6.5. Adaptability
The digital transformation has changed, and will continue to change, many aspects of work. Numerous jobs now involve computer use, and in the future, it is likely that people will need to adapt to working with computers in new ways, such as in “teams” comprising both human and artificial intelligence.
Learning new things, thinking creatively and problem solving are likely to be especially valuable traits for adapting to and gaining from workplace digitalisation. In all countries, people aged 25-34 score more highly in terms of readiness to learn and creative thinking than those aged 55-65. In Finland, the Slovak Republic, Slovenia and the Russian Federation, women generally score more highly than men, while in Belgium and Japan the opposite is observed. Age and gender-related differences are generally more considerable across countries than within individual countries; this underlines the role of cultural and societal factors in shaping personal characteristics and the need for tailored policy responses (OECD, 2017a).
Governments can play an important role in helping workers and employers to adapt to changes driven by digitalisation. In 2016, OECD governments spent almost 0.4% of GDP on active labour market policies, on average. Chief among these in many countries is training to help people gain the skills needed for work, including ICT skills. As the digital transformation continues, skills needs are likely to change as more routine tasks become automated (see page 6.1). Other schemes can help people find and try potentially suitable jobs.
The digital transformation also brings new business opportunities. Government support can help people start companies based around digital technology, although spending on start-up incentives is much less than on most other schemes, except in Spain and France.
Alongside the adaptability of workers themselves, and of managers who play a key role in determining how employers adopt new technologies and adapt to the digital transformation, social partners (trade unions and employers’ organisations) can also help in ensuring that workers and companies reap the benefits of technological change. Through social dialogue and collective bargaining, they can spread best practices in terms of technology usage and help employers to adapt working hours and the way work is organised. Furthermore, social partners can help to enhance labour market adaptability, for example by providing training and reskilling in the case of mass layoffs. On average, 32% of wage earners in OECD countries with the right to bargaining were covered by collective agreements in 2016. In a time of rapid change, and despite declines in membership and coverage, the role of social partners in finding tailor-made solutions, managing transitions, and anticipating and filling skills needs may be more important than ever.
OECD governments spend 0.13% of GDP on training for unemployed people and workers at-risk of involuntary unemployment. The digital transformation may significantly increase the need for support.
Definitions
Readiness to learn is based on six PIAAC questions relating to inquisitiveness and investigation, desire to learn, and problem solving.
Public expenditure on active labour market policies relates to central and local public authority spending on schemes aimed at individuals who do not work but would like to do so, or who are at risk of involuntary job loss (“targeted persons”).
Training includes targeted institutional and workplace-based training.
Employment incentives include schemes where the employer covers the majority of the labour cost, and job rotation/sharing schemes where a targeted person substitutes for an employee for a fixed period.
Direct job creation relates to new jobs where the majority of the labour costs are funded by public funds for a limited period.
Placement and related services are typically provided by the public employment service or other publicly financed bodies. They include employment counselling, referral to opportunities for work, information services and so on.
Start-up incentives encourage targeted persons to start businesses or to become self-employed.
Collective bargaining is defined as “all negotiations which take place between an employer, a group of employers or one or more employers’ organisations, on the one hand, and one or more workers’ organisations, on the other, for determining working conditions and terms of employment; and/or regulating relations between employers and workers; and/or regulating relations between employers or their organisations and a workers’ organisation or workers’ organisations” (ILO, 1981).
Measurability
The readiness to learn and creative thinking indicator was developed using exploratory factor analysis. It relies on six PIAAC items related to openness to new experiences and creative thinking, including “relate new ideas into real life” and “like learning new things”. Detailed methodology in Grundke et al. (2017).
OECD data on public expenditure on labour markets are based mainly on information about individual labour market programmes appearing in state budgets and the accounts and annual reports of bodies implementing the programmes.
Source: OECD (2017a), calculations based on Survey of Adult Skills (PIAAC) Database, June 2017. See 1.
← 1. The readiness to learn and creative thinking indicator is built using exploratory state-of-the-art factor analysis. It relies on six items related to openness to new experiences and creative thinking. The detailed methodology can be found in Grundke et al. (2017).
The data for the following 23 countries from the first round of PIAAC refer to the year 2012: Australia, Austria, Belgium (Flanders), Canada, Czech Republic, Denmark, Estonia, Finland, France, Germany, Ireland, Italy, Japan, Korea, the Netherlands, Norway, Poland, the Russian Federation (excluding Moscow), Slovak Republic, Spain, Sweden, the United Kingdom (England and Northern Ireland) and the United States. Data for the remaining countries refer to 2015 and are sourced from the second round of the first wave of the PIAAC survey.
For the Russian Federation, the PIAAC sample does not include the population of the Moscow municipal area. The data published, therefore, do not represent the entire resident population aged 16-65, but rather the population of the Russian Federation excluding the population residing in the Moscow municipal area.
Source: OECD, Labour Market Programmes Database, October 2018. See 1.
← 1. For Greece, Italy, Luxembourg and Spain, data refer to 2015.
OECD data on public expenditure on labour markets are based mainly on information about individual labour market programmes appearing in state budgets and the accounts and annual reports of bodies implementing the programmes.
Public expenditure on active labour market policies relates to spending by central and local public authorities on schemes aimed at the following “targeted persons”: unemployed (i.e. not in work, actively seeking), inactive (i.e. would like to work, not actively seeking) or employed but at risk of involuntary job loss.
Placement and related services are typically provided by the public employment service or other publicly financed bodies. They include employment counselling and case-management, referral to opportunities for work, information services and so on.
Training includes targeted institutional and workplace-based training of targeted persons.
Employment incentives include incentives where the employer covers the majority of the labour cost, and job rotation/sharing schemes where a targeted person substitutes for an employee for a fixed period.
Direct job creation relates to new jobs where the labour cost is majority funded by public funds for a limited period.
Start-up incentives encourage targeted persons to start businesses or to become self-employed.
Source: OECD, Collective Bargaining Coverage Database, October 2018. See 1.
← 1. For the Czech Republic, Denmark, Finland, Italy, Korea, Portugal, Slovak Republic, Slovenia and Sweden, data refer to 2015 instead of 2016.
For Estonia, data refer to 2001 and 2015.
For France, data refer to 2004 and 2014.
For Greece, data refer to 2013 instead of 2016.
For Hungary, Ireland and Luxembourg, data refer to 2014 instead of 2016.
For Israel and Poland, data refer to 2012 instead of 2016.
For Latvia, data refer to 2002 instead of 2000.
For Lithuania, data refer to 2002 and 2015.
For Mexico, data refer to 2012.
For Norway, data refer to 2002 and 2014.
For Switzerland, data refer to 2001 and 2014.