Adult education policies, participation and social inequalities and their relationship

A cross-country comparison of Germany and Austria

Mémoire (de fin d'études) , 2020 , 82 Pages

Pédagogie - Système scolaire, Politique d'enseignement et de l'école

This thesis specifically investigates incentive structures and the removal of barriers to enhance a more equal participation in adult education to prepare the workforce for upcoming challenges related to the future of work. It aims at contributing to this discussion and indicates how adult education policies can be used to possibly reduce socio-economic inequalities in accessing adult education and provide workers with the skills needed in the (future) labour market. This topic seems to be largely unexamined and no systematic cross-country comparison has been conducted in the past. While human capital accumulation in terms of primary education has been well investigated, research in the context of post-schooling phase, investigating the direct correlation between investing in (adult) education and its returns, largely remains a black box.

Two European countries that are considered particularly affected by automation will be examined, namely Austria and Germany. Whereas in Austria, 16.6% of jobs are at high risk of automation and 29.7% at risk of significant change, the figures for Germany are slightly higher with 18.4% of jobs at high risk of automation and 35.8% at risk of significant change, compared to an OECD average of 14% and 32% respectively. Additionally, both countries share similar institutional structures and are therefore considered comparable.

This thesis aspires to shed light on government intervention and the adult education market to possibly reduce social inequalities by setting incentives and removing barriers to enhance overall (and a more egalitarian) participation in adult education.
Finally, it seeks to contribute to the scientific and societal debate as well as to provide approaches on how to design effective, efficient, and egalitarian adult education policies. The research question to be answered aims at empirical hypothesis testing and is of explanatory nature.

Table of Content

Executive Summary

List of Figures

List of Tables

List of Abbreviations

1. Introduction and Research Question
1.1. Future of Work and the Importance of Adult Education
1.2. Research Question

2. Theory and Hypotheses
2.1. Defining Adult Education
2.2. Human Capital and Rational Choice Theory
2.2.1. Human Capital Theory – An Introduction
2.2.2. Human Capital Theory – Important Actors
2.2.2.1. From a State Level’s Perspective
2.2.2.2. From an Individual’s Perspective: Matthew Effect
2.2.3. Rational Choice Theory – Individual’s Balance of Costs and Benefits
2.2.4. Adult Education as an Important Asset in the Context of HCT
2.3. Three Levels Determining Participation in Adult Education
2.3.1. Micro / Individual-Level
2.3.2.1. Socio-Economic and Cultural Dimension
2.3.1.2. Psychological Dimension
2.3.2. Meso / Educational Institutions-Level
2.3.3. Macro / Country-Level
2.3.3.1. Varieties of Capitalism and Welfare Regime Literature
2.4. The Role of Public Policy Frameworks
2.4.1. Individual Demand for Adult Education
2.4.2. Employer Demand and Support for Adult Education
2.4.3. Public Demand for Adult Education
2.5. Adult Education Policies
2.5.1. Analysing Austria's and Germany's Lifelong Learning Strategies..
2.5.1.1. Key Characteristics
2.5.1.2. Key Policies and Objectives
2.6. Hypotheses

3. Methodology
3.1. Research Design
3.1.1. Most Similar Research Design
3.2. Data Collection Methods and Analytical Sample
3.3. Composition of the Sample and Description of the Variables

4. Quantitative Analyses and Results
4.1. Overall Participation in Adult Education
4.2. Participation in Adult Education by Different Sub-Groups
4.3. Incentives to Participate in Adult Education
4.4. Barriers That Prevent Participation in (Further) Adult Education Activities
4.5. The Impact of Austria’s Lifelong Learning Strategy LLL:2020

5. Conclusion and Discussion
5.1. Elaboration on the Results and Further Discussion
5.2. Limitations and Further Research

6. Policy Recommendations

7. Literature
7.1. Publications
7.2. Online-Sources
7.3. Data

8. Appendix
8.1. Tables
8.2. Sample Statistics
8.3. Description of Sample Sizes for Analysis of the “Most Important Barrier” Identified by Respondents
8.3.1. Sample Size for AES German Sample,
8.3.2. Sample Size for AES German Sample, 2012
8.3.3. Sample Size for AES Austrian Sample, 2016
8.3.4. Sample Size for AES Austrian Sample, 2011
8.4. Description of Multicollinearity (Barriers)
8.5. Overview of The Results of the Statistical Analysis for the Hypotheses
8.6. R-Code

Executive Summary

Adult education systems are powerful mechanisms at the disposal of governments to help individuals adapt to altering labour market needs and changing skill requirements. Ideally, they should permit regular re- and upskilling in a knowledge-based economy, help overcome barriers and incentivise individuals to participate in adult education. This thesis focuses on Austria and Germany, two European countries that are not only most similar with regards to their welfare regimes and capitalist systems, but also particularly affected by automation. It aims at determining to what extent Austria’s 2011 and Germany’s 2019 lifelong learning strategies are reflective of and influence individuals’ incentives to enhance participation in adult education as well as identifying barriers preventing participation, particularly of underrepresented group. The research question is being examined through the lens of human capital and rational choice theory and addressed in three steps: To begin with, the two strategies are analysed. In a next step, regression analyses of Adult Education Survey (AES) data provide insights regarding participation in training as well as incentives and barriers faced by different sub-groups. Ultimately, it is investigated whether the Austrian strategy had a positive impact and the findings are extrapolated to the German case.

It is found that, albeit to a limited extent, the two strategies are reflective of individuals’ incentives to enhance participation in adult education and remove barriers that prevent participation. The relevance to focus on underrepresented groups is highlighted in both strategies. However, the results from the regression analyses indicate that in some aspects the different groups are considered too generic. Examining the issue against the tenets of human capital and rational choice theory, it is found that the responsibility to invest in adult education should not only and never exclusively be laid on individuals. Instead, employers and the state should invest in enhancing the incentives and reducing the barriers that individuals face. Especially in the future of work context, devising well-targeted incentives and removing barriers to increase equity and inclusiveness in access to training is key.

To the authors’ best knowledge, this study is the first systematic cross-country comparison that takes an individual learner-centred perspective to shed light on how to design adult education systems that incentivise people to participate considering socio-economic background factors.

List of Figures

Figure 1: Types of learning format (Boeren, 2011; authors’ own creation.)

