In 2022, the ETF Skills Lab Network of Experts had its first live event at the University of Gastronomic Sciences in Pollenzo, titled “Foreseeing the Unpredictable: How new skills needs can be anticipated in times of high uncertainty and change”.

Participants included experts and practitioners from ETF partner countries, EU member states, and other organisations including Eurofound, UNIDO, and the Union for the Mediterranean (UfM).

This challenge was based on the fictional country of SkillLand. In this scenario, SkillLand is heavily reliant on imported fossil fuels for its energy needs, with a low share of employment in the energy sector and a low percentage of energy developed from renewable resources.

The team's challenge was to make SkillLand a global leader in renewable energy production and technology that attracts investment and generates a high number of jobs which are taken by skilled workers at all professional levels. This was to be done by imagining a system that allows to identify and develop sector-specific skills, so as to equip people with the skills they need to grasp the opportunities offered by promising and rapidly growing sectors.

Three main difficulties were identified:

• Information about new skills needs in the sector is poor, relying mostly on international literature that is not always in line with the specificities of the country. Even if specific analyses are carried out, they are not systematic and regular, and therefore quickly become obsolete.
• Cooperation among actors in the sector exists but is not optimal. While companies need to respond to market forces, the public administration is hampered by bureaucracy and “cultural” inertia. More openness to innovation is needed.
• Reforming or updating the education and training systems takes an average of 4 to 5 years, while companies need to have workers with the skills needed on renewable energies to be immediately deployed.

Team Red proposed a three-pillar approach to these issues:

1. Capacity-building by securing international funds to support knowledge transfer from academia to businesses, while also crowding in private funds through public funds guarantees.
2. Creating a department of Education & Outreach which would work beyond addressing skill needs by changing the perception of career aspirations. This aims to inspire youths and women to be active actors of change, attracting talents and entrepreneurs.
3. Developing a Data & Intelligence body as the focal point for sectoral data beyond skills and employment, and a central hub bringing together employers, employees, unemployed, educators and local/regional authorities.

Despite recent improvements in its economic situation, the fictional country QualiLand still struggles with high inactivity rates, particularly among women and vulnerable groups, and difficult labour market entry for young people.

The overall level of education has increased over the last few decades, the country remains characterised by a high skills mismatch on both the vertical and horizontal levels, with frequent overqualification and underqualification, and a loose connection between offer and demand. Participation in lifelong learning is modest, hindering the capacity of individuals and enterprises to adapt to the the rapidly evolving economy.

The team's challenge here was to ensure that everyone acquires the right skills at all levels of education, in line with specific labour market needs, allowing them to adapt to rapidly changing conditions and contexts. This was to be done by developing an integrated, dynamic, incentivising and recognisable system that facilitates relevant learning at all levels of education. 

Two main challenges were identified here:

• The very high mismatch relates first of all to young people. There is a problem in the labour market in the school-to-work transition, which seems to be triggered by the difference between skills taught and skills expected in the workplace. 

• High skills mismatch also relates to adult workers, especially given the fast changing working environments and the tasks needed to perform different jobs. In Qualiland, this might also be related to structural changes which led to the closing down of many firms that employed people with lower VET-level qualifications, and with new jobs being created mostly in service sectors requiring different sets of skills. The latter requires a higher level of socio-emotional skills as well as cognitive skills form all employees.

Two models were proposed here, both highlighting the need for a continuum between research, policy, education providers and the labour market. 

and

Etherland is a middle-sized country, with an economy in transition. In the last twenty years, it has experienced rapid development, based primarily on the improved educational outcome of its population. Employment has improved throughout the years, however at a much slower pace compared to the level of education.

Despite these improvements, Etherland still has moderate unemployment, especially among young people, many of whom emigrate in search for better jobs and lives. However, the country is rich in mineral resources and is developing well in the service sector. ICT is becoming prominent and the quality of life, the favourable weather conditions, the well-structured education system and the rather advanced health sector are factors that can potentially revert the emigration trend and even attract talents, if the right policies are put in place.

The Government of Etherland wants to take action and adopt policies that can attract and retain talents. To do so, it has set up a Group of Experts who must present concrete proposals based on scientific data that provide information on employers’ needs, the sentiment of workers, and other factors that can be gathered through data analytics.

The aim here is to make Etherland a country that is capable of developing policies to attract and retain talents based on the gathering, use and analysis of big data. This is to be done by developing education and employment policies that adapt to both the new emerging needs of the labour market and the new needs of individuals, thanks to the use of big data gathered through different methods and techniques.

Three main challenges have emerged:

• Representativeness. Especially in developing and transition countries, Big Data is weaker in capturing labour market developments, due to factors like low digital coverage of the labour market and high levels of informality. Moreover, given that the share of informal jobs tends to be lower for higher-skilled positions, job vacancy data in developing countries will tend to be more biased towards these high-skilled jobs.
• Securing resources and expertise. Big Data analysis requires specific technical and domain expertise and a dedicated hard- and software infrastructure. Developing such tools and infrastructure requires long-term vision and commitment over time. 
• Limited application in public policy. Before it can be used effectively, big data needs to be managed and filtered through data analytics - tools and methodologies that can transform massive quantities of raw data into “data about the data” for analytical purposes. Only then is it possible to detect changes that may be useful for policymaking. While Big Data analytics are widely used in the private sector, their application is still limited in the public policy domain.

Two models emerged here:

This hackathon challenge is but one example of the impact of working as a network. As Xavier Matheu de Cortada, Director ad interim, said in his opening speech:

“The role of networks is fundamental in our work supporting the skills ecosystem in partner countries, and helping education and training systems preparing for the future to meet labour market needs.”