Skill mismatch

The research issues. Economic theory suggests that labor market deregulation is of key importance to competitiveness, growth and employment. The intuition is that as firms are left free to hire and fire at low costs, they can improve the average quality of matches between workers and jobs, move to most promising specializations, increase their productivity and profits, and hence improve the overall competitiveness of the economic system, its growth potential and employment score. Building on this, labor market deregulation has become the hallmark of labor market policies during the last decades in most OECD countries. Key to this way of reasoning is the unproved assumption that workers’ turnover is beneficial to the quality of matches between workers and firms. This is an empirical matter for which the European Center for the Development of Vocational Training (Cedefop, 2009) has identified five priorities: (i) improve measurement of skills and skills mismatch; (ii) examine the persistence of skill mismatch and its impacts; (iii) improve understanding of skill mismatch processes, its dynamics and the consequences of skill mismatch; (iv) focus on skill mismatch for vulnerable groups on the labor market; and (v) improve data availability and use. LABOR is active on this wide topic and aims to propose an innovative measure of skill mismatch, which holds the promise of accounting for the complex, dynamic and continuous processes of human capital accumulation, depreciation and upgrading that unfold in the labor market well beyond formal education or, as it has been recently proposed, general skills in terms of literacy and numeracy. This calls for the innovative use of specific types of micro data on which LABOR holds a specific expertise, known as longitudinal matched employer-employee (LMEED), as an intermediate step to identify the effect of changes in the employment protection legislation on the quality of matches.

Team. Fabio Berton, Francesco Devicienti and Matteo Richiardi.

Funding. The project is funded under the chapter for ‘Excellent Young PIs’ by the University of Torino and Compagnia di San Paolo Bank Foundation for the period from July 2015 to June 2017.