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Dr Ellen Boeren & Professor John Holford
VOCATIONALISM VARIES (A LOT): A 12 COUNTRY
MULTIVARIATE ANALYSIS OF PARTICIPATION IN FORMAL
ADULT EDUCATION
ABSTRACT
To encourage adult participation in education and training, contemporary policy-makers
typically encourage education and training provision to have a strongly vocational
(employment-related) character, while also stressing individuals’ responsibility for
developing their own learning. Adults’ motivation to learn is not, however, purely vocational
– it varies substantially, not only between individuals but between populations. This paper
uses regression analysis to explain motivation among 12,000 learners in formal education and
training in twelve European countries. Although vocational motivation is influenced by
individual-level characteristics (such as age, gender, education, occupation), it turns out that
the country in which the participation takes place is a far stronger explanatory variable. For
example, although men’s vocational motivation to participate is higher than women’s in all
countries, Eastern European women have significantly higher levels of vocational motivation
than men in Western Europe. This supports other research which suggests that, despite
globalisation, national institutional structures (social, economic) have continuing policy
significance.
INTRODUCTION
Contemporary education policies show extensive common patterns. For adults, they generally
encourage a close alignment between education and training and paid employment (Dehmel.
2006; Field, 2006). They imply that the development of vocational competence is important
for economic growth and competitiveness (individual, organisational and national): we term
this presumption that education and training should serve the needs of paid employment
“vocationalism”, whereas citizenship and social cohesion were stronger valued in earlier
decades (Holford et al., 2008). At the same time, policies are based on the belief that
individual adults’ decisions should play a major, even a preponderant, role in shaping public
resource allocation. The logic behind both these assumptions – as many have pointed out – is
essentially ‘neoliberal’. On the one hand, the principal purpose of education is to enable
Dr Ellen Boeren & Professor John Holford
individuals to earn their living: investing in themselves is ‘one way free men can enhance
their welfare’ (Schulz 1971: 26). On the other, markets allocate more efficiently than state
bureaucracies, and government should allow them to function freely except where there is
manifest ‘market failure’. (Crouch 2011)
At the heart of this strategy lies a paradox: policy-makers wish to encourage vocationalism;
but they assume that individuals, left alone, will naturally choose vocational courses. In fact,
half a century of research has shown that adults’ motivations to participate in adult education
and training are by no means only vocational (Houle 1961, Boshier, 1973). The risk that
people’s ‘natural’ inclinations may be insufficiently vocational may in part explain (or at any
rate contribute to) the increasing deployment of mechanisms, across a range of policy areas,
to encourage adults to want, feel they need, or (in the economic sense) demand vocational
training: financial and tax incentives, vocational training as a condition of welfare benefits,
preferential public funding for vocational provision, publicity and marketing campaigns, and
so forth (Billett, 2011).
Of course, this area is fraught with terminological inexactitude. Two particular points should
be noted. First, in many (though not all) English-speaking countries, it has been common in
recent years to use the term ‘lifelong learning’ to refer to provision of education and training
for adults. For a period, European Union (EU) language was guilty of similar elisions (Jarvis
2010). In this paper, the focus is on formal adult education and training, which belongs to the
overarching idea of ‘lifelong learning’ and ‘adult learning’,as explained below. Second, we
use ‘vocational’ not in the narrow sense associated with shaping individuals’ professional
identities (as when Weber (1946) wrote of science or politics ‘as a vocation’), but in the
(perhaps more common) sense in which it is employed in the term ‘vocational education and
training’. In this respect, it incorporates education and training which contributes to
developing ‘occupational fields’ and ‘vocational identities’ (Billet 2011).
Perhaps in reaction to more simplistic assumptions about the ubiquity of globalisation, and
easy assumptions about ‘policy-borrowing’ from ‘high-performing’ nations, a body of
literature has emerged emphasising the importance of context – particularly national context
– in shaping educational performance. In relation to policy, for instance, Holford et al. 2008)
found ‘significant diversity in approaches to lifelong learning in post-communist regimes’,
and that ‘labour market conditions are central in defining the nature of lifelong learning in
Dr Ellen Boeren & Professor John Holford
any particular country’ (p. 133). The ‘diversity of national context,’ they argued, ‘means that
a single model of lifelong learning across the EU is unlikely to be achieved’ (p. 132). In the
same vein, ‘striking’ differences in participation between countries led Boeren et al. (2012) to
question ‘the feasibility of a one-size-fits-all EU policy with specific targets and policy
measures’ (p. 81). Saar and Ure (2013) and Hefler and Markowitsch (2013) have broadened
and deepened this analysis: on the one hand by exploring the basis and utility of various
country typologies, and on the other by exploring – from an evolutionary or historical
perspective – the features of seven types of formal adult education.
We know, therefore, that adults participate in education and training for a spectrum of
reasons, and that rates of participation vary by country (Boateng, 2009). But do patterns of
motivation vary between national populations? This might have policy significance: if, for
instance, the people of country X are more vocationally motivated than the people of country
Y, might this call for different kinds of policies to be applied? In fact, recent European
research (Boeren et al., 2012) has shown that adults’ vocational motivation varies
significantly – and in relatively predictable ways – between countries. It is much more
pronounced in Eastern than Western Europe. But in addition to this broad distinction,
countries appear to be clustered together in smaller groupings. In Western Europe, Austria
and Belgium show distinct similarities, but differ from a second cluster of countries
(England, Ireland and Scotland). In Eastern Europe, Bulgaria and Lithuania are ‘rather
distinct’, another cluster also emerges: Hungary, Russia, the Czech Republic, Estonia and
(perhaps) Slovenia (Boeren et al. 2012). When, therefore, international organisations
encourage policies to encourage adults to choose vocational learning, or when national
governments engage in ‘policy learning’ – for example, the European Union now encourages
member states to exchange ‘good practices’ in the field as part of developing national
‘lifelong learning strategies’ (Council of the European Union 2011) – can they safely take
advantage of such findings to make more context-specific policy prescriptions?