Figure 2: Motivational/incentive structures: job-related versus personal advancement (BMBF, 2018; Statistik Austria, 2017; authors’ own creation)

Figure 3: The educational market (Boeren et al., 2010; authors’ own creation)

Figure 4: Distribution of funding between the stakeholders in Germany and Austria, 2009 (Lassnigg et al., 2012; UIL, 2019; authors’ own estimates)

Figure 5: Selected barriers on personal/individual and educational institutional level (BMBF, 2018; authors’ own creation)

Figure 6: Key success factors, building blocks for success, system-level indicators and outcomes of adult education policies (EU Commission, 2015; authors’ own creation)

Figure 7: LLL:2020 in a nutshell (Republik Österreich, 2011; authors’ own creation)

Figure 8: National skills strategy (BMAS & BMBF, 2018; authors’ own creation)

Figure 9: Analytical model highlighting the main variable of interest (authors’ own creation)

Figure 10: Overview of the four hypotheses upon which the analysis is based

Figure 11: Barplot for participation in adult education in Austria and Germany, 2012 and 2016 (BMBF, 2018; Statistik Austria, 2017 & 2012; authors’ own creation)

Figure 12: Barplot for participation in adult education (Y) and skill-intensity of jobs (X). Austrian sample, 2011 and 2016 (Statistik Austria, 2017 & 2012; authors’ own creation)

Figure 13: Barplot for participation in adult education (Y) and skill-intensity of jobs (X). German sample, 2012 and 2016. Column % (BMBF, 2018; authors’ own creation)

Figure 14: Barplots for incentives to participate in adult education by year. Austrian and German sample, 2011 and 2016. Column % (BMBF, 2018; Statistik Austria, 2017 & 2012; authors’ own creation)

Figure 15: Results from multinomial regression analyses for incentives to participate in adult education on job-related, personal or both levels (Y) and skill-intensity of jobs (X). Austrian sample, 2011 (Statistik Austria, 2012; authors’ own creation)

Figure 16: Results from multinomial regression analyses for incentives to participate in adult education on job-related, personal or both levels (Y) and skill-intensity of jobs (X). Austrian sample, 2016 (Statistik Austria, 2017; authors’ own creation)

Figure 17: Results from multinomial regression analyses for incentives to participate in adult education on job-related, personal or both levels (Y) and skill-intensity of jobs (X). German sample, 2012 (BMBF, 2018; authors’ own creation)

Figure 18: Results from multinomial regression analyses for incentives to participate in adult education on job-related, personal or both levels (Y) and skill-intensity of jobs (X). German sample, 2016 (BMBF, 2018; authors’ own creation)

Figure 19: Barplot for barriers that prevent (further) participation in adult education (Y) and skill-intensity of jobs (X). Austrian sample, 2011 and 2016. Column % (BMBF, 2018; Statistik Austria, 2017 & 2012; authors’ own creation)

Figure 20: Barplot for barriers that prevent (further) participation in adult education (Y) and skill-intensity of jobs (X). German sample, 2012 and 2016. Column % (BMBF, 2018; Statistik Austria, 2017 & 2012; authors’ own creation)

Figure 21: Barplots for barriers that prevent (further) participation in adult education (Y) and skill-intensity of jobs (X). Austrian sample, 2011/2 and 2016. Column % (Statistik Austria, 2017 & 2012; authors’ own creation)

Figure 22: Barplots for barriers that prevent (further) participation in adult education (Y) and skill-intensity of jobs (X). German sample, 2011/2 and 2016. Column % (BMBF, 2018; authors’ own creation)

Figure 23: The results at a glance

Figure 24: Composition of sample for AES data, German sample, 2016 (BMBF, 2018; authors’ own creation)

Figure 25: Composition of sample for AES data, German sample, 2012 (BMBF, 2018; authors’ own creation)

Figure 26: Composition of sample for AES data, Austrian sample, 2016 (Statistik Austria, 2017; authors’ own creation)

Figure 27: Composition of sample for AES data, Austrian sample, 2011 (Statistik Austria, 2011; authors’ own creation)

List of Tables

Table 1: System classification in OECD countries: VoC and welfare state typology (Esping-Andersen, 1990; Hall & Soskice, 2011; authors’ own creation)

Table 2: Types of Policies (Desjardins & Rubenson, 2013; authors’ own creation)

Table 3: Analysis of the characteristics of German and Austrian lifelong learning strategies (BMAS & BMBF, 2019; Republik Österreich, 2011; authors’ own creation)

Table 4: Total and final sample size (BMBF, 2018; Statistik Austria, 2017 & 2012; authors’ own creation)

Table 5: Cross-table of participation in adult education in Austria and Germany, 2011/2 and 2016 (BMBF, 2018; Statistik Austria, 2017 & 2012; authors’ own creation)

Table 6: Cross-Table and Chi-Square test of participation in adult education (Y) and skill-intensity of jobs (X). Austrian sample, 2011 and 2016. Column % (Statistik Austria, 2017 & 2012; authors’ own creation)

Table 7: Cross-Table and Chi-Square test of participation in adult education (Y) and skill-intensity of jobs (X). German sample, 2012 and 2016. Column % (BMBF, 2018; authors’ own creation)

Table 8: Cross-Table and Chi-Square test of incentives (Y) and skill-intensity of jobs (X). Austrian sample, 2012 and 2016. Column % (Statistik Austria, 2017 & 2012; authors’ own creation

Table 9: Cross-Table and Chi-Square test of incentives (Y) and skill-intensity of jobs (X). German sample, 2012 and 2016. Column % (BMBF, 2018; authors’ own creation)

Table 10: Cross-Table and Chi-Square tes tof barriers (Y) and skill-intensity of jobs (X). Austrian sample, 2012 and 2016. Column % (Statistik Austria, 2017 & 2012; authors’ own creation)

Table 11: Cross-Table and Chi-Square test of barriers (Y) and skill-intensity of jobs (X). German sample, 2012 and 2016. Column % (BMBF, 2018; authors’ own creation)

Table 12: Distribution of reported barriers that prevent participation in (further) adult education activities in Germany and Austria in 201/2 and 2016 (BMBF, 2018; Statistik Austria, 2017 & 2012; authors’ own creation)

Table 13: Key policies to overcome barriers as identified in the German and Austrian lifelonglearning strategies (BMAS & BMBF, 2019; Republik Österreich, 2011, authors’ own creation)

Table 14: Results from logistic regression models (DV: Participation in NFE and FED and IVs), German and Austrian sample (BMBF, 2018; Statistik Austria, 2017 & 2012; authors’ own creation)

Table 15: Results from logistic regression models (DV: Institutional and individual-level barriers and IVs), German and Austrian sample (BMBF, 2018; Statistik Austria, 2017 & 2012; authors’ own creation)

Table 16: Sample statistics of independent variable and participation in adult education for German respondents, 2012 and 2016 (BMBF, 2018; Statistik Austria, 2017 & 2012; authors’ own creation)

Table 17: Sample statistics of independent variable and participation in adult education for Austrian respondents, 2012 and 2016 (BMBF, 2018; Statistik Austria, 2017 & 2012; authors’ own creation)

Table 18: The results at a glance, extended version

List of Abbreviations

Abbildung in dieser Leseprobe nicht enthalten

1. Introduction and Research Question

1.1. Future of Work and the Importance of Adult Education

Globalisation, digitalisation, automation, demographic and structural changes as well as the growing diversification of work arrangements have profound impacts on the world of work. These mega-trends are affecting the quantity and quality of jobs that are available and the skill requirements that workers (will) need on the labour market. It is expected that skills requirements as well as more than 35% of occupations will undergo substantial changes, according to all indications at an accelerated pace in the upcoming decades (Butler, 1994; Dolphin, 2015; Nedelkoska & Quintini, 2018; OECD, 2019 a). Rapidly changing skills requirements raise the risk of skills mismatch and shortage, ultimately impacting qualification requirements in the field of adult education (Butler, 1994). To help individuals adapt to changing labour market needs, it is important for governments to design and implement adult education systems, which permit regular reskilling and upskilling for employment security and/or to find new employment in a knowledge-based economy (Durazzi & Geyer, 2019).

