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Abstract
The quality of jobs of economics graduates was studied in terms of educational mismatch. The returns of over‐education on earnings and on the job‐search were also investigated. The discussion regards the second wave of a longitudinal survey of a random sample of economics graduates from the University of Naples ‘Parthenope’, a major school of economics in southern Italy. Over‐education was measured using two different indicators of educational mismatch, one based on an objective parameter and the other on the same parameter combined with a subjective one. A probit regression with selection was carried out to analyse the influence of a set of control variables (such as family, background, employment geography and characteristics of job, work history, gender and channels used to enter the labour market) on over‐education. The same variables were used to study the returns of over‐education on earnings and on the job‐search. The probability of being over‐educated was significantly affected by gender, attainments in Higher Education (HE), channels used to enter the labour market, job location and job sector applied for. Females, lower HE achievers and graduates working in trade/sales or information systems sectors were more likely to be over‐educated than other subjects, whereas use of further education to enter the labour market decreased the probability of being over‐educated. Over‐educated workers were found to have a high probability of low earnings. Over‐education and low earnings induced workers to change jobs.
Acknowledgements
We would like to gratefully acknowledge the anonymous referees for their helpful comments on previous drafts of this paper. This study was supported by the 2005 Endowment Funds of the Department of Statistics and Mathematics for Economic Research of University of Naples ‘Parthenope’ in the framework of the research ‘The Transition from University to Work’.
Notes
1. This work is coordinated by C. Quintano and it is the result of the common work of the authors. R. Castellano is the author of Sections ‘Introduction’, ‘Overview of the labour market for Italian graduates’ and ‘Data’, and A. D'Agostino is the author of sections ‘Definition of variables and methodology’, ‘Empirical results’ and ‘Final discussion and conclusion’.
2. The ISTAT indicates that the employment rate three years after graduation in the economics and statistical science is 80.6% and the list is only exceeded by engineering (90.8%) and political and social science (85.6%) (ISTAT Citation2004).
3. According to the first quarter bulletin ISTAT Citation2007, the unemployment rate in the south of Italy is 10.9% compared to an average of 6.2% in the whole country. In the Campania region, it is 11.3%.
4. The ninth largest in Italy, and the number of students enrolling each year is increasing in absolute and relative terms.
5. Item non‐response is not present in all variables considered in the empirical analysis.
6. The weights obtained were very close to one since weighted and non‐weighted percentages were very similar.
7. Sample survey through the Web is a relatively new method of collecting data in economic and social sciences. Its use is increasing because it is often faster and cheaper than traditional methods like the telephone or face‐to‐face interviews (Cobanoglu, Warde, and Moreo Citation2001; Couper Citation2000). Used by few Italian universities: Universities of Padua and Florence (Fabbris and Giusti Citation2001), and the University of Pisa (www.diogent.net).
8. This is the definition used in Di Pietro and Cutillo (Citation2006).
9. Ungaro and Verzicco (Citation2005) also define an educational mismatch index combining those two parameters.
10. Di Pietro and Urwin (Citation2006) found that 68% of graduates needed the formal requirement of a university degree for their jobs.
11. This survey methodology was chosen mainly because it would be difficult to measure earnings by an on‐line questionnaire without incurring measurement errors. We therefore opted for acquiring earning classes directly.
12. We do not use mark at graduation as a continuous variable since we are interested in specific marks at graduation. In Italy graduates can only apply for certain jobs if their mark at graduation was 105/110. A mark under 99/110 is very low mark and can penalise the transition from university to work. Since we only considered variables that had an effect significantly different from zero in at least one of the three models shown in section ‘Empirical results’, we did not consider other individual variables (such as graduate's age, time spent at University, etc.).
13. The definition of this variable is similar to that proposed by Granovetter (Citation1974) and revised by Costa, Gianecchini, and Guitta (Citation2004).
14. In Italy, a good master's degree or training course can facilitate transition from university to work as the master organisation or the firm or office where the training is organised have channels into the labour market. The contacts they have are connected to the specific university degree and consequently reduce the risk of over‐education.
15. In many similar studies, cited in the references, information on firm size and on time spent in the job training is always included. We did not have this information.
16. No interaction terms between variables were introduced in the three models because their effects were not significantly different from zero. Probably this was also due to too small sample size.
17. The empirical results presented in the paper were obtained using the specific packages for the probit regression analysis with selection implemented in STATA (Citation2001).
18. This result confirms that of Fullin (Citation2001) who suggested that families provide protection against the risks of the labour market.
19. In Italy, gender discrimination exists in different forms, for example, difficulty in finding first job and lower pay.