Applying macro-structural analysis, Boeren et al. (2012) found welfare regime theory
(Titmuss, 1974; Esping-Andersen, 1989; Desmedt et al., 2006; Fenger, 2006) had some
power to explain these clusterings. Austria and Belgium, which have particularly low
vocational motivation, have ‘conservative-corporatist’ welfare regimes. England, Ireland and
Scotland’s ‘Anglo-Celtic’ welfare states are marked by a greater degree of liberalisation, and
higher (though still below average) vocational motivation. In Eastern Europe, by contrast,
Dr Ellen Boeren & Professor John Holford
vocational motivation was universally above average. This appeared to be linked to economic
performance, which may also provide a partial explanation of the two clusterings: economic
development has been slower in Bulgaria and Lithuania than in the Czech Republic, Estonia,
Russia and Slovenia.
Recent literature on adults’ motivation to learn suggests that participation is best understood
through a ‘bounded agency’ approach (Rubenson & Desjardins, 2009; Boeren et al., 2012).
Structure and agency both matter: macro structural insights from country level analysis are
best combined with individual characteristics of adult learners who are, after all, the agents of
individual choice (Coleman, 1990).
In this paper, therefore, we reanalyse the data used by Boeren et al. 2012 to explore how far
clusterings in vocational motivation by can be explained by socio-economic, socio-
demographic and country level variables. Our main aim is to increase understanding of the
role countries play in the lifelong learning, especially in formal adult education and training.
“Countries,” in this context, refers in effect to the institutional formations which distinguish
one nation-state’s educational activities from another’s. We include a set of Eastern European
countries which – when the project was designed – were new to the EU.
The sample used by Boeren et al. (2012) was large (1000 in each of twelve countries); it
differed not only by country, but also in other ways, such as age and educational attainment
(see Figs 1-4). For example, the Eastern European samples contained much larger proportions
of younger respondents: in the samples from Belgium, Scotland and England adults aged 45
and over were common, while in the Estonian sample 50 percent of adult learners were aged
under 25. This paper explores whether the apparently higher level of vocational motivation
found in Eastern European countries is simply a function of the samples, or whether country
level differences and clusterings persist even when we control for micro level variables such
as age, gender, job status and educational attainment. The central research question is:
How far do socio-economic, socio-demographic and country level variables explain
the variation in vocational motivation across a sample of 12,000 adult learners in
formal adult education in 12 European countries?
Dr Ellen Boeren & Professor John Holford
The paper begins by outlining the main theoretical perspectives on socio-economic, socio-
demographic and country level aspects of lifelong learning. It then describes the research
methodology, sets out the results and discusses their significance.
THEORETICAL BACKGROUND
Two main strands stand out in the literature on adult participation in lifelong learning:
psychological and sociological. These dimensions were discussed in detail by Boeren et al.
(2010). In our analysis, ‘sociological’ (socio-economic and demographic) variables are
‘regressed’ towards a psychological dependent variable: motivation. We therefore begin with
a brief survey of the relevant literature on two levels: macro and micro. The macro level will
help in understanding the different characteristics of countries
MACRO LEVEL
The Bounded Agency Model of participation in adult education was developed in the light of
empirical evidence which showed that different ‘welfare regimes’ produce different barriers
to participation (Rubenson & Desjardins, 2009). Countries’ structural features – institutions
of various kinds – relate not only to whether adults participate in lifelong learning, but also to
how they participate. Boeren et al.’s (2012) analysis of motivational variation among adult
learners in Eastern and Western Europe includes factors related to the labour market, the
family and the educational system. (Educational systems have often been omitted from
research on welfare state regimes, yet they seem particularly salient for lifelong learning
(Aiginger & Guger, 2006).)
Labour market
National labour markets differ. Eastern European countries, previously under Communist-led
governments, have transformed – or are transforming – into market-oriented economies,
comparable in many ways to those of the West. They sometimes said to be ‘catching up’ –a
process to which specific vocational training may well contribute (Cazes & Nesporova, 2004;
EBRD, 2013). The extent of transformation differs (Schiff et al., 2006). In Bulgaria and
Lithuania, for instance, it has been less marked than in Estonia or Slovenia. Whereas Western
Europe is now strongly service-oriented, a significant proportion of employment across
Eastern European countries remains agricultural, particularly in Lithuania and Bulgaria
Dr Ellen Boeren & Professor John Holford
(Holford et al., 2008). National systems of social security – welfare benefits, and job security
– may also influence motivation to learn: for instance, social security benefits may be
conditional on undertaking training. The continental ‘conservative-corporatist’ countries
(Austria and Belgium in our sample) are strongly stratified: access to welfare and benefits
(both pensions and unemployment benefit) depend largely on performance in the labour
market (European Commission, 2012). As a result, their labour markets are relatively
ineffective at generating social inclusion; those in work, however, are relatively well-off, and
have more opportunities to participate in education and training.
Family structures and quality of life
Surveys of ‘life values’ and ‘social aspects’ reveal that quality of life is generally lower in
Eastern European countries (Borooah, 2006). Families in Lithuania and Bulgaria tend to be
larger, while their housing conditions are typically poorer than in Western Europe. Measured
levels of happiness, and of trust in the political system, are quite low. Crime and violence
tends to cause more concern than in Western countries. Scores on cognitive and social
motivation are quite similar across Europe, but poorer living conditions in Eastern European
countries may well contribute to stronger vocational motivation to participate in education
and training.
