 35; the second examined the effect of the educational category on
each outcome and the third additionally included the same background controls as the multiple
regression models. Year of birth was once again included to control for birth cohort effects. All
analyses were carried out separately for men and women.
Such a strategy risks underestimating the effect of type of education, particularly in the English
context, where the average age of finishing further or higher education is lower than in Germany
and may have had an immediate impact. So some of the differences at age 21, and some of the
change from age 21 to age 25 may be due to type of further education.
Political interest and self-rated health were treated as ordered categorical variables. In order to
ensure measurement equivalence, threshold invariance was imposed between men and women and
between age 25 and age 35 (Little, 2013). Models were fit using the WLSMV estimator.
16
Section 3 : Descriptive statistics
Before proceeding to the more complex statistical analysis, we undertook two sets of descriptive
analyses. Firstly, an analysis of the characteristics of the educational categories to identify any
associations between social and educational background factors and the educational categories
followed in the two samples. Secondly, the outcome variables were analysed by educational
category, again to observe any patterns in the data before embarking on the statistical modelling.
Descriptive analysis of educational categories
Descriptive analyses of the different educational categories were undertaken, to examine
commonalities and differences between the two samples.
Prior education
It is evident from Figure 3-1 that prior education is strongly linked to the type of post-compulsory
trajectory pursued – in both countries. The German figure is slightly artificial since the Abitur can
only be achieved within the Gymnasium, and is a precondition of University education. What is of
interest therefore is that in the English school system which is not so heavily stratified, GCSE
attainment is playing much the same role in determining the post-compulsory route of these young
people as the stratified education system in Germany.
Figure 3-1: Characteristics of trajectories by young people’s prior education
England
Germany
100
90
80
70
60
% 50
40
30
20
10
0
100
90
80
70
60
% 50
40
30
20
10
0
academic vocational
5+ GCSEs A-C
mixed
no further
ed
<5 GCSEs A-C
academic vocational
Abitur
mixed
no further
ed
No Abitur
Sex
In terms of sex, there are some differences between the samples in the two countries with a much
larger proportion of men in the academic and no further education categories in England, while
women comprise the larger share of the no further education category in the German sample. More
men than women are present in the mixed category in the German sample, while this category is
equally balanced in the English sample.
17
Figure 3-2: Characteristics of trajectories by young people’s sex
England
Germany
60
60
50
50
40
40
% 30
% 30
20
20
10
10
0
0
academic vocational
female
mixed
no further
ed
academic vocational
male
female
mixed
no further
ed
male
Parental education
Figure 3-3 shows the influence of parental education on young people’s trajectories. For example,
the largest proportion of parents with “no school qualifications” belong to the young people who
left the education system in both countries. Conversely, those whose parents achieved a university
or higher degree are more prevalent in the academic and mixed education categories.
Figure 3-3: Characteristics of trajectories by highest parental education
England
Germany
70
70
60
50
40
%
30
20
10
0
60
50
40
%
30
20
10
0
academic vocational
No school quals
Further ed
mixed
no further
ed
Some school quals
Uni/higher degree
academic vocational
No further ed
Vocational training
mixed
no further
ed
Semi-skilled
University
It is clear that particularly at each end of the educational spectrum, two conclusions can be drawn:
firstly, although the educational systems in England and Germany differ greatly, the demographics of
the people who follow each educational trajectory are very similar: in both countries parental
education plays a significant role in determining which post-compulsory trajectory our young people
follow. It remains to be seen whether any of these background factors are equally important in
determining people’s social outcomes.
18
Descriptive analysis of outcomes
The following figures show the graphs for the samples in England and Germany, with the
percentages of each type of education achieving the outcome in each year for which data is
available, enabling us to gain a first sense of any changes over time. In the case of political interest
and self-rated health, the outcome was dichotomised from the initial scales show the percentage of
people who respectively were interested/very interested in politics or reported good/very good
health. The other outcome variables presented were already binary.
Health and wellbeing
Self-rated health
It can be seen from Figure 3-4 that there are not huge differences between the different educational
categories in terms of their health. Since we are looking at people aged between 16 and 40, this is
perhaps not that surprising, as the majority of people in these two countries will be enjoying good
health in this age range. Despite this, it can be observed that in both countries, the academic and
mixed categories are slightly more likely to report better health, with disparities becoming more
noticeable over time as the sample ages, while those in the vocational category report levels of
health more similar to those who have left the education system.
Figure 3-4: Proportion of people reporting good or very good health in England (n=732) and
Germany (n=878)
England
Germany
100
90
80
70
60
% 50
40
30
20
10
0
100
90
80
70
60
% 50
40
30
20
10
0
91
93
95
97
00
academic
02
04
06
08
vocational
92 95 97 99 01 03 05 07 09
mixed
no further ed
Smoking
The pattern that emerges in terms of people’s smoking habits differs between the two countries. In
Germany, a similar pattern is observed as with health, with people in the academic and mixed
categories less likely to smoke and those in the vocational category as likely to smoke as those who
left education. In England, however, there is a discernibly different pattern. Vocational students,
along with academic and mixed students have lower levels of smoking; those who left school at 16
however, are more likely to smoke than their peers who remained in education. In both countries
there is an overall reduction in smoking over time as the sample ages.
19
Figure 3-5: Proportion of people who smoke in England and Germany
England
Germany
100
90
80
70
60
% 50
40
30
20
10
0
100
90
80
70
60
% 50
40
30
20
10
0
91 93 95 97 00 02 04 06 08
academic
99
vocational
mixed
01
04
06
08
10
no further ed
Life satisfaction
The average levels of life satisfaction do not differ considerably in either country, however, a
discernible pattern emerges, indicating that those with academic or mixed qualifications report
higher levels of satisfaction with life than those with vocational and no further education. In both
England and Germany, the levels of satisfaction diverge over the course of the sample, revealing that
as the sample ages, there are greater differences between categories than initially observed.
Figure 3-6: Mean life satisfaction reported by people in each educational category in England and
Germany
England
Germany
6.0
8.0
5.5
7.5
5.0
7.0
4.5
6.5
4.0
6.0
96 97 98 99 00 02 03 04 05 06 07 08 10
academic
vocational
91 93 95 97 99 01 03 05 07 09
mixed
no further ed
Civic participation
Political interest
The descriptive analysis of the variable “interest in politics” again shows a clear pattern in that
respondents with no or vocational education show lower interest than those with an academic or
mixed achievement. Again those with vocational education are much closer to those with no further
20
education than to those with higher education. In both England and Germany a slight increase in the
proportion of people interested in politics can be seen over time.
Figure 3-7: Proportion of people who reported a high level of political interest in England and
Germany
England
Germany
100
90
80
70
60
% 50
40
30
20
10
0
100
90
80
70
60
% 50
40
30
20
10
0
91 92 93 94 95 96 01 02 03 04 05 06 07 08 10
academic
vocational
91 93 95 97 99 01 03 05 07 09
mixed
no further ed
Voting
The dominant pattern can again be seen in the proportions of people who voted in two general
election years in England and Germany. Respondents in academic or mixed categories were more
likely to vote in both elections than those in the vocational or no further education categories. It is
interesting that although in both countries, voter turnout was higher in the earlier year, the
proportion of people voting in both countries increases for the second election. This may be
evidence of an increased engagement with politics as the sample ages.
Figure 3-8: Proportion of people who vote in England and Germany
England
Germany
100
90
80
70
60
% 50
40
30
20
10
0
100
90
80
70
60
% 50
40
30
20
10
0
academic vocational
1997
mixed
2005
no further
ed
academic vocational
2005
mixed
no further
ed
2009
21
In sum, with the exception of smoking in the English data, a clear pattern emerges, with those
following an academic or mixed trajectory in post-compulsory education having on the whole better
outcomes than those who pursued vocational education or left the education system. It is
noteworthy, particularly in the German context, that the outcomes for those on the vocational track
are not too different from those who left after compulsory education. These results seem to
contradict our initial hypothesis that the German vocational system would be advantageous to its
apprentices in terms of increasing their three capitals, translating into better outcomes in
comparison with those that left education than would be found for their English counterparts. We
therefore undertook more advanced statistical analysis in an attempt to understand these
descriptive results.
22
Section 4 : Results
Linear panel models
Table A-1 to Table A-10 in the Appendix provide the full results of the multivariate analyses, for each
outcome, within each country, disaggregated by gender. Here we outline the key findings from the
analyses for each outcome and present the statistically significant results for Model 3 for each
outcome in tabular form to aid the reader.