While these changes affect the whole workforce, low-skilled adults, self-employed and those whose jobs are at highest risk of automation bear the brunt of adjustment costs. However, these workers are the ones that least participate in adult education (OECD, 2019 a). They are three times less likely to have participated in on-the-job training in the past year compared to workers in non-automatable jobs (OECD, 2018). Numerous studies confirm this tendency, including Dengler and Matthes (2018), Frey and Osborne (2017) and Zika et al. (2018). Less qualified adults often find themselves in a so-called ‘low skills trap’ as many have low-level positions, frequently step in and out of unemployment and often expect limited returns to training, such as higher wages or access to better jobs (Burdett & Smith, 2002). Additionally, individuals tend to choose to conduct adult education in occupations that are quite closely-related in skill content to their previous training and hence does not represent an actual reskilling (Nedelkoska & Quintini, 2018). In general, adult education is given significant importance for economies and the professional chances of employees (Brandenburg, 1980; Sauter, 1984; 1982). Thus, policies that focus on underrepresented groups not only increase overall participation rates and address skills gaps, but are likely to reduce inequalities in access to adult education and socio-economic inequalities at large (European Commission, 2015).

Due to the drastic work-related changes, the international community, including the ILO, the OECD and the EU have called for a universal entitlement to lifelong learning (European Commission, 2000; Global Commission on the Future of Work, 2019). Governments, workers and employers as well as educational institutions have a mutual responsibility in building an effective and appropriately financed lifelong learning ecosystem (ibid.). The benefits of adult education are considered to be manifold and affect not only the learners themselves, but likewise employers and the wider community (European Commission, 2015). Key success factors include granting equity of access for all and to significantly improve participation by those who are least likely to participate (ibid.).

Consequently, the topic of how to design (inclusive) adult education policies in Europe is already and continues to be one of the main puzzles to be solved. There is an imminent need to create a fair and egalitarian environment for the workforce. This thesis specifically investigates incentive structures and the removal of barriers to enhance a more equal participation in adult education to prepare the workforce for upcoming challenges related to the future of work (ibid.). It aims at contributing to this discussion and indicates how adult education policies can be used to possibly reduce socio-economic inequalities in accessing adult education and provide workers with the skills needed in the (future) labour market. This particular topic seems to be largely unexamined and no systematic cross-country comparison has been conducted in the past. While human capital accumulation in terms of primary education has been fairly well investigated, research in the context of post-schooling phase, investigating the direct correlation between investing in (adult) education and its returns, largely remains a black box (Robinson, 2015). Two European countries that are considered particularly affected by automation will be examined, namely Austria and Germany. Whereas in Austria, 16.6% of jobs are at high risk of automation and 29.7% at risk of significant change, the figures for Germany are slightly higher with 18.4% of jobs at high risk of automation and 35.8% at risk of significant change, compared to an OECD average of 14% and 32% respectively (OECD, 2019 b). Additionally, both countries share similar institutional structures and are therefore considered comparable. This thesis aspires to shed light on government intervention and the adult education market to possibly reduce social inequalities by setting incentives and removing barriers to enhance overall (and a more egalitarian) participation in adult education. Finally, it seeks to contribute to the scientific and societal debate as well as to provide approaches on how to design effective, efficient, and egalitarian adult education policies.

1.2. Research Question

Investigating Austria’s and Germany’s lifelong learning strategies, it will be assessed to what extent these attempt to provide adequate incentive structures and reduce barriers to enhance participation rates in adult education, especially of underrepresented groups, i.e. those groups that are least likely to participate. Since Austria has its strategy in place since 2011 and Germany just recently, in 2019, published its first national adult education strategy, this thesis takes a policy-learning approach (BMAS & BMBF, 2019; Republik Österreich, 2011). As such it extrapolates the assessed impact of the Austrian lifelong learning strategy to the case of Germany.

The research question to be answered aims at empirical hypothesis testing and is of explanatory nature. It reads as follows: “To what extent are Austria’s 2011 and Germany’s 2019 adult education strategies reflective of and influence individuals’ incentives to enhance participation as well as barriers that prevent participation in adult education activities, especially of underrepresented groups?”

To gain a deeper understanding of the question’s dimension, several sub-questions are examined:

1. Is participation in adult education related to socio-economic and demographic status and therefore reflects social inequalities?
2. What are incentive structures to enhance participation in adult education? Did they change over time? Are they related to the workers’ skill-intensity level?
3. What are principal barriers that prevent people from participating in adult education? Are these related to socio-economic and demographic status as well as labour-market related factors? Did they change over time?
4. Did the Austrian lifelong learning strategy have an impact on the perception of principal barriers in adult education? Is policy learning to Germany possible?
5. What are potential policy recommendations for German adult education policies?

The remainder of this thesis is organised as follows: The next chapter attempts to comprehensively review theoretical literature to derive hypotheses. It forms the theoretical base of this work and thereupon analyses the Austrian and German lifelong learning strategies. The main analytical section starts at chapter 3, entailing a description of the data and methodology and ultimately presenting results of the descriptive, logistic regression and multinominal regression analyses (cf. section 3 and 4). The results of the analyses subsequently allow to answer the aforementioned sub-questions and the research question itself. The thesis concludes by listing policy recommendations based on the empirical findings.

2. Theory and Hypotheses

2.1. Defining Adult Education

One issue in the context of adult education is the absence of a universally applied definition (Becker, 1991; Kruppe & Baumann, 2019). While the Austrian lifelong learning strategy (LLL:2020) per se does not include a definition of adult education, in a cross-country comparison on vocational training, it is referred to as the following: Adult education comprises general education on the one hand and job-related education on the other hand. Job-related adult education builds upon a primary professional degree or represents an addition to it (Republik Österreich, 2011; Tritscher-Archan et al., 2012). The German adult education strategy elaborates on adult education as follows: “The [...] partners understand adult education as the continuation or resumption of structured learning after the end of an initial period of education and entry into working life” (BMAS & BMBF, 2019, p. 3). Already in 1970, the German Education Council defined adult education along these lines, which is referred to by several recent publications (Bilger, 2006; Dehnbostel, 2008; Deutscher Bildungsrat, 1970; Dollhausen & Muders, 2016; Kupper, 2008; Nuissl, 2009; Strunk, 1997; Walter, 2014).

Since the EU published its Memorandum on Lifelong Learning in 2000, the references to adult education have included formal, non-formal and informal learning activities (European Commission, 2000). The European Commission (EC) defines lifelong learning, a concept that is often used in the context of adult education as “all learning undertaken throughout life, with the aim of improving knowledge, skills and competence, within a personal, civic, social or employment-related perspective” (Commission of the European Communities 2001, p. 4). Thus, adult education aims at improving one’s educational biography and primarily refers to organised forms of learning, while lifelong learning refers to learning activities in general and also includes informal learning (Thiele et al., 2016).

