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Are EU skills policies flying half-blind? How to better measure skills in the digital transformation
by Clara Behrend, Mathias Cuypers | 08.02.2023

Have you ever considered which data the New Skills Agenda for Europe needs and which is currently used? You would expect this data to be reliable and comprehensive. Only in such a case can stakeholders take appropriate decisions. However, when looking at the data, indicators and measures available and used to support the monitoring of skill requirements for digital transformation, we can say that these policymakers and stakeholders are driving partially blindfolded. To fully understand changes in skill demand and resulting needs for adapting the skill supply, data collection, use, and preparation need to be improved.

The BEYOND 4.0 project has considered what is needed to bring some eye-sight to the skills debate.

The gaps in current policy thinking and in recommendations for future skill-related policies that facilitate digital inclusion and digital transformation can best be overcome by focusing on the skill requirements for workers, as well as skill supply.

The demand and supply of certain occupations and qualifications are not enough to effectively understand changes in skill requirements and skill supply. Instead, you need a look at skill categories that represent the types of skills needed and used in a job or possessed by workers. These need to be measured by surveys that are recent and allow international comparisons. All parties need to understand that it is not only the separate skill categories (e.g. methodological, personal, social skills) that need to come into focus but more and more the interactions of these categories. Especially, since digital skills are mainly used in combination with other categories of skills. Skill use, skill needs and needs for the adaptation of training and education require this new perspective.

Our study (report D6.3, based on tasks 6.1-4) suggests one set of requirements for adequate skill data that are used as the yardstick against which existing data on skills were examined so that gaps in and requirements for better data are identified, and one resulting set of requirements for the improvement of the current data on skills that policymakers and stakeholders in the domain of the New Skills Agenda need to consider.

What do we suggest for collecting the right and best skill data?

  • a distinction should be made between skills and related concepts. Related concepts, such as qualifications or occupations, should not be used as a proxy for skills, if possible.
  • In the comparison between job-based and task-based approaches in the debate on skill change, we consider task-based approaches to be better suited to reflect a dynamic understanding of job profiles.
  • To understand the reasons for skill changes, overarching dimensions are relevant, such as technological change and work organisation.
  • Skills can be distinguished according to their content, which is why it is meaningful to have a distinction between several skill categories, such as the BEYOND 4.0 categorisation.
  • Interacting skills, that is, digital skills in combination with other skills categories, required to do a specific task, need to be measured.
  • Meeting skill needs is a collaborative task of different actors. All perspectives must be understood and reflected in the data (employers, workers, educational side).
  • The variety of interests is also related to the skill-matching perspective. Data on both, skill needs and skill supply of the actors, should be collected. While doing so, a distinction between the various types of skill mismatches must be considered, and data on all of them are relevant.
  • The disaggregation of data on skill gaps and skill supply for groups at risk of exclusion (education level, age, gender, recent migration from another country, disability) of the labour market should be possible.
  • For the European skills intelligence to be useful, harmonised data for all European member states should be available.

We have an abundance of data on occupations and qualifications to measure supply and demand in the labour market. However, data on skill categories needed for jobs and used in the workplace as well as skills taught in education, are missing and hardly measured. Policymakers and other stakeholders only see half of the skills’ reality when taking decisions on skill strategies, education and training.

On this basis, several requirements for better data are proposed to European data producers and data agencies. It is recommended to …

  • … consider the purpose of what the data is going to be used for and by whom.  To this end, all relevant stakeholders must be included already in the process of designing data collection skills.
  • … provide (or at least map sources for) more fine-grained data at the sector and regional levels and ensure that for each perspective and topic (e.g. skill shortages, skill gaps, skill demand and skill supply), there are data available at an adequately disaggregated level in European quantitative surveys that facilitates the respective unit of analyses at the country, regional, sectoral, company levels.
  • … collect quantitative and other types of data that allow for within-company insights. Ideally, employers and employees from the same companies need to be asked within one survey, so that company strategies can be directly linked to changes in the jobs of employees in the companies.
  • … collect and/or map non-survey data on skills in a centralised way.
  • … further, facilitate linking existing datasets through better coordination and harmonisation at the European level. One important use is the linking of employer surveys with employee or labour-force surveys. Also, the linking of information on the skill needs of employers and/or employees with data on the supply side of skills, such as qualification contents, skills assessment surveys and graduate surveys, is important.
  • … consider expansion on the sample sizes of EU surveys, especially employer surveys, to allow for better analysis while maintaining data protection and statistical validity when linking datasets. Here, policymakers and data producers on national and European levels need to work together to find a viable solution.
  • … provide a more flexible solution to data protection in datasets, where researchers can choose the trade-off of data detail, maintaining data privacy while gaining a level of detail for the specific research topic.
  • … align measurements between surveys better to enable the comparison of measurements of the same subject between countries and EU-wide.
  • … clearly define the skills and skill concepts measured and differentiate between tasks, skills, and related measures such as qualifications and occupations.
  • … standardise skill categories comparable to other international standardisations and provide a continuous measurement of skill needs and skill supply using this categorisation.
  • … directly assess skills. Surveys using this method, which provides the best validity of data measuring, such as the PIAAC survey, need to be further funded and elaborated for more skill categories.
  • … to provide more timely data. One potential solution to collecting more timely data on skills could involve the inclusion of questions on skills needed in jobs and on current skills of workers in surveys that are conducted more frequently, such as the EU-LFS or the EWCS and the ECS.
  • … to measure interacting skills from different categories needed in combination to do one task. It is not yet possible to identify the interaction of skills with existing data except for the problem-solving skills in technology-rich environments testing of the PIAAC. Data producers could, for example, add questions to surveys that ask about specific combinations of skills needed within jobs or ask which tasks respondents have and which skill categories they need to do them.
  • … to provide data on skills for an inclusive future. Policymakers need to support according to processes and data producers need to include according to questions into skill surveys (most already include the measurement of educational attainment, age, and gender while immigration status and work-relevant disabilities and chronic illnesses are not asked for).
  • … to tailor the presentation, dissemination and accessibility of data to suit the needs of the different key stakeholders. Good examples are the Skills Intelligence Platform and Europass.