Health and wellbeing
Self-rated health
For women, in the English sample, there are few observed effects of post-compulsory education on
self-rated health: only those women aged 35 who followed an academic route report better health
compared with those who left education at 16. This is not sustained when background controls are
included.
For men, all types of post-compulsory education are associated with better self-rated health at 25,
but this is not sustained for their health at age 35. The positive effect of post-compulsory education
compared to no further education remains at age 25 once controls are introduced, with good GCSE
results linked to better health at 25 and 35 and higher social class to better health at age 25.
Vocational and mixed routes have a marginal indirect effect on health at 35, mediated by prior
health reports at age 25.
Table 4-1: Linear panel results for self-rated health (England)
Women
Men
Effect on self-rated health at 35
Academic
Vocational
Mixed
0.450
-0.241
-0.290
-0.219
0.148
-0.009
Self-rated health at 25
Parental education
Parental social class
5+ GCSEs A-C
Living with biological parents at 16
0.216 *
0.076
-0.092
0.132
-0.575 *
0.323 **
0.240
-0.266
0.552 **
-0.146
Effect on self-rated health at 25
Academic
Vocational
Mixed
Parental education
Parental social class
5+ GCSEs A-C
Living with biological parents at 16
0.432
0.114
0.261
0.277 +
-0.269
0.277
-0.114
0.419 +
0.392 *
0.500 *
-0.285 +
0.450 **
0.434 *
0.069
In the German data only women aged 25 who followed a vocational path, report better health
compared to their peers who left education. The addition of background controls removes this
relationship, leaving only men with better educated parents reporting better health at age 25.
23
Table 4-2: Linear panel results for self-rated health (Germany)
Women
Effect on self-rated health at 35
Academic
Vocational
Mixed
Self-rated health at 25
Parental education
Paternal social class
Abitur
-0.106
-0.027
0.009
0.328
Men
-0.225
-0.011
-0.185
***
0.331 ***
0.087
-0.062
-0.076
Effect on self-rated health at 25
Academic
Vocational
Mixed
-0.154
0.242
0.360
0.237
0.247
-0.070
Parental education
Paternal social class
Abitur
-0.027
0.095
-0.255
0.191 *
-0.070
-0.290
Smoking
In England, women and men both show a reduced propensity to smoke at both ages if they
continued in education post-16, no matter which route they took, in line with the descriptive
analyses reported in Section 3. When background factors are added, GCSE attainment is associated
with reduced smoking for both men and women at age 25 and only men and women who have
vocational qualifications are still less likely to smoke. This is probably due to the close relationship
between GCSE attainment and academic and mixed trajectories which we have already observed.
Smoking at age 25 is strong predictor of smoking at age 35, for both genders. The mediation analysis
reveals that vocational education has an indirect effect on men and women’s smoking at 35,
mediated by smoking habits at 25.
Table 4-3: Linear panel results for smoking (England)
Women
Effect on smoking at 35
Academic
Vocational
Mixed
-0.386
0.002
-0.412
Smoking at 25
Parental education
Parental social class
5+ GCSEs A-C
Living with biological parents in childhood
0.883
-0.243
0.091
0.093
0.188
Men
0.146
-0.016
0.122
***
0.850 ***
0.192
-0.382 *
-0.244
0.146
Effect on smoking at 25
24
Academic
Vocational
Mixed
-0.276
-0.488
-0.006
Parental education
Parental social class
5+ GCSEs A-C
Living with biological parents in childhood
-0.091
0.251
-0.759
-0.198
*
***
-0.221
-0.436 *
-0.463 *
-0.309 *
0.160
-0.510 **
-0.383 *
In the German results, a slightly different picture emerges. Women in 2010 were less likely to smoke
if they had academic or mixed qualifications than if they had left education. This result remained
even when earlier smoking and background controls were introduced into the model. Having an
Abitur also reduced women’s propensity to smoke, in 1999.
Men who had academic qualifications were much less likely to smoke in both years than their peers
who left education. As can be seen in Table 4-4, once background controls were included, having an
Abitur was the strongest predictor of reduced smoking, but only in 1999. As might have been
expected from the descriptive results, people with a vocational education were no less likely to
smoke than their peers with no further education.
Table 4-4: Linear panel results for smoking (Germany)
Women
Men
Effect on smoking in 2010
Academic
Vocational
Mixed
-0.790
-0.202
-0.593
*
-0.724
-0.113
-0.067
Smoking in 1999
Parental education
Paternal social class
Abitur
0.724
0.046
-0.163
0.160
***
0.716
-0.071
-0.123
0.169
Effect on smoking in 1999
Academic
Vocational
Mixed
0.297
0.210
-0.061
0.010
0.203
0.154
Parental education
Paternal social class
Abitur
0.061
-0.076
-0.606
-0.036
-0.101
-0.608
***
***
***
Life satisfaction
In England, women who pursued an academic education between the ages of 16 and 25 were more
likely to enjoy greater life satisfaction at age 35 than their peers who had left education. This result
remains significant when the baseline is introduced in Model 2 and when background factors are
added in Model 3. No such effects were found for life satisfaction at age 25, although GCSE
attainment is associated with greater life satisfaction at this age 25.
25
For men, education seems to have little effect. There is a marginal effect (p<0.1) of vocational
education at age 35, again associated with increased life satisfaction, and which again holds with the
addition of other variables. Parental social class is significantly associated with higher life
satisfaction at age 25, while GCSE attainment is marginally associated with greater satisfaction with
life at age 35.
Table 4-5: Linear panel results for life satisfaction (England)
Women
Effect on life satisfaction at 35
Academic
Vocational
Mixed
0.648
0.207
0.327
Life satisfaction at 25
Parental education
Parental social class
5+ GCSEs A-C
Living with biological parents in childhood
0.331
0.049
-0.193
0.024
-0.070
Effect on life satisfaction at 25
Academic
Vocational
Mixed
-0.233
-0.213
-0.265
Parental education
Parental social class
5+ GCSEs A-C
Living with biological parents in childhood
0.348
-0.295
0.433
0.320
*
***
Men
0.045
0.315
0.033
0.138
-0.181
0.194
0.301
0.119
+
*
+
0.137
0.064
0.124
+
*
0.076
0.395
-0.054
0.135
*
In Germany, there are no effects of post-compulsory education on life satisfaction for either men or
women at either age. For men, having an Abitur is linked to greater life satisfaction at age 25, and
having a father from a higher occupational class is associated with higher life satisfaction at age 35.
Conversely, higher levels of parental education are associated with a reduced satisfaction with life at
age 35.
Table 4-6: Linear panel results for life satisfaction (Germany)
Women
Effect on life satisfaction at 35
Academic
Vocational
Mixed
0.047
-0.061
-0.103
Life satisfaction at 25
Parental education
Paternal social class
Abitur
0.416
0.024
-0.098
0.152
Men
0.073
-0.061
-0.017
***
0.304 ***
-0.182 *
0.182 *
0.240
26
Effect on life satisfaction at 25
Academic
Vocational
Mixed
0.198
-0.013
0.075
-0.091
-0.156
0.119
Parental education
Paternal social class
Abitur
-0.112
-0.071
0.125
-0.129
-0.041
0.297 *
In sum, we see that continuing in education after the compulsory leaving age has some positive
effects, particularly for smoking, which it could be argued is due to it being a more objective
measure than self-reported health or life satisfaction. It is also clear that prior education is important
in almost every case. In all models behaviours at the first time point are strong predictors of
behaviours at the second time point. It remains to be seen whether these outcomes are stable from
an even earlier time point when we come to the results of the latent growth models.
Civic participation
Political interest
In England, women who gained academic qualifications post-16 or a mixture of academic and
vocational qualifications reported higher levels of interest in politics at age 35 than their peers who
left school at 16. A similar pattern emerges for women aged 25 in the second model; a result that
remains with the introduction of background controls. None of the background factors have a
significant effect on women’s political interest at either age.
For men, the story is a little different. An academic or mixed route post-16 is associated with greater
political interest at age 35 than for those without post-compulsory education. Model 2 shows that a
mixed education is still important for political interest at 35, while academic education is linked to
greater interest at age 25. With the introduction of background controls in Model 3, an academic
education remains significant for higher levels of political interest at 25 and indirectly at 35. Unlike
for women, higher levels of parental education and GCSE attainment are linked to more interest in
politics at age 35.
