Skill mismatch: measurement issues and consequences for innovative and inclusive societies
Aree / Gruppi di ricerca
Partecipanti al progetto
Descrizione del progetto
Economic theory dating to back to the late eighties and early nineties suggests that labor market deregulation is of key importance to competitiveness, growth and employment (e.g. Bertola 1990, Rogerson 1987). As firms are left free to hire and fire at low costs, it is argued, they can improve the average quality of matches between workers and jobs, move to most promising specializations, increase their productivity and profits, boost their innovation activity, and hence improve the overall competitiveness of the economic system, its growth potential and employment score. Building also on the good economic performance of the US under Ronald Reagan and of the UK under Margaret Thatcher during the eighties, labor market deregulation was the prescription to heal “inflexible Europe” (Oecd 1994) from its low growth and high unemployment disease, and a policy actually implemented in a huge variety of countries around the world in the last decades (Berton et al. 2012). Labor market flexibility is still understood as a crucial issue to overcome the employment consequences of the current economic crisis. Key to this way of reasoning is the (unproved) assumption that workers’ turnover is beneficial to the quality of matches between workers and firms.
Understanding whether this is actually the case is the ultimate objective of the project, and requires to start with a better measure of the quality of matches, and hence of any potential mismatch. Match quality is usually defined and measured in terms of distance between workers’ education and the level of education typically required in their occupations (Freeman 1976). This makes the issue of mismatch equivalent to that of over- or undereducation (Büchel et al. 2003, Leuven and Oosterbeek 2011). This is a major area where the current state-of-the art in scientific analysis reveals theoretical and empirical gaps. While the traditional measure of mismatch appears a good approximation at labor market entry, its appropriateness fades away as working careers evolve. Indeed, as time goes by, the distance between a worker’s skills and those demanded by her current job may increase or decrease as a result of skill obsolescence and acquisition, on the worker’s side, and modifications in skill requirements due to technological change, on the job’s side. Central to the current project is the idea that a worker’s overall experience in the labor market contains crucial (but still unexplored) information to better identify the worker’s stock of skills, and hence to measure any relevant mismatch between the worker’s skills and those required by the current job. As a worker accumulates experience in the labor market, skills are acquired through a combination of learning-by-doing, of formal on-the-job training, of contacts with colleagues with experience in a similar job, of exposition to new working environments requiring different combinations of skills, and so on. Hence, a worker’s entire labor history provides the relevant information on a worker’s current stock of human capital, over and above the stock acquired at school. The existing literature does not recognize this fact, but for a few analysis of on-the-job formal training programs (Brunello et al. 2007), and their effects on productivity (Jones et al. 2009).
Within this framework, the PI and the research team aim at pursuing several objectives through this scientific project.
(1) The first goal is to propose an innovative and more comprehensive 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. This calls for the innovative use of specific types of micro data, known as longitudinal matched employer-employee (LMEED). The availability of LMEED, which generally originate from administrative records, allows the detailed observation of individual careers along with those of people working in the same firm at the same time. The set-up of appropriate LMEEDs represents a substantial part of the proposal, spanning from its start-up phase (see below) to the first part of the scientific project. The optimal metric used to compute the new measure of mismatch, which extends the traditional one, will be identified according to state-of-the-art literature on measures of distance.
(2) The second goal is the validation of the proposed measure of mismatch. In order to understand whether the improved measure performs significantly better than the traditional education-based one, we will test their consistency with the implications of well-established search and matching theories of the labor market.
(3) The third objective is to identify separately three dimensions of mismatch within the proposed measure: educational mismatch (corresponding to the traditional measure of skill mismatch), experience mismatch (i.e. the distance between a worker’s actual experience and her current job), and peers mismatch (i.e. the distance between each worker’s current job and her previous exposure to colleagues employed in similar sectors/occupations). Using balance sheet information (that we plan to integrate into LMEEDs), we can write firms’ profits as a function of (among the others) these three components of mismatch, and get an estimation of their relative importance under the hypothesis of maximization of profits.
(4) Fourth, the research will provide fresh empirical evidence on the central assumption that workers’ mobility across firms, sectors and occupations is beneficial to the job matching quality at the individual level. In this perspective, the key issue will be to prevent any potential bias due to reverse causality. To what extent, indeed, does workers’ mobility shape mismatch, and to which one, instead, is mismatch a determinant of workers’ mobility? Search-type models (e.g. Mortensen and Pissarides 1994) predict that wrong matches are the first to separate, but shorter-lived employment relationships (a likely consequence of deregulation) are in turn not neutral to the accumulation of skills (Acemoglu and Pischke 1999, Berton and Garibaldi 2012, Lazear 2009) and hence potentially to job match quality. The scientific project will address this methodological issue through the identification of quasi-experimental situations following exogenous shocks to workers’ mobility (e.g. through changes in the employment protection legislation). In these respect the proposed project appears particularly innovative, as much of the literature on the relationship between mobility and mismatch relies upon cross-sectional evidence and does not provide comparative analysis (Cedefop 2012).
(5) Finally, to the extent that part of the skill mismatch originates in the overall labor market experience of an individual, integral part of the research is a thorough understanding of how labor market institutions affect human capital formation and upgrading. Complementary to this understanding will be the analysis of industrial relations. Interaction among employers’ associations and unions, at the firm level in particular, shapes the way that labor market institutions at the macro level (e.g. employment protection legislation) affect economic outcomes. For this reason, whenever possible, data on local-level bargaining information will be integrated into LMEEDs, and used in the many steps of the proposed analyses.
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