Educational system
Labour market and employment structures also relate to educational structures (Holford et al.,
2008). There appear to be links between countries’ compulsory schooling and adult education
and training systems (Desmedt et al., 2006). Of the countries studied, Austria and Belgium
have strongly stratified schooling systems: children are split between ‘academic’ and
‘vocational’ tracks around the ages of 12-14 years. As Brunello (2001) argues, strong
differentiation leads teenagers to receive more specialised education than those in
comprehensive schooling systems; their need for specialised job-related training in adulthood
may be lower.
The length of compulsory education also seems likely to affect adult participation in
education (Eurydice, 2011). In some countries the school leaving age is 18; in many it is 15
or 16. While there seems no particular association between vocational motivation and
national school leaving age, this is complicated by variations in such factors as the age at
which education becomes compulsory (ranging from 5 to 7 years), the total number of years
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spent at school, and the length of the school year. Shorter compulsory initial education
presumably reduces how much can be studied, and may be associated with a need for more
vocational training during adulthood.
Education systems in Eastern Europe have, of course, changed markedly over the last two
decades, paralleling changes in the economy (Hantrais, 2002; Temple 2010). Studies of pupil
performance (such as the Progress in International Reading Literacy Study (PIRLS), the
Trends in International Mathematics and Science Study (TIMSS) and the Programme for
International Student Assessment (PISA)) reveal that attainment levels in Eastern European
countries are catching up with the West (Van Damme, 2008). PIRLS, for instance, shows that
the reading abilities of Bulgarian primary school children are the same as those in Flanders
(547), while Lithuania’s children do significantly better than Scotland’s (537 versus 527).
TIMSS shows similar patterns (Van den Broeck et al., 2004). Comparisons over time are
particularly revealing: TIMSS scores in mathematics and sciences decreased by 13 and 17
points respectively in Flanders, while the Lithuanian scores rose by 30 and 56 points over the
period 2003-2007. Western European countries appear to have difficulties in maintaining
standards, while younger Eastern Europeans seem to have improved their scores on these
types of assessments. More research and data collection in the next few years will make it
clearer whether the gap between Eastern and Western European education will narrow.
Adult education systems themselves do also vary across counrties. As Hefler and
Markowitsch (2013) point out, the concept of formal adult education is fluid. In fact, they
argue that different adult education systems can be characterised by two main components:
variety in provision and in length and content of the programmes (see Figure 1). Exploring
this analysis, an East-West division emerges. Eastern European countries have limited adult
education provision, mostly concentrated around long courses (70 percent of courses take
more than 200 hours). Western European countries,in contrast, have more variety in the
number of different types of institutions offering adult education, but courses also vary in
content and length, with considerably more shorter courses and courses focussing on ‘leisure
aspects’.
MICRO LEVEL
Dr Ellen Boeren & Professor John Holford
Motivation is of course a concept relating to the effort a person is willing to undertake (Deci
& Ryan, 2002). In this paper we are concerned not only with a particular application (to
education and training) of the broader concept, but with a particular subset of this: why does
an adult learner participate in a specific type of programme? (Keller, 1987). Houle (1961)
distinguished three types of adult learner, based on their motivation: (a) those interested
primarily in achieving a concrete goal, usually related to improving their status in the labour
market, or obtaining a qualification; (b) those participating primarily because of the social
interactions within the group of learners; and (c) those participating primarily because of a
strong cognitive interest in the subject of the course. Houle’s theorization is still commonly
used by researchers in the field: e.g. Boeren et al. (2012) and Robert (2012).
Age
Analyses of differential participation suggest age is one of most strongly determining
characteristics (Desjardins et al., 2006; Boeren et al., 2010). International bodies like the
European Union and the OECD (Organisation for Economic Co-operation and Development)
define older workers as those aged 55 and above; however, research shows there is already a
sharp decline in participation after the age of 45 (Desjardins et al. 2006). This may be
associated with ‘stereotyping’ of older adults: this appears to have a negative impact on their
participation in learning activities as well as in the labour market (Chasteen et al., 2002; Gray
and McGregor, 2003; Van Dalen et al., 2010). Gaillard and Desmette (2010) found that
people’s categorization of themselves as ‘older workers’ lowers not only their aspirations at
work, but also their willingness to learn, develop and undertake training, and increases how
likely they are to retire early. Such stereotyping reduces older adults’ productivity,
adaptability and loyalty (Greller & Stroh, 2004; Van Dalen et al., 2009).
Age discrimination arises especially where employers do not have strong age management
strategies (Snape & Redman, 2003; Bennington, 2004; Gartska et al., 2005; Conlin &
Emerson, 2006; Macnicol, 2006; Wood et al., 2008). Kyndt et al. (2011) showed that younger
employees (under 45) felt they received more encouragement from management to
participate in training than those aged over 45. Across most OECD countries, those over age
55 participate less both in education and training and in the labour force: as the workplace is
one of the major providers of lifelong learning opportunities for adults, lower labour force
participation explains (in part) older adults’ lower participation in training.
Dr Ellen Boeren & Professor John Holford
In addition to the literature relating to age in vocational education and training, psychological
literature also suggests that some aspects of the capacity to learn decrease with age (Matzel et
al., 2008). Cognitive abilities, memory and concentration decline, and may lead to learning
processes being perceived as harder or less attractive. This may also (in part) explain older
adults’ lower participation in education and training.
