Skills Gap – Measuring skills mismatch

Skills supply (skills possessed by the labour force) and skills demand (skills demanded by employers) are central concepts in the economic analysis of employment structure and dynamics. The interaction between supply and demand has in turn led analysts to look into another dimension, skills mismatch — a concept that is rapidly gaining in importance for the economic and social policy discussion. At a macro level, skills mismatch relates to the gap between supply and demand for skills. At a micro level, skills mismatch — considered as a condition of workers, jobs or vacancies — can be defined in several ways: ‘vertical mismatch’(3), i.e. the mismatch between formal education and job requirements measured against a benchmark(4); or ‘horizontal mismatch’, for example mismatches between the worker’s field of education and job requirements. Mismatches have also been analysed in terms of over-skilling and under-skilling of workers(5). Finally, the fourth dimension, skills development, refers to training activities, particularly vocational training and the adult education system, that aim at reducing mismatches on the labour market.

Measuring the skills match is the most complex aspect of monitoring skills and human capital in an economic system. The analysis of mismatches must take into account the rise of qualification requirements that has occurred on labour markets over the last few decades. Jobs that today require a university qualification used to be filled with workers who had no such qualification 30 years ago. While the nature of many tasks has indeed changed, an increased demand for university qualifications also occurs due to the phenomenon called degree inflation or credentialism when employers require a qualification merely because there is a sufficient pool of graduates to draw from. In other words, the fact that a qualification is currently required for a job is not sufficient proof that certified skills are really necessary for that job.

Most studies in this area use derived and/or combined data based on existing indicators of skills supply and demand. For example, estimations of imbalances and skills matches are available from Cedefop’s macroeconomic skills forecasting exercise (and a related Occupation Skills Profiles project); from macroeconomic estimation exercises of skills mismatches based on EU-LFS or PIAAC; from the one-off European Skills and Jobs (ESJ) survey providing data on self-reported skills match; and from the European Working Conditions Survey (EWCS), which is limited to a few self-reported questions on needs for further training. At the same time, certain approaches assess skills match via dedicated variables in statistical surveys, either for employers or employees.


The possibility of compiling indirect measures of skills mismatch depends on the level of detail available from the collected data. Thus improvement of statistical measures of skills supply and demand would lead directly to a better capacity to assess the extent to which skills supply and demand match. Skills mismatches can be indirectly assessed across several dimensions:
• the match between the supply and demand for skills on an aggregate (country) level via, for example, ‘Beveridge curve’ analysis — a graphical representation of the relationship between the unemployment rate and the job vacancy rate measured as the number of unfilled jobs expressed as a proportion of the labour force;
• the match between the supply and demand of skills in terms of qualifications levels, orientation or fields of study for different socioeconomic groups and different economic activities;
• the match between the level of directly tested skills (using, for example, PIAAC) and the requirements of the job (as reported by employees, employers or a combination of the two);
• the match between the supply and demand for skills combining self-reported data from employees and employers working for the same enterprises via, for example, linked employee and employer surveys. This approach is being explored by Cedefop and the OECD (and is currently being discussed as a potential option for PIAAC Cycle II) but it has not yet been fully tested empirically.
The skills match analysis can be based either on normative measures (what qualifications are deemed by experts most relevant for a given occupation), or statistical measures (what is most common for a given occupation).

5. Weaknesses of existing skills statistics

Although the ESS and different EU entities already collect substantial data that could be used for assessment of skills systems in the EU, there seems to be scope for further development, mainly in the direction of harmonising skills-related concepts and operationalising them in data.

Thus a number of key gaps in existing data can be identified as follows.

Firstly, there is a lack of a commonly accepted methodology (i.e. specific statistical definitions, sources and methods) for capturing skills in data, which may yield to non-harmonised and incomparable statistics.

One of the challenges relates to defining types and levels of skills. A first commonly used distinction is between basic and advanced levels of skills (a more granular classification is given in the European Qualifications Framework, EQF)(42). Another way to discriminate between types of skills is to consider generic versus specific skills. In the domain of digital skills, the distinction is between ICT user versus ICT professional skills. Recently, much attention has been given to so-called soft or transversal skills, such as problem solving or social-emotional skills, including exploration by the OECD to capture transversal skills in PIAAC using personality trait assessment instruments. In addition, in studies based on Cedefop ESJ survey(43), only a few bundles of skills could be decoupled, since a large sub- set of them (e.g. communication, team working, problem solving etc.) tended to be evaluated similarly in terms of their importance.

Secondly, certain data weaknesses derive from a compromise between data coverage (level of detail, reflected in for example sample size restrictions or frequency of data collection) and data relevance. As a result, datasets that cover a high number of countries over longer periods produce rather aggregate measures of skills supply (mostly limited to labour force by qualifications in terms of educational attainment) and skills demand (mostly limited to employment by occupation), while data collections more specifically targeted at measuring skills are collected with a low frequency.

Thirdly, the availability of statistics on skills development presents important limitations. In this domain, one notable weakness is underrepresentation of indicators derived from business statistics. While social statistics on skills development are rather developed (though fragmented) in the ESS, business data about training offered by employers is represented in the ESS to a more limited extent. The only exceptions are the Community Survey on ‘ICT usage and e-commerce in enterprises’ that has information on companies that upgrade the ICT skills of their employees, and the CVTS that collects information on continuing vocational training in enterprises.

Chosen excerpts by Job Market Monitor. Read the whole story at Statistical approaches to the measurement of skills — The state of play in Europe within the European Statistical System – Product – Eurostat



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