Chapter 4. The Participation of adult migrants in lifelong learning activities

Lifelong learning is a crucial ingredient of skills policies, in that it might facilitate re-skilling (in response to changing skills demands) and prevent age-related skills decline (in response to longer working careers). Migrants might have more incentives and a higher need to participate in adult training, but might also face higher financial or non-financial barriers to participation. This Chapter shows that migrants participate less in lifelong learning than natives, but the differences are not very large, and are mostly accounted for by differences in observable individual characteristics. On the other hand, migrants are more likely to report not having been able to participate in training activities they were interested in, largely because of financial barriers and family responsibilities. Migrants therefore appear to express a high demand for existing training opportunities, and indeed the data show that, once they are able to gain access to training opportunities, migrants tend to spend more time than natives in such activities.

    

The statistical data for Israel are supplied by and under the responsibility of the relevant Israeli authorities. The use of such data by the OECD is without prejudice to the status of the Golan Heights, East Jerusalem and Israeli settlements in the West Bank under the terms of international law.

Investing in initial formal education is crucial for everyone, as it improves the ability to learn, and thus to upgrade one’s skills in response to a changing economic environment. Yet it is becoming increasingly evident that initial education is no longer sufficient. Adults need to continue developing their skills throughout their entire working career, and more effort should be devoted to improve the quality of adult education and training systems, particularly of programmes targeted to the most vulnerable segments of the population, such as displaced workers or unemployed adults.

Three main factors lie behind the increased importance attributed to continuous skills development during a lifetime. First, rapid and often disruptive technological change has created the need to train many workers in the use of new technologies, or in the new ways of organising work made possible by such technologies (OECD, 2017[1]). Second, the opening of markets to globalisation and the corresponding increased competition from lower-income countries has pushed many firms in developed countries to invest in innovation and in high value-added activities in order to maintain a competitive edge; but these strategies have often required the upskilling of the current workforce. Finally, longer working lives, related to increases in life expectancy and the corresponding need to reform pension systems to preserve financial sustainability, imply that workers are more likely to face technological, organisational or strategic changes in the course of their careers – and are thus more likely to need to update their skills to new environments.

In summary, adequate lifelong learning initiatives are needed because, in a more uncertain world, workers will likely find themselves at a higher risk of displacement if their skills no longer match the demand of the labour market. In this respect, the effectiveness of education and training systems would benefit from greater investments in exercises that aim to anticipate the demand for certain skills and from more effective diffusion of the available information about the skills that are more likely to be in high demand in the labour market (OECD, 2016[2]).

PIAAC allows examining issues related to lifelong learning because it contains information on education and training activities undertaken by adults participating in the Survey. This chapter takes a somewhat narrower definition of lifelong learning, which is best suited to the information contained in PIAAC. In particular, the focus is on adult training during working career, i.e. after the completion of formal education. For this reason, the analysis excludes 16-19 year old individuals enrolled in upper-secondary education, as well as 20-24 year old individuals enrolled in tertiary education.

It is beyond the scope of this chapter to provide a comprehensive review of the issues related to adult training (and of the vast literature that has investigated the topic). However, it is useful to provide a brief sketch of the basic economics principles behind adults’ investment in education and training as a way to contextualise the thinking on this issue.1

The provision and impact of adult education and training

Somewhat contrary to initial education, which is mainly general in nature, adult education and training generally falls into one of two types: general or firm-specific training. The first helps workers acquire skills that are portable across different firms and jobs. For this reason, employers have generally little interest in financing this kind of training, as they might not be able to reap the returns on the investment if, for example, the worker moves to a different job or firm. At the other end of the spectrum, training in fully firm-specific skills is by definition not transferable. In this case, both the employer and the employee have an interest in sharing the cost of training, as both will reap the returns as long as the worker stays in his or her current job.

The theoretical justification for policy interventions in providing adult training is tied to the presence of market imperfections. An obvious example is imperfections in the credit market, whereby credit-constrained individuals are not able to borrow to finance profitable investments in their human capital. Labour market imperfections are also relevant in the case of adult training. Firms might underinvest in training their employees if they cannot fully exploit the benefits of training (the so-called hold-up problems that arise when wage bargaining takes place after training has been completed, with the investment now being a sunk cost from the firm’s point of view). When workers move from one firm to another, and the new firm is able to pay trained workers less than their productivity, the extra profits of the new firm are obviously not enjoyed by the former employers who invested in training their employees.

From a macroeconomic perspective, in models of endogenous growth, skills acquired on the job have positive externalities (e.g. in the learning-by-doing model of Romer, (1986[3]), that once again justify public intervention. Finally, if not on efficiency grounds, government intervention can be justified based on equity considerations. There is strong empirical evidence that training is disproportionately taken up by individuals who are already highly skilled or who have high education qualifications (OECD, 2012[4]). Equally, there is the possibility that certain categories of workers are discriminated against when it comes to accessing training opportunities (Milburn, 1996[5]; European Union Agency for Fundamental Rights, 2011[6]; Costello and Freedland, 2014[7]).

Public programmes aimed at subsidising or providing adult education or on-the-job training are an important part of the portfolio of “active” labour market policies (ALMP) that are supposed to help unemployed, displaced and other disadvantaged individuals to achieve better labour market outcomes. Skills upgrading and other interventions, such as job-search assistance, counselling and wage or employment subsidies aim to improve the employability and earnings of targeted individuals. In recent years, such programmes have been the object of a large research effort aimed at evaluating their effectiveness and their ability to achieve their stated objectives.

The OECD Employment Outlook 2004 (OECD, 2004[8]) tried to evaluate the impact of training on subsequent labour market outcomes. Participation in training was found to be associated with a greater likelihood of actively participating in the labour market, and with a decrease in the risk of being unemployed. Positive impacts on wage growth were detected only for young or highly educated employees, while benefits in terms of subjective and objective measures of employment security extended to older and low-educated workers.

A more extensive meta-analysis of the literature on the evaluation of active labour market policies is provided in Card, Kluve and Weber (2010[9]; 2017[10]). They conclude that the benefits of these kinds of programmes tend to materialise only two to three years after the end of the programme, and that this is especially true for programmes that emphasise the accumulation of human capital, such as adult training programmes. This makes intuitive sense, as participants are typically not able to accept job opportunities while they are enrolled in training. However, investments in human capital do pay off in the medium term. Similar conclusions are reached in the OECD Employment Outlook 2015 (OECD, 2015[11]), which also stresses how mixed programmes, combining job-search assistance or work experience with education and training have often the most consistent impact, and that training programmes focusing on specific skills tend to offer larger returns in terms of earnings.

Impact of adult education and training on migrants

The impact of active labour market programmes are often found to be stronger for women and those who are long-term unemployed (Card, Kluve and Weber, 2017[10]). Unfortunately, there is less evidence on the effects of these programmes on migrants. Butschek and Walter (2014[12]) provide a meta-analysis of studies evaluating the impact of ALMPs specifically targeted to migrants, or for which separate estimates of the impact on migrant participants are provided. Unfortunately, only estimates of the short-run impact of ALMPs are provided. However, about one-third of the studies surveyed in the meta-analysis found positive short-term returns to training for migrants, while half of them failed to detect a statistically significant impact.

Adult education and lifelong learning are arguably even more important for individuals who emigrated from their home country. Education and training are, in fact, key to the economic and social integration of migrants. Integration can only occur when every person has acquired an adequate level of knowledge and skills and then uses those skills to contribute to his or her local community and wider society. Chapter 2 documented that skills gaps between foreign-born and native-born adults are substantial in a large number of countries and that migrants are largely over-represented at the bottom of the skills distribution. Differences in skills levels imply that the full integration of migrants into the labour markets of host countries, and into society as a whole, requires some form of training, as the set of skills migrants bring with them to the host country is typically different and unlikely to match the needs of the host country’s labour market. While language is the most obvious skill for which migrants might need training, it is certainly not the only one.

The participation of migrants in adult education and training is not only important to ensure that they upgrade their skill set, but also as a way for them to certify the skills that they have already acquired but that may not be recognised in their host country. This might happen when formal education qualifications that migrants had earned in their home country are not officially recognised in the host country, and might be one reason for the presence of over qualification, which is well-documented in the literature (Battu and Sloane, 2002[13]; Lindley, 2009[14]; Aleksynska and Tritah, 2013[15]; Piracha, Tani and Vadean, 2012[16]; Joona, Datta Gupta and Wadensjö, 2014[17]; Visintin, Tijdens and van Klaveren, 2015[18]). A devaluation of previous school or labour market experience might mean that migrants have to start their career from scratch in the host country, and the lack of recognition of previously acquired qualifications or work experience can be a considerable barrier to accessing high-skilled jobs.

Previous research has clearly demonstrated that the skills content of one’s occupation, as well as the characteristics of the firm one is employed at, strongly affect opportunities to participate in lifelong learning. In particular, adults employed by large and innovative firms and those working in skills-intensive occupations are much more likely to participate in training (Bassanini et al., 2007[19]). Similarly, a lack of opportunities to practice certain skills can cause skills atrophy, or accelerate the natural decline of information-processing skills over a lifetime (Reder, 1994[20]; Reder, 2009[21]; Paccagnella, 2016[22]). Migrants might therefore end up trapped in a situation in which they are employed in jobs that do not demand much of their skills – and, precisely because of that, they are not offered opportunities to develop the skills they already have.

On the other hand, qualifications (and the lack of recognition of those qualifications) alone are unlikely to be the only explanation behind differences in labour market outcomes of migrants, as recent surveys of adult skills have also shown that adults holding the same formal qualifications often have very diverse levels of proficiency in information-processing skills, especially when comparing qualifications earned in different countries (OECD, 2013[23]).

