Employment and Social Developments in Europe (ESDE) 2023

3. Assessment of policies to address skill shortages and improve matching

3.4. Building labour market resilience: anticipating and matching skills policies

Skills intelligence meaningfully blends quantitative and qualitative information on labour market needs and skills, allowing skills anticipation. Several tools and methods ⁽³⁵³⁾ are already in place across the Member States ⁽³⁵⁴⁾ that allow for methodologically accurate and relevant skills anticipation outputs (Table 3.5).

The main approaches use skills assessments/surveys and labour market indicators to map the current state of skills demand and supply. Skills forecasts typically apply an economic model where skills are proxied by occupations and/or qualifications. These provide expected developments of future skills demand and supply, based on assumptions informed by past trajectories. Technological and skills foresight activities are used to gain a better understanding of the possible futures that may lie ahead for societies, economies and labour markets. Including more qualitative methodologies allows for exploration of the steps needed to move towards the desired future. Less traditional methods are then necessary to deepen understandings of the implications of technological change and emerging skill needs. These include explorations of automated skills intelligence methods that use Big Data and AI-driven analyses (e.g. those building on online job advertisements), as well as patent data, scientific databases, and online course websites. ⁽³⁵⁵⁾

A growing number of Member States apply skills anticipation methods in their policy-making cycles (Table 3.5). ⁽³⁵⁶⁾ At least one skills anticipation method is used in each Member State, with some also implementing complementary methods. Skills forecasts remain the backbone of skills intelligence generation in most countries, run at national, regional, or even sectoral level, either regularly or periodically. 2019’s Skills Forecast continues to serve skills intelligence needs in Member States without their own regular forecasts, as well as complementing national sources by allowing harmonised comparison of information across Member States.

The global market is increasingly volatile and vulnerable to a wide range of factors and shocks, which may have prompted the recent uptake of foresight methods. Several Member States have expanded their foresight capacity to support their understanding of emerging skills needs in different scenarios/futures. As part of the shift towards more sophisticated approaches, Big Data-powered skills intelligence is rapidly expanding and is now broadly applied by PES. Some Member States resort to employer surveys to map sectoral trends, while graduate tracking or tracing surveys are a growth area to enable the understanding of skills supply and education-to-work transitions.

There is room for innovation and better addressing of local needs. This is true both of countries with skills intelligence systems in development and also those at a more mature stage. Next-generation skills intelligence needs to be more user-centred, focus on transitions (green, digital, others), and provide better insight into the links between all forms of learning and skills.