Table 4-7: Linear panel results for political interest (England)
Women
Men
Effect on political interest at 35
Academic
Vocational
Mixed
-0.272
0.071
-0.019
-0.099
-0.259
0.231
Political interest at 25
Parental education
Parental social class
5+ GCSEs A-C
Living with biological parents in childhood
0.700 ***
-0.048
0.061
0.196
-0.067
0.502
0.585
-0.387
0.538
0.209
***
***
*
Effect on political interest at 25
27
Academic
Vocational
Mixed
Parental education
Parental social class
5+ GCSEs A-C
Living with biological parents in childhood
0.903 *
0.109
0.734 *
0.070
0.094
0.177
-0.323
0.717
-0.011
0.279
***
-0.315
0.406
0.343
-0.376
In Germany, women aged 35 with an academic education reported a greater interest in politics than
those with no further education. For women aged 25, both an academic and a mixed trajectory are
linked to higher levels of political interest. Vocational trainees however, have a similar level of
interest to their peers who left education. When background factors are included, having an
academic education still has a direct effect on levels of political interest at age 25 and an indirect
effect at age 35. Having an Abitur and a father of a higher occupational class are also associated with
greater interest in politics.
Men who continued in education are no more interested in politics at age 35 than their peers who
left education, whichever route they took. There is a relationship however between men with
academic and mixed qualifications and higher levels of political interest at age 25. Vocational
trainees are no more interested in politics at age 25 than those that did not further their education.
With the introduction of background factors, achieving an Abitur is highly significant of higher levels
of political interest at age 25, and post-compulsory education is no longer significant. Men with
fathers from the higher occupational class are more interested in politics at age 35 than those from
lower classes.
Table 4-8: Linear panel results for political interest (Germany)
Women
Men
Effect on political interest at 35
Academic
Vocational
Mixed
0.453
-0.069
0.199
0.069
-0.027
-0.233
Political interest at 25
Parental education
Paternal social class
Abitur
0.592 ***
0.058
-0.165
0.011
0.460 ***
-0.057
0.253 **
-0.035
Effect on political interest at 25
Academic
Vocational
Mixed
0.499 *
0.080
-0.008
-0.118
-0.171
0.195
Parental education
Paternal social class
Abitur
0.100
0.212 *
0.301 *
0.130
-0.035
0.674 ***
28
Voting
In England, women’s voting habits were not affected by either post-compulsory education or
background factors in any of the models. However, men with academic or mixed qualifications were
more likely to vote in both elections than those who had left school at 16. These effects, however,
disappeared in Model 3, when background controls were added. In this final model, GCSE
attainment was the only significant predictor of voting in 2005, when the sample was aged between
35 and 40 years of age.
Table 4-9: Linear panel results for voting (England)
Women
Men
Effect on voting in 2005
Academic
Vocational
Mixed
0.028
-0.006
0.246
Voting in 1997
Parental education
Parental social class
5+ GCSEs A-C
Living with biological parents in childhood
0.549
-0.022
0.042
0.059
0.269
Effect on voting in 1997
Academic
Vocational
Mixed
0.146
-0.125
0.170
0.250
0.251
0.438
0.004
0.024
0.356
0.050
0.014
0.139
0.349
0.165
Parental education
Parental social class
5+ GCSEs A-C
Living with biological parents in childhood
-0.141
-0.088
0.055
***
0.581 ***
0.025
0.010
0.617 **
-0.113
The results for the German data were different from the English ones. Once again, it was women
with academic or mixed qualifications who were more likely to vote in 2009 than their peers who
had left education. There is no such effect observed in Model 2, for either election. The introduction
of controls in Model 3 revealed that women whose fathers were in a higher social class were less
likely to vote in 2005 while those who had achieved an Abitur were more likely to vote in 2009.
For men, the effects were slightly different. Men who had taken an academic route through postcompulsory education were more likely to vote in 2009 than those who had not continued in
education. An academic education had a similar effect on voting in 2005. In the third model with
controls, there are no effects of post-compulsory education on voting in either election. Instead,
men with better educated parents and those who had achieved an Abitur were more likely to vote in
the 2005 election.
29
Table 4-10: Linear panel results for voting (Germany)
Women
Effect on voting in 2009
Academic
Vocational
Mixed
0.079
0.027
0.261
Voting in 2005
Parental education
Paternal social class
Abitur
0.565
0.025
0.007
0.541
Men
0.278
-0.169
0.291
***
*
0.543
-0.017
-0.219
0.394
Effect on voting in 2005
Academic
Vocational
Mixed
0.353
-0.276
0.103
0.260
-0.032
-0.398
Parental education
Paternal social class
Abitur
0.250
-0.358
0.301
0.400
-0.017
0.665
*
***
**
*
30
Latent growth models
The results presented in the following tables are the average results over the twenty data sets
created from multiple imputation and for analyses on men and women separately. The objective of
these models was to investigate individual changes in outcomes over time and whether these
changes differed depending on the type of education undertaken.
Health and wellbeing
Self-rated health
Results for the piecewise linear model for the English data are given in Table 4-11 and show that
results for men and women are very similar. Men rate their health higher at age 21 than women
and, for the group overall, there is no significant average change, indicated by the slopes from 21 to
25, and from 25 to 35 being near zero and not significantly different from zero. There is, however,
significant variance at age 21, and in change from 21 to 25 (for men) and from 25 to 35. Our goal,
then, is to find out what predicts this variability in change and to see whether it is, at least in part, a
function of post-compulsory education.
Table 4-11: Piecewise model of trajectories of self-rated health for age 21 to 35 (England)
Mean
Initial Status (age 21)
S1 (change 21-25)
S2 (change 25-35)
*p < .05, ** p < .01, ***p<.001
Variance
Men
Women
Men
Women
0.191**
0.000
0.554***
0.473**
-0.005
0.013
0.033***
0.001
-0.009
0.005
0.004**
0.004**
Table 4-12 gives the results for the model with the inclusion of post-compulsory education and
background variables predicting all three growth parameters. In terms of any effects of postcompulsory education, only academic education has a marginal effect for women’s initial status at
21. Otherwise, the type of post-compulsory education has no significant effects on initial status, or
on change for either period, for men or women. There are just a handful of significant effects of
background controls and given the number of tests carried out some caution needs to be exercised
in interpreting these few positive results. In terms of the effect on initial status, birth year has a
negative effect for men (those born later haver lower self-rated health), and parental class has a
positive effect at age 21.
For change from 25 to 35 (S2), living with both biological parents at 16 has a negative effect for
women: those who did tended to decline in self-rated health. For men, parental class had a marginal
negative effect, with higher class having a slight decline in health in this period.
31
Table 4-12: Effect of type of post-compulsory education and background controls on changes in selfrated health (England)
Variable
Initial Status
S1
S2
Men
Women
Men
Women
Men
Women
Birth year
-0.085**
-0.013
0.026+
0.009 -0.004
-0.004
Parental Education
-0.103
0.212
-0.006
-0.029
0.009
0.001
Parental Class
0.238*
-0.066
0.033
0.022 -0.029+
-0.006
5+ GCSE A-C
0.086
0.100
0.027
0.018
0.005
0.008
Bio Parents
0.157
0.132
-0.038
0.011 -0.006
-0.053*
Academic
0.064
0.391+
0.043
-0.021 -0.001
0.012
Vocational
0.199
0.166
-0.005
-0.049
0.014
-0.007
Mixed
0.285
0.073
-0.016
-0.012
0.006
-0.002
* p < .05, ** p < .01, + p < .10
Model fit: Chi-square = 942.15, df = 386, CFI = .936, RMSEA = .063, WRMR = 1.685, # parameters =
124.
In Germany, the results for the piecewise linear model (in Table 4-13) again show similarities
between the results for men and women. Self-rated health shows little change between the age of
22 and 25. However, there are small significant differences in the S2 (age 25 to 36). There is
significant variance at age 22, and in change from 25 to 36 for both men and women, although in
this second time period, the variance is very small.
Table 4-13: Piecewise model of trajectories of self-rated health for age 22 to 36 (Germany)
Mean
Men
Variance
Women
Men
Women
Initial Status (age 22)
0
0.064
0.612***
0.440*
S1 (change 22-25)
0.007
0.014
0.020
0.019
0.031***
0.029***
0.003***
0.002***
S2 (change 25-36)
*p < .05, ** p < .01, ***p<.001
Table 4-14 gives the results for Model 3. Vocational and mixed education have a significant positive
effect on women’s initial health at 22. In terms of effects on change, birth year has a negative effect
for men (those born later haver lower self-rated health) in S1 (22 to 25) and vocational education
has a negative effect on women’s health. In the period between age 25 and 35, year of birth has a
positive effect for men (those born later have higher self-rated health) while having achieved the
Abitur has a negative effect on men’s health. There are no significant effects on women’s health in
this time period.