The participation of migrants in adult education and training

While migrants might have stronger incentives to participate in adult education and training, they might also face higher barriers to participation. Both native-born and foreign-born adults face financial and non-financial barriers to participation, although probably to a different extent. The former include the direct as well as the opportunity cost of participation. As migrants are generally less wealthy than native-born adults (Mathä, Porpiglia and Sierminska, 2011[24]) they might face stronger credit constraints, preventing them from investing in potentially rewarding training activities. Non-financial barriers might include a wide range of factors, such as lack of time due to family or work commitments, lack of information about training opportunities, discrimination or programmes’ lack of adaptation to the specific needs of migrants (Milburn, 1996[5]; Zegers de Beijl, 2000[25]; Sheared et al., 2010[26]) or institutional barriers, such as those related to the design of welfare systems, to the rules governing access to training opportunities, or (more specifically in the case of migrants) to the laws governing the rights of foreigners to live in the host country (Costello and Freedland, 2014[7]).

Lower levels of proficiency can discourage migrants from practicing their skills, or prevent them from accessing jobs that require engagement in cognitively-demanding tasks. As already mentioned, skills use can be an important source of learning (learning-by-doing), leading to further skills development, or at least to a deceleration of the natural process of skills loss related to ageing. The role of workplaces (and of different types of work organisations) as learning environments has recently been highlighted by Boeren (2016[27]) and Lorenz et al. (2016[28]). The degree to which workplaces are conducive to employees’ learning is typically thought to depend on the interaction between a variety of factors that can be internal or external to the firm. External factors can include sector-specific characteristics such as the degree of competition and the extent of technological change, which could provide different incentives to firms to increase productivity by increasing the skills of their workforce. Internal factors refer to the managerial and organisational choices undertaken by each firm. Other than policies directly related to training, they can include broader human resource policies that provide incentives to workers to invest in their human capital, such as the degree of autonomy given to workers, or the presence of performance-related pay. To the extent that skills partly determine whether workers are more or less likely to be employed by firms providing an environment more or less conducive to skills development, existing skills gaps between migrants and natives can themselves be seen as barriers to further skills development.

Research on the provision, take-up, barriers to participation, and costs and benefits of adult training among migrant populations is often made difficult by a lack of suitable and comparable data, particularly across countries. By its very nature, adult education is difficult to measure, particularly as a significant portion of training and learning is often informal, or provided on the job and by employers. This chapter takes advantage of data from the OECD Survey of Adult Skills (PIAAC). PIAAC was primarily conceived as a way to measure the information-processing skills of the adult population; but it also includes a background questionnaire that elicits detailed information on the socio-demographic characteristics of the respondents, including their education and labour market career.

Data from PIAAC allow for a description of patterns of participation in formal and non-formal learning activities of migrant and native adults. They also allow for an investigation of the differences in individual dispositions to training and in the barriers that prevent participation in such activities. PIAAC also contains information on the actual tasks performed on the job and on the use of information-processing skills at work and at home. Together with actual measured proficiency, this allows for a rich characterisation of cross-country and within-country differences in skills proficiency, skills practices, and skills development among native and migrant adults.

While the Survey of Adult Skills has been conducted in 33 countries, the analysis described in this chapter is restricted to 27 of them, mainly due to insufficient sample sizes of the sub-population of migrants, which in some countries is extremely small. Twenty out of these 27 countries participated in the first round of the survey, conducted in 2011/12: Australia, Austria, Belgium (Flanders), Canada, Cyprus2,3, the Czech Republic, Denmark, Estonia, Finland, France, Germany, Ireland, Italy, the Netherlands, Norway, the Russian Federation4, Spain, Sweden, the United Kingdom (England and Northern Ireland) and the United States. Data for Chile, Greece, Israel5, Lithuania, New Zealand, Singapore and Slovenia were collected in 2014.

Participation in adult education and training in OECD countries

Participation in lifelong learning activities refers to participation in formal adult education and training and/or non-formal education and training activities. This is also the definition used by the European Commission in the Education 2020 strategy, according to which at least 15% of the adult population (aged 25-64) should participate in at least one “lifelong learning” activity (Eurydice, 2011[29]).

PIAAC asked respondents whether they participated in formal and non-formal education and training activities in the 12 months prior to the interview. Formal education refers to courses leading to the award of a formal qualification by a recognised educational institution. As the PIAAC target population includes all individuals aged 16 to 65, some exclusions were put in place to avoid classifying young adults enrolled in the normal cycle of initial education as participants in formal adult education activities. As a consequence, 16-19 year-old respondents enrolled in a course leading to an ISCED 3 qualification (equivalent to upper secondary education), and 20-24 year-old respondents enrolled in a course leading to an ISCED 4 qualifications (equivalent to tertiary education), were excluded from the sample and not counted as participants in adult education.

Participation in non-formal education and training is more difficult to capture, as it is not always easy to define what constitutes informal learning. The binary variable indicating participation in non-formal education and training was constructed combining four separate items in the background questionnaire that asked whether the respondent participated in: open or distance education; on-the-job training or training by supervisors or co-workers; seminars or workshops; and courses or private lessons not otherwise reported.

Participation in lifelong learning activities varies substantially across countries (Desjardins, 2015[30]). In the sample of countries that is analysed in this chapter, participation rates in any kind of adult education and training (whether formal or informal) range from 20% in Greece and the Russian Federation to 67% in New Zealand (the cross-country average is 50%). Formal adult education and training accounts for a small share of overall participation in lifelong learning: on average, only 11% of survey respondents participated in formal education in the 12 months prior to the interview, while 46% participated in at least some informal learning activity.

The vast majority of training activities were job-related: 83% on average across participating countries, ranging from 76% in Slovenia to 88% in Australia, Denmark, France, Norway and the United Kingdom (England and Northern Ireland). The relative incidence of non-job-related training was 20% among migrants and 16% among native-born adults. Among migrants, the incidence of training activities non-job-related was particularly high in Finland, the Netherlands, Spain and Sweden (between 26% and 29%) and particularly low in the Czech Republic (9%).

Organised on-the-job training sessions were the kind of learning activities most frequently reported, by 30% of respondents on average across participating countries. This share increases to 37% on the subsample of respondents that were either currently working at the time of the interview, or that reported to having been in paid employment in the 12 months prior to the interview.

The large cross-country differences in participation rates suggest that different institutional arrangements play a role in determining individuals’ participation in adult training. The role of institutions and public policy frameworks has been discussed by Desjardins and Rubenson (2013[31]). Rubenson and Desjardins (2009[32]) suggest the possibility that different configurations of welfare states are related to participation in lifelong learning. Indeed, observed clusters of countries based on participation in lifelong learning can be directly mapped into clusters of welfare typologies identified in the sociological literature (Esping-Andersen, 1990[33]; Fenger, 2007[34]; Saar, Ure and Holford, 2013[35]; Blossfeld et al., 2014[36]; Busemeyer, 2014[37]). Participation rates are highest in Nordic countries and lowest in Southern European countries, while Anglo-Saxon and Central and Eastern European countries are clustered in the middle of the ranking.

Figure 4.1 shows participation rates for native and migrant adults. On average, the participation rates of migrants are four percentage points lower than those of natives. The gap is more pronounced in Estonia (16 percentage points), Germany (14 percentage points), Slovenia (12 percentage points), and France and the United States (10 percentage points). In about half of the countries in the sample the differences in participation rates are not statistically significant.

Figure 4.1. Participation rates in lifelong learning
picture

Note: Countries are sorted in ascending order of overall participation rates. Countries for which the difference in participation rates between foreign-born and native-born adults is not statistically different from zero are marked in a lighter tone.

1. See notes 2 and 3 at the end of this Chapter.

Source: (OECD, 2015[38]) Survey of Adult Skills (PIAAC) (2012, 2015), www.oecd.org/skills/piaac/publicdataandanalysis

 StatLink http://dx.doi.org/10.1787/888933845909

Restricting the sample to respondents holding a job (either at the time of the interview or in the 12 months prior to it), native-born adults were about 7 percentage points more likely than foreign-born adults to participate in on-the-job training (38% versus 31%), with a gap particularly pronounced in Germany (16 percentage points), Spain (15 percentage points), and Flanders (Belgium), Denmark and Estonia (13 percentage points). Singapore is the only country in which foreign-born adults were more likely to participate in on-the-job training than native-born adults (by 5 percentage points) (see Annex Table 4.A.2).

The cross-country correlation between migrants’ and natives’ participation rates is extremely high, at 91%, suggesting that countries’ institutional characteristics matter much more than individuals’ migrant background. This does not imply that institutions are the only determinant of participation in lifelong learning. Within countries, a central role is played by individual characteristics, such as age, gender, educational attainment and employment status.

Across the 24 countries that participated in the first round of PIAAC, the average participation rates in adult education and training were as high as 74% among adults who scored at Level 4/5 in the literacy assessment, and as low as 33% among adults who scored at Level 1 (OECD, 2013[23]). The relationship between literacy skills and participation in lifelong learning is strong in all participating countries. This relationship highlights the vicious cycle for low-skilled adults: if they do not benefit from adult learning, their skills remain weak or deteriorate over time, which makes it even harder for them to participate in learning activities. By contrast, high-skilled adults benefit from a virtuous cycle, as they tend to take advantage of opportunities to further develop or update their already-high skills.

Other individual characteristics that have been consistently found to be positively related to participation in adult learning include age (younger adults are more likely to invest in training), educational attainment, socio-economic status, being employed, and being in a white-collar occupation (Bassanini et al., 2007[19]; Desjardins, 2015[30]). Participation in lifelong learning is therefore best seen as the result of complex interactions between individual characteristics and the cultural, institutional and social environment (Boeren, Nicaise and Baert, 2010[39]; Boeren, 2016[27]).

Figure 4.2. Individual determinants of participation in lifelong learning
picture

Note: The figure shows the average of selected coefficients from separate linear regressions (one for each country, on the subsample of foreign-born and native-born adults) of the probability of participation in lifelong learning on a set of age dummies, a set of educational qualifications dummies, literacy proficiency scores, an a dummy for being in paid employment. The coefficients have been multiplied by 100 to express the effect in percentage points. For age, the reference category is 25-34 years old. For educational attainment, the reference category is below upper secondary. The coefficients for literacy proficiency have been multiplied by the standard deviation of literacy proficiency scores across all the countries that have participated in PIAAC.

