Youth Labour-Market Performance, Institutions and Vet Systems: A Cross-Country Analysis

Abstract

Youth are a vulnerable category of workers, since they are in a delicate phase of their working life, the first entry in the labour market. Young graduates and early school leavers are involved in the school–to–work transition process, whose duration considerably varies across countries. In this paper we explore the impact of labour-market and educational institutions on youth labour-market performance across OECD countries for the 1985–2012 period. We extend to 2012 the CEP–OECD dataset and match it with series about youth population, schooling and the vocational education and training participation rates from OECD and World Bank. We estimate a dynamic panel model, building upon Bassanini and Duval, “Employment Patterns in OECD Countries: Reassessing the Role of Policies and Institutions? OECD Social, Employment and Migration Working Papers n. 35, Paris, 2006, including unemployment rate in the analysis, articulating the analysis upon various age groups (15–24, 20–24) and distinguishing between male and female workers. The tax wedge, changes in union density, the minimum wage, educational attainment and the level of economic activity stand out as the key determinants of youth employability (minimum wage has not however the sign most often expected in the literature). VET programmes participation also matter, although only in the short-run. There are also some interesting differences across age and gender groups. In particular, labour-market institutions seem to have a stronger impact for women

Manual for the classification of intervention best-practices with rural NEETs

“…More pronounced increases in unemployment rates, especially among youths in the southern European countries, were observed since they were more severely hit (Quintano et al, 2018). The different institutional environments may explain the cross-country youth disparities (Caroleo et al, 2017). The countries differ considerably in terms of: the efficiency of their school-to-work transition systems (e.g., the period between the end of compulsory schooling and full-time employment involving many actors from education systems to the institutions operating on the labour market); labour market regulations; and, labour market flexibility.…”

“…There are several key factors associated with a higher probability of becoming a NEET. These include: personal characteristics, such as belonging to an older age group, being a female and/or being part of an ethnic and religious minority; low educational level or its poor quality in terms of provision; exclusion or suspension from school; migrant status; poor physical or mental health; disability; early marriage and early childbirth; having experienced unemployment; and, having unemployed friends (Caroleo et al, 2017;Basta et al, 2019;Quintano et al, 2018;Vancea & Utzet, 2018;Salvà-Mut et al, 2018;Ciccarelli & Fabrizi, 2017;Tamesberger & Bacher, 2014). The NEETs rate varies significantly across European countries (and sometimes also within the countries themselves), where identical personal characteristics can easily correspond to starkly different propensities to being NEET (Caroleo et al, 2020).…”

“…The probability of being/becoming NEET differs sharply across European countries. (Caroleo et al, 2017; During the last decade, the recession exacerbated the economic disparities across all countries. More pronounced increases in unemployment rates, especially among youths in the southern European countries, were observed since they were more severely hit (Quintano et al, 2018).…”

“…The countries differ considerably in terms of: the efficiency of their school-to-work transition systems (e.g., the period between the end of compulsory schooling and full-time employment involving many actors from education systems to the institutions operating on the labour market); labour market regulations; and, labour market flexibility. Each of these, either singularly or in combination, may affect the length of unemployment spells and gaps in experience among youth (Caroleo et al, 2020;Zimmermann et al, 2013;Caroleo et al, 2017 These questions can arise in any process of assessment, mapping or classification of various kinds of interventions, including in those dedicated to the vulnerable and socially excluded rural youth. A solid methodology can guide researchers and practitioners in this process by proposing some "keys" for understanding and evaluating the interventions.…”

Then and now: analyzing Filipino youth education and work decisions following the 12 basic education reform

“…The reality is that aspirations do not always translate to outcomes. Some of the reasons point to labor supply often exceeding labor demand [Manacorda et al 2017]; education not translating to sufficient skills needed by the industry [Gropello et al 2010] due to coordination failure between the academe and the industry [Orbeta 2002]; or the labor markets are simply inefficient due to information asymmetry [Lockwood 1991], with labor market incentives penalizing young people and favoring older and more experienced workers [Caroleo et al 2017]. Thus, many young people face the problems of unemployment, marginalization, and further inequality, resulting in disillusionment or stasis (Heissler [2011]; Naafs and Skelton [2018]).…”