Measuring skills mismatch: sheepskins or banana skins ? Mark Keese

1. Measuring skills mismatch: sheepskins or banana skins? Mark Keese Employment, Labour and Social Affairs, OECD Cedefop Workshop on “Skill Mismatch: Identifying Priorities for Future Research”, 30 May 2008, Thessaloniki

2. Outline of presentation • What should we be measuring? • How should we be measuring this? • What remains to be done?

3. What should we be measuring? • Concept of skill mismatch is straight forward: a gap between the skills required in jobs and the skills possessed by workers or the non-employed • Skill shortages or mismatch is not the same as labour shortages which may arise because of: • Limited geographical mobility • Ageing populations • Economic boom • But which type of skills should we be measuring? • Depends on the policy issues at stake • For use by public employment services • For career guidance • For assessing performance of education and training systems

4. How should we measure skill mismatch? • There are many ways to measure skill mismatch: • Use of Beveridge curves (vacancies vs job seekers) • Using admin data • Or survey data • Employer reports of recruitment difficulties • Matching of actual educational qualifications of workers with “average” or “required” qualifications in their jobs • Matching of measured generic skills (e.g. literacy, numeracy) with use of these skills in jobs • Self-assessment by workers of own skill adequacy

5. Each measure of skill mismatch has advantages and disadvantages

6. What remains to be done? • Need to do further work on improving and testing the theory behind skill mismatch • Policy implications will differ according to the theoretical basis for why skill mismatch arises and for why it may persist • Has a consensus been reached on whether over-education reflects “sheepskin effects” or other unmeasured skills, etc.? • How robust are our measures of skill mismatch over time, across countries and according to changes in method and? Or do we risk stepping on a banana skin by drawing firm policy conclusions on the basis of any one study? • Need to develop and improve comparisons of skill mismatch within countries • Better longitudinal data is required to examine persistence in mismatch at the individual level • Time series at the national level are required to examine trends over time and the impact of the business cycle

7. What remains to be done? • Develop and improve international comparability of skill mismatch measures to isolate the impact of institutional and policy settings: • Which features of national education and training systems are associated with better or worse outcomes in terms of skill mismatch? • Do strict employment protection rules, minimum wages or family-unfriendly employment policies generate labour rigidities, reduce labour mobility and worsen skill mismatch? • Need to improve our measures of skill • More direct measures of skill are required • Surveys of adult skills such as the OECD’s PIAAC survey will be of considerable help here • The PIAAC survey will not only test literacy and numeracy skills but will also provide measures of other generic skills being used in jobs

8. Conclusions • Measuring and understanding skill mismatch is a highly policy-relevant area for research • But first we need to answer the fundamental questions of what do we want to measure, for whom and why • We also need to carefully distinguish structural trends from “fads”, e.g. see the swings in the US policy debate about over-education, the bursting of the “dotcom” bubble along with expectations of severe shortages in IT specialists