Source: (OECD, 2015[38]) Survey of Adult Skills (PIAAC) (2012, 2015), www.oecd.org/skills/piaac/publicdataandanalysis

 StatLink http://dx.doi.org/10.1787/888933845928

Figure 4.2 shows how various individual characteristics are related to the likelihood of participating in lifelong learning, on average across countries (see Annex Table 4.A.3 for country-specific results). The figure shows the average of estimated coefficients from linear regressions run separately for each country, on separate samples of native-born and foreign-born adults. The regressions estimate the effects of age, educational attainment, literacy proficiency, employment status and gender on participation in lifelong learning. The coefficients on gender are not reported, as none of them was statistically different from zero. The figure reports the coefficients associated with a one standard-deviation increase in literacy proficiency and with being 45 to 54 years old (the reference category is 25-34 years old), having a tertiary qualification (the reference category is below upper secondary), and not being in paid employed (the reference category is being in paid employment).

Across participating countries, being more educated, more proficient in literacy, and in paid employment increases the likelihood of participating in lifelong learning, while being older reduces the likelihood of participating. This is true for both native and migrant adults. However, within countries, natives and migrants differ in the strength of association between such personal characteristics and the likelihood of participation in adult education and training.

In a majority of countries, the negative effect of age on participation rates is larger for migrants than for natives. For example, in Greece and Denmark older migrants (aged 45-54) are about twenty percentage points less likely than younger migrants (aged 25-34) to participate in lifelong learning; for natives of the same age the difference is about twice as small.

In all countries, having a tertiary degree strongly increases the likelihood of participating in lifelong learning among both natives and migrants. In some countries, the estimated effect is stronger among native-born adults. In New Zealand, for instance, a tertiary degree is associated with a 21 percentage-point increase in the probability that native-born adults participate in lifelong learning, but only a 6 percentage point increase in the probability that foreign-born adults participate. However, in other countries, namely Australia, Austria, the Slovak Republic, Slovenia, Sweden and the United States, the size of the estimated coefficient does not differ greatly across the two groups. In a few countries, notably Chile, the Czech Republic, Greece and the United Kingdom, the estimated effect is stronger among foreign-born than among native-born adults.

Similarly, there is no clear pattern when considering the impact of literacy proficiency. In most countries, and across both groups, the increasing likelihood of participation related to a one standard-deviation higher score in literacy is between 5 and 10 percentage points, with no differences according to the migration background of respondents.

In all countries, not being in paid employment strongly reduces the likelihood of participation in lifelong learning, among both natives and migrants. However, in most countries this effect is much stronger among native-born adults (Greece is a notable exception in this regard). In Italy and Spain, for example, being in paid employment has no effect on the likelihood that migrants participate in adult education and training; but among native-born adults in Italy, not being employed is associated with a reduction of 11 percentage points in the probability of participating.

A more extensive analysis was conducted on the subsample of migrants, in order to assess the impact on the likelihood of participation in lifelong learning activities of the number of years spent in the country of destination and of whether the highest educational qualification was acquired in the country of destination or abroad (see Table 4.1.). The analysis is restricted to a subset of countries for which the necessary information was available on a sufficient number of observations.

Table 4.1. Extended analysis on the subsample of foreign-born

 

More than 5 years in the country

Foreign qualification

 

Model 1

Model 2

Model 3

Model 4

Coef.

S.E.

Coef.

S.E.

Coef.

S.E.

Coef.

S.E.

Australia

 

 

 

 

-0.31

0.034

-0.06

0.042

Austria

-0.20

0.059

-0.04

0.066

-0.15

0.047

-0.06

0.047

Canada

-0.05

0.025

-0.03

0.024

-0.16

0.023

-0.08

0.021

Chile

0.27

0.115

0.09

0.080

 

 

 

 

Cyprus (1)

0.11

0.055

0.12

0.057

 

 

 

 

Denmark

-0.21

0.031

-0.10

0.037

-0.08

0.048

-0.07

0.045

England/N.Ireland (UK)

-0.04

0.050

0.00

0.062

-0.04

0.089

-0.05

0.097

Finland

-0.16

0.077

-0.37

0.094

 

 

 

 

Flanders (Belgium)

-0.07

0.085

-0.08

0.079

0.13

0.069

0.14

0.065

France

-0.23

0.062

-0.18

0.066

0.04

0.062

-0.05

0.057

Germany

-0.14

0.084

0.00

0.085

-0.21

0.070

-0.17

0.061

Ireland

-0.01

0.036

0.03

0.036

 

 

 

 

Israel

0.15

0.080

0.23

0.080

 

 

 

 

Italy

0.16

0.037

0.20

0.038

 

 

 

 

Netherlands

-0.22

0.077

-0.11

0.085

 

 

 

 

New Zealand

0.03

0.035

0.04

0.038

-0.06

0.039

-0.03

0.036

Norway

-0.16

0.036

-0.13

0.043

0.14

0.075

0.12

0.069

Singapore

-0.08

0.050

-0.01

0.043

 

 

 

 

Slovenia

-0.05

0.080

-0.02

0.085

 

 

 

 

Spain

-0.02

0.052

-0.01

0.050

 

 

 

 

Sweden

-0.11

0.055

-0.12

0.058

0.14

0.084

0.01

0.075

United States

-0.07

0.070

0.01

0.061

 

 

 

 

Note: The table shows selected coefficients from separate linear regressions on the subsample of foreign-born adults in each country. Models 1 and 3 only control for a constant and the respective variable of interest (a dummy for having spent more than 5 years in the host country in the case of Model 1 and a dummy for having obtained the highest educational qualification abroad in the case of Model 3). Models 2 and 4 further control for gender, age, age squared, a set of educational attainment dummies, literacy scores and a dummy for being in paid employment.

1. See notes 2 and 3 at the end of this Chapter.

Source: (OECD, 2015[38]) Survey of Adult Skills (PIAAC) (2012, 2015), www.oecd.org/skills/piaac/publicdataandanalysis

 StatLink http://dx.doi.org/10.1787/888933846479

In the vast majority of countries, there is a negative association between length of stay in the host country and the likelihood of participating in lifelong training. Chile, Israel and Italy are the main exceptions to this pattern. On average, migrants who have been living in the host country for more than 5 years are about 5 percentage points less likely to have participated in training in the 12 months prior to the seurvey. This makes intuitive sense, as newly arrived migrants are more likely to participate in training to accelerate their integration into the host country’s labour market. However, much of the difference is explained away after controlling for a range of observable characteristics such as age, gender, literacy proficiency and educational qualifications: the estimated coefficients shrink in most countries (with the notable exception of Finland) and often becomes statistically not different from zero.

Having a foreign qualification is positively associated with participation in lifelong learning in Flanders (Belgium), Norway and Sweden, and negatively associated in Australia, Austria, Canada and Germany. However, after controlling for other observable characteristics, Germany is the only country in which such negative association remains statistically significant.

Probably unsurprisingly, individual characteristics have a strong impact, within each country, on the probability of participating in lifelong learning, over and above migration background per se. Moreover, the characteristics of the migrant population vary greatly across countries, as shown in Chapter 1. Countries have different histories of immigration, such that the age of the migrant population differs across countries. In addition, the rules governing the right of migrants to enter a country also differ, ultimately resulting in large disparities across countries in the education, proficiency and employability of the migrant population.

For this reason, any analysis of the differences in participation rates between migrants and natives should try to control as much as possible for differences in relevant individual characteristics. Figure 4.3 shows the results of a series of regression analyses, run separately for each country, that aimed to estimate differences in the participation rates between natives and migrants, controlling simultaneously for relevant individual characteristics such as age, education, literacy proficiency, gender and employment status.

Figure 4.3. Differences between migrants and natives in the probability of participating in lifelong learning
picture

Note: The figure shows coefficients from separate linear regressions (one for each country) of the probability of participation in lifelong learning on a foreign-born dummy, controlling for age, age squared, a set of educational qualifications dummies, literacy proficiency scores, and a dummy for being in paid employment. The coefficients have been multiplied by 100 to express the effect in percentage points. Statistically significant differences are marked in a darker tone.

1. See notes 2 and 3 at the end of this Chapter.

Source: (OECD, 2015[38]) Survey of Adult Skills (PIAAC) (2012, 2015), www.oecd.org/skills/piaac/publicdataandanalysis

 StatLink http://dx.doi.org/10.1787/888933845947

In a large majority of countries, even after controlling for differences in observable individual characteristics, foreign-born adults are found to be less likely to participate in lifelong learning. However, in most countries, the estimated gap in participation is narrow (less than 4 percentage points) and not statistically different from zero. To put the magnitude of the estimated effects in perspective, the average baseline participation rates is around 50%, approaching 70% in the Nordic countries, the Netherlands and New Zealand.

Germany is the country where being foreign-born is most strongly associated with a reduction in the likelihood of participating in lifelong learning, with an estimated negative effect of 6.3 percentage points. The overall baseline participation rate in Germany is 53%. By contrast, in Finland, where the baseline participation rate is among the highest, at 66%, migrants are 8.6 percentage points more likely than natives to participate in lifelong learning (after controlling for other observable individual characteristics).

Figure 4.3 also highlights how the magnitude of the differences in participation rates between natives and migrants shrinks significantly after controlling for observable characteristics (as seen by comparing adjusted and unadjusted gaps). This is likely because, in many countries, migrants are less educated and less proficient (and less likely to be employed) than native-born adults – and these are the characteristics that favour participation in lifelong learning activities. Once these differences between the two groups are taken into account, participation rates among migrants turn out to be similar to those of natives.

One possible implication of this finding is that foreign-born adults are not discriminated in their access to lifelong learning opportunities, as observable characteristics like education and skills largely explain the differences in participation rates. On the other hand, it could be argued that the direction of causality runs in the opposite direction and that promoting participation in adult training is key to improve the skills and the employability of migrants.

It could also be argued that the policy objective should not be limited to closing the participation gap between native-born and foreign-born adults. Migrants, especially the ones recently arrived in the host country, are likely to need more training than natives, for instance in order to improve their proficiency in the language of the host country. Unfortunately PIAAC does not allow measuring precisely language proficiency (and therefore the need for language training). An indirect measure of the need of language proficiency can be derived by looking at migrants who declared to be native speakers of the language of the PIAAC assessment (which generally coincided with the official language of the host country). However, it turns out that migrants who are native speakers in the language of the PIAAC assessment are, if anything, slightly more likely than other migrants to participate in lifelong learning.

