Literacy and participation in society

The literacy skills required to participate in personal, social, political and economic life are becoming increasingly complex, especially with increased digitalisation and development of information and communication technologies. As a result, it is increasingly important to be able to understand text in different formats, expression levels and styles of argumentation to participate in a society that keeps developing based on these technologies (Stromquist, Reference Stromquist2009).

The literature considering the impact that general literacy has on individuals’ life, however, is relatively limited. Clark and Dugdale (Reference Clark and Dugdale2008) present evidence that literacy has a strong association with happiness and success at the individual level in the U.K., where individuals with low literacy skills have a greater likelihood of being unemployed, in low-paid employment and dependent on state benefits, while being less likely to have been promoted or to receive on-the-job training. Low literacy is also associated with poor housing, poor health, depression, social exclusion and low feelings of social cohesion (Clark & Dugdale, Reference Clark and Dugdale2008). Similarly, in the U.S. and more widely in OECD countries, low literacy skills have negative implications for educational achievement, employment, health, wage levels, professional development, informal learning and cultural and civic engagement (Gioia, Reference Gioia2008; Kirsch et al., Reference Kirsch, De Jong, Lafontaine, McQueen, Mendelovits and Monseur2003; Kakarmath et al., Reference Kakarmath, Denis, Encinas-Martin, Borgonovi and Subramanian2018). Individuals are also largely dependent on basic skills, such as literacy and numeracy, to function in modern workplaces. The ability to read documents, communicate in writing, obtain information and make simple numerical operations are minimum requirements for most office jobs and are also increasingly important in manual labour. This is also reflected by lower income levels among individuals with lower basic skills (McIntosh & Vignolest, Reference McIntosh and Vignoles2001). Bynner et al. (Reference Bynner, McIntosh, Vignoles, Dearden, Reed and Van Reenen2001) found that improving basic skills among adults in the U.K. was associated with better performance in the labour market and less unemployment, having fewer physical and mental health issues, becoming more active citizens, obtaining more liberal and less dissimilatory attitudes and having lower probability of having children that struggle at school. As literacy is a crucial factor in the social and educational process that determines the outcomes of adult lives, the benefits of improving literacy are evident at all levels in society (Clark & Dugdale, Reference Clark and Dugdale2008).

Active labour market policy

The welfare state plays a key role in assisting individuals who experience larger struggles in their lives, essentially aiming to promote welfare in the population, ensuring physical and material well-being. This is combined with measures promoting participation and productivity, contributing to the sustainability of the welfare state. An essential part of welfare provision is, according to Esping-Andersen (Reference Esping-Andersen1990), to ‘decommodify’ citizens: referring to ‘the degree to which individuals, or families, can uphold a socially accepted standard of living, independently of market participation’ (Esping-Andersen, Reference Esping-Andersen1990:37). He further argues that social spending is a crucial government responsibility and claims the free market only serves those who are able to perform in it, and that welfare is reduced as people are treated as commodities. Andersen points to spending related to pensions, unemployment and illness as particularly important decommodifying measures (Esping-Andersen, Reference Esping-Andersen1990). In recent years there has been increased interest in the idea of ‘re-commodifying’ individuals, where welfare dependency is seen as a threat to a sustainable welfare state (Pierson, Reference Pierson2001).

This increased focus on ‘re-commodifying’ individuals has led to a growing interest in developing the active aspect of the welfare state as opposed to the passive elements, where social service-related payments often are complemented with assistance, training and other services that are meant to enable social service recipients. Hemerijck (Reference Hemerijck2018), representing the social investment paradigm, argues that traditional redistributing welfare measures should be complemented with ‘cost-efficient ex-ante preventative capacitating interventions’. He argues for a balanced strategy when it comes to policies with functional characteristics he calls flow, stock, or buffer, referring to policies that function to 1) ease the flow in transitions associated with employment and life-course, 2) maintain and increase the quality and stock of human capital and capabilities and 3) ensure that no one falls into deep poverty by providing minimum income protection, which also contributes to buffer business cycles (Hemerijck, Reference Hemerijck2018).