Employment and educational attainment
Analysis of Labour Force Survey data shows that adults with no formal qualifications are
least likely to be in employment (Riddell & Weedon, 2012). Early-school leavers are also
vulnerable in the labour market, often becoming trapped in a cycle of low-paid work
interspersed with periods of unemployment (Illeris, 2006; 2011). Individuals with no (or
limited) formal qualifications tend to have more negative attitudes towards education (Tett et
al., 2008). In general, adults with no (or poor) formal qualifications are underrepresented in
lifelong learning, even in countries with high participation rates (Desjardins et al., 2006;
Nesbit, 2006; Robert, 2012). They are much more likely to be economically inactive, and are
over-represented among the long-term unemployed (Nixon, 2006; Nicaise, 2010; Federighi et
al., 2012). They are found disproportionately in lower-paid and more monotonous jobs with
limited autonomy or flexibility and fewer opportunities for training (Ashton, 2004). Schindler
et al. (2011) argue that training needs are strongly related to job requirements, which
generates a vicious spiral: the more highly qualified find work in higher-quality skill-
intensive occupations, which themselves offer more training opportunities. Poor
qualifications make finding work more difficult; unemployment leads to poverty, social
exclusion, and typically poorer health and well-being (Hoskins et al., 2010). Overall, there is
a strong correlation between labour market participation and lifelong learning. The workplace
itself generates many opportunities, so the proportion of adults engaged in lifelong learning is
likely to be greater among the employed. While the unemployed participate less, those
without work who do participate in education or training may do so for vocational reasons –
for instance, to obtain paid work.
Gender
Although participation rates among men and women are quite similar, there are differences
between their motivations, the types of courses in which they participate and the barriers they
have to overcome. Women participate more for leisure-oriented reasons, while men’s motives
are much more job-related. Women are limited by a ‘glass ceiling’ effect: employers seem
Dr Ellen Boeren & Professor John Holford
less prepared to invest in their development. Women also bear the main burden of family
responsibilities (Koelet, 2005; Laurijssen, 2012). It is widely accepted that participation in
education is strongly gendered (Leathwood & Francis, 2006).
Our research draws not only on this theoretical background, but also on clusterings of
countries and regions developed in previous work within the EU-funded project, ‘Towards a
Lifelong Learning Society in Europe: the Contribution of the Education System’ (LLL2010)
(Holford et al., 2008; Boeren et al., 2012). Building on desk-based research, a country
typology was constructed. . Empirical data were then used to validate this typology.
Empirical comparative research is time consuming and expensive (Hantrais, 2009). We
therefore also sought to use the data to understand how the different levels of analysis relate
to one other: in this case, how the country and individual levels relate. Through such
exploration of countries and regions, we can throw light on how far generally accepted
propositions (e.g. that the likelihood of participation in education decreased among older
adults), are valid across a range of diverse contexts. This should increase our understanding
of how generalizable and robust theories of participation are.
DATA AND METHODS
Having outlined the main theoretical perspectives on the determinants of adults’ participation
in lifelong learning, we now seek to control these variables (gender, educational attainment,
age and labour market status) – across twelve European countries – to identify empirically
which contribute most strongly to adults’ vocational motivation to learn.
Data Context
Our data are drawn from an international database of 12,000 participants in formal adult
education during the year 2007.1 Formal adult education was defined as officially-recognised,
1 The data were gathered as part of the project, ‘Towards a Lifelong Learning Society in Europe: the Contribution of the Education System’ (LLL2010). We are grateful to the European Union’s 6th Framework research programme, which funded the research, and to our colleagues in the LLL2010 consortium. For further results from the project, see in particular Holford et al. (2008), Riddell, Markowitch & Weedon (2012), and Saar, Ure & Holford (2013).
Dr Ellen Boeren & Professor John Holford
credential-based, education or training. Typically, this might involve recognition by a
national ministry of education, or in a national qualifications framework. What official
recognition means in application, of course, varies according to national context, but in
effect, these are the forms of adult education most similar to compulsory education. Informal
workplace learning and in-company training outside the regular formal adult education
system (for instance, in-company courses which do not lead to recognised qualifications)
were excluded. The countries (or sometimes regions if they were part of a larger country, but
had their own educational policies in place, e.g. for Belgium, only Flanders took part in the
study) covered were Austria, Belgium (Flanders), Bulgaria, Czech Republic, England,
Estonia, Hungary, Ireland, Lithuania, Russia, Scotland and Slovenia.
Sampling
Stratified quota sampling was used: in every country 1000 participants were surveyed across
four educational levels, based on the International Standard Classification of Education: 250
at ISCED levels 1 and 2 (comparable to primary and lower secondary education), 250 at
ISCED level 3 (comparable to higher secondary education), 250 at ISCED level 4
(comparable to post-secondary but non-tertiary education) and 250 at ISCED level 5
(comparable to bachelors and masters courses in higher education). Within each ISCED
block, sampling was random. In practice, the target of 250 participants at each level was not
achieved in every country, and was exceeded in others. The sample was reweighted to the
original sampling plan of 4 x 250 in 12 countries.
Questionnaire
Participants completed a questionnaire with closed questions. These focused mainly on
motives to participate, experience of the classroom environment, barriers, and course
characteristics such as teaching methods and enrolment requirements. Previous educational
experience was also mapped, together with socio-economic and socio-demographic
characteristics. Adult learners at ISCED Levels 1 and 2 completed the questionnaire face to
face with a trained interviewer; learners at higher levels generally completed the form on an
individual basis, in the classroom or at home.
Quality procedures
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The use of a survey questionnaire in diverse cultural and institutional environments, and in a
range of languages, presents particular problems. The LLL2010 research team, which
included nationals of each of the countries in which it was used, adopted a number of quality
assurance measures. A glossary of terms for the core variables in the questionnaire was
created and discussed within the team: this ensured that as far as possible all members shared
understandings of the survey questions, and could translate and apply these in nationally
meaningful and contextualised forms. Wherever possible, the questionnaire contained
validated items from other international surveys: for example, socio-demographic variables
were measured in exactly the same way as in the Eurostat Labour Force Survey and Adult
Education Survey. Motivation scores were measured based on Boshier’s Education
Participation Scale, which is widely validated in the adult education literature.
