Skills - Experimental statistics
Why do we need indicators about skills mismatches?
The European Commission communication 'A New Skills Agenda for Europe' defines among others policy priorities and sets out actions to be undertaken with the aim to make better use of people's existing skills. It should be ensured that the skills available in the labour market match the needs of businesses and the economy.
This undertaking includes finding ways to improve the matching between skills and labour market needs as well as bridging the gap between education and work. Skills mismatch indicators should measure the gap between demand and supply of skills (macro-level) as well as conditions of workers, jobs or vacancies (micro-level).
However, currently no official statistics and indicators for measuring skills mismatch exist. There is a growing interest in this type of statistics and in response to this demand, Eurostat proposes two new indicators as a first attempt to measure skills mismatch using ESS data.
Why are these indicators published as experimental statistics?
These statistics are published as experimental statistics because no general agreement on the way to measure skills mismatch exists. Firstly, it is difficult to compare the supply and demand of labour because the 'supply' comes from the side of individual persons and the 'demand' from the side of businesses. Secondly, proxies like level and field of educational (supply side) and rates /occupation (demand side) need to be used and the use of these proxies could be challenged.
How are the skills mismatch indicators produced?
The following two skills mismatch indicators are provided:
This indicator aims at understanding how many high-skilled persons (meaning persons who have completed tertiary education level based on the ISCED classification) are employed in occupations (based on the ISCO classification) that do not require tertiary education. Data are broken down by economic activities (based on the NACE classification).
This indicator aims at understanding how many employed persons are working in occupations (based on the ISCO classification) that do not correspond to the field of education they have attended (based on the ISCED-F classification). Data are broken down by field of education.
The indicators are derived by combining available figures using EU Labour Force Survey (EU-LFS).
The use of a single source ensures consistency, comparability and reliability, and the methodology behind is based on the literature in the field (‘The skill matching challenge: Analysing skill mismatch and policy implications’ – Cedefop 2010) and on existing empirical exercises (‘Employment and labour demand’ – Eurostat 2016 and EU-LFS ad hoc module 2000).
Access the statistics
- Detailed data on over-qualification rate by economic activity for the period 2008 to 2019.
- Detailed data on the rate of horizontal skills mismatch by field of education for the period 2014 to 2019.
To help Eurostat improve these experimental statistics, users and researchers are kindly invited to give us their feedback:
- Do you have any suggestions on how to improve the two indicators proposed by Eurostat to measure skills mismatch (for example regarding sample size problems for horizontal skills mismatch)?
- What other definitions or sub-indicators based on existing data collections could be used to analyse skills mismatch in Europe in order to better identify gaps between skills supply and demand? Would you recommend analysing other "mismatch dimensions" for policy purposes? If so, which ones?
- In your opinion, how can the two new indicators support policy makers in the area of skills? Which policy implications should be drawn from the data? It should be also taken into account that some of the mismatches existing in the labour market could be the result of a voluntary choice.