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The skill matching challenge: Analysing skill mismatch & policy implications

How to measure skill mismatch is a core concern. Three alternative methods have been used: systematic job evaluation (objective measure), worker self-assessment (subjective measure) and empirical method (where data sets do not contain a direct question on the phenomenon). Each method has weaknesses. The report argues that over and underskilling may be superior measures to over and undereducation, especially if we are concerned with potential welfare losses. Skills obsolescence can be measured in several ways, but little consensus exists on which method is the most appropriate, while few data sets contain questions that can be used to assess skills obsolescence. Good data are probably the most crucial prerequisite to supporting timely, effective and evidence-based skill mismatch policies. The current shortcomings of data sets may take years to remedy. Ideally, new matched employer-employee panel data, with information on labour demand and supply, should be developed. Collecting these data is very expensive and several years of data are needed for full research potential. Individual or household data can be used instead, but with a comprehensive coverage of the various elements of mismatch, which is not currently the case. Remedying existing data sets is cheaper than developing new ones. An alternative cost-effective method for collecting Europe-wide data on mismatch could be a new module containing questions on mismatch, possibly introduced in several existing European large panel surveys at regular but infrequent intervals (such as once every three years). We support the view that alternative means of data collection should also be considered for issues that may be too detailed to be informed through conventional interviews.