Active labour market policy is defined by the European Commission as: ‘All social expenditures (other than education) which are aimed at improvement of the beneficiaries’ prospects of finding gainful employment or to otherwise increase their earning capacity. This category includes spending on public employment services and administration, labour market training, special programmes for youth when in transition from school to work, labour market programmes to provide or promote employment for unemployed and other persons (excluding young and disabled persons) and special programmes for the disabled’ (EC, 2017). The core aim of ALMPs is therefore to increase employment and help individuals find more suitable employment (EC, 2017). Given that this is the sector within the welfare state with most contact with individuals who are struggling, there can be large potential benefits from ALMP assistance, not only when it comes to employment, but also functioning in society, empowerment and quality of life.

The role of ALMPs in promoting basic capabilities

To understand how ALMPs could help to promote the functioning and participation of people with low literacy skills, we draw on the concept of capabilities. The capability approach was introduced by Amartya Sen, as ‘an intellectual discipline that gives a central role to the evaluation of a person’s achievements and freedoms in terms of his or her actual ability to do the different things a person has reason to value doing or being’ (Sen, Reference Sen and Martinetti2009, 16; Robeyns, Reference Robeyns2017). The capability approach focuses on the health, education and social support an individual can enjoy, their real freedoms to do things such as work, travel and be politically active, and the level of wellbeing obtained through choosing from options open to them (Robeyns, Reference Robeyns2017:8). Literacy skills can be seen as a fundamental ability necessary for developing basic capabilities. Social workers also report that individuals with low literacy skills tend to avoid reporting about their problems and need for extra assistance due to feelings of embracement (Greenberg & Lackey, Reference Greenberg and Lackey2006). This group may therefore have insufficient skills to both function in society and make use of the support systems in place for them.

In theory, the welfare state, and ALMPs in particular, could serve to fill this gap and be the driving force helping people with low literacy skills function fully and participate in society either by giving them opportunities to improve their literacy skills (e.g. through training or learning on the job), or by helping them to participate despite their lack of skills (e.g. by improving their prospects of employment). However, low literacy skills may also pose a crucial barrier to making use of the opportunities offered through ALMPs. A common critique of the social investment approach is that it mostly benefits those already best off, failing to reach the most disadvantaged individuals with the largest needs. This is often referred to as the ‘Matthew effect’. Firstly, participation in ALMPs often requires certain levels of cognitive and non-cognitive skills. Secondly, scarce slots in ALMP programmes are given to individuals with better prospects, often referred to as ‘creaming’. The latter is examined by Bonoli and Liechti (Reference Bonoli and Liechti2018), who found mixed results: access bias is associated with ALMP programmes related to job subsidies and training, and is more common in conservative countries and not in the Nordic countries. Under the social investment approach, it has also become common to let certain benefits be conditional on specific obligations to be fulfilled. Handler (Reference Handler2003) argues that as local office workers are responsible for administrating whether obligations have been fulfilled and deciding which sanctions to impose in the event of non-fulfilment, exclusions are inevitable, where the inability to keep track of obligations and fulfil these represent a serious barrier to actually receiving help, and where agency workers become more concerned with sanctioning individuals who fail to comply than to really try to help the very-hard-to-employ.

In a review article on the relationship between ALMP participation and unemployment, Filges et al. (Reference Filges, Smedslund, Knudsen and Jørgensen2015) find mixed results, with some indication that ALMP participation may increase the chances of finding employment. Card et al. (Reference Card, Kluve and Weber2010) have conducted a review of the literature considering the impact of different types of ALMP services on outcomes such as time in unemployment, employment and earnings. They find that training is associated with positive medium-run effects, however no effect in the short run. They further find subsidised public sector employment programmes to be largely ineffective; however, job search assistance appears to be associated with more positive outcomes, especially in the short run. Escudero (Reference Escudero2018) finds that ALMPs matter at the aggregate level, with a negative impact on the unemployment rate, especially for individuals with low education. A review article, written by Puig-Barrachina et al. (Reference Puig-Barrachina, Giró, Artazcoz, Bartoll, Cortés-Franch, Fernández and Borrell2020), conclude based on thirty-six articles that ALMPs have a positive impact on health and quality of life, with particularly positive associations with mental health. Van der Wel and Halvorsen (Reference Van der Wel and Halvorsen2015) find generous ALMP and welfare arrangements to be associated with higher employment commitment, which is also evident for groups that traditionally have weaker labour market attachments, such as individuals with low education, among others. Voßemer et al. (Reference Voßemer, Gebel, Täht, Unt, Högberg and Strandh2018), on the other hand, find that the effect unemployment has on well-being and health is more negative in countries with higher ALMP spending. They argue the effect of ALMPs can depend on whether job creation measures resemble regular employment and if the individual experiences the training as useful for labour market participation. A negative impact can result from programmes being involuntary, often experienced as paternalistic. Partly in line with this, Wulfgramm (Reference Wulfgramm2011) shows that a German activation programme could offset the detrimental effect of unemployment on life satisfaction, but mostly if the participants see the programme as a good match with their personal skills and their ambitions for future employment.

