University of Edinburgh€¦ · Web viewContemporary education policies show extensive common...

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


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


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,


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?


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


(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


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


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.


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


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).


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


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


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


Educational attainment 116.382 6 .072 .000

Countries 206.137 17 .280 .000


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


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


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 -,


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.


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.


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


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


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.


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.


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.


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.


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.


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.


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.


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.

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