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Do high-quality traineeship help to find better jobs? Evidence from a survey on the participants in the youth guarantee program

Author

Listed:
  • Cristina Lion
  • Vanessa Lupo
  • Katia Santomieri
  • Veronica Sciatta

Abstract

Job quality is a key issue in the agenda of policy makers at the international and European level: a strong commitment towards decent work has strengthened and a particular attention to young generation has been devoted. Since 2014 the European Youth Guarantee Program has been strongly investing in active labor market policies, with the aim to combat youth unemployment and inactivity: noncurricular traineeship is one of the most widespread measure supported by the program and the issue of its quality has become more and more relevant. Against this background,the paper has analysed the relationship between the quality of traineeship that young people have experienced and the quality of their job: the hypothesis is that participating in a high quality traineeship entails a better transition towards decent jobs. We use data of a sample survey carried out in 2017 by ANPAL on 20,000 young people who have registered to the Youth Guarantee program. Preliminary results suggest that the quality of traineeship is important in promoting better job for young people.

Suggested Citation

  • Cristina Lion & Vanessa Lupo & Katia Santomieri & Veronica Sciatta, 2020. "Do high-quality traineeship help to find better jobs? Evidence from a survey on the participants in the youth guarantee program," Working Papers 0053, ASTRIL - Associazione Studi e Ricerche Interdisciplinari sul Lavoro.
    • Handle: RePEc:ast:wpaper:0053
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    More about this item

    Keywords

    quality of work; non-curricular traineeship; NEETS; Youth Guarantee;
    All these keywords.

    JEL classification:

    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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