In the last decades the emphasis on lifelong learning has grown. It has become one of the European Union Horizon 2020 strategy priorities for education. The EU Agenda stipulates that 15% of adults aged 25-64 should be taking part in lifelong learning in 2016. Data from the European Union Labour force survey (EU LFS) has been used to monitor the participation rate in lifelong learning. These data indicate considerable country differences in the participation rate in adult education or training. For example, the participation rate in lifelong learning as of 2016 ranged from below 2.5% in Romania and Bulgaria to more than 20% in Iceland, Finland, Denmark, Sweden and Switzerland (Eurostat, code: trng_lfs_01. Data extracted on 08.01.2018). As such, still many EU countries have failed to reach the 15% - target.

Against this background, the aim of our paper is to explore the barriers to participation in lifelong learning. In doing so, we search for ways of broader inclusion of adults in lifelong learning. More specifically, we look at these barriers from the perspective of individuals, while, at the same time, acknowledging that institutions may (structurally) impose barriers to individuals. Hereby, we adhere to previous literature that has focused on barriers for the participation in lifelong learning (Cross 1981; Chapman et al. 2006; Laal 2011; Boeren 2016; among others). In particular, we build upon the bounded agency model proposed by Rubenson and Desjardins (2009). This model is based on the assumption that welfare state regimes can affect a person’s capability to participate through the way it constructs structural conditions and helps individuals overcome both structurally and individually based barriers. It also suggests taking into account the interaction between structurally and individually based barriers to participation. Applying this theoretical perspective to the Nordic model of adult education, Rubenson and Desjardins (2009) indicate that the major difference between Nordic and non-Nordic countries is not mainly the existence of barriers to participation, but also the ability of these countries to overcome a variety of barriers to participation.

Eve-Liis Roosmaa and Ellu Saar (2016) use the bounded agency model and classify barriers into (1) situational barriers; (2) institutional barriers; and (3) dispositional barriers. Situational barriers related to the particular life situation of the individuals. Based on Desjardins, Rubenson and Milana (2006), these include mainly time constraints, which is strongly determined by the family situation (Merriam 2005), or the job-related time allocation. Institutional barriers exist at the supra-individual level (e.g. region or country-level). These barriers are dominantly related to high perceived costs of lifelong learning (Rubenson & Desjardins, 2009). These high costs can be the result from (lack of) policy support towards lifelong learning. Dispositional barriers refer to the attitudes and dispositions towards lifelong learning.Eve-Liis Roosmaa and Ellu Saar (2016) explore the influence of various individual-level characteristics and country-level features on the occurrence of these three types of (perceived) barriers to participation in lifelong learning. This is much in line with our preferred methodology. However, the study of Eve-Liis Roosmaa and Ellu Saar (2016) has focused only on adults who do not want to participate in learning. We wish to apply the bounded agency model on participants in lifelong learning. Furthermore, we acknowledge that lifelong learning is a heterogeneous good. From this we argue that barriers to participation in lifelong learning can be imposed differently to participants across formal- vs. non-formal learning activities, or work-related vs. not work-related lifelong learning.

Previous studies have made use of data available from the Eurobarometer survey (Rubenson & Desjardins 2009), the Adult education survey (Roosmaa & Saar 2016), and the Survey of Adult Skills (PIAAC) (Desjardins 2015). We mainly use EU LFS data, but also include variables from other surveys such as the Adult Education Survey (AES) and the Continuing Vocational Training Survey (CVTS). We wish to identify the main barriers to participation in lifelong learning. Therefore, at a first stage, we gathered variables that are able to measure these barriers. With regard to situational barriers, we include gender, age; family situation (numbers of children below 10 year-olds); income (in deciles) and it squared term; working history (employed vs. unemployed); economic inactivity; and usual weekly working hours. Next, with regard to institutional barriers, we include a subjective measure of the cost of lifelong learning; the cost of lifelong learning offered by firms (in percent share of total labour costs); and average actual individual consumption expenditure in thousands of euros. Several approximates have been used for mapping dispositional barriers, like field of study and level of education; the capitalization of education in current job; and years of potential utilization of education on the job and its squared term. In a second stage of the research we have included all variables in a Probit regression model. This model is among the most commonly used models for binary outcomes (Long & Freese 2006). We have subsequently added variables underlying the above-discussed three sets of barriers. Doing so, we have estimated three models. Model 1 only includes situational barriers; Model 2 includes situational and institutional barriers; Model 3 includes situational, institutional as well as dispositional barriers. In the third step, we will explore the application of mixed level models or SEM-models in a next phase of the research in order to account for the different levels in the model (namely individual-level vs. institutional- or country-level). Finally, we will estimate these models for various forms of lifelong learning participation to see if we will find differences in the influence of the barriers on participation in various forms of lifelong learning.

Results from the Probit models indicate that the likelihood to participate in lifelong learning declines when living in a household with children aged below 10. This particularly holds for mothers. Furthermore, the participation decision is negatively influenced by gender (males are less likely to participate than females); by age (older persons are less likely to participate than young adults); and by the perceived cost of lifelong learning (persons, who perceive lifelong learning as ‘costly’ are less likely to participate). On the contrary, participation in lifelong learning increases among adults with tertiary educational attainment. From these findings, we conclude that situational barriers are strong determinants for the likelihood of lifelong learning participation. This is line with previous research that shows that the situational barriers tend to dominate in the case of nonparticipants who wanted to participate in lifelong learning (Rubenson & Desjardins, 2009). However, there is clearly an interaction with institutional barriers and dispositional barriers. For example, those persons with tertiary educational attainment have more likely a paid job, higher income, and, therefore, they may perceive lifelong learning as less costly. By definition, unemployed persons are excluded from on-the-job learning or training opportunities, while, at the same time, employers’ average spending on training is positively correlated with participation of adults in lifelong learning. Employed workers (compared to the unemployed) then may face fewer costs of lifelong learning, whereas the employer finances the costs of lifelong learning. It is exactly this kind of interactions that is scope for further research for the construction of this paper.

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