RESULTS
The questionnaire contained 18 motivational statements, measuring their relevance for adult
learners’ participation (see Boeren et al., 2012). Principal component analysis on the entire
European dataset revealed two main dimensions of motivation: a cognitive-social dimension
and a vocational dimension, which means that items in the same dimension correlate to each
other, while it is not impossible that specific individuals scored high on both cognitive-social
and vocational items. The Cronbach Alphas of both dimensions were above .700, suggesting
these constructs provide reliable bases for further investigation (Mortelmans & Dehertogh,
2008). The results, including all factor loadings, are presented in Table 1.
TABLE 1: DATA REDUCTION FOR ‘RELEVANCE’ – 2 COMPONENTSC1 C2
to learn more on a subject that interests me .527 -.154to earn more .095 .529because my employer required me to enrol in the programme
-.018 .685
to participate in group activities .612 .240to contribute more to my community .691 .213to gain awareness of myself and others .755 .034to get a break from the routine of home and work .468 .128to do my job better .323 .423because someone advised me to do it .063 .527to start up my own business .150 .465because I was bored .225 .270
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because I was obliged to do it. e.g. to claim benefits, to avoid redundancy
-.003 .695
to get a job .157 .561to learn knowledge/skills useful in my daily life .597 .099to contribute more as a citizen .694 .234to meet new people .686 .177to be less likely to lose my current job .078 .688to obtain certificate .204 .368Cronbach’s Alpha = (T).816 & (C1) .801 & (C2) .739; Kaiser-Meyer-Olkin = .843; Bartlett’s p < .001, variance explained 36 percent
The results of this principal component analysis were saved in a standardized form, resulting
in a mean of 0 and a standard deviation of 1 for each component. Scores were compared
across countries and analysed by means of cluster analysis. As Boeren et al. (2012) show,
four clusters emerged: (a) Belgium and Austria, (b) Scotland, England and Ireland, (c) the
Czech Republic, Estonia, Hungary, Russia, and Slovenia, and (d) Bulgaria and Lithuania. The
variation in vocational motivation across countries was particularly strong, with Eastern
European countries in general scoring higher than the Western European countries.
To clarify our understanding of vocational motivation, we sought to control whether other
variables (i.e. other than the country level) contribute to the variation in vocational
motivation. We controlled not only for socio-demographic and socio-economic variables
(age, job, gender and educational attainment), but also for the country level. Figures 2-5 show
how the sample was distributed by these four control variables in each country
The Adjusted R-square indicates how much of the variance is explained by the independent
variables (Field, 2009). Age contributed less than 4 per cent. The inclusion of the variable
whether the adult learner had a job or not generated no major increase (+ 0.1 per cent). The
gender effect was also quite small (+0.5 per cent). The adult learner’s educational attainment
contributed rather more (+2.8 per cent), but the strongest increase came with the inclusion of
the country level (+20.8 percent).
TABLE 2: VOCATIONAL MOTIVATION - CONTROLLING FOR OTHER VARIABLES
F df Adjusted R-square p
Age 119.763 3 .038 .000
Job 91.309 4 .039 .000
Gender 83.138 5 .044 .000
Dr Ellen Boeren & Professor John Holford
Educational attainment 116.382 6 .072 .000
Countries 206.137 17 .280 .000
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Figure 2: sample gender distribution by country Figure 3: sample educational attainment distribution by country
Figure 4: sample age distribution by country Figure 5: sample labour market status distribution by country
Dr Ellen Boeren & Professor John Holford
This shows that differences in motivation are most strongly explained by differences between
countries, rather than by differences in individuals’ characteristics. In general, as noted by
Boeren et al. (2012), motivational variance across countries shows similar patterns to welfare
state typologies. Adult learners in Western European countries score lower on the vocational
dimension than those in Eastern countries; scores in Bulgaria and Lithuania are especially
high, where scores were higher than in the other Eastern European countries.
In order to demonstrate these differences, and to show that differences between countries go
beyond the different age distributions in the country samples, we examined the mean for
vocational motivation for each of the four clusters (see Figs 6-9).
Age (see Fig 6)
The literature suggests participation in formal education and training declines quite sharply
after the age of 45 (Desjardins et al., 2006); we therefore divided the samples by age, creating
a ‘middle group’ containing those aged between 38-42 (those born between 1965 and 1969 in
a survey conducted in 2007-2008). We included an older group, aged 42-67, and two younger
groups: those born in the 1970s and those born in the 1980s (i.e., aged 28-37 and 18-27 at the
time of the survey).
While adults’ vocational motivation to learn might be expected to decrease with age,
differences in motivational scores between the various age groups were not significant in
Eastern Europe. (In the Czech Republic, Estonia, Hungary, Russia, and Slovenia: F=2.026;
df=3; p=.108. In Bulgaria and Lithuania: F=0.302; df=3; p=.824.) In the Anglo-Celtic cluster,
differences were also quite small: only the oldest group differed significantly from the
youngest (F=9.324; df=3; p=.000). The ‘conservative-corporatist’ cluster (Austria and
Belgium) showed the largest differences between the youngest and oldest groups
(F=107.261; df=3; p=.000).
While the differences between age groups within one cluster are interesting, comparing the
scores for the same age groups across clusters is also revealing. While one would expect
those born in the 1980s to have stronger vocational motivation (being at the earlier stages of
their careers), we found that younger people in Austria and Belgium scored negatively
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compared to the overall mean of all adult learners in the pooled European dataset. That is, the
youngest adult learners in Austria and Belgium had lower levels of vocational motivation
than the oldest learners in both clusters of Eastern European countries. The difference
between the scores of those born in the 1980s across the four clusters is clearly significant
(F=242.381; df=3; p=.000); the same applies in all age groups. This result clearly indicates
that vocational motivation exists as an interplay between individual as well as country level
characteristics, and that motivation is thus more than simply a age-related concept.
