Across the EU, 21% of workers are over-qualified and 25% report being over-skilled for their current roles, with 10% being under-qualified and 9% reporting being under-skilled (Do Adults Have the Skills They Need to Thrive in a Changing World?: Survey of Adult Skills 2023). These skills mismatches, jointly with skills shortages, can have an impact on economic growth, as they limit businesses' productivity (“Labour Market Mismatch and Labour Productivity: Evidence from PIAAC Data” and “Adult skills and productivity: New evidence from PIAAC 2023”), and decrease job satisfaction and wages for mismatched workers (Do Adults Have the Skills They Need to Thrive in a Changing World?: Survey of Adult Skills 2023).
Over-qualified workers in EU countries typically face a 11% wage penalty compared to their well-matched peers in the same occupation and industry, and they are nearly 3 percentage points less likely to report high life satisfaction. Over-skilled workers earn 2% less than their well-matched counterparts, while under‑skilling is linked to a 0.2-percentage point decrease in life satisfaction (Do Adults Have the Skills They Need to Thrive in a Changing World?: Survey of Adult Skills 2023).
The success of interventions to tackle these skills mismatches and shortages depends on having accurate and comprehensive data about current and future skill needs. To support governments in this endeavour, the present report analyses national and whole-of-economy Skills Assessment and Anticipation (SAA) exercises, identifies the key design features of these exercises, and provides options and examples for each feature. In addition, it looks at how the use of the results of these exercises may be fostered in higher education.
The report relies on an analysis of 17 SAA exercises in Australia, Austria, Canada, Estonia, Finland, France, Germany, Hungary, Ireland, Italy, the Netherlands, Singapore, Slovenia, Spain and Sweden. It is based on new evidence gathered primarily through desk research, supplemented with interviews with international experts and institutions. The examples reviewed provide a good overview of the different design features of SAA exercises.
Key findings include:
SAA exercises can focus on different units of analysis, have varying geographical and sectoral coverage, and consider different time horizons, with choices dependant on the exercise’s objective. While obtaining information on specific skills shortages and mismatches seems desirable, most exercises rely on occupational analysis since such information is more easily obtained. Such results are often further matched to job requirements to translate the information into qualification or skill shortages. However, compared to directly analysing skill needs, this risks missing changes in skill needs within occupations.
SAA use a range of methodologies, both quantitative and qualitative. Quantitative approaches generally offer more consistent and comparable results, and can be more easily replicated. However, they tend to be more suited to identifying labour shortages and surpluses than skill mismatches, as information on skills, particularly supply, is limited. Recent innovations in SAA have focused on identifying skills demand using big data, such as job postings. However, identifying skills supply is more complex, particularly for skills provided by higher education. Skills developed in higher education programmes are often defined at the institution level, with the documentation of such skills not being as formalised as in other education and training levels, such as Vocational Education and Training (VET), where skills developed are generally aligned with learning outcomes of qualifications in the National Qualification Framework (NQF). Efforts are underway using new technologies to identify skills supply from Higher Education Institutions’ (HEIs’) documentation, such as programme descriptors. Quantitative approaches may also miss important information not yet visible in the data and therefore harder to model, such as technological innovations that will have an impact on skill requirements at work. Quantitative analysis is therefore frequently complemented with qualitative information, either as an input to the SAA exercise or to validate the quantitative assumptions, methodology, or results.
Given the extensive information required and the multiple potential uses of SAA exercise results, it is crucial to engage as many relevant stakeholders as possible. This serves to validate the results but also ensures the exercise meets their needs and that they contribute with relevant information.
Implementing SAA exercises is costly and often involves the involvement of multiple experts, making the availability of sustainable funding crucial. Costs can be minimised by leveraging existing work and avoiding duplication. Many SAA exercises are integrated into a wider SAA system, where a core exercise is subsequently developed to better meet the diverse needs of different users. This can happen, for example, when expert groups further develop results for a specific region or sector or when core exercise results are matched with relevant higher education information to produce higher education-specific information.
SAA exercises can serve multiple policy purposes, from training and education planning to industrial planning and migration. In higher education, SAA exercises typically inform the number of places available and the content of higher education programmes as well as the field choices of prospective students. Regulatory approaches, such as quotas or basing programmes on qualifications included in an NQF, and financial incentives on higher education supply or demand, can promote the responsiveness of higher education provision to SAA results. Career guidance or information provision can encourage prospective students to take SAA results into account when making their choices.
High-performing SAA systems generally involve multiple SAA exercises providing information for different time horizons and units, which mostly build from a core SAA exercise and high stakeholder engagement. They combine quantitative and qualitative methods to make the most of both approaches and ensure the use of SAA outputs by presenting them clearly and by supporting SAA users in accessing and understanding the information.