An alternative, though always indirect, way to capture the need or the demand for adult training is to look at the actual time spent in lifelong learning activities by adults that had the opportunity to participate. PIAAC elicits information on the estimated amount of hours spent in non-formal learning activities during the 12 months prior to the interview. While this information is only available for non-formal activities, it is nonetheless informative, as formal adult education accounts for only 11%, on average, of overall participation in lifelong learning. Non-formal learning activities differ from formal learning activities in that they do not lead to a formal qualification. They include open or distance education (e.g. online courses), on-the-job training or training by supervisors or co-workers, seminars or workshops, and private courses/lessons.

On this metric there is evidence that foreign-born adults express a higher demand, meaning that they spend more time in learning activities than native-born adults do. Differences between foreign- and native-born adults in the amount of hours spent in non-formal learning activities are substantial in about half of the countries, as shown in Figure 4.4. In Denmark, Flanders (Belgium), Finland, the Netherlands and Lithuania foreign-born adults who participated in non-formal learning activities spent about 60 percent more hours than native-born adults. The gap does not change substantially after controlling for observable characteristics. With the exception of Israel, in all the countries where the gap is in favour of natives, the estimated difference is generally small and not statistically different from zero.

Figure 4.4. Differences in hours of participation in non-formal learning between migrants and natives
picture

Note: The graph shows percentage differences in the amount of hours spent by foreign-born and native-born adults in non-formal learning activities. The bars show adjusted differences, controlling for age, age squared gender, educational attainment, literacy proficiency and employment status. Statistically significant adjusted differences are shown in a darker tone.

1. See notes 2 and 3 at the end of this Chapter.

Source: (OECD, 2015[38]) Survey of Adult Skills (PIAAC) (2012, 2015), www.oecd.org/skills/piaac/publicdataandanalysis

 StatLink http://dx.doi.org/10.1787/888933845966

Intentions to participate and barriers to participation

The literature on participation in lifelong learning has underlined the role played by the demand side, i.e. the intention to participate. Baert, De Rick and Van Valckenborgh (2006[40]) argue that a positive attitude towards lifelong learning is a precondition to express an intention to participate. Once such an intention has been formulated, an adult will search for an opportunity in the education and training market. Lack of participation might be due to either the lack of a suitable offer, or to some other barriers.

PIAAC collects information on whether respondents wanted to participate in an informal learning activity in the previous 12 months, but ended up not doing so. Respondents were also asked why they did not participate and were given eight reasons to choose from: did not have the prerequisites; excessive financial cost of the learning activity; lack of employer’s support; too busy at work; inconvenient time or place of the activity; need to care for children or other family responsibilities; unexpected reasons; and other reasons.

Information on the share of adults who were prevented from participating in lifelong learning activities (meaning that they had formulated an intention to participate, but were somehow hindered from actually participating) can be useful for designing policies that help remove such constraints.

Figure 4.5 shows the incidence of “constrained” adults in each country, and among native- and foreign-born adults. Unmet demand for lifelong learning is highest in New Zealand, the United States, Singapore and Denmark (in descending order), while it is lowest in Greece, Lithuania and the Czech Republic (in ascending order). Countries with high participation rates tend also to be countries with high unmet demand (the correlation coefficient equals 0.76). Unmet demand is generally higher among foreign-born adults; however, in many countries, there are no significant differences between native- and foreign-born adults in the degree to which they reported unmet demand for adult training.

Figure 4.5. Constraints on participation in informal learning activities
picture

Note: The figure shows the percentage of adults who reported being willing to participate in some informal learning activity in the 12 months prior to the interview but who, in the end, did not start that learning activity.

1. See notes 2 and 3 at the end of this Chapter.

Source: (OECD, 2015[38]) Survey of Adult Skills (PIAAC) (2012, 2015), www.oecd.org/skills/piaac/publicdataandanalysis

 StatLink http://dx.doi.org/10.1787/888933845985

Figure 4.6 looks more closely at differences between native- and foreign-born adults by reporting both unadjusted differences between the two and adjusted differences, estimated after taking into account differences in the composition of the two groups according to age, gender, educational attainment, employment status and literacy skills.

In about half of the countries the adjusted gap is either not statistically significant or less than five percentage points. The gap is especially sizeable (at 12 percentage points) in Finland. In Greece, Norway, Italy and England/Northern Ireland (UK) the gap is between 7 and 8 percentage points.

Figure 4.6. Differences between foreign- and native-born adults in constraints on participation in informal learning activities
picture

Note: The bars report the estimated difference (after controlling for age, age squared, gender, educational attainment, literacy skills and employment status) between foreign- and native-born adults in the probability of reporting a constraint in participation in an informal lifelong learning activity (i.e. a willingness to participate in some informal learning activity in the 12 months prior to the interview that, in the end, did not materialise into actual participation). Statistically significant differences are shown in a darker tone. The diamonds represent the unadjusted differences. Estimated regression coefficients have been multiplied by 100 to express the gaps in percentage points.

1. See notes 2 and 3 at the end of this Chapter

Source: (OECD, 2015[38]) Survey of Adult Skills (PIAAC) (2012, 2015), www.oecd.org/skills/piaac/publicdataandanalysis

 StatLink http://dx.doi.org/10.1787/888933846004

Barriers to participation

Importantly, PIAAC asks respondents about the reasons behind the inability to participate in training. The five most frequently reported reasons are: excessive cost of participation (reported by 17% of respondents, on average across countries), lack of employer’s support (7%), excessive amount of things to be done at work (“too busy at work”, 29%), inconvenience of time or place (11%), and child care or family responsibilities (15%). Some 14% of respondents reported a residual category (“Other reasons”) and 4% reported “unexpected reasons. Only 3% of respondents reported that they were unable to participate in training because they lacked the prerequisites (2.8% of native-born and 4.5% of foreign-born adults reported so). This might be because the focus is on non-formal learning activities, which are likely to require fewer (formal or non-formal) prerequisites than learning activities that lead to a formal qualification.

Figure 4.7 presents the average, across countries, of the shares of native-born and foreign-born respondents that have reported various reasons that prevented them from undertaking lifelong learning activities. Results for individual countries are provided in Annex Table 4.A.4.

Figure 4.7. Reasons for not participating in lifelong learning activities
picture

Note: The graph shows the cross-country averages of the shares of respondents that have reported various reasons for not having being able to start a lifelong learning activity, despite their willingness to do so.

Source: (OECD, 2015[38]) Survey of Adult Skills (PIAAC) (2012, 2015), www.oecd.org/skills/piaac/publicdataandanalysis

 StatLink http://dx.doi.org/10.1787/888933846023

In the vast majority of countries, being busy at work is the most frequently cited reason for not participating in training, among both natives and migrants. Native-born adults were generally more likely than foreign-born adults to report so. A notable exception is the United States, where 37% of migrants reported that they were too busy at work to participate in non-formal learning activities, compared with 25% of natives. Also in Canada and New Zealand migrants were slightly more likely than natives to report being busy at work as a reason for not participating in training.

The second most frequently cited barrier is the excessive financial cost of the learning activity. In this case, foreign-born adults were more likely to report so than native-born adults. The share of migrants who cited financial considerations ranges from 8% in Finland to 40% in Greece. Only in the United States native-born adults were significantly more likely than foreign-born adults to report excessive financial costs as the reason that prevented them from participating.

Migrants were also more likely than natives to be constrained by child care or other family responsibilities. This could be due to a lack of access to a family network capable of helping with childcare, or to other barriers (linguistic, financial or bureaucratic) to access formal childcare. The share is highest in Chile (27%) and lowest in Denmark (8%).

After taking into account differences in observable characteristics, the estimated differences between natives and migrants in the probability of citing the various types of barriers to participation become not statistically significant in most cases (see Table 4.2). But some consistent patterns across countries are observed. For example, foreign-born adults were generally more likely than native-born adults to cite financial constraints. This is especially the case in Austria, Estonia, Norway and Spain. In all of these countries, the difference between the two groups is around 8 percentage points. In the United States, by contrast, foreign-born adults were about 9 percentage points less likely to cite financial constraints.

Migrants were less likely than natives to report a lack of employer support as the reason for not participating in training. The differences between the two groups of adults are statistically significant in Germany, Greece, the Netherlands and Spain, ranging between 2 and 7 percentage points. Similarly, migrants in Australia, Canada, Estonia, Finland, France and Norway were less likely than natives to find the time or location of training inconvenient, with statistically significant differences ranging from 2 to 7 percentage points.

In most countries, there are no significant differences between migrants and natives in the likelihood of reporting being too busy at work as a reason for not participating in lifelong learning activities. Only in Chile, Greece and Norway were foreign-born adults less likely – by eight percentage points – to cite that reason. By contrast, migrants in Canada were 4 percentage points more likely, and those in the United States were 14 percentage points more likely than natives to report that they were too busy at work to participate in training.

A more mixed picture emerges when looking at the burden imposed by childcare or other family responsibilities. Foreign-born adults in Denmark, England and Northern Ireland (UK), Finland, Germany, Singapore, and the United States were more likely than native-born adults to report being constrained by family responsibilities. In Estonia, Flanders (Belgium) and Spain native-born adults were more likely to report so.