32
Table 4-14: Effect of type of post-compulsory education and background controls on changes in selfrated health (Germany)
Variable
Initial Status
S1
S2
Men
Women
Men
Women
Men
Women
Birth year
0.064
0.021
-0.034*
-0.010
0.013*
0.005
Parental Education
0.123
0.080
-0.007
-0.029
-0.002
-0.012
Paternal Class
0.068
-0.008
-0.019
0.026
0.016
0.001
Abitur
-0.162
-0.002
0.002
-0.088
-0.045*
0.000
Academic
-0.023
0.340
0.040
-0.134
-0.005
-0.008
Vocational
0.213
0.318*
-0.023
-0.113*
-0.016
0.001
Mixed
-0.221
0.768*
0.090
-0.173
-0.058
0.012
*p < .05, ** p < .01, ***p<.001
Model fit: Chi-square = 1084.88, df = 392, CFI = .940, RMSEA = .063, WRMR = 1.852, # par = 118
Smoking
The second health outcome we looked at is smoking. Since smoking is a binary variable, we decided
to use a WLSMV estimator with delta parameterisation in Mplus, fitting a probit model to the
smoking response variable. For some reason, the multiple group model (where men and women
were the two groups) did not run, and neither did models with a quadratic growth function. We
therefore ran models for men and women separately and confined it to a piecewise linear growth
model. Additionally, Model 3 would not run in the German dataset.
Table 4-15 presents the unconditional linear piecewise model separately for men and women. The
trajectories, though, are flat. For both men and women, the S1 slope, representing change from age
21 to age 25 is not significantly different from zero, and similarly the S2 slope, representing change
from 25 to 35, is also not significantly different from zero for both groups. Moreover, the variances
of the slopes are not significant either for men and women, indicating that there are few individual
differences in change over time. The only variances that are significant are the intercepts for both
men and women, indicating significant individual differences in initial smoking status at age 21.
Table 4-15: Piecewise model of trajectories of smoking for age 21 to 35 (England)
Mean
Men
Initial Status (age 21)
0
S1 (change 21-25)
0.024
S2 (change 25-35)
-0.038
+ p < .10, *p < .05, ** p < .01, *** p<.001
Variance
Women
0
Men
Women
0.961***
1.028***
-0.183
0.013+
1.261
-0.254
0.010
0.156
It is not surprising therefore when post-compulsory education and background controls are added to
the model that the only significant effects are found in the initial smoking status at age 21 (see Table
4-16). Men who followed vocational and mixed educational trajectories are less likely to smoke at
age 21 than those who left education. Those who have more than five GCSEs at grades A-C and
those living with both biological parents at age 16 are less likely to smoke at age 21. Those born later
are more likely to smoke. For women, vocational education is again a predictor of less smoking at
33
age 21, and those with more than five GCSEs at grades A-C are also less likely to smoke (although of
marginal significance, p < .10). None of the predictors of change, S1 and S2, are significant. These
variables, then, do little by way of predicting smoking uptake or quitting, and type of further
education does not seem to play a role.
Table 4-16: Effect of type of post-compulsory education and background controls on changes in
smoking (England)
Variable
Initial Status
S1
S2
Men
Women
Men
Women
Men
Women
Birth year
0.120**
0.004
-0.017
-0.026
-0.006 -0.003
Parental Education
-0.067
-0.026
-0.044
0.015
0.006 -0.195
Parental class
0.183
0.124
-0.015
0.117
-0.051
0.220
5+ GCSEs A-C
-0.609***
-0.356+
0.056
-0.550
-0.076 -0.225
Bio Parents
-0.412*
-0.270
0.034
-0.048
-0.019
0.108
Academic
-0.060
-0.426
-0.025
-0.095
-0.009 -0.625
Vocational
-0.500**
-0.585**
0.042
-0.283
-0.046 -0.358
Mixed
-0.667**
-0.337
0.088
0.044
-0.034 -0.557
+p < .10,*p < .05, ** p < .01, *** p <.001
Model fit: Men: Chi-square = 221.71, df = 171, RMSEA = .026, CFI = .999; Women: Chi-square =
204.50, df = 171; RMSEA = .024, CFI = .999
In Germany, the simple model tells a slightly different story (see Table 4-17). Women are more likely
to be smoking at age 25 than men. However, there is a small but significant average increase in
smoking between the ages of 25 and 36 for men, and significant variances for both men and women
in initial status, indicating that smoking habits vary widely between individuals in the sample. The
analysis failed to converge when background controls were added so we are unable to determine
the contribution of post-compulsory education to these results.
Table 4-17: Single linear growth model of trajectories of smoking for age 25 to 36 (Germany)
Mean
Initial Status (age 25)
S1 (change 25-36)
*p < .05, ** p < .01, *** p<.001
Variance
Men
Women
Men
Women
0
0.325***
0.888***
0.982***
0.020**
0.036
0.001
0.002
Life satisfaction
The results for the final outcome in the health domain, life satisfaction, are presented in Table 4-18.
The average life satisfaction is fairly high for both men and women at age 21 (life satisfaction is
measured on a 7 point scale in the BHPS), however there is significant variance among the men in
the sample at this age. On average there is no significant change between the ages of 21 and 25 (S1)
or between 25 and 35 (S2) for either men or women. However, there are significant variances in the
rate of change for men and women between 25 and 35.
34
Table 4-18: Piecewise model of trajectories of life satisfaction for age 21 to 35 (England)
Mean
Initial Status (age 21)
S1 (change 21-25)
Variance
Men
Women
5.169***
5.047***
0.613***
0.056
0.014
-0.009
Men
Women
0.365
-0.007
S2 (change 25-35)
-0.003
-0.008
0.005***
0.006***
+ p < .10, *p < .05, ** p < .01, *** p<.001
Model fit: Chi-square = 319.33, df = 224, p<0.001, RMSEA = .034, CFI = .918, SRMR = .092, BIC =
34100.45, # par = 80
When post-compulsory education and background factors are introduced to the model, these
variables have no effect on life satisfaction for men (see Table 4-19). For women, those who are
born later have a slightly increased satisfaction with life at the age of 21. In terms of change, higher
parental class predicts a decrease in life satisfaction between 21 and 25 for women, while vocational
education predicts a slight upward change in satisfaction with life for women between 25 and 35.
There are marginal increases for women in this time period for those with more than five GCSEs at
grades A-C and those who followed a mixed trajectory.
Table 4-19: Effect of type of post-compulsory education and background controls on changes in life
satisfaction (England)
Variable
Initial Status
S1
S2
Men
Women
Men
Women
Men
Women
Birth year
0.089
0.178*
-0.023
-0.022
-0.001 -0.005
Parental Education
0.062
0.220
0.008
0.027
-0.022 -0.025
Parental class
0.037
-0.038
0.023
-0.124**
-0.005
0.025
5+ GCSEs A-C
-0.163
0.219
0.070
-0.036
0.007
0.036+
Bio Parents
0.238
0.256
-0.004
0.001
-0.008 -0.036
Academic
0.050
0.200
-0.015
-0.004
0.027
0.047
Vocational
0.274
0.103
-0.071
-0.055
0.026
0.052*
Mixed
0.006
0.099
0.009
-0.035
0.014
0.056+
+p < .10,*p < .05, ** p < .01, *** p <.001
Model fit: Chi-square = 510.53, df = 386, p<0.001, RMSEA = .030, CFI = .915, SRMR = .071, BIC =
31349.28, # par = 124
The results for the piecewise model for the German dataset are in Table 4-20. As with the English
data, the initial satisfaction with life is high (the German life satisfaction is on a scale of 0 to 10).
There is an average decrease in life satisfaction for women between the ages of 25 and 36, although
the increase is not large. There are significant variances within the life satisfaction of men and
women in each period of measurement, with the largest variances apparent at age 21.
It was hoped therefore that some of this variance would be explained by the variables introduced
into the model in Model 3. However, as can be seen from the results in Table 4-21, there are few
significant results and none of the predictors have an effect on the slopes between 21 and 25 or 25
and 35. A significantly increased life satisfaction is observed at age 21 for women who have achieved
the Abitur, and a significantly decreased rating of satisfaction with life for those women at age 21
who have a combination of vocational and academic qualifications. There are no significant
predictors for men’s initial life satisfaction at age 21.