Hypotheses

Based on the theories and empirical evidence presented above we expect that countries with higher investments in ALMP have a narrower gap based on skill level when it comes to participation in society. Through ALMPs, individuals with low literacy skills who are struggling can get assistance in finding appropriate employment and developing skills needed in the labour market and society. If, however, we fail to identify this relationship, it may indicate that low literacy can be a crucial obstacle to making use of ALMPs, in line with the Matthew effect and the issue of creaming.

Data

We use data from the Programme for International Assessment of Adult Competencies (PIAAC), a survey that includes nationally representative samples of around 250,000 individuals, aged between fifteen and sixty-five, from thirty-one OECD countries, with one cycle of data collected between 2011-2015. The survey focuses on measuring key cognitive and workplace skills that individuals need to participate in society and that economies need to prosper. The survey was conducted face-to-face under the supervision of trained interviewers. A background questionnaire was administered in a computer-aided personal interview format (CAPI), followed by a general literacy, numeracy and problem-solving skills assessment, which was administrated on computer or paper, depending on the computer skills of the respondent. PIAAC uses a multistage adaptive design for testing skills, which ensures that each respondent receives questions at estimated proficiency levels, allowing PIAAC to obtain more reliable information about each individual’s skill level quite rapidly (OECD, 2013). The data-capturing social expenditures are from the OECD Social Expenditure Database. In our analyses, we use data on respondents from twenty-five countries: Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Great Britain, Greece, Ireland, Italy, Japan, South Korea, Netherlands, New Zealand, Norway, Poland, Slovakia, Slovenia, Spain, Sweden, and the United StatesFootnote ¹ .

Dependent variables

The analyses are conducted on nine dependent variables sorted into four groups of variables, where all variables are recoded into dummy variables. The first group reflects labour market participation and is measured by 1) unemployment (1=not employed but looking for employment and 0=out of the labour force or employed), and 2) out of the labour force (1=not employed and not looking for employment and 0=unemployed or employed). The second group of variables reflects quality of life, measured by 1) self-rated health (1=poor health and 0=fair, good, very good or excellent health), and 2) job dissatisfaction (1=being dissatisfied or extremely dissatisfied with current employment and 0=being satisfied, extremely satisfied or neither satisfied nor dissatisfied with current employment). The third group of dependent variables reflects digital participation and is measured by 1) low email use (1=use email less than once per week and 0=use email at least once per week), and 2) low use of internet to understand issues about the world such as health, finance, environment etc. (1=use internet for this purpose less than once per week and 0=reported use at least once per week). The fourth group of variables reflect participation in lifelong learning, meaning participation in learning activities at age twenty-five and above, with a focus on: 1) formal education (1=currently enrolled in a formal educational programme at any level and 0=not enrolled), 2) informal education (1=participated in open or distance education, seminars, workshops or took private lessons during the last year and 0=no participation in such activities), and 3) on-the-job training (1=attended any on-the-job training during the last year and 0=no participation in such activities). Table 1 provides descriptive statistics for all included variables.

Table 1. Descriptive statistics

Independent variable

The variable capturing low literacy distinguishes individuals who are only capable of solving tasks at level 1 or below and not tasks at level 2 from the other respondents. Level 1 reflects tasks that require the respondent to read short pieces of text and locate single pieces of information that are identical to the information given in the question. At this level, the participant is expected to be able to recognise basic vocabulary, determine the meaning of sentences and read paragraphs of text. At level 2 the participants are required to make matches between text and information that are not identical, and that may require paraphrasing or low-level inferences (OECD, 2013). The variable for low literacy is therefore coded into a dummy variable =1 if literacy skills <=225 and =0 if >225 (OECD, 2013).