Table 4.2. Differences between migrants and natives in the reasons cited for not participating in lifelong learning

Country

Too expensive

No employer’s support

Too busy at work

Inconvenient schedule

Family responsibility

Australia

0.03

-0.01

-0.02

-0.05

0.05

Austria

0.08

-0.01

0.00

0.02

-0.03

Canada

0.01

-0.02

0.04

-0.03

0.01

Chile

-0.04

0.09

-0.18

-0.01

0.14

Cyprus (1)

0.06

-0.00

0.059

-0.02

-0.10

Czech Republic

0.16

0.03

-0.13

-0.02

0.10

Denmark

-0.03

-0.03

0.03

0.01

0.03

England/N.Ireland (UK)

-0.01

-0.01

0.03

-0.03

0.07

Estonia

0.08

0.01

-0.04

-0.05

-0.04

Finland

0.00

-0.03

0.00

-0.07

0.06

Flanders (Belgium)

0.03

0.04

-0.04

0.09

-0.08

France

0.04

-0.02

-0.05

-0.02

0.03

Germany

0.02

-0.04

0.02

-0.03

0.07

Greece

0.11

-0.03

-0.08

-0.04

0.06

Ireland

0.04

0.01

-0.02

0.01

0.00

Israel

-0.05

0.03

-0.06

-0.03

0.04

Italy

0.03

-0.02

0.02

0.00

0.03

Lithuania

0.01

0.03

-0.01

-0.02

0.00

Netherlands

0.05

-0.07

-0.01

0.05

-0.02

New Zealand

0.00

0.01

0.02

0.02

-0.03

Norway

0.09

0.00

-0.08

-0.05

0.04

Russian Federation

-0.15

-0.02

-0.13

0.08

0.15

Singapore

-0.03

0.00

-0.01

-0.00

0.08

Slovenia

-0.05

-0.03

-0.01

0.04

0.05

Spain

0.07

-0.02

0.02

0.00

-0.05

Sweden

0.00

0.02

0.04

-0.04

0.02

United States

-0.09

-0.01

0.14

-0.02

0.05

Note: Estimated marginal effect of being foreign-born on the probability of reporting the indicated reason for not participating in a lifelong learning activity. Multinomial probit model that controls for age, gender, educational attainment, literacy skills and employment status. Multinomial logit model for Chile, Cyprus1, Greece and Lithuania. Statistically significant marginal effects (at the 10% level) are shown in bold.

1. See Notes 2 and 3 at the end of this Chapter

Source: (OECD, 2015[38]) Survey of Adult Skills (PIAAC) (2012, 2015), www.oecd.org/skills/piaac/publicdataandanalysis

 StatLink http://dx.doi.org/10.1787/888933846498

Opportunities to practice

This section looks at the frequency with which native- and foreign-born adults engage in practices that require using information-processing skills. As discussed above, practicing is important not only as a way to develop skills, but as means of preventing (or at least slowing) the decline in proficiency over a lifetime (Reder, 1994[20]; Reder, 2009[21]; Paccagnella, 2016[22]). In addition, there is evidence that the skills content of the tasks performed at work varies within occupations, and that it is an important determinant of wages (Autor and Handel, 2013[41]; Quintini, 2014[42]).

PIAAC asked respondents about the frequency with which they perform a wide range of tasks, requiring the use of reading, writing, numeracy or ICT skills, both at work and at home. The analysis in this chapter uses this information to construct an index of intensity of reading practices (at work and at home). The index is standardised across countries to have a mean of zero and a standard deviation of one to facilitate the interpretation of results.

Previous work has shown that proficiency and education are generally positively related to the frequency of skills practice (OECD, 2013[23]; Quintini, 2014[42]). This makes intuitive sense, as more skilled individuals are more likely to work in occupations that require a more intense or more frequent use of their skills. Similarly, more skilled individuals might find it easier or more enjoyable to engage in reading, writing or numeracy practices outside work. As discussed regarding participation in lifelong learning more broadly, adults with more skills individuals are also more likely to have opportunities to practice their skills, which can exacerbate existing differences in proficiency.

Figure 4.8 confirms that migrants generally read less frequently at work than natives. Differences are especially large (more than half of a standard deviation) in Italy, Slovenia and Germany. This is likely because migrants are sorted into more manual or routine occupations, where they are required to perform tasks that do not require them to read intensely. The unadjusted gap is not statistically significant in Cyprus, the Czech Republic, Lithuania and Australia.

When accounting for differences in observable characteristics, such as age, gender, literacy proficiency and educational attainment, the gap shrinks considerably. However, it remains substantial – between 20% and 40% of a standard deviation – in a range of countries, namely Austria, France, Germany, Greece, Ireland, Italy, Slovenia and Spain.

Further controlling for occupation dummies reduces the magnitude of the effect in most (but not all) countries. The estimated gap remains large (above 15%) in Italy, Slovenia, Germany, Greece, Austria, France, Ireland, and Chile. This possibly suggests the existence of migrants’ segregation in the characteristics of tasks they carry out at work, even within narrowly defined occupations.

Figure 4.8. Differences between foreign- and native-born adults in the use of reading skills at work
picture

Note: The graph reports the estimated difference between foreign- and native-born adults in the index of use of reading skills at work. Adjusted gaps control for differences in age, age squared, gender, educational attainment, literacy skills and employment status. The bars further controls for occupation dummies at the 1-digit level (ISCO2008). Statistically significant differences are marked in a darker tone (in the bars).

1. See notes 2 and 3 at the end of this Chapter.

Source: (OECD, 2015[38]) Survey of Adult Skills (PIAAC) (2012, 2015), www.oecd.org/skills/piaac/publicdataandanalysis

 StatLink http://dx.doi.org/10.1787/888933846042

A different picture emerges when examining the frequency of practice outside work (Figure 4.9). Differences between migrants and natives are generally smaller, and in some countries, migrants are more likely than natives to read outside of work, especially after controlling for observable characteristics. In Finland, Ireland, Lithuania, New Zealand, Sweden and Singapore foreign-born adults are significantly more likely than native-born adults to read outside of work, with differences between 10% and 25% of a standard deviation. Only in France, Germany and Slovenia are migrants found to be at a significant disadvantage when it comes to reading at home.

Figure 4.9. Differences between foreign- and native-born adults in the use of reading skills in everyday life
picture

Note: The graph reports the estimated difference between foreign- and native-born adults in the index of use of reading skills in everyday life. Adjusted gaps control for differences in age, age squared, gender, educational attainment, literacy skills and employment status. Statistically significant differences are marked in a darker tone (in the bars). Countries are sorted according to the unadjusted gap.

1. See notes 2 and 3 at the end of this Chapter.

Source: (OECD, 2015[38]) Survey of Adult Skills (PIAAC) (2012, 2015), www.oecd.org/skills/piaac/publicdataandanalysis

 StatLink http://dx.doi.org/10.1787/888933846061

Interpreting the results on reading practices is complicated by the fact that PIAAC contains no information on the language that adults use in such reading activities. While it can be safely argued that most reading at work is done in the language of the host country (and therefore in the same language as the PIAAC literacy assessment), the same does not hold for reading at home. Indeed, migrants might read more at home because they can do so in their native language. An alternative explanation is possible, though: if they are less proficient in the host-country language, migrants might just need more time to read the same amount of material.

More insights into this issue can be gained with a regression analysis in which literacy proficiency is regressed on a full set of interaction terms between the foreign-born dummy and the frequency of reading at home. The regression also controls for the usual observable characteristics (age, age squared, gender, educational attainment, occupational status), as well as for whether the respondent took the cognitive assessment in his or her mother tongue. This exercise allows verifying if the relationship between the frequency of reading at home and literacy proficiency is different across the two groups of native-born and foreign-born adults. Indeed, if migrants mainly read in their native language at home, one would expect a smaller effect of reading practices on proficiency.

The opposite turns out to be true, though, as shown in Annex Table 4.A.5: the coefficients on the interaction terms are positive in the vast majority of countries (and they are not statistically significant in the few cases in which they are negative), which means that migrants benefit more than natives from reading at home (or, more precisely, that in the case of migrants reading at home is more strongly related to literacy proficiency than it is for natives). If this were the case, it would be unlikely that most reading at home is done in the migrants’ native language, as it is hard to think of reasons why reading in a foreign language would help improve performance in a literacy assessment administered in a different language. Unfortunately, the design of PIAAC does not allow for disentangling whether it is reading practice that improves literacy proficiency or it is literacy proficiency that makes people more likely to engage in reading outside of work, so the arguments put forward in this paragraph should be taken as reasonable interpretations of the patterns observed in the data more than as a “proof” of some hypothesis about the benefits of practicing reading on literacy proficiency.

In sum, while it can be safely concluded that migrants face obstacles in the labour market, and are less likely to be offered the same opportunities for practicing and developing their skills, results concerning the frequency of reading outside of work are harder to interpret. Reading at home is likely to be beneficial, but it is hard to tell how much it compensates for less reading at work.

Conclusions

In a rapidly changing environment, participation in adult training and lifelong learning activities is crucial for adapting and upgrading skills to meet changing demand, especially as increases in life expectancy have been accompanied by a lengthening of the working life. This is even more important for migrants, who often face additional challenges and have to adapt their skills to the needs of the host country.

Designing more effective systems of adult training and increasing participation in lifelong learning activities is a priority in many OECD countries, particularly in those where participation rates are low. Indeed, the cross-country variation in the design and effectiveness of systems of adult education and training is remarkable. OECD countries also differ substantially in their migration policies, and this often translates into large differences in the characteristics of the foreign-born population (see Chapter 2).

On average, migrants tend to participate less than natives in lifelong learning activities. However, patterns of participations seem to be much more influenced by country-level characteristics than by immigration background. Foreign-born adults living in countries with effective education and training systems and with traditionally high rates of participation tend to take part in lifelong learning activities much more than do native-born adults living in countries with less-developed training systems.

Differences in participation rates between native- and foreign-born adults vary across countries, but are generally not very large, especially after controlling for basic individual characteristics, such as age, education or literacy proficiency. In the majority of countries, differences in participation are small and not statistically different from zero; in Finland and the Slovak Republic, migrants are even significantly more likely than natives to participate in lifelong learning activities. The Survey of Adult Skills (PIAAC) also finds that, depending on participation, migrants generally spend more hours than natives in training activities.

However, significant differences remain, and data contained in the Survey of Adult Skills (PIAAC) allows for a more detailed picture of the issues surrounding participation in lifelong learning, which could help countries design more effective training policies for their foreign-born population.

Immigrants are more likely to report that they are not able to participate as much as they would like. They more often cite financial barriers and obstacles related to childcare and other family responsibilities as the reasons that prevent them from participating.

