Chapter 1. More and better adult learning for a changing world of work

The world of work is changing and the ability of individuals, firms and economies to reap the benefits of these changes will depend critically on the ability of individuals to maintain and acquire new skills throughout their working lives. Adult learning systems have a key role to play in supporting individuals in this process. While all countries will have to step up their efforts to improve the future-readiness of their systems, some countries are facing a greater urgency than others. Low adult skill levels, as well as demographic and structural changes are increasing the pressure on adult learning systems to get ready for the future. This chapter looks at the driving forces behind the urgency for each country to take action.


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.

1.1. Adult learning systems as a key lever to address skill challenges

New technologies, digitalisation, globalisation and population ageing are changing the quantity and quality of jobs that are available and the skill-sets they require. To reap the benefits of these changes, skill development systems must be future-ready, i.e. ready to support people in acquiring and maintaining the relevant skills needed in a changing world of work.

Given that the majority of people affected by these changes are already in the workforce, adult learning systems play a key role in up- and re-skilling to meet new skill needs. Yet, this is also where the challenge lies. Today as in the past, adult learning remains the “weak link in the lifelong learning agenda” (OECD, 2005[1]). In many countries adult learning systems lack focused policy attention and resources, putting in doubt their readiness to address future skill challenges. In contrast, countries with advanced adult learning systems have understood their usefulness in supporting economic and social adjustment processes (Desjardins, 2017[2]).

A key challenge is that adult learning systems are difficult to define and delineate. They consist of a range of sub-systems with different actors, objectives, inputs, activities and degrees of organisation, ranging from opportunities to acquire formal basic and general education, through non-formal learning in the workplace, to leisure-oriented liberal adult education1 (Desjardins, 2017[2]). Each of these sub-systems has further overlaps with other areas, such as initial education or wider labour market policy. Future-ready adult learning systems therefore require improving the readiness of all elements of the system, and improved coordination between them, to prepare people for the future world of work.

This report focuses on adult learning that is job-related, i.e. adult education and training that is expected to have some effect on performance and productivity at work. Job-related adult learning subsumes: 1) formal education and training, which leads to a formal qualification; 2) non-formal education and training that doesn’t necessarily lead to formal qualifications, such as structured on-the-job training, open and distance education, courses and private lessons, seminars and workshops; and 3) informal learning, i.e. unstructured on-the-job learning, learning by doing or learning from colleagues.

1.2. Urgency – results from the PAL dashboard

The PAL dashboard identifies the main drivers behind the degree of urgency for adult education and training, including current skill levels, demographic change, automation and structural change, and globalisation. The full set of indicators used to assess the urgency of training need across countries is shown in Table 1.1.

Table 1.1. Urgency – PAL indicators


Adult skills

Population ageing

Numeracy and/or literacy skills

% of adults with low literacy and/or numeracy proficiency (0/1 level)

Old-age dependency ratio 2015

Population aged 65+ as % of population aged 15-64, 2015

Problem-solving skills

% of adults with low problem-solving skills in technology-rich environments

Old-age dependency ratio 2050

Population aged 65+ as % of population aged 15-64, 2050

Automation and structural change


Risk of automation

% of workers facing a significant risk of automation (>50%)

Trade openness

Total trade (export + import) as a % of GDP

Structural change

Lilien index (structural change over last 10 years – sectors)

Trend in trade openness

10-year change in total trade (export + import) as a % of GDP



Workers engaged in meeting foreign demand

% of business sector jobs sustained by foreign final demand



Trend in workers engaged in meeting foreign demand

10-year change in the % of business sector jobs sustained by foreign final demand

Note: See Annex B for details on the data sources used for each indicator.

The PAL dashboard suggests that there are large differences between countries in the urgency of getting their adult learning systems ready for the future (Figure 1.1). Across the different sub-dimensions, the highest urgency can be observed in Portugal, followed by Lithuania and Spain. The lowest levels of urgency are observed in Australia, New Zealand and Norway. It should be noted however that levels of urgency are relative and even countries with low scores may still have strong reform needs. The following sections describe the performance of countries on specific indicators.

Figure 1.1. Results of the Urgency dimension
Urgency index (0-1)

Note: The index ranges between 0 (least urgent) and 1 (most urgent). Switzerland was excluded due to missing data.