According to Eurostat (2019 a), formal education refers to any type of schooling to attain an official qualification, recognised in the National Framework of Qualifications. Non-formal education in contrast does not certify any formal qualification and might take any form also outside educational institutions. In that sense it potentially caters to all population groups (ibid.). Informal learning in comparison is less organised and less structured and may take place in a social context (ISCED, 2012). This study focuses on formal and non-formal adult education as especially the latter is gaining importance in the future of work context (cf. Figure 1).

Figure 1: Types of learning format (Boeren, 2011; authors’ own creation.)

Abbildung in dieser Leseprobe nicht enthalten

2.2. Human Capital and Rational Choice Theory

2.2.1. Human Capital Theory – An Introduction

For a country and its labour market it is essential to maintain a robust, flexible and capable workforce to remain intact and absorb shocks. The aforementioned trends in the future of work context depict possible shocks and stimuli for a country's labour market. From a state’s perspective, one prerequisite for a well-functioning labour market is the continuous availability of a large workforce. From a private sector perspective, it is critical to accumulate the highest possible level of skills and innovation (Becker, 1991; Sengenberger, 1987).

Economists elaborated that, for a country to keep its competitiveness, one has to consider not only material, but also human capital. Human capital theory (HCT) assumes that a certain investment allows for a higher return than the initial input and that investments may be undertaken in another form than monetary input. Schultz (1960) is considered to be one of the founders of HCT. He suggests treating education as an investment in human beings and to consider its consequences as a form of capital. Education thus enhances people’s skill levels, which leads to a higher-skilled workforce and increases the production capacity of a market. An increase in human capital is also associated with creating ‘optimised’ citizens and improving the societal standard of living (Olaniyan & Okemakinde, 2008). Becker (1975) proposed setting incentives for actors in the market to invest in adult education. Potelienė & Tamašauskienė (2013) outline three ways of investing in human capital, which also represent the returns to education, including (1) the private return, (2) the social return and (3) the labour productivity return. Overall, it is primarily the state and individuals undertaking the investment and, depending on the context, also the private sector.

2.2.2. Human Capital Theory – Important Actors

2.2.2.1. From a State Level ’s Perspective

There is empirical evidence for the significance of skills towards an improved well-being, positive health outcomes, increased life expectancy, and enhanced social cohesion (Hartog & Van den Brink, 2007; Robinson, 2015). Hence, HCT is essential for policy-makers. Modern economic growth theory includes human capital as one factor to calculate the macroeconomic output per person. Modern growth theory stipulates that a significant amount of worldwide annual growth in labour productivity can be traced back to increased human capital (Fernald & Jones, 2014). Buttler & Tessaring (1993) hypothesised that human capital will likely be the most important factor of production and thus requires a continuous increase in highly qualified workers, especially in the light of technological change and automation of routine tasks (Autor et al., 2003). Investing in human capital has the potential to positively impact innovation capabilities (Acemoglu & Robinson, 2012; Russ, 2017). Babalola (2003) lists three positive spill-over effects, namely (1) knowledge transfer between generations; (2) transfer of know-how on innovation; and (3) development of novel ideas. Nevertheless, human capital is also essential for effectively complementing technology (Andrews et al., 2018).

2.2.2.2. From an Individual’s Perspective: Matthew Effect

According to HCT, a person will decide to invest in education if they expect to gain individual benefits that are higher than their initial investment (Beicht et al., 2004). Malloch (2003) uses the following formula to calculate a person’s educational investment: total human capital = (ability + behaviour) x effort x time investment.

Thus, it can be derived that the individual’s investment in education can be divided into ability and behaviour, effort and time investment. The formula makes clear that there is no causal relationship between investing in education, the return and the role of ability and behaviour, which is influenced by social background factors that have to be taken into account. Several studies show that the socio-economic status of one’s family is positively correlated with receiving a school diploma, which in turn increases the likelihood and ability to participate in adult education (Becker, 1991; Welch, 1975). Some scholars argue that participation in (adult) education displays a pattern of cumulative advantage, whereby those who are already better endowed also participate more (cf. Matthew effect, Kilpi-Jakonen et al., 2015). This pattern is often explained by employers’ incentives, since investing in higher educated employees indicates a greater enhancement in employers’ productivity (ibid). Further, occupations in which highly educated tend to work are often knowledge-intensive and therefore likely to require more training.

Returning to the above-outlined formula, the sum of the equation represents the individual return, which may be as diverse as the individuals themselves. Recent research has identified four dimensions of benefits to adult education, including income, professional advancement, prevention of unemployment and return to gainful employment after inactivity (Pollak et al., 2016). Training also potentially has positive effects in terms of higher employment stability, labour market transitions and labour mobility (Blundell et al., 1999). In sum, HCT considers that people always try to achieve the maximum possible return and minimum costs to reach their individual aim (Boeren et al., 2010). This principle is founded in rational choice theory (RCT).

2.2.3. Rational Choice Theory – Individual’s Balance of Costs and Benefits

The rational choice (RC) approach holds a long tradition in research since the 1980s. The base goes back to the 15th/16th century to well-known philosophers and economists such as Machiavelli, Hobbes, Hume, and Sir Karl Popper. Based on the assumption of a homo economicus, it follows that individuals choose from a series of available actions those which enable the best possible realisation of their goals. Thus, the consequences of actions have a specific utility value on which the individual’s choice between action alternatives depends (Kunz, 2004; Walter, 2014).

However, RCT fails to explain individuals’ decision-making processes comprehensively. Blossfeld and Müller (1996) criticise RCT for attempting to explain individuals’ rational actions based on the assumption of universal, timeless human preferences and for considering structural parameters as exogenous. From a sociological perspective, the RC approach should consider social norms and structural constraints, which might influence individuals’ cost-benefit calculations (ibid.). There are different approaches to explain collective events: According to the ‘structural-individualistic approach’, social behaviour is explained on the basis of individual behaviour and the social context in which they act (Esser, 1991; Hill, 2002). Schauenberg (2001) considers social and family-related conditions, on which basis families decide on the educational attainment of their children. The differences in the assessment of costs and benefits of educational decisions are referred to as secondary origin effects (Baur et al., 2015). The ‘declaration model’ similarly argues that individual behaviour is oriented towards class-related (social or psychological) norms, which form the base for an individual’s decision-making process (Paulus & Blossfeld, 2007). This model assumes that people from disadvantaged backgrounds are unable to identify the benefits of education adequately or do not consider them as relevant (ibid.).

In the context of adult education, the RC approach emphasizes the subjectively expected benefit. Accordingly, the expectations attached to participating in adult education influence the decision to participate. However, this rationale is only valid if it deviates from a routine decision, which is the case when people engage in an adult education activity and/or identify reasons to participate for the first time (Behringer, 1999). The following condition holds: “Individuals will only undergo additional […] training […] if the costs (tuition and training course fees, foregone earnings) are compensated by sufficiently higher future earnings” (Blundell et al. 1999, p. 2 f.). While further, enhancing labour market prospects shows to be one of the main motivational or incentive factors throughout society, for low-skilled people the main reason to participate is to prevent unemployment (Dieckhoff, 2007; Pollak et al., 2016). Hence, low-skilled people in particular are considered to participate in adult education activities to maintain employment security (Ambos, 2005) (cf. Figure 2).

