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Overeducation at a glance. Determinants and wage effects of the educational mismatch, looking at the AlmaLaurea data

Author

Listed:
  • Floro Ernesto Caroleo

    (University of Naples "Parthenope”, IZA)

  • Francesco Pastore

    (Seconda Università di Napoli, IZA)

Abstract

This paper provides the first available evidence on overeducation/overskilling based on AlmaLaurea data. We focus on jobs held 5 years after graduation by pre-reform graduates in 2005. Overeducation/overskilling are relatively high – at 11.4 and 8% – when compared to EU economies. Ceteris paribus they tend to be more frequent among children of parents with lower educational levels, through school tracking. Most arts degrees and social sciences, but also Geology and Biology are associated to both types of the educational mismatch. The quality of education is also a factor, suggesting that in addition to the low demand for skills, one should also reckon the inability of the educational system to provide work-related skills. Moreover, we find a non-conditional wage penalty of 20% and 16% and a conditional one of about 12% and of 7%, respectively. Heckit returns a sample selection corrected penalty slightly higher, supporting not only the job competition and job assignment models, but also the human capital model.

Suggested Citation

  • Floro Ernesto Caroleo & Francesco Pastore, 2014. "Overeducation at a glance. Determinants and wage effects of the educational mismatch, looking at the AlmaLaurea data," Working Papers 58, AlmaLaurea Inter-University Consortium.
    • Handle: RePEc:laa:wpaper:58
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    More about this item

    Keywords

    University-to-Work Transition; Overeducation; Overskilling; Sample Selection Bias; AlmaLaurea; Italy.;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • I2 - Health, Education, and Welfare - - Education
    • J13 - Labor and Demographic Economics - - Demographic Economics - - - Fertility; Family Planning; Child Care; Children; Youth
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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