Source: OECD. See Annex B and C for details on data sources and methodology.

1.2.1. Adult skills

Current adult skill levels are one of the key drivers behind the need for future-ready adult learning systems. A high-skilled workforce is essential for firms and countries to reap the benefits of technological advances and take advantage of possibilities to move up global value chains. Better skills are also important to protect adults from any potentially negative effects of automation and globalisation, notably by enabling participation in lifelong learning. Despite the importance of up-skilling adults with low skills, this group is heavily under-represented in adult learning (see Chapter 2), not least because they do not meet basic entry requirements. In addition, low-skilled adults are often not employed in a standard full-time employment relationship making access to training more difficult (OECD, Forthcoming[3]). Improving basic skills has the potential to put adults on a virtuous circle of further skills acquisition through their work lives.

Therefore, the dashboard includes indicators of the proportion of adults who lack basic skills in different countries (Figure 1.2). On average across the countries for which data is available, 26% of adults are able to complete only very basic reading and/or mathematical tasks. An even higher number (37%) of adults have no or very limited skills in using digital technology and communication tools to navigate and solve problems in their everyday life (so-called digital problem solving skills). In both of these dimensions, Chile and Turkey are outliers with particularly high proportions of adults with low skills and in need of up-skilling opportunities. They are followed – with some gap – by a number of Mediterranean countries. At the other end of the spectrum, Japan displays the smallest share of adults with low literacy and/or numeracy skills (9%), while Norway has the smallest share of adults with low digital problem solving skills (22%). This illustrates that all countries have a sizable population of adults with low basic skill levels, although with considerable differences in the size of the challenge they face.

Figure 1.2. Adults with low basic skill levels
% of adults aged 25-64

Note: Adults scoring at or below level 1 in literacy and/or numeracy. Digital problem solving refers to adult with no computer experience, who failed the ICT core test or scored at level 1 or below in PIAAC’s problem-solving in technology-rich environments. Belgium refers to Flanders only, United Kingdom to England and Northern Ireland.

Source: PIAAC (2012, 2015).

1.2.2. Automation and structural change

Structural change creates employment opportunities in some occupations and industries and decreases opportunities in others, and thereby changes the types of skills that are needed in the labour market. In the past decade, some countries have seen their economies transform more rapidly than others, implying a greater need to re-train workers. The dashboard captures these changes through the Lilien index, which measures the extent to which employment in different sectors of the economy grows or shrinks at different speeds (OECD, 2012[4]). The higher the score on the Lilien index, the more profound the transformation of the economic structure between 2005 and 2015. Figure 1.3 shows that Ireland, Korea, Lithuania and Spain have experienced the biggest changes over the past decade, while a number of countries have seen relatively small changes to their employment structure, including the Czech Republic, the United States and the Netherlands.

Figure 1.3. Structural change 2005-2015
Lilien Index

Source: OECD calculations based on OECD national accounts database.

For many countries, the biggest changes to their economic structure may be yet to come. There is a vibrant public debate about the impact of technology on jobs in the future. It is likely that cutting-edge technology will be able to automate more and more complex tasks at accelerating speed, fundamentally changing the skills that are required for many jobs. Some jobs may even become entirely redundant. Recent OECD research suggests that, should current cutting-edge technology become widespread, 32% of jobs across the 32 countries analysed are likely to see significant changes in how they are carried out and a further 14% of jobs could disappear altogether (Nedelkoska and Quintini, 2018[5]). This risk of significant job loss and change in job tasks as a result of new technologies, and hence in the skills needed in the labour market is captured in the dashboard by the share of jobs with significant automation risk, i.e. jobs with more than 50% automatable tasks (Figure 1.4). This share varies markedly between countries from more than 60% of jobs in Lithuania and the Slovak Republic to less than 40% of jobs in the Nordic countries, New Zealand, the United Kingdom and the United States.

Figure 1.4. Risk of job automation
% of workers facing significant risk of automation (>50% of tasks at risk of automation)

Note: Significant risk is defined as having a risk of automation over 50%, low risk as having a risk of automation of at most 50%. Belgium refers to Flanders only, United Kingdom to England and Northern Ireland.

Source: Nedelkoska and Quintini (2018[4]) using PIAAC data (2012, 2015).