35
Table 4-20: Piecewise model of trajectories of life satisfaction for age 21 to 36 (Germany)
Mean
Men
Initial Status (age 21)
7.040***
S1 (change 21-25)
0.006
Variance
Women
7.091***
-0.002
Men
Women
2.244***
1.962***
0.121***
0.063***
S2 (change 25-36)
-0.014
-0.039***
0.009***
0.008***
*p < .05, ** p < .01, ***p<.001
Model fit: Chi-square = 413.57, df = 224, p<0.001, CFI = 0.949, RMSEA = .044, SRMR=.067, BIC =
48455.96, # par = 80
Table 4-21: Effect of type of post-compulsory education and background controls on changes in life
satisfaction (Germany)
Variable
Initial Status
S1
S2
Men
Women
Men
Women
Men
Women
Birth year
0.019
-0.002
-0.009
-0.002
0.002
0.004
Parental Education
-0.116
-0.079
-0.051
0.021
0.004
-0.004
Paternal Class
-0.002
-0.180
0.006
-0.006
0.021
0.012
Abitur
0.298
0.449*
0.042
-0.024
0.010
0.018
Academic
0.122
-0.143
-0.053
0.087
0.036
0.007
Vocational
-0.283
-0.032
0.046
-0.002
0.022
-0.008
Mixed
0.059
-0.852*
0.023
0.150
-0.002
0.015
+ p<0.10, *p < .05, ** p < .01, *** p <.001
Model fit: Chi-square = 634.78, df = 406, p<0.001, CFI = .944, RMSEA = .036, SRMR = 0.054, BIC =
48637.29, # par = 122
Civic Participation
Voting
Since there are several general elections within our timeframe, with voting practices in between
being recalled by respondents, we decided to focus on responses in the year of those elections.
However, this created some issues with structuring the data. Table A-11 (in the Appendix) shows
how old each year of the birth cohort was when each general election took place. We have
information on voting across the 21 – 35 age range, but there is a confounding between age, year of
birth and the particular election concerned. For example, data for ages 23 – 25 only concern the
1997 election and only those born between 1972 and 1974, whereas data for ages 32 – 34 only
concern the 2005 election and those born between 1971 and 1973. This is not something we can do
anything about, but we should bear in mind that changes in age also reflect changes in the election
and somewhat different groups of participants. In using multiple imputation to handle missing data
we must assume that there is no systematic difference between these groups of respondents (which
is perhaps a reasonable assumption). The numbers for each age are also given in Table A-11 and it
shows they range from 66 at age 34 to 214 at age 22. The number of cases providing data for at least
one age is 643 and therefore there is a very high proportion missing at each age.
We were not able to impute missing values since the analysis fails to converge; probably due to the
high proportion of missing data at each age and the pattern for different groups of participants. We
36
decided therefore just to look at responses at the three general elections, 1997, 2001 and 2005, and
to exclude 1992, since we have data from all our year of birth cohorts for these three elections.
Moreover, rather than structuring the data by age, we structured the data by year of the general
elections and used year of birth as a covariate.
Table 4-22 provides the ‘unconditional’ model for the three general elections where a linear model is
fitted and where year of birth is added as a covariate to the intercept (i.e. whether they voted in the
1997 general election) when our participant age was between 22 and 27 years. The model provides
an excellent fit to the data. However, the average slope coefficient, reflecting change in the
propensity to vote between 1997 and 2005, is not significant for either men or women, indicating
that there is no mean level change, and neither is its variance significant for either men or women,
indicating that there are no significant individual differences in change over time. Year of birth is not
significantly related to the propensity to vote in 1997.
Table 4-22: Change in voting over three general elections (England)
Mean
Men
Initial Status (whether vote in 1997)
S1 (change over time)
Variance
Women
0
-0.021
Men
Women
0
0.255
1.594
0.379
0.013
3.331
Initial Status on birth year
0.003
-0.059
+p < .10,*p < .05, ** p < .01, *** p <.001
Model fit: Chi-square = 7.59, df = 4, CFI = .993, RMSEA = .054, WRMR = .764, # par = 14
In view of the lack of significant variance in the unconditional model, it is hardly surprising that
background variables and type of further education do not predict propensity to vote or changes in
the propensity to vote. The results are in Table 4-23 and it can be seen that in the English data there
are no significant effects, either for men or for women.
Table 4-23: Effect of type of post-compulsory education and background controls on changes in
voting (England)
Variable
Initial Status
Men
Women
-0.043
-0.072
0.087
0.031
0.120
0.021
0.269
0.337+
0.211
0.102
0.211
0.115
0.158
-0.084
0.277
0.157
S1
Men
Women
Birth year
0.023
-0.143
Parental Education
-0.041
-0.027
Parental Class
-0.057
0.341
5+ GCSEs A-C
-0.110
0.329
Live with Bio Parents
-0.106
0.391
Academic
-0.111
-0.104
Vocational
-0.080
0.200
Mixed
-0.136
0.401
*p < .05, ** p < .01, + p < .10
Model fit: Chi-square = 14.66, df = 14, CFI = .998, RMSEA = .009, WRMR = .532, # par = 46
No comparable analysis could be done with the German dataset, since there was only data available
at two time points.
37
Political interest
The question was asked in every wave of the BHPS, except for a four year period between 1997 and
2000 corresponding to waves 7 – 10. The consequence of this gap is that when the data are
restructured by age there is a significant amount of missing data due to the varying ages for which
there is no information for particular year of birth cohorts.
Table A-12 (in the Appendix) gives the numbers providing valid responses to the political interest
question in the BHPS by age and year of birth. Among the core sample of 732 participants, 20 failed
to provide any data for ages 21 to 35 and therefore have been excluded from the sample, giving n =
712. The effect of the missing data in waves 7 - 10 is evident: for ages 22 – 30, at least one year of
birth cohort fails to provide any data, and for ages 25-27, four year of birth cohorts fail to provide
any data. The consequence is that the number of cases at each age varies considerably and is
structured by year of birth.
When we tried to impute missing data including background variables, the model failed to converge.
We were, however, able to reach a solution by just including the political interest variables and so
we created the datasets for MI by merging these political interest datasets with the data for the
background variables that had been obtained from multiple imputations on the health status data.
The results for fitting an unconditional piecewise model are shown in Table 4-24. The model fits the
data extremely well. The results show that men have a higher interest in politics at age 21 than do
women, although there is significant variability in initial status for both men and women. Average
change between 21 and 25 shows a marginal decrease for men while average change between 25
and 35 shows a significant if small increase – also for men. There is some significant variance
between 25 and 35 for men but again, the variance is small. It seems that level of political interest is
well established by age 21 and, thereafter, it remains relatively stable.
Table 4-24: Unconditional piecewise model of political interest (England)
Mean
Men
Initial Status (age 21)
S1 (change 21-25)
0.250*
-0.054+
Variance
Women
0
-0.019
Men
1.198***
0.002
Women
0.608***
-0.007
S2 (change 25-35)
0.030*
0.001
0.007**
0.002
+ p < .10, *p < .05, ** p < .01, *** p <.001
Model fit: Chi-square = 1126.70, df = 194, CFI = .969, RMSEA = .114, WRMR = 2.259, # par=76
38
This is further supported when we add background variables and type of post-compulsory education
to the model, as is shown in Table 4-25. For women, having academic or academic and vocational
qualifications predicts higher political interest at age 21, as does having more than five GCSEs. Year
of birth predicts lower political interest, with those born later having less interest at 21. None of the
variables predict men’s initial political interest at age 21. There are no predictors of change in S1,
which is to be expected given the lack of observed change in this period in Model 1. The change
observed in men’s political interest between 25 and 35 is explained in part by the additional
variables; birth year, parental education and (marginally) GCSE attainment predict an increase in
political interest in this time period.