In many countries, migrants are more likely to be employed in occupations that do not require them to practice their literacy skills. While this is probably partly due to the fact that they tend to be less proficient to begin with, this lack of practice makes it harder for migrants to catch up with natives. On the other hand, migrants are more likely to engage in literacy-related practices outside of work. While this is a positive finding, it is unlikely to compensate for the lack of practice on-the-job, especially if reading at home is in the migrants’ native language rather than in the host-country language.

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[5] Milburn, F. (1996), “Migrants and minorities in Europe: implications for adult education and training policy”, International Journal of Lifelong Education, Vol. 15/3, pp. 167-176, http://dx.doi.org/10.1080/0260137960150303.

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Annex 4.A. Additional Tables
Annex Table 4.A.1. Rates of participation in lifelong learning

 

Native-born

 

Formal education

Formal and non-formal

Formal and non-formal, job-related

Formal and non-formal, non-job-related

 

%

S.E.

%

S.E.

%

S.E.

%

S.E.

Countries

 

 

 

 

 

 

 

 

Australia

16.6

(0.01)

55.3

(0.01)

49.5

(0.01)

5.8

(0.00)

Austria

6.7

(0.00)

49.9

(0.01)

41.1

(0.01)

8.9

(0.00)

Canada

12.3

(0.00)

59.2

(0.01)

50.5

(0.01)

8.6

(0.00)

Chile

13.1

(0.01)

46.9

(0.02)

39.4

(0.01)

7.5

(0.01)

Cyprus (1,2)

5.6

(0.00)

37.6

(0.01)

31.4

(0.01)

6.2

(0.00)

Czech Republic

6.1

(0.00)

49.1

(0.01)

42.3

(0.01)

6.8

(0.01)

Denmark

15.3

(0.01)

66.9

(0.01)

59.0

(0.01)

8.0

(0.00)

England/N. Ireland (UK)

14.8

(0.01)

55.0

(0.01)

49.1

(0.01)

6.0

(0.00)

Estonia

10.6

(0.00)

55.0

(0.01)

43.6

(0.01)

11.5

(0.00)

Finland

16.1

(0.01)

65.8

(0.01)

55.8

(0.01)

10.0

(0.00)

Flanders (Belgium)

7.8

(0.00)

48.7

(0.01)

39.7

(0.01)

9.1

(0.00)

France

5.2

(0.00)

37.2

(0.01)

33.1

(0.01)

4.0

(0.00)

Germany

7.5

(0.00)

55.2

(0.01)

47.8

(0.01)

7.3

(0.01)

Greece

5.5

(0.00)

20.5

(0.01)

16.3

(0.01)

4.2

(0.00)

Ireland

13.5

(0.01)

49.8

(0.01)

42.7

(0.01)

7.2

(0.00)

Israel

17.0

(0.01)

51.6

(0.01)

39.7

(0.01)

11.9

(0.01)

Italy

5.3

(0.00)

24.5

(0.01)

20.5

(0.01)

4.0

(0.00)

Lithuania

6.1

(0.01)

33.7

(0.01)

27.7

(0.01)

6.0

(0.00)

Netherlands

15.0

(0.01)

65.2

(0.01)

54.5

(0.01)

10.7

(0.00)

Norway

15.6

(0.01)

63.3

(0.01)

56.3

(0.01)

7.0

(0.00)

New Zealand

17.1

(0.01)

66.7

(0.01)

57.1

(0.01)

9.6

(0.01)

Russian Federation

5.8

(0.00)

19.8

(0.02)

15.6

(0.01)

4.1

(0.01)

Singapore

10.0

(0.00)

55.7

(0.01)

46.9

(0.01)

8.7

(0.01)

Slovenia

11.6

(0.01)

49.7

(0.01)

37.3

(0.01)

12.4

(0.01)

Spain

13.3

(0.01)

47.2

(0.01)

36.3

(0.01)

10.9

(0.00)

Sweden

12.4

(0.00)

66.8

(0.01)

54.8

(0.01)

12.1

(0.01)

United States

13.9

(0.01)

61.2

(0.01)

52.3

(0.01)

8.9

(0.01)

 

Foreign-born

 

Formal education

Formal and non-formal

Formal and non-formal, job-related

Formal and non-formal, non-job-related

 

%

S.E.

%

S.E.

%

S.E.

%

S.E.

Countries

 

 

 

 

 

 

 

 

Australia

17.5

(0.01)

54.4

(0.01)

46.0

(0.01)

8.4

(0.01)

Austria

7.1

(0.01)

43.5

(0.02)

34.1

(0.02)

9.4

(0.01)

Canada

17.5

(0.01)

53.4

(0.01)

43.6

(0.01)

9.7

(0.01)

Chile

10.6

(0.05)

53.3

(0.07)

40.6

(0.05)

12.7

(0.06)

Cyprus (1,2)

6.0

(0.01)

37.1

(0.02)

32.2

(0.02)

4.9

(0.01)

Czech Republic

10.4

(0.04)

43.6

(0.07)

39.9

(0.07)

3.7

(0.01)

Denmark

24.1

(0.01)

60.2

(0.01)

50.8

(0.01)

9.5

(0.01)

England/N. Ireland (UK)

20.5

(0.02)

57.0

(0.02)

46.7

(0.02)

10.2

(0.01)

Estonia

4.2

(0.01)

38.9

(0.01)

30.7

(0.01)

8.3

(0.01)

Finland

19.6

(0.03)

68.1

(0.04)

48.2

(0.04)

19.9

(0.02)

Flanders (Belgium)

9.6

(0.02)

41.4

(0.03)

31.5

(0.02)

9.9

(0.02)

France

6.5

(0.01)

27.6

(0.02)

22.3

(0.01)

5.3

(0.01)

Germany

11.3

(0.01)

40.9

(0.02)

33.6

(0.02)

7.3

(0.01)

Greece

5.5

(0.01)

20.6

(0.02)

16.1

(0.02)

4.5

(0.01)

Ireland

18.1

(0.01)

52.6

(0.02)

44.3

(0.02)

8.3

(0.01)

Israel

11.5

(0.01)

47.3

(0.02)

36.4

(0.02)

10.9

(0.01)

Italy

8.3

(0.02)

22.2

(0.02)

17.0

(0.02)

5.2

(0.01)

Lithuania

4.7

(0.02)

26.8

(0.04)

22.5

(0.04)

4.3

(0.03)

Netherlands

17.8

(0.02)

58.4

(0.03)

43.1

(0.03)

15.3

(0.02)

Norway

21.1

(0.02)

66.5

(0.02)

54.2

(0.02)

12.3

(0.01)

New Zealand

17.5

(0.01)

67.7

(0.01)

58.5

(0.01)

9.2

(0.01)

Russian Federation

8.9

(0.02)

22.5

(0.03)

17.6

(0.03)

5.0

(0.01)

Singapore

11.2

(0.01)

59.4

(0.02)

50.4

(0.02)

9.0

(0.01)

Slovenia

6.6

(0.01)

37.6

(0.02)

31.3

(0.02)

6.3

(0.01)

Spain

11.7

(0.01)

40.6

(0.02)

29.4

(0.02)

11.2

(0.01)

Sweden

21.1

(0.02)

58.9

(0.02)

42.2

(0.02)

16.7

(0.02)

United States

13.0

(0.01)

50.8

(0.02)

41.3

(0.02)

9.6

(0.01)

Note: The table reports the percentage of respondents that have participated in different forms of lifelong learning.

1. Note by Turkey: The information in this document with reference to “Cyprus” relates to the southern part of the Island. There is no single authority representing both Turkish and Greek Cypriot people on the Island. Turkey recognises the Turkish Republic of Northern Cyprus (TRNC). Until a lasting and equitable solution is found within the context of the United Nations, Turkey shall preserve its position concerning the “Cyprus issue”.

2. Note by all the European Union Member States of the OECD and the European Union: The Republic of Cyprus is recognised by all members of the United Nations with the exception of Turkey. The information in this document relates to the area under the effective control of the Government of the Republic of Cyprus.

Source: Survey of Adult Skills (PIAAC) (2012, 2015)

 StatLink http://dx.doi.org/10.1787/888933846517

Annex Table 4.A.2. Participation in on-the-job training, employed adults

 

On the job training, employed

 

Native-born

Foreign-born

Difference

 

%

S.E.

%

S.E.

%

S.E.

p-value

Countries

 

 

 

 

 

 

 

Australia

42.7

(1.11)

39.3

(1.84)

-3.4

(2.19)

0.1

Austria

27.9

(0.88)

20.3

(1.80)

-7.6

(1.99)

0.0

Canada

44.7

(0.67)

33.9

(1.29)

-10.8

(1.47)

0.0

Chile

36.2

(1.43)

40.5

(6.32)

4.3

(5.77)

0.5

Cyprus (1,2)

21.5

(0.90)

24.5

(2.66)

3.0

(2.98)

0.3

Czech Republic

49.5

(1.46)

41.5

(8.09)

-8.0

(8.00)

0.3

Denmark

47.4

(0.79)

34.6

(1.70)

-12.7

(1.89)

0.0

England/N. Ireland (UK)

47.0

(1.22)

38.7

(2.59)

-8.3

(2.89)

0.0

Estonia

45.8

(0.83)

32.7

(1.82)

-13.1

(1.86)

0.0

Finland

54.9

(0.88)

46.6

(3.85)

-8.3

(3.95)

0.0

Flanders (Belgium)

38.3

(0.89)

25.1

(2.82)

-13.2

(2.95)

0.0

France

24.1

(0.66)

14.2

(1.51)

-9.9

(1.65)

0.0

Germany

44.6

(1.16)

29.0

(2.62)

-15.6

(2.78)

0.0

Greece

12.3

(0.91)

7.9

(2.04)

-4.4

(2.15)

0.0

Ireland

42.6

(1.17)

37.8

(2.13)

-4.7

(2.52)

0.1

Israel

34.1

(1.04)

32.7

(1.73)

-1.4

(1.89)

0.5

Italy

20.4

(1.11)

13.7

(2.88)

-6.7

(2.81)

0.0

Lithuania

30.7

(1.14)

28.0

(5.56)

-2.7

(5.66)

0.6

Netherlands

51.8

(0.91)

45.8

(3.05)

-6.0

(3.08)

0.1

Norway

37.8

(0.79)

37.6

(2.32)

-0.2

(2.27)

0.9

New Zealand

50.3

(1.04)

49.5

(1.73)

-0.8

(1.99)

0.7

Russian Federation

14.0

(1.22)

15.3

(4.11)

1.3

(4.01)

0.7

Singapore

38.4

(0.83)

43.1

(1.77)

4.7

(1.81)

0.0

Slovenia

37.7

(1.03)

26.4

(2.44)

-11.3

(2.68)

0.0

Spain

37.6

(0.95)

22.7

(2.11)

-14.9

(2.37)

0.0

Sweden

38.9

(1.06)

27.1

(2.02)

-11.8

(2.31)

0.0

United States

49.8

(1.17)

38.0

(2.90)

-11.7

(2.92)

0.0

Note: The table reports the percentage of employed respondents that have participated in on-the-job training.

