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Skill mismatch: measurement issues and consequences for innovative and inclusive societies

Fondazione CSP
Ente finanziatore
Fondazione della Compagnia di San Paolo
Settore ERC
SH1_13 - Labour and demographic economics
01/07/2015 - 31/12/2017
Fabio Berton

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) The fourth objective of the project will then be to provide novel empirical evidence on the extent of skill mismatch in Europe. We will highlight the cross-national differences in mismatch at entry (where the education-based measure ought to be more relevant) and the evolution of country-specific mismatch at later stages of a worker’s career (where we expect the more comprehensive measure to be more informative). In order to do this a dedicated international network, representative of the skill formation regimes in Europe (see Busemeyer and Trampusch 2012), will be identified and established since the start-up phase. This (scientific) approach to the construction of the proper international network serves the twofold purpose (i) of overcoming potential limitations in terms of external validity that would arise were the analysis carried out on a limited or wrong set of countries, and (ii) of granting an easier access to the needed LMEEDs.

(5) Fifth, 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).

(6) The sixth objective hinges upon the impact that skill mismatch has on innovative societies. This requires a better understanding of the way an economy’s productivity and innovation activity is affected by the nature and extent of measured mismatch in the workforce. Arguably, the most relevant domain where the complex relationship between human capital formation and productivity unfold is at the micro level, and at the firm level in particular. This is also where our knowledge of the interrelationship between productivity, innovation and the skill formation of the workforce is particularly lacking. Existing research only considers the impact that educational mismatch has on firm productivity, with mixed results (e.g., Kampelmann and F. Rycx, 2012). Progress in the area can be obtained with an extended focus to the measurement of mismatch and the linkage of LMEEDs to detailed information on a firm’s financial information (balance sheets) and innovation activity (e.g. through the issuing of patents, investments in R&D activities, etc.), a strategy that we adopt in the project.

(7) 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. Comparative analysis will play a key role to this end. Indeed, commonalities emerging among the countries involved in the scientific project will be considered as the implications triggered by labor market reforms irrespective of the specific traits distinguishing the skill formation regimes under analysis. On the contrary, peculiarities will be a likely consequence of those country-specific institutions. 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.

The risks and the uncertainties involved in these activities are largely managed during a start-up phase, which will forerun the scientific project. During the start-up phase, the PI and the research team will:

(a) Set up a specific LMEED for Italy, merging balance sheet, innovation and firm-level bargaining information with individual workers’ and firms’ histories. A pilot study revealed that the construction of this ideal LMEED is successful in more than 95% of cases. Challenges in managing this extremely huge amount of information will provide the PI and the research team with a useful expertise to take advantage of during the scientific project.

(b) Improve the traditional education-based measure of mismatch using standardized data sources. Readily available sources like EU-SILC, for instance, allow improving education-based measures of mismatch by including some information on actual experience, i.e. of what we called above “experience mismatch”. In the view of the scientific project, this allows to preempt some of the methodological issues, in particular as far as metrics matters are concerned.

(c) Identify the ideal international network – representative of the skill formation regimes existing in Europe – by surveying the available LMEEDs and related access policies.  

On top of its groundbreaking scientific nature, the present project represents an added value also in terms of policy impact. The European Commission’s communication “New skills for new jobs” (European Commission 2008) and the Europe 2020 initiative “Agenda for new skills and jobs” (European Commission 2010), witness that skill formation plays a primary role within the European strategy towards Europe 2020. The European Center for the Development of Vocational Training (Cedefop 2009) has recognized this issue through the identification of five priorities for future research: (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. The impact and consistency of the proposed project on priorities (i), (ii), (iii) and (v) are evident from the summary description of the research activities and objectives given above. That on priority (iv) emerges once one considers that the distinction between an education-based approach to the measurement of mismatch and a wider approach incorporating information on workers' labor market career is not only likely to produce a more realistic picture of the economy-wide extent of skill deficiencies. It is also expected to be highly relevant when focusing on key groups of the population. The distinction between the two approaches brings also an important gender perspective. There are high risks that the traditional education-based approach deliver misleading measures of mismatch for women, as they are typically more educated than men are, and tend to be more segregated in firms, sector and occupations with low average levels of formal qualifications. In contrast, the measure of mismatch that incorporates information on the worker's longitudinal experience in the labor market career has the potential to account for (i) the more discontinuous careers typically held by women, e.g. during child raising periods; (ii) any sectoral or occupational segregation patterns; (iii) gender differences in the provision of formal training within the firm, as well as any gendered differences in informal peer learning within the firm. The career-based approach is also better placed than the education-based approach for informing the debate of skill deficiencies in active ageing societies. For, mature workers have probably learned to perform their jobs to competency levels that is only poorly measured by the levels of educations they acquired decades earlier. The opposite is expected to hold for young workers who have just entered the labor market. Cohort effects are also likely to matter, as older workers have traditionally relied less on school learning and more on on-the-job learning-by-doing than their younger counterparts.

Assessing the role of labor market institutions on the formation and consequences of skill mismatch, with a particular reference to EPL dispositions, unionization and the general characteristics of a country’s system of industrial relations, are thus a central concern of innovative and inclusive societies. The project’s findings will provide key additional perspectives to evaluate to what extent the EU is actually on the way to become “the most competitive knowledge-based economy in the world”. Doing so requires that specific attention is paid in the years to come to the processes that lead to human capital accumulation and mismatch throughout the entire worker’s life, and to public policies designed to fostering such processes.


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Berton, F. and Garibaldi, P. (2012) “Workers and firms sorting into temporary jobs”, Economic Journal, 122: F125-F154.

Brunello, G., Garibaldi, P. and Wasmer, E. (eds., 2007) “Education and training in Europe”, Oxford: Oxford University Press.

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Lazear, E. (2009) “Firm-specific human capital: a skill-weights approach”, Journal of Political Economy, 117: 914-940.

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Risultati e pubblicazioni

F. Berton, A. Carreri, F. Devicienti and A. Ricci (2019), ‘Workplace unionism, collective bargaining, and skill formation. New results from mixed methods’, IZA Discussion Paper No. 12712, revised and resubmitted for the British Journal of Industrial Relations

F. Berton, F. Devicienti and S. Grubanov-Boskovic (2017) ‘Employment protection legislation and mismatch: evidence from a reform’, IZA Discussion Paper no. 10904

F. Berton, A. Carreri and F. Devicienti (2017), ‘Rent-sharing, sindacato e contrattazione di secondo livello: il caso italiano’ in C. Dell’Aringa, C. Lucifora and T. Treu (eds.) Salari, produttività e disuguaglianze, Bologna: Il Mulino

S. Dughera (2020), "Skills, preferences and rights: evolutionary complementarities in labour organization", Journal of Evolutionary Economics, 30: 843-866

F. Berton, F. Devicienti and L. Pacelli (2016) ‘Human capital mix and temporary contracts: implications for productivity and inequality’, Politica Economica-Journal of Economic Policy, 32(1): 27-46


Ultimo aggiornamento: 31/10/2022 17:11
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