Overeducation at a glance. Determinants and wage effects of the educational mismatch, looking at the AlmaLaurea data
AbstractThis paper provides the first available estimates of the impact of overeducation on wages of AlmaLaurea university graduates. The analysis focuses on jobs held 5 years after the graduation attained in 2005. Overeducation / overskilling are relatively high when compared to those in similarly advanced economies, and persistent over the years after graduation. Ceteris paribus they tend to be more frequent among children of parents with lower educational levels, through school tracking. The degrees more frequently associated to overeducation are: Agriculture, Arts, Education, Languages, Physical Education, Political Sciences, and Psychology. Working while studying and having started the university later than the curricular years are also factors. Moreover, we estimate a conditional wage penalty of about 10% of the median wage when we adopt the "to get" (overeducation) and of about 6.7% when we adopt the "to do" (overskilling) definition. However, the personal attributes that dispose individuals to be mismatched might also reduce the probability of finding a job. Controlling for this source of sample selection bias by using the Heckit procedure, we find that the wage penalty associated to overeducation / overskilling goes up to 35 and 74 percent, respectively. This is support for the job competition and the job assignment models versus the search theoretical model, suggesting that the non-employed would be more likely overeducated / overskilled if they found a job.
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Bibliographic InfoPaper provided by CRISEI, University of Naples "Parthenope", Italy in its series Discussion Papers with number 18_2012.
Date of creation: 19 Oct 2012
Date of revision:
School-to-Work Transition; University graduates; AlmaLaurea; Overeducation; Overskilling; Earnings; (Ordered) Probit; sample selection bias; Heckit; Italy.;
Find related papers by JEL classification:
- C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
- 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; Longitudinal Data; Spatial Time Series
- 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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