Figure 6: age and vocational motivation by clusters
Labour market status (see Fig. 7)
To analyse labour market status, the sample was divided between those who were in paid
work at the time of the survey, and those who were not. In general, the unemployed had
slightly higher vocational motivations than those in employment, suggesting participation in
formal adult education may be seen as a stepping stone to future employment. However,
comparing the unemployed across country clusters, clear differences again emerged
(F=334.922; df=3; p=.000). Although the unemployed scored more highly than those with a
job in each separate country cluster – although most clear in the Austrian-Belgian cluster -,
Dr Ellen Boeren & Professor John Holford
those within the Eastern European clusters scored more highly than those in the Western
European clusters. This result suggests – as with age – that the effect of the country level is
stronger than the effect of labour market status.
Figure 7: labour market status and vocational motivation by clusters
Gender (see Fig 8)
Within each country cluster, men scored more highly on vocational motivation than women.
However, if we compare men (F=346.288; df=3; p=.000) and women (F=623.303; df=3;
p=.000) across country clusters, we notice – again – that the country level asserts itself. Men
in Western European countries had lower vocational motivation than women in Eastern
European countries: country is more important than gender in explaining variation in
vocational motivation across our 12 European countries. While one usually assumes that men
participate because of job related reasons, this assumption is only true if one explores the
results within separate countries. Comparing Belgian men with Lithuanian women gives a
completely different result.
Dr Ellen Boeren & Professor John Holford
Figure 8: gender and vocational motivation by clusters
Educational attainment (see Fig 7)
In order to explore the influence of educational attainment, we distinguished those who had a
degree (ISCED Level 5 qualification from a tertiary educational institution) from those who
did not. In the Bulgarian-Lithuanian cluster, those with a degree had a somewhat stronger
vocational motivation to participate; in the other three clusters, the opposite was found.
Comparing degree-holders (F=286.643; df=3; p=.000) and those without a degree
(F=611.201; df=3; p=.000), it is again clear that the country level variable is stronger.
Dr Ellen Boeren & Professor John Holford
Figure 9: educational attainment and vocational motivation by clusters
DISCUSSION AND CONCLUSIONS
Our principal conclusion is that a clustering of countries according to respondents’ scores on
motivational statements – particularly statements relating to vocational motivation – remains
valid after controlling for individual socio-demographic and socio-economic sampling
characteristics. This supports literature which suggests macro-structural factors – educational
system and labour market – contribute to differences in motivational scores between
countries. Differential patterns of motivation between countries in our study represent much
more than mere sampling differences. This provides an empirical demonstration of the
‘bounded agency’ approach (Rubenson & Desjardins 2009). As Boeren et al. (2012) argued,
participation in adult lifelong learning is too often analysed in separate country contexts, with
a focus on individual level variables. Motivational theories are typically based on small-scale
qualatitive research in single countries. – Houle’s (1961) study is both exemplary and
representative: undertaken in the USA, it was based on 22 in-depth interviews. His theory has
Dr Ellen Boeren & Professor John Holford
been, of course, been tested using quantitative scales, but by allowing multi-country
comparisons, our research adds substantial new insights to the knowledge base.
Having presented the results, what do these findings imply? First, they challenge sharply the
widespread assumption that policies to encourage participation in adult lifelong learning
should or can rely on the existence of broadly comparable levels of vocational motivation
internationally. Patterns of adult motivation to learn vary very significantly between
countries. This points to the pervasiveness and power of national institutional structures and
cultures.
Second, they show the very different challenges countries face in pursuing common goals
such as building a ‘learning society’. Vocational motivation to learn is weaker in some
countries than in others (Boeren et al. 2012).It therefore seems likely that vocationalism will
have varying effectiveness as a policy lever. The motivational patterns revealed in this paper
suggest that countries will and must take varied policy paths, even when they agree on the
goal. How far a learning society should be based on vocational education alone is, of course,
ultimately a normative matter: our findings provide some basis for empirically-informed
questioning of today’s vocationalist policy ‘commonsense’.
Third, they raise questions about the use of indices (such as the EU’s lifelong learning
participation index) as a foundation for policy-design, particularly at the national level. The
lifelong learning participation index is no more than a descriptive tool; it allows no
multivariate exploration of other variables related to participation, and thus provides a very
weak evidence-base for policy purposes. Data are cross-sectional, not longitudinal, which
limits researchers to explore changes over time.
Finally, our finding suggest the need for deeper understanding of participation in adult
lifelong learning based on studies which combine psychological and sociological approaches
to participation with insights from social policy (Hudson et al., 2008). Adult education and
training systems are deeply embedded in national social and institutional structures, in how
state, market and family structures deliver social rights, and in patterns of social stratification.
Research should take account of different elements of welfare (such as social security,
employment, housing, education and health systems, not only in the theoretical framework,
but it is also recommended that follow-up research includes specific variables measuring
Dr Ellen Boeren & Professor John Holford
factors at the level of the education and labour market system): fortunately the specific
country codes contained within cross-sectional micro-datasets such as the Eurostat Adult
Education Survey and the Labour Force Survey permit comparative micro-macro analysis.
Dr Ellen Boeren & Professor John Holford
REFERENCES
Aiginger, K. & Guger, A. (2006). The European social model: from obstruction to advantage.
Progressive Politics, 4(3).
Ashton, D. (2004). Political economy of workplace learning. In H. Rainbird, A. Fuller & A.
Munro (Eds.), Workplace Learning in Context (pp. 21-37). London: Routledge.