Table 4-25: Effect of type of post-compulsory education and background controls on interest in
politics (England)
Variable
Initial Status
S1
S2
Men
Women
Men
Women
Men
Women
Birth year
-0.083
-0.086*
-0.012
0.009
0.020*
0.005
Parental Education
0.098
0.049
-0.030
-0.021
0.044*
0.004
Parental class
0.262
0.254+
-0.022
-0.049
-0.022
0.001
5+ GCSEs A-C
0.508
0.342*
-0.041
0.005
0.071+ -0.003
Bio Parents
-0.437
-0.156
0.071
-0.003
0.013
0.008
Academic
0.489
0.608*
0.011
0.010
-0.011
-0.030
Vocational
-0.200
0.188
0.032
-0.024
0.009
0.005
Mixed
0.721+
0.602**
-0.116
-0.004
0.028
-0.022
+ p < .10, * p < .05, ** p < .01
Model fit: Chi-square = 1503.03, df = 386, CFI = 0.966, RMSEA = .089, WRMR = 1.967, # par = 124
In Germany, women are less interested than men in politics at age 21 although there is a slight
average increase in their interest between the ages of 25 and 36. Men also experience slight
increased interest from 21 to 25 and from 25 to 36. There is significant variability in the results
between men in the sample at each time period and between women initially, and in the second
period, between 25 and 36.
Table 4-26: Unconditional piecewise model of political interest for age 21 to 36 (Germany)
Mean
Men
Initial Status (age 21)
0
S1 (change 21-25)
0.029*
Variance
Women
Men
Women
-0.329***
0.852***
0.508***
0.010
0.025***
0.005
S2 (change 25-36)
0.027***
0.014**
0.004***
0.003***
*p < .05, ** p < .01, *** p <.001
Model fit: Chi-square = 641.17, df = 225, CFI = .984, RMSEA = .065, WRMR = 1.596, # par=79
The introduction of background variables in Model 3, produces different results from the English
dataset. Men and women who have achieved the Abitur have increased political interest at age 21,
with additional marginal increases for women following either an academic or a vocational path. In
terms of change between 21 and 25, year of birth predicts increased interest (those born later have
greater interest in politics) and there is a marginal effect of parental education (higher education
results in increased political interest) on interest for men. There is little change in the period from 25
39
to 36, with only a marginal decrease in interest for men due to parental education and a marginal
decrease in interest for women with vocational education and training.
Table 4-27: Effect of type of post-compulsory education and background controls on changes in
political interest (Germany)
Variable
Initial Status
S1
S2
Men
Women
Men
Women
Men
Women
Birth year
-0.094*
-0.032
0.035**
0.016*
0.002
-0.006+
Parental Education
0.008
0.102
0.035+
0.011
-0.017+
-0.004
Paternal Class
0.113
0.088
-0.027
-0.013
0.015
-0.004
Abitur
0.851***
0.446***
-0.065
-0.012
-0.016
-0.015
Academic
0.191
0.359+
-0.033
0.029
0.015
0.024
Vocational
-0.089
0.182+
0.001
-0.004
0.009
-0.018+
Mixed
0.023
0.154
0.061
0.004
-0.020
0.007
+ p<0.10, *p < .05, ** p < .01, *** p <.001
Model fit: Chi-square = 905.96, df = 407, CFI = .979, RMSEA = .053, WRMR = 1.493, # par = 121
40
Section 5 : Discussion and conclusion
We set out to discover whether the type of post-compulsory education (that is academic education
or vocational education and training) followed by young people influenced their social outcomes
later in the life course and whether these effects differed in contrasting educational systems. We
reasoned that the content of academic education in both countries would result in greater social
capital and hypothesised that people with academic qualifications would enjoy benefits in terms of
both their health and wellbeing and their civic participation. The results of our analyses show that
overall, this hypothesis was correct: people who followed an academic or mixed trajectory (which
includes academic qualifications) through post-compulsory education were more likely to report
better outcomes in both of the domains studied.
Our second question centred on whether the system in which this education was achieved made a
difference. We hypothesised that young people who had trained in the dual system of VET in
Germany would enjoy similar outcomes to their peers with academic education but that this was
less likely to be the case for VET students in England. Our analysis instead found that while no form
of post-compulsory education has a negative effect on young people’s civic outcomes, vocational
trainees in both countries had outcomes closely aligned to their peers with no further education.
Furthermore, VET in England, not Germany was clearly associated with improved health outcomes.
We hypothesised that the later age at which German young people moved from compulsory to postcompulsory education may affect how much difference is observed at this stage. The results for
several outcomes for Germany were not as strong as those for the English cohort, which may be
explained by this timelag. A further explanation may be that the comparison group in England
(those not continuing in education at 16) are more disadvantaged in comparison to their more
educated peers than their counterparts in Germany as this too may have had an effect on the
strength of the results. Finally, we hypothesised that since the German school system is stratified at
a young age and such stratification is highly dependent on the parents’ experience of the education
system, that we would find more of an effect of parental education on the outcomes in Germany
than in England. Instead, in both countries, young people’s prior achievement were stronger
predictors in most models than their parents’ social background. While it is certainly the case that
this is an indirect manifestation of parental educational and social background, it was as pronounced
in the English context as in Germany.
Health and wellbeing
Our analyses showed that health outcomes were only little affected by educational trajectory
followed. There are several reasons for it: the first one may be the fact that the health system in
both countries is so advanced that it diminishes differences across different groups of people. A
second reason is associated with the age range of respondents. Although we followed participants
over an age span of 15 to 20 years, the oldest ones in our sample were about 40 years old. This
might be too early to find significant health effects between groups. Similarities between the
different educational groups could be seen in the initial descriptives in Figure 3-4, although these
were not disaggregated by gender at this stage. Health at this age is fairly stable, as evidenced by
the latent growth models for both countries, so despite the literature on the health benefits of
education, it is unlikely that we would find much of great significance in this age group. It is
therefore perhaps more surprising that we found significant effects of all types of post-compulsory
education on men’s health in the English data, and that background factors made positive changes
too. In light of these results, it is worth bearing in mind who the reference group is. Perhaps it is the
case in England that those who left school at age 16 were at a greater health disadvantage than their
peers who stayed on in education than is the case for the comparable groups in Germany. A third
41
reason might be the higher probability of healthy persons to stay in the sample for 15 to 20 years, or
for those who are less healthy to attrit.
In terms of smoking, the descriptive statistics presented in Figure 3-5 revealed stark differences
between the two countries, particularly with regard to the vocational education and training
category, which in the English sample was removed from the no further education category, rather
than positioned beside it as in the German sample. The linear panel models additionally showed
that these differences persisted for both sexes in the disaggregated analysis. The difference in
educational contexts of vocational education and training between the two countries could provide
the key to these differences. Firstly, the dual system of vocational education and training in Germany
is based largely in the workplace, unlike in England where much of VET is college-based. It could be
that the VET trainees in Germany are picking up workplace habits, just as those who had left school
at 16 in England. On the other hand, VET students in England were still in an educational habitus,
and in many cases could be studying in the same establishments as those on the academic and
mixed track.
As shown by the latent growth models for life satisfaction in England and Germany in Table 4-18 and
Table 4-20 and by the descriptive results presented in Figure 3-6, the levels of overall satisfaction
with life were already fairly high at the age of 21 and were subject to little change over time. It is
therefore unsurprising that post-compulsory education had little effect in general, although
interesting that for women in England with academic qualifications there was an observed increase
in satisfaction with life. Considering that the economic outcomes would differ considerably between
those graduating from University in the 1990s compared to those leaving education with very few
qualifications, it is noteworthy that there are not greater differences between these two groups.
Civic Participation
We hypothesised that all types of education would increase civic engagement through the
development of the three capitals in the learning process and that the dual system of VET in
Germany was more likely to have an effect on civic participation than VET in England. Our analysis
instead found while no form of post-compulsory education had a negative effect on young people’s
civic outcomes, vocational education, in most cases, does not increase young people’s interest in
politics compared to those who leave the educational system. While this was half-expected in the
English context, it was an unexpected result for the dual-system of Germany.
While cross-sectional studies have repeatedly found associations between increased levels of
education and increased levels of political interest, such studies are unable to take into account
factors from childhood and family, which may have an effect over and above the educational effect.
The use of panel data in our own research has enabled us to investigate this association in more
depth. In both countries, the educational pathway pursued after completing compulsory education
is heavily dependent on the type of school in Germany and on exam results age 16 in England.
Furthermore, we find that much of the initial effect of post-compulsory education on civic outcomes
can be explained by socio-economic factors, since the impact of post-compulsory education is
affected in every case by the addition of background factors such as school-level education, social
class and parental education. This concurs with recent research indicating that gaps in political
interest by educational achievement already exist from as early as 12 years of age (Keating et al.,
2015).