2.2.4. Adult Education as an Important Asset in the Context of HCT

A transmission or diffusion of skills through institutionalised adult education represents an important factor for economic development, augmenting the productivity of the labour force and increasing individuals’ potentials to be successful in the labour market (Schömann & Becker, 1998). In the future of work context, adult education is considered a future investment and collective good, benefitting participants, employers, the state and society at large (Buttler, 1994; Pischke, 2001).

Through the application of HCT, adult education becomes economised (Desjardins, 2014). However, if adult education is left to function according to the market principle, further segmentation will likely be the consequence as employers might primarily invest in those workers whom they consider valuable for their productivity and want to maintain as employees (Gillen et al., 2010). To incentivise employers to fulfil their part, and to increase the level of participation in adult education, public policies should therefore ensure that demands other than the employers’ ones are met (Desjardins, 2014).

Figure 2: Motivational/incentive structures: job-related versus personal advancement (BMBF, 2018; Statistik Austria, 2017; authors’ own creation)

Abbildung in dieser Leseprobe nicht enthalten

2.3. Three Levels Determining Participation in Adult Education

This section investigates the three primary levels of actors in terms of their contribution and characteristics for adult education: The individual, institutional and country-level (Boeren et al., 2010). To participate in an adult education activity, a successful match must occur between the different levels: The government who has an interest in people participating in adult education and therefore regulates the demand and supply interactions between individuals and educational institutions, by providing financial stimuli, reducing costs, and offering services. Educational institutions provide training offers targeted at the demands of individuals and employers (cf. Figure 3). Investigating influential factors at the micro-, meso-, and macro-level for participation in adult education, particular focus will be placed upon barriers and incentives taking a learners’ centred perspective.

Figure3: The educational market (Boeren et al., 2010; authors’ own creation)

Abbildung in dieser Leseprobe nicht enthalten

2.3.1. Micro / Individual-level

Employers and individuals represent actors on the demand side of the educational market. Currently, the discourse is shaped in the direction of the individual’s obligation to write their own professional biography (Boeren et al., 2010). Participation in adult education is unequally divided between different societal groups and depends on individual attributes, which can be distinguished between a socio-economic and cultural as well as a psychological dimension (ibid.).

2.3.1.1. Socio-Economic and Cultural Dimension

Depending on a person’s socio-economic and/or cultural background, costs and benefits of participating in adult education are judged differently. Analysing characteristics that determine participation in adult education, Desjardins (2014, p. 5) finds the following: “Among the most important socio-demographic characteristics revealing unequal distribution are age, the extent of education […], the level of literacy proficiency adults have attained, and adults’ socioeconomic status as reflected by their parents’ level of education”. Many of these aspects are confirmed throughout literature (Kruppe & Baumann, 2019). The EU finds that most inequalities in participation are due to i.a. the individual’s employment status, prior educational attainment, and occupational skill-intensity (Cedefop, 2015 a). Mania (2018) defines the following so-called ‘underrepresented educational groups’: Single parents, people aged 55+, low-skilled, illiterate, unemployed, adults with learning difficulties, young adults without formal education, people with disabilities, migrants, welfare recipients, and convicts. Low-skilled workers are defined as individuals who have not (yet) acquired any vocational qualification (Ambos, 2005; Kruppe & Baumann, 2019). Referring to the above-outlined Matthew effect, people with lower educational attainment and in less skill-intense jobs display a lower participation rate compared to their counter-groups (Buttler & Tessaring, 1993; Dollhausen & Muders, 2016; Rosenbladt et al., 2008). Thus, through adult education, the discrepancy between lower and higher-skilled workers increases (Bremer, 2014; Faulstich, 2003). There is consensus regarding the low participation rates of unemployed adults and people with a migrant background (Jürges & Schneider, 2004; Walter, 2014). In terms of migration, another difficulty is that foreigners, asylum-seekers and people with a migration background are often not represented in statistics due to language barriers (Ambos, 2005). There is evidence that older people participate less in adult education. However, especially for those, participation in adult education is necessary, as their prior education is likely to be outdated and not up-to-date in terms of technological skills (Bilger et al., 2017; Cedefop, 2015 a; Pollak et al., 2016; Schmid & Kailer, 2008). While studies from the past century suggest lower participation of female respondents, several other studies find that the participation of women and men is relatively equal or even higher for women than for men (Breen et al. 2012; Dollhausen & Muders, 2016; Walter, 2014). Several studies reveal that in the context of adult education, employer engagement is essential (Bilger et al., 2017; Dollhausen & Muders, 2016). Since large employers are more likely to offer adult education activities, their employees are more likely to participate than others and unemployed sub-groups (Pollak et al., 2016).

2.3.1.2. Psychological Dimension

Psychological approaches explain participation in adult education at individual level as (self-)motivated action (Boeren et al., 2010). Keller (1987) shows that positive attitudes towards learning, recognising the advantages of learning, and confidence in one’s abilities are vital for motivating people to participate in educational activities. Even though the dominant reason for participation is job-related, other reasons such as social contacts or personal development are important. As adults decide upon their participation, motivation is of key importance when it comes to the initiation of a learning process and for the successful participation of the learner (Gorges, 2015). Studies show that intrinsic motivation results in better performance in learning activities (Schunk & Zimmermann, 2012). Among the reasons for the non-participation of underrepresented groups are insufficient information, negative prior learning experiences, lower self-regulated learning competencies and lack of basic competencies such as literacy and numeracy skills as well as self-confidence (Kohl, 2019; Rubenson & Desjardins, 2009).

2.3.2. Meso / Educational Institutions-Level

The importance of educational institutions has come to be increasingly appreciated as they can influence the distribution of benefits and realisation of upward mobility (Eltis et al., 2009). Dessus (2001) considers that the importance of human capital to economic development depends on the educational infrastructure, the initial human capital, and the ability of the system to distribute services equally (Potelienė & Tamašauskienė, 2013). According to Boeren et al. (2010), interventions at the level of educational institutions can be seen as catalysts in generating higher demand on the individual-level. It is sometimes wrongly assumed that the offer is tailored to meet the needs of all individuals. However, programme formats often focus on the middle-class, since adults must often have to pay enrolment fees (Nicaise, 2003; Boeren et al., 2010). Instruments to overcome barriers, including the recognition of prior learning, counselling of disadvantaged groups, and collaboration with relevant stakeholders therefore deserve explicit attention.

The Equality of Educational Opportunity Report was a pioneering attempt to measure the impact of institutional-level variables. Coleman et al. (1966) conclude that schools’ characteristics had little impact on the differences in students’ performance as inequalities were determined by socio-economic characteristics. Because of his focus on material aspects, Coleman’s conclusions were criticised. A second wave of investigations arose in the 1970s (Brookover et al., 1979; Rutter et al., 1979). Mortimore (1988) come to the conclusion that it is the social environment and learning climate which impact students’ performance (Darkenwald & Valentine, 1986). Schuetze & Slowey (2002) conclude that flexible access to the curriculum, alternative study methods, financial aid, the provision of support services (such as child care, study advice, job and welfare services) and related benefits (such as public transport tickets or Internet access) incentivise adults to participate. In contrast, institutional barriers that might prevent people from participation include scheduling problems, inconvenient distance and insufficient information (Boeren, 2016; Cross, 1981; Darkenwald & Merriam, 1982). Although it has been shown that the factors at the educational institutions-level can affect the attraction of learners, the development of policy goals, benchmarks and indicators remain weak at this level (Eurydice, 2013).