Intervening variables

The OECD labour market policy database groups social expenditures related to ALMP into six underlying categories: 1) public employment services and administration, 2) training, 3) private sector employment incentives, 4) supported and sheltered employment, 5) direct job-creation and 6) start-up incentives. Due to availability of data, this paper will focus on total spending on ALMP as well as three underlying categories: 1) public employment services and administration, 2) training and 3) private sector employment incentives. Spending levels are analysed in terms of purchasing power parity (PPP) per capita in 2015 constant U.S. dollars, where spending levels are given in terms of $10.000.

Control variables

The analyses include control variables both at the individual and national level. At the individual level, the variables capture the respondents’ 1) age, 2) gender (1=male and 0=female), 3) partner (1= lives with partner and 0=does not live with partner), 4) educational level, captured by dummy variables for low education (1=highest education level is primary education) and medium education (1=completed lower secondary school or started upper secondary school but didn’t finish) and with high education as reference category (1=completed education upper secondary school or higher), 5) parents’ education (1=at least one parent has attained upper secondary education and 0=neither has attained education at this level), 6) unemployment (1=unemployed, but looking for work), 7) out of labour force (1=unemployed and not looking for employment) and 8) migrant status, reflected by four dummy variables reflecting a) first-generation migrant, b) second-generation migrant, c) one parent born outside country, where d) native-born is the reference category. At the country level, the control variables included are 1) total social expenditure as percent of GDP; an important control variable as it controls for general welfare generosity and makes sure the estimates are not picking up the effect of other social spending, and 2) other ALMP spending categories when single sub-categories are analysed, which contribute to isolate the effect of the spending category of interest and 3) unemployment rate, which controls for the need for ALMP services, where generous spending can reflect larger social problems, which means that some of the ALMP spending may have to be divided by a greater number of people. ALMP spending levels are included in the analyses in terms of $10.000.

Statistical Analysis

Table 2 presents the relationship between low literacy and the dependent variables individually for each country included in the analysis, as well as for the full sample. From this analysis, we will establish the overall relationship between low literacy and participation within our sample and the variation between countries. The results are obtained through multilevel logistic regression with the variables reflecting indicators of participation as dependent variables, low literacy as independent variable, as well as the control variables mentioned above. All analyses presented in Table 2-7 are performed as multilevel analyses with individuals at level 1 and countries at level 2. Multilevel modelling enables us to estimate the effects of both individual-level characteristics, contextual characteristics at the country-level, as well as the cross-level interactions between these (Snijders & Bosker, Reference Snijders and Bosker2011). Tests for multicollinearity are also conducted. For the models without of the labour force and unemployment as dependent variables, control variables related to sector and employment status are omitted. For the model with job dissatisfaction as dependent variable, the variable reflecting employment status is omitted. The analyses conducted with the lifelong learning variables are performed on a subsample of individuals aged twenty-five and above to capture adult students enrolled in life-long learning activities. Furthermore, the analyses that look at job training and job dissatisfaction are performed on a subsample of employed individuals only. The results are presented as odds ratios throughout all models.

Table 2. Relationship between low literacy and dependent variables (OR)

Table 3 presents the relationship between the outcome variables and total ALMP to establish what the relationship looks like for the general population. The inclusion of control variables and subsamples follows the same setup as in Table 2. Tables 4-7 present the main models, one for each group of dependent variables as presented above, where Table 4 reflects labour market participation, Table 5 looks at quality of life, Table 6 looks at digital participation and Table 7 presents analyses of the impact on lifelong learning. An interaction term is included between the dummy variable reflecting low literacy skills and each ALMP variable, capturing the differences in impact from policy initiatives among individuals with low literacy skills compared to those at a more adequate level. A random slope is also included at the lower level of the interaction (i.e. low literacy skills), following Heisig and Schaeffer (Reference Heisig and Schaeffer2019). In Figures 1 through 11 we also present graphs with marginal effects for each of the statistically significant relationships between the ALMPs and the outcome variables identified in the analyses, also illustrating the interaction effect.

Table 3. Associations between total ALMP expenditure and outcomes

Odds ratios (SE in parentheses).

*** p<0.01, ** p<0.05, * p<0.1.

Figure 1-6. Graphs and marginal effects (ME) for significant relationships identified in Tables 4-6.

Figure 7-11. Graphs and marginal effects (ME) for significant relationships identified in Table 7.