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An Analysis of the Determinants of Over-Education Among Italian Ph.D Graduates

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Abstract

The paper examines the determinants of over-education among Italian Ph.D graduates drawn from the four cohorts 2004, 2006, 2008, 2010 surveyed by the Italian National Institute of Statistics (ISTAT). We study over-education through the definitions of over-skilling, over-qualification and a combination of the two phenomena (genuine over-education). The analysis spans the years 2004–2014 allowing us to investigate job education mismatch before and during the severe economic crisis. The results show that socio-demographic variables do not exert a relevant influence on Ph.D graduates’ over-education contradicting what the empirical literature has found for college graduates. More than socio-demographic variables, those variables related to the doctoral educational path turn out to be positively strategic for the outcome in the labor market. In particular, an experience abroad is always a positive driver to overcome any kind of job mismatch. Similarly, job related characteristics are also relevant determinants of over-education with jobs within the academia or the research sector more often associated with successful labor matching. Conversely, accessing jobs via informal channels or working as self-employed increases the risk of over-education. The investigation of the effect of the recent economic crisis emphasizes how the recession makes the labor market more selective with Ph.D and job-related variables, worsening the risk of incurring in job-education mismatch. Finally, we discuss some policy implications in the light of the empirical findings of our analysis.

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Notes

  1. The law 240/2010, which came into force on 2010, has reformed profoundly the university system. Focussing on the effects that mostly affect Ph.D recipients, who largely ended up working into the academia, the access system towards an academic career has been modified causing a greater precariousness for newcomers. Positions to enter the academic career are generally more scarse and researchers are forced to attain a professorship by a few years in order to avoid losing their job. Indeed, perspectives of career into academia deeply worsened.

  2. Indagine sull’inserimento professionale dei dottori di ricerca (ISTAT 2009, 2014). The present analysis uses the ISTAT Microdata for research purposes, available from ISTAT upon request.

  3. In the first survey the employment conditions of the respondents are assessed 3 and 5 years after Ph.D graduation (for those who were awarded the title in 2006 and 2004 respectively), while in the second survey the professional outcome is examined 4 and 6 years after graduation (for those who were awarded the title in 2010 and 2008 respectively). This survey design allows only to predict medium term outcomes. Thus, we cannot assess to what extent the professional status at the time of the survey may be correlated with future job opportunities. We thank an anonymous referee for this remark.

  4. As to the definition of the fields of study, Hard Sciences includes Mathematics and Computer Science, Physics, Chemistry, Earth Sciences, Biology; Technical Sciences includes Civil Engineering and Architecture, Industrial and IT Engineering; Socio-political Science and Humanities includes Philology and Literature, History Philosophy and Psychology, Political and Social Sciences.

  5. For the definition of the variable parents class we followed ISTAT (2003).

  6. The format of microdata used in this analysis does not report the punctual age of respondents; it reports age by class at the time of Ph.D completion. Because of different coding of age classes across surveys, we have been only able to build a dummy variable taking value of one if the title has been obtained at the age of 29 or earlier and zero otherwise.

  7. The first survey (2009) reports some information about the courses and other activities (workshops, seminars, etc.) attended during the doctoral program. Unfortunately, the second survey (2014) does not contain any information on these aspects.

  8. In the dataset of micro-data used in this analysis, the only information available at the university level is the province where the university awarding the title is located.

  9. The difference between matched and non-matched groups has been assessed by performing t tests on the equality of means.

  10. In labeling the interplay between over-skilling and over-qualification as “genuine overeducation”, we follow Gaeta et al. (2016) that propose the following taxonomy: “genuine matching” (no overskilling, no overeducation), “apparent matching” (overskilling but no overeducation), “apparent overeducation” (overeducation but not overskilling) and “genuine overeducation” (overskilling and overeducation). Using a slightly different labeling, a similar approach is adopted in Mavromaras et al. (2010, 2013), Pecoraro (2014), Iammarino and Marinelli (2015) and Di Paolo and Mañé (2016). We thank an anonymous referee for this suggestion. Table 5 in the Appendix refers the means of the variables used in the analysis for the two groups of workers (matched and non-matched) defined in terms of genuine over-education.

  11. Nevertheless, the probit model and the probit model with sample selection return similar estimated coefficients in terms of sign and significance.

  12. Nevertheless, we cannot exclude that socio-economic background influences labor market outcomes in a more indirect way. For example, by influencing the probability of getting a job as, according to our estimates, parents’ social class is highly significant in the selection equation. In addition, the indirect impact of socio-economic background may reveal by influencing the channels to access work (Gaeta 2015) or the educational choice pattern starting already from high school (Brunello and Checchi 2007; Caroleo and Pastore 2013).

  13. We performed a Wald test for the joint significance of the coefficients of the variable children and of the interaction between children and sex, rejecting the null hypothesis that both the coefficients are simultaneously equal to zero.

  14. 80% of Ph.D graduates with an income during the doctoral studies were granted a scholarship from the University.

  15. The variables chosen as instruments perform as expected in predicting the probability of being employed: being married and having children prompt individuals to work but can be an obstacle if the worker is a woman.

  16. The variable sex and the related interaction terms are statistically significant in the selection mechanism of the estimation framework outlined in Eqs. 1–3.

  17. For the sake of brevity, the results of the robustness check are not included. However, they are available upon request.

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Authors and Affiliations

  1. Department of Economics and Social Sciences (DiSES), Università Politecnica delle Marche, Piazzale Martelli, 8, 60121, Ancona, Italy

    Barbara Ermini, Luca Papi & Francesca Scaturro

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  1. Barbara Ermini
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  2. Luca Papi
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  3. Francesca Scaturro
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Correspondence to Francesca Scaturro.

Appendix

Appendix

Table 5 Variables and summary statistics, by mismatch type (mean)
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Table 6 Determinants of over-qualification. All, before and after the crisis
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Table 7 Determinants of genuine overeducation. All, before and after the crisis
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Ermini, B., Papi, L. & Scaturro, F. An Analysis of the Determinants of Over-Education Among Italian Ph.D Graduates. Ital Econ J 3, 167–207 (2017). https://doi.org/10.1007/s40797-017-0053-3

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