1.2.3. Globalisation

An increasingly globalised world has a profound impact on the skills that are in demand in the labour markets of advanced economies. Globalisation can lead to greater specialisation and hence different skill-sets needed in the labour market. In fact, evidence from advanced economies suggests that increasing participation in global value chains raises the demand for those high-level skills which are needed to specialise in high-tech manufacturing industries and in complex business services (OECD, 2017[6]) Increasingly global value chains can also led to jobs being offshored, especially at the low-end of the skills spectrum. However, it is difficult to disentangle the effects of technological progress, automation and globalisation on driving these changes, as these megatrends mutually reinforce each other.

Countries differ in the extent to which they are integrated into global-value chains. One indicator of such integration is the percentage of business sector employment that is sustained by foreign, rather than domestic, demand. This ranges from 15% of business sector employment in the United States to more than 50% in the small open economies of Hungary (59%), Ireland (65%) and Luxembourg (81%). Data also shows that economies are becoming increasingly integrated in global value chains: between 2004 and 2014 the percentage of business sector employment sustained by foreign demand increased by more than 10% on average.

Other indicators of globalisation included in the scoreboard show similar patterns. Trade openness, defined as exports and imports as percentage of GDP, is highest in Hungary, Ireland, Luxembourg and the Slovak Republic. It is lowest in Australia, Japan and the United States. As the integration into global value chains, trade openness has increased over the past decade, with supposed impacts on the skills needs of the labour market and hence the adult learning system.

Figure 1.5. Integration into global value chains
% Business sector jobs sustained by consumers in foreign market

Note: Business sector jobs sustained by foreign final demand as percentage of total business sector employment.

Source: OECD Science, Technology and Innovation Scoreboard 2017.

1.2.4. Population ageing

Finally, an ageing population is an often overlooked driver of changing skill demand and supply and adult learning policy, due to its slow-moving nature. However, population ageing impacts training needs in a number of important ways. First, it increases the need for individuals to maintain and update their skills over the life-course in the context of longer working lives. Furthermore, the retirement of large cohorts can lead to significant shortages of qualified labour in some countries; a gap that can be filled through training of the existing workforce amongst other measures. Finally, population ageing is likely to contribute to further shifts in the structure of the economy, as demand for goods and services changes, an example being an increased demand for health and elderly care services (OECD, 2017[7]).

According to United Nations (UN) population statistics projections, all countries included in the PAL dashboard will see a significant increase in ratio of the elderly population (aged 65 and over) to the working-age population (aged 15-64). Some countries that already have a high share of older people are projected to see this share increase further. For example, in Japan today, there are two adults aged 65+ for every five adults in the working-age population. In 2050, this is projected to rise to three older adults for every four adults of working age. Greece, Korea and Spain are forecasted to experience the greatest demographic change and hence the greatest additional pressure on their adult learning systems (Figure 1.6).

Figure 1.6. Population ageing
Population aged 65+ as % of population aged 15-64

Note: Projections are based on the medium scenario of possible future growth of the world population

Source: UN world population prospects (2017)


[2] Richard Desjardins (ed.) (2017), Political Economy of Adult Learning Systems, Bloomsbury Academic, London, New York.

[5] Nedelkoska, L. and G. Quintini (2018), “Automation, skills use and training”, OECD Social, Employment and Migration Working Papers, No. 202, OECD Publishing, Paris,

[7] OECD (2017), “Future of Work and Skills. Paper presented at the 2nd Meeting of the G20 Employment Working Group”, (accessed on 18 July 2018).

[6] OECD (2017), OECD Skills Outlook 2017: Skills and Global Value Chains, OECD Publishing, Paris,

[8] OECD (2016), Getting Skills Right: Assessing and Anticipating Changing Skill Needs, Getting Skills Right, OECD Publishing, Paris,

[4] OECD (2012), Structural Change and Growth: Trends and Policy Implications, OECD Publishing, Paris, (accessed on 23 August 2018).

[1] OECD (2005), Promoting Adult Learning, (accessed on 26 July 2018).

[[3] OECD (Forthcoming), Employment Outlook 2019, OECD Publishing, Paris.


← 1. This report focuses on job-related adult learning and hence excludes sub-systems that have no direct labour market relevance.

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