42
Conclusion
Our results lead us to two conclusions: firstly, that it is important to look at the type of education
that people undertake, not just the level of qualifications or the number of years of study. Our
research shows that with only one exception people with academic qualifications enjoy more
positive outcomes in comparison with their peers who left education at the earliest opportunity.
While no form of post-compulsory education has a negative effect on outcomes, it is striking,
particularly with regard to the dual system in Germany, that the outcomes of those pursuing a
vocational education are generally little different to those who have no further education.
Secondly, we can conclude that whether the formal stratification of the German school system or
the examination-led stratification of the English system, that the channelling of young people into
different educational routes is dependent on their educational achievement and their social
background. While our results show that education can increase young peoples’ capital, it is also
true that the capital that people bring to the education system matters as much, if not more, than
the capital that results from it.
The policy implications of this study are that there needs to be a greater emphasis in both countries
on countering social inequalities in terms of health and civic participation at earlier stages of the
learning process since currently, both in England and Germany, the educational systems are
perpetuating differences in social outcomes rather than reducing them.
Acknowledgements
This research was funded by the Economic and Social Research Council under grant ES/J021326/1.
43
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46
Appendix A
Figure A-1: UK policy interventions affecting youth transitions in the late 1980s and the 1990s
1986 GCSEs replaced O levels
1988 National curriculum introduced
Youth training guarantee – all 16 and 17 year olds to be in education, employment or
training- YTS programme
All benefits for unemployed young people between the ages of 16 and 18 removed to
encourage them to engage in youth training or stay on in education
NVQs introduced
1990 Youth Training (YT) replaces YTS
1992 Polytechnics become universities
1993 GNVQs introduced (after one year pilot)
1995 Modern Apprenticeships introduced
1996 Dearing Report on vocational qualifications
1997 Dearing Report on Higher education
National Traineeships introduced
Investing in Young People announced (aiming to increase post-16 participation in
education)
1998 Education Action Zones introduced
New Deal for Young People
Minimum wage legislation for 18+
1999 Investing in Young People renamed ConneXions
Educational Maintenance Allowances introduced in pilot form – rolled out post-2000,
scrapped in England in 2011.
47
Table A-1: Linear panel model results for self-rated health (England)
Women
Model 1
Effect on self-rated health at 35
Academic
Vocational
Mixed
Year of birth
Self-rated health at 25
Parental education
Parental social class
5+ GCSEs A-C
Living with biological parents in childhood
Model 2
Model 3
Men
Model 1
Model 2
0.450
-0.241
-0.290
0.312
0.244
0.378
0.063
0.115
0.159
-0.219
0.148
-0.009
-0.099
0.329
-0.085
0.323
0.240
-0.266
0.552
-0.146
0.559
-0.215
-0.213
*
0.428
-0.250
-0.300
-0.150
*
-0.145
0.232
Mediated effects via self-rated health at 25
Academic
Vocational
Mixed
Effect on self-rated health at 25
Academic
Vocational
Mixed
Year of birth
Parental education
Parental social class
5+ GCSEs A-C
Living with biological parents in childhood
+ p<0.1, * p<0.05, ** p<0.01, *** p<0.001
*
*
-0.148
0.216
0.076
-0.092
0.132
-0.575
*
*
-0.119
*
Model 3
**
*
0.132
0.014
0.048
0.561
0.148
0.365
0.432
0.114
0.261
0.750
0.385
0.660
-0.027
-0.018
0.277
-0.269
0.277
-0.114
-0.059
+
***
*
**
**
**
0.112
0.144
0.159
+
+
0.419
0.392
0.500
+
*
*
-0.044
-0.285
0.450
0.434
0.069
+
**
*
48
Table A-2: Linear panel model results for self-rated health (Germany)
Women
Model 1
Effect on self-rated health at 35
Academic
Vocational
Mixed
Year of birth
Self-rated health at 25
Parental education
Paternal social class
Abitur
Model 2
-0.175
0.026
0.076
-0.086
-0.054
0.014
0.011
0.022
0.327
Model 3
-0.106
-0.027
0.009
***
Mediated effects via self-rated health at
25
Academic
Vocational
Mixed
Effect on self-rated health at 25
Academic
Vocational
Mixed
Year of birth
Parental education
Paternal social class
Abitur
* p<0.05, ** p<0.01, *** p<0.001
0.017
0.328
***
Men
Model 1
Model 2
Model 3
-0.252
0.076
-0.333
-0.274
-0.011
-0.246
-0.225
-0.011
-0.185
0.081
0.111
0.339
-0.059
0.077
0.123
-0.270
0.248
0.194
-0.032
*
***
0.111
0.331
0.087
-0.062
-0.076
***
0.059
0.083
-0.039
-0.154
0.242
0.360
0.064
0.252
-0.254
0.237
0.247
-0.070
-0.024
-0.027
0.095
-0.255
-0.088
-0.080
0.191
-0.070
-0.290
*
49
Table A-3: Linear panel model results for smoking (England)
Women
Model 1
Effect on smoking at 35
Academic
Vocational
Mixed
Year of birth
Smoking at 25
Parental education
Parental social class
5+ GCSEs A-C
Living with biological parents in childhood
-0.913
-0.485
-0.628
0.030
**
*
*
Model 2
Model 3
Men
Model 1
-0.323
-0.012
-0.322
-0.386
0.002
-0.412
-0.578
-0.407
-0.585
0.069
0.882
0.069
0.883
-0.243
0.091
0.093
0.188
0.044
***
Mediated effects via smoking at 25
Academic
Vocational
Mixed
Effect on smoking at 25
Academic
Vocational
Mixed
Year of birth
Parental education
Parental social class
5+ GCSEs A-C
Living with biological parents in childhood
* p<0.05, ** p<0.01, *** p<0.001
-0.400
-0.431
-0.169
-0.669
-0.536
-0.347
-0.044
**
**
-0.276
-0.488
-0.006
-0.057
-0.091
0.251
-0.759
-0.198
***
*
*
*
Model 2
Model 3
-0.082
-0.027
0.030
0.146
-0.016
0.122
-0.022
0.842
-0.039
0.850
0.192
-0.382
-0.244
0.146
***
*
*
***
***
*
-0.128
-0.377
-0.343
*
+
*
*
-0.589
-0.452
-0.730
**
*
**
-0.221
-0.436
-0.463
0.079
*
0.076
-0.309
0.160
-0.510
-0.383
*
**
*
50
Table A-4: Linear panel model results for smoking (Germany)
Women
Model 1
Effect on smoking in 2010
Academic
Vocational
Mixed
Year of birth
Smoking in 1999
Parental education
Paternal social class
Abitur
-0.858
-0.087
-0.993
Model 2
*
*
-0.761
-0.223
-0.557
0.054
0.718
Men
Model 1
Model 3
*
***
-0.790
-0.202
-0.593
0.046
0.724
0.046
-0.163
0.160
*
***
-1.114
-0.044
-0.336
Model 2
***
-0.700
-0.140
-0.048
0.131
0.718
Model 3
*
***
-0.724
-0.113
-0.067
0.113
0.716
-0.071
-0.123
0.169
***
Mediated effects via smoking in 1999
Academic
Vocational
Mixed
Effect on smoking in 1999
Academic
Vocational
Mixed
Year of birth
Parental education
Paternal social class
Abitur
+ p<0.1, * p<0.05, ** p<0.01, *** p<0.001
-0.138
0.189
-0.608
0.297
0.210
-0.061
-0.577
0.134
-0.400
-0.005
0.010
0.061
-0.076
-0.606
-0.073
***
*
0.010
0.203
0.154
-0.083
-0.036
-0.101
-0.608
***
51