In Germany, the institutional structure of adult education is highly differentiated (Offerhaus et al., 2016). This applies to the large number of providers and supporting organisations, their interests and thematic priorities as well as to the question to whom offers are accessible. The latter applies to adult education in enterprises and activities funded by the Federal Employment Agency in accordance with the Social Code III. In Austria, the adult education market is segmented into in-company adult education; labour market training and second-chance education (Zweiter Bildungsweg). In terms of the plurality of educational providers, Austria is in a similar situation as Germany. A high number of providers are affiliated with the social partners and are non-profit organisations. However, numerous private sector organisations did emerge in the course of the past few years (Lassnigg, 2011; Lassnigg, 2005). Furthermore, they are divided into national associations, focussing either on general education or occupation-oriented education. Some large associations collaborate with the Austrian Library Association established in 1972 to form the Conference of Austrian Adult Education (KEBÖ) (Lenz, 2005). In such a context, the Austrian adult education market has created ‘insiders’ and ‘outsiders’ due to the gap between traditional and new providers, especially in terms of access to public finances (Lassnigg, 2011). Similar to Germany, the highest share of adult education in Austria is in-company training. In 2009, Germany spent about 5 billion Euros on adult education (4% of the education budget), while Austria spent 23.5 million Euro (0.15% of the entire education budget) (Lassnigg et al., 2012; UIL, 2019). Figure 4 displays an overview of the distribution of funding between the stakeholders in Germany and Austria in 2009 (Boeren & Field, 2019). The primary financial source and provider of adult education in both countries are employers (Eurostat, 2019 b).

Figure 4: Distribution of funding between the stakeholders in Germany and Austria, 2009 (Lassnigg et al., 2012; UIL, 2019; authors’ own estimates)

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Apparently, various educational institutions and stakeholders in the adult education market operate both in Germany and Austria in a highly uncoordinated and fragmented manner. This makes it difficult for individuals to find their optimal individual learning path. Hence, it is not necessarily the (lack of) policy measurements, or the number of adult education providers that are insufficient, but the barriers on institutional-level, which individuals encounter along the way to commit to adult education. Figure 5 summarises the barriers on personal/individual and educational institutional level, on which basis parts of the empirical analysis will be conducted.

Figure 5: Selected barriers on personal/individual and educational institutional level (BMBF, 2018; authors’ own creation)

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2.3.3. Macro / Country-Level

The fact that participation rates between countries differ is related to various ways governments offer support to tackle barriers, provide information and guidance and engage employers and unions (OECD, 2018; Walter, 2014). Hence, one can assume that state systems influence adults to participate in adult educational activities. With regards to participation rates in Europe, one can distinguish four groups (ibid.). Scandinavian countries (participation rates of ~50% or higher); Anglo-Saxon (participation rates of ~35-50%); Western European countries, including Austria and Germany (participation rates between ~20-35%) and the final group including Southern European countries, and some Eastern European countries (participation rates ~ <20%) (Desjardins & Rubenson, 2009). The grouping aligns with welfare regime types, regarding cultural developments, educational and skill formation systems, as well as labour market and economic contexts (Boeren et al., 2010).

2.3.3.1. Varieties of Capitalism and Welfare Regime Literature

The cross-country differences in adult education can be analysed in the context of Esping-Andersen’s (1990) welfare regime typology (Desjardins & Rubenson, 2009). In the welfare state literature, Austria and Germany are both classified as ‘pure’ Conservative or Corporatist cases with medium social stratification and decommodification (Arts & Gelissen, 2002; Castles & Mitchell, 1993; Esping-Andersen, 1990; Ferragina & Seeleib-Kaiser, 2011). The role of the state is not to interfere until family capacities are exhausted. Rubenson and Desjardins (2009) use the welfare state typology and resort to the so-called ‘bounded agency model’, explaining the appearance of barriers to participation in adult education and their reciprocity. Each welfare regime affects these barriers through public policies and thus, welfare states have an impact on dispositional barriers. In the Varieties of Capitalism (VoC) typology, Germany and Austria are also both classified as coordinated market economies (CMEs) (Hall, 2015; Hall & Gingerich, 2004; Hall & Soskice, 2001). Within the VoC paradigm, it is often argued that adult education systems provide the basis for divergent institutional outcomes of political economies (Thelen, 2004). CMEs heavily rely on non-market or strategic forms of interaction in the coordination of the employers’ relationships with others. Such support is i.a. provided through employer associations, trade unions, and legal or regulatory systems facilitating information-sharing and collaboration (Hall & Soskice, 2001). These ‘institutions’ implicate that employers can coordinate with other actors through strategic interaction. From their perspective, the existence of such educational institutions contributes to a safe and eased investment in the training of workers (Graf, 2009).

Table 1: System classification in OECD countries: VoC and welfare state typology (Esping-Andersen, 1990; Hall & Soskice, 2011; authors’ own creation)

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2.4. The Role of Public Policy Frameworks

Major institutional and public policy frameworks, such as those relating to labour market or welfare state institutions can either alleviate or exacerbate barriers to participation in adult education and affect incentives in positive or negative ways (Desjardins & Rubenson, 2013). Also, policies in other areas that are not seemingly relevant for adult education (including early childhood education and care and other family policies) can indirectly affect investments in adult education in unintended ways (ibid.). Policy-makers have at their disposal demand- and supply-side policies to boost investment and redress inequalities in access to adult education (cf. Table 2). This in turn means that they can impact the behaviour of stakeholders in relation to the provision and take-up of adult education opportunities. While supply-side policies include the targeting of subsidies to providers, demand-side policies would target subsidies directly to individuals, such as training vouchers. Institutional and public policy frameworks condition the provision and take-up of adult education and hence the constraints discussed in the previous sections (Desjardins & Rubenson, 2013). There is the assumption that, despite the apparent need, there is an underinvestment in adult education both by employers and employees (OECD, 2018; Walter, 2014).

State intervention should firstly facilitate optimised resource allocation and prevent market failure, manifesting itself for instance in not reaching all societal groups (Milana & Desjardins, 2007). However, Hall (2006) argues that there is no guarantee for a state to succeed since the costs of intervention could exceed those of the market failure and a private solution might be more cost-efficient. Additionally, governments do not always possess all relevant information to enter the market (Lassnigg, 2000). For example, little knowledge is available regarding the skills needs in prospective labour markets. However, there are services dedicated to provide information, such as those drawn from skills anticipation systems (OECD, 2018). Secondly, state intervention should reduce socio-economic inequalities (Fouarge et al., 2013). Patterns of inequality in adult education participation reflect broader structural socio-economic inequalities, i.a. in income, educational attainment and the distribution of qualifications. Such patterns tend to mirror the distribution of resources and power, and exemplify prevailing notions of justice, rights, responsibilities and entitlements (Desjardins, 2014). Desjardins et al. (2013) find a correlation between increased participation in learning activities and a reduction of economic inequality. Thus, adult education policies also have the potential to reduce social inequalities.