Bennington, L. (2004). Prime age recruitment: the challenges for age discrimination
legislation. Elder Law Review, 3.
Beveridge, W. (1942). Social insurance and allied services. Basingstoke: Macmillan.
Billett, S. (2011). Vocational education: purposes, traditions and prospects. Dordrecht
Heidelberg London New York: Springer.
Boateng, S. K. (2009). Significant country differences in adult learning. Luxembourg:
Eurostat.
Boeren, E., Holford, J., Nicaise, I. & Baert, H. (2012). Why do adults learn? Developing a
motivational typology across twelve European countries. Globalisation, Societies and
Education, 10(2), 247-269.
Borooah V.K. (2006). How much happiness is there in the world? A cross country study.
Applied Economics Letters, 13(8), 483-488.
Boshier, R. W. (1973). Educational participation and dropout: a theoretical model. Adult
Education, XXIII, 255-82.
Brooke, L., & Taylor, P. (2005). Older workers and employment: managing age relations.
Ageing and Society, 25(3), 415–429.
Brunello, G. (2001). On the complementarity between education and training in Europe. IZA
Discussion Paper No. 309. Bonn: IZA.
Dr Ellen Boeren & Professor John Holford
Cazes, S. & Nesporova, A. (2004). Labour market in transition: balancing flexibility and
security in Central and Eastern Europe. Geneva: International Labour Organisation.
Chasteen, A. L., Schwarz, N, & Park, D. C. (2002). The activation of aging stereotypes in
younger and older adults. Journal of Gerontology: Psychological Sciences, 57(B), 540-547.
Coleman, J. (1990). Foundations of social theory. Cambridge, MA: Harvard University
Press.
Conlin, M. & Emerson, P. (2006). Discrimination in hiring versus retention and promotion.
Journal of Law, Economics and Organization, 22(1), 115-136.
Council of the European Union (2011). Council Resolution on a renewed European agenda
for adult learning. Official Journal of the European Union C 372/1, 20 December 2011.
Crouch, C. (2011). The strange non-death of neoliberalism. Cambridge: Polity Press.
Deci, E. & Ryan, R. (2002). Handbook of self-determination research. Rochester: The
University of Rochester Press.
Dehmel, A. (2006). Making a European area of lifelong learning a reality? Some critical
reflections on the European Union’s lifelong learning policies. Comparative Education,
42(1), 49-62.
Desjardins, R., Rubenson, K. & Milana, M. (2006). Unequal chances to participate in adult
learning: international perspectives. Paris: UNESCO.
Desmedt, E., Groenez, S. & Van den Broeck, G. (2006). Onderzoek naar de
systeemkenmerken die de participatie aan levenslang leren in de EU-15 beïnvloeden. Leuven:
HIVA.
Duncan, C. & Loretto, W. (2004). Never the right age? Gender and age-based discrimination
in employment. Gender, Work & Organization, 11 (1), 95-115.
Dr Ellen Boeren & Professor John Holford
European Bank for Reconstruction and Development (EBRD) (2013). Stuck in transition?
Transition report 2013. London: European Bank for Reconstruction and Development.
Esping-Andersen, G. (1989). The three worlds of welfare capitalism. Cambridge: Policy
Press.
European Commission (2012). European economy: benchmarking unemployment benefit
systems. Brussels: European Commission.
Eurydice (2011). Adults in formal education: policies and practices in Europe. Brussels:
Eurydice.
Federighi, P. (Ed.) (2012). Enabling the low skilled to take their qualifications one step up.
Florence: Universita degli Studi di Firenze.
Fenger, H.J.M. (2007). Welfare regimes in Central and Eastern Europe: incorporating post-
communist countries in a welfare regime typology. Contemporary Issues and Ideas in Social
Sciences, 3(2), 1-30.
Field, J. (2006). Lifelong learning and the new educational order. Stoke on Trent: Trentham.
Gaillard, M. & Desmette, D. (2010). (In)validating stereotypes about older workers
influences their intentions to retire early and to learn and develop. Basic and Applied Social
Psychology, 32(1), 86–98.
Garstka, T. A., Hummert, M. L. & Branscombe, N. R. (2005). Perceiving age discrimination
in response to intergenerational inequity. Journal of Social Issues, 61(2), 321–342.
Gray, L. & McGregor, J. (2003). Human Resource Development and older workers:
stereotypes in New Zealand. Asia Pacific Journal of Human Resources 41(3), 338-353.
Greller, M.M. & Stroh, L.K. (2004). Making the most of ‘late-career’ for employers and
workers themselves: becoming elders not relics. Organizational Dynamics, 33(2), 202–212.
Dr Ellen Boeren & Professor John Holford
Hantrais, L. (2002). Central and East European states respond to socio-demographic
challenges. Social Policy and Society, 1(2), 141-150.
Hantrais, L. (2009). International comparative research: theory, methods and practice.
Basingstoke: Palgrave-Macmillan.
Hefler, G. & Markowitsch, J. (2013). Seven types of formal adult education and their
organizational field: towards a comparative framework. In E. Saar, O.B. Ure & J. Holford
(Eds). Lifelong learning in Europe: national patterns and challenges (pp.82-116).
Cheltanham: Edward Elgar Publishing.
Holford, J., Riddell, S., Weedon, E., Litjens, J. & Hannan, G. (2008). Patterns of lifelong
learning. policy & practice in an expanding Europe. Wien: LIT Verlag.
Hoskins, B., Cartwright, F. & Schoof, U. (2010). Making lifelong learning tangible. The
ELLI index Europe 2010. Gütersloh: Bertelsmann Stiftung.
Houle, C.O. (1961). The inquiring mind. Madison, WI: University of Wisconsin Press.
Hudson, J., Lowe, S. & Kühner, S. (2008). The short guide to social policy. Bristol: The
Policy Press.