Table A-5: Linear panel model results for life satisfaction (England)
Women
Model 1
Effect on life satisfaction at 35
Academic
Vocational
Mixed
Year of birth
Life satisfaction at 25
Parental education
Parental social class
5+ GCSEs A-C
Living with biological parents in childhood
0.605
0.146
0.269
0.088
Model 2
*
0.576
0.190
0.275
0.052
0.335
*
***
Mediated effects via life satisfaction at 25
Academic
Vocational
Mixed
Effect on life satisfaction at 25
Academic
Vocational
Mixed
Year of birth
Parental education
Parental social class
5+ GCSEs A-C
Living with biological parents in childhood
+p<0.1, * p<0.05, ** p<0.01, *** p<0.001
Men
Model 1
Model 3
0.648
0.207
0.327
0.052
0.331
0.049
-0.193
0.024
-0.070
*
0.306
0.329
0.186
-0.051
***
+
Model 2
Model 3
0.260
0.312
0.150
0.045
0.315
0.033
-0.051
0.151
-0.061
-0.042
-0.045
0.091
-0.127
-0.015
0.077
0.120
0.348
-0.295
0.433
0.320
*
-0.041
0.138
-0.181
0.194
0.301
0.119
+
*
+
0.001
-0.013
0.009
-0.233
-0.213
-0.265
+
+
+
+
*
0.321
0.130
0.254
0.137
0.064
0.124
0.004
0.002
0.076
0.395
-0.054
0.135
*
52
Table A-6: Linear panel model results for life satisfaction (Germany)
Effect on life satisfaction at 35
Academic
Vocational
Mixed
Year of birth
Life satisfaction at 25
Parental education
Paternal social class
Abitur
Women
Model 1
Model 2
0.149
-0.107
-0.029
0.092
-0.070
-0.031
0.029
0.000
0.423
Mediated effects via life satisfaction at 25
Academic
Vocational
Mixed
Effect on life satisfaction at 25
Academic
Vocational
Mixed
Year of birth
Parental education
Paternal social class
Abitur
* p<0.05, ** p<0.01, *** p<0.001
***
Model 3
Men
Model 1
Model 2
Model 3
0.047
-0.061
-0.103
0.284
-0.101
0.288
0.264
-0.048
0.208
0.073
-0.061
-0.017
0.062
0.052
0.318
-0.005
0.416
0.024
-0.098
0.152
***
0.150
-0.010
0.057
***
0.055
0.304
-0.182
0.182
0.240
***
*
*
-0.046
-0.080
0.063
0.136
-0.087
0.006
0.198
-0.013
0.075
0.062
-0.169
0.252
-0.091
-0.156
0.119
0.069
0.057
-0.112
-0.071
0.125
0.032
0.019
-0.129
-0.041
0.297
*
53
Table A-7: Linear panel model results for political interest (England)
Women
Model 1
Effect on political interest at 35
Academic
Vocational
Mixed
Year of birth
Political interest at 25
Parental education
Parental social class
5+ GCSEs A-C
Living with biological parents in childhood
0.587
0.176
0.658
-0.018
Model 2
*
*
-0.135
0.083
0.082
-0.272
0.071
-0.019
0.713
-0.243
0.642
-0.032
0.699
-0.030
0.700
-0.048
0.061
0.196
-0.067
-0.017
***
Mediated effects via political interest at 25
Academic
Vocational
Mixed
Effect on political interest at 25
Academic
Vocational
Mixed
Year of birth
Parental education
Parental social class
5+ GCSEs A-C
Living with biological parents in childhood
* p<0.05, ** p<0.01, *** p<0.001
Men
Model 1
Model 3
***
Model 2
***
**
0.312
-0.226
0.485
0.111
0.455
Model 3
*
***
0.447
0.078
0.422
1.038
0.135
0.824
0.020
***
**
0.903
0.109
0.734
0.025
0.070
0.094
0.177
-0.323
*
*
-0.099
-0.259
0.231
0.142
0.502
0.585
-0.387
0.538
0.209
***
***
*
0.254
-0.035
0.141
*
***
0.900
-0.032
0.352
***
0.717
-0.011
0.279
-0.277
*
-0.268
-0.315
0.406
0.343
-0.376
54
Table A-8: Linear panel model results for political interest (Germany)
Women
Model 1
Effect on political interest at 35
Academic
Vocational
Mixed
Year of birth
Political interest at 25
Parental education
Paternal social class
Abitur
0.948
0.038
0.451
-0.036
***
Model 2
Model 3
Men
Model 1
0.392
-0.088
0.142
0.453
-0.069
0.199
0.367
-0.046
0.234
0.138
0.001
-0.110
0.115
0.083
0.464
-0.056
0.572
***
Mediated effects via political interest at 25
Academic
Vocational
Mixed
Effect on political interest at 25
Academic
Vocational
Mixed
Year of birth
Parental education
Paternal social class
Abitur
* p<0.05, ** p<0.01, *** p<0.001
0.972
0.220
0.541
0.034
***
*
-0.060
0.592
0.058
-0.165
0.011
***
0.314
0.050
-0.005
*
0.499
0.080
-0.008
*
0.039
0.100
0.212
0.301
Model 2
Model 3
0.069
-0.027
-0.233
0.100
0.460
-0.057
0.253
-0.035
***
**
-0.058
-0.083
0.095
0.494
-0.102
0.740
0.069
*
*
***
*
**
-0.118
-0.171
0.195
0.075
0.130
-0.035
0.674
***
55
Table A-9: Linear panel model results for voting (England)
Effect on voting in 2005
Academic
Vocational
Mixed
Year of birth
Voting in 1997
Parental education
Parental social class
5+ GCSEs A-C
Living with biological parents in childhood
Women
Model 1
Model 2
Model 3
Men
Model 1
0.343
-0.008
0.531
0.118
0.027
0.325
0.028
-0.006
0.246
0.556
0.050
0.593
-0.072
-0.029
0.553
-0.030
0.549
-0.022
0.042
0.059
0.269
0.020
Mediated effects via voting in 1997
Academic
Vocational
Mixed
Effect on voting in 1997
Academic
Vocational
Mixed
Year of birth
Parental education
Parental social class
5+ GCSEs A-C
Living with biological parents in childhood
* p<0.05, ** p<0.01, *** p<0.001
***
***
Model 2
*
*
Model 3
0.219
-0.148
0.216
-0.141
-0.088
0.055
0.045
0.590
0.068
0.581
0.025
0.010
0.617
-0.113
***
0.085
-0.070
0.147
***
**
0.110
0.123
0.266
0.406
-0.063
0.371
0.146
-0.125
0.170
0.574
0.285
0.639
-0.078
-0.071
0.004
0.024
0.356
0.050
-0.044
**
**
0.250
0.251
0.438
-0.038
0.014
0.139
0.349
0.165
56
Table A-10: Linear panel model results for voting (Germany)
Women
Model 1
Effect on voting in 2009
Academic
Vocational
Mixed
Year of birth
Voting in 2005
Parental education
Paternal social class
Abitur
0.750
-0.076
0.978
0.036
*
*
Model 2
Model 3
Men
Model 1
0.455
0.070
0.780
0.079
0.027
0.261
1.036
-0.151
0.559
0.005
0.586
Mediated effects via voting in 2005
Academic
Vocational
Mixed
Effect on voting in 2005
Academic
Vocational
Mixed
Year of birth
Parental education
Paternal social class
Abitur
* p<0.05, ** p<0.01, *** p<0.001
***
-0.004
0.565
0.025
0.007
0.541
0.074
***
*
Model 2
Model 3
0.476
-0.207
0.397
0.278
-0.169
0.291
0.119
0.525
***
*
0.236
-0.176
0.072
0.101
0.543
-0.017
-0.219
0.394
***
0.168
-0.027
-0.251
0.499
-0.249
0.337
0.353
-0.276
0.103
1.056
0.110
0.299
0.054
0.047
0.250
-0.358
0.301
-0.082
*
**
0.260
-0.032
-0.398
-0.056
0.400
-0.017
0.665
**
*
57
Table A-11: General election year by age and year of birth (n in parentheses) (England)
Age
21
1970
1971
22
23
24
26
27
28
29
30
31
32
33
34
1997
2001
2005
(124)
(102)
(92)
(81)
1992
1997
2001
2005
(115)
(83)
(74)
(66)
1973
1974
1975
115
35
1992
1972
N
25
1997
2001
2005
(95)
(85)
(77)
1997
2001
2005
(98)
(86)
(69)
1997
2001
2005
(111)
(101)
(86)
1997
2001
2005
(90)
(78)
(65)
214
111
98
95
161
203
86
85
139
178
69
77
66
81
58
Table A-12: Number of participants for political interest by age and year of birth (England)
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
1970
137
112
109
103
97
103
0
0
0
0
91
88
87
83
85
1971
105
97
90
91
92
0
0
0
0
74
70
65
64
65
67
1972
107
99
98
99
0
0
0
0
84
79
74
74
77
72
70
1973
104
95
97
0
0
0
0
83
79
74
68
71
69
67
65
1974
114
121
0
0
0
0
99
93
91
87
87
86
84
84
0
1975
84
0
0
0
0
76
69
66
59
64
62
62
58
0
45
651
524
394
293
189
179
168
242
313
378
452
446
439
371
332
59