Table 2: Types of Policies (Desjardins & Rubenson, 2013; authors’ own creation)

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2.4.1. Individual Demand for Adult Education

When aiming to foster participation in adult education, governance and institutional structures should devise policies that incentivise individual demand for adult education (Desjardins, 2014). Such policy responses entail the provision of quality information and guidance on available opportunities, potential rewards and associated risks through optimised institutional set-ups (one-stop shops; career guidance). Further, they encompass improving access by promoting a positive learning culture; recognising prior learning; freeing up time from family- and job-related obligations (statutory educational and parental leave); providing flexible and innovative learning provisions (e.g. on a part-time basis, distance-learning) and mitigating financial constraints or providing financial incentives (tax incentives, subsidised loans or training/time account schemes) (OECD, 2018).

2.4.2. Employer Demand and Support for Adult Education

Public policies not only have a key role in managing skill supply, but also in fostering its demand. Even though employers play a crucial role in providing and financing adult education, underinvestment in the skills of their employees is common (Froy et al., 2009; OECD, 2018). Hence, a key role for public policy vis-a-vis structurally-based constraints is to engage stakeholders in the process (Desjardins, 2014; OECD, 2018). Examples are to foster employer and union engagement and investment (e.g. through awareness campaigns, financial incentives or flexible work arrangements), promote the pooling of risks with other stakeholders, and coordinate skill requirements with educational offers (ibid.). Trade unions can also play a key role in promoting and managing training provisions (OECD, 2018).

2.4.3. Public Demand for Adult Education

Employers are likely to invest in the skills of employees who already have a decent skill-base (Nedelkoska & Quintini, 2018). This tendency may contribute to greater inequality in accessing adult education opportunities and support (Desjardins et al., 2006). Without public policy frameworks, inequity in access to adult education opportunities can be exacerbated. Besides, public policies have an essential role in sustaining a universal, high-quality and flexible skill-base in knowledge-based economies (Desjardins, 2014; Rees, 2013). Examples of adequate policy responses are to foster flexible pathways as well as diverse learning methods and community-learning; promote relevant and responsive provisions to individual, employer and public demand; and foster active citizenship as well as active ageing.

2.5. Adult Education Policies

Many countries have been implementing comprehensive lifelong learning strategies incorporating various priorities for adult education (European Commission, 2015). Since the 1990s, the EU has started to emphasise the growing importance of education (Guimaraes, 2017). More recent developments have highlighted the importance of harmonising systems in each member state with the European Qualification Framework (European Commission, 2015). Additionally, the Council of the EU and the EC have set priority areas to work on by 2020, including the fostering of lifelong learning and enhancement of transparency and recognition of skills and qualifications to facilitate learning, promote employability and enhance labour market and social mobility (Milana & Holford, 2014).

Even though national lifelong learning strategies display a rather diverse scope of key policy actions, common objectives include improving access to adult education (e.g. by promoting a positive learning culture), promoting flexible and innovative models for adult education (based on new technologies, distance learning), recognising prior education, reshaping institutional setups for effective delivery and monitoring, enhancing employer and union engagement and investment, and articulating lifelong learning around broader socio-economic objectives (such as to modernise the economy, improve competitiveness, and reduce unemployment) (ibid.). Many strategies link these policy actions to improving adults’ employability and well-being with economic competitiveness. According to the European Commission (2015), three system-level indicators to measure the success of policies are (1) increased participation in adult education, (2) improved skills, and (3) competences and higher quality of learning. For an overview of possible policy actions, outputs and outcomes cf. Figure 6.

Figure 6: Key success factors, building blocks for success, system-level indicators and outcomes of adult education policies (EU Commission, 2015; authors’ own creation)

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2.5.1. Analysing Austria’s and Germany’s Lifelong Learning Strategies

In the next step, Austria’s and Germany’s lifelong learning strategies will be investigated regarding the above-outlined policy elements as well as incentives and barriers that enhance and prevent participation in adult education.

2.5.1.1. Key Characteristics

Published in 2011, Austria’s LLL:2020 is guided by five core principles: (1) life phase orientation, (2) placing learners at the centre (flexibility of learning), (3) lifelong guidance (facilitating the learning process), (4) competence orientation (recognition of informal learning), and (5) promotion of participation in lifelong learning (enhancing the motivation to learn) (Republik Österreich, 2011). It pursues a holistic approach and includes all levels of the education system from early childhood to adult education until and beyond the retirement phase (ibid.). The strategy is structured along ten lines of action. Each line is presented on the basis of a vision, the current status, the objectives and measures. The action lines include the provision of adult education and second-chance education free of charge, the promotion of learner-friendly environments, and the offer of guidance to improve one’s work-life balance (cf. Figure 7). Further, there are five core principles and four basic principles (cf. Table 3). The four basic principles include gender and diversity, equal opportunities and social mobility, quality and sustainability, as well as performance and innovation (ibid.). The LLL:2020 is a coherent platform, regardless of diverse responsibilities (Eurydice, 2018).

Figure 7: LLL:2020 in a nutshell (Republik Österreich, 2011; authors’ own creation)

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Regarding the case of Germany, the recently published national skills strategy is part of the domestic pillar of the skilled-workers strategy (Fachkräftestrategie) (BMAS & BMBF, 2019). It is structured along ten objectives for action, including the commission of concrete tasks to the 17 partners of the strategy and other stakeholders (cf. Figure 8). It explicitly refers to its target groups, which include individuals and organisations and alludes to other policies, including the ‘Qualification Opportunities Act’ (Qualifizierungschancengesetz) and the ‘Upgrading Training Assistance Act’ (Aufstiegsfortbildungsförderungsgesetz). A secretariat was established in the Federal Ministry of Labour and Social Affairs (BMAS) that functions as a link between the BMAS and the Federal Ministry of Education and Research (BMBF) and is the primary stakeholder in the implementation process of the national skills strategy (cf. Table 3).

Figure 8: National skills strategy (BMAS & BMBF, 2018; authors’ own creation)

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Table 3: Analysis of the characteristics of German and Austrian lifelong learning strategies (BMAS & BMBF, 2019; Republik Österreich, 2011; authors’ own creation)

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2.5.1.2. Key Objectives and Policies

The key objectives and policies as identified in the two respective lifelong learning strategies will be analysed in the following (cf. Table 13 in Appendix). Thereby, particular emphasis is placed on overcoming barriers and setting incentives for underrepresented groups. Both strategies touch upon aspects of the above-outlined key success factors as visualised in Figure 6. To improve learners’ disposition towards adult education, key policies in the German and Austrian strategies focus on information provision, motivational and targeted counselling aspects, establishing a learning culture and recognising prior learning. Austria aims at a harmonised counselling system with one-stop-shops as centralised contact points. Providing the right information is not only key to planning and participation in adult education but also functions as a motivational boost. Additionally, there should be a transparent and customised portfolio system to document participation. Particular focus is placed on career starters and low-skilled individuals. Simultaneously, the LLL:2020 aspires to establish a lifelong learning culture by means of community-based learning and mutual recognition and accreditation of qualifications. Similarly, Germany also focuses on a uniform and digitised information provision and counselling to boost participation. The German national skills strategy also aims at providing a plan of action for unemployed people to reintegrate them on the labour market within three months. Additionally, both strategies aim to rely on information provision by the social partners to overcome barriers that prevent participation.

