TRAILS · Enabling data analytics for actions tackling skills shortages & mismatch

TRAILS proposes a holistic approach to identifying the conditions for skills gaps and shortages across European countries and to contributing to policy actions. Its approach involves all conceptual and theoretical models of optimal skills match and utilization by workplaces, new methodologies for measuring skill gaps, and understanding optimal ways to skills upskilling and reskilling. It involves novel primary datasets, state-of-the-art big datasets from administrative records matched to firms and workplaces, innovative inquiries with large secondary datasets that are merged with taxonomies and other sources to enable pathbreaking analyses, field and stated preference experiments, along with boost samples of groups of particular interest, such as platform workers and other stakeholders. All these elements will produce a canvas that enables a new framework for identifying the conditions and elements required for developing skills matched to the needs. It will allow the inspection of relevant behavioural, societal, and cultural dimensions affecting individuals' participation and choice of training and learning programmes. Moreover, the project will propose aims and objectives for basic and transferable skills that are most conducive to European labour mobility towards the green and digital jobs of the future. Finally, the project will propose ways to optimize the use of new technology-enhanced ways to bridge the gap between labour supply and demand in matching skills to needs. Via the usage of AI methods and the large-scale piloting, the project will propose ways that empower skills profiling and intelligence to raise awareness of training programmes matched to the needs of individuals and the jobs of the future for European labour markets. Ultimately, a TRAILS web-based tool will be developed to bring together new datasets and insights for policy makers and to raise citizens’ awareness regarding transferable skills and training programmes.