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

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

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 What should we be measuring?

There are many ways to measure skill mismatch:  Use of Beveridge curves (vacancies vs job seekers) o Using admin data o 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 How should we measure skill mismatch?

Each measure of skill mismatch has advantages and disadvantages

1. 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? 2. 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 What remains to be done?

3. 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? 4. 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 What remains to be done?

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 Conclusions