To enhance equity of access for all, key policy actions focus on special target group assistance, low-threshold access, basic skill development and recognising prior learning. The Austrian LLL:2020 aims at fostering social mobility by a harmonised accreditation structure of skill acquisition. Additionally, it aims at providing equal access through needs-oriented and harmonised financial schemes. The LLL:2020 further elaborates on individual learning accounts to incentivise private investments in education. In terms of transition support and measurements targeted at low-skilled workers, the LLL:2020 fosters the establishment of individualised offers and personalised case-management to enhance participation in adequate education activities and boost employability. Another group that is directly addressed are people in their post-occupational life phase. The LLL:2020 further mentions the importance of embedding basic skills as necessary for re- and upskilling. The German national skills strategy sets the goal to close financing gaps, i.a. by investigating a possible extension of the education bonus after 2020.¹ Besides, it aims at guaranteeing universal access through low-threshold and broad access, for example through platforms. Similar to the Austrian strategy, the German strategy also refers to the relevance of basic skills development as a foundation for equal access to adult education offers.

To deliver high quality and needs-based training, key policies focus on a combination of providing time flexibility and financial assistance, ensuring quality of training and provisions aligned to skill needs. Since the 1990s, Austria grants the right to publicly subsidised educational leave for employees (Bildungskarenz). The LLL:2020 further aims at providing flexible working time arrangements, including needs-based care offers. Similarly, the national skills strategy also aims at examining measures of publicly subsidised educational leave. In terms of quality provision, both strategies focus on assuring quality of learning providers, educational institutions and teachers. While the LLL:2020 aims at providing good practices to ensure a nationwide quality standard, the national skills strategy focuses on providing information on (future) skills demands and innovative approaches to adult education.

To coordinate effective lifelong learning policies, measures are taken to ensure a successful implementation and monitoring of the process as well as a harmonised, coordinated provision of policies. While the LLL:2020 aims at establishing transparent targets based on indicators to ensure a successful implementation and monitors the process through a task force, the national skills strategy aims at organising so-called ‘policy development labs’ to explore individual objectives in greater depth. In October 2020, the task force will present a final report on the achievement of objectives and further recommendations. Until 2020, the level of implementation is measured by predefined benchmarks and documented in annual internal reports (Eurydice, 2018). To ensure a harmonised provision of policies, the LLL:2020 aims at establishing a uniform coordination process in the context of the so-called ‘national platform for lifelong learning’, including the harmonisation of regional and national strategies. The national skills strategy aims at promoting collaboration among all stakeholders including the state, businesses, workers, and social partners. The intended measures and actions are assigned to the main responsible stakeholders who then investigate the feasibility and implementation of instruments.

2.6. Hypotheses

Based on the reviewed literature and inspected lifelong learning strategies, the following hypotheses can be derived:

- Hypothesis 1: Adult education is considered to display a pattern of cumulative advantage, whereby already privileged groups are more likely to participate in activities. Particular focus is placed on skill-intensity, i.e. those working in skill-intense jobs are assumed to be more likely to participate. Further, full-time employees working in large organisations, those with high prior educational attainment, natives and young people as well as men are more likely to participate in adult education activities in Austria and Germany in 2011/12 and 2016.
- Hypothesis 2: Compared to privileged sub-groups, underrepresented groups are more likely to identify job-related rather than personal incentives, encouraging them to participate in adult education. Underrepresented groups are considered to be in more pressing need to participate for job-related reasons (such as employment security) rather than already privileged sub-groups who may instead consider adult education as a personal investment.
- Hypothesis 3: Compared to other non-participants, underrepresented groups are more likely to face barriers on an individual-level in accessing adult education activities, as they might be confronted with higher personal burdens and inequalities of opportunity.
- Hypothesis 4: In its LLL:2020, Austria emphasises the reduction of the barriers of “costs” and “lack of support”. Hence, these barriers were less often mentioned in 2016 compared to 2011 by respondents, assuming a successful implementation of the strategy and a corresponding public discourse.

3. Methodology

This chapter illustrates the methods that will be employed to answer the research question(s) and to test the above-outlined hypotheses. It includes (1) the research design, (2) the data collection method and analytical samples as well as (3) the composition and description of the samples.

3.1. Research Design

As the aim of this thesis is to identify supporting factors to enhance participation in adult education and reduce barriers that prevent participation as well as to identify if these supporting factors are reflected in Austria’s and Germany’s adult education strategies, an explanatory cross-sectional cross-national research design is employed. Explanatory research involves developing causal explanations (Kowalczyk, 2016). One may speak of a cross-sectional research when all variables of a set of units are measured at the same time (Dooley, 2009). Three assumptions need to be fulfilled in order to speak of causality: association, time order, and non-spuriousness (ibid.).

This thesis is conducted as a mixed-methods study. Firstly, the Austrian and German lifelong learning strategies were analysed in section 2.5.1. Subsequently, inequalities in participation in adult education and barriers that prevent participation are analysed by making use of binary logistic regressions, whereby the binary outcome variable is regressed against a set of explanatory variables (Allisson, 2009; Pampel, 2000). Logistic regression analysis allows to describe a correlation between a dependent dichotomous variable and independent variables. A binomial distribution of errors will be taken as a base of analysis (Hosmer et al., 2013). Results are presented as odds ratios. Investigating the incentives to participate in adult education, multinomial logistic regression is used to model the nominal outcome variable, in which the log odds of the outcomes are modelled as a linear combination of the predictor variables (Long & Freese, 2006). The results from the multinomial logistic regression are presented as predicted probabilities at their means. The logistic regression analyses will be based on data from the Adult Education Survey (AES) (cf. Section 3.2.), using the software R.

In a subsequent step, the identified key policy actions in the lifelong learning strategies will be matched with the results from the logistic regression analysis to assess if, in the case of Austria they were effective and in the case of Germany, they are likely to be effective. As the Austrian lifelong learning strategy was published in 2011, and data from the AES is available for 2011 and 2016, one can assess potential changes in participation and barriers over time that might be associated with the policies identified in the LLL:2020.

[...]

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¹ Specific, vulnerable groups are eligible under certain conditions to receive state funding to cover 50% of their adult education costs (BMBF, 2019).

• Université: Hertie School of Governance
• Auteurs: Viktoria Arnold (Auteur), Finn Koenemund (Auteur)
• Année de publication: 2020
• Pages: 82
• N° de catalogue: V925183
• ISBN (ebook): 9783346245601
• ISBN (Livre): 9783346301284
• Langue: anglais
• mots-clé: adult education, lifelong learning, Austria, Germany, logistic regression analysis, incentive structures, barriers, Adult Education Survey