1. See Note 1, 2 in Table 4.A.1

Source: Survey of Adult Skills (PIAAC) (2012, 2015)

 StatLink http://dx.doi.org/10.1787/888933846536

Annex Table 4.A.3. Individual correlates of participation in lifetime learning

 

45-54

Tertiary education

Literacy (1 SD)

Not employed

 

Native-born

Foreign-born

Native-born

Foreign-born

Native-born

Foreign-born

Native-born

Foreign-born

 

Coef.

S.E.

Coef.

S.E.

Coef.

S.E.

Coef.

S.E.

Coef.

S.E.

Coef.

S.E.

Coef.

S.E.

Coef.

S.E.

Countries

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Australia

-0.02

(0.03)

-0.05

(0.04)

0.26

(0.03)

0.29

(0.04)

0.09

(0.01)

0.08

(0.01)

-0.25

(0.02)

-0.16

(0.03)

Austria

-0.08

(0.03)

-0.17

(0.05)

0.27

(0.03)

0.24

(0.06)

0.09

(0.01)

0.08

(0.02)

-0.14

(0.02)

-0.05

(0.04)

Canada

-0.08

(0.02)

-0.10

(0.04)

0.21

(0.02)

0.18

(0.04)

0.08

(0.01)

0.10

(0.01)

-0.22

(0.01)

-0.14

(0.02)

Chile

-0.16

(0.02)

-0.15

(0.13)

0.32

(0.03)

0.69

(0.17)

0.05

(0.02)

0.09

(0.06)

-0.11

(0.02)

-0.03

(0.10)

Cyprus (1,2)

-0.07

(0.03)

-0.21

(0.08)

0.33

(0.03)

0.34

(0.07)

0.01

(0.01)

0.02

(0.03)

-0.15

(0.02)

-0.12

(0.06)

Czech Republic

0.03

(0.04)

-0.20

(0.08)

0.23

(0.04)

0.36

(0.11)

0.05

(0.02)

0.09

(0.04)

-0.29

(0.03)

-0.31

(0.09)

Denmark

-0.09

(0.02)

-0.21

(0.04)

0.21

(0.03)

0.18

(0.05)

0.07

(0.01)

0.06

(0.01)

-0.22

(0.01)

-0.08

(0.03)

England/N.Ireland (UK)

0.01

(0.02)

0.01

(0.06)

0.25

(0.02)

0.35

(0.07)

0.07

(0.01)

0.03

(0.02)

-0.25

(0.02)

-0.11

(0.06)

Estonia

-0.13

(0.02)

-0.14

(0.06)

0.26

(0.02)

0.24

(0.05)

0.07

(0.01)

0.03

(0.02)

-0.19

(0.02)

-0.26

(0.03)

Finland

-0.09

(0.02)

-0.11

(0.10)

0.26

(0.03)

0.15

(0.10)

0.06

(0.01)

0.04

(0.03)

-0.23

(0.02)

-0.09

(0.07)

Flanders (Belgium)

-0.05

(0.03)

-0.11

(0.08)

0.35

(0.03)

0.17

(0.08)

0.04

(0.01)

0.07

(0.02)

-0.16

(0.02)

-0.06

(0.06)

France

0.01

(0.02)

-0.10

(0.04)

0.24

(0.02)

0.19

(0.04)

0.05

(0.01)

0.04

(0.01)

-0.14

(0.01)

-0.07

(0.03)

Germany

-0.05

(0.02)

-0.15

(0.06)

0.22

(0.04)

0.25

(0.06)

0.10

(0.01)

0.09

(0.03)

-0.16

(0.02)

-0.02

(0.04)

Greece

-0.13

(0.03)

-0.20

(0.06)

0.24

(0.02)

0.36

(0.07)

0.04

(0.01)

0.06

(0.03)

-0.08

(0.02)

-0.16

(0.05)

Ireland

-0.04

(0.02)

-0.03

(0.05)

0.32

(0.03)

0.19

(0.06)

0.04

(0.01)

0.06

(0.02)

-0.20

(0.02)

-0.11

(0.04)

Israel

-0.13

(0.03)

-0.10

(0.05)

0.24

(0.03)

0.16

(0.07)

0.10

(0.01)

0.08

(0.02)

-0.11

(0.02)

-0.17

(0.04)

Italy

-0.04

(0.03)

-0.12

(0.06)

0.36

(0.03)

0.26

(0.10)

0.06

(0.01)

0.03

(0.03)

-0.12

(0.02)

0.00

(0.07)

Lithuania

-0.05

(0.03)

-0.16

(0.17)

0.31

(0.03)

0.38

(0.11)

0.07

(0.01)

0.05

(0.06)

-0.17

(0.02)

-0.29

(0.08)

Netherlands

-0.06

(0.02)

-0.10

(0.07)

0.25

(0.02)

0.18

(0.07)

0.04

(0.01)

0.03

(0.03)

-0.26

(0.02)

-0.17

(0.06)

New Zealand

-0.09

(0.03)

-0.02

(0.04)

0.21

(0.03)

0.06

(0.05)

0.05

(0.01)

0.10

(0.02)

-0.18

(0.02)

-0.22

(0.04)

Norway

-0.10

(0.02)

-0.11

(0.06)

0.23

(0.02)

0.09

(0.06)

0.04

(0.01)

0.03

(0.02)

-0.23

(0.02)

-0.10

(0.05)

Russian Federation

-0.14

(0.02)

-0.25

(0.07)

-0.01

(0.04)

0.38

(0.11)

0.01

(0.01)

0.06

(0.03)

-0.05

(0.02)

-0.06

(0.09)

Singapore

-0.17

(0.02)

-0.11

(0.04)

0.26

(0.03)

0.18

(0.06)

0.08

(0.01)

0.08

(0.01)

-0.18

(0.02)

-0.26

(0.03)

Slovenia

-0.09

(0.02)

-0.03

(0.07)

0.37

(0.03)

0.37

(0.07)

0.06

(0.01)

0.07

(0.03)

-0.12

(0.02)

-0.13

(0.05)

Spain

-0.10

(0.02)

0.04

(0.06)

0.30

(0.02)

0.16

(0.05)

0.06

(0.01)

0.07

(0.02)

-0.12

(0.02)

0.04

(0.04)

Sweden

-0.07

(0.02)

-0.12

(0.06)

0.22

(0.03)

0.22

(0.06)

0.07

(0.01)

0.06

(0.02)

-0.19

(0.02)

-0.07

(0.05)

United States

-0.11

(0.03)

-0.04

(0.05)

0.31

(0.03)

0.32

(0.07)

0.06

(0.01)

0.06

(0.03)

-0.25

(0.02)

-0.17

(0.05)

Note: The table shows selected coefficients from separate linear regressions (on the sample of native- and foreign-born) of the probability of participation in lifelong learning on a set of age dummies, a set of educational attainment dummies, literacy scores, a gender dummy, and a dummy for being in paid employment. For age, the reference category is 25-34 years old, and for educational attainment the reference category is below upper-secondary.

1. See Note 1, 2 in Table 4.A.1

Source: Survey of Adult Skills (PIAAC) (2012, 2015)

 StatLink http://dx.doi.org/10.1787/888933846555

Annex Table 4.A.4. Reasons for not participating in lifelong learning activities

 

Native-born

 

Too expensive

No employer support

Too busy at work

Inconvenient time or place

Family responsibility

Countries

%

S.E.

%

S.E.

%

S.E.

%

S.E.

%

S.E.

Australia

17.60

(1.79)

6.54

(1.06)

27.25

(1.52)

12.42

(1.33)

19.29

(1.47)

Austria

10.08

(1.25)

3.00

(0.59)

34.66

(1.72)

14.38

(1.41)

14.84

(1.25)

Canada

18.80

(0.99)

6.16

(0.61)

28.59

(1.02)

12.94

(0.86)

15.32

(1.01)

Chile

15.75

(1.39)

7.51

(0.98)

26.56

(2.33)

12.05

(0.99)

16.10

(1.34)

Cyprus (1,2)

11.03

(1.30)

2.76

(0.59)

29.39

(1.77)

11.44

(1.22)

30.75

(1.67)

Czech Republic

13.72

(1.70)

9.84

(1.81)

36.28

(3.29)

6.98

(1.41)

11.79

(1.79)

Denmark

13.88

(0.93)

15.09

(1.00)

26.43

(1.27)

9.85

(0.84)

5.01

(0.64)

England/N. Ireland (UK)

20.05

(1.49)

8.23

(1.01)

29.09

(1.77)

9.33

(0.89)

12.21

(1.01)

Estonia

17.45

(0.91)

7.06

(0.64)

29.91

(0.97)

16.24

(0.83)

10.49

(0.69)

Finland

6.46

(0.64)

9.45

(0.82)

28.72

(1.45)

21.69

(1.24)

8.63

(0.76)

Flanders (Belgium)

4.92

(0.79)

5.73

(0.89)

32.48

(1.81)

17.63

(1.57)

20.00

(1.38)

France

17.23

(1.02)

17.82

(1.00)

22.13

(1.14)

3.90

(0.47)

7.54

(0.67)

Germany

9.05

(0.83)

10.49

(1.16)

32.48

(1.43)

14.50

(1.07)

13.51

(1.23)

Greece

28.59

(2.44)

2.74

(0.99)