Illeris, K. (2006). Lifelong learning and the low skilled. International Journal of Lifelong
Education, 25(1), 15-28.
Illeris, K. (2011). The fundamentals of workplace learning: understanding how people learn
in working life. London: Routledge.
Jarvis, P. (2010). Adult education and lifelong learning: theory and practice. New York:
Routledge.
Keller, J.M. (1987). Strategies for stimulating the motivation to learn. Performance and
instruction, 26(8), 1-7.
Dr Ellen Boeren & Professor John Holford
Koelet, S. (2005). Standvastige verschillen. Een analyse van theoretische benaderingen over
de verdeling van het huishoudelijk werk tussen vrouwen en mannen op basis van
tijdsbudgetonderzoek. Doctoraal proefschrift, Onderzoeksgroep TOR, Vakgroep Sociologie,
Vrije Universiteit Brussel.
Kyndt, E., Michielsen, M., Van Nooten, L., Nijs, S. & Baert, H. (2011). Learning in the
second half of the career: stimulating and prohibiting reasons for participation in formal
learning activities. International Journal of Lifelong Education, 30(5), 681-699.
Laurijssen, L. (2012). Verdeeld tussen arbeid en gezin. Een panelstudie naar de context en
dynamiek van de keuze voor deeltijds werk. Brussel: VUBpress.
Leathwood, C., & Francis, B. (Eds.). (2006). Gender and lifelong learning: Critical feminist
engagements. London: Routledge.
Lynch, L. (2002). Too old to learn? Lifelong learning in the context of an ageing population.
In D. Istance, H. Schuetze & T. Schuller (Eds.), International perspectives on lifelong
learning. From recurrent education to knowledge society. Ballmoor: SRHE and Open
University Press.
Macnicol, J. (2006). Age discrimination: an historical and contemporary political
analysis. Cambridge: University Press.
Matzel, L.D., Grossman H., Light K., Townsend, D.A., & Kolata, S. (2008). Variations in
age-related declines in general cognitive abilities of Balb/C mice are associated with
disparities in working memory span/capacity and body weight. Learning & Memory, 15(10),
733–746.
Mortelmans, D., Dehertogh, B. (2008). Factoranalyse. Leuven, Acco.
Nesbit, T. (2006). What’s the matter with social class. Adult Education Quarterly, 56(3), 171-
187.
Dr Ellen Boeren & Professor John Holford
Nicaise, I (2010). A smart social inclusion policy for the EU: the role of education and
training. Paper presented at the Belgian Presidency Conference on Education and Social
Inclusion, Ghent, 28 29 September, 2010.
Nixon, D. (2006). I just like working with my hands: employment aspirations and the
meaning of work for low‐skilled unemployed men in Britain's service economy. Journal of
Education and Work, 19(2), 201-217.
OECD (2006). Live longer, work longer: a synthesis report. Paris: OECD.
Riddell, S. & Weedon, E. (2012). Lifelong learning and the wider European socioeconomic
context. In S. Riddell, J. Markowitsch & E. Weedon (Eds.), Lifelong learning in Europe:
Equity and efficiency in the balance (pp. 17-38). Bristol: Policy Press.
Robert, P. (2012). The socio-demographic obstacles to participation in lifelong learning
across Europe. In S. Riddell, J. Markowitsch & E. Weedon (Eds.), Lifelong learning in
Europe: Equity and efficiency in the balance (pp. 87-101). Bristol: Policy Press.
Rubenson, K. & Desjardins, R. (2009). The impact of welfare state regimes on constraints to
participation in adult education. A bounded agency model. Adult Education Quarterly, 59(3),
187-207.
Schiff, J, Egoume-Bossogo, P., Ihara, M., Konuki, T. & Krajnyak, K. (2006). Labor market
performance in transition; the experience of central and eastern european countries. IMF
Occassional paper 248. Washington: IMF.
Schindler, S., Weiss, F. & Hubert, T. (2011). Explaining the class gap in training: the role of
employment relations and job characteristics. International Journal of Lifelong Education,
30(2), 213 232.
Schultz, T.W. (1971) Investment in human capital: The role of education and of research.
New York: Free Press.
Dr Ellen Boeren & Professor John Holford
Snape, E. & Redman, T. (2003). Too old or too young? The impact of perceived age
discrimination. Human Resource Management Journal, 13(1), 78-89.
Temple, P. (2010). Accountability in Eastern Europe: becoming like everywhere else? In B.
Stensaker & L. Harvey (Eds.), Accountability in Higher Education: Global Perspectives on
Trust and Power (pp.93-110). Abingdon UK: Routledge.
Tett, L. & Maclachlan, K. (2008). Learners, tutors and power in adult literacies research in
Scotland. International Journal of Lifelong Education, 27(6), 659-672.
Titmuss, R.M. (1974). Social policy. London: Allen and Unwin.
Van Dalen, H., Henkens, K. & Schippers, J. (2009). Dealing with older workers in Europe: A
comparative survey of employers' attitudes and actions. Journal of European Social Policy
19(1), 47-60.
Van Dalen, H. P., Henkens, K. & Schippers, J. (2010). Productivity of older workers:
perceptions of employers and employees. Population and Development Review, 36(2), 309–
330.
Van Damme, J. (2008). PIRLS 2006: Vlaanderen in de wereld. Leuven: CO&E.
Weber, M. (1946). Politics as a vocation. In H.H. Gerth and C. Wright Mills (eds.) From Max
Weber: Essays in sociology (pp. 77-128). New York: Oxford University Press.
Wood, G., Wilkinson, A. & Harcourt, M. (2008). Age discrimination and working life:
perspectives and contestations – a review of the contemporary literature. International
Journal of Management Reviews, 10(4), 425–442.