18.70

(2.36)

11.46

(1.50)

18.05

(1.58)

Ireland

19.46

(1.21)

4.56

(0.68)

22.40

(1.17)

10.02

(0.98)

18.11

(1.08)

Israel

25.00

(1.66)

3.65

(0.74)

30.04

(1.79)

12.18

(1.19)

16.79

(1.15)

Italy

14.52

(1.51)

3.47

(0.82)

38.79

(2.27)

5.23

(1.02)

18.66

(1.80)

Lithuania

24.81

(1.61)

7.59

(1.05)

31.61

(2.04)

12.64

(1.60)

8.71

(1.27)

Netherland

13.31

(1.21)

10.21

(0.98)

30.11

(1.74)

8.14

(0.90)

11.01

(1.05)

Norway

8.25

(0.98)

11.86

(1.00)

33.78

(1.40)

10.17

(1.07)

10.08

(1.13)

New Zealand

13.90

(1.06)

6.44

(0.68)

29.05

(1.25)

10.34

(0.91)

19.80

(1.27)

Russian Federation

25.42

(2.83)

4.64

(1.11)

27.78

(2.78)

14.83

(2.77)

12.23

(2.37)

Singapore

14.62

(1.17)

6.39

(0.77)

40.22

(1.72)

10.52

(0.96)

13.48

(1.08)

Slovenia

26.04

(1.87)

8.51

(0.99)

16.25

(1.32)

13.43

(1.31)

12.41

(1.14)

Spain

8.86

(0.86)

3.09

(0.48)

28.83

(1.40)

8.40

(0.83)

22.26

(1.07)

Sweden

11.51

(0.98)

7.81

(0.77)

25.48

(1.32)

13.08

(1.05)

12.10

(1.06)

United States

25.12

(1.41)

3.97

(0.52)

25.34

(1.50)

11.67

(0.91)

15.95

(1.24)

 

Foreign-born

 

Too expensive

No employer support

Too busy at work

Inconvenient time or place

Family responsibility

Countries

%

S.E.

%

S.E.

%

S.E.

%

S.E.

%

S.E.

Australia

19.86

(2.24)

5.34

(1.17)

27.45

(2.85)

7.74

(1.29)

22.23

(2.39)

Austria

17.71

(3.31)

1.89

(1.09)

30.45

(3.61)

13.24

(3.11)

15.80

(2.84)

Canada

20.51

(1.59)

4.38

(0.83)

31.38

(1.72)

9.63

(1.06)

18.60

(1.36)

Chile

13.26

(2.40)

18.58

(5.02)

10.14

(2.68)

12.81

(6.34)

27.18

(5.79)

Cyprus (1,2)

18.37

(4.69)

3.02

(1.37)

31.83

(5.45)

9.01

(2.16)

20.15

(3.45)

Czech Republic

33.28

(12.69)

10.70

(10.81)

18.78

(8.58)

4.41

(2.92)

21.64

(11.20)

Denmark

14.08

(2.05)

11.29

(1.71)

24.84

(2.07)

9.24

(1.41)

7.84

(1.43)

England/N. Ireland (UK)

19.81

(3.11)

7.02

(1.74)

29.46

(3.19)

5.11

(1.63)

19.89

(3.08)

Estonia

27.80

(2.89)

8.24

(1.99)

22.77

(2.50)

9.05

(1.79)

4.82

(1.51)

Finland

8.14

(3.29)

7.35

(3.00)

21.23

(4.31)

11.96

(3.19)

17.00

(4.58)

Flanders (Belgium)

9.75

(3.33)

8.09

(3.48)

24.37

(6.24)

21.65

(5.74)

15.74

(4.59)

France

24.68

(3.20)

13.36

(2.32)

14.18

(2.94)

1.27

(0.76)

9.80

(2.10)

Germany

12.88

(2.98)

6.11

(1.76)

28.39

(4.24)

9.47

(2.30)

21.10

(3.90)

Greece

40.81

(7.01)

0.00

(0.00)

9.84

(3.78)

7.86

(3.10)

23.27

(5.30)

Ireland

24.40

(2.44)

5.39

(1.25)

19.33

(2.25)

9.76

(1.73)

20.40

(2.36)

Israel

21.02

(2.69)

6.82

(1.73)

23.54

(2.34)

8.72

(1.78)

20.02

(2.89)

Italy

18.34

(4.87)

2.79

(1.62)

37.74

(7.30)

5.83

(4.00)

18.77

(5.42)

Lithuania

20.77

(8.50)

8.63

(4.56)

32.77

(10.33)

9.77

(8.44)

9.45

(9.80)

Netherland

19.39

(4.64)

3.08

(1.83)

26.96

(5.12)

11.42

(3.46)

12.32

(3.83)

Norway

16.41

(2.80)

9.86

(2.11)

20.86

(2.94)

4.44

(1.50)

18.66

(2.50)

New Zealand

15.21

(1.84)

7.69

(1.51)

31.22

(2.23)

11.86

(1.50)

16.76

(2.10)

Russian Federation

10.27

(5.35)

2.70

(1.57)

9.74

(4.18)

18.39

(6.31)

27.80

(12.92)

Singapore

11.73

(1.64)

7.09

(1.04)

38.91

(2.27)

7.89

(1.51)

24.15

(2.16)

Slovenia

22.20

(4.55)

4.80

(2.46)

13.78

(4.12)

16.82

(4.33)

17.17

(4.33)

Spain

18.41

(2.91)

0.71

(0.43)

26.40

(3.18)

7.08

(1.69)

17.43

(2.34)

Sweden

16.18

(2.60)

7.68

(1.53)

24.69

(3.00)

7.28

(2.07)

13.11

(2.22)

United States

15.46

(3.26)

3.18

(1.10)

36.88

(3.98)

9.20

(2.20)

21.95

(3.25)

Note: The table reports the share of respondents that have reported various reasons for not having being able to start a lifelong learning activity, despite their willingness to do so.

1. See Note 1, 2 in Table 4.A.1

Source: Source: Survey of Adult Skills (PIAAC) (2012, 2015)

 StatLink http://dx.doi.org/10.1787/888933846574

Annex Table 4.A.5. The relationship between literacy proficiency and reading at home

 

Index of reading at home

Reading at home x Foreign-born

Foreign-born dummy

Countries

Coef.

S.E.

Coef.

S.E.

Coef.

S.E.

Australia

14.20

(1.07)

4.90

(2.15)

-8.36

(2.65)

Austria

10.32

(0.94)

8.18

(2.29)

-6.40

(3.20)

Canada

14.67

(0.71)

1.26

(1.41)

-22.98

(1.63)

Chile

10.35

(1.11)

0.92

(3.56)

-6.73

(5.13)

Cyprus (1,2)

1.64

(0.99)

5.85

(2.21)

-8.29

(3.43)

Czech Republic

11.17

(1.38)

5.57

(5.51)

-3.93

(8.91)

Denmark

14.85

(1.11)

4.68

(2.07)

-19.91

(4.62)

England/N. Ireland (UK)

13.91

(1.16)

5.17

(3.01)

-13.34

(3.69)

Estonia

10.71

(0.91)

-2.68

(1.90)

-16.09

(1.66)

Finland

16.39

(1.06)

-12.42

(12.14)

-8.25

(8.62)

Flanders (Belgium)

10.91

(0.91)

4.97

(3.32)

-20.95

(3.82)

France

11.59

(0.77)

4.21

(2.03)

-16.92

(1.90)

Germany

15.53

(1.12)

-4.72

(2.84)

-10.32

(3.88)

Greece

7.27

(1.22)

3.12

(3.95)

7.35

(4.21)

Ireland

12.20

(1.06)

2.42

(2.29)

-6.18

(2.49)

Israel

14.74

(0.98)

-1.36

(2.24)

-10.92

(2.64)

Italy

10.10

(1.03)

3.40

(3.48)

-13.32

(4.31)

Lithuania

11.31

(1.29)

-1.18

(6.81)

-12.38

(6.60)

Netherland

12.13

(0.90)

5.84

(3.23)

-22.66

(4.11)

Norway

12.84

(0.99)

6.46

(3.19)

-14.59

(5.38)

New Zealand

12.89

(1.03)

0.36

(2.36)

-6.37

(2.81)

Singapore

13.34

(0.97)

-0.06

(1.71)

-15.98

(1.77)

Slovenia

9.79

(1.03)

2.48

(2.31)

-5.14

(3.87)

Spain

11.26

(0.81)

2.74

(2.28)

-18.86

(2.31)

Sweden

14.40

(1.18)

1.16

(2.88)

-29.14

(3.58)

United States

8.82

(0.89)

1.62

(1.86)

-16.53

(3.18)

Note: The table shows coefficients from country-specific regressions of literacy proficiency on the index of reading at home, on a dummy for foreign-born, and on the interaction between the two. The regressions also control for a quadratic polynomial in age, a set of educational attainment dummies, a gender dummy, a dummy for being in paid employment, and a dummy for being native speaker in the language of the assessment.

1. See Note 1, 2 in Table 4.A.1

Source: Survey of Adult Skills (PIAAC) (2012, 2015).

 StatLink http://dx.doi.org/10.1787/888933846593

Notes

← 1. A more extensive review is provided in Bassanini et al. (2007[19])

← 2. Note by Turkey: The information in this document with reference to “Cyprus” relates to the southern part of the Island. There is no single authority representing both Turkish and Greek Cypriot people on the Island. Turkey recognises the Turkish Republic of Northern Cyprus (TRNC). Until a lasting and equitable solution is found within the context of the United Nations, Turkey shall preserve its position concerning the “Cyprus issue”.

← 3. Note by all the European Union Member States of the OECD and the European Union: The Republic of Cyprus is recognised by all members of the United Nations with the exception of Turkey. The information in this document relates to the area under the effective control of the Government of the Republic of Cyprus.

← 4. The data for the Russian Federation do not include the population of the Moscow municipal area.

← 5. The statistical data for Israel are supplied under the responsibility of the relevant Israeli authorities. The use of such data by the OECD is without prejudice to the status of the Golam Heights, East Jerusalem and Israeli settlements